Benedict Evans on AI: Agentic Coding, Commoditization, and the Future

Agentic coding went from being kind of useful to really changing everything. It was going to be magic. And in 20 years time, we'll just say, "Well, of course that's how it is. Computer's always done that." >> Every year, Silicon Valley awaits Benedict Evans presentation. Former A16Z partner, author of one of the industry's most read newsletter, The Mind Behind AI eats the world. >> We are in this extreme scarcity. Like, we can't spend $10 trillion a year

on AI infrastructure cuz there isn't$10 trillion a year there to spend on it. >> The big part of your thesis is this idea that models are going to end up as commodities. I don't think foundation models are a product. I don't think a chatbot is a product. I think the value will be further up. >> Explain the reasoning that they're a bit in what they look like. >> There's like three or four building blocks you can

put on the table. One of them is >> 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 use the world. Uh you know, you since wrote it almost a, you know, year and a half ago. At at this point we're get you always begin your presentation with your you know what are the big questions but I'm curious this time

first before getting into the what are questions going forward I want you to reflect on what have we learned since you originally uh made the presentation what's played out um and let's reflect back on >> 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 a s of a sense of kind of competitive tension that goes beyond just make a bigger model faster

with more about more more compute. Um we've had several iterations of open AI strategy in particular from sort of everything all at once yesterday to oops no maybe we should double down on coding. Um clearly agentic coding started working and so all the focus in tech has kind of narrowed in massively onto that as something that has absolute product market fit in the sense that like the customers are pulling it out of your hands. Um and

um and of course that comes with the supply crunch around capacity and price imbalance imbalance of supply demand capacity capex pricing that we see at the moment. Um so that's kind of the big shift like we had a moment of like this is kind of sort of working and kind of exciting but we're not quite sure what we're going to do with it to like right it works for coding um will it work for anything else

like 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. Um otherwise um you know the chartman numbers keep coming up, the models keep getting bigger, the capex keeps growing, the usage keeps growing, people using this more. But most of the sort of fundamental questions you might have had two or three years ago didn't really have answers. Like we don't know if there'll be a winner

in the models. We don't know if they can capture value up the stack. We don't know how much the models can do. Um we don't see a way that consumers will use this daily rather than weekly with the technology we have right now. So all all of those questions are still open. >> Yeah. And just on on the on the coding, how could could we have figured could we have foreseen that that would have been the

the the use case that really would have taken off or what's sort of a reflection on that? >> Well, um you deterministically you could have said, well look who's messing about with this stuff? Software developers. What are software developers going to try and make work software development? Um so you know at a very kind of simplistic naive level well yeah the stuff that should work is software develop first is software development just as like kind of

I often compare this moment to like the internet in like 9798 but it's also like the PCs in the early 80s or the late '7s. 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. Um, and the first thing that people are doing with LLMs, in a sense, LLMs are computers, is to make more compute. Um, and

so that's not terribly surprising. I think the shift is been at the beginning of this year clearly that agentic coding went from being kind of useful to really changing everything. And I'm not sure you could have you clearly there were people who were going to say, well, this is going to be able to do absolutely anything. And so they will say, well, yes, look, I told you. Um, but I don't think anyone kind of kind of

could have deterministically predicted exactly when that was going to happen and that it was going to be coding it would work first. >> And and what have we learned about sort of uh, you know, say more about what this means for engineers, junior engineers, senior engineers, sort of the the jobs discussion, how teams are organized, uh, etc. What have we learned so far? >> I don't think we've learned anything. I mean, you know, this this didn't

this didn't this didn't work six months ago. >> Yeah. and everyone is scrambling around trying to work out what it means. And you know, 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. Um, you know, it's going to take a couple of years for this all to settle down. You know, if nothing else because of

the pricing, you know, we've got this enormous crunch between the demand and the supply and hence the pricing. Um, so we don't know what, you know, what a team is going to look like. I think people are asking new questions around, you know, the sort of the obvious one of, you know, 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's sort of becomes much more real now in software development because you actually are automating a bunch of stuff that used to be done by people. So those questions are kind of now rather than theoretical. But I don't think

