AI Eats the World? A Reality Check with Benedict Evans

21:22And 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, you know, all pieces of enterprise software that A16Z is invested in, how often does like 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.

21:54Well, no, that's not how it works. Any more than it worked like that for cloud. Like you didn't say, well, our company is standardized on AWS. Like you don't even know what cloud that SaaS product runs on. That's the whole point. It's a practical way. 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 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.

22:31So all of that kind of taken together, well, the models are kind of 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 will be a bunch of edge and a bunch of open source. go and ask me ask Martin Cassado what he thinks I don't know he probably has a better way of saying he doesn't know than I do but we don't know so where is 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 whole other business models as well like Google selling ads so they've got a different attitude to pricing to open AI and so 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.

23:35Like, right now, we're in this period of extreme disequilibrium of supply and demand and price 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 bits is infinite. It's grown 1500, 2000x in the last 15 years. But you've still got your supply and demand price equilibrium and you've 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.

24:05And of course, developers will also swap back and forth. Now, this is, you know, 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 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 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 just say as 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 capex and like a trillion dollars of capex So, like, those multiples are going to move around.

24:53And then what? Yeah.

24:57Erik Torenberg: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? So, I think one way to, Some of the questions we've already talked about, how far do the stack of the models go? Can the models differentiate? 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.

25:40Obviously, this is what Apple's going to be talking about in a couple of weeks. How much can you push onto the device where the compute is free or free to you. Anyway, it doesn't have marginal cost for the developer. Another classic question is, it's almost like the questions 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 is paying for and how do those things kind of get reconfigured.

26:33And 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 this reminds me a lot of is there's something I wrote when I was at A16Z, which I called Content Isn't King. And I also wrote something that said Netflix isn't a tech company.

27:08And the point I was kind of getting at is that if you look to Netflix, this whole thing is enabled by stuff that the tech industry has 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. 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 all the questions that matter for Netflix have become media industry questions.

27:38This is obviously the great tension point about Tesla. It's a car company. It's a technology company. And so what I'm kind of getting at 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? But, you know, 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.

28:14But 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, 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.

28:49So 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, 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, you know, in what ways will the characters of the models change?

29:22We 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 don't, you know, I think I'm pretty safe in saying that, you know, Anthropik'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?

29:56Yeah. In which field? Like law, law, bank, I don't know where, but something.

30:02Erik Torenberg:If you had to guess, what are the use cases outside of coding that could potentially yield daily activity?

30:11So, I should say, the sort of presentation that I published a couple of weeks ago, there were 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 is the margins, what happens to the margins of the companies and everything else. And the third section I call 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, you know, the Yogi Berra quote that, you know, predictions are hard, especially about the future.

30:44And 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 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?

31:17Or do you do more for the same money? Or do you do more for more money? Because it becomes so much cheaper. Do you, 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-based 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, like, what stuff was just completely impossible, like totally cost-prohibitive, so that nobody even thought about it?

31:57And 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, 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.

32:29The problem with these kind of making predictions like this is that 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, we know, internet will destroy the value of physical distribution, it turned out that meant completely different things for newspapers and movie studios. 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 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.

33:05And 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? Which is, you know, advertising is a trillion dollars and retail is$25 trillion. So, you know, this is a reasonable size term. And so the thing that I've always used to think about was that Google and Meta and Amazon and 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.

33:41I'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 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.

34:21Because 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, this 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?

34:57And 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.

35:30That 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'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.

36:22So 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, you know, do sentiment analysis on calls into the call center and tell me which customers are angry. You know, 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'd live in a parallel universe.

37:03You 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.

37:08Erik Torenberg:It seems like, yeah, one of the questions we're now asking is, what was unreasonably expensive to do before that now is possible? Is it something crazy like rebuilding YouTube from scratch or rewriting Linux from scratch? Yeah, it's funny. The pair 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, 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.

37:49And it's to shift that level of abstraction and to kind of spot problems that 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 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.

38:23And 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 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. But if you kind of go back and think about all the pictures you've seen since you joined Essex, you can see, how many of them were things where people in the industry knew that was a problem?

38:51Like 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 that this new thing would fix that for them. And that's kind of the problem with the idea that, you know, middle manager in 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.

