AI Eats the World: Benedict Evans on the Next Platform Shift

AI is reshaping the tech landscape, but a big question remains: is this just another platform shift, or something closer to electricity or computing in scale and impact? Some industries may be transformed. Others may barely feel it. Tech giants are racing to reorient their strategies, yet most people still struggle to find an everyday use case. That tension tells us something important about where we actually are. In this episode, technology analyst and former a16z partner Benedict Evans joins General Partner Erik Torenberg to break down what is real, what is hype, and how much history can guide us. They explore bottlenecks in compute, the surprising products that still do not exist, and how companies like Google, Meta, Apple, Amazon, and OpenAI are positioning themselves. Finally, they look ahead at what would need to happen for AI to one day be considered even more transformative than the internet. (00:00) Intro (01:07) AI's Impact, Platform Shifts and Historical Comparisons (03:12) Generative AI: Potential and Challenges (06:12) AI's Market Dynamics and Investment (08:28) AI Deployment and Use Cases (10:22) AI's Future and Speculations (19:33) Generative AI in Practice (29:27) New Behaviors and Market Opportunities (31:29) Understanding Law Firms' Needs (32:05) The Role of User Interfaces (33:40) Machine Learning and Interns (35:26) The Evolution of Tech Products (39:43) The Competitive Landscape of AI (43:17) The Future of AI Models (45:27) Impact on Various Industries (46:49) Apple's Unique Position (50:08) Strategic Questions for Tech Giants (58:44) Reflecting on AI's Potential Resources: Follow Benedict on LinkedIn: https://www.linkedin.com/in/benedictevans/ Benedict's ‘AI eats the world’ presentation: https://www.ben-evans.com/presentations Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details, please see [a16z.com/disclosures](http://a16z.com/disclosures).

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

• 0:00Benedict Evans?

  ChatGPT has got 800 or 900 million retweet active users. And if you're the kind of person who is using this for hours every day, ask yourself why five times more people look at it, get it, know what it is, have an account, know how to use it, and can't think of anything to do with it this week or next week. The term AI is a little bit like the term technology. Once something's been around anywhere for a while, it's not AI anymore. Is machine learning still AI? I don't know. In actual general usage, AI seems to mean new stuff. And AGI seems new scary stuff. AGI seems to be a little bit like this. Like either it's already here and it's just more software, or it's five years away and will always be five years away. We don't know the physical limits of this technology. And so we don't know how much better it can

• 0:42Benedict Evans?

  get. You've got Sam Altman saying, "We've got PhD level researchers right now." And Demis Sisypas says, "No, we don't. Shut up." Very new, very, very big, very, very exciting. World's changing. Things tend to lead to bubbles. So yeah, if we're not in a bubble now, we will be.

• 0:57Speaker 01

  Is AI just another platform shift or the biggest transformation since electricity? Benedict Evans, technology analyst and former A16Z partner, has spent years studying waves like PCs, the internet, and cell phones to understand what actually changed and who captured the value. Now he's turned that same lens on AI, and the picture is far more complex than benchmarks or hype cycles suggest. Some industries may be rewritten from the ground up, others may barely notice. Tech giants like Google, Meta, Amazon, and Apple are racing to reinvent themselves before someone else does. Yet for all the excitement, most people still struggle to find something they truly need AI for every single day. A disconnect Benedict thinks is an important signal about where we really are in the curve. In today's episode, we get into where bottlenecks emerge, why adoption looks the way it does,

• 1:42Speaker 01

  what kinds of products still haven't shown up, and how history can actually guide us here. And finally, what would have to happen over the next few years for us to look back and say, AI wasn't just another wave, it was bigger than the internet?

• 1:57Erik Torenberg?

  Benedict, welcome back to the A and Z Podcast.

• 1:59Benedict Evans?

  Good to be back.

• 2:00Erik Torenberg?

  We're here to discuss your latest presentation, AI Eats the World. So for those who haven't read it yet, maybe we can share the high level thesis and maybe contextualize it in light of recent AI presentations. I'm curious how your thinking has evolved.

• 2:13Benedict Evans?

  Yeah, it's funny, one of the slides in the debt references a conversation where I had with a big company's CMO, who said, we've all had lots of AI presentations now. The Google one, and we've had the Google one and the Microsoft one, we've had the Bain one and the BCG one, we had the one from Accenture and the one from our ad agency. So now what? So this sort of 90 odd slides, there's a bunch of different things I'm trying to get at. One of them is, I think, just to say, well, if this is a platform shift or more than a platform shift, how do platform shifts tend to work? What are the things that we tend to see in it? And how many of those patterns can we see being repeated now? And of course, some of the patterns that come out of that are things like bubbles, but others are that lots of stuff changes inside the tech industry.

• 2:56Benedict Evans?

  And there are winners and losers and people who were dominant tend up becoming irrelevant. And then there are new billion trillion dollar companies created. But then there's also, what does this mean outside the tech industry? Because if we think back over the last waves of platform shifts, there were some industries where this changed everything and created and uncreated industries. And there are others where this was just kind of a useful tool. So, you know, if you're in the newspaper business, the last 30 years looked very different to if you were in the cement business where the internet was just kind of useful, but didn't really change the nature of your industry very much. And so what I tried to do is give people a sense of, well, what is it that's going on in tech? How much money are we spending? What are we trying to do? What are the unanswered questions?

• 3:40Benedict Evans?

  What might or might not happen within the tech industry? But then outside technology, how does this tend to play out? What seems to be happening at the moment? How is this manifesting into tools and deployment and new use cases and new behaviors?

• 3:58Benedict Evans?

  And then as we kind of step back from all of this, again, how many times have we gone to all of this before? It's funny, I went on a podcast this summer and I, the sort of opening line, I said something like, well, I'm a centrist, I think this is as big a deal as the internet or smartphones, but only as big a deal as the internet or smartphones. And there's like 200 YouTube commenters under these saying this more and he doesn't understand how big this is. And I think, well,

• 4:19Erik Torenberg?

  This is pretty big.

• 4:20Benedict Evans?

  The internet was kind of a big deal. It was kind of a big deal. And, you know, I sort of finished the day by looking at elevators because I live in an apartment building in Manhattan and we have an attended elevator, which means it's, there's a hand, there's no buttons, there's an accelerator and a brake and the doorman gets in and drives you to your floor. This is streetcar. And in the 50s, Otis deployed automatic elevators and then you get in and you press a button. And they marketed it by saying, it's got electronic politeness, which means the infrared beam. And today, when you get into an elevator, you don't say, ah, I'm using an electronic elevator. It's automatic. It's just a lift, which is what happened with databases and with the web and with smartphones.

• 5:05Benedict Evans?

  And I kind of think now, it's just funny, I did, I've done a couple of polls on this in LinkedIn and threads of is machine learning still AI? The term AI is a little bit like the term technology or automation. It only kind of applies when something's new. Once something's been around for a while, it's not AI anymore. So our databases certainly aren't AI. Is machine learning still AI? I don't know. And there's obviously there's like an academic definition where people say this guy's an idiot. No, of course I'm going to explain the definition of AI. But then in actual general usage, AI seems to mean new stuff.

• 5:34Erik Torenberg?

  Yeah. And AGI seems, you know, like new scary stuff.

• 5:39Benedict Evans?

