A rational conversation on where AI is actually going | Benedict Evans

My most controversial opinion is that I think that AI is as big a deal as the internet or mobile and only as big a deal as the internet or mobile. What's

your just the coming job apocalypse?

Every time we have a new technology, it automates away a bunch of jobs and then that automation unlocks a bunch of new jobs and you don't know the new job cuz it doesn't exist yet. We've had that process over and over again.

Even just looking at the most advanced AI companies throughout big open AI, everyone's increasing headcount. You

talk to these doomers on Twitter and they would act like every big company is going to buy Chat GBT tomorrow and then in two weeks time they'll fire all their stuff. These people are morons. You

stuff. These people are morons. You

can't predict which things are going to be exposed. You can't look at a senior

be exposed. You can't look at a senior partner at a law firm and say, "Well, 17% of their work could be automated.

This is horshit."

I'm curious if you're following the anti- AI sentiment.

It's a big fuzzy mess. Yes, this will change a bunch of stuff and we'll need to worry about it, but that's kind of a constant. We've always had that. What

constant. We've always had that. What

would be a couple things you recommend people do to be more successful in this future? Don't stick your head in the

future? Don't stick your head in the sand and say, "I hate all of this stuff." That gives you a great feeling

stuff." That gives you a great feeling of moral superiority and you can go on Blue Sky and shout at everybody about how evil AI is. Like great, I'm happy for you. But that's not going to help.

for you. But that's not going to help.

What helps is you diving into this and coming out understanding what you can do with today. My guest is Benedict Evans.

today. My guest is Benedict Evans.

Benedict was a longtime partner at A16Z as their in-house analyst and resident thinker. Before that, he was a longtime

thinker. Before that, he was a longtime equity researcher. And for the past six

equity researcher. And for the past six years, he's been an independent analyst tracking the most important tech trends and sharing what he's learning. Most

recently, as you'd expect, he's spending all his time on how AI is changing our lives. And in his words, AI is eating

lives. And in his words, AI is eating the world. In this conversation, we go

the world. In this conversation, we go deep on what we're still not pricing in on the impact that AI is going to have on our lives and our work, the rise of

anti-AI sentiment, the impact on jobs, where in the value chain most of the value will acrue, and tons more. If you

are worried about AI or just confused about where things are heading, this conversation will teach you a lot and also make you feel better. Before we get into it, don't forget to check out lenny'spass.com

for a year free of some of the most amazing, hottest, most well-crafted AI products in the world, available exclusively to Lenny's newsletter subscribers. With that, I bring you

subscribers. With that, I bring you Benedict Evans.

Benedict, thank you so much for being here. Welcome to the podcast.

here. Welcome to the podcast.

Thank you for inviting me.

You just put out this deck called AI is eating the world. I want to ask you kind of the the flip side of this of we all know it's a big deal like knowing that what do you think people are still not

fully pricing in when they think about the change that they're going to experience to their lives and their work? Um, an interesting way of thinking

work? Um, an interesting way of thinking about it, I did a um, a podcast last year with someone where I said, you know, I my most controversial opinion is that I think that AI is as big a deal as

the internet or mobile and only as big a deal as the internet or mobile because clearly there's a bunch of people in tech who think no, this is more like the industrial revolution or something. And

there are a whole bunch of people underneath saying, well, he thinks this is just as big as does he not understand how big this is? And I'm like, smartphones were quite a big deal. The

internet was quite a big deal. We

wouldn't be doing this if it wasn't for the internet. So there's like one layer

the internet. So there's like one layer of but then if you dig into that like if you're going to make the internet comparison it's like we're in 1997. Like

it's very exciting.

Most stuff kind of doesn't work yet.

Most of the stuff that people are going to do hasn't been built yet and it's not really clear how any of it's going to work when it does work. And the people who have have already got it who have

already taken whichever pill it is I forget which sort of imagine that everybody in the world is already there and the truth is you've got this kind of very wide distribution. So there's

people in tech who bought their cluster of Mac minis and you know don't use Google anymore. And then you look

Google anymore. And then you look outside tech and setting aside the idiots who think that this isn't real.

Um you know most people are using who are using this are using this every week or two maybe. Um so you've got that kind of spread of adoption and that spread of

maturity of how well this works. And

then within that you can make sort of specific points about well how are the models going to work and do the model labs have pricing power and where's the value going to be and you know has open

AAI won the whole thing or you know is anthropic got it this week and so then you can kind of get into calling those races where again it's like being in 1997 and saying well is it going to be

excite or yahoo and the answer was no generally so there's a sort of a fractual point here there's like the sort a super high level that like this is going to change

absolutely everything. I don't think

absolutely everything. I don't think it's particularly productive to say well is it 20% bigger than the internet or 100% those aren't productive conversations but it's one of those fundamental changes but then you don't

know how any of it is going to work. Um

in fact I just published this I do a presentation every six months and I just published one yesterday and one of the comments was Benedict this is 80 slides are saying we don't know which is like slightly facitious but also kind of

true. This episode is brought to you by

true. This episode is brought to you by our season's presenting sponsor, WorkOS.

What do OpenAI, Anthropic, Cursor, Versell Replet Sierra Clay and hundreds of other winning companies all have in common? They are all powered by work OS. If you're building a product

work OS. If you're building a product for the enterprise, you've felt the pain of integrating single signon, skim, arback, audit, logs, and other features required by large companies. work OS

turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SAS.

Literally every startup that I'm an investor in that starts to expand up market ends up working with Work OS. And

that's because they are the best.

Whether you are a seedstage startup trying to land your first enterprise customer or a unicorn expanding globally, work OS is the fastest path to becoming enterprise ready and unblocking

growth. It's essentially Stripe for

growth. It's essentially Stripe for enterprise features. Visit works.com to

enterprise features. Visit works.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions. Workos allows

you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience. Go to works.com to

developer experience. Go to works.com to make your app enterprise ready today.

So, if we're in this 1997 timeline uh for AI, I know it's I know so much of your messages we don't know where it's going exactly yet. I don't know. Now, do

you have a sense of just like the timeline to okay, now things are going to be radically changing? Like where are we in that cycle? You talk about all these different cycles we've been through. Like how far far are we from

through. Like how far far are we from just like wow it's all different now?

Well, unquestionably we're already in that moment in software. And then

there's a conversation about well what does agentic and AI software development two separate things that merge together mean for the future of software industry? You know, there's one extreme

industry? You know, there's one extreme which is no one really believes which is, you know, hey, you'll just like v code your own stripe and no one actually believes that although you don't believe that, but like clearly there's a whole bunch of questions about what this means

for the software industry and how much stuff you'll be able to do yourself or how much more software there will be and that's, you know, whole that's one whole conversation. But the other extreme is,

conversation. But the other extreme is, you know, if you're in a law firm, this is all very interesting.

Um, but what am I how how exactly do we use this? And how do we work out how not

use this? And how do we work out how not to be the next story that we've submitted something with hallucinations in it? And how many associates are we

in it? And how many associates are we going to hire next year? Uh, what does this mean for us? One of the analogies I used in the presentation is imagine you're seeing imagine you're an

accountant seeing the first software spreadsheets in the late '7s. This is

mind-blowing. you know, you change the interest rate here and all the other numbers change and it does a week of work for you in like 30 seconds. And we

can talk about what that meant for the accounting industry, but clearly if you're an accountant, this is obviously mind-blowing. But if you were a lawyer

mind-blowing. But if you were a lawyer looking at that or a journalist looking at that, you'd think, well, that's very clever and my accountant should see this, but that's not what I do. I might

use it for my time sheet next week if it didn't cost 10 or $15,000 to get the Apple 2 and the monitor and the printer to run it, which is what it cost if you adjust, but that's not what I do. And

you need a word processor, which actually came like very shortly afterwards. And so that's sort of the

afterwards. And so that's sort of the moment that we're in of there's some people like software development are develop software developers are the accountants seeing visi like oh my god

this changes everything like before viscal and after visalc before before cl code and after claw code a lot of other people are picking it up using it to

varying degrees but slightly puzzled so you there's a bunch of survey data that I put in in the in the presentation that like even if you look at like 13 to 18 year olds or something, it's still like

kind of 15 20% of people are daily active users and another 20% are weekly active users and then the other 60% of those people in that demographic on you

say they are not using this. So there's

a sort of very widespread of who gets it and a very wide which I think also maps this is kind of almost a separate point maps to the sort of jagged frontier

question of where does this work where does it not work can you tell where it's going to work is it intuitive to know where it would work can you tell after it worked can you can you can you can

you work out for yourself what you would do with this and all of those intersect if you're a software developer a lot of other people were like people having moment or they're not or we're in again

we're in that kind of 1997 moment of okay what is this along those lines something you've been writing a bit about is this like unexpected investment in professional services consulting services/forward

deployed engineers uh all the AI labs at least the two big ones open anthropic are like investing in buying massive comp like consultancies and PE firms talk about just what's happening there

why why that's happening well it's funny I was kind of groping for a joke last night when I wrote my newsletter and couldn't quite get to land it but as you know something like you know you know the joke that a machine learning scientist is a

statistician who lives in San Francisco and there's something in there of like a forward deployed engineer is like an Accenture outsourced software developer who lives in San Francisco or works in

San Francisco. I mean, you know, joking

San Francisco. I mean, you know, joking apart, if you have any experience of professional services, like companies do not have lots of people sitting around

waiting to do a build a big new project or do a big new piece of analysis or build a big new piece of technology or a new product or work out how they're going to redesign their stores or, you know, work out where the stores should

be or try and work out why the churn is too high. Oh no. All of those kinds of

too high. Oh no. All of those kinds of questions are things reasons why you hire Bane BCG McKenzie on one side or

Accenture Infasis whoever on the other or you hire a branding agency or you hire an AR firm of architects or whatever. And it's always like well we

whatever. And it's always like well we could hire some architects but why on earth would we want to have 15 architects on staff when we we just go and hire an architecture firm? We just

go and hire an ad agency. And so you're supposed to like completely reimagine all of the internal workflows of your company and work out which of them could

be automated really quickly with AI.

