The most rational take on AI you’ll hear this year

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 next 2. Where value will actually accrue in the AI stack 3. The anti-AI backlash, and where it may lead 4. The surprising boom in consulting and professional services at AI companies 5. Why distribution is becoming the ultimate moat as software gets easier to build 6. 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/lenny Vanta—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:* • Andreessen Horowitz: https://a16z.com • AI Eats the World: https://youtu.be/niJpDnNtNp4 • VisiCalc: https://en.wikipedia.org/wiki/VisiCalc • McKinsey & Company: https://www.mckinsey.com • Bain & Company: https://www.bain.com • Accenture: https://www.accenture.com • Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox • Benedict’s post on LinkedIn about Excel: https://www.linkedin.com/posts/benedictevans_younger-people-may-not-believe-this-but-activity-7303217994459938816-PNqu • The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder/CEO of Every): https://www.lennysnewsletter.com/p/inside-every-dan-shipper • Dario Amodei on X: https://x.com/DarioAmodei • Marc Andreessen: The real AI boom hasn’t even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom • Frame.io: https://frame.io • Food Marketing Institute: https://en.wikipedia.org/wiki/Food_Marketing_Institute • Llama: https://www.llama.com • Steven Sinofsky on X: https://x.com/stevesi • Drake meme: https://imgflip.com/memegenerator/343699919/Drake-Hotline-Bling-Transparent-Background • Ex-Google CEO Gets Booed While Discussing AI in Commencement Speech | WSJ News: https://www.youtube.com/watch?v=tNH43a1EI7s • Jonathan Swift’s quote: https://www.goodreads.com/quotes/9838985-you-cannot-reason-a-person-out-of-a-position-he • George Carlin’s quote: https://www.brainyquote.com/quotes/george_carlin_391403 • Fujitsu: https://global.fujitsu • O*NET OnLine: https://www.onetonline.org • Pete Holmes’s website: https://peteholmes.com • The Seventh Seal: https://www.imdb.com/title/tt0050976 • Ericsson R310s phone: https://en.wikipedia.org/wiki/Ericsson_R310s • i-mate phone: https://en.wikipedia.org/wiki/I-mate *Recommended books:* • Three Men in a Boat: https://www.amazon.com/Three-Men-Boat-Jerome-K/dp/1512099899 • Nature’s Metropolis: Chicago and the Great West: https://www.amazon.com/Natures-Metropolis-Chicago-Great-West/dp/0393308731 _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email [email protected]._ Lenny may be an investor in the companies discussed.

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

• 0:00Speaker 00

  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.

• 0:06Lenny Rachitsky?

  What's your gist on the coming jobpocalypse?

• 0:09Speaker 00

  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.

• 0:17Lenny Rachitsky?

  Even just looking at the most advanced AI companies throughout, like OpenAI, everyone's increasing headcount.

• 0:22Speaker 00

  You talk to these doomers on Twitter and they would act like every big company is going to buy ChatGPT tomorrow. And then in two weeks' time, they'll fire all their staff. These people are morons. 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.

• 0:40Lenny Rachitsky?

  I'm curious if you're following the anti-AI sentiment?

• 0:43Speaker 00

  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.

• 0:50Lenny Rachitsky?

  What would be a couple of things you recommend people do to be more successful in this future?

• 0:56Speaker 00

  Don't stick your head in the sand and say, I hate all of this stuff. That gives you a great feeling of multi superiority. And you can go on 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 with it.

• 1:15Lenny Rachitsky?

  Today, my guest is Benedict Evans. Benedict was a longtime partner at A16Z as their in-house analyst and resident thinker. Before that, he was a longtime 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,

• 1:59Lenny Rachitsky?

  this conversation will teach you a lot and also make you feel better. Before we get into it, don't forget to check out lennysproductpass.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.

• 2:23Lenny Rachitsky?

  Benedict, thank you so much for being here. Welcome to the podcast.

• 2:27Speaker 00

  Thank you for inviting me.

• 2:29Lenny Rachitsky?

  You just put out this deck called AI is 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?

• 2:46Speaker 00

  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 as big a 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 as, 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. Like it's very exciting.

• 3:28Speaker 00

  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 this 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 who think that this isn't real.

• 4:06Speaker 00

  You know, most people are using, who are using this are using this every week or so, 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 OpenAI won the whole thing? Or, you know, has 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 Excite or Yahoo?

• 4:42Speaker 00

  And the answer was no, generally. So there's a sort of a fractal 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%? Like, they, they, 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, Benedict, this is 80 slides of saying, we don't know, which is like slightly facetious, but also kind of true.

