Why Everyone Is Wrong About AI (Including You) | Benedict Evans

Benedict Evans has been calling tech shifts for decades. Now he says forget the hype: AI isn't the new electricity. It's the biggest change since the iPhone, and that's plenty big enough. We talk about why everyone gets platform shifts wrong, where Google's actually vulnerable, and what real people do with AI when nobody's watching. Evans sees patterns others don't. This conversation will change how you think about what's actually happening versus what everyone says is happening. ----- Approximate Timestamps: (00:00) Introduction (01:04) What's your Most Controversial Take On AI? (05:11) Platform Shifts - The Rise Of Automatic Elevators (10:07) Profit Margins In AI (26:37) What Are The Questions We Aren't Asking About AI (39:41) What Benedict Uses AI For (44:21) Thinking By Writing (47:35) Can AI Make Something Original? (52:31) Advice for Students In The Age Of AI? (59:32) Who Will Win The AI Race? (1:11:09) What Is Success For You? ----- Thanks to our sponsors for this episode: SHOPIFY: Sign up for your one-dollar-per-month trial period at ⁠www.shopify.com/knowledgeproject ⁠ ReMarkable for sponsoring this episode. Get your paper tablet at ⁠⁠reMarkable.com⁠⁠ today NOTION MAIL: Get Notion Mail for free right now at ⁠⁠notion.com/knowledgeproject ----- Upgrade: Get a hand edited transcripts and ad free experiences along with my thoughts and reflections at the end of every conversation. Learn more @ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/membership⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ------ Newsletter: The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it’s completely free. Learn more and sign up at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠ ------ Follow Shane Parrish X ⁠⁠⁠⁠⁠@ShaneAParrish⁠⁠⁠⁠⁠ Insta ⁠@farnamstreet⁠ LinkedIn ⁠Shane Parrish Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  It seems to me right now you could do like a double-blind test of the same prompt given to Grok, Claude, Gemini, Mistral, DeepSeek. I bet most people wouldn't be able to tell which is which.

• 0:11Speaker 01

  Benedict Evans is a technology analyst known for his insightful takes on platform shifts in the tech industry. He sees AI differently than others. He's spent decades spotting patterns others miss and dives into how people really use AI.

• 0:26Speaker 00

  Why is it that somebody looks at this and gets it and goes back every week, but only every week? The very high-level threat to Google is that you have this moment of discontinuity in which everybody resets their priors that we consider as their defaults. And so it's no longer just the default that you go and use Google. There's this sort of question for Apple around, does this actually change the experience of what a smartphone is? What the ecosystem is? Does it end up kind of getting Microsofted? In the sense that...

• 1:03Speaker 01

  I want to start with your most controversial take on AI.

• 1:07Speaker 00

  It's funny, my, I suppose my take on AI, controversial take on AI, rather like my controversial take on crypto is being a centrist. In that it seems to me very clear this is like the biggest thing since the iPhone. But I also think it's only the biggest thing since the iPhone. And there's a bunch of people who think, no, it's much more than that. It's a minimum, it's more like computing. And then you've got people going around saying, no, this is more like, you know, the electricity or the industrial evolution or, you know, transhumanism or something. My sort of base case is to say this is kind of another platform shift and all the new stuff will be built around this for the next 10 or 15 years. And then there'll be something else.

• 1:51Speaker 00

  And so the impact on employment will be kind of like the impact on employment from the other platform shifts and the impact on the economy and productivity and intellectual property. And there'll be, there'll be a whole bunch of different weird new questions, just like there were a bunch of different weird new questions before. And then in 10 years' time, it'll just be software.

• 2:12Shane Parrish?

  Put this in historical context for us with other platform shifts. Everybody's saying this time is different, which everybody does at each platform shift, I would imagine. What's the same?

• 2:21Speaker 00

  Well, that's the, there's a, there's a famous book about financial bubbles called This Time Is Different. Because people always say this time is different, and it always is. Like, the dot com bubble was different to like the late eighties and the Japanese financial bubble was different to, you know, pick any other bubble you want, they're always different.

• 2:42Speaker 00

  But that doesn't mean that they're a bubble. And the same thing here, I have a diagram I use a lot from 1995. This research firm made a diagram of something called, they called cyberspace.

• 2:56Speaker 00

  Because it wasn't clear it was just going to be the internet. It was clear that everyone was going to have some kind of computer thing connected to some kind of network. But remember the phrase information superhighway?

• 3:06Shane Parrish?

  Yeah.

• 3:07Speaker 00

  Which sort of conveys that it would be centralized and controlled by cable companies and phone companies and media companies, which is sort of how everything had always previously worked. It wasn't clear, no, it was going to be the internet. It wasn't clear the internet was going to be kind of radically decentralized and permissionless and anyone could do what they wanted. It wasn't clear the internet was going to be the web and only the web because there were all these other things going on. If you look at Mary Meeker's first big public internet report from 1995, she has a separate forecast for web users and email users and she thought email users would be way bigger. It wasn't clear like that was all one thing. And then it wasn't clear that it was about the browser. It wasn't clear that the browser wasn't where the value capture was because Microsoft crowbarred its way into dominance in browsers, but that turned out not to matter. And then all the value is in site advertising and social, which were five years later and 10 years later.

• 3:53Speaker 00

  And so like you can be very, very clear that this is the thing and then still be completely unclear how it's going to work. The same thing with, with mobile internet. Just funny, mobile internet now is kind of like saying black and white television on color television. Desktop internet, mobile internet, black and white TV, color TV. No one really says mobile internet anymore. It's like talking about e-commerce. You're starting to have people talk about physical retail and retail. And, but it wasn't clear, you know, I was a telecoms analyst in 2000 and. It was very clear mobile internet was going to be a thing. It was not clear that there would be basically small PCs. Like that was the fundamental shift to the iPhone is it's a small Mac. It's not a phone with better UI, it's a small Mac. And it wasn't clear that the telcos would get no value.

• 4:38Speaker 00

  It wasn't clear Microsoft and Nokia would get no value. It wasn't clear it would take 10 years before it took off. And it wasn't clear it would replace the PC as the center of the tech industry. I mean, everyone was talking about, well, what's a mobile use case? What would you do? You'll do some things on your mobile phone, but what? But obviously your PC will be how you use the internet. And of course, that's not how it worked. And so we kind of forget, because now we don't see it, because now it just kind of became part of the air we breathe, how weird and strange and different all these things are. There's something I love talking about, which is is the rise of automatic elevators. So until the 50s, elevators were manually operated. They were basically vertical street carts. They were trams. They were pub trains.

• 5:21Speaker 00

  And you have a driver who has a lever with an accelerator and a brake. If you've been into a New York co-op, you may have seen one of these. They call it an attended elevator. There's a lever, you push it that way to go down, middle to stop, that way to go up. And then in the 50s, Otis creates the autotronic, I think it's called the autotronic elevator, which had electronic politeness, which basically meant the infrared thing that stops the door closing. But if you get into an elevator now, you don't say, ah, I'm going to use an automatic elevator with electronic politeness. It's just lift. We kind of forget how weird and different all the other things were. And yes, this is new and weird and different in a bunch of kind of strange, confusing, confounding ways we can probably talk about. But we sort of forget that other things were weird and strange and different too.

• 6:03Shane Parrish?

  Is this the first major platform shift where the incumbents have an advantage? Because they have the data?

• 6:10Speaker 00

  I'm pretty sure people thought Microsoft had an advantage on the internet. And Google and Meta had an advantage on mobile. And everyone thought IBM was going to win PCs. Once IBM made a PC, that was it, it's all over now. And we kind of forget that there were PCs before. And then IBM made one and that kind of became the standard, but then IBM lost it.

• 6:33Shane Parrish?

  So what happens with the incumbents? Do they grab on to using the technology instead of adopting it? Because adopting it would mean killing the golden goose. Like, what happens in a platform shift with incumbents?

• 6:43Speaker 00

  The master of my college at Cambridge said that history teaches us nothing except that something will happen. And, you know, there's always the example and the counter example. So with any new kind of, any new platform shift. And a platform, the term platform shift itself is, you know, it's a useful term, but you have to be careful not to be trapped by your terminology. And get into this sort of arguments about, well, is it a platform shift or was not a platform shift? And how do you define a platform? Like, shut up.

• 7:12Speaker 00

  The, the thing is, when any, with any of these sort of fundamental technology changes, the incumbents always try and make it a feature and they try and absorb it. And the same thing outside of technology. Existing companies try and absorb it and they use it to automate the stuff they're already doing. And then over time, you get new stuff. You unbundle, basically incumbents intake and you unbundle existing companies because of something that's possible because of this new technology. So you can always kind of jump into the new thing. And sometimes the new thing kind of really is just a feature. And sometimes it's no, it's a fundamental change in how everything works. And sometimes that sort of contingent, you know, there's this whole sort of parlor game, like drinking game that historians play about kind of historical inevitability.