That's a project. That's a project that needs like five or 10 people to sit down and spend a month or two working it out.

And then actually doing it is another project. Okay. So we need to plug these

project. Okay. So we need to plug these three vertical systems into these two horizontal systems and build in a bunch of new workflows and train people to do that. Well, guess what? Who's going to

that. Well, guess what? Who's going to do that? Because you don't have a bunch

do that? Because you don't have a bunch of people sitting around not doing anything. So, on the one side, this is

anything. So, on the one side, this is part of the the model of some PE firms, which is that they provide support to their portfolio companies to do stuff.

And on the other side, that's why you hire, depending on what you're trying to do, you hire Bane or you hire Accenture or you hire publicists to help you work

that out. What's really just funny about

that out. What's really just funny about this trend is you would think AI is going like consultants were going to be gone. No, we don't need all these people

gone. No, we don't need all these people anymore. AI is going to do their work.

anymore. AI is going to do their work.

Instead, like the most cutting edge AI labs are the ones most investing in these folks. It's I think it's pretty

these folks. It's I think it's pretty surprising.

Well, one of the strands in my presentation, so I split the presentation into three sections.

There's a section on capital, which is basically where is all this capex going and are the model labs going to have differentiation? And then there's a

differentiation? And then there's a section on deployment, which is basically what does it mean for the software industry? And then the third

software industry? And then the third section is how does this change stuff?

And one of the sort of sort of strands I tried to pull together in the section on change is um what's the hard part of the job? Is

the hard part of the job writing the code line by line? Is the hard part of the job like giving you the school or making the PowerPoint

or is the hard part of the job something else? Is it the task or the job? And you

else? Is it the task or the job? And you

know, pulling that apart, sometimes the task is the job. Like the classic example is like a an elevator attendant.

I live in a building that has an attended elevator. We have a manual

attended elevator. We have a manual elevator. There's no button. There's a

elevator. There's no button. There's a

there's a there's a lever in the door and drives you to your floor. It's a

vertical speed car. Um it's like one of those trams in San Francisco. They drive

you to the store to your floor. Um and

then those all got automated after the 50s and now you get and you press a button and pressing the button is a job.

So there were some things where the the the button the job was a task and the task got automated. What happens much more and this is why people talked about like the jeans paradox is this pricey

elasticity because jeans paradox is just pricey elasticity applied price elasticity. If you make it cheaper to do

elasticity. If you make it cheaper to do something what happens do you do the same for less money or do you do more for the same amount of money or do you do more for more money because you've got new ROI.

And if you look at something like the history of accounting or indeed professional services like you know this is a joke I made on Twitter back when it was Twitter was like young people won't believe this but before invest before

Excel junior investment bankers worked really long hours and now thanks to Excel Goldman's associates all work at lunchtime on Fridays. It's like well why is that not what happened? You could

make the same point in software development. You know before IDED and

development. You know before IDED and libraries and operating systems developers had to write all the code.

Now, if you write an iPhone app, 90% of the code is written for you by Apple.

Like, Apple wrote the modem driver and the graphics drivers and, you know, the file system. You don't need to write any

file system. You don't need to write any of that. So, we've got like a tenth as

of that. So, we've got like a tenth as many engineers now. Well, no. And so,

then you kind of have to look at an industry and work out, well, which is it? And what is the hard part? One of

it? And what is the hard part? One of

the the analogies that occurred to me here is to look at the history of e-commerce, which is that what Amazon does is it gets you the skew. If you

know what the skew is, if you know what skew you want, you want that microphone stand. You know this part number, you

stand. You know this part number, you can go to Amazon and get it. If you

don't know what microphone to get, probably shouldn't start on Amazon.

Multiply that by many, many, many product categories. And so what Amazon

product categories. And so what Amazon does is get you the skew, but knowing what skew you want is another job. You

know, the claw code can write you the code, but what code do you want? It can

make you the features, sure, but what features do you want? Who's your

customer? What's the right product for that customer? How you going to take it

that customer? How you going to take it to market? And long way of answering

to market? And long way of answering your question, why do you hire McKenzie?

Are you hiring them to get a 75 slide deck?

Well, narrowly Claude Co will make a really, really crappy version of that.

And you'll get all these kind of AI grifters on LinkedIn and and Twitter and so on saying, "Hey, I made a McKenzie deck with Claude." and you look at it and you think, "Yeah, that's a bunch of dog crap. That's not what you'd get if

dog crap. That's not what you'd get if you from McKenzie." But even if it was, that's not what you paid them for. What

you actually pay Bane to do is to go and walk all over your enterprise, your company, and work out, yes, but why is it that you didn't do that? And how do

the politics of this work? And what do you actually need to do? And let's go and talk to your customers and work out what they actually think as opposed to what's on the first page of Google. is

all the other stuff and the PowerPoint is just like the task but that's not what you hired them for. The same with you know Amazon versus the retailer the same with software development. So

you've got that kind of split. The other

analogy that occurred to me here was looking at like the sort of class of industry that got steamed by the internet because they had those two things and you could split the part. So

you had the physical manufacturing or physical distribution and then you had the other the the thing what was the actual thing like classic examples will be newspapers and recorded music. So

record companies do not think of themselves as being in the business of manufacturing small pieces of plastic but that was that was what they actually did and when that went away they were screwed. Um same thing for newspapers.

screwed. Um same thing for newspapers.

Newspapers did not think of themselves as like manufacturing and trucking companies. When you decouple that then

companies. When you decouple that then that becomes a problem. But often you kind of can't decouple that or that wasn't really the problem or you make that thing cheap and then all this other

stuff happens as well. And so all of this is just vastly more complicated than saying well hey you know we're just going to automate the accountants or we're going to automate the the consultants. Um I mean there's there's

consultants. Um I mean there's there's two charts in the presentation of the number of people employed as accountants which went up right the way through the 20th century and has gone up again since the beginning of the 21st century. So

you have adding machines and punch cards and mainframes and databases and ERP and cloud with spreadsheets and PCs and the number of accounts keeps going up. And

so why is that? Well, it's not it must be more it's more complicated than automation. Even just looking at the

automation. Even just looking at the most advanced AI companies, Anthropic, OpenAI, I just had Dan Shipper from every on the podcast. Everyone's just

increasing headcount. Like the companies you would think would be least likely to add humans are adding many, many humans.

And to your point, it's really complicated. What's your just kind of

complicated. What's your just kind of gist on the job, the coming job apocalypse? You know, like Daria's

apocalypse? You know, like Daria's talking about all the entry level people are no more jobs. Just like

Yeah. Um I mean there's a narrow point here which is that I would place I don't like argument from authority.

And I don't think the fact that you run an AI lab suddenly gives you or rather and if you're going to use argument from authority then it should be relevant to the field. So like I'm interested in

the field. So like I'm interested in Dario's opinions on where models are going to go in the next 6 to 12 months.

I'm not particularly interested in opinions on theories of labor and market value and competitive comparative advantage like yeah maybe he had a course on that at university. So did I.

So I think one needs to be a little bit cautious on like well Dario says and that's setting aside like the cynical view that he you know he's just doing that pump stock which I don't I don't believe at all. So this kind of comes back to my point about you know platform

shifts. um every time we have a new

shifts. um every time we have a new technology um it automates away a bunch of jobs and then that automation whether it's price elasticity and the enablement of the fact that they became automated

unlocks a bunch of new jobs and so you know you go back to 1800 like 90% of us were peasants and our major concern was would like the crops going to fail

because then we'll all go hungry or worse and so ever since then we've been automating jobs and creating new jobs and you can always see the job that's going to go going to go away and you don't know the new job because it doesn't exist yet and it's like

something that sounds dumb anyway like you know like railway engineer what's a railway um why would that be a thing who would care who would want to go that fast um and so we've had that process over and over again this is what any

first year economics student would tell you um we've had this process over and over again since 1800 and each time you go through it you get a bunch of frictional pain and dislocation and a bunch of people lose their jobs and a

bunch of towns get hollowed out and it's all it all sucks but you know when you come through on the other side we're all richer and we're not worried about the crops failing anymore and you know this is the process of the last 200 years so

then the question is is there some a prior reason why this would be different to those because like the internet removed a bunch of jobs PCs removed a bunch of jobs there aren't many people working as type setters anymore um or

telephone operators or typists um the internet removed a bunch of jobs and generally the jobs that go away are crap jobs seen retrospectively and the new jobs are better because you know GDP

keeps going up so is AI different and so Then there's kind of a couple of answers to this. One theory is well this is going to be way quicker and certainly the adoption of AI is quicker than previous technologies because but this

is kind of because you're standing on the shelves of giants. So like you don't need to wait for everyone to buy a piece of expensive hardware to like buy a phone or a PC or wait for the telco to deploy broadband. It's already there. So

deploy broadband. It's already there. So

of course chat GBT can get 900 million chat users because there's already 900 million people on the internet. Like in

like when Mark Andre launched Netscape in what was it 9394 there were like 50 to 100 million PCs on Earth. So no, you didn't have 900 million users then. But

and so the but the point is then he didn't need to wait for like phone networks or microchips and before that you didn't need to wait for electricity and you didn't need to wait for like mass production. So

there's all you're always kind of standing on the shoulders of giants.

