Why Everyone Is Wrong About AI (Including You) | Benedict Evans

Transcript from https://podmenti.com/t/61cc317a0fd19571

It seems to me right now you could do like a double blind test of the same prompt given to Groc, Claude, Gemini, Mistral, Deep Seek I bet most people wouldn't be able to tell which is which.
Benedict Evans is a technology analyst known for his insightful takes on platform shifts in the tech industry.
He sees AI differently than others.
He spent decades spotting patterns others miss and dives into how people really use AI. Why is it that somebody looks at this?
And gets it and goes back every week.
But only every week. The very high level threat to Google is that you have this moment of discontinuity in which everybody resets their price that we considers their defaults. And so it's no longer just the default that you go and use Google. There's this sort of question for Apple around does this n actually change the experience of what a smartphone is, what the ecosystem is.
Does it end up kind of getting microsofted?
In the sense that
I wanna start with your most controversial take on AI.
It's funny, my I suppose my take on AI, controversial take on AI rather like my controversial take on
Um crypto is being a centrist.
In that
Seems to me.
Very clear this is like the biggest thing since the iPhone.
But I also think it's only the biggest thing since the iPhone.
And there's a bunch of people who think no, it's much more than that. It's
But a minimum it's more like computing.
And then you've got people going around saying no, this is more like, you know, the electricity or the industrial revolution or, you know, transhumanists or something.
my sort of base case is to say this is kind of another platform shift and all the new stuff will be built around this for the next ten or fifteen years.
And then there'll be something else.
And so the impact on employment will be kind of like the impact on employment from the other platform shift and the impact on the economy and productivity and intellectual property. And there'll be there'll be a hundred whole bunch of different weird new questions.
just like there were a bunch of different weird new questions before and then in ten years time it'll just be software.
Put this in historical context for us with other platform shifts. W everybody's saying this time is different, which everybody does at each platform shift, I would imagine. What's the same? Well that's the c there's a there's a
famous book about um
Financial bubbles.
Called This Time is different.
Because people always say this time is different and it always is. Like the dot com bubble was different to like the late eighties and the Japanese financial bubble was different to, you know, pick any other bubble you want. They're always different.
Um but that doesn't mean they're normal bubble.
And the same thing here. I have a diagram I use a lot from um
Nineteen ninety five, this research firm made a diagram of something called they called cyberspace.
Um, because it wasn't clear it was just gonna be the internet. It was clear that everyone was gonna have some kind of computer thing.
connected to some kind of network. But remember the phrase information super highway? Yeah. Which sort of conveys that it would be centralized and controlled by cable companies and phone companies and media companies, which is sort of how
Everything it always previously would. It wasn't clear no, it was going to be the internet. It wasn't clear the internet was going to be kind of radically decentralised and permissionless, and anyone could do what they wanted. It wasn't clear the internet was going to be the web.
And only the web,'cause there were all these other things going on. If you look at Mary Meeker's first big public internet report from nineteen ninety five, she has a separate forecast for web users and email users, and she thought email users would be way bigger. It wasn't clear like that was all one thing.
And then it wasn't clear that it was about the browser.
It wasn't clear that the browser wasn't where the value capture was,'cause Microsoft craybared its way into dominance in browsers, but that turned out not to matter.
And then all the value is inside advertising and social, which were five years later and ten years later.
And so like you can be very, very clear that this is the thing and then still be completely unclear how it's gonna work. The same thing with with with mobile internet.
Just funny, mobile internet now it's kind of like saying black and white television on colour television. Desktop internet, mobile internet, black and white TV, colour TV. No one really says mobile internet anymore.
It's like talking about e commerce. You're starting to have people talk about physical retail and retail.

And but it wasn't clear you know, I was a telecoms analyst in two thousand.
And and it was very clear mobile internet was going to be a thing. It was not clear that there would be basically small PCs.
That was the the fundamental shift of the IFAD is it's a small back.
It's not a phone with better UI, it's a small Mac.
And it wasn't clear that the telcos would get no value. It wasn't clear Microsoft and Nokia would get no value.
It wasn't clear it would take ten years.
Before it took off.
Um and it wasn't clear it would replace the PC, as I said, for the tech industry. I mean everyone was talking about well, what's a mobile use case?
What would you do? You'll do some things on your mobile phone, but what?
But obviously your PC will be how you use the internet. And of course that's not how it works.
And so I we kind of forget because now we don't see it. Because now it just kind of became part of the air we breathe, how weird and strange and different all these things are. There's something I love talking about, which is is um the rise of automatic elevators.
So until the fifties elevators were manually operated. They were basically vertical streetcars. They were tramps. They were pub trains.
And you have a driver who has a lever with an accelerator and a brake. If you've been into a New York co op, you may have seen one of these and they call it an attended elevator.
There's a lever, you push it that way to go down, middle to stop, that way to go up. And then in the fifties OTIS creates the autotronic I think it's called the Autotronic Elevator, which had electronic politeness.
Which basically meant the infrared thing that starts the talk, I think. But if you get into an elevator now you don't say, Oh, I'm going to use an automatic elevator with electronic politeness. It's just lift.
We kind of forget how weird and different all the other things were. And yes, this is new and weird and different in a bunch of kind of
strange, confusing, confounding ways we can probably talk about.
But we sort of forget that other things were weird and strange and different too.
Is this the first
major platform shift where the incumbents have an advantage.
Because they have the data.
I'm pretty sure people thought Microsoft had an advantage on the internet.
And Google and um Meta had an advantage on mobile.
And everyone thought IBM was gonna win PCs.
I once IBM made a PC, that was it. It's all over now.
And we f kind of forget l that there were PCs before
And then IBM made one and that kind of became the standard, but then IBM lost it.
So what happens with the incumbents? Do they grab on to using the technology instead of adopting it because adopting it would mean killing the golden goose? Like what happens in a platform shift with incumbents? The master of my college at Cambridge um said um that history teaches us nothing except that something will happen. And you know, there's always the example and the counterexample.
So with any new kind of any new platform shift and and a platform the term platform shift itself
is you know, it's a useful term, but you have to be careful not to be trapped by your terminology and get into this sort of arguments about well, is it a platform shift or is it not a platform shift? And how do you define a platform? Shut up.

The the thing is when any with any of these sort of fundamental technology changes, the incumbents always try and make it a feature. And they try and absorb it.
And the same thing outside of technology, um, existing companies try and absorb it and they use it to automate the stuff they're already doing.
And then over time you get new stuff. You unbundle
both the incumbents intake and you unbundle existing companies because uh something that's possible because of this new technology. So you can always kind of jump jump into the new thing. And sometimes the new thing kind of really is just a feature. And sometimes it's no it's a fundamental change in how everything works.
And sometimes that sort of contingent.
You know, there's this whole sort of parlor game like drinking game that historians play about kind of historical inevitability. You know, well what would have happened if that battle had been lost or if that politician had been assassinated or not assassinated.
And it depends. Sometimes the answer is well no nothing then then everything would have been completely different. And sometimes the answer is well no then you know. What if Napoleon had won at Waterloo, well then he'd have lost another battle six months later. Like nothing would have changed the whole environment had changed.
Um what if, you know, the revolution hadn't happened in spring of nineteen seventeen, then it would have happened in the summer or the autumn.
Sometimes it's like really clear. I mean I always think the um
The Kodak example.
Uh it was kind of interesting.
