Jeremy, welcome to New York City podcast.
And we've been we've been friends or friendly online, I
think for a while. You are the the founder of fast
dot AI, which is this incredible course that's online.
We both taught large online courses.
So we kind of have have talked about that.
You're the founder of Answer dot AI before that, I think you were
at Cagle, right? And you're Australian.
You have an interest in biomedicine.
And I think we're also into, I mean peace and trade broadly
internationalism and and so on. Give me the spiel is that is
Danielle. Everything is that or give me
Jeremy on Jeremy. Yeah, no, pretty much.
I mean, I say maybe fast AI, most people know us for the
course because that's how most people interact with us, but
that was only one quarter of it. So fast AI was all about trying
to avoid a kind of massive centralization of power and
inequality due to what my wife and I saw in 2012 is likely to
be a rapid growth of AI. And so we want to say.
Similar to open AI's mission. In theory, except we actually
were open. Yeah.
So we. Yeah.
So we basically decided to get AI into the hands of as many
people as possible, including people with few resources.
And so we did a lot of research to figure out how to make AI
more accessible because at that time, only 5 labs in the world.
And yeah, the techniques to actually use AI in practice were
not published. They were kind of like little
recipes. Yeah, so my wife Rachel actually
asked earlier when he was presenting in like 2012 or
something about some of his work.
And it's like, OK, so how did you actually do that bit?
What weights did you use? How you know what fine shooting
is to use? He's like, oh, we don't we don't
publish any of that. That's our bag of tricks.
So we were like, OK, this is not OK.
Like this is this technology is going to change the world and
it's requires a bag of tricks that you have to go to Stanford
to learn, you know, So we figured out all the tricks and
built a lot more tricks of our own.
And then, you know, everybody then tried to make it all about
money. So then Google eventually
started creating TP us and stuff instead of saying like, oh, you
can't. I remember Jeff Dean saying
there's no point trying to do stuff with AI unless you're at
Google because only we have this a compute.
Yeah. And we beat them in a global
competition to train Imagenet. You.
Mean a taggle. No at that fast AI.
Oh really? I didn't actually know.
That yeah, yeah. There was a global competition
called Donbench and we competed against Intel.
They had like a cluster of 1000 Donbench DAWN Bench EE NCH.
By the way, I love I, I, I'm friendly with Jeff Dean.
I think he's amazing. And so.
Yeah, yeah, yeah. So, so that, that's actually
pretty. I mean, I'm sure he was
impressed that you're able to do so much.
Oh, yeah, no, he was, he was great about it.
You know, they, they published a post, they published a paper and
they credited us. And there's no hard feelings,
you know, but we just want it. It's just we wanted to say like,
no, you don't have to be a rich Google person to.
You know how is that? Happening.
Actually, maybe you can talk about that because like that's a
little surprising to me because you know, obviously DeepSeek has
brought costs down recently, but back then was it did you like
obviously Google had massive amounts of clean data and huge
compute resources and so on. Why could how could the student
projects be competitive with Google during Dawn Bench?
Because these big labs suffer from being over resourced.
So in fact not as bad now, but particularly around that time
and for the next few years at Google you're explicitly
rewarded for using more compute. Where else we were like, hey, we
don't have much money. Like we, we made no revenue, we
had no grants. It was just my wife and I put
our own money into fast AI can. You explain that to me.
How are they rewarded for? Using so they were basically if
you could use more TP us that's like a a a good tick on your
performance. No, really.
Yeah, wow. OK.
So, you know, we came along and said, hey, like so for example.
It's it's because they wanted people to use the TP us since
they were. Yeah, and they wanted to like
show off how big their, their rig was.
And like you look at our big rig and these people using our big
rig to do these big things. Well, for example, in in Dawn
Bench, it was an image recognition competition, be as
fast as you can to train a model.
And the images were 224 by 224 pixels.
And we thought like, OK, well, 90% of the time, the 1st 90% of
training, we're going to train on 64 by 64 pixel downsized
versions. Yeah, makes perfect sense.
They look the same. You know the last 10% were used
bigger ones. That 4X or 16X delta.
Nobody else thought of that. You know, this is one of the
many tricks we used and why would anybody like an open AI or
Google try and do that? Because it's like, Oh, well, now
we're not using our amazing DP us well.
It's it's interesting because you know, that's actually I,
I'm, I'm actually going to put out a little little comic on
this actually on that, which is, you know, that meme about a
secret third thing is, is people will say, Oh, you're not an X or
AY, but a secret third thing. And they'll say it sarcastically
like, Oh, you must be a Democrat or Republican.
You're not a secret third thing, right.
But actually, if you think about like, like a, like an image zero
or one, one pixel is not enough to describe the complexity of an
image. You need not just a secret third
thing, but a secret 4th and 5th and thousandth and millionth and
so on, right? Pixels.
But you know, there is, there is a minimum necessary complexity,
right? And it's interesting because
obviously if you go all the way down to like a, you know, if you
have the number of pixels all the way down to just one, you're
not going to get enough, right? So it's an empirical question.
Going from 2:56 to 64, it still works.
I don't know, maybe going to 32 it still works.
Maybe going to a fave icon, it even kind of still works.
I don't know if you did that, if you.
Absolutely. We did.
And but I first just did it visually, you know, I just
downscaled it and I looked and I was like, can I still see what
that is? Right.
And if I couldn't see it, then I thought computer probably won't
be able to do as well. What was it?
Was it like, was it 16? Was it 32?
Was kind of. 6464. Yeah.
OK, Yeah, at at 32 you. It's Squint.
Yeah, it's it's, you can kind of see it's maybe a dog, but you
can't see what kind of dog it is.
I see. Interesting.
Yeah. OK, so OK, I want to.
Well, there's actually, I love, I love this.
So first of all, I want to actually show you something.
It'll jump around whatever. I want to show you something
that we have done that I think is a compliment to Fast AI and
this also. So I taught a MOOC in 2013
called Startup Engineering. I'm a big fan of it, yeah.
OK, great. So I did that with Vijay Pandey,
my my colleague is. I'm a big fan of Vijay as well.
Great. So he he's now at the bio fund.
We've invested a lot of bio stuff together.
So we have that overlap as well We're.
Interested. So you and Steve Huffman created
those two fantastic courses. I don't know if you ever looked
I. Don't know Steve Huffman's
course? What's?
His yeah, so similar thing. They were both like kind of end
to end, like how to make stuff. Oh, OK, got it.
And. That's the Reddit founder, Yeah,
Yeah, he's my friend also. I didn't actually.
Know he did, of course. Yeah, so and neither of them are
really available anymore and they're, you know, I free.
Web Development course by Steve Huffman.
Interesting. We need a, we need a, we need a
modern one. Alright, OK, so how about this,
maybe I'll do a refresher and we can we'll send it to the fast AI
people put it online and something like that.
I think that I do think a 2025 version.
So actually, you know, let me tell you what I'm planning to do
next on this. Well, so the reason I taught
that course very similar I think in some ways to your, you know,
kind of kind of thing is I know there's a lot of talent on the
Internet, right? And actually really around the
world. And you know how like the, you
know, the kinds of the dark matter and like the Hubble
telescope and you can find the dark matter around the globe or
or or not the globe in the universe, right?
So. Like gravitational lensing?
Yeah, exactly. That's right.
And so you need like a special telescope to see that, right.
So by analogy, just a fun analogy, the mobile telescope,
like the, the phones that billions of people now have,
allow us to find if, if the, if the Hubble telescope allows us
to find the dark matter, the mobile telescope, so to speak,
allows us to find the dark talent around the world, right?
Basically people who really have nothing other than their phone
and their hunger to learn, right?
