I'm here with Benedict Evans. We worked together at a six and
Z more than 10 years ago. Benedict is, you know, well
known newsletter author, probably needs no introduction
for people watching this. We're here in Singapore.
We just came here for an AI conference.
You do about 1/4 newsletter, 3/4 conference nowadays or speaking
that that's brought out here, right?
Yeah, and newsletters down like 175, something like that, you
said? Yeah, something like that.
Wobbles a bit from day-to-day. And it started out you, you
started as a mobile analyst and you became like a broader tech
analyst. Is that is that the evolution?
Yeah, that's one way to put it. I mean, I think.
You're at orange, is that right? A long time ago.
Yes, long time ago. Yes, just when it was all
becoming horribly French, there's there's like as we were
chatting before this and I said, like the thing in tech is that
the point that you understand something is, is often the point
that you should be moving on to pay attention to something else.
I started my career in the.com bubble as an equity analyst and
I was covering mobile stocks. And at that time mobile was kind
of dynamic and exciting and sexy and disruptive and they turned
into water companies. They were going.
Into water companies, utilities. Oh, utilities.
Yeah. OK.
They were going to connect to everybody in the world and then
they did. And like now what they would
like Marc Andreessen's phrase. They were like the dog that
caught the truck, right? And I went and worked in
strategy and a bunch of media and telecoms things.
And yeah, I was analyzing, looking at smartphones because
that was suddenly become the center of the industry and no
one understood it. Now, like it happened, I'm time
to look for different questions. Well, it's, it's funny because I
think when we were overlapping, it was right in the middle of
the, the smartphone dividend, the smartphone explosion.
And just to, you know, we actually there's a few things.
One is the smartphone dividend. That's a useful concept, right?
Like that the rise of a billion smartphones meant that
everything that went into them became cheaper.
And that enabled VR headsets, that enabled drones, right?
All this stuff. Yeah, all the components that
came out of it, Yeah. So smartphone sales and now from
memory like 1 and a quarter, 1 1/2 billion units a year and all
the supply chain from that, all of those components is then
available off the shelf if you want to buy 5000 of them or
10,000 of them, all the Wi-Fi chips and the batteries and the
cameras and and all the other bits.
And before, if you wanted to put computer into something, you'd
basically need to use PC components.
So ATMs and so on, all basically PCs, like elevators are
basically PCs, and that has size and power and cost constraints.
And then smartphones become the thing and then all those
components are available. And so that's what gets you
drones and connected light bulbs and all the other bits and
pieces around the edge of that. One of the things that I think
people don't appreciate is they think, for example, like the
consumer, they think like the military has like special gear
and it's got its own kind of supply chain.
And often the military supply chain is often just a subset of
the consumer supply chain, because you sell a billion units
into this and maybe you have 100,000 or 1,000,000 units of a
military thing. It's, it's actually, it's almost
kind of the reverse now in that it used to be.
So that what the way I think about this is like in the past,
like before we were born, the intelligence agencies would get
the cool new stuff 1st and then the military would get it and
then big corporations would get it and eventually consumers
would get it like 30 years afterwards.
So this is like the connection, yes.
It's like microwaves were invented for NASA, right?
And eventually consumers get them.
Yeah, or like GPS was invented to guide missiles and now it's
used for tagging cat photos. And the shift is like a
combination of the stuff getting cheap enough that it can be for
consumers instead of you needing a billion dollars to have one,
and then the scale of consumers once it gets cheap enough.
And so now the way it works is the consumers get the new stuff
and the military gets it 10 years later because that's how
long it takes to. The bureaucracy to similar to.
That A, the bureaucracy B, to harden it and productize it and
turn it into what you need if you're going to get shot out or
it's going to be cold or hot or warm or whatever it is.
Yeah, that's it's funny. Does it really improve through
that process? I know people think it does, but
I'm not sure it does relative to the the the cost of not using
the pretty good product versus whatever improvements come from
the delay to harden it. I'm not sure if it actually.
I don't know. I think this is, but there's
clearly there's a sort of a process if you have to put it
into a fighter jet, yes. You don't replace the avionics
in a fighter jet every six months, right?
Well, but but yeah, you know, that's the kind of the core of
it is the, the cutting edge of the innovation is for consumers
and then that flows back through to everything else.
That's right. Well, you know, I was going to
say maybe in China you do maybe in China, like I think what
happened with the consumer drones.
They got good at quadcopters and that's LED them to their new
form. Have you seen E hang?
It's like the Chinese flying cars.
And, you know, I, I played this clip like a year, year and a
half ago and people said, you know, the TL1, like we want to
fly in cars. We've got 140 characters.
And I was like, a lot of people didn't riff on that, but I was
like, we want to fly in cars. We got them in Chinese
characters. OK.
And The thing is when we put that up there, people are like,
that's not a car. It's, you know, it's a right,
that's not a car, it doesn't have wheels, but it's solved the
problem differently, right? And actually I think was one of
your lines. It's like unfair comparisons are
often the best kind of comparisons, right?
Yeah, I remember seeing a bunch of flying cars when we were at
Andreessen Horowitz, I think Marc Andreessen, he said.
It's like they were all like houseboats, and a houseboat is a
crap house and a crap boat. Yes, that's right.
And, you know, think of it, thinking of it as a flying car
is like the wrong term. It's better to think of it as
like a much a small, much better, much cheaper helicopter.
Yes, maybe, but the point is that they now the other thing is
it's been for short hops and like city city where you fly
over the traffic and they've got this.
Uber was going to do this by the way, before they decapitated
Uber. Like the low altitude economy
was something they were thinking about.
And a lot of things get like cut off in the West and then they
appear fully formed in China, like consumer drones, for
example, you know, Chris Anderson, he was very early on
drones and that got blocked by the FAA.
And so consumer drones were hobbled in the US and that's why
DJI arose in China. So lots of things get blocked in
the US and they arise in China because of that.
Anyway, coming back up. So smartphones, I mean, I think
you and Horace did you of a simple.
I was on his pod a while ago. I think you're two of the best.
He's also like European or something like that.
French he was. He was at Nokia.
I mean, there's an interesting kind of like information.
Do you know him? Yeah, yeah, he's a great guy.
Part of it was it was like, and there was a moment in time when
there weren't many people doing mobile who really understood
this and were industry analysts and were able to talk in public,
right? Yes.
So there were people inside Nokia or Goldman's or or Bain or
wherever who had all the data, but they couldn't publish the
data and they weren't allowed to say stuff in public, right?
Or if they were writing analysis, it was analysis for
public markets, investors or something, right?
And so there were very few people who were like, knew that
you could go and take Apple's reports and make a chart of unit
sales and make a chart of ASP and knew what ASP was, right?
Or knew what AP was right. Now there's like an explosion of
this. So there's huge numbers.
And particularly if you look at AI now, there's like 10 people
who do a really, really good 200 page deck of every possible AI
chart. Is that right?
Interesting. Yeah.
And so that whole thing shifted. But at the time, yes, it was me
and chorus and like Ben Baharin. Well, like the only and.
Stratectory. Kind of, yeah, Yeah.
Exactly. Well, like, you know, a handful
of people who understood this and could do the charts and were
allowed to do the charts. And so that was sort of, you
know, being at the right place at the right time got me a lot
of attention. Yeah, it's interesting.
I think like you and Ben Thompson Stratechory and I'm not
sure if Horace had a newsletter, but you guys were newsletters
before Subsec productized. It's sort of like Rogan was
podcast before that became productized as a category.
And are you in Subsec? Were you on ghost?
No. Yeah.
No, I'm you. Got your own custom?
I'm still on my old cobble together stack of MailChimp plus
member full plus Squarespace. Why?
Why don't you? You don't want to move to
something. It's just a pain to move.
It's a heavy lift to move platform and you sit and do the
analysis and you're like, this is a good use of like a week of
my time, right? Maybe.
Maybe it might be at this point sub stack's pretty good, but I
mean, it's you. Know you're well, there's
there's a separate sub stack thing thing which is do you want
it to be on your, your newsletter or your sub stack?
Yes, that's true. Yeah, because it's a platform.
And you get the advantage of I mean this is something we can
talk about. It's Chris Dixon's line have
come for the tool state for the network, right.
You go on subset, they will get you new subscribers because it
won't get you subscribers. Yes.
On the other hand, they now they control who your readers are and
you don't, which is always a thing of a network.
Well. I mean used to still mail out.
There that you do, but then them they're trying to get you to use
their website and their algorithm to decide who reads
and what that's true. So there's always these kind of
questions, like, do you want to go with the people who will give
you an audience? And in exchange for that,
they're deciding that they'll give you the audience.
It's always a trade off for the distribution.
Yeah, I think Ghost is another option.
Yeah, yeah. And I think.
Ghost and Beehive are the 2. Others that people use ghost is
like, you know, I saw ghost and it's very early and I just
thought it was so good for for what it was like it was.
I mean not even for it's just a very polished thought through
for an open source product. It's unusually polished.
John Nolan's very, very good. It's funny, you know, like on
that, but there's a bunch of things we can talk about.
But the, the whole newsletter thing, it's sometimes there's
things that are like newsletters or podcasts that are what are
considered lower case in technology before they become
upper case. Like for example, Odeo, you
know, like what Twitter was Twitter was a podcasting company
before it became Twitter and the time constant.
They just got the time constant wrong where you needed, which is
hard to predict that microblogging would take off
1st. And then it required like
Airpods and everybody being online for a long time and
maybe, you know, COVID before podcast really exploded and the
term was around in lowercase. And even argue it's needed like
5G or 4G, something like that. Yes, if you're if you're
listening to it on the car, in the car, then you need a half
fast enough network. Yes, bandwidth is a constraint,
yes. And then the time works.
So what do you think is lowercase today that's going to
become uppercase? Like what's that?
What's what exists in tech that people are like, Oh yeah, that
exists. That that's going to go big.
I, I have some ideas. I want to hear yours.
Interesting question. I think there's probably the
answer. If I was a consultant in trying
to whiteboard this is I would be looking around AI because that's
a new platform and you know, there's a lot all the white, the
old white space got filled in and now you've got a whole bunch
of new white space. So deterministically, there
should be a bunch of those things here.
AI, which I'm sure we'll talk about, does feel very sort of
mid 90s in that you're like mid 90s Internet in that like, well,
this is a browser, how do you use it?
What's it for? How would you get to it?
How does this work? Where's the value?
Where's the value capture going to be?
I'm not sure that there's like maybe 1 answer is like I'm too
old and I'm not like spending too much time looking for like
weird weird stuff around the edges.
The last one of these that I spotted personally was Sheehan
which. Is it Sheehan?
Or I'm told it's Sheehan. Is that right to speak to people
there? Like I haven't worked out her
team yet. That was an interesting one that
it was. Maybe you could also say it was
the last of the ones that you could spot.
Because suddenly, wait, what is this thing that's at the top of
the iPod at the iPhone App Store charts all the time?
Oh, I see. Yeah.
Suddenly that thing exploded and that's like probably the same,
probably the largest apparel that pure player apparel
retailer on earth. Yeah, and like Sheehan and Temo
and yeah, that's right, they're now getting hit with the tariff
stuff and you. Know yeah, tariffs plus plus the
de minimis rule in the US, but. That's only the US market and
that's not, you know, I don't know what fraction of their
sales that. Yeah, Yeah.
It's like a third of their sales or a whole quarter of their
sales or something. So that was like that was that
was a thing that was interesting.
I'm not sure. There's not like a new thing
that I'm watching that I've noticed recently.
I'm sure there will be. You know, I, I, I keep looking.
So I have a few we we were talking about the glasses,
right? Like I think smart glasses are
sort of like the most predictable thing after the
iPhone. Yeah, that's I put in a
different category. I was sort of thinking like,
what stuff that's being used now that people haven't quite
noticed it's being used yet. I see so well I I guess that.
Glasses glass is definitely your next thing.
Sure. So so I guess I would, I would
sort of bundle VR headsets, AR headsets, you know, like that
with glasses and and say that that's just glasses are sort of
the next version for goggles. But OK, so that's one that that
that we agree on, except the question is, as you said, is it
going to be watches or phones? How big does that get right?
I think I think, you know, just like podcasts grew to mean like
a video podcast and so on. The robot dogs are interesting.
They are fun to play with and they're getting way cheaper now,
right? They went from the Boston
Dynamics kind of things. So the home robot as a toy, I
think is probably going to become more and more popular
like a Christmas present kind of thing at first, right?
Because I see kids playing with them and they just love them
just as a toy. And the, you know, it's kind of
like the robot dog, the drone as like a starting to become a like
a Christmas present kind of thing.
I think that that becomes a thing and eventually, like we
were talking about this at the Museum of the Future in the UAE,
they clad these so that it doesn't, it's not just like a
skeleton of a robot dog, but it actually looks like a animal.
And that completely changes your perception of it, right?
So I think that that'll be a thing.
And with respect to AI, and so let's do, I mean, there's AI,
there's Bitcoin, there's China, there's drones, there's biotech,
there's actually several different areas that I'm
tracking. I'm tracking eventually these
very singularities, whatever. It's not all really actually
singularities in the technical sense of going to Infinity, but
ramps, curves, curves. That's right.
Yeah. With AI, there's, you know, one
way of thinking about it is like now we're 2 1/2 years in, let's
say, let's call the ChatGPT moment, right?
And it's interesting because it, I, I think what people really
overestimated was how much it's agentic intelligence versus
amplified intelligence like that say you still have to prompt it.
So prompting is like higher level programming.
Number one, you still have to verify the output.
And that means you kind of need to know what it is you're
looking for. For example, if it spits out a
bunch of mathematical symbols in an area of math that you don't
know, then you have to be Terrence Tao to verify it.
