A rational conversation on where AI is actually going | Benedict Evans

My most controversial opinion is that I think that AI is as big a deal as the internet on mobile. And it only is big a deal as the internet on mobile. What's your just done? They're coming job-park lips. Every time we have a new technology, it automates away a bunch of jobs. And then that automation unlocks a bunch of new jobs. And you don't know the new job because it doesn't exist yet. We've had that process over and over again. Even just looking at the most advanced AI companies throughout Big Open AI, everyone's increasing headcount. You talk to these consumers on Twitter and they would act like "Every big company is going to buy Chachi VT tomorrow and then in two weeks' time they'll fire all their staff." These people are more on- You can't predict which things are going to be exposed. You can't look at a senior partner with a law firm and say, "Well, 17% of their work would be automated." This is bullshit. I'm curious if you're following the anti-AI sentiment. It's a big fuzzy mess. Yes, this will change a bunch of stuff and we'll need to worry about it. But that's kind of a constant. We've always had that. What would be a couple of things you recommend people do to be more successful in this future? Don't you think you're heading to sand and say, "Hey, told me of this stuff." That gives you a great feeling of moral superiority and you can go in blue sky and chat at everybody about how evil AI is like. "Great. I'm happy for you." But that's not going to help. What helps is you diving into this and coming out understanding what you can do it. Today, my guest is Benedict Evans. Benedict was a long-time partner at A16Z as their in-house analyst and resident thinker. Before that, he was a long-time equity researcher. And for the past six years, he's been an independent analyst tracking the most important tech trends and sharing what he's learning. Most recently, as you'd expect, he's spending all his time on how AI is changing our lives. And in his words, AI is eating the world. In this conversation, we go deep on what we're still not pricing in on the impact that AI is going to have on our lives and our work. The rise of anti-AI sentiment, the impact on jobs, where in the value chain most of the value will accrue and tons more. If you're worried about AI or just confused about where things are heading, this conversation will teach you a lot and also make you feel better. Before we get into it, don't forget to check out Lenny'sProductPass.com for a year free of some of the most amazing, hottest, most well-crafted AI products in the world, available exclusively to Lenny's newsletter subscribers. With that, I bring you Benedict Evans. Benedict, thank you so much for being here. Welcome to the podcast. Thank you for infrashing me. You just put out this deck called AI Eating the World. I want to ask you kind of the flip side of this. We all know it's a big deal. Knowing that, what do you think people are still not fully pricing in when they think about the change that they're going to experience to their lives and their work? An interesting way of thinking about it. I did a podcast last year with someone where I said, "My most controversial opinion is that I think that AI is as big a deal as the Internet or mobile." And it only is bigger deal as the Internet or mobile. Clearly, there's a bunch of people in tech who think, "No, this is more like the industrial revolution or something." And there are a whole bunch of people underneath saying, "Well, he thinks this is just as big as. Does he not understand how big this is?" And I'm like, "Small phone is for quite a big deal. The Internet was quite a big deal. We wouldn't be doing this if it wasn't for the Internet." So there's one layer of. But then if you dig into that, if you're going to make the Internet comparison, it's like we're in 1997. Like it's very exciting. Most stuff kind of doesn't work yet. Most of the stuff that people are going to do hasn't been built yet and is not really clear how any of it's going to work when it does work. And the people who have already got it, who have already taken whichever pill it is, I forget which, sort of imagine that everybody in the world is already there. And the truth is you've got this kind of very wide distribution. So there's people in tech who bought their cluster of Mac minis and you know, don't use Google anymore. And then you look outside tech and setting aside the idiots you think that this isn't real. You know, most people are using. Who are using this, are using this every week or two, maybe. So you've got that kind of spread of adoption and that spread of maturity of how well this works. And then within that you can make sort of specific points about well, how are the models going to work? And do the model labs have pricing power? And where's the value going to be? And you know, is Open AI on the whole thing? Or you know, is Anttropic God of this week? And so they can kind of get into calling those races where again, it's like being in 1997 and saying, well, is it going to be exciting or Yahoo? And the answer was no, generally. So there's a sort of a fractual point here. There's like the sort of the super high level that like, this is going to change absolutely everything. I don't know if it's particularly productive to say, well, is it 20% bigger than the internet or 100%? Like, those aren't productive conversations. But it's one of those fundamental changes. But then you don't know how any of it's going to work. In fact, I just published this. I do a presentation every six months and I just published one yesterday. And one of the comments was, "Fenetit, this is 80 Slides of Say, We Don't Know," which is like, slightly forcibly, but also kind of true. This episode is brought to you by our season's presenting sponsor, Work OS. 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Work OS allows you to build faster with the light-full APIs, comprehensive docs, and a smooth developer experience. Go to WorkOS.com to make your app enterprise-ready today. So if we're in this 1997 timeline for AI, I know so much of your messages we don't know where it's going exactly yet. I don't know. The other sense of just like the timeline to, okay, now things are going to be radically changing. Like, where are we in that cycle? You talk about all these different cycles. We've been through like how far we from just like, wow, it's all different. Well, unquestionably, we're already in that moment in software. And then there's a conversation about, well, what does agentic and AI software development three separate things that most together mean for the future of software industry? You know, there's one extreme which is no one really believes which is, you know, how you'll just like, vibrate your inscribed. And we know it actually believes that, although very few don't believe that. But clearly there's a whole bunch of questions about what this means for the software industry and how much stuff you'll be able to do yourself or how much more software there will be. And that's one whole conversation. But the other extreme is, you know, if you're in a law firm, this is all very interesting. But what exactly do we use this? And how do we work out how not to be the next story that we've submitted something with hallucinations in it? And how many associates are we going to hire next year? What does this mean for us? One of the analogies I used in the presentation is, I can imagine you're seeing, imagine you're in account and seeing the first software spreadsheets in the late 17s. And this is mind-blowing. Now you change the interest rate here. And all the other numbers change. And it does a week of work for you in like 30 seconds. And we can talk about what that meant for the accounting industry. They're clearly, if you're an accountant, this is obviously mind-blowing. But if you were loyal looking at that or journalists looking at that, you'd think, well, that's very clever and my accountant should see this. But that's not what I do. I might use it for my time sheet next week if it didn't cost $10,000 or $15,000 to get the Apple II or the monitor and the printer to run it, which is what it costs if you adjust the replacement. And you need word process. So which actually came very shortly afterwards. And so that's sort of the moment that we're in of there's some people like software development or software developers are the account and seeing physical. Like, oh my god, this changes everything. Like before physical can ask to physical. Before, before, before, before, before, before, and after, okay. A lot of other people are picking it up using it to varying degrees, but slightly puzzled. So, you know, there's a bunch of survey data that I put in the presentation that I even if you look at like 13 to 18 year olds or something, they'd still like kind of 15 to 20% of people are daily active users. And another 20% are weekly active users. And then the other 60% of people of those people in that demographic, how long you say they are not using this. So there's a sort of very widespread of who depths it and a very wide, which I think also maps. This is kind of almost a separate point. Maps to the sort of jagged frontier question of where does this work, where does it not work. Can you tell where it's going to work? Is it intuitive to know where it would work? Can you tell after it worked? Can you work out for yourself what you would do with this? And all of those intersective yourself are developers. A lot of other people are like, people having a moment or they're not, or we're in the game. We're in that kind of 1997 moment of, okay, what is this? Along those lines, something you've been writing a bit about is this like unexpected investment in professional services, lash consulting services, for deployed engineers. All the AI labs, at least the two big ones open and anthropic or like investing in buying massive. like consultancies and e-firms, talk about just what's happening there and why that's happening. - Well, I was kind of groping for a joke last night when I wrote my newsletter and couldn't quite get to the land it. But as you know, something like, you know, we know the joke that a machine learning scientist is a statistician who lives in San Francisco. And there's something in there of like a forward deployed engineer is like an Accenture, outsource software developer who lives in San Francisco or works in San Francisco. I mean, you know, joking apart, if you have any experience of professional services, like companies do not have lots of people sitting around waiting to do a bit, build a big new project or do a big new piece of analysis or build a big new set of technology or a new product or workout, how they're going to redesign their stores or