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

0:00 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. But you're just on the coming job apocalypse. Every time we have a new technology, it automates away a bunch of jobs and then that automation unlocks a bunch of new jobs. And you don't know the new job because it doesn't exist yet. We've had that process over and over again. Even just looking at the most advanced AI company Throughout big company. Everyone's increasing headcount. You talk to these gymers on Twitter and they would act like every big company is going to buy ChatGPT tomorrow and then in two weeks' time they'll five all their stuff. These people are more. You can't predict which things are going to be exposed. You can't look at a senior partner at a law firm and say, well, 17% of their work could be automated. I'm curious if you're following the anti-AI sentiment. It's a big fuzzy mess. Yes, it will change a bunch of stuff and we'll need to worry about it. But that's kind of a constant. We've always had that. What would be a couple things you recommend people do to be more successful in this future. Don't stick your head in the sand and say I hate all of this stuff. That gives you a great feeling of moral superiority and you can go in blue sky and shout at everybody about how evil AI is like great. Happy for you. But that's not gonna help. What helps is you diving into this and coming out understanding what you can do with it. Today my guest is Benedict Evans.

1:17 Benedict was a longtime partner at A sixteen Z 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.

1:47 The rise of anti-AI sentiment, the impact on jobs, where in the value chain most of the value will accrue, and tons more. If you are worried about AI, or just confused about where things are heading, this conversation will teach you a lot and also make you feel better. Before we get into it, don't forget to check out Lenny'sProductPass.com. For a year free. Of some of the most amazing hotest. Most well crafted AI products in the world, available exclusively to Lenny's newsletter subscribers.

2:17 With that, I bring you Benedict Evans. Benedict, thank you so much for being here. Welcome to the podcast. Thank you for inviting me. You just put out this deck called AI is eating the world.

2:32 I wanna ask you kinda the 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

2:42 the change that they're gonna experience to their lives and their work. Um an interesting way of thinking about it. I did a um a podcast last year with someone where I said, you know, I 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,'cause clearly there's a bunch of people in tech who think no, this is more like the industrial revolution or something. And are there 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. They 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

3:22 If you're going to make the internet comparison, it's like we're in nineteen ninety seven. Like it's very exciting. Most stuff kind of doesn't work yet. Most of the stuff that people are going to do hasn't been built yet, and it's not really clear how any of it's going to work when it does work. And the people who have who've already got it, who have already taken whichever pill it is, I forget which.

3:45 Sort of if they imagine that everybody in the world is already there. And z The truth is you've got this kind of very wide distribution. So there's people in tech, you've bought their Cluster of Mac Minis. And you know, don't use Google anymore.

4:00 And then you look out side tech and setting aside the idiots you think that this isn't real. Um you know, most people are using who are using this are using this every week or two, maybe. Um 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

4:23 Well, How are the models gonna work? And do the model labs have pricing power? And where's the value going to be? And You know, has open AI won the whole thing, or you know, is Anthropic got it this week. So then you can kinda get into calling those races where again it's like being in nineteen ninety seven and saying, Well, is it gonna be Excite or Yahoo? And the answer was no.

4:44 Generally. So there's a sort of a fractual point here. There's like the sort of the super high level that like this is going to change absolutely everything. I don't think it's particularly productive to say, well, is it twenty percent bigger than the internet or a hundred percent? Those aren't productive conversations. But it's one of those fundamental changes. But then you don't know how any of it's gonna work. Um in fact I just published it.

5:05 I do a presentation every six months and I just published one yesterday. And one of the comments was Benedict, this is eighty slides are saying we don't know, which is like Slightly facetious, but also kinda true. This episode is brought to you by our season's presenting sponsor, Work OS. What do OpenAI, Anthropic, Cursor, Vercelle, 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 felt the pain of integrating single sign on, skim.

5:36 Rback, 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 upmarket 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, Or unicorn expanding globally. Work OS is the fastest path to becoming enterprise ready and unblocking growth.

6:05 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 dot com to make your app enterprise ready today. So if we're in this nineteen ninety seven timeline. Uh 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

6:37 Okay, now things are gonna be radically changing. Well, like where are we in that cycle? You talk about all these different cycles we've been through, like how flaw far are we from just like wow, it's all different. Well Unquestionably, we're already in that moment in software. And then there's a conversation about well, what does Agentich and AI software development.

6:57 Two separate things at merge together. mean for the future of the software industry. Yeah, there's one extreme which is no one really believes, which is, you know, hey you'll just like vibecode your own stripe. And we no one actually believes that, although probably 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, whole that's one whole conversation.

7:19 But the other extreme is, you know, if you're in a law firm This is all very interesting. Um, but what am I how what 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 gonna hire next year? Uh what does this mean for us?

7:40 One of the analogies I used here 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 blank. No, you change The interest rate here.

7:53 And all the other numbers change. And it does a week of work for you in like thirty 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 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. I might use it for my time sheet next week.

8:14 If it didn't cost ten or fifteen thousand dollars to get the Apple II 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. Okay. And you need word processor, which actually came like very shortly afterwards. And so that's sort of the moment that we're in of the some people like

8:30 Software development are develop software developers are The accountant seeing this account. Like oh my god. This changes ever thing, like before Vitalcalc and after VitalCalk, before before clock code and after clock code. A lot of other people are

8:45 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 like even if you look at like thirteen to eighteen year olds or something. It's still like kinda fifteen, twenty percent of people are daily active users and another twenty percent are weekly active users. And then the other sixty percent of peop of those people in that demographic, how long you say they are not using this? So there's a through

9:10 very wide spread of who gets it and a very wide s which I think also maps This is kind of almost a set for a 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 gonna work? Is it intuitive to know where it would work? Can you tell after it worked? Can you can you and can you can you work out for yourself what you would do with this? And all of those intersect if you're a software developer. There's a lot of other people who are like people having a moment or they're not, or we're we're in again, we're in that kind of nineteen ninety seven moment of

9:43 Okay, what is this? Along those lines, something you've been writing a bit about is This like unexpected investment in Professional services, slash consulting services, slash forward deployed engineers. Uh all the AI labs, at least the two big ones, open anthropic, are like investing in buying massive

10:00 Comp like consultancies and P E firms. Talk about just what's happening there, why is why that's happening. Well I'm I was kind of groping for a joke last night when I I wrote my newsletter and couldn't quite get to land it, but it's you know, something like you know the we know the joke that uh a machine learning scientist is a statistician who lives in San Francisco.

10:17 And there's something in there of like a forward deployed engineer is like an Accenture outsource software developer. He 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

10:38 waiting to do a bit build a big new project or do a big new piece of analysis or build a big new s piece of technology or new product or work out how they're going to redesign their stores or we know work out where the stores should be or try and work out why the churn is too high. Oh no All of those kinds of questions are thing reasons why you hire Bain B C McKinsey on one side or Accenture in face, whoever on the other Or you hire a branding agency, or you hire an arch firm of architects, or whatever. And it's always like, Well, we could hire some architects.

