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The a16z Show
AI Eats the World? A Reality Check with Benedict Evans
01:02:57
Erik Torenberg speaks with tech analyst Benedict Evans about the current state of AI, what has changed over the past year, and which questions remain unanswered. The conversation covers coding agents, foundation models, AI infrastructure spending, software economics, and the tension between today's AI excitement and the long-term realities of technology adoption. Evans discusses why coding has emerged as AI's first breakout use case, how previous platform shifts can help frame the current moment, and why many of the most important questions about AI remain unresolved. Along the way, they explore the future of software, enterprise adoption, consumer behavior, and whether AI models ultimately capture value themselves or become infrastructure for the next generation of applications.
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- [00:01]
A
Mobile didn't need to wait for the Internet. The Internet didn't need to wait for PCs. And PCs didn't need to wait for consumer electronics and semiconductors and so on. So you've always got this accelerating adoption.
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B
Benedict Evans is a tech analyst known for his presentation AI Eats the World. He sees AI differently than the world, spotting patterns others miss and dives into how people really use AI.
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A
They built this amazing piece, incredibly sophisticated, very expensive global infrastructure with enormous growth in use all the time. And it changed all of our lives and we all pay for it. And they didn't make any money from it because all the value moved up the stack. The place that's got product market fit right now is coding. Anthropic's gone from whatever it was 9 billion run rate at the end of last year to $47 billion run rate now. That's all software, isn't it? So what happens when someone else in some other field gets something worked? One of the characteristics of tech is that the moment that you understand something and you know what's going to happen is the moment you should move on to something else.
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B
You know, Google said that the risk of under investing is riskier than over investing.
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A
Investors are kind of looking at all
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B
these companies and saying every major technology platform shift creates the same separating what we know from what we're guessing. AI is already changing software development, reshaping infrastructure spending, and forcing companies to rethink products and workflows. But many of the biggest questions remain. Who captures value, what becomes a product, what gets automated, and what entirely new categories emerge? Benedict Evans has spent years studying how previous technology waves unfolded from PCs and the Internet to smartphones and cloud computing. In this conversation, we discuss what AI has already changed, what remains uncertain, and how to think about the next phase of the AI transition. Benedict, welcome back to the Async Z podcast.
- [01:51]
A
Thank you.
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B
Last time you were here, we were discussing the first iteration of your presentation, AI Eats the World. You wrote it almost a year and a half ago. At this point, you always begin your presentation with what are the big questions? But I'm curious this time. Before getting into the questions going forward, I want you to reflect on what have we learned since you originally made the presentation? What's played out and let's reflect back
- [02:12]
A
what's changed in the last year. So I think we have much more of a sense of diverging product strategy. We have much more of a sense of kind of competitive tension that goes beyond just make a bigger model faster with more computer. We've had several iterations of OpenAI strategy in particular, from sort of everything all at once yesterday to oops, now maybe we should double down on coding. Clearly agentic coding started working and so all the focus in tech has kind of narrowed in massively onto that as something that has absolute product market fit in the sense that like the customers are pulling it out of your hands. And of course that comes with the supply crunch around capacity and price imbalance, imbalance of supply, demand, capacity, CapEx pricing that we see at the moment. So that's kind of the big shift. Like we had a moment of. This is kind of sort of working and kind of exciting, but we're not quite sure what we're going to do with it. Right. It works for coding. Will it work for anything else? Yes, almost certainly. But that's what's working right now. And so that's become. We've got this kind of much narrower focus. Otherwise the Chartman numbers keep coming up, the models keep getting bigger, the capex keeps growing, the usage keeps growing, people using this more. But most of the sort of fundamental questions you might have had two or three years ago didn't really have answers. Like, we don't know if there'll be a winner in the models, we don't know if they can capture value up the stack. We don't know how much the models can do. We don't see a way that consumers will use this daily rather than weekly with the technology we have right now. So all of those questions are still open?
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B
Yeah. And on the coding, could we have foreseen that that would have been the use case that really would have taken off? Or what's your reflection on that?
- [03:49]
A
Deterministically you could have said, well, look who's messing about with this stuff. Software developers. What are software developers going to try and make work? Software development. So at a very kind of simplistic, naive level. Well, yeah, the stuff it should work is software developer. First is software development. Just as like I often compare this moment to like the Internet in 97, 98, but it's also like the PCs in the early 80s or the late 70s. It's incredibly exciting. It's not quite clear what it's for and it doesn't quite work yet. And clearly the first thing that people did with PCs was make computers. And the first thing that people are doing with LLMs, and in a sense LLMs are computers, is to make more compute. And so that's not terribly surprising. I think this shift has been at the beginning of this year, clearly that agentic coding went from being kind of useful to really changing everything. Clearly there were people who would say, well, this is going to be able to do absolutely anything. So they will say, well, yes, look, I told you. But I don't think anyone kind of determines, predicted exactly when that was going to happen and that it was going to be coding. It would work first.
- [04:47]
B
And what have we learned? Say more about what this means for engineers, junior engineers, senior engineers, the jobs discussion, how teams are organized, et cetera. What have we learned so far?
- [04:57]
A
I don't think we've learned anything. I mean, this didn't work six months ago and everyone is scrambling around trying to work out what it means. And you can get very, very into the noise and the detail and what did somebody say at a party yesterday? So, oh my God, that's how it's all going to work. No, it's going to take a couple of years for this all to settle down. If nothing else because of the pricing, this enormous crunch between the demand and supply and hence the pricing. So we don't know what a team is going to look like. I think people are asking new questions around the sort of the obvious one of do you hire junior people and if so, what are they doing? And why were you hiring junior people in the past and were you actually hiring to do the thing that they did or were you hiring them to do something else? And so if you automate away a class of stuff that used to get done by people, then what will happen? And that sort of becomes much more real now in software development because you actually are automating a bunch of stuff that used to be done by people. So those questions are kind of now rather than theoretical. But I don't think anybody can possibly say they kind of know what the market structure is going to look like or what the career of a software engineer is going to be in three years time. I think you'd be insane to think that you could know that yet.
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B
Yeah, talk about OpenAI. Talk about what's most surprised you. How have you kind of made sense of their sort of strategy development and the questions that they have going forward?
