WEBVTT

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Something about AI that causes
the people in the field I would say to

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become both excessively utopian
and excessively apocalyptic. Having said

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that, I think what's actually happened
is an enormous amount of technical

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progress that built up over time.
And like for for example, we now know the

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neural network is the correct
architecture. And I I will tell you like

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there was a 60-year run where that was
like a you know, or even 70 years where

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that was controversial. And so so
the way I think about what's happening is

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basically I think I think about
basically the the period we're in right

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now is it's I call it 80-year
overnight success. Right, which is like

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it's an overnight success cuz it's like
bam, you know, chat GPT hits and then

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and then 01 hits and then you know, open
claw hits and like you know, these are

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open these are these are like overnight
like radical overnight transformative

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successes, but they're drawing on an
80-year sort of wellspring backlog, you

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know, of of of of ideas and thinking.

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It's not just that it's all brand new,
it's that it's an unlock of all of these

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decades of like very serious hardcore
[music] research. If I were 18, like

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this is a hundred this is what I would
be spending all of my time on. This is

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like such an incredible
conceptual breakthrough.

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Before we get into today's episode,
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Now let's get into it.

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Everyone, welcome to the Latent Space
podcast. This [music] is Alessio,

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founder of Kernel Labs, and I'm joined
by Swyx, editor of Latent Space. Hello,

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and we're in a16z with A uh Martin
Casado, welcome. Yes. Yes, A and what,

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half of 16? >> [laughter] >> A1. Exactly.
Uh apparently this is the the final few

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days in your your current office. You're
moving across the road. Uh we're yeah,

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we have a we have we have some we have
some projects underway, but yeah, this

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is actually this is the original. We're
in actually the original office. We're

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in the we're in the we're in the
we're in the whole thing. Yeah.

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>> Great. Thank you.
So I have to come out.

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This is a you know, I just wanted
to pick a spicy start. In October 2022, I

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just made friends with Rune and

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I wanted to give him something to sort
of be spicy about and I said uh you'll

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never not be funny that A16Z was
constantly going the future is where the

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smart people choose to spend their time
and then going deep into crypto and not

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in AI and that was in October 2022
and Rune says there was an internal

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meeting in A16Z to reorient around Gen AI.
Obviously you have, but was there a

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meeting? What what was that?
I mean, I don't look, I've been doing AI

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since the late '80s. So I don't know
like all that. As far as I'm concerned,

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this stuff is all Johnny-come-lately.

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Yeah, yeah, I mean look, we've been
doing AI our entire existence. I mean,

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we've been doing AI machine learning
deep you know, deep learning. We've been

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doing this stuff way from
the beginning. But obviously,

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AI is just core to computer science.
I I I actually view them as like quite

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>> [snorts] >> uh quite continuous. Um you
know, Ben and I both have computer science

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degrees. Um you know, we we both Ben
Ben and I actually both are old enough to

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remember the actual AI boom in the 1980s.

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There was like a there was a big AI boom
at the time. Um and there was a there

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was one under names like expert systems
um and with the era of like Lisp and

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Lisp machines. Um I I coded in Lisp.
I was coding in Lisp in 1989. Where that

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was the the language of the AI future.

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Um yeah, so this is something that we're
like completely you're completely

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comfortable with. We've been doing
the whole time and are very enthusiastic

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about. Is there a strong like this time
is different because uh my closest

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analog was 2016-17.
There was there was an AI boom and it

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petered out very very quickly. Uh >> Well,
it just it it just didn't sort of invest.

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>> Sort of. Sort of. Investment investment
excitement. Although that's really when

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the the Nvidia phenomenon really
it was it I would say it was in that

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period when it was very clear that it
at the time it the vocabulary was more

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machine learning, but it it was very
clear at that time that machine learning

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was hitting some sort of takeoff point.

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Yeah. Well, and as you guys you guys
have talked about this at length on your

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on your thing, but you know, if you
really track what happened, I think the

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real story is it was it was the AlexNet
uh basically breakthrough in like 2013

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that was the that was the real knee
in the curve. Um and then it was obviously

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the transformer breakthrough in '17.

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Yeah. Um and then everything that
followed. But but you know, look,

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machine learning, you know, there were
you know, look, uh I mean, look, I've

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been working, you know, I've been working
with one of my you know, kind of projects

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working with Facebook since 2004. Um and
on the board since 2007. And of course,

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you know, you know, they they started
using machine learning very early. Um

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and you know, I've used it basically,
you know, for like 20 years for, you

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know, content, you know,
feed optimization and advertising

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optimization and obviously many,
you know, [snorts] financial services, you

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know, many many many companies, many
different sectors have been doing this.