11:11 But why on earth would we want to have fifteen architects on staff when we we just go and hire an architecture firm. We just go and hire an ad agency. And so We're supposed to like

11:22 completely reimagine all of the internal workflows of your company and work out which of them could be automated really quickly with AI. That's a project. That's a project that needs like five or ten people to sit down and spend a month or two working it out. And then actually doing it is another project. Okay, so we you need to plug these three vertical systems into these two horizontal systems and building a bunch of new workflows and to train people to do that. Well, guess what, who's going to do that? Because you don't have a bunch of people sitting around not doing anything.

11:53 So On the one side, this is part of the 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 publicists. To help you work that out.

12:13 What's really just funny. about this trend is you would think A I is going like consultants were gonna be gone. No. We don't need I all these people anymore. AI is gonna do their work. Instead, like the most cutting edge AI labs are the ones most investing in these folks. It's it I think it's pretty surprising. Well, one of the strands in my presentation, so I split the presentation into three sections. There's a section on capital, which is basically where is all this capace going and are the model labs gonna have differentiation, and then there's a section on deployment, which is basically what does it mean for the software industry.

12:42 And then the third section is how does this change stuff? And one of the sort of sort of strands I tried to pull together in the section on change is Um What's the hard part of the job? It's a hard part of the job writing the code line by line.

12:59 It's a hard part of the job. Like giving you the scoop. Or making the power point. Or is the hard part of the job something else? Is it the task or the job? And you know, pulling that apart.

13:12 Sometimes the the task is the job. Like the classic example is like a an elevator attendant. If I live in a building that has an attended elevator, we have a manual elevator. It's there's no button, there's a there's a there's a lever in the doorman drives you to your floor. It's a vertical speed car. Um it's like one of those trams in San Francisco. They drive you to the store to your floor. Um

13:31 And then those all got automated after the fifties. And now you get and you press a button, and pressing the button is a job. So there were some things where the d the the button the job. was a task and the task got automated. Well what what happens much more, and this is why people talked about like the Jevons paradox, is There's price elasticity.

13:50 apply price dist. If you make it cheaper to do something, what happens? Do you do the same for less money? Or do you do more for the same amount of money? Or do you do more for more money because you've got a new ROI. And if you look at something like the history of accounting or indeed professional services, like Yeah, this is a joke I made on Twitter back when it was Twitter was like young people won't believe this, but before invest but before Excel Junior investment bankers work really long hours. And now thanks to Excel, Goldman's Associates all the will uh work at lunchtime on Fridays. It's like, Well, why is that not what happened?

14:20 You could make the same point in 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, ninety percent of the code is written for you by Apple. Like Apple wrote the modem driver and the graphic drivers and you know the file system. You don't need to write any of that. So we've got like a tenth as many engineers now. Well no. And so then you kind of have to look at an industry and work out well which is it and what is the hard part. One of the the analogies that occurred to me here

14:51 is to look at the history of e commerce. Which is that what Amazon does is it gets you the skew if you know what the skew is. If you know what school you want, you want that microphone stand. Yeah, this part number. You can get it on Amazon and get it. If you

15:03 Don't know what microphone to get. Probably shouldn't stall on Amazon. Multiply that by many, many, many product categories. And so what Amazon does is get you the scu, but knowing what school you want is another job. You know, the Claw Code can write you the code, but what code do you want?

15:22 It can make you the features, sure. 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 a question. Why do you hire McKinsey? Are you hiring them to get a seventy five slide deck?

15:39 Well narrowly Claude co work will make a really, really crappy version of that. And then you get all these kind of AI grifters on LinkedIn and 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 doc crap. That's not what you'd get if you from McKinsey. But even if it was, that's not what you paid them for. What you actually pay Bain to do is to go and walk all over your in and try your company and work out

16:03 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.

16:29 The an other analogy that occurred to me here was looking at like the sort of class of industry Let go. steam loaded by the internet because they had those two things and you could split the part. So you had the physical manufacturing or physical distribution and then you had the other the th the thing, what was the actual thing, like Classic examples here will be newspapers and recorded music.

16:47 So record companies uh 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. Um 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.

17:05 But often 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 gonna automate the accountants or we're gonna automate the the consultants. Um, I mean there's there's two charts in the presentation of the number of people employed as accountants, which went up right the way through the twentieth century and has gone up again since the beginning of the twenty first century. So you have adding machines and punch cards and mainframes and databases and ERP and cloud with Fs and P Cs and the number of accounts keep going up. And so why is that?

17:42 But it's not it's must be more it's more complicated than automation. Even just looking at the most advanced AI companies through pick, open AI. I just had Dan Shipper from Every on the podcast. Everyone's just increasing headcount. Like the companies you would think would be least

17:56 likely to add humans or adding many, many humans. And since your point it's really complicated. What's your just kinda just on the job. The coming job poclips, you know? Like Dario's talking about all the entry level people are no more jobs just like Yeah, um I mean there's a narrow point here, which is that I would place I don't like argument from orthosy.

18:16 And I don't think the fact that you run AI lab suddenly gives you or rather a an and if you're going to use argument from authority, then it should be relevant to the field. So like I I'm interested in Dario's opinions on where models are gonna go in the next six to twelve months. Not particularly interested in opinions on series of labour and market value and competitive comparative advantage. Like yeah, maybe he had a course on that at university, so did I. So I think one needs to be a little bit cautious on like Walderio says. Uh, and that's setting aside like the cynical view that he's you know, he's just doing that on the start, which I don't I don't believe at all.

18:50 So it kinda comes back to my point about, you know, platform chips. Um Every time we have a new technology Um 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.

19:04 And so, you know, you go back to eighteen hundred, like N ninety percent of us were peasants. And our major concern was we're like the the are the crop's gonna fail,'cause then we'll all go hungry. Well was. And so ever since then we've been automating jobs and creating new jobs and you can always see the job that's gonna go gonna go away. And you don't know the new job'cause it doesn't exist yet and it's like something that sounds dumb anyway. Like, you know.

19:26 I Railway engineer. What's a railway? Um, why would that be a thing? Who would care who would want to go that fast? Um And so we've had that process over and over again. This is what any first year economic student would tell you. Um we've had this process over and over again since eighteen hundred, 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.

19:50 But you know, when you come through on the other side we're all richer and we're not worried about the clock failing anymore. And you know, this is the process of the last two hundred years. So then the question is is there some a priority reason why this would be different to those? 'Cause like the internet. removed a bunch of jobs. PC's removed a bunch of jobs. There aren't many people working as typesetters anymore.

20:09 Um or telephone operators or typists. Um the internet with me to vantage jobs and generally the jobs that go away are crap jobs. seen recrossively and the new job the better because you know GDP keeps getting up. So is AI different? And so then there's kind of a couple of answers to this. One theory is well, this is gonna be way quicker.

