I think generative AI is at its heart a con. And seeing these ultra-rich, ultra-powerful people lie through their teeth turns my stomach.
The word 'con' is a strong word.
Well, what do you call something where from the very beginning they've sold it in the terms of magic, but it's just a half-assed autocomplete machine? They are misleading the entire world.
You are the first person that I've spoken to that has that opinion.
Well, the fact that this is happening is insane. And the fact it's not a scandal is insane. And I've been in the tech industry for 16 years now, and I love technology and I'm enthusiastic about it, but I don't like being misled. And this is the largest non-consensual push of technology in history.
So, we're going to play a game, Ed. I have the things that you consider to be myths about the AI industry.
Let's play it. The AI industry is creating enormous economic growth. No, it's not. All of these companies run at a horrifying loss. OpenAI lost $20.9 billion last year. None of these people can just say, 'Yeah, we're on the path to making this profitable,' because they can't.
Next one. AI will replace all human jobs.
That just isn't happening. There's no economic data to support it.
Next, the United States needs to spend trillions to beat China in the AI race.
What's the race to do? For us to constantly piss our pants worrying about China? But people keep saying, 'What if these models fall into the wrong hands?' They're already in the wrong hands. Mark Zuckerberg, Sam Altman, Dario Amodei.
Mark Zuckerberg says, 'We'll continue to invest aggressively in infrastructure to meet the demand.'
God, Meta is a monstrosity. Makes me think of Shrek with Lord Farquaad: 'Some of you may die, but that's a risk I'm willing to accept.' If only these people gave a [__] about poverty or actual problems in the world versus are we buying enough GPUs.
If this continues, what does the future look like? [__]
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Ed Zitron, there are a number of things that you believe that a lot of other people don't believe, right? You have, I think, a couple of controversial opinions and opinions that are in contrast to the other guests that I've sat here with. What exactly are those opinions, Ed?
I think generative AI is at its heart a con. I don't think it is sold as honest software. I think that they overstate both what it can do, what it will do, and the underlying financials to the point that they are misleading the entire world. And they're actively exploiting the weaknesses in journalism, in our economies, and indeed within the responsible parties with sell-side analysts, governments, and all over the shop.
The word 'con' is a strong word.
Yeah. I mean, what do you call something where from the very beginning they've sold it in the terms of magic as this thing that will replace all jobs, that will cure cancer, 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 got a finance experience, he's not got a 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. It can't even do the basics. It can't even do search well. Whenever you ask an AI person, 'Well, what's your setup?' they describe this Pee-wee's Playhouse thing of like, 'Well, you got a harness here and you got to use the right prompt. Well, you don't want to use that prompt. You want to use this prompt here with this model, but don't use this model for the beginning, but at the end you're going to want to use this model.' And this is meant to be artificial intelligence. It's meant to be smart. It's meant to be autonomous. It's meant to be something that you set and forget.
We have the sort of six leading AI companies on the table here: Anthropic, Amazon, Nvidia, Microsoft, OpenAI, Google. You're saying that their fundamental business model is a con.
Well, their revenues are not really coming from AI. Up until fairly recently, none of their revenues were coming from AI. Like dribbles a bit. Right now, 70% of all AI revenues across those three companies are from OpenAI and Anthropic, two unprofitable, unsustainable companies that literally cannot afford to exist without these very same companies giving them money. Amazon sent $50 billion to OpenAI this year. They sent $5 billion to Anthropic. Google sent $10 billion to Anthropic. And in the next three and a half years, OpenAI and Anthropic, based on actual sell-side analyst evaluations, their estimates that inform whether stock is going to go up or down after earnings, they are expecting $400 or more billion dollars of revenue, 30 or something percent of cloud growth just from these two unprofitable companies that will need to be given the money from somewhere. And on top of that, these companies have such low respect for the average investor, for the analyst, for everyone really, that they don't even disclose their AI revenues. The few times they do, they use something called a run rate, an annualized run rate, which means well, nothing. They never define it. It can mean month 12. It can mean month 13. It can mean last four weeks times 13. It's different every time, and they never define it. And then they sometimes just don't mention it. So, you've got this big thing that is meant to be the biggest, most influential change to software ever. And whenever you ask them about it, when you say, 'What? How much are you making from this?' they go, 'Oh, I couldn't possibly say. I'm too shy.' These are public companies, or at least the ones that aren't Anthropic and OpenAI. When they have good news, they'll tell you. And when they don't tell you something, well, that actually speaks volumes.
Have you used these tools, the AI tools, Gemini, Anthropic, ChatGPT, etc., and you found no value in them?
There's some value, but it's not... they have spent over a trillion dollars in capex.
What does capex mean for you?
Capital expenditures. So when you are a business and you have operating expenses like electricity, for example, those come right off immediately. Capital expenditures are long-term investments that are theoretically one-off. So a data center, or indeed the GPUs you put inside an AI data center.
Okay. So you've got a data center.
And then you have these GPUs, which are like computer chips. So AI GPUs are much bigger, much more power intensive. They take a bunch of high-bandwidth memory, and because of how many of them you need — you need thousands of them, tens of thousands, hundreds of thousands in some cases — you need a bunch of power. So an example: OpenAI and Oracle are building a data center in Texas, in Abilene, Texas. 1.2 GW, called Stargate Abilene. Within that, with each one of the eight buildings, there'll be 50,000 Nvidia GB200 GPUs. So, the city of Bristol takes about 7,800 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. The 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 labor into this one spot. And all of these data centers cost hundreds of billions of dollars. All of these companies other than Microsoft have now had 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 OpenAI.
One of the rebuttals to that would be that the adoption, the customer adoption of people using OpenAI and Anthropic has been absolutely insane. These are the fastest growing products in all of history, especially as it relates to sort of technology. If we just focus in on technology, they are, you know, hundreds and hundreds of millions of people, billions of people are using these tools every single day for things that they have subjectively decided are problems they need solving. So, you know, money is a lagging indicator of value. So, one would argue that they're just investing ahead of the monetization options.
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, Copilot's bugging you. When you use Amazon, whatever Rufus AI is, it has opinions on what socks you're buying. This is the largest non-consensual push of technology in history. ChatGPT, for example. Every single media outlet has been screaming about this for 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, and that's partly because Google fell behind in search, and also because it's better at 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? 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. AI GPUs are not useful for other things either. It's a directionless egregore of capitalism, this headless beast that lumbers around desperate to seek out growth everywhere in the hopes that if it harasses people and scares people and demonizes labor enough, people will be forced to use it.
The reason I pause is because I just think about my own company. Obviously, everybody thinks about their own personal situation. So, you have people listening now that don't use any AI tools. Then you'll have people that are using it for everything from coding new software tools to everything they write to, you know, images, whatever. And when you look at the stats around enterprise adoption, it says 88% of organizations regularly use AI at least once for one particular business function. And I'd say in our company, 95% of people use one of these AI tools like Anthropic or ChatGPT or Gemini every day, right? And that exists on some kind of spectrum of like the super users that are using it probably, you know, every hour of every day for almost everything to, you know, someone maybe high in the executive team that's using it less because their job doesn't require of it as much, right? And when you look out into the world at, you know, at how the world is changing from a content perspective, if we're looking at generative AI, it is obvious that these tools are being widely adopted. Part of the symptom is the AI slop you see all over the internet, right? So, I don't know, this idea that it's not being used, I struggle with.