workout where the stores should be or try and work out why the churn is too high. Only all of those kinds of questions are things the reasons why you hire Bain BC GM McKinsey on one side or Accenture in Faces, whoever on the other, or you hire a branding agency or you hire an Occurrent of Architects or whatever. And it's always like, well, we could hire some architects. But why on earth would we want to have 50 architects on staff when we could just go and hire an Occurrent of them? We just go and hire an ad agency. And so you're supposed to like completely reimagine all of the internal workflows of your company and work out which of them could be automated really quickly with AI. That's a project. That's a project that needs like five or 10 people to sit down and spend a month or two working it out and then actually doing it is another project. You're okay. So we need to plug these three vertical systems into these two horizontal systems and build a bunch of new workflows and train people to do that. Well, yes, well, who's going to do that? Because you don't have a bunch of people sitting around not doing anything. So on the one side, this is part of the model of some PE firms, which is that they provide support to their portfolio companies to do stuff. And on the other side, that's why you hire, depending on what you're trying to do, you hire a bank or you hire Accenture or you hire publicists to help you work that out. What's really just funny about this trend is you would think AI is going, like consultants were going to be gone. No, we don't need all these people anymore. AI is going to do their work. Instead, like the most cunning AI hat labs are the ones most interesting in these folks. I think it's pretty surprising. Well, one of the strands in my presentation, so I split the presentation into three sections as a section on capital, which is basically where is all this capate going and all the model labs going to have differentiation. And then there's a section on deployment, which is basically what does it mean for the software industry. And then the third section is how does this change stuff. And one of the sort of strands I tried to pull together in the section on change is, what's the hard part of the job? Is the hard part of the job writing the code line by line? Is the hard part of the job like giving you the school or making the PowerPoint? Or is the hard part of the job something else? Is it the task or the job? And you know, pulling that part, sometimes the task is the job. Like the classic example is like an elevator attendant. Is I live in a building that has an attended elevator? We have a manual elevator. There's no button. There's a lever in the dorm and drives you to your floor. It's a vertical speed cut. It's like one of these trends in terms of Cisco. They drive you to the store, to your floor. And then there's all good automated after the 50s. And now you get a new presser button and pressing the button is a job. So there was some things where the button, the job was a task and the task was automated. Well, what happens much more and this is why people talk about like the Javons' Power Dogs is this pricey elasticity. Because Javons' Power Dogs is just pricey elasticity, apply pricey elasticity. If you make it cheaper to do something, what happens? Do you do the same for less money? Or do you do more for the same amount of money? Or do you do more for more money? Because you've got a new ROI. And if you look at something like the history of accounting or indeed professional services, like you know, since the Javon made on Twitter back when it was Twitter was like young people won't believe this, but before before Excel, junior investment bankers worked really long hours. And now thanks to Excel, Goldman's associates all the real work at lunchtime on Fridays. It's like, well, why is that not what happened? You could make the same point in software development, before IDE's and libraries and operating systems, developers had to write all the code. Now, if you write an iPhone app, 90% of the code is written for you by Apple. Like Apple wrote the modem driver and the graphics drivers and the file system. You don't need to write any of that. So we've got like a tense as many engineers now. Well, no. And so then you kind of have to look at an industry and work out, well, which is it? And what is the hard part? One of the analogies that occurred to me here is to look at the history of e-commerce, which is that what Amazon does is it gets you to school if you know what the school is. If you know what school you want, you want that microphone stand. This part number, you can go to Amazon and get it. If you don't know what microphone to get, probably shouldn't start on Amazon. Multiply that by many, many, many product categories. And so what Amazon does is get you the school, but knowing what school you want is another job. You know, the clawed coke and white you the code, but what code do you want? It can make you the features. Sure, but what features do you want? Who's your customer? What's the right product for that customer? How you gonna take it to market? And long way of answering the question, why do you hire McKinsey? Are you hiring them to get a 75 slide deck? Well, narrowly clawed toe work will make a really, really crappy version of that. And you'll get all these kind of AI grifter on LinkedIn and Twitter and so on and so on. Hey, I made a McKinsey deck with clawed and you look at it and you think, yeah, that's a bunch of dot crap. That's not what you get from McKinsey. But even if it was, that's not what you pay them for. What you actually pay Bain to do is to go and walk all over your company and work out. Yes, but why is it that you didn't do that? And how do the politics of this work? And what do you actually need to do? And let's go and talk to your customers and work out what they actually think, as opposed to what's on the first page of Google, is all the other stuff and the PowerPoint is just like the task. But that's not what you hired them for. The same with, you know, Amazon versus the retailer, the same with software development. So you got that kind of split. The other analogy that occurred to me here is looking at like the sort of class of industry that got steam-moaded by the internet because they had those two things and you could split the part. So you had the physical manufacturing or physical distribution and then you had the other, the thing, what was the actual thing? Like classic examples of your newspapers and recorded music. So record companies do not think of themselves as being in the business of manufacturing small pieces of plastic. But that was what they actually did. And when that went away, they were screwed. Same thing for these papers. Newspapers did not think of themselves as light manufacturing and trucking companies. When you decouple that, then that becomes a problem. But often you kind of can't decouple that. Or that wasn't really the problem. Or you make that thing cheap. And then all this other stuff happens as well. And so all of this is just vastly more complicated than saying, well, hey, we're just going to automate the accountants. So we're going to automate the consultants. I mean, there's two charts in the presentation of the number of people employed as accountants, which went up right the way through the 20th century and has gone up again since the beginning of the 21st century. So you have adding machines and punch cards and main trains and databases and ERP and cloud, with spreadsheets and PCs and the number of accountants you'd go up. And so why is that? But it's more complicated than automation. You've been just looking at the most advanced AI companies and through up like OpenAI. I just had Dan Shipper from every on the podcast. Everyone's just increasing head count. Like the companies you would think would be least likely to add humans or adding many, many humans. And since your point, it's really complicated. What's your just kind of just on the job, the coming job apocalypse, you know? Like Daria is talking about all the entry level people are no more jobs, just like-- Yeah. I mean, there's a narrow point here, which is that I would place-- I don't like argument from authority. And I don't think the fact that you run AI Lab suddenly gives you-- or rather-- and if you're going to use argument from authority, then it should be relevant to the field. So like, I'm interested in Daria's opinions on where models are going to go in the next six to 12 months, not particularly interested in opinions on series of labor and market value and competitive advantage. Like, yeah, maybe he had a course on that at university, so did I. So I think one needs to be a little bit course is on like, well, Daria says. And that setting aside, like the cynical view that he's just doing that stop, which I don't believe at all. So it kind of comes back to my point about, you know, platform shifts. And then that automation, whether it's price elasticity and the enablement of the fact that they became automated, unlocks a bunch of new jobs. And so, you know, you go back to 1800, like, 90% of us were peasants. And our major concern was, like, the crops going to fail, because then we'll all go hungry. Well, worse. And so ever since then, we've been automating jobs and creating new jobs. And you can always see the job that's going to go away. And you don't know the new job, because it doesn't exist yet. And it's like something that sounds dumb. Anyway, like, you know, like, railway engineer. What's a railway? Why would that be a thing? Who would want to go that fast? And so we've had that process over and over again. This is what any first year economic student would tell you. We've had this process over and over again since 1800. And each time you go through it, you get a bunch of frictional pain and dislocation. And a bunch of people do your job, then a bunch of towns get hollowed out, and it's all, it all sucks. But, you know, when you come through on the other side, we're all richer and we're not worried about the crops failing anymore. And, you know, this is the process of the last 200 years. So then the question is, is there some A prior? reason why this would be different to those. Because like the internet removed a bunch of jobs, PC's removed a bunch of jobs. There aren't many people working as typesetters anymore, or telephone operators or typists. The internet removed a bunch of jobs. In generally, the jobs that go where crap jobs seem retrospectively and the needs jobs are better because GDP keeps going up. So is AI different? And so then there's kind of a couple of answers to this. One theory is, well, this is going to be way quicker. And certainly the adoption of AI is quicker than previous technologies because this is kind of because