Lenny's Podcast: Product | Career | Growth - A rational conversation on where AI is actually going | Benedict Evans

Episode Date: May 31, 2026

Benedict Evans is an independent analyst and former partner at Andreessen Horowitz, where he spent years as their in-house “thinker” tracking the most important technology trends. For the past six years, he’s been publishing deeply researched presentations on where tech is heading, most recently focused on AI’s transformation of the economy. His work is read by founders, investors, and operators trying to make sense of a noisy field. His most controversial opinion: AI is as big a deal as the internet or mobile—and only as big.In our in-depth conversation, we discuss:1. Why we’re in “1997” for AI—early, exciting, and deeply uncertain about what comes next2. Where value will actually accrue in the AI stack3. The anti-AI backlash, and where it may lead4. The surprising boom in consulting and professional services at AI companies5. Why distribution is becoming the ultimate moat as software gets easier to build6. Why the right question about your job isn’t “What percent can AI do?” but “Is this a task or a job?”7. Why things will probably be okay—and what you need to do to prepare—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyVanta—Automate compliance, manage risk, and accelerate trust with AI: https://vanta.com/lenny—Episode transcript: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Benedict Evans:• LinkedIn: https://www.linkedin.com/in/benedictevans• Newsletter: https://www.ben-evans.com/newsletter• Website: https://www.ben-evans.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Benedict Evans(02:19) What people aren’t pricing in about AI’s impact(06:24) Why we’re in the 1997 moment of AI(09:44) The unexpected boom in professional services and consultants(17:44) Why distribution is becoming the ultimate moat(23:17) The coming job transformation: what’s real vs. panic(27:33) Why AGI definitions keep shifting(38:11) Where value will accrue: models vs. applications(42:55) Distribution wars: Google, Meta, Apple, and OpenAI(48:12) The anti-AI sentiment and backlash(53:11) How to raise kids in an AI future(58:27) What jobs to steer toward or away from(59:20) The question nobody’s asking about AI(1:06:25) How to be successful in this coming future(1:08:43) AI corner(1:11:43) Lightning round—Referenced: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [e-mail].—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

My most controversial opinion is that I think that AI is as big a deal as the internet or mobile, and only as big a deal as the internet or mobile. What's your just on the coming job apocalypse? Every time we have a new technology, it automates a way 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 DEMAs on Twitter, and they would act like every big company is going to buy chat GPT tomorrow, and then in two weeks time they'll fire all their staff. These people are more. You can't predict which things are going to be exposed. You can't look at a senior partner at a law firm and say, well, 17% of their work could be automated. This is horseshit.

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 stick your head in the sand and say, I hate all of this. stuff. That gives you a great feeling of moral superiority and you can go on blue sky and

shout 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 with. 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 are 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's productpass.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 inviting me. You just put out this deck called AI Eating the World.

I want to ask you kind of the flip side of this of we all know it's a big deal. Like 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, you know, my most controversial opinion is that I think that AI is as big a deal as. the internet or mobile, and only is bigger deal as the internet or mobile, because 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. Does he not understand how big this is? And I'm like, smartphones were 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 like one layer of, but then if you dig into that, like, if you're going to make the internet comparison, it's like we're in 1997. 7th. 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 it's 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 is 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've bought their cluster of Mac minis and 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 who are using this are using this every week or shoot, 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, has open AI won the whole thing? Or, you know, is Anthropic got it this week? And so then you can kind of get into calling those races where, again, it's like being in 1997 and saying, well, is it going to be excited or Yahoo? And the answer was no, generally. So there's a sort of a fractural point here. There's like the sort of the super high level that, like, this is going to change absolutely everything. I don't think it's particularly productive to say, well, is it 20% bigger than the internet or 100%?

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 it. I do a presentation every six months and I just published one yesterday. And one of the comments was, well, it's been to this is 80 slides of saying we don't know, which is like slightly facetious but also kind of true. This episode is brought to you by our season's presenting sponsor WorkOS. What do OpenAI, Anthropic, Cursor, Versel, Replets, Sierra, Clay, and hundreds of other winning companies all have in common?

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WorkOS allows you to build faster with delightful 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. Do you have a sense of just like the timeline to, okay, now things are going to be radically changing? Well, like, where are we in that cycle? You talk about all these different cycles we've been through, like how far are we from just, like, 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 Agenic and AI software development

two separate things that merge together mean for the future of the software industry? There's one extreme which is no one really believes, which is, you know, hey, you'll just like Vibode your own stripe. And no one actually believes that, although really 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, you know, 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 am I, how 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 in the presentation is like imagine you're seeing, imagine you're an accountant seeing the first software spreadsheets in the late 70s. This is mind-blowing. 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. But clearly if you're an accountant, this is obviously mind-blowing. But if you were a lawyer looking at that or journalists looking at that, you'd think, well, that's very clever in 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 2 and the monitor and the printer to run it, which is what it costs if you adjust to inflation. But that's not what I do. And you need a word processor, which actually came like very shortly afterwards. And so that's sort of the moment that we're in of there's some people like software development are developed software developers are the accountancy of VisiCalc. Like, oh my God, this changes everything. Like before VisiCalc and after VisiCalc, before, Clauccoe. an article code. 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, in the presentation