  Yeah, it's funny, there's, I was thinking about this, there's an old theologian's joke that the problem for Jews is that you wait and wait and wait for the Messiah and he never comes. And the problem for Christians is that he came and nothing happened, you know, the world didn't change. There was still sin, all practical purposes, nothing happened. And AGI seems to be a little bit like this, like either it's already here and so you've got Samuel and saying, we've got PhD level researchers right now. And Demis Sissibus says, what? No, we don't. Shut up. And so either it's already here and it's just more software or it's five years away and will always be five years away.

• 6:13Erik Torenberg?

  Yeah, yeah. Let's compare back to previous platform shifts because some people look at, you know, some of the internet and say, hey, there were net new trillion dollar companies, Facebook and Google, that were created from it and just sort of all sorts of new emerging winners, whereas they look at something like mobile and say, hey, you know, there were big companies like Uber and Snap and Instagram and WhatsApp, but these were billion dollar outcomes or tens of billion dollar outcomes. But really, the big winners were, were in fact, Facebook and Google. And so in some sense, mobile perhaps was sustaining. You feel free to quibble with the definition of sustaining disruptive, but sustaining in the sense that maybe more of the value went to incumbents, companies that existed prior to the shift. I'm curious how you think about AI in light of that in

• 6:56Erik Torenberg?

  terms of is more of the gains going to come to net new companies like OpenAI and Anthropic and others that follow? Or are more of the gains going to be captured by Microsoft and Google and Meta and companies that existed prior?

• 7:08Benedict Evans?

  So I think there's several answers to this. One of them is like, you kind of have to be careful about like framings and structures and things because you end up arguing about the framing and the definition rather than arguing about what's going to happen. And they're all useful, but they've all got holes in them. And, you know, what mobile did was, you know, there's a bunch of things that it changed fundamentally. It shifted us from the web to apps, for example. And it gave everybody on the world a pocket computer. So even today, there's less than a billion consumer PCs on earth and there's something between five and six billion smartphones. And it made possible things that would not have been possible without it, whether that's TikTok or arguably, I think, things like online dating. And you can map those against dollar value. You can also map those against

• 7:52Benedict Evans?

  kind of structural change in consumer behavior and access to information and things. And I think you could certainly argue that Meta would be a much smaller company if it wasn't for mobile, for example. So you can kind of argue the puts and calls on this stuff a lot. There's certainly not all platform shifts are the same. And, you know, you can do the sort of standard sort of teleology of say, well, there were mainframes and then PCs and then the web and then smartphones. But you kind of want to put SaaS in there somewhere and you kind of want to put open source in there and maybe you want to put databases. And so these are kind of useful framing. But they're not predictive. They don't tell you what's going to happen. They just kind of give you one way of understanding what seems some of the patterns that we have here. And of course, the big debate around generative AI is just another platform shift, or is it something more than that?

• 8:36Benedict Evans?

  And of course, the problem is we don't know and we don't have any way of knowing other than waiting to see. So this may be as big as PCs or the web or SaaS or open source or something. Or maybe as big as computing and then you've got the very over excited people living in group houses in Berkeley who think this is as big as fire or something. Well, great. But this is pretty new companies. I mean, you go back to the mobile. There was a time when people thought that blogs were going to be different to the web. Which seems weird now, like Google needed like a separate blog search. This was seriously, this was a thing. There was a time when it was really not clear, and I think you kind of generalize this point. You go back to the internet in the mid nineties. We kind of knew this was going to be a big thing. We didn't really know it was going to be the web. So before that, we didn't know it was going to be the internet.

• 9:18Benedict Evans?

  We knew there were going to be networks, but it wasn't clear it was going to be the internet. Then it wasn't clear it was going to be the web. Then it wasn't really clear how the web is going to work. And when Netscape launched, like Mark Zuckerberg was in junior high or elementary school or something and Larry and Sergey were students and like Amazon with a bookstore. So you can know it, but not know it. And you could make the same point about smartphones. Like it was, we knew everyone was going to have an internet connected thing in their pocket. But it was not clear it was basically going to be a PC from this has been PC company from the eighties and a search engine company. It was not clear it wasn't going to be Nokia or Microsoft. See, I think you have to be super careful in making kind of deterministic predictions about this. What you can do is say, well, when this stuff happens, everything changes.

• 10:04Benedict Evans?

  And that's happened five or 10 times before.

• 10:06Erik Torenberg?

  I'm curious how you got conviction in this idea or we got the prediction that, hey, AI is going to be as big as the internet, which of course is pretty big, but I'm not yet I Benedict, I'm not yet at the conviction that it's going to be any bigger. I'm curious what sort of inspires that sort of, you know, sort of statement. And then also what might change your mind either way, you know, that it might not be as big as the internet. Because of course that internet was obviously very big, but also that, hey, perhaps it might be bigger.

• 10:34Benedict Evans?

  Well, so I think, you know, I don't want to, I made a diagram of kind of S-curves kind of going up the slide and someone said, well, what's the axis on this diagram? You know, I don't want to kind of get into, you know, is this is it 5% bigger than than internet or is it 20% bigger? I think the question is more like, is it another of these industry cycles or is it a much more fundamental change in in what technology can be? Is it more like computing or electricity as a sort of structural change rather than here's a whole bunch more stuff we can do with computers? I think that's sort of the question. And there's a funny sort of disconnect, I think, in in looking at debates about this within tech, because, you know, I watched this, this is one of the OpenAI livestreams a couple of weeks ago. And they spend the first 20 minutes talking about how they're going to have like human

• 11:18Benedict Evans?

  level PhD level AI researchers like next year. And then the second half of the stream is, oh, and here's our API stack that's going to enable hundreds and thousands of new software developers just like Windows. And in fact, literally quote Bill Gates. And you think, well, those can't kind of both be true. Like, either I've got a thing which is a PhD level AI researcher, which by implication is like a PhD level CPA. Yeah. Or I've got a new piece of software that does my taxes for me. And well, which is it? Either this thing is going to be like human level and some, and that's a very, very challenging, problematic, complicated statement. Or this is going to let us make more software that can do more things a software couldn't be.

• 12:00Benedict Evans?

  And I think there's a real like schizophrenia in conversations around this because like scaling laws and it's going to scale all the way. And meanwhile, I'm going to here, look how good it is at writing code. And again, like, well, is it writing code or do we not need software anymore? Because in principle, if the models keep scaling, nobody's going to write code anymore. You'll just, I say to the model, like, hey, can you do this thing for me? Yeah. Is it a little bit of a hedge or like a sequencing thing or? Well, it's some of it's a sequencing thing, but, you know, in principle, if you think this stuff is going to keep scaling, like, why are you investing in a software company? Yeah. Like, you know, people just have this like God in a box that can do everything. And, and, and I think this is, this is the, the, the kind of the funny kind of challenge. And this is, I think, the only, the fundamental way that this is different from previous platform shifts.

• 12:44Benedict Evans?

  Is that with the internet or with mobile or being deemed with mobile mainframes, like you didn't know what was going to happen in the next couple of years. You didn't know that I'm what Amazon would become and you didn't know how Netscape was going to work out. And you didn't know what next year's iPhone was going to be and 10 years ago when we cared about that. You kind of knew the physical limits, like you knew in 1995, you knew that telcos were not going to give everybody gigabit fiber next year. And you knew that the iPhone wasn't going to like have a year's battery life and unroll and have a projector and fly or whatever. But we don't know the physical limits of this technology because we don't really have a good theoretical understanding of why it worked so well. Nor indeed do we have a good theoretical understanding of what human intelligence is. And so we don't know how much better it can get.