you're standing on the shoulders of giants. So like, you don't need to wait for everyone to buy a piece of expensive hardware to like buy a phone or a PC or wait for the telecoach to deploy broadband. That's what you do. So of course, chat GPT can get 900 million which chat users because there's already 900 million people on the internet like in bike. When Mark had recently launched an escape in what was it? 93 94. There were like 50 to 100 million PCs on earth. So no, you didn't have 900 million users then. But so the point is then he didn't need to wait for like phone networks. Or microchips. And before that, you didn't need to wait for electricity and you didn't need to wait for a mass production. So there's always kind of standing on the shoulders of giants. There's always a likely compounding effect. So yeah, this is faster but the internet was faster too. I think the other answer to this and this kind of comes back to professional services point is like, you know, you talk to these dimmers on Twitter and they would like act like, you know, every big company is going to buy chat GPT tomorrow. And then in two weeks time, they'll fire all their stuff and these people are more on something. One of many reasons why the Germans were more on something like a complete failure to understand the way the world works. And that was like the starting point why they then did not understand anything else. You know, typical big company enterprise self-self-cycle, you'll know this better than me. Enterprise self-self-cycle cycle is like 18 months. It's your lucky. You know, this is always the problem. The enterprise self-cycle is shorter than the venture back self-help funding cycle. Longer, longer, longer, longer, like it takes you longer to get an enterprise deal than it takes you to get between apps. And this was always a problem of God. You know, particularly for sectors like aerospace or healthcare or something. So I know people aren't going to tear out SAP and replace it with XYZ. Maybe in five, in like three, five, 10 years, yes, that whole estate will look radically different and all those jobs will have changed. But it will take, you know, T3, 4, 5, 10 years and it will take time sector by sector and it will take time for people to work out. Oh, you could do that thing with this. And one of the companies I always remember with the we looked at when I was at Andrews and Horowitz as a company called frame.ai, which is video editing, video collaboration. And there's nothing there that you couldn't have done at least five years earlier and maybe 10 years earlier. And actually, there's kind of a bad example because I've relies on a bunch of like, a bunch of stuff like cutting edge web technologies. Like if you go around and like pick 10 random SaaS companies that were started the day before TattuPT launched. How many of them could have been founded at any point in the previous 15 years? Like somebody, the delay was somebody realizing, oh, we could that problem exists inside that industry. And oh, this is the way that we would solve it. It didn't all happen the day after Google Docs. It took like 10, 15, 20 years for people to invent all that stuff and work out that you could do that with this. And so all of that is like the way I've said, well, yes, it is going to be quick. But actually, no, it will kind of take a while for people to work out how to completely change how their business works. Your view is so comforting because it's, you know, basically it's like, okay, this is a huge deal, but we've been through many transformations before and it's going to be okay. Well, I have a slide towards the end of the presentation, which I know the title is something like, you know, this is going to be completely different from everything else, just like everything else. And then the next slide is an IBM ad from the 50s, which has got this sea of white men holding up with in white shirts and ties, all holding up flydrels. And the the the ad is said as the slogan on the title of the ad is, it's an IBM ad, it says an IBM electronic calculator. This is before it was called computer. It's an electronic calculator. It's the size of a fridge. It's like having 150 extra engineers. Like how many people listening to this content list like their company slogan is basically will give you 150 extra engineers. I mean, isn't that like the whole picture of Claude Goat? 150 extra engineers for free. All not free. That's like a lot of money. And yeah, that's what I gave you. And so yes, we keep going through this over and over and over again, just to kind of make that tangible. I mean, obviously we couldn't be doing this with the, without the internet. So there's a slide in my presentation, which is we could maybe talk about, but it's a slide or chart showing how many products are stocked in supermarkets in America since the 50s. And the point of the slide is to say that barcades allowed supermarkets to stock way more stuff because they could keep track of it. But making that chart, I had to know there was a thing called the food marketing institute. And I had to have found out that they published a number for how many schools they were in supermarkets every year. And then I had to realize they've been around since the 50s. And if I could like, dog long enough, I might be able to bake a whole time series and I could make whole chart. Now imagine doing that in 1994. First of all, you would have no idea that exists. You really need to go and find a library where they publish that number and that the numbers in that report, you'd have no idea. Then you need to find a library that had them. So you're going to spend like three days on the phone and spend like $50 on like long distance phone calls to find a library that has these. Or maybe you call the food marketing institute and they say, yeah, sure, if you buy a, you know, I will sell them to you for $500 each. So then you know, you're going to get on a tree. Maybe you live in New York or like that someone that has this and you two weeks later, you've got the chart and you look at it. And then the other side of this is the life of an analyst is he's fend all day making a chart and you look at it and go, oh, that's not really interesting. So you've been two weeks to make the chart and then you look at it and go, yeah, I'm not going to use that. And for me, this was like two hours in Google. And so we like, we like forget how big a deal the internet was. That's a long way of saying it. But like we forget we've had these absolutely enormous changes and then we don't see it because it's like that's the world the world's always been. What's different potentially this time just to be human your code is it's different. This is everything's going to change like just like literally like last time. Like the big difference obviously is a GI might emerge and super intelligence where that is could it, you know, does the work of humans can do a lot of this stuff for us can actually replace jobs just like thoughts on that element of this transformation we're going through. I don't know. This is one of the ways I've struggled to write about AI is like certainly in like 2023, early 24, like all the questions were questions you could have asked in late December 2022. And like, no questions didn't really change. And the strategies didn't really change. And I think the AGI question is kind of the same. I mean the thing that the observation one can make like, you know, we have no theory of what human intelligence is. We have no theory of why these models work so well, we have no theory of how much better they will get. So we all just kind of vibes forecasting is to what all hand. And then you can have like the 2am, you know, don't talk to lots of you students talking about, hey man, like is this consciousness? Maybe we aren't conscious either. We just think we are. Yeah, great. Thank you. I think the one thing one can observe today is so we have no idea. We don't know. We can guess, but we don't really know how that where this is going to end up. What I think you can say today is that there's a lot of kind of redefinition of terms. So I think quite I used to in my presentation last year was, there's an AI scientist called Larry Tesla who said AI is whatever machines can't do yet because once machines can do it, people say, well, that's your software. And so certainly, I mean, I didn't do a poll on social media every now and then asking, is machine learning still AI? Because I've certainly heard people say, well, that's not AI, that's just image recognition. That's not AI. That's just sentiment analysis. So AI is a bit like the word technology. It's like, if it's new, then it's technology. But in the 60s, airline is jet airline is a technology now jet airline or isn't tech. And so there's a sort of sense of AI is like a moving target is whatever just started working. And I think the point here is now clearly you can see people redefining a GI to mean the stuff that works now. So is AI? What's the definition now? It's like it can do a certain percentage of economically valuable work. Well, that's a very different thing to it has a soul and it's fucking alive. Because the database could do that, like, you know, and I be a mainframe in 1975 could do a meaningful percentage of economically valuable work that was previously done by people. And in turn, there was a whole bunch of other stuff that it couldn't do that we didn't do then. We didn't know existed. It says there's a lot of like kind of creative redefinition here. Super intelligence. I'm not sure is super intelligence more than a GI or less than a GI. Because last year, I thought super intelligence was like really good, but not as good not actual AGI. And now it's like, oh, no, no, we've already got AGI but super intelligence. That's really hard. It's like all these terms are like, what do you mean? What even is funny? I was having an argument on a hack in the use this morning. Remember the idea. You remember the argument, which is never, never could use the time. But you remember the argument of like, you know, people would argue about whether crypto is blockchain or whether blockchain is crypto. There isn't a right answer to that. Let's just be sure. You know, it's important to understand what you mean when you say that, but there isn't like a correct answer to this. Are we going to get to something that has human level intelligence? I really know. I don't think we have any way of answering that question. Maybe, maybe not. We can make arguments either wait. Meantime. Does it mean in the meanwhile? We've got this thing that's clearly kind of a completely transformed to a technology. And maybe the serious point here is you don't have to believe even if like the models stopped getting better tomorrow. If this is it and we hit a brick wall tomorrow, this