anybody can possibly say they kind of know what the market structure is going to look like or what the career of a software engineer is going to be in three years time. I think it would be you'd be insane to think that you could know that yet. >> Yeah. the talk about uh open AI uh talk about what's most uh surprised you or how have you kind of made sense of their sort of strategy development and

and the questions that they have going forward. >> Well, you know, it's always been such a such a a tranquil drama-free environment. So, you know, it's and you know, obviously they've had the issue with with with Fiji Simo having to take a medical leave um which kind of shuffled things up a bit. Look, clearly the second half, last quarter of last year, this 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? You know, ask chat GP 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 what it looked like. And then um um Anthropic with having less capital raised said, "No, we're going to focus on coding." And they got coding working. Um whether that was like a deliberate strategy or kind

of they stumbled into it is you know for other people to say but like clearly that worked but the question kind of still remains. It's like the stuff that's working right now is software development and some things in some other fields and then there's a lot of people who are kind of excited about using this around the edges and using this for some things and there's clearly this kind of very widespread between people in the valley

who bought you know a cluster of Mac studios and are running open claw all day versus um you know those other 40% of people who say yeah it's kind of useful um I used it last week for something and I'm like how do you br how do you bridge that and I don't think that question you soft software is a place where that's really really bridge jumped over that bridge and I don't think and then there's

a lot of other places where people are kind of scratching their heads and using it up to a point and then there's a lot of places where corporations are using it to automate some like 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 you know I go and talk to, you know, companies outside America

and outside of tech and talk to consultants and um, you know, investors. They're looking at those one at a time point solutions. Um, so like I'm speak 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 low margin business.

So that's a big deal and so they want to use LLMs to get better cash flow forecasting. That's very different thing from kind of going to catch EBT or Claude and saying hey you know give me a summary of my meetings this week. >> Yeah. H can you share how uh how did this compare with mobile um or other sort of platform in terms of you know use user early user adoption on on sort of the

you know weekly or daily user. >> So I think there there's there's 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 um the internet or cellular networks like mobile data. Mobile internet didn't need to wait for it kind of needed to wait for cellular data but it didn't need to wait for

like the internet to happen and the internet didn't need to wait for PCs and PCs didn't need to wait for consumer electronics and semiconductors and so on. So you've always got this accelerating adoption and you know when when when your boss my old boss Mark Andre was working on Netscape there were like doubledigit millions of PCs on the entire planet. So like no you couldn't have 900 million weekly active users because there 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 how 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 done in the last hour might still be there. That just doesn't happen anymore. And 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 a that's the weekend

to make that work. I mean, I remember this trying to get this stuff to work. And the same thing with the internet, like you know, you've got to get a floppy disc that has TCP IP on and you know, it's slow and none of the stuff that you need to do existed and the same with mobile. And we're kind of at that stage and of course it's not clear which of these things are going to work.

And that's the same thing now like a browser's going to work, is it going to be this? Is it going to be that? How's this 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 the work in to get something to work and just turning that into a thing where you can just press a button in real hands. I think the the third

point point here is and you know there's a much more tangible observation is that the the pricing crunch that we've already mentioned looks to me a lot like what happened with mobile data instead of 200910 where um suddenly people got bills for like5 $10,000 of data on the one side and on the other hand if you had flatweight data which is kind of what happened in the US with the iPhone the AT&T launches AT&T singular launched

the iPhone with flatweight data and then unfortunately ely everybody buys iPhones and starts and then they get 3G and people start watching YouTube and the whole network goes down because they just don't have capacity to do that. It's funny, there are still people in tech who don't understand that cellular networks have have marginal cost and like 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 infrastruct the pricing system aligned with the underlying cost and aligned with perceived value which they kind of did with with with capp bundles and fair use and throttling and so on. But the other side of that com and that's exactly you know you where you see now it's like on the one hand you're paying $20 a month and you get 10 grand worth of tokens and on the other