There's always like a compounding effect. So yeah, this is faster but the

effect. So yeah, this is faster but the internet was faster too. Um I think the other answer to this and this kind of comes back to the professional services point is like you know you talk to these doomers on Twitter and they would like

act like you know every big company is going to buy chat EBT tomorrow and then in two weeks time they'll fire all their stuff and these people are morons and this is one of many reasons why why doomers were morons but a complete failure to understand the way the world

works and that was like the starting point why they then didn't understand anything else you know typical big company you know enterprise software sales cycle you'll know this better than me enterprise software sales cycle is like 18 months if you're lucky

You know this is always the problem. The

enterprise sales cycle is shorter than the the venture back start software funding cycle longer longer rather longer like it takes you longer to get an enterprise deal than it takes you to go between wraps and this was always the problem you know particularly for you

know sectors like aerospace or healthcare or something. So like no people aren't just going to tear out SAP and replace it with XY Z. Maybe in five in like three five 10 years yes that

whole estate will look radically different and all those jobs will have changed but it will take you know t three four five 10 years and it will take time sector by sector and it will

take time for people to work out uh you could do that thing with this.

One of the companies I always remember that we looked at when I was at Andre and Horitz is a company called um frame.io which is video editing, video video collaboration. And there's nothing

video collaboration. And there's nothing there that you couldn't have done at least 5 years earlier and maybe 10 years earlier.

And actually, that's kind of a bad example because that relies on a bunch of like a bunch of stuff like web cutting edge web technologies. But if

you go out and like pick pick 10 random SAS companies that were started the day before Tatbt launched, how many of them could have been founded at any point in the previous 15 years?

like somebody to the the delay was somebody realizing oh we could that problem exists inside that industry and oh this is the way that we would solve it. It didn't all happen the day after

it. It didn't all happen the day after Google Docs. It took like 10 15 20 years

Google Docs. It took like 10 15 20 years for people to invent all that stuff and work out that you could do that with this. So all of that is like the way of

this. So all of that is like the way of saying well yes it is going to be quick but actually no it will kind of take a while for people to work out how to completely change how their business works. your view is so comforting

works. your view is so comforting because it's you know basically it's like okay this is a huge deal but we've been through many transformations before and it's going to be okay. Well, I have a

slide towards the end of the presentation which and I know the title is something like, you know, this is going to be completely different from everything else just like everything else. And then the next slide is an IBM

else. And then the next slide is an IBM ad from the 50s which has got this sea of white men holding up with in white shirts and ties all holding up flight rules.

And the the the ad it said the slogan on the title of the ad is it's an IBM ad.

It says an IBM electronic calculator.

This is before it was called a computer.

It's an electronic calculator. It's the

size of a fridge is like having 150 extra engineers.

Like how many people listening to this company list like their company's slogan is basically we'll give you 150 extra engineers. I mean isn't that like the

engineers. I mean isn't that like the whole pitch of Claude called code? 150

extra engineers for free or not free.

That's like a lot of money. Um so and yeah that's what it gave you and so yes we keep going through this over and over and over again just to kind of make that tangible. I mean obviously we couldn't

tangible. I mean obviously we couldn't be doing this with the without the internet. So there's a slide in my

internet. So there's a slide in my presentation which is we could maybe talk about but it's a slide or chart showing how many products are stocked in supermarkets in America since the 50s.

And the point of the slide is to say that barcodes allowed supermarkets to stock way more stuff because they could keep track of it.

But making that chart, I had to know there was a thing called the Food Marketing Institute. And I had to have

Marketing Institute. And I had to have found out that they published a number for how many SKUs there were in supermarkets every year. And then I had to realize they'd been around since the

50s. And if I like dug long enough, I

50s. And if I like dug long enough, I might be able to make a whole time series and I could make whole chart. Now

imagine doing that in 1994.

First of all, you would have no idea that exists. you really need to go and

that exists. you really need to go and find a library where they public where they and they publish that number and then the numbers in that report. You'd

have no idea. Then you need to find a library that had them. So you're going to spend like three days on the phone and spend like $50 on like longistance phone calls to find a library that has these. Or maybe you call the Food

these. Or maybe you call the Food Marketing Institute and they say, "Yeah, sure. If you buy a, you know, we'll sell

sure. If you buy a, you know, we'll sell them to you for $500 each." So then you know you're going to get on a maybe you live in New York or like there's somewhere that has this and you two weeks later you've got the chart and you look at it and then the other side of

this is the life of analyst is you spend all day making a chart and you look at it and go that's not very interesting.

So you spend two weeks to make the chart and then you look at it and go yeah I'm not going to use that and for me this was like two hours in Google

and so we like we we like forget how big a deal the internet was. That's a long way of saying it, but like we forget we've had these absolutely enormous changes and then we don't see it because it's like that's the world the world has

always been. What's different

always been. What's different potentially this time just to even though your code is it's different this is everything's going to change like just like just like last time like the

big difference obviously is uh AGI might emerge and super intelligence where that is uh could you know does the work of humans can do a lot of this stuff for us can actually replace jobs just like

thoughts on that element of this transformation we're going through I don't know this is one of the the ways I've struggled to write about AI is like certainly in like 2023 three early 24

like all the questions were questions you could have asked in like December 2022 like the questions didn't really change and the strategies didn't really change and I think the AGI question is kind of

the same um I mean the thing that the the observation one can make like you know we have no theory of what human intelligence is we have no theory of why these models work so well we have no theory of how much better they will get so we're all just kind of vibes

forecasting as to what will happen um and then you can have like the 2 a.m.

you know doped out philosophy students talking about hey man like is this consciousness maybe we aren't conscious either we just think we are like yeah great thank you I think the one thing

one can observe today is so we have no idea we don't know we can guess but we don't really know how the where this is going to end up what I think you can say today is that there's a lot of kind of

redefinition of terms so I think a quote I used in my presentation late last year was an AI scientist called Larry Tesla who said AI is whatever machines can't do yet because once machines can do it.

People say, "Well, that's just software."

software." And so certainly, I mean, I I did do a poll on social media every now and then asking, "Is machine learning still AI?"

Because I've certainly heard people say, "Oh, that's not AI. That's just image recognition." That's not AI, that's just

recognition." That's not AI, that's just sentiment analysis. So AI, it's a bit

sentiment analysis. So AI, it's a bit like the word technology. It's like if it's new, then it's technology. But in

the 60s, airlines, jet ainers were technology. Now a jet ainer is in tech.

technology. Now a jet ainer is in tech.

And so there's a sort of sense of AI is like a moving target is whatever just started working. And I think the point

started working. And I think the point here is now clearly you can see people redefining AGI to mean the stuff that works now. So is AEI what's the

works now. So is AEI what's the definition now? It's like it can do a

definition now? It's like it can do a certain percentage of economically valuable work. Well, that's a very

valuable work. Well, that's a very different thing to it has a soul and it's alive. Um because a

it's alive. Um because a database can do that. like you know an IBM mainframe in 1975 could do a meaningful percentage of economically valuable work that was previously done by people and it turned out there was a

whole bunch of other stuff that it couldn't do that we didn't do then we didn't know existed so there's a lot of like kind of creative redefinition here super intelligence I'm not sure is super intelligence more than AGI or less than AGI because last year I thought super

intelligence was like really good but not as good not actual AGI and now it's like oh no no we've already got AGI but super intelligence that's really hard it's like all these terms are like what I what even it's funny I was I was having an argument on hacking news this

morning. You remember the idea, you

morning. You remember the idea, you remember the argu which is never never a good use of time but you you remember the argument of like you know people would argue about whether crypto is blockchain or whether blockchain is

crypto there isn't a right answer to that let's just be sure you know it's important to understand what you mean when you say that but there isn't like a correct answer to this are we going to get to something that has human level

intelligence I we don't know I don't think we have any way of answering that question maybe maybe not you can make arguments either way meantime it does mean in the

meanwhile we've got this thing that's clearly kind of a you know completely transformative technology and maybe the serious point here is you like you don't have to believe even if like the model stopped it getting better tomorrow if

this is it and we hit a brick wall tomorrow this is an incredibly useful technology that's going to change the world and get rolled out over the next 10 years so you don't have to believe in any of that stuff to believe that this is giant deal

something that's definitely changed I had um your former boss Mark Anderson on the podcast and we didn't actually talk about this during the conversation and he brought it up before we started recording and I never got to it is he

had this insight that the the opportunity set for companies now is so much larger. We used to have no trillion

much larger. We used to have no trillion dollar companies. Now we have we're

dollar companies. Now we have we're going to have dozens of trillion dollar companies. Just like the size companies

companies. Just like the size companies can grow to or is going up so much and valuations also go up along with that.