It's being used. Here's the thing with the slop. Before we had AI slop, we had SEO slop because Google incentivized doing the lowest common denominator that would rank well in search. There's a whole story about how they pulled back spam guards thanks to Prabhakar Raghavan, which we can get into, where they made the internet worse by allowing worse content to rank higher. It's why when you used to Google, oh, best washing machine, there's 11 different horrible blogs that read like somebody got a concussion. They are built to rank rather than be read by humans, or built to be good, made good. So AI helps weaponize that at scale. Yeah, you can make a bunch of generic slop. We've had slop for years. We've just found a slop machine. But then also there's the problem of cost. So when you use AI services, you burn tokens, and it's per million tokens. So that'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.
And every word, let's just say for ease it's a word, you're paying per word.
About a word, yeah. And it's per million tokens. So you'll be charged per million input tokens, the stuff you feed into it like a document or a bunch of 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 — SemiAnalysis actually found this, a big analyst group — they found that on a $200 a month ChatGPT subscription, you can burn $14,000 worth of tokens, and on Anthropic's you can burn $8,000 for 200 bucks. That is how most... and even on the 20 buck a month service, you can burn $400. Now, most people don't realize that. Most people have no idea what AI costs. Most people just think, 'Oh, it's 20 bucks a month.' No. All of these companies run at a horrifying loss. OpenAI lost $20.9 billion last year because people can burn as many tokens as they want. And when they tried to move everybody on the enterprise side, so companies bigger than 150, onto actually paying the cost of AI in around March of 2026, to quote Sam Altman, they said, 'Uh, people have a big problem with it. I think it's a huge issue,' which is not really what the heir apparent tech genius is meant to be saying. But the point is enterprises immediately started freaking out. Uber burned through their entire annual token budget in three months. So suddenly, after everyone saying AI is the most productive thing ever, it's amazing, it's changing everything, the moment people actually had to pay for it, they go, [snorts] 'I don't know, actually. Um, maybe it's... obviously we all love it. It's all great, right? But it's costing too much. So we 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 who's a power user of these tools, I could be costing Anthropic or OpenAI $1,000, but they're only charging me $100, let's say. So they are having to subsidize $900 of my usage because of the electricity costs and the costs at their data centers. And so your assertion here is that that is unsustainable.
Yes. And just to be clear, they're probably not one for $1. It might be 30 to 4. We don't know. I think it's unprofitable. These companies don't disclose them. Even in their audited financials, they play funny games with how they categorize things. But nevertheless, yes. And on top of that, the way that you stand up inference, which is the thing that creates the output within these data centers, you're not just saying, 'Okay, turn the inference machine on. Let's go.' You are standing up the GPUs necessary to take in the demand, and if you buy too much, 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 cancel, 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. And really, the simplest way to explain it is: if they were actually profitable, if they actually 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 codebase and they [__] up a bunch of stuff, they break a bunch of stuff — you're paying for that. You're paying for it whether it works or not, unless of course you're using one of these subscriptions.
I think the really interesting point is: are they spending ahead of the value showing up, which is I imagine what they would argue, or are they spending all of this money and subsidizing all of their users in a way that's unsustainable and that will never be justified? Like, does it, you know, because you think back through the history of technology, you often get people losing money to grab market share, right? And they're also focusing on bringing the costs down and making it more profitable for them as well. But they can't afford to underinvest.
If they were bringing the cost down, they would have brought the cost down, which they have not. It seems to be getting more expensive. In fact, every inference provider doesn't seem to be profitable. Even the companies renting out GPUs don't seem to be profitable. I imagine that it wasn't like they started out and they were like, 'Shit, this is unprofitable at the beginning. We know it. Screw it. We'll keep doing it anyway.' I don't think it's some big conspiracy. They probably thought at some point, yeah, this will go profitable. The chips will catch up. Customers will pay for the overwhelming value, because you don't know in 2023 where it's going to be in 2026. You assume it's going to go up. That's the nature of venture capital. They should have stopped in like 2024 when OpenAI lost over $5 billion. They should have been like, 'Yep, this is not going to work.' But they kept going because it helped number go up so much. It helped stock values pump. It helped everyone pump. It helped Nvidia pump, Microsoft, everyone. And not from the revenues. Because here's the funny thing about Google, Microsoft, and Amazon. People for years have been saying their AI bets have paid off. Wow, their AI bets have paid off. As these companies refuse 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, because revenue go up, everyone went, 'It's AI,' because these companies wouldn't spend a trillion dollars for no reason, right? Except in fiscal year 2026, which just ended for Microsoft — annoying, I know — they made total, according to Bloomberg, about $34.33 billion. $24.1 billion of that was from OpenAI, so that leaves them with about $10 billion in a year when they spent $115 billion on capital expenditures, as Nvidia spent $175 billion next year. The math does not make sense. I imagine their plan was, okay, this is just going to get exponentially more valuable, and at some point the costs will be outpaced by the return. Problem is that large language models need a bunch of money to train them. They need constant data flows through. They need customized data. It's just this big expensive monster. And when you try and talk to people about it and you try and say, 'Hey, look, this is really bad. Nvidia has sold $215.9 billion in the last fiscal year worth of GPUs mostly,' and you try and go, 'Yeah, that's to support like $22 billion of revenue total in the entire world outside of these two companies that literally require money being fed into them, sometimes by Nvidia, to keep alive.' When you tell people that, they go, 'Well, companies just lose money, right?' Because 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 that with this idea that the ultra-wealthy, the ultra-powerful didn't get there through big brains, they didn't get there through anything other than luck and opportunism and getting an MBA perhaps with the right people, that they just got there because they're regular people and they just happened to be in the right place at the right time — reconciling 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'll read it. And one of the things it talks about is how the innovation that ends up taking out or transforming an industry often starts worse, doesn't make economic sense, none of your customers are asking for it, and this is typically why we end up ignoring it. So like you've got horse and carriages in the 1800s, amazing form of transport according to the 1800s, you know, people of the 1800s. And then you have this thing called cars come along. Now the problem with cars is they broke down all the time. It's kind of like AI hallucinates now. 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 you had to employ someone to walk in front of it waving a red flag. Obviously, it's worse. It's like a worse solution. However, these things that are disruptive innovations, they have a higher ceiling of growth, and so they eventually overtake the horse. And when I think about that analogy in the context of all of this, I go, okay, it's imperfect at the moment. The economic models aren't perfectly ironed out. They're still figuring out how to make it cheaper, the infrastructure, etc. But if you think about the rate of improvement versus other, you know, let's say coding — how much could I train a human coder to improve and to increase their output versus an AI agent — one would go... if you just imagine any rate of improvement in these AI tools, at some point, if you just imagine a 5% rate of improvement per month, at some point it's, you know, and then you imagine a 5% reduction in cost, which is what we did with the internet, what we did with cars, yeah, but Moore's law.
Moore's law is not with GPUs. So let me actually explain. So Nvidia invented — I think it was in the 2000s — they put out something called CUDA, which is the underlying software library and the way to run software on GPUs. It took them a solid decade or more to make it something where they could do data analytics, one of the early things, MapReduce and such. And then when AI came along, they'd had lots of experience with it. But nevertheless, this company has got more money, more attention, more geniuses behind them, more people focused on making their things more efficient than anyone could ever ask for.
And Nvidia, for anyone that doesn't know, makes the chips.
They... so, and that CUDA thing I mentioned, they were the ones with CUDA, and CUDA allowed generative AI to grow.
And chips are needed. Those are the things that go into the data centers.