hand like you know you messed about for a couple of days and you get a bill for 10 grand and you're like what the hell is this? Um that's exactly what you know you see little literally see these stories now um which is exactly what happened in kind of 2009 8 9 10 also what happened in like 2001 and 2 and three with GPS um but I think the other interesting part of that analogy is or

that comparison is that since then mobile data traffic has risen by something like one and a half to 2,000 times and the mobile networks collectively have revenue of about a trillion dollars and they spend about 200 billion dollars 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 they would do they would do mobile banking um which now seems absolutely insane um 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 the absolutely central question for for LLMs is can the model do the whole thing or do you have to have 300 apps built on top of it can you just go to the model and say do my taxes for me or do you need to have a tax thing that uses that might use some AI in 10

10 different ways inside it um and if not then what is it to be a foundation 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 trillion2 trillion dollars of capex coming

down the pipe and the models get 100x 200x efficient more efficient every year and then there's new models and will the models use more tokens or less tokens but wherever like we'll get to a different equilibrium and why would that equilibri would 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 um 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 were they had all these levers to go up the stack back. Um, and of course they have network effects which models don't have. So that's sort of the

the question is do they end up like the infrastructure layers or do they end up like the operating system layers and capture value um and actually get to decide what gets built or do they end up I mean the irony of there of course is Netscape where um you know Mark Andre famously said that he was going to turn Windows into a set of badly debugged device drivers and um then Microsoft kind of crowbar 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 the kind a swirling mass of questions about how this settles out, which comes back right through to all all my kind of answers 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 um you know, software where 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 seems like uh they have better margins and are curing a lot of the value but

it's unclear if that will you remain the same or if u there will be sort of applications uh you know if we'll look more like the internet h how would you even begin to predict you know the answer to to s >> well so so two answers to this I mean there's all these sorts of quotes about how history works and you know my favorite one is history teaches us nothing except that something will happen and

you know you can always expose factor say well of course it worked out like that but it was generally wasn't obvious at the time and you know in particular I remember you know like sort of 15 years ago a lot of really really clever people in tech looked at the iPhone and Android and said you know this is open versus closed again and Android is 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 all of these comparisons are useful none of them are predictive um and you know it's always obvious in hindsight I you know it's funny I I' I've done a couple of podcasts recently and I've published this presentation and there's like a there's like a 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 bunch of 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 foundation models of a product I don't think a chatbot is

a product I think the value will be further up but the other side of this is like when you're at this stage in the cycle you there's there's there's many paths and you don't know which of those paths it's going to be. And to try and say, well, I think it's going to be that one is, you know, you might be right, but you do have to kind of be conscious of of like how uncertain this

is and how many different paths it could take. Um, that's the nature of this part of the cycle is all bets are open. You know, we get to the point where the scurve 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's a moment where it was clear, right, this is what's happening. Now, we move on to next quest. The next question. 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 what what what you know this year's iPhone looks like. I I don't pay attention to their market share in China like it happened. Next question. >> You mentioned the prediction of you don't think foundation models are are the product you think it'll move up. Explain that that that the re the reasoning there

there a bit and what that 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 um 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 isn't doesn't seem to be a network effect. There doesn't seem to be sort of levers you can pull and a strate

where Instagram is or YouTube is or Google searches. And we don't see an equivalent of that for LLMs. Now you have different emphasis. you know this 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 um 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. It needs to have the right data and it needs to be configured and controlled and have the right user interface and people need to have kind of sat down and thought about how this should work

because generally people who are good at using the tool and doing the job that needs the tool are not the same people who are good at deciding what the tool should be. So you know people who are really really good at you know designing print publications are not the people who should create and design. That's a different set of skills and you know people who are really really good at doing financial advice are not the right

people to design Turboax. Those are different people with different skills. So um and you have kind of groping around the middle of this. So you now have you know Claude for this, Claude for that and you have skills and so on. To me this is kind of like well one question is well who builds the skill? Another question is like well you know that seems to be a bit like what you get if you do file