And we can offer them a course, and that's like a Skyhook and a
Bootstrap. That's what Fast AI was about as
well. Like we really reached out to
parts of India and Africa and stuff that had nothing.
So we had like a guy from the Ivory Coast who was like asking
like, is there some way to get this on CDs, 'cause we don't
have Internet here. And yeah, turned out like one of
our biggest markets was in Lagos.
It's amazing. So actually I, I, I have a fair
number of folks in, in Nigeria, basically anywhere there's
Anglophones around the world, in India, Nigeria, in the
Philippines, right? There's actually all these
Anglophones meaning just, I, I do want to translate into other
languages and so on. But I think that's like the V1.
Right. Go ahead.
Yeah, No, I mean it. And it, it, it was just like,
it, it, there's all this talent around the world and it drives
me crazy that it, it's not being used.
You know, they're like picking coffee beans or whatever.
And, and as you say, like they've got like so many of them
were saying like how I'm training a particularly when
collab, Google Paylab came along, they're like, I'm
training a neural net on my phone, you know, through collab,
you know, can you help me do this or that?
And I'm just like, oh, this is great, you know?
And so there was a, there was a young woman from Bangladesh, one
of our first courses who contacted me.
And she was like, Jeremy, you don't probably don't even know
who I am, but I'm in Bangladesh and I'm a teenager.
And she was like, I want to know if what I'm doing is OK because
I feel shame. And she said, I don't know
anybody else in my province that does anything with AII.
Don't know any other girls that use computers.
Everybody thinks I'm weird. I want you to know.
I want to know if you think it's OK for me to do AI.
Oh, she just needed the social encouragement.
And I, and I, and I wrote back and I said, not only is it OK,
but like, you know, you're going to put your province on the map,
you know? And you know what, like a couple
of years later, she wrote to me from Google in Silicon Valley.
And she said, hey, thanks to you, I'm now a Google Scholar.
They flew me over to San Francisco.
What I like to do is I like to find these folks, mention them,
train them, stand them up, and now they're leaders in their own
communities. It's a, you know, quote, teach a
man, teach a man to fish or teach a man to recognize an
image of a fish, you know, right, so to speak, right.
Actually, you know, you can use that.
That's a good one liner. You know, 'cause you open with
the you open with the bird thing from from XKCD.
So teach a man to recognize an image of a fish or woman.
You know, right? You know, the fish specifically
you need to know is the tench, tench, Tench.
Anybody who's understands computer vision knows about the
tench. Yeah, because tench is the first
Imagenet category. So anybody who's ever worked for
the image net. Right.
Yeah, yeah. So teach a man to recognize a
tench, Yes, Yeah, that's. Good.
That's right. Actually, that's like replaced
Lena. Yes, exactly.
Yes, that's right. OK, so let's see now why don't
you give me the Jeremy life story So like before, so I know
fast AI know Kaggle, I know answer AII know the COVID and
and you know, masks what what's like What's the what's the so?
Before Kaggle. So yeah, so Anthony and I kind
of got Kaggle started in Melbourne in Australia, and then
we flew out here. He had this crazy idea that
venture capitalists in America would put money into our little
start up. And I thought it was crazy.
I thought there's no way. But he was right and I was
wrong. It's like, OK, I'll, I'll come.
I'll give it a go. But you know.
Does Cagle have some? Is it?
Is it an Australian is just sort of just like a funny word,
made-up word? Just a made-up word.
OK, Yeah. Like Google Cagle.
OK, Yeah. And, and yeah, we, we, we spoke
to some of your old colleagues. We spoke to Mark Marc Andreessen
and it was interesting. At that time Andres and Horowitz
hadn't done anything in machine learning and in the end they
were very good about it. They passed on our round and
they said, look, we don't know anything about machine learning.
Maybe it's going to be a big deal, but we don't have anybody
here that can judge that or not. But you know, so we ended up
with like an old closler and other folks put the money in.
But before that I had two startups in that I ran out of
Australia. 1 was or Fastmail which became a very popular
global e-mail company and then the other was called optimal
decisions which if you're insurance you would definitely
know and if you are not you definitely wouldn't.
It basically trans changed how insurance companies price away
from using just actuarial methods to using optimization
based methods. Like convex optimization or
something like that, no. Yeah, yeah.
Just, you know, pretty classic optimization, But the key thing
was to model elasticity and competitor price, not just risk.
Because if all you do is model risk, all you can do is cost
plus pricing, which as you know, is economically very suboptimal.
So we make insurance companies a lot more profitable, which I
have no pride over. In hindsight.
I don't know why I spent years of my life working on that.
But yeah, originally, I don't know, like coming out of school
I was a bit lost to be honest, because like, I was interested
in stuff that nobody else was interested in.
So I was interested in like spreadsheets and databases and
PCs. This is a bit over 30 years ago.
I didn't know any other adults or kids that were interested in
any of those things. You know, Australia.
Yeah. OK.
And there weren't any university courses you could go to that
were about data. So I ended up doing philosophy,
but I actually ended up not going to any classes because I
happened to get a job at McKinsey and Company where they
really appreciated this odd set of skills I had.
So tell me about South McKenzie is actually interesting to me
because there's the, let me give the negative and the positive
view of McKenzie. So the negative view of McKenzie
is, oh, you know you're hiring overpriced consultants to tell
you to fire people and blah, blah, blah, blah, blah, right?
And the positive view is it's something that takes young
people and gives them lots of different kinds of business
experience and, you know, lets them actually see the actual
numbers of lots of businesses and actually trains people to
make, of course, good slide decks and good presentations,
but really to communicate well and understand the gears and
nuts and bolts of businesses. And actually, when I've hired
former McKinsey and Bain and so on people, they've actually done
fairly well. They're they're very good
non-technical athletes like power users or what have you,
right? I don't know.
Give me your thoughts on that. Oh, I mean, you know, I was in
this unusual. Situation.
Sorry to be negative, I didn't mean.
It's just like the pro and oh, I love, I love like please, like
challenge me. OK, go.
If I say something worth challenging, challenge me
because otherwise it's boring for everybody listening too and
boring for me. Look, I started there when I was
19, so. Oh.
Really. Wow, That's, that's interesting.
Yeah. So I was years younger than
everybody else and for me it was eye opening and it was great
because suddenly there were people who cared about what I
did. And you're right, they're
generally non-technical people. Just one of the reasons why as a
19 year old I could be really successful there, you know.
Did you feel you levelled up when you were there?
Yes and no. It, it's funny you say it's this
kind of polarizing thing. It was polarizing in my life
too, right? Because at one level it's like I
felt like, OK, I need to learn business because I didn't know
any of that stuff. And I I wanted to create my own
companies. Yeah, you're very commercial for
a professor. Yeah, he's professor type.
Yeah, yeah, yeah. Well, I mean, I never went into,
I've never been a professional academic in my life.
Right. But you've got, you've got the.
I think we both have that disposition.
Yeah, sure. No, absolutely.
And so I was trying to learn business and by being at
McKinsey, I, I did learn a lot about how business worked, but
also in a lot of ways it's a very conservative organization
because I, I was telling my colleagues at the time, Hey,
this new Internet thing, I think it's going to be big, you know,
and they'll be like, I don't know, Jeremy, this computer
stuff, it's, this is pretty nerdy.
It's like, what's it for? I'm like, I don't know exactly,
but I feel like like very early 90s, I feel like a, it's going
to impact business. And they're just like, no, look,
let me explain how business works.
You know, business is about relationships and strategy and
capital and, you know, and in the end, like they were wrong,
you know, and I didn't have the trust in myself.
At the time, you didn't know whether you were wrong.