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And so, it's like one of these things.

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It's like it's not a sing it's not
a single thing. Like it's it's like it's

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like layers, right? Um and and the
layers arrive at different paces. And

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but they kind of build up. Yeah. Uh they
kind of build up over time. And then and

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then yeah, and then look, in retrospect,
it was 2017 was kind of the, you know,

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the key the key point with the
transformer. And then and then as you

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guys know, there was this really weird
like four-year period where it's like

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the the transformer existed
and then it was just like >> [snorts]

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>> It's cool. Yeah. Well, [laughter] but
but it was just but but between 2020 but

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between 2017 and 2021, I mean, that
was the era of which like companies like

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Google had internal chatbots, but they
weren't letting anybody use them. Yeah.

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Right. And then, you know, and then
OpenAI developed ChatGPT or GPT-2, and

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then they told everybody this is way
too dangerous to deploy, right? We're you

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know, we can't possibly let normal
people normal people use this thing. And

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then do you guys I'm sure remember AI
Dungeon? Um It's a So, the old for there

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was like a year where like the only way
for a normal person to use GPT-3 was in

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in AI Dungeon. Yeah. And so, you you
we would do this. You'd go in there and

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you'd pretend to play Dungeons and
Dragons. In reality, you're just trying

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to talk to talk to GPT. And so, there
was this, you know, there was this long,

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you know, and the I you know, the big
big companies you know, big companies

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are cautious. And you know, the big
companies were cautious. It It By the

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way, it took OpenAI you know, they they
they talk about this. It took OpenAI

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time to actually adjust, you know,
kind of re- redirect their research path.

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>> I think it was at Rosewood, right?
Uh that the dinner they founded OpenAI was

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right there. Right. But that that
dinner would have taken place in 20

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>> 18 18 The formation
of OpenAI is late as

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2018? I I sorry no I'm I'm I'm I'm wrong.
Probably should be 20 They just

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celebrated their 10-year anniversary
so it it is 2025. Yeah. That's so 2015.

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Yeah, 2015 yeah 2015.
But then Alec Radford did GPT-1 in

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>> What? Probably 17 18 yeah.
>> 17 18 so it yeah for the and then and

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then they didn't really and then GPT-3

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was what? 20 20 20 2020 Because that
became co-pilot immediately yeah. Even

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OpenAI which has been you know the leader
of the of this thing in the last

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decade you know even they had to adapt
and and and lean into the new thing and

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so yeah I I think it's just this process
of basically sort of wave after wave

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layer after layer you know building
on itself and then you kind of get these

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catalytic moments where where the whole
thing pops. And obviously that's what's

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happening now. Is it useful
to think about will there

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be an AI winter because there's always
these patterns? Like is it endless

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summer? It's something I constantly
think about because do I get do I just

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like just get endlessly hyped and just
trust that I will only be early and

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never wrong? Or >> [laughter]

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>> Or are we will there be a winter?

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So there's something about say
the following there's something about AI

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that has led to this repeated pattern.

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And and and you you guys know this It's
summer winter summer winter summer

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winter summer winter
and it goes back 80 years.

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80 years. So the original neural network
paper was 1943 right which is which is

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amazing that it was it was far back
that long. And then there was you have you

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guys ever talked about this on your show
but there was this there was a big there

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was an AGI conference at Dartmouth

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University in 1955. 55 Yeah. And they

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got an NSF grant to for the all the AI
experts at the time to spend the summer

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together and they figured if they had
10 weeks together they could get AGI

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>> [laughter] >> out the other end.
And they got there by

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the way they got the grant they got the 10
weeks and then you know 1955 you know

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no no AGI. And like I said
I lived through the 80s

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version of this where there was a big
a big boom and a crash. And so so there is

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this thing there there is something
about AI that causes the people in the

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field I would say to become both
excessively utopian and excessively

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apocalyptic Um and it's
we're probably on both sides

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of like the the the boom bust cycle
you you kind of see that play out.

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Having said that, I think what's
actually happened is like just at in you

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know in we now know in retrospect like
an enormous amount of technical progress

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that built up over time. And like
for for example, we now know that neural

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network is the correct architecture.
And I I will tell you like there was a

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60-year run where that was like a you
know or even 70 years where that was

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controversial. And and we now know
that that's the case. And so we we now you

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know everything we're building on today
is sort of derived from the original

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idea 1943. And so so in retrospect we
now know that like these these guys were

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right they you know they would get
the timing wrong and they thought you know

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capabilities would arrive faster or they
were it could be turned into businesses

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sooner or whatever. But like they were
fundamentally the scientists who worked

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on this over the course of decades were
fundamentally correct about what they

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were doing. >> [snorts] >>
And and and the payoff from from from

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all their work is happening now.