new in Excel like these are templates and they'll take you so far but a certain point you know people outgrow the templates. Um there's a slide in my presentation which is a quote from that somebody said to me on Twitter years ago you 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

there's this kind of fuzzy swirly place of like do you need dedicated software? Do you need horizontal software? Do you need vertical software? But you can't just do everything in Excel. There's always, you know, you know, we've all like seen the department that runs along on a 10 megel file. Now, I run my business in numbers, but on a spreadsheet, but like there's a certain point where like you outgrow that. Um, and so following that on,

well, can the model labs build all of that? Well, of course not. No more than like Microsoft or Apple could build every Windows app or every iPhone app. So then, do the model labs have leverage? Are they are they 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 do the C all the pieces of

enterprise software that A6Z is invested in um 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 or open AAI because we we standardize on claude well no that's not how it works anymore than worked like that for cloud like you didn't say well you know our company standardized on AWS like you don't even know what company what what cloud that that

that SAS product one is on that's the whole point it's abstracted away it's not your And so the foundation models seem to look more like that. They seem to more look more like the hyperscalers in that sense in that they don't have you know they might have competitive advantages that further up the stack you don't have leverage you don't have a network effect you don't have control. Um that sort of prompts me to incidentally to to

say well maybe the right comparison here is with semiconductors where with each generation it just gets more expensive and so you have fewer players. Um, so all of that kind of taken together, well, the models are kind of diff commodities and the chatbot isn't the right UI or the right product and the companies aren't going to be able to build all of that stuff themselves. So therefore, they're low-level infrastructure. And so then, well, do they have

pricing power? Well, you're going to have, pick a number, three to six companies making a Frontier model, spending no one knows, no one honest knows, like something between $200 billion and $2 trillion a year on building these models. Plus, there'll be a bunch of edge and a bunch of open source. 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 um Google selling ads so you know they've got a different attitude to pricing to to open AI. Um and so like I think the 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. Right now we're in this period of extreme dis equilibrium of supply and demand and price and capace 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 2x in the last 15 years. But you still got your supply and dem price equilibrium and

you still got a murderous price war between Telkos in most parts of the world because fundamentally you're selling kind of a commodity um to people who will swap back and forth and 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 we 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. Um, 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 what tell prove what's going to happen this time. But it doesn't mean at least you should sort

of ask the questions and you should certainly I'll just say as a sort of a primary observation like this situation right now is transitory. 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 and then what >> going back to your your it's a good

segue to your point you made earlier of like hey you know we know you know Apple's Apple one next question to to 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 answer what so so some of the questions we've already talked about like well how far do the stand do the models go can the

models differentiate and so on I think another is obviously is 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 um you know how much can you push onto the device where the compute is free or free to you anyway doesn't have marginal cost for the developer. Another classical question is it's almost like the question is move out of technology. So if you're looking at a law firm or a cons consultancy um 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 were 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 Bane BCG McKenzie 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. Um, and so like well what does AI mean for fin what does this mean for finance both for that 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 um for big law firms um and for advertising?

and you kind of probably can know some of those questions. Um, 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 something I wrote when I was at A6C, 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 the tech industry is built. But all the questions for Netflix are 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 that that that all the questions that matter to Netflix have become media industry questions. You this is obviously the great tension point about Tesla. Is it a car company? was it was technology company. Um, 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 clients actually buying from them. Same thing for like what does generative video mean for Hollywood? Like, you know, Ben Affleck probably knows a lot more about this than I do. he built a company and sold it for like 100 billion hundred million dollars. So obviously he does but like you know that 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. Um and then the third level which is I think and I probably should have said this earlier what 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 Telos 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 cuz a PC cost like $3,000. So, you kind of knew the basic physical limits of what what could and couldn't happen. And with generative AI, obviously, we don't like we might like like

look at our phones when we get off this recording and there's a push notification that says that like Open AI'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, but like 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 up you