Or I was sure I was wrong and I just kept trying to figure out
why I'm so wrong. And I felt really upset with
myself for being stupid that they everybody else can see it.
It's so obvious that they're just like, look, Jeremy, let me
try to explain it. I just couldn't get it.
So I wish I had, you know, I stayed in consulting for 10
years. Oh really?
Wow. I should have done it just too,
because that's enough. And like what I really learnt
there was a sales. Like it's really grateful
learning sales. What did you like?
I don't know. What are the top 3 Five things
you learn in McKinsey like sales?
OK, Yeah. So I was and, and, and at IT
county. So I went from from there to IT
county. What what I learned was like,
OK, it's all about change and influence, right?
So it's not just sales, but it's a kind of sales.
It's like you're trying to sell an idea or you're trying to sell
a piece of work, whatever. So we were very careful about
mapping out the organization, you know, so it's like, OK, we
want to sell this piece of work next, or we want to help our
client sell this idea. OK, who's everybody in the
organization who's in any way a stakeholder who could have an
opinion, who could cause this to succeed, who could cause this to
fail? Like, OK, who do we know?
Who knows that person and like extremely kind of careful and
optimized process of creating change for human management,
human connections. We brought professional actors
in, like play the role of different types of clients and
we would then interact with them and then, you know, then talk
about what the results were. It was just way more intense
human optimization than I'd ever conceived of.
I'd always thought of that human side as being like, oh, some
people are charismatic, you know, or, oh, some people are
just good at convincing people. It's like, no, they're, they're
skills. There's a science, there's a,
there's a logic, There's a, there's like a different kind of
logic to programming a computer. But if you want to get an
organization to do a thing, you know, you have to know how to
map it out and how to react. You know, in some ways it felt.
Yeah. But in some ways it felt cold
and kind of calculating and horrible to be like, oh, this
human being. I don't seeing that as a human
being. I'm seeing them as like this cog
and this machine. And I'm going to use this
process. But it totally worked, you know,
And so it made me after a while, I, I changed my view of it.
I was like, you know what? Like getting organizations to do
things is important. It is also.
Important and so if that involves treating people as
machine parts sometimes because humans are very predictable, you
know, and so if you learn how to manage different types of humans
and different types of situations and like, you know,
so like you get the one person, it'd be your kind of inside bowl
who's like super who, who. And they've recognized that they
can use you to advance their career.
And then you talk to them specifically about how they can
advance their career and then they tell you who's going to get
in the way. And then you get 3 more people.
And then you use that to put pressure on the 5th person who
is well known to, you know, be somebody who likes following
rather than leading. And you know, you structure it
out, it'll play out. And at the end, it's like, OK,
it happened, you know? It's funny, like the way you
know, do you know Mark Craney at a 16 ZI don't know if you don't
have him, he's a very different personality than you, but he
also he's like a gruff Mormon a few words, but he's like a sales
genius actually, right, and very similar.
Like the way I think about it, that kind of reconciles all of
it is it's a nested set of like win, win relationships all the
way up to the organization level, right.
Like the best kind of sales is when you are genuinely selling
them something that will improve their business or their, their
product or something in some way, right.
And then it will also improve at a nested level of the career of
this person who approves it. And so and so it's almost like a
like a venture investment all the way through.
And that is actually what I think is the reason that that
will work is that's the most consistent kind of thing where
even if you're flipping them to do it, they will like it in the
medium to long run. Yeah.
And if you're trying to have a dent on the world, you know, and
you've got good ideas and develop good things, but you're
unable to influence anybody to buy it or use it, then you're
not going to have a dent on the world.
Like that's actually, you know, it's funny, one of the I mean,
there's a lot of great things about your course, but one of
the best is the domain name fast at AI, right?
Like I learn AI fast. Amazing.
OK. That's what I want, right.
So that's like an example of sort of an inbuilt marketing
kind of thing, which is great, right.
And I'm sure there was some thought into that, because lots
of people could have. Named it.
Oh, yeah. We did a lot of marketing stuff
there. We we also, as far as I know, we
were the first company in the world to do AB tests on our
homepage. Oh, Zara.
Interesting. I think we were also the first
to have all the free e-mail accounts.
A little photo would be added to every e-mail message, marketing
the surface like we did a lot of little.
Things like that. Things like that, little viral
things that today everywhere. Yes, so, OK, great.
Actually I want to show you something which is so we took.
So let me describe problem and then solution and get your, your
your thoughts right. So you and I have both taught
large online courses, right? And the typical thing that
happens with a large online course is people, it's a little
bit like signing up for for like a workout, right?
People aspirationally want to do it and then.
They want to have done it. They want to have done it
exactly. That's right and.
Then they want to be the kind of person that would have done
that. That's.
Right. And there's something good out
of that, right? But what happens is they sign up
for and the problem is allocating the time or then if
they have the time, the energy or the discouragement or what
have you, there have been various mechanisms and so on to
try to solve that, address that, right?
There's like cohort based learning and you know, and so
on. And those things work to an
extent. What's so great?
Yes, so that that can work. But let me show you something
that we did, which we call a learn a thon.
When should you use a random forest?
What is a confusion matrix? Don't know.
What about collaborative filtering?
Don't know. When should you use a random
forest tabular data? And if you have a lot of like
noisy features, what is the confusion matrix?
It's like a table of actual answers against like the
predicted answers and then comparing, you know like how
often it gets it right and then when and how much it gets it
wrong. What is collaborative filtering
recommendation algorithm by clustering people or items or
things by similarity. So basically we're going to do
a, you know, updated version of that.
But basically, so the fastest, so essentially, literally we
took because what it's like about 10 hours, 11 hours of
videos, right? So over 2 days, we said, OK, you
really want to do fast AI. OK, sign up, come here. 9:00 AM
on on Saturday morning and 9:00 to 9:00 Saturday, 9:00 to 9:00
Sunday. They watch every single video
start to finish. No phones, right?
And then when it was time to go and type things in, you know,
laptops out, do that. Absolutely.
And it drives me crazy because so many people tell me like, oh,
Jeremy, I I started your course. I meant to finish.
You know, I've tried three times.
I haven't managed to finish. I always think, like, look,
yeah, you could just put aside one weekend and just binge it,
you know, get it done. Yes, exactly.
And I want to did I, did I show you the fellowship video?
OK, hold on, take a look at this.
OK, Global meritocracy is finally here because we're
awarding $100,000 in funding for the new Network School
Fellowship, and anyone from anywhere can apply, and they
might well ask how. Well, you see, we've set up shop
on an island right off the coast of Singapore in the new Special
Economic Zone, and it has an enlightened immigration policy
that means it's the perfect place to assemble a global
community of tech founders and AI creators.
And that's what we've done. We've set up housing, food, Co
worker fitness classes, yoga, fast Wi-Fi, office pods, a
state-of-the-art gym, healthy snacks, Starlink, A makerspace,
a content studio, guest lectures from the most successful
founders and investors in the world, nomad visas and help with
everything else you might need. And we have funding too, if
you're good. So go and apply for the Nervous
Tool Fellowship now, atns.com. The only connection you need is
an Internet connection. That's very inspiring.
I want to come. Great, SO.
Also, Malaysia's awesome, so go to Malaysia.
That's right. So basically the combination of
Singapore, Malaysia and the new Singapore Johor Special Economic
Zone, You know it, it was one of the things where there was
theory and then somebody had to put that into practice, right?
So the theory is like Singapore has a lot of capital but doesn't
have a lot of land. Malaysia's actually improving a
lot, but but it is. I mean, Malaysia's got a good
education system. It's a strong.