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A
Well, it's always been such a tranquil, drama free environment. So it's obviously they've had the issue with Fiji Suma having to take a medical leave, which kind of shuffled things up a bit. Look, clearly the second half, last quarter of last year, their question was right, well, the models are the models, but what else? And how do we get people to other stuff with this? So we'll do ads, we'll do E commerce, we'll do shopping carts, we'll do payments, we'll do a browser, we'll do a social video app, you know, everything. Ask ChatGPT for 15 ideas for what we could do to build value on top of infrastructure. And then we'll do all of that. It's almost literally what it looked like. And then anthropic with having less capital raised said, no, we're going to focus on coding. And they got coding working. Whether that was like a deliberate strategy or kind of they stumbled into it is for other people to say, but clearly that worked. And then so OpenAI kind of swing around and okay, well clearly that's the thing, but the question kind of still remains. Like the stuff that's working right now is software development and some things in some other fields. And then there's a lot of people who are kind of excited about using this around the edges and using this for some things. But it's very unclear how it is that this is instantiated as product and taken to the other 90% of people. We still see in the data that sort of 10% of people are daily active users and 30, 40% of people are weekly active users. And if you're only using this once a week, then you haven't achieved nirvana yet. And there's clearly this kind of very widespread between people in the valley who bought a cluster of Mac studios and are running OpenClaw all day versus those other 40% of people who say, yeah, it's kind of useful. I used it last week for something and I'm like, how do you, how do you bridge that? Software is a place where that's really, really bridge. Jumped over that bridge. And I don't think. And then there's a lot of other places where people are kind of scratching their heads and using it up to a point. And then there's a lot of places where corporations are using it to automate some specific back office process where you're not asking the user to work out what they do with a new tool. Instead you're saying, okay, here's the problem that we can solve. And I go and talk to companies outside America and outside of tech and talk to consultants and investors. They're looking at those one at a time point solutions. So like I'm speaking so a couple of days ago to a commodities company and they want to use LLMs to get better predictions on their cash flow because they deal with all sorts of small producers and they don't necessarily know when their invoices are going to get paid. And it's a very low margin business, so that's a big deal. And so they want to use LLMs to get better cash flow forecasting. That's very different thing from kind of going to ChatGPT or Claude and saying, hey, you know, give me a summary of my meetings this week.
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B
Can you share how did this compare with mobile or other sort of platform shit in terms of early user adoption on sort of the, you know, weekly or daily user?
- [09:17]
A
So I think there's a bunch of different ways to answer this. One of them is like, we're always standing on the shoulders of giants and the growth is always compounding. So mobile didn't need to wait for the Internet or cellular networks, like mobile data. Mobile Internet didn't need to wait for cell. It kind of needed to wait for cellular data, but it didn't need to wait for like the Internet to happen. And the Internet didn't need to wait for PCs and PCs didn't need to wait for consumer electronics and semiconductors and so on. So you've always got this accelerating adoption. And you know, when your boss, my old boss Marc Andreessen, was working on Netscape, there were like double digit millions of PCs on the entire planet. So like, no, you couldn't have 900 million weekly active users because they weren' million PCs. So there's always that acceleration. So that's one point. I think the second point is like at the early stage of any of these shifts, it's not really clear how it's going to work and nothing works. So, you know, like, I'm just about old enough to remember this. I'm not sure how old you are, but like, you know, anyone in their 30s doesn't really remember a time when it was completely normal that you'd be working and then everything on the screen would just freeze and you just have to crawl under your desk and unplug the computer and then pray that like some of what you've done in the last hour might still be there. That just doesn't happen anymore. Back to, you know, the 80s and like you bought a sound card. Well, that's $300. You won't have sound on your computer. Okay, that's $300. And it's like that's the weekend to make that work. I mean, I remember this, you know, trying to get this stuff to work. And the same thing with the Internet, like, you know, you've got to get a floppy disk that has TCP IP on and you know, it's slow and none of the stuff that you need to do existed. And the same with mobile and we're kind of at that stage and of course it's not clear which of these things are going to work. And that's the same thing. Now, like a browser's going to work, is it going to be this, is going to be that. How's this all going fit together? And there's a gap between what's incredibly exciting and the small number of people who are willing to put their work in to get something to work and just turning that into a thing where you can just press a button and it all happens. I think the third point here is, and there's a much more tangible observation, is that the pricing crunch that we've already mentioned looks to me a lot like what happened with mobile data in 2009 10, where suddenly people got bills for like 5, $10,000 of data on the one side, and on the other hand, if you had flat rate data, which is kind of what happened in the US with the iPhone, the AT&T launches, AT&T singular, launch the iPhone with flat rate data. And then unfortunately everybody buys iPhones and then they get 3G and people start watching YouTube and the whole network goes down because they just don't have capacity to do that. It's funny, there are still people in tech who don't understand that cellular networks have marginal cost. They have to add more capacity and that costs more money. And so the networks kind of had to SCRA like the cost curve aligned with the infrastructure pricing system, aligned with the underlying cost and aligned with perceived value, which they kind of did with capped bundles and fair use and throttling and so on. But the other side of that, and that's exactly what you see now, it's like on the one hand you're paying $20 a month and you get 10 grand worth of tokens. And on the other hand you messed about for a couple of days and you get a bill for 10 grand and you're like, what the hell is this? That's exactly what you see, always literally see these stories now, which is exactly what happened in kind of 2009, 8, 9, 10, also what happened in 2001 and 2 and 3 with GPRS. But I think the other interesting part of that analogy is, or that comparison is that since then mobile data traffic has risen by something like one and a half to 2,000 times. And the mobile networks collectively have revenue of about a trillion dollars and they spend about $200 billion a year on capex and the stocks have been flat for 20 years and all the cool stuff got built by somebody else and they kind of all thought that they were going to build all the cool stuff. Like I worked for a phone company that had a banking license because they thought they would do mobile banking, which now seems absolutely insane. But that's kind of the point is they built this amazing piece of global, incredibly sophisticated, very expensive global infrastructure with enormous growth in use all the time. And it changed all of our lives and we all pay for it. And they didn't make any money from it because all the value moved up the stack. And this is of course absolutely, as I said earlier, this is the absolutely central question for LLMs is can the model do the whole thing or do you have to have 300 apps built on top of it? Can you just go to the model and say do my taxes for me or do you need to have a tax thing that uses it, that might use some AI intent to have 10 different ways inside it? And if not, then what is it to be? A foundational model provider is this just commodity infrastructure that gets sold at marginal cost, which is somehow seems to be a very difficult concept for people to grasp right now because you can sell all the tokens you can make so you can price it at roi. But over the next couple of years we've got like a trillion or $2 trillion of capex coming down the pipe and the models get 100x200x more efficient every year. And then there's new models and will the models use more tokens or less tokens? But wherever we're like, we'll get to a different equilibrium. And why would that equilibrium be one where the model companies have pricing power when the models are all kind of the same, doing kind of the same thing with the same chips? Why would they have pricing power? And I think that's. So it's a long answer to your question, but you go back and look over time like chip companies didn't Capture the value, ISPs didn't capture the value, mobile network operators didn't capture the value, Windows and iOS did. But they were doing something else. They had all these levers to go up the stack and of course they had network effects, which models don't have. So that's sort of the question is do they end up like the infrastructure layers or do they end up like the operating system layers and capture value and actually get to decide what gets built, or do they end up. I mean, the irony of there, of course, is Netscape, where Mark Andreessen famously said that he was going to turn Windows into a set of badly debugged device drivers. And then Microsoft kind of crowballed their way into the market, but it turned out that web browsers weren't the point because all the value was somewhere else. And so that's kind of the more these kind of swirling mass of questions around how this settles out. Which comes back right through to all of my kind of answer to your question. It's like, you know, some of this stuff, but you don't know how it's going to work.