And his point is just people haven't really groed just how large companies can get now. Like everyone's hitting 100 million AR in like five five months, six months. Just thoughts on that. Yeah, I

months. Just thoughts on that. Yeah, I

mean this was his whole software is he eating the world thesis from you know 15 years ago whenever it was yeah you know the TAM it gets progressively bigger because you can address larger and larger parts of the economy and so you

know if you think about the kind of the classic platform shift framing that you know mainframes are I think peak mainframe install base was something like 70 80,000 units I mean slightly

fuzzy term what exactly is a main frame and what's the difference at what point does it become two main frames as one but something like that that order of magnitude and then when the internet kicks off there are as I said 50 to 100

million PCs on earth maybe today there are something over a billion one to one and a half billion but obviously a lot of those are corporate it's like 7 800 million consumer PCs in the world there's about 5 and a half six billion

mobile smartphones in the world which is and which is why you can have 900 million weekly IT users on JTP and so there was this narrative like five years ago like well we've run out

of people so like this the next thing can't be in order of magnitude bigger um which was true up to a point but that was like the wrong model because clearly what's happening now is you're moving in another direction is you're just you

know branching out and automating big big new ways of the economy. Um now the you know the back to your job point you know you could argue well we're just going to replace all the people with AI

and like all the money will go to to to Sam Waltman and you know Mark Mark can buy himself another Gulf Stream. I think

the add to the fleet. I think the kind of the the other answer is you know it's back to the lump of labor fallacy and you know the last 200 years that you know each of these technologies removes a bunch of jobs, creates a bunch of new

jobs, creates a bunch of new value, unlocks prosperity for all of us and that's painful as you go through it but it always creates more value. And so

here you could you could certainly make an analog to you know the useful analog to the electricity industry is just saying how that electricity became part of absolutely everything and software

has been kind of slowly working its way out. You know the anal here would be

out. You know the anal here would be electricity in factories and then electricity sort of slowly spreads out and so that would be the point again that you know it slowly spreads out to do more and more things um and so you

know more and more value and a bigger bigger and bigger um contribution to the economy. Um it also of course disappear

economy. Um it also of course disappear disappears inside things and you know the other side this is the point of my capital section in the presentation is um you know there's this quote from Sam Alman where he said you know we're going

to be selling electricity we're going to be selling AI AI intelligence on a meter like water or electricity and you look at this and think you know my dear sweet child you need me to explain the margin

structure of the utility industry to you um because guess what when you watch television the TV company isn't paying a percentage of your monthly bill to the electricity company, you know, when you

wash your clothes, Bosch isn't paying a percentage of the price of the washing machine. Um, and you know, clearly this

machine. Um, and you know, clearly this is like the much more kind of specific tactical question at the moment is do we even end up with three giant

models or does be does it become hundreds of models and open models and local models and so on. And even if we do end up with, you know, say, pick a number, three to six to 10 giant

foundation models that cost hundreds of billions of dollars a year. Um, fine. Do

they get all the value from that? Now, I

started my career as a telecoms analyst and so, you know, still pay attention to it a bit. Global mobile industry has revenue of about a trillion dollars a year, maybe a bit more now. And it

spends about $200 billion a year on capex every year. Total telecoms is about 300. Mobile is about 200. It's

about 300. Mobile is about 200. It's

about 15 to 20% of revenue every year.

And if you look at a chart of mobile data consumption, it's an exponential curve like perfect curve going straight up. And then the number now I think it's

up. And then the number now I think it's about, you know, 1500 to 2,000 times what it was in 2010 globally.

And the stocks have gone nowhere in 25 years because it's an Xgrowth low margin commodity utility

where they're selling this inc this objectively amazing piece of global technology infrastructure that has enormous complexity and enormous sophistication, but all the cool stuff

is made by you. It's made by the people listening to this podcast. It's made by somebody else. This was that like kind

somebody else. This was that like kind of pivotal moment where the Telos thought that they would do all the stuff that you did on your iPhone.

And not only do they not do it, but Apple doesn't do it either. It's all

further up the stack. Um, and so this is, you know, the kind of the elemental question right now around Foundation models is does the model do the whole thing? Can you do you just go to the

thing? Can you do you just go to the chatboard and get the chatbot to do the whole thing? Can the model companies

whole thing? Can the model companies keep building these like clawed for X, clawed for Y things, which to me look very much like what you see if you hit file new in Excel? It's like the tablets, but like all of those are

actually billion-dollar companies as well.

And if not, no. Does it all have to be apps? Quote unquote, whatever app means.

apps? Quote unquote, whatever app means.

And if it all has to be apps, who builds those? Well, they can't all get built by

those? Well, they can't all get built by the model labs, just as they didn't all get built by Microsoft. And so, if they're all built by other companies, does the models foundation models have leverage up the stack the way Windows

did? Or is this more like AWS where like

did? Or is this more like AWS where like if you're I don't know an engineering company or a law firm buying a piece of software, you don't care which cloud it runs on and you don't have to like standardize on AWS because that's where

all the software is and like the developers all standardize on AWS because all the customers use AWS.

That's not how it works. That's how

Windows OS works, but that's not how how works. And so it does sort of seem to me

works. And so it does sort of seem to me that like if if the chatbot isn't the UX and it needs to be apps and the model companies aren't going to build that and the models themselves are basically

commodities as at least as you can see them as users then why would the model companies have pricing power and wouldn't all the value be further up the stack? Aren't you basically have you

the stack? Aren't you basically have you got like three to six companies selling a commodity at marginal cost? Now

obviously the semi- analyst guys are like no no no no there's going to be infinite pricing power forever. I'm

sorry I'm exaggerating, but like I think you have to really important to kind of draw a distinction between where are we now where you have radical price equilibrium and you know you've got

these you know what's the guy the open claw guy spent $1.5 million on tokens last last month. Um but that's like somebody getting like a 50 grand mobile

data bill in 2010.

Um that's temporary. What is the steady state equilibrium point where all of these lines the lines on the chart kind of get lined up and we don't have this kind of weird crazy stuff going on and

then will you have pricing power or have you got like three or four or five companies kind of all selling the same thing and so then you should have a pricing price you should have lower pricing and

lower margins and the value got stack I am so excited to tell you about this season's supporting sponsor Vanta. Vanta

helps over 15,000 companies like Cursor, Ramp, Dualingo, Snowflake, and Atlassian earn and prove trust with their customers. Teams are building and

customers. Teams are building and shipping products faster than ever thanks to AI. But as a result, the amount of risk being introduced into your product and your business is higher than it's ever been. Every security

leader that I talk to is feeling the increasing weight of protecting their organization, their business, and not to mention their customer data. Because

things are moving so fast, they are constantly reacting, having to guess at priorities, and having to make do with outdated solutions. Vanta automates

outdated solutions. Vanta automates compliance and risk management with over 35 security and privacy frameworks, including SOCK 2, ISO 27,0001, and

HIPPA. This helps companies get

HIPPA. This helps companies get compliant fast and stay compliant. More

than ever before, Trust has the power to make or break your business. Learn more

at vanta.com/lenny.

And as a listener of this podcast, you get $1,000 off Vanta. That's

vanta.com/lenny.

A really interesting takeaway here is that your sense is over time the foundational model companies, Anthropic, OpenAI, others will their margins will get squeezed. They will not be as

get squeezed. They will not be as successful as they are today. And the

bigger opportunity is in the application layer, the people building on the models, the rappers.

Yeah. I mean, this is a very sort of deterministic thesis which is the models companies crucially what I said is the models don't seem to have network effects. So

there doesn't seem to be a winner takes all effect where one of these will run away ahead of the others. So you should have competition indefinitely. You have

competition indefinitely. You don't have you don't have different primary like really radical differentiation in what the product is.

then why would you have pricing power and meanwhile if the if you need to have thousands of applications that are all different built by different people those can't all be built by the model people

so it should end up looking more like cloud than it looks like Windows now that may be completely wrong and you know one of the points I make in the presentation is like imagine having this

conversation about the internet in 1997 like what would you have got right and or indeed having it about mobile in 2000 you you would not most of you would have missed almost all of it. You certainly

would have said that like a has been PC company from Cryptoino would win the whole thing and no one would have said that. Um and a search company with like

that. Um and a search company with like a weird logo like search what's that got to do with mobile like no forget it you're an idiot. So like we should presume we don't know but they're all you know the sort of basic building

blocks of like well but why would they have pricing power? Um, I don't know. I

had a when I was a baby analyst in like 99 went to see a.com company in the UK that was trying to do online selling computer cards components online like and um like they had this whole model

and this whole story in the brand and like the whole thing and we went up to see them and we're on the train back from Birmingham and this sort of sort of senior banker called David Tate um we're

all sitting talking about it and Taty says it's a low margin reseller one time sales you can say.com com all you like. It's a

low margin reseller with and I think that's the kind of the crux of this is they're they're undifferentiated commodity infrastructure providers.