• 5:15Lenny Rachitsky?

  This episode is brought to you by our season's presenting sponsor, WorkOS. What do OpenAI, Anthropic, Cursor, Vercel, Replit, Sierra, Clay, and hundreds of other winning companies all have in common? They are all powered by WorkOS. If you're building a product for the enterprise, you've felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand up market ends up working with WorkOS. And that's because they are the best. Whether you are a seed-stage startup trying to land your first enterprise customer

• 5:59Lenny Rachitsky?

  or a unicorn expanding globally, WorkOS is the fastest path to becoming enterprise-ready and unblocking growth. It's essentially Stripe for enterprise features. Visit workos.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions. 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 it's, 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? Like, where are we in that cycle?

• 6:41Lenny Rachitsky?

  You talk about all these different cycles we've been through, like, how far are we from just like, wow, it's all different now?

• 6:46Speaker 00

  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?

• 6:57Speaker 00

  Three separate things that merge together mean for the future of the software industry. You know, there's one extreme, which is no one really believes, which is, you know, how you'll just like vibe code your own stripe. And we know it actually believes that, although probably you don't believe that, but like 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.

• 7:26Speaker 00

  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 the presentation is like, imagine you're seeing, imagine you're an accountant seeing the first software spreadsheets in the late seventies. 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, but clearly if you're an accountant, this is obviously mind blowing. But if you were a lawyer looking at that or a journalist looking at that, you'd think, well, that's very clever and my accountant should see this, but that's not what I do.

• 8:11Speaker 00

  I might use it for my timesheet next week. If it didn't cost $10,000 or $15,000 to get the Apple two and the monitor and the printer to run it, which is what it costs if you adjust replacement. 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 or software developers are the accountant seeing VisiCalc. Like, oh my God, this changes everything, like before VisiCalc and after VisiCalc, before, before Claude Code and after Claude Code.

• 8:43Speaker 00

  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, 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, 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 this sort of jagged frontier question of where does this work, where does it not work, can you tell where it's

• 9:24Speaker 00

  going to work, is it intuitive to know where it would work, can you tell after it worked, can you, can you, can you, can you work out for yourself what you would do with this? And all of those intersect with your software developers, a lot of other people were like, people 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?

• 9:44Lenny Rachitsky?

  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, 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, why that's happening.

• 10:06Speaker 00

  Well, it's funny, I was kind of groping for a joke last night when I wrote my needs letter and couldn't quite get to land it, but it's, 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 outsourced 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 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,

• 10:47Speaker 00

  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, BCG, McKinsey on one side or Accenture, Emphasis, whoever on the other, or you hire a branding agency, or you hire an architect 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 could 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

• 11:32Speaker 00

  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. Okay, so you need to plug these three vertical systems into these two horizontal systems and build in 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 Bain, or you hire Accenture, or you hire Publicis to help you work that out.

• 12:13Lenny Rachitsky?

  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 cutting edge AI labs are the ones most investing in these folks. It's, I think it's pretty surprising.

• 12:29Speaker 00

  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 CapEx 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 scoop 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 apart, sometimes the task is

• 13:13Speaker 00

  the job. Like, the classic example is like an elevator attendant. Because I live in a building that has an attendant elevator, we have a manual elevator. There's no button, there's a lever and the doorman 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, 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 this price elasticity. Because 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?

• 13:56Speaker 00

  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 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 IDEs 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

• 14:38Speaker 00

  a tense as many agents 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 the SKU if you know what the SKU is. If you know what SKU 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.

• 15:11Speaker 00

  Multiply that by many, many, many product categories. And so what Amazon does is get you the SKU, but knowing what SKU you want is another job. You know, Claude Co can write you the code, but what code do you want? They 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 are you going to 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, Claude Co work will make a really, really crappy version of that. And you will 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 crap. That's not what you'd get if you from McKinsey. But even if it was, that's not what you paid them

• 15:56Speaker 00

  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 was looking at like the sort of class of industry that got steamrolled by the internet because they had those two things and you could split apart.

• 16:38Speaker 00

  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 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 was, 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 saying, well, hey, you know, we're just going to automate the accountants or we're going to automate the consultants. I mean, there's, there's two charts in the presentation of the number of people employed

• 17:23Speaker 00

  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 databases and ERP and cloud, there's proteins and PCs and the number of accountants keep going up. And so why is that? Well, it's not, it must be more, it's more complicated than automation.

• 17:45Lenny Rachitsky?

