Ep 91: Top AI Analyst Unpacks Today's AI Hype Cycle

Benedict Evans, one of tech's most widely-read analysts, joins Jacob Effron. The conversation centers on Benedict's core thesis that comparing AI's scale to past platform shifts (the internet, mobile, PCs) is analytically useless, and that the more productive move is studying how those previous technologies actually evolved economically to reason about where AI's value will accrue. He argues the one genuine difference this time is that we don't know AI's physical or scientific limits, unlike past shifts where the boundaries were at least knowable, and that this uncertainty is what fuels both AGI hype and doomerism without resolving anything. Benedict unpacks why capabilities remain jagged, meaning usage is jagged too, why coding became the first real enterprise use case thanks to scalable verification, and why most consumer and enterprise use cases still have to be invented by entrepreneurs rather than emerging spontaneously once models improve. He also lays out why foundation model labs may end up structurally like TSMC rather than Windows, valuable but bounded rather than owning the entire stack, walks through why automation has historically meant more work rather than less (using a hundred years of rising accountant headcount as evidence), and explains why industries like Uber and Airbnb, or Caterpillar and the internet, show just how unevenly this kind of technology actually lands. Throughout, he offers candid, historically grounded takes on OpenAI's product sprawl versus Anthropic's narrow coding bet, Apple's stumbled AI moment, and why most companies, unlike Silicon Valley, have far bigger priorities than AI on their minds.

• 0:00Speaker 00

  You can wave your hands and say, no, this is like electricity. Okay, fine, it's like electricity. Well, what happened with electricity? And there was a time before electricity and people before electricity weren't dumb either.

• 0:08Speaker 01

  You think it's irrational that like the heads of these labs are talking so much about job loss?

• 0:12Speaker 00

  We get deep into logical fallacies here. So you've got Jeff Hinton, was 10 years ago, saying, stop training radiologists. And the problem here is he doesn't actually understand what radiologists do.

• 0:20Speaker 01

  Given this discussion we've had on the foundation models, would you be doing anything different in terms of like focus?

• 0:26Speaker 00

  I have a lot of sympathy for Sam Altman.

• 0:28Speaker 01

  I'm Jacob Effron from Redpoint, and this is Unsupervised Learning, a podcast where we probe the sharpest minds in AI about what's happening in the ecosystem today and where the future is headed. Today, we had an awesome conversation with Benedict Evans. He's one of my favorite thinkers in the space. I love reading his newsletters, presentations, and had the chance to sit down with him and talk through a bunch of things. We talked about where value will accrue in AI, as well as what he thinks the ultimate valuations of the foundation models might look like. We talked about why consumer AI applications aren't really working today, and the kind of jagged edge of capabilities and their implications on enterprise adoption overall. And we hit on a lot of his really interesting takes on job usage, whether this is an enabling technology or not. Just awesome to get to talk with one of my favorite thinkers about a bunch of these things. I think folks will really enjoy the conversation. Without further ado, here's Benedict.

• 1:13Speaker 01

  Well, very excited to do this. I feel like you're one of the most thoughtful people on tech. I love reading the presentations you do. You've obviously have a huge influence on the ecosystem at large. And today, excited to kind of dig into your opinions on the ecosystem as we have it today, the future of the model labs, where value accrues. But where I really want to start with is the hype in the current moment, because I feel like you have maybe a more subdued take. It's funny to call a subdued take, being that this is as big a deal as the internet and mobile, but maybe not civilization altering like the industrial revolution. But I want to start there. What are you like paying attention to that might change your mind on that? Because I'm sure a lot of our listeners will disagree.

• 1:50Speaker 00

  I'm not sure how analytic you can get about saying, well, is this as big as mobile or as big as the internet? Or is big as computer? Was mobile bigger than PCs? Like, I struggle to kind of how you can kind of quantify those things. I think the place that I'd come from is just to say, look, the internet was kind of a big deal and did absolutely change everything. And so was mobile and so was PCs. And yes, I think you certainly kind of can say that the web was bigger than PCs because PCs was like 50 or 100 million devices. You know, when Marc Andreessen launched Netscape, there were like 75 million, 100 million PCs in the world. And now there's 5 billion smartphones. So clearly it's got bigger over time.

• 2:33Speaker 00

  I suppose the thing I sort of struggle with is that nothing like this has ever happened before. And we've not had technology wiping out great swathes of jobs before. And we're automating stuff in a way that this has never been done before. And I kind of, I was, I was, I was talking to somebody the other day at a big company, and they were, you know, very smart guy, PhD in all sorts of computer sciencey stuff. And they said, you know, big companies have never had to deal with people building tools and systems for themselves before. And I thought, so have you heard of Shadow IT? Have you heard of Excel? Yes, this is different, but it's always different. And we do go through these big changes and they're always different and they're always bigger.

• 3:14Speaker 00

  But that doesn't mean you shouldn't kind of go back and look and say, well, what is it that happened the last five times? We had a world changing piece of technology. I don't think there's much percentage in arguing about which one is bigger or smaller. I think what's interesting is to say, well, what happened the last five times we had something like this? And yes, you can wave your hands and say, no, this is like electricity. Okay, fine, it's like electricity. Well, what happened with electricity? And there was a time before electricity and people before electricity weren't dumb either. And people were kind of scratching their heads and trying to work out what to do with this. And so it's just useful not to imagine that nothing ever has happened, never happened before. And to kind of sit and scratch your heads and think, well, what are the patterns that you can see here? I wrote something at the end of last week, talking about token pricing.

• 3:56Speaker 00

  And one of the sort of blocks of the essay was, well, how did mobile work? How did semiconductors work? How did fiber work? How did operating systems work? And none of those have predictive power. Like, you can't prove that this is going to work in a certain way by arguing about how close it is to mobile. But you should go back and look at what happened at mobile and think, okay, so what does this tell us about what might happen? So you can point to semiconductors and say you had this escalating cost structure, Wachslaw, that said that, you know, the cost of a cutting edge fab doubles every four years. And that's, and foundation models look a little bit like that. You can look at mobile and say, well, mobile had marginal cost.

• 4:39Speaker 00

  Like adding more, a lot of people in tech don't understand this, like mobile networks have marginal cost. You add, if you double the number of traffic, you have to build a bunch more base stations and put a bunch more equipment on the base stations. And so you have marginal cost. And that looks a lot like the supply crunch we've had in the last six months, where everyone got an iPhone and started watching YouTube and the networks fell down. And they had to change pricing schemes. But the interesting part of that comparison is that mobile data traffic in the last 15 years has gone up by like one or 2,000 times. And it's a trillion dollar industry and they spend $200 billion a year on capex and they don't make any money. I mean, a bit of money, but like all the value is other people. You know, they didn't get to do Uber. They don't run your banking. They don't do YouTube. That's all other people. And all the value went up stack. And obviously, that's one of the questions about, about, about AI.

• 5:23Speaker 00

  You can point to cloud. You can point to operating systems. Sam Altman said, we're going to be like Windows, but like LLMs don't have network effects. So it doesn't look like they're going to turn out like Windows. But the point is to say, look, there's a bunch of different competitive dynamics, industry dynamics. What happens at different layers as a stack? Can you compete up? Can you compete down? And you can learn from all of those.

• 5:45Speaker 01

  Yeah. I think one thing that makes, you know, challenging to figure out like the, you know, long-term macro impact of all this is just, obviously, the capabilities of these models are not static. But you pointed to some of the big outstanding questions in the space. It feels like the biggest is just, you know, what capabilities do we ultimately get to on the model side? I'm curious, like, what would change your mind that actually this is a bigger deal than, that's something more akin to the industrial revolution? Is it a model capability, you know?

• 6:08Speaker 00

  Well, so I think, you know, you could make a very obvious kind of counterargument, which is just to point out that each of these compound. Part of that is that's why it's happening quicker. But like mobile internet didn't need to wait for the internet.

• 6:19Speaker 01

  Right.

• 6:19Speaker 00

  And the internet didn't need to wait for PCs and PCs didn't need to wait for semiconductors. And semiconductors didn't need to wait for, I don't know, specialty chemicals or, you know, whatever you think the enabling technology is. Photolithography. And that didn't need to wait for electricity. And electricity had a whole manufacturing industry that already existed and so on. So there's always this kind of compounding effect. And, you know, I mean, another way of saying this is big as the internet is to say, well, the internet was 10 times bigger than PCs. And so maybe this is 10 times bigger than the internet. The big, like a difference, like the actual tangible difference to a previous platform shift is that we don't know the physical limits of this.

• 7:00Speaker 00

  So we do not have a good scientific understanding of why the models work so well and what will happen next. Which was whereas with, like, you go back to 2010, you didn't know what the next iPhone would be, but you knew it wouldn't have like a retinal implant and a one-year battery life and flight and cost $5. You know, in the internet, like 1995, you didn't know, you had no idea how the internet was going to work. You probably thought you did, but you didn't. But you knew that PCs are like $2,000 and $3,000 and most people don't have one. And telcos aren't going to give every household on earth fiber to the home next month. Like, you kind of knew the basic kind of physical parameters. And so that's clearly a kind of a conceptual difference. And, but I think then there's, then you can kind of say, okay, we've got this, this stuff we don't know.

• 7:43Speaker 00

  Fine. Then you can kind of break that apart. So there's, there's maybe there's one kind of binary question, which is, you know, there's, there's like a, there's an urban legend, may or may not be true, that during the Cuban Missile Crisis, there's a legend, there's a rumor starts that the missiles have launched and everyone on the stock exchange starts selling. And one veteran trader goes out and starts buying and he says, well, it's binary.