• 13:26Benedict Evans?

  So you can do, you could do a chart and you could say, well, you know, this is the raid map for Maydans and this is the raid map for DSL. And this is how fast DSL will be. And then you can make some guesses about how quickly telcos will deploy DSL. And then you can say, well, clearly we're not going to be able to replace broadcast TV. We're streaming in 1998. But we don't have an equivalent way of modeling this stuff to know what is the fundamental capability of it going to look like in three years. Which gets you to these kind of slightly vibes based forecasting where no one really knows. So, you know, Jeff Inton says, well, I feel like, and Demis Hassabis says, well, I feel like, but no one knows.

• 14:06Erik Torenberg?

  And then Karpathi goes into Rakesh's podcast and says, I feel like, you know, it's a decade out.

• 14:11Benedict Evans?

  Yeah, I know. Well, I saw this, this meme of, of what's his name, Ilya Sutskever, but like, well, he says like, the answer will reveal itself. And somebody like memed, I was, I'm going to say Photoshop, but of course it wouldn't have been Photoshop, turned him into a Buddhist monk, wearing like an orange, like an orange outfit. The future will reveal itself. Well, but this is the problem. We don't know. We don't have a way of modeling this.

• 14:32Erik Torenberg?

  Yeah. And so let's connect this to sort of the, you know, the upfront investment that some of these companies are making. If he goes, we don't know, you know, is there a risk of over investment leading to some, you know, potential, uh, you know, bubble like mechanics? Or how, how, how do you think about that, that question?

• 14:50Benedict Evans?

  Well, deterministically, very new, very, very big, very, very exciting, world-changing things tend to lead to bubbles. Yeah. And you don't think anybody would dispute that you can see some bubbly behaviour now. And you can argue about what kind of bubble, but again, that doesn't have very much predictive power. And one of the features of bubbles is that when everything's going, everything goes up all at once and everyone looks like a genius and everyone leverages and cross-leverages and does circular revenue and that's great until it's not. And then you get a kind of a ratchet effect as it goes back down again. So yeah, if we're not in a bubble now, we will be. I remember Marc Andreessen saying, you know, 1997 was not a bubble, 98 was not a bubble, 99 was a bubble.

• 15:34Benedict Evans?

  Are we in 97 now or 98 or 99? You know, if we could predict that, you know, we'd live in a parallel universe. I think, you know, but to the, there's, I suppose, maybe kind of two more specific, more tangible answers to this. The first of them is, we don't really know what the compute requirements of this stuff are going to be. And forecasting that, except like more, and forecasting that feels a lot like trying to forecast like bandwidth use in the late 90s. Imagine if you were trying to do the algebra on that. You say, well, this many users, you know, how much bandwidth does a webpage use? How will that change? How will that change if bandwidth gets faster? What happens with video? What kind of video? What bandwidth? What bitrate of video?

• 16:19Benedict Evans?

  How long do people watch a video? How much video? And then you'd like, you could build the spreadsheet and it would tell you what bitrate would, what global bandwidth consumption would be in 10 years. And then you could try and use that to back calculate how many routers is this going to sell? And you could get a number, but it wouldn't be the number. You know, there'd be a hundredfold range of possible outcomes from that. And you could, you know, you could make the same point about algebra of consumption now. So, you know, right now, we have a bunch of rational actors saying, well, this stuff is transformative and a huge threat. And we can't keep up with demand for it now. And as far as we know, the demand is going to keep going up.

• 17:00Benedict Evans?

  And, you know, we've had a variety of quotes from all of the hyperscalers, basically saying the downside of not investing is bigger than the downside of over-investing.

• 17:10Benedict Evans?

  That kind of thing always works well until it doesn't. Yeah. And I saw a slightly strange quote from Mark Zuckerberg saying, well, if it turns out that we've over invested, we can just resell the capacity. And I thought, let me just like stop you there, Mark. Because if it turns out that you can't use your capacity, everybody else is going to have loads of spare capacity as well. Yeah. All these people now who are desperate for more capacity, if it turns out we can get the same results for hundreds of the compute, that will be true for everyone else too, not just you. Yeah. So, yeah, you know, in a investment cycle like this, you tend to get over investment.

• 17:51Benedict Evans?

  But then after that, there's very limited predictions you can make about what's going to happen. I think the more useful kind of way to look at this is to think, well, you've got these kind of transformative capabilities that are already increasing the value of your existing products, if you're Google or Meta or Amazon. And you're going to be able to use them to build a bunch more stuff. And why would you want to let somebody else do that rather than you doing it, as long as you're able to keep funding and selling what you're building?

• 18:34Benedict Evans?

  Yeah. And it will turn out that, you know, we have an evolution of models in the next year that means you can get the same result for hundreds of the compute that you're using today, bearing in mind that it's already going down, like to pick your numbers 20, 30, 40 times a year. Yeah. But then the usage is going up. So you're in this very, as I said, it's like trying to predict bandwidth consumption in the late 90s, early 2000s. You can throw all the parameters out, but it doesn't get you to something useful. You just kind of need to step back and say, yeah, but is this internet thing any good?

• 19:07Erik Torenberg?

  Well, yeah, because I'm curious if the bottlenecks are, if you see them as more on the supply side or the demand side, you know, more technical constraints, or is just, is AI any good? Are there enough use cases to justify the type of spend? What are you seeing and what are you predicting?

• 19:24Benedict Evans?

  So maybe two answers to this question. The first of them is, I think we've had this sort of bifurcation of what all the questions are. So there were now very, very detailed conversations about chips and then very, very detailed conversations about data centers and about funding for data centers and then about what is a new enterprise SaaS company built on AI? What margins will it have? And how much money does it need to raise? And so there are venture capital conversations. And so there are many different conversations within which, like, I don't know anything about chips, you know, I can spell ultraviolet, but like, I don't know what like an ultraviolet process is. It's like, it's more, it's more, more violet. So I don't know.

• 20:06Benedict Evans?

  And so you've got this, you know, it's like the Milton Friedman line, no one knows how to build a pencil. You've got, you know, we've got this, you know, it's turned into deployment. I think a second answer might be, I think there's two kinds of AI deployment, generative AI deployment. One of them is there are places where it's very easy and obvious right now to see what you would do with this, which is basically software development, marketing, point solutions for many very boring, very specific enterprise use cases. And also basically people like us, which are people who have kind of very open, very free form, very flexible jobs with many different things and people who are always looking for ways to optimize that.

• 20:51Erik Torenberg?

  Yeah.

• 20:52Benedict Evans?

  And so you get people in Silicon Valley who are like, you know, I spend all my dead time in chat GPT. I don't use Google anymore. You know, I've replaced my CRM with this. And you kind of, and then you obviously people who like, if you're writing codes, this works really well. If you're in marketing, you know, all these stories of big companies where, you know, they're making 300 assets where they would have made 30. And then Accenture and Bain and McKinsey and Infosys and so on, sitting and solving very specific problems inside big companies. Then there's a whole bunch of other people who look at it and they're like, it's okay.

• 21:27Benedict Evans?

