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

My most controversial opinion is that I think that AI is as big a deal as the internet or mobile and only as big a deal as the internet or mobile. What's your just 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 [music] jobs and you don't know the new job cuz it doesn't exist yet. We've had that process over and over again. >> Even just looking at the most advanced AI companies throughout big open AI, everyone's increasing headcount. You talk to these doomers on Twitter and they would act like every big company is going to buy Chat GBT tomorrow and then in two weeks time they'll fire all their stuff. These people are morons. You can't predict which things are going to be exposed. You can't look at a senior partner at a law firm and say, "Well, 17% of their work could be automated. This is horshit." >> I'm curious if you're following the anti- AI sentiment. >> It's a big fuzzy mess. Yes, this will change a bunch of stuff and we'll need to worry about it, but [music] 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 on Blue Sky and shout at everybody about how evil AI is. Like great, I'm happy for you. But that's not going to help. What helps is you diving into this and coming out understanding what you can do with today. My guest is Benedict Evans. Benedict was a longtime partner at A16Z as their in-house analyst [music] and resident thinker. Before that, he was a longtime equity researcher. And for the past six years, he's been an independent [music] 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 [music] 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 [music] on the impact that AI is going to have on our lives and our work, the rise of anti-AI sentiment, [music] the impact on jobs, where in the value chain most of the value will acrue, and [music] 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'spass.com lenny'spass.com for a year free of some of the most amazing, hottest, most well-crafted AI products in the world, available exclusively to Lenny's newsletter subscribers. With that, I bring you Benedict Evans. Benedict, thank you so much for being here. Welcome to the podcast. >> Thank you for inviting me. >> You just put out this deck called AI is eating the world. I want to ask you kind of 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 the change that they're going to 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 because clearly there's a bunch of people in tech who think no, this is more like the industrial revolution or something. And there are a whole bunch of people underneath saying, well, he thinks this is just as big as does he not understand how big this is? And I'm like, smartphones were quite a big deal. The internet was quite a big deal. We wouldn't be doing this if it wasn't for the internet. So there's like one layer of but then if you dig into that like if you're going to make the internet comparison it's like we're in 1997. Like it's very exciting. 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 have already got it who have already taken whichever pill it is I forget which sort of imagine that everybody in the world is already there and the truth is you've got this kind of very wide distribution. So there's people in tech who bought their cluster of Mac minis and you know don't use Google anymore. And then you look outside tech and setting aside the idiots who 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 well how are the models going to work and do the model labs have pricing power and where's the value going to be and you know has open AAI won the whole thing or you know is anthropic got it this week and so then you can kind of get into calling those races where again it's like being in 1997 and saying well is it going to be excite or yahoo and the answer was no generally so there's a sort of a fractual point here there's like the sort a super high level that like this is going to change absolutely everything. I don't think it's particularly productive to say well is it 20% bigger than the internet or 100% those aren't productive conversations but it's one of those fundamental changes but then you don't know how any of it is going to work. Um in fact I just published this I do a presentation every six months and I just published one yesterday and one of the comments was Benedict this is 80 slides are saying we don't know which is like slightly facitious but also kind of true. This episode is brought to you by our season's presenting sponsor, WorkOS. What do OpenAI, Anthropic, Cursor, Versell, Replet, Sierra, Clay, and hundreds of other winning companies all have in common? They are all powered by work OS. If you're building a product for the enterprise, you've felt the pain of integrating single signon, skim, arback, audit, logs, and other features required by large companies. work OS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SAS. Literally every startup that I'm an investor in that starts to expand up market ends up working with Work OS. And that's because they are the best. 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Like how far far are we from just like wow it's all different now? >> Well, unquestionably we're already in that moment in software. And then there's a conversation about well what does agentic and AI software development two separate things that merge together mean for the future of software industry? You know, there's one extreme which is no one really believes which is, you know, hey, you'll just like v code your own stripe and no one actually believes that although you don't believe that, but like clearly there's a whole bunch of questions about what this means for the software industry and how much stuff

