a16z Podcast - AI Eats the World? A Reality Check with Benedict Evans

Episode Date: June 4, 2026

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.   Resources: Follow Benedict Evans on X: https://x.com/benedictevans Follow Erik Torenberg on X: https://x.com/eriktorenberg Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow 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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. 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.

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 stack. The place that's got product market fit right now is coding. And so it'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 at?

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. Google said that the risk of underinvesting is riskier than overinvesting. investors are kind of looking at all these companies and saying, every major technology platform shift creates the same challenge, 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 open. 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 Acing Z podcast Thank you

Last time you were here we were discussing the first iteration of your presentation AI used 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. 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 compute. We've had several iterations of OpenAI strategy in particular from sort of everything all at once yesterday to oops, no, maybe we should double down on coding. Clearly, agented coding started working, and so all the focus in tech has kind of narrowed in massively onto that as something that has an absolute product market fit in the sense that, like, the customers are pulling out out of your hands. And of course, that comes with the supply crunch around capacity and price imbalance, imbalances 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 all going to do with it. It works for coding. We'll 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 Charmonde numbers keep coming up. The models keep getting bigger. The CAPEX keeps growing. The usage keeps growing. People are using this more. but most of the sort of fundamental questions you might have had two or three years ago

don't really have answers. 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. Yeah.

And on the coding, could we have foreseen that that would have been the use case that really would have taken off? What's a reflection on that? But deterministically, you could have said, well, look, who's messing about with this stuff? Software developers, what a software developer is going to try and make work software development. So at a very kind of simplistic, naive level, well, yeah, the stuff that should work is software about first, it's 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, but 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 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 agented coding went from being kind of useful to really changing everything. Clearly there were people who were going to 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 kind of could have determinously predicted exactly when that was going to happen

and that it was going to be coding it would work first. 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? 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. You know, 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 of the supply and hence the pricing. So we don't know what a team's 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're actually all-operating 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. 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. Well, it's always been such a tranquil, drama-free environment. Obviously, they've had the issue with Fiji Sumo 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 do other stuff with this? So we'll do ads, we'll do e-commerce, we'll do shocking. cartes, we'll do payments, we'll do a browser, we'll do a social video app, you know, everything. Ask chat GPT for 15 ideas for what we could do to build value on top of infrastructure and then we'll do all of them. 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 opening high kind of swing around and okay, well, clearly that's the thing. But the question kind of, kind of, you know, still remains. 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 open crawl 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 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 the 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 the new tool. Instead, you're saying, okay, here's a 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 speak 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 a very different thing from kind of going to chat to EBT or Claude and saying, hey, you know, give me a summary of my meetings this week. 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? 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 cellular data. We 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, Mark 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't 900 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. Go 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 as 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 it going to be that? How is it all going to 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 in your hands. 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 instead of 2009-10, where suddenly people got bills for like $5,000, $10,000 of data on the one side. And on the other hand,

if you had flat-ray data, which is kind of what happened in the US or the iPhone, the AT&T launches, AT&T single launch the iPhone with Labrata, 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 the cellular networks have marginal cost. They have to add more capacity, and that costs more money. And so the networks kind of had to scramble

to get, like, the cost curve aligned with the infrastructure, the 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 $10,000 worth of tokens. And on the other hand, you know, you messed about for a couple of days and you get a bill for $10,000. And you're like, what hell is this? That's exactly what, you know, you see, they'll 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 GPS. 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 two thousand times. And the mobile networks collectively have revenue of about a trillion dollars. And they spent about $200 billion a year on CAPEX. And the socks 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 like 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 stack.