that even if you look at like 13 to 18 year olds or something, it's still like kind of 15, 20% of people are daily active users, and another 20% are weekly active users. And then the other 60% of those people in that demographic count on, you say they are not using this. So there's a sort of very widespread of who gets 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 of your software developed, there's a lot of other people where like

people are having a moment or they're not or we're in again 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 slash consulting services slash forward deployed engineers uh all the AI labs at least the two big ones open anthropic are like investing in buying massive like consultancies and PE firms talk about just what's happening there why that's happening well it's funny I was kind of groping for a joke last night when I wrote my newsletter and couldn't quite get to 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 build a big new project or do a big new piece of analysis or build a big new piece of technology or a new product or work out how they're going to redesign their stores or, you know, work out where the stores should be or try and work out why the churn is too high. All of those kinds of questions are things, reasons why you hire Bain B.C.G. McKinsey on one side

or Accentia, Info, C, whoever, on the other, or you hire a branding agency, or you hire an firm of architects or whatever. And it's always like, well, we could hire some architects, but why on earth would we want to have 15 architects on staff when we just go and hire an architecture firm? 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 ten people to sit down and spend a month or two working it out. And then actually doing it is another project. Okay, so you 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, guess 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 Bain or you hire Accentia 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 edge AI labs are the ones most investing 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, there's a section on capital, which is basically where is all this capix going, and are 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 scoo or making the PowerPoint? Or is the hard part of the job something else? Is it the task or the job?

And pulling that part, sometimes the task is the job. Like the classic example is like an elevator attendant. I live in a building that has an attended elevator. We have a manual elevator. There's no button. There's a lever and the dormant drives you to your floor. It's a vertical speed car. It's like one of those trams in San Francisco.

they drive you to the store, to your floor. And then those all got automated after the 50s, and now you get it, and you press a button, and pressing the button is the job. So there were some things where the button, the job was a task, and the task got automated. What happens much more, and this is why people talked about, like the Jevons Paradox,

is the Jevons Paradox, is just price elasticity, applied price 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, this is a joke I made on Twitter back when it was Twitter was like young people won't believe this, but before Excel, junior investment bankers worked really long hours. And now thanks to Excel,

Goldman's Associates all the work at lunchtime on Fridays. It's like, well, why is that not what happened? You could make the same point as software development. You know, before IDEEs 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, you know, 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 use a SCOO if you know what the SCOO is. If you know what screw you want, you want that microphone stand. You know, 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 scoo, but knowing what school you want is another job. You know, the Claude Co can write you the code, but what code do you want? They can make you the features. Sure, what features do you want? Who's your customer? What's the right product for that?

customer, how you're going to take it to market, and long way of answering a question, why do you hire McKinsey? Are you hiring them to get a 75 slide deck? Well, narrowly, Claude, co-work will make a really, really crappy version of that. And you get all these kind of AI grifters on LinkedIn and Twitter and so on saying, hey, I made a McKinsey deck with Claude, 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 enterprise, 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. It's 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've 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-willed by the internet because they had those two things and you could split apart. 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 it will be 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 has. That was what they actually did. And when that went away, they were screwed. Same thing for newspapers. Newspapers did not think of themselves as like 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 say, well, hey, you know, we're just going to automate the accountants, or 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 mainframes and ERP and cloud, spreadsheets and PCs and the number of accounts keep going up. And so why is that? But it's not, it must be more, it's more complicated than automation. Even just looking at the most advanced AI companies, anthropic, Open AI, I just had Dan

Shipper from every on the podcast. Everyone's just increasing headcount. 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 you're just kind of just on the job, the coming job apocalypse, you know? Like Darya's 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 an 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 Dario's opinions on where models are going to go in the next six to 12 months, not particularly interested in opinions on a series of labour and market value and competitive comparative 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 cautious on, like, well, Dario says. And that's setting aside, like, the cynical view that he's, you know, he's just doing that hump the stock, which I don't believe at all.

So it kind of comes back to my point about, you know, platform shifts. Every time we have a new technology, it automates a way of a bunch of jobs. 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 go back to 1800, like 90% of us were peasants. And our major concern was, like, are the grots going to fail? Because then we'll all go hungry or 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 here. 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 their jobs and a bunch of towns get hollowed out. And 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 crop's 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. PCs 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. And generally, the jobs that go away are crap jobs, seen retrospectively, and the new jobs are better because, you know, GDP keeps going up. So is AI different? And so then there's kind of a couple of... 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, but 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 telco to deploy broadband. It's already there. So, of course, chat, GPT, you can get 900 million with chat users, because there's already 900 million people on the internet. Like, in, like, when Mark had recent 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 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 like mass production. So there's all, you're always kind of standing on the shoulders of giants. There's always like the 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 the 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 TVT tomorrow and then in two weeks time they'll fire all their stuff and these people are morms. It's one of many reasons why the Duma's were more on. But I have a complete failure to understand the way the world works.