you'll be able to do yourself or how much more software there will be and that's, you know, whole that's one whole conversation. 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 how exactly do we use this? And how do we work out how not to be the next story that we've submitted something with hallucinations in it? And how many associates are we going to hire next year? Uh, what does this mean for us? One of the analogies I used in the presentation is imagine you're seeing imagine you're an accountant seeing the first software spreadsheets in the late '7s. This is mind-blowing. you know, you change the interest rate here and all the other numbers change and it does a week of work for you in like 30 seconds. And we can talk about what that meant for the accounting industry, but clearly if you're an accountant, this is obviously mind-blowing. But if you were a lawyer looking at that or a journalist looking at that, you'd think, well, that's very clever and my accountant should see this, but that's not what I do. I might use it for my time sheet next week if it didn't cost 10 or $15,000 to get the Apple 2 and the monitor and the printer to run it, which is what it cost if you adjust, but that's not what I do. And you need a word processor, which actually came like very shortly afterwards. And so that's sort of the moment that we're in of there's some people like software development are develop software developers are the accountants seeing visi like oh my god this changes everything like before viscal and after visalc before before cl code and after claw code [snorts] a lot of other people are picking it up using it to varying degrees but slightly puzzled so you there's a bunch of survey data that I put in in the in the presentation that like even if you look at like 13 to 18 year olds or something, it's still like kind of 15 20% of people are daily active users and another 20% are weekly active users and then the other 60% of those people in that demographic on you say they are not using this. So there's a sort of very widespread of who gets it and a very wide which I think also maps this is kind of almost a separate point maps to the sort of jagged frontier question of where does this work where does it not work can you tell where it's going to work is it intuitive to know where it would work can you tell after it worked can you can you 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 a lot of other people were like people having moment or they're not or we're in again we're in that kind of 1997 moment of okay what is this this >> along those lines something you've been writing a bit about is this like unexpected investment in professional services consulting services/forward deployed engineers uh all the AI labs at least the two big ones open anthropic are like investing in buying massive comp like consultancies and PE firms talk about just what's happening there why why that's happening >> well it's funny I was kind of groping for a joke last night when I wrote my newsletter and couldn't quite get to land it but as you know something like you know you know the joke that a machine learning scientist is a statistician who lives in San Francisco and there's something in there of like a forward deployed engineer is like an Accenture outsourced software developer who lives in San Francisco or works in San Francisco. I mean, you know, joking apart, apart, if you have any experience of professional services, like companies do not have lots of people sitting around waiting to do a build a big new project or do a big new piece of analysis or build a big new piece of technology or a new product or work out how they're going to redesign their stores or, you know, 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 things reasons why you hire Bane BCG McKenzie on one side or Accenture Infasis whoever on the other or you hire a branding agency or you hire an AR firm of architects or whatever. And it's always like well we could hire some architects but why on earth would we want to have 15 architects on staff when we we just go and hire an architecture firm? We just go and hire an ad agency. [snorts] And so you're supposed to like completely reimagine all of the internal workflows of your company and work out which of them could be automated really quickly with AI. That's a project. That's a project that needs like five or 10 people to sit down and spend a month or two working it out. And then actually doing it is another project. Okay. So we need to plug these three vertical systems into these two horizontal systems and build in a bunch of new workflows and train people to do that. Well, guess what? Who's going to do that? Because you don't have a bunch of people sitting around not doing anything. So, on the one side, this is part of the 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 Bane or you hire Accenture or you hire publicists to help you work that out. What's really just funny about this trend is you would think AI is going like consultants were going to be gone. No, we don't need all these people anymore. AI is going to do their work. Instead, like the most cutting edge AI labs are the ones most investing in these folks. It's I think it's pretty surprising. 