  Even just looking at the most advanced AI companies, and through like OpenAI, 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 are adding many, many humans. And so to your point, it's really complicated. What's your just kind of just on the job, the coming jobpocalypse, you know, like Daria is talking about all the entry level people are no more jobs, just like.

• 18:08Speaker 00

  Yeah. I mean, there's a narrow point here, which is that I would place, I don't like arguments 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 Dario's opinions on where models are going to go in the next six to 12 months, not particularly interested in opinions on theories of labour and market value and 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 setting aside, like, the cynical view that he's, you know, he's just doing that pump stock, which I don't, I don't believe at all. So it kind of comes back to my point

• 18:52Speaker 00

  about, you know, platform shifts. Every time we have a new technology, it automates away 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 know, you go back to 1800, like, 90% of us were peasants. And our major concern was, would like, are the crops 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, 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, who would want to go that fast? And so we've had that process over and over again. This is what any first

• 19:36Speaker 00

  year economics 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 lose their jobs, and 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 priori 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?

• 20:21Speaker 00

  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, 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, ChatGPT can get nine hundred million with chat users because there's already nine hundred million people on the internet like in like, when Mark Andreessen launched Netscape in, what was it, ninety three, ninety four, there were like fifty to a hundred million PCs on earth. So no, you didn't have nine hundred million users then. But and so the but the point is then he didn't need to wait for like phone networks or microchips.

• 21:00Speaker 00

  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 a compounding effect. So yeah, this is faster, but the internet was faster too. Um. 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 gamers on Twitter and they would like act like, you know, every big company is going to buy ChatGPT tomorrow. And then in two weeks' time, they'll fire all their staff. And these people are morons. And this is one of many reasons why why the gamers were morons, but like 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 eighteen months if you're lucky.

• 21:41Speaker 00

  You know, this is always the problem. The enterprise sales cycle is shorter than the 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 the problem of, you know, particularly for, you know, 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, two, three, four, five, ten years, and it will take time sector by sector, and it will take time for people to work out.

• 22:18Speaker 00

  Oh, you could do that thing with this. And one of the companies I always remember whether we looked at when I was at Andreessen Horowitz is a company called Frame dot A I, which is video editing, video, video collaboration. And there's nothing there that you couldn't have done at least five years earlier and maybe ten years earlier. And actually, this is kind of a bad example because that relies on a bunch of like, well, a bunch of stuff like cutting edge web technologies. But if you go around and like, pick, pick 10 random SaaS companies that were started the day before ChatGPT launched, how many of them could have been founded at any point in the previous 15 years? Like somebody, the, 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.

• 23:03Speaker 00

  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.

• 23:18Lenny Rachitsky?

  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.

• 23:28Speaker 00

  Well, I have a slide towards the end of the presentation, which, and I know the, the, the title is something like, 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 floodwales.

• 23:48Speaker 00

  And the, the, the ad is, the slogan of 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.

• 24:03Speaker 00

  Like, how many people listening to this company 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, or not free, that's like a lot of money.

• 24:16Speaker 00

  So, 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 with, 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.

• 24:37Speaker 00

  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 scoops there were in supermarkets every year. And then I had to realise they'd been around since the 50s. And if I dug 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 number's in that report, you'd have no idea. Then you need to find a library that had them.

• 25:18Speaker 00

  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, we'll sell them to you for $500 each. So then, you know, you're going to get on a trip, maybe you live in New York or somewhere that has this. And 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 you spend all day making a chart and you look at it and go, oh, that's not very interesting. So you've spent two weeks to make the chart and then you look at it and go, yeah, I'm not going to use that.

• 25:47Speaker 00

  And for me, this was like two hours in Google.

• 25:52Speaker 00

  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.

• 26:03Lenny Rachitsky?

  What's different potentially this time, just to, even though your quote is it's different. This is, everything's going to change, like, just like, literally like last time. Like, the big difference, obviously, is AGI might emerge and superintelligence where that is, could, 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.

• 26:27Speaker 00

  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 the 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 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 2am, you know, Dota 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.

• 27:10Speaker 00

  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 the 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 in my presentation late last year was is an AI scientist called Larry Tesler, 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 do a poll on social media every now and then, asking, is machine learning still AI? Because I've certainly heard people say, oh, 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, airliners, jet airliners were technology.

• 27:54Speaker 00

  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 a 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.

• 28:37Speaker 00

  Superintelligence, I'm not sure, is superintelligence more than AGI or less than AGI? Because last year I thought superintelligence 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 superintelligence, that's really hard. So all these terms are like, what are you, I didn't, what even, it's funny, I was having an argument on Hackenews this morning. We remember the idea, you remember the arg, 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.