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And so so the way I think about what's
happening is basically I think I think

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about basically the the the period we're
in right now is it's I call it 80-year

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overnight success. Right. Which is like
it's an overnight success cuz it's like

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bam you know chat GPT hits and then
and then 01 hits and then you know Claude

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hits and like you know these are open
these are these are like overnight like

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radical overnight transformative
successes but they're drawing on an

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80-year sort of wellspring backlog you
know of of of of ideas and thinking.

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It's not just that it's all brand new
it's that it's an unlock of all of these

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decades of like very serious hardcore
research um and thinking. Look, there

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were AI researchers who
spent their entire lives.

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They got their PhD. >> [laughter] >>
They worked for they researched for 40

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years. They retired. In a lot of cases
they passed away and they never actually

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saw it work. Yeah. So sad. It is it is
sad. It is sad. Jeff Hinton was like the

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last guy. Yeah [laughter] yeah well
there were guys out there Alan Newell I

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mean there was tons of John McCarthy
John McCarthy was like one of the

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inventors of the field. He's one
of the guys who organized the Dartmouth

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conference and you know he taught
at Stanford for 40 years and passed you

00:09:21.200 --> 00:09:24.117
know passed away I don't know
whatever 10 10 years ago or something.

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Never never actually got got to see
it happen. But like it is amazing in

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retrospect like these guys were
incredibly smart and they worked really

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hard and they were correct. So anyway so
then it's like okay you know as they say

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history doesn't repeat but it rhymes.

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It's like okay does that mean that
there's going to be another like you

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know basically boom bust cycle
and I I will tell you like look like

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in a sense I guess everything goes
through cycles and you know people get

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overly enthusiastic and overly depressed
and there's there's a time there's a

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timelessness to that.
Having said that there's just no question.

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So The four most the four
most dangerous words in the

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>> is different. This time is different.
Do you know the 12 most dangerous words in

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investing? >> No.
The four most four most dangerous

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words in investing [laughter]
are this time is different.

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So the 12 most dangerous words. And so
like I'll tell you what's different like

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Now it's working. Like like there's just
no I mean look there's just no question.

00:10:09.520 --> 00:10:12.917
And by the way I'll just give you
guys my take like LLMs like from from

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basically the chat GPT moment through to

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spring of 25 I think you could still
I think well intentioned well informed

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skeptics could still say oh this is just
pattern completion and oh these things

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don't really understand what they're
doing and you know the hallucination

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rates are way too high and you know
this is going to be great for creative

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writing and creating you know
Shakespearean sonnets and you know as as

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rap lyrics or whatever like it's going
to be great at all that stuff but we're

00:10:36.720 --> 00:10:39.597
not going to be able to harness this to
make this relevant in you know coding or

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in medicine or in law or in you know
kind of fields that you know kind of

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really really matter.
And I think basically it was the reasoning

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breakthrough. It was 01 and then R1
that basically answered that question

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basically said oh no we're going
to be able to actually turn this into

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something that's going to work
in the real world and then and then it

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obviously the coding breakthrough over
the basically the coding breakthrough

00:10:55.640 --> 00:10:57.837
that kind of catalyzed over the holiday
break was kind of the third step in

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that. Where it's like [snorts] all
right if if you know if Linus Torvalds is

00:11:00.840 --> 00:11:04.197
saying that AI coding is now
better than he is like >> [laughter]

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>> Like that that's that's
never happened before.

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>> That's the benchmark yeah. That's never
happened before and so now we know that

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it's it's going to sweep through coding
and and then and then we we know you

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know we know that if it's going
to work in coding it's going to work in

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everything else right? It's just that
that cuz that's that's like that's like

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that's like the hardest in many ways
that's the hardest example and now

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everything else is going to be
a derivative of that. And then on top of

00:11:22.480 --> 00:11:24.877
that we just got the agent breakthrough
but you know with open claw which is

00:11:24.960 --> 00:11:27.837
fantastic which is amazing
and incredibly powerful. And then we just

00:11:27.920 --> 00:11:31.157
got the the the auto research you know
know, the self improvement you know

00:11:31.240 --> 00:11:33.557
we're now into the self improvement
breakthrough. And so the so the way I

00:11:33.640 --> 00:11:36.317
think about it is we've had
four fundamental breakthroughs in

