---
author: a16z
date: '2026-06-08'
guest: ''
layout: post.njk
source: https://www.youtube.com/watch?v=ktl8mNiWqMM
speaker: a16z
tags:
- artificial-intelligence
- automation
- software-development
- technology-adoption
title: 本尼迪克特·埃文斯论AI发展、编程应用落地与技术浪潮的演进
summary: 本尼迪克特·埃文斯在播客中探讨AI演进，指出编程成为当前显现产品市场契合度的首个用例，但模型商业价值、赢家归属及向更广泛经济领域扩展仍存疑问，认为AI变革终将如历史浪潮般化为理所当然的技术底座。
insight: ''
draft: true
series: ''
category: ai-application
area: tech-engineering
project: []
people:
- Benedict Evans
companies_orgs:
- a16z
- OpenAI
products_models:
- ChatGPT
media_books:
- AI Eats the World
status: evergreen
---
Agentic coding 从有点用变成了真正改变一切。它曾经像是魔法。而在二十年之后，我们只会说：“嗯，事情理所当然就是这样。计算机历来就是这么干的。”
Original English
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."
Original English
>> 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.
Original English
>> 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.
Original English
>> 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.
Original English
>> Explain the reasoning that they're a bit in what they look like.
Original English
>> There's like three or four building blocks you can put on the table. One of them is
Original English
>> Benedict. Welcome back to the Acing Z podcast.
Original English
>> Thank you.
Original English
>> 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
Original English
>> 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.
Original English
>> 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?
Original English
>> 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.
Original English
>> 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?
Original English
>> 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.
Original English
>> Yeah.
Original English
>> 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.
Original English
>> 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.
Original English
>> 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
Original English
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.
Original English
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.
Original English
>> 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.
Original English
>> 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.
Original English
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.
Original English
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.
Original English
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.
Original English
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
Original English
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
Original English
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
Original English
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
Original English
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.
Original English
>> 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
Original English
>> 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
Original English
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
Original English
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,
Original English
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.
Original English
>> 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.
Original English
>> 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
Original English
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.
Original English
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.
Original English
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,
Original English
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
Original English
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.
Original English
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.
Original English
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.
Original English
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
Original English
>> 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.
Original English
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.
Original English
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.
Original English
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.
Original English
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
Original English
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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?
Original English
>> Yeah.
The
I
mean
Original English
>> which
field
like
law
bank
I
don't
know
where
but
something
Original English
>> if
you
had
to
guess
what
are
the
use
cases
outside
of
coding
that
could
potentially
yield
you
know
daily
activity.
Original English
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
Original English
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.
Original English
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
Original English
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
Original English
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
Original English
>> 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
Original English
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.
Original English
>> 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.
Original English
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
Original English
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.
Original English
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
Original English
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.
Original English
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.
Original English
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.
Original English
>> 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?
Original English
>> Well, in a sense, I mean maybe kind of an 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.
Original English
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.
Original English
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.
Original English
>> 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.
Original English
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.
Original English
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.
Original English
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.
Original English
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.
Original English
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?
Original English
>> 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
Original English
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.
Original English
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.
Original English
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?
Original English
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
Original English
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.
Original English
>> 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?
Original English
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,
Original English
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
Original English
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?
Original English
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
Original English
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.
Original English
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?
Original English
>> 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
Original English
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
Original English
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
Original English
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?Original English
>> Why
do
most
people
look
at
chat
GPT
and
not
really
think
of
anything
to
do
with
it
today?Original English
>> 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.Original English
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.Original English
>> Thanks.
Great
to
chat.