• 29:06Speaker 00

  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 make arguments either way. Meantime, in the meanwhile, we've got this thing that's clearly kind of a completely transformative technology. And maybe the serious point here is you don't have to believe, even if like the models start to 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.

• 29:45Lenny Rachitsky?

  Something that's definitely changed, I had a former boss, Mark Andreessen, on the podcast, and we didn't actually talk about this during the conversation and he brought it up before we started recording and Amber 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 is going up so much. And valuations also go up along with that. And his point is just people haven't really grogged to just how large companies can get now. Like everyone's hitting 100 million ARR in like five months, six months. Just thoughts on that?

• 30:22Speaker 00

  Yeah. I mean, this was his whole software eating the world thesis from 15 years ago, whenever it was. Yeah. You know, the TAM 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 platform sheer framing that, you know, mainframes are, I think peak mainframe installed base was something like 70, 80,000 units. I mean, slightly fuzzy term, what exactly is a mainframe? And what's the difference? 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 to 100 million PCs on earth, 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 700, 800 million consumer PCs in the world. There's

• 31:06Speaker 00

  about five and a half, six billion mobile smartphones in the world. Which is, and which is why you can have nine hundred million weekly ad viewers on GPT. And so there was this narrative of 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 branching out, automating big, big new swathes of the economy. Now, 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 Altman and, you know, Mark can buy himself another Gulfstream. I think the, add to the fleet, I think the kind of the other answer was,

• 31:50Speaker 00

  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 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, 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 know, more and more value and a bigger and bigger contribution to the economy.

• 32:36Speaker 00

  It also caused disappearance inside things. And, you know, the other side of the issue, the point of my capital section in the presentation is, you know, there's this quote from Sam Altman where he 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 modern 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.

• 33:20Speaker 00

  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 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 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.

• 34:04Speaker 00

  And 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-gross, low-margin 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 this podcast. It's made by somebody else. This was that kind of pivotal moment where the telcos 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.

• 34:49Speaker 00

  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, do you just go to the chatbot and get the chatbot to do the whole thing? Can the model companies keep building these like Claude for X, Claude 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.

• 35:12Speaker 00

  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 built by other companies, does the models, foundation models have leverage 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 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 Windows or iOS works, but that's not how how cloud works. And so it does sort of seem to me that like, if if the chatbot isn't the UX and it needs to be apps, and the model companies aren't going to build that.

• 35:56Speaker 00

  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 marginal cost? Now, obviously, the semi analyst guys are like, no, no, no, no, there's going to be infinite pricing power forever. I'm sorry, exaggerating. But like, 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 tokens last month. But that's like somebody getting like a 50 grand mobile data bill in 2010. That's temporary.

• 36:41Speaker 00

  What is the steady state equilibrium point? Where all of these lines, the 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 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 go up stack.

• 37:04Lenny Rachitsky?

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• 37:45Lenny Rachitsky?

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• 38:28Speaker 00

  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 effects. 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 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 you know, one of the points I make in the

• 39:13Speaker 00

  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 know, you would not, you know, most 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 I wouldn't have said that. And a search company with like a weird Lego, 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 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 99, went to see a dot com company in the UK that was trying to do online selling computer cards competence

• 39:54Speaker 00

  online. And like, they had this whole model and this whole story and 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 just sort of sort of senior banker called David Tate. We're all sitting talking about it. And Tatey says it's a low margin reseller, one time sells. Like, you can say dot com all you like, it's a low margin reseller with. And I think that's the kind of the crux of this is they're 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 no mobile 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, that's what it looks like now.

• 40:39Lenny Rachitsky?

  This is great. So I know, I know you're not an investor. I know you didn't actually do investing at a 16Z, even though you work for a 16Z.

• 40:45Speaker 00

  Partner.

• 40:46Lenny Rachitsky?

  Partner. Just sit around and pontificate. Partner. Would you, are there companies you would invest in? Like, if there are a couple of companies you'd invest in now, is there some on the list or categories even?

• 40:56Speaker 00

  You know, I mentioned briefly that I was an analyst. I was a, I was a sell side equity analyst. I was not a very good sell 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, you know, I don't, you know, there's, there's

• 41:16Speaker 00

  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 IQ meme. And, you know, the guy with fifty and the guy with two hundred are both saying, Jeff Bezos, 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.

• 42:03Speaker 00

  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. Like 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, right, what does this change? Like, doesn't change anything. 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 failed to make the jump. Maybe eBay, 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.