00:11:36.400 --> 00:11:39.557
functionality. LLMs reasoning, agents um

00:11:39.640 --> 00:11:44.277
and then and and then now RSI. Um and
they're all actually working. Um and so

00:11:44.360 --> 00:11:47.357
I'm I'm just as you can I'm
jumping out of my shoes. [laughter]

00:11:47.440 --> 00:11:50.677
Like like this is like this is it.
Like this is the culmination of 80 years

00:11:50.760 --> 00:11:53.637
worth of worth of work and this is the
time it's becoming real. Yeah. Yeah, I

00:11:53.720 --> 00:11:57.037
I'm completely convinced. I think the
anxiety that people feel is like during

00:11:57.120 --> 00:12:00.557
the transistor era you had Moore's law
and it's like, all right, we understand

00:12:00.640 --> 00:12:03.717
why these things are getting better.
We understand the physics of it. With AI

00:12:03.800 --> 00:12:08.117
it's it's so jagged and like the jumps
where like like you said it's like in 3

00:12:08.200 --> 00:12:11.917
months you have like this huge jump like
and people are like, well, this can't

00:12:12.000 --> 00:12:13.837
keep happening, right?
But then it keeps happening.

00:12:13.920 --> 00:12:17.837
>> It'll keep happening. And so like how
do you think about also timelines of like

00:12:17.920 --> 00:12:20.877
what's worth building? I think we always
have this question with guests which is

00:12:20.960 --> 00:12:24.437
like, you know, should you spend time
building harness for a model versus like

00:12:24.520 --> 00:12:27.837
the next model just going to do
it one shot in the latest space.

00:12:27.920 --> 00:12:31.117
And how does that inform like how you
think about the shape of the technology?

00:12:31.200 --> 00:12:34.237
You know, you talk about how it's
a new computing platform. If you have a

00:12:34.320 --> 00:12:37.477
computing platform then like every 6
months it like drastically changes in

00:12:37.560 --> 00:12:40.717
what it looks like. It's hard to build
companies on top of it. Yeah, so so a

00:12:40.800 --> 00:12:43.757
couple things. So one is like, look,
the the Moore's law was what we now call a

00:12:43.840 --> 00:12:47.277
scaling law. Like Moore's law was a
scaling law and for your younger viewers

00:12:47.360 --> 00:12:50.277
Moore's law was every chip chip chips
either get twice as powerful or twice as

00:12:50.360 --> 00:12:53.117
cheap every every 18 months.
And that and that and that you know,

00:12:53.200 --> 00:12:55.597
that it's gotten more complicated in the
last few years, but like that that was

00:12:55.680 --> 00:12:59.477
like the 50-year trajectory of of of the
computer industry. And then and then by

00:12:59.560 --> 00:13:02.957
the way, that's what took the mainframe
computer from a $25 current dollar thing

00:13:03.040 --> 00:13:05.317
into you know, the phone in your pocket
being you know, a million times more

00:13:05.400 --> 00:13:08.037
powerful than that. Like that,
you know, for for 500 bucks.

00:13:08.120 --> 00:13:10.797
And so that that was a scaling law.
And then and then and then key to any

00:13:10.880 --> 00:13:13.997
scaling law including Moore's law
and the AI scaling laws is, you know,

00:13:14.080 --> 00:13:17.317
they're not really laws, right? They're
they're they're they're predictions. But

00:13:17.400 --> 00:13:20.157
when they work they become self-fulfilling
predictions because they they

00:13:20.240 --> 00:13:23.077
they set a benchmark and and then
the entire industry, right? All the smart

00:13:23.160 --> 00:13:26.157
people in the industry kind of work to
make sure that that that actually happens.

00:13:26.240 --> 00:13:28.677
And so they they kind of motivate
the breakthroughs that are required to to

00:13:28.760 --> 00:13:31.637
keep that going. And in and in and
in chips that was a 50-year that was a

00:13:31.720 --> 00:13:34.477
50-year run, right? And it it
was amazing and it's still happening in in

00:13:34.560 --> 00:13:37.797
some areas of of chips. I think the same
thing is happening with the the core

00:13:37.880 --> 00:13:41.557
scaling laws the core scaling laws
in in in AI. You know, they're they're not

00:13:41.640 --> 00:13:44.717
really laws, but like they they are
basically they're predictions and then

00:13:44.800 --> 00:13:48.157
they're motivating catalysts for the
research work that is required to be and

00:13:48.240 --> 00:13:50.757
and and by the way also
the investment dollars

00:13:50.840 --> 00:13:53.517
you know, required to basically
keep you know, keep the curves going.