And that was like the starting point why they then didn't understand anything else. You know, typical big company, you know, enterprise software sales cycle, you'll know this better than me. Enterprise software sales cycle is like 18 months if you're lucky. You know, this is always the problem. The enterprise sales cycle is shorter than the venture backed software funding cycle. longer, longer, rather, longer. Like, it takes you longer to get an enterprise deal than it takes you to go between

wraps. And this was always a problem with, 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 X, Y, Z. Maybe in five, in like three, five, ten years, yes, that whole estate will look radically different and all those jobs will have changed. But it will take, you know, three, four, five, ten 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.

One of the companies I always remember whether we looked at when I was at Andreessen Horowitz as a company called Frame.a, which is video editing, video collaboration. And there's nothing new 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 relies on a bunch of like, a bunch of stuff like cutting-age web technologies. If you go out and pick 10 random SaaS companies that were started the day before TAT TVT 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 of saying, 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, and 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 in white shirts and ties, all holding up flood rules. And the ad is, 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 a computer.

It's an electronic calculator. It's the size of a fridge. It's like having 150 extra engineers. How many people listening to this company list, like their company's slogan is, basically will give you 150 extra engineers. I mean, isn't that like the whole picture? of Claude.

150 expert engineers for free, or not free, that's like a lot of money. And yeah, that's what it 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 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 barcodes 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 scoos there were in supermarkets every year. And then I had to realize they've been around since the 50s. And if I think like dug long enough, I might be able to make 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 know, 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 like, life of an analyst is you spend all day making a chart and you look at it and go, oh, that's not very interesting. So you spend 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'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, even though your quote is, it's different. Everything's going to change just like, just like last time. Like, the big difference, obviously, is AGI might emerge and super intelligence, where that is, 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 like December. 2022. And our 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're all just kind of vibes forecasting as to what will happen. And then you can have like the 2 AM, you know, dope to our philosophy 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 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 a quote I used to in my presentation late last year was 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 just software.

And so certainly, I mean, I did 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, it's a bit like the word technology. It's like if it's new, then it's technology. But in the 60s, airline is jet airlin is a technology. Now a jet airliner 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 AGI to mean the stuff that works now. So is AEI, 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 can do that. Like, you know, an IBM mainframe in 1975 could do a meaningful percentage of economically valuable work that was previously done by people.

And it turned out there was a whole bunch of other stuff that it couldn't do that we didn't do then. We didn't know existed. So there's a lot of like kind of creative redefinition here. Super Intelligence. I'm not sure. Is super intelligence more than AGI or less than AGI? 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 even, what even, it's funny, I was having an argument on hack and use this morning. You remember the idea, you remember the argument, which is never, never a good use of 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 don't know.

I don't think we have any way of answering that question. Maybe. Maybe not. You can I make arguments either way. Meantime, in the meanwhile, we've got this thing that's clearly kind of a completely transform into technology. And maybe the serious point here is you don't have to believe, even if the model stopped are 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 your world and get rolled

out over the next 10 years. So you don't have to believe in any of that stuff to believe that this is a giant deal. Something that's definitely changed. I had a former boss Mark Gendryson on the podcast. And we didn't actually talk about this during the conversation and he brought it up before we started recording and I never got to it. Is he had this insight that the opportunity set for companies now is so much larger. We used to have no trillion-dollar companies. Now we have, we're going to have dozens of trillion-dollar companies. Just like the size companies can grow to or is going up so much. And valuations also go up along with that. And his point is just people haven't really grok to just how large companies can get now. Like everyone's hitting 100 million

error in like five months, five months, six months. Just thoughts on that. Yeah. I mean, this was his whole software teaching the world thesis from, you know, 15 years ago, whenever it was. Yeah, you know, the dam gets progressively bigger because you can address larger and larger parts of the economy. And so, you know, if you think about the kind of the classic platforms here framing that, you know, mainframes are, I think peak mainframe install base was something like 70, 80,000 units. I mean, slightly, a funny term, what exactly is a mainframe? And what's the different way? At what point does it become two mainframes as one? But something like that, that order of magnitude.

And then when the internet kicks off, there are, as I said, 50, 200 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. It's like 7,800 million consumer PCs in the world. There's about five and a half, six billion mobile smartphones in the world, which is why you can have 900 million weekly IT users on JCPT. 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 magnitude 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, you know, granting out and automating big, big news weights of the economy. Now, you know, back to your job point, you know, you could argue, well, we're just going to replace all the people with AI and, like, all the money will go to Sam Hortman.