know in what ways will the characters as models change we don't know and that is different to previous platform shifts where you did know the sort of fundamental constraints um and so that will that will kind of spin off your 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'm think I'm pretty safe in saying that you know when swap it's gone from whatever it was 9 billion run rate at the end of last year to $47 billion run rate That's all software, isn't it? So what happens when someone else in some other field gets something working? >> Yeah. The I mean >> which field like 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 you know daily activity. M so there there I should say the sort of presentation that I published a couple of weeks ago there's sort of three sections and one of them is talking about capital and capex and infrastructure and foundation models and differentiation which is the stuff we talked about and the second is well how would you build software with this and what does this do for the software industry

and what would software look like if you and what what does the margins 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 um and I opened it with again what appears to upset certain category of person where I said you know the yogi bear quote that you know predictions are hard especially about the future um and I think there's a

sort of a back test point which is imagine asking these kind of questions about the internet in 1997 what would you have got what would you have not got but I think one way you can look at this is to say well this is automation that this makes a class of thing that people used to do that couldn't be automated now you can automate that and say then well what does that mean and I proposed like

three or four way the sort buttons to press. First one is is this just price elasticity, which is really what the jeans paradox is. Like if you make it cheaper to do stuff, do you do the same amount of stuff for less money or do you do the same more for the same money or do you more do more for more money? Um because it becomes so much cheaper. Um 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? Like is there something that now was was a barrier to entry in a cost space 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 cost totally cost prohibitive that 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 engineers make trains possible and it wouldn't matter how many horses you buy you couldn't have a train or like an express train. um much more contemporary example would be to point to something like YouTube or indeed

point to Spotify like you know Spotify says you know step one of the you 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 you all the music that there is which is something that was just completely impossible. Um, this is all the problem 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 but like you don't actually know what it's going to mean industry by industry. Um, so like if we'd been back in the late '9s 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 like newspapers got completely

screwed by this and movie studios are kind of not really changed very much. So again it depends. The other kind of part of this is you know try you can there are some places where I think you kind of can like 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 because you know advertising is

a trillion dollars and retail is $25 trillion so you know is a reasonable sized tam 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 skew 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 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 um and with um 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 by and obviously no is like a a difficult tricky term to use. What do you mean when you say no? But at a minimum like a much a very different level of statistical correlation of of what um an AI system would be able to do which is of course why you see the ad numbers and the you know the conversion rates shooting up in the every quarter from from from Google and and and um

and Facebook because they're rolling all of this into their ad systems and their um recommendation engines and their um prediction algorithms and like the 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 is to say like you kind of look at um 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 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, um, look at my Instagram and suggest a winter coat I should buy that will change my look, but not too much. And again, like 3 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 the going back right to the beginning like whenever you get a new technology you start by doing the old thing but more spreadsheets like more powerpoints, more email, better email. Um 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 benile observation but we 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? Um I mean I think the you know the enterprise version of this would be you know you've got all Zoom calls with clients recorded and you've got all the flows of emails in and

out of Salesforce and you can see all of the telemetry and the metrics and the analytics of how people use our product. So how should we change our prices to improve our churn? And again, that's something that an LLM might be able to do, which is very different to saying, 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. Um, 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 um 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. 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 it seems like yeah, one of the questions we're now asking is what sort of follows is um sort of what was in unreasonably expensive to do before that now is is possible and may you know is it so I don't something crazy like rebuilding YouTube from scratch or rewriting Linux from scratch or >> yeah it's

funny I mean you know the other the the paired fallacy of course is the new thing comes along and says well we're going to build the new thing with the the old thing with the new thing um of course we're going to we're going to build office with open source we're going to rebuild it on the web And you know, it turns out like, you know, 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. Um, and it's to shift that level of abstraction and and 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 sounds kind of useful. and the stuff where you think