And they're specific chips, 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 how you'd measure that, because a junior software engineer can still experience things and learn things from context, from how people deal with problems. And the way that people deal with problems is not as simple as looking at the code or reading some emails. It's context 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? Is it better at C++?
I'd say productivity. So just like, yeah, shipped. If we did it in the context of coding, it would be like shipped code.
That's the thing that would be like, 'He's the best writer in the world because his newsletter's really long.' That's an insane way of evaluating it. With coding, it would be — I mean, it's even difficult to evaluate because is the software out there better is actually a great way of evaluating it. And I would say uniformly not. I would say the standard of software across Google, Microsoft, Amazon, Meta, especially — God, Meta is a monstrosity — is worse. GitHub, GitHub, someone posted on Twitter earlier today, we should get a notification when GitHub is up rather than when it's down because that would be more reliable. Microsoft, one of the largest companies in the world, and they can barely wipe their own ass when it comes to GitHub. The quality of software is going down, weirdly enough, as more people use LLMs and more businesses demand — and I really do mean demand — that people use these services.
So on this point of if we go back to this horse and carriage and car analogy, say that we're at whatever point today, if you imagine any rate of improvement in the technology, which we have seen since ChatGPT came out — I remember when ChatGPT came out and I was in Asia and I was there showing it to my fiancée, I was like, 'Look, it can do this,' and it was hallucinating once in a while and getting things wrong. I actually don't have that experience anymore. I have moments where I believe its reasoning is weak, but I don't have outright hallucinations anymore.
See that? I disagree. So,
give me an example of what you define as a hallucination.
Okay, great one. So, I have a Bloomberg terminal. The very useful thing they have on there is ask B. So, when you do a Bloomberg inquiry to like look up what we think Nvidia's revenue is going to be next quarter, it runs something called BQL, which is its own programming language. Now, instead of having to learn that, you can just type into ask B and it will generate it and run it for you. And so, you get pulled up and you know where the data is coming from. It deals with hallucinations real well. The other day, I was like, you know what, we get a little spicy. I'm going to look up the growth rate of stocks of Microsoft, Google, Meta, and Amazon over the course of five years, I think it was.
And I was about to — I copy-pasted it over to something, looked at it in Excel. I was about to write it in the newsletter. It went: Microsoft stock's never been $575 a stock.
You know what? When it's a cute little thing like, oh, it's a stock price and I can't acquire it, no harm, no foul. That's fine. But when you're talking about, I don't know, like a transcribing tool for a doctor or a financial model that a hedge fund is dependent on, at that point it becomes a little more dangerous. And the thing is, a hallucination with a software package. For example, you're refactoring a code base and it leaves a door open security-wise, or it just breaks something and you — I don't know, maybe you've been vibe coding for six months. You haven't really been coding with your own hands for a while. Maybe you've forgotten a few things. You had this slop to look for [__] — 'I'm not doing it.' And so the problems become multiplicative. And I don't really know how you train them out of that. And they've certainly not succeeded. So on one hand they have got better, but one of the main ways they evaluate them getting better are benchmarks that are adjusted specifically for large language models because you can't just have them do tasks. They've got better at that. They found some tasks they can have them do on. But even them, like 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 Vectara hallucination leaderboard, shows that hallucination rates on simple summarization tasks have plummeted from around 21.8% four years ago down to 0.7% roughly on today's top frontier models like Gemini and ChatGPT. Again, the point of nuance here is that these are on simple tasks, which is kind of what I've experienced. I've experienced that on day-to-day things that it hallucinates less. Again, rate of improvement thinking. So if I just imagine the trajectory to continue, there is going to become a time where hallucinations become rarer than they are today, increasingly. And also what I would say is when I think about other technologies, there's two more points. Other technologies at their inception, when they first came to the world like the internet, also had technical difficulties. I remember growing up with 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, this is crap. This is technology crap. All the...
I don't know, mate. I loved it.
Yeah, [laughter] 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, right? Whereas that's not actually the alternative in the working world. Like if I wanted to do, let's say, a simple writing task, I should compare AI to my alternative way of doing that simple writing task, which is both measured in my time, right, and my ability to hallucinate as a person who doesn't know everything, or if I'm hiring someone, an intern who might also be prone to hallucination or have gaps in their knowledge. So it's not actually like we're comparing — we should compare AI to perfection. It's AI to the other alternatives. And if someone hallucinates 0.7% of the time, but knows way more and is faster, maybe on a net basis, that's a good trade. Maybe I should use AI.
So, let's start with an example. Someone I love dearly, Matt Hughes, my editor, lives in 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 support, and he's a great editor, but he's also someone who gets into the guts of it and has the experiences of it. He's a decorated tech journalist and on top of that a wonderful, loving being with empathy and joy in his heart for the stuff he loves and absolute [__] venom for the people he hates. That's — I can't get that from a large language model. But on top of that, I 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? And on the thing is, are you really paying an intern for something basic? Are you really going to them and saying, 'Yeah, can you look up what the date is?' No, you're doing that on Google. Whatever the task is, you are trying to also train an intern. The point of an intern is to train them and turn them in, take them out of Pinocchio status, but it's also an intern learns. An intern gets context, an intern learns your habits.
AI gets context and learns.
No, it doesn't. It doesn't learn. I mean, it doesn't. The way it learns is you create a giant CLAUDE.md file that it sometimes doesn't read, sometimes does read. You create a harness. You put — it's like Pee-wee's breakfast machine from Pee-wee's Playhouse. You have to do all these contortions to mitigate the hallucinations. And even then at the end, how much effort have you put in?
But so, okay, this is an extreme simplified example. If I went on my Claude now and said, 'What's my dog's name?'
It would know my dog's name.
Jesus Christ. This company raised 95...
I'm using an extreme simplified example to show that it can remember things from the past. Obviously, it knows much more complex things as well, but I just use that as an example. So, we accept the fact that it does have memory of the past.
It has files it can access that have stuff on it, but that's not the same as memory. And it's also just — okay, so it remembers your dog's name. It might remember your habits. It might be able to read things you've said before. Does it know your moods? Does it know what's going on in the world around it? Does it have good days and bad days? Is it there for you? Because it's just a [__] text machine. And the thing is, the intern example. An intern is something that can grow. It's something that you invest in. That's not something you do through feeding files and text to it. The way that we store memories ourselves, the way in which we accrue experiences is a mile 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, right? 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. 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. And we get, you know, because I've heard this debate about like, can AI be creative, right? I think like the way to answer that question is like, it's about the output. When I ask it to do a creative thing, does it give me the answer? Not is the process the same as a human process, because actually, no, who cares? What people care about, they pay for the outcome, the product.
I actually disagree about the process because Matt Hughes, for example,
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 [__] these — Jesus Christ, they can't do...' like just the misanthropy of just the horrible, cynical people of asset managers like Blackstone, just learning about them and being like, 'It can't be this,' and having a back and forth with him that is fundamentally different because we were both learning together, and the learning process was as much about creating the output as the output itself. When you learn something, you're not creating the average, which really is what these things do, of the documents it could find. You're not getting particularly novel outputs. If I needed a generic slop output, sure, but I've 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 generic reports, the same, 'Oh, we noticed this analysis,' things that you can find on any kind of AI slop out there.
What you described to me there, what I heard anyway, is there's two points of value you're getting from your time with Matt. I mean, there's many more, but you said you're learning, and then you're getting this blog edited — 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, what if the revenue number was wrong?