Country very underrated and it's improving a lot and you can
basically live a pretty good life there I I think and it's
right next door, right? So Malaysia has land and has
less? You literally drive there you.
Literally drive there. I literally drive back and forth
all the time, right? In fact, we're just like 30
minutes from Singapore. Basically.
You're just literally, you know, just go over the bridge pop.
You can see, you can see Singapore directly from from it,
right? So, and we'll have probably have
a ferry or something back and forth that'll get down to like
15 minutes or so. Yeah.
So I want like these autonomous boat kind of things, right?
So why not? Yeah.
So those knock on road, let's get let's get that right.
So this is something, what you're seeing in that video is
something I've wanted to do for more than 10 years, right?
And you just have to build all the overnight thing 10 years in
the making. So certainly anybody who's like
doing fast AI, who's taking the deep learning courses, we're
looking for the kinds of people who completed your course and we
can fund them and help them build things.
And in particular, the thing about, so let me explain kind of
the motivation behind what we're doing in network school, right?
So a, it's very hard obviously now to get student visas,
skilled worker visas into the US.
It's, I mean, even like people who are tourist visas, like
they're getting strip searched or crazy things happen.
You saw, there's actually some Australian or what have you,
like some terrible thing happened to them or that right?
And I. Think every almost every country
now has some stories, examples of people, citizens of their
country that have been screwed around.
Tourist visas. Student visas, Skilled worker
visas like the and. In Southeast Asia, these
countries are now competing for that talent with their digital
visas, with their startup visas. Exactly.
It's. So smart.
This is exactly that's right. And this is the thing I was
like. I want Australia to get on that
boat too. You know, we've had this global
talent visa in Australia, which is pretty good.
It's so yeah, I have it. Everybody's needs to do this.
You know the the country's offering digital nomad visas,
right? Yeah.
So there's this weird thing where the US is taking itself
out of the global economy. Yeah, just as everybody else.
Everybody else is diving in and. Exactly.
That's all of America's big value creators are tech.
That's right, exactly. And they're globally mobile
because there's no silicon in Silicon Valley.
No, we're not like mining. So our team answer AI is fully
distributed. So we folks in in Turkey, Japan,
earlier Ireland. If you ever want to look at
them, we can host them in every school for a week or a month or
something like this. And one of the things you want
to do is like Co location for remote teams.
That's a nice idea because like, we've got together for the first
time ever in person here in Singapore.
Oh, great. And, and, and we're all like,
oh, it's so nice to spend a week together.
Eric Reese and I at Answer AI, we did something a bit unusual.
We decided to only have one policy.
And our only policy at Answer AI is to only have one policy.
OK, what is that policy? The policy is to only have one
policy. Oh, it's very mad.
Is this like one of those recursive kind of things?
Go ahead. I'm done, Dave.
We only have one policy and it's to only have one policy, so it
can't have no policies because that's a policy.
OK. OK.
So we have no policies other than the policy that we're only
going to have one policy. I see.
OK. Got it.
Why? Well, policies, they're like
ideologies. They're like, they're these
fixed things which say like, oh, you can turn your brain off now
because, because we've decided X, you know, in this situation,
this is how you meant to behave. Like, I am equally sceptical of
ideologies and policies and all of these cognitive shortcuts
that basically say like, oh, I believe in this thing because
that's what my ideology says, you know, or.
Yes. So let me give an analogy or a
way of thinking about this that I have from the Network State
book, which is, you know, like programming paradigms, you can
have imperative programming, functional programming,
declarative programming and so on and so forth, right?
And for certain problem demands, you know, certain style, it just
makes it very easy and concise to solve that problem domain,
right? But then you also want like a
multi paradigm language like like Python or something like
with Haskell, you know, you can just do everything as F of G of
H of X and you can actually get far with that.
But it's sometimes nice to do things in an imperative style or
what have you, right. And and so that's how I think
about political paradigms, right?
Like I never announced she is, you know, I'm not a big UFC guy,
but like Ultimate Fighting Championship is some people are
using grappling, some boxing, some Muay Thai and it's
situational as to just solve this with a kick or a punch,
right? Just solve this as functional or
imperative. And I think like Lee Kuan Yew
was someone who is like that, where he understood many
different political schools of thought and then he just like
applied the right technique that was sort of self consistent in
that school of thought for that situation, right?
And so that's like the beyond ideology thing, which is you're
aware of a lot of these different things and you
situationally figure out which one is appropriate and you use
that because. Andrew, you know, you're
constantly curious and interested and you know what you
care about is doing a good job. You know, rather than being
consistent with other members of your tribe, most humans are
mainly interested in being consistent with other members of
their tribe. That's right, the number one
driving force. And, and the thing about that is
there's a, there's a meta rationality to that.
I think it's kind of like, do you know, like evolutionary game
theory, right? So like you can imagine you have
two populations of people who are conformists and dissidents,
so to speak, right? And the distance are constantly
exploring and they're taking high risk and sometimes they
fall off a Cliff and sometimes they have reward and the tribe
follows them, right? And the conformists are just,
you know, they're like, this is this is risk capital.
And this is just, you know, stay home money or what have you, so
to speak, right? So you can make an argument for
a portfolio strategy as to why you want a small number of
dissidents who are sometimes wrong or they're wrong or
contrarians or whatever you want to call entrepreneurs, right?
And then most people should actually like go the tribe so
they don't run off a Cliff, but they could actually find, you
know, a better, better pasture or something over here.
That's that's one way of thinking about the respective
balance. Go ahead.
Yeah. I mean, I'm kind of curious
about this because like globally, somehow every
jurisdiction has settled on the same education system and the
education system teaches children to be conformist.
Yes. If you, if you, you know the
test tests whether you can feedback the things you are
taught in the way that you are taught them, you will get
rewarded if you do what you're told.
And like, I'm kind of curious about how much of this thing we
see in the world is because every single child basically in
the Western world at least has learnt this same.
Do you know their behaviors? Have you heard that it comes to
the Prussian educational system? Yeah.
OK Do you know what preceded that?
No. OK, so there's this great book
we can put it on the screen called called the Craft
Apprentice. OK.
And one of my macro kind of theories of the world is that
history is running in reverse. And I can show you a bunch of
graphs on that or what have you. But literally like AU curve
where in many ways our future is more like our past, like more
like, let's say the 1850s and then eventually the 1750s and
the 1950s. Like there's a lot of U curves
which have their minimum or maximum in 1950.
And I can I can show you some graphs on that.
And so one premise of that is like prior to the Prussian
educational system, which was which is what we currently know
K through 12 and so on, that was all set up.
It was inspired by Bismarck after German unification to have
all the children get basically the same software in their
heads. It's like, you know how with
Windows, you have like the default install that comes off
the factory and then you have like, you know, Windows premium
ultimate, maybe for college graduates, and then you have the
service packs from, you know, mainstream media.
That's how I kind of think about right, right.
And, and there's a reason for that because then everybody kind
of has the same references. They they salute the flag and
you know, they've just got the same basic install and they can
interoperate, right? There's there's a rationale for
that. It's how you, it's a softer part
of constructing a nation. In fact, arguably that's even as
important as quote, the hardware part, right?
Which is like the physical territory and, and the people
and so on. But before that, there's a
different system, which was all based on apprenticeship.
And they would start working from an early age and they would
just learn practical skills very, very early on.
Or they'd be like Jebediah and Abigail would have 12 kids and
they'd all be working on the farm and they'd be like mini
industrial robots, so to speak, picking fruit or something like
that, you know, mending fences very, very early, early on.
So the entire concept of extended adolescence wasn't
there. The concept of being on your
parents health insurance till 26 or whatever, it wasn't there.