I'm not quite sure that 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 never 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 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 an earlier point this is why I don't see the I think that this is the problem with the idea the model will do the whole thing

and if you kind of you kind of go back and think about all the pictures you've seen since you joined S6C how many of them were things where people in the industry knew that was a problem like 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 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 you know 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 could work out the right way to build a tool to solve it. Does this imply a less consolidated SAS environment than than before AI? Maybe

less bundling or single behemoths like the Microsoft Enterprise. >> Gosh, way to bring me back down to earth. Like is a SAS industry going to be less consolidated, Benedict? That's all great, but like tell us about the stocks. Um, 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. Um, and so there will be more competition. there 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 um are you going to go to you know outcomebased pricing it's really hard to like tie each button press in a piece of enterprise software to P&L sometimes you can in Salesforce

or something there's an awful lot of software it would be really hard to say well you know the the work I did today did this to DPS therefore this is what we should pay for it um this is what we should pay for that piece of software I don't think that makes sense and long Anyway, but what does the pricing structure look like over time versus now? Um, there will be more competition. It will be easier

to build stuff and quicker to build stuff. The way that I sort of thought about I suppose this is there's maybe kind of two framings to think about this that are kind of useful. One of them is to say that if you think about the sort of enterprise software fleet today, you've got like three buckets. You've got like your big iron horizontal systems, so SAP and workday and your CRM and your capital management software and your

payroll management software and so on. And then you've got um vertical software and typical big US company has like three to 400 SAS apps and then like another thousand apps that they 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 and so and stuff kind of moves back and forth between those and like in principle

every SAS app is doing something that you could have done in SAP or you could have done in Excel like you could have like managed your graduate recruiting in workday, but at a certain point like if you're friends conversation the other day, like if you're PWC um and you hire however many thousand graduates every year to train to be accountants, um 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, like cuz why would you buy software for that? And then like then there's a space in the middle. Do you do it in workday? Do you do in Excel? Do you do it in a

dedicated app? And now you add chat EVC to that. Do you do that in an LLM? Is there an LLM tool that means you can do that in Salesforce where you couldn't do it before? Or you can do it in your vertical app that you couldn't do before? Um, 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 10meg Excel spreadsheet that

someone built 15 years ago. No one knows it works. No one know how it works, but they're still using that. So it kind of it's it arrives within this kind of broad fragmented complicated landscape and it's another set of options for how you would do that task. So this is kind of one framing to think about to think about it. I think the other framing to think about this is does the yellow lamb 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 and what I should say on the call to call 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 you know go look at Salesforce and workday and all of our email and Google Analytics and and and then synthesize something that you couldn't have done before. So the the the the tension in both cases is where do you put the probabilistic software

that can make mistakes and where do you put the deterministic system software that can't answer these kind of questions. So where do you put the database and where do you put the LLM and is it like which is at the top and which is at the bottom. The answer is probably both depending on on what you what you what you're doing and where it goes. Um all of which is a long way of saying like what

does this do to software and the answer is more software like way more software. I mean all software companies exist to solve problems created by other software companies. That was the joke in in security. Like all security software is exist to solve problems created by other security software. And like clearly that's that's what we went through with SAS. Like SAS gave us an order of magnitude, two orders of magnitude more software. Um and we should probably

expect that with this. Um what that gets to you to the SAS apocalypse is all the investors are kind of looking at all these companies and saying well we don't really know which of these companies are going to get screwed by all of this. Some of them must be like obviously there must be some you know go through the end of this and you know x% of all the SAS companies that are out there are going

to get wiped out by this but you don't know which ones so you probably shouldn't derate the whole thing by 50%. But clearly you're going to like go I'm not sure I'm going to be long software at the moment until I have some idea of what the hell's going on. >> You 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 the right shape of software for the future? >> Well, in a sense, I mean maybe kind of a an interesting turn on this is this is both what what strategy consultants do and software companies do

is they kind of look at what's going on inside a company and say, well, this is a crap way of doing it. This would be a better way of doing it. It would achieve your objectives better. And a software company kind of encodes that in software. and you know a strategy consultancy kind of encodes that in you know workflows and or charts and processes and training and you know objectives and you know maybe tells them to

buy some software to do that thing or maybe now increasingly maybe builds them that software as well. Um I think um 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 an eflow chart of and explain to you. Um that's what that's a big chunk