• 8:05Speaker 00

  Either the rumor is true, in which case we're all dead anyway, or it's not, in which case the stocks are cheap. And, you know, if this stuff, like, if, you know, if, if, if AGI really does, you know, get a happen and ASI, whatever, pick your acronym or sort concept, if all of that happens, then we've got bigger problems than worrying about middle-class unemployment. Like, we're all Peps, fine. Otherwise, then, okay, let's worry about, let's try and work out what this means for enterprise software. Those are kind of the

• 8:33Speaker 01

  I love that comparison. It's like, you're not even, it's like, it's kind of unknowable right now, what the shape of model progress looks like. There are some, all the shape that we're worrying about and the implications it would have for everything else, there's so much bigger fish to fry, if that's the case. That it's almost like, you wouldn't, you probably wouldn't be writing about enterprise software in the, in the context of which

• 8:50Speaker 00

  Yeah, exactly. It's like, yeah, exactly. You know, either you can worry about that stuff. A lot of these conversations end up in hunt for metaphors. It's like, you know, it's like nuclear weapons. It's like this. It's like that. A lot of these conversations also end up sounding like the stuff you say at 2 o'clock in the morning, if you're a slightly drunk philosophy grad student. It's like, hey, man, have you thought that like, maybe we don't have structural understanding either? Like, well, great, thank you. I can't do anything with that. I can't analyze that. This is like the Descartes fallacy. Descartes thought that you could like deduce the nature of God from first principles. No, you can't. We don't know. We'll find out. Meanwhile, let's build some stuff and work out what works. I mean, you go back to, you know, there's a little bit of an echo of this. I mean, you go back to, you ever read the John Perry Barlow Declaration of Independence of Cyberspace?

• 9:35Speaker 01

  No, I haven't.

• 9:36Speaker 00

  God, you're so young. So John Perry Barlow is like one of the lyricists for the Grateful Dead, I think. So I'm too young to really know this. But anyway, so he wrote this beautifully poetic thing. And it's like, you giants of of steel and iron and stone, you have no sovereignty on the internet. And people kind of thought this was going to end war.

• 9:55Speaker 01

  Yeah.

• 9:56Speaker 00

  Because everyone would understand each other now. And so you have to remember, there was an awful lot of this kind of wild eyed millenarian utopianism around the internet, too. Some of which was true, some of which wasn't. Meanwhile, let's build some software and work out what people do with it. And that gets you like then. So you set that whole argument aside, say, right. Open Air kind of looks like Netscape. Chatbots, this is this a web browser like? And again, you don't want to be too rigid on the metaphor, but you have this stuff that's amazing and it doesn't work. And I think this is kind of a more interesting way of thinking about where we are now. That go back to like trying to use the internet in the mid 90s or trying to do mobile internet, never mind 2010, 2002.

• 10:37Speaker 00

  And it's kind of sort of works and it's obviously really cool and amazing and it's going to change everything. And I have this very vivid memory of of sitting in a meeting with a mobile software company in like 1999. And I had my Palm five and I had a Nokia phone that had GPRS and an infrared port. And so we were trying to connect my Palm five to the internet over GPRS over infrared to this Nokia. And we're sitting there going, oh, my God, this is so cool. And like, but nobody, it didn't actually work and no one is actually going to do that for another 10 years. Obviously, this isn't another 10 years, but it is that sort of moment of like. What are the building blocks here? Where's the value capture? What are the points of leverage?

• 11:20Speaker 00

  What are the experiences that people are actually going to use to get billions of people to use this every day? As opposed to 10 million people using it all day and a billion people using it once or twice a month, which is kind of where we are now. I'm exaggerating a bit. We've got like sign of like 10, 15% of people are daily active users right now. And even daily active is you're still, you're using this once or twice a day. You're not using it all the time. And then there's another 20 or 30%, 40% of people who are like weekly active users or monthly active users. And you remember from social, like weekly active user is bullshit. So like, if you're using this once or twice a week, you're clearly not in the dream of Silicon Valley that this changes computing.

• 12:03Speaker 01

  Yeah.

• 12:03Speaker 00

  It's a useful new tool sometimes. And how do we bridge that? And I think this is kind of a more interesting split, like you've got software development where this has product market fit unquestionably. Yeah, poof, works, changes everything in software development. Fine. And then we can talk about that. Then you've got like a bunch of people who are kind of like us, like they're knowledge workers who are work for themselves, a flexible free phone, got lots of stuff going on. And you're using this a lot. And I think this reminds me of quite a lot of people who use Nation. And then you've got all these other people who are like. Yeah, it's kind of useful. Yeah, that was cool.

• 12:42Speaker 00

  But aren't using it all the time. And how does that change?

• 12:45Speaker 01

  Yes.

• 12:45Speaker 00

  The analogy I I always use here is imagine you're an accountant seeing the first software spreadsheets in the late 70s. This is life changing. Now imagine you're a lawyer seeing it. Okay. That's cool.

• 12:56Speaker 01

  Yeah.

• 12:56Speaker 00

  I could use that for my timesheet next week, but that's not what I do every day. And I think that's kind of the split. And that's, we're talking before we started recording about like being in the Silicon Valley bubble. If you're in the Valley bubble and you've got five Mac minis in a cluster. And, you know, you're leaving, you're walking around all day, holding your laptop open. So your Claude code can carry on running. You're not really, you're not the normal user here. The normal user, you're you're the guy using Telnet to connect to the internet, connecting to Telnet over to using Telnet on the internet in 1992. Or you're using dial up on Netscape one in 1994. Was it Mosaic even before that in 1994? You're not like getting the real experience.

• 13:37Speaker 01

  Probably the hardest part of the whole thing is figuring out like just, you know, I feel like that makes a ton of sense for current model capabilities. And it's like, you probably could have looked two years ago or a year ago, and you would have been like, God, this stuff has product market fit in, you know, some product market fit in consumer, but doesn't really work anywhere else. And then the coding stuff starts to work. I don't know what you make of the fact that the people at the labs who are closest to the research all seem super bullish on, you know, kind of continued capability improvement that might then start to change some of this.

• 14:02Speaker 00

  So I think one of the questions here, and this is like a UX interface deployment kind of question is.

• 14:12Speaker 00

  How many of the problems that we might want to automate, would you struggle to explain to an actual person? I mean, put this another way, like presuming the model capability, presuming this, here's this thing you want to do with Claude or ChatGPT and it kind of can't do it. Or you can't work out how to tell it to do it. Would that work if you had a person at the other end? How hard would it be for you to explain what you want? What would that be like? Would that be the right way of doing it? I mean, I'm like, you know, I'm not neither the lawyer nor the accountant. I'm like a weird outlier in that I work for myself and I don't have anybody working for me. And partly because of that, I don't have lots of repetitive tasks because I've structured what I do so that I don't have lots of repetitive tasks.

• 14:57Speaker 00

  Because I can't do them because I don't have a bunch of interns. So now I get Claude and Copilot and ChatGPT. And now I've got a bunch of, now I've got this thing, but I don't actually have the tasks to give to it. What I'm kind of getting at is, is there is the model capability and then there's, there is your ability to isolate and describe the things that you could give to it.

• 15:19Speaker 01

  Yeah.

• 15:19Speaker 00

  And that's a different problem. This is, I think, is particularly a problem with voice and chat. But it's quite hard to work out that you have that problem and to work out that you could give that problem to this system. With every new technology, it's harder and it takes longer to make the new things that you could only do with the new thing. As opposed to automate doing more of the old thing. Like with every new technology, you start by taking this thing you're already doing and doing it more and faster with the old technol, with a new technology. And forcing the new technology to fit the old way of doing it. And it takes longer to think of the completely new thing.

• 15:57Speaker 01

  Yeah.

• 15:58Speaker 00

  That's only possible because of this. And generally, the completely new thing isn't automating a whole bunch of stuff you're already doing. It's something else. One of the sort of analyses I did in the presentation I published in May now, which is feels like a lifetime ago.

• 16:13Speaker 01

  Dog years.

• 16:14Speaker 00

  Yeah, dog years, exactly. AI years. Was, you know, a number of people working as accountants in the USA in the course of the 20th century is a straight line up and to the right. Even as the tech industry progressively automates over and over again, like punch cards and adding machines and mainframes and all the stuff that tech industry has done, number of accountants keeps going up. And there's two things you can say about this. One of them is, is now this, this cliche, the Jevons paradox that everyone rushed to look up on Wikipedia last year. Which basically is price elasticity. Like if you make it cheaper to do stuff, do you do the same work for less money? Or do you do more work for the same money or more work for more money? Because you might have a different ROI. But I don't think that's what that is. Like, I don't think that if you are like, you know, five years in at PWC now, you're doing exactly what you'd have been doing if you'd been five years in at PWC in 1970, but more.

• 16:58Speaker 00

  It wasn't called PWC there. Anyway, it's high waterhouse, something else. Anyway, you're not doing what you were doing then, but more. You're doing the stuff that you were doing then is now 10 seconds once a week.

• 17:07Speaker 01

  Yeah.

• 17:07Speaker 00

  But this is his problem. It's just making the model better. Fine. Okay. Even if you had a model that actually was AGI, whatever AGI means, and now people are trying to redefine AGI stuff that was working two years ago, but fine. Presume you have a model that is actually a person. What does that mean?

• 17:26Speaker 00

  How would you, what do you, where does that fit? What do you do with that? How does that fit into an organization? And it's not as necessarily as obvious and easy to answer that as it seems.

• 17:38Speaker 01

  If you take like a accounting or consulting or something, you know, and you think about like, I think maybe a counter argument to this would be. You had tools in the past that could do one specific part of a, of a large set of skills that someone might do. And yeah, cool. So people, you know, found other skills that they could go do. You could imagine, and certainly it seems like pretty models, maybe decently close to being, you know, as good at a set of skills as like your average college grad would be. For, you know, who has never worked in a consulting firm. We'll take that. That was my first job out of college. So I think the the concern would be anything that like might be reinvented into, you know, that a 22 year old with no experience in that job might do, the model could just do out of the box.