- [15:35]
B
Yeah, it's unclear whether it looks more like the Internet or software where a lot of the value or just better margins happen at the application layer or sort of the cloud where it seems where sort of existed at the hardware layer. And right now, so far it seems like Nvidia and going up, it seems like they have better margins and are accruing a lot of the value. But it's unclear if that will, you know, remain the same or if there will be sort of applications, you know, if we'll look more like the Internet. How would you even begin to predict, you know, the answer to such.
- [16:15]
A
Well, so two answers to this. I mean, there's all these sorts of quotes about how history works. And you know, my favorite one is history teaches us nothing except that something will happen. And you know, you can always expose facto say, well, of course it worked out like that, but it was generally wasn't obvious at the time. And you know, particularly I remember, you know, like sort of 15 years ago, a lot of really, really clever people in tech looked at the iPhone and Android and said, you know, this is open versus closed again. And, and we're just going to crush the iPhone, which of course isn't what happened. And then I can go and explain why. But all of these comparisons are useful. None of them are predictive. And it's always obvious in hindsight. It's funny, I've done a couple of podcasts recently and I published this presentation and there's a class of comment on this stuff, which is to say, Benedict, you're not doing your job. You're supposed to tell us what's going to happen. You're supposed to make predictions, and all you seem to do is say, well, we don't know. And there's kind of two problems with that. One of them is there's a broadcast of places where I actually do say, like, I don't think this is going to work. I Think it's going to work like that? Like, I don't think foundation models are a product. I don't think a chatbot is a product. I think the value will be further up. But the other side of this is, like, when you're at this stage in the cycle, there's many paths and you don't know which of those paths it's going to be. And to try and say, well, I think it's going to be that one is, you know, you might be right. But you do have to kind of be conscious of how uncertain this is and how many different paths it could take. That's the nature of this part of the cycle is all bets are open. We get to the point where the S curve kind of curves up and it narrows in. And there was a moment when Windows Phone might have worked. In hindsight, no, it probably wasn't going to work, but there was a moment when it wasn't clear how mobile was going to work. And there was a moment where it was clear, this is what's happening now. We move on to next quest, the next question. And I think the kind of the. I'm sorry, I'm kind of monologuing a bit, but, like, one of the characteristics of tech is that the moment that you understand something and you know how it works and what's going to happen is the moment you should move on to something else. You should always be looking for the places where we don't know what the answers are. Because, you know, I haven't updated my Apple spreadsheet in like five years because we know what happened they wanted, like, I don't care what the next year's, what this year's iPhone looks like. I don't pay attention to their market share in China. Like it happened. Next question.
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B
Yeah, but just to flesh out, you mentioned the prediction of you don't think foundation models are the product you think will move up. Explain that the reasoning there a bit and what they could look like.
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A
So I think there's like three or four building blocks you can put on the table. One of them is, is that it's not clear how you could build a model that was fundamentally better than everybody else's model in some sort of sustainable, differentiated way. There doesn't seem to be a network effect. There doesn't seem to be sort of levers you can pull and a strategy, a position you can get into. Where Instagram is or YouTube is or Google searches. We don't see an equivalent of that for LLMs. Now you have different Emphasis. Maybe this one's better than that. Maybe you like this one more than that. There doesn't seem to be a sort of fundamental differentiation, fundamental competitive difference between the models except your willingness to spend money. Second problem is the chatbot itself is like a kind of a weird limited v1UI. And there's some things and some people and some kind of task where it works really well, but there are most of the others. You need a bunch of other stuff. You need tooling, and it needs to be set up right, and it needs to have the right data, and it needs to be configured and controlled and have the right user interface. And people need to have kind of sat down and thought about how this should work. Because generally people who are good at using the tool and doing the job that needs the tool are not the same people who are good at deciding what the tool should be. So, you know, people who are really, really good at designing print publications are not the people who should create InDesign. That's a different set of skills. And, you know, people who are really, really good at doing financial advice are not the right people to design TurboTax. Those are different people with different skills. And you have kind of groping around the middle of this. So you now have Claude for this, Claude for that, and you have skills and so on. To me, this is kind of like, well, one question is, well, who builds the skill? Another question is like, well, that seems to be a bit like what you get if you do file new in Excel. Like, these are templates and they'll take you so far. But at a certain point, people outgrow the templates. There's a slide in my presentation, which is a quote that somebody said to me on Twitter years ago. They said they were a consultant and half of the jobs were telling people who used Excel to use a database, and the other half, we're telling people to use the database to use Excel. So this is kind of fuzzy, swirly place of like, do you need dedicated software? Do you need horizontal software? Do you need vertical software? But you know how I just do everything in Excel? There's always, you know, we've all, like seen the department that runs along on a 10 mega Excel file. I run my business in numbers, but on a spreadsheet. But like, there's a certain point where, like, you outgrow that. And so following that on, well, can the Model Labs build all of that? Well, of course not. No more than like Microsoft or Apple could build every Windows app or. So then do the model Apps have Leverage, are they windows, are they iOS? And again, well, is there a network effect? Like if you're a law firm right now and you buy a piece of software, all the pieces of enterprise software that A16Z is invested in, how often does the law firm or the manufacturing company or the bank say oh well, does this use Claude or does it use OpenAI? Because we standardize on Claude. Well no, that's not how it works any more than it did work like that for cloud. You didn't say, well our company standardize on AWS. You don't even know what company, what cloud that SaaS product runs on. That's the whole point. It's abstracted away. It's not your problem. And so the foundation models seem to look more like that. They seem to look more like the hyperscalers in that sense, in that they don't have, they might have competitive advantages, but further up the stack you don't have leverage, you don't have a network effect, you don't have control. That sort of prompts me to incidentally to say, well maybe the right comparison here is with semiconductors where with each generation it just gets more expensive and so you have fewer players. So all of that kind of taken together. Well, the models are kind of diff commodities and the chatbot isn't the right UI or the right product and the companies aren't going to be able to build all of that stuff themselves. So therefore they're low level infrastructure. And so then, well, do they have pricing power? Well, you're going to have pick