of the value of Bane BCG McKenzie is that they have license to come into a company and talk to everyone and talk to the people you're not allowed to talk to that are in a different org and not get fired and to go and work out how this actually works as opposed to how it's supposed to work and why it is that people aren't doing the strategy because actually guess what their bonus targets depend on them

not doing the strategy and work all of that out and be a team that's ready to come in from the outside and give you the answer and then you can blame them or have that kind of that that pre-baked solution. Um that's not you know you that's that that's these are sort of problems in organizational management and how people function and how people can explain what they do that are very hard to write down and very

hard to kind of bake into a claude skill and say there you are like make a PowerPoint. Um, 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? Um, 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. >> To that end, do 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. They're things I struggle to have a strong opinion on because you know they're not kind of not deep deep into the weeds of of how enterprise infrastructure gets bought. I I wonder how new some of these questions are. Um I remember um Chris Dixon saying like 10 15 years ago that you know APIs is a new BD and software wouldn't need come. Software companies could just you know open up

your APIs and like well like what's old is new. Um you know you don't need an API anymore. You just have an MCP server and like people will just plug in the agent will just plug into that. Um I don't know. I think the challenge with a lot of this stuff is that all the the decisions are really exception handling. Like the question is always what can you not automate? What requires someone to make a decision

um 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. Um 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. Um the way that I used in the deck was to talk about what's a task versus what's 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. Um they do spend almost none of their time doing the same things but like to the client it's kind of the same thing. Um it just gets done in a

completely different way with a whole bunch of different tasks. Um, and one of the sort of the more sort of either profound or kind of abstract ways to think about this is where is it that you want the ab the average? Where is it that what you want is the way that everybody will do this? That's the way everyone would do it. That's what anyone would say. That's what anyone would make. That's what any associate would

make. That's what anybody would give me. That's the answer anyone would give. Versus where is that not what you want? Where is it that you want the answer to a new question or a different answer or a different idea? Um because LLMs are 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. Various people including you know uh 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 um Microsoft, Meta and Google are all on in line to spend

over 50% of revenue on capex this year. And you know we think of telecoms as being capital intensive. telecom spend instead of 15 to 20% of revenue on capex. Um 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 dollars. I think from memory depends which exactly who you ask. Um, so $700 billion a year is not an impossibly large amount of money as of 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 um for any length of time. Um, and so there's a

certain point at which like that growth has to slow down because like there isn't any more money. Um now clearly you can talk about ROI and your ability to produce returns from that investment. Um 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 inf 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 um 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 we've got a bunch of multiples. So, um there's far more demand than supply. On the other hand, the efficiency is increasing massively.

Um 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 runs across all of it is is the model is only relevant for 3 to six months, six to nine months, whatever you want to say. And the model costs how many billion dollars.

Um, and how much infrastructure do you need to do that? Um, I don't think that mass is really shaken out yet. I mean, obviously you can, you know, there's a bunch of very clever semiconductor analysts who spend lots of time trying to put numbers on this. It is kind of like trying to put numbers on bandwidth, internet bandwidths in the late 90s. like you kind of know what the rows in the spreadsheet are, but you don't

really know where the values are. All you can really say is, well, look, it can't be you. There's there's clearly physical limits on this. Um I think you know another way to answer the question is like if you're Google or Meta or Microsoft um 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 um so on the one hand your

returns on the investment at the moment are hugely positive. Um, on the other, you can't let other people get away with this without you participating because then your company's gone and you are 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. Um, so if this is the future of compute, then you need to be

participating in it. Um but obviously at the same time the CFO is sitting there saying well yeah that's great but um how much participation are we talking about here and I don't think we you know it's clearly at a certain point that curve is going to have to taper off cuz like there's nowhere else it can go. >> Do you think there's going to be a reckoning around token maxing? Uh 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. Um, which is kind of what happened with mobile, you know, in 2010. Like, you know, you you got a $10,000 bill and you would have said, "Wait, wait, I thought this was a flat rate bundle." Like, what happened? Um, so there's like you've got you've

got you obviously you've got a bunch of of like silly/mateful stories. Um, I think there's also a a point of like I think but what what maybe what's slightly more interesting as a question is um and clearly there's going to be a point in which as I've said se several times we're at a moment of kind of massive dise equilibrium and the pricing has got to get back into alignment with the cost and the usage has

got to get it into alignment with the pricing and the ROI. The challenge is it's a bit tricky at this early stage. It's quite hard to know what the ROI is. It's it's rather like giving everybody the internet in the late '9s and saying okay go off be more productive and if you look at like there's a survey from deote there's also a survey from the fed that's in my presentation where if you go and ask