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 capex going and are the model labs going to have differentiation? And then there's a section on deployment, which is basically what does it mean for the software industry? And then the third section is how does this change stuff? And one of the sort of sort of strands I tried to pull together in the section on change is change is um what's the hard part of the job? Is the hard part of the job writing the code line by line? Is the hard part of the job like giving you the school or making the PowerPoint or is the hard part of the job something else? Is it the task or the job? And you know, pulling that apart, sometimes the task is the job. Like the classic example is like a an elevator attendant. I live in a building that has an attended elevator. We have a manual elevator. There's no button. There's a there's a there's a lever in the door and 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 and then those all got automated after the 50s and now you get and you press a button and pressing the button is a job. So there were some things where the the the button the job was a task and the task got automated. What happens much more and this is why people talked about like the jeans paradox is this pricey elasticity because jeans paradox is just pricey elasticity applied price elasticity. If you make it cheaper to do something what happens do you do the same for less money or do you do more for the same amount of money or do you do more for more money because you've got new ROI. And if you look at something like the history of accounting or indeed professional services like you know this is a joke I made on Twitter back when it was Twitter was like young people won't believe this but before invest before Excel junior investment bankers worked really long hours and now thanks to Excel Goldman's associates all work at lunchtime on Fridays. It's like well why is that not what happened? You could make the same point in software development. You know before IDED and libraries and operating systems developers had to write all the code. Now, if you write an iPhone app, 90% of the code

is written for you by Apple. Like, Apple wrote the modem driver and the graphics drivers and, you know, the file system. You don't need to write any of that. So, we've got like a 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 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 skew you want, you want that microphone stand. You know this part number, you can go to Amazon and get it. If you don't know what microphone to get, probably shouldn't start on Amazon. Multiply that by many, many, many product categories. And so what Amazon does is get you the skew, but knowing what skew you want is another job. You know, the claw code can write you the code, but what code do you want? It can make you the features, sure, but what features do you want? Who's your customer? What's the right product for that customer? How you going to take it to market? And long way of answering your question, why do you hire McKenzie? Are you hiring them to get a 75 slide deck? deck? Well, narrowly Claude Co will make a really, really crappy version of that. And you'll get all these kind of AI grifters on LinkedIn and and Twitter and so on saying, "Hey, I made a McKenzie deck with Claude." and you look at it and you think, "Yeah, that's a bunch of dog crap. That's not what you'd get if you from McKenzie." But even if it was, that's not what you paid them for. What you actually pay Bane to do is to go and walk all over your enterprise, your company, and work out, yes, but why is it that you didn't do that? And how do the politics of this work? And what do you actually need to do? And let's go and talk to your customers and work out what they actually think as opposed to what's on the first page of Google. is all the other stuff and the PowerPoint is just like the task but that's not what you hired them for. The same with you know Amazon versus the retailer the same with software development. So you've got that kind of split. The other analogy that occurred to me here was looking at like the sort of class of industry that got steamed 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 the thing what was the actual thing like classic examples will be newspapers and recorded music. So record companies do not think of themselves as being in the business of manufacturing small pieces of plastic but that was that was what they actually did and when that went away they were screwed. 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. But often you kind of can't decouple that or that wasn't really the problem or you make that thing cheap and then all this other stuff happens as well. And so all of this is just vastly more complicated than saying well hey you know we're just going to automate the accountants or we're going to automate the 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 20th century and has gone up again since the beginning of the 21st century. So you have adding machines and punch cards and mainframes and databases and ERP and cloud with spreadsheets and PCs and the number of accounts keeps going up. And so why is that? Well, it's not it must be more it's more complicated than automation. Even just looking at the most advanced AI companies, Anthropic, OpenAI, I just had Dan Shipper from every on the podcast. Everyone's just increasing headcount. Like the companies you would think would be least likely to add humans are adding many, many humans. And to your point, it's really complicated. What's your just kind of gist on the job, the coming job apocalypse? You know, like Daria'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 authority. And I don't think the fact that you run an AI lab suddenly gives you or rather and if you're going to use argument from authority then it should be relevant to the field. So like [snorts] I'm interested in Dario's opinions on where models are going to go in the next 6 to 12 months. I'm not particularly interested in opinions on theories of labor and market value and competitive comparative advantage like yeah maybe he had a course on that at university. So did I. So I think one needs to be a little bit cautious on like well Dario says and that's setting aside like the cynical view that he you know he's just doing that pump stock which I don't I don't believe at all. So this kind of comes back to my point about you know platform shifts. 