• 7:56Speaker 00

  You know, well, what would have happened if that battle had been lost? Or if that politician had been assassinated or not assassinated? And it depends. Sometimes the answer is, well, no, nothing then, then everything would have been completely different. And sometimes the answer is, well, no, then, you know, what if Napoleon had won at Waterloo? Well, then he'd have lost another battle six months later. Like, nothing would have changed, the whole environment had changed. What if, you know, the revolution hadn't happened in spring in 1917? Then it would have happened in the summer or the autumn. Sometimes it's like really clear. I mean, I always think the, um, the Kodak example here is kind of interesting.

• 8:30Shane Parrish?

  Tell me about it.

• 8:31Speaker 00

  Because you know, like, it's like the cliche that people say, oh, Kodak had digital cameras and they didn't get it, or they ignored it, or they didn't want to do it because it would destroy their business. But then you go and look at it and that's like, well, that was nineteen seventy five and the thing they had was the size of a refrigerator. I know that was not a consumer product. And it took until the late nineties before the technology was actually viable as a consumer product. So of course, like, they didn't do it in the seventies because you couldn't. What actually happens is, once it starts happening, Kodak go all in on digital cameras. At one point, they were the best selling digital camera vendor in the USA. And if you look at their annual ports at the time, they think this is going to be great because they're going to sell way more photo printers. So they're selling these inkjet photo printers, people are going to produce way more photos, so they're going to take way more, they're going to print them all.

• 9:15Speaker 00

  Two things that screw Kodak, one of them is smartphones. And you can't argue that what actually screws Kodak is not the camera, it's the social media and it's not printing anymore. Which is, that's what killed, that's one side. The other side of it is that film was this high margin product where they had a bunch of unique intellectual property. And digital cameras are a low margin commodity, where they were competing with the entire consumer electronics industry with no differentiation. And so even if they go, even at the point being, even if you go all in into that market, it's still a crappy market where you've got no differentiation. So you can kind of, you know, you can, you can kind of put all of these things on the table and shuffle them around and say, well, in hindsight, obviously BlackBerry was screwed.

• 10:01Speaker 00

  And in hindsight, obviously, Google was going to be able to make the jump, and in hindsight, in hindsight, and.

• 10:06Shane Parrish?

  Yeah, maybe. Is there a parallel between the second point you made about Kodak and Google today, where, you know, they have a high margin in search business and a low margin AI business?

• 10:17Speaker 00

  So I think it's, I'd be nervous about knowing what the margins are in AI. Because we've had, you know, depending on who you ask, like, the price to get a given result has probably come down by two orders of magnitude. But then, that's, that was the state of the art two years ago, and now there's a new thing which is more extensive. And so there's an awful lot of kind of shifting planes and shifting, you know, there's a lot of. There's a lot of algebra and all the variables and the algebra will changing at the moment. So it's kind of hard to quite to know what that is. I think the the the the obvious Google threat right now is that Google shows you a bunch of links and results and ideas.

• 11:01Speaker 00

  And those could now be solved in a different way. Well, let me kind of go back a second.

• 11:08Speaker 00

  The very high level threat to Google is that you have this moment of discontinuity in which everybody resets their price and we see we consider their defaults. And so it's no longer just the default that you go and use Google. And for this search or that search, like maybe Bing is 10% better on that search. In fact, as we saw from the the the Google TAC trial last year, actually, Bing is Google is still the best search engine by quite a long margin relative to the other traditional search engines. But what we have now is like a reset of the playing field. And Google has a whole bunch of advantages as to why they might win in that playing field. But there's a reset both of what the product is and and and how you sell it and your org structure around selling it.

• 11:49Speaker 00

  And do you have the right politics and the right org structure to build that and the right incentives and internal conflicts? And then the consumer behavior kind of gets reset as well.

• 11:56Shane Parrish?

  My understanding of AI is that so much is data driven, like you have proprietary data sources. You have better data, so you can train better models. That gives you one of the key inputs.

• 12:07Speaker 00

  So I'd actually, I think it's actually the opposite, which is that everyone's kind of using the same data, which is you need such an enormous amount of generalized text that the amount that Google has or that Meta has is not actually enough to move to be a kind of a fundamental difference in what you can train with.

• 12:28Shane Parrish?

  So you don't think like YouTube as a repository is an advantage for like a significant?

• 12:35Speaker 00

  So it depends.

• 12:37Shane Parrish?

  Pushback, yeah.

• 12:38Speaker 00

  So it depends. So the models that we're training now, we're training on text. So that's not really being trained on YouTube. We saw this lawsuit around book copyright with Meta that they downloaded a torrent of pirated books. Because guess what? That's they don't have enough start text and it's not the right kind of text.

• 12:56Shane Parrish?

  Friend.

• 12:57Speaker 00

  They don't have lots of prose. They've got lots of short snippets of text. So I think the generality of L L M's is you just need such an enormous amount of data that everyone kind of needs all the text that there is. And all the text that there is is kind of equally available to anyone.

• 13:12Shane Parrish?

  So we've kind of like the data is a level playing field effectively.

• 13:16Speaker 00

  Yes, because you need so much more. And it's also not necessarily the kind of data that you have. So obviously, Google has, you know, an enormous repository of scraped data because they read the web all the time. But anyone else with a billion dollars can go out and do that.

• 13:32Shane Parrish?

  Right.

• 13:33Speaker 00

  Or you can go and download the common crawl from A W S.

• 13:36Shane Parrish?

  How far away do you think we are from autonomous sort of A I making A I better? So no human intervention, but A I sort of going out in the real world, getting feedback, adapting itself and making itself better.

• 13:51Speaker 00

  There is this sort of like people parody of like the fumpf, like you puff, like suddenly magically this thing grows and becomes amazing and learns everything. I don't think we're at that stage now. I don't think anyone really knows when it would happen. So it's very sort of impressionistic. I think another answer might be you kind of have to be very careful looking at headlines and thinking like, what exactly is that telling me?

• 14:20Speaker 00

  So Anthropic has done a bunch of things where they say like, the A I was threatening to blackmail me.

• 14:27Shane Parrish?

  Yeah, I saw that.

• 14:28Speaker 00

  And you read the story and you think, okay, you asked what's basically a story generating machine? Please tell me a story of what you would do if in this situation where most people would probably say X and the machine says probably X. And you say, my God, it said it would do X. It's like, well, yeah.

• 14:50Speaker 00

  It would blackmail.

• 14:51Shane Parrish?

  Based on human behavior.

• 14:52Speaker 00

  It would blackmail you. Okay, how would it do that?

• 14:55Shane Parrish?

  Yeah.

• 14:55Speaker 00

  What do you mean it would blackmail you? It's kind of like, the reductio ad absurdum of this is you write, this is a point that somebody else made, that like, you write murder is good on a piece of paper and you put it in a photocopier and you press go. And you say, my God, the machine says murder's good. Well, no, you told the machine to say that. And that's what these anthropic studies are. They're basically, you tell the machine to say a thing and then it says it. Like, well, you haven't proved anything.

• 15:25Shane Parrish?

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• 16:10Shane Parrish?

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• 17:37Shane Parrish?

  for up to 100 days with a satisfaction guarantee. If it's not the game changer you were hoping for, you'll get your money back. Get your paper tablet at remarkable.com today. Where do you stand on regulation of AI?

• 17:52Speaker 00

  So I think regulation of AI is sort of the wrong level of abstraction. Talking about regulating AI as AI is the wrong level of abstraction. It's like saying we're going to regulate databases or regulate spreadsheets or regulate cars. Well, we do, but not like that. When you regulate stuff, there are trade-offs. You learn about this in your first year in economics class. Like, regulation has costs and consequences, and it's not necessarily, you know, there's always a trade-off. And often you're making product decisions or engineering decisions that do actually have trade-offs. There's like a three-way trade-off of like, what's good for the product? What's good for the consumer? What's good for competition? What's good for the company? What's good for the consumer?

• 18:33Shane Parrish?

  I think the regulatory stuff is interesting in the framework of multiple countries sort of competing for superintelligence. How would you advise a country to prepare for AI? If I'm the President of the United States and I call you and I say, Benedict, you have five minutes I need, what do I need to prepare for? What can I do to put our country in the best position possible for AI?

• 18:56Speaker 00

  Well, what's your objective? Is your objective to have a nice press release?

• 19:01Shane Parrish?

  No, it's to dominate AI.