Once you should have defined deterministic ways of knowing those numbers, you should not rely on them. Even with the terminal running BQL, which I trust, I will double, triple, treble check everything just to be sure, partly because also the process of learning for me — I don't want just a report I go like that. I want something that I fully understand and also understand the context around it. I don't think that 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, the more likely there are things to be wrong with it. If you are with Matt Hughes, for example, I can trust he's got it right. I can trust he understood, and I can trust that I can have a back and forth with him that will inform me if I've missed something. I can read the stuff that he's read and actually trust him because there's a big trust part as well.
What is the basis of your trust in Matt? Could it be his historical performance?
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. 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 leaderboard, it continually delivers for people, and that's why we've seen the fast...
I mean, is that what that board says?
Well, it's saying like, is it getting it wrong? Is it hallucinating?
Simple tasks. How are those defined?
That's the thing though, because this is actually a very illustrative thing of the AI industry. They are the whataboutist masters. They have like, 'Well, look, we've got this benchmark that says we're good at this, and look, the number's higher.' What's the number mean? No. What does that mean? And I'm not using this as a critic against you. It's when you can't give a direct answer, you give a side answer. When you as the LLM industry want to prove your worth, you can't just be like, 'Just use the product.' When the first iPhone came out, I was at Penn State at the time. Oh, I felt like the apes at the beginning of 2001. [__] official voicemail. It was immediate. And I showed it to tech friends. I showed it to the most normal people in the world. And everyone was like, 'Holy [__], this is...' They were on RAZRs. 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...
It was. And I also showed it to a bunch of people because I'm aware that I had bias when I just love gadgets.
But I remember the famous Steve Ballmer, who was the CEO of Microsoft, interview where he was told about the iPhone and he bursts out laughing. [laughter] '$500 fully subsidized with a plan. I said that is the most expensive phone in the world and it doesn't appeal to business customers because it doesn't have a keyboard, which makes it not a very good email machine. You can get a Motorola Q phone now for $99. It's a very capable machine. It'll do music. It'll do internet. It'll do email. It'll do instant messaging. So, I kind of look at that and I say, well, I like our strategy. I like it a lot.' He burst out laughing, mocking it because it was so disruptive. It was way more expensive and it was way different. No keyboard.
Well, phones used to be insanely expensive and the carriers would cover them, but you had to sign a long contract. You were still spending 500 bucks. But the thing I'm getting at is you didn't have to explain to someone why — perhaps you'd have to get past the cost part, but you could just be like, 'Look how good this is.' And then once the app — the iPhone 3G with the App Store — people were like, 'Oh [__], this could actually change things.' Mobile web, even though it was a monstrosity, it was so bad at first. Even then, you...
could get your emails and you could just look at them. BlackBerries were also expensive and were still actually kind of cool, but the way they worked was not like consumer software. They didn't have the classic GUI. iPhones felt like that. It felt like a cell phone designed even like a computer. It was obvious. It was obvious from the beginning. Everyone I was dating a girl in the center of Pennsylvania at the time, and everyone I showed it to was like, "Wow, this is incredible." That to me is the obvious thing with AI to this day when you're like, "Okay, why is it so amazing?" People still dither. People are still like, "Yeah, you can't run a business fully with it without this weird system of pulleys and levers and such."
But how come then when you look at the stats around ChatGPT's growth, 100 million active users in just the first 60 days after launching. For comparison, TikTok took 9 months. Instagram took 2.5 years. And the internet itself, the worldwide web, took roughly 7 years to reach that scale. Over 60% of US adults are integrated into AI tools in their daily and regular routines within 3 years of the launch, reaching 40% of the population. And that same milestone took the internet 5 years and personal computers nearly 12.
So like this is the, I think this is the part that's giving me dissonance is like what I showed my fiance ChatGPT. Okay. It didn't really work. But as a sole entrepreneur whose English isn't her first language, who has to write lots of text, lots of copy and generate lots of images, and was paying a graphic designer to help her make certain images that she, you know, couldn't make herself because she doesn't have the skills, she would describe it as being transformative for her business.
What I'm hearing from you is that it's not transformative and there's no value in it for people. But if she was sat here now, she'd say it's transformative. Would she pay the per million token rate? Would you pay the actual rate? Because that's the thing. If this was sold at its honest cost, yeah, I would actually, if 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 would be the honest cost if they weren't subsidized?
The actual per million token cost? The actual API cost they should charge.
Do you know how much that is relative to...
Oh god, it depends. It depends on the model. But there's actually kind of a point I want to make about the thing you said with the internet earlier. So when I first got on the internet, 33.4 kilobit per second modem, even back then I was like, if this was faster. And that was like immediate, just like if this was faster, because it was slow. You'd go on like Happy Puppy or something. Download would take all bloody day waiting for Shareware to download. Immediately like, if I could do this faster it would be better. And even back then I'm like, man, you could probably do video camera stuff with this stuff that eventually happened. And actually there's this guy called Jim Covello from Goldman Sachs in a report he did in 2024 that was, too much spend for not enough return, paraphrasing there. 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's 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. We're not sure if we can fully not trust it. Not in the I'm scared way. I mean, just like we're not sure that this is going to be a disruptive world-changing thing. It has potential, but we're going to go slow. It's really expensive. This is an R&D effort. We're not going to expose consumers to it." And actually being like, calling them like, I don't know, language models and no, no 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 to 2022, "This is the best thing since sliced bread. This is changing everything. This is going to do all your work. This is going to take your job. You're going to talk to Bing and it's going to tell you to leave your wife." All of these crazy things. And what's funny is when the writer, uh, Kevin Roose, I think it was, um, he was speaking to Kevin Scott, the CTO of Microsoft, about it. And Kevin Scott goes, "You know, I'm just glad we're having this conversation." Instead of being like, "Settle down, Beavis. It's a website. Hey, the website told you something. It's just LLMs." They talked it up. And that's because everyone is talking about what they wish this was rather than talking about what it can actually do. This makes it scary to people deliberately. So, it makes it environmentally destructive. Look at the gas turbines poisoning black neighborhoods. I think it's in Louisiana. It's one of Musk's data centers. Look at the incredible energy draws. It is raising power bills. And also, it is creating inflation across all consumer electronics because of the massive RAM.
Do you know what's interesting? I almost feel like so much of what you're saying is true and also it can be true that this technology is going to profoundly change the world. 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.
Which I found really funny, especially the name, The Rot Economy, and you talked about the rot-com bubble.
Yes. Talking about how AI is of 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, right? And so I do, I kind of, and that's what bubbles do, right? Huge hype, 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 that the internet's impact on the economy has been no greater than the fax machine." In 1995, astrophysicist Clifford Stoll famously, I wrote about this in my book, wrote famously in Newsweek, "Do our computer pundits lack all common sense? The truth is, no online database will replace your daily newspaper. No CD-ROM can take the place of a competent teacher. Commerce and businesses will shift from offices and malls to networks and modems. Baloney. So, how come my local mall does a roaring business and the cyber mall gets zero business?" And then I'll give you one more from Krueger, who is the award-winning economist. He said, "The growth of the internet will slow drastically as it becomes apparent most people have nothing to say to each other."
That may actually be the worst one of those predictions. Like, hang around any bar in middle America. Honestly, the best conversation, but it's just all the same. I actually, so Clifford Stoll, actually his piece was interesting because there were some boner points in it, but he made points about how like an overwhelming amount of bad information out there is bad for society. He's completely right saying how online education would not be a great replacement for regular education. I think we've seen that. But there is an economic difference that's vastly, it's just completely different. So the dot-com bubble was actually two bubbles. There was the website bubble which was just trash on trash on trash. It was just like, I think what was it? Pets.com bought a e-greeting card company for like a billion dollars. It was insane crap happening that was so small. The big thing that people are thinking about is the dark fiber.