20:27 And certainly the adoption of AI is quicker than previous technologies because but this is kinda because you're standing on the shelters of giants. So like you don't need to wait for everyone to buy a piece of expensive hardware to like buy by a phone or a PC or wait for the telco to deploy broadband, it's already there. So of course Chat GPT can get nine hundred million with chat users,'cause there's only nine hundred million people on the Internet, like in bike When Mark Edweeson launched Nescape in what was it, ninety three, ninety four, there were like fifty to a hundred million PCs on Earth.

20:51 So no, you didn't have nine hundred million. He's it's then. But and so the but the point is then he didn't need to wait for like Fine networks. Or microchips. And before that you didn't need to wait for electricity and you didn't need to wait for like mass production. So there's all you're always kinda standing on the shoulders of giants. There's always like a compounding effect. So yeah, this is faster, but the internet was faster too.

21:11 Um, I think the other answer to this, and this kind of comes back comes back to the professional services point, is like, you know, you talk to these gimmers. on Twitter and they would like act like, you know, every big company is going to buy chat GPT tomorrow and then in two weeks' time they'll fire all their stuff. And these people are mons. And it's one of many reasons why while Jumits were morts. But like a complete failure to understand the way the world works. And that was like the starting point where they then didn't understand anything else. In a typical big company, you know, Enterprise Selfware Sales Cycle, you'll know this better than me. Enterprise Selfir Sales Cycle is like Eighteen months if you're lucky.

21:41 You know, this is always the problem. The enterprise sales cycle is shorter than the the the venture back start funding cycle. Longer. Longer. 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 building, particularly for you sectors like aerospace or healthcare or something. So I know people aren't gonna just gonna tear out SAP and replace it with XYZ. Maybe in five in like three, five, ten years, yes, that whole s 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.

22:18 Oh, you could do that thing with this. Um one of the companies I always remember whether we looked at when I was at Andrews and Horitz is a company called um frame.io 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 that's kind of a bad example'cause that relies on a bunch of like what a

22:39 a bunch of stuff like web cutting edge web technologies. Like if you go around and like pick pick ten 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 fifteen years? Like somebody it took 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. It took like ten, fifteen, twenty 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 gonna 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.

23:20 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 gonna be okay. Well I have a slide towards the end of the presentation. Which and I know the the the the title is something like this is going to be completely different from everything else, just like everything else.

23:37 And then the next slide is an IBM ad from the fifties, which has got this sea of white men. Holding up with in white shirts and ties, all holding up flowers. And the the the ad it sa the 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 a hundred and fifty extra engineers.

24:03 Like how many people listening to this company like their company slogan is basically we'll give you hundred and fifty expand engineers. I mean, isn't that like the whole picture of clawed code? Hundred and fifty eight screen engineers for free or not free, that's like a lot of money. Um So and yeah, that's what he gave you. And so yes, we keep going through this over and over and over again. just to kind of make that tangible. I mean obviously we couldn't be doing this with the internet 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 stopped in supermarkets in America since the fifties.

24:37 And the point of the slide is to say that barcodes allowed supermarkets to stock way more stuff'cause they could keep track of it. But making that chart. I had to know there was a thing called the Food Marketing Institute. And I had to have found out that they published a number for how many schools there were in supermarkets every year. And then I had to realise they've been around since the fifties. And if I like

24:58 dug long enough I might be able to bake a whole time series and I could make whole chart. Now imagine doing that in nineteen ninety four. First of all, you would have no idea that exists. You really need to go and find a library where they public where they th and that they publish that number. And then the number's in that report. You'd have no idea. Then you need to find a library that had them. So you're gonna spend like three days on the phone and spend like fifty dollars on like long distance phone calls to find a logo that has these. Or maybe you call the feed marketing institute and they say, Yeah, sure, if you buy a

25:27 You know, I will sell them to you for five hundred dollars each. So then you know you're gonna get on a trip. Maybe you live in New York or someone that has this and you Two weeks later you've got the chart and you look at it, and then the other side of this is the life of an analyst is 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 gonna use that. And for me this was like two hours in Google.

25:52 And so we like we 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. 'Cause it's like that's the world the world's always been. What's different potentially this time, just to even know what your quote is, it's different. This is everything's gonna change like just like just like last time. Like the big difference, obviously, is uh AGI might emerge and superintelligence, where

26:16 That Is Uh Could it you know, does the work of humans can do a lot of this stuff for us, can actually replace jobs. Just like thoughts on that.

26:25 element of the this transformation we're going through. I don't know, this is one of the the the the ways I struggle to write about AI is like certainly in like twenty twenty three early twenty four. Like all the questions were questions you could have asked in like December twenty twenty two. Line those questions didn't really change.

26:41 And the strategies didn't really change. And I think the AGI question is kind of the same. Um I mean the thing that the 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 vibe forecasting as to what will happen. Um and then you can have like the two AM, you know, Dope T philosophy students talking about, Hey man, like

27:05 Is this consciousness? Maybe we aren't conscious either, we just think we are. Yeah, great, thank you. I think the one thing one can observe today Is so we have no idea. We don't know. We can guess, but we don't really know how the where this is going to end up.

27:18 What I think you can say today is that there's a lot of kind of redefinition of terms. So I think uh quite argued in my presentation uh late last year was an AI scientist called Larry Tesla who said AI is whatever machines can't do yet because once machines can do it, people say well that's just software. Yeah. And so certainly I mean I did a do a poll on on social media every now and then asking is machine learning still AI? 'Cause 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.

27:48 So AI it's a bit like the word technology. It's like if it's new, then it's technology. But in the sixties airliners, jet airliners were technology. Now we're jet airliner wasn't checked. And so there's a sort of sense of AI is like a moving target, is whatever just started working. And I think that m point here is now clearly you can see people redefining AGI. To me, the stuff that works now.

28:09 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. Um because a database can do that. Like, you know, an IBM mainframe in nineteen seventy five 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 and we didn't know existed. So there's a lot of like kind of creative redefinition here. Superintelligence. I'm not sure is superintelligence more than AGI or less than A GI? 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.

28:49 So like all these terms are like what are I don't even w what even it's funny, I was I was having an argument on Hacker News this morning. You remember the idea the arg which is never never a good use of time. But you you remember the argument of like, you know, people would argue about whether crypto is blockchain or whether blockchain is crypto. There isn't a right answer to that, let's just be sure. You know, it's important to understand what you mean when you say that, but there isn't like a correct answer to this. Are we gonna get to something that has human level intelligence?

29:16 I no we don't know. I don't think we have any way of answering that question. Maybe. Maybe not. You can I make arguments either way. Meantime, does does it mean in the meanwhile. we've got this thing that's clear kind of a you know completely transformative technology. And maybe the serious point here is you like you don't have to believe, even if like the model stop getting better tomorrow.