Tell me about it. Because you know, like it's like the cliche that people say, Oh, Kodak had digital cameras and they didn't get it, or they ignored it, or they didn't want to do it because it would destroy their business. But then you go and look at it and that's like, Well that was nineteen seventy five and the thing they had was the size of a refrigerator.
I know that was not a consumer product.
And it took until the late nineties before the technology was actually viable as a consumer product.
So of course like they didn't do it in the seventies'cause you couldn't. What actually happens is once it starts happening, okay, don't go all in on digital cameras. At one point they were the best selling digital camera vendor in the USA.
And if you look at their annual ports at the time, they think this is gonna be great because they're gonna sell way more photo printers.
So they're selling these inkjet photo printers, people are gonna produce may w way more photos, so they're gonna take way more print they're gonna print them all.
Two things that screw cut up. One of them is cat is smartphones.
And you could argue that what actually screw is Kodak is not the camera, it's the it's the social media and it's the not printing anymore.
Which that's what kill that's one side. The other side of it is that
film with this high margin product where they had a bunch of unique intellectual property.
And digital cameras are a low margin um commodity.
where they were competing with the entire consumer electronics industry with no differentiation. And so even if they go even at the point being, even f even if you go all in into that market, it's still a crappy market where you've got no differentiation.
Um
You know, you can you can kind of put all of these things on the table and shuffle them around and say, Well, in hindsight, obviously Bybus Scoot. And in hindsight, obviously Google was going to be able to make the jump. And in hindsight and in hindsight and
Yeah, maybe. Is there a parallel between the second point you made about Kodak and Google today, where you know they have a high margin search business and a low margin AI business?
So I think it's I'd be nervous about knowing what the margins are in AI because we've had, you know, depending on who you ask, like the price to get a given result has probably gone but come down by two orders of magnitude. But then that's but that was the state of the art two years ago and now there's the new thing which is more expensive.
Um and so there's a l awful lot of kind of shifting planes and shift you know, there's there's a lot of the the the there's a lot of algebra and all the variables and the algebra all changing at the moment. So it's kind of hard to quite to know what that is.
I think the the the kind of the the the obvious Google threat right now
Is that
Um Google
Shows you a bunch of
Links.
And results and ideas.
And those could now be solved in a different way.
Um
The very high level threat to Google is that you have this moment of discontinuity in which everybody resets their price and we s we considers their defaults.
And so it's no longer just the default that you go and use Google and for this search or that search, like maybe Bing is ten percent better on that search. In fact as we saw from the micro the the the Google.
T A C trial last year actually, Go Bing is Google is still the best search engine by quite a long margin relative to the other traditional search engines. But what we have now is like a reset of the playing field.
Um
And Google has a whole bunch of advantages as to why they might win in that playing field.
But there's a reset both of what the product is and and and how you sell it and your org structure around selling it and do you have the right politics and the right org structure to build that and the right incentives and internal conflicts. And then the consumer behavior kind of gets reset as well. My understanding of AI is that so much is data driven. Like you have proprietary data sources, you have
uh better data as you can train better model. That gives you like one of the key inputs. I would assume it's actually I think it's actually the opposite which is that everyone's kind of using the same data.
Which is you need such an enormous amount of generalized text.
that the amount that Google has, or that Meta has, is not actually enough to m to be a kind of a f a fundamental difference.
In what you can train with.
So you don't think like YouTube as a repository is
An advantage for like a significant advantage.
It depends. Push back, yeah. So it depends. So the models that we're training now, we're training on text.
So that's not really being trained on YouTube. We saw this lawsuit around book copyright with Meta that they downloaded a torrent of pirated books.
'Cause guess what? That's they don't have enough style t text. And it's not the right kind of text. They don't have lots of prose.
They've got lots of short snippets of text. So I think the generality of LLMs is you just need such an enormous amount of data that everyone kind of needs all the text that there is.
And all the text that there is is kind of equally available to anyone.
So we've kind of like the data is a level playing field effectively. Yes, because you need so much more. And it's also
not necessarily the kind of data that you have.
So obviously Google has, you know, an enormous repository of scrape data'cause they read the web all the time.
But anyone else with a billion dollars can go out and do that.
Right. Or you can go and download the common call from AWS.
How far away do you think we are from autonomous
sort of AI making AI better. So
uh no human intervention, but AI
sort of going out in the real world, getting feedback, adapting itself and making itself better.
There is this sort of like people parody of like the fump.
Like poof, like suddenly magically this thing grows and becomes amazing and and learns everything. Um
I don't think we're at that stage now. I don't think anyone really knows when it would happen. So it's very sort of impressionistic.
I think
Another answer might be you kind of have to be very careful looking at headlines and thinking like
What exactly is that?
Telling me.
Um so Anthropic has done a bunch of things where they say like
The AI was threatening to blackmail me.
Yeah.
I saw that. And you read the story and you think
Okay, you asked what's basically a story generating machine.
Please tell me a story of what you would do.
If in this situation where most people would probably say X and the machine says probably X and you say, My God, it said it would do X. It's like well
Yeah.
It would blackmail based on human behavior. It would blackmail you. Okay, how would it do that? Yeah. What do you mean it would blackmail you? It's kind of like the reductio adaptum of this is
Um you write this is a point somebody else made, that like you write murder is good on a piece of paper and you put it in a photocopia and you press go and you say
My God, the machine says murder's good. Well
No, you told the machine to say that.
And that's what these anthropic studies are. They're basically you tell the machine to say a thing
And then it says it.
Like well, you haven't proved anything.
Mm.
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Where do you stand on regulation of AI?
So I think regulation of AI is sort of the wrong of abstraction.
Talking about regulating AI as AI.
is the wrong level of abstraction. It's like saying we're going to regulate databases or regulate spreadsheets or regulate cars. Well we do
But not like that.
When you regulate stuff, there were trade offs.
Like you learn about this in your first year in economics class. Like regulation has costs and consequences and it's not necessarily um
You know, there's there's always a trade off.
And
often you're making product decisions or engineering decisions that do actually have trade offs.
There's like a three way trade off of like what's good for the product, what's good for the consumer, what's good what's good for competition, what's good for the company, what's good for the consumer. I think the regulatory stuff is interesting in the framework of
Multiple countries sort of competing for superintelligence.
How would you advise a country to prepare for AI if I'm the president of the United States and I call you
And I say Benedict.
Uh you have five minutes. I need
What do I need to prepare for? What can I do to put our country in the best position possible? Well what's your objective? Is your objective
to um have a nice press release.
No, it's to dominate AI. A long time ago I used to get these questions about like how can we replicate Silicon Valley.
And I always feel like the answers to those quest questions as much as possible is you can't
I mean, occasionally there are things you can do, like you can create funding structures, you can make it easy, you can you know, you can try and jump start start up ecosystems, you can try and jump start funding funding availability. But most of the answer is things like getting out of the way.
I think the the idea of trying to create national champions is very hard.
Now that alm almost kind of becomes an economists question rather than a technology question. How do you create national champions? Where is that work? Where is that not work? I'm sure there's a bunch of books and papers about, you know, why where does industrial policy w work? Where does it not work?
from a technology analyst perspective, I think of this in terms of
A, what are you doing that would make this harder? And B, think of this as just more startups.
What are the things that we're doing that make it harder?
to develop that uh eco without picking a winner, it's not about picking a company and lacking them. If you do like this this this ridiculous law that California had a year or two ago.