And now the reason that that stuff got introduced in part is
because I think in the, in the late 1800s, with the advent of
like industrialization in factories, these kids were no
longer under the supervision of their parents or people the
parents knew. They were under the supervision
of factory owners who would push them too hard, right?
Like these were like the child labour factories, you know, and
so and so forth. And that was a dis alignment
between like the interest of the factory owner and interest of
the kids. That's when the child labour
laws were passed and so on. I mean, that took a long time.
It took a long time. It's like, what was it like
60-70 years? Britain was the first in the
world to introduce child labour laws but.
Yes. Still took much longer than it
should have. That's right, this little
Dickensian kind of era or what have you, right.
So then, so now there's a good to that at first, but then
that's what actually led to the modern era of adolescence.
And you know, I'm having fun as a kid for a long period of time.
And now we have this extremely extended adolescence and
training period where some people are like students as
doctors all the way up into their 30s before they start
their career. And they're almost middle age
before they, you know, and I think that the, the, the
corrective to that is 'cause everything good, you can always
overdo it, right? And so you can go from quote,
you know, like being opposing to child labor to not allowing
people to even work until their, their 30s as a doctor, for
example, right. So I think the opposite of that,
the thesis antithesis synthesis is when the kid is at home and
they're under the supervision of their parent, but they're able
to start earning online by doing development, software
development and so on. Even 1012 years ago, I had a
bunch of kids. Some of my best students at at
Stanford 1012 years ago were, were kids who had actually
earned their first dollar doing online programming in their
teens. Right.
And it's not Even so much about the amount of money it is that
it's that the market is a greater.
This is how I think with with have you seen the grade
inflation graphs? Yeah.
So like, you know, you put that on screen, but basically kind of
crazy. Everybody gets a 4.0.
Basically students are the customers.
So they're basically buying a job.
And so how do you, how do you deal with that?
And my answer is the market is a grader, right?
So now you have kids that are doing software, they can't hurt
themselves like in a factory, they're under supervision
because they're working remote at home, but they're also like
apprenticing, right? I think we network school.
We also want to make that happen where now they're in a friendly
environment along a bunch of other adults.
So they're they can run around and and roam and so on.
And then they can level up. They can be next to an
electrical engineer, next to a mechanical engineer as they're
building robots and stuff like that and just help them with
small things, right? And they start to see what the
like what adults are doing. And it's not just being, you
know, sitting at a desk the whole day, right?
So let me pause there. That's kind of how I'm thinking
about part of the future education.
Maybe you have some thoughts? I have a lot of thoughts.
Yeah. So I mean, I, I know a lot of
kids who are in that kind of interesting group who are
basically ready to go to university when they're like 11
or 12 and adults all try to stop them.
Oh, interesting. It's like we don't for some
reason. Well, the vast majority of
adults I deal with don't want children to learn.
When they're ready to learn, they have to learn at the speed
which they're expected. To learn they want a speed
limit. Yeah, and they assume any kid
that's keen to learn more, it must be the parents fault that
they're pushing them. Kids, kids are not allowed to
have curiosity and drive and passion.
But actually not every kid learns everything at the same
speed. Yeah.
So I'm very interested in like, how do we help this, that talent
at the much younger age, not because I want to like make them
more productive or whatever, but it's because I know so many of
these kids are equally unhappy when they're artificially held
back. And I want to all, you know,
help them all have the opportunity to, to have that
excitement of feeling like they're achieving their
potential, that they're, that they're just really happy with
the things they're building. So I've got a kid, you know,
she's 9. And she's, we, we let her
basically have whatever opportunities she wants, you
know, and she chooses her curriculum and she chooses what
she does. And she's happy for us to
provide her some guidance as well, you know?
But we don't force her to do anything.
And yeah, she's got this great cohort of friends all around the
world now who learn in this way and are all doing it at their
own speed. Obviously with AI, there's a lot
of opportunities to help more and more of these kinds of kids
develop as they're ready, you know, and, and get a much more
customized, personalized, dynamic education experience,
one that's not focused on conformity or authority.
You know, sometimes my daughter comes back.
She's like, she does lots and lots of extracurricular things.
You know, one of them is trampolining, and she comes back
from trampolining. Sometimes she'll be like, oh, I
got a Gold Star for good behavior.
Isn't that great? And I would say, like, I don't
know, I'm not sure I want you to have great behavior.
You know, why do you think that's so important to have
great behavior? Well, well, of course it depends
obviously like a layer of dissidence and so on on top of a
fundamentally pro social attitude is good.
But if if people are like anti social and they're littering or
they're, you know, yelling in the street, that's.
No, exactly. It's, it's it's not necessarily,
you know, being the best behaved kid in the class and getting the
Gold Star that week is not necessarily the great thing.
It's not, it's something not something I want her to be proud
of. Right, Right.
You know, yeah, she's incredibly pro social, she's incredibly
kind, she's incredibly generous. But that doesn't mean she has to
do everything she's told as soon as she's told to do it.
That's. Right.
And, and this is, it's funny you see us because.
Basically, particularly for a girl, like, like, like, like
girls are particularly taught to, to like fit in and do what
they're told. And I don't want her to be
somebody in society who just fits in and does what she's
told. I think, I think this concept of
like the balance and so on where it's like, you know, as you
said, they're pro social and they're kind, but they also
don't obey every single command and so, so.
Yeah, I, I tend to focus on empathy with my daughter, which
maybe ends up in a similar place.
You know, just like, particularly for younger kids,
empathy doesn't necessarily come as easily.
So I have to kind of say like, OK, you thought that was funny.
Now can you try to imagine what that person's situation was?
Do you think they would have found it funny if you were them
in that situation? Has anything similar happened to
you before? And eventually someone just
said, wow, did I just do that thing to them that that other
person did to me? That made me sad.
Like, oh wow, I feel so sad. I didn't want to make upset that
person. It, it's funny because you know,
sometimes you can get to like, just like with religions, you
can often get to a similar behavior pattern by different
kinds of religion. So I had a, a recent tweet a
little bit viral on, on, on actually that exact topic of
empathy. And essentially what I said is
because I was, I was talking to conservatives and I was saying,
look, empathy is actually a useful concept even for a
completely cold blooded capitalist, right?
Why? Because you have to understand
other guy's point of view and their win win, right?
And a lot of the like, especially in today's America,
they've gotten themselves in the in the mental state.
They think everybody's exploiting them, everybody's
ripping them off, right? And that like Australia's an
enemy and Canada's an enemy and Vietnam is an enemy and
whatever, right? And it's like, you know, lots of
people are just neutral, right? They're just business partners
or they're just like living their lives.
And you don't have to like fight.
And you can't fight the entire world.
And you also have to have some understanding of, OK, what's
their win? And how can we get to a win win?
And often a win win is more profitable for both parties
involved and and so on and so forth, right?
So. You can.
And actually, altruism is programmed into us like this is
something we've discovered, like evolutionarily being programmed
into all of us. To not be altruistic is to fight
against your basic instincts. And that's really dangerous
because when you fight against things that evolution has
programmed you to do, you're creating a new unstable
equilibrium, you know? So why has that happened?
Well, presumably there were plenty of groups that had no
altruism in their villages. You know, just genetically they
didn't have that as part of their DNA.
They can. Cooperate and they died out.
They died out, you know. And so we, we as a species, you
know, we're not perfect, right? But you don't want to
underestimate the power of what we're born with, you know, when
we're born, you know, altruism is not weakness.
Altruism is, is strength. These are the people that
survived. And if you want to fight against
that, then you're fighting against a basic survival
instinct. Also, it's, it's nigh on
impossible to design and organize such a complex system.
They, they arise over a very long period of time to create
these marvelously stable equilibria.