CFOs where have you seen 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 um more productivity you can make more slides more quickly you can 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 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, well, how long does this take? I think the other answer to problem here, of course, is consumer surplus, which is to say that um which is kind of what

happened with Excel in that, you know, guess what? you know, if 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. Um, so some of what happens is that, um, these things become competitive necessities and everybody has to buy it, use it. Um, 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 I mean, if you're, you know, if you're at McKenzie and, you know, doing that or or Bane or BCG and doing that piece of analysis used to take a week and now it takes a day, um, 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 with with with investment banks and financial analysis. You just went did way more analysis with probably fewer people and charged customers the same amount of money. part of your your 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 uh

you know in the in the fastest time in history is these foundation model companies. Um so given that what advice might you have for them either either collectively or we can pick on someone individually um in order to in order to adapt? >> It's not that I know that they're going to become commodities. my position is more more now well like hey here is a 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 um and that's as far as I would commit to that um I think the you know the the raising all this money I kind of go back to my point about mobile which again has no predictive value but 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 do you know well what's the return on capital and the answer is well it depends which mark whether you're in America or Europe or or India or China um but meanwhile like that was a worthwhile thing to do and it produced a return for somebody but then it didn't ended up not controlling the whole thing and other people ended up getting more value from that um than

they did um you know I don't have the number in my head what is Google's net income last year was what $50 billion or something what was net income for you know the total telecoms industry I should really subscribe to Bloomberg then I could just answer these questions instantly. Um but like you're a pretty safe bet that Google, Meta, Amazon, um Microsoft, Apple produce more profits than the entire telecoms industry. Um so this is a um

is a puzzle is you're build 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 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 um 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 that aren't software development I mean that's a good business um is that the only business there's you know pick a number of that many hundreds of billions millions of dollars it is to make the software industry more productive great then

what I mean that's you know that's worth a trillion dollars maybe but but then what like 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 consultancies where you know 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 um so you

go to Bane BCG Mackenzie or Infosys and cognizant and IBM app Accenture or private equity shelters. So there is this sort of sense of like on the one hand you so I'm sort of trying to work out the answer as I speak but like on the one hand you're building this big these bigger and bigger models and you've 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? >> Last question. Is there anything from the presentation that you want to make sure uh listeners leave with? The thing that I I used last year and I I used again is an IBM ad I found from the early 50s which has got a picture of a sea of of engineers all holding up

slide rules and it's an IBM ad and it says you know an IBM electronic calculator gives you 150 extra engineers and that's like how many pictures have you seen at A6Z where that was the pitch. Um, and we kind of remember like we go through these waves of these these fundamental technology changes every 10 or 15 or 20 years and they're all amazing and change everything and are completely unlike anything that's happened before. And so AI

is amazing and transformative and completely unlike anything that's happened before. Mobile was quite a big deal too and so was the internet and so were PCs and so was computing. Those were all also very big deals where it was hard to tell what was going to happen. 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. Um, and you know, there'll be a bunch of stuff that we're not very happy about. Um, 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. Um, I mean, here we are. We've been on this call for an hour and our

computers didn't crash and we're streaming HD video to each other and it's like, well, of course that worked. In fact, I'm also doing it with my iPhone. So, my iPhone is streaming to my Mac over Wi-Fi streaming video here and like it just works like it's magic and we don't notice it anymore. And I think that's really my kind of oneline 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 great place to wrap. The presentation is called AI Essets the World. It's on Benedict Evans website. It is excellent. Uh 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. Great

to chat.