• 18:13Speaker 00

  Yeah. So the, the, the, the, the way to frame this is like, we've been automating higher and higher level human functions. So we started in the 19th century with human beings as beasts of burden.

• 18:22Speaker 01

  Yeah.

• 18:23Speaker 00

  We automate legs.

• 18:24Speaker 01

  Yep.

• 18:24Speaker 00

  And then we automate arms.

• 18:25Speaker 01

  Yep.

• 18:25Speaker 00

  And then we automate fingers.

• 18:27Speaker 01

  All we have left is our brains, but it feels like.

• 18:29Speaker 00

  But like you, you've, you've progressively automated everything all the way up to the top of the stack. I don't think, I think that's kind of an un-falsifiable statement. And I think the problem is you could have said that in 1950. Because you kind of didn't, there, all this other stuff happened that you couldn't see then, that it turned out you couldn't automate. And of course, we don't know where this stuff is going to go and what it will be able to automate and won't automate. So as I said, you know, there's a, there's like an un-falsifiable statement that says like, you know, the only way you can falsify it by coming back and kind of waiting five years and seeing what happens. But like, it may be, yes, that we get a thing that actually can do anything that people can do by definition. Well, clearly then everything changes, but right now we don't have that. We have something that's very jagged.

• 19:13Speaker 01

  Yeah.

• 19:14Speaker 00

  And we don't know whether it would go all the way to that. And we certainly can't just presume, well. I mean, this is the thing that drives me crazy about this chart of like, you know, the AI can do something that takes people 17 hours, 18 hours, 24 hours.

• 19:29Speaker 00

  Yeah, but there's also stuff that takes people five minutes that it can't do.

• 19:32Speaker 01

  Yeah.

• 19:32Speaker 00

  That's not, it's not linear like that. Intelligence isn't linear like that. So, you know, it may be that, yes, this goes all the way to do everything that anybody could possibly do. But at the moment it can't. And we don't know when or if it will be able to do that. Go back and look at all the other times that you automated absolutely everything that your 25 year old was doing. And what is it that they're doing now? And how did that change? I mean, this is like, you know, the, going back to the 19th century, this is a lump of labor fallacy. That you could always see the jobs that will go away and you don't know the new jobs.

• 20:04Speaker 01

  Yeah.

• 20:05Speaker 00

  And the new jobs will be doing things with completely different kind of skills that it didn't occur to you existed or that you needed or wanted. These are all un-falsifiable statements.

• 20:14Speaker 01

  Yeah. But I guess, given all that, do you think it's irrational that like the heads of these labs are, are, you know, talking so much about job loss? So, you know, it seems like it's a probability distribution of kind of an unknowable of how much better these models are going to get. And there's some part of the probability distribution where this stuff does really matter, right?

• 20:29Speaker 00

  Yeah. I mean, we, we get deep into logical fallacies here. So you've got Jeff Hinton, whatever it was, 10 years ago, saying, stop radio, training radiologists. And the problem here is he doesn't actually understand what radiologists do. So the narrow problem is actually machine learning couldn't do what he thought it could do. And the broader problem is, but that isn't what radiologists did anyway.

• 20:48Speaker 01

  Yeah.

• 20:49Speaker 00

  And, you know, I mean, you you you started in consulting, like, the client generally isn't buying a PowerPoint.

• 20:57Speaker 00

  The PowerPoint might be the tangible expression of the project, but that isn't what they're paying you for. They're buying all sorts of other stuff. And the same thing as an accountant or the same thing as a lawyer. The the thing I wonder here is about consumer surplus. So whatever the enterprise version of consumer surplus is. So for the sake of argument, like in nineteen eighty, you know, your lawyer goes and sends their power legal to the basement into the library.

• 21:23Speaker 01

  Yep.

• 21:23Speaker 00

  And they come back after a day with two precedents. Great. Today, five years ago, your accountant, your your your associate would have gone to the database and they had found twenty precedents. And so did the opposition.

• 21:37Speaker 01

  Right.

• 21:38Speaker 00

  And so the client gets billed the same amount of money for the same number of hours. You deliver the same number of pages in the filing. Your filing has exactly the same likelihood of winning that it did then, and you get kind of the same result you would have got then.

• 21:51Speaker 00

  Consumer surplus, like you've you've done kind of a bunch more stuff for the same price to deliver the same result, which is, you know, this is the extreme. This is the iPhone, you know, the iPhone is ten thousand dollars, hundred thousand dollars of consumer electronics disappeared. So there will certainly be some cases where that's what's happened. I think there's another way of of of looking at this here, maybe, which is just to think about how massively variable this will be. So there's a chart I I've used for a while. You know, like the Mark and Reese loves the software is eating the world thing. The Uber doesn't sell software to taxi companies and Airbnb doesn't sell software to hotels. Really important observation, fine. What did Uber do to taxis and compare that to what Airbnb did to hotels? Turns out, you go and look in the like, say, look in New York, I mean, it's a bit fuzzy

• 22:35Speaker 00

  because it depends by the city, but in New York, taxi market is down by, I forget the number, three quarters. Uber is gone like this, and the result is there's a bigger market with more people. And I mean, the market looks different, the average number of rides per driver is different and so on. But the point is, it unlocked all this new demand and demolished the yellow cab business. Fine. Now look at Airbnb.

• 22:55Speaker 00

  Maybe slowed down the growth of hotels a bit.

• 22:58Speaker 01

  Yeah.

• 22:58Speaker 00

  It's maybe ten, five, ten, fifteen percent the size of the hotel market. And the reasons why are, well, hotels and cabs are different.

• 23:06Speaker 01

  Right.

• 23:07Speaker 00

  Half of hotel business is business, there's a confidence business.

• 23:10Speaker 01

  As long as the models have jagged capabilities like this, where they work a lot in some cases, not others.

• 23:14Speaker 00

  The models are jagged, and then the use cases are jagged, and some of the use cases are physical and not just about processing information. So I wrote an essay a couple of weeks ago about like trying to predict job impact, because like clearly this will have an impact on employment. But trying to do these like star charts of radar charts of which ones where you say, ah, well, you know, Opus eight point six can do ninety three percent of what a first year law firm associate is doing. This is a delusional statement. Because you can't measure what the law firm student is, the associate is doing like that. You also can't measure whether the model can do it. This is actually the expert in the system fallacy, where you people kind of thought that you could measure how you recognize a cat from a dog.

• 23:59Speaker 00

  And you say it's the same mistake, you can't measure what a law associate does. What I was making was like, first of all, like the the the accountancy job changed, which is the content we made earlier. Secondly, there are industries that your analysis would say are completely unaffected that will get demolished by something else like journalism.

• 24:14Speaker 01

  Right.

• 24:15Speaker 00

  Is the for the sake of argument, the internet didn't change what it was to be a journalist at all, but it demolished the local advertising business, which wasn't in your analysis at all.

• 24:22Speaker 01

  Right.

• 24:22Speaker 00

  And the same thing for Uber, like you wouldn't have looked at the smartphone. Nobody was, I mean, I was in the mobile business, we were talking about location all the time. Nobody saw that Uber opportunity.

• 24:32Speaker 01

  Right.

• 24:32Speaker 00

  And so you kind of go and back test these analysis where you say, well, this job is exposed this much and this job is exposed this much. It's horse shit. You know, it's like the joke about the physicists who are predicting which horse is going to win a race, and they say, first, we're going to presume that the horse is a perfect sphere. You know, it's like, great, if you presume that, if you define this into something that you can understand, then you can understand it, but it isn't that, it's something else.

• 24:55Speaker 01

  Right. I guess it's really this question of like, yeah, are there still capabilities that like, you know, entry level workers will have that models don't?

• 25:01Speaker 00

  Yeah, so the, you know, the, have you ever come across Anselm's proof? Anselm is this medieval theologian. And Anselm says, okay, first proposition, God by definition is that by which nothing could possibly be greater than God. Okay, that seems reasonable. Second proposition, something that exists is greater than something that doesn't exist.

• 25:21Speaker 00

  Okay. Therefore, God exists. And thirty seconds later, everyone in the room says, Anselm, dude, that's fucking bullshit.

• 25:29Speaker 00

  Because like you could prove anything like that, like, well, a horse that exists is better than a horse that doesn't exist. They're like, we'd better if a unicorn existed than not exist. Like, you can't prove stuff like that. But it took like a thousand years of people arguing about this to work out why that was wrong.

• 25:43Speaker 01

  Yeah.

• 25:43Speaker 00

  And the reason I mention it is like, if you define AGI as necessarily inevitable and necessarily going to kill us all, then AGI is necessarily going to kill us all. Great. But you haven't proved anything.

• 25:52Speaker 01

  Yeah. So what do you pay attention to like in the interim to, you know, obviously, it it feels like almost, uh, there there's always a a set of people that, you know, the models can do something more, and it's like, well, it still can't do X or Y. Like, one thing I really appreciate about you is like you have a nuanced perspective on this, you kind of take the facts that are out there. What like, you know, what are the things you're paying closest attention to or would start to adjust to your view on on some of this?

• 26:14Speaker 00

  I think there's that I kind of pulling apart, like, what are the sort of interesting sets of questions? Firstly, there is like a capital conversation. So what's going on in chips? What goes on in data centers? When do chips catch up with supply and demand? Like, what's this CEO of SK Hynix the other day said? He reckons, you know, supply and demand is out of whack until twenty thirty. Well, it's like, well, he would say that, wouldn't he?

• 26:36Speaker 01

  Good for his business.