You know, and this is what kind of terrifies me at the moment is
there are so many opportunities to destabilize the equilibrium
right now, you know, with with technology and the connectivity
we have. And historically, each time you
get a previously stable equilibrium is damaged,
sometimes ending up with, you know, hundreds of years of
societal misery. And so I always just like, I'm
definitely very keen to see change and growth, but I want
people to understand the power of where we're at and know how
hard it was to get there. And, and to know enough history
to know that, you know, creating, you know,
destabilizing an equilibrium creates a power vacuum.
And there are certain people who are extremely motivated and good
at taking advantage of power vacuums.
And the pair, the people you definitely don't want to
empower, you know, I don't know, like somehow Singapore did an
amazing job. Like the one country in the
world that like, I think they just got lucky with Lee Kuan
Yew, you know what I mean? They ended up with a guy who's
kind of incorruptible. He doesn't have a huge chip on
his shoulder. He just cares about outcomes.
Most places around the world in that situation end up with, you
know, basically a, you know, deeply insecure, chip on their
shoulder power hungry person. You know, it's funny about Lee
Kuan Yew, which I think is very underappreciated.
Is he like he could argue his case in English?
I think this is the most underappreciated aspect of Lee
Kuan Yew because he would argue his case in English that he
could argue on the global stage, right?
Other people understood at least his point of view.
He can make it cogently, he could do it in short form, he
could do it in long form, sound bites and then, you know, long
speeches extemporaneously or in policy papers.
And he made sure that Singapore won the argument.
If you win the argument, then you often don't have to fight,
right? Because there's like that swing
vote in the middle. He's like, you know what, he has
a point here. We should do it his way and so
on and so forth, right? And I feel that, for example,
there's other other folks in East Asia who delivered
comparable economic results to LKY, right?
For example, in, in South Korea or in Taiwan or what have you.
But they couldn't make their argument in English, right?
That's a really exceptional aspect of they could speak in
Korean, they could speak in Chinese, but like they couldn't,
they couldn't make their case on a global stage, right?
And and I think that's very underrated and it's something I
think about a lot because so let me let me actually slightly
counter argue with you on the power vacuum thing, which is
there, right. I think that we are about to
enter a period where the the future is China versus the
Internet. Should I elaborate on what I
mean by that? I know versus the Internet.
China versus the Internet. So the 20th century was sort of
a symmetric thing, you know, almost like basketball at the
Final four plays. And it then ends up as US versus
USSR. Everybody slugs it out, right?
Sean Mcmeekin has this book called Stalin's War where he
kind of makes the point that World War One, World War 2 can
be seen almost as like a 30 years war, like an extended bar
brawl with people like smashing chairs over each other's heads
all around the world, right? And then it kind of lands up as
the US versus USSR, right, with Japan and Germany eliminated
and, and, and, and other powers too, UUSUK, France, blah, blah,
right? I think this century is going to
be different where it's a not a symmetric thing, but asymmetric
like China and the Internet are. I think the balancing things and
China is obvious and I think the Internet is not obvious.
What do you mean with China is obvious?
China, if you take the quote American empire, I think China
inherits the manufacturing and the money and the military, not
all the money, but the manufacturing, the military and
really the might of it globally like what the alliances and so
on. The world is after this tariff
thing recentralizing around China, right?
Quickly it'll. Be interesting to see how
eminent that is, but it's it's something very deep happening
there. Yeah.
So, so I think what's going? To happen.
And it's not just economically, also culturally.
You know, America's cultural power has been enormous.
It, it has been. That's right.
And now in Australia, I'm seeing people being like, oh, America's
kind of cringe now. It's cringe now, that's right.
But I think that the other air that the less visible, but as
important air is the Internet, which it has the people, the
values and the language. OK.
And the reason I say that is the only thing that has economic
scale comparable to China is actually the Internet like so
that's that why why am I into crypto?
I'm into crypto because everybody in the Internet is
equal, meaning you're peer-to-peer.
You can send packets back and forth.
You have the same property rights, you have the same
contract law, right? You have the same monetary
policy. And so whatever you were born
into, you can opt in to a system of law that is superior to the
one that you were born into. And it's like emigrating to at
least half of what a government is, right?
It's not the land, it's not the physical territory yet.
I'll come to that. But it's at least the property
rights. And you have to have some
sacrifice. You have to buy some of the coin
or whatever. You start interacting with this,
now you have like a system of law that's often superior to the
one that you inherited, whether is in Nigeria or is in, you
know, Lebanon OR something like that.
These places have destroyed currencies.
They don't guard property rights.
Now you can finally save because you know, the, the, the, the
blockchain protects your savings, right?
So I think that the Internet has half of what we want, which is
it has a system of government and with all these blockchains,
multiple systems of government. And it actually compare it, one
of the ways to think about it is, you know, with early
America, it didn't actually think of itself as America at
first. They were British colonists,
right? They're they're, you know, like
the the, you know, Virginia colony, Massachusetts colony,
and they had a land of native people, but they didn't have a
government, right? Because the government was in
London and took a while for them to develop a sense of national
consciousness and realize, oh, that's actually not our
government. Our government is here, right?
So they had land people in government, they became America,
right? And I think the Internet is
evolving in the opposite way. It has a people and actually as
a government in the form of the blockchain, but doesn't it
doesn't need to have land. I think that's the next step.
And hopefully it won't be verses unfortunately, Jinping has moved
into a power vacuum in China. Prior to that, actually, China
was much more of a democracy than people realized.
Talk about this. Well, I think a lot of people
don't understand how the political situation in China
worked. So there was a lot of voting,
but unlike most Western democracies, the voting was
entirely within the the party party.
And people might think, oh, that's not very big.
It's. Actually, 100 million people.
Chase Guy was very big. Yeah.
And then you go. To the and and it's not and like
of my so I spent a lot of time in China and with a lot of
really great people in China, young people and the vast
majority of the best of the people, most what they wanted to
do was to get into the party. Well, the the not commenting on
whether this is good or bad, but it ends up with a kind of a
democracy of, you know, the the hardest working, most
intellectually capable people. Can I make a provocative
comment? So there's a book called The
Party Decides. The point of that book was the
American Union Party decides who's actually running on the
Democrat and Republican side. For many years there people have
said a choice non echo or whatever, right?
And so there's a similarity to that where there were quote,
smoke filled rooms where the candidate was determined.
And certainly with the recent Democrat primary, it was
something where basically the party determined who was
running. And and so and so and then
there's a whole disaster, the whole Biden comma thing.
So there's more similarity to the American system for many
years where there was essentially A uniparty that
decided like who who the candidates were.
Then then some would argue and now I'd say in a sense we've had
true democracy burst forth, but that's some people conceptualize
this democracy. Let me pause there.
Yeah. So yeah, so that's another whole
kind of worms I'll leave aside for a moment, which is that
actually, yeah, there's there's actually a lot more conspiracies
in the world than people realize.
Hey, there's a lot of smoke filled rooms.
I've been in plenty of them. Yeah.
But I think I just wanted to mention is the the the the thing
that was missing in what you said is the is the key power for
me. The key issue for me which is
the presence of positive feedback loops.
And I want to say positive feedback loop.
I don't mean good feedback loop, I mean a feedback loop which
goes back and causes more of itself.
So power and wealth. Like like viral reproduction?
Something like that. But like power and wealth are
naturally positive feedback loops.
Getting more power puts you in a position to be able to get more
power. Getting more wealth puts you in
a position to get more wealth. And then you've got the cross
correlation. Getting more power helps you get
more wealth. Getting more wealth helps you
get more power. I talked to him earlier about
the importance of a stable equilibrium.