If you treat this as like nuclear weapons
And you say this is incredibly dangerous and we need to have it under extremely tight control so that nobody does anything bad with it.
Which is basically the EU approach. Go back to economics class.
Yeah. Policies have trade-offs.
To govern is to choose.
You're making a choice when you do that and you're choosing that has costs.
Personally, like most people in tech, I think the idea that this is all gonna kinda produce bioweapons and take over the world and kill us all is just idiotic. Like I think and I think it's just a bunch of kind of childish logical fallacies within that.
But you have to be conscious. You say if we're going you know, the the the the kind of the Biden approach to to generative AI very explicitly was to say this is sort of social media two point oh.
Mm like social media to one was terrible and destructive and bad and I think there's a
I don't agree with that. I think there's a huge dose of moral panic within that. But be that as it may, if you make a decision that says we are deliberately and explicitly going to make it really hard to build models and really hard to start a company that builds models and really hard to do anything with any of this stuff, then guess what?
It's kind of like you know the mail election in New York today. Like if you make it really hard and expensive to build houses, houses will be more expensive.
You've made that choice.
If you do that, you cannot then complain that houses are more expensive. You can choose that, but you can't complain. Why do you think as a society we don't understand that?
Part of this is that like in most non emotive fields we kind of do
Uh you understand people understand that, you know, if you make you know more employment regulation tends to produce slower growth but more protection for employment and you're c you're choosing a trade off.
Yeah, people kind of I think everybody on both sides of that equation understands that that that's the trade off and you're choosing one versus the other. The point is you can have a fully functioning free market and you can regulate some of the
negative externalities of free markets that anybody in any part of the economic spectrum understands there are negative externalities to free markets. You can also have like a government provided alternative. You can have the government do the fire department.
Where you have some of the kind of most obvious gaps between the US and Europe, it seems to me, sometimes, or in places where you kind of have neither.
So you the US neither has a government controlled healthcare system, nor a free market healthcare system. Do you see what I mean?
You have neither a government controlled housing, which you have in like in weird places like Singapore.
Nor free market in housing.
So you kind of break the free market. So you stop the price signaling. This is like the great insight of Hayek is that pricing is a signal. Pricing is an information system. It's telling people what's wanted.
It's not it's it's not just a signal of worth, it's a signal of demand. There's a a fascinating book I read a while ago called Red Plenty.
Which is about Soviet central planning in the sixties, seventies, eighties.
And it's about sort of what happens when you have a central planning that just cannot cope with the level of complexity of a sophisticated economy in the sixties and seventies, as opposed to let's make grain and tractors and locomotives in the twenties and thirties and steel, which already kind of works.
But once you actually have a sophisticated industrial economy, central planning can't handle the complexity.
And so you try and create incentives and structures around that while not having pricing.
And that just doesn't work.
I suppose there's a sort of a generalized point which is like
A market economy is a system.
And if you pull a lever here, something will move there.
And you can't just pull lever here and say, Well, I don't want that to move because it's a democracy.
It will move anyway.
And so you have to understand how the system works and understand what consequences you want from that and what your parameter parameters are within this.
One of the things that I admire about you is that you're sort of known for spotting patterns. I have a theory on how to learn pattern matching, and I'd love to hear your your pushback on this.
My theory on how we learn is I call it the learning loop. We have an experience
We reflect on that experience and we create a compression and that compression becomes our takeaway. So we can watch a movie.
uh read a book and you come away with a compression of it, but you can work backwards from that compression to the experience.
But what we consume most of the time is other people's compressions. So like when people read your newsletter, they're consuming a compression of the work that you've done.
uh but not the actual raw work. So in a way it's an illusion of knowledge if if you haven't done the work in that area.
It's funny, I'm I'm I have a a draft.
thinking about like what L L L Ms do to web search and publishing and discovery and e commerce and like big foot of
hand wavy, fuzzy, all of that stuff.
And
I was sort of thinking about this and there's a book written by a French academic.
sort of twenty years ago or something called How to Talk About Books You Hadn't Read.
Which sounds very kind of.
Um
Snipe.
Um
But kind of his point is that like
the the book you read when you were seventeen and you really didn't get it.
Mm-hmm. And if you read it now, you'd get it. And there's the book that like he's got this kind of list of like there's the books that everybody else has read, so it's as you've read them. There's the books that like you've read three other books by that writer, so you don't really need to read this one too. Um you get it. Like you do need you know, do you need to read another Malcolm Gladwell book? Like if you or you've kind of got the Malcolm Gladwell experience. So there's this sort of generalized sense of pattern.
And
Accumulation.
of what you've seen, what you've half seen, what you half remember.
There's also I think, you know, what your your your viewers, listeners might notice is I I kind of have two modes, two or three modes.
I have a mode that's sort of discursive and a slightly rambling and free associating and I'll kind of spiral off in different directions and hopefully come back to the point.
Um
And then there's a mode where I want to try and pin the thing down.
And break it apart and say, What are the two, three, four things that are happening here, which is what you see in the slides. This and then this and then that. And as that capture that that's a way of trying to understand what this is.
I I try and work out what I think about this, how I understand it, how you can break it apart by kind of pinning it down.
The thing about data is
And the thing about the slides and the analysis is is like I'm always asking who cares.
Mm-hmm.
And I'm always asking, yes, but what actually matters here?
Why are you showing me this slide?
Why am I showing you this chart?
And so you kind of have to ask like, Well what are the actual
Questions.
What are the questions we're not asking on AI that we should be asking?
I mean, we're asking okay, well there's some people who are saying all of Alcatra's gonna be in the w in the p in their in their models.
There's this kind of funny split between people who are just talking about the models getting better and everybody else who's saying, Well, all the value's going to be in the application layer and you know, all of the companies and let's fund Cursor and let's fund all of this stuff. And why isn't there a consumer breakout yet? And other people are saying, What do you mean there isn't a consumer breakout? Everyone's using ChatGPT to which John says, Well
Not exactly, which is my data point. Some people are using chat GPT, most people look at it and don't get it still.
Just fascinating. There's this kind of core where's the value capture question.
Then there's like a bunch of questions we could have asked two years ago where we don't have an answer.
Will the error rate ever be controllable or manageable? Will you ever get to a model that knows when it's wrong? Which to me seems just like given a statistic statistical system seems like a contradiction in terms. But maybe there's a bunch of kind of
You could make a list of like a dozen questions we could have asked in early twenty three.
We don't really have answers to any of those.
I mean there were some people who were asking, are these things commodities? Will China catch up to a tree answer even then was obviously yes, of course, which is what happened, which deep sea kind of demonstrated.
But we don't have that many new questions since then.
The thing that I puzzle about right now is first of all, there's this whole nexus, as I said, of like what is LL what is SEA for LLMs.
You know, we have infinite product, infinite retail, infinite media. How will you choose what to buy? What happens if I go to an LM and say what matters should I buy? What life insurance what life insurance should I get? How does that walk that
That poses dozens of questions we don't know yet.
Then there's the question around like
the differentiation w in the LLMs as product.
Like it seems to me right now you could do like a double blind test of the same prompt given to Groc, Claude, Gemini
Um
Mystery, deep sink.
Do a double blind test, I bet most people wouldn't be able to tell which is which.
That question of like is there product differentiation, can there be product differentiation around the LLM as consumer product?
'Cause right now the model's the commodities, but chat GPT is way, way, way, way, way more usage.