And, you know, Mark can buy himself another goal stream. I think the, add to the fleet, I think the kind of the, the other answer is, you know, it's back to the lump of labor fallacy. And, you know, the last 200 years that, you know, each of these technologies removes a bunch of jobs, creates a bunch of new jobs, creates a bunch of new jobs, creates a bunch of of new value unlocks prosperity for all of us, and that's painful as you go through it, but it always creates more value. And so here you could certainly make an analog to, you know, the useful analog to the electricity industry is just saying how that electricity became part of absolutely everything. And software has been kind of slowly working its way out.

You know, the analog here would be electricity in 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 knew more and more value in a bigger and bigger contribution to the economy. It also, of course, disappears inside things. And, you know, the other side of the point of my capital section in the presentation is, you know, there's this quote from Sam Altman where we said, you know,

we're going to be selling electricity, we're going to be selling 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 margin 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 so on? And even if we do end up with, you know, say, pick a number three to six to ten 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 career as a 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 a year on CAPEX every year. Total telecoms is about 300.

mobile is 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 the number now I think it's about, you know, 1,500 to 2,000 times what it was in 2010 globally. And the stocks have gone nowhere in 25 years because it's an X-gross, low-margin commodity utility

where they're selling this increase, 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 this podcast. It's made by somebody else. This was that kind of pivotal moment where the telcove 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 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 chat board and get the chat board 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 billion 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 by other companies, does the models, their notion models have leverage up the stack the way Windows did? Or is just more like AWS, where like, if you're, I don't know, an engineering company or a law firm buying a piece of software, you don't care which cloud 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 WindowsOS 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 going to build that, and the models themselves are basically commodities, 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 marginal cost? Now, obviously, the semi-analyst guys are like, no, no, no, no, no, there's going to be infinite pricing power forever. I'm sorry, exaggerating. But I think you have to really important to kind of draw a distinction between where are we now where you have radical price disequilibrium and you know you've got these you know what's the guy the open claw guy spent one and a half million dollars on token last last month but that's like somebody getting like a 50 grand mobile data bill in 2010 that's temporary what is the steady state equilibrium point where all of these lines aligners on the chart kind of get lined up and we don't have this kind of weird crazy stuff going on and then you will you have pricing power or have 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. You should have lower pricing and lower margins and the value should get up stack. I am so excited to tell you about this

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And the bigger opportunities in the application layer, the people building on the models, the rappers. 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 effect. So there doesn't seem to be a winner-takes-all effect, where one of these will run away ahead of the others. So you should have competition indefinitely. If you have competition indefinitely, 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 the, 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 cloud than it looks like Windows. Now, that may be completely wrong. And, you know, one of the points I'm making the presentation is, like, imagine having this conversation about the internet in 1997. Like, what would you? you've got right. Or indeed having it about mobile in 2000. You know, you would not, you know, you would have missed almost all of it. You certainly would have said that like a has-been PC

company from Cryptino would win the whole thing. I know one would have said that. And a search company with like a weird logo. Like, search? What's that got to do with mobile? Like, no, forget it. You're an idiot. So I, we should presume we don't know. But they're all, you know, this sort of basic building blocks. Like, well, but why would they have pricing power? I don't know. I had a, when I was a baby analyst in like 99, we went to see a dot-com company in the UK that was trying to do online selling computer cards components online. And like they had this whole model and this whole story in the brand and like the whole thing and we went up to see them and we're on the train back from Birmingham. And this sort of senior banker called David Tate. We're all sitting

talking about it. And Tatee says it's a low margin reseller one time sells. You can say dot com all you like. It's a low margin reseller. would. 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 flat panel screen? Like there's nobel prizes in flat panel screens. They're still a low margin commodity. I look forward to be proving wrong, proven wrong, but like, hey, that's what it looks like now. This is great. So I know you, I know you're not an investor. I know you didn't actually do investing at A16C even though you work for A16C.

partner. Partner. Just sit around and pontificate partner. Would you, are there companies you would invest in? Like if there are a couple companies you'd invest in now, is there some on that list or categories even? You know, I mentioned briefly that I was an analyst. I was a saleside equity analyst.

I was not a very good saleside 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, you know, I don't, you know, there's there's 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 ikewarm and you know

the guy with 50 and the guy with 200 are both saying like jeff bezzles 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, 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. So for Google, it didn't change anything. For meta, this was great. This is a way better way to do social than on PC. It's like, you've got a camera and notifications, and it's on your phone all the time with you. Amazon, like, what does this change? Like, doesn't change everything.

I mean, I'm massively oversimplifying here. But the point is, now, meanwhile, Yahoo mail fails to make the jump. There are companies that were already kind of dying that fail to make the jump. Maybe eBay, 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, well, Stevensonowski at A16 and Z, he used to run Windows.