29:35 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 world out over the next ten years. So you don't have to believe in any of that stuff to believe that this is a giant deal. Something that's definitely changed. I had um your former boss, Mark Andrewson on the podcast. And we didn't actually talk about this during the conversation and he brought it up before we started recording and I never got to it. Is he had this insight that the the opportunity set for companies now is so much larger? We used to have no trillion dollar companies. Now we have

30:04 We're gonna have dozens of trillion dollar companies. It's like the size companies can grow to or is going up so much. Evaluations also go up along with that. And his point is just people haven't really groked at just how large companies can get now. Like everyone's hitting a hundred million AR in like five month five months, six months. Just thoughts on that. Yeah, I mean this was his whole software's eating the world thesis from

30:27 No, fifteen years ago, whenever it was. Yeah, you know, the TAM it 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 share framing that, you know Mainframes are I think peak mainframe install base was something like seventy, eighty thousand units. I mean slightly fuzzy term, what is that mainframe and what's the difference? But something like that. That order of magnitude.

30:53 And then when the internet kicks off, there are, as I said, fifty to hundred 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 seven, eight hundred million consumer PCs in the world. There's about five and a half, six billion mobile smartphones in the world. And which is what you can have

31:13 Nine hundred million weekly IT buses on Ch P T. And so there was this narrative like five years ago, right? Well, we've run out of people. So like the the next thing can't be an order of magnitude bigger. Um, 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, if you're just you know, branching out and automating big big news weight of the economy. Um No, they no, they back to your job point.

31:36 You know, you could argue, well, we're just gonna replace all the people with AI and like all the money will go to to to to Sam Waltman. And You know. Mark Mark can buy himself another gold screen. add to the fleet. I think the kind of the the other answer was, you know, it's back to the lump of labour fallacy and you know that the last two hundred years that you know

31:54 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 it always creates more value. And so here you could you could certainly make an analogue to you know, the useful analogue 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 analogy 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.

32:30 Um and so you knew more and more value and a bigger b bigger and bigger um contribution to the economy. Um also of course disappear disappearance inside things. And you know the other side the the the point of my capital section in the presentation is um You know, there's this quote from Sam Altman where he said, You know, we're gonna be selling electricity We're gonna be selling AI AI intelligence. On a meter like water or electricity, and you look at this and think, you know, my dear sweet, you need me to explain the margin structure of the utility industry to you.

32:59 Because guess what? When you watch television, the T V 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. Um And you know, clearly this is like the much more kind of specific tactical question at the moment is Do we even end up with three giant models or does it be does it become hundreds of models and open models and local models and sell them?

33:26 And even if we do end up with, you know, say pick a number three to six to ten giant foundation models that cost hundreds of billions of dollars a year. Um 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.

33:44 Global mobile industry has revenue of about a trillion dollars a year, maybe a bit more now. And it spends about two hundred billion dollars a year on CapEx every year. Total telecoms is about three hundred, mobile is about two hundred. About fifteen to twenty percent of revenue every year.

33:58 And if you look at a chart of Mobile data consumption, it's an exponential curve, right? Perfect curve going straight up. And I the number now I think it's about you know. fifteen hundred to two thousand times what it was in two thousand ten, globally. And the stocks have gone nowhere in twenty five years.

34:14 because it's an X gross low margin util commodity utility. Where They're selling There's incre this objectively amazing piece of global technology infrastructure that has enormous complexity.

34:29 And in normal 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 like 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 stack.

34:49 Um 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 chapel and get the chap ball to do the whole thing? Can the model companies keep building these like clawed for X, Clawed for Y things? Which to me look very much like what you see if you hit file new in Excel. Like the tablets, but like all of those are actually.

35:09 billion dollar companies as well. And if not, no, does it all have to be apps, quote unquote, whatever app means. And if it all has to be apps, who builds those? Well, they can't all get built by the model labs, just as they didn't all get built by Microsoft. And so if they're all bought by other companies, does the models, found ocean 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 file it runs on. And you don't have to like standardise on AWS because that's who all the software is, and like the developers all standardise on AWS because all the customers use AWS. That's not how it works. That's how Windows OIS works, but that's not how how Carbide works.

35:48 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 gonna build that. And the models themselves are basically commodities. as at least as you can see them as users.

36:02 Then why would the mobile 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, there's gonna be Internet 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 f last month.

36:32 Um But that's like somebody getting like a fifty grand mobile data bill. In two thousand ten. Um, that's temporary. 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.

36:51 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 dis you should have lower pricing and lower margins and the value should have stack. I am so excited to tell you about this season's supporting sponsor, Vanta. Vanta helps over fifteen thousand companies, like Cursor, ramp.

37:13 Duolingo, Snowflake, and Atlassian earn and prove trust with their customers. Teams are building and shipping products faster than ever thanks to AI. But as a result, the amount of risk being introduced into your product and your business is higher than it's ever been. Every security leader that I talk to is feeling the increasing weight. of protecting their organization, their business, and not to mention their customer data. Because things are moving so fast, they are constantly reacting, having to guess at priorities, and having to make do with outdated solutions.

37:45 Vanta automates compliance and risk management with over thirty-five security and privacy frameworks. including SOC two, ISO twenty seven thousand one, and HIPAA. This helps companies get compliant fast and stay compliant. More than ever before, trust has the power to make or break your business. Learn more at vanta.com slash Lenny

38:06 And as a listener of this podcast, you get one thousand dollars off Vanta. That's vanta dot com slash Lenny. A really interesting takeaway here is that Your sense is over time the foundational model company's Anthropic Open AI. others will their margins will get squeezed. They will not be as

38:22 successful as there today and the bigger opportunity is in the application layer, the people building on the models, the rappers. Yeah, I mean this is a very sort of deterministic Thesis. Which is the models companies crucially what I said is the models don't seem to have no over effects. So there doesn't seem to be a winner takes all effect where one of these will run away ahead of the other. So you should have competition indefinitely. You have competition indefinitely, you don't have c you don't have differenti primary, like really radical differentiation of what the product is.

38:52 Then why would you have pricing power? And meanwhile, if the if you need to have thousands of applications that are all different built by different people, those can't all be built by the model people. So it should end up looking more like clouds than it looks like Windows. Now that may be completely wrong and you know one of the Points I make in the presentation is like imagine having this conversation about the internet in nineteen ninety seven, like what would you have got right? And or indeed having it about mobile in two thousand.

39:21 You know. you would not mean most you would have missed almost all of it. You certainly would have said that like a has been P C company from Cryptino would win the whole thing. I know one wouldn't say that. Um and a search company with like a weird Lego. Like search? What's that gotta do with mobile? No, no, forget it. You're an idiot.

39:37 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? Um I don't know. I had a When I was a baby analyst in like ninety nine, went to see a

39:50 dot com company in the UK that was trying to do online selling computer cars. Components online. And um 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 were on the train back from Birmingham. And

40:03 Just sort of sort of senior banker called David Tate. Um We're all sitting talking about it. And Tatie says it's a low margin reseller, one time sells. Mm.