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 and so you know you go back to 1800 like 90% of us were peasants and our major concern was would like the crops going to fail because then we'll all go hungry or worse and so ever since then we've been automating jobs and creating new jobs and you can always see the job that's going to go going to go away and you don't know the new job because it doesn't exist yet and it's like something that sounds dumb anyway like you know like railway engineer what's a railway 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 economics student would tell you um we've had this process over and over again since 1800 and each time you go through it you get a bunch of frictional pain and dislocation and a bunch of people lose their jobs and a bunch of towns get hollowed out and it's all it all sucks but you know when you come through on the other side we're all richer and we're not worried about the crops failing anymore and you know this is the process of the last 200 years so then the question is is there some a prior reason why this would be different to those because like the internet removed a bunch of jobs PCs removed a bunch of jobs there aren't many people working as type setters anymore um or telephone operators or typists um the internet removed a bunch of jobs and generally the jobs that go away are crap jobs seen retrospectively and the new jobs are better because you know GDP keeps going up so is AI different and so Then there's kind of a couple of answers to this. One theory is well this is going to be way quicker and certainly the adoption of AI is quicker than previous technologies because but this is kind of because you're standing on the shelves of giants. So like you don't need to wait for everyone to buy a piece of expensive hardware to like buy a phone or a PC or wait for the telco to deploy broadband. It's already there. So of course chat GBT can get 900 million chat users because there's already 900 million people on the internet. Like in like when Mark Andre launched Netscape in what was it 9394 there were like 50 to 100 million PCs on Earth. So no, you didn't have 900 million users then. But and so the but the point is then he didn't need to wait for like phone networks or microchips and before that you didn't need to wait for electricity and you didn't need to wait for like mass production. So there's all you're always kind of standing on the shoulders of giants. There's always like a compounding effect. So yeah, this is faster but the internet was faster too. Um I think the other answer to this and this kind of comes back to the professional services point is like you know

you talk to these doomers on Twitter and they would like act like you know every big company is going to buy chat EBT tomorrow and then in two weeks time they'll fire all their stuff and these people are morons and this is one of many reasons why why doomers were morons but a complete failure to understand the way the world works and that was like the starting point why they then didn't understand anything else you know typical big company you know enterprise software sales cycle you'll know this better than me enterprise software sales cycle is like 18 months if you're lucky You know this is always the problem. The enterprise sales cycle is shorter than the the venture back start software funding cycle longer longer rather longer like it takes you longer to get an enterprise deal than it takes you to go between wraps and this was always the problem you know particularly for you know sectors like aerospace or healthcare or something. So like no people aren't just going to tear out SAP and replace it with XY Z. Maybe in five in like three five 10 years yes that whole estate will look radically different and all those jobs will have changed but it will take you know t three four five 10 years and it will take time sector by sector and it will take time for people to work out uh you could do that thing with this. One of the companies I always remember that we looked at when I was at Andre 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 5 years earlier and maybe 10 years earlier. earlier. And [snorts] actually, that's kind of a bad example because that relies on a bunch of like a bunch of stuff like web cutting edge web technologies. But if you go out and like pick pick 10 random SAS companies that were started the day before Tatbt launched, how many of them could have been founded at any point in the previous 15 years? like somebody to 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 10 15 20 years for people to invent all that stuff and work out that you could do that with this. So all of that is like the way of saying well yes it is going to be quick but actually no it will kind of take a while for people to work out how to completely change how their business works. your view is so comforting because because it's you know basically it's like okay this is a huge deal but we've been through many transformations before and it's going to be okay. Well, I have a slide towards the end of the presentation which and I know the title is something like, you know, this is going to be completely different from everything else just like everything else. And then the next slide is an IBM ad from the 50s which has got this sea of white men holding up with in white shirts and ties all holding up flight rules. rules. And the the the ad it said the slogan on the title of