And this is of course absolutely, as I said earlier, this is one of 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 you my taxes for me? Or do you need to have a taxing that users? That might use some AI in 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, two trillion dollars of capics coming down the pipe. And the models get 100x, 200x, more efficient every year. And then there's new models, and will the models use more tokens or less tokens? But wherever 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 know, 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, you know, Mark Andreessen famously said that he was going to turn Windows into a set

of badly debug device drivers. And then Microsoft kind of crowbarred 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 I think that's kind of the more, these kind of swirling mass of questions about 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. Yeah. Yeah, it's unclear whether it looks more like the, you know, internet or sort of, you know, software where a lot of the value or just better margins happen at the application layer or sort of the cloud where it seems the, you know, where sort of existed at the hardware layer. And right now,

so far it seems like Nvidia and going, you know, up. It's, it's, you know, it's, you know, it's, it seems like they have better margins and are occurring a lot of the value, but it's unclear if that will remain the same or if there will be sort of applications, you know, if it will look more like the internet. How would you even begin to predict, you know, the answer to such? Well, so to answer to this, I mean, there's all these sorts of quotes about how history works, and, you know, my favorite one is history teaches there's nothing except that something will happen. And, you know, you can always expose factors 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 looks at the iPhone and Android and said, you know, this is open versus closed again 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, you know, all of these comparisons are useful. None of them predictive. And, you know, it's always obvious in hindsight. I, you know, it's funny, I've done a couple of podcasts recently and I published this presentation. And there's like a, there's like a class of comment on this stuff, which is to say, you know, 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 class 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 Fland Ocean models of a product. I don't think a chapod 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, there's many parts. And you don't know which of those parts 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 like how uncertain this is

and how many different parts it could take. That's the nature of this part of the cycle, there's all the bets are open. You know, we get to the point where the S curve kind of curves up and it narrows in. And, you know, there was a moment when, you know, Windows phone might have worked. In hindsight, no, it probably wasn't going to work.

But, you know, there was a moment when it wasn't clear how mobile was going to work. And there was a moment where it was clear. Right, 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 want. like I don't care what the next year's I what what you know this year's iPhone looks like I don't pay attention to their market show in China like it happened next question yeah the um but just to flesh it out you mentioned the prediction of you don't think foundation models are the product you think it'll move up explain that that the reasoning there are a bit in what they could look like so I think there's like three or four like building blocks you can put on the table. One of them 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 in a strategy, a position you can get into where Instagram is or YouTube is or Google searches. And we don't see an equivalent of that for LLLM's. Now you have different emphasis, you know, maybe this one's better than that, maybe you like this one more than that. But 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 V1 UI. 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 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 they should work because generally people who are good at using the tool and doing the job that needs the tool and not the same people who are good at deciding what the tool should be. So people who are really, really good at designing print publications

and not the people who should create indesign. That's a different set of skills. and people who are really, really good at doing financial advice and not the right people to design turbotax. There's different people with different skills. And you have kind of groping around the middle of this, so you have clawed for this, clod for that, and you have skills and so on.

To me, this is kind of like, well, one question is, well, who builds a skill? Another question is like, well, that seems to be a bit like what you get if you do file in you in Excel, like these are templates, and they'll take you so far. but at a certain point, you know, people outgway the templates. There's a slide in my presentation, which was a quote that somebody said to me on Twitter years ago. He said they were a consultant,

and half of the jobs were telling people who used Excel to use a database, and the other half were telling people who used a database to use Excel. So this is kind of fuzzy, swirly place of like, do you need dedicated to software? Do you need horizontal software? Do you need vertical software? But you know how it just do everything in Excel. We've all like seen the department that runs along on a 10-Mexel file.

No, I run my business in numbers, but on his spreadsheet. but there's a certain point where you outgrow that. And so following that on, well, can the model labs build all of that? Well, of course, not. No more than Microsoft or Apple could build every Windows app or every iPhone app. So then do the model labs 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, like all pieces of enterprise software that A16 is invested in, how often does, like, the law firm or the manufacturing company or the banks say, oh, well, does this use Claude, or does it use OpenEI, because we standardise on Claude. Well, no, that's not how it works.

Any more than it did work like that for Cloud. Like, you didn't say, well, our company standardised on AWS. Like, you don't even know what company, what cloud that South product runs on. That's the whole point. It's a subtracted away. It's not your problem. And so the foundation models seem to look more like that.

They seem to look more like the hypers in that sense, in that they don't have, you know, 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 with some 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 different commodities

and the chat port 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 their 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 will be a bunch of edge and a bunch of open source. I know. Go and ask Martin Consolto 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, you know, Google's selling ads.