Dark fiber, dark fiber was all of the wires 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 just simplifying to bring that to people's houses. And there was the assumption that well, that would all get lit up and people would want it immediately. Didn't really happen. Now the post-dot-com bubble thing people say is, well, but after that there was demand from the...
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.
Predominantly subsidized and most people experience it and yet are not paying the real cost.
On top of that we already have all of the possible marketing in the world. We have the largest most disingenuous marketing campaign in the history of man pushing this up the hill. We have the apex predator of cloud software, Microsoft. They can only get single-digit billions from selling AI software. And Christ almighty, outside of OpenAI and Anthropic, we barely get $22 billion. And the thing is, $22 billion is a large amount to you and me. It's not a large amount of money when you've spent a trillion-plus dollars, when you have Anthropic and OpenAI with $1.1 trillion worth of cloud commitments. And on top of that, how does this turn into a post-dot-com bubble thing? A data center built today is going to be as expensive to run in 2050 as it is today. Unless there's some breakthrough in electricity, but again that's not happening with AI. AI is not doing that unless there's some breakthrough in GPU technology. But we already have Broadcom, Nvidia, Etched, we have every major chip company, ARM, trying to do something about this. And no one seems to magically be able to make this profitable or indeed even less costly. Even Nvidia with Vera Rubin, their more expensive new GPU system. Even then they're like, "Yeah, 10x more efficient. It's more dollars per megawatt." They're all coy about it. They don't just say, "Yeah, we worked with OpenAI and Anthropic and we found it reduced their cost by 50%." Easiest thing in the world if it was true. And that's because it's not happening. And this isn't a case where...
So are you saying there's not going to be the demand for, let's say, let's, you know, there's different types of AI, generative AI, we can...
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, they'll go, um, um, sir, you forgot to give us homework and also AI, it's working on curing cancer. When it's just like, no, that's not LLM. Stop giving them credit. The similarity though is they all need GPUs, all these...
And that's the funny thing. All those data centers that we're building, all of them are for just generative AI. They're not for all of the other stuff. They're not for the cool stuff. AI has been around for a long time. Google, a lot of the good stuff that comes out of Google from the search side is AI, but pre-generative.
How would you run the type of AI that sits in a robot, let's say one of the Optimus robots, if you didn't have a GPU? So Matic, Matic has this cleaning robot for example. That thing has not got a little GPU in it. What it has, and may indeed have used some GPUs, but nowhere near as many as they need for generative AI, to run the training data into it so it's able to clean a house. But when the little bugger's going around cleaning my floor, Turdsley I call him, goes around mopping my floor, it's not like burning money the whole time. But when it comes to these massive amount of data centers, Sightline Climate said in February there's 190 gigawatts of data centers in planning, don't know about under construction, that works out at about 12 million megawatts, that's 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? Where most of the demand is coming from? Amazon feeding money to OpenAI or Anthropic, Microsoft feeding money to OpenAI and Anthropic, Google feeding money to OpenAI and Anthropic. Well, Google hasn't fed it to OpenAI yet, but they're a pretty big customer, billions of dollars. The conceit is that we are building these effigies to capitalism, these giant GPU data centers, and people are being told, well, it's for AI, you know, the thing that's done all this other stuff that's unrelated. Or the worst thing I've seen is like, oh, you don't like, you like online banking. Well, you do like data centers. There's a big difference between a data center for regular non-GPU compute for standing up a server, a content delivery system like Akamai or something that brings the website to you, or how Meta runs Facebook. That is not the same. It takes way less power, mostly CPU driven, compared to these giant GPU data centers that are for one thing, one thing only.
But I was doing the research and looking at some of these notes here. It does say that for tougher types of AI systems designed to solve concrete physics, biology, and spatial problems, they require some of the most intense data center infrastructure on the planet.
AI systems like DeepMind's AlphaFold, the protein folding company, used for genomic sequencing and climate forecasting, etc., run on high-performance computing clusters. These require immense precision and continuous heavy computing data centers.
Yeah. Training the brains for self-driving cars requires billions of miles of simulated physics environments. The AI isn't generating text. It's learning to navigate 3D spaces and gravity and relies on data centers, right? And the thing is those data centers, they might have GPUs in them. We had GPUs used for this HPC, the high-performance computing, before generative AI. And yeah, that's how AI has been trained before. That's how Tesla did it. I believe they've had their own data centers when it comes to training the autopilot system, for better or for worse. That's how we've done it before. Again, that is not why we're building these data centers. These data centers are being built to sell to AI, generative AI companies to either train systems or run inference. These things are being built in this brainless way where it's just, well, actually maybe this is a good way of illustrating the conceit because everyone saw Google, Microsoft, Amazon and Meta give Nvidia over, call it, 800 something billion dollars, because everyone saw that, they went, well, they wouldn't do that for no reason. They went, we got to build more of these things. There must be all this demand. Even though the demand, 70% or more of all that demand comes from these two companies who are funded by these three companies, and that's the funny thing. The reason that they don't want to break out their AI revenues is because it will 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 buy a bunch of GPUs and they said, "I want in on that." They must have diverse customers, right? They wouldn't just have two unprofitable failsons that they're propping up with. Christ, they've raised $27 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? 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...
Well, I'll tell the story of a real... a guy called Prabhakar Raghavan. Previously, one of the heads of ads at Google. In 2019, Google called something called a code yellow, which is when they said, "We've got a problem." And it was material weakness in query numbers, which means the amount of times people were searching on Google Search. A guy called Ben Gomes, internal at Google, then the head of Google Search, says, wait a minute, to increase this number, we're 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 Thakur was another engineer, was saying, yeah, can we please tell Sundar this because this doesn't seem good. We can't just increase the amount of queries, that would just mean that people would have to search more, which would make the product worse.
But it would make them more money, you're saying?
Yes, because you'd show them more ads. So if you're spending more time on Google because Google's worse...
But is this linked to AI doing code?
I'll get there. So this is the problem, is that this guy called Prabhakar Raghavan, who was the head of ads at the time, was pushing, pushing and saying, no, we need to make more queries happening to make it happen. Nick Fox, who was there as well, I believe is actually taking over Google Search, got to make him go up, this is our new reality. Sometime in early 2020, Prabhakar Raghavan takes over Google Search from then. And this is, this is what I believe, can't prove it, if you go and look around the various SEO sites, Search Engine Journal and the various forums, Google stripped back a lot of the suppression of spammy sites so that people would be on Google more. And then over the course of time, Google wanted to create more queries and Google Search became much worse. It's why people always do like plus Reddit or from Reddit or what have you. It's because the actual underlying search results of Google had got worse. And then generative AI came along and Prabhakar got put to run part of Gemini. And Google also was having trouble getting people back on Google. And what did they think they'd do? Well, everyone's talking about this AI thing. We'll just put it right at the top so people have to stay at Google. And actually, they'll use it more because instead of searching websites and doing that annoying thing where they click away from Google, they'll just only use Google. Instead of generating answers, by which I mean giving you search results you click through, now Google is the answer. Is it right? God knows. It might tell you to eat rocks, might tell you to eat poisonous mushrooms. Maybe it'll give you a little few links you could click through. But the ideal situation was that AI was the ultimate form of Google's evil, which was...