the ad is it's an IBM ad. It says an IBM electronic calculator. This is before it was called a computer. It's an electronic calculator. It's the size of a fridge is like having 150 extra engineers. extra engineers. Like how many people listening to this company list like their company's slogan is basically we'll give you 150 extra engineers. I mean isn't that like the whole pitch of Claude called code? 150 extra engineers for free or not free. That's like a lot of money. Um so and yeah that's what it gave you and so yes we keep going through this over and over and over again just to kind of make that tangible. I mean obviously we couldn't be doing this with the without the internet. So there's a slide in my presentation which is we could maybe talk about but it's a slide or chart showing how many products are stocked in supermarkets in America since the 50s. And the point of the slide is to say that barcodes allowed supermarkets to stock way more stuff because they could keep track of it. But making that chart, I had to know there was a thing called the Food Marketing Institute. And I had to have found out that they published a number for how many SKUs there were in supermarkets every year. And then I had to realize they'd been around since the 50s. And if I like dug long enough, I might be able to make a whole time series and I could make whole chart. Now imagine doing that in 1994. First of all, you would have no idea that exists. you really need to go and find a library where they public where they and they publish that number and then the numbers in that report. You'd have no idea. Then you need to find a library that had them. So you're going to spend like three days on the phone and spend like $50 on like longistance phone calls to find a library that has these. Or maybe you call the Food Marketing Institute and they say, "Yeah, sure. If you buy a, you know, we'll sell them to you for $500 each." So then you know you're going to get on a maybe you live in New York or like there's somewhere 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 analyst is you spend all day making a chart and you look at it and go that's not very interesting. So you spend two weeks to make the chart and then you look at it and go yeah I'm not going to use that and for me this was like two hours in Google Google 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 because it's like that's the world the world has always been. What's different potentially this time just to even though your code is it's different this is everything's going to change like just like just like last time like the big difference obviously is uh AGI might emerge and super intelligence where that is uh could you know does the work of humans can do a lot of this stuff for us can actually replace jobs just like thoughts on that element of this transformation we're going through >> I don't know this is one of the the ways I've struggled to write about AI is like certainly in like 2023 three early 24 like all the questions were questions you could have asked in like December 2022 2022 like the questions didn't really change 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 observation one can make like you know we have no theory of what human intelligence is we have no theory of why these models work so well we have no theory of how much better they will get so we're all just kind of vibes forecasting as to what will happen [snorts] um and then you can have like the 2 a.m. you know doped out philosophy students talking about hey man like is this consciousness maybe we aren't conscious either we just think we are like 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 what I think you can say today is that there's a lot of kind of redefinition of terms so I think a quote I used in my presentation late last year was 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." [snorts] And so certainly, I mean, I I did do a poll on social media every now and then asking, "Is machine learning still AI?" still AI?" Because I've certainly heard people say, "Oh, that's not AI. That's just image recognition." That's not AI, that's just sentiment analysis. So AI, it's a bit like the word technology. It's like if it's new, then it's technology. But in the 60s, airlines, jet ainers were technology. Now a jet ainer is in tech. And so there's a sort of sense of AI is like a moving target is whatever just started

working. And I think the point here is now clearly you can see people redefining AGI to mean the stuff that works now. So is AEI what's the definition now? It's like it can do a certain percentage of economically valuable work. Well, that's a very different thing to it has a soul and it's alive. [snorts] Um because a database can do that. like you know an IBM mainframe in 1975 could do a meaningful percentage of economically valuable work that was previously done by people and it turned out there was a whole bunch of other stuff that it couldn't do that we didn't do then we didn't know existed so there's a lot of like kind of creative redefinition here super intelligence I'm not sure is super intelligence more than AGI or less than AGI because last year I thought super intelligence was like really good but not as good not actual AGI and now it's like oh no no we've already got AGI but super intelligence that's really hard it's like all these terms are like what I what even it's funny I was I was having an argument on hacking news this morning. You remember the idea, you remember the argu 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 crypto there isn't a right answer to that let's just be sure you know it's important to understand what you mean when you say that but there isn't like a correct answer