It might be gibberish, it might be real, who knows, right.
And so the prompting and verifying are actually the
bottlenecks in many areas. Now Karpati and I, you know the
Andrej Karpati, we were just having a discussion on this like
a week or so ago. And the thing about verifying is
if you're using the GPU's that we have built in and you're
looking at images or video or front end code, right, like the
like the user interface, your eye can just instantly pick out
and you can verify pretty quickly.
So for that side of things, AI is quite good.
Anything that's images, video, your ear can also pick out
audio, right? And front end.
But when it's back end stuff, right, when it's like database
code, when it's like crypto, when it's mathematical equations
that you don't have like GPU's, you can't just like hit it with
your eyes and and quickly detect it, right?
That whether it's whether it's correct or not, you have to deep
read it carefully, right? So it can generate reams of
text, But then you have to verify it exactly.
That's right. Maybe you have some thoughts on
that? Well, so it's funny, I was
talking to you, John Boltwick the other day and he said
Benedict, you think in slides so.
That we do too. We both think in slides.
So I have a slide, yes. And, and maybe there's sort of
a, I'll talk about the slide and there's sort of an observation
around it. I think a lot of discussion of
LLMS is sort of hunting for the like, what's the right, what
would the right way to conceptualize this?
It's like with machine learning, the right way to conceptualize
it was this is pattern recognition.
And we're still sort of hunting for the right way to
conceptualize LLMS. The slide is that traditional
software is deterministic and does things that are easy to
explain to machines. In fact, automation machine
tools, sewing machines, typewriters, adding machines,
things that are easy to explain to a computer.
There may be things that are very hard for people to to to
do, but they're easy to explain. So it's hard for you to drill a
hole 100 times or to calculate a mortgage in your head, but it's
easy for you to write down the logical steps to explain how you
do this. So that's traditional software
like databases, data processing, the whole sixties, 70s mainframe
thing. Machine learning is stuff that's
hard to explain to a computer, so it's hard to explain why that
credit card transaction is weird.
It's hard. Or how to move your hands or
something. Yeah, it's hard to explain why
that's a picture of a dog and a cat, but you think it's easy
until you try and do it. And then it's like you tried to
make a mechanical horse. It always falls over until some
robotics comes along. So that was machine learning.
I also think that as a kind of quiz for you, do you think
machine learning is still AI or is that now just software?
Well, so the way that. I think there's a process right
once it's been around for a while.
It's AI anymore. It's funny.
So I think like within the field, technically the division
would be machine learning would be, you know, everything up to
linear logist regression and you know, SVMS, all that kind of
stuff. And then right at the point you
start doing deep learning and you have large neural networks
now, you start getting into what people would call modern AI.
So ML is almost like the boundary of understandability,
you might say, right where you can write clean equations and
like really understand what's going on.
And to me, the most surprising and confusing.
I still don't feel like I know what the phenomenon is, but I
still find it magical. It's something called the double
descent problem. Do you know what that is?
Basically, normally when you're fitting to data, you want to
have the fewest possible parameters because you can
overfit, right? And so your error goes down, and
then your error starts going up on the holdout set.
So you train your model in machine learning, and you want
the minimum number of parameters to be able to explain the
training data and predict the test data.
And if you overfit, then you're no longer predicting out of
sampled stuff. But double descent is when you
do AI, you get actually a second wind when you start going to a
very highly parameterized model and the error actually drops
again, right? And which is just a really weird
phenomenon that there's papers on this and so on.
And it's one of the most counterintuitive things about
the whole thing that just having these gigantically parameterized
models would generalize. Well, right, Because it violates
that. That's the biggest difference.
Go ahead. There's other things people
might say is the biggest. Difference.
Well, that's why I mean, I think there's, there's one of the ways
I, I sort of sort of think about the, the term AI is that people
kind of use it like technology, the word technology.
Yeah, that's right. That anything new is technology,
anything your parents had. Is the technology I'm a stickler
for precision? So there's, there's different
ways that you can say, what do we mean by the word AII feel
like AI has almost become like the word metaverse, where like
you don't know what somebody means when they say it.
Continue my slides. So the first point is there's
deterministic software, which is stuff that's easy to explain,
right? There's machine learning, which
is stuff that was hard to explain, which basically machine
learning solves this. And now an LLM is maybe stuff
that's easy to explain to an intern.
It's something where if you had to go away and have a have a
like a kick off meeting and spend half an hour working out
how we're going to do this project, then an LLM probably
can't do that. But interesting.
But if it's something that you could explain in 10 seconds or
20 seconds, then an LLM is going to be able to do that.
And part of the problem is, are you able to explain it even to
yourself? Could you explain it to another
human being? Can you actually kind of shut
your eyes and conceptualize how is it that I'm going to explain
what it is that I want this thing to?
Do it's. So what you're saying is very
important because there's several different angles I want
to take off of that. You know, in one, since I had
this tweet, we're living in the age of the phrase, right?
So the prompt for the AI or the 140 character tweet or actually
in crypto, like 14 words, 13 words, 12 words can be your,
your crypto reset phrase, right? These like are phrases of power
right in, in AI, in social and crypto, right?
Like this strings of characters that do a lot, you know, and
they're spells, they're spells, right?
And the thing about it is the, the crisper you are as a
manager, like, you know, if you're, if you're, if you're a
really good engineering manager, you're great at prompting AI
because crucially, you don't just say, Hey, code this, you
say, hey, you know, try and use, you know, React for this.
You can use React native for, you know, the iOS and Android
interfaces. Use Tailwind, use it.
The more in a sense vocabulary terms you have, the better you
can prompt something with and if you use the vocabulary terms
correctly. And what that meant is, for
example, I realized with Dolly, you know, when that was first,
you know, before the chat CPT moment, I was like, wow, art
history is now an applied subject.
Knowing like Cezanne and Picasso and what you know, these various
kinds of obscure styles, Suddenly you can be like boom,
style it like this, style it like this.
And it'll do that, right? You could.
Say the same thing for music, like what exactly is it that's
being done there? And there is a word for that.
So and you have to you have a word.
That's right. Exactly.
So you can upload a track and you can say what style is this?
How do you caption this? Right.
Ever seen, you know, like the restaurants with the fancy menus
and they don't say. They don't say tomatoes.
They seem like heirloom. There's a word.
There's things that are theoretically subjective, yeah,
but there are within the professions there is a
particular term for doing that particular thing.
Exactly. It's like the difference between
like red versus burgundy and you know, Crimson and what have you.
They've got precise words, which means something and then you can
summon greater precision with those precise words.
And So what a way of thinking about, you know, what you're
saying is that, and I've written about this, AI is like
undocumented AP is, right? So normal API, every function is
like written out. And it's like, you can do this
and you can do that. And so I've got 20 functions and
here's everything is there, right?
With AI, it can do lots of things that even the people who
wrote it up. So, so there's, it's much more
mysterious as to what it can do. You just have to try things
right? So the way I was thinking about
this from a different angle was to think about Guis.
Oh yeah. Yeah, what a GUI is doing
several things that a GUI is doing.
One of them is, it's telling you all the features that the
developers have created. And part of the reason that was
a revolution is a, you knew what they were and you didn't need to
memorize keyboard commands. But B, you can actually have
more stuff because you're not constrained by the number of
keyboard commands that you can write down.
So you can have hundreds of functions instead of like, you
know, you can just put them all. You can just add more shit to
the menus. Yes.
But the other part of it is that the GUI is telling the user a
whole bunch of accumulated decision and institutional
knowledge about what the right things to do at this point would
be. And so if you're in a workflow
as opposed to just a blank screen, you know, it's one thing
if you're in like Photoshop or Excel.
Yeah, it can. It can prompt you on the prompt.
But if you're in a workflow in Salesforce, then there's a
decision taken. This is I'm going to offer the
use of these five options here and not 750 options.
And with a, with a prompt, you don't have any of that.
So you've got to shut your eyes and think for a minute of like,
well, what would I do here? And you don't have that help.
This is, you know, Carpathy has talked about this also, but I do
think there's room for AIOS, right?
Like in in a sense, and we can talk about crypto in a second,
but I think and crypto are both actually operating system level
innovations. And for example, maybe someone
just does it as an app or like like a downloadable thing and
just does it as a layer on top of the Mac.
But if you have the full context of all the actions that are
happening on your Mac, you can suggest which apps to use,
suggest which apps to download, suggest, hey, you probably want
to change these keyboard settings.
And so like there's, you know, it's funny to put it this way,
but Clippy is finally vindicated Clippy, but for everything,
right? And because Clippy can now be
really, really, really, really smart, right?
Like, you know, it was Anderson's line.
It's like everything in tech works.
It's just when right. And, and even the thing that's
interesting about the Clippy thing is somebody also made a
point, which is that you actually want to put faces on
your AI avatars, on your AI agents.
So you could pick from Clippy or 10 other kinds of things.
And the, and the reason you want to do that, this is counter
intuitive, but people like you and I can use ChatGPT and, and
you know, clawed and what have you, because we're familiar with
interfaces, But the reason they're actually intuitive to
100 million people is they're used to chatting with another
human on the other side. So they're already modelling the
chat box as being a human like response because they've been
using WhatsApp or Facebook Messenger or Instagram chat or
something like that for a long time, right.
But when it's outside of that chat box environment and it's
like suggesting on the screen, you kind of want to face to pop
up so they can associate, OK, this person is suggesting this
because that's who they are. And, and, and they kind of map
that personality onto the AI agent.
And so you can choose from different kinds of clippies that
would give you prompts on what to do or, or it just does it for
you. That's their possibility.
But I don't think people like it when they does it.
They want they want to be able to approve it.
Before they do it, I think there's a sort of sense in here
of how people people conceptualize what this thing is
and how it works. I remember John Brotherow at
Google showing me a chart, a Google Trends chart of best
versus cheap. Best versus cheap?
So the best does this and the cheap does that.
And what are the axes? Crossing over time.
So Google Trends. So what's the frequency of the
word best? So it starts with like cheap
phones and it goes to best phones, yes.
And so the thesis was that this was shifting from the Internet
as price comparison where you'd already knew what you wanted and
it's at the top of the and that's the bottom of the funnel
to the Internet as recommendation, curation,
suggestion. Is that what you're looking for?
Oh, that. Suggestion so interesting so let
me see if I can understand the psychology so when they're.
Used to it. So it starts from 20, it starts
from 2004. So in 2004 you go on the
Internet and you already know what you want and you look for
the cheap, what is the cheap X? And then you put in a scoop or
you put in a product or something.
Whereas over time that goes down.
And. Best goes up and crosses it.
It's a perfect X on the chart. Unfortunate.
And then the thesis is you're going further up the funnel.
You're looking more and more for I want someone on the Internet
to tell me the best X or Y, but previously you'd have got that
from the magazine or newspaper or something.
There's two or three. There's two things about that.
The first is, you know, Andy Grossing about the paired
metrics. So Andy Grove, whenever
anybody's optimizing, like sales, for example, they will
usually sort of recruiting, they'll start by optimizing
quantity. But there's, you know, you can
sometimes optimize quantity and then quality drops off, right?
So quantity is easy to measure. It's like just the number of
people we hired or whatever, but quality is how good were they,
right? And so that's the the second
paired metric is usually a quality metric that and so
quantity is cheap, right? And people start with cheap and
then quality is best and they go to best.
So that's another lens on this 1/3 lens.
What I thought my explanation, maybe it's different than what
actually happened was when people are just trying out a
space, they just want the cheap version to try it out.
And once they've committed to a space at like for example, they
cheap digital camera, cheap drone or something like that, I
want to try it out, right? And they want to try it out at
low cost. Try it before you buy.
And then once they're committed to a space, then they're like, I
want the best drone out there now because I'm, I want it.
Well, the, the analysis then would be cheap drone versus best
drone, right. But I think the that's.
What I thought you were saying yes, but but you're saying
cheapest, best overall. Yes, but I'd love to see a
category by category. I wouldn't be surprised to see
that happen category by category, but maybe not.
Well, there's a. Different plot point there,
which is sort of what I was talking about on in our in our
panel this morning, which is this infinite product.
So how do you know what to buy? And it used to be that you'd
start with a magazine and then you'd go to the Internet to find
the cheap place to buy it. Or you know what you wanted and
now you go to the Internet to. Find.
Like where's the, what's the right place to do this?
So the Internet has become much more kind of a default.
But actually the thing that the thought that prompted me to that
was you can also go and play with Google trends and, and I
did a chart played with like how, why, where what like more
kind of basic questions. And you really need to be inside
Google to do that analysis properly.
But it's that sense of how much are people doing conversational
queries into Google as a opposed to typing keywords into Google
and things that are not really a Google query, like what is a is
not. But it probably doesn't help
Google. But that's still how people use
it. People were trained for years to
not to remove all prepositions, to remove all that stuff and
just do keyword ease, and now were trained the opposite, to
write full and complete English sentences.
Like prompting is the new searching, but it's a completely
different, you know, behavior, right?
Go ahead. Well, so this is one of the,
there's a sort of tangential point of that.
Like one of the like the early, easy, obvious things that people
have deployed with AI with LLMS on the Internet is different
kinds of, is sort of natural language queries or different,
not so much language, but like different kinds of query.
So that the canonical one people talk about is Walmart saying now
you can search for what should I buy to take on a picnic, which
isn't a database query. And for Google, for Walmart or
for Amazon, five years ago that search just wouldn't work
because unless there's a product that's like tag with Picnic,
it's not going to come up. Whereas now there's an LLM with
a world model, they can pull it. Yes, that has some sense of what
how you might answer that question.
Yes. Is it a world model?