So, you know, they've got a different attitude to pricing to open AI. And so, like, I think the challenge here is, like, 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. Like right now we're in this period of extreme disequilibrium of supply and demand and price and capates 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. It's grown 1,500,000 X in the last 15 years. But you still got your supply in the 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 force.

Now, this is, you know, I'm happy to say that this might be completely. 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 get subsumed into the model, or they have leverage further up the stack. And, you know, 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 that it's, 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, is a sort of primary observation, like, this situation right now is transitory.

You know, we're in this extreme scarcity. And then we have a pricing system and we have a free market and we have a surge of capax and like a trillion dollars of capax. So like those multiples are going to move around. And then what? 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, Apple's, Apple one.

Next question. As a segue, what are some of the next? questions that you're most focused on or that we should be paying most attention to? So I think one way to want, some of the questions we've already

talked about, how far up to the standard of the models go? Can the models differentiate? And so on. I think another is obviously is 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 computer is free or free to you? Anyway, it doesn't have marginal cost for the developer. Another class 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 and 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 is paying for. And how do those things kind of get reconfigured? And so, like, well, what does AI mean for finance? what does this mean for finance, based for that internal like hiring structure and the kind of products you can create and like 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 that reminds me a lot of is something I wrote when I was at A16C, 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 TV, 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. Like, 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 the, that, that, that all the questions that matter for Netflix, have become media industry questions. This is obviously the great tension point about Tesla, is a car company, which is a technology company. And so what I'm kind of getting at is, is that, you know, 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? 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 question is 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 at 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, you know, 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 the PC costs 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. Like, we might, like, look at our phones when we get off this recording, and there's a push notification that says,

like, Open AI's new model is out and it's like 2% of the price because they work 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, you know, pick, or, you know, in what ways all the characters of models change? We don't know. And that's 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 when Swapix'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. In which field?

Like law or law bank? I don't know where, but something. If you had to guess what are the use cases outside of coding that could potentially yield daily activity. So, I should say, the sort of presentation that I published a couple of weeks ago, there was 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 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 it appears to upset a certain category of person where I said, you know, the Yogi Berra quote that, you know, predictions are hard, especially about the future. And I think this is 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 ways the buttons to press. First one is, it's 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 more for the same money, or do you 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 base at 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 like the sort of the final question would be like what stuff was just completely impossible like 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. You know, Spotify says, you know, step one of the, you know, look at the last 25 years of the music business.

The first half is what happens if you don't have to buy a $15 CD to get that track? But then the second half is what if $15 a month gets all the music that there is, which is something that was just completely impossible. This is all, the problem with these kind of making predictions like this is like on the one hand you're going to say stuff that's kind of clever and obvious, but like you don't actually know what it's going to mean industry by industry. So like if we'd been back in the late 90s and we'd said, well, internet will destroy the value of physical distribution.

It turned out that meant completely 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. Like, sorry for the people that annoys. But the other part of this is, you know, 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 in e-commerce and brands and marketing and everything that we buy? Cheers, you know, advertising is a trillion dollars in retail is 25 trillion dollars. So, you know, it's a reasonable size to tamp.

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. Because Amazon doesn't really know what a toilet seat is and doesn't know that people don't buy two. Actually, they should know that. That should be a frequency analysis, but they don't.

And with an LLM, like 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 in 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 saying are more likely to be things you'd like to buy, and so they have these enormous, this sudden acceleration in their ad revenue. And all of which is to say, like, 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, 10 years ago, that certainly wouldn't work. 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. And those kinds of shifts in like what the computer knows, what it can automate, what suggestions it can make. Going back right to the beginning, like 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 know, you've got all our Zoom calls with clients recorded and you've got all the flows of emails in now to 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. do, which is very different to saying, you know, do sentiment analysis on calls into the call center and tell me which customers are angry. You know, you go 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 put it 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. You know, VCs would have, you know, it wouldn't be a one in 10 hit rate. It would be a 10 out of 10 hit rate.