So ChatGPT is at the top of the outstore rank, Gemini bubbles between like fifty and a hundred.
Um, none of the others were in the top one hundred. Same in Google Trends, same in the usage number, same in the revenue. There's revenue for corporate APIs. Corporate is a whole other story.
But as a consumer thing, it's like chat GPT is now like the brand.
It's the default. It's the Google. You use it'cause you've heard of it. And none of the others have broken it.
Is that where we are now?
Is there but but then if you look at the products, the products not any of the model's all the same.
The underlying model's the same. The product's all the same.
It's really hard to tell the difference, except like they've got different colour schemes and different icons. Different branding. Different branding.
But the iPod is all the same. And this reminded me of looking at um
Browsers.
And browsers are all the same.
The rendering engine underneath might be different, just as the LLM might be different. But you've got an out an input box. An output box.
And the AppleBox wonders what the window engine gave you. And the only innovation in browsers in the last twenty five years is basically tabs and merging search in the app address bot.
Yeah. And it's like
There's people trying to do it now, but it hasn't worked. It hasn't got traction. And is that sort of how L Ms will work in that it's about the distribution and the brand is not actually about the product or the model.
Or is it maybe more like social?
In the yeah, photo showing is a commodity.
But you there's a big difference between Instagram and Flickr.
Yeah. And all the other people that try to do photo sharing.
And so you have to that would almost be an argument that it's sort of winner take all, right? It it's very hard for
uh like use cloud as an example. It'd be very hard for Cloed to compete if
They don't have enough
um usage to continuously make the investment. Well that's a slight this is a slightly different
Thing.
So the winner there doesn't appear to be
A sort of self reinforcing cycle in which more people use it because more people use it. The product gets better because more people use it. So more people use it, which is what you have with operating systems because you have more apps, therefore more users, therefore more apps. So what you have with Google search that Google has all the feedback from how people use it that makes the search engine better.
There's no apparent equivalent in LLMs right now.
There's no reason why the LMs get better because more people use them.
Now that may come.
You have that the open AI and people have been doing memory.
where it remembers what else you've asked, but that seems more like a switching cost than a network effect. And also it might be easier for you to just ask it what it knows about you and then tell Claude.
Or vice versa. So it's not clear if
That
But it we are that sort of stage where you're looking at the browser and saying, Is there a way that you can create stickiness here? Or that you can create a network effect on the browser? Or is it just that the browser itself is a commodity? Um now capital
is not a winner take all effect.
In that convention. Oh, and anyway, there's a different kind of when it takes all effect.
Um I mean I wouldn't conventionally think of capital as a network effect.
Um or is it it's not a product that's in it it's not something that's inherent in the product. It's something else.
It may be that yes, Chat GP the open air has more money so they can make their model better.
There's like six rabbit holes I wanna go down before we move on to something new. If open AI can
I I liked your point about sort of at the point where AI gets better because people are using it, then there's a huge advantage to being open there.
At that point though.
Um whoever's in the lead would sort of think then you could get kind of a runaway.
Um, but we should kind of go back and think about MySpace.
Um, because you have like
And the s it would be my space, you know, in the early phases of these things, and you see the same thing with the early PC industry, you got a dozen of them.
And there's often an early leader.
that falls away later. Let's say MySpace was the early leader that fell away later. Then you get a late stage where the S curve is kind of flattened out.
where all the network effects have kind of solidified and the product quality has solidified. It was very easy actually to get people to switch back and forth between MySpace and Facebook and and Bebo and and and Friends Reunited and whatever the um um Orcut and all these other things.
In the early days. Right. Then you kind of get this this separation out.
But then of course then you get then Instagram comes along.
And then TikTok comes along. Right. And so as soon as you have something that's a different proposition, it turned out to be extremely easy to pull that away.
you know, you go Google Google lost to YouTube and they had to buy YouTube, Facebook lost to to Instagram and WhatsApp and they had to buy them both.
So those are quite fragile and quite narrow when it's all effects, or at least th they they they appear to be.
We don't know what that would be or what the modalities would look like. Modalities, so that's a great meaningless word, it's like saying societal. We don't know what that would look like and therefore we can't we don't know how rigid it would be or how it would work because we don't have it yet. You can't as as I'm sure you know, you're not they're not retraining the models all the time with the data.
So you don't have that kind of one away effect as like continuous flow and more queries produces, you know, better results.
Um so it's kind of tricky to do that yet.
I wanna come back to something you said. You said some people look at Chat TPT and don't get it. Yeah, I think this is really important.
There's a whole bunch of survey data on how people how many people are using this stuff. You've got the numbers from so so so so J OpenAI produced say, well, we've got this many weekly attributes. It's funny thing about social is
People when social happened, people would talk about registered users. You remember in the early days of the internet, people would talk about hits?
Yeah.
web page has seven items in the menu bar, that's seven gifts, so that's seven hits, so hits with meanless, and you have to switch to page impressions, and then it's which users and then it's monthly active users. And on social people said, Well hang on, if you're using Instagram once a month you're not using it. It's daily active users or nothing. And weekly active users that we don't like either. Now OpenAI is doing weekly active users. And Sam Waltman was a social media start founder. He knows this.
It's a bullshit number. You look at survey data, and I had the did the slide in the last presentation I did of like five different surveys from the US.
From late last year, earlier this year. And it's all roughly the same. It's like something around ten percent.
give or take three or four percent of people, depending on the survey, are using this.
Can't say they're using this every day.
Another sort of fifteen to twenty percent of people say they're using it every week.
And then the say you've got like say ten percent of people using it every day.
say fifteen or twenty percent of people using it every week. Another twenty or thirty percent of people who say I use it every month or two.
And another twenty or thirty people of a percent of people who said, Yeah, I had a look, I didn't get it.
And then you have this survey where people say seventy percent of people are using AI and like Wait what
You mean there's a whole other rabbit hole, which is you know, people say, Well, you did you use Snapchat's face filters also you're using AI. What do we mean by AI? So which is let's be specific, let's talk about you are you using a consumer facing LLM chatbot, like you're going to chat GPT or call and asking questions. Like that's the number we want to look at.
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And
To me there's a there's a bunch of you could matrix this. So some of this is
It's early.
There's a counterpoint here which if people do the chart and they say, Oh my God, it's so fast.
It's like it's faster than smartphones, yes, because you didn't need to buy a thousand dollar smartphone. Right. It's faster than PCs, yes,'cause uh you know what PCs cost in the eighties adjusted for inflation? It's like five grand. Yeah.
But it's free for a lot of them. It's free. It's a website. You just go there. Of course it's got fast production. And there's way more people online as well.
So even the absolute numbers are faster than they were for Facebook twenty years ago, fifteen years ago,'cause there's way more people online now. Yeah. So so that's
That's again an example of my unfair but relevant comparison. You're sort of standing on the shoulders of Jones.
So of course you can get to way more people quicker, but do you have to keep asking well what yes, but
Why do so many more people look at this and not get it? Or even worse.
Uh the not getting it I can kinda see.
Because people look at everything and don't get it.
Why is it that somebody looks at this?
And gets it and goes back every week.
But only every week.
Right. Why is it they can only think of something to do with this once a week? I worry about those people.
I mean I I'm just thinking if these numbers are accurate, the ten percent, the fifteen
Yeah.
Ninety percent of the people that I spend the most time with are within that ten percent. Well I'm not.