It is. It's at least a web model.
It's a different kind of query anyway.
It's not. You're not doing a SQL query,
you're doing something else. That's right.
And I think, you know, one of the things that's interesting is
the computers are we knew they were very good at that first
kind of deterministic computation, the sequel query,
the calculation. That's what they're built for,
doing math, right? And now they've gotten good at
probabilistic kinds of things, right?
So this would be like system 1 and system 2 thinking, right?
Probabilistic is like the quick impression.
And then, you know, this is like the logical calculation.
So it's actually good at the heart.
The thing that's harder for humans is the, you know, like
long involved mathematical because you can do that
errorlessly. And now it can also do the other
kind of thing. And so it does suggest that
there would be some synthesis of that eventually where an AI can,
I mean, this is like AI tool use or what have you.
Like it detects that it needs to go to system 1 and and it starts
invoking Python for that. And this is getting better, but
it's surprisingly not amazing 2 1/2 years in right when when it
needs to go deterministic. Well, so I, I wrote last long
thing I read about this was about looking at deep research,
which Open AI launched. And one of the kind of traps in
looking at the new thing is to test it based on what was
important to the old thing. So you know, to look at the
Apple 2 and say, does this match the main the up time of a
mainframe? No, So it's useless.
Well, no, but that's not the right question.
Can you write and build an Excel model on an iPhone?
No, but that's not the point. It can still replace PCs and,
and the reason I mentioned this is so, so, so, so deep research,
open air launch that thing and it's whatever it was $100 a
month or whatever. So, but then you look at the
marketing page and the marketing page shows it doing a research
project about mobile, which as we said, I know a lot about and
it got the answers wrong and it's.
That's verifying. See, you knew you could tell
that it was wrong. Well, but it looked.
It looked. Exactly.
So this is the thing and it got stuff wrong in several levels.
People think remembering now what I wrote like 2 months ago.
And so there was a specific, it was make a table which shows
mobile smartphone adoption in a bunch of countries and then the
operating system market share. And then this is like an intern
teaching moment because first of all, what does adoption mean?
Does that mean unit sales share installed base App Store sales?
Like what? What?
What do you, what metrics specifically you asking me for?
Yeah. Then it had given a source for
the number it had come up with, which was Statista.
And Statista is an aggregator that steals other people's data
and who polishes it. Yeah.
And when you jump through a bunch of registration hoops, you
discover that the actual source was, I think Kantar.
It's an it's an ad agency. It's OK.
It's part of group. I thought it was part of one,
that it's it's consumer survey data.
So it's it's a proper, proper, proper company.
Yeah. So it was actual proper consumer
survey data, but the two things that so then when you go to the
Canton chart page, you discover that deep research had got the
numbers the opposite, so it'd flip percentages.
I see. And then it had also said,
right? Because it didn't have.
Actually it just it didn't remove.
It had copied them from the website.
Well, I see and then the other source it gave was stat counter
and stat. Counter was just using the same
wrong. Data, which is a traffic
measure. So that's not going to tell you
an option because high end phones get used more and iPhones
get used more So, and there's a bunch of things in here where
you'd like. This is what I'd expect from an
intern, right? I would go back and say, no,
this is what I mean by adoption and this is a good data source.
And that isn't right. And it's like a great first
version. The problem is I had to copy the
number out wrong, which is not what I would expect from an
intern, or at least not a good intern.
But secondly, I'd have to be a mobile analyst to know any of
these. Things.
And that's a verifying thing that I was.
Getting at this is this is the kind of the core of it is all
these people were looking at deep research and saying this is
fantastic for researching things you don't know anything about.
I was like. No, no it's not.
Yeah, it's fantastic if you need a bunch of material about
something you know a lot about. Exactly.
So that's things that's why I think AI in its current
incarnation is better thought of as amplified intelligence
because the better the more you know about a field, the better
you are at prompting because you got better vocabulary and the
better you are at verifying because you know more facts
about it and you have more cross cutting checks.
And that is less true for the visual area.
But just identifying that as a very important limitation where
you have a completely different system you can use for the
visual stuff, which is just your eyes, right?
You don't have to use the you know, we have just different
hardware for quickly seeing, you know, this way, the hands or
something like that. Whereas if that.
Was it's a monkey brain. It's a monkey brain, exactly
right. So that's now an interesting
question. This is, you know, Carpathy.
I was discussing this is, is there some way to turn some or a
subset of the non visual things into visual cues where you could
see it was wrong immediately? So I'll give you a small and
simple example. Let's say you generate an audio
file, right? You know, like a spectrogram of
an audio file, right? You could maybe immediately see
if there's some artifact there, right?
That's a trivial example. Right.
So I think this it's a fascinating concept.
I'm I would wonder whether that's the right split.
OK, it's at least one split I found useful for now, but what
are you thinking? Well, so the split I was
thinking was that the natural language generation to make text
is perfect, so the text is always grammatically correct.
That is true, yes, but the model underneath, like the facts
presented by the knot in the text, might be wrong.
Yes. And that's sort of deceptive to
us because we see the text is correct and it looks confident.
Yeah, that's right. Whereas in an image, like you
ask it for a picture of somebody and everything's perfect except
the person's got six hands. I'm not sure conceptually what
is it that that's flattened? Is that you're seeing two things
in one layer or is it that? Do you see what I mean?
I I see what you mean. I think it.
Is or is it that it's a different level of?
Well, maybe then maybe there's a different point here, which is
if you ask for an image of a car.
Yeah. And the car, like I actually did
this ask for a fantasy 1960s French sports car, right?
It will look French. It will look like a sports car.
It will have 4 wheels. It might have two steering
wheels. Yes, that's right.
The two steering wheels is the equivalent of a grammatical
mistake or spelling mistake in the text generator.
Yes, because. However, it may also be that the
balance of the car is all wrong and it would flip over if it
tried to go round a corner. But you'd have to be an
automotive expert to know that. Yes.
So I'm saying that like there's. I'm sure you're saying levels of
error. That's right.
What you're saying is the 2 steering wheels is like a
spelling error, but spelling errors are very rare for AI,
whereas the two steering wheels is a common error, right?
And I think that has to do with just the way diffusion models
work versus how Transformers work.
That'd be like 1 high level answer I'd give where it's doing
like kind of it's more local with the diffusion model and you
can be locally correct with the steering wheel but globally
incorrect, whereas locally correct with spelling is usually
correct. That's like maybe once that's
useful. That's one answer.
The second is that with there's only a small space.
I think like, for example, we are optimized to recognize faces
so we can detect very subtle differences in faces.
But if I gave you like 5 different sheets of like static
noise, right? Even if there are very clear
patterns, like mathematically these are all like Fourier
transforms of the same object and this is the one, they're
just like total noise to you. A computer would be like these
12 are the same and this one is odd one out, right?
So in a sense, our eyes are optimized for a very low
dimensional set of things, which are the things that occur in the
real world. Like those are the things we can
pick out, right? Which is also that our eyes are
like, dogs are better at motion than us.
Yeah. So even eyes are different
depending on the species. That's right.
So, so, so because of that, we actually have a like because
they can't detect patterns in static.
That's like too high dimensional space.
I think text is kind of like that because it can describe one
of the most, I mean surprising things to me about how AI has
evolved. We were talking about this
question before is I was surprised you could get so much
mileage out of pure text. The reason is.
So much Watson. So much mileage out of pure
text, right? And the reason I was surprised
by that is, you know, you'd think.
You mean like reasoning and all stuff that looks like reasoning?
Reasoning and also spatial manipulation like like picking
like like having cameras, having eyes, seeing the world,
reasoning about it like a baby and so and so forth.
It is amazing how much of that world humans have assigned
machine readable labels to with text And the way that you know,
it's just, it's just very surprising how well that worked.
Like language, what I'm trying to say is in a few, in like 40
words, you can describe, it's like code.
You can describe many, many, many different kinds of things
in like 40 words, right? And, and it's just more general.
It's one of those things where if you're sometimes when you're
really close to a space, you're actually more surprised by a
breakthrough than if you're farther away.
And I, I should say like, you know, even looking seeing all
the style transfer stuff in the mid twenty 10's and seeing image
net and seeing the benchmarks and so on and so forth.
I was surprised that it got you know, a markup chain is well, if
you saw this stuff before GPD 3, right, it was like semi
coherent, but it didn't look like it was converging on
something. You know, it just looked like,
you know, it repeat itself many times and what have you.
And the fact that it broke through to what it did just
based on language was so counter intuitive.
And it's, I think it's because it's such a high dimensional
thing. It captures so many different
aspects of the world. Like anything you can perceive
in the world, there's a word for it.
There's many words for it. And then we also have billions
of people who've been typing those words for two decades,
right. So in a sense, like the entire
Internet, the video games and social media were like this
bootstrapper for for AI anyway. So on the on the other hand, AI
is very bad at spatial stuff. You know this thing called ARC?
Francois Shillet has this benchmark.
Yeah, that I know, Francois. Yeah, and so he is.
His benchmark actually got beaten by the recent, you know,
ChatGPT release and he's got like a new one.
And it's almost like a tetrisy kind of thing that's got some
degree of logic and spatial type stuff that AI finds it hard, but
humans still find it easy. It's kind of like maybe the next
generation CAPTCHA and it's it's visual more than it is verbal,
right? So for a reason.
Is it something that would be hard to explain in words?
Yes, I think kind of it's, it's about like this is here and and
it's almost like minesweeper, you know minesweeper where you
click and it expands and so on and so forth.
I, I think AI because it started with words, it doesn't do well
with the spatial side of things. Now on their hand, what the
Chinese are working on in particular is physical robotics.
Obviously Elon's working on it and so on and so forth.
But China's way ahead on the physical supply chain.
So like physical AI is robots and those definitely have
cameras and XYZ and spatial and rotation and so on and so forth.
So there's some eventual fusion, you know, like the the self
driving cars have gathered hundreds of 1,000,000 billions
of miles at this point. So there's some fusion of the
web, which is words and the world, which is, you know,
spatial. That will get you like a
completely, you know, maybe a fusion set where it can reason
about the world as it is. It knows how tall Everest is
because someone, some robot has hiked it.
You know, like Google Street View.
You might eventually imagine a bunch of humanoids walking the
world just like that, you know? I I wrote a thing years ago
about Street View and Yahoo, and the sort of thing I was kind of
poking away at is that basically every big Internet system is a
mechanical Turk. And the question is, where do
you put the people? Where the humans, Yes.
And with Google search, the people are everybody, a
everybody making a link on a web page and B everybody using
Google. That's true.
Whereas with Yahoo they tried to like, have a bunch of people in
an office. Yeah, doing it in the middle.
Making a hierarchical list of all the websites on the
Internet, which was became impossible.
Yeah. And with Street View, you just
pay a bunch of people to drive down every street in the world,
which is actually not impossible.
It's just expensive. It's.
Just expensive. It's really, it's an interesting
computation. It's not obvious that it would
be feasible to it. It's funny, you know, the Yahoo
thing, Yahoo, you know, I think got started in like the early
mid 90s, right? I think 94 ish, 93, something
like that. Yeah.
And the thing about it is it like Yahoo had to kind of get to
its limit before it was obvious that you needed something like
Google because like web pages had to be suffused with at the
time they put on page spam and so on and so forth.
You had to kind of top out. You had to get enough web pages
that the hierarchical model broke down.
You had to get enough economic value that people were really
incentivized to game the system and so on and so forth.
Before, you know, maybe Yahoo could have self disrupted, but
before something like Google was there, Yahoo almost built out
enough of the web economy to make something like Google
necessary, you know anyway, so one thing I wanted to talk
about, I want to go through various other areas, but what is
AI disrupted? What is AI going to disrupt?
Right. So what is it already disrupted?
So search has taken points off of Google share, you know, like
stack Overflow, you know, their queries are down image search
because now image search is image generation, obviously
video, obviously many different kinds of specialty apps will,
you know, things that are, for example, like various sales
tools that make templated emails and things like that.
Those all, you know, change. I, I, I'm not sure Salesforce, I
mean, Salesforce is, you know, certainly they're using AI, but
the entire Salesforce model, like spamming people with
e-mail, I'm not sure that's going to last in the age of AI
because you can spam so many of them now, right.
So, so those are some of the, you know, obviously robotics,
obviously protein folding and and whatnot.
What is it going to disrupt that people haven't thought about
yet? And I can give some ideas.
Well, one answer is we don't know.
It's like trying to say that ask that question about the Internet
in 1994. Sure.
And the joke is always that newspapers thought the Internet
would be great because they'd save on printing.
And they. At first, probably was good for
them. Yes, they did.
Yes. I did a slide in my last
presentation I did because it struck me that people would
always say, well, you know, Uber didn't sell software to taxi
companies and Airbnb didn't sell software to hotels.
They redefined what those things were.
So I went and did a chart of, well, what happened to taxis
versus what happened to hotels. And that's actually the other
one, surprisingly. What happened?
Taxing down is. Uber demolishes taxis.
Obviously Airbnb is mostly additive to hotels.
Why is that? I think this is Airbnb is a
different kind of experience in a hotel.
It's not the substitutional experience.
Yeah, it's complimentary. Yeah, half of business, half of
hotels are business. There's another whole bunch of
conferences. There's a bunch that's about
like, I mean just OK, so 2 examples like my fiance works
for goes to fly to Milwaukee. She arrives in town at 10:00 at
night. She needs a gym.
She's got a client meeting the next morning and then she's got
a flying back to New York and she doesn't want to go and stay
in some random strangers hotel which which you've got no idea
what it's going to be like. She wants, you know, a very
specific brand promise from you. Mean Brandon Strange Airbnb She
won't stay in a hotel. Yeah, she will stay in a hotel.