• 19:04Speaker 00

  A long time ago, I used to get these questions about like, how can we replicate Silicon Valley? And I always feel like the answers to those questions as much as possible is like, you can't. I mean, occasionally, there are things you can do, like you can create funding structures, you can make it easy, you can, you know, you can try and jumpstart startup ecosystems, you can try and jumpstart funding availability. But most of the answer is things like getting out of the way. I think that the idea of trying to create national champions is very hard. Now, that almost kind of becomes an economist's question rather than a technology question. How do you create national champions? Where has that worked? Where has that not worked? I'm sure there's a bunch of books and papers about, you know, why, where does industrial policy work? Where does it not work? From a technology analyst perspective,

• 19:49Speaker 00

  I think of this in terms of, A, what are you doing that would make this harder? And B, think of this as just more startups.

• 19:57Shane Parrish?

  What are the things that we're doing that make it harder to develop that ecosystem? Without picking a winner, it's not about picking a company and backing them. It's like-

• 20:06Speaker 00

  Well, if you do like this, this, this ridiculous law that California had a year or two ago, if you treat this as like nuclear weapons, and you say this is incredibly dangerous, and we need to have it under extremely tight control so that nobody does anything bad with it.

• 20:22Shane Parrish?

  Which is basically the EU approach.

• 20:23Speaker 00

  Go back to your economics class. Policies have trade-offs. To govern is to choose. You're making a choice when you do that, and you're choosing that has costs. Personally, like most people in tech, I think the idea that this is all going to kind of produce bioweapons and take over the world and kill us all is just idiotic. Like, I think, I think it's just a bunch of kind of childish logical fallacies within that. But you have to be conscious. You say, if we're going, you know, the, the, the kind of the Biden approach to, to generative AI, very explicitly was to say this is sort of social media 2.0. Like, social media to one was terrible and destructive and bad. And I think there's a, I don't agree with that. I think there's a huge dose of moral panic within that. But be that as it may, if you make a decision that says we are deliberately and explicitly

• 21:08Speaker 00

  going to make it really hard to build models, and really hard to start a company that builds models, and really hard to do anything with any of this stuff, then guess what? It's kind of like, you know, the mayoral election in New York today. Like, if you make it really hard and expensive to build houses, houses will be more expensive.

• 21:26Speaker 00

  You've made that choice. If you do that, you cannot then complain that houses are more expensive. You can choose that, but you can't complain.

• 21:33Shane Parrish?

  Why do you think as a society, we don't understand that?

• 21:36Speaker 00

  Part of this is that, like in the most non-emotive fields, we kind of do. You understand, people understand that, you know, if you make, you know, more employment regulation tends to produce lower growth, but more protection for employment. And you're choosing a trade off. If people kind of, I think everybody on both sides of that equation understands that that's a trade off and you're choosing one versus the other. The point is, you can have a fully functioning free market and you can regulate some of the negative externalities of free markets that anybody in any part of the economic spectrum understands. So negative externalities to free markets. You can also have like a government provided alternative. You can have the government do the fire department. Where you have some of the kind of most obvious gaps between the US and Europe, it

• 22:19Speaker 00

  seems to me sometimes are in places where you kind of have neither. So the US neither has a government controlled healthcare system nor a free market healthcare system. Do you see what I mean? You have neither government controlled housing, which you have in like in weird places like Singapore, nor a free market in housing. So you kind of break the free market. So you stop the price signaling. This is like the great insight of Hayek is that pricing is a signal. Pricing is an information system. It's telling people what's wanted. It's not, it's not just a signal of worth, it's a signal of demand. There's a fascinating book I read a while ago called Red Plenty, which is about Soviet central planning in the sixties, seventies, eighties. And it's about sort of what happens when you have a central planning that just cannot

• 23:04Speaker 00

  cope with the level of complexity of a sophisticated economy in the sixties and seventies as opposed to let's make grain and tractors and locomotives in the twenties and thirties and steel, which already kind of works. But once you actually have a sophisticated industrial economy, central planning can't handle the complexity. And so you try and create incentives and structures around that while not having pricing. And that just doesn't work. I suppose there's a sort of a generalized point, which is like a market economy is a system. And if you pull a lever here, something will move there. And you can't just pull a lever here and say, well, I don't want that to move because it's a democracy. It will move anyway, and so you have to understand how the system works and understand what consequences you want from that and what your parameters are within this.

• 23:47Shane Parrish?

  One of the things that I admire about you is that you're sort of known for spotting patterns. I have a theory on how to learn pattern matching, and I'd love to hear your pushback on this. My theory on how we learn is, I call it the learning loop. We have an experience, we reflect on that experience, and we create a compression, and that compression becomes our takeaway. So we can watch a movie, read a book, and you come away with a compression of it. But you can work backwards from that compression to the experience. But what we consume most of the time is other people's compressions. So like when people read your newsletter, they're consuming a compression of the work that you've done, but not the actual raw work. So in a way, it's an illusion of knowledge if you haven't done the work

• 24:32Shane Parrish?

  in that area.

• 24:33Speaker 00

  It's funny, I have a draft thinking about like what Ella Lems do to web search and publishing and discovering e-commerce and like Bigfoot and wavy fuzzy, all of that stuff. And I was sort of thinking about this, and there's a book written by a French academic, sort of 20 years ago or something, called How to Talk About Books You Haven't Read, which sounds very kind of snide. But kind of his point is that like, there's the book you read when you were 17 and you really didn't get it. And if you read it now, you'd get it. And there's the book that like, he's got this kind of list of like, there's the books that everybody else has read. So it's as though you've read them. There's the books that like, you've read three other books by that writer. So you don't really need to

• 25:17Speaker 00

  read this one too. You get it. Like, you do need, you know, do you need to read another Malcolm Gladwell book? Like, you've kind of got the Malcolm Gladwell experience. So there's this sort of generalized sense of pattern and accumulation of what you've seen, what you've half seen, what you half remember. There's also, I think, you know, what your viewers, listeners might notice is I kind of have two modes, two or three modes. I have a mode that's sort of discursive and slightly rambling and free associating, and I'll kind of spiral off in different directions and hopefully come back to the point. And then there's a mode where I want to try and pin the thing down and break it apart and say, what are the two, three, four things that are happening here?

• 26:00Speaker 00

  Which is what you see in the slides is no, like, what is it? It's this and then this and then that. And as that capturing that, that's a way of trying to understand what this is. I try and work out what I think about this, how I understand it, how you can break it apart by kind of pinning it down. The thing about data is, and the thing about the slides and the answers is, is like, I'm always asking, who cares?

• 26:25Speaker 00

  And I'm always asking, yes, but what actually matters here? Why are you showing me this slide? Why am I showing you this chart? And so you kind of have to ask, like, well, what are the actual questions?

• 26:37Shane Parrish?

  What are the questions we're not asking on AI that we should be asking?

• 26:41Speaker 00

  I mean, we're asking, okay, well, there were some people who were saying, oh, all of our capture is going to be in the, in the, in the, in the models. There's this kind of funny split between people who are just talking about the models getting better. And everybody else is saying, well, all the value is going to be in the application layer. And, you know, what are the companies? And let's fund CASA and let's fund all of this stuff. And why isn't there a consumer breakout yet? And other people are saying, what do you mean there isn't a consumer breakout? Everyone's using chat GPT, to which I'll say, well, not exactly, which is my data point. Some people are using chat GPT. Most people look at it and don't get it still, just fascinating. There's this kind of core, where's the value capture question? Then there's like a bunch of questions we could have asked two years ago where we don't have an answer. Will the error rate ever be controllable or manageable? Will you ever get to a model that knows when it's wrong?

• 27:25Speaker 00

  Which to me seems just like, given a statistical system, seems like a contradiction in terms, but maybe. There was a bunch of kind of, you could make a list of like a dozen questions we could have asked in early twenty three. We don't really have answers to any of those. I mean, there were some people who were asking, are these things commodities? Will China catch up to a true answer? Even then was obviously yes, of course, which is what happened, which Deep Sea kind of demonstrated. But we don't have that many new questions since then. The thing that I puzzle about right now is, first of all, there's this whole nexus, as I said, of like, what is L L, what is S E O for L L M's? You know, we have infinite product, infinite retail, infinite media. How will you choose what to buy? What happens if I go to an L L M and say, what mattress should I buy? What life insurance, what life insurance should I get? How does that work?

• 28:10Speaker 00

  That, that poses dozens of questions we don't know yet. Then there's a question around like the differentiation in the L L M's as product. Like, it seems to me right now, you could do like a double-blind test of the same prompt given to Grok, Claude, Gemini, Mistral, DeepSeek. Do a double-blind test, I bet most people wouldn't be able to tell which is which. That question of like, is there product differentiation? Can there be product differentiation around the L L M as consumer product? Because right now, the models are commodities, but Chat G P T is way, way, way, way, way more usage. So Chat G P T is at the top of the App Store rank. Gemini bubbles between like fifty and a hundred.