Interesting. Tell me more about that. Well here, actually I'll I'll preface this conversation with
My kids don't use Google anymore. They have they use it to find phone numbers.
or local businesses or places uh distance.
Everything else they they basically have default to chat GPT now. Again, I'm going to all AI conversations seem to be analogy. So I'll and it's like it's like nuclear weapons. No, it's not.
The comparison I think is interesting here. It was not perfect, but it's interesting is to look at early spreadsheets.

Software spad sheets, spad sheets with paper. Dan Berkeley and creates Physical and the other guy, I can't remember the other guy's name, you create Physical.
In the late seventies.
And I think to get an Apple II to run it with a screen and everything costs like fifteen grand adjusted for inflation.
And you show this to an accountant and then it's like you can change the interest rate here.
And all the other numbers change.
And we see that now and we're like, Yes.
Nineteen seventy eight, that was a week of work. Almost literally That was like amazing. Yeah, it would do a week of work in half an hour. Yeah.
All plus.
And he has all these stories about accountants who would, you know
they would be given a one month project and they'd get it done in a week and then they'd like go and play golf for three weeks because they partly because they could they didn't actually want to tell the client I needed it in a week because the client would think they hadn't done it properly.
So I look at chat GPT and I think right, I don't write code.
I have
Zero use of something that will like h for me.
I don't really do brainstorming.
Okay.
I don't do a summarization of things. I don't
Two.
the things where it's sort of out of the box.
Easy and obvious.
And then there's a sort of mental load of okay, I've got to kind of try and think of what things am I doing that it could do for me.
And that most people don't think like that.
So there's a sort of I said a moment again, there's like a matrix. There's a matrix of like
Who has the kinds of tasks that it's good at, obviously.
Who is good at has the kind of tasks that it's good at? Not obviously.
Who is good at thinking?
About new tools for the things that they're doing.
Who isn't?
If you're kinda blown away you don't reflexively use AI.
Salesforce.
And you had a button that said draft me an email to apply to this client.
Then that gets massive adoption.
Well that's that feature that we talked about.
Is it the chatbot as product where you get this blank screen
And you kinda have to look at it and you scratch your head and you have to think, Well, what is it that I would do with this? And then you have to form new habits around it.
Or is it that it's wrapped in a product in UI where somebody else has said
It would be really useful for this, wouldn't it? And then you look at it and go, Oh yeah, that would I I could do that.
Do you think it's better with qualitative or quantitative uh
I think it is
Presently.
And I'm gonna get a binary statement. I think today it has zero value for quantitative analysis.
Oh interesting.
Let me let me qualify that.
Do the numbers need to be right?
Or roughly right. Because what all of these things do is they give you something that's roughly right. And roughly
Yes, a spec spectrum, but it's always.
Depends how big the circle you're measuring is.
Yeah.
Um
You know, this is the line about pi that you know, we can calculate it, the like then however many digits we have is like enough to calculate, you know, the diameter of the gala of the universe or something, but people still adding more numbers.
So, you know, there's a little bit of Xenos paradox in here, you know, like you get infinitely close.
At a certain point it doesn't matter, and this is at a high level, this is some of the AGI argument that if the thing gets infinitely close to reasoning without ever actually reasoning, does it matter?
Like at a certain point the thing if the thing is always right if thing if if the thing is only wrong once in a billion years, does it matter that it's not that it's not always right?
The problem today is it's not wrong once in a billion years. It's wrong a dozen times a page. You don't wanna spit that out and give it to somebody. And I don't know. Yeah, yeah. So I had a very early example of this and I was going to speak at an event at the beginning of twenty twenty three and the conference people had asked me for a long biography of myself and I didn't have I don't have still don't have one.
And so they'd made one and they'd used it with chat GPT and not told me and they just sent it to me to check and I looked at it and I said, What the fuck is this bullshit?
That's twenty twenty. That's like generations ago. And th that that's not relevant to the point I'm gonna make. The point I'm gonna make is
A it was always the right kind of.
biography. It was the right kind of degree, the right kind of university, the right kind of experience, the right kind of jobs. It just wasn't actually right things.
But B, I could take that and fix it.
Mm-hmm.
So for them it was useless.
Right.
For me it was completely useful. I like to spend thirty seconds fixing it instead of spending an hour scratching my head, which is
What they say, right or wrong.
Depends.
It's a very kind of French philosopher kind of question. Is the answer is the prompt does it does the prompt have errors?
It kind of depends on
Or you want it.
Okay, well that I don't have use cases.
Where I want something that's roughly right.
I don't have use cases where I want a list of ten ideas, or I want it to brainstorm, or I want it to draw off me an email, or want it to write code, or I don't want it to generate some images. You know
friend who works at a consultancy and they want pencil sketches of concepts and now they can just use mid journey to make those. That's great. Does that sketch like does that person at the back have three legs, not anymore no?
And if they did it wouldn't matter, you could photoshop that out.
I don't do that. That's I don't create images. So I don't have a good mapping of the stuff this is good for early against the stuff that I do. And the stuff that it that it maybe would be useful for is the stuff where it's actually not yet very good.
And the things where you would mitigate that by saying, Well, I would fix it
I don't
Do those things.
Okay, so th this is a good thing because I wanted to come back to something you said. You said you think by writing.
And in a world where you're taking something generated by AI
And editing it. That's different than writing. Talk to me about thinking by writing.
I why actually my u my my Chat GPT use case, which is more mental model than a practical thing, is I write something and I think is that I I what I was always would ask in the past is kind of your point about pattern recognition is
I look at something and say, Am I adding value here? Am I saying something useful? Am I saying something different? Am I asking the key question? Am I pushing further? Am I pushing the on am I asking the next question rather than just answering the obvious questions? Now I can just say, Is this what ChatGPT would have said?
And if the answer is this is what chat GPT would have said, then I don't publish it.
Not because people can get it from Chat G V T, but because anyone would have said that.
That's a perfect
analysis in the sense that it raises the baseline of what qualifies as insight.
The difference is the slope.
of the insights. And so you wouldn't say it if chat GPT is gonna say it and push back on this by all means. Uh but the the chat GPT level of insight to use an example, it could be Cloak, it could be Croc, could be any of them.
is increasing
At a faster pace than most people.
And eventually those slopes intersect and it's probably intercepted already or intersected already with
You know, maybe up to intern level.
And next year it might be masters level or it might even far surpass that and it hasn't some demands in terms of math. Um the year after it might and so maybe it's like five years before it it passes Benedict.
Uh
And maybe it's four years before it passes somebody else, and maybe it's like passed me a way long time ago. So I think that's a
There's the two or three.
Direction is we c we could take that.
One of them is
That's an interesting
Theoretical philosophical question.
is originality. Which is to say
Alpha guy who could do original moves.
Because it could do all the moves and do it do moves that no one had done before, not knowing what people had done before. But it had an external scoring system. It knew that that move was good. Mm-hmm. They'cause it had feedback. Yeah, it had a feedback loop because every move has a score.
They can evaluate the score every move.
um parable of the monkeys and typewriters, or you know the Borgia's Infinite Library is there's no feeble.
Yeah. Yes, the Bobby's influence library contains new masterpieces generated at random. Well, the monkeys with targetwriters would generate new masterpieces, but there's no feedback lead, so there's no way of knowing.
You'll see this with music now. You can generate new music, it can generate new stuff that you wouldn't know.
for an L and M variance is bad.