I would always say, it's, you know, incumbents always try and make the new thing, a feature. And sometimes they're right, sometimes it's a feature. Actually, along those lines, something I wanted to get your take 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 of, you know, the Drake meme of like, he says, I don't like that. I do like this. It's like, no, I don't like seen GPT app is I do like harnesses.

So, yeah, 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 the distribution is what matters. And, you know, I wrote a thing about SAT TPT earlier this year, opening out earlier this year. Like, how do they compete? Well, there's an obvious comparison here that a lot of people made is with web browsers. They're fundamentally web browser.

And it is a distinction here, I think, between the web browser as product and the web browser rendering engine. And 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 what's the last innovation in browser design? Like tab browsing, which is 20 years ago, 25 years ago. It's like, and everyone out and then somebody tries to innovate in browser design and it never works because like you found the platonic ideal. It's like trying to innovate in smartphone design. Like, you know, it's a glass rectangle. Like 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 that it turns out that winning browsers doesn't matter anyway because the value is further up stack. And so Microsoft will bely browsers for like five, six years, and it doesn't matter. It doesn't get them anything. And so clearly what's happening now is Google is using distribution to drive Gemini. And what's the difference between Gemini and Trot? And if you're using this stuff all day, then you know.

But like normal person, there's no difference. And the same thing with meta. Like, if you look at survey data on which LAMS people use, even before, like, the new thing, like the Lama thing, like, meta was like behind, it was up there between chat TPT and Gemini, which if you're in tech, 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, people called it, you know, everything everywhere yesterday. And so they were just kind of trying everything to kind of work out how they would get that. Like, how can we get flywheel? How can we get distribution?

How can we get something that sticks? How can we get people something that people uses? Before Google and Meta and Amazon spray it everywhere and get everybody using that one? And then you've got like the inertia and the power of the default. And like, why would you switch? I see MetApple is kind of the last penny to drop here. there was this sort of slightly weird opening ideal and now there's even a weirdest story

that open A, I want to sue Apple. Good luck with that. The funny thing about the Apple deal thing is just not to go off on a tangent, but if you go back and watch the WWDC from 2024, the whole second half of it is Apple Intelligence, that was like the most compelling vision of a personal AI assistant.

I've 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 tool users, a genetic, on-device, AI with no prompt injection and no hallucinations,

and a completely standardized API system across 10,000 apps with intents that all work perfectly. Well, that sounds good to me, but I'm not surprised they couldn't ship it. But nobody else has shipped that. But that vision was great. You know, I really want to see what happens

at WWGC in a month. Like, do they actually ship that now? Powered by Gemini. But that's also another point. It's like, okay, there's going to be the AI intelligence, whatever we call, 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 model is just like the dumb thing underneath, the funny way of putting it, the dumb thing underneath that powers the feature. The model is the commodity that powers different decisions about what the feature should be and what different distribution. And in that situation, of course, Apple's 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 Android. So again, distribution questions.

Interesting. Google Ios next week, so we'll see what they launch. Oh, no, they launched, they launched the Android. They did just show you how, like, how module is it today. Well, no, they launched you last week. I mean, which is, it's like, it just illustrates how much we stopped paying attention to Android and iPhone, an iPhone. Like Google did a whole big thing last week. They've got, they were replacing Chromebooks with Google Books,

and they've got a new Android intelligence powered by Gemini, that will roll out to like the five people who bought a pixel phone. She don't work for Google. I'm going to go in a slightly different direction. Something that I'm curious if you're following is just the anti-AI sentiment that feels like is growing. It feels like if you've seen these surveys, AI is 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 were 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 goes over time? It's interesting, and it's a big sort of fuzzy mass of 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, objectively, but it did, and this is a question. The water thing is weird because it's just, like, completely fake.

And I should call to 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. I actually went and dug into this that the Livermore Lab

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 they capped the well and gave all the water for the data center, then you're really pissed off.

But like that's a planning problem. That's not a data center problem. In generality, yes, this is, you know, data centers of what, like 5% of US energy and might grow at 1% a year for the next five years, one percentage point a year. But the water stuff is just nonsense. And then you get into more tangible, like,

well, what is happening with this? Is it taking jobs away? Where you can watch a bunch of three-hour podcasts of economists talking to each other. And the 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, you know, 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 look exposed to AI and fields that don't look exposed to AI. So there's a lot of like econometric argument about this. And I mean, there's a border 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 use of information. They give us these weird studies of, like, people, how many people use 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 TPT. 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 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, hints, 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? Like, what can we see? And the answer right now, I think, is like, there's no clear consensus that we're seeing an 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 is an issue, whether it's because of AI or whether it's because of Trump and terrorists. It's a different question. Then you get like, like, you know, people who draw book covers for young adult romance novels are very upset that now. You can get a picture of a naked woman on the back of a dragon flying through over a volcano without paying them. So there's, I'm sorry, being deliberately unkind, but there's a little, you know, people are particularly like novelists, people who write e-books. There's a huge culture war over whether it's okay to use AI.