• 28:55Speaker 00

  None of the others are in the top one hundred. Same in Google Trends, same in the usage numbers, same in the revenue. There's revenue for a corporate API. Corporate is a whole other story. But as a consumer thing, it's like Chat G P T is now like the brand. It's the default. It's the Google. You use it because you've heard of it and none of the others have broken it. Is that where we are now? Is there, but then if you look at the products, the products, not any of the models are all the same. The underlying model is the same, the product's all the same. It's really hard to tell the difference, except like they've got different color schemes and different icons. Different branding. Different branding, but the eye products are all the same. And this reminded me of looking at browsers. And the browsers are all the same. The rendering engine underneath might be different, just as the L L M might be different. But you've got an app, an input box, an output box.

• 29:38Speaker 00

  And the output box wonders what the winner will give you. And the only innovation in browsers in the last 25 years is basically tabs and merging search in the address bar.

• 29:46Shane Parrish?

  Yeah.

• 29:47Speaker 00

  And there's like, there's like the browser project. There's people trying to do it now, but it hasn't worked, it hasn't got traction. And is that sort of how L L Ms will work in that it's about the distribution and the brand is not actually about the product or the model? Or is it maybe more like social in that, yeah, photo sharing is a commodity, but you, there's a big difference between Instagram and Flickr.

• 30:08Shane Parrish?

  Yeah.

• 30:08Speaker 00

  And all the other people that try to do photo sharing. And so you have to really.

• 30:12Shane Parrish?

  That would almost be an argument that it's sort of winner take all, right? It's very hard for, like, use Claude as an example, it'd be very hard for Claude to compete if they don't have enough usage to continuously make the investment.

• 30:28Speaker 00

  Well, that's a slight, this is slightly different thing. So the winner, there doesn't appear to be a sort of self reinforcing cycle in which more people use it because more people use it. The product gets better because more people use it, so more people use it, which is what you have with operating systems, because you have more apps, therefore more users, therefore more apps. So what you have with Google search, that Google has all the feedback from how people use it that makes the search engine better, you have a network effect in social media that you're there because you're there, because your friends are there because you're there. There's no apparent equivalent in L L Ms right now. There's no reason why the L L Ms get better because more people use them. Now, that may come. You have that, the OpenAI and people have been doing memory, where it remembers what

• 31:11Speaker 00

  else you've asked, but that seems more like a switching cost than a network effect. And also, it might be easier for you to just ask it what it knows about you and then tell Claude or vice versa. So it's not clear if that, but we are at that sort of stage where you're looking at the browser and saying, is there a way that you can create stickiness here? Or that you can create a network effect on the browser? Or is it just that the browser itself is a commodity? Now, capital is not a winner takes all effect in that convention. So it always, at any rate, it's a different kind of winner takes all effect. I mean, I wouldn't conventionally think of capital as a network effect. Or is it, it's not a product that's in, it's not something that's inherent in the product. It's something else. It may be that, yes, chat G P, the OpenAI has more money so they can make their model better.

• 31:54Shane Parrish?

  There's like six rabbit holes I want to go down before we move on to something new. If OpenAI can, I liked your point about sort of at the point where AI gets better because people are using it, then there's a huge advantage to being open.

• 32:05Speaker 00

  But we don't have any, we don't have visibility on what that would be yet.

• 32:08Shane Parrish?

  At that point, though, whoever's in the lead would sort of.

• 32:12Speaker 00

  If it did, then you could get kind of a runaway. But we should kind of go back and think about MySpace. Because you have like, in this, it would be MySpace, you know, in the early phases of these things. And you see the same thing with the early PC industry. You've got a dozen of them. And there's often an early leader that falls away later. And so MySpace was the early leader that fell away later. Then you get a late stage where the S curve is kind of flattened out, where all the network effects have kind of solidified and the product quality has solidified. It was very easy actually to get people to switch back and forth between MySpace and Facebook and Bebo and Friends Reunited and whatever the, um, Orkut and all these other things. In the early days.

• 32:52Shane Parrish?

  Right.

• 32:53Speaker 00

  Then you kind of get this, this separation out. But then, of course, then you get, then Instagram comes along. And then TikTok comes along.

• 33:01Shane Parrish?

  Right.

• 33:01Speaker 00

  And so as soon as you have something that's a different proposition, that turned out to be extremely easy to pull that away. You know, Google, Google lost to YouTube. They had to buy YouTube. Facebook lost to Instagram and WhatsApp and they had to buy them both. So those are quite fragile and quite narrow winner tastes all effects, or at least they, they, they appear to be. We don't know what that would be or what the modalities would look like. Modalities, sorry, that's a great meaningless word. It's like saying societal. We don't know what that would look like and therefore we can't, we don't know how rigid it would be or how it would work because we don't have it yet. You can't, as I'm sure you know, you're not, they're not retraining the models all the time with the data. So you don't have that kind of runaway effect as like continuous flow and more queries produces, you know, better results.

• 33:45Speaker 00

  So it's kind of tricky to do that yet.

• 33:48Shane Parrish?

  I want to come back to something you said. You said, some people look at chat T P T and don't get it.

• 33:52Speaker 00

  Yeah, I think this is really important. There's a whole bunch of survey data on how people, how many people are using this stuff. You've got the numbers from, so, so, so, so chat, OpenAI predicts, say, well, we've got this many weekly active users. Funny thing about social is people, when social happened, people would talk about registered users. You remember in the early days of the internet, people would talk about hits? Yeah. And then we realized that if your webpage has seven items in the menu bar, that's seven gifs, so that's seven hits. So hits was meaningless and you had to switch to page impressions. And then it's registered users. And then it's monthly active users. And on social, people said, well, hang on, if you're using Instagram once a month, you're not using it. It's daily active users or nothing. And weekly active users, we don't like either. Now, OpenAI is doing weekly active users. And Sam Altman was a social media startup founder. He knows this. It's a bullshit number.

• 34:33Speaker 00

  You look at survey data, and I had to did this slide in the last presentation I did of like five different surveys from the US from late last year, earlier this year. And it's all roughly the same. It's like something around 10%, give or take three or four percent of people, depending on the survey. They're using this, say they're using this every day. Another sort of 15 to 20% of people say they're using it every week. And then there's, say, you've got like, say, 10% of people using it every day, say 15 or 20% of people using it every week. Another 20 or 30% of people who say, I use it every month or two. And another 20 or 30% of people who said, yeah, I had a look, I didn't get it. And then you have this survey where people say 70% of people are using AI. And like, wait, what do you mean?

• 35:18Speaker 00

  There's a whole other rabbit hole, which is, you know, people say, well, did you use Snapchat's face filters? Also, you're using AI. What do we mean by AI? So which is, let's be specific. Let's talk about, are you using a consumer facing LLM chatbot? Like you're going to ChatGPT or Claude and asking questions. Like that's the number we want to look at.

• 35:34Shane Parrish?

  Most people don't think about their inbox as a system, but I do. Email used to be the thing that helped me run my business. Lately, it felt like the thing getting in the way of it. I'd spend too much time reading through low priority messages, trying not to miss the one or two that actually mattered. And it was draining my focus. Then I started using Notion Mail and everything changed. Notion Mail is the inbox that thinks like you. It's automated, personalized and flexible to finally work the way that you work. With AI that learns what matters to you, it can organize your inbox, label messages, draft replies, and even schedule meetings. No manual sorting required. I've created custom views that split my inbox by urgency and topic, so I can focus without distraction.

• 36:18Shane Parrish?

  And I use snippets to fire off my most common emails, follow-ups, intros, and scheduling, without rewriting anything. The best part is, it works seamlessly with my Notion Workspace, and is powered by Notion, the tool trusted by over half of Fortune 500 companies. Notion is known for powerful connectivity, intuitive functionality, and the ability to supercharge productivity. Get Notion Mail for free right now at notion.com/knowledgeproject, and try the inbox that thinks like you. That's all lowercase letters, notion.com/knowledgeproject. To get Notion Mail for free right now, when you use our link, you're supporting our show, too. notion.com/knowledgeproject.

• 37:01Speaker 03

  This episode is brought to you by Michelob Ultra. Good times mean competitions between you and your friends, like seeing who can throw some pigskin the farthest, or rush the field the fastest. Winner gets an ultra. Because it's not just a superior light beer, it's also giving fans superior access to their favorite sporting events. Superior is worth playing for. Visit michelobultra.com/superioraccess to enter. Enjoy responsibly. Copyright 2026 Anheuser-Busch InBev Michelob Ultra Beer, St. Louis, Missouri.