Originality is is is a lower score.
So what's the feedback loop for original but good?
Now it might be
that that's the same sort of false question as saying, is it really reasoning or is it just right?
Ninety nine times, you know, nine nine nine followed by many zeros. Does it actually understand or is it just always right without understanding? Does it actually know that's original and different, or does it not? And that's a that's a kind of a
puzzling I don't think we know the answer to that and it may be the one on one question, but it's kind of a a puzzle
As two
How would these how far can these things make things that are both different?
From the training data and good.
And is knowing that this is different but good, is it really different, or is it just match the pattern on a longer um frequency? You see what I mean.
And how much could you actually have predicted that given enough data that it's not actually outside the pattern, it just kinda looks like it is.
If you're zoomed in more and if you zoom out more, then that is f matching the pattern. How would you know that people will it's like you know, thinking about music, like
How would you know that people would like punk?
You could get you can very easily imagine um generative AI system that can make you more stuff that sounds like yes, or more stuff that sounds like Pink Floyd. It might not sound like good Pink Floyd for Floyd, but you could imagine it would make more

Light the grateful dead.
You know what Grateful Dead fans say when they run out of drugs.
This mu this music's terrible.

You can imagine that the the you can imagine I'm being unkind, but like you can imagine the challenge is knowing that now people are really fed up.
of seventies pro group.
And they would really like something else.
And that something else would be punk.
And that would work.
It's knowing that people in the forties were really fed up of the war and would want luxury and that Christian Dior's new look would work and would express that. Could an LM do that thing? How much variance do you need? I don't know. It's an interesting thought experiment to ask that question.
Th there's a completely different place to take this, which is to say
This is an appeal for boutiques and in person events and the unique and the curated in the individual.
There's a shop I always used to always talk about. I'm not sure if it's actually still there. There's a shop in Tokyo that just sells one book. In Ginza that just sells one book and they change what it is once a month.
It may may have closed ten years ago. I've been talking about it for twenty years. But the point is you don't go into the shop and have to work out what booked by, but you have to know the shop exists.
Hm. Or you can be Amazon.
And you go.
Five hundred million skills.
And
They know they got everything.
Like actually there's some stuff they don't have'cause it they want to be individual.
They don't have LDMH. But for the sake of argument, Amazon has everything, but you can't go to Amazon and say what's a good book.
Or you know, what's a good lamp? They have all the lamps. You can't just go to it and say what lamp should I buy?
Right.
All of retailing and merchandising and advertising is about where are you on that spectrum and what else do you do? Do you do you spend the money on rent or advertising or shipping?
Mm-hmm.
And how does that work?
And the more it there's a sort of a polarisation between, well, if I know I want the thing, I can get it within twelve hours, but how do I know I want the thing?
And as I alluded to earlier, an LM is one might uh paradoxically an LM could suggest you the unique individual thing. Would the L M also create the unique individual thing? That's uh a high order and a second
That's that's a different question. It's a question further down the pipe.
But the more that the LLM can do what everybody would probably do or say what everyone would probably say, then the more you push to other places.
That makes a lot of sense. I mean, there's always gonna be a market for inside, whether it comes from
Lm or people. You you have to be
Providing insight. You know, our world as quote unquote content creators is a very wide spectrum of people who do very different stuff. And there's, you know, there's people doing AI slot and there's people doing, you know, the the what is it called, the, you know, the passive income thing.
But there's people who do very different kinds of content coming from different places for different reasons. You know, Scott Galloway does very different kind of stuff to me.
Mary Mika does very different kind of stuff. You do very different kind of stuff to me. It's just part of that is about who you are and your story and the authenticity of it. And some of it is about no one cares who you are, but you're saying interesting stuff. And some of it's the recommendation algorithm and or something else.
There's a book by um Zola about the creation of department stores called The Bonheur de Dame, which means the happiness of women. And it's basically about a nineteenth century Jeff Bezos calling a an apartment store department store into existence out of thin air through force of will.
And lucky invent fixed prices so that you can have discounts.
and lost leaders.
And mail order and advertising and, you know, he puts the the slow moving expensive stuff at the top of the store and he puts food and make up on the bottom of the store. Um there's nothing new under the sun.
And meanwhile the shopkeepers on the other side of the street are saying, like you've seen what that maniac's doing now. He's selling hats and gloves in the same shop. He's got no moles, so be selling fish next and of course he's got it.
Like there's like the whole plot point is about um loss leaders. So there's like you you can again, you can kind of step back and think, Well, people have freaked out about industrial not industrialized mass produced product before. People have freaked out about there being too much content. There's the line that Erasmus was the last person to have read every book.
There's too much AI content slop on the internet now. Like yeah. How many books do you think were being published in nineteen eighty? Do you think everyone was reading all the books then? Yeah.
Same thing, just different scales, I guess. W what advice would you give students today?
Well when I was a student we were all supposed to be learning Japanese.
I think that was just the tail end of that.
Yeah, I was sort of
lucky to have a a sort of very expensive and old fashioned and handcrafted education.
That was all about learning how to learn.
And learning how to think.
I think there are skills that people use to sneer at that
probably shouldn't have been sneered at and certainly shouldn't know. I mean, I'm old enough to remember when people would just sort of smugly say, Well, I'm not computer literate.
Mm. As though that was like there's like being a car mechanic or something.
Um, I don't know how to do that. That's not my problem, that's somebody else's problem.
And I don't think anyone now partly this is because of mobile, I don't think anyone thinks like that anymore.
Should you learn to code?
No, I think you should find out if you want to learn how to code. I think this is like saying should you learn an instrument.
Or should you, you know, go
They take theater classes.
That you m that may or may not be what you should be doing. Of course, what does learned code need in ten years' time? That's a different question. But
Should you
Yeah, I don't think you should presume you will or won't be a software engineer. I think you should presume that you will need to be curious and that you'll have many careers.
And different kinds of jobs.
I think you should be focusing on learning how to think. But I don't know. I think you should be presuming that everything will change.
Everybody says something like learning how to think, I feel like you would have a really good what does that mean? Like break that down for me because it probably means different things to different people.
Every now and then I I'm slightly perplexed to get an email asking for career advice'cause I think if you look at my LinkedIn and I sort of
Company shut down, company shut down, company shut down, like last it a year there, that didn't work.
Coming from the UK and seeing the US system, I never really liked the US idea that like
If you want a good job, you should be doing maths and business and engineering.
Now that may be how people hire.
Students here.
But I never liked the idea that you're like studying philosophy.
or studying history or studying literature is useless because you're learning about history. That's not what I learned. Yes, you know
throw off lots of analogies about history, none of which are actually things I studied at university. What I learned studying history at Cambridge was how to ask what the next question is, how to break this apart.
How to read a hundred books or fifty books in a week and find the bits that you need, how to synthesize lots of information, how to ask, well, what does that actually mean as opposed to what it looks like it means? Do you believe this? Is this credible or should you just jettison that idea? Um how do you put this together and think about how you would explain something?
And that's what my friends who studied English did, or my friends who studied philosophy did, or my friends who studied engineering did. That was what you were
being taught how to do. You wouldn't be you weren't being taught to be an English language to to be a historian or to
And
I'd hesitate to think that you know you can only build a company if you've
had it or only work for Goldman's or McKinsey or a big law firm if you had a particular kind of
education a particular kind of degree, I think he should be looking for
Um
What's gonna challenge you and push you?