But I'm saying here, I'm saying here, that's not the fact that coders could code on Google that's made Google worse. That's human decisions have made it worse.
Yes. And then there's the instability of Google's platform, which is actually, I should have probably led with that, a problem across the whole tech industry.
Okay. So you're saying that the, you're saying Google is going down more.
Yes. Google is less stable. Google Docs is a bugfest right now and has been for a while. Google Sheets, same deal. And the thing is, you're right, I'm being a little unfair. This is everyone. It's the same with Microsoft. It's the same with Amazon. It's the same across.
How do we quantify that outside of anecdotes? Like, is there a way to...
You're right. I mean, GitHub downtime is the best example. Amazon Web Services went down, I think, two or three times this year because of AI tools. And honestly, you're right, it is kind of hard to quantify outside of anecdotes. But I challenge anyone listening to this, go and use a website these days and tell me how well it works. Tell me how buggy it is. Tell me how many problems. Even with my iPhone, the supposed best UX in town. Even the iPhone is a flipping mess these days.
Okay, so the research says the short answer is yes. Tech downtime and software outages have demonstrably increased over the last few years and industry data points directly to the explosion of AI-assisted coding as a primary culprit. The problem is hitting the tech industry from two entirely different directions. The code itself is getting buggier and the sheer volume of AI activity is literally crashing the underlying infrastructure.
Yeah, that's because GitHub, people are just writing a bunch of code, pushing it, and thus there's just more code on there.
Yeah, it's a real mess as well because open source has had this problem as well because it's well-meaning people. They're like, I learned a bit of code with an LLM. I'm going to go out and do some stuff. I'm going to make this project better. And these people barely understand what they're shipping. Or maybe they understand a bit of code and they say, "Oh, Dunning-Kruger, this... I'm going to... I 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.
Because we're going, okay, look, I 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. And that's human nature, is to get sort of, to take shortcuts, to spend less energy on an activity if you can, right? But the AI's still making the mistake and we're still making all the promises of AI. That's the thing. This thing is meant to be this autonomous... you say it can't be perfect. I don't know. Based on what Sam Altman has been saying for the last few years, clammy Sam Altman's been promising the world, saying this will replace software engineers. Dario Amodei himself has been saying, oh yeah, 50% of white-collar labor is going to go away in the next few years. These people are promising the world. Again, if they were saying it would be smaller and they were like, yeah, it does have issues and we must be... none of this, oh, what if it wakes up and it's super powerful. Just like, yeah, it's probabilistic. It's going to make mistakes and if you don't know what you're doing, you don't really know what you're looking at. You're going to miss those mistakes and it's going to get multiplicatively worse as you go when you don't know what you're doing. So, yeah, human nature is part of it, but so is the marketing. So are the promises.
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My car that drives itself, that is AI technology.
I sat here with Dara from Uber and he was saying that I think in a couple of years' time we won't need drivers for Uber because the cars will drive themselves, 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.
Yes, or they're guessing in a way that's very good for them. Think about it from the perspective of Microsoft. Satya Nadella, he's not going to be like, yeah, we don't know if this is going to work, mate. Of course he's going to talk his book and he's going to say, yeah, this is going to replace all workers because 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. It'll make him more powerful because the things aren't catching up. Dara 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 socioeconomic problems that will come from it. I think there are actual real problems that will emerge. And also...
What kind of problems? Well, I mean, socioeconomically there are, like you said, one of the largest employment centers in the world. I mean, just the economics of cabs would fall apart, but again, we are nowhere, nowhere, nowhere near that. We're not even close. Waymo has had to do the smallest rollouts and the most controlled things because the problem with pretty much every AI system, but especially driving, is not the getting 95% of the way. It's those edge cases. It's raining, which is a big problem for them in San Francisco. It's a kid runs across the road but they're wearing a high-viz thing, doesn't even notice it's a child.
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 than human drivers in some ways, but they might also... I was in Vegas the other day and I was in a hotel and I watched a bunch of Zoox cars just get stuck.
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 happen outside of a hotel when I got out of a Waymo in San Francisco. Just stopped at the... and then a bunch of cars and another Waymo got stuck behind it. And these are kind of, these are...
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 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 and it doesn't seem like they're rushing it, which is good, and they're not promising the world.
I do agree. Listen, I'm a big fan of, a big fan of taxi drivers generally, in part because I spend a lot of time in taxis and I think I'm not just getting in there because I want to get from A to B. I'm getting in there for lots of other reasons.
Yeah. However, when I look at the stats around what is more dangerous, driving myself or having an autonomous vehicle drive me, there's a 68% lower overall crash involvement rate when you're in an autonomous vehicle. Autonomous vehicles experience roughly 2.1 police-reported crashes per million miles compared to humans that are at roughly 4.68 per million miles. So, a 55% reduction when you get in an autonomous vehicle. And autonomous vehicles show an 80 to 81% reduction in crashes resulting in injuries versus human drivers.
So you're 85% less likely to be involved in a single-vehicle crash like hitting a wall or a tree if you're in an autonomous vehicle.
Versus being driven by...
I agree. But so it's safer.
Also, that data, what's the sample size of human drivers? I mean, we've got many, many, many, many, many, many more years of drivers and many, many, many more years of accidents. And also, man, does that not have anything to do with generative AI? If we were just talking about that, we'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 is 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, well the profession, right, I should say some of the skills within the profession will be relegated to AI to do.
Here's the thing. Lawyers, for example, great example, always in... legal partners talking about AI, never the associates. The associates are the ones that go out and find the precedent, they're the ones that go and do the grunt work, they're the ones who are pulling motions half the time. The partner is the one, it might be the litigant, it may be the client-facing. But the ones that are actually doing the day-to-day work, I'm not hearing from them. I'm not hearing associates being like, "This is awesome." I'm hearing a bunch of well-paid people that have sat on ChatGPT and gone, "Yeah, yeah, I'm the greatest lawyer ever." They're not the ones that I want to hear from, the actual workers. White-collar labor disruption is not happening. OpenAI had a study that came out, I think like a week ago, that said there was no connection between spending on AI tokens and revenue per employee. Like, this is OpenAI, and that's the thing.
What does that mean? Could you explain that to me?
As in the more tokens you spend has no correlation at all with the amount of money you make. It's the second report they've put out. The other one was like hallucinations are mathematically guaranteed, kind of almost is the one thing I respect about that company, that occasionally they just put out a study. It's like, yeah, kind of sucks. But the people that are having their lives disrupted work-wise are art directors. It's people, art directors, transcribers, translators, who have bosses that don't care about the output. It's what they consider cheap work. And the problem is those people would have automated your work away anyway. They would have sold it. They would have taken the cheapest offer. They would have sold it to the global South. They would have taken the shittiest option they could. That is something that AI is doing. And again, those people are not paying the actual cost of AI. They're using a subscription. The actual white-collar labor force might have some things that are slightly changing, but there is no evidence of productivity gains. In fact, if there were, they would be screaming it from the rooftops. There was an Oxford Economics study last year where it's like, oh, young people are finding less jobs because of AI. We actually read the study, which multiple journalists did not. It was a single line that said, yeah, we saw some correlation. Didn't give a number. Didn't actually say what the correlation was. We are so conditioned to believe that the rich and powerful know what they're doing that we internalize these narratives about, like, well, previous booms lost a lot of money. Well, technology takes time to do stuff, and they are intentionally playing on those mythologies. They are playing on these knowing that journalists, analysts, investors will believe them. And this is partly because our realities are defined by stock prices. Because the stock prices of these companies went up, we're like, "Ah, 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, um, they'll get better.