• 26:37Speaker 00

  Yeah, he's got a good business. So there's all of that stuff, data centers, time to power, grid deployment, et cetera. Secondly, there is the sort of fairly, what seems to me really incontestable fact that right now there's something between three and six companies making models and they're all kind of the same. And, you know, someone's ahead this week, someone's ahead next month, you know. But the fact that SpaceX is like a paradoxical statement, like, the fact that SpaceX managed to jump straight back up to almost at the top of the leaderboards after completely flunking out is a really negative signal for SpaceX. He has that for a paradoxical statement. Because what that says to me is not very hard, if you're willing to spend a couple of billion dollars and hire the right people. You know, this stuff remains, they do not appear, there are not yet fundamental barriers to entry.

• 27:22Speaker 00

  Barriers to entry in the sense that they were for search or social or mobile.

• 27:26Speaker 01

  The capital, you know, I mean, at this point, like, there's only so many companies that can uh can bear the cost to train a Frenchman.

• 27:32Speaker 00

  Well, there are, but it's not two.

• 27:34Speaker 01

  Right. But like, it's gonna, you keep going up, right?

• 27:36Speaker 00

  Well, so this was my semiconductor comparison that we made earlier. What happened with semiconductors is it got harder and more expensive with each generation. And so we shrank from dozens of companies of the cutting edge just to just one, and then two one step behind. That may be how this evolves, we're not there yet.

• 27:48Speaker 01

  Yeah.

• 27:48Speaker 00

  Of course, if the curve slows down, then everything else changes.

• 27:51Speaker 01

  Compute is like the major moat of these businesses today.

• 27:53Speaker 00

  As long as scaling continues, then you you should sort of expect the number of cutting edge companies to shrink down. Then there's a then there's a question of, well, what what where do you need to be on what is it, the Pareto curve?

• 28:05Speaker 01

  Yeah.

• 28:05Speaker 00

  Like, how many kind of use cases need to be absolutely the cutting edge? How many, well, put another way, how many come use cases need to be there, have an ROI to be there? The other extreme at the end of the curve is like dictation works for free on your phone.

• 28:17Speaker 01

  Yep.

• 28:18Speaker 00

  And what's in the middle? Like, does Amazon, where on that curve does Amazon need to be to do how much, or what to get what ROI on recommendations or review summarization, whatever? And so there's, there will be people all the way along that curve, and presumably, as long as you, the further, the cheaper you get, the more commoditized it gets. And you get to like, you know, you've got dynamic real time bidding across thirty neo claps.

• 28:39Speaker 01

  Yeah.

• 28:39Speaker 00

  And so how much stuff is at the head of the curve that needs that?

• 28:45Speaker 00

  How much value, how competitive is the head of the curve remain? And of course, you know, there may be a paper that says, hey, guess what? You can have a model that's ten exercise for a tenth of the price. So it may be that you get some price collapse, even as everything scales.

• 28:57Speaker 01

  Yeah.

• 28:57Speaker 00

  We don't know. But then, like, how much of the value can the model capture? And this is kind of what we were talking about earlier, about the the sort of how do you explain it to the model question? And the thing I was thinking about, I I do a weekly newsletter and I wrote a column about this last night, so it's sort of fresh in my mind, is like, most people are three things. Most people aren't tool builders. Most people don't see the problem that the tool would be solving. And most people aren't in a position to build the tool, even if the other two weren't true. And so I kind of explain what I mean by that. Like, if you think about like most enterprise, half of enterprise software, you look at it and you don't get it, the customer looks at it and they don't get it. You don't see why that's a problem, you don't see why we need that, we don't understand why we would do that.

• 29:41Speaker 00

  Think about how many tools we all use every day, where the first time you saw it, you thought, what's the point of that?

• 29:45Speaker 01

  Yeah.

• 29:46Speaker 00

  So now you're, you've got the job, you've got that problem every day, very often you don't see that you've got that problem. And then even if you see it, most people, the the skill of being like a really great salesperson is completely different to the skill of being really good at designing a new piece of, say, sales enablement software. The skill of being a really good video editor is completely different to the skill of being really good at designing a completely different way to edit video. I mean, the person who is, who does do that probably is, does know a lot about video editing, but most people aren't that person.

• 30:16Speaker 00

  So the skill of actually working out what the software should be, what the tool should be, how it should be done, what the right structures and workflows, and how the network should work. Think about every great software pitch you've ever seen, now like, oh, wow, that's a really clever way of doing it.

• 30:29Speaker 01

  Yeah.

• 30:29Speaker 00

  I would never have thought of that. And yet you're expecting like random middle managers in the back office of big companies to dream that up like this afternoon, if you give them thought. And then like the third piece is very often it's regulated data. And even if it's not, it's, you know, there's 1,500 people who are touching this data and it needs to go into a system of record and there's money attached to it. So you can't like have random people just like building tools and like replacing SAP or replacing these systems. And so everything gets more fuzzy. So like, there's a sort of, there's like an improvised space of CSVs and Excel and Tableau and Perl and so on. And then you've got your big iron horizontal systems and then you've got four or 500 SaaS apps inside every big company.

• 31:12Speaker 00

  And AI kind of shuffles all of those around. And so like, if you're PWC and you hire a thousand graduates a year, 10,000 graduates a year, you use software. If you're Redpoint and you maybe hire a couple of people every year, you've got Google Sheets.

• 31:26Speaker 01

  Yeah.

• 31:27Speaker 00

  And then there's something in between and there's a point where you say, hey, maybe we should use Notion or may, maybe we should hot build this software, maybe we should get this thing. And then this horrible moment where you see, hey, maybe we should use Workday, which is like the ultimate test for AGI, if it can navigate Workday successfully. And that, none of that is binary. Like, and so then, okay, there will be this new SaaS app that somebody built using AI. And maybe or maybe not, it does stuff using AI that you couldn't do before. But also you'll be able to do that in SAP now.

• 31:57Speaker 01

  Yeah.

• 31:57Speaker 00

  And also you'll be able to get Excel to do that. And also maybe you'll be able to use Co-Work to do that. And so which do you choose? Well, how did you choose whether to buy software or use Excel or use SAP before?

• 32:08Speaker 01

  Yeah.

• 32:09Speaker 00

  Same question.

• 32:10Speaker 01

  Yeah. I mean, I thought there's two parts to your argument, like, you know, I think you've, you know, probably said, you know, the foundation models are, you know, I mean, I don't even take it as like not necessarily valuable. It's more just like will not capture most of the value of this, of this ecosystem. I, I'm still unsure whether you think there'll actually be, you know, whether these long-term as anthropic and open ago public will be like.

• 32:29Speaker 00

  So, I mean, you know, again, without arguing by analogy, I will point to analogy. TSMC has a monopoly on the cutting edge of semis. You don't write apps for TSMC.

• 32:41Speaker 01

  Yeah, one of the 10 biggest companies in the world, though.

• 32:43Speaker 00

  Yes, but their net income last year was half of Apple's. It's a great company, very profitable company. Uber does not write apps for TSMC. You have multiple layers in the stack, different abstraction layers, different ways of building that, and the right people to build an enterprise go to market for creativity software, different people to the right people to build. This is why we have a stack. This is why AWS doesn't own the entire tech industry. It's why every app on your iPhone isn't made by Apple.

• 33:14Speaker 01

  Right. But I think obviously if you're the AWS of, you know, the AI space and the opportunity is as massive as it appears, that's still quite a valuable business.

• 33:21Speaker 00

  Yeah, great business.

• 33:22Speaker 01

  Yeah.

• 33:23Speaker 00

  This is the thing, Windows is a great business, TSMC is a great business, AWS is a great business. None of them own the whole stack all the way up to the top. Now, you can turn around and say, yes, but Benedict, like, you'll just be able to go to Claude and say, invent 10 new enterprise software applications and then go build them and then go and work out the right way of building that. You could build the whole thing. And again, we're back to Anaselm. You're back to, well, if I define this as something that can do absolutely everything, can it do absolutely everything? Yes. Okay, but that's the question. You can't, you can't win the argument by defining it away.

• 33:55Speaker 01

  What do you make of what's happened in the coding space?

• 33:57Speaker 00

  It's funny to see obviously software jobs exploding.

• 33:59Speaker 01

  Yeah. More just from the model providers, you know, competing pretty effectively against application companies.

• 34:05Speaker 00

  Um, well, so I think it's kind of interesting to think about what happened with SaaS here. Whereas with SaaS, we went from probably an order of magnitude, maybe several orders of magnitude increase in the amount of software that there was. And so, you know, in the PC era, typical big company has, I don't know, a dozen, couple of dozen apps in the data center. And now the typical big American company has four to 500 SaaS applications, plus several thousand legacy stuff they've accumulated over the last 30 years. And that was just go to market. And so that meant you could automate all of these things that you could not have justified getting on-prem software for before. And so at a minimum, we should presume, okay, it's going to be way cheaper and way quicker to build new software, plus it can do this whole class of thing that your software couldn't do at all before. Therefore, there will be more software.

• 34:48Speaker 01

  Yep.

• 34:49Speaker 00

  And some of that, some of the incumbents will fall away. Some of them will fail to make that jump. Some big complex expert systems will get automated by, away by some very simple machine learning system. Some stuff will get bundled, unbundled. This is what happened, right? What happened to people's soft, what happened to Siebel systems? Like, there will be that kind of platform shift, people die, fine. Second question is, it was the point I was making earlier about, does this mean you have less or more software? How much stuff you no longer need the SaaS app? You'll just do it inside Excel with Copilot or you'll do it inside Claude instead?

• 35:20Speaker 01

  Certainly we'd all agree there's more software, but I guess that maybe the point I was trying to make on Claude Code is like, that is an example of the, you know, model providers moving into the application layer and winning it pretty effectively.

• 35:29Speaker 00

  Yeah. I mean, I suppose this is, it's funny, this is like all the conversations about, you know, what should be integrated in macOS or what should be integrated in Windows. So, and I wrote, wrote stuff about this years ago, that like, you go back to the mid 80s and spreadsheets didn't do printing or didn't do charts, particularly didn't do charts.