She's not staying in Airbnb. The other side of this is I
think there's a more general point and same thing.
I arrived in Singapore at 2:00 this morning.
I'm not going to go and work out whether this Airbnb is any good.
I'm going to stay in a hotel. Sure.
I think there's a, there's a, there's a more general point,
which is that like everything is probably disruptive to someone
at some point in the value chain, but it kind of depends on
the industry quite how much and in what sense.
So like the iPhone demolished the existing cellular industry
didn't really have any effect on telcos.
Telcos kind of hoped that they were going to do all these
services, but that was never going to happen.
But telco mobile operators today are basically the same companies
that they were 20 years ago with more basically the same share
price because their business was not in anything that the iPhone
changed except that they're providing massively more data
than they were in the past. The business is basically owning
sites and owning spectrum and connecting them up and selling
that to consumers. The same thing with like online
travel booking completely demolished the travel agent
industry, didn't really change the airline business and
airlines had to do a bunch of stuff around loyalty and pricing
and maybe pricing became much more transparent and so on.
But at the end of the day, their business is owning or leasing
aeroplanes and. There's front end change.
Buying fuel and owning landing slots and maintaining the
aircraft. And so there now, of course,
the, the, the, the counter argument would be you could have
looked at taxis and say, well, clearly that's not going to get
changed by the Internet, except maybe you'll be able to book a
taxi more efficiently until it becomes long and changes it.
But the point is you can't, there's this sort of very naive
view that says, oh, well, there are, the software will just
destroy everything, right, right.
And it the answer is, well, it kind of depends.
It's path's moment that's true. Yeah.
And like one of the the ways that I sort of think about this
is that like the tech industry kind of it comes and changes
everything in the industry and resets how it works and then
leaves and goes off and works. And so you know the joke about
how consultants are seagulls. Yeah, they come, they fly.
In crap everywhere make lots of noise and fly out, right?
And so if you think about what happened to books or music, no
one in the tech industry cares about music anymore, right?
Like. Well, yeah, Spotify does, yeah.
Spotify but. It's not.
It's not the main main event. Yeah, recorded music is like $20
billion a year. It's like a rounding error in
the scale of the tech industry. It has no streaming means.
It has no strategic leverage for Apple or Google.
Suno is interesting though. So the so the AI, yeah.
Yeah but but but for the last 20 years, 20 years ago the Internet
completely screwed the music industry and since then it left
and doesn't care. Same thing in books.
Like all the conversations around books right now, some of
which were about Amazon, are book industry conversations.
I think there's something similar happening now with video
generation and Hollywood. Like everybody in Hollywood,
like all over the panic and now everyone is sitting and looking
at this and thinking, OK, well, this saves a bunch of second
unit stuff. So when we.
Think all like all the questions for What does this mean are
questions for people in LA. So one one way of thinking about
it is conversation is proportional to derivative
rather than absolute value. So let's say you have a, a
sigmoid that's going like like this and then it flattens out,
right? So when it's like a nullity or
ubiquity, you know, when it when it, when it doesn't exist or
when it's everywhere, when 0% or 100%, it's just not notable.
It's not worth talking about, right?
People use Uber or Dropbox a lot more today than when they were
talking about Dropbox and Uber a lot, right?
So the conversation is maximum at the time of maximum growth
and then it's just much less because now it's like not
notable, it's just a feature of the environment, right?
So you can do Google engrams? Yeah, that show exactly this, I
think. That'd be a great.
That'd be a. Great.
So you can do them for like steel or and some of.
These OK railroads, yeah, carload of.
Steel because it starts in 1800. And of course, some of them, you
look at it and you go, oh, I'm actually seeing a chart of World
War 2, yeah, where you see steel suddenly does that, or shipping
suddenly does that and. That's not obvious, right?
Because conversations or like attention is focused on change
rather than absolute value. Well, I always used to do AI,
always used to be fascinated by elevators.
I get these kind of autistic autism spectrum fascinations
about things, and there's a chart I did of the number of
people employed in the US as elevator attendance, which is a
perfect bell curve. Interesting.
Yeah, it's all curves up and down.
And this is because first half of the 20th century you.
Didn't have any. You deploy a lot of elevators,
right second-half of the 20th century, they become automatic
right button and you can go and find all this advertising.
Why were they at the beginning? Was it just like switch word?
Operators, I would tell you what.
Was it technical enough? There was no, but there was the.
Well, if you think about what it actually takes to have an
automatic, automatic elevator system in a building, you've got
to have all the dispatching. You've got to have the
dispatching and the queuing. I see.
There's an interim stage. We have an elevator attendant
who would just stand in the elevator and you would say I
want buff floor 5, please. And they'd press the button for
five, right? But if you get in, you know and
originally. Elevator.
What was it originally before the buttons?
There was a lever that's an accelerator and a brake.
Oh, so it's like a? Car.
Almost exactly. It's a streetcar elevator.
It's a vertical streetcar. I didn't know that.
So there's a fantastic book I have called The Cultural History
of Elevators, which is all about how weird this was so.
It was a vertical train. Yes, it's a vertical train.
Wow. And.
That's how people thought about it.
Yeah. And so an elevator attendant,
you can kill people. And there's this wonderful story
I tell everybody, which is that you you press the buzzer to
summon the elevator, but it's literally you're just ringing a
bell and a light goes on in the elevator car.
And there's this story from the the War Department.
It's like hailing a taxi. Yeah, there's a story from War
Department or ringing for a servant.
There's a story from the War Department in DC, which is that
you would buzz more based on how senior you work.
So imagine you're like a Lieutenant and you get into the
elevator on the 2nd floor and you want to go to the 10th
floor, but on the way the buzz rang.
It rings four times. So he has to stop on the 6th
floor and go down to the first floor and then a major gets in.
So now you're. So theoretically there's Paul of
10. It could be in a prior day in
the elevator going up and. Down.
So interesting. And we don't see any of this
now, which is your point about conversation.
Yeah, you don't get into an elevator now.
And so it's an, it's an electronic elevator, right?
It's automatic, right? It's just an elevator.
It's simply said something like there's a phrase which is
civilization advances as you can do more things without thinking
about them. Like the quote just work right
the. Classic one is light.
People with yeah, electricity. Light gets cheap.
Yes, that's right. And I think, you know, the age
of Internet now sometimes what happens is these things get
really ubiquitous and they're out of the conversation.
And then there's this. Now that you can treat them as
like at 100% adoption, then the new thing arises.
For example, all of the craziness of the last 10 years
is in part a function of the fact that social media got such
ubiquity in the early 20 tens such that it was no longer the
novelty was, oh, I'm on social media, I'm using it.
How do I use this Twitter app or whatever?
Everybody knows what Twitter is. Everybody knows how to use it.
They know what a like is, whatever, whatever.
And then you start getting. Then you get the 2nd order
effects the. 2nd order effects. That's right.
So it's almost like it's like installing a device driver and
then you can install the next one and the next one.
But it's like the device driver is the percentage of the
population that's adopted something.
And once it gets to 100% or 90 something, then you can like,
I'll give you an example, like during the pandemic, there's
just the assumption that everybody had a mobile phone,
right? And they could QR code scan this
and the Indonesia, that was a really big thing, right?
That's how you'd show your health.
That made QR codes work in the West as well.
Yeah, that's right. But basically, obviously 10
years ago, you know, 10 years beforehand, they wouldn't be
able to do that. They would have to have some
other paper system or something like that.
In 2010, you couldn't assume everybody on the planet had a
smartphone. It was, it was getting big, but
it wasn't yet there. It's certainly 15 years ago,
nobody would have it, right. So that was something where the
ubiquity of something maybe sometimes the next step is comes
from that ubiquity or or you, you could give two or three
things at the same time. Yeah, I mean, you could think
about TV and radio, all forms of mass media in the past.
And you know, the growth of pop music requires recorded music
and requires radio. And, you know, the great of mass
democracy kind of goes hand in hand with literacy and TV
newspapers, right, That you need newspapers before you can have
other stuff has to happen, right, For that, for that to
come. And then, of course, you have
backlash. It was sort of think there's
something interesting in looking at stuff like the Arts and
Crafts movement in the late 19th century because he's a bunch of
people who say we hate all this mass manufactured stuff That's,
yeah, handcrafted things. It's funny that that's not a
statement that would make any sense in 1800.
Well. Well, it's funny because there's
this what you're talking about, like people were farmers that
are artisans and are like, Oh my God, this automation is
disrupting us. We hate it so much.
We want to go back to the old ways and and now it's funny is
those manufacturing jobs that all these workers were so mad
about in the late age, hundreds and early 1900s, all the
strikes, all communism and so on.
Those are now the things are looked back on romantically by a
lot of mega types where they are like, oh, that was such a great
job. I wish I had that.
I hate this information job kind of thing.
I hate this, you know, these, these these desk jobs and so on
and so forth. So it's interesting because
there's a romanticization sometimes of the past thing,
even as millions of people are exiting that for the next thing.
Now, this is a little more complicated, obviously, by the
fact that China has a lot of those, quote, manufacturing
jobs, but yet a lot of them are being automated in China as well
with the robots. So it's funny, the thing that
people were so mad about that they were getting seemingly
pushed into, which was manufacturing out of farming
into manufacturing, are the things that at least some
fraction of this generation wants to go back to.
Or they think they do. You know, I think it's.
Interesting. Some of those things, I mean
the, the Luddites are those one of these sort of misunderstood
movements because a lot of what the Luddites are about is
self-employed high status artisans losing that status and
being pushed into low status commodity jobs.
So this is going to be the big thing with I think.
Have you seen the elephant graph?
So the elephant graph and some people dispute the graph, but I
think it it's probably gesturing at something that's right.
It shows percentiles or deciles of the world in terms of income.
And it shows over the last 20 something years, I think from 91
to 2008 or something like that, where the growth went like whose
incomes rose and basically most of the world.
So the, the lower 10% in Africa didn't gain that much, but like
maybe from the 10 to 20% through the 70 to 8% had huge growth.
Then it drops off and the 8 to 90% to almost zero and then it
picks up again at the very top, right.
And so that means is the like global, you know, elite in every
country did great and so did China, India, Vietnam, Eastern
Europe, all these countries are no longer socialist, communist,
etcetera, right? But the Western middle class
didn't. And that is a big part of, I
think the silence ability now when we're looking at it is, you
know, in America, they have, you know, obviously red versus blue.
But one way of thinking about it is starting in, you know,
certainly in 2008, there's a ramp where China flips US
manufacturing. And so China puts all this
pressure on red America and that leads to Trump and trade war.
And you've seen that that graph of print media disruption,
right? That's the Internet suddenly
rising after 2008 to flip blue America and it takes all the ad
revenue away and it's all not shattering.
It's also Craigslist, it's classified ads, a bunch of other
things. So the Internet disrupts blue
America and that leads to wokeness in in the 20 tens, I
think, and and also techlash, right, which is the anti tech
movement. So we look at it as red and
blue. There's also China and the
Internet over here where the Internet is disrupting blue and
China is destructing red. So the thing I think that's
coming next is AI disrupts blue America and robots disrupt red
America. And so Chinese robots and
Internet AI and so that artisan movement kind of thing is going
to accelerate where people are going to be mad about that
happening. I think on balance, there's
going to be a lot more productivity in the rest of the
world. But it's possible, for example,
that a job that's at let's say 200K or something like that in
the US and there's somebody in, in India or Mongolia or Vietnam
or something who's at $2000 a year that that equilibrates at
like 20 K, right? For like somebody supervising
medical results or something like that, right?
Where the licensure is no longer as important the the Western
licensure, the Western state doesn't can't really protect it
as much because it's all on the Internet.
And that's a boon for everybody who's a customer of that.
Like healthcare costs go down around the world.
You've got a great doctor on tap at any time.
Most people benefit from it. But those people who lost, you
know, relative status, relative money and that get super angry.
And I think the burning of the way MO's and like the extreme
anti AI sentiment that I see among some people is is kind of
a precursor to that. Let me know your thoughts.
So. I think this is a general
observation that like when Europeans live in Europe,
probably something similar in Asia, when Europeans live in
Europe, we all feel different. So like Germans are very
different to Italians and different to British people,
different French people and so on.
And when Europeans live in America, they all feel European
and America is buried is, is in a different place to the
aggregate of, of, of, of, of Europe.
And the US has its own sort of political culture and political
questions that are different to the questions in France or
Germany or Britain. I do think some of what's
happened and I don't wouldn't call myself political analyst,
but I think some of what's happened is that certainly in
the US to the some extent the UK, there were coalitions,
particularly there were on the progressive side or the left
side. There was a coalition of urban
upper middle class, highly educated people with a certain
set of social attitudes and working collar, blue collar
blood working class blue collar people.
Has been totally. Busted in a different part of
the country, often with rather different social and political
attitudes and the same thing, I think in the US and the
Republican Party on the right, you know, the coalition of sort
of. Wall Street Journal reading
Capitalist. Yeah, like Mitt Romney and.
Military. Guys that that is split apart
completely and all of those, you know, centre right, economically
conservative, socially liberal people who are Republicans kind
of don't have a political party anymore and equally people who
are sort of Bloomberg. Central, you know.
Sort of Bloomberg centralists, centrists kind of don't have a
political party anymore and there's a lot of those
coalitions have kind of broken apart now.
What you have in a bunch of European countries is partly
because of proportional representation is it's viable to
have half a dozen different parties and the US and the UK
because of the first past the post system you don't have
multiple. It's never been viable to have
five different political parties at different points in the
spectrum in the same way the US has got the UK has got this kind
of weird hangover sent Liberal Party, which is no one has ever
been quite clear what it was for sort of in the middle quote
called Liberal Party. It's there's an interesting sort
of sideline there, which is the Liberal Party in the UK in the
19th century was one of the two parties of government and it was
socially liberal and economically conservative.