There's a lot of science to it but there's a lot of science in mobile. I

mean what do you play for flat panel screen? Like there there's Nobel prizes

screen? Like there there's Nobel prizes in flat panel screens. They're still a low margin commodity. I look forward to be proving wrong proven wrong but like hey that's that's what it looks like now. This is great. So I know I know

now. This is great. So I know I know you're not an investor. I know you didn't actually do investing at A6Z even though you work for A6Z partner.

Partner just sit around and pontificate partner. Would you are there companies

partner. Would you are there companies you would invest in? Like if there are a couple companies you'd invest in now is there some on that list or categories even?

You know I I I mentioned briefly that I was an analyst. I was a I was a sellside equity analyst. I was not a very good

equity analyst. I was not a very good sellside equity analyst but partly because I was not interested in talking to clients. Partly because I was not

to clients. Partly because I was not interested in share prices which would seem to be like a disqualification to be an equity analyst. Um, and you know, I

don't, you know, there's there's there's like a huge difference between being right and being early, and there's a huge difference between the right company and the right price. Now, you

know, deterministically, you can look across the market and say, well, you know, it's like, you know, the bell curve IQ meme and, you know, the guy with 50 and the guy with 200 are both saying, Jeff Bezel, smart guy, I buy

stock.

And you know you can certainly like overthink all of this and you know you can look at you know Google, Apple, Facebook, Amazon and say it's hard to

see a problem for them really with all of this. You you can certainly see

of this. You you can certainly see questions for all of them um and one of them may drop the ball but it's worth you know kind of remembering what happened in mobile.

You know the internet was this like a big obvious platform shift. The funny

thing about mobile is that some companies missed it completely. And for

some of them, it really didn't change anything. Like for for Google, it didn't

anything. Like for for Google, it didn't change anything. For Meta, this was

change anything. For Meta, this was great. Like this is a way better way to

great. Like this is a way better way to do social than on PC because like you've got a camera and notifications and it's on your phone all the time with you. Um

Amazon, like what does this change? Like

doesn't change everything. I mean I'm I'm I'm massively oversimplifying here, but the point is now meanwhile Yahoo mail fails to make the jump. There are

companies that were already kind of dying that failed to make the jump.

maybe eBay, you could kind you can argue about individual names. The point is that like we went through that shift and it didn't change anything for half the industry um half the internet industry.

And so I think you know that you could kind of propose a little bit of that here. Um it's what what Stephen Silowski

here. Um it's what what Stephen Silowski at A6Z used to run Windows would always say is you know incumbents always try and make the new thing feature and and sometimes they're right sometimes it's a feature. Actually along those lines

feature. Actually along those lines something I wanted to get your take on.

There's this thread that's been happening across a bunch of guests which is around distribution becoming a bigger and bigger moat because as software is

easier to build, everyone's launching products. Uh, everyone's trying to

products. Uh, everyone's trying to compete for attention. It's getting

harder and hard. It's always been hard to get people's attention, but it's just like the noise in the market is just going up like crazy. And to me, that tells me distribution is becoming a more

and more uh valuable uh skill and asset.

And it also tells me incumbents are going to be a lot more successful because they already have distribution versus a startup that's trying to break through.

Yeah. I mean, there's like a version of, you know, the Drake meme of like he says, "I don't like that. I do like this." It's like,

this." It's like, you know, I don't like seeing GPT2 rappers. I do like harnesses.

rappers. I do like harnesses.

So, yeah, I I did I did spend some time talking about this in the presentation I did at the end of last year that if the product is a commodity, then the distribution is what matters. And you

know I wrote a thing about about sat earlier this year open earlier this year like how do they compete? Well if

there's an obvious comparison here a lot of people made is with web browsers the fundamentally web browser and you there's a distinction here I think between the web browser as product and the web browser rendering engine the

rendering engine can be better or worse but the browser product is just like a really thin wrapper for a rendering engine. Like there's an input box and an

engine. Like there's an input box and an output box and like what else? And which

is like what's the last innovation in browser design like tab browsing which is 20 years ago 25 years ago because it's like and every now and then somebody tries to innovate in browser design and it never works because like you found the platonic ideal. It's like

trying to innovate in smartphone design like you know it's a it's a glass rectangle like there's nothing you can do there. And so what happened of course

do there. And so what happened of course is that Microsoft uses distribution to break their to break in. Um then of course what also happens is setting aside the lawsuit is that it turns out that winning browsers doesn't matter

anyway because the value is further upstack and so Microsoft wins browsers for like five six years and it doesn't matter it doesn't get them anything. Um

and so clearly what's happening now is that Google is using distribution to drive um to drive Gemini and like what's the difference between Gemini and for and and and like if you're you know if you're using this stuff all day then you

know but like normal person there's no difference and the same thing with meta like you look at survey data on which which people use even before like the

new news thing like the llama thing like meta was like behind it was up there between chat GPT and Gemini which if you're in tech you people have completely written it off But it was

like they'd sprayed it on every service surface and it wasn't that bad. It was

fine. So distribution of an adequate product when the field is basically commodity distribution on brand become a big deal. You can see that in you could

big deal. You can see that in you could see that in like the strategy open AI strategy late last year was you know people called it you know everything yesterday. Um, and so they were just

yesterday. Um, and so they were just kind of trying everything to kind of work out how they would get that like how can we get a flywheel? How can we get distribution? How can we get

get distribution? How can we get something that sticks? How can we get people something something that people uses before Google and Meta and Amazon spray it everywhere and get everybody using that one? And then you've got like

this inertia and the power of the default and like why would you switch?

Obviously met Apple is kind of the last penny to drop here. Um, there was this sort of slightly weird opening ideal and now there's even weirder story that open. I want to sue Apple.

open. I want to sue Apple.

Good luck with that. Um, the funny thing about the Apple deal thing is just not to go off on a tangent, but like if you go back and watch the WWGC from 2024, like the whole second half of it is Apple intelligence. That was like the

Apple intelligence. That was like the most compelling vision of a personal AI assistant. I've still still the most

assistant. I've still still the most compelling vision I've seen. They then

couldn't ship it, but then neither was anybody else. and you watch it again and

anybody else. and you watch it again and you're like, "Okay, so you want tool using a gentic ondevice AI with no prompt injection and no hallucinations

and a completely standardized API system across 10,000 apps with intents that all work perfectly and like like well that sounds good to me, but like I'm not surprised they couldn't ship it, but nobody nobody else has shipped that."

But like that vision was great. You

know, I really want to see what happens at WWGC in a month. Like do they actually ship that now powered by Gemini? But that's also another point is

Gemini? But that's also another point is like okay there's going to be the the AI intelligence whatever we call it Gemini intelligence on Android and then there's going to be Apple intelligence on iOS

which is powered by Gemini but it's not going to be the same set of products the model is just like the dumb thing underneath the funny way of putting it the dumb thing underneath that powers the feature the model is the commodity that powers different decisions about what the feature should be and what

different distribution and in that situation of course um Apple's got like a billion devices that can run this on edge and and Google has wonderful marketing slogan coming soon to our most

powerful devices meaning it won't work on most Androids.

So again distribution questions interesting Google IO's next week so we'll see what they launched.

Oh no they launch they launched they launched a um Android just shows how how like how was it today? Yeah.

Well no they launched it last week. I

mean which is it's like it just illustrates how much we we stopped paying attention to Android and an iPhone. Like Google did had a whole big

iPhone. Like Google did had a whole big thing last week. They've got they're replacing Chromebooks with Google books and they've got a new Android intelligence powered by Gemini that will roll out to like the five people who

bought a Pixel phone.

You don't work for Google.

Yeah. Um I want to go in a slightly different direction. Something that I'm

different direction. Something that I'm curious if you're following is just the anti- AI sentiment that is feels like is growing. Feels like if you've seen these

growing. Feels like if you've seen these surveys, AI is like less popular than ICE. People are trying to stop data

ICE. People are trying to stop data centers from being built. I think Eric Schmidt just did a commencement speech and people were booing him every time he mentioned AI. Just like where do you

mentioned AI. Just like where do you think what do you think is going on?

Where do you think this this goes over time?

It's interesting and it's a big sort of fuzzy mess of different stuff. I think

there is like tangible like my electricity bill went up which applies actually in a very small number of places objectively but it did and this

is a question the water thing is weird because it's just like completely fake um and I should qualif explain what I mean here um data centers use water for

cooling it's mostly closed loop but the number of data centers relative to the total amount of water use in the USA is tiny I actually went and dug into this at the Livermore lab did did a study at

the end of 2024 where they estimated US data center water consumption and it came out at about 0.017% of US water consumption.

Now obviously if you live in a small town and you've got one well and like they capped the well and gave all the water to the data center then you're really pissed off but like that's like that's a planning problem. That's not a data center problem. You know in

generality yes this is you know data centers are what like 5% of US energy and might grow at 1% a year for the next five years one percentage point a year

but the water stuff is just nonsense and then you get into more tangible like well what is happening with this is it taking jobs away where you can watch a bunch of three-hour podcasts of econom e

economists talking to talking to each other and main answer is we really don't know yet. There's a bunch of charts that

know yet. There's a bunch of charts that kind of say yes and a bunch of charts that kind of say no. And

clearly there's a slowdown in employment of you know 18 to 24 year olds but that seems to be the same for people who do and don't have degrees and the same for people in fields that are look exposed

to AI and fields that don't look exposed to AI. So there's a lot of like

to AI. So there's a lot of like econometric argument about this and and I mean there's there's a broad border point here. In fact, which is different

point here. In fact, which is different point here that like we have very little data on what's going on in AI from anyone. The model labs don't tell us

anyone. The model labs don't tell us anything like they don't give us any meaningful usage information. They give

us these weird studies of like people how many people used this for this and that. They don't give us a daily active

that. They don't give us a daily active use number. We do not have a daily

use number. We do not have a daily active user number for a forhat.