• 42:46Speaker 00

  It's what Stephen Sinovski at A60Z, he used to run Windows. What would we say is, you know, incumbents always try and make the new thing a feature. And, and sometimes they're right, sometimes it's a feature.

• 42:56Lenny Rachitsky?

  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 harder. 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.

• 43:34Speaker 00

  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 inside, you know, I don't like seeing GPT wrappers. I do like harnesses.

• 43:46Speaker 00

  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 the distribution is what matters. And, you know, I wrote a thing about, about SAGPT earlier this year, opening eye 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. The fundamentally web browser, and it is a distinction here, I think, between the web browser as product and 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? My tab browsing, which is 20 years ago, 25 years ago.

• 44:29Speaker 00

  It's like, and everyone now 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, 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 wins 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 that Google is using distribution to drive, to drive Gemini. And like, what's the difference between Gemini and Tort? And, and, and like, if you're, you know, if you're using this stuff all day, then you know, but like normal person, there's no difference.

• 45:09Speaker 00

  And the same thing with Meta, like you look at survey data on which, which LMS people use, even before like the new, new thing, like the Loma thing. Like Meta was like behind, it was up there between ChatGPT and Gemini, which if you're in tech, people have completely written it off, but it was like they 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, OpenAI 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 a flywheel?

• 45:51Speaker 00

  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 sprayed 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? Obviously Meta, Apple is kind of the last penny to drop here. That was this sort of slightly weird opening ideal. And now there's even weirder story that OpenAI want to sue Apple. Good luck with that.

• 46:18Speaker 00

  The funny thing about the Apple deal thing is just not to go off on a tangent, but like, if you go back and watch the WWDC from 2024, and the whole second half of it is Apple Intelligence, that was like the most compelling vision of a personal AI assistant. I'm 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 tool using a Gentic on device, AI with no prompt injection and no hallucinations and a completely standardized API system across 10,000 apps with a tense that all work perfectly. And like, well, that sounds good to me, but like, I'm not surprised they couldn't ship it. But nobody, nobody else has shipped that. But like, that vision was great. You know, I really want to see what happens at WWDC in a month. Like, do they actually ship that now powered by Gemini?

• 47:03Speaker 00

  But that's also another point. It's like, okay, there's going to be the, the AI intelligence, whatever we call it, 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. If 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, and Google has this wonderful marketing slogan, coming soon to our most powerful devices.

• 47:38Speaker 00

  Meaning it won't work on those Androids. So again, distribution questions.

• 47:42Lenny Rachitsky?

  Interesting. Google iOS next week, so we'll see what they launch.

• 47:45Speaker 00

  Oh, no, they launched, they launched, they launched the Android. It just shows how, how, like, how.

• 47:50Lenny Rachitsky?

  Was it today?

• 47:51Speaker 00

  Well, no, they launched it last week. I mean, which is, it's like, it just illustrates how much we've stopped paying attention to Android and iPhone. And iPhone, like, Google did a whole big thing last week. They've got, they're replacing Chromebooks with Google Books. And they've got a new Android Intelligence powered by Gemini that will roll out to light the five people who bought a Pixel phone.

• 48:10Speaker 00

  She don't work for Google.

• 48:11Lenny Rachitsky?

  Yeah. I want to go in a slightly different direction. Something that I'm curious if you're following is just the anti-AI sentiment that is, feels like is growing. Feels like if you've seen 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 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, this goes over time?

• 48:38Speaker 00

  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, rejectively. But it did, and this is a question. The water thing is weird because it's just like completely fake. And I should qualify, 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 at the Livermore Lab, did a study at the end of 2024 where they estimated US data center water consumption.

• 49:21Speaker 00

  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 capped the well and gave all the water to the data center, then you're really pissed off. But like, that's, that's a planning problem. That's not a data center problem. You know, in generality, yes, this is, you know, data centers have what, like 5% of US energy and might grow 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 main answer is we really don't know yet.

• 50:05Speaker 00

  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, eighteen to twenty four 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 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 usage 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 ChatGPT.

• 50:51Speaker 00

  It's crazy. And all the data comes from academic economists trying to back stuff out of BLS surveys. Or consultancies and and and marketing agencies like spending a whole bunch of money to survey twenty thousand 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 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? Like, what could 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. Like, 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.

• 51:32Speaker 00

  Then you get like, like, kind of neat things 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 this is, I'm sorry, I'm being deliberately unkind, but there's a little. You know, there's, there's, you know, people particularly like novelists, people who write ebooks. 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, if you saw the number that like thirty, forty percent of your podcasts are generated by AI. So there's a lot of, there's a big fuzzy mass of 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

• 52:17Speaker 00

  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 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 they won't reasonably do. So you get this kind of wide, it was a long way to answer your question, but you've 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 are kind of a fuzzy mess in the middle.