But in the 19th century, what we now call economically
conservative in the 19th century meant pro free trade and against
regulation, right, Whereas now economically conservative is the
other way round. Yes.
So all of those labels kind of shift and move and change in the
different things at the time. It's interesting, Meg, I'd say
Meg is arguably against, certainly against free trade,
but they're also against sense regulation.
So it's like half right. But but it's finished what
you're saying. But I agree with you.
Of course the labels do change. Yeah, the labels change.
The coalition's broke apart, break apart.
I think there's always this tension in looking at
progressive ideas and saying, because if you look at the last
100 years, the social progress, progressive ideas have always
won. Like nobody today says like
being gay should be illegal. Like so you know, a little bit
like what we were saying about AIA while ago today you you, you
could deterministically say that what is woke today in 30 years
time will be what every far right conservative agrees with,
like. Yeah, people have said that
kind. Of thing theoretically in 50
years, you know, maybe, maybe not, but there's also you also
have these kind of overreaches around this.
It does strike me that one of the differences between the Usus
and UK politics is that what happened in the last in my
lifetime is that the right for want of a better term won the
economic argument that state ownership and government control
of the economy is bad right and the left one the social
arguments that like gay marriage is OK.
Well, it's. And and so on.
And in what happened in the UK was the the right embraced that
and the Conservative Party is the party that brought in gay
marriage in the UK, whereas in the left in the US it's kind of
the other way around, the Republicans kind of and Tony.
Blair sort of brought in, kind of.
Yeah, and he brought in level economics, whereas what happened
in the US is that the Republican Party in the US never kind of
accepted that it had lost the social arguments.
Well, it's interesting. I think from 1950, like the
moment of 1950, you do have something where because
communism fell basically because Nazis was defeated, the world
moved socially to the left. And then when communist as
communism was defeated, it moved economically to the right.
And so thus, for example, like the immigrant billionaire or gay
billionaire is like in a sense. Can be right wing.
Well, they're far, well, they're far to the left of 1950 socially
and they're far to the right in an economic right in a sense of
1950 economically because 1950, yes, the Soviet Union had 100%
taxes because it's communism, but the US had 90% marginal tax
rates. And you really couldn't get rich
mid century in the US. You could be a corporation man,
you could work for NASA or GM, General Motors, General Mills,
General Electric, but you're sort of funneled, channeled into
like these gigantic things. You had more freedom in the US
than other places, but you're still very stultified.
It was it was too capital intensive to be an entrepreneur
and so on. And then gradually with I think
the transition was the mirror moment where that's begun, A
decentralization arc and history is running in reverse since that
moment. But and so I think a lot of
things are happening this century that are like a reversal
of things in the past. I think it would be interesting,
and I have no opinion about this at all, but it would be
interesting to ask what is behind the growth in
billionaires? Oh, is this an unlocking of a
new kind? Is this a wave of company
creation? So I, I, you see what I mean?
Yeah, I do have this on this. Which is is is your point is why
are there new billionaires? Is that because there were a
bunch of new companies and they're first generation owners
and where did those come from? And certainly some of them came
from Google and you know, winner global winner takes all effects
and some of them didn't. I don't know.
I mean, I, I, I, I, I'm, I'm not sure how much value I can, I can
I kind of add to that conversation.
There's a bunch of kind of economic statistical question as
well. I just not spent the time
looking. I can I can give some thoughts
on that, which is that has AU curve right?
Where for example, like who is the richest guy in the Soviet
Union? Like didn't exist Communism, you
know, basically Stalin, you know, didn't need money because
you could just requisition anything I think.
The well, the Soviet Union is kind of a bad example of
creating billionaires. No, no, just cut the country up
and gave it to 20 people. Well.
No, no. Well, that's right, but that's
starting in the 90s, right? Then it wasn't, that was Russia
then, right? But basically the number of like
independently wealthy men who could do things in the US, for
example, a lot of the, the, the great fortunes, the robber
barons and and captains of industry were forced into
foundations. That's why you have the Ford
Foundation, Carnegie Foundation, Mellon Foundation, Rockefeller
Foundation, because in 1930s Roosevelt didn't want any other
powers besides him. So he, you know, went after
Andrew Mellon, all these people, Ida Tarbell went after
Rockefeller and those fortunes were corralled and basically
controlled by the state in these foundations in the Soviet Union,
in Communist China, they were just seized, right?
So basically, let me give the, the, the normal way of talking
about this is inequalities rising.
And that's terrible, right? Another way of thinking about it
is what is the state, right? The state is in a sense it's
like all the people who are it's citizens and they kind of crowd
fund the state, right? And the question is, are they do
they have a choice in doing that?
Can they opt out of that? Like what set are they part of?
You know, for example, if they're on the Franco German
border, can they call themselves part of the German side or the
French side? You know, how about the Polish,
the Polish German border with that kind of thing?
And how much does the state take and how powerful is it?
And mid century, because of mass media and mass production, the
the states were more centralized they've ever been in history.
I can show a bunch of graphs on that.
That's not just that's a quantitative thing.
So you had these Gaiga states, you had fewer sovereign units on
the planet than at any time before or since like only like
50 UN countries. So there's like 196.
So things have decentralized since then.
If you go backwards in time, you go to like Germany under
Bismarck, you've got all these principalities.
Go to France before the revolution, you have all these
things, Italy before Garibaldi, you have all of these little,
you know, city States and so on, right.
So you go backwards time and forth time is decentralized and
the same, the same thing happens where you've got lots of
fortunes, you've got lots of, you know, individual potentates
and what have you, right. So in a sense, like the world is
sort of returning to what it used to be, with a big exception
being China. I think China is the like the,
the 20th century centralized state that will keep scaling
into the, into the century. So anyway, the reason I just say
that is I, I think there is something real going on, which
is that the state is just take capturing less of the wealth of
its individuals. People are sort of breaking away
on the borders of it and then being able to do their own
thing. And so it's like Elon, not NASA,
It's like Travis not taxing medallions and so on.
And there's a good to that where there's a lot more room for
individual initiative, but there's a bad to that as well,
which is then people don't feel as bought in on the collective
project. And they're not like included in
it. It's some guy's thing.
It's not their thing. It's not like America lands on
the moon or it's Elon, OK, fine. You know, And they don't feel as
bought in, right? So it's complicated kind of
thing. I think we're going to have to
renegotiate all that stuff in the future.
I think there's a there's a lot of this outside, again outside
sort of US politics, which is that partly because the US, the
part of the nature of the US economy, partly because the US
is a big domestic market, partly because the successful Internet
companies are in the US and have global winner takes all effects.
People outside the US for the front of the first time think,
well, we've got all these giant company U.S.
Companies that are running stuff in our country and that was kind
of true for like General Motors or Coca-Cola.
That's much more direct. But not.
Really. Yeah.
Yeah, right. You know, General Motors sold
cars, but you had a lot of your own car companies as well, and
ITM didn't decide how you built roads or anything.
And there's certainly a sort of a you know, you go to European
events now and there's people saying, well, do we need our own
Google? And one level, those are like
dumb questions, but they're dumb questions about like a real
issue, which is you have this other layer of stuff that you're
using which didn't used to be globalized and used to be
subject to local democratic control.
And now, well, it's not quite clear how that works.
Yeah, yeah. So, so, so actually it's very
important. I mean, where you're hitting on
there is, I think one of the core questions, and I'll
actually ask it in reverse, which is, are those American
companies basically, is the Internet American right now?
On one level you'd say that's a weird question.
Of course, there's two parts to that is are they American?
But also is they're not. They're not in our country.
If you're Swedish or Italian, it's not a Swedish company.
That's right, that's right. So so like you know, my view is
the Internet is to America, but America was to Britain.
It is like the version 3 point O and because the early Americans
actually consider themselves as you know British right, all the
folkways and stuff came from Britain and the.
American War of Independence is essentially a civil war.
Yeah, exactly. That's right.
So they had a people and they had a land, but they didn't have
a government, right? Because the government was in
London, right. And when they had all three,
they became Americans. They had a sense of self and I
think with the Internet, we have actually a lot of tribes that
actually have a people and a government, but not land.
And the reason they have a government is they have a
blockchain, they have a social network they have with with
moderators or forums. And now increasingly they have
like an AI agent or like a central Oracle or something like
that where it almost takes the role of like a God, which they
all ask questions to, right. So you think of every large
enough online community that has its own social network, whether
it's a discord or a forum or something like that, its own
cryptocurrency, which has its, you know, smart contracts and,
and currency and its own AI, which is sort of like its Oracle
or search of all the community's knowledge, right?
And that's like a digital community that actually has a
fair amount of strength. And then because, you know,
where are your communications happening?
They're happening online, whereas your, where your
transactions are online, more and more of your wealth is
stored online like crypto's at trillions of dollars now.
It wasn't, it wasn't that 15 years ago is at 0 basically.
And so the significance of these cloud communities I think is
under appreciated. And eventually they're going to
be able to have enough money to crowd fund territory.
And so the because the tension between your primary identity is
online, your social network is online, your currency is online,
your your information is online and then not being grouped
offline. That'll resolve, in my view, in
terms of the descent of the cloud to the land.
So it's interesting. I mean, I probably take a sort
of more more prosaic view of this, but listening to you talk,
I am reminded of like distant memories of being at university
and looking at social history. And you know, there are a lot of
social history is about the kind of the, the joining into groups,
yes. And so the, the, the joining, I
think about why you joining and how, what is this sort of form
of sort of self, what direction? Yes.
Why do people want to fund monasteries?
Why do people form lay brotherhoods around the church?
Why do people like there's a whole 19th century British thing
of like all sorts of social joining.
Why do people want to join militias?
And you know, why do they want to form all these kind of former
Guild? Why do they want to form all of
these kind of different social groups and social clubs and ways
of getting together? And what are they trying to
achieve? And some of it is about, you
know, self-defense, you know, or pretty not, not in a kind of
military sense, but about, you know, forming your group to
protect your group's interests. Some of it is about establishing
status. Some of it is about, you know,
self-expression and self actualization, you know, kind of
classic Maslow hierarchy stuff. But it's not new to have lots of
communities. What is new is that they're not
necessarily kind of physically like Co located and they're not
necessarily centred around, I mean things like women's
suffrage, you know, they're not necessarily centred around a
movement or some. Specific, I think the objective,
I think they will be. I think they will be but.
Well, there may be, but we've had those in the past, you know
the Cornwall League or women's suffrage, all of those, yes, you
know veganism, slavery, anti slavery movements and so on.
So those senses of, you know, social organization and joining
and grouping in clubs in different forms, in different
aspects of society for different reasons.
It's kind of a, a recurrent pattern of human society.
And now it gets expressed, which is the sort of thing we always
talk about is, you know, the in the Internet is human behaviour
and it expresses and channels it in new ways.
And that's everything from, you know, people being horrible on
Twitter or doing terrible things on the Internet through to
people forming groups, clubs and societies on Discord or Reddit
or whatever it. Is that's right.
You know, by the way, I have, I have an explanation which you
might find funny as to I, I used to wonder why are people so
crazy on Twitter? Why are they so crazy on social
media? Because, you know, like starting
fights and stuff. Just as a sidebar, I was able to
explain it in the following way. You know, you know, the
Unabomber in the early 90s. Yeah.
So he blew up all these people. But, you know, the reason he did
that was to get an op-ed in the Washington Post, right?
So he killed those people for the distribution.
He killed all those people just to get his message out there.
So when you realize there's people like that, then it
actually makes it more understandable how many crazy
people there are on social media.
If, if someone is willing to kill all these people to get,
you know, just his message out there, a lot of other people are
willing to be very nasty on social media to get their
message. Out there, I always thought a
lot of it was about context collapse, which is sort of
actually, yeah, buzzy people doesn't mean anything.
I felt like some of it was you don't know who that person is
and you haven't understood what they've said and what else they
think, and you presume they think X.
It's like it's lossy compression.
You kind of compress 3 paragraphs.
There's no sub clause, there's no nuance.
You can't say of course I'm not a Nazi and you know some of it
is also which? And in fact, they can't take
that for granted, because you're not.
Maybe you are. Yeah, well, yeah.
Or basically they're like, you know, they have no context on
you, they can't read 5000 posts, they don't know where to trust
you and and so sort. Of yes, some of it is also just
morphs is Morgan Hassell I think.
Yeah, Morgan Hassell. Yeah.
You write a book that quoted me that gets endlessly requoted.
Well, I'd said something like like the Internet means that
basically you're confronted with people who disagree with you.
Yes, and you all the time. And you didn't realize there
were all these people who like, the particular thing I always
found was weird was there were people who were like very, very
far left. There were people who are
communists and they're like, you'll say something that isn't
communist and they'll be like amazed.
They were like, the thing was always, I always thought it was
weird. Is like, I can, I think it's
weird that you're a communist because at this stage you have
to be an idiot to be a. Communist.
Yeah, right. But it's even more weird that
you don't know that most people aren't.
Yeah, yeah, yeah, yeah. They're like shocked by it.
They're like amazed that anyone doesn't agree with their tiny
minority opinion. Yes, that's right.
And a lot of Twitter was that it was like, you're amazed that I
don't think everybody should own a car.
You're amazed that I don't agree with.
I'm not that I don't necessarily share your opinion on every.
Possible. That's right.
And I think the way that's gonna reconcile is you're gonna get a
lot more, I think smaller. I mean, in a sense, Twitter
doesn't exist anymore, right? X.