It seems like, yeah, one of the questions we're now asking is what, or you're sort of, if I was unreasonably expensive to do before that now is possible and, you know, is it something crazy, like rebuilding YouTube from scratch or rewriting Linux from scratch? Yeah, it's funny. I mean, you know, the other, the, the, the, the, the, the, the, the, the, the, The paired fallacy, of course, is the new thing comes along and says, well, we're going to build the old thing with the new 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, like, you know, guess what? Look at Google Docs. It's called 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, you know, 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 like kind of fill a hole in the universe. And like 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, you know, where, you know, 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 problem existed. and no one, including the people who have that problem, 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 another point. This is why I don't see the, I think,

that this is a problem with the idea the model will do the whole thing. If you kind of go back and think about all the pictures you've seen since you've joined as a student see, 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 was actually took like two years to explain to them and persuaded them that that problem actually existed at all

and that this new thing would fix that for them. And that's kind of the problem with the idea that middle manager in finance is going to use this tool to solve this big global industry problem. Like, 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. Does this imply a less consolidated SaaS environment than before AI?

Maybe less bundling or single behemoths like the Microsoft Enterprise? Gosh, way to bring me back down to Earth. This is a SaaS industry going to be less consolidated, Kennedy. That's all great. But tell us about the stocks. What are the kind of building rocks 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&L. Sometimes you can. I own Salesforce or something. There's an awful lot of software. It would be really hard to say, well, you know, the work I did today

did this to the PPS, therefore this is what we should pay for it. This is what we should 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 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 these 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 U.S. company has like three to 400 SaaS apps and then like another thousand apps that they've built, built themselves 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 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 your... This is a conversation the other day. Like if you're PWC and you hire however many thousand graduates every year to train to the 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 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 chat EPC 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 it before? Do you use the LM to build yourself a tool for that just as you might have a company department that runs on a 10 mega Excel spreadsheet that someone built 15 years ago. No one knows how it works. No one knows how it works, but they're still using that. So it kind of 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 for think about it, to think about it. I think the other framing to think about this is does the L&M 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 do here, what I should send to the cost of the customer.

So it's a feature. It's a button that's controlled. and has tooling and 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 go look at sales force 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 terministic software that can't answer these kind of questions? So where do you put the database and where do you put the LLM and which is at the top and which is the problem? the bottom, and the auto is probably both, depending on what you're doing and where it goes.

All of which is a long way of saying, like, what is it's due 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 a joke in security. All security software exists to solve problems created by other security software. And clearly, 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.

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 de-rate the whole thing by 50 percent, 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. 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? Do you think we need more experimentation, iteration from these vertical AI startups to get this is the right shape of software for the future? 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. It's they kind of look at what's going on inside of 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 out in software, and a strategy consultancy kind of encodes that in, you know, workflows and all charts and processes and training and, you know, objectives and, you know, maybe tell us and 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 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 flow chart of and explain to you. That's a big chunk of the value of B and B, CG, McKinsey, is that they have licensed to come into a company and talk to everyone

and talk to the people who are not allowed to talk to they're 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 I 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 pre-baked 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 broad skill and say, there you are, like, make you a 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? 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. So I think these are kind of interesting ideas.

There's 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 is a new BD and software wouldn't need software companies

could just open up your APIs and like, well, like what's old is new. You know, you don't need an APO anymore. You just have an MCP server and like 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? or 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 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. We 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 is 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 sort 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 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 a different idea because LLMs are going to be very good at anything where you can describe how people do and where what you want is the way anybody would do that and not so good 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. Yeah, that's helpful for me.

I want to zoom back out from the weeds gearing towards closing here and a couple last questions. One is, in various people, including CEO of Google, 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? Well, there's a financial gravity problem in that Microsoft, Mesa, and Google are all in line to spend a 50% of revenue on CAPEX this year. And, you know, we think of telecoms as being capital intensive. Telecoms spend sort of 15 to 20% of revenue on CAPEX. And so, you know, $700 billion is the guidance from the big four companies this year. Well, you know, telecoms is 300, mobile is 200, total telecoms is 300. Oil and gas, depending on which definition and which bits of it you're counting,

is anything from $700 billion to a trillion, I think, from memory. I depends on which, 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, like, those companies could not spend $1.5 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, like, that growth has to slow down because, like, there isn't any more money. Now, clearly you can talk about ROI and your ability to produce returns from that investment. And, you know, clearly the capital markets are willing to fund that up to a point. But, like, pick a number at random. like 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's a finite, there are kind of like 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 like we've got a bunch of multiples. So there's far more demand of supply.