  And you go and look at the usage data and you see, okay, ChatGPT has got eight or nine hundred million weekly active users. Five percent of people are paying. And then you go and look at all the survey data and you know, it's very fragmented and inconsistent, but it all sort of points to like, something like ten or fifteen percent of people in the developed world are using this every day. Another twenty or thirty percent of people are using it every week. And if you're the kind of person who is using this for hours every day, ask yourself why five times more people look at it, get it, know what it is, have an account, know how to use it, and can't think of anything to do with it this week or next week. Why is that?

• 22:12Benedict Evans?

  Is it because it's early? And it's not like a young people thing either, incidentally. And so is that just because it's early? Is it because of the error rates? Is it because you have to map it against what you do every day? And one of the analogy I always used to use, which isn't in the current presentation, I've been used in previous presentations, is imagine you're an accountant and you see software spreadsheets for the first time. This thing can do a month's of work in ten minutes, almost totally.

• 22:43Benedict Evans?

  You want to change, you want to recalculate that DCF, that ten year DCF with a different discount rate. I've done it before you finished asking me to. And that would have been like a day or two days or three days of work to recalculate all those numbers. Great. Now imagine you're a lawyer and you see it. And you think, well, that's great. My accountant should see it. Maybe I'll use it next week when I'm making a table of my billable hours, but that's not what I do all day. And Excel doesn't use do things that a lawyer can do every day. And I think there's this other class of person that's like, I'm not sure what to do with this. And some of that is habit, some of that is like realizing, no, instead of doing it that way, I could do it this way.

• 23:27Benedict Evans?

  But that's also what products are. Like every entrepreneur who comes into A16Z, when I was there from two thousand and fourteen to two thousand and nineteen, and I'm sure now, like, you could look at any company that comes in and say, that's basically a database. That's basically a CRM.

• 23:45Benedict Evans?

  That's basically Oracle or Google Docs. Except that they realized there's this problem or this workflow inside this industry and worked out how to use a database or CRM or basically concepts from five, 10, 20 years ago and solve that problem for people in that industry and go in and sell it to them and work out how they can get it to use it. And so this is why, you know, you look at data on this, that, you know, depending on how you count it, the typical big company today has four to 500 SaaS apps in the US. Four to 500 SaaS applications and they're all basically doing something you could do in Oracle or Excel or email.

• 24:26Benedict Evans?

  And that's the other side, I'm monologuing, I'm afraid, but like this is the other side of what is, what do you do with these things? Do you just go to the bot and ask it to do a thing for you? Or does an enterprise salesperson come to your boss and sell you a thing that means now you press a button and it analyzes this process that you needed, that you never realized you were even doing?

• 24:50Erik Torenberg?

  Yeah.

• 24:51Benedict Evans?

  And I feel like that's, I mean, that's why there are AI software companies.

• 24:57Erik Torenberg?

  Right.

• 24:58Benedict Evans?

  Really? And isn't that what they're doing? They're unbundling ChatGPT, just as the enterprise software company of 10 years ago was unbundling Oracle or Google or Excel.

• 25:06Erik Torenberg?

  Do you have the view that, you know, what Excel did for, for, for accountants, um, you know, we're, we're sort of, AI is now doing for, for coders, um, and developers, but hasn't quite figured out that sort of, you know, daily critical workflow for, for other job positions. And so it's unclear for people who aren't developers, you know, why I should be using this for many, many hours a day or?

• 25:28Benedict Evans?

  I think there's a lot of people who don't have tasks that work very well with this.

• 25:34Erik Torenberg?

  Yeah.

• 25:35Benedict Evans?

  And then there's a lot of people who need it to be wrapped in a product and a workflow and tooling and UX and someone to come and say, hey, have you realized you could do it with this?

• 25:46Benedict Evans?

  I had this conversation with, um, in the summer with, with, with Balaji, who's another base, former A16Z person. And he was making this point about validation, that can you, because these things still get stuff wrong and people in the valley often kind of hand wave this away. But, you know, there are questions that have specific answers where it needs to be the right answer or one of a limited set of right answers. Can you validate that mechanistically? If not, is it efficient to validate it with people? So, you know, the marketing use case, it's a lot more efficient to get a machine to make you 200 pictures and then have a person look at them and pick 10 that are good than to have people make 10 good images or 100, even more, if you're going to make 500 images

• 26:28Benedict Evans?

  and pick 100 that are good, that's a lot more efficient than having a person make 100 images. But on the other hand, if you're doing something like data entry, and I wrote something about this, about OpenAI launched deep research. OpenAI launched deep research, their whole marketing case is it goes off and collects data about the mobile market. I used to be a mobile analyst, the numbers are all wrong.

• 26:48Benedict Evans?

  Their use case of look how useful this is, their numbers are wrong. And in some cases, they're wrong because they've literally transcribed the number incorrectly from the source. In other cases, it's wrong because they've used a source that they shouldn't have used. But like if I'd asked an intern to do it for me, then an intern would probably have picked that. And to the point about verification, if you're going to do data entry, if I'm going to ask a machine to copy 200 numbers out of 200 PDFs and then I'm going to have to check all 200 of those numbers, I might as well just do it myself.

• 27:20Erik Torenberg?

  Yeah.

• 27:21Benedict Evans?

  So you've got like a whole swirling matrix of how do you map this against existing problems? But the other side of it is, how do you map this against new things that you couldn't have done before?

• 27:38Benedict Evans?

  And this comes back to my point about platform shift, because you know, I see people looking at ChatGPT or looking at generative AI and saying, well, this is useless because it makes mistakes. And I think that's kind of like looking at like an Apple II in the late 70s and saying, could you use these to run banks? To which your answer is no. But that's kind of the wrong question.

• 27:58Erik Torenberg?

  Right.

• 27:58Benedict Evans?

  Like, could you build video edit, professional video editing inside Netscape? No. But that's the wrong question.

• 28:06Erik Torenberg?

  Right.

• 28:06Benedict Evans?

  And later, yeah, 20 years later, you can, but that meanwhile, it does a whole bunch of other stuff, and the same with mobile. Like, can you, can you use mobile to replace, you know, your, you know, your five-screen professional programming? No, therefore it can't replace PCs. Well, guess what? Five billion people have got a smartphone and seven or eight hundred million people have got a consumer PC. So it kind of did.

• 28:24Erik Torenberg?

  Yeah.

• 28:25Benedict Evans?

  But did a different thing. And the point of this is like the new thing, this is, you know, the disruption framing you mentioned earlier, the new thing is generally not very good or terrible at the stuff that was important to the old thing, but it does something else.

• 28:36Erik Torenberg?

  Right.

• 28:36Benedict Evans?

  And a lot of the question is, okay, it may not be very good at doing. There's a class of old tasks that generative AI is good at. There's also a lot, many more old tasks that generative AI is maybe not very good at. But then there's a whole bunch of other things that you would never have done before. The generative AI is really, really good at. And then how do you find those or think of those? And how much of that is the user thinking of it faced with a general purpose chatbot? How much of that is the entrepreneur saying, hey, I've just realized that there's this thing that I can do that you couldn't do before. And here you are, I've given you a product with a button that will do it for you.

• 29:17Erik Torenberg?

  Right.

• 29:18Benedict Evans?

  And that's why there are software companies.

• 29:20Erik Torenberg?