• 52:55Speaker 00

  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, the worrying about missile 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.

• 53:12Lenny Rachitsky?

  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?

• 53:33Speaker 00

  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 and no one knows 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 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 TVT around, you know, the collapse of gatekeepers, the, you know, should you really believe what that influencer on TikTok says?

• 54:18Speaker 00

  And, you know, where exactly are you getting your understanding of what's going on in Israel? And all of those kinds of social media, internety media consumption kinds of questions.

• 54:32Speaker 00

  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 Carlin line, you know, that anyone who drives faster than you is a maniac and anyone who drives slower is an idiot. And that certainly applies to parenting. So I'm, you know, like everybody thinks, there's 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 in three, six, twelve, eighteen months' time. I'd settle for him not breaking his chronobook again.

• 55:09Lenny Rachitsky?

  I like that you're just general vibe is it's gonna be okay, guys. It's gonna be okay.

• 55:14Speaker 00

  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, 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. I remember in the whole wave of the panic around social media, I dug up, so a whole bunch of books in the late seventies about databases. There was a whole panic about databases. And again, half of it was

• 55:52Speaker 00

  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 news, deep fake news issue, for example, there's like a dumb reaction to this, which is to say, haven't you heard of Photoshop? Which is true, that a 15 year old kid couldn't use Photoshop to make hardcore pornographic nudes of every girl in their high school and send them to the whole school in one afternoon.

• 56:28Lenny Rachitsky?

  And turn them into video.

• 56:30Speaker 00

  Exactly. Even more. 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 nineties is it's great. You can read, 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, you know, somebody 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 all the bad people and all of our own worst instincts and every problem in society. And so that will happen again with AI. You know, we can deep fake news, 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

• 57:14Speaker 00

  about, have you, do you know about the post office scandal in the UK? No. 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, very often Indian, Indian immigrants, second generation Indian people. And so the post office like 15 years ago rolled out a new computer cell 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 Fujitsu that had a bunch of bugs in it that showed shortfalls in cash. And the post office looks at this and says, aha, we knew these people were stealing from us. Hundreds of people get prison, bunch of suicides, bunch of bankruptcies, people lose their homes. Meanwhile, people from the post office and people from Fujitsu are going to court and

• 57:59Speaker 00

  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.

• 58:28Lenny Rachitsky?

  So maybe following that, right, and coming back to the kids thing and the jobs thing. Are there, 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?

• 58:39Speaker 00

  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.

• 58:49Speaker 00

  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 whatever. You know, there were certainly 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, 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.

• 59:21Lenny Rachitsky?

  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?

• 59:33Speaker 00

  Sure. I mean, we talked about like value capture, like obviously this is a whole, everyone is, 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 today will continue or that of course they will. So I think that's maybe a question that not enough people ask. 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, but what is just the thing that becomes a button or makes you versus what are people actually hiring you for? It's like kind of a useful way of thinking about this. And clearly there are going to be some jobs where no, that is just a task. And that job gets sort of made to away, but there's a bunch where that kind of isn't the question.

• 1:00:15Speaker 00

  The way I actually pulled that together at the end of the deck was a chart of a global recorded music revenue. Which is, you may know, is kind of a U shaped curve, more or less. So it's dropped by about half from two thousand to two thousand fifteen or so. And since then, it's come back about to about seventy five percent of the peak. Adjusted translation. 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 fifteen dollars to get a CD to get that track. And the second half of the chart is saying, what happens if fifteen dollars a month gets you all the music that there is? So it's kind of a completely different sort of question.

• 1:00:56Speaker 00

  And you could, you know, that's a way that you could look at it 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?

• 1:01:09Speaker 00

  With every 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 redefine 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. Cause like, obviously Spotify look crazy and Uber look crazy and Airbnb look crazy. But that's a 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?

• 1:01:54Speaker 00

  What has this changed? What wasn't possible before? What gets unlocked as opposed to just doing the old thing, but more of it?

• 1:02:02Lenny Rachitsky?

  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 codes to zero percent of your code is AI.

• 1:02:28Speaker 00

  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, this whole, there's this sort of US government called data set called ONET or something like that. Which 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 horse shit.

• 1:02:57Speaker 00

  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 with expert systems is like, for anyone who doesn't know, like you try to recognize a picture of a cat. And so you start building up logical steps. So you make an age 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.