is an incredibly useful technology that's going to change you world and get world out over the next 10 years. So you don't have to believe in any of that stuff to believe that this is John deal. Something that's definitely changed. I had a former boss Mark Andrews in on the podcast. And we'd actually talk about this during the conversation and keep brought it up before we started recording. And I never got to it. Is he had this insight that the opportunity set for company. companies now is so much larger. We used to have no trillion dollar companies. Now we have, we're gonna have dozens of trillion dollar companies. Just like the size companies can grow to or is growing up so much. Invaluations also go up along with that. And this point is just people haven't really grow up to just how large companies can get now. Like everyone's hitting a hundred million, they are like five months, six months, just thoughts on that. - Yeah, I mean, this was his whole software's eating the world he says from, like, 15 years ago, whenever it was, yeah, the time it gets progressively bigger because you can address larger and larger parts of the economy. And so, if you think about the kind of the classic platform share framing that, you know, mainframes are, I think peak mainframe install base was something like 70,000 to 80,000 units. I mean, slightly fuzzy term. What exactly is a mainframe? What's the difference? At what point does it become to you mainframes as one? But something like that, that quarter of magnitude. And then when the internet kicks off, there were, as I said, 50 to 100 million PCs on us, maybe today there are something over a billion, one to one and a half billion, but obviously a lot of those are corporate, like 7,800 million consumer PCs in the world. There's about 5.5 billion mobile phones in the world, which is, and which is why you can have 900 million WTR TV users on JTPT. And so there was this narrative like five years ago, right, well, we've run out of people. So, like, the next thing can't be in order of energy bigger, which was true up to a point, but that was like the wrong model because clearly what's happening now is you're moving in another direction, is you're just branching out and automating big, big new suites of the economy. Now, back to your job point, you could argue, well, we're just gonna replace all the people with AI and all the money we'll go to to Sam Wartman and Mark can buy himself another Goldstream. I think the add to the fleet, I think the kind of the other answer is, it's back to the lump of labor fallacy and the last 200 years that each of these technologies removes a bunch of jobs, creates a bunch of new jobs, creates a bunch of new value, unlocks prosperity for all of us, and that's painful as you go through it, but it always creates a more value. And so here you could certainly make an analog to the useful analog to the electricity industry, is just saying how that electricity became part of absolute everything. And software has been kind of slowly working its way out. You know, the analog here would be electricity and factories and then electricity sort of slowly spreads out. And so that would be the point again that, you know, it slowly spreads out to do more and more things. And so you know, more and more value and a bigger and bigger, a bigger contribution to the economy. It also calls dissipation, dissipation is inside things. And you know, the other side of the point of my capital section in the presentation is, you know, this is secquate from Sam Wartman, we said, you know, we're going to be selling electricity. We're going to be selling AI, AI, intelligence, on a meter like water or electricity. And you look at this and think, you know, my dear sweet child, you need me to explain the modular structure of the utility industry to you. Because guess what? When you watch television, the TV company isn't paying a percentage of your monthly bill to the electricity company, you know, when you wash your clothes, Bosch isn't paying a percentage of the price of the washing machine. And you know, clearly this is like the much more kind of specific tactical question at the moment is, do we even end up with three giant models or does it become hundreds of models and open models and local models and sell them? And even if we do end up with, you know, say, pick a number three to six to 10 giant foundation models that cost hundreds of billions of dollars a year, fine, do they get all the value from that? Now I started my growth at telecoms analyst and so, you know, still pay attention to it a bit. Global mobile industry has a revenue of about a trillion dollars a year, maybe a bit more now. And it spends about 200 billion dollars a year on CapEx every year. Total telecoms is about 300 mobile, there's about 200. About 15 to 20% of revenue every year. And if you look at a chart of mobile data consumption, it's an exponential curve, like perfect curve going straight up. And at the number now, I think it's about, you know, 1500 to 2000 times what it was in 2010, globally. And the stocks have gone nowhere in 25 years because it's an X-grace low margin you to commodity utility where they're selling this objectively amazing piece of global technology infrastructure that has enormous complexity and enormous sophistication. But all the cool stuff is made by you. It's made by the people listening to podcasts. It's made by somebody else. This was that kind of pivotal moment where the telco thought that they would do all the stuff that you did on your iPhone. And not only do they not do it, but Apple doesn't do it either. It's all further up the stack. And so this is, you know, the kind of the elemental question right now around foundation models is does the model do the whole thing? Can you just go to the chapel and get the chapel to do the whole thing? Can the model companies keep building these like clod for X, clod for Y things? Which to me, look very much like what you see if you hit file new in Excel. It's like the templates, but like all of those are actually building dollar companies as well. And if not, no, does it all have to be apps? Quote, unquote, whatever app means. And if it all has to be apps who builds those, well, they can't all get built by the model labs just as they didn't all get built by Microsoft. And so if they're all worked by other companies, does the models, the notion models have leveraged up the stack the way Windows did? Or is this more like AWS where like, if you're a, I don't know, an engineering company or a law firm buying a piece of software, you don't care which side it runs on. And you don't have to like standardize on AWS because that's where all the software is. And like the developers all standardize on AWS because all the customers use AWS. That's not how it works. That's how Windows or iOS works, but that's not how it works. And so it does sort of seem to me that like, if the chatbot isn't the UX and it needs to be apps and the model companies aren't gonna build that and the models themselves are basically commodities as at least as you can see them as users. Then why would the model companies have pricing power? And wouldn't all the value be further up the stack? Aren't you basically, have you got like three to six companies selling a commodity at multiple cost? Now obviously the semi analyst guys are like, No, no, no, no, no, no, no. There's gonna be an infinite pricing driver forever. I'm sorry, I'm exaggerating. But I think you have to really important to kind of draw distinction between where are we now, where you have radical price, dissequilibrium. And you know, you got these, you know, what's the guy? The open goal guy spent one and a half million dollars and token the last month. But that's like somebody getting like a 50 grand mobile data build in 2010. That's temporary, what is the steady state equilibrium point where all of these lines, lines on the chart kind of get lined up and we don't have this kind of weird, crazy stuff going on. And then will you have pricing power or if you got like three or four or five companies kind of all selling the same thing. And so then you should have a pricing, price, this you should have lower pricing and lower margins and the value should be up stack. I am so excited to tell you about this season's supporting sponsor, Vanta. Vanta helps over 15,000 companies like Cursor, Ramp, Duolingo, Snowflake, and Atlassian earn and prove trust with their customers. Teams are building and shipping products faster than ever things to AI. But as a result, the amount of risk being introduced into your product and your business is higher than it's ever been. 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Yeah, I mean, this is a very sort of deterministic thesis, which is the models companies, crucially, what I said is the models don't seem to have no other effects. So there doesn't seem to be a winner takes all the fat, well, one of these will run away ahead of the others. So you should have competition indefinitely. You have competition indefinitely. You don't have, you don't have different primary, like really radical differentiation of what the product is. Then why would you have pricing power? And meanwhile, if you need to have thousands of applications that are all different, built by different people, those can't all be built by the model people. So it should end up looking more like clouds than it looks like windows. Now, that may be completely wrong. And one of the points I make in the presentation is like, imagine having this conversation about the internet in 1997, like, what would you have got right? And, or indeed, having it about mobile in 2000, you would have missed almost all of it. You certainly would have said that like a has been PC company from crypto-china would win the whole thing. I know everyone said that. And a search company with like a weird logo. So what's that goal to do with mobile? Like, never forget it, we would need it. So we should presume we don't know. But there are all these sort of basic building blocks of like, well, but why would they have pricing power? I don't know, I had a, when I was a baby analyst in like '89, we went to CEO.com company in the UK that was trying to do your online selling computer cards components online. And like, they had this whole model and this whole story in the brand by the whole thing. we went up to see them and were on the train back from Birmingham and this sort of senior banker called David Tate. We're all sitting talking about it and Tate says it's a low margin reseller, one-tone sales. Now you can say Doc Holmourley like it's a low margin reseller and I think that's the kind of the crux of this is they're undifferentiated commodity infrastructure providers. There's a lot of science to it but there's a lot of science in mobile. I