  Right. And, and on, on mobiles, you know, some of the new use cases, you know, we're, you know, getting in strangers cars. You know, we mentioned Lipton Uber or sort of, you know, dating people you met via an app or um sort of um, you know, lending your spare bedroom out, um, you know, et cetera. And, and those were net new companies that, that, that, you know, were built around those behaviors. And I think for AI, there's still questions of, you know, what are those net new behaviors? We're, we're starting to see some in terms of, you know, people engaging and talking with, you know, chatbots instead of humans or, or, um, or in addition. And then there's a question of, hey, are these done by the model providers that they currently exist? Or are these done by, you know, net new companies, both on, you know, sort of enterprise and consumer?

• 30:02Benedict Evans?

  Well, this is always the question is how far up the stack does a new thing go? And, you know, I was, I was talking about this with another former, former A16Z person. He pointed out that like in the, the mid nineties, um, people kind of argued that, well, you know, the operating system does all of it. And the, at Windows apps are basically just kind of thin win thirty two wrappers.

• 30:20Erik Torenberg?

  Yeah.

• 30:21Benedict Evans?

  And, you know, Office is basically just, you know, a thin win thirty two wrapper. Like all the important stuff is being done by the OS, whether it's, you know, the document management and printing and storage and display, which all stuff that used to be done by apps. Like in DOS, the apps had to do printing, the apps had to manage a display. We moved to Windows, like ninety percent of the stuff that the app used to do is now being done by Windows. And so Office is just like a thin wind thirty two wrapper and all the half stuff has been being done by the OS. And it turned out, well, that was, again, it's like frameworks are useful, but that's not made, maybe not a useful way of thinking about what's going on. And the same thing now, like, how much does this need single dedicated understanding of how that market it works, or what that market is and what you would do with that?

• 31:03Benedict Evans?

  I mean, I remember when we were at A sixteen Z, there was an investment in a company called Everlaw, which is cloud, legal discovery in the cloud. And so machine learning happens. And so now they can do translation. Are they worried that lawyers are going to say, well, we don't need you guys anymore. We're just going to go and get a translate app and a sentiment analysis app from AWS. Like, no, that's not how law firms work. Law firms want to buy a thing that solves that want to buy legal discovery, software management, you know, they don't want to go out and write their own, do API calls. I mean, very, very big law firms might, but you know, typical law firm isn't going to do that. People buy solutions, they don't buy technologies. And the same thing here, like, how far up the stack do these models go? How much can you turn things

• 31:47Benedict Evans?

  into a widget? How much can you turn things into an L M request? And how much node does it turn out that you need that dedicated UI? Funny thing is, you can see this around Google, because Google had this whole idea that everything would just be a Google query, and Google would work out what the query was. And guess what? You know, now you want you, there's Google flights is not a Google query. You know, they use certain point. And one of the, one of the interesting things about this, and I think it's interesting to think about what a GUI is doing, that some of what a GUI is doing, and the obvious thing that a GUI is doing, is that it enables Office to have five hundred application, five hundred features, and you can find them all. Well, at least it's, you don't have to memorize

• 32:28Benedict Evans?

  keyboard commands. You can now have effectively infinite features, and you can just keep adding menus and dialog boxes, and eventually you run out of screen space for dialog boxes. But you can have hundreds of features without people needing to memorize keyboard commands. But the other side of it is, you're in that dialog box, or you're in that screen, in that workflow, in Workday, or Salesforce, or whatever the enterprise software is, whatever any software, or the airline website, or Airbnb, or whatever it is. And there aren't six hundred buttons on the screen, there's seven buttons on the screen, because a bunch of people at that company have sat down and thought, what is it that the users should be asked here? What questions should we give them? What choices should there be at this point in the flow?

• 33:10Benedict Evans?

  And that reflects a lot of institutional knowledge and a lot of learning and a lot of testing, a lot of really careful thought about how this should work. And then you give somebody a raw prompt and you just say, okay, you just tell the thing how to do the thing. And you're like, but you've kind of got to shut your eyes, screw your eyes up and think from first principles. How does this all of this work? It's kind of, I always used to talk about machine learning as giving you infinite interns. You know, imagine you've got a task and you've got an intern and the intern doesn't know what venture capital is.

• 33:47Erik Torenberg?

  How helpful are they going to be?

• 33:47Benedict Evans?

  And like they're, and they don't know the company's published quarterly reports. And that we've got a Bloomberg account that lets us look up multiples. And that then you should probably use PitchBook for this data. And rather than using Google, this is my point about deep research, like, no, you should use this source and not that source.

• 34:13Benedict Evans?

  Do you want to have to work that out from scratch? Or do you want a bunch of people who know a lot about this stuff to have spent five years working out? What the choices should be on the screen for you to click on it? I mean, it's the old user interface saying the computer should never ask you a question that you should have to work out, that it should know by itself. You go to a blank, raw chatbot screen. It's asking you literally everything. It's not just asking you one question. It's asking you absolutely everything about what is it is that you want? And how you're going to work out what, how to do it.

• 34:44Erik Torenberg?

  The, and so, you know, you're mentioning chat, you know, you wrote about how chat GPT isn't sort of a product as much as a chatbot, as disguised as a product. I am curious, you know, when we sort of look back at this sort of, you know, platform shift. Do you think that? There will be another sort of iPhone, sort of Excel-esque product that kind of defines the feature, the sort of platform shift in a way that ChatGPT won't, or is it sort of that the world has to catch up to how to use ChatGPT or something like that?

• 35:19Benedict Evans?

  So both of these can be true, because there was a lot of like, it took time to realize how you would use Google Maps and what you could do with Google and how you could use Instagram. And all of these products have evolved a huge amount over time. So some of it is like you grow towards realizing what you could do with this. Like you realize that's just a Google query now. You realize that you could just do it like that. And you realize I spent, you know, hours doing this and I just realized, oh, I could actually just make a table.

• 35:43Erik Torenberg?

  Yeah.

• 35:44Benedict Evans?

  The other side of it is then, but you're still then expecting people to work it out themselves from first principles. And, you know, it's kind of useful to have somebody really, a hundred, a thousand, 10,000 really clever people sitting and trying to work out what those things are and then showing it to you as a product. I think another side of this is like, you know, there were always these precursors. So like there were lots of other things before Instagram.

• 36:11Erik Torenberg?

  Yeah.

• 36:12Benedict Evans?

  You know, YouTube didn't start as YouTube. It started as video dating, I think. There were lots of attempts to do online dating that all kind of worked until Tinder kind of pulled the whole thing inside out. And so there were always lots of things, what's the phrase, local maximum. In fact, this is where we were, particularly with the iPhone. Before, because I was working in mobile for the previous decade, it didn't feel like we were waiting for a thing. It felt like it was kind of working. Like every year, the networks got faster and the phones got better and you got a little bit better every year. And we had apps and we had app stores and we had 3G and we had cameras and stuff seemed to be, you know, every year was a bit better. And then the iPhone arrives and it just, you

• 36:56Benedict Evans?

  know, but you know. Blow the chart, you've got this line doing this and then there's a line that does that. Although, remember, also, the iPhone took two years before it worked, because the price was wrong and the feature set was wrong and the distribution model didn't quite work. And so, yeah, you can think everything is going well and then something comes along and you realise, no, oh, no, no, no, that's... Which is the same for Google. Search was a thing before Google, it just wasn't very good. So there was lots of social stuff before Facebook and that was the thing that catalyzed it. So I just think deterministically, this whole thing is so early that it feels like, of course, there are going to be dozens, hundreds of new things. Otherwise, A, C, D should just shut down and give the money back to

• 37:41Benedict Evans?

  the LPs.