• 1:03:25Speaker 00

  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. Well, anyway, we can't. You can't kind of look at a senior partner at a law firm and say, well, seventeen percent of their work could be automated. Like, this is horseshit. You can't do that. I think the other side of the fallacy, though, is to talk about taxi drivers. So, you know, if we'd been having this conversation in nineteen ninety seven, it's like the Uber test. Imagine we're in nineteen ninety seven. 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. Their printing bills will go down.

• 1:04:09Speaker 00

  Well, yes, but no. But the other side is, well, obviously, like 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 I need a personal trainer? Now, that might be complete nonsense. But that's how these things work. Like, the stuff that you don't think is, you can't predict which things

• 1:04:53Speaker 00

  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 like, 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 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 where Uber demolished the taxi business and made it much bigger as well. The town became much bigger and everyone switched. Airbnb's impact on 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 Milwaukee

• 1:05:37Speaker 00

  next week. She's gonna land at eight o'clock at night. She wants to go to her hotel. She wants to have room service. She needs a bathroom bath. She needs, you know, she needs a gym at six in the morning. And then she gets seven in the morning. She's gonna drive to the client's site. She's not gonna stay in Airbnb. Like, absolutely zero chance she's gonna stay in an Airbnb. And half of the hotel business is business travel. And you know, as soon as you actually get into anything, then it gets complicated. I remember somebody on social media said a problem with Benedict is everything, his answer to everything is it depends. It's like, yeah, it does, it depends. So there, you know, it's back to my 1997 point. You can say some of this, but you have to have that humility.

• 1:06:20Lenny Rachitsky?

  Yeah, coming back to this phrase you used, 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?

• 1:06:48Speaker 00

  Well, I should just kind of wind back on what you just said. It's like, as Keens 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 One. Great, but if you know, if you're, if you're a 19 year old in 1914, you've got a, you know, one in three chance of not coming back. So, um, 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.

• 1:07:27Speaker 00

  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. Cause 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 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.

• 1:08:09Speaker 00

  Going to the interviewer and say, well, I think AI is bullshit and I'm never going to use it is probably not the right move.

• 1:08:17Speaker 00

  So you know, you can, that, 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.

• 1:08:33Lenny Rachitsky?

  I think that is actually very actionable and, and very consistent advice on the podcast is just, just do stuff, build it, don't just sit around and pontificate and be, 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've used AI and use AI in your work or life? That is really interesting, something that other people might, might be inspired by.

• 1:09:00Speaker 00

  I don't know. I, I struggle with this question because I'm sort of the lawyer looking at chat GPT.

• 1:09:07Speaker 00

  So, you know, the stuff that I would do, that I would automate are sort of precise information retrieval tasks, which is precisely the thing that this is kind of worst at. And, you know, that's not a criticism, it's just an observation, the kind of, the, the kind of stuff that I would want a machine to do for me is the stuff that AI 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, you're well at that. Here's a picture of this room repainted at this light and this table and this rug. No change the color of the rug, there's a kind of a cluster of stuff where it works.

• 1:09:43Speaker 00

  But I mean, 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'm, I, I, I struggle to find many, many examples of those where I need it. But then, you know, I'm a kind of a unique weird job, you know, I, you know, 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 where people 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 completely transform everything. But I actually don't make switches every day.

• 1:10:23Lenny Rachitsky?

  I went to a stand up comedy show with 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 this Bohemians like, no, I don't want to. I don't want to do all these ugly things. I want to be creative. Make art.

• 1:10:44Speaker 00

  Yeah, well, I mean, there's, there's, you know, variations of all of this, you know, it's like, I don't want the AI to do the stuff I do for fun. I want to do the stuff, the boring stuff that I don't do fun.

• 1:10:53Lenny Rachitsky?

  Yeah.

• 1:10:53Speaker 00

  And, you know, finding that mesh, I mean, you know, joking apart, this is going to come back to kind of my chatbot, chatbot point. That, you know, the chatbot is a blank screen in a jagged edge. And 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, I dictate. I dictate as a voicemail. And that's automatically transcribed. Is that still AI or is that just voice recognition? Probably an L L M. There's probably an L L M in there. Okay. So maybe that's AI. Well, okay. So, so what? At a certain point, it's just automation.

• 1:11:28Lenny Rachitsky?

  What do you use for that? For voice, voice transcription?

• 1:11:31Speaker 00

  So I actually find Apple Notes, the app or 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, I'm happy with that.

• 1:11:42Lenny Rachitsky?

  All right. Final question before we get to our very exciting lightning round. Is there anything else that you wanted to share? Anything else you want to leave listeners with?