• 35:48Speaker 01

  Yeah.

• 35:48Speaker 00

  So charts were a separate program. In fact, you can go back to the late, early 90s and you can find these group tests of 30 or 40 spell check apps. So the way it worked is you write, save your document in your Word or WordPerfect or whatever, and then you open the spell check app. And the spell check apps were like $200 and $300 each. And they would have this grid of like, do they support Word 6 or do they only support Word 5? And do they, do they support WordPerfect and so on? And you'd run that. And of course, over time, that gets integrated and then it becomes wavy red lines. And printing, of course, was like, that was a whole thing. They got integrated into the operating system and the Word, all the DOS apps, all they should do printing. And then it gets integrated in the operating system. So there is this question of what sits naturally at what should, what naturally gets integrated down into your enabling layer and what should be a separate application?

• 36:33Speaker 00

  This was of course the antitrust argument about Internet Explorer. But today, if you bought an iPhone and it didn't have a web browser, that would be insane. So it changes over time. And so there is this question, well, what naturally gets integrated into the lower level substrates and what naturally gets unbundled into separate things? Where you really need to understand, have deep vertical domain knowledge and go to market and everything else that builds a vertical software app. And this is the same question of, well, why isn't that part of AWS? And the answer is, well, it depends.

• 37:00Speaker 01

  Maybe to push back for a second, like, in the case of coding, like, the model to some extent is the product, right? And so that feedback loop that you have between the model, the real world usage of the model, you know, the, then makes the underlying product better. We know, whereas like AWS is just like the compute itself, whether it's used in pharma or whether it's used in, you know, a grocery store. It's not like that usage fundamentally changes the, the product in any way. Compute is compute. You know, in this case, it would be very hard to, and it has been very hard for other players, and I think both Windsor, Curse, or they've all concluded like, God, we need to own the underlying model layer to have an effective product in this space. And ultimately, just as an application on top, we're going to get totally hosed.

• 37:37Speaker 00

  Yeah, but isn't that the conversation that we had with the hyperscalers? Which things should be naturally be part of the platform and which parts are dedicated vertical tools? Which parts are naturally part of Windows?

• 37:50Speaker 01

  Yeah.

• 37:50Speaker 00

  And which should are naturally better if they're part of the operating system and which parts aren't?

• 37:55Speaker 01

  The one big market we've had so far in LLM world has actually proven to be very good for the, for the model providers to provide themselves.

• 38:01Speaker 00

  I think there's an interesting, you know, there's, there's a couple of like things you can ask about the specificity of why is it that it's coding that worked?

• 38:09Speaker 01

  Yeah.

• 38:09Speaker 00

  Is it just that it's that, that all these people are coders? Is it like, well, we fix the problem that we see in front of us? Is it that there's something specific about LLMs and code that makes it work particularly well? Is it that, that you see the problem in front of you and so that's what you work on?

• 38:28Speaker 01

  Yeah.

• 38:29Speaker 00

  Versus, is there something specific about this? Is it, you know, my point about tool builders? Is it that the tool builders are building the tool for their task? You know, the one place where the tool builders are developers is development tools. So I don't know. Is it, is it, and I don't think we have a good, I may be, I'd love to know, is there some theoretical, there are theoretical, theoretical arguments that says that code is uniquely well-suited to LLMs?

• 38:54Speaker 01

  A hundred percent. Code to math, you know, very verifiable. You can run, run them, you know, millions of times.

• 38:59Speaker 00

  Well, this is another point. This is another point is where, if you, we're going back to the conversation about automation. Where do you have enough training data and where is validation scalable? And the more that you get into like complex consulting projects, the harder it is to have scalable, repeatable validation.

• 39:19Speaker 01

  Yeah.

• 39:20Speaker 00

  Because you're getting a long way away from something that's tangible and specific and objective.

• 39:25Speaker 01

  And I think this is like exactly, you know, I think there's a lot of folks that have been saying, and this whole Neolab push lately of folks spinning out and saying, God, there's got to be a more data efficient way to train models. And ultimately, if like, the way we did code is the only way to do everything else, maybe we get there over time and we, the whole economy is just data labelers and over time you can, you can do enough data labeling to, to get there. But like, it feels like that will be a real slog relative to, to something that's more algorithmically efficient.

• 39:48Speaker 00

  So, so pushing in a different direction, sort of places that it is, you know, given that we don't know how the models will work, what are places where it's conceptually questionable that they might, whether they could work?

• 40:00Speaker 01

  Yeah.

• 40:01Speaker 00

  And one of them is in the conversation about where there isn't training data, where the data is implicit, where it's implicit knowledge, maybe even where it's hard actually to explain how you do it, why you do it, which comes right back to the beginning of machine learning. It's hard to explain. Well, you can't turn it into a statistics problem.

• 40:18Speaker 01

  Right.

• 40:19Speaker 00

  Where is it hard to turn this from a logic problem into a statistics problem? And secondly, where do you not want the average? Because in principle, what all of these things are doing is how would most people probably do this? And you know, in software, you are saying, how would most people probably build an app that does X, Y and Z with these criteria and you want the way most people would probably do it?

• 40:46Speaker 00

  And the more that you get into, no, I kind of don't want it the way most people would probably do it. I'm going to do something different, then the more you get kind of the models, there's a kind of conceptual question for the models. And the way I used to think about this was that if you go back to something like AlphaGo, AlphaGo can do model, do moves that no one's done before, but it's got scalable verifying system. It can check whether the moves win. Whereas if you were to create like a completely new enterprise strategy, how would you check that was completely different from what anyone had ever done before? How would you check whether that made any sense?

• 41:26Speaker 01

  Right. And it's all about, I mean, people are creating these, you know, these very basic RL environments today, right? Where it's, you know, it's like, hey, you go through the Salesforce app and update things correctly and use that to train models. And it feels very clear that over time, I think the only solution to a lot of this stuff is going to need to be in simulation, right? And like you kind of are simulating like complex, you know, business situations or economies.

• 41:46Speaker 00

  Yeah. So just sort of thinking out loud here, there's like,

• 41:51Speaker 00

  what there's like, the dumb criticism is, you know, the model can't do the well, there's a short term criticism which says the model can't do that. The longer term question is, can you get enough training data for the model to be able to do that? And enough feedback loops for the model to be able to do that? The third step is, yes, but do you want the product of the training data? Do you want the average?

• 42:18Speaker 00

  And then you can push this different places because, I mean, I have the sort of imagine if slides towards the end of my presentation, the last presentation. And I was sort of thinking like, there's a consumer version and an enterprise version. Consumer version is step one, here's a picture of a coat, where can I buy that? Okay, that wouldn't have, 10 years ago, the science fiction, now that's an engine, now that's a product problem.

• 42:39Speaker 01

  Yep.

• 42:39Speaker 00

  That would work. Coats may be hard, but yeah, in principle, that will work. Here's a, here's a vase, where can I buy this? Here's a lamp, where can I buy this? Step two, give me 10 options for a coat like that and explain how I would choose. Okay, that's now solvable. Step three, go look on my Instagram and suggest a coat

• 42:59Speaker 00

  that matches my look, that would improve my look, that would give me a radical change that I might like. That can probably do now.

• 43:06Speaker 01

  Yeah.

• 43:07Speaker 00

  But the further you get to not what's obvious, but good, the harder it is to do that. The enterprise version of this would be something like, step one is, listen to our Zoom recordings with clients and tell me if there are new concerns emerging in the last couple of months.

• 43:24Speaker 01

  Yep.

• 43:25Speaker 00

  Step two would be,

• 43:28Speaker 00

  go look at our, you know, step two or three would be, go look at our app telemetry and everything in Salesforce and all of our client calls and then do a competitive market review. And then

• 43:42Speaker 00

  spin up a user survey, automate the questions, work out the list of users, run the conference calls, do all the calls, synthesize the results, pull it all back together again, and then tell me why do I need to change my churn? How do I change my churn? Now, that's an example, incidentally, of something where you probably do need to be at the head of that pricing curve. And it's not a commodity question. It's also something that one random person in the company isn't going to be able to spin up in Claude. You're going to, that's, that's a one month project just to kick that off, just to get that going. But these are sort of, the further you get along that curve, the more you're not doing, this is what anyone would tell me. And the more you're getting to variance.

• 44:22Speaker 00

  To make that much simpler, like you can get a machine learning, you can get an AI music model to make you a lot more bad jazz or bad bark or bad pop music. You can get it to make you more stuff that sounds like Taylor Swift. Apologies to any Taylor Swift fans listening. You can make more Taylor Swift, fine. But imagine inventing punk.

• 44:42Speaker 01

  Yeah.

• 44:43Speaker 00

  Imagine inventing hip hop.

• 44:46Speaker 01

  Right.

• 44:46Speaker 00

  And where does that come from?

• 44:49Speaker 01

  99% of things in enterprise probably not inventing hip hop like that. Example you just gave, I think you probably could do with LLMs today. And then people are, it's not perfect today, but you know, you have your AI interviewers and you have like, you know, there's, there's enough iteration on it that it doesn't feel.

• 45:01Speaker 00

  The funny, yeah, the funny thing about AI interviewers is, I was talking to somebody from Bain a while ago, who are talking about using this for interviews and for synthetic customers. It's like, there's a common pattern, a kind of complete sidebar here. There's a common pattern in people talking about what AI means for XYZ industry. If you're not in that industry, you'd think you'd do this with it. And the people in the industry are like, no, no, no, no, no, we do this. This is the thing we can do with it. So like in advertising, there's a whole thing about you can make the ads with it. And Martin Sol is like, no, no, no, no, no. You use it to automate all the really boring back office crap.

• 45:32Speaker 01

  Yeah.

• 45:32Speaker 00

  You automate the faxes, the faxes we're sending to Google to book the ads, almost literally. And the same thing in consulting, like, do we use this to make PowerPoints? Well, kind of, but what we really use it for is to automate the research, customer research.