• 37:42Erik Torenberg?

  Right.

• 37:43Benedict Evans?

  The fan models will just do the whole thing. And I don't think you're going to do that, at least I hope not.

• 37:47Erik Torenberg?

  No, no, no. If we have any regrets from the last few years, it's not going bigger. I think we didn't fully appreciate how much specialisation there would be across, whether it's voice or image generation, or take any sort of sub-sector that there would be, net new companies created that would be better than the model providers. There would be even multiple model providers that in every category, one thing we've always, in the Web 2 era, we've always been on the category winner, right? And the category winner would take most of the market. But these markets are so big and there's so much expertise and specialisation that, one, there can be winners in every category, it's not just

• 38:30Erik Torenberg?

  the model providers taking everything, but that even in every category, including the model providers, there can be multiple winners and increasing specialisation. And the markets are just big enough to contain multiple winners.

• 38:42Benedict Evans?

  I think that's right. And I think the categories themselves aren't clear.

• 38:49Erik Torenberg?

  Right.

• 38:50Benedict Evans?

  And many things, you think this is a category and it turns out, no, it was actually that whole other thing. And the categories get unbundled and bundled and recombined in different ways. I remember I was a student in 1995 and I think I had like four or five different web browsers on my PC, web servers on my PC. Because, I mean, Tim Berners-Lee's original web browser had a web editor in it, because he thought this was kind of like a network drive and it was a sharing system, not really a publishing system. So you would have your webpages on your PC and you'd leave your PC turned on and that would be how your colleagues would look at your Word documents.

• 39:25Benedict Evans?

  Or your webpages. And so again, like, we just don't know how. And I just kind of keep coming back to this point. I feel like most of the questions we're asking at the moment are probably the wrong question. And picking up on a strand within what you just said, though, the interesting, one of the things I'm sort of thinking about a lot is looking at OpenAI. Because I'm sort of fascinated by disconnections. And we've got this interesting disconnect now, which is that, you know, if you look at the benchmark scores. So you've got these general purpose benchmarks where the models are basically all the same. And if you're, yes, if you're spending hours a day and then you've got this opinion about, oh, I like Claude's tone of voice more than, I like GPT and I like GPT five point one more than GPT four point nine or whatever the hell it's called. If you're using this once a week, you really don't notice this stuff. And the benchmark scores are all roughly the same.

• 40:11Benedict Evans?

  And but the usage isn't, it's basically the only, Claude has basically no consumer usage, even though on the benchmark score it's the same. And then it's ChatGPT and then halfway down the chart, it's Meta and Google. And the funny thing is, you know, that you read all the AI newsletters, then Meta's lost, they're out of the game, they're dead. Mark Zuckerberg is spending a billion dollars a researcher to get back in the game. But from the consumer side, well, it's distribution. And the interesting thing here is that you've got, what I'm kind of circling around is. If the model for a casual consumer user certainly is a commodity. And there's no network effects or winner takes all effects yet. They may, those may emerge, but we don't have them yet.

• 40:56Benedict Evans?

  And things like memory aren't network effects or stickiness, but they can be copied.

• 41:04Benedict Evans?

  How is it that you compete? Do you just compete on being the recognized brand and adding more features and services and capabilities and people just don't switch away? Which is kind of what happened with Chrome, for example. There's not a network effect for Chrome, but and it's not actually any better much, maybe it's a bit better than Safari, but you know, you use Chrome because you use Chrome. Or is it that you get left behind on distribution? Or network effects that emerge somewhere else?

• 41:39Benedict Evans?

  And meanwhile, you don't have your own infrastructure. So I suppose what I'm getting at is like you've got these eight or nine hundred million weekly active users, but you don't have, but that feels very fragile because all you've really got is the power of the default and the brand. You don't have a network effect. You don't really have feature lock in. You don't have a broader ecosystem. You also don't have your own infrastructure. So you don't control your cost base. You don't have a cost advantage. You get a bill every month from Satcha. So you've kind of got to scramble as fast as you can in both of those directions to on the one side, build product and build stuff that on top of the model, which is our earlier conversation, is it just the model?

• 42:22Erik Torenberg?

  Yeah.

• 42:22Benedict Evans?

  Now you've got to build stuff on top of the model in every direction. It's a browser. It's a social video app. It's an app platform. It's this, it's that. It's like, you know, the meme of the guy with the map with all the strings on it.

• 42:34Erik Torenberg?

  Yeah.

• 42:35Benedict Evans?

  You know, it's all of these things. We're going to build all of them yesterday. And then in parallel, it's infrastructure. Like, and you know, we do, we've got to deal with OpenAI. We're sorry, deal with Nvidia, with Broadcom, with AMD, with Nvidia, with Oracle, and well, with petri dollars, because you're kind of scrambling to get from this amazing technical breakthrough and these eight hundred, nine hundred million wows to something that has like really sticky, defensible, sustainable business value and product value.

• 43:10Erik Torenberg?

  Yeah. And so as you're evaluating the competitive landscape among the hyperscalers, what are the questions that you're asking, that you think are going to be most important in determining, you know, who's going to gain, you know, durable competitive advantages or how this competitive is going to, competition is going to play out?

• 43:32Benedict Evans?

  Well, this kind of comes back to your point about sustaining advantage. And we talked about Google, like if we think about the shift to particularly shift to mobile, for Meta, this turned out to be transformative, like it made the products way more useful.

• 43:44Erik Torenberg?

  Yeah.

• 43:46Benedict Evans?

  For Google, it turned out mobile search is just search.

• 43:52Benedict Evans?

  And maps changed probably and YouTube changed a bit. But basically for Google search, Google search is search. And the web search is just means more people doing more search more, more of the time.

• 44:03Benedict Evans?

  And the default view now would seem to be, well, Gemini is as good as anybody else. Next week, like the new model, I haven't looked at the benchmarks for G P T five point one, which is out today. Is it better than Gemini? Probably. Will it still be better next month? No.

• 44:20Benedict Evans?

  So that's a given, like you've got a frontier model, fine. What does that cost? It costs you, pick a number, two hundred and fifty billion dollars a year, a hundred billion dollars a year. What's this? What's this earlier conversation about cap base? Okay, so Google can pay that because they've got the money. They've got, they've got the cash flow from everything else. And so you do that and your existing products get, you optimize search, you optimize your ad business, you build, you know, you build new experiences. Maybe you invent the new, the iPhone of A I. Maybe there is no iPhone of A I. Maybe someone else does it and you do an Android and just copy it.

• 44:54Benedict Evans?

  So, fine, it's a new mobile. We'll just carry on search as such. A I is A I. We'll do the new thing. We'll make it a feature. We'll just carry on doing it. The matter that feels like there are bigger questions on what this means for search or what it means for content and social and experience and recommendation, which makes it all that more imperative that they have their own models, just as it is for Google.

• 45:18Benedict Evans?