40:14 Well 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 we play for flat panel screen? Like there's there's no bell prizes in flat panel screens. They're still a low margin commodity.

40:35 I look forward to be fooding wrong. Proven wrong, but like hey, that's that's what it looks like now. This is great. So I know you I know you're not an investor. I know you didn't actually do investing at A sixteen C, even though you work for A sixteen Z. Partner. Partner. Just sit around and pontificate. Partner.

40:50 Would you are there companies you would invest in? Like if there are a couple of companies you'd invest in now is Is there some on that list or categories even? You know, I I I mentioned briefly that I was an analyst. I was a I was a sell side equity analyst. I was not a very good sellside 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 equary analyst. Um

41:12 And I you know, I don't You know there's there's There's a like a huge difference between being right and being early, and there's a huge difference between the right company and the right price. Now, you know, deterministically you can look across the market and say, Well, you know, it's like you know that the bell curve I came. And you know, the guy with fifty and the guy with

41:34 Two hundred are both saying I 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.

41:52 Yeah, you can certainly see questions for all of them. Um, and one of them may drop the ball. But it's worth, you know, kind of remembering what happened in mobile. Yeah, 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. It's like for for Google, it didn't change anything.

42:13 For Meta this was great. Like this is a way better way to do social than on PC'cause like you've got a camera and notifications and it's on your phone all the time with you. Um, Amazon, like, what does this change? Like Doesn't change everything. I mean I'm I'm 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 you bet you kinda but you can argue about individual names. The point is that like you we went through that shift and it didn't change anything for half the industry. Um healthy internet industry. And so I think you know that you could kinda propose a little bit of that here. Um it's what while Steven Sinovsky at a at A six in Z who used to run Windows would always say is you know, incumbents always try and make the new thing a feature.

42:54 And And sometimes they're right, sometimes it's a feature. Actually along those lines something I wanted to get your take on. There's this Thread that's been happening across a bunch of guests.

43:03 Which is around distribution becoming a bigger and bigger moat. Because as software is easier to build. Everyone's launching. Products, uh everyone's trying to compete for attention. It's getting harder and hard. It's always been hard to get people's attention, but it's just Like the noise in the market is just going up like crazy.

43:21 And to me, that tells me Distribution is becoming a more and more Uh valuable uh skill and asset. And it also tells me incumbents are gonna be a lot more successful because they already have distribution versus a startup that's trying to break through. Yeah. I mean there's like a version of, you know, the Drake meme of like he says I don't like that. I do like this. Yeah. You know, I don't like thing GPT wrappers. I do like harnesses.

43:44 Yeah. Mm. So Yeah, I d 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, so I've read to think about about

43:57 Yeah, open AI earlier this year. Like how do they compete? Well, it's it's there's an obvious comparison here that a lot of people made is with web browsers. The fundamentally web browser and you know there's 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.

44:22 And like what else? Which is like what's the last innovation in browser design, like tab browsing? Just twenty years ago, twenty five years ago. And every now and then somebody tries to innovate in browser design and it never works,'cause like you found the platonic ideal. It's like trying to innovate in smartphone design. Like you know, it's a yeah, it's a it's a gl it's a gloss rectangle, like there's nothing you can do there. And so what happened, of course, is that Microsoft uses distribution to break the work to break in.

44:44 Um Then of course what also happens is setting aside the lawsuit is that it turns out the winning browsers doesn't matter anyway, because the value is further up stack. And say Microsoft wheels browsers for like five, six years, and it doesn't matter. It doesn't get them anything. Um So really what's happening now is that Google is using distribution to drive um to drive Gemini and like what's the difference between Gemini and Court? 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.

45:09 And The same thing with Meta, like if you look at survey data on which which LMs people use, even before like the new sh new thing, like the Llama thing. Yeah. Meta was like j j behind it was up there between Chat GPT and Gemini. Which if you're in Turkey people have completely written it off, but it was like they've sprayed it on every service surface.

45:29 It wasn't that bad. It was fine. So distribution of an adequate product when the Field is basically commodity distribution on brand. become a big deal. You can see that in you could see that in the like the strategy, open AI strategy like last year was you know, what people called it, you know, everything everywhere. Yes today.

45:46 Um 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, how can we get distribution, how can we get something that sticks, how can we get people something that something that people use it. Before Google. And meta and Amazon spray it everywhere and get everybody using that one. And then you've got like the inertia and the power of the default, and like why would you switch? I've seen Meta Apple is kind of the last penny to drop here. Um

46:10 That was this sort of slightly weird opening ideal. And now there's an even weirder story that opened a want to see Apple. Good luck with that. Um 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 WW DC from twenty twenty four, like the whole second half of it is Apple intelligence. That was like the most compelling vision of a personal AI assistant I've still still the most compelling vision I've seen. They then couldn't ship it, but then neither was anybody else. And you watch it again and you're like, Okay, so you want to using a genetic on device

46:41 AI with no prompt injection and no hallucinations and a completely standardized UP API system across ten thousand apps with intents that all work perfectly and like my Well, that sounds good to me, but like I'm not surprised they couldn't chip it. But yeah, nobody nobody else has shipped that. But like that vision was great. You know, I I really want to see what happens at WWG in a month of like do they actually ship that now? Powered by Gemini. But that's also another point, is like okay, there's the gonna be the

47:06 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. The model's just like the dumb thing underneath the funny way of putting it, the dumb thing underneath that powers the feature. The mobile is the commodity that powers different decisions about what the feature should be and what different distribution.

47:27 And in that situation, of course, um Apple's got like a billion devices that can run this on Edge. And and Google has his wonderful marketing slogan, coming soon to our most powerful devices. Meaning it won't work on those annoys. So again, distribution. Questions. Interesting. Google IO's next week so we'll see what they launch.

47:45 Oh no, they launched they launched App they launched the um Android and it just shows how how modern is it today. Well no, they launched it last week. I mean it which is it's like it just illustrates how much we've we've stopped paying attention to Android and iPhone and iPhone. Like Google did hard a public thing last week. They got they're replacing Chromebooks with Google Books. And they've got a new Android intelligence powered by Gemini that will roll out to like the five people who bought a Pixel phone. She don't work for Google.

48:11 Yeah. Um I'm gonna 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 see these surveys, AI is like less popular than ICE.

48:25 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 ho this goes over time? It's interesting. And it's a big sort of fuzzy massive different stuff.

48:43 I think. There is like tangible like my electricity bill went up which applies Actually in a very small number of places. Objectively. But it did, and this is a question.

48:57 The water thing is weird because it's just like completely fake. Um I should qualify. Explain what I mean here. Um, 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.

49:16 Done did a study at the end of twenty twenty four where they estimated US data center water consumption. And it came out at about naught point naught one seven percent 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 like that's a planning problem. That's not a data center problem. And you know in generality, yes, this is you know, data centers have what, like five percent of US energy and might grow at one percent a year for the next five years.