And give you the ability to learn and think in different ways.
But again, this is me. You know, what are the skills that you have? How does your brain work? How do you think about things? And it took me twenty years to work out what I was good at.
So I'm not sure that you can know that as a student.
So you have to try and find what you're good at.
As well as, you know, learning to think. Maybe learning to think is what I do.
Maybe that's not what you should be doing. You should be learning what is it that you should be learning to do. What are the things that you're good at? Try all the different things. I don't know. It sounds like a like in a university commencement speech. No, I don't fucking know. Um but you don't know what you're going to be good at. So you kind of want to try and like create options for yourself.
What did you learn about investing working at A sixteen Z?
So uh there's a bunch of like um
Maxims or sayings. That's probably I wouldn't want to dignify them as like theses or anything else, but there's a whole bunch of maxims and sayings in Venture.
Um
Which you know, we could have a podcast talking about but there are better people to give you a
podcast talking about the mechanics of venture, but like
You have you're understanding what startups are and how they work and how the machine works. And startups are an industry and Silicon Valley is like a machine for creating startups.
And still too many people kind of look and say, Well that was a dumb idea and say, Well
That's the wrong question. The question is
If it you look at a start up and you think, Could it work?
And if it did work, what would it be? And could those people make it work. And then you understand more, you like the mechanics of well, how does social media work and how do people build companies and what is it like to create a startup, which is a whole other conversation.
I think something else that I learnt was
Um calibration. This is sort of again another metaphor I I always think of, which is that if you go to like a really great art gallery, like you go to the Memo or you know, the Met or the Louvre or or something.
Everything that was a mouthpiece.
If you go to a smaller, weirder art gallery.
Like London's a gallery in London called the Wallace Collection. Or I was in Rome a couple of weeks ago and I went to one of the sort of old
Aristocratic palaces.
And
There's palace is like it's like ten or fifteen rooms.
Of of pictures.
And they've got like
A quite good tinter at uh
And a maybe Tation.
And a Raphael.
You see it glowing across the room.
Yeah, like I
That's why he's Raphael.
And it's the same when you see lots and lots of startups. Like, Oh, that's why he's my so chip.
Oh no, that's why this is Blocks like we get ten minutes in and like
I've got another forty five minutes I've got to pretend to be interested and polite, so the founder has a good experience. Um
If saying that
Contrast and texture.
And
Seeing what good looks like, seeing what worked, what didn't work, what people tend to say, how this tend to work. Pattern recognition as much as anything else.
you also get there's a whole all sorts of other kind of cultural context you get around it. You know, Silicon Valley is can be very high school.
You know, I always used to say it's it's an industry tan. And I always used to say it was like being a college tan, where there's one subject.
So everybody you meet
is doing the same thing. And so in some ways that's very powerful.
You you know, everybody around you're doing a you want to do a PhD? Everyone around you're doing a PhD. Here's the world expect on the subject. Of course you are. It's like being a middle class kid. Of course you're going to university. What do you mean you're not going to everyone's going to university. Of course you're going to do great work. Of course you're going to start a company. And you're surrounded by the people who've done it.
Um, you wanna get a CTO who's done it five times, you wanna get the head of grace he's done it five times. Like they're all there.
The other side of that is you'll never meet anybody who isn't working on exactly the same stuff and isn't interested in what you're working on.
So you have no external context, you and you have no external perspective. The newest theatre is in LA, then you know no Chicago, I think. So theatre's in Like
You wanna go to an art gallery, you gotta go to a light.
Who's the best positioned right now? So from the outside looking in, you know?
Stock is is going all in on AI. Elon seems to be going all in on AI.
Uh, which of sort of the the leading companies do you think
Like A, why why are they all of a sudden shifting and and really you know, they were dabbling in it before, but now they're
They're really committing, you know, tens and hundreds of billions of dollars. And then who's the best positioned in this sort of space? If you had to pick one and invest your entire net worth in it, who would it be? Well, that's that's that's several different questions. Let's pick'em apart. Like this was a technology that had been kind of floating around.
before ChatGPT three point five and everyone kind of thought it didn't work very well. And then Chat GPT was no actually it works well enough.
And then there's this explosion of interest since then. And so I think last year the Google
Google, Microsoft, AWS, and Meta spent about two hundred and twenty billion dollars at GapAce last year. And will probably spend something over three hundred this year. And so that's basically more than doubled.
Almost triple, I think, from a couple of years ago. So there's enormous surge in capex in investment in this. And we've got these stories about like well, Meta bought scale half forty nine percent of scale. I for fifteen billion dollars apparently looked at C that's uh
Both of the other.
recent open i spin out sa safe superintelligence and what's the other one, thinking machines, which are both basically pre product, pre revenue labs.
with a somebody from OpenAI at mul multiple tens of billions valuations.
Um and he apparently like Sam Altman complained that Mark is offering people a hundred million dollars to join.
Um St. Mark's in in Beast mode.
situation in that his own models aren't actually very good, but it's got this very kind of weird relationship with OpenAI.
OpenAI, um Sam Altman is I was gonna say polarizing figure, but actually opinions about him tend to be fairly unanimous.
Um and tend to be fairly negative. Like everybody who's ever worked with him quit. OpenAR itself still kind of sets the agenda.
But much less so than it was two years ago.
Yeah, I wouldn't want to do like a detailed like calling the scores on whose models are good and whose labs are good, but you know, objectively Google is clear like firing on all cylinders now and is doing very is is making great models. Llama seems felt Lama forwards about it'cause seems to have been an embarrassment and so
Metro's kind of sc scrambling to catch up. Apple is a slightly different position in that
they are always sort of taking the position that they don't want to be first, they want to do it right, and that they don't need to be doing whatever the latest
'Cause the internet thing is like they don't have a YouTube. I think Craig Federig said in an interview after WWE C We don't have a YouTube. You just call it YouTube but like we don't have a YouTube, we don't have a car sharing service, we don't do grocery delivery.
We also don't have a chat bottom.
Okay, that was wasn't quite the question. The question for Apple is how much does in would integrating an LLM into the operating system change the experience of what it is, potentially shifting the competitor balance with with with with Pixel
Which at the moment has
Basically it only gets bought by people who work for Google and people who work for the TechRest and like literally no one else buys pixels.
Well, because Google doesn't want to t to compete with something. Um could go down to go down a whole smartphone industry rabbit hole. There's this sort of question for Apple around does this net actually change the experience of what a smartphone is, what the ecosystem is?
Does it end up kind of getting microsofted?
In the sense that
you're going to still g for the time being, you're still gonna buy a smartphone.
It's not
At all apparent there's gonna be another device. And if there is, it's a long way away and it might be an Apple device as well. But you're still gonna buy a smartphone. You're still gonna buy the nice one with a good battery and the fast chip to run the IO models and a good screen and the best camera, which will still be an iPhone, because Apple still has the best chip team and a whole bunch of other hardware advantages.
But everything you do on it will be from someone else. And it won't be someone else in the sense that it's an app from the app store. It will be someone else in the sense that it's a model running in the cloud. Which is what happened to Microsoft in the two thousands, which was everyone had to get on the internet. To get on the internet you needed a computer.
You weren't gonna buy a Linux computer, you probably wouldn't buy Mac either. So everyone bought a Windows P C but they were using it to do to do web stuff, not Microsoft stuff.
Say Microsoft kind of lost.