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...
I know. I get what you mean. What they are fundamentally misleading people about is how possible it is. How many actual signs they have, because they don't have the signs. If they had the signs, as in the signs of this getting cheaper, as in the signs of this being able to autonomously do work without the 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 there really are some... I'm sorry, there are some people on Twitter, because the other thing about this is this is really unique to the AI industry. I've never seen it any other industry outside of maybe like sports teams. The attachment that some people online have to these companies, if you dare, dare to criticize Anthropic, it's almost this religious attachment. Good example was this week, Bloomberg reported that OpenAI was on track to hit $40 billion in annualized revenue. Month times 12, four weeks times 13, we don't know. They don't define it. I saw multiple people online going, actually it's 60 billion. It's actually 60 billion. I heard from someone... It is like a cult, and it's a cult of software driven around growth. And this idea that by backing the right horse you will have some grand thing, and OpenAI in particular, in particular Mr. Altman, they have been fermenting this, that Thibault as well, the Thibault, one of the guys at OpenAI, they ferment this thing online, they build this kind of parasocial relationship with both the large language model themselves and the companies, and one's allegiance to the companies is so important, it's truly vile. If only these people gave a... about, I don't know, Medicare for all or poverty or 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.
Because you know the AI doomers that have come here and told, you know, some of the original founding fathers of AI like Geoffrey Hinton have told me that what they're building is highly, highly dangerous and that it will be fundamentally disruptive to society. And it's interesting because some of the CEOs who you've mentioned, their historical narrative was also, by the way, this is really dangerous and there is a significant chance it could... we could... up the planet. And what we've seen is this slow pivot away from it because now they're getting booed and they're being attacked.
It sounds like it's not going to change anything and it's no, there's just not... it's now, it's not dangerous at all. But that's the funny thing.
And that's why I'm saying like, you're, you're now... I actually think there might be a couple PR people at these big AI companies thinking, thank God for Ed, some of it, because you're like, you're saying actually don't worry, everything's going to be fine, it's not going to take your job, it's not going to disrupt the economy, it's just a fad, there's no technology.
And I think they don't think that. Here's the thing, I think Altman and Amodei are some of the most deeply corrupt and cynical people in the world. I don't... of course they were going to say from the... it was early 2023, Altman said we're a little bit scared about what we're creating. Oh, shut up. I'm just... I hear that and I feel so frustrated because I've met so many of these rich liars, these people. And you know why he wants to say that? So you'll invest in his company and buy the software. So you'll be scared that if you don't use AI today, you'll be left behind in the future, which is their continual narrative that if you don't get on the train today, then you'll be left behind. By the way, every single scam and con starts with rushing you. Every single trick in history begins with saying you must do this now. And the best piece of advice I ever got was if anyone tries to rush you and it's not literally a mortal thing like you are bleeding or on fire or the house is on fire, slow down. And yet all of these companies saying it's so scary. And now they're talking about slowdowns. But you ever noticed that Amodei and Altman, they say, "Oh, maybe we should slow down progress." And then they don't. Right now Altman's saying, "Oh, we slowed down progress because we're so delayed." No, they're out of compute. Now they're doing it. I can guarantee you, by the way, the PR people do not like me. I know for... I know I don't think OpenAI's PR people are super fond of me.
But I bet there's elements of what you're saying because you're calming people. You 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...
Can I... so the timeline, and I sit here and what I do is I log their quotes over time and I read them out from 2015 to 2026 and the change you see is them going from there could be extinction, that's the narrative, the early narrative. Elon said it himself, he says it's the single most dangerous thing in 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 is intelligence for everyone. It's suddenly... and all the... and whenever Dario comes out and says, "By the way, it's really dangerous," they attack Dario.
They're like, "Dario, shut the... up."
Honestly, I've been saying, "Dario, shut the... up" for years. But it's... but the thing is, I get your point where it's like, I don't think they've changed to calm the public down so much as they're desperate to not get regulated, which is laughable. We don't regulate tech. We don't regulate... America doesn't regulate... We are in the... We are still trapped in the hands of Milton Friedman, Margaret Thatcher, and Ronald Reagan. We're still stuck in the neoliberalistic hellscape, which is growth at all cost, free market capitalism. So, no, no one's regulating. The regulation of these companies should have been, I don't know, breaking up. Put these bastards to the side. Break up these... for sure. We shouldn't have companies this big. It makes things worse.
But these technologies are dangerous.
I mean, they're dangerous, but not in the ways they've been warning about. Let's... if we think about cyber hacking, right? And just to be clear, those cyber hacking things that happened were not a result of... they were like, break out of the sandbox, and then they set the sandbox up wrong. They set up the server they were on wrong. But I mean, you know, advanced AI models could very easily, 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.
In at scale and arguably at a higher intelligence and faster and wider than humans, a human hacker could theoretically. So that's dangerous.
Well, here's the funny thing. We don't know how much compute was spent to do the Hugging Face attack, the OpenAI 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 already in the wrong hands. Mark Zuckerberg, Sam Altman, Dario Amodei. The wrong hands are the hands of those who are running these companies. We should not be training these models to do these things. I don't know why the... we're doing it other than they've...
run out of other things they can train on. There's a ton. And the fact that they can do it, it's kind of interesting. But you do would you agree that it's an intelligence and I'll call it that. You know, you might disagree with that terminology, but an intelligence that can go out onto the internet and click around and take actions is inherently there's risks associated with that.
Well, the second part I agree with the risks. We've had people running automated scripts, hacking scripts for a while. We've had hackers doing that for years and years and years. This is brute forcing it with a bunch of compute and yet it is dangerous. These companies are doing something dangerous. That is not what Geoffrey Hinton and others have been warning about. They've been saying, "Oh, these things could destroy society. They could manipulate people." When you actually look at the underlying things, not so much. Geoffrey 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 cyber security side. I agree this is dangerous. These people should not have access to so much compute. They clearly don't know what to do with it. There's a really easy way of dealing with this. It's not letting them use so much compute. It's regulating that part out of existence.
What if the Chinese do it? The Chinese 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, to use an annoying Sam Altman term. We let this happen because we let these companies be unregulated and use as much compute as they want. We had these enablers allow them to burn as much compute as they want. And also, for all of these dire warnings about AI dangers, that one seems...
You still haven't done anything?
Okay, we're going to play a game, Ed.
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.
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. So, let's do this.
What does it say? And what's your one?
It says the AI industry is creating enormous economic growth.
No, it's not. It's nowhere in the data.
Okay. Like it's just... May I do a second sentence?
Go ahead.
Pretty much all of the economics is either Nvidia feeding money to companies like CoreWeave, or these three companies feeding money to these ones to spend it with them.
Okay. And what evidence do you have that it's not causing economic growth? Just to be clear, other than the spend on semiconductors, so the speculative investment in GPUs and data center infrastructure, that's happening.
But as far as spend on AI goes, barely cracking a hundred billion. And most of that is just these two running their services and paying these three companies, Oracle, Core, and others.
But a hundred billion is a lot of money for a relatively new technology.
Not when you've spent $300 billion in equity funding, and if we're going with just these three, I think $600 billion in capital expenditures.
Yeah, I get that. That means it's not profitable. But the hundred billion is an expression of consumer demand.
When the compute is mostly driven by subscriptions that are subsidized, no, it's not. When you're giving someone $20 or $40 for a dollar, they're going to use it more. If this was all on a per million token basis, we'd be having a different conversation.