to this are we going to get to something that has human level intelligence intelligence I we don't know I don't think we have any way of answering that question maybe maybe not you can make arguments either way meantime it does mean in the meanwhile we've [snorts] got this thing that's clearly 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 stopped it getting better tomorrow if this is it and we hit a brick wall tomorrow this is an incredibly useful technology that's going to change the world and get rolled out over the next 10 years so you don't have to believe in any of that stuff to believe that this is giant deal >> something that's definitely changed I had um your former boss Mark Anderson 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 we're going to have dozens of trillion dollar companies. Just like the size companies can grow to or is going up so much and valuations also go up along with that. And his point is just people haven't really groed just how large companies can get now. Like everyone's hitting 100 million AR in like five five months, six months. Just thoughts on that. Yeah, I mean this was his whole software is he eating the world thesis from you know 15 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 shift framing that you know mainframes are I think peak mainframe install base was something like 70 80,000 units I mean slightly fuzzy term what exactly is a main frame and what's the difference at what point does it become two main frames as one but something like that that order of magnitude and then when the internet kicks off there are as I said 50 to 100 million PCs on earth maybe today there are something over a billion one to one and a half billion but obviously a lot of those are corporate it's like 7 800 million consumer PCs in the world there's about 5 and a half six billion mobile smartphones in the world which is and which is why you can have 900 million weekly IT users on JTP >> and so there was this narrative like five years ago like well we've run out of people so like this the next thing can't be in 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 is you're just you know branching out and automating big big new ways of the economy. Um [snorts] now the you know the back to your job point you know you could argue well we're just going to replace all the people with AI and like all the money will go to to to Sam Waltman and you know Mark Mark can buy himself another Gulf Stream. I think the add to the fleet. I think the kind of the the other answer is you know it's back to the lump of labor fallacy and you know the last 200 years that you know each of these technologies removes a bunch of jobs, creates a bunch of new jobs, creates a bunch of new value, unlocks prosperity for all of us and that's painful as you go through it but it always creates more value. And so here you could you could certainly make an analog to you know the useful analog to the electricity industry is just saying how that electricity became part of absolutely everything and [snorts] software has been kind of slowly working its way out. You know the anal here would be electricity in factories and then electricity sort of slowly spreads out and so that would be the point again that you know it slowly spreads out to do more and more things um and so you know more and more value and a bigger bigger and bigger um contribution to the economy. Um it also of course disappear disappears inside things and you know the other side this is the point of my capital section in the presentation is um you know there's this quote from Sam Alman where he said you know we're going to be selling electricity we're going to be selling AI AI intelligence on a meter like water or electricity and you look at this and think you know my dear sweet child you need me to explain the margin structure of the utility industry to you um [snorts] um [snorts] because guess what when you watch television the TV company isn't paying a percentage of your monthly bill to the electricity company, you know, when you wash your clothes, Bosch isn't paying a percentage of the price of the washing machine. Um, [snorts] and you know, clearly this is like the much more kind of specific tactical question at the moment is moment is do we even end up with three giant models or does be does it become hundreds of models and open models and local models and so on. And even if we do end up with, you know, say, pick a number, three to six to 10 giant foundation models that cost hundreds of billions of dollars a year. 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. Global mobile industry has revenue of about a trillion dollars a year, maybe a bit more now. And it spends about $200 billion a year on capex every year. Total telecoms is about 300. Mobile is about 200. It's about 15 to 20% of revenue every year. And if you look at a chart of mobile data consumption, it's an exponential curve like perfect curve going straight up. And then the number now I think it's about, you know, 1500 to 2,000 times what it was in 2010 globally. And the stocks have gone nowhere in 25 years years because it's an Xgrowth low margin commodity utility commodity utility where they're selling this inc this objectively amazing piece of global technology infrastructure that has enormous complexity and enormous sophistication, but all the cool stuff is made by you. It's made by the people listening to this podcast. It's made by somebody else. This was that like kind of pivotal moment where the Telos thought that they would do all the stuff that you did on your iPhone. And not only do they not do it, but Apple doesn't do it either. It's all further up the stack. 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?