a number, three to six companies making a frontier model spending. No one knows, no one honest knows. Like something between $200 billion and $2 trillion a year on building these models. Plus there'll be a bunch of edge and a bunch of open source. I know. Go and ask me Martin Casad, what he thinks. I don't know. I mean he doesn't know either. He probably has a better way of saying he doesn't know than I do. But we don't know. So where's this going to settle down? You're going to have, as it might be half a dozen companies that are all competing to sell this stuff. And so where is the price discipline going to come from? Particularly when some of them have got like whole other business models as well, like Google selling ads. So they've got a different attitude to pricing, to OpenAI. And so I think the challenge here is there's a difference between where we are right now and where this should end up, which is Kind of a first year economics student kind of conversation. Right now we're in this period of extreme disequilibrium of supply and demand and price and capex and capacity. But just because demand for tokens is infinite, that doesn't mean that you can't get to a different price equilibrium. Because of course that's what happened with mobile data. Like demand for this is infinite has grown 1500 2000x in the last 15 years. But you still got your supply and demand price equilibrium and you still got a murderous price war between telcos in most parts of the world because fundamentally you're selling kind of a commodity to people who will swap back and forth and of course developers will also swap back and forth. Now this is, I'm happy to say that this might be completely wrong. It may be that we get to a world in which there's only two companies that can make an LLM and they have pricing power, or we get to a world in which most of what we do gets subsumed into the model or they have leverage further up the stack. And it's kind of my point about iOS versus Android. Just because you can say, well it worked like that the last three times, that doesn't prove what's going to happen this time. But it doesn't mean at least you should sort of ask the questions and you should certainly, I'll just say as a sort of a primary observation, like this situation right now is transitory. We are in this extreme scarcity and then we have a pricing system and we have a free market and we have a surge of capex and like a trillion dollars of capex. So like those multiples are going to move around and then what?
- [24:56]
B
Yeah, I mean going back to your, it's a good segue. To your point you made earlier of like hey, you know, we know, you know Apple's Apple won. Next question, As a segue, what are some of the next questions that you're most focused on or that you know, we should be paying most attention to?
- [25:15]
A
So I think one way to answer some of the questions we've already talked about like well how far do the stat of the models go, can the models differentiate and so on. I think another is obviously at what point are there do we see more and more classes of use case where the models are good enough and we don't need the most expensive, fastest, biggest, heaviest model in the cloud and you can use an older model, you can use an open source model, you can have a model running on device. Obviously this is What Apple's going to be talking about in a couple of weeks, you know, how much can you push onto the device where the compute is free, or free to you anyway, doesn't have marginal cost for the developer. Another classical question is it's almost like the question is move out of technology. So if you're looking at a law firm or a consultancy or an investment bank, or basically anyone in professional services, where you traditionally have this pyramid structure and you can automate a great chunk of what the people at the bottom of the pyramid are doing, what happens? And the only thing you can say there is, if you have never worked at a law firm or never worked at Bain, BCG, McKinsey, probably not going to have a good idea of how this works, because you probably don't really know what it is that all those associates are doing. And you also don't really know what it is that the client was paying for. And how do those things kind of get reconfigured? And so like, well, what does AI mean for finite? What does this mean for finance based for that internal hiring structure and the kind of products you can create and the margin structure? What does it mean for consultants? What does it mean for the big four, for the Big three, for Accenture, for big law firms and for advertising? And you probably can know some of those questions, but if you're not kind of in that industry, you don't really know the answers. The thing this reminds me a lot of is something I wrote when I was at a 16Z, which I called content isn't king. And I also wrote something that said Netflix isn't a tech company. And the point I was kind of getting at is that if you looked at Netflix, this whole thing is enabled by stuff that the tech industry is built. But all the questions for Netflix are LA questions like what shows, how many shows, what kind of shows, what should you pay the talent? Should you aim for awards, should you do movies, should you buy sports? What kind of sports? These are all Los Angeles questions. These are not San Francisco questions. No one in San Francisco even knows what the right questions are. They're media industry questions. And this was kind of my point that all the questions that matter for Netflix have become media industry questions. This is obviously the great tension point about Tesla, is that a car company, was it a technology company? And so what I'm kind of getting at is that what does this stuff mean for law? Is kind of a question for lawyers as much as it is, or people who understand a lot about law firms and how they actually work and what they're actually doing and what the client's actually buying from them. Same thing for like, what does generative video mean for Hollywood? Like, Ben Affleck probably knows a lot more about this than I do. He built a company and sold it for like $100 million. So obviously he does, but, like, you know, so that's kind of a second question, which is the questions move outside of AI and they become sort of half AI questions, half something else kind of questions. And then the third level, which is, I think, and I probably should have said this earlier, the way that all of this is sort of fundamentally different from previous platform shifts is that with, you know, 3G or the iPhone or the web or whatever it was, you didn't know what was going to happen next, but you knew the physical limits. Like 1995, you knew that telcos weren't going to give everybody in the world broadband next week. And you knew that everyone in the world wasn't going to go out and buy a PC because a PC cost like $3,000. So you kind of knew the basic physical limits of what could and couldn't happen. And with generative AI, obviously we don't. We might look at our phones when we get off this recording and there's a push notification that says OpenAI's new model is out and it's like 2% of the price because they worked something out. I mean, I don't think it's very likely at this stage, but we don't know those kinds of questions. So how much bigger will the models get? How much better, how much faster, how much cheaper? How much pick or in what ways will the characters of the models change? We don't know. And that is different to previous platform shifts where you did know the sort of fundamental constraints. And so that will kind of spin off new questions. And in a sense, this is something I pointed to earlier. I said like, the place that's got product market fit right now is coding. Nothing else has equivalent product market fit right now. I think I'm pretty safe in saying that Anthropic's gone From whatever it was 9 billion run rate at the end of last year to $47 billion run rate now. That's all software, isn't it? So what happens when someone else in some other field gets something working? Yeah, Law will. Law bank. I don't know where, but something, if
- [30:03]
B
you had to guess, what are the use cases outside of coding that could potentially yield daily activity?