00:13:53.600 --> 00:13:55.677
And and look it's going to be
complicated and it's going to be

00:13:55.760 --> 00:13:57.917
variable and there you know, there going
to be walls that are going to look like

00:13:58.000 --> 00:13:59.837
they're fast approaching and then
they're going to be you know, engineers

00:13:59.920 --> 00:14:01.437
are going to get to work and they're
going to figure out a way to punch

00:14:01.520 --> 00:14:04.837
through the walls. And obviously that's
you know, that's been happening a lot.

00:14:04.920 --> 00:14:06.917
You know, and then look there's going
to be times when it looks like the walls

00:14:07.000 --> 00:14:09.197
have you know, the the the laws have
petered out and then they're going to

00:14:09.280 --> 00:14:12.277
they're going to pick up again and surge
and then and then and then it it appears

00:14:12.360 --> 00:14:15.677
what's happening to the eyes there's now
multiple you know, multiple scaling laws.

00:14:15.760 --> 00:14:18.597
Um there's multiple areas of improvement
and and I think you know, I don't know

00:14:18.680 --> 00:14:20.637
how many more there already have to be
discovered, but there are probably some

00:14:20.720 --> 00:14:23.437
more that we don't know about yet. You
know, like for example, there's probably

00:14:23.520 --> 00:14:27.437
some scaling law around um world
models and that we don't fully kind of

00:14:27.520 --> 00:14:30.277
acquisition of data at scale in the real
world that we don't fully understand

00:14:30.360 --> 00:14:32.997
yet. So that that that one will
probably kick in at some point here.

00:14:33.080 --> 00:14:35.717
There's a bunch of really
smart people working on that. Um

00:14:35.800 --> 00:14:38.757
And so yeah, I I think the expectation
is that you know, the the scaling laws

00:14:38.840 --> 00:14:41.437
generally are going to continue.
Yeah, the the pace of improvement will

00:14:41.520 --> 00:14:45.437
continue to move really fast.
Um To your question on like what to build.

00:14:45.520 --> 00:14:48.197
So I'm a complete believer the scaling
laws are going to continue. I'm a

00:14:48.280 --> 00:14:51.077
complete believer the capabilities
are going to keep getting amazing

00:14:51.160 --> 00:14:53.717
you know, leaps and bounds.
The part where I kind of part ways a

00:14:53.800 --> 00:14:56.397
little bit with I would what I
would describe as the AI purist

00:14:56.480 --> 00:14:59.517
you know, which is which I would
characterize as like the people who are

00:14:59.600 --> 00:15:01.757
many ways the smartest people in the
field, but also the people who spend

00:15:01.840 --> 00:15:03.797
their entire life like in a lab.

00:15:03.880 --> 00:15:06.237
Um [snorts] and have to have I would
say have very little experience in the

00:15:06.320 --> 00:15:09.357
outside world. Um the nuance
I would offer is the outside

00:15:09.440 --> 00:15:13.077
world of 8 billion people
and institutions and governments and

00:15:13.160 --> 00:15:15.317
companies and economic systems and

00:15:15.400 --> 00:15:17.957
social systems is really complicated. Um

00:15:18.040 --> 00:15:22.597
and and doesn't you know, it it
8 billion people making collective

00:15:22.680 --> 00:15:27.077
decisions on planet Earth is not
a simple process of like just like

00:15:27.160 --> 00:15:29.357
You see this happening now it's like
a bunch of the AI CEOs have this thing

00:15:29.440 --> 00:15:31.597
which is just like they're well there's
just this they just all have this kind

00:15:31.680 --> 00:15:33.877
of thing when they talk in public where
they're just like well there's this this

00:15:33.960 --> 00:15:36.637
obvious set of things that society needs
to do. Mhm. [snorts] And then they're

00:15:36.720 --> 00:15:38.997
like society's not doing
any of those things.

00:15:39.080 --> 00:15:41.797
Right? And it's like how can society not
you know what whatever their theory is

00:15:41.880 --> 00:15:45.197
how can society not see XYZ? And the
answer is well society is number one

00:15:45.280 --> 00:15:48.277
there's no single society it's like 8
billion people. And they like all have a

00:15:48.360 --> 00:15:50.477
voice and they all have a vote like
at the end of the day of how they they

00:15:50.560 --> 00:15:53.997
react to change and then you know it
just like it's just human reality is

00:15:54.080 --> 00:15:55.917
just really complicated and messy.

00:15:56.000 --> 00:16:00.557
Um and and and so the specific answer to
your question is like as usual it depends.