This is crazy. Um, and all the data comes from academic economists trying to back stuff out of BLS surveys or consultancies and and and marketing agencies like spending a whole bunch of money to survey 20,000 people and

saying, "What are you doing with this stuff?" Like we don't have like good

stuff?" Like we don't have like good data on what's going on and how many people are really using this. But but

but to the employment question, hence like there's a lot of people like looking through all the stuff that the US census collects and trying to work out well where can we see this? Can we

see productivity? Like what could we see? And the answer right now I think is

see? And the answer right now I think is like there's no clear consensus that we're seeing an impact on jobs. But of

course politically that doesn't matter like if you're a student and you can't get a job and that clearly is an issue whether it's because of AI or whether it's because of Trump and terrorists is a different question. Um then you get

like like kind of niche things like you know people who draw book covers for um young adult romance novels are very upset that now you can get a picture of a naked woman on back of a dragon flying through over a volcano um without paying

them. So there's there's I'm sorry I'm

them. So there's there's I'm sorry I'm being deliberately unkind, but there's a little you there's there's and you know people particularly like novelists, people who write ebooks, uh there's a

huge culture war over whether it's okay to use AI. There's this whole sort of AI slop question and you know you saw the number that like 30 40% of new podcasts generated by AI. So there's a lot of

there's a big fuzzy mass of questions.

Some of this I think is is a little bit like the backlash we had around social but much more compressed and like social some of the backlash around social was true and some of it

was sort of true and some of it wasn't you know always like exemplified in the whole like Facebook sells your data thing which is just a not true and b the people who believe it are absolutely

adamant that of course it's true and you're you're obviously a lunatic for suggesting otherwise. You know, it's

suggesting otherwise. You know, it's like the line from from Jonathan Swift that you can't reason somebody out of an idea they won't reasonably do. Um, so

you get this kind of wide it was a long way to answer your question, but you got this kind of wide kind of spread of ideas just as you kind of did with social. There's like 20 different

social. There's like 20 different things. Some of which are really real

things. Some of which are really real and some of which are really not real and a lot of which are kind of a fuzzy mess in the middle.

All of which means that that meanwhile you've got Trump saying he wants a new executive order on on dangerous models which I actually don't think is is the thing that drives the backlash. You know

that worrying about missile cyber I don't feel like that's you know a main street America conversation but that's the thing that got Trump interested in this stuff. Again let me go kind of in a tangential direction.

Something that I like to ask ask folks that have kids that come on the podcast especially people that are thinking so deeply about where things are going.

Knowing what you know about just where the world is heading, what AI is going to do to the future? How are you changing the way you raise your kids, just what are you teaching them differently potentially that might help them in the future?

I don't know. I think there's a curve here in that if you've got kids who are going onto the job market in the next year or two, then everything is up in the air and no one knows how knows how this is going to work. If you've got

kids who are going onto the job market in like five years, then who knows? Um, but stuff will have settled down a lot by then in probably

unpredictable ways. So, I could be a lot

unpredictable ways. So, I could be a lot more worried if I had a 21-year-old. You

know, I don't. I've got, you know, kid in his sort of early teens. So, it's a diff those those questions vary. Then

you've got a lot of the questions that were the same before chat TV around you know the collapse of gatekeepers the you know should you really believe what that

influencer on Tik Tok says and you know where exactly you getting your understanding of what's going on in Israel and all of those kinds of social mediary internety media consumption

kinds of questions. Um

I don't know there are people who are like super super intentional about you know every minute of their child's life.

Um I'm not I'm kind of recall you know the George Carlin line you know that anyone who drives faster than you is a maniac and any anyone who drives slower is an idiot and that certainly applies to parenting. Um, so I'm, you know, like

to parenting. Um, so I'm, you know, like everybody thinks they're somewhere in the middle, but, you know, I don't have, you know, a deeply systematic and widespread and coherent like plan for

this is what my child is going to be doing in 3, 6, 12, 18 months time. Um,

I' I'd settle for him not breaking his Chromebook again.

I like that your just general vibe is it's going to be okay, guys. It's going

to be okay.

Yeah. Uh, I don't know if you I think if you you know, maybe this is cuz I'm British and we haven't had political violence in 500 years. And um I think, you know, if I came from Iran, I'd have

a different attitude to to being calm about the future. Um I think there's a layer of like yes, this will change a bunch of stuff and we'll need to worry

about it, but that's kind of a constant.

We've always had that. I remember in the whole wave of um the panic around social media, I dug up there were a whole bunch of books in the late '7s about databases. There was a whole panic about

databases. There was a whole panic about databases and again half of it was true.

Like um you know if everybody's like police records and arrest or if all police records and all government records are online then that's different. If you think about, for

different. If you think about, for example, the deep needs, deep fake needs issue, for example, there's like a dumb reaction to this, which is to say, um,

haven't you heard of Photoshop?

Which is true, but a 15-year-old kid couldn't use Photoshop to make hardcore pornographic nudes of every girl in their high school and send them to the whole school in one afternoon

and turn them into video.

Exactly. Even Well, yeah, even more. And

now they can. So, like that is different. It's kind of like, you know,

different. It's kind of like, you know, the challenge of social. You know, the thing people would say in the '9s is it's great. You can be, you know, the

it's great. You can be, you know, the only gay kid in your village and you can find other gay people and you can find your tribe. And guess what? It turned

your tribe. And guess what? It turned

out you could also be the only Nazi in your village or the only pedophile in your village or the somebody who wanted to look at child porn. And like, yeah, now you can find the other people who like looking at child porn and they'll tell you it's great. So, oops. Um, we

connected everybody and unfortunately that meant we connected all the bad people and all of our own worst instincts and every problem in society.

And so that will happen again with AI.

You know, we can deep fake news are like the obvious thing we can see now. There

will be a whole bunch more of this stuff. Um, but there's also and you know

stuff. Um, but there's also and you know something of kind of technical audience should know about. Have you do you know about the post office scandal in the UK?

Nope. Okay. So sidebar here. So in the UK post offices are mostly franchises run by small business people. So they're

run by like pharmacies classically. Tip

very often Indian immigrants second generation Indian people. Um, and the post office like 15 years ago rolled out a new point of sale computer system. So

they have a separate counter in the back. That's the post office. And so the

back. That's the post office. And so the post office rolled out this new computer system built by them by Fujitsu that had a bunch of bugs in it that showed short halls in cash. And the post office looks at this and says, "Aha, we knew these

people were stealing from us." Hundreds

of people get prison. Bunch of suicides, bunch of bankruptcies, people lose their homes. Meanwhile, people from the post

homes. Meanwhile, people from the post office and people from Fujitsu are going to court and swearing there's no bugs in the system and nobody else has had this problem. This is 1970s technology.

problem. This is 1970s technology.

And that's really the point that every wave of technology comes with ways that you can ruin people's lives either deliberately or by accident. This is the whole thing of Chinese mass surveillance

is deliberate. This is maybe people

is deliberate. This is maybe people should go to prison, maybe not. But like

we have this with every technology. we

have a bunch of ways that you can ruin people's lives and you have to be conscious of that and also kind of not panic about it.

So maybe following that thread and coming back to the kids thing and the jobs thing. Are there is there like a

jobs thing. Are there is there like a job you are steering your kid away from and is there a job you kind of think you want to steer them towards?

I don't know about that since it's probably a little bit early yet. He's

not quite at the like I want to be a fireman stage. Um but

fireman stage. Um but that might be a great job.

Yeah. And certainly you know if I look at my career you know I started as an equity analyst and then I went and worked in industry and then I was a consultant like you know the days when you kind of knew what your career would was going to be or over you know there were clearly some people where you want to be an architect you want to be a

software engineer you know you want to be X or Y I don't know I think you know the only the only kind of thinking I have here is that you have like you slowly work out there's a bunch of skills that you have and there's a bunch

of like jobs that make that makes you good at and then there's a bunch of stuff that people will pay you for and you want to get at least two of those and preferably all three.

Okay. So, zooming out a little bit, let me ask you a a meta question. What's a

question about AI that you think nobody's asking yet or not enough people are asking that we should be asking ourselves?

Sure. I mean, we talked about like value capture like obviously this is a whole everyone is is is asking like you know I'm not sure how many people are asking whether model labs have pricing power. I

think a lot of people are just presuming that the situation today will continue or that of course they will. So I think that's maybe a question that that not enough people ask. I think the question I posed towards the end of my

presentation which we talked about earlier is like what's the task and what's job? What is just the thing that

what's job? What is just the thing that becomes a button or make scoo versus what are people actually hiring you for is a kind of a useful way of thinking about this. And clearly there are going

about this. And clearly there are going to be some jobs where no that is just a task and that job gets automated away.

But there's a bunch where that kind of isn't the question. The way I actually pulled that together at the end of the deck was a chart of global recorded music revenue, which as you may know is

kind of a U-shaped curve, more or less.