• 37:31Speaker 00

  And to me, there's a bunch of, you could matrix this. So, some of this is, it's early. There's a counterpoint here, which if people do the chart, and they say, "Oh, my God, it's so fast." It's like, it's faster than smartphones, yes, because you didn't need to buy a $1,000 smartphone. Right. It's faster than PCs, yes, because you know what PCs cost in the 80s, adjusted for inflation? It's like five grand. Yeah, it's free for a lot of them. It's free, it's a website, you just go there. Of course, it's got faster adoption, and there's way more people online as well. So even the absolute numbers are faster than they were for Facebook 20 years ago, 15 years ago, because there's way more people online now. Yes. So, so that's, that's again, an example of my unfair but relevant comparison. You're sort of standing on the shoulders of giants. So, of course, you can get to way

• 38:13Speaker 00

  more people quicker, but you have to keep asking, well, what, yes, but? Why do so many more people look at this and not get it? Or even worse, the not getting it, I can kind of see, because people look at everything and don't get it. Why is it that somebody looks at this, and gets it, and goes back every week? But only every week.

• 38:34Shane Parrish?

  Right.

• 38:34Speaker 00

  Why is it they can only think of something to do with this once a week?

• 38:38Shane Parrish?

  I worry about those people. I mean, I I'm just thinking if these numbers are accurate, the 10%, the 15%, you know, 90% of the people that I spend the most time with are within that 10%.

• 38:50Speaker 00

  Well, I'm not.

• 38:51Shane Parrish?

  Interesting. Tell me more about that. Well, here, actually, I'll I'll preface this conversation with my kids don't use Google anymore. They have, they use it to find phone numbers or local businesses or places at distance. Everything else, they they basically have defaulted to ChatGPT now.

• 39:09Speaker 00

  Again, I'm going to, all AI conversations seem to be analogies, so I'll.

• 39:13Shane Parrish?

  A hundred percent.

• 39:14Speaker 00

  And it's like, it's like nuclear weapons. It's like, no, it's not.

• 39:18Speaker 00

  The comparison I think is interesting here, it was not perfect, but it's interesting is to look at early spreadsheets. For software spreadsheets, spreadsheets were paper. Dan Bricklin creates, and I can't remember the other guy's name, he created Physical in the late 70s. And I think to get an Apple II to run it with a screen and everything costs like 15 grand, adjusted for inflation. And you show this to an accountant and it's like, you can change your interest rate here and all the other numbers change. And we see that now and we're like, yes, 1978, that was a week of work, almost literally.

• 39:51Shane Parrish?

  That was like amazing.

• 39:52Speaker 00

  Yeah, it would do a week of work in half an hour.

• 39:54Shane Parrish?

  Yeah.

• 39:54Speaker 00

  Or less. And he has all these stories about accountants who would, you know, they would be given a one month project and they'd get it done in a week. And then they'd like go and play golf for three weeks because they, partly because they could, they didn't actually want to tell the client, I only did it in a week because the client would think they hadn't done it properly. So I look at ChatGPT and I think, right, I don't write code. I have zero use for something that will like code for me. I don't really do brainstorming.

• 40:19Shane Parrish?

  Okay.

• 40:19Speaker 00

  I don't do summarization of things. I don't do the things where it's sort of out of the box, easy and obvious. And then there's a sort of mental load of, okay, I've got to kind of try and think of what things am I doing that it could do for me? And that, most people don't think like that. So there's a sort of, I said a moment ago, there's like a matrix, there's a matrix of like, who has the kinds of tasks that it's good at, obviously?

• 40:49Speaker 00

  Who is good at, has the kind of tasks that it's good at? Not obviously. Who is good at thinking about new tools for the things that they're doing? Who isn't? If you

• 40:59Shane Parrish?

  I'm kind of blown away. You don't reflexively use AI.

• 41:02Speaker 00

  If you were using Salesforce and you had a button that said, draft me an email to reply to this client.

• 41:10Shane Parrish?

  Yeah.

• 41:10Speaker 00

  Then that gets massive adoption.

• 41:13Shane Parrish?

  Well, that's

• 41:13Speaker 00

  But that's

• 41:14Shane Parrish?

  Feature that we talked about earlier.

• 41:15Speaker 00

  But that's a different thing.

• 41:16Shane Parrish?

  Yeah.

• 41:17Speaker 00

  Is it, is it the chatbot as product where you get this blank screen? And you kind of look at it and you scratch your head and you have to think, well, what is it that I would do with this? And then you have to form new habits around it. Or is it that it's wrapped in a product and UI where somebody else has said, it would be really useful for this, wouldn't it? And then you look at it and go, oh yeah, that would, I could do that.

• 41:40Shane Parrish?

  Do you think it's better with qualitative or quantitative?

• 41:42Speaker 00

  So I think it is presently, and I'm going to get in a binary statement. I think today it has zero value for quantitative analysis.

• 41:53Shane Parrish?

  Oh, interesting.

• 41:53Speaker 00

  Because if, well, let me, let me qualify that. Do the numbers need to be right or roughly right? Because what all of these things do is they give you something that's roughly right. And roughly is a spectrum, but it's always

• 42:07Shane Parrish?

  You don't want pi to be 3.1.

• 42:10Speaker 00

  Depends how big the circle you're measuring is.

• 42:13Shane Parrish?

  Right.

• 42:15Speaker 00

  You know, this is the line about pi that, you know, we can calculate it the like, then however many digits we have is like enough to calculate, you know, the diameter of the galaxy of the universe or something, but people still adding more numbers. So, you know, there's a little bit of Xeno's paradox in here, you know, you get infinitely close. At a certain point, it doesn't matter. And this is at a high level, this is some of the AGI argument that if the thing gets infinitely close to reasoning without ever actually reasoning, does it matter? Like at a certain point, the thing, if the thing is always right, if the thing, if the thing is only wrong once in a billion years, does it matter that it's not, that it's not always right? The problem today is, it's not wrong once in a billion years. It's wrong a dozen times a page.

• 42:49Shane Parrish?

  You don't want to spit that out and give it to somebody.

• 42:51Speaker 00

  And I don't know.

• 42:52Shane Parrish?

  Yeah, yeah.

• 42:53Speaker 00

  So I had a very early example of this and I was going to speak at an event at the beginning of 2023. And the conference people had asked me for a long biography of myself and I didn't have, I don't have, still don't have one. And so they'd made one and they'd used it with chat GPT and not told me. And they just sent it to me to check and I looked at it and I said, what the fuck is this bullshit?

• 43:10Shane Parrish?

  That's twenty twenty, that's like generations ago.

• 43:14Speaker 00

  This isn't the point. And that's not relevant to the point I'm gonna make. The point I'm gonna make is, A, it was always the right kind of biography. It was the right kind of degree, the right kind of university, the right kind of experience, the right kind of jobs. It just wasn't actually right things. But B, I could take that and fix it. So for them, it was useless.

• 43:32Shane Parrish?

  Right.

• 43:33Speaker 00

  For me, it was completely very useful. I spent thirty seconds fixing it instead of spending an hour scratching my head, which is why I say, right or wrong, depends. Which is a very kind of French philosopher kind of question. Is the answer, is the prompt, does the prompt have errors? It kind of depends why you want it.

• 43:52Speaker 00

  Okay, well, that, I don't have use cases where I want something that's roughly right. I don't have use cases where I want a list of ten ideas, or I wanted to brainstorm, or I wanted to draw a few email, or wanted to write code, or I don't want it to generate some images. You know, a friend who works at a consultancy and they want pencil sketches of concepts, and now they can just use Midjourney to make those, that's great. Does that sketch, like, does that person at the back have three legs? Not anymore, no. And if they did, it wouldn't matter, you could Photoshop that out. I don't do that, that's, I don't create images. So I don't have a good mapping of the stuff this is good for early against the stuff that I do.

• 44:35Speaker 00

  And the stuff that it, that it maybe would be useful for is the stuff where it's actually not yet very good. And the things where you would mitigate that by saying, well, I would fix it, I don't do those things.

• 44:50Shane Parrish?

  Okay, so thi- this is a good thing, because I wanted to come back to something you said. You said you think by writing. And in a world where you're taking something generated by AI and editing it, that's different than writing. Talk to me about thinking by writing.

• 45:06Speaker 00

  I, why should my, my, my chat GPT use case, which is more mental model than a practical thing, is I write something and I think it's such a, I, what I was, was, would ask in the past is kind of your point about pattern recognition is, I look at something and say, am I adding value here? Am I saying something useful? Am I saying something different? Am I asking the key question? Am I pushing further? Am I pushing, am I asking the next question rather than just answering the obvious questions? Now I can just say, is this what chat GPT would have said? And if the answer is, this is what chat GPT would have said, then I don't publish it. Not because people can get it from ChatGPT, but because anyone would have said that.