00:16:00.640 --> 00:16:03.997
Um you know it it depends. Look there's
no question people are going to like

00:16:04.080 --> 00:16:06.277
there's no question there're going to be
companies it's already happening. There

00:16:06.360 --> 00:16:08.397
are companies that think that they're
building value on top of the models and

00:16:08.480 --> 00:16:10.317
then they're just going to get blitzed
by the by the next model. There's no

00:16:10.400 --> 00:16:12.917
question that's happening. But I think
there's no question also that just the

00:16:13.000 --> 00:16:17.357
process of adaptation of any technology
into the real in into the real messy

00:16:17.440 --> 00:16:21.237
world of humanity is is just going to be
messy and complicated. It's it's not

00:16:21.320 --> 00:16:23.677
going to be simple and straightforward
it's going to be messy and complicated.

00:16:23.760 --> 00:16:27.197
And there are going to be a lot
of companies and a lot of products um and

00:16:27.280 --> 00:16:30.477
in in in fact entire industries that are
going to get built that to basically

00:16:30.560 --> 00:16:33.567
actually help all of this technology
actually reach real people.

00:16:33.650 --> 00:16:35.037
>> [snorts] >> The amount
of capital going into these

00:16:35.120 --> 00:16:38.717
companies I mean Dario talked about it
on the Door Cash podcast and Door Cash

00:16:38.800 --> 00:16:41.477
was like why don't you just buy 10x more
GPUs and he's like because I'm going to

00:16:41.560 --> 00:16:46.357
go bankrupt if the model doesn't exactly
hit the the performance level. How do

00:16:46.440 --> 00:16:49.477
you think about that also as a risk
on you know you guys are investors at

00:16:49.560 --> 00:16:51.957
Inflection and Inflection AI
and Thinking Machines and World Apps it

00:16:52.040 --> 00:16:55.157
seems like we're
leveraging this gaming loss

00:16:55.240 --> 00:16:59.197
at a pretty high rate. Like how
comfortable I guess do you feel with the

00:16:59.280 --> 00:17:01.237
downside scenario? Like and say like

00:17:01.320 --> 00:17:06.437
things peter out you think you can kind
of like restructure these build outs and

00:17:06.520 --> 00:17:08.997
you know capital investments? Yeah
so I should start by saying so I lived

00:17:09.080 --> 00:17:12.157
through the dot com crash and I
can tell you stories for hours

00:17:12.240 --> 00:17:15.917
about the dot com crash and it was
horrible. No it was awful. It was it was

00:17:16.000 --> 00:17:19.557
it was apocalyptic. By the way the a lot
of the dot-com crash was actually at the

00:17:19.640 --> 00:17:22.397
time it was actually a telecom crash.
It was a bandwidth crash. Like the the

00:17:22.480 --> 00:17:24.597
thing that actually crashed that wiped
out all the money with the tele- the

00:17:24.680 --> 00:17:26.677
telecom companies. Global Crossing.

00:17:26.760 --> 00:17:31.517
Global Global Yes. I'm from Singapore
and they laid so much cable over over

00:17:31.600 --> 00:17:34.997
our oceans. Actually, there was a
scaling law in the dot-com era and it

00:17:35.080 --> 00:17:38.557
was literally the the US Commerce
Department put out a report in 1996 and

00:17:38.640 --> 00:17:41.317
they said internet traffic
was doubling every quarter.

00:17:41.400 --> 00:17:44.677
Uh and and actually in 1995 and 1996
internet traffic actually did double

00:17:44.760 --> 00:17:46.997
every quarter. And so that became
the scaling law. And so what all these

00:17:47.080 --> 00:17:49.557
telecom entrepreneurs did was they went
out and they raised money to build fiber

00:17:49.640 --> 00:17:52.997
anticipating that the demand for bandwidth
was going to keep doubling every quarter.

00:17:53.080 --> 00:17:55.117
Doubling every quarter though is like,
you know, grains of chess on the

00:17:55.200 --> 00:17:58.557
chessboard. Like at some point
the numbers become extremely large, right?

00:17:58.640 --> 00:18:01.437
And and and it really and really what
happened was the internet the internet

00:18:01.520 --> 00:18:04.277
by the way continuously kept growing
basically since inception and it's, you

00:18:04.360 --> 00:18:07.517
know, it's it's continuously grown. It's
never shrunk. And it's grown really fast

00:18:07.600 --> 00:18:10.197
compared to anything else, you know,
in in in human history. But it wasn't

00:18:10.280 --> 00:18:12.517
doubling every quarter as of 1998-1999.