So, it's dropped by about half from 2000 to 2015 or so, and since then has come back about to about 75% of the peak um adjusted for inflation. And the way that

I look at this is to say, and that's driven by streaming. And I kind of looked at this and said, well, the first half of this chart is saying, what happens if I don't have to pay $15 to get a CD to get that track? And the

second half of the chart is saying, what happens if $15 a month gives you all the music that there is? So, it's kind of a completely different sort of question.

And you could, you know, that's the way that you could look at Uber or the way you could look at at at Airbnb, all these kinds of companies. is that to

begin with you do the old thing but more with every new technology you do the old thing but more of it on the new place.

So, you know, you put flicker on mobile, you print out your emails, and then you make new things that are only possible with a new thing. And then maybe you go a bit further and you kind of completely redefine the question and you make something that isn't that at all. You

know, Spotify is not an online music store. It's something else.

store. It's something else.

And right now, you know, those questions, you only even know what the question is after it's been asked and you built a billion dollar thing that lots of people use because like obviously Spotify look crazy and look

crazy and Airbnb look crazy. But that's

a sort of I think the the way to get at what this means is you have to get past we do the old stuff but more and you have to get to what do you do that's

different that's because of this what does this change what wasn't possible before what gets unlocked as opposed to just doing the old thing but more of it.

Yeah. just to support this kind of general theme you have of it's like we don't know what is going to happen like this is unprecedented if you if you were to zoom out like a few years ago maybe

three years ago four years ago the last profession you think would be automated is engineering and coding it's like that feels like the hardest thing that's like we're going to need people to build these things now it's like the most

transformed role of any role like you went from writing all your code to 0% of your code is AI it's almost like you didn't realize you didn't realize it was boring manual labor that could be automated. You thought it was something

automated. You thought it was something else. It's funny. I mean, I I was

else. It's funny. I mean, I I was looking at this this whole there's a sort of US government called own data set called Onet or something like that which tries to kind of analyze every single job and then people try and kind

of score it and they try and say, well, you know, this profession is X or Y% exposed to AI and AI can do Z% of it today. I think this is just the most

today. I think this is just the most ridiculous bunch of deluded horseshit.

And there's two reasons for this. The

first reason is that this is like ironically this is the logical systems problem. The expert systems problem. The

problem. The expert systems problem. The

problem of expert systems is like for anyone who doesn't know like you try to recognize a picture of a cat and say you start building up logical steps. So you

make an edge detector and then you make a third detector and you make an eye detector and you make an ear detector and 15 years later you've got 700 steps and it doesn't work. Um, and this is

what happens when you try and look at a profession and sort of break it down by which bits can be automated and which can't. You can't describe a profession

can't. You can't describe a profession like that or at anyway we can't. You

can't kind of look at a senior partner at a law firm and say, well, 17% of their work could be automated like this is horshit. You can't do that. Um, I

is horshit. You can't do that. Um, I

think the other side of the fallacy though is to talk about taxi drivers.

So um you know if we've been having this conversation in 1997 it's like the Uber test. Imagine we were in 1997 what will

test. Imagine we were in 1997 what will be crushed by the internet? Well

newspapers will be fine. They'll just

because they'll save money on the printing bills. This is like a joke but

printing bills. This is like a joke but people said that newspaper the internet will be great for newspapers. Their

printing bills will go down. Well yes

but no. But the other side is well obviously like taxi drivers you couldn't automate that with the internet. It's

got nothing to do with the internet.

Maybe you'd have internet booking but like no that's not going to change anything. And of course, it completely

anything. And of course, it completely changes the whole thing. And so, uh, like the the example I saw the other day was like things that won't be affected

by AI, personal trainers. Okay.

So, I take my iPhone and I balance it on the metal piece with the camera pointed at me and I ask an AI to build me a training routine and watch me and tell me if I'm doing it right. Why do I need

a personal trainer? Now, that might be complete bonsense. Um, but that's how

complete bonsense. Um, but that's how these things work. Like the stuff that you don't think is ex you can't predict which things are going to be exposed necessarily or you know a lot of the big

companies are things that didn't look like that would work and didn't like look like that was exposed. The other

side of this of course is this is one of the charts at the end of my presentation is comparing Uber and Airbnb because this is like the cliche from Mark Andre that like Uber doesn't sell software to taxi companies, Airbnb doesn't sell

software to hotels. Okay, now let's go and look at the market impact. Well, a

whole bunch of cities where Uber demolished taxi business and made it much bigger as well made the town became much bigger and everyone switched.

Airbnbs impact hotel hotels if you actually go and look at the numbers is pretty marginal. They carved out this

pretty marginal. They carved out this whole other business and maybe they slowed down the growth of hotels bit.

But you know my wife flies to Milwaukee next week. She's going to land at 8:00

next week. She's going to land at 8:00 at night. She wants to go to a hotel.

at night. She wants to go to a hotel.

She wants to have room service. She

needs a bathroom bath. She needs, you know, needs a gym at 6:00 in the morning and then she's get 7 in the morning, she's going to drive to the client site.

She's not going to stay in an Airbnb.

Like absolutely zero chance she's going to stay in an Airbnb. And half of the hotel business is travel is business travel. And you know, you can as soon as

travel. And you know, you can as soon as you actually get into anything, then it gets complicated. I remember somebody on

gets complicated. I remember somebody on social media said the problem with Benedict is everything his answer to everything is it depends. It's like

yeah, it does. It depends.

it does. It depends.

So there were, you know, it's back to my 1997 point. You can say some of this.

1997 point. You can say some of this.

Um, but you have to have that humility.

Yeah. I'm coming back to this uh phrase you use, presume radical uncertainty is a nice uh core thesis here. So knowing

all this just it's hard to tell. We

don't know exactly where it's going. Uh

things are going to change a lot, but it'll probably be okay broadly. just a

lot of people listening are pretty worried about their jobs and their careers and how much the world changes.

What would be a couple things you recommend people do knowing what you know to be more successful in this future?

Well, I I should just kind of wind back on what you just said is like as Kees tells us in the long run we're all dead.

So, you know, it's all you know like on average um you know on average nobody died in World War I. Great. But if you know if you're if you're a 19 year old

in 1914 you you got a you know one in three chance of not coming back. So um

yes you know clearly there's a bunch of professions where this is a major question and particularly if you're an associate or want would have been thinking about being an associate this is a major question and it's very unclear how those professions are going

to play out. It's very unclear what the you know happens to the pyramid structure of professional services. The

answer I the only answer I think one can have is you know don't stick your head in the sand and say I hate all of this stuff because that gives you a great feeling of moral superiority and you can

go on blue sky and shout at everybody shout at each other about how evil AI is like great I'm happy for you but that's not going to help what helps is you

diving into this completely submerging yourself in it and coming out understanding what you can do with it how this changes things how can you how you can be a great hire. And that may

still not help, but you know, if you're going into a law firm and they're like, well, we hired 100 associates last year and this year we're only going to hire 50. Going to the interview and say,

50. Going to the interview and say, well, I think AI is and I'm never going to use it. Is probably not the right mood.

So, you know, you can that that that may not be particularly comforting, but I don't think there's there's an alternative is, you know, you have to dive into this and absorb it and internalize it and think about what it

means, just as, you know, you and I did with mobile and with with the internet.

I think that is actually very actionable and and very consistent advice on the podcast is just just do stuff. Build it.

Don't just sit around and pontificate and be be pissed at what's happening. to

uh close us out. I'm going to take us to AI corner, a recurring corner of the podcast. Uh and the question to you is

podcast. Uh and the question to you is just what's one way you've used AI and use AI in your work or life that uh is really interesting, something that other people might might uh be inspired by.

I don't know. I I struggle with this question because I'm sort of the lawyer looking at chat GBT.

So, you know, the stuff that I would do that I would automate are sort of precise information retrieval tasks, which is precisely the thing that this is kind of worst at.

And, you know, that's not a criticism.

It's just an observation. The kind of the kind of stuff that I would want a machine to do for me is the stuff that AI kind of can't do for me very very well at the moment. I use it for proof reading. I use it, you know, for images.

reading. I use it, you know, for images.

I used it redecorating my apartment.

That work fantastically well at that.

Here's a picture of this room. Repaint

it. Add this light and this table and this rug. Um, no, change the color of

this rug. Um, no, change the color of the rug. There's a kind of plants for

the rug. There's a kind of plants for stuff where it works. Um, but I mean, a couple of years ago, somebody said AI is good at stuff that computers are bad at and bad at stuff that computers are good

at. And that's

at. And that's I I I struggle to find many examples of those where I need it. But then, you know, I'm a kind of a unique weird job.

you know, I', you know, sit at my desk all day, you know, trying to synthesize a whole bunch of other stuff into a whole bunch of new ideas. That's not

particularly common where people spend their time. I struggle to find AI use

their time. I struggle to find AI use cases. I am the accountant looking at

cases. I am the accountant looking at spreadsheet and thinking, well, that's very clever and this is clearly going to completely transform everything. But I

actually don't make spreadsheets every day. I went to a standup comedy show uh

day. I went to a standup comedy show uh of Pete Holmes. I don't know if you know him and he made this joke that uh we want AI to do like clean the poop off the street and do all these like hard things that nobody wants to do but

instead it's like oh let me help you write let me help you create imagery it's like this bohemians like no I don't want to I don't want to do all these ugly things I want to be creative make art yeah well I mean there's there's there's

you know variations of all of this you know it's like I don't want the AI to do the stuff I do for fun I want to do the stuff that the boring stuff that I don't do for fun um and you know finding that mesh I mean you joking apart. This is going to come

back to kind of my chat boy chatbot point that you know the chatbot is a blank screen in a jagged edge like what am I supposed to do and what will work and that's a big problem and the solution to that problem is to wrap it

in in use cases. Part of it is also like AI just disappears. So most of what I write now I dictate. I dictate as a voice memo and that's automatically transcribed. Is that still AI or is that

transcribed. Is that still AI or is that just voice recognition? It's probably an LLM. There's probably an LLM in there.