• 45:47Speaker 01

  Right.

• 45:47Speaker 00

  Cause now you can do that query, you can do that survey in a day instead of a week.

• 45:51Speaker 01

  Yeah.

• 45:51Speaker 00

  Which is my consumer surplus point. You do the same, same spend, same survey, but you deliver it in a week instead of, in a day instead of a week.

• 45:58Speaker 01

  Yeah, it makes sense. I mean, I guess, given this discussion we've had on the foundation models, like, if you were running one of these companies, like, would you be doing anything different in terms of like focus or, you know, what they're?

• 46:10Speaker 00

  I have a lot of sympathy for Samuel here.

• 46:14Speaker 00

  That may be a unique and controversial statement. You've got a lot of plates to juggle.

• 46:19Speaker 01

  Yeah.

• 46:19Speaker 00

  Because you have all, I mean, you can make your list, you could, in fact, you could ask Claude, your chat GPT to make your list of what are all the problems? Do you make a device? Oops.

• 46:31Speaker 00

  Sorry about that.

• 46:34Speaker 00

  Okay, let's go. I'm going to spend a lot of time on calls with lawyers now.

• 46:39Speaker 00

  How do you think about, of course, OpenAI and Anthropic don't have infrastructure. So there's an infrastructure question, how do we get infrastructure? How do we get the funding for the infrastructure? Do we make our own chips? Yes. Okay. How long is that going to take? You know, work your way all the way through those layers of the stack. It's like you're trying to invent, it's like you're Bill Gates and you've got to invent, you've got to build PCs and enterprise software and broadband networks in like 1985 or 1980. You've got to, you know, so many different parts of that stack are completely unclear and all happening at once. There's a sort of, well, obviously this is what you would do. And so you can see the two strategies, you can see the strategies across companies. So if you're an incumbent, you try and make the new thing a feature.

• 47:19Speaker 01

  Yeah.

• 47:19Speaker 00

  And so that's what we saw at Google, spray the whole thing over everything is what you see it with. And probably mostly successfully is what if you're Microsoft, well, we don't have this capability. So we'll buy it, rent it, whatever, spread all over everything. One of the steps in enterprise AI deployment was like, step one is deploy Copilot. Step two, oops, step three, let's do something else. If you're Apple, clearly they fumbled massively two years ago. If you've played with a beta of the new series, like this is as good as ChatGPT was six months, nine months ago. And it's a consumer deployment and use case story there. If you are OpenAI or Anthropic, you don't have a legacy business, you don't have a way to make it a feature, you don't have distribution, you don't have infrastructure. What you do have is cutting edge science, but you've kind of got to build all of the rest.

• 48:02Speaker 00

  And so I think what OpenAI did second half of last year was what can we build on top of a commodity foundation model? Is it a browser? Is it a social video app? Is it an app store? Is it another app store? Is it a third app store? Is it shopping? Is it ads? Everything. If you're Anthropic, less money, more focus, more worried about evil stuff, fine, whatever.

• 48:25Speaker 00

  Much more narrowly focused on coding. Stumble into coding is something that works, right? Okay, everyone else now rushes towards coding. Like, it's like the scene in Monty Python with, you know, in Life of Brian with Jehovah, you know, like you're throwing rocks at coding. Coding!

• 48:40Speaker 00

  But is that the next thing? I don't know. I mean, part of the, the other thing we could, one could have said about coding is coding is actually a pretty small industry. I mean, if, I mean, I mean, it's weird, every, I point this out, people in tech get really upset with me. There's millions of people writing software, fine. Imagine if we had a use case that really, really worked that like a billion people wanted to do. Like the, we couldn't, like the infrastructure, you couldn't possibly support it. I mean, this is one of the interesting differences from every other previous platform shift is it's more expensive rather than cheaper. It has marginal cost. And so you can't make a consumer app and get a hundred, a hundred million users and then work out the revenue model. Which is why it's all enterprise as opposed to all consumer. But if you're running, you're running a model company, I mean, obviously OpenAI has had all sorts of like weird political stuff going on, but like.

• 49:25Speaker 00

  You're shepherding your researchers, you're shepherding capital, you're shepherding infrastructure, you're herding cats in all sorts, shepherd is the wrong word, you're herding cats in all sorts of different directions. You've got to work out, well, what is the path to sustainable competitive differentiation? When every six, four to six weeks, there's another model at the top of the leaderboard? And there's absolutely no sign of what you would do that would mean you would have sustainable competitive differentiation? Do we have a path to network effects here? Do we, can we build product layers on top of this that mean everyone has to use our product even though the model underneath doesn't seem to be very different? Can we focus our model in on one capability and try and pull ahead in that capability?

• 50:10Speaker 00

  So you could argue what Claude has done is focused on getting coding really, really good. And maybe they have a competitive lead there. But you also have to be continually conscious that everything is changing every three to six months. No one really knows what this is going to look like in three or four years' time, and no one really knows what the building blocks are.

• 50:28Speaker 01

  So do you think then running all these experiments makes sense if you're OpenAI, just given how uncertain the cell is?

• 50:33Speaker 00

  I think it, well, the problem is both strategies make sense. Now, and I think if you're OpenAI and you've got all this capital, it kind of made sense to do or try all of that stuff. If you're in topic and you had less capital and you're more worried about like takeoff and evil stuff happening, then it made more sense just to focus in narrowly on, on coding. You know, we didn't know that coding was going to work. And we kind of, maybe you could have said in hindsight, obviously it was going to work, but clearly like it flipped at the beginning of this year from kind of working to actually working. We, and again, we've had this conversation, is that, was that inevitable? Was that the thing? But you, you come back to this question, like, the, the thing that sort of fascinated me about OpenAI in the last couple of days is the new app launches.

• 51:15Speaker 00

  And if it tells you one thing, it's that like writing the code isn't the hard part because it's such a disastrous, chaotic, confused mess. And there's like this weird, like, we're going to give everybody co-pilot and also work and work is different to chat and it's called chat, but chat's hidden. And when you do chat, then it appears in this weird pop-up, but you can't use this in that. And then you hide it and then this happens and this works on cloud and that doesn't work. I'm like, really? How did you shit that? How did that happen? There's a step back from all of this, which is like, before Microsoft and Google and Meta and so on had the winner takes all effects, they had to execute their way into that.

• 51:51Speaker 01

  Yeah.

• 51:52Speaker 00

  I mean, you kind of knew that then what the winner takes all effects would be completely obvious. Like, remember when people thought that maybe like social would win in different countries? Like Bebo would win in Europe and Facebook would in America? And like Uber would win, it would like, like ride share was going to be city by city. And so you're like, you, you knew what, you kind of knew what the network effect was, but you weren't sure. But also you had to execute your way into getting it. And remember that like MySpace was there first. And remember that when Facebook was happening, people, Google did wave, buzz, thing, other thing.

• 52:21Speaker 01

  Yeah.

• 52:22Speaker 00

  And Google has all the data. So there's an execution, you can get very deterministic about, well, I want like the network effects and commoditization and so on. But there is an execution story there as well. Like you actually have to like make the thing happen.

• 52:34Speaker 01

  Yeah.

• 52:35Speaker 00

  It's like, you know, Mark said, history is not a material person. Like history isn't a person. So you can, Mark tries this whole thing about history works like this and these are inevitable forces. And then he says, yeah, but, but history isn't a person. So even more, somebody actually has to do it.

• 52:45Speaker 01

  Yeah.

• 52:46Speaker 00

  So you have to actually make that thing happen.

• 52:48Speaker 01

  Yeah. No, I love that. I mean, I guess, you know, on, on the opening eye side, I think, you know, they have, obviously they had all these meta execs come in and I think you talked about how they kind of tried to run a similar playbook.

• 52:57Speaker 00

  Yeah, it felt a bit cargo culty.

• 52:59Speaker 01

  Yeah. What, what went wrong there?

• 53:01Speaker 00

  You know what a cargo cult is? I was never, is what this reference?

• 53:03Speaker 01

  You know, it's something I've said a bunch and I don't actually think I have any idea what the origin is.

• 53:06Speaker 00

  It's a World War II thing. So this war in the Pacific, there are all these like islands in the Pacific where like, you know, people have seen a European ship every six months or something and every year or something. But no one, these are not people who are exposed to modern mass-produced industrialized society. And the Americans arrive and they build an airbase and a base and a ship and a port and a pier. And there's all this stuff. And like America in particular, like there's a infinite money and so all the supplies, they handle all the stuff out.

• 53:36Speaker 01

  Yeah.

• 53:37Speaker 00

  And like canned food and like saucepans and like everything you want. And then the war ends and like the Americans just leave. There's this island somewhere in the Pacific that like, there's like 10,000 airplanes and trucks and stuff just dumped into a lagoon because there was just, it wasn't worth shipping it all back to America and scapping it. So there's like, I'm slightly, I'm only slightly exaggerating. There's like hundreds of brand new trucks and aircraft dumped in this lagoon. So the point is like, suddenly the airfield is empty and there's no more planes coming. So what do you do? Well, you go up into the control tower and you speak and you line up in parade and you run a flag up the flagpole because that's what the Americans did and the plane came. I mean, this is absolutely true.

• 54:20Speaker 00

  This is this phenomenon in the 50s, well, late 40s and 50s. You didn't actually understand what it was that was causing the cargo to come.

• 54:27Speaker 01

  Yeah.

• 54:28Speaker 00

  So you imitate the forms.

• 54:30Speaker 01

  Interesting.

• 54:31Speaker 00

  And I've gone down a rabbit hole again, but like the point here is like, you joined Meta in 2010, way after it had product market fit. And now you're at, so what do you do? Well, you make an app store. You build an ad business, you build a new commerce thing and you know how to, you're doing all of those playbooks, but you're not at the part where you know what works. And what you actually want is the people who are Meta when it was just Harvard.