Is fragmented and. Exactly.
It's a tower available moment, right?
So Twitter no longer exists. There's X and there's Truth and
there's Gab and blue sky on the left and Mastodon and Threads
and and then the crypto ones like Farcaster Lens.
Noster a lot of stuff went to things that didn't look like
that, so stuff went to LinkedIn. TikTok.
Or it went to TikTok or it went to Instagram.
And people make fun of LinkedIn like there isn't a bunch of
bullshit on Twitter. But you know the I realized that
an awful lot of corporate people were sitting quietly using
LinkedIn when, yes, it didn't feel that they could use
Twitter. Yeah, because basically The
funny thing is it's interesting, something about LinkedIn means
people are artificially polite. And something about X or
Twitter, especially Twitter, I think even more than X in some
ways meant that they were artificially negative hostile,
right. And The funny thing about it is
artificially hostile reads to people as more sincere.
Like, that's to say, of the two, there's something about the
artificially polite, like, like, for example, a good review is
not a rave review. A good review is.
I love Ben's book. It was great, but he could
improve XY and Z. That's like, the best review
you'll get. Yeah, You know what I mean?
Usually. Whereas A hater will be like,
just complete crap on you. Right.
So the negative is generally much more negative than the
positive is positive. And so when you see a LinkedIn
style post, it's often like super positive and it feels fake
immediately. But people don't apply the same
filter. They think negative is real, but
they don't think negative could also be fake.
It's like. A mental, you know there.
Was a thing that went viral a while ago, Some surgeon who'd
got a, they'd got a review and it was like, he saved my life.
He's the most wonderful surgeon in history.
It's amazing. It's wonderful. 4 out of five
stars. Yeah, yeah, yeah, yeah, yeah,
exactly, exactly. Wow, what did I have to do to
get 5 stars? Yeah, exactly.
That's right. Like, you know, I forgot to give
the mint chocolate under the pillow or something.
Yeah, Yeah. OK.
So like, you know, let's do, let's change gears.
Let's talk about just survey of tech, just things, you know, you
can tell me you've been thinking about this, you have anything
about this. So we talked about like gadgets.
So we talked about, you know, the glasses we talked about we
haven't. Actually, did we talk about
glasses on the podcast or on the car in the?
Car. We talk about glasses a little
bit on the pod, but basically, well, tell me.
Tell me your thoughts on glasses.
Oh so. ARARVR Glasses.
Yeah, XR glasses. Yeah.
So I've made this point a while a bunch on online as far as I
can see, like you have the VR experience, you think it's
amazing. It's not clear to me that this,
my base case of VR is that it may end up like games consoles
in that you see a games console, it's amazing.
Most people don't buy it. There's a portion of people that
don't understand that games is actually quite a small industry
in terms of number of people. It's a lot of money, but a lot,
there's like 2 or 300 million people play games, console
games. And so it may be that VR, you
have the experience, it's amazing.
You put it down, you walk away. Most people don't buy it no
matter how good the hardware gets.
I think it's much easier to see something like what I'm wearing
now being a universal device at the level of a smartphone.
Clearly we don't have the optics for that yet.
We may it's. Improving every year though.
It is. Yeah, it is.
Yeah, the. Question is, it's is that next
five years time? Is that 2 years time?
Is that 10 years? It's not clear.
Yet, yeah, there's, there's there's a few people I know who
just like, they almost subscribe to the space in the sense of
they're constantly just getting the latest glasses, usually out
of China, and they're just trying them out, right?
Or getting prototypes. There's various prototypes
people are making. And this is something that I
feel there's some value in tracking because it's almost
being ignored by the world, right?
Now it is because it's like it's, it's, it's one.
Of the hit that Gartner Hype cycle things.
Yeah, curve that's bumping on the bottom and hasn't quite
happened. Yeah, or it's a through after
the hype of. Metaverse, And there's a subset
of that, which is OK. Clearly you want a wide field of
view. Do you need to have something
that looks like it's 3D, like it's really there?
So do I need to have glasses that could put something on the
table in front of us that looked like it was there?
And that's radically harder, Yeah, having a really good heads
up display that could put a public that could put and that
could put like an iPad display hovering in front of me.
I think it helps a lot with things like repair, like for
example you open the hood of a car.
And well, that, but that's still a Hut that's still like a
hovering label over the thing versus does it need to work in
broad daylight? Does it need to have black?
Does it need to be able to occlude a bright white table
like this? Right?
Maybe, maybe not. I think there's a range of
outcomes there where you maybe it ends up like a watch that
it's to be clearly to begin with, it'll be a smartphone
accessory just to have the. Computer in the battery, yes.
But does it end up like a watch where there's hundreds of
millions of people who have it, but the smartphone is the main
device, right? Or does it end up, no, actually
a couple of billion people? Are wearing this.
Let me ask you another question. Does does a watch top out?
And because The thing is wearables are another thing that
has huge traction and it's kind of like there's a lot, a lot, a
lot. We could fill this table, this
whole room now with IoT health stuff, right?
Because there's watches, there's rings like the ring, there's,
you know, wristbands, but. It depends.
It depends on the question. Is the the Mark Zuckerberg board
Oculus? Is that Mark Zuckerberg board
Oculus because he thinks this is the next smart thing?
He didn't buy it to be a games device or 100 million people
using it. He bought it because he thinks
this is an Xbox smart thing. Yeah, because also he had been
hit by the. Platform.
So hard yeah, he wants to own the platform for sure makes.
Sense so there's I I my base case is that VR might be might
crap out at 50 or 100 million people and I've struggled to see
it being 5 billion. I can see glasses being a couple
of 100 quite easily once it worked the optics are there I
can imagine it being 5 billion. I think that's harder, but.
ARAR AR/XR is probably bigger than VR, but as we were talking
about, VR is very, you know, the the thing new thing they're
doing with for controlling military drones like you.
Know there's loads of vertical stuff.
We're absolutely. That's gonna nail it,
Definitely, no question. That's right.
So all the telepresence have, yeah.
And you know, the guy up the telephone pole, the guy in the
oil wearing glasses. Yes, absolutely.
That will be a. That is a thing already.
That's right. And I think have you seen this
movie? It's called Surrogates.
It's actually, you know, pretty good sci-fi movie from like
almost 1015 years ago. And essentially like people are
like they stay at home and they pilot a good looking version of
themselves as a surrogate walking around outside.
So you can take more risks and so on.
Because if that thing gets in a car crash wherever, nobody cares
and then they could just do another surrogate and runner
inside, right. So I do think what are the use
cases for like a proper the VR, the VR control of a remote
thing. So it starts with I think
drones. And have you ever done a VR
headset with a drone? It's an experience.
You should definitely try it. It's a wild moment because it
really does feel like you're flying right, which is very cool
and an interesting experience. So yeah, I think it starts with
drones, but I think it eventually gets to something
where you've got gloves and maybe an omnidirectional
treadmill or something like that.
There's various kinds of things like that.
And you are able to control a humanoid anywhere, right?
So you control a humanoid and you can, you know, clamber up a
telephone pole and fix something.
You and you're training the AI as you're doing this, right?
You you could have a maintenance worker with skill in the art.
Yeah. You know, and we're not there
yet. It'll be years before we're
there. But eventually you have all
these humanoids around where you can just go into this, like
animate the suit and start doing things, you know?
So that's a pretty important use case for VR, like physical
telepresence. You have to nail a bunch of
technology for that. But I could go through the
gloves. I could go through the haptics.
A lot of those things are moving forward, Right.
And, you know, a lot of people are pouring money into this.
That's something I give a lot of credit to Zach for.
He's just, you know, he's just continuing this, you know, like,
I don't know how many 10s of billions of dollars have been
put into this. He's probably put the thick end
of 100 billion into that. Something along those.
Lines like it's 75 to 100. Yeah, I mean they are actually
selling a fair number of units now.
It just hasn't come close to keeping up with the the spend.
Yeah, the sales, the the sales are just bouncing along.
It's like it's not good enough to break out of VR enthusiasts.
Yeah. And it's it's funny you you go
back to what you said about Twitter.
There's almost like a test, which is if you say that
something probably isn't working yet and you get a bunch of
people shouting at you on social media there, and that proves
you're right because if it was working, they wouldn't care.
Yeah. Yeah, Yeah, that's right.
Well, so. If you went on social media and
said nobody uses TikTok, then people would just say this guy's
an idiot, you're on social media and say yeah, and there aren't
actually any consumer use cases for drones.
You'll get like the 10 people who love their.
Drones, OK, there's one exception which I will argue
with you on, which is crypto, yes, right.
So that is something where people will say there's no use
for crypto, you will say. Yes, but there there's just a
huge number of idiots on every. Side that's also true, yes,
right, that's right so so OK, so we did.
So I don't say there's no useful use case for crypto.
I have the most unpopular position possible, which as I
say, it's kind of useful but not completely useful, which means I
get both sides screaming. At me, yeah, that's right.
That's just perfect position. So actually, what has Ben Evans
on crypto then? I'll tell you biology on crypto.
There's several answers to that question.
One of them is, and this is sort of more an observation, which I
hope you you won't tell me I'm wrong, is like there's a bunch
of clever people working away building like all the tourists
left, like the whole line of tea thing was, was all nonsense.
And that all that that all all the tourists left the the
tourists and the grifters basically all moved on to AI.
Yeah, a lot of them, yes. And all the kind of people
trying to build content brands saying this is all wonderful,
all this is all bullshit, they all moved off to AI.
There's a bunch of people sitting and doing like abstruse,
very clever, very technical stuff.
There's a bunch of stuff working or being built that may work
around a financial, the finance industry, around finance rails,
around stable coins, various kinds of financial instruments,
most of which is storing money or speculating in money or
moving money around. Yeah, there is a thesis that you
could build Instagram on this, that this is sort of an open
source computer in which you could write software that
consumers would use. And I have a bunch of questions
about how that would work, whether that would work, whether
you would need to abstract the open, sort the crypto stuff away
so that the consumers didn't see it.
And if you did that, then why would they care?
Totally. And but none of that's kind of
there yet. Like they're all billion scale
consumer apps built on blockchain yet.
So there's a sort of watch this space around that.
And then there's the finance side, which I think is sort of
theoretically very interesting, but I struggle to get very
interested in it. Just personally, it's not what
I'm interested in and I struggle to see ways that I could add
value in talking about it. So I kind of pay attention to
it. And every now and then I point
out, like my newsletter on Sunday, I pointed to the Shopify
and Stripe and, and said, like, there's stuff happening here.
Yeah, and you should pay attention to this.
And there's people still interested in trying to build
things. So if you've just written this
off as all bullshit, you're kind of wrong.
Right. But as a writer and an analyst,
I haven't moved it on to something that I feel I should
write about. Totally.
So OK, so that's very helpful. It's always helpful for me to
kind of triangulate on an area, you know, on the.
So here is my basic view. You may have heard me say this
12 years ago. I think this is still true.
Crypto is good for transactions that are very large, very small,
very fast, very international, very automated, very complex, or
then to be very transparent. And the reason for that is like,
for example, a Starbucks swipe like of a credit card is none of
those things. It's not very large or very
small. It's like a mezzanine
transaction. It doesn't need to be very
automated because you can just talk to the, you know, cashier
and see your receipt. It's not international.
Both you and them are in the same room at the same time.
It doesn't need to be transparent.
You don't need a receipt on the blockchain for everybody to see
and so on and so forth, right? So the reason people think about
the coffee transaction and the thing about crypto is it's one
of the most common transactions people do.
They pay for their coffee every day, right?
So it's like, I don't know, 10% of your transactions, 20% or
maybe coffee because it's very few things you buy every day.
Coffee is one of those things people buy every day.
So where crypto really shines is the the alternative forms of
traffic. Actually, let me take your
mobile example, right? The Internet can do telephony,
but that was actually the thing that was best served by the
existing system, right? We still have like local
telephone calls, right? You can still use the telephone
network to place telephone calls where the Internet shine was.
And telephone calls were sort of like mezzanine amounts of
information, right? Especially local was like
between people in the same country, wasn't very
international where the Internet shine was, for example, moving
really large files like Dropbox or very small files like tweets,
right? Being very international, like
across borders, being very automated.
So it wasn't a human on both sides of the call, right?
It, it's shown for, you know, being very transparent.
You're broadcasting the web page here, but it's not a phone call
just between two people and so on and so forth, right.
So that I think is a good analogy where you like, yes, now
today, eventually the Internet took over long distance
telephony because that was Skype and then WhatsApp and what have
you. But even still today, telephony
as well captured by the current system, right?
And like the existing phone lines still exist.
That I think is a useful analogy for crypto where crypto, for
example, if you have, if you're a power user of money, right?
If I want to receive or send a wire to a startup in Japan,
USCC, I can do that in seconds and then I can refresh the page.
They can refresh the page and they can see it's cleared,
right? Yep, that is a real use case.
It's international wire transfers from anybody to
anybody with and by the way, the bank account set up also is
instant, right? So think about what we've done.
We've taken it from days to get AUS and Japanese bank account
set up to seconds. We've taken it from paying money
to do that to for the for the transfer itself to free.
We've taken it from taking multiple days for a wire
transfer to clear to seconds. And we also by the way, the up
time, it's not nine to five banking hours.
You can do it, you know, 24/7 and you can do it on any device,
right? That's a lot of improvements
just for the the important use case of international buyer
transfers, right? Then you also have the digital
gold use case that one you'll only believe in if I mean, I can
just point to the graph, Bitcoin is appreciated from .1 cents per
per Bitcoin to $100,000. So like there's enough people
who believe in it for gone up 100 million X, right?
Yeah. It's also, I mean digital gold.