How can you get a stable equilibrium in a situation where
somebody getting ahead can let them get more ahead?
Right, There's a the. Compounding interest.
There's a huge tension here, right?
And this is where democracy and capitalism and the market
economy come into a huge, a huge problem, right?
Which is if if you allow those positive feedback loops that
happen, then you end up with people who have incredible
riches and incredible power because they're on the right
side of that feedback loop you. Know.
Yes, OK. And so it's a natural is that
equilibrium and it's not compatible with actual market
forces or with democracy because you're now in a situation where
you can like you can buy the media, you know, you can, or
nowadays like the social networks or whatever you can,
you know, all the odds in your favor.
And that is not, again, that's not a resilient state to be in.
So somehow many societies in the world have managed to create
sophisticated complex equilibria that have avoided this for
decades, you know, but it's not the natural state of things.
The natural state of things is for there to be, you know, 1
incredibly wealthy and powerful person that you know is there
because of the pair of positive feedback.
OK, so let me let me disagree with that in two ways and then
maybe a counter argument. Counter argument.
The 1st is there's this saying like shirt sleeves to shirt
sleeves in three generations, right?
Which is to say that like this guy, he starts a factory, his
son inherits it, and his discipit grandson puts a fortune
up his nose and, you know, does drugs and, you know, basically
spends down the whole thing, right?
And this is like the resource curse concept where when people
get too wealthy or too powerful, these get extremely lazy.
They forget cause and effect, especially if they're two or
three generations out and they don't even know what hard work
resulted in that fortune in the 1st place.
And these blow the whole thing up.
And that's actually what's happening with the US right now.
Like in in many ways, I think the people who are currently
running the US government are not founders.
They're heirs. They've inherited the system
that, like better people set up decades and decades ago.
They don't even understand how it works.
It's like a factory they've inherited.
And they don't understand how it produces widgets or how it
maintains global order, global peace.
And they just think I'm big and powerful and they don't
understand why it exists. I think that's true, but it
doesn't matter because the, what the data shows is that over
multiple hundreds of years periods, the wealthy families
say the wealthy families and, and at like highest levels of
power, you know, you like, if you look at the history of the,
you know, English royal family, whatever, or Chinese emperors,
like they stay there for hundreds of years, you know, and
they create, they create feudal systems underneath themselves,
which are critical for establishing loyalty and all
that. That's the more natural state of
things that things fall into. Unless you can.
OK, so I'm on a counter argument set from an argument that I
think is interesting at least maybe to to, you know, maybe
you'll disagree. So if you have an heir or you,
you let's say you have a like a Genghis Khan, right?
They have two. Like they have a child, they've
got half their DNA, then another child, they've got a fourth,
then their child, they've got an eighth, right.
And most of the time people don't have an exponentially
increasing number of children. So that means that that fortune,
for example, would or, or whatever it is, it's very hard
to pass a fortune down many generations #1 and #2 is that
person almost doesn't even exist anymore because their genes are
being split up, diluted. Like, does the person even?
In what sense is somebody who's only 116th part of the same
family? Right.
I I think you're dramatically though over emphasizing the
importance of beings over context.
So like if. They're 4 generations down.
How is they've got a bunch of descendants, right?
The vast major of their descendants must like.
What does it even mean to say, a family across four or five
generations that family doesn't like?
Argues not how power is transferred, right?
So power is transferred by picking an air and then they
have an air and they have an air.
And then as soon as there's like a lack of a clear air, then you
get 100 years of war and then somebody wins.
And now they have another, you know, err, err, err, like they,
that's the thing. They, they, they generate the
system of hierarchical loyalty and, and, and they do, like you
can see, historically, the people do maintain it.
But but I made two points. First is most of their errors
are not inheriting that fortune, so the the majority of the
family or the descendants or whatever are not right because
it would be divided. And the second is even this 4th
or 5th generation guy is now like 132nd Genghis Khan or, or
what have you. And so they may just not have
the, the zeal or the energy of the original Genghis, right?
Say lose. And then there's a new guy who,
who takes over, right? So basically what I'm saying is
it's almost like there's there's a huge tax like a 50% tax every
generation that makes it very hard to keep concentrating the
same stuff in the same because the same people don't even exist
3 or 4 even with there's some inbred but you think.
You've got the premise wrong. The premise is that what better
there is the genes. And what I'm saying is no
biology. What matters is the power of the
positive feedback loop. Power gets begets power.
It doesn't matter if my I'm 5 generations away from Genghis
Khan. What matters is I'm the king of
England or I am the king of France, right?
But, you know, like, you've, like, you saw what happened in
China. Hundreds of years of terrible
emperors, opium addicts destroying the country, they
still maintain the power, right? And the country went from like
during the Tang dynasty, the, you know, the vast majority of
GDP in the world was in China, Cultural Center was was in
China, scientific center was in China.
And then through power concentration, the civilization.
Sure. So.
So we don't want that to happen I guess.
So, so let me agree with you on that.
And I do think that there needs to be alternatives and so on and
so forth. I'll just make one other point,
which is if that person is only 132nd or 164th Genghis Khan,
then there were 31 or 63 other people or families that rose.
So, so like the mobility is actually there.
If there's, it's if it's a sufficiently exogamous society,
then all these folks did rise to become rulers because their
bloodlines actually did get up there.
So basically, what I'm essentially what I'm not what
I'm agreeing with you is the title got passed down, but the
family doesn't even exist beyond 564, whatever number of
generations, right? The family just gets diluted
out. Does that make any sense?
Yeah, but but that's what I'm saying.
It doesn't matter, right? What matters is that you that
the, the positive feedback loop created a power and wealth
concentration that was maintained for hundreds of years
and most people in the country suffered, right?
And that's a thing that we want to avoid.
And it's incredibly difficult to avoid because that's the natural
state of things. It's positive.
Positive feedback loops. Maybe this is an empirical
question. We can look at different
trajectories, but I think it is difficult to maintain that power
and wealth concentration without zeal.
And that zeal, if it's not there, what, people get fat and
happy a few generations out? Like we've seen that.
I mean, maybe, maybe we're just think of different kinds of
examples, right? And for example, in tech, it's
almost entirely quote, new money, right?
And what I find is that people who've inherited fortunes are
just lethargic, right? They don't have that energy.
So we are seeing this Internet disruption, right?
This dark talent that's hungrier.
I would always invest in that. I'd always back that because
it's hungrier and it wants it, right?
Whereas so I'm only seeing anti compounding.
I'm only seeing I guess. Yeah, no.
And I agree with all that, but I'm trying to get you to think
about the end state. OK, go, go.
Like I agree with everything you're saying, right?
But what I'm trying to say is, OK, consider the positive
feedback loop here, right. You've with AI now you've got
the ability to create more power, you know, and, and more
wealth and we're more connected. Like we could literally end up
with a global dictator, and we could literally end up with a
permanent underclass representing 99.99% of the
world. So let's talk about how we
prevent that, right? Because I, because this is
something I do think about, right?
So my view is, and you may, may or disagree with this or not, or
is that we got people got more left than they expected.
Now they're getting more right than they expect more MAGA and
then they're going to get more China than they expected.
Like basically, I think what's going to happen is China's
rolling up a lot of alliances like the EU is doing deals with
China. All its historical rivals in
Southeast Asia are now just all folding in.
So the whole global economy is re centralizing around China.
And America has not just become isolationist, they've isolated
itself from the world. And the most punishing, they've
sort of self-imposed, the most punishing sanctions of all time
on themselves. Like a rogue state, North Korea,
Iran would face this kind of embargo, but it was like
self-imposed. So they think it's going to make
them strong. It's really kind of crazy stuff,
MAGA Maoism or whatever, right? So as a consequence, I think a
lot of power gets centralized in China and.