• 45:42Shane Parrish?

  That's a perfect analysis in the sense that it raises the baseline of what qualifies as insight. The difference is the slope of the insights. And so you wouldn't say it if ChatGPT is going to say, and push back on this by all means. But the ChatGPT level of insight, to use an example, it could be Claude, it could be Grok, it could be any of them, is increasing at a faster pace than most people. And eventually those slopes intersect and it's probably intersected already with, you know, maybe up to intern level. And next year it might be master's level, or it might even far surpass that. And it hasn't some domains in terms of math.

• 46:27Shane Parrish?

  The year after it might, and so maybe it's like five years before it passes Benedict.

• 46:33Shane Parrish?

  And maybe it's four years before it passes somebody else. And maybe it's like passed me way long time ago.

• 46:39Speaker 00

  So I think there's a, there's two or three directions we could take that. One of them is, it's an interesting theoretical philosophical question, is originality. Which is to say AlphaGo could do original moves because it could do all the moves and do moves that no one had done before, not knowing what people have done before, but it had an external scoring system and neither that move was good.

• 47:05Shane Parrish?

  They Because it had feedback.

• 47:07Speaker 00

  Yeah, it had a feedback loop because every move has a score. They can evaluate the score of every move. The classic parable of the monkeys and typewriters, or you know, the Borgias infinite library is there's no feedback loop.

• 47:18Shane Parrish?

  Yeah.

• 47:18Speaker 00

  Yes, the Borgias infinite library contains new masterpieces generated at random. Well, the monkeys with typewriters would generate new masterpieces, but there's no feedback loop. So there's no way of knowing. You'll see this with music now. You can generate new music, it could generate new stuff that you wouldn't know. For an LLM, variance is bad. Originality is a lower score. So what's the feedback loop for original but good? Now, it might be that that's the same sort of false question as saying, is it really reasoning? Or is it just right 99 times, you know, nine, nine, nine followed by many zeros? Does it actually understand? Or is it just always right without understanding? Does it actually know that's original and different? Or does it not? And that's a, that's a kind of a puzzling. I don't know that we know the answer to that.

• 48:00Speaker 00

  And it may be the wrong, wrong question, but it's kind of a puzzle as to how would these, how far can these things make things that are both different from the training data and good? And is knowing that this is different but good, is it really different? Or is it just matching the pattern on a longer frequency? Do you see what I mean? And how much could you actually have predicted that, given enough data, that it's not actually outside the pattern? It just kind of looks like it is if you're zoomed in more. And if you zoom out more, then that is matching the pattern. How would you know that people will? It's like, you know, thinking about music, like, how would you know that people would like punk? You could get, you can very easily imagine a generative AI system that can make you

• 48:43Speaker 00

  more stuff that sounds like, yes, or more stuff that sounds like Pink Floyd. It might not sound like good Pink Floyd for Floyd, but you could imagine it would make more stuff that sounds like, like the Grateful Dead. You know what Grateful Dead fans say when they run out of drugs? This music's terrible.

• 49:01Speaker 00

  You can imagine that if the, you can imagine, I'm being unkind, but like, you can imagine the challenges, knowing that now people are really fed up of 70s prog rock and they would really like something else. And that something else would be punk and that would work. It's knowing that people in the 40s were really fed up of the war and would want luxury and that Christian Dior's new look would work and would express that. Could an LLM do that thing? How much variance do you need? I don't know. It's an interesting, like, thought experiment to ask that question. There's a completely different place to take this, which is to say, this is an appeal for boutiques and in-person events and the unique and the curated and the individual. There's a shop I always used to always talk about, I'm not sure if it's actually still

• 49:46Speaker 00

  there, the shop in Tokyo that just sells one book, in Gensu that just sells one book, and they change what it is once a month. It may, may have closed 10 years ago. I've been talking about it for 20 years. But the point is, you don't go into the shop and have to work out what book to buy, but you have to know the shop exists.

• 49:59Shane Parrish?

  Hmm.

• 50:00Speaker 00

  Or you can be Amazon and you've got five hundred million SKUs. And they know they've got everything, like actually there's some stuff they don't have because they want to be individual. Like they don't have L M H. But for the sake of argument, Amazon has everything, but you can't go to Amazon and say, what's a good book? Or, you know, what's a good lamp? They have all the lamps. You can't just go to it and say, what lamp should I buy?

• 50:18Shane Parrish?

  Right.

• 50:18Speaker 00

  All of retailing and merchandising and advertising is about where are you on that spectrum and what else do you do? Do you do you spend the money on rent or advertising or shipping? Or what? And how does that work? And the more, there's a sort of a polarization between, well, if I know I want the thing, I can get it within twelve hours. But how do I know I want the thing? And as I alluded to earlier, an L L M is one might, paradoxically, an L L M could suggest you the unique individual thing. Would the L L M also create the unique individual thing? That's a high word and a second, that's, that's a different question. It's a question further down the pipe.

• 50:58Speaker 00

  But the more that the L L M can do what everybody would probably do, or say what everyone would probably say, then the more you push to other places.

• 51:08Shane Parrish?

  That makes a lot of sense. I mean, there's always going to be a market for insight, whether it comes from L L M's or people. You, you have to be providing insight.

• 51:16Speaker 00

  You know, our world as quote unquote content creators is a very wide spectrum of people who do very different stuff. And there's, you know, there's people doing A I slop and there's people doing, you know, the, the, what is it called? The, you know, the passive income thing. But there's people who do very different kinds of content, coming from different places for different reasons. You know, Scott Galloway does very different kind of stuff to me. Mary Mika does very different kind of stuff to me. You do very different kind of stuff to me. It's just part of that is about who you are and your story and the authenticity of it. And some of it is about, no one cares who you are, but you're saying interesting stuff. And some of it's the recommendation algorithm or something else. There's a book by Zola about the creation of department stores called Bonheur des Dames, which means the happiness of women.

• 51:59Speaker 00

  And it's basically about a nineteenth century Jeff Bezos collinger, an apartment store and department store into existence out of thin air through force of will. And like he invents fixed prices so that you Can have discounts and lost leaders and mail order and advertising. And, you know, he puts the the slow moving expensive stuff at the top of the store and he puts food and makeup on the bottom of the store. Um, there's nothing new under the sun. And meanwhile, the shopkeepers on the other side of the street are saying like, have you seen what that maniac's doing now? He's selling hats and gloves in the same shop. He's got no morals. He'll be selling fish next. And of course, he's got the counter. Like, there's like, the whole plot point is about, um, lost leaders. So it was like, you can, again, you're going to step back and think, well, people have freaked out about industrial industrialized mass produced product before.

• 52:43Speaker 00

  People are freaked out about there being too much content. There's a line that Erasmus was the last person to have read every book. And there's too much A I content slop on the internet now, like, yeah, how many books do you think were being published in nineteen eighty? Do you think everyone was reading all the books then?

• 52:57Shane Parrish?

  Yeah, same thing, just different scales, I guess. Wha- what advice would you give students today?

• 53:03Speaker 00

  Well, when I was a student, we were all supposed to be learning Japanese. I think that was just the tail end of that. You know, I was sort of lucky to have a sort of very expensive and old fashioned and handcrafted education. That was all about learning how to learn and learning how to think. I think there are skills that people used to sneer at that probably shouldn't have been sneered at and certainly shouldn't now. I mean, I'm old enough to remember when people would just sort of smugly say, well, I'm not computer literate.

• 53:33Shane Parrish?

  Hmm.

• 53:34Speaker 00

  As though that was like, there's like being a car mechanic or something. I don't know how to do that. That's not my problem. That's somebody else's problem. And I don't think anyone now, partly this is because of mobile, I don't think anyone thinks like that anymore. Should you learn to code? No, I think you should find out if you want to learn how to code. I think this is like saying, should you learn an instrument? Or should you, you know, go take take theatre classes? That you, that may or may not be what you should be doing. Of course, what does learn to code need in ten years' time? That's a different question, but should you? I don't think you should presume you will or won't be a software engineer. I think you should presume that you will need to be curious and that you'll have many careers and different kinds of jobs. I think you should be focusing on learning how to think.

• 54:18Speaker 00

  But I don't know, I think you should be presuming that everything will change.

• 54:21Shane Parrish?

  Everybody says something like learning how to think, I feel like you would have a really good, what does that mean? Like, break that down for me, because it probably means different things to different.