I think it's also something that there's a kind of country
mapping here because some of what you're talking about is a
much bigger problem in say in the US than it is in countries
with less with different banking systems.
Some of it is also. Sepa, you guys have Sepa in
Europe and. It's like you send the money, it
arrives for free like it's also this is the point about PayPal.
But the SEPA is worse than Europe though.
SEPA would not work for a wire transfer to Brazil, for example.
So you saw the same issue, though I think there's.
Another point which is like I remember reading about people in
Argentina, yes, literally keeping their money in bricks.
Exactly. That's right.
So it's Argentina, Nigeria. Lebanon where you you actually
can't trust your government, Yes, and there are kind of.
Places. There's a lot of places like
that, unfortunately, yes. There's also a bunch of places
where nobody's worried about that they sent you for 100
years. Exactly, that's right.
So so the more middle class, stable and so on you are the
like basic crypto is for the power user of money and the
powerless, right? The purpose person who's like
reinventing what a bank account even is and the person who's
just trying to hang on to a bank account.
So it's like a U-shaped coalition, right, Similar to the
people who actually benefited most from the global economy.
Remember I said it was like the the elephant graph, right?
You had the basically 10th to 80th percentile of the world who
grew and you have the top 1% who grew and the the Western middle
class didn't, right. That coalition is actually also
the Cryptic coalition. It's like the people who are
just, you know, intranet as, as Tim Ferriss put it, James God,
what's, what's his name? Jason Bournes of the Internet,
right? Like just Internet hackers who
are just trying to move money. Like, for example, I'll give a
concrete example. Brian Armstrong, you know, my
friend, CEO of Coinbase, one of the reasons he got into crypto,
he had a few different life experiences that led him there.
One was actually lived in Argentina for a while.
So we saw like what a failed state would be like.
And the second, though, was actually being an Airbnb
engineer. So The thing is Airbnb even
still today has the problem of transactions that are very
large, very international, right and and also very one time low
trust, right? Because you've got like somebody
from Denmark staying with someone from Japan and it's,
it's a one time transaction of maybe on the order of $1000.
There's actually a fair amount of money.
And like the wire system is simply not set up for that
frequency of use between unrelated parties.
And there's a lot of friction on something like that.
And to a surprising extent, Airbnb had a lot of forex risk,
like, you know, because they had to hold currencies and all these
different things. And the thing you thought was a
solved problem, like just moving money from one country to
another, it's like, well, Airbnb has to do.
It's accounting in USD, but it's got income in, you know, if
they're, if they're an American company and they've got somebody
transferring money from Denmark to Japan there there's three
currencies in that transaction just right there, right?
So there's at least three currency pairs which fluctuate
and you've got at least two or three banking systems and all
the delays and fees, you start to see if people are like, wow,
this sucks so much. We need an Internet first
banking system, right? We need something which is
payments as packets, right? So that was the second thing
that motivated Brian to do it, right?
There's other things as well, right?
But so where, where would I put crypto today, right?
I'd say there's at least three applications.
There's more, but I'd say at least three that are at the
trillion or multi 100 billion range.
And those are a digital gold, right.
Just whether you believe in gold or not, like that's that's
there, people do, people do even if you just consider an
insurance. It's the thing that people are
doing. It's the thing that people are
doing. That's right.
B is it's like even if you didn't believe in luxury cars,
that's a market, right? So there's a market for it,
right, OK. B is international wire
transfers. I think stablecoins are now
there at this point. There are now one 2% is $250
billion silver coins have passed Visa, they passed MasterCard,
right And then third is actually crowdfunding, right.
So if you look at the largest crowdfundings of all time, most
of them are crypto. And the reason is that capital
formation online, like if you think about something like
Kickstarter or what have you, it's actually more
geographically limited and more limited by the credit card
rails. And you might think, for
example, it's not that easy for somebody in Brazil and Japan and
India to put 5000 bucks into your Kickstarter, right?
They the credit card rails being accepted might be fraud hit.
Go ahead. Yeah.
I was just to say, I wonder with some of the there's a certain
amount of swapping paper for paper in some of that.
Go ahead. Well, in the sense of here is a
new crypto project, Yes, a bunch of people who've speculated, oh,
totally made a bunch of crypto money for sure put their paper
gains in Bitcoin into this new crypto.
Project, that's right. That's right.
But but, but I'd say you're right.
A bunch of it is like that. Which is what a lot of NFTS was.
Yes, that's right. But but even if we even if
you're just totally right, what was funded off just the mechanic
of crowdfunding, Yeah, shows that that mechanic for capital
information, what they spent it on, I'd agree with you.
Many of those projects didn't go somewhere.
Some of them went really far. Like Ethereum was a really that
paid for all the rest in a sense, you know, if all the ones
went to zero, that was so successful.
But but just the mechanic of capital formation where you have
so, so that gets me to #4 right. If you look at now, you may, you
may, you may sort of disbelieve. So at least those three markets,
gold wire transfers, crowdfunding, those are very
large markets. Those are $100 billion trillion
dollar markets. Then you go to like other cases.
Now if I just look at trade volume, right, crypto today is
actually the number 4 Stock Exchange in the world in terms
of volume #1 I see #2 NASDAQ #3 Shenzhen #4 crypto and it's
rising fast. The thing that has held it back
for almost 15 years is the doing the obvious things was
pathologized, meaning it like literally yesterday or like like
a day or two ago, we finally fully legalized, very clearly
legalized putting a dollar on chain right now that we can put
a dollar on chain very clearly such to the point that Amazon
and Walmart are like OK, congressional legislation is
perfectly good. Let's go time right now.
We can finally put an equity on chain and we can put a fund
interest on chain. We can put every paper kind of
thing on chain. That is a very big deal, right?
That means that crowdfunding thing I talked about says that
an Internet company can issue Internet equity and anybody in
the world can be part of that cap table.
Whether you choose to accept them or not is another thing.
But the capital formation mechanism, it's now possible for
somebody in Japan or Brazil or or Mexico to invest in your
company. Once you have Internet equities,
Internet capital markets, that is now within sight.
Now that we have the Sablecoin thing, boom, done.
There's nothing, you know, now it's just a mechanical thing to
get the legal system going to make the on chain equities work
and there's already work on that.
So that is a big deal, right, Because the US doesn't want to
be the center of global financial empire anymore, right?
It's like it's, it's very conflict about this, but with
the tariffs and the trade war and you know, tourist visas,
work visas, student visa bans and so on, it like is very
conflict about whether he even wants foreign money coming in
to, to America, right. And they've got remittances
taxes coming up like one for 5%. So U.S. financial markets I
don't think are going to be there in the same way by 2035.
I think Chinese markets are rising.
Chinese stocks are rising. That's going to be one thing
that's there. But I think the Internet capital
markets will take over from American capital markets and
that's a very, very big application.
Let me go through a few more. Is this interesting?
So far, no. It's it's interesting.
I mean I, I, I mean sort of think about I.
Mean we've got numbers now, yeah.
Yeah, yeah, there was a thing that I was, so I'm getting away
from the microphone because I, we were chatting about it in the
car this morning. I have a sort of a mental Venn
diagram of like stuff I'm feel I can add something to.
Sure. Stuff that I feel I understand
and stuff where there's an audience.
Yes. And the challenge I always had
in writing about crypto, this is like a kind of a practical
question as an as an analyst is all AI, all all kind of crypto
questions. It felt like they were either
very, very technical conversations about it was kind
of like writing about Linux. So and I should always think
that like crypto reminds me a lot of open source and you are
either it is open source, but in just in the sense of the
general. Movement.
It was sort of. It reminded me a bit of like
either I write something about like the new kernel memory
management thing in Linux where I don't understand it and the
people who do aren't interested in what I'm going to say and no
one else cares. Yes.
It just it gets better or it was like, imagine what will happen
when it's like talking about open source in the early 90s.
Imagine what's what is going to happen when software is free and
there's not there was I've I've struggled and it's actually it's
a it's a, it's a, it's a thing. I've also had writing about AI
because I want to kind of it's not, it's not a specific about
what you think about this. It's what I'm most good at.
I think all the stuff that I write that people seem to like
most is kind of talking about the product strategy of how is
this going to work? Who's going to win?
Who's not going to win? How is a corporation or consumer
going to buy this? What would you do with it?
Right? And I struggled for a while to
to write about our lands on that point because it was either
like, what are the 30 new papers this year or like this is going
to transform humanity, right? And it was kind of hard to find
anything in the middle. Is in in the weeds or super
macro but the mezzo. Super kind of messianic, but not
much about like product strategy in the middle.
And I have the same challenge in writing about crypto in that
it's either very, very technical, OK, I've got
something for you or it's yeah, imagine in 30 years or it's
about finance where I don't you don't care that much about it.
Yeah, it's not just that I don't care.
It's like I would have to spend 6 months to get to the point
that I know what all the acronyms for moving money
between banks are. I have an opinion about them.
So I've never like seen Well, is that is it?
It's, it's in a kind of in a completely different analogy.
It's also like talking about chips, you know, should I,
should I get to the point that I understand what's going on in
chips? Is that a good use of my time?
Would I be able to say anything of value there?
And I so far I've kind of felt no, there's a bunch of people
who know way more about that. Like the semis analyst guys have
got it. So it, it, it so let me let me
actually empathize with you in a certain way, which is I was
actually a very late user of social media, right?
I only got on Twitter in like December 2013, OK, Which is like
like a decade. Hello Boomer.
Hello, Boomer. Exactly.
That's right. No, I mean, The thing is I got
onto Facebook very early because it just was like moving around
universities or what have you at the time.
But I didn't really use it. And the reason is that until
2013, I essentially believed that there was absolutely.
I was just a very private person, you know, I was just
like, you know, it's, it's weird because I now post a lot or what
have you. I was just a very private person
and I never, I didn't give any public talks until late 2013 and
so on. And I just thought social media
was a complete waste of time. And all that mattered was
genomics and math. And you know, like, like hard,
like what people call hard tech now.
Like I was doing genomics and robotics and I'm, you know,
proud of that work. I think is is important stuff.
And I didn't see the utility in tweeting my breakfast and I
didn't see the utility in just, you know, petting each other's
fur, which is a lot of what people do on Facebook or
whatever, you know, right. So I didn't see the value in any
of that. And it was only once all of that
was what bootstrapped the space. All of the fur petting got
hundreds of millions of people on there, all of the breakfast
tweeting and so on until, you know, what actually made it
useful and interesting to me was I saw somebody tweeting a
summary of a genomics conference at Cold Spring Harbor that I
didn't have the time to attend. And they gave a much better
account of it than any layman would have.
It's like, you know, like someone tweeting a mobile thing
and you're like, oh, that's those are really great details
and you're skilled in the art, right?
And and then I was like, oh, wow, I can get like really
detailed information. You're OK.
Now this is valuable to me as a reader, right?
What's my point? My point is, I think the
parameter that you want to track when you're looking at crypto is
block space. Have you heard that parameter
before? OK, That is the most important
parameter in crypto that people outside crypto don't realize
governs crypto. Block space is to crypto what
bandwidth is to the web. So if you think about the early
Internet or the early web, I should be more precise in the
90s like it was very bandwidth constraints.
It's 28857 six modems. And so that's why like Google
was 10 blue links and I think Amazon even had many images at
all. And in fact, you remember 6°, it
was a social network, right? So that was a text based social
network. It didn't take off because
without images, people didn't really.
Yeah. You got nothing to.
Share. You got nothing to share exactly
right. ICQ was a chat app that did
work. AOL Instant Messenger worked
because that was just text that could be sent on that low
bandwidth thing. It was only in the 2000s that
you started to get more graphical things when bandwidth
increase, like Facebook, the reason it took off at Harvard,
everybody had AT1 connection being at Harvard and they, they
finally had digital cameras so you could have photos.
And as digital cameras propagated out, so did Facebook,
right. And you go further and further
and like, you know, the Internet only or Internet Explorer only
got disrupted by Firefox in like the late 2000s, right?
It was only really by the early twenty 10s that you had the full
JavaScript stack of like jQuery and then only later for React
and what have you. So this concept that we have
today of like a mobile web app or you can download JavaScript
and run an app in the browser on a phone was a vision in the 90s,
but it took a long time together because bandwidth had to
increase for that, right? So what's the analogy here?
Block space, basically block space is the amount of storage
that you have on a blockchain. Like, think of a blockchain as
like an armored car for data, right?
Because this is data that people want to corrupt, right?
In a sense, if it's a file on disk, it's important to you.
If it's a file online, it's important to others.
And if it's a file on chain, it's really important to others.
And it's so important that they might try to screw with it.
And so Bitcoin came up with like an armored car for data where
you could guard the -1 or plus one of who had what Bitcoin.
And over time, that block space increased so that you could do
some basic smart contracts on Ethereum.
And now it's increased enough that you can blast millions of
stablecoin transactions a day on like base and Solana and so on
and so forth. And so you should conceptualize
it as why hasn't this happened yet?
And instead think of, OK, these applications are gated by the
amount of block space. And so they're coming online
similar to the amount of bandwidth.
You had like text only apps, then you had images, then you
had videos. And like Netflix only did
streaming video in like the early 20 tens, right?
I mean, we think about all that as recent.
No, I don't I don't I. Don't.
That's the way of thinking about it.
Yeah, I don't have a problem with the idea that you couldn't
build Instagram on this because the infrastructure isn't fast.
Block space wasn't. Current.
Yes. Yet I think there's a bunch of
like, interesting conceptual questions around, well, what
would happen when we got there? Yeah.
So here's a few things. Interesting.
There will be also kind of you're sort of speculating five
years in advance. Yeah.