• 1:11:48Speaker 00

  No, I think, you know, I've, I've, I've modeled 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 Evans wisdom.

• 1:12:03Speaker 00

  Some of which may even be useful. Somebody answered, someone unsubscribed 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.

• 1:12:17Lenny Rachitsky?

  Well, with that Benedict, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

• 1:12:22Speaker 00

  Sure.

• 1:12:23Lenny Rachitsky?

  First question, what are two or three books that you find yourself recommending most to other people?

• 1:12:28Speaker 00

  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. It's 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 I Ching. 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

• 1:13:12Speaker 00

  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 broadband. It's all the same kind of, it's those 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 grownups. Please read something other than Lord of the Rings, if you're going to name another company. It's like, I saw this, 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 than all that science fiction. Read about different things, read about things you don't know about.

• 1:13:53Lenny Rachitsky?

  Kind of along those lines. You have a favorite recent movie or TV show that you've really enjoyed?

• 1:13:57Speaker 00

  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 was 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.

• 1:14:26Lenny Rachitsky?

  Favorite recent product that you recently discovered that you really love? Could be a gadget, could be an app.

• 1:14:32Speaker 00

  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, the CEO of the company 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 love this.

• 1:14:51Lenny Rachitsky?

  You want to share the brand or you want to keep it, keep it secret?

• 1:14:54Speaker 00

  Okay, we'll keep it secret. I don't know. I think one comes, 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 shift, a platform shift. It's like all the white space went for cool new apps. And now we haven't quite got, issue, this is to the earlier point. We don't have breakout AI 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.

• 1:15:29Lenny Rachitsky?

  For consumer, yeah.

• 1:15:30Speaker 00

  For consumer, no. I just, we're like, keep getting these ads for voice recorders, like somebody selling like a business class size, like hardware voice recorder.

• 1:15:39Speaker 00

  But like, I didn't get it. Like I've got the voice recorder on my phone.

• 1:15:43Lenny Rachitsky?

  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 work or in life?

• 1:15:51Speaker 00

  I suppose I've mentioned earlier, apparently I mostly say it depends.

• 1:15:56Lenny Rachitsky?

  That's going to be the title.

• 1:15:59Speaker 00

  It'll probably be okay.

• 1:16:01Lenny Rachitsky?

  Yeah, okay, that's, 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 somewhere that you own a lot of old phones. Is that true?

• 1:16:12Speaker 00

  Uh, it is, yes, as a, um, I kept, I mean, I was telecom analyst and mobile analyst 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 from everything else, um, before it kind of resulted. 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 like differentiate based on like the shape and then everything converges on one shape. And it's kind of the same with phones, like everyone, everything converged on one shape.

• 1:16:54Speaker 00

  Before that, it was all this innovation. So, yeah, like I have like, like a whole bunch of PDAs and smartphones.

• 1:16:59Lenny Rachitsky?

  Like, how many phones are we talking about?

• 1:17:01Speaker 00

  I don't know, like 20 or 30.

• 1:17:03Lenny Rachitsky?

  Okay, okay, okay. It's not so crazy. What's like the oldest one? What's the oldest one you got?

• 1:17:07Speaker 00

  So I have one of those, as you told me, I'd have got the box down. I have one of those Ericsson Sharkfin flip phones from like 98 or something, which is very, very, again, like hardware design, visual design, trying to differentiate. I've got an iMade phone from 2001 and a J-Phone phone from 2001 that has a camera. So I came back from Japan in 2001 and my phone had a color screen and a camera. And like, I just had like endless client meetings and people just wanted to see the phone with a color screen. Like it's mind blowing. Didn't work outside Japan. I plunked 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 that like, we thought there'd be all these different shapes and sizes.

• 1:17:52Speaker 00

  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. Although all these different ideas for what it would look like and we didn't realize it was all going to converge on one device.

• 1:18:03Lenny Rachitsky?

  Benedict, this was amazing. I learned a ton. I feel better after this conversation. Two final questions. Where can folks find you online? Where do they find this presentation? And how can listeners be useful to you?

• 1:18:13Speaker 00

  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 yet? 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, you know, the moment you really understand something is the moment you have to push on to 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.

• 1:18:56Speaker 00

  No, 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, 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.

• 1:19:15Lenny Rachitsky?

  And by the way, the domain is ben-evans.com if folks want to check it out and E V A N S dot com. Benedict, thank you so much for being here.

• 1:19:24Speaker 00

  Thanks a lot.

• 1:19:26Lenny Rachitsky?

  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 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 lennyspodcast.com. See you in the next episode.