This is a whole sort of AI slot question. And, you know, if you saw the number that like 30, 40% of new podcasts, generated by AI. So there's a big, fuzzy massive 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 B, the people who believe it are absolutely adamant that, of course, it's true. And you're obviously a lunatic for suggesting otherwise. You know, it's like the line from Jonathan Swift that you can't reason somebody out of an idea that weren't reasonably to do. So you get this kind of wide, it was a long way on to go to this question, you got 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 is kind of a fuzzy mess 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 the thing that drives the, backlash, you know, they're worrying about myth or cyber. I don't feel like that's, you know, a main street America conversation. But that's the thing that got Trump interested in this stuff again. Let me go kind of in a tangential 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 oven no-one house knows how this is going to work. If you've got kids that are going on to the job market in like five years, then who knows? But stuff will have settled 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. I've got a kid in his early teens. So it's a different, those questions vary. Then you've got a lot of the questions that were the same before chat, TBD around, you know, the collapse of gatekeepers, the, you know, should you really believe what that influencer 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 media, internety, 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 kind of recall, you know, the George Carlin line, you know, that anyone who drives faster than you is a maniac and anyone who drives slow is an idiot. And that certainly applies to parenting. So, you know, like everybody thinks there somewhere in the middle. But, you know, I don't have, you know, a deeply systematic and widespread and coherent like plan for this. This is what my child is going to be doing in 3, 6, 12, 18 months time.

I'd settle for him not breaking his crown book again. I like the church as general vibe is, 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 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. But that's kind of a constant.

We've always had that. Remember in the whole wave of the panic around social media, I dug up, there were 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, if all police records and all government records are online, then that's different. If you think about, for example, the deep nudes, deep fake nudes issue, for example, there's like a dumb reaction to this, which is to say, haven't you heard of Photoshop?

Which is true, but a 15-year-old kid couldn't use Photoshop to make hardcore pornographic news of every girl in their high school and send them to the whole school in one afternoon. And tournament of 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, you know, the only gay kid in your village, and you can find other gay people and you can find your tribe. 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 person who 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 own worst instincts

in every problem in society and so that will happen again with AI you know we can deep fake news are like the obvious thing we can see now there will be a whole bunch more of this stuff but there's also and you know something a kind of technical audience should know about it do you know about the post office scandal

in the UK? Nope okay so sidebar here so in the UK post offices are mostly franchises run by small business people so they're run by like pharmacies, classically. Deput, very often Indian immigrants,

second generation Indian people. And so the post office, like 15 years ago, rolled out a new point-to-sell computer system. So they have a separate counter in the back. That's the post office. And so the post office rolled out this new computer system built by them by Fujitsi that had a bunch of bugs in it

that showed shortfalls in cash. The post office looks at this and says, aha, we knew these people were stealing from us. Hundreds of people get prison. A bunch of suicides, bunch of bankruptcies, people lose their homes. Meanwhile, people from the post office and people from Fijitsu

are going to court and swearing there's no bugs in the system and nobody else has had this problem. This is 1970s technology. That's really the point, that every wave of technology comes with ways 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 thread and coming back to the kids thing and the jobs thing, are there, is there like a job you're 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. That might be a great job. 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 an industry and then I was a consultant. Like, you know, the days when you kind of knew what your career was going to be or over, you know, there was some people where you want to be an architect, you want to be a software engineer, you know, you want to be X or Y.

I don't know, I think, you know, the only kind of thinking I have here is that you have, like, you 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 zooming 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 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 situation today will continue or that of course they will. So I think that's maybe a question that there's not enough people are. I think the question I posed towards the end of my presentation, which we talked about earlier, is like, what's the task and what's job? What is just the thing that becomes a button or makes goo versus what are people actually hiring you for? It's like kind of a useful way of thinking about this. And clearly there were going to be some jobs where, no, that is just the task. And that job gets sort of made it away. 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. 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. Adjusted for inflation. And the way that I look at this is to say, 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 $15 to get a CD to get that track?

And the second half of the chart is saying, what happens if $15 a monthly? see all the music that there is. So it's kind of a completely different sort of question. And you could, you know, that's the way that you could look at Uber, the way you could look at Airbnb, all these kinds of companies. Is it to begin with you do the old thing but more? Without any new technology, you do the old thing, but more of it on the new place. So, you know, you put Flickr on 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 read a find the question and you make something that isn't that at all. You know, Spotify is not an online music store. It's something else. And right now, you know, those questions, you only 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 like, obviously Spotify look crazy and people look crazy and everybody 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 because of this? What is his change? What wasn't possible 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 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 is AI. It's almost like 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 a sort of US government called own data set called O-NET or something like that. We tries to kind of analyze every single job and then people try and kind of score it. And they try and say, well, you know, this profession is X. or Y percent exposed to AI and AI can do Z percent of it today. I think this is just the most ridiculous bunch of deluded horseshit. And there's two reasons for this. The first reason is that this is like, ironically, this is the logical systems problem, the expert systems problem.