mean what do you play for a panel screen? There's low barrisons in flat panel screens. There's still low margin commodity. I look forward to being proven wrong but like hey that's what it looks like now. This is great. So I know you're not an investor. I know you didn't actually do investing at a 16-seven, though you work for a 16-seven partner. Just sit around and pontificate. Partner. Would you are the companies you would invest in? Like if there are a couple companies you'd invest in now is there some on that list or a category is even? You know I mentioned briefly that I was an analyst. I was a sales side equity analyst. I was not a very good sales side equity analyst but partly because I was not interested in talking to clients partly because I was not interested in share prices which would seem to be like a disqualification to be an equity analyst. And I don't you know there's a like a huge difference between being right and being early and there's a huge difference between the right company and the right price. Now you know deterministically you can look across the market and say well you know it's like you know like the bell curve I came in and you know the guy with 50 and the guy with 200 are both saying Jeff Bezos or smart guy I buy stock. And you know you can certainly like overthink all of this and you know you can look at you know Google Apple Facebook Amazon and say it's hard to see a problem for them really with all of this. You can certainly see questions for all of them and one of them may drop the ball but it's worth you know kind of remembering what happened in mobile. You know the internet was just like a big obvious platform shift. The funny thing about mobile is that some companies missed it completely and for some of them it really didn't change anything. For Google it didn't change anything. For measure this was great. This is way better to do social than on PC because you've got a camera and notifications and it's on your phone all the time with you. Amazon right what does this change like from the change everything. I mean I'm massively over simplifying here but the point is now meanwhile Yahoo Mail fails to make the jump. The companies that were already kind of dying that fail to make the jump maybe you can't be you can argue about individual names. The point is that like we went through that shift and it didn't change anything for half the industry. Half the internet industry and so I think you know that you could kind of propose a little bit of that here. It's what Steve is an also get at the A6 and Z used to run Windows or what I would say is you know incumbents always try and make the new thing feature and sometimes it's a feature. Actually along those lines something I wanted to get your kick on. There's this thread that's been happening across a bunch of guests which is around distribution becoming a bigger and bigger moat because as software is easier to build everyone's launching products everyone's trying to compete for attention. It's getting harder and hard. It's always been hard to get people's attention but it's just like the noise in the market is just going up like crazy and to me that tells me distribution is becoming a more and more valuable skill and asset and it also tells me incumbents are going to be a lot more successful because they already have distribution versus a startup that's trying to break through. Yeah I mean there's like a version if you know the Drake meme of like he says I don't like that I do like this inside. You know I don't like seeing GPT rappers I do like harnesses. So yeah I did I did spend some time talking about this in the presentation I did at the end of last year that if the product is a commodity then distribution is what matters and you know I showed that I've waited to think about that. It's actually GPT earlier this year opening it earlier this year I had a compete well if there's an obvious comparison here that a lot of people made us with web browsers. The fundamentally web browser and it is a distinction here I think between the web browser as Todd and web browser rendering engine in the rendering engine can be better or worse but the browser product is just like a really thin wrapper for a rendering engine like there's an input box and an output box and like what else and which is like also lost innovation in browser design like tab browsing just 20 years ago 20 progress okay it's like and everyone out there and somebody tries to innovate in browser design and it never works because like you found the platonic ideal. You slide down trying to innovate in smartphone design like you know it's a it's a it's a glass rectangle though there's nothing you can do there. And so what happened of course is that Microsoft uses distribution to break their work to break in then of course what also happens is setting aside the lawsuit is it turns out the winning browser doesn't matter anyway because the value is further out stack and so Microsoft wins browsers for like five six years and it doesn't matter and doesn't get anything. And so clearly what's happening now is the Google is using distribution to drive to drive Gemini and like what's the difference between Gemini and Traut and and and if you're you know if you're using this stuff all day then you know but like no person there's no difference and there's same thing with Meta like you look at survey data on which which our arms people use even before like the new new thing like the Lama thing like Meta was like to jump behind it was up there between chat TpT and Gemini which if you're in tech you people have completely written it off but it was like they'd sprayed it on every service surface. It wasn't that bad it was fine so distribution of an adequate product when the field is basically commodity distribution on brand become a big deal. You can see that in you could see that in the like the strategy open AI strategy late last year was you know what people called it you know everything everywhere yesterday and so they were just trying to find everything to kind of work out how they would get that like how can we get a fly we all how can we get a distribution how can we get something that sticks how can we get people something that some people uses before Google and Meta and Amazon spray it everywhere and get everybody using that one and then you've got like yes inertia and the power of the default and like why would you switch obviously Meta Apple is kind of the last penny to drop here that was just sort of slightly weird opening ideal and now there's even weirdest story that open AI want to see Apple. All right good luck with that. The funny thing about the Apple deal is that it's just not to go off an attention but like if you go back and watch the WDC from 2024 like the whole second half of it is Apple Intelligence that was like the most compelling vision of a personal AI assistant I've still still the most compelling vision I've seen. They then couldn't ship it but then neither was anybody else and you watch it again and you're like okay so you want to using a genetic on device AI with no prompt injection and no hallucinations and a completely standardized API system across 10,000 apps with attempts at all work perfectly and like well that sounds good to me but like I'm not surprised they couldn't ship it but yeah nobody nobody else to ship that but like that vision was great you know I really want to see what happens at WDC in a month the like do they actually ship that now powered by Gemini but that's also another point is like okay this is going to be the AI intelligence whatever we call a Gemini Intelligence on Android and then there's going to be Apple Intelligence on iOS which is powered by Gemini but it's not going to be the same set of products the models just like the dumb thing underneath the funny way of putting it the dumb thing underneath the power of the feature. The models that can model to the power of different decisions about what the features should be in more different distribution and in that situation of course apples got like a billion devices that can run this on edge and Google has this wonderful marketing slogan coming soon to our most powerful devices meaning it will work on most adroids so again distribution questions interesting Google iOS next week so we'll see what they'll launch. Oh no they launched they launched the Android and it just shows how how how models are today well no they launched it last week I mean it's like it just illustrates how much we've stopped paying attention to Android and I've been like Google did had a whole big thing last week they've got they were placing Chromebooks with Google books and they've got a new Android Intelligence Power by Gemini that will vote out to light the five people who bought a pixel change. You don't worth Google. Yeah I'm going to go in a slightly different direction something that I'm curious if your following is just the anti-AI sentiment that is feels like as growing feels like if you see these surveys AI is like less popular than ice people are trying to stop data centers from being built I think Eric Schmidt just did a commencement speech and people are booing him every time he mentioned AI just like where do you think what do you think is going on where do you think this hope this goes over time. It's interesting and it's a big sort of fuzzy massive different stuff I think there is like tangible like my electricity bill went up which applies it actually in a very small number of places which actively but it did and this is a question the water thing is weird because it's just like completely fake and I should call explain what I mean here data centers use water for cooling it's mostly closed loop but the number of data centers relative to the total amount of water use in the USA is tiny and she went and dug into this that the Livermore Lab done that did a study at the end of 2024 where they estimated US data center water consumption and it came out at about 0.017% of US water consumption. Now obviously if you live in a small town and you've got one well and like they kept the well and gave all the water to the data center then you're really pissed off but like that's like that's a planning problem that's not a data center problem in you know in generality yes this is you know data centers of what like 5% of US energy it might grow 1% a year for the next five years 1% point a year but the water stuff is just nonsense and then you get into more tangible like water is happening with this is it taking jobs away where you can watch a bunch of 3 hour podcast of economic. economists talking to each other and main answer is we really don't know yet. There's a bunch of charts that kind of say yes and a bunch of charts that kind of say no. And clearly there's a slowdown in employment of 18 to 24 