00:18:12.600 --> 00:18:15.077
And so there was this gap in the
expectation of what they thought was the

00:18:15.160 --> 00:18:18.477
scaling law versus reality. And that's
actually what caused the dot-com crash,

00:18:18.560 --> 00:18:21.797
which was they they wait over companies
like Global Crossing wait overbuilt

00:18:21.880 --> 00:18:26.197
fiber. Which is sort of the By the way,
fiber, telecom equipment, you know, so

00:18:26.280 --> 00:18:28.997
all that all the networking gear, you
know, and then and then by the way, the

00:18:29.080 --> 00:18:31.557
actual physical data centers. Like that
was the beginning of the of the of the

00:18:31.640 --> 00:18:34.997
data center build and then and then data
center overbuilt. And so you had the but

00:18:35.080 --> 00:18:38.717
it was it was literally I think it
was like $2 trillion got wiped out, right?

00:18:38.800 --> 00:18:42.077
It was like >> Jesus It was like
a big It was And by the way,

00:18:42.160 --> 00:18:46.277
the other the other subtlety in it was
the internet companies themselves never

00:18:46.360 --> 00:18:48.917
really had any debt cuz tech tech
companies generally don't run on debt,

00:18:49.000 --> 00:18:50.397
but the telecom companies run on debt.

00:18:50.480 --> 00:18:52.997
Physical infrastructure companies run
on debt. And so the companies like Global

00:18:53.080 --> 00:18:55.757
Crossing not just raised a lot of equity,
they also raised a lot of debt.

00:18:55.840 --> 00:18:57.957
So they're highly levered. And so then
you just do the thing of just like,

00:18:58.040 --> 00:19:00.277
okay, you have a highly levered thing
where you're you're just over over

00:19:00.360 --> 00:19:03.877
you're overbuilding capacity. Demand
is growing but not as fast as you hoped.

00:19:03.960 --> 00:19:06.837
And then boom, bankrupt, right? And then
and then it's and then it's like they

00:19:06.920 --> 00:19:09.557
say about the hotel industry, which
is it's always the third owner of a hotel

00:19:09.640 --> 00:19:12.557
that makes money. Right?
It has to go bankrupt twice.

00:19:12.640 --> 00:19:15.757
Right? You have to wash out all of the
over-optimistic exuberance before it

00:19:15.840 --> 00:19:17.967
gets to actually a stable
state and then it makes money.

00:19:18.050 --> 00:19:19.357
>> [gasps] >> So,
by the way, all of those data

00:19:19.440 --> 00:19:22.557
centers and all of those all the fiber
that they're in use it's all in use

00:19:22.640 --> 00:19:26.877
today. But 25 years later. >> [snorts] >>
But it took it actually the elapsed time

00:19:26.960 --> 00:19:30.597
was it took 15 years. It took 15 years
from 2000 to 2015 to actually fill the

00:19:30.680 --> 00:19:34.957
fill up all the capacity. The cautionary
warning is the the overbuild can happen.

00:19:35.040 --> 00:19:37.517
Um and and and and you know you you
get into this thing where basically

00:19:37.600 --> 00:19:40.397
everybody everybody who basically has
any sort of institutional capital is

00:19:40.480 --> 00:19:43.357
like wow it's just I I don't know how
to invest in these crazy software things.

00:19:43.440 --> 00:19:46.557
But for sure I can put build data
centers and for sure I can buy GPUs and

00:19:46.640 --> 00:19:50.597
I can deploy you know the compute
grids and and all these things.

00:19:50.680 --> 00:19:53.477
Um and so you know you if you're
a pessimist you could look at this and you

00:19:53.560 --> 00:19:56.877
could say wow this is like really set
up to be able to basically replicate you

00:19:56.960 --> 00:19:59.277
know what we went through we went
through in 2000. Obviously that would be

00:19:59.360 --> 00:20:02.437
bad. The counterargument which
is the one I I agree with which is the

00:20:02.520 --> 00:20:05.877
counterargument on the other side is a
couple things. One is the companies that

00:20:05.960 --> 00:20:08.437
are investing all the the companies
that are investing the money are like the

00:20:08.520 --> 00:20:12.077
bluest chip of companies. So that back
back in the in the like Global Crossing

00:20:12.160 --> 00:20:15.317
was like an it was like an entrepreneur
it's like a a new venture. But like the

00:20:15.400 --> 00:20:19.157
money that's being deployed now at scale
is Microsoft and you know and Amazon and

00:20:19.240 --> 00:20:23.917
Google right and Facebook and Nvidia
and you know these these these and and now