  For Amazon, okay, well, on the one side, it's commodity infra and we'll sell it as commodity infra. And on the other side, I mean, maybe, maybe, maybe stepping back. If you're not a hyperscaler, if you're a web publisher, a marketer, a brand, an advertiser, a media company, you could make a list of questions. Like you don't even know what the questions are right now. What is this, what happens if I ask a chatbot a thing instead of asking Google? Even if it's Google, from Google's point of view, well, I'll ask Google chatbot, it's fine. But as a marketer, what does that mean? What happens? If I ask for a recipe and the LLM just gives me the answer, what does that mean if my business is having recipes?

• 46:02Benedict Evans?

  Do you have a kind of split between, and this is also an Amazon question? How does a purchasing decision happen? How does this decision to buy a thing that I didn't know existed before happen? What happens if I wave my phone at my living room and say, what should I buy? Where does that take me in ways that it wouldn't have taken me in the past? So there's a lot of questions further downstream, and that goes upstream to Meta and to stomach stent for Google. It's a much bigger question in the long term for Amazon. Do LLMs mean that Amazon can finally do really good at scale recommendation and discovery and suggestion in ways that it couldn't really do in the past? Because of this kind of pure commodity retailing model that it has.

• 46:44Benedict Evans?

  Apple, Apple's sort of off on one side. You know, interestingly, they produced this incredibly compelling vision of what Siri should be two years ago. It just turned out that they couldn't make it. Interestingly, nobody else could have made it either. You go back and watch the Siri demo that they gave and you think, okay. So we've got multimodal, instantaneous, on-device, tool-using, agentic, multi-platform e-commerce in real time with no prompt injection problems and zero error rates. Well, that sounds good.

• 47:15Benedict Evans?

  Has anyone got that working? Like, no. OpenAI, Google and OpenAI don't have that working. I mean, Google, I don't think Google or OpenAI could deliver the Siri demo that Apple gave two years ago. I mean, they could probably do the demo, but they couldn't like consistently reliably make it work. I mean, that demo, that product isn't in Android today.

• 47:33Benedict Evans?

  And Apple, I mean, Apple to me has the most kind of intellectually interesting question, which is.

• 47:40Benedict Evans?

  So I saw Craig Federighi make this point, which is like, we don't have our own chatbot. Fine, we also don't have YouTube or Uber.

• 47:49Benedict Evans?

  Explain why that is different, which is a harder question to answer than it sounds like. And of course, the answer is if this actually fundamentally changes the nature of computing, then it's a problem. If it's just a service that you use like Google, then that's not a problem. Which is kind of the point about, you know, where does Siri go? But the interesting counter example here would be to think about what happened to Microsoft in the 2000s, which is the entire dev environment gets away from them and no one builds Windows apps after like 2001 or something. But you need to use the internet. To use the internet, you need a PC. And what PC are you going to buy? Well, like Apple's like not really a player at that time. And of just getting back into the game, Linux is obviously not an option for any normal person.

• 48:31Benedict Evans?

  So you buy Windows PC. So basically, Microsoft loses the platform war and sells an order of magnitude more PCs. Like, well, not selling them, but an order of magnitude more Windows PCs as a result of this thing that Microsoft lost. And then it takes until mobile that like then they lose the device as well as the development environment. So here's this kind of question is if all the new stuff is built on AI and I'm accessing it in an app that I download from the App Store, to what extent is this a problem for Apple?

• 49:00Benedict Evans?

  And what would have to, you would need a much more fundamental shift in what it was that was happening for that to be a problem for Apple. And even if you take like the, you know, not the like the full like the Rapture arrives and we'll just kind of go and live sleep in pods like the guys in Up. Not Up, um, yes, what is it? The one with the robot that's capturing the trash? Which one is that? Wally. Wally. Wally, yeah. You know, the guys in the pods in that movie. Maybe we'll be the people, maybe we'll be like that in which case, fine. But like there's a sort of a mid case, which is like the whole nature of software changes and there are no apps anymore. And you just go and ask the LLM a thing. Fine. What is the device on which you ask the LLM a thing? Well, it's probably going to have a nice big color screen. And it's probably going to have like a one day battery life, probably use a microphone, probably a good camera.

• 49:47Benedict Evans?

  Kind of sounds like an iPhone.

• 49:51Benedict Evans?

  Am I going to buy the one that's a tenth of the price and just use the LLM on it?

• 49:57Benedict Evans?

  No, because I'll still want the good camera and the good screen and the good battery life. So it's not, there's a bunch of kind of interesting strategic questions when you start poking away. Well, what does this mean for Amazon? Those are completely different questions to what does it mean for Google? Or what does it mean for Apple? What does it mean to Facebook? Or what does it mean to Salesforce? What does it mean to Uber? And then right back to what we were saying at the beginning of this conversation, what does this mean for Uber? Well, their operations get X percent more efficient and now the fraud detection works. And, okay, maybe the autonomous cars, different conversation. But presumably autonomous cars, that's a whole other conversation. Otherwise, as Uber,

• 50:39Benedict Evans?

  what does this change? Well, not a huge amount.

• 50:43Erik Torenberg?

  I want to zoom out a little bit, this whole framing. So you've been doing these presentations for a while now. You've bumped them up two times because there's so much is changing. And one of the things you do in each presentation is your frame is for asking really great questions and chronicling what are the important questions to be asking. I'm curious, as you reflect, maybe post ChatGPT in 2022, or GPT-3, rather, the questions you were asking then, and you reflect on to now, to what extent do we have some direction on some of those questions? Or to what extent are they the same questions or new and different

• 51:21Erik Torenberg?

  questions? Or what is sort of your, if I woke up in a coma after reading your original presentation, let's say, the one after GPT-3 launch came out, and then seeing this one now, what were the most surprising things or things that we learned that updated those questions?

• 51:41Benedict Evans?

  So I think we have a lot of new questions this year.

• 51:48Benedict Evans?

  So I feel like you could make a list of as it might be half a dozen questions in spring of twenty three, like open source, China, Nvidia, scaling continue, what happens to images, how long does OpenAI's lead remain? And those questions didn't really change in twenty three and twenty four. And most of those questions are kind of still there, like the Nvidia question hasn't really changed, you know, the answer on China, the answer on, you know, will how many models will there be? The answer is, okay, there's going to be anybody who can spend a couple of hundred, you can spend a couple of billion dollars can have a frontier model. And that was pretty obvious, you know, in twenty three. It took a while for everyone to understand that. And big models and small models, will they

• 52:32Benedict Evans?

  have small models running on devices? No, because the small models, the capabilities keep moving too fast for the small models to shrink the small model onto the device. But those questions kind of didn't change for two, two and a half years. I think we now have, I think, a bunch of more product strategy questions. As you see, real consumer adoption and OpenAI and Google building stuff in different directions, Amazon going in different directions, Apple trying and obviously failing and then trying again to do stuff. There's some sense of like, there is something more going on in the industry than just, well, let's just build another model and spend more money. Yeah. There's more questions and more decisions now. There's also more questions outside of

• 53:15Benedict Evans?

  tech in certainly on like the retail media side of how do you start thinking about what you would do with this? And again, you know, classic framing in my deck is like, step one is you make it a feature and you absorb it and you do the obvious stuff. Step two is you do new stuff. Step three is maybe someone will come and pull a whole industry inside and completely redefine the question. And so you could kind of do like an imagine if here of like, step one is, you know, you're a manager at a Walmart in the Bay Area or DC or whatever it is. Step one is find me that metric. Step two is build me a dashboard.