year olds, but that seems to be the same for people who do and don't have degrees. And the same for people in fields that are look exposed to AI and feel that they don't look exposed to AI. So there's a lot of like econometric argument about this. And I mean, this is a board of point here. In fact, which is a different point here, that like we have very little data on what's going on in AI. From anyone, the model labs don't tell us anything. They don't give us any meaningful usage information. They give us these weird studies of like people, how many people used this for this and that. They don't give us a daily active use number. We do not have a daily active user number for a chat TFT. It's crazy. And all the data comes from academic economists trying to back stuff out of BLS surveys. Or consultancies and marketing agencies like spending a whole bunch of money to survey 20,000 people and saying, what are you doing with this stuff? Like we don't have like good data on what's going on and how many people are really using this. But to the employment question, hence like there's a lot of people like looking through all the stuff that the US Census collects and trying to work out, well, where can we see this? Can we see productivity? That what can we see? The answer right now, I think, is like there's no clear consensus that we're seeing impact on jobs, but of course, politically, that doesn't matter if you're a student and you can't get a job. And that clearly isn't it? Whether it's because of AI or whether it's because of Trump and terrorists, it's a different question. Then you get like kind of niche things like people who draw book covers for young adult women's novels are very upset that now you can get a picture of a naked woman on back of her dragon flying through a revolcano without paying them. So I'm sorry, I'm being deliberately unkind, but there's a little, there's a, and you know, particularly like novelists, people who write e-books, there's a huge culture war over whether it's okay to use AI. This is whole sort of AI slot question and you know, as you saw the number that like 34% of your podcast is generated by AI. So there's a lot of, there's a big fuzzy master questions. Some of this I think is a little bit like the backlash we had around social, but much more compressed. And like social, some of the backlash around social was true and some of it was sort of true and some of it wasn't. You know, always like exemplified in the whole like Facebook sells your data thing, which is just a not true and be the people who believe it are absolutely adamant that of course it's true and you're obviously elunatic for suggesting otherwise. You know, it's like the line from Jonathan Swift that you can't reason somebody out of an idea that went recently to do. So you get this kind of wide, it was a long way off to the rest of you, but this kind of wide kind of spread of ideas just as you kind of did with social. There's like 20 different things, some of which are really real and some of which are really not real and a lot of which kind of a fuzzy message in the middle. All of which means that meanwhile you've got Trump saying he wants a new executive order on dangerous models, which I actually don't think is to say that it's a backlash. You know, they're worrying about missile cyber. I don't feel like that. So you know, a main street, America, compensation for that. So the thing that got Trump interested in this stuff again. And they go kind of in a 10 general direction, something that I like to ask folks that have kids that come on the podcast, especially people that are thinking so deeply about where things are going, knowing what you know about just where the world is heading, what AI is going to do to the future. How are you changing the way you raise your kids? Just what are you teaching them differently, potentially that might help them in the future? I don't know. I think there's a curve here in that if you've got kids who are going on to the job market in the next year or two, then everything is up in the air now and how it's nice how this is going to work. If you've got kids who are going on to the job market in like five years, then who knows? But staff will settle down a lot by then in probably unpredictable ways. So I could be a lot more worried if I had a 21 year old. You know, I don't know, I got you know, I'm a kid in his early teens. So it's a different, those those questions vary. Then you've got a lot of the questions that were the same before to ITVG around, you know, the collapse of gatekeepers, the, you know, no, should you really believe what that influence on TikTok says? And you know, where exactly you're getting your understanding of what's going on in Israel and all of those kinds of social and media or internet, media consumption kinds of questions. I don't know, there are people who are like super, super intentional about, you know, every minute of their child's life. I'm not. I'm kind of recall, you know, the George Carden line, you know, that anyone who drives faster than you is a maniac and anyone who drives slow, it was an idiot. And that's certainly applies to parenting. And so I'd be like everybody thinks they're somewhere in the middle, but you know, I don't have, you know, a deeply systematic and widespread and coherent like plan for this is what my child is going to be doing him three, six, twelve, 18 months time. I'd settle for him not breaking his growing book again. I like that you're just general libers. It's going to be okay, guys. It's going to be okay. Yeah. I don't know if you, I think if you, you know, maybe this is because I'm a British and we haven't had political violence in 500 years. And I think, you know, maybe if I came from Iran, I'd have a different attitude to being calm about the future. I think there's a layer of like, yes, this will change a bunch of stuff and we'll need to worry about it. Remember in the whole way of the panic around social media, I dug up, so a whole bunch of books in the late 70s about databases. There was a whole panic about databases. And again, half of it was true. Like, you know, if everybody's like police records and a restaurant, or if all police records and all government records are online, then that's different. If you think about, for example, the deep needs, deep fake needs issue, for example, there's like a dumb reaction to this, which is to say, you heard a Photoshop, which is true, but a 15 year old kid couldn't use Photoshop to make hardcore pornographic needs of every girl in their high school and send them to the whole school in one afternoon. And to them in the video. Exactly. Even well, yeah, even more. And now they can. So like that is different. It's kind of like, you know, the challenge of social, you know, the thing people would say in the 90s is, it's great. You can read, you know, the only gate yet in your village and you can find other gay people and you can find your drive. And guess what? It turned out you could also be the only Nazi in your village or the only pedophile in your village or the only somebody wanted to look at child porn. And like, yeah, now you can find the other people who like looking at child porn and they'll tell you it's great. So oops. We connected everybody and unfortunately that meant we connected to all the bad people and all of our worst instincts in every problem in society. And so that will happen again with AI. You know, that we can deep fake needs are like the obvious thing we can see now. There will be a whole bunch more of this stuff. But there's also a kind of technical audience you know about, have you heard, do you know about the post office scandal in the UK? No. Okay. So side by here. So in the UK post offices are mostly franchises run by small business people. So they're run by like pharmacies classically. Very often Indian Indian immigrants, second generation Indian people. And supposed office like 15 years ago rolled out and you can point to sell computer system. So they have a separate counter in the back of the post office. And so the post office rolled out this new computer system built by the budget for budget see that had a bunch of bugs in it that showed shortfalls and cash. And the post office looks at this and says, ah, we knew these people were stealing from us. Hundreds of people get present. Bunch of suicides, bunch of bankruptcies, people lose their homes. Meanwhile, people from the post office and people from Fijietsu are going to court and swearing there's no bugs in the system and nobody else has had this problem. This is 1970s technology. And that's really the point that every wave of technology comes as a way that you can ruin people's lives either deliberately or by accident. This is the whole thing of Chinese mass surveillance is deliberate. This is maybe people should go to prison maybe not. But like we have this with every technology. We have a bunch of ways that you can ruin people's lives and you have to be conscious of that and also kind of not panic about it. So maybe following that, the rate and coming back to the kids thing and the jobs thing. Is there like a job you are steering your kid away from? And is there a job you kind of think you want to steer them towards? I don't know about that. It's probably a little bit early yet. He's not quite at the like I want to be a fireman stage. But, ah, yeah. And certainly, you know, if I look at my career, you know, I started as an equity analyst and then I went and worked in industry and then I was a consultant. Like, you know, the days when you kind of knew what your career would get was going to be or over, you know, there were sickly some people were like, you want to be an architect, you want to be a software engineer, you know, you want to be ex or why. I don't know. I think, you know, the only the any kind of thinking I have here is that you have like the slowly work out. There's a bunch of skills that you have. And there's a bunch of like jobs that make that makes you good at. And then there's a bunch of stuff that people will pay you for. And you want to get at least two of those and preferably all three. Okay. So zoom me out a little bit. Let me ask you a meta question. What's a question about AI that you think nobody's asking yet or not enough people are asking that we should be asking ourselves? Sure. I mean, we talked about like value capture. Like obviously this is a whole. Everyone is is asking like, I'm not sure how many people are asking whether model labs have pricing power. I think a lot of people are just presuming that situations they will continue or that of course they will. So I think that's maybe a question that the not enough people ask. I think the question I pose towards the end of my presentation, which we talked about earlier is like, what's the task and