39:19Erik Torenberg: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, Benedict. That's all great, but tell us 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 we don't really know what that margin structure is going to look like are you going to go to 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 own Salesforce or something it'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 EPS, 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.

40:33One of them is to say that if you think about the 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 three to four hundred SaaS apps and then like another thousand apps that they've built, bought or 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.

41:05And 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, if you're PwC and you hire however many thousand graduates every year to train to the accountants, you've 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 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 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 it before?

42:04Do you use the LM to build yourself a tool for that, just as you might have a company department that runs on a 10 meg Excel spreadsheet that someone built 15 years ago? 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 framework 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.

42:36So 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 the call to 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 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.

43:08So the tension in both cases is where do you put the probabilistic software that can make mistakes and where do you put the deterministic 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 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, way more software. I mean, all software companies exist to solve problems created by other software companies.

43:36That was the joke in security. All security software exists to solve problems created by other security software. And clearly, that's what we went through with SAS. SAS gave us an order of magnitude, two orders of magnitude, more software. And we should probably expect that with this. And what that gets to the SaaS apocalypse is 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.

44:10But 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. Yeah.

44:22Erik Torenberg: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 this to 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, is they kind of look at what's going on inside a company and say, well, this is a crap way of doing it.

44:51This 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 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.

45:30That'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 they'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 pre-baked solution.

46:08These 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, 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.

46:44Erik Torenberg: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?

47:04So, I think these are kind of interesting ideas. There's things I struggle to have a strong opinion on because they're 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 10-15 years ago that APIs is a new BD and software companies could just open up your APIs and what's old is new you don't need an API anymore, you just have an MCP server and people 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?

47:49What 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. 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.

48:23Like 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 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 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.

49:00That'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 a different answer or a 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.

49:32Erik Torenberg:Yeah, that's helpful framing. 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 under-investing is riskier than over-investing. 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, 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, you know,$700 billion is the guidance from the big four companies this year.

50:19Well, 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$1 trillion, I think, from memory. I think that 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$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 that growth has to slow down because there isn't any more money.

51:03Now, 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 of supply.

51:44On 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're always chasing the next model. And so this is kind of the line that goes 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, there's a bunch of very clever semiconductor analysts who spend lots of time trying to put numbers on this.

52:19It is kind of like trying to put numbers on bandwidth, internet bandwidth in the late 90s. You kind of know what the rows in the spreadsheet are, but you don't really know where the values are. All you can 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 and you have a sort of FOMO problem in that

52:55so 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 there's nowhere else it can go.

53:37Erik Torenberg: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. 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? Obviously you've got a bunch of silly slash painful stories. I think there's also a point of like, maybe what's 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 massive disequilibrium.

54:24and 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.

54:52So 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 us this much money. Those things, 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 the problem here, of course, is consumer surplus, which is to say that, which is kind of what happened with ASL, in that, you know, guess what?

55:34You know, 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. 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 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.

56:13And, 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 that money.

56:28Erik Torenberg: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? 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.

57:08I think the raising all this money, I kind of go 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 is done by somebody else. And then you can do, you know, well, what's the return on capital? And the answer is, well, it depends 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 then it ended up not controlling the whole thing and other people ended up getting more value from that than they did.

57:43You know, I don't have the number in my head. what was 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, hey, aren't we just building AGI?

58:20We're going to build God in a box, which some people do believe, although it's hard to analyze, but maybe. So we'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 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? 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 partnering with 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 actually running a real company.

59:11So 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, sorry, 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? Yeah. Yeah.

59:40Erik Torenberg: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 I found from the early 50s, which has got a picture of a sea of engineers all holding up slide rules. And there's an IBM ad and And it says, you know, an IBM electronic calculator gives you 150 extra engineers. And that's like, how many pitches have you seen at A16Z where that was the pitch? And we kind of remember, like, we go through these waves of these fundamental technology changes every 10 or 15 or 20 years.

1:00:23And 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. 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.

1:00:56And, you know, 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. 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 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. 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.

1:01:30It's going to be magic. and in 20 years' time will just say, well, of course, that's how it is. Computers have always done that. Yeah.

1:01:36Erik Torenberg: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. Great. Thanks. Great to chat. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X and A16Z and subscribe to our sub stack at a16z.substack.com.

1:02:15Erik Torenberg:Thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. It should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com forward slash disclosures.

1:02:56Thank you.