49:45 Top one percentage point, yeah. 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 economic economists talking to each other you're talking to each other, and main answer is we really don't know yet.

50:04 There's a bunch of charts that kind of say yes and a bunch of charts that kind of say no. And Th 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 all look exposed to AI and feels that don't look exposed to AI. So there's a lot of like econometric argument about this. And and I mean there's a sort of broader point here.

50:31 In fact, which is a different point. That like We have very little data on what's going on in AI. From anyone. The model labs don't tell us anything. Like 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.

50:48 We do not have a daily active user number for for chat GPT. It's crazy. Um And all the data comes from academic economists trying to back stuff out of BLS surveys.

50:58 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 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 can we see? And the answer Right now, I think is like there's no clear consensus that we're seeing an impact on jobs but of course politically that doesn't matter. If you're if you're a student and you can't get a job. And that clearly is an issue, whether it's because of AI or whether it's because of Trump and terrorists. It's a different question. Then you get like like kind of niche things like, you know, people who draw book covers for um young adult women's not also very upset that now you can get a picture of a naked woman on back of it.

51:40 Dragon flying through over a volcano um without paying them. So there's I'm I'm sorry, I'm being deliberately unkind, but there's a little Yeah, there's the does it and you know, people particularly like novelists, people who write ebooks. Uh there's a huge culture war over whether it's okay to use AI. There's this whole sort of AI slot question and you know, if you sure the number that like thirty, forty percent of new podcasts generated by AI. So there's a lot of there's a big fuzzy mass of questions. Some of this I think is is a little bit like the backlash we had around social, but much more compressed. And like social, some of the backlash around social was true and some of it was sort of true and some of it wasn't. You know, always like exemplified in the whole like Facebook sells your data thing. Which is just

52:23 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 from Jonathan Swift that you can't reason somebody out of an idea they won't reason it to

52:37 Um So you get this kind of wide it was a long way on Christmas, but you've got this kind of wide kind of spread of ideas, just as you kind of did with social. There's like twenty different things, some of which are really real, and some of which are really not real. And a lot of which are kind of f a fuzzy mess in the middle. All of which means that that meanwhile you've got Trump saying he wants a new executive order on d on dangerous models. Which I actually don't think is is the thing that drives the backlash, you know, the worrying about myth or cyber, I don't feel like that's, you know, a main street America conversation.

53:09 But that's the thing that got Trump interested in the self again. Well maybe go kind of in a tangential direction. Something that I I like to ask fo 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 gonna do to the future.

53:27 How are you changing the way you raise your kids? Just what are you teaching them differently potentially that might help them in the future? I don't know. I think there's a curve here in that if you've got kids who are going on to the job market in the next year or two. then everything is up in the air and no one knows how knows how this is gonna work. If you've got kids who are going onto the job market in like Five years.

53:49 Then who knows? And but Starfall have settled down a lot by then in pr probably unpredictable ways. So I could be a m a lot more worried if I had a twenty one year old. No, I don't have got you know kid in his sort of early teens. So it's a Diff those those questions vary.

54:05 Then you've got a lot of the questions that were the same before Chat TBT around, you know, the collapse of gatekeepers, the, you know No, should you really believe what that influencer on TikTok says? 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, internet y media consumption kinds of questions. Um I don't know, there are people who are like super, super intentional about, you know, every minute of their child's life.

54:39 Um 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 any w anyone who drives slower is an idiot. And that certainly applies to parenting. Um So I'm you know, like everybody thinks they're somewhere in the middle, but you know, I don't have, you know, a a deeply systematic and widespread and coherent like plan for this is what

55:00 My child is going to be doing in three, six, twelve. Eighteen months time. Um I'd I'd settle for him not breaking his Chrome book again. Mm-hmm.

55:09 I like that your just general vibe is it's gonna be okay, guys. It's gonna be okay. Yeah. I don't know if you th I think if you you know, maybe this is'cause I'm British and we haven't had political violence in five hundred years, and Um, I think you know, maybe if I came from Iran I'd have a

55:26 different attitude to to being calm about the future. Um, 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 um

55:43 Yeah. I dug up there a whole bunch of books in the late seventies about databases. There was a whole panic about databases. And again, half of it was true. Like Um

55:55 You know, if everybody's like Police records And arrest rec and w if if all police records and all government records are online. Then that's different. If you think about, for example, the deep news.

56:08 Deep fake news issue, for example. There's like a dumb reaction to this, which is to say, um Haven't you heard of Photoshop? Yeah. Which is true. But a fifteen year old kid couldn't use Photoshop to make hardcore pornographic news of every girl in their high school and send them to the whole school in one afternoon.

56:28 And turn them into video. Exactly, even well. Yeah, even more. And now they can. So like that is different. It's kind of like, you know, the challenge of social, you know, the thing people would say in the nineties is it's great, you can be you know, the only gay kid in your village and you can find other gay people and you can find your tribes. And guess what? It turned out you could also be the only Naucy in your village or the only pedophile in your village, or the only 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. Yeah.

56:56 Um 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 all happened again with AI. You know, the we can deep fake news are like the obvious thing we can see now. There will be a whole bunch more of this stuff. Um but there's also and you know something of kind of technical audience should know about. Have you uh do you know about the post office scandal in the UK? Nope.

57:18 Okay. Fight. Side bar here. So in the UK post offices are mostly franchises run by small business people. So they're run by like pharmacies classically. Teb very often Indian b Indian immigrants, second generation Indian people. Um

57:32 And the post office, like fifteen years ago, rolled out a new component cell computer system. So they'd have a separate counter in the back that's the post office. And so the post office rolled out this new computer system built by them for Jit f by Fujitsu that had a bunch of bugs in it that showed shortfalls in cash. The post office looks at this and says, Aha, we knew these people were stealing from us. Hundreds of people get prison.

57:52 Munti suicides. Bachruppes, people lose their hose. Meanwhile, people from the post office and people from Fiji Su are going to court and swearing there's no bugs in the system and nobody else has had this problem. This is nineteen seventies 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.

58:14 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 win people's lives and you have to be conscious of that and also kind of not panic about it. So maybe following that th 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?

58:36 And is there a job you kind of think you want to steer them towards? I don't know about that. He's not quite at the like I want to be a fireman stage. Um but Yeah. And certainly, you know, if I look at my career, you know, I started as an equity analyst and then I went and worked in an industry and then I was a consultant. Like, you know, the the the days when you kind of knew what your career would get was going to be, or whatever. You know, there were clearly some people where uh you wanna 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

59:04 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. Okay, so zooming out a little bit, let me ask you a a meta question. What's a

59:27 question about AI that you think nobody's asking. Yet or not enough people are asking that we should be asking ourselves. Sure. I mean we talked about like value capture. Like obviously this is a whole everyone is is is asking like Right. 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 the situation today will continue or that of course they will. So I think that's maybe a question that that not enough people ask.