what's job, but what is just the thing that becomes a button or make school versus what are people actually hiring before is kind of a useful way of thinking about this. And clearly there were going to be some jobs where no, that is just a task. And that job gets sort of made it a way. But there's a bunch where that kind of isn't the question. The way I actually pull that together at the end of the deck was a chart of a global recorded music revenue, which as you may know is kind of a U-shaped curve, more or less. And so it's dropped by about half from 2000 to 2015 or so. And since then has come back to about 75% of the peak for just a translation. And the way that I look at this is just and that's driven by streaming. And I kind of looked at this and said, well, the first half of this chart is saying what happens if I don't have to pay $50 to get a CD to get that track. And the second half of the chart is saying, what happens if $15 a month, let's see all the music that there is? So it's going to completely different sort of question. And you could, you know, that's a way that you could look at you or the way you could look at Airbnb, all these kinds of companies. Is it to begin with you do the old thing but more? With every new technology, you do the old thing but more of it on the new place. So, you know, you put Flickr or mobile, you print out your emails. And then you make new things that are only possible with a new thing. And then maybe you go a bit further and you kind of completely redefine the question and you make something that isn't that of all, you know, Spotify isn't not a non-line music store. It's something else. And right now, you know, those questions, you only know you've even know what the question is after it's been asked and you've built a billion dollar thing that lots of people use. There's obviously Spotify look crazy and you feel like crazy and Airbnb look crazy. But that's the sort of, I think, the way to get at what this means is you have to get past, we do the old stuff but more. And you have to get to what do you do that's different that's because of this. What does this change? What was impossible before? What gets unlocked? As opposed to just doing the old thing but more of it. Yeah, just to support this kind of general theme you have of it's like we don't know what is going to happen. Like this is unprecedented. If you if you were to zoom out like a few years ago, maybe three years ago, four years ago, the last profession you think would be automated is engineering and coding. It's like that feels like the hardest thing that's like we're going to need people to build these things. Now it's like the most transformed role of any role. Like you went from writing all your code to 0% your code as they are. It's almost like you didn't realize you didn't realize it was boring manual labor that could be automated. You thought it was something else. It's funny. I mean, I was looking at this as whole. There's this sort of US government called own data set called own edge or something like that. We've tried to kind of analyze every single job and then people try and kind of score it and they try and say, well, this profession is x or y% exposed to AI and AI can do z% of it today. I think this is just the most ridiculous bunch of deleted horse shit. And there's two reasons for this. The first reason is that this is like, I want to say this is the logical systems problem. The expert systems problem. The problem with expert systems is like, really, really doesn't know. Like you try to recognize a picture of a cat and say you start building up logical steps. So you make an edge detector and then you make a third detector and you make an eye detector and you make an ear detector and 15 years 80, you've got 700 steps and it doesn't work. And this is what happens when you try and look at a profession and sort of break it down by which bits can be automated in which cart. You can't describe a profession like that or anyway, we can't. You can't kind of look at a senior partner or a law firm and say, well, 17% of their work could be automated. Like this is horse shit, you can't do that. I think the other side of the fallacy though is to talk about taxi drivers. So, if we've been having this conversation in 1997, it's like the Uber test. Imagine we're in 1997, what will be crushed by the internet? Well, newspapers will be fine. They'll just, because they'll save money on the printing bills. This is like a joke, but people said that. These papers, the internet will be great for these papers. They're printing bills will get down. Well, yes, but no. But the other side is, well, obviously, I taxi drivers. You couldn't automate that with the internet. So, what other people do with the internet? Maybe you'd have internet booking, but like, no, that's not going to change anything. And of course, it completely changed this whole thing. And so, like the example I saw the other day was like things that won't be affected by AI personal trainers. Okay. So, I take my iPhone and I balance it on the metal piece with the camera pointed at me. And I ask an AI to build me a training routine and watch me and tell me if I'm doing it right. Why do I need a personal trainer? Now, that might be complete nonsense. But that has these things work. Like, the stuff that you don't think is, you can't predict which things are going to be exposed necessarily. Or, you know, a lot of the big companies are things that didn't look like that would work and didn't look like that was exposed. The other side of this, of course, is this is one of the charts at the end of my presentation is comparing Uber and Airbnb because this is like the cliché from Mark and recent that like Uber doesn't sell software to taxi companies. Airbnb doesn't sell software to hotels. Okay. Now, let's go and look at the market impact. Well, the whole bunch of cities were Uber to monitor taxi business and made it much bigger as well. The tan became much bigger when everyone switched. Airbnb's impact hotel hotels, if you actually go and look at the numbers, is pretty marginal. They carved out this whole other business. And maybe they slowed down the growth of hotels a bit. But, you know, my wife flies to Millwalkie next week. She's going to land at eight o'clock at night. She wants to go to her hotel. She wants to have a room service. She needs a bathroom at bath. She needs, you know, she needs a gym at six in the morning. And then she gets seven in the morning. She's going to drive to the client sites. She's going to understand Airbnb. Like, absolutely zero chance. And half of the hotel business is travel. It's business travel. And you know, you can see, as soon as you actually get into anything, then it gets complicated. I remember somebody on social media said a problem with bandages. Everything, his answer to everything is it depends. It's like, yeah, it does. It depends. So there were, you know, it's back to my 1997 point. You can say some of this. But you have to have that humility. Yeah. I'm coming back to this phrase used, presumably radical uncertainty is a nice court thesis here. So, knowing all this, just it's hard to tell. We don't know exactly where it's going. Things are going to change a lot, but it'll probably be okay, broadly. Just a lot of people listening are pretty worried about their jobs and their careers and how much the world changes. What would be a couple things you recommend people do, knowing what you know to be more successful in this future? Well, I should suggest kind of wind back on what you just said. It's like, it's Keens tells us in the long run we were all dead. So, you know, it's all, you know, like on average, you know, on average, nobody died in World War One. Great. But if you're 19 year old in 1914, you've got one in three chance of not coming back. So, yes, clearly there's a bunch of professions where this is a major question and particularly if you're an associate or would have been thinking about being an associate, this is a major question. And it's very unclear how those professions are going to play out. It's very unclear what the, you know, happens to the pyramid structure of profession services. The only answer, I think one can have is, you know, don't stick your head in the sand and say, hey, to all of this stuff because that gives you a great feeling of moral superiority and you can go on blue sky and shout it and everybody, shout at each other about how evil AI is like great. I'm happy for you, but that's not going to help. What I helps is you diving into this completely submerging yourself in it and coming out understanding what you can do with it, how this changes things, how can you, how you can be a great hire. And that may still not help. But, you know, if you're going into a law firm and they're like, well, we hired a hundred associates last year and this year we're only going to hire 50, going to the interview and say, well, I think AI is bullshit and I'm never going to use it is through all the way not the right mood. So, you know, you can, that that may not be particularly comforting, but I don't think there's an alternative is, you know, you have to dive into this and absorb it and internalize it and think about what it means just as, you know, you and I did with mobile and with the internet. I think that is actually very actionable and very consistent and based on the white gasses just do stuff, build it, don't sit around and pontific it and be pissed at what's happening. To close this out, I'm going to take us to AI corner, a recurring corner of the podcast. And the question to you is just what's one way you use AI and use AI in your work or life that is really interesting, something that other people might might be inspired by. I don't know. I start off with this question because I'm full of the lawyer looking at chat TFT. So, you know, the stuff that I would do that I would automate are sort of precise information, retrieval task, which is precisely the thing that this is kind of worse at. And, you know, that's not a criticism, it's just an observation, the kind of the kind of stuff that I would want a machine to deform here is the stuff that AI can't deform you very, very well at the moment. I use it for proofreading, I use it, you know, for images, I used it for redecorating my apartment, that worked fantastic, you will have that. Here's a picture of this who I'm repainted at this light and this table and this rug, no change color of the rug. There's a kind of plan of stuff where it works. But a couple of years ago somebody said AI is good at stuff that computers are bad at and bad at stuff that computers are good at. And that's, I struggle to find many, many examples of those where I need it. But then, you know, I'm kind of a unique way job, you know, I sit on my desk all day, you