Okay, fair. Fine. Cool. Next one.
The United States needs to spend trillions to beat China in the AI race. Let's see. What AI race? That's actually my point. 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 GPUs. Kakashi and Jario, two amazing analysts I love, they've been on this for years. It's like China's already had Nvidia GPUs that they're not meant to have for years. But also, to do what? They already got the 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. 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.
Robotics is not what we're talking about. Robotics is a very different thing. And even then,
robotics will be powered by AI.
I mean, yes, but there are tons of different kinds of AI. We're talking explicitly about generative AI. And that's what I, for the MythBusters piece, that was definitely about generative AI.
Okay. But what about robotics? Like the thing is the Optimus robot that Elon's working on at Tesla.
The one where even in the demo of the hand, they had to have a guy controlling it, it wasn't doing it autonomously. Here's the thing. If they can beat all these challenges, yeah, robotics would be really cool. I don't know how long that's... that's one I'd actually be willing to believe in a couple decades.
Have you seen them Chinese robots? I know you've seen them.
The 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.
Robots are cool. I'm not going to pretend I don't think robots are cool. I wish they were building robots and actually doing cool stuff. I wish the tech industry still made fun stuff and interesting stuff. Instead, we get these large language models.
But with AI plus robotics, you know, I was in San Francisco and I went to this massive incubator there. And when I'd gone there three years earlier, it was all software startups. And when I went back three years later, it was all these robot startups. And I remember saying to the founder of the incubator, I was like, "Why is everything robots now?" There was this one robot where it was just the arm and it had a frying pan on it.
And the whole thing is it cooks for you.
So he was showing me it cooking whatever, and he goes, "Well, you know the arm." He goes, "The hardware part, the physical parts, that's always been fairly cheap."
He goes, "The expensive part was the intelligence, and now that's come down to pennies." So what you're seeing is this explosion in the robotics industry because robotics is a function of intelligence plus hardware. We've always had the...
And a ton of data though as well, and the data is very expensive.
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. And also a lot of human jobs that you might think of, like, I don't know, a dishwashing robot, for example.
Yeah. Some guy at a restaurant isn't paying 10, 20 grand for a robot to replace the job that they're already not paying enough for.
The point is, yeah, it could if you can replace the jobs. That is not what we're talking about with this.
Yeah. I just ask these questions not because I'm trying to be like... I actually, I'm trying to form my own opinion on these things. And I do think, you know, as it's written there, it says AI will replace all human jobs. Obviously not. Obviously that's... But I'm 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.
Right. The thing is, with this particular thing that I know that this is from, it's a specific blog I wrote. I was explicitly talking about generative AI, though. I was explicitly talking about people when they say this, they are referring to that.
So you're not talking about agentic AI, which...
Agentic AI is LLMs. Agentic AI is just a fancy way of saying an LLM talking to another LLM with a harness on top. That is still LLM. Agentic AI is one of the 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 LLM. It's still an LLM talking to other LLMs, taking screenshots and putting them in LLMs and stuff.
Oh god. Yeah.
Okay, but you know, I could make the case that... I'm just thinking about my personal usage. I definitely use agents to do things that I would have previously asked people to do. It's not to say that I didn't... I still don't hire, because we're hiring like crazy.
And I still... in that particular function, I'm thinking about like the chief of staff role. So my chief of staff would have triaged all of my inboxes previously and put them somewhere and told me about them, or maybe once upon a time shown me a piece of paper back in the day. I guess now my chief of staff is no longer doing that job.
You still have a chief of staff though.
This is what I'm saying. They're doing other things, right?
But the thing is, again, what you were describing is fairly basic automation. I don't know what the tasks are. Triage email. They didn't spend a trillion dollars on triaging email. Like that's the promise. If they'd spent $10 billion and this was much smaller, and you'd go, "Cool software. Yay." A lot of the things that people are impressed with, like script stuff as well, it's just LLMs doing Python. You should be impressed by Python. Python is incredible. You can scrape websites. You can download... It's awesome. But the point I'm making is none of this would be anywhere near as much of a problem if they didn't ask for all of the attention, all of the money, and promise the world. It's their promises that are the problem, and the journalists who went along with it, and the analysts, and the Twitter people who went along with this saying that this would change everything and replace everything and leaving the realm of reality.
Is there any technological innovation through history that was really, really game-changing where that didn't happen?
I mean, people overpromised that.
I mean, they overpromised on the businesses, but I've read through a great many pieces about the early internet. A lot of people were excited but hesitant. They were worried that there was not enough demand, but they were still like, oh yeah, this could have potential ramifications if it happened. People were not super negative about the internet. A lot of the skeptics were saying we're worried about an overload of bad information. Look at where we are. A lot of people were worried about the social consequences of everyone talking online, which they were correct about. With the economic things, they were specifically talking about like the globe, which I think made hundreds of thousands of dollars and had like a, I think, a billion dollar market cap, but they were talking.
Yeah, there was massive hype in the dot-com era.
I read a lot of those stories. The hype was nowhere near it. You didn't have articles everywhere that were saying if you don't get online, you'll be left behind. You didn't have professional consequences. Nick Searle mentioned his blog earlier. He described this thing, global AI is viscerating global decision-making, where he said that you have businesses you work at where if you don't say that you're more productive with AI, whether or not it's true is irrelevant, you have professional consequences, you can get fired. There are people having to AI-wash their jobs by saying AI did it, otherwise their bosses who don't do 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, was it, decentralization of media in general has caused this as well, and also the fact that day trading, there's so many different things that are different, it's crazy.
I do think AI is different from the internet in part if you just measured it on the speed of adoption, especially if we just think about generative AI and AI...
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. And I do agree that there's way more hype, and we again going back to this point that we're clustering AI in this big category of lots of different things.
There's generative AI. There's like real-world AI.
Generative AI is explicitly what I'm talking about here. When bosses are saying you need to use AI, they're not saying I need you to go and buy a Unitree robot. They're saying use LLMs so that I... And that's the thing. They have this theory, the era of the business idiot, where it's like we are ruled by people that don't do work because nobody who actually does a bunch of work, who really is productive, is harassing someone who works for them for not being productive enough.
They don't have the time. They're doing work.
Someone who is sitting there with the ingratiation machine that's telling them that every beautiful idea out of their messy little skull is amazing. Yeah. They're going, "Damn, this thing says I'm a genius. Why are you not using the genius machine to do more work?" And yeah, if you're a boss that goes to lunch, leaves lunch, and sometimes reads your emails, LLMs are magic.
I do, you know, one of the most compelling arguments I have for the overhype of AI: in a world where everybody has access to these tools, whatever the tools can do would largely be commoditized. What one could say is the human taste, judgment, you could say it's people skills, whatever you want to say, is now going to be the valuable thing because the scarce and the hard becomes the most valuable through history and the commoditized becomes the least valuable. So the very nature that we're commoditizing the generation of content or whatever you want to call it, code, means that's actually not where the value will accrue 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 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 ChatGPT or Anthropic, you know, Claude to make your LinkedIn posts, let's say, they will be LinkedIn posts because everybody else is using them. And actually, a great LinkedIn post now is someone who doesn't use them and makes something that's like irreplaceably human, right? And deeper and more personal, N-of-one lived experience.
All these things that AI can't do. And I think that's a compelling argument that actually the commodity tools produce commodity outcomes.
So everyone has access to these things, and what's changed, like really, like what...