So my view is, I'm not sure if it'll be exactly Instagram, you
know? On well, it would and I, I, I
think we can be sure it wouldn't be exactly Instagram, right,
right, right, but just kind of conception.
What is the app you could build? Consumer applications you could
use? I mean, this is the phrasing I
remember you using years ago that one should think of of 1
should think of a blockchain as a distributed virtual machine.
Yes, and it's another. Layer it is exactly that's.
Right. And every layer of abstraction
is always slower and crapper than running on the bare metal,
except that it allows you to do a bunch of stuff that you can't
do if you want on the bare metal.
That's exactly right. That's exactly right.
And The thing is, block chains are in a sense one of the
frontiers of operating systems research.
Like in the same way like there's operating system like
Windows, there's a browser which is itself an operating system
because you can run apps in it. It's got a full programming
language like that's how Chrome layered.
Were you at A16Z when Martin Cassado was our?
Yeah, we overlapped just a bit and we invested a bunch of
things together, Yeah. Well, Martin had this great
observation. You remember when YC said that
like for 1/4 of their the companies 90% of the code was
written with AI? Yeah.
And he responded to this by saying yes, but if you write an
iPhone out 90% of your code is written by Apple.
Yes. And so there were all those
levels of abstraction. Prompting is just a higher level
of programming. That's right.
Yeah, exactly. And so there's a, the, I suppose
the another way of answering your question is like the
finance stuff is there. I can see it.
I get it. I'm not sure I can add any value
to that. It's interesting and I will tell
people it's kind of interesting, but you pay attention to this.
I think you'll be. A leader, Go ahead, Sir.
The building more generalized consumer applications on it is
conceptually more more interesting to me is something
that I could make money telling other people about.
Yes, except that it isn't happening yet, and it probably
will. At a certain point the curve
will tell it curve up. The blocked space will expand.
The stuff will get faster and cheaper and can store more stuff
and people you will be people will be able to build stuff on
this deterministically. It won't be exactly Instagram.
I think that's just kind of a useful mental model for thinking
that you could build. Something.
Like that you could build consumer network apps like that
on this, this at that point, then I think you have a bunch of
kind of new interesting questions like, well, is it a
good idea to have a social network where all the users have
a vote? What would that look like?
What problems does that? Right, right, right, right.
Yes. And well, Dallas are that
already this. Is yeah, exactly, which struck
me the other day that all the arguments against that are
basically all the argument and saying no, you need ACEO in
charge. It basically all the same
arguments to say no, you don't want mass democracy, you need
you need a king. And you can have some balance
like representative democracy, right?
So you have the vote and they vote for somebody for mixed.
Constitutions which again like look at Africa to see how Latin
America to see how mixed constitutions.
Work out well, I'm saying representative actually, where
you have a leader, but they've got a fixed term and there's
both of them, for example. All of that stuff is
fascinating. I, it's like we don't have it
yet and I and no one's going to pay me to go to a conference and
give a presentation. Totally, totally.
So it's kind of tough for me to write about.
Yeah, totally. I will say all I just say is to
put on your radar if you go to likesnapshot.org or vote Agora,
there are actually very large treasuries where all that voting
stuff is happening on chain cryptographic voting.
And so and so that's that's growing like stablecoins kind of
people that ignored stablecoins for a while just kept
compounding. So the the on chain voting stuff
is there. But what I will say is that I
think just like I was like a late adopter of social media
since I just it, it had to get to a certain level of
significance before I cared about it for the kinds of things
I care about. Just I think the kinds of people
interesting in crypto are either A, they're engineers and they
just like the developers or the power users.
B, their financiers, right? Or, or in some sense financiers
or day traders, whatever it is, both the high and the lower.
And then C in the part we didn't say is just like they're
political, right? It's like a political
motivation. So I kind of being like being a
Protestant or a Catholic. They have a certain worldview.
Also very open source. Yeah, that's right.
Exactly. So, so like I have that, you
know, we both like enterprise SAS type stuff, product type
stuff, that kind of discussion. And but I also like a bunch of
other things and you like you like art museums and, and things
like that, which I'm like, OK, that's cool.
You know, go have fun, right? So we have, we have our own Venn
diagram kind of thing, right? So, OK, so switching gears, I
think you'll be more interested in crypto as block space
increases. And once crypto wallets, let me
actually give you an example of something which it's used for
useful for right now, where the block space increased enough,
you know, open router that that allows you to try a bunch of
different AI models and just use a crypto to pay for all of it.
OK, So this way you don't have to have 500 different accounts
at 500 different because there's so many different AI models, you
don't necessarily set up accounts and all that stuff,
right? So it just takes all that
account set of process and you just have one account, you pay
crypto and it settles it with all these other guys, right?
That isn't kind of completely transiental.
Thing that just occurs to me as you were speaking is, you know,
LM Arena as this distributed voting system.
The thing I always thought would be interesting would be to flip
that and say can you pass a double-blind test?
Yeah, if you take a model that's on the top 20, yeah, and give me
a bunch of responses, how many people would pass a double-blind
test? You know which is which.
Well, The thing is probably some kinds of question.
You would tell very easily, but an awful lot.
I bet most people probably. Wouldn't, so the most
fundamental one would be like what is the private key to this
or like a basically what is the private key to this wallet?
That's something that depending on how it's set up, we were
talking about this in the car, but basically another major use
case for crypto is AI makes everything fake.
Crypto makes it real again, because AI can fake all kinds of
stuff and give you this very convincing thing on like the,
the, the deep resourcing where it said 40% of the phones or
whatever you're saying, but it cannot fake the private key.
So it cannot show a nonzero Bitcoin balance or nonzero
Ethereum balance without actually having the the
cryptographic solution there. So.
But it could probably just tell you that the balance is 0,
because it might be. Yeah, sure, sure.
But what I mean about that is like, for example, all kinds of
let me give you, you know, Captchas, right, websites.
So AI can bust a lot of captchas.
Now it can get through. It can am IA robot, it can
figure it out, get through. But if you had to log in with a
crypto wallet that had $1.00 in it or $10.00 or $100, AI can't
fake that. It cannot fake the possession of
that cryptography, right? Like to give you 1.
Here's one motivating example for why crypto will get.
Maybe this argument will convince you, maybe not.
If it's fine, you know, Google login, you agree, Is it billions
of users, right? But Google login, when you log
into a website, you only can log in basically with your e-mail
address and the permissions to your Google account.
There's something very obvious that somehow even Google with
all of its strength has not been able to implement, which is an
international balance, a spendable balance, right?
Google login could not have, for their reason, a spendable
balance across different countries.
They've solved that for Google itself, where everybody can pay
Google and subscribe to Google with a zillion credit cards in
all these different countries, but somehow they couldn't make
it work so you could log into a third party site with a
spendable balance. Crypto did solve that.
Just that alone means that every Google and Facebook login will
eventually be either augmented or replaced by a crypto login.
So I'm going to pick up something you said, which I you
mentioned, which I mentioned in the car around what's fake and
what's real. Yeah.
So if you're buying an apartment and well, so going back a step,
I think most of what most people follow on Instagram is no longer
their friends. It's interest graph.
Yes, that's right. And so do you care if that photo
is a photo of a real thing or not?
Sometimes you really do, and sometimes you really don't.
Exactly. Yes.
And I think that's kind of interesting.
It's a sort of not so much generative search as generative
content. Exactly.
If you're decorating your apartment and you want a mood
board and you can specify some styles and you can say I like
this and this and this and this, and it gives you more and you
look and you say more, more like that or more like this.
It doesn't necessarily matter at all if those images are real.
It does if like maybe you want to buy that table and it that
table doesn't exist, it just looks like those kinds of tables
or it looks like those kinds of chairs or whatever.
But if what you're looking for is no, I want to be more like
this or more like that, and you keep going until you get a mood
board of exactly what you want, doesn't may not matter at all
whether those images are real. That's right.
So, so if it's Pinterest on the one hand, then just inspiration
or what have you. But if it is.
If it's shoppable, then maybe it does, unless you it's then
there's an extreme case here, which is they'll just send that
dress to she and she will make it for you.
That's right. Or, or let's say, you know,
there's some a photo of a fire somewhere, right?
And quite a lot of times people will post photos of fires and
it's from like some like a concrete example, the Brazilian
fires from a few years ago. There's like a fake photo like
from that that Macron tweeted out because he he was told it
was a photo of the Brazilian fires.
But someone was able to show that it was actually like a like
AI think it was like a Reuters image or something, but from a
photographer who died years ago. Yeah, it wasn't that image.
Well, this is The funny thing about people complaining about
deep fakes. It's like we don't.
The problem isn't the picture, the problem is the label.
This the label exactly that's right so so The thing is that
with with crypto you can do what I call chain of custody right
block chain of custody where you can have a camera and by the way
this is also important in scientific work as well there's
this huge replication crisis with all these labs and data and
you. Know fake the data.
Yeah, exactly or, or or something, right.
So you could have, you know, there's something called pre
registration of studies, where like if you're doing a study,
you have to describe in some places who you're doing it on
what you're doing. It's like monitored to make sure
that people report the results, whether they're positive or
negative, right? So let's say it's, you know,
it's a study or it's a camera. You can have like a like either
crypto software or hardware in there such that when the frames
of images are recorded, they're instantly hashed and put on
chain either directly or as a digest of some kind, right?
That basically is like tamper proofing such that before the
data is even like collected or analyzed, this Internet
connected thing is doing something.
Now it's possible maybe to hack the firmware and mess with that,
but it would be pretty hard to. Depending on how you do this, it
would be pretty hard to do that. Right.
You also have this on Google and so on in front of watermark.
Yeah, generated images. So the challenge is if the image
isn't water March that doesn't, that won't stop people believing
it. True.
That's right. But but I think over time this
type of stuff where it'll gain traction at first our crypto
oracles for prediction markets because if you're making a
financial decision, I don't know if you've seen that stuff.
Alex Tabrock has talked about this.
When people have money on the line, their partisanship reduces
and they actually get a different chip in their head.
Really. Like, is this true or not?
They're trying to dispassionately figure it out,
right? They're not just cheering my
tribe, your tribe, whatever. And the is this true chip?
It basically means, OK, I'm going to double click into this,
I'm going to verify this, I'm going to look at this.
And that's where like Oracle's come in.
They're like feeds of data that have some degree of
verification. And right now they're like
mostly price data, but people use it for weather data.
They use it for this, that and the other, right, All these
different feeds of information that people trade on.
And over time, I think those feeds, once you can guard price
data, weather data, you know, health data, etcetera,
eventually you can guard any kind of data.
And then now you've got like a chain of custody for data, like
the scientific data rough off of it anyway, why don't we we
should wrap, but this is actually awesome conversation.
Anything, what's your latest stuff?
What should people go and check out?
Anything. Well, I've been publishing A
newsletter every week since 2013, and I always welcome all
subscribers to that. You should write Where is there
gonna be a Bendick book? Google, Benedict Evans.
My parents had good SEO. Book is interesting.
I've had publishers approach me every now and then about doing a
book. I have to work out what it would
actually be and why it would be worth reading.
Honestly, if you just, I don't know, maybe a history of tech,
like because all your slide decks are very good, right?
And there's one of the things I learned from, you know, my
friend novel, like the Navalomanac, right, that sold
1,000,000 copies. Why did it sell million copies?
I was surprised, but he was surprised by that.
It was Eric Jorgensen went and curated novel's old content and
turned into a book. And I was really surprised.
I was like, wait a second, isn't that all available on Twitter
for free already? Didn't people already see it?
They did. However, if you say what is the
one work that represents like the best of nobles thought over
years, you know, just to see his latest tweets is not the entry
point for that. You want to kind of collect all
of them, sort them, filter them, organize them thematically,
style them, and so on and so forth.
And I think you could have a pretty good book.
If you do that, let me know. Well, that's one thing on the on
the list And yes, the other thing is I, I used to do an
annual presentation. I've now shifted my cadence.
So I did a, a new AI presentation last month that I
published, which I, I was just in town to present.
And then I will do another one in the autumn, the fall for
American listeners. Great.
On a sort of e-commerce advertising, marketing brand,
like all the other stuff that's being transformed by AI right
now. And in general, what do I do?
I try and work out what's going on and how to explain it and how
I can explain it. And then I go and do
presentations and speak at events and talk to companies and
I do slides for money. Well.
That is similar. I do a lot of slides too.
I do a lot of speak. So you know, I've mentioned the
cloud communities thing and materializing those cloud
communities. So that's what I'm working on
atns.com like network school. So if people are interested in
this kind of stuff, we we talk about that there.
So subscribe to bendixnewsletter@isthatbenedictevans.com.
Ben, Ben Dash. Evansben-evans.com OK, great.
And and then if you want to check out network school, come
to ns.com. Sure, there you.
Gobenedictevans.com is another Benedict Evans, no, who is a
photographer really. And so my profile picture is
taken by him because I used to get his e-mail.
This is an obviously, this is. Obviously this is a.
In this case, there's a contact form on my website.
And he won't sell it to you. And I redesigned it.
I don't even didn't ask. I redesigned my website recently
so it's clear of who I am. But it was quite generic.
And people would go to the contact form and they would say,
hey Benedict, we really liked your work photographing Harvey
Keitel. Would you like to go to Mexico
next week and take pictures of Robert De Niro?
And I would look at it that's. So funny.
Forward. Well, you know it's funny.
You know, it's funny, there's actually probably as maybe even
more biology stream of Austins that are, but because there's
like 12 people last I checked in like the SF Bay Area alone with
my, my first and last name, you know, so just I, I I feel your
pain. OK.
Well, this is great. Really great seeing you in a
while. And we should do this more.
Yeah. Great.
Thank you, Sir.