59:52 I think the question I pose towards the end of my presentation, which we talked about earlier, is like what's the task and what's job, what is just the thing that becomes a button or make the skew versus what are people actually hiring you for? is that 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 dog gets automated away, but there's a bunch where that kind of isn't the question. The way I actually pull that together at the end of the deck was a chart of a global recorded music revenue.

1:00:22 Which as 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 has come back about to about seventy five percent of the peak. Um adjusted translation.

1:00:36 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. The second half of the chart is saying what happens if fifteen dollars a month gives you all the music that there is. So it's kind of a completely different sort of question. And you could, you know, that's the way that you could look at Uber or the way you could look at at Airbnb, all these kinds of companies. Is it to begin with you do the old thing but more?

1:01:09 With every new technology, you do the old thing but more of it on the new place. So, you know, you put Fical mobile. You print our UMLs. 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.

1:01:26 Yeah something else. Yeah. And right now, you know, those questions you only even know what the question is after it's been asked and you built a billion dollar thing that lots of people use'cause like obviously Spotify look crazy. And people look crazy and everybody look crazy. But that's a sort of I think

1:01:42 the the the way to guide what this means is you have to get past We do the old stuff but more. And you have to get to What do you do that's different that's because of this? What is this change? What wasn't possible before? What gets unlocked? As opposed to just doing the old thing but more of it.

1:02:02 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're to zoom out like a few years ago. Maybe three years ago, four years ago, the last profession you think would be automated.

1:02:16 is engineering and coding. It's like that feels like the hardest thing. That's like we're gonna need people to build these things. Now it's like the most transformed role of any role. Like You went from writing all your code to zero percent of your code as AI 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 I was

1:02:35 looking at this is whole there's a sort of US government called O data set called ONET or something like that. We try 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 p profession is X or Y percent exposed to AI and AI can do Z percent of it today. I think this is just the most ridiculous bunch of deluded horseshit. And There's two reasons for this.

1:03:00 The first reason is that this is like ironically, this is the logical systems problem. The expat system's 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 fifteen years later you've got seven hundred steps and it doesn't work.

1:03:23 Um And this is what happens when you try and look at a profession and sort of break it down by which bits can be automated and which can't. You can't describe a profession like that. Or at any rate, 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 bullshit. You can't do that. Um I think the other side of

1:03:45 the the fallacy though. is to talk about taxi drivers. So um You know, if we've been having this conversation in nineteen ninety seven. It's like the Uber test.

1:03:57 Imagine we're in nineteen ninety seven, what will be crushed by the internet? Well newspapers will be fine, they'll just'cause 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 if her debt will go down. 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.

1:04:20 And so uh Like the 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? Yeah.

1:04:36 And I ask an AI to build me a training machine and watch me and tell me if I'm doing it right. Why do we need a personal trainer? Now that might be complete bonsons. Um

1:04:48 But that's how these things work. Like the stuff that you don't think is ex you can't predict which things are going to be exposed necessarily. Or you know, a lot of the big companies are things that didn't look like That would work and didn't 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.

1:05:08 And recently. 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 it would demolish a taxy business and made it much bigger as well. Might neither t the tan became much bigger when everyone switched.

1:05:23 Airbnb's Impact Hotel hotels, if you actually go and look at the numbers, it's pretty marginal. They carved out this whole other business and maybe they slowed down the growth of hotels a bit. But you know My wife flies to Milwaukee next week.

1:05:38 She's gonna land at eight o'clock at night, she wants to go to a 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's getting seven in the morning, she's gonna drive to the client's site. She's not going to stay in NB and B. Like absolutely zero chance she's going to stay in N B. And half of the hotel business is travel. And you know, you can 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

1:06:07 Yeah. It does, it depends. So there were you know, y it's it's back to my nineteen ninety seven point. You can Save some of this? Um

1:06:18 But you have to have that humility. Yeah, I'm coming back to this uh phrase you use, r presume radical uncertainty. Is a nice uh Core thesis here. So knowing all this, just

1:06:28 It's hard to tell. We don't know exactly where it's going. Uh, things are gonna 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

1:06:45 be more successful in this future. Well, I I should just kind of wind back on what you just said. It's like if Keyn tells us in the long run we're all dead. So You know it's all you know, like on average Um

1:06:58 You know, on average nobody died in World War One. Great, but if you know, if you're a if you're a nineteen year old in nineteen fourteen, you you've got a you know, one in three chance of 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 one would would have been thinking about being an associate, this is a major question. And it's very unclear how those professions are going to play out. It's very unclear what the you know happens to the pyramid structure of professional services. The answer I the only answer I think one can have is You know

1:07:33 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 I are. It's like great, I'm happy for you. But that's not gonna help. What helps is you diving

1:07:50 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 gonna hire fifty.

1:08:10 Go into the interview and say, Well, I think AI is bullshit and I'm never going to use it is probably not the right move. 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

1:08:28 internalize it and think about what it means just as, you know, you and I did with mobile and with with the internet. I think that is actually very actionable and and very consistent advice on the podcast is just Just do stuff. Build it. Don't just sit around and modify it and be Be pissed at what's happening. Two uh

1:08:45 Close us out. I'm gonna take us to AI Corner, a recurring Corner of the podcast. Uh And the question to you is just what's one way you

1:08:53 Used AI and use AI in your work. We're life. That yeah, is really interesting. Something that other people might might uh be inspired by. I don't know. I I struggle with this question because I'm sort of the lawyer looking at chat GPT.

1:09:07 So, you know, the stuff that I would do that I would automate are sort of precise information retrieval tasks, which is uh precisely the thing that this is kind of worth 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 today I kind of can't do for me very very very well at the moment. I use it for proofreading, I use it, you know, for images, I used it redecorating my apartment. That worked fantastically well at that. Here's a picture of this room, repaint it, add this light and this table and this rug. Um no change the colour of the rug. There's a kind of planet stuff where it works.

1:09:42 Um 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. Yeah. And that's I'm I I I struggle to find many K many examples of those where I need it. But then you know, I'm a kind of a unique weird

1:10:00 Job. You know I Yeah, sit at my desk all day. Night.

1:10:04 trying to synthesize a whole bunch of other stuff into a whole bunch of new ideas. That's not particularly common way for people to spend their time. I struggle to find AI use cases. I am the accountant looking at the spreadsheet and thinking, Well that's very clever and this is clearly going to completely transform. Everything. But I actually don't make sweet sheets every day. I went to a stand up comedy show with

1:10:25 Uh of Pete Holmes. I don't know if you know him. And he made this joke that uh 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 Bohemian's like, No, I don't wanna I don't wanna do all these ugly things. I wanna be creative.

1:10:43 Make art. Yeah, well I mean there's 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 me to do the stuff that the boring stuff that I don't do for fun. Yeah. Um and you know, finding that next I mean, you know, Jake, this kind of comes back to kind of my my chat boy chatbot point. that you know the chat board is a blank screen in a jagged edge. 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.