That
And so that would be the concern for Apple would be you'll still buy your new iPhone and you'll buy the iPhone Air this autumn because it will be thinner and lighter and it will be a lovely phone.
And you'll use it.
To do chat GP.

But the counter argument would be to say, Yeah, you'll do use it to do chat GPT.
And DoorDash.
And Uber.
Mm. And Instagram. And that cool new game. And that other new cool game that's enabled by LMs. And to do TikTok and to do and and and and and and it will be kind of the same except there'll
So there's a sort of you see what I mean, there's this sort of slight fuzziness around what the bear case for Apple actually is.
Does it sort of end up like Microsoft did? How bad is that exactly? What does that mean? Yeah.
If we all get wearing something like this.
That's lit up by AI, then that's a bigger shift. But it's
Very unclear.
How really is.
Um just for the optics.
There's another axis here, which is what happens to Google search.
Where does that money go?
How do you map?
the search activity that goes to an LLM.
that and how do you map that against where the revenue is and also from the other side, how do you map that against where the publishers are?
How do you think about whether you just shift your habit and you're actually using Chat GPT as Google?
It's basically doing what Google does, but you've shifted the brand and you're going to that search box instead of the other search box.
I don't think you can count them out in absorbing that in
So who would you out of the
public companies, you had to put your money in one of the top
Like seven or then there's a valuation question. And I a long time ago I was a public markets analyst, and I was bad at being a public markets analyst, equities analyst for a bunch of reasons. One of which was I was never interested in share prices. Well, we've given the standard disclaimer. You're forced to. What would you um
It's it's hard to see iPhone sales slipping from what we see now.
Hm.
Even half the cool sexy Google stuff is in the Google app on the iPhone. Mm-hmm.
I think Meta and Google though is this sort of big question around where the ad revenue goes and how much the ad revenue gets pulled away to different places.
I think Instagram is probably in a very good place there.
in terms of changing what advertising looks like and how that works.
I mean I I had a slide in my presentation which was like
Um
What Meta and Amazon want to do is to make LLM's commodity in for the solder cost.
Yeah.
N this is why Meta made it open source,'cause they want to make it commodity intro that sold a cost and they differentiate on top with Meta stuff with Facebook social
Instagram y stuff and they want the model itself to be just infrastructure. Amazon would also like it to be commodity infrastructure that sold its cost because that's what AWS is. They sell commodity infrastructure at cost and they do it better than anybody else. And they make a lot of money from doing that. Go to Amazon's financials and basically all the money comes from AWS and the ads. People who complain about AWS.
Don't haven't realise the ads make it. Amaz did fifty billion, sixty billion dollars of ad revenue lost yet.
Um
So Amazon seems to be fine, but there's a bunch of stuff to navigate around how does this change how people buy stuff on Amazon.
Who does that leave? Microsoft.
It was this line from Bismarck that the great man is somebody who hears God's footsteps through history and grabs onto his coat as he walks past.
And like Satcha is like tried to first of all he tried to grab onto VR onto VR and AR with whole hollow than saying that we don't talk about that anymore. Now it's AI. Their own models are not really ranking.
I mean they hired Mustafa, but like they're still struggling. They've got this weird contentious relationship with Sam Alman and Open AI and it's basically not their models.
On the other hand.
Like they're gonna sell an awful lot of Azure.
To run all this stuff.
Which again is this tension. Is it is it that everybody's used just uses Chat GBT to do the thing? Or is it that someone is gonna come to you with a great accounting product to run Farnum Street and it runs on Azure and it uses
Some L L M. Who cares which one it is? It's just it's just better.
You know, you can edge to your bank and it does the cool stuff, and you can edge to that and it does the cool stuff. You know, my use case for an LLM is do my fucking invoicing for me.
It's not even that. It's
Work out why exactly it is that that client's ERP doesn't like my bank account and not have me spend the next three months bouncing emails back and forth with somebody in India about wanting to figure this out on getting this done. That would be a great use case. That LMs can't do that yet. If they could that would be great.
But that we're not there yet.
So Microsoft and Google are in this sort of position of being the incumbent.
Both you know, how can I put this like and give you a more systematic again, I'm sort of thinking my way through to the answer to your question.
For Google and Microsoft, they're in it they have an incumbent business that is potentially disrupted pretty profoundly by this, but they also have a cloud business that sells all the new stuff for this.
Amazon has an incumbent business that doesn't get disrupted by this, at least much less obviously. And a cloud business that will be very happy selling all of this stuff. Um Meta
doesn't have a cloud business selling this stuff and has a bunch of new ways to make money from all of this new stuff, except they've got to have some better models.
Apple.
Is this a competitive threat to the iOS ecosystem?
A lot of stuff would have to happen first.
And they'd have to drop a lot more balls.
Um before that was to happen. And meanwhile they're still gonna sell you the nicest glowing rectangle to do all of this stuff.
And then we're talking about glasses and VR, which is a whole other two hour conversation about when when does that happen. There's still people poking around in crypto, like Web Three. Web three, remember, maybe that someone said that people still working on crypto are like those Japanese soldiers on islands in the Pacific. You don't know the war's over.
Um
But there's still people working on crypto, so that's like another disruptor thing coming down the pipe.
Who are the other incumbents? Um
Netflix is a T V company.
Maybe the car company.
Elon Musk.
There's a there's a whole
Tesla conversation fascinates me because Tesla Bears bulls think it's a software company and Tesla
But I think it's a car company. And at the moment it's a car company.
I mean yes, they launched autonomous driving.
But what did they launch?
They launched half dozen.
Existing model cars with test drivers.
Doing a GFN.
Drive that everyone else was doing ten years ago.
Is that gonna scale?
Is are they finally gonna get the flywheel of having all the camera data, meaning that will work with just cameras?
We've been I don't there's there's a conversation you could have asked ten years ago. In fact I wrote stuff ten years ago. Oh, there will it take all effects in autonomous cars. We'll test to get it working with cameras before everybody else gets it working with LiDAR. Well Waymo's got it working with fifty grand of LiDAR or whatever that that stat costs. It's tens of thousands of dollars of extra stuff on the car. So they've got it working with all of that stuff.
Tesla does not w does not have it working with cameras. Will Tesla get it working with cameras?
Before Waymo can get rid of the Lidar.
Uh we could have had that conversation. I literally was on podcasts.
seven, eight years ago having those conversations.
We don't know the answer.
Maybe.
We don't know.
But the interesting Tesla point is people always looked at it and said it's the iPhone of cars. No, it's not. What's happening is that cars are becoming Android.
With no iPhone. Mm-hmm and Tesla is just selling an is is a in that metaphor, Tesla is just another Android phone maker.
And they're competing with the whole Chinese industrial policy to make more and there's gonna just gonna be a flood outside the US as they're protected by tariffs. Everywhere else it's very clear what's happening is there's just a flood of E Vs where they're just as good as Teslas.
We always end on the same question, which is what is success for you?
We live in the luckiest time, you know. We are not worried about rockets landing on our heads. We're not worried about our children dying from diseases. We're not worried that the bank might be closed tomorrow and all of your money's gone. We're doing something interesting that we enjoy and that pays the rent that we want to be able to pay.
I get paid to fly around the world and give flies for money.
So I think I'm doing okay. I could always be doing more. Um
But I'm
Always looking for
The next question. Like I'm always trying to be curious.
This was a great conversation. Thanks for taking the time to
Thank you.
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