• 53:54Benedict Evans?

  Step three is it's Black Friday and I'm running, managing a Walmart outside of DC. What should I be worried about? Like, and that might be the wrong one, but it's like, you know, step one for Amazon is you bought light bulbs, so here's, so you bought bubble wraps, so here's some packing tape. But what Amazon should actually be doing is saying, hmm, if this person is moving home, we'll show them a home insurance ad. Which is something that Amazon's correlation systems wouldn't get because they wouldn't have that in their purchasing data. And we're still very much at the like, we're still starting, we're still on the step one of that, but thinking much more, what would the step two, step three be?

• 54:38Benedict Evans?

  What would new revenue be for this other than just like simple dumb automation? What would new things that we would build with this be? Where would this actually like, might, might actually kind of redefine or change what the market might look like?

• 54:53Benedict Evans?

  And that's obviously a big question for anyone in the content business.

• 54:57Erik Torenberg?

  Yeah.

• 54:58Benedict Evans?

  You know, what does it mean if I can just go and ask an L L M this question? What kinds of content were predicated on Google routing that question to you? And what kind of questions, what kind of content isn't really that question? Like, do I want a bolognese recipe? Or do I want to hear Stanley Tucci talking about cooking in Italy? Like, do I just want the, do I want that skew? Or do I want to work out which product I should buy? Which is, Amazon is great at getting you the skew, terrible at telling you what skew you want. Do I just want the slide deck? Or do I want to spend a week talking to a bunch of partners from Bain about how I could think about doing this?

• 55:43Benedict Evans?

  Do I just want money? Or do I want to work with a 16Z's, you know, operating groups?

• 55:50Erik Torenberg?

  Yeah.

• 55:50Benedict Evans?

  Like, what is it that I'm doing here? And I think the, the L L M is starting, thing is starting to crystallize that question in lots of different ways.

• 56:01Erik Torenberg?

  Yeah.

• 56:01Benedict Evans?

  Like, what am I actually trying to do here? Do I just want a thing that a computer can now answer for me? Or do I want something else that isn't? Because the L L M's can do a bunch of stuff that computers couldn't do before.

• 56:12Erik Torenberg?

  Right.

• 56:13Benedict Evans?

  Is that thing that the computer couldn't do before my business? Or am I actually doing something else?

• 56:20Erik Torenberg?

  We're about to figure out what is the, in a much more granular way, what is the true job to be done for many, many of these?

• 56:27Benedict Evans?

  Yeah. And you know, going back to the internet, there was, you know, the sort of observation about newspapers is that newspapers looked in the internet and they talked about, you know, expertise and curation and journalism and everything else and didn't really say, well, we're a light manufacturing company and a local distribution and trucking company. And that was the bit that was the problem. And until the internet arrived, like that wasn't a conversation you thought about. And then the internet suddenly makes that clear and suddenly creates an unbundling that didn't exist before. And so there will be those kinds of like, you didn't realize you were that before, until an LLM comes along and points to, someone comes along with an LLM and says, oh, I can use this to do this thing that you didn't really realize was the basis

• 57:10Benedict Evans?

  of your defensibility or the basis of your profitability. I mean, it's like, you know, the joke about, you know, US health insurance that like, the basis of US health insurance profitability is making it really, really boring and difficult and time consuming. That's where the profits come from. Maybe it isn't. I don't know. I don't know. But for the sake of argument, say that's, that's your defensibility. Well, an LLM removes boring, time-consuming, mind-numbing tasks. So what industries are protected by having that and they didn't realize that? And these, you know, it's like, you could have asked these questions about the internet in the mid 90s or about mobile a decade later. And generally, you'd have half of the questions you'd have asked would have been the wrong questions in hindsight.

• 57:51Benedict Evans?

  I remember as a baby analyst in 2000, everyone kept saying, what's the killer use case for 3G? What's a good use case for 3G? And it turned out that having the internet in your pocket everywhere was the use case for 3G. But that wasn't the question that people were asking. And I'm sure that will be the thing now. Is there's so much that we will, that will happen and get built where you go and you realize, oh, that's how you would do this. You can turn it into that. And I'm sure you've had this experience seeing entrepreneurs, you know, you get every now and then they come in, they pitch the thing, you're like, oh, okay. You can turn it into that. It didn't, I didn't realize it was that.

• 58:36Erik Torenberg?

  Yeah, no, a hundred percent. My, my, my last question will get you out of here is um. If, if we're talking in two or three years from now, or you're, you're doing a presentation, you say, oh, this is actually bigger than the internet, or, or maybe this is like, like computing. What would need to be true? What, what would need to happen? What, what would, uh, what would evolve our thinking?

• 58:56Benedict Evans?

  I mean, I, I kind of, you know, sort of come back to my point about, you know, Jews and Christians, like the Messiah came, nothing happened.

• 59:09Benedict Evans?

  We forget, I mean, there's maybe two, two ways, very brief ways to think about this. One of them is, I think we forget how enormous the iPhone was and how enormous the internet was. And you can still find people in tech who claim that smartphones aren't a big deal.

• 59:25Benedict Evans?

  And this was the basis of people complaining about me like this idiot, he thinks like generative AI is big as those silly phone things like.

• 59:33Benedict Evans?

  Come on. I think another answer would be like.

• 59:39Benedict Evans?

  I don't want to get into the argument about, you know, what is the gross rating capability and benchmarks and, and all, you know, you can see lots of five hour long podcasts of people talking about this stuff. But the stuff we have now is not a replacement for an actual person.

• 59:58Benedict Evans?

  Outside of some very narrow and very tightly constrained guardrails, which is why, you know, Demis's point that like it's absurd to say that we have PhD level capabilities now.

• 1:00:12Benedict Evans?

  What, we would have to be seeing something that would really shift our perception of the capability of this stuff. So that it's actually a person as opposed to it can kind of do these people like things really well sometimes, but not other times. And it's, you know, it's a very tough conceptual. Kind of thing to think about because you know, I'm deliberate, I'm conscious, I'm not giving you a falsifiable answer, but I'm not sure what a falsifiable answer would be to that. When would you know whether this was AGI? You know, it's the Larry Tesla line, AI is whatever doesn't work yet. As soon as people say it works, people say, well, that's

• 1:00:54Benedict Evans?

  just not AI, that's just software. It's a, you know, it's a, it's an, and it becomes like a kind of a slightly drunk philosophy, Brad student, a kind of conversation as much as it is a technology conversation. Like, what would it, have you ever considered, Eric, that maybe we're not either?

• 1:01:14Erik Torenberg?

  Ooh.

• 1:01:16Benedict Evans?

  As a thought. It's, I, I, all I can say to give a tangible answer to this question is what we have right now isn't that. Will it grow to that? We don't know. You may believe it will. I can't tell you that you're wrong. We'll just have to find out.

• 1:01:36Speaker 01

  I think that's a good place to, to wrap. The, the presentation is AI is the world. We'll, we'll link to it. It's fantastic. Benedict, thanks so much for coming on the podcast to discuss it.

• 1:01:44Benedict Evans?

  Sure. Thanks a lot.

• 1:01:47Speaker 01

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.substack.com. Thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies

• 1:02:29Speaker 01

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