• 54:31Speaker 00

  So every now and then I'm slightly perplexed to get an email asking for career advice, because I think if you looked at my LinkedIn, and like, sort of, company shut down, company shut down, company shut down, like, lasted a year there, that didn't work. Coming from the UK and seeing the US system, I never really liked the US idea that like, if you want a good job, you should be doing maths and business and engineering. Now, that may be how people hire students here, but I never liked the idea that you're like, studying philosophy, or studying history, or studying literature is useless because you're learning about history. That's not what I learned. Yes, you know, I throw lots of analogies about history, none of which are actually things I studied at university. What I learnt studying

• 55:15Speaker 00

  history at Cambridge was how to ask what the next question is, how to break this apart, how to read a hundred books or fifty books in a week and find the bits that you need, how to synthesise lots of information, how to ask, well, what does that actually mean as opposed to what it looks like it means? Do you believe this? Is this credible, or should you just jettison that idea? How do you put this together and think about how you would explain something? And that's what my friends who studied English did, or my friends who studied philosophy did, or my friends who studied engineering did. That was what you were being taught how to do. You weren't being taught to be an English like, to be a historian, or to... And I'd hesitate to think that, you know, you can

• 55:58Speaker 00

  only build a company if you've had it, or only worked for Goldman's, or McKinsey, or a big law firm. If you had a particular kind of education, a particular kind of degree, I think you should be looking for what's going to challenge you and push you, and give you the ability to learn and think in different ways. But again, this is me, you know, what are the skills that you have? How does your brain work? How do you think about things? And it took me, god, twenty years to work out what I was good at. So I'm not sure that you can know that as a student. And so you have to try and find what you're good at as well as, you know, learning to think. Maybe learning to think is what I do. Maybe that's not what you should be doing. You should be learning.

• 56:41Speaker 00

  What is it that you should be learning to do? What are the things that you're good at? Try all the different things. I don't know. It sounds like a, like in a university commencement speech now. I don't fucking know. But you don't know what you're going to be good at. So you kind of want to try and like create options for yourself.

• 56:57Shane Parrish?

  What did you learn about investing, working at a sixteen z?

• 57:01Speaker 00

  So there's a bunch of like maxims or sayings. That's probably, I wouldn't want to dignify them as like theses or anything else. But there's a whole bunch of maxims and sayings in venture, which, you know, we could have a podcast talking about. There are better people to give you a podcast talking about the mechanics of venture. But like, you have, you're understanding what startups are and how they work and how the machine works. And startups are an industry and Silicon Valley is like a machine for creating startups. And still too many people kind of look and say, well, that was a dumb idea. And it's like, well, that's the wrong question. The question is, if you look at a startup and you think, could it work? And if it did work, what would it be? And could those people make it work? And then you understand more, you like the mechanics of, well, how does social media work?

• 57:45Speaker 00

  And how do people build companies? And what is it like to create a startup? Which is a whole other conversation. I think something else that I learnt was calibration. This is sort of, again, another metaphor I always think of, which is that if you go to like a really great art gallery, like you go to MoMA or, you know, the Met or the LIVRE or something, everything there is a masterpiece. If you go to a smaller, weirder art gallery, like London, there's a gallery in London called The Wallace Collection. Or I was in Rome a couple of weeks ago and I went to one of the sort of old aristocratic palaces. And this palace is like, it's like 10 or 15 rooms of pictures.

• 58:23Speaker 00

  And they've got like a quite good tintoretto and a maybe Titian and a Raphael. You see it glowing across the room. And like, oh, that's why he's Raphael. And it's the same when you see lots and lots of startups, like, oh, that's why he's Marcel Chint. Oh, no, that's why this is bollocks. Like, you get ten minutes in and like, God, I've got another forty five minutes. I've got to pretend to be interested and polite. So the founder has a good experience. It's seeing that contrast and texture and seeing what good looks like, seeing what worked, what didn't work, what people tend to say, how this tend to work. There's

• 59:05Speaker 00

  pattern recognition as much as anything else. You also get, there's a whole, all sorts of other kind of cultural contexts you get around. You know, Silicon Valley is kind of a very high school. You know, I always used to say it's, it's an industry town. And I always used to say it was like being a college town where there's one subject. So everybody you meet is doing the same thing. And so in some ways, that's very powerful. You, you know, everybody around you's doing a, you want to do a PhD, everyone around you's doing a PhD. Here's the world expert in the subject. Of course you are. It's like being a middle class kid. Of course you're going to university. What do you mean you're not going to, everyone's going to university. Of course, you're going to do great work. Of course, you're going to start a company. And you're surrounded by the people who've done it. You want to get a CTO who's done it five times. You want to get the head of grace who's done it five times. They're all there. The other side of that is you'll never meet anybody who isn't working on exactly the same stuff and isn't interested in what you're

• 59:50Speaker 00

  working on. So you have no external context. You have no external perspective. The nearest theatre is in LA, then, you know, no, Chicago, I think. Like, if there's a theatre in LA, you want to go to an art gallery, you've got to go to LA.

• 1:00:01Shane Parrish?

  Who's the best positioned right now? So from the outside looking in, you know, Salk is, is going all in on AI. Elon seems to be going all in on AI. Which of sort of the, the leading companies do you think, like, hey, why, why are they all of a sudden shifting and, and really, you know, they were dabbling in it before, but now they're, they're really committing, you know, tens and hundreds of billions of dollars. And then who's the best positioned in this sort of space? If you had to pick one and invest your entire net worth in it, who would it be?

• 1:00:33Speaker 00

  Well, that's, that's, that's several different questions.

• 1:00:35Shane Parrish?

  Let's pick them apart.

• 1:00:36Speaker 00

  Like, this was a technology that had been kind of floating around before ChatGPT 3.5. And everyone kind of saw that it didn't work very well. And then ChatGPT works, now actually it works well enough. And then there's this explosion of interest since then. And so I think last year, the Google, Google Microsoft, AWS and Meta spent about $220 billion of CapEx last year. And we'll probably spend something over 300 this year. And so that's basically more than doubled, almost tripled, I think, from a couple of years ago. So there's enormous surge in CapEx in investment in this. And we've got these stories about like, well, Meta bought 49% of Scale.ai for $15 billion. Apparently looked at both of the other recent OpenAI spinouts, Safeway at Superintelligence.

• 1:01:18Speaker 00

  And what's the other one? Thinking Machines, which are both basically pre-product, pre-revenue labs with somebody from OpenAI at multiple tens of billions of valuations. And he apparently, like Sam Altman complained that Mark is offering people $100 million to join. So Mark's in beast mode. Microsoft has this kind of weird situation in that his own models aren't actually very good, but it's got this very kind of weird relationship with OpenAI. OpenAI, Sam Altman is, I was going to say polarizing figure, but actually opinions about him tend to be fairly unanimous and tend to be fairly negative, like everybody who's ever worked with him quit. OpenAI itself still kind of sets the agenda, but much less so than it was two years ago.

• 1:01:59Speaker 00

  And I wouldn't want to do like a detailed like calling the scores on whose models are good and whose labs are good. But, you know, objectively, Google is clearly like firing on all cylinders now and is doing very, is making great models. Llama, Llama 4 was about, it seems to have been an embarrassment and so Meta is kind of scrambling to catch up. Apple is in a slightly different position in that they have always sort of taken the position that they don't want to be first. They want to do it right and that they don't need to be doing whatever the latest consumer internet thing is. Like they don't have a YouTube. I think Craig Federighi said in an interview after WWDC, like, we don't have a YouTube. He just called it YouTube, but like, we don't have a YouTube, we don't have a car showing service, we don't do grocery delivery. We also don't have a chatbot. Okay, that wasn't quite the question.

• 1:02:42Speaker 00

  The question for Apple is how much does integrating an LLM into the operating system change the experience of what it is? Potentially shifting the competitive balance with Pixel, which at the moment has, basically it only gets bought by people who work for Google and people who work for the tech press and like literally no one else buys Pixels. Well, because Google doesn't want to compete with Samsung. We could go down a whole smartphone industry rabbit hole. There's this sort of question for Apple around, does this actually change the experience of what a smartphone is? What the ecosystem is? Does it end up kind of getting Microsofted? In the sense that you're going to still, for the time being, you're still going to buy a smartphone. It's not at all apparent there's going to be another device. And if there is, it's a long way away and it might be an Apple device as well.