• 54:59Speaker 01

  Yeah.

• 55:00Speaker 00

  Not the people who are Meta when five billion people were using it.

• 55:04Speaker 01

  And obviously, you know, on the consumer side, I feel like you've, you know, talked about how, you know, these products have like some relatively shallow usage today across the board. I think one of the hard parts that you've, you know, discussed before is like, if you're at one of these labs and you're on the product team, you're kind of waiting for the model capabilities to figure out like what to go build. And so it's a hard way to build product. And then at the same time, I can't tell whether we're just like a few model capabilities away from like a completely different set of consumer use. I mean, the killer ones always felt like computer use. Like if you really got computer use to work, would people just use these, these products way more?

• 55:34Speaker 00

  I'm not sure about that. I just remember how many people, for how many people their main device is a smartphone. Most people don't use a computer as much as they use a smartphone. So I'm a little bit hesitant about that one. It does feel very geeky to me, even setting aside security and failure rates and you might accidentally delete all of your stuff, which again is like, you know, I was using. I mean, it's, it's a point about PCs in the, the early 80s. It's like, it was completely normal that you'd be working, even in the mid 90s, late 90s, you'd be working and you look up at your screen and you realize it's frozen and nothing is. And your, your solution is you crawl under your desk and you unplug your PC and then you wait for it to turn on and then you hope that you haven't lost more than an hour of work.

• 56:14Speaker 01

  Yeah.

• 56:15Speaker 00

  That was just normal, completely normal that was had. And that's, that's exactly the same now of like, you know, I, I told coworker to clean out my inbox and it deleted my inbox. Hey, it's cleaned up. Great. So there's, there's, there's a consumer, like, the takeoff here is like, again, these are unfulsifiable statements. Is it just that the models will get better? Or is it that that's not quite what the problem is? Is it that most people don't very often have the kind of things that mesh very well with this? Set aside the enterprise side of like, you're seeing an SAP, it's a pain in the ass to do this thing. Great, you can't use ChatGPT for that because it's not authorized, it's not plugged in, you're not allowed to do that. Fine, that's, that's a CIO conversation.

• 56:58Speaker 00

  You're a consumer, how do you work out what this is? How do you bridge it? You get, you open ChatGPT and, and this, I know, I don't think I'm, I don't think I know the answer here. I mean, obviously I don't know the answer, but like I'm, I'm ambivalent here because I remember like portals on the internet in the late 90s where you give people a web browser and you can't just give them a browser with a URL bar and a blank screen. You've got to help them.

• 57:25Speaker 01

  Yeah.

• 57:26Speaker 00

  And eventually you didn't need to help them. And some of that is, that wasn't really about broadband or better tech. That was about the ecosystem kind of maturing and people forming new habits. And so when you see you open a chatbot now and it's got these little tiles of like, you could do this, you could do this, you could do this. Is that like the portal then of like the handholding of helping people work out what to do with this? I don't know. An awful lot of what you were doing on the internet was stuff you're already doing. Like now buy an air conditioner, book a holiday, buy this thing, find this thing, watch that video. It's stuff you kind of already knew. It's like the spreadsheet point, like if you're an accountant, you already knew about spreadsheets. You're the thing you already knew you could do.

• 58:08Speaker 00

  How much of what you do with an LLM is stuff that you're already doing and you realize, oh, I could do that much better with this? This is obviously the search conversation and booking a holiday conversation, whatever, or my, my point about find me a coat. How much of it is the other side of that, that an entrepreneur is going to realize, ah, no, this is what you do? I mean, this is Flickr and Instagram.

• 58:33Speaker 01

  Yeah.

• 58:34Speaker 00

  And TikTok. That's not like a consumer thinking, oh, I want a way to share pictures. Somebody has to invent that. That's not organically emergent behavior out of consumers. It's not like you just give enough, everybody enough broadband and they'll start sharing pictures.

• 58:51Speaker 01

  Well, I'm talking about the enterprise side. I've obviously seen people do this with, you know, the labs leaning into these like deployment companies, right? And like the idea of like going out there and like, do you think that will be successful?

• 58:59Speaker 00

  So again, we're going in lots of different directions. Like step one of enterprise deployment was give everyone a co-pilot. Step two was, oh crap, that didn't work.

• 59:07Speaker 01

  Yeah.

• 59:08Speaker 00

  For like a small portion of people, like if you've given them Claude, maybe great, like my wife lives in Claude, fine. But most people know for the reasons we've talked about, that doesn't make sense. Step two is pilots and everyone has done now, done a whole bunch of pilots and deployed about half of them and they, they work fine. But that's kind of a one at a time thing. And then the conversation becomes like, is that it? That feels like that's not it. Well, we automated this one pain point one at a time.

• 59:37Speaker 01

  Yeah.

• 59:39Speaker 00

  Feels like that's not the right way to think about this. How do we actually change structurally how we do things around this new, this new technology? We're still sort of at the point of like, we successfully PDFed all of our catalogs and put them on our website. Great, that worked. How many people have downloaded them? Loads. And it's like, what's our ROI? Our printing bill's collapsed. We've got way more customers. Great, okay, but that's probably not the endpoint. And so that's what all of these things are. And this is of course why consulting exists. Because most people do not have those skills and they don't have it set up as a process. And they don't have all those people sitting around not doing anything. And so you hire Bain or BCG or McKinsey, or you hire Accenture, IBM, Cognizant, Deloitte, PWC and so on.

• 1:00:22Speaker 00

  Or you hire WPP, Publicis and so on, or you hire Edelman or whoever it is for these different kinds of problems. And they come to you and help you work it out. This is why, or lawyers. This is, this is why professional services exist, because you don't have all of that, that talent in-house. But working out what all of that re-imagination would be is a project. It's a big, difficult project. This is what used to be called digital transformation and now enterprise transformation. I always used to joke that if you say digital transformation three times, then a partner from Accenture will appear in a puff of smoke. It's like my most reliable last line at conferences, especially enterprise conferences. The whole conference grind is for halt for 30 seconds after I say that. But like, yeah, like there's a reason why they've got like 800,000 people.

• 1:01:05Speaker 00

  And that will probably go to half, that will probably half or whatever the number is. But like, no company is going to have all of those skills there just sitting around, ready to reimagine and stuff. I think the one thing that occurred to me there though, is that there's a sort of symmetry or an overlap between what an enterprise software company does, a vertical enterprise software particularly does, and what a consulting firm does. And that what they're, a strategy consulting firm, management consulting like Bain, BCG, McKinsey and so on. What they're doing is they're kind of going and looking at your company and saying, you're kind of doing it like this. But actually you could do it like this and that would work way better.

• 1:01:41Speaker 01

  A hundred percent. I mean, that's what a lot of these vertical AI companies are doing today, right? You have to have it, what they call this forward deployed, which is, you know, reinvention of.

• 1:01:47Speaker 00

  But that's what any vertical software company is doing.

• 1:01:49Speaker 01

  Yeah.

• 1:01:49Speaker 00

  Well, at least half of them, that's what they're doing. They're saying, well, you were doing it like this and we've worked out you could do it like this and that would work way better. And that's also what Bain does, what McKinsey does, what BCG does. But just in a kind of a different modality, which is why all of these companies are sort of sitting and scratching their heads and trying to work out. Like, you know, I mean, you know, there was a joke that a machine learning scientist is a statistician who lives in Silicon Valley. And there's, there must be, I haven't quite worked out the right equivalent for a forward engine, deployed engineer.

• 1:02:17Speaker 01

  Yeah.

• 1:02:17Speaker 00

  Is like an Accenture body shopper. If you've got a job, you've got a job at OpenAI. But they're kind of coming, they're climbing the mountain from opposite directions. It's like, do you want a software company that's, that's got a bunch of forward deployed engineers? Or do you want a consulting company that's got a bunch of, of software engineers? Because they're kind of doing the same thing from different directions.

• 1:02:37Speaker 01

  I think this point of basically, obviously in the enterprise, you know, people, you can't, you don't just give people these models and they figure out exactly how to use them. And there's, there's needed to be this like big translation layer. And then I think on the consumer side, it's certainly been that to date. And I think the question, you know, to the point you're making on Instagram earlier is like, will some great entrepreneurs come along? Or, you know, maybe within these companies and find a way that's like a little more, you know, that has some latent behavior within people that they want to do.

• 1:02:59Speaker 00

  Yeah, like everything has to actually be invented. All of the use cases have to be invented. Very few of them are just purely spontaneous grassroots consumer behavior. All, every app on our phone, somebody had to think, well, that would be a good idea. And then everybody looked at it and said, that's the dumbest thing I've ever heard. I mean, remember how dumb Uber looked? Remember how dumb Instacart looked? I was in the pitch at A16Z for Instacart. Like, will the economics work? Because this makes sense, will people do this? I mean, you know, the story about, what's his name, the founder? That he, he didn't have a car. And so he set up this bare bones website that emailed him orders. And then he would get an Uber and go to the supermarket and do the shop and get an Uber to people's home to deliver them.

• 1:03:43Speaker 01

  Tough for the unit economics.

• 1:03:44Speaker 00

  To work out whether this would work.

• 1:03:46Speaker 01

  Yeah. That's hilarious. Well, what do you make of Sora, like some of these early consumer app tries?

• 1:03:50Speaker 00

  So I think Sora fits into a classic pattern of consumer social, which is you have to come up with some new mechanic, some new piece of Maslow's hierarchy, some new way of feeling that gets attention. But then it has to be more than a gimmick. And TikTok found that in one form and Snap found that. And name another one. Because it's really hard, actually. Snap finally found two or three, like disappearing messages and stories and so on. But like, it's work, like we, you could kind of argue that social has flattened out at the top of the S curve. The way smartphones did or the way PCs did, that like everything's been invented.