- [30:10]
A
So I should say the sort of Presentation that I published a couple of weeks ago. There's sort of three sections and one of them is talking about capital and CapEx and infrastructure and foundation models and differentiation, which is the stuff we talked about. And the second is, well, how would you build software with this? And what does this do for the software industry? And what would your software look like if you what happens to the margins and the companies and everything else? And the third section I called change, which is kind of getting to this point, and I opened it with again, what appears to upset a certain category of person, where I said the Yogi Berra quote, that predictions are hard, especially about the future. And I think there's a sort of a back test point which is imagine asking these kind of questions about the Internet in 1997. What would you have got? What would you have not got? But I think one way you can look at this is to say, well, this is automation, that this makes a class of thing that people used to do that couldn't be automated. Now you can automate that. And so then, well, what does that mean? And I propose like three or four way buttons to press. First one is, is this just price elasticity, which is really what the Jevons paradox is like. If you make it cheaper to do stuff, do you do the same amount of stuff for less money or do you do more for the same money or do you more do more for more money because it becomes so much cheaper? Was there something that you couldn't do before that now becomes cheap? Was there something that was expensive and you were doing as a barrier to entry, like owning a printing press as a newspaper? Is there something that now was a barrier to entry and a cost basis, a barrier to entry that now goes away? Is there something that gets unlocked in your business model or in your competitive space because this thing became cheap? And then the final question would be what stuff was just completely impossible, totally cost prohibitive, so that nobody even thought about it? And now that's within reach. And the example I used to give here was like, well, steam engines make trains possible and it wouldn't matter how many horses you buy, you couldn't have a train or like an express train. Much more contemporary example would be to point to something like YouTube or indeed point to Spotify. Spotify says step one of the look at the last 25 years of the music business. The first half is what happens if you don't have to buy a $50 CD to get that track. But then the second half is what if $15 a month gets you all the music that there is, which is something that was just completely impossible. The problem with these kind of making predictions like this is on the one hand, you're going to say stuff that's kind of clever and obvious, but you don't actually know what it's going to mean, industry by industry. So if we'd been back in the late 90s and we'd said Internet will destroy the value of physical distribution, it turned out that meant different things for newspapers and movie studios. Like newspapers got completely screwed by this and movie studios are kind of not really changed very much. So again, it depends. Sorry for the people that annoys. But the other kind of part of this is there are some places where I think you kind of can ask more useful questions. And the one that sort of intrigues me is to say, well, how does this change advertising and E commerce and brands and marketing and everything that we buy? Advertising is a trillion dollars and retail is $25 trillion. So, you know, this is a reasonable sized TAM. And so the thing that I've always used to think about was that Google and Meta and Amazon don't really know what that product is. They know it's a scoop. They know what the publisher typed in the metadata field, and they know that people who bought this also bought that, but they don't know why, and they don't really know what those things are. Which is why you get these jokes about, you know, hey, Amazon, I bought a toilet seat cover. I'm not collecting toilet seats. Gazamond doesn't really know what a toilet CDs and doesn't know that people don't buy two. Actually, they should know that that should be frequency analysis, but they don't. And with an LLM, in principle, you would kind of know what those things are and why people buy them and what other things people buy. And obviously no is like a difficult, tricky term to use. What do you mean when you say no? But at a minimum, a very different level of statistical correlation of what an AI system would be able to do. Which is of course why you see the ad numbers and the conversion rates shooting up every quarter from Google and Facebook, because they're rolling all of this into their ad systems and their recommendation engines and their prediction algorithms, and you get shown more stuff that you would like and the ads that you're selling are more likely to be things you'd like to buy. And so they have these enormous sudden acceleration in their ad revenue. And all of which is to say you kind of look at how these systems work. And right now they say, well, people who bought that could buy this. And you should now be able to say, like, the slide I had in the presentation was like, here's a picture of a coat. What is it? Where can I buy that? And like, five years ago, that really wouldn't. Ten years ago, that certainly wouldn't have worked. Five years ago, probably wouldn't work now. That should work. And then you can say, okay, suggest 10 other coats like that with different prices and tell me where I can buy them and suggest the pros and cons of each one, and you'll kind of get that too. And then you can push one step further and say, look at my Instagram and suggest a winter coat I should buy. That will change my look, but not too much. And again, like, three years ago, that would have been total science fiction. And now you think, yeah, you could probably build something like that that would kind of work. Those kinds of shifts in what the computer knows, what it can automate, what suggestions it can make. Going back right to the beginning, whenever you get a new technology, you start by doing the old thing. But more. More spreadsheets, more PowerPoints, more email, better email. But the important stuff is not doing the old thing, but more it's doing something new that you couldn't have done with the old thing. I mean, it's a pretty banal observation, but you kind of lose sight of it. And so what are the new things that you can only do with this as opposed to automating the old stuff? I mean, I think the enterprise version of this would be you've got all our zoom calls with clients recorded, and you've got all the flows of emails in and out of Salesforce, and you can see all of the telemetry and the metrics and the analytics of how people use our product. So how should we change our prices to improve our churn? And again, that's something that an LLM might be able to do, which is very different to saying, do sentiment analysis on calls into the call center and tell me which customers are angry. You get kind of multiple shifts in the layer of abstraction around what analysis you can do. And of course, that then creates new companies and destroys old companies and creates new businesses and everything else. But again, we're in 1997 and I'm trying to predict Uber and Airbnb, and if I could actually do that, There's a sort of general point here, which is if we could actually predict what was going to happen, we live in a parallel universe. Know, VCs would have, you know, it wouldn't be a 1 in 10 hit rate, it would be a 10 out of 10 hit rate.
- [37:09]
B
It seems like. Yeah, one of the questions we're now asking is what sort of follows is sort of what was unreasonably expensive to do before that now is possible? And you know, is it, I don't know, something crazy like rebuilding YouTube from scratch or rewriting Linux from scratch or.