On the other hand, the efficiency is increased. 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're always chasing the next model. And so this is kind of the line that we want to 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,

semiconductors an analyst who spend lots of time trying to put numbers on this. It is kind of like trying to

put numbers on bandwidth, internet bandwidth in the late 90s. You don't know what the rows in the spreadsheet are,

but you don't really know where the values are, or you could really save, well, look, it can't be. There's clearly

physical limits on this. I think, you know, 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. And you have a sort of, you know, 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 META 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've, 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. 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.

Well, obviously, you know, you've had people like using the most expensive model to dick around on the internet. Just kind of what happened with mobile, you know, in 2010. Like, you know, you got a $10,000 bill and you said, wait, wait, I thought this was a flat rate bundle. Like, what happened? So there's like, you've got, you've got, obviously you've got a bunch of, like, silly slash meaningful stories.

I think there's also a point of like, I think maybe what's, maybe what's, you know, what slightly more interesting as a 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 it 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, wherever you've 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, but it's not the same as saying,

well, we made this new thing with AI and it had this revenue or it saved 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 a problem here, of course, is consumer surplus, which is to say that it's kind of what happened with their cell. In that, you know, guess what? If a DCF takes you a week, then you probably only do one or two DCS. And if a DCF takes you 10 seconds, then you do 50 DCS.

But you probably 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 away. So you don't get to charge more for it. You know, I mean, if you're at McKinsey and, you know, doing that or Orban 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 like your cost base hasn't changed either. So like, you know, which is exactly the way to think about, you know, what happened with investment banks and financial analysis. You just did way more analysis with probably fewer people and charged customers to save them out money.

Part of your, a big part of your thesis is this idea that models are going to end up as commodities. And yet the, you know, the layer that's raising the most money, you know, 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? It's not that I know that they're going to become commodities.

My position is more now. Well, okay, 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,

it's 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 is done by somebody else. And then you can do, you know, what's the return on capital? And the answer is, well, it depends on which mark,

whether you're in America or Europe or India or China. But meanwhile, like, that was a worthwhile thing to do and it produced a return for somebody, but that 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 of my hands. Google's net income last year

was what, $50 billion or 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 a pretty safe bet that Google meta, Amazon, Microsoft Apple produce more profits than the entire telecoms industry. So this is a puzzle.

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 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 and 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, carry, 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 software development? I mean, that's a good business. is that the only business? If there's a near, you know, pick a number of how many hundreds of billions of dollars it is to make the software industry more productive, great.

Then what? I mean, 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, you know, partnering private equity, partnering with the consultancies,

where, you know, exactly as we've been discussing, guess what is actually quite hard to work out what to do with this stuff if you're actually running a real company. So you go to B.CG McKinsey or Inphasis and cognizant to an IBM and Accenture or private equity shareholders. So there is this sort of sense of like on the one hand you're 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 chat GPT and not really think of anything to do with it today? 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? It's a 70, 80 slide presentation and many of them could be kind of 20 minute conversations. The thing that I used last year,

and I used again, is an IBM ad ad I found from the early 50s, which has got a picture of a sea of engineers or holding up slide rules. And there's an IBM ad and it says, an IBM electronic calculator gives you 150 extra engineers. And that's like, how many pictures have you seen at A16Z

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 net, and so were PCs, and so was computing.

Those were also very big deals where it was hard to tell what was going to happen. And 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, you know, there'll be a bunch of stuff that we're not really 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. 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 videos for 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's like it just works. It's 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. Yeah. that's a 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. Thanks. Thanks for listening to this episode of the A16Z podcast. If you like 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 Podcast. and Spotify, follow us on X and A16Z and subscribe to our substack at A16Z.com. Thanks again for listening and I'll see you in the next episode.

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