LLM. There's probably an LLM in there.

Okay. So maybe that's AI. Well, okay.

So, so what um at a certain point it's just automation.

What do you use for that for voice voice transcription?

So I actually find Apple notes the app or the one built into the iPhone works fine. I mean I'm conscious of the people

fine. I mean I'm conscious of the people want others but like I mean I dictate it. There it is. It worked. So I'm I'm

it. There it is. It worked. So I'm I'm happy with that.

All right, final question before we get to our very exciting lightning round. Is

there anything else that you wanted to share? Anything else you want to leave

share? Anything else you want to leave listeners with?

No, I think you know I've I've I've monologued plenty and I've gone through a bunch of stuff in the deck. Go read

the deck and um sign up to my newsletter and then you will get many more mags of brilliant Benedict Evans wisdom. Um some

of which may even be useful. Somebody un

someone unsubscribed from my newsletter and they said you didn't you didn't give me any actionable stock ideas and I'm like well on one level that's completely true. On the other level maybe not.

true. On the other level maybe not.

Well with that Benedict we've reached our very exciting lightning round. I've

got five questions for you. Are you

ready?

Sure. First question, what are two or three books that you find yourself recommending most to other people? It's

a tough one for me because I just read an enormous amount of books and then I can't remember which ones I've read. Um

I I I I sometimes often joke that there's a there's a classic British comedy from the late 19th century called Three Men in a Boat, which is like my itching. It's like we're having trouble

itching. It's like we're having trouble hanging a picture. Well, there's a section about that. You know, we're having trouble doing this. Oh, well,

there's a story about that. All of which are hilarious. Um so three men in a boat

are hilarious. Um so three men in a boat is my iting. There's a book by I think William Cronin about the economic history of Chicago which is fascinating and actually very relevant to technology because it's talking basically about

standardization and packetization and logistics and channel conflict and network dynamics and um network neutrality. So like when the meat

neutrality. So like when the meat packers of Chicago um reach the point that it's cheaper to ship a cow from New York to Chicago, kill it, pack it and then ship it back to New York than to

kill it in New York. and you know the pricing of refrigerator cars and it's exactly like reading about broadband.

It's all the same kind of those kind of business issues which is fascinating.

What else have I read? I don't know.

Read books. Read different books generally. Read books for grown-ups.

generally. Read books for grown-ups.

Please read something other than Lord of the Rings. If you're going to name

the Rings. If you're going to name another company like I saw this and what was the latest like Peter Teal company?

I was like read another book.

Everything is named after a character from this one book. There's more than one book in the world. If there is more than one book, then all about science fiction. Read read about different

fiction. Read read about different things. Read about things you don't know

things. Read about things you don't know about.

Kind of along those lines. You have a favorite recent movie or TV show that you've really enjoyed?

I don't know. I've dropped so badly off the the the current media treadmill and I just spend most of my time watching classics which are like always the ones that you're supposed to have seen and that all seem intimidating and then you

watch them and you're like, "Oh, that was actually really good."

Um, I watched The Seventh Seal recently, which is like one of those joke Woody Allen terrifying, boring movies, and it was brilliant. It was really

was brilliant. It was really interesting, and it's like it's only like an hour. So, go watch one of those movies that you are supposed to have seen or hadn't seen.

Favorite recent product that you've recently discovered that uh you really love. Could be a gadget, could be an

love. Could be a gadget, could be an app.

I was speaking at a partner meeting for a company earlier this this week. What's

today? Monday. No, last week. and um met the founder of the company who has a very famous the CEO of the company has a very famous name and admired his shoes and didn't say anything but then went in Google like half an hour later yeah okay

I'll buy a pair of those you want to share the brand or you want to keep it keep it secret okay we'll keep it secret I don't know I think one comes one comes in waves of new products and you know

you get into waves of new things and um like when's the last time there was a cool app like iPhone apps that was you know all that white space went I mean it's partly a function of product ships a platform ships like all the white

space went for for cool new apps and now we haven't quite got actually this is a to the earlier point we don't have breakout a consumer AI apps yet because I think because of marginal cost more than anything else so you can't make it

free and get 50 million users and then have a revenue bottle um but we don't have those breakout things yet for consumer yeah for consumer no I just weird I keep getting these ads for voice recorders like somebody's selling like a business

card size like hardware voice recorder I'm like But like I didn't get it. Like I've got a voice recorder on my phone.

Yeah. All kinds of cool stuff coming.

Okay, two more questions. Uh, do you have a favorite life motto that you find yourself coming back to often in work or in life?

I suppose I've mentioned earlier apparently I mostly say it depends. Um,

that's going to be the title.

No, it'll probably be okay.

Yeah. Okay. I that's that's the vibe I get. I like that. I like that it's

get. I like that. I like that it's probably going to be okay. Not for sure.

Um, okay. Final question. I saw

somewhere that you own a lot of old phones. Is that true?

phones. Is that true?

Uh, it is. Yes. As a um I kept I mean I was a telecoms analyst and mobile analyst and I I kept all my phones up to a point. Now they're kind of

a point. Now they're kind of uninteresting. But as you you may

uninteresting. But as you you may remember like before the iPhone, particularly outside the USA, there was this huge creativity and expansion in what phones look like because everyone was basically innovating around a little teeny tiny gray square. So everyone was

trying to differentiate from everything else. um before it kind of result. It's

else. um before it kind of result. It's

kind of like cars actually. It's like

cars before street before like wind tunnels. Cars all look different and

tunnels. Cars all look different and everyone's trying to innovate around because you've got the same four wheels and the same engine and everyone's trying to like differentiate based on like the shape and and then everything converges on one shape and it's kind of the same with phones like everyone

everything converged on one shape.

Before that there was all this innovation. So yeah, like I have like

innovation. So yeah, like I have like like a whole bunch of PDAs and and smartphones and how many phones we talking about?

I don't know like 20 or 30. Okay. Okay.

It's not so crazy. What's like the oldest one? What's the oldest one you

oldest one? What's the oldest one you got?

So, I have one of those I should have you told me I'd have got the box down. I

have one of those Ericson um Shark fin flip phones from like 98 or something which is very again like hardware design visual design trying to differentiate.

I've got an iMode phone from 2001 and a Jane phone from 2001 that has a camera.

So, I came back from Japan in 2001 and my phone had a color screen and a camera and like I just had like endless client meetings and people just wanted to see the phone with a color screen. Like it's

mind-blowing. Didn't work outside Japan.

I plugged it in the other day. It still

charges up. I mean clearly I can't do anything with it. Um, and like I mean there's there's a little bit of an analogy in there as well and that like we thought there'd be all these different shapes and sizes and before the iPhone people kind of imagined like

well some people will have like a little pocket PC and some people have a keyboard and you have like folding although all these different ideas for what it would look like and it we didn't realize it was all going to converge on one device.

Benedict, this was amazing. I learned a ton. I feel better after this

ton. I feel better after this conversation. Two final questions. Where

conversation. Two final questions. Where

can folks find you online? Where do they find this presentation? And how can listeners be useful to you? If you can Google me, as I always say, my parents had good SEO. So, Google Benedict Evans.

Um, and so there's a website where there's I publish all the presentations that I've done and sign up for my newsletter which comes out every week.

Otherwise, how can they be useful to me?

Like I'm always trying to understand stuff and I'm always trying to ask different questions. The worst thing in

different questions. The worst thing in tech is to like carry on talking about the same stuff. It's like, you know, the moment you really understand something is the moment you have to push on to something else. And so I'm always trying

something else. And so I'm always trying to think like, no, am I just talking about the same thing over and over again? Like last year I just spent

again? Like last year I just spent probably too much time saying, "But these models still hallucinate. Stop

telling me they don't hallucinate."

And they do. They still hallucinate. You

know, you push them, push them a little bit further, any question, and you'll still get like, "Nope, that's not true."

Um, but that doesn't mean they're not useful. So you have to kind of keep

useful. So you have to kind of keep pushing keep pushing myself. So that's

always the challenge for me is is how do I push? Um, and then yes, if you want me

I push? Um, and then yes, if you want me to come and present to your board in the Caribbean, um, then let me know.

And by the way, the domain is bend-ans.com if folks want to check you out. And it's evs.com.

out. And it's evs.com.

Mendic, thank you so much for being here.

Thanks a lot.

Bye everyone. Thank you so much for listening. If you found this valuable,

listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider

podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at

lennispodcast.com.

lennispodcast.com.

See you in the next episode.