And along with that, interestingly, you're seeing a
huge this kind of cultural isolationism happening in
America also like quite difficult to undo potentially.
Extremely difficult because they.
Don't want to end up like Japan pre the Maiji Restoration.
You know, they thought they're powerful, they thought they're
strong, but actually they separate themselves in society
and become weak. That's a good outcome.
I actually I think it's quite. That's a good outcome.
Yeah, fair enough. I think, I mean, because that's
actually something where they give up the Empire, but they're
just like, you know, a country or.
What? Stay isolationistic, except
they've got nuclear weapons. Well, that's.
That's a problem. And The thing is, I think, you
know, there's a lot of people who'll say like, actually both
on the left and the right, who'll say we need to, you know,
a Republic, non empire or we need to shut down, you know, And
the problem is that first of all, maybe you'll agree with
these things. I'll give a view and then maybe
you shoot at it, right. I think the first thing at least
that I start with is American Empire is real.
And it was spectacular in the sense of arguably for all it's
false, one of the greatest of all time.
It did have capitalism, democracy, World Peace in many
ways, then lost its way, especially recently.
And and now you've got a very common kind of thing where the
folks on the left think, oh, the US is bombing lots of countries
that should stop doing that. Folks on the right think the US
is, is, is being exploited by all these foreigners abroad.
It's it's being cheated. We've deindustrialized.
We need to stop all that, bring all those jobs.
OK, fine. So this group thinks the US is
harming the world. This group thinks the world is
harming the US. Both of them think they want to
shut down the empire, bring the troops home, you know, and so
on. OK.
Remember also like it during that heyday of the 50s, you
know, the American top match world tax rate was like 80%,
like 90%. Ninety.
Yeah, there's like they're working very hard to avoid this
positive feedback. I loop I mentioned, you know,
redistricting the wealthiest. So on that point, just to talk
about that, the at that time though, power was completely
centralized in the US government, right?
So you almost have like a toothpaste tube squeezing, where
like if you avoid centralization on one axis, you often get it in
another kind of thing. It's kind.
Of right so cause. 'Cause the people who want power
will find ways to get it. Yeah, you can have total
centralization of government power or you can have
totalization of corporate power or maybe military power.
And so or you can have checks and balances.
And where where I think the world is going to go is a
billion person Chinese superstate.
And then eventually like 1000 million person network states,
like, and then I think India is going to be in the middle.
I think there's other countries are going to be in the middle
and so on and so forth. But that's that's where I think
things go by like 2040 or so, right.
And and so hopefully that gives I I'm not saying they're all a
million person network states. Some could might be bigger, some
might be smaller, but I but I do think that we'll have a lot of
choice of jurisdictions. I mean that that that would be
nice. That that's at least a hope.
Yeah. Go ahead.
I just got to say, keep thinking about the positive feedback
problem because I think it still has it, you know, it, it feels,
you know, rosy to the level of being like, well, that's that
seems not in line with how power dynamics.
I guess, I guess my biggest argument against that is
arbitrage because it's very difficult to get or let me give
a game theoretic argument, right, which is going back to
your sales example, right? If you have two people, you have
4 possible outcomes and a win, lose thing.
You can have win, win, win, lose, lose, win, lose, lose,
right? If you have three people, you
have two to the third, so 88 possible grams win, win, win,
win, win, lose, right? And you have K people, you have
two to the K possible outcomes where you know any of them can
win and n -, K can lose and so on for any value of N&K.
OK, So this is how I think about like managing a startup, right?
The startup, if you have 100 people, what you don't want is
political behavior where some subset of them loses and the
other subset wins. You want to have a single thing
which aligns everybody, and that's like equity, and that's
like the exit. So they all know if I work
together, we all get the maximum payoff when it's all win, win,
win, win across the board, right?
However, there's limits to the to to how large you can make
that, right? You might make that 100 people
might make that 1000. You might make it even a million
people like cryptocurrencies of getting it to 10s or hundreds of
millions of people, right? But I don't think you can get to
everybody. And the reason you can't get to
everybody is at some point there is an incentive to breakaway to
dis line is what I call network defect, right?
And so that is the counterweight to kind of I think what you're
saying about infinite compounding is?
Actually, if you're allowed to go ahead, if you're allowed to,
like, I mean, like, yeah, it's like, oh, you know, the people
in Wessex could have left or whatever.
It's like, no, they're in a futile state and they would have
got killed. And there's violence and, and
like if you had AI in the mix, then you can have like absolute
global surveillance and power and total control.
Right. So now OK.
So here it's it's fine. In theory, you could go and do
something else. In practice, if you even talk
about it, you get shot in the face.
Yeah, so, so the right so the practical way where I do agree
with you is the Chinese drone Armada, right?
Because they can manufacture huge numbers of robots and those
robots are they're no longer like human beings who can
defect, right, Because they can't defect.
All this concepts I've been talking about the game, the
principal agent problem goes away and just one guy pushing a
button and it's like a machine that just enacts our action
around the world, right. That is definitely something
which changes these dynamics. That is actually something where
you could have centralization and power for a long time, and
that is actually something we should think of as the most
important thing to build counterweights to going 35.
So I think your network states idea can hit that too.
So fast AI, you've got this Practical Deep Learning for
Coders Part 1. Part 2.
We've done a new course called How to Solve it with Code and
we've got a whole new platform for it which we basically he
beta tested it. We opened up sign ups for 24
hours kind of reasonably quietly, 1000 people signed up
within 24 hours. So then we closed it.
We did that and the reactions we got were amazing.
Like we've had hundreds of people come back and say, this
changed my life. I've got a new job.
Well, it's not open for everybody, but it's, it's solve
it dot fast dot AI. So we're trying to figure out
how to mail make the most of this because we've come up we've
we've, we've created something clearly extraordinary.
Basically the, the the fundamental idea.
I don't know how familiar with the Polya book, but it's
basically like. It's a bag of tricks for solving
math problems. Yeah, but it's more than a bag
of tricks. It's actually a fundamental
idea, which is that to do things iteratively, step by step.
And when you apply that idea to coding and then you bring AI
into the mix as well, you can. We, we've kind of come up with
this way of solving problems with code and AI where you're
constantly in control of the AI. You never get into that
situation where the AI is kind of controlling you.
Yeah. So, yeah.
So we've, we, we, like I said, we, we this was from months ago.
We haven't let anybody use it for months because we've been
running it and testing it. Yeah.
So it's a bit of a long story, but basically it's it's a whole
different way of thinking about problem solving, which is the
exact opposite of the whole vibe coding kind of.
It's like, let's think step by step for humans.
Yeah, let's think step by step for human plus AI together.
The AI sees all of your thinking.
You see the AI is thinking. You write code.
The AI write code. You're constantly focused on
learning and iteratively improving, you know, vibe
coding. It's just like one shot thing
where you don't learn anything. You get up more and more
technical debt. So it's actually it's
interesting like my Co founder Eric Grease has this lean
startup approach, which it turns out is really similar to the
Polya approach. Again, it's like highly
iterative learning based. So we're hoping that through
this Solvit course that we're going to eventually build
something like your startup engineering, but start using
this solvent approach and with with the help of AI to allow and
then to create like 1000 new startups from that course.
And then work with investors to give each of them, you know, a
start financially and maybe hopefully build the next
generation of of founders. Amazing.
And I think you know would be good.
I want to actually talk about the network school fellowship
with your fast AI folks because I think a lot of them could
benefit from applying or whatever.
So, OK, awesome. Thank you very much, Jeremy.
Thank you, Sir.