1:11:09 Part of it is also like AI just disappears. So most of what I write now I dictate. I dictate is a voicemail. And that's automatically transcribed. Is that still AI or is that just voice recognition? Probably an L L M there's probably an L M in there. Okay, so maybe that's AI. Well Okay, so So what?

1:11:27 Um at a certain point it's just automation. What do you use for that? For voice voice transcription? So I actually find Apple notes. The Apple the one built into the iPhone works fine. I mean I'm conscious of the people want others, but like I mean I dictate it. There it is. They worked, so I'm I'm happy with that. Alright, 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? No, I think you know, I've I've I've monologued plenty and gone through a bunch of stuff in the deck. Go leave the deck and um sign up to my newsletter and then you will get many more mags of brilliant Benedict Heaven's wisdom. Um

1:12:03 Some of which may even be useful. Somebody on sub 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. Well, with that, Benedict, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

1:12:22 Sure. First question, what are two or three books that you find yourself recommending most to other people? Tough one. For me'cause I just read an enormous amount of books and then I can't remember which ones I've read. Um

1:12:35 I I I I sometimes often joke that the the the classic British comedy from the late nineteenth century called Three Men in a Boat, which is like my I Ching. Like we're having trouble hanging a picture, well, there's a section about that. You know, we're having trouble doing this, ah well, there's a story about that. All of which are hilarious. Um So Three Men in a Boat is my Ai 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 um network neutrality.

1:13:08 So like when the meat packers of Chicago um reach the point that it's cheaper to ship a cow from New York to Chicago, kill it, pack it and then ship it back to New York than to kill it in New York. and the di the pricing of refrigerator cars. And it's exactly like reading about broadband. All the same kind of besides those kind of business issues, which are fascinating. What else have I read? I don't know. Read books. Read different books.

1:13:31 Generally read books for grown ups. Please read something other than Lord of the Rings. If you're going to name another company. I saw this line and what was the latest like Peter Teal 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. If there is more than one book, then all that science fiction.

1:13:50 Read read about different things, read about things you didn't know about. Kind of along those lines. You have a favorite recent movie or TV show you've really enjoyed? No, I've dropped so badly off the the the the current media treadmill and I just spend most of my time watching Classics which are like what was 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. Um

1:14:13 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 w go watch one of those movies. that you are supposed to have seen or hadn't seen. Favorite recent product you've recently discovered. That uh you really love.

1:14:30 Could be a gadget, could be an app. I was speaking at a partner meeting for a company Alias. This week, what's today? Monday, no last week and um met the founder of the company who has a very famous n the the CO of the company has a very very famous name and admired his shoes and

1:14:47 Didn't say anything but then went and Google like half an hour later, Yeah, okay, I'll buy a pair of those. You wanna share the brand or you wanna keep it keep it secret? Okay. We'll keep it secret. I don't know. I think one comes in one comes in waves of new products and you know you get into waves of new things. And um like

1:15:04 When's the last time there was a cool app? Like I find apps. That was you know, all that white space went. I mean it's partly a function product ships, uh a platform chips like all the white space went for for cool new apps and uh we haven't White got you this is a to the earlier point. We don't have breakout a consumer AI apps yet.

1:15:20 Because but I think because of marginal cost more than anything else, you can't make it free and get fifty million users and then have a revenue bubble. Um but we don't have those break out things yet. For consumer. Yeah. For consumer, no. I just weird, I keep getting these ads for voice recorders, like somebody selling like a business card size. Like hardware voice recorder on my the the

1:15:39 But like I don't get it. Like I've got a voice recorder on my phone. Yeah. All kinds of cool stuff coming. Okay, two more questions. Uh The other favorite life motto.

1:15:48 that you find yourself coming back to often in work or in life. I suppose both mentioned earlier, apparently I mostly say it depends. Yeah. No it'll probably be okay.

1:16:01 Yeah. Okay. I that's that's the vibe I get. I like that. I like that it's probably gonna be okay. Not for sure. Um, okay, final question. I saw someone that you'll own a lot of old phones. Is that true? Uh it is, yes, as a um I can't.

1:16:16 I mean I was a telecom analyst and mobile analyst and I I kept all my phones up to a point. Now they're kind of un uninteresting but as you 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 grey square. So everyone was trying to differentiate from everything else. Um before it kind of results. It's kind of like cars, actually. It's like cars before street before like wind tunnels, cars all look different. And everyone's trying to innovate around because you've got the same four wheels and the same engine and everyone's trying to like differentiate based on like the shape. And and then everything converges on one shape. And it's kind of the same with phones. Like everyone Everything converged on one shape but before that it was all this innovation. So yeah, like I I I have like

1:16:57 Like a whole bunch of PDAs and and smartphones and how many phones are we talking about? I I don't know, like twenty or thirty. Okay, okay, okay. It's not so crazy. What's like the oldest one? What's the oldest one you got? So I have one of those I should have you told me I'd have got the box down. I have one of those Ericsson um shark fin flip phones. from like ninety eight or something, which is not again like hardware design, vis visual design trying to differentiate. I've got an iMade phone from two thousand and one and a J phone phone from two thousand and one that has a camera. So I came back from Japan in two thousand and one and my phone had a color screen.

1:17:32 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 blow. It didn't work outside Japan. I plugged it in the other day. It still charges up. I mean clearly I can't do anything with it. Um And like I mean there's there's a little bit of an analogy in there as well. And like we thought there'd be all these different uh shapes and sizes. And before the iPhone, people kind of imagined like well, some people will have like a little pocket PC and some people have a keyboard and you have like folding uh or the all these different ideas for what it would look like and it all we didn't realise it was all gonna converge on one device.

1:18:03 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? If you can Google me, as I always say, my parents had good SEO. So Google Benedict Heavens. Um, and so there's a website with this I publish all the presentations that I've done and sign up for my newsletter, which comes out every week. Otherwise, how can they be useful to me like I'm always trying to understand stuff and I'm always trying to ask different questions. The worst thing in tech is to like carry on talking about the same stuff.

1:18:37 It's like You know, the moment you really understand something, it's 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

1:18:49 probably too much time saying but these models still hallucinate. Stop telling me they don't hallucinate. They do. They still living snakes. Yeah. Yeah, push him push him a little bit further, any question, and you'll still get like nope, that's not true. Um, 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 the the challenge for me is is how do I approach

1:19:10 Um and then yes, if you want me to come and present to your board in the Caribbean, um then let me know. And by the way, the domain is bands dot com, if folks wanna Yeah, check it out and E V A N. Benedict, thank you so much for being here.

1:19:24 Thanks a lot. Hi, 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 Lenny's Podcast dot com.

1:19:47 See you in the next episode.

What you see above is a preview of the first minutes. One unlock costs 10 credits and covers this episode forever: full segment and word-level timestamps on this page, plus .txt, .srt, .vtt and word-level JSON downloads, as many times as you like.