The problem of expert systems is like anyone who 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 later 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 and which can't. You can't describe a profession like that. Or anyway, we can't.

You can't kind of look at a senior partner at a law firm and say, well, 17% of their work could be automated. This is horseshit. You can't do that. I think the other side of the fallacy, though, is to talk about tax. drivers. So, you know, 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. Newspaper, the internet will be great for newspapers that printing bills will go down. Well, yes, but no. But the other side is, well, obviously, our taxi drivers, you couldn't automate that with the internet. It's got nothing to 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 changes the 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 we need a personal trainer? Now, that might be complete Bonsons. but that's how 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 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 cliche from Mark and recent

that 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 demolished a taxi business and made it much bigger as well. The town became much bigger and everyone switched. Airbnb's impact hotels, if you actually go and look at the numbers, it's 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 Milwaukee next week.

She's going to land at 8 o'clock at night. She wants to go to a hotel. She wants to have room service. She needs a bath for a bath. She needs, you know, he needs a gym at 6 in the morning. morning and then she gets seven in the morning she's going to drive to the client's site. She's not going to stay in an Airbnb. Like absolutely zero chance she's going to stay in an Airbnb.

And half of the hotel business is travel. And as soon as you actually get into anything, then it gets complicated. I remember somebody on social media said a problem with Benedict, 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 you use, presume radical uncertainty is a nice core 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 of things you recommend people do,

knowing what you know to be more successful in this future. Well, I should just kind of wind back on what you just said. It's like, as Keynes tells us, in the long run, we're all dead. So, you know, it's all, you know, like on average, you know, on average, nobody died in World War I. Great. But if you know, if you're a 19-year-old in 1914, you've got a, you know, one in three chances of not coming back. So, yes, you know, 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 professional services. The answer, the only answer I think one can have is, you know, don't stick your head in the sand and say, I hate all of this stuff, because that gives you a great feeling of moral superiority, and you can go on blue sky and shout at 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 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 you can be a great hire. And that may still not help. But, you know, if you're going to a law firm and they're like, well, we hired 100 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 probably not the right mood. So, you know, you can, that, that may not be particularly comforting, but I don't think there's, 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, with the internet. I think that is actually very actionable and very consistent advice on the podcast is just, just do stuff, build it, don't to sit around and pontific it and be be pissed at what's happening. To close us 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've used AI and used AI in your work or life?

That is really interesting, something that other people might be inspired by. I don't know. I struggle with this question because I'm thought of the lawyer looking at JATVT. 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 me is the stuff that I kind of can't do for me very, very, very well at the moment. I use it for proofreading. I use it, you know, for images. I used it redecorating my apartment. That worked fantastic. Be well at that.

Here's a picture of this room. Repaint it at this light and this table and this rug. No change of color of the rug. There's a kind of plants 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 examples of those where I need it. But then, you know, I'm kind of a unique weird job.

You know, I sit at my desk all day, you know, trying to synthesize a whole bunch of other stuff into a whole bunch of new ideas. That's not particularly common way. for people to spend their time. I struggle to find AI use cases. I am the accountant looking 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 of 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 that nobody wants to do. But instead it's like, oh, let me help you write. Let me help you create imagery. It's like those bohemians.

It's like, no, I don't want to do all these ugly things. I want to be creative. Make art. 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. Yeah. And, you know, finding that mesh.

I mean, you know, joking apart, this is going to come back to kind of my chatbot point, that, you know, the chat port is a blank screen in a jagged edge. Like, what am I supposed to do and what will work? And that's a big problem. And the solution to that problem is to wrap it in. in use cases. Part of it is also like AI just disappears. So most of what I write now dictate, I dictate as a voice marrow and that's automatically transcribed. Is that still AI or is that just voice recognition? There's probably an LLM in there. Okay, so maybe that's AI. Well,

okay, so what? At a certain point, it's just automation. What are you used for that for voice transcription? So I actually find Apple notes, the Apple, the one built into the iPhone, well, it's fine. I mean, I'm conscious of the people want others, but like, I mean, dictate it. There it is. They worked. So I'm happy with that. All right. Final question before we get to a very exciting lightning round. Is there anything else that you want to share anything else you want to leave listeners with? No, I think, you know, I've monologued plenty and I've gone through a bunch of stuff in the deck. Go read the deck and sign up to my newsletter and then you will get many more mags of brilliant Benedict Heaven's 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? It's 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 there's a classic British comedy from the late 19th century called Three Men in a Boat, which is like My I Ching. Like we're having trouble hanging a picture, well, there's a section about that. You know, we're having trouble doing this. Oh, well, there's a story about that, all of which are hilarious.