- [37:28]
A
Yeah, it's funny. I mean the, you know, the other, the paired fallacy, of course is the new thing comes along and says, well, we're going to build a new thing with the old thing. With the new thing thing, of course we're going to build office with open source, we're going to rebuild it on the web. And it turns out, guess what, look at Google Docs, it's got like 20% of the market. Because that's actually not the point. What's interesting is to do something else is to do something new. And it's to shift that level of abstraction and to kind of spot problems that have never existed. I mean, it's the experience you get sitting in pitches all day at a venture firm is there's some stuff where you think, well, that sounds kind of useful. And there's stuff where you think, I'm not quite sure why that would work, but there are some things that kind of fill a hole in the universe and as soon as somebody explains it to you, you think, wow, why did nobody do that before? Why did no one see that that thing existed? And that's where part of the fun part of looking at startups is. And that's what people will do with this. People will suddenly work out a way that you could turn that, that will work out that that problem existed and no one, including the no one, realized that problem existed. And then they'll go out and make a thing to solve it. This is also, incidentally, going back to an earlier point. This is why I don't see the model. I think that this is the problem with the idea. The model will do the whole thing. And if you kind of go back and think about all the pictures you've seen since you joined a 16C, how many of them were things where people in the industry knew that was a problem? Quite often the answer is actually no. Actually no one in the industry thought that was a problem. And it actually took like two years to explain to them and persuade them that that problem actually existed at all and this new thing would fix that for them. That's kind of the problem with the idea that middle manager and finance is going to use this tool to solve this big global industry problem? No, because no one knew that industry problem was there, let alone could work out the right way to build a tool to solve it.
- [39:20]
B
Does this imply a less consolidated SaaS environment than before AI? Maybe less bundling or single bit behemoths like the Microsoft Enterprise.
- [39:27]
A
Gosh, wait, to bring me back down to earth, is the SaaS industry going to be less consolidated? Benedict, that's all great, but tell us about, about the stocks. What are the kind of building blocks that we can put down here? So obviously it's going to be way cheaper and quicker to build software. Obviously there's going to be a bunch of stuff you could do with software that you just couldn't do before at all. And so there will be more competition. And of course this comes with a new margin structure. But as per our conversation earlier, we don't really know what that margin structure is going to look like. Are you going to go to, you know, outcome based pricing? It's really hard to tie each button press in a piece of enterprise software to the P and L. Sometimes you can in Salesforce or something. It was an awful lot of software. It would be really hard to say, well, the work I did today did this to the eps. Therefore this is what we should pay for it. This is what we used to pay for that piece of software. I don't think that makes sense. Anyway, what does the pricing structure look like over time versus now? There will be more competition. It will be easier to build stuff and quicker to build stuff. The way that I sort of thought about, I suppose this is there's maybe kind of two framings to think about this that are kind of useful. One of them is to say that if you think about the sort of enterprise software fleet today, you've got like three buckets. You've got like your big iron horizontal systems. So SAP and Workday and your CRM and your capital management software and your payroll management software and so on. And then you've got vertical software and typical big US company has like 3 to 400 SaaS apps and then like another thousand apps that they've built, bought or built and sell internally running on prem. And then in the middle you've got this kind of fuzzy improvised space of Excel and email and the shared file system and stuff kind of moves back and forth between those. And like in principle every SaaS app is doing something that you could have done in SAP or you could have done in Excel. You could have managed your graduate recruiting in workday, but at a certain point, like if you, you're conversation the other day, like if you're PwC and you hire however many thousand graduates every year to train to be accountants, you probably got a piece of dedicated software that you built for yourself, or maybe you hired Accenture to build and you probably hate it. But anyway, you've got this piece of dedicated hiring software or you bought something. If you are a company that hires five graduates a year, you're doing that in email and a shared Google sheet because why would you buy software for that? And then like, like then there's a space in the middle. Do you do it in workday? Do you do it in Excel? Do you do it in a dedicated app? And now you add chatgpt to that. Do you do that in an LLM? Is there an LLM tool that means you can do that in Salesforce where you couldn't do it before, or you can do it in your vertical app that you couldn't do before? Do you use the LLM to build yourself a tool for that? Just as you might have a company department that runs on a 10megXL spreadsheet that someone built 15 years ago. No one know how it works. No one knows how it works, but they're still using that. So it arrives within this kind of broad, fragmented, complicated landscape and it's another set of options for how you would do that task. So this is kind of one framing to think about it. I think the other framing to think about this is does the LLM go at the top of the stack or the bottom of the stack? So on the one hand, bottom of the stack is a feature inside Salesforce. So you're in Salesforce. Look at the history with this customer. Look at the context of every other sales call we've done. Look at our business objectives and suggest an email or suggest me what I should, I should say on the call to the call to customer. So it's a feature, it's a button that's controlled and has tooling and where guardrails and everything else that are driven by that particular use case. The other way to look at it is the example I gave earlier, which is, you know, go look at Salesforce and Workday and all of our email and Google Analytics and, and, and, and then synthesize something that you couldn't have done before. So the tension in both cases is where do you put the probabilistic software that can make mistakes and where do you put the Deterministic system software, software that can't answer these kind of questions. So where do you put the database and where do you put the LLM and is it which is at the top and which is at the bottom? And the answer is probably both, depending on what you're doing and where it goes. All of which is a long way of saying, what does this do to software? And the answer is more software. Like way more software. I mean, all software companies exist to solve problems created by other software companies. That was the joke insecurity. All security software exists to solve problems created by other security software. And clearly that's, that's what we went through with SaaS. Like SaaS gave us an order of magnitude, two orders of magnitude more software. And we should probably expect that with this. What that gets to the SaaS Apocalypse is all the investors are kind of looking at all these companies and saying, well, we don't really know which of these companies are going to get screwed by all of this. Some of them must be like, obviously there must be some, you know, go through the end of this and you know, x percent of all the SaaS companies that are out there are going to get wiped out by this. But you don't know which ones. So you probably shouldn't derate the whole thing by 50%. But clearly you're going to like go, I'm not sure I'm going to be long software at the moment until I have some idea of what the hell's going on.
- [44:22]
B
Yeah, you said in your talk with Ben Thompson that software is. Someone sat down and designed a workflow and said this is the right way of doing this from now on. But you also said that a process grows out of the way, just a business runs. Does that just take time? Or do you think we need more experimentation, iteration from these vertical AI startups to get that this is the right shape of software for the future.