• 1:04:33Speaker 00

  And like Sora was like, I don't know, it was like, you know, all the stupid things. Like the weird experiments people tried with smartphones, you know, modular smartphones and things like, you know, you clip on this or clip on that. Like a second screen or eating screen on the back. You can maybe, like, there's a moment where like, okay, now all of this social stuff has been done. And then of course TikTok comes along and does something else. But I think that's the frame that I would fit it into that narrative rather than specifically an AI narrative. It was another attempt at a cool social thing and it turned out that it kind of didn't work. I mean, it's the same thing with Midjourney. We remember like a year or two ago, we all spent a month playing with Midjourney.

• 1:05:09Speaker 01

  Yeah.

• 1:05:09Speaker 00

  And now, now like Meta's got a new image model, how I tried it.

• 1:05:15Speaker 00

  The image generation, I mean, there's another, maybe there's another point to make here, which is like what happened with drones and 3D printing. Which is like one Christmas, whatever, whenever it was 10 years ago, we all bought a drone for Christmas. And then three days later, we say, okay, I've seen the roof of my house now.

• 1:05:33Speaker 00

  Very cool. Same with like, well, like 3D printers. Okay, I made a little Eiffel Tower. Like there's no consumer use case for 3D printing. There is no consumer use case for drones. There's a hobbyist case, there's a bunch of people who think it's really cool. There was this brief idea, like it's not a thing that, that everyone has. And so I think that was kind of where video creation ended up. Now, somebody may need to pull the whole thing inside out. And now like virtual try-ons, if you could get virtual try-ons working, that would be image generation, but it wouldn't be Midjourney.

• 1:05:59Speaker 01

  Yeah.

• 1:06:00Speaker 00

  You know, here's, look at my Instagram and go look at the latest lookbook from this brand. Make 10 videos of me in different looks. I'm like, that's probably completely insane, but like that would be a, that would be a product.

• 1:06:14Speaker 01

  Yeah.

• 1:06:14Speaker 00

  Midjourney, I mean, Midjourney wasn't a product in the sense that like you had to log into it with Discord. But like, it also wasn't like, it was, it had the same problem as drones. It's like it didn't translate into a tangible, specific thing that you would do, that solved a problem.

• 1:06:27Speaker 01

  Yeah. That's super interesting. I feel that's a, a common theme throughout our conversation. I always like to end with kind of a quick fire round where I just stuff a bunch of things into the end that we haven't discussed. And so maybe to start, I'm curious, like, you know, I feel like you've been pretty consistent in your view on this technology. What's one thing you've like changed your mind on in the last year around AI?

• 1:06:45Speaker 00

  The basic thing mistake that I still see people making is not to understand that this is an enabling technology that makes lots of different things possible and still look at it as something that makes text and pictures. And not understand, no, this will like make fraud detection work better. Or this will solve all sorts of like obscure back office processes inside companies you use every day that you've never thought of. And so you'll get all sorts of better experiences and better products that won't come to you looking at AI and won't come to you saying this was chat GPT. And you still get people kind of saying, but you know, I asked it a question and the answer wasn't quite right. Well, like, great. This is kind of like, it's like looking at the internet and saying it's kind of slow and anyone can say anything like, yes.

• 1:07:28Speaker 01

  Yeah.

• 1:07:29Speaker 00

  But well, more, it's like looking at the internet in like 1995 and saying, well, but, but, but like, there's an interview you can find online with David Bowie and a British journalist called, TV journalist called Jeremy Paxman in like 1994 or something. And David Bowie is saying, this is going to change your world. And Jeremy Paxman says, but it's just like random people talking, talking to each other on chat rooms. Because this is a mistake, it's not the chat rooms, it's the network. And it's the same thing here, it's not the text and the pictures, it's the enabling technology.

• 1:07:54Speaker 01

  Yeah. But I think obviously a lot of what you write about compares what's happening in AI, you know, to other technological revolutions for context. And I think one interesting thing is, you know, many people at the forefront of AI at these labs are like in their twenties. They don't necessarily have the same context around some of these, a bunch of the most on the podcast. Like, I guess if you could impart a few messages to them on, you know, maybe on those eras, like what would they be?

• 1:08:14Speaker 00

  So, I mean, I think we've talked about a bunch of this stuff that like, you know, stuff is unclear and stuff will change. And all the acronyms and me, I've, the slide in my presentation is here are a whole bunch of acronyms and companies and concepts and ideas and technologies from 1995 that failed. Same thing from 2010 or 2005. We should just presume a bunch of stuff that we're working on now won't work. And some of it you're very conscious of. I mean, this was the conversation about OpenAI last year. Of course, some of this wasn't going to work, but it was worth trying. I mean, I think just like last week, OpenAI gave up on the web browser.

• 1:08:45Speaker 01

  Yeah.

• 1:08:46Speaker 00

  And I had that on my slide as, is this a thing that will work? Probably not. No, we should presume MCP will get replaced by something else or may do and so on. So that's kind of one point, just presume you're in this stage of radical uncertainty where everything is turning over and changing. Secondly, like, you know, I always have this, this slide I show of the, so from Goldman CIA survey of the percentage of enterprise workflows that are in the cloud and the number is like 30% after 20 years. Like it takes time and it's hard and complicated and people have other priorities. I do a poll on social media every now and then, is machine learning still AI? And some people get very upset, like there's this academic definition that they've dreamt up of what AI is. But like AI is whatever's just, is new, it's like technology. It's not technology once it's been around for a while.

• 1:09:29Speaker 00

  But the, the, the, the point I was sort of thinking is like, I gave this presentation to, I think it was a, like a European water company. And I gave the presentation on machine learning and the end the CIA says, CIA, CEO says, this is very interesting. Maybe innovation should be one of our top five priorities next year. And if you're in tech, that sounds insane. And then you think, but they're a water company. Let's, let's think about what their priorities might be. Regulation, earthquake, there's lead in the pipes, they need to rip out the Huawei equipment. There's a repricing coming up next year. Like they've got other stuff. There's a drought. That reservoir, the dam is leaking. Oh crap, we're going to need to like, screw the rest of our budget. So like most people have other stuff going on.

• 1:10:14Speaker 00

  And then maybe a much more tangible thing, other people's jobs are hard too. Everybody else's job is hard. Every other industry has a bunch of stuff they're trying to work out that you probably don't know about. I remember having a conversation with the head of now the head of one of the world's biggest ad agencies at an event years ago. And I said, you know, trying to be clever, like, what are you worrying about? You worried about machine learning? This is before OpenAI. He said, yeah, I'm also worried that one of our countries doesn't have a good head of creative. I'm worried that this big client is going to re-put us up for review. I'm worried that my head of XYZ is in a conflict with this other person. Everyone has all these other stuff going on. And AI isn't the only thing. And if you think about, you know, this is, you know, talking about

• 1:10:58Speaker 00

  previous lessons, like, you go back and think about what industries the internet affected and didn't affect. If you're the newspaper, fine, you got demolished. Now imagine you're Caterpillar. What did the internet change for Caterpillar? Okay, it was really useful. In the end of the day, your business is making big pieces of metal. Yeah. What did the internet change for an aggregates company? Do you even know what an aggregates company is? Your job is digging sand out of the ground, putting it on trains and then on trucks and then giving it to people. How much did the internet change your industry? Well, in the end, kind of not very much. How much is AI going to change that industry?

• 1:11:32Speaker 01

  Depends how well the physical AI stuff works, I guess.

• 1:11:35Speaker 00

  Yeah, but do you want to put, are you going to put humanoid robots in the three-story high trucks? Or the trucks going to get connected to GPS and drive themselves around? They probably already are. The trucks will get autonomous, fine. Okay. How much does that actually change your industry? Like, guess what? There are some industries where it's not going to change anything. And it won't be predictable. There'll be some, maybe, you know, there'll be some that you think of now where you're completely wrong. There'll be a bunch of industries where the internet actually didn't change a damn thing.

• 1:12:03Speaker 01

  Well, I want to make sure to leave last word to you. Anything we haven't talked about here that you think our listeners should take away, or anywhere you want to point them. We'll certainly link to your presentations, but anything else?

• 1:12:12Speaker 00

  Yeah, well, my parents had good SEO. There's a story I was thinking about recently, which I didn't use writing about tokens, which was in like 1999, the bank I worked for was going to look at working on the IPO of a company that sold electronics and software on the internet. They'd previously been a catalog company. And it wasn't clear Amazon would do this. And the guy had, the founder had started in his bedroom, aged 16, and they'd like turn over 100 million quid, and they were going to float for seven, eight hundred million, which was insane even then. And so we all go up to Birmingham to see it, and then we're on the train back. And we're all talking about the founder, and the board's kind of weird, and the valuation, and a veteran banker called David Tate, I

• 1:12:56Speaker 00

  think he's retired now, I should look him up. David Tate sort of opens one eye and says, it's a low margin reseller, one time sales, and goes back to sleep. I mean, sometimes it is kind of simple. It's a low margin reseller, one time sales.

• 1:13:11Speaker 01

  This has been so much fun to get to talk to you about all this stuff. I really appreciate taking the time to come on the pod.

• 1:13:15Speaker 00

  Sure. Thanks for having me.

• 1:13:16Speaker 01

  I'm Jacob Effron, and this has been Unsupervised Learning, a podcast where I get to talk to the smartest people in AI and ask them tons of questions about what's happening with models and what it means for businesses in the world. As I hope is clear, I have a ton of fun doing this. It's a nights and weekends project, in addition to my day job as an investor at Redpoint. But our ability to get these incredible guests on really comes from folks like you subscribing to the podcast, sharing it with friends. It's really what ultimately makes this whole thing work. And so please consider doing that. And thank you so much for your support and listening. We'll see you next episode.