know, trying to synthesize a whole bunch of other staff into a whole bunch of new ideas. That's not particularly common where people spend their time. I struggle to find AI use cases. I am the accountant, luckily at the spreadsheet and thinking, "Well, that's very clever." And this is clearly going to complete, transform everything. But I actually don't make spreadsheets every day. I went to a stand-up comedy show at Pete Holmes. I don't know if you know him. And he made this joke that we want AI to do, like, clean the poop off the street and do all these like hard things than nobody wants to do. But instead it's like, "Oh, let me help you write. Let me help you create imagery." It's like, "There's bohemian's." No, I don't want to do all these ugly things. I want to be creative. They're tart. Yeah, well, I mean, there's variations of all of this, you know. It's like, "I don't want the AI to do the stuff I do fun." I want to do the stuff, the boring stuff that I don't do fun. And finding that mesh. I mean, you know, joking apart, this kind of come back to kind of my chatbot point, that, you know, the chatbot is a blank screen in a jagged edge. I want to my space to do and what will work. And that's a big problem. And the solution to that problem is to wrap it in use cases. Part of it is also like AI just disappears. So most of what I write now I dictate. I dictate is a voicemail. And that's automatically transcribed. Is that still AI? Or is that just voice recognition? Probably an LLM in there. Okay, so maybe that's AI. Well, okay. So, so what? A certain point is just automation. We'll use for that for voice, voice transcription. So I actually find Apple notes. The Apple, the one built into the iPhone works fine. I mean, I'm conscious of the people want others. But like, I mean, I dictate it. There it is. They worked. So I'm happy with that. All right. Final question before we get to a very setting, lightning round. Is there anything else that you wanted to share anything else you want to leave a listener with? I think, you know, I've monologed plenty and I've gone through a bunch of stuff in the deck. Go leave the deck and sign up to my newsletter. And then you will get many more mags of brilliant Benedict Evans wisdom. Some of which may even be useful. Somebody answered someone unsubscribe from my newsletter. And they said you didn't, you didn't give me any actionable stock ideas. And I'm like, well, on one level, that's completely true. On the other level, maybe not. Well, with that Benedict, we've reached our very exciting lightning round. I've got five questions for you. Are you ready? Sure. First question, what are two or three books that you find yourself recommending most to other people? This is a tough one for me because I just read an enormous amount of books and then I can't remember which ones I've read. I sometimes often joke that the classic British comedy from the late 19th century called "Three Men and About," which is like my itching. We're having trouble hanging a picture. Well, there's a section about that. We're having trouble doing this. Oh, well, there's a story about that. All of which are hilarious. So "Three Men and About" is my itching. There's a book by William Cronon about the economic history of Chicago, which is fascinating and actually very relevant to technology because it's talking basically about standardization and packetization on logistics and channel conflict and network dynamics and network neutrality. So when the meat packets of Chicago reach the point that it's cheaper to ship a cow from New York to Chicago, kill it, pack it and then ship it back to New York and kill it in New York. And the pricing of refrigerator cars and it's exactly like reading a badball event. It's all the same kind of business issues, which is fascinating. What else? I read. I don't know. Read books. Read different books. Generally, read books for grown-ups. Please read something other than "Lord of the Rings" if you're going to name another company. It's like I saw this song and what was the latest like "Tried a Teal Company" but like read another book. Everything is named off a character from this one book. There's more than one book in the world. There is more than one book than all that science fiction. Read about different things. Read about things you don't know about. Kind of along those lines. You have a favorite recent movie or TV show and you've really enjoyed. And now I've dropped so badly off the current media treadmill and I just then most of my time watching classics, which are like all the ones that you're supposed to have seen and that all seem intimidating and then you watch too many. I like, oh, that was actually really good. Now, "Water 7 Seal" recently, which is like one of those Jake Woody Allen terrifying boring movies and it was really interesting and it's like, no, it's only like an hour. So go watch one of those movies that you are supposed to have seen or hadn't seen. Favorite recent product you've recently discovered. You really love. It could be a gadget, it could be an app. I was speaking at a partner meeting for a company earlier this week, what's today, Monday, no last week and met the founder of the company who has a very famous CEO of the company who has a very famous name and admired his shoes and didn't say anything but then went and Googled like half an hour later, yeah, okay, I'll buy a pair of this. You want to share the brand or you want to keep it secret? Okay. I don't know. I think one comes in waves of new products and you get into waves of new things and like, once in a while there was a cool app like iPhone apps, that was all that white space went. I mean, it's partly a function of product chips, a platform chips like all the white space went for the cool new apps and we haven't quite got issue of this to the earlier point. We don't have breakout a consumer AI app yet because I think because of marginal cost more than anything else, you can't make it free and get 50 million users and then have a revenue model. But we don't have those breakout things yet for consumer. For consumer. I just weird, I keep getting these ads for voice recorders, like somebody selling like a business call, like hardware voice recorder and like that. But like, I didn't get it. Like, I've got the voice recorder on my phone. Yeah. All kinds of cool stuff coming. Okay. Two more questions. The only favorite life motto that you find yourself coming back to often in worker and life. I just first mentioned earlier, apparently I mostly say it depends. That's going to be the title. It'll probably be okay. Yeah. Okay. That's the vibe I get. I like that. It's probably going to be okay. Not for sure. Okay. Final question. I saw somewhere that you're on a lot of old phones. Is that true? It is. Yes. I kept, I mean, I was talking on sound on the phone. I was on the phone. I kept all my phones up to a point. Now they kind of are uninteresting. But as you may remember, like before the iPhone, particularly outside of the USA, there was this huge creativity and expansion in what phones look like because everyone was basically innovating around a little teeny tiny gray square. So everyone was trying to differentiate from everything else before it kind of results. Kind of like cars, actually. It's like cars before street before like wind tunnels. Cars all look different and everyone's trying to innovate around because they've got the same four wheels and the same engine. Everyone's trying to like differentiate based on light and shape. And then everything converges on one shape. And it's kind of the same with phones. Like everyone, everything converged on one shape for that. It was all this innovation. So yeah, like I have like a whole bunch of pdi and smart phones and how many phones we're talking about? I don't know, like 20 or so to you. Okay. Okay. Okay. It's not so easy. What's like the oldest one? What's the oldest one you get? So I have one of those. I should have easily told me I'd have got the box down. I have one of those Ericsson shark fin flip phones from like 98 or something, which is very not very again, like hardware design, visual design, trying to differentiate. I've got an I made phone from 2001 and a J-Fane from 2001 that has a camera. So I came back from Japan in 2001 and I phone had a color screen and a camera. And like I just had like endless client meetings that people just wanted to see the phone with a color screen. Like it's in my blood. It didn't work outside Japan. You start to plunk within the other day. It still charges up. I mean, I can't do anything with it. And like, I mean, there's a little bit of an analogy in there as well. And like we thought there'd be all these different shake sizes. And before the I-Fane, people kind of imagined like, well, some people will have like a little pocket PC and some people have a keyboard and you have like folding, or there were all these different ideas for what it would look like. And it all we realized it was all going to convert to modivites. Ben addict. This was amazing. I learned a time. I feel better after this conversation. Two file questions working folks finding online. Where do they find this presentation? And how can listeners be useful to you? If you can Google me, as I always say, my parents had good SEO. So Google bandedict heavens. And say there's a website with this I publish all the presentations that I've done and sign up for my newsletter, which comes out every week. Otherwise, how can they be useful to me? Like, I'm always trying to understand stuff. And I'm always trying to ask different questions. The worst thing in tech is to like Harry on talking about the same stuff. It's like, you know, the moment you really understand something, it's the moment you have to push onto something else. And so I'm always trying to think like, no, am I just talking about the same thing over and over again? Like last year, I just spent probably too much time saying that these models still hallucinate. Stop telling me they don't hallucinate. They do. They still have hallucinate. You know, you push them, push them a little bit further. Any question in you'll still get like, no, that's not true. But that doesn't mean they're not useful. So you have kind of keep pushing it, keep pushing myself. So that's full with the challenge for me is how to approach. And then yes, if you want me to come and present to your board in the Caribbean, let me know. And by the way, the domain is bendashevans.com. If you want to check out and evan.com. Thank you so much for being here. Thanks a lot. Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts. Spotify or your favorite podcast app. Also, please consider giving us rating or leaving review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny'spodcast.com. See you in the next episode.