So Three Men in a Boat is my eyeing. There's a book by, I think, William Cronin, about the economic history of Chicago, which is fascinating and actually very relevant to technology, because it's talking basically about standardization and packetization and logistics and channel conflict and network dynamics and network neutrality. So like when the meat packers 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 than to kill it in New York. And the pricing of refrigerator cars.

And it's exactly like reading about Bourbent. It's all the same kind of business issues, which is fascinating. What else have I read? I don't know. Read books. Read different books. Generally read books for grown arts. please read something other than Lord of the Rings

if you're going to name another company and I saw this like and what was the latest like Peter Thiel company I was like read another book everything is named after a character from this one book there is more than one book in the world there is more than one book

and all about 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 I don't know I've dropped so badly off the current media treadmill

and I just spend most of my time watching classics, which are like always the ones that you're supposed to have seen and that all seem intimidating, and then you watch them, and you're like, oh, that was actually really good. I watched The Seventh Seal recently, which is like one of those Jake, Woody Allen, terrifying, boring movies,

and it was brilliant, it's really interesting, and it's like, 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 recently discovered that you really love? It could be a gadget, could be an app. I was speaking at a partner meeting for a company earlier this week, what's

today, Monday know last week, and met the founder of the company as a very famous network, the CEO of the company, has a very famous name and admired his shoes and didn't say anything, but then went to Google, like half an hour later, yeah, okay, I'll buy him over this. You want to share the brand or you want to keep it secret? Okay, we'll keep it secret. I don't know. I think one comes in waves of new products and you know, you get into waves of new things. And, like, when's the last time there was a cool app, like, iPhone apps, that was, you know, all that white space went. I mean, it's partly a function of product ships, a platform chip. All the white space went for cool new apps.

And now we haven't quite got, actually, this is a, to the earlier point, we don't have breakout a consumer AI apps 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. Yeah. For consumer, no. I keep getting these ads for voice recorders. Like somebody's selling like a business card size, like hardware voice recorder.

But like I didn't get it. Like I've got a voice recorder on my phone. Yeah. All kinds of cool stuff coming. Okay, two more questions. Do you have a favorite life motto that you find yourself coming back to often in worker in life? I suppose I've mentioned earlier, apparently, I mostly say it depends. That's going to be the title.

It'll probably be okay. Okay, that's the vibe I get. I like that. I like that. It's probably going to be okay. Not for sure. Okay, final question. I saw someone that you own a lot of old phones.

Is that true? It is, yes. I was a telecoms analysts and I kept, I mean, I was a telecoms analysts and I kept all my phones up to a point. Now they're kind of uninteresting. But as you may remember, like before the iPhone, particularly outside the USA, there was this huge creativity and expansion in what phones looked like because everyone was basically innovating around a little teeny tiny gray square. So everyone was trying to differentiate and everything else.

Before it kind of results, it's 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 you've got the same four wheels and the same engine. Everyone's trying to differentiate based on the shape. And then everything converges on one shape. And it's kind of the same with phones.

Like everything converged on one shape. Before that, it was all this innovation. So yeah, I have like a whole bunch of PDAs and smartphones. How many phones are you talking about? Yeah. I don't know, like 20 or 30. Okay, okay, okay. It's not so crazy.

What's like the oldest one? What's the oldest one you got? So I have one of those, I should have you told me. I'd have got the box down. I have one of those Erickson, Shark Finn flip phones from like 98 or something, which is very not. Again, like hardware design, visual design trying to differentiate. I've got an I-made phone from 2001 and a J-Fane phone from 2001 that has a camera. So I came back from Japan in 2001 and a phone had a color screen.

and a camera. And I just had like endless client meetings and people just wanted to see the phone with a color screen. It didn't work outside Japan. I plugged it in the other day. It still charges up.

I mean, clearly 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 shapes and sizes. And before the iPhone, 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, all these different ideas for what it would look like in it, or we didn't realize it was all going to converge on one device. Benedict, this was amazing. I learned a ton. I feel better after this conversation.

Two final questions. Where can 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 Benedict Evans and so there's a website where 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 carry on talking about the same stuff. It's like, the moment you really understand something, it's a 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,

but these models still hallucinate. Stop telling me they don't hallucinate. And they do, they still hallucinate. You know, you push them, push them a little bit further, any question, and you'll still get like, no, that's not true. But that doesn't mean they're not useful. So you have to kind of keep pushing myself. So that's always the challenge for me is, is how do I push? And then, yes, if you want me to come and present to your board in the Caribbean, then let me know. And by the way, the domain is

ben-dash-evins.com. Yeah, check you out in eV-A-N-S.com. Medindig, 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 on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lenniespodcast.com.

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