- [44:40]
A
Well, in a sense, I mean, maybe kind of an interesting turn on this is this is both what strategy consultants do and software companies do is they kind of look at what's going on inside a company and say, well, this is a crap way of doing it. This would be a better way of doing it that would achieve your objectives better. And a software company kind of encodes that in software and a strategy consultancy kind of encodes that in workflows and org charts and processes and training and objectives and you know, maybe tells them to buy some software to do that thing or maybe now increasingly maybe builds them that software as well. I think another Thing to talk about here is how much of what's done inside an organization is implicit and not documented and not in the training data and not something that anybody in that company could actually kind of sit down and draw you a neat flowchart of and explain to you. That's what, that's a big chunk of the value of Bain BCG McKinsey is that they have license to come into a company and talk to everyone and talk to the people you're not allowed to talk to that are in a different org and not get fired and to go and work out how this actually works as opposed to how it's supposed to work and why it is that people aren't doing the strategy. Because actually, guess what, their bonus targets depend on them not doing the strategy. And work all of that out and be a team that's ready to come in from the outside and give you the answer. And then you can blame them or have that kind of that prebate solution that's not, you know, these are sort of problems in organizational management and how people function and how people can explain what they do that are very hard to write down and very hard to kind of bake into a Claude skill and say there you are, like make your PowerPoint. And so there's a sort of broader how does this always work challenge here of how do you get people to use these technologies? How do people adopt new tools? How do you work out how to help people adopt new tools and work out what new things you would do with them? Which is also what happened with cloud and the web and mobile and the Internet and PCs and spreadsheets and so on.
- [46:45]
B
Do you think there's some kind of co evolution between AI native software and new types of interfaces, for example new customer service AI platforms that might not have had as much human facing UI or system of record software being built without a front end at all because its primary user will be AI agents querying it directly.
- [47:02]
A
So I think these are kind of interesting ideas. They're things I struggle to have a strong opinion on because you know, they're not kind of not deep, deep into the weeds of how enterprise infrastructure gets bought. I wonder how new some of these questions are. I remember Chris Dixon saying like 10, 15 years ago that APIs are the new BD and software wouldn't need software. Companies could just open up your APIs and like well what sold is new. You don't need an API anymore. You just have an MCP server and people will just plug, the agent will just plug into That I don't know. I think the challenge with a lot of this stuff is that all the decisions are really exception handling. The question is always what can you not automate? What requires someone to make a decision and some judgment and have an opinion about it? Because maybe that hasn't been written down or that didn't happen before or it doesn't look quite the way it happened before. I think there's a sort of, you know, there are various ways of kind of think about separating out what gets automated and what doesn't. The way that I used in the deck was to talk about what's a task versus what's a job job. Very often the tasks that are used to accomplish the job might change without the job itself changing very much or without the thing that the job is selling to the client changing very much. Like if you think about what accountants did 50 years ago and what accountants do today, they do spend almost none of their time doing the same things. But to the client it's kind of the same thing. It just gets done in a completely different way with a whole bunch of different tasks. And one of the more of either profound or kind of abstract ways to think about this is where is it that you want the average where is it that what you want is the way that everybody will do this? That's the way everyone would do it. That's what anyone would say, that's what anyone would make. That's what any associate would make. That's what anybody would give me. That's the answer anyone would give versus where is that not what you want? Where is it that you want the answer to a new question or different answer or different idea? Because LLMs are going to be very good at anything where you can describe how people do it and where what you want is the way anybody would do that. And not so good at where you can't really explain why you did it like that and where you're doing it differently to the way people would normally do it.
- [49:34]
B
Yeah, that's helpful for Amy. I, I want to zoom back out from the weeds gearing towards closing here and a couple last questions. One is various people, including, you know, CEO of Google, have said that the risk of underinvesting is riskier than overinvesting. Is there any level of Capex where that stops being true and are we getting there now?
- [49:52]
A
Well, there's a financial gravity problem in that Microsoft, Meta and Google are all in line to spend over 50% of revenue on CapEx this year. And we think of telecoms as being capital intensive. Telecoms spend sort of 15 to 20% of revenue on CapEx. And so $700 billion is the guidance from the big four companies this year. Well, telecoms is 300, mobile is 200, total telecoms is 300. Oil and gas, depending on which, which definition and which bits of it you're counting, is anything from 700 billion to a trillion dollars. I think from memory. I think depends exactly who you ask. So $700 billion a year is not an impossibly large amount of money. It's what big global infrastructure costs. It's just a lot of money. Clearly those companies could not spend one and a half trillion next year, or if they did, they'd have to borrow it. And they certainly couldn't sustain that level of spending for any length of time. And so there's a certain point at which that growth has to slow down because there isn't any more money. Now, clearly you can talk about ROI and your ability to produce returns from that investment, and clearly the capital markets are willing to fund that up to a point. But pick a number at random. We can't spend $10 trillion a year on AI infrastructure because there isn't $10 trillion a year there to spend on it. So there are kind of laws of physics caps on the amount of money that's available. I'd hesitate to say something more tangible than that at the moment. I mean, I kind of almost go back to what I said at the beginning, that we've got a bunch of multiples, so there's far more demand than supply. On the other hand, the efficiency is increasing massively. We don't know what the next model will be. We don't know where edge or open source come in yet, when edge and open source come in yet. And meanwhile you are always chasing the next model. And so this is kind of the line that runs across all of it is the model is only relevant for three to six months, six to nine months, whatever you want to say. And the model costs how many billion dollars and how much infrastructure do you need to do that? I don't think that mass is really shaken out yet. I mean, obviously you can, you know, there's a bunch of very clever semiconductor analysts who spend lots of traffic time trying to put numbers on this. It is kind of like trying to put numbers on bandwidth, Internet bandwidth in the late 90s. Like, you kind of know what the rows in the spreadsheet are, but you don't really know where the values are. All you could really say is, well, look, it can't be. There's clearly physical limits on this. I think another way to answer the question is like, if you're Google or Meta or Microsoft to some extent Amazon, some extent Apple, this is sort of an existential problem problem and you have a sort of a FOMO problem in that. So on the one hand, your returns on the investment at the moment are hugely positive. On the other, you can't let other people get away with this without you participating because then your company's gone and you don't want to end up like Microsoft in the 2000s or IBM in the 90s or indeed measure in the 2010s where they are kind of continually getting shafted by Apple. So if this is the future of compute, then you need to be participating in it. But obviously at the same time the CFO is sitting there saying, well yeah, that's great, but how much participation are we talking about here? And I don't think we, you know, it's clearly at a certain point that curve is going to have to taper off because, like, there's nowhere else it can go.
- [53:38]
B
Do you think there's going to be a reckoning around, token maxing? Is it possible that companies have been overshooting AI usage and when they do proper ROI studies, they'll pull back?