• 1:03:27Speaker 00

  But you're still going to buy a smartphone. You're still going to buy the nice one with a good battery and the fast chip to run the AI models and the good screen and the best camera, which will still be an iPhone because Apple still has the best chip team and a whole bunch of other hardware advantages. But everything you do on it will be from someone else. And it may be someone else in the sense that it's an app from the App Store. It will be someone else in the sense that it's a model running in the cloud, which is what happened to Microsoft in the 2000s, which was everyone had to get on the internet. To get on the internet, you needed a computer. You weren't going to buy a Linux computer. You probably wouldn't buy a Mac either. So everyone bought a Windows PC, but they were using it to do web stuff, not Microsoft stuff. So Microsoft kind of lost that. And so that would be the concern for Apple would be you'll still buy your new iPhone

• 1:04:11Speaker 00

  and you'll buy the iPhone Air this autumn because it will be center on lighter and it will be a lovely phone. And you'll use it to do ChatGPT. But the counter argument would be to say, yeah, you'll use it to do ChatGPT and DoorDash and Uber and Instagram and that cool new game and the other new cool game that's enabled by LLMs. And to do TikTok and to do and, and, and, and, and, and it will be kind of the same, except they'll. So there's a sort of, you said, Remy, and there's this sort of slight fuzziness around what the bare case for Apple actually is. Does it sort of end up like Microsoft did? How bad is that exactly? What does that mean? Yeah. If we all go to wearing something like this that's lit up by AI, then that's a bigger shift.

• 1:04:56Speaker 00

  But it's very unclear how close that really is, just for the optics. There's another axis here, which is what happens to Google Search? Where does that money go? How do you map the search activity that goes to an LLM? That, and how do you map that against where the revenue is? And also from the other side, how do you map that against where the publishers are? How do you think about whether you just shift your habit and you're actually using ChatGPT as Google? It's basically doing what Google does, but you've shifted the brand and you're going to that search box instead of the other search box. I don't think you can count them out in absorbing that in.

• 1:05:35Shane Parrish?

  So who would you, out of the public companies, you had to put your money in one of the top like seven?

• 1:05:41Speaker 00

  Well, then there's a valuation question, and a long time ago, I was a public markets analyst. And I was bad at being a public markets analyst, equities analyst for a bunch of reasons. One of which was I was never interested in share prices.

• 1:05:53Shane Parrish?

  Well, we've given the standard disclaimer, you're forced to, what would you?

• 1:05:56Speaker 00

  Um, it's, it's hard to see iPhone sales slipping from what we see now. Even half the cool sexy Google stuff is in the Google app on the iPhone. I think Meta and Google, there is this sort of big question around where the ad revenue goes and how much the ad revenue gets pulled away to different places. I think Instagram is probably in a very good place there in terms of changing what advertising looks like and how that works. I mean, I had a slide in my presentation, which was like, well, Meta and Amazon want to do is to make L L M's commodity infra that sold at cost.

• 1:06:35Shane Parrish?

  Yep.

• 1:06:36Speaker 00

  This is why Meta made it open source, because they want to make it commodity infra that sold at cost. And they differentiate on top with Meta stuff, with Facebook, social, Instagrammy stuff. And they want the model itself to be just infrastructure. Amazon would also like it to be commodity infrastructure that sold at cost, because that's what AWS is. They sell commodity infrastructure at cost, and they do it better than anybody else. And they make a lot of money from doing that. Go to Amazon's financials and basically all the money comes from AWS and the ads. People who complain about AWS don't haven't realized the ads make it. Amazon did fifty billion, sixty billion dollars of ad revenue last year.

• 1:07:04Speaker 00

  So Amazon seems to be fine, but there's a bunch of stuff to navigate around. How does this change how people buy stuff on Amazon? Who does that leave? Microsoft, there was this line from Bismarck that the great man is somebody who hears God's footsteps through history and grabs onto his code as he walks past. And like a statue is like tried to, first of all, he tried to grab onto V R and A R with whole, hollow lens thing that it is. Well, we don't talk about that anymore. Now it's A I, their own models are not really ranking. I mean, they hired Mustafa, but like they're still struggling. They've got this weird contentious relationship with Sam Altman and OpenAI, and it's basically not their models. On the other hand, like they're going to sell an awful lot of Azure.

• 1:07:46Speaker 00

  To run all this stuff, which again is this tension, is it, is it that everybody is just uses chat G P T to do the thing? Or is it that someone is going to come to you with a great accounting product to run Farnham Street? Yeah. And it runs on Azure and it uses some L L M. Who cares which one it is? It's just, it's just better. You know, you can edge to your bank and it does the cool stuff and you can edge to that and it does the cool stuff. You know, my use case for an L L M is do my fucking invoicing for me. It's not even that. It's work out why exactly it is that that client's E R P doesn't like my bank account and not have me spend the next three months bouncing emails back and forth with somebody in India about one thing. Figured this out, yeah. On getting this done. That would be a great use case.

• 1:08:31Speaker 00

  That L L M's can't do that yet. If they could, that would be great, but that we're not there yet. So Microsoft and Google are in this sort of position of being the incumbent, both, you know, how can I put this, like, give you a more systematic, again, I'm sort of thinking my way through to the answer to your question. For Google and Microsoft, they're in, they have an incumbent business that is potentially disrupted pretty profoundly by this, but they also have a cloud business that sells all the new stuff for this. Amazon has an incumbent business that doesn't get disrupted by this, at least much less obviously, and a cloud business that will be very happy selling all of this stuff. Meta doesn't have a cloud business selling this stuff and has a bunch of new ways to make money from all of this new stuff, except they've got to have some better models.

• 1:09:12Speaker 00

  Apple, is this a competitive threat to the I O S ecosystem? A lot of stuff would have to happen first and they'd have to drop a lot more balls before that was to happen. And meanwhile, they're still going to sell you the nicest glowing rectangle to do all of this stuff. And then we're talking about glasses and V R, which is a whole other two hour conversation about when, when does that happen? There's still people poking around in crypto, like Web three, Web three, remember, maybe that someone said that people still working on crypto are like those Japanese soldiers on islands in the Pacific. You don't know the wars over, but there's still people working on crypto. So that's like another disruptive thing coming down the pipe. Who are the other incumbents? Netflix is a TV company. It was a car company. Elon Musk.

• 1:09:58Speaker 00

  There's a whole Tesla conversation fascinates me because Tesla bears, bulls think it's a software company and Tesla bears think it's a car company. And at the moment, it's a car company. I mean, yes, they launched autonomous driving, but what did they launch? They launched half dozen existing model cars with test drivers doing a geofence drive that everyone else was doing 10 years ago. Is that going to scale? Is, are they finally going to get the flywheel of having all the camera data, meaning they will work with just cameras? We've been, I don't, there's a conversation you could have asked 10 years ago. In fact, I wrote stuff 10 years ago. Are there winner takes all effect in autonomous cars? Will Tesla get it working with

• 1:10:41Speaker 00

  cameras before everybody else gets it working with LiDAR? Well, Waymo has got it working with 50 grand of LiDAR or whatever that that stat costs. It's tens of thousands of dollars of extra stuff on the car. So they've got it working with all of that stuff. Tesla does not, does not have it working with cameras. Will Tesla get it working with cameras before Waymo can get rid of the LiDAR? We could have had that conversation. I literally was on podcasts seven, eight years ago, having those conversations. We don't know the answer. Maybe. We don't know. But the interesting Tesla point is people always looked at it and said it's the iPhone of cars. No, it's not. What's happening is that cars are becoming Android with no iPhone. And Tesla is just selling, and is, is

• 1:11:23Speaker 00

  in that metaphor, Tesla is just another Android phone maker. And they're competing with the whole Chinese industrial policy to make more, and there's going to just going to be a flood outside the US if they're protected by tariffs. Everywhere else is very clear what's happening is there's just a flood of EVs with that are just as good as Tesla's.

• 1:11:38Shane Parrish?

  We always end on the same question, which is, what is success for you?

• 1:11:42Speaker 00

  We live in the luckiest time, you know, we are not worried about rockets landing on our heads. We're not worried about our children dying from diseases. We're not worried that the bank might be closed tomorrow and all of your money's gone. We're doing something interesting that we enjoy and that pays the rent that we want to be able to pay. I get paid to fly around the world and give slides for money. So I think I'm doing okay. I could always be doing more.

• 1:12:07Speaker 00

  But I'm always looking for the next question. Like, I'm always trying to be curious.

• 1:12:14Shane Parrish?

  This was a great conversation. Thanks for taking the time to. Right. Thank you. Thanks for listening and learning with us. Be sure to sign up for my free weekly newsletter at fs.blog/newsletter. The Farnham Street website is also where you can get more info on our membership program, which includes access to episode transcripts, my repository, ad-free episodes, and more. Follow myself and Farnham Street on X, Instagram, and LinkedIn to stay in the loop. If you like what we're doing here, leaving a rating and review would mean the world. And if you really like us, sharing with a friend is the best way to grow this community. Until next time.