- [53:47]
A
Well, obviously you've had people using the most expensive model to dick around on the Internet, which is kind of what happened with Mobile in 2010. You got a $10,000 bill and you said, wait, wait, I thought this was a flat rate bundle. What happened? So there's like, you've got, obviously you've got a bunch of silly, painful stories. I think there's also a point of like. I think maybe what's slightly more interesting is the question is, and clearly there's going to be a point in which, as I've said several times, we're at a moment of kind of massive disequilibrium and the pricing has got to get back into alignment with the cost and the usage has got to get it into alignment with the pricing and the roi. The challenge is it's a bit tricky. This early stage is quite hard to know what the ROI is. It's rather like giving everybody the Internet in the late 90s and saying, okay, go off, be more productive. And if you look at like there's a survey from Deloitte, there's also a survey from the Fed that's in my presentation where if you go and ask CFOs, where have you seen the benefits and Most of the benefits so far have been stuff that's pretty hard to measure. So, like better analytics, better customer support, more productivity. You could make more slides more quickly. You could do the analysis more quickly. It's kind of tough to put a financial value on that. It has a financial value value, but it's not the same as saying, well, we made this new thing with AI and it had this revenue or it saved us this much money. Those things just obviously those things take longer. It's harder to build a new revenue line than to give this to everybody and have them use it to make spreadsheets more quickly. So there's a little bit of like, well, how long does this take? I think the other answer to problem here, of course, is consumer surplus, which is to say that this is kind of what happened with ACL in that guess what? If a DCF takes you a week, then you probably only do one or two DCFs. And if a DCF takes you 10 seconds, then you do 50 DCFs. You really can't charge any more money for that. So some of what happens is that these things become competitive necessities and everybody has to buy it and use it. But the cost saving or the productivity gain that you get from it just kind of gets competed well. Right? So you don't get to charge more for it. I mean, if you're at McKinsey and doing that, or Bain or BCG and doing that piece of analysis used to take a week and now it takes a day, you probably do five times more analysis and charge your customer the same. And your cost base hasn't changed either. Which is exactly the way to think about what happened with investment banks and financial analysis. You just did way more analysis, this with probably fewer people and charge customers the same amount of money.
- [56:29]
B
A big part of your thesis is this idea that models are going to end up as commodities. And yet the layer that's raising the most money in the fastest time in history is these foundation model companies. So given that, what advice might you have for them, either collectively or we can pick on someone individually in order to adapt.
- [56:52]
A
It's not that I know that they're going to become commodities. My position is more now, well, here is a chain of argument that says that deterministically it looks like these things will be commodities. And explain to me why they won't be. And that's as far as I would commit to that. I think the raising all this money, I kind of get back to my point about mobile, which again has no predictive value. But it's a worthwhile observation is that the mobile industry is very big and spends a lot of money on infrastructure and isn't very profitable and all the cool stuff stuff is done by somebody else. And then you can do, well, what's the return on capital? And the answer is, well, it depends which market, whether you're in America or Europe or India or China. But meanwhile, that was a worthwhile thing to do and it produced a return for somebody, but it ended up not controlling the whole thing. And other people ended up getting more value from that than they did. I don't have the number in my head. Google's net income last year was what, $50 billion or something? Something. What's net income for the total telecoms industry? I should really subscribe to Bloomberg then I could just answer these questions instantly. But pretty safe bet that Google, Meta, Amazon, Microsoft, Apple produce more profits than the entire telecoms industry. So this is a puzzle is you're driving the frontier forward. You're kind of caught in this trap that you have to keep competing because otherwise they'll do it it and you'll fall behind. You've also got this thing that we haven't talked about at all, which is, you know, hey, aren't we just building AGI? Like we're going to build God in a box, which, you know, some people, they do believe, although it's kind of hard to. It's hard to analyze, but maybe. So, you know, Carrie, you're going to carry on building this stuff, but the practical question is, well, how do you get things that people want to use that aren't software, that aren't. That aren't software development? I mean, that's a good business. Is that the only business? Pick a number of how many hundreds of billions of dollars it is to make the software industry more productive. Great, then what? That's worth a trillion dollars. Maybe, but then what? How do you expand this into the rest of the economy, into everybody else? Which is why you get these conversations about partnering private equity, partnering with consultancies, where exactly as we've been discussing, guess what, it's actually quite hard to work out what to do with this stuff if you're running a real company. So you go to Bain, BCG, McKinsey or Infosys and Cognizant and IBM and Accenture, or private equity shareholders. So there is this sort of sense of like on the one hand, so I'm sort of trying to work out the answer as I speak, but like on the one hand, you're Building these bigger and bigger models and you kind of feel like you've got to keep doing it. But on the other hand. Yes, but what are people doing with it? Why do most people look at ChatGPT and not really think of anything to do with it today?
- [59:38]
B
Yeah, last question. Is there anything I forgot to ask you or anything else from the presentation that you want to make sure listeners leave with?
- [59:50]
A
It's a 7080 slide presentation and many of them could be kind of 20 minute conversations. The thing that I, I used last year and I, I used again is an IBM ad I found from the early 50s which has got a picture of a sea of, of engineers all holding up slide rules. And there's an IBM ad and it says you IBM electronic calculator gives you 150 extra engineers. And that's like how many pitches have you seen at a 16Z where that was the pitch. And we kind of remember we go through these waves of these fundamental technology changes every 10 or 15 or 20 years and they're all amazing and change everything and are completely unlike anything that's happened before. And so AI is amazing and transformative and completely unlike anything that's happened before. Mobile was quite a big deal too and so was the Internet and so were PCs and so was computing. Those were all also very big deals where it was hard to tell what was going to happen.
- [60:44]
B
And
- [60:46]
A
so we should sort of presume as a base case, okay, well we're going to go through that again and you know that will produce a bunch of things that ruin people's lives and it will put a bunch of people out of work and there'll be a bunch of stuff that we're not very happy about and there'll be a bunch of stuff that we all think is great. And then in 20 years time we'll kind of forget that there was a world when computers couldn't do that. That I mean here we are, we've been on this call for an hour and our computers didn't crash and we're streaming HD video to each other and it's like, well of course that worked. In fact, I'm also doing it with my iPhone. So my iPhone is streaming to my Mac over WI fi streaming video here and it just works like it's magic and we don't notice it anymore. And I think that's really my kind of one line description of how all of this is going to end up. It's going to be magic and in 20 years time we'll just say, well of course, that's how it is. Computers have always done that. That.
- [61:36]
B
Yeah, that's a great place to great place to wrap. The presentation is called AI Eats the World. It's on Benedict Evans website. It is excellent. There's a lot more that we didn't get to, so definitely go check it out. Benedict, this has been a great conversation. Thanks so much for coming to the podcast.
- [61:50]
A
Great. Thanks. Great to chat.
- [61:55]
B
Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like 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 X16Z and subscribe to our substack@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 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 discussed in this podcast. For more details, including a link to our investments, please see a16z.com disclosures.