00:20:24.000 --> 00:20:25.997
you know by the way Open AI and Anthropic
which are now at like you know

00:20:26.080 --> 00:20:28.677
really serious size um you
know as companies with you know

00:20:28.760 --> 00:20:32.237
very serious revenue. These are very
large scale companies with like lots

00:20:32.320 --> 00:20:36.517
lots of cash lots of debt capacity that
they've they've never used. And so this

00:20:36.600 --> 00:20:39.877
is institutional in a way that that
really wasn't at the time. And then the

00:20:39.960 --> 00:20:43.397
other is at least for now every dollar
that's being put into anything that

00:20:43.480 --> 00:20:47.477
results in a running GPU is being turned
into revenue right away. Like so and you

00:20:47.560 --> 00:20:50.357
guys know this like everybody's starved
for capacity. Everybody's starved for

00:20:50.440 --> 00:20:53.317
compute capacity and then you know all
the associated things memory and and and

00:20:53.400 --> 00:20:57.157
interconnect and everything else.
Um data center space. And so every dollar

00:20:57.240 --> 00:20:59.997
right now that's being put in the ground
is turning into revenue. And and it and

00:21:00.080 --> 00:21:02.597
in fact I actually think there's
an interesting thing happening which is

00:21:02.680 --> 00:21:06.397
because everybody's starved for capacity
the models that we actually have that we

00:21:06.480 --> 00:21:09.317
can use today are inferior versions of
what we would have if not for the supply

00:21:09.400 --> 00:21:11.877
constraints. That's true.
If if right, suppose a

00:21:11.960 --> 00:21:14.837
hypothetical universe in which GPUs
were 10 times cheaper and 10 times more

00:21:14.920 --> 00:21:17.917
plentiful, the models would be much
better cuz you would just allocate a lot

00:21:18.000 --> 00:21:19.957
more money to training and you'd just
build better models and they would be

00:21:20.040 --> 00:21:23.477
better. And so we're we're actually
getting the sandbag version of the

00:21:23.560 --> 00:21:26.117
technology. >> Yeah,
no everything we use is quantized

00:21:26.200 --> 00:21:30.277
because the the labs have to keep
the the full versions. Right.

00:21:30.360 --> 00:21:32.037
>> [laughter] >> We're not
even getting the good stuff.

00:21:32.120 --> 00:21:36.477
Yeah. But but getting the good stuff
is is just even if technical progress

00:21:36.560 --> 00:21:40.157
stops, once there's like a much bigger
build of like GPU manufacturing capacity

00:21:40.240 --> 00:21:42.197
and memory, you [clears throat] know,
all all the things that have to happen

00:21:42.280 --> 00:21:45.077
in the course of the next five or 10
years. Once it happens, even the current

00:21:45.160 --> 00:21:47.957
technology is going to get going to get
much better. And then as you know, like

00:21:48.040 --> 00:21:50.597
there's just like a million ways to use
this stuff. Like there's just like a

00:21:50.680 --> 00:21:53.277
million use cases for this. Like it
it you know, this isn't just sending

00:21:53.360 --> 00:21:55.997
packets across a a thing, whatever, and
hoping that people find something to do

00:21:56.080 --> 00:21:58.717
with it. This is just like, oh, we apply
intelligence in every domain of human

00:21:58.800 --> 00:22:02.917
activity and then it works like
incredibly well. Yeah. Um here's what I

00:22:03.000 --> 00:22:06.877
know. Here's what I know. Um in the next
three or four years, it's like somewhere

00:22:06.960 --> 00:22:08.957
between three or four years out,
basically everything is selling out. So

00:22:09.040 --> 00:22:12.397
like the the entire supply chain is is
is is sold out or or selling out. And so

00:22:12.480 --> 00:22:15.757
there there's no like it I we're
just going to have like chronic

00:22:15.840 --> 00:22:19.077
supply shortage for you know, for years
to come. Um there's going to be a

00:22:19.160 --> 00:22:22.117
response from the market that's going
to result in an enormous, you know, it's

00:22:22.200 --> 00:22:25.277
happening now, an enormous flood of
investment in a new fab capacity and you

00:22:25.360 --> 00:22:28.277
know, every everything else to be able
to do that. At some point the supply

00:22:28.360 --> 00:22:31.397
chain constraints will unlock, you know,
at least to some degree. That will be

00:22:31.480 --> 00:22:34.437
another accelerant to industry growth
when that happens cuz the products will

00:22:34.520 --> 00:22:36.437
get better and everything
will get cheaper.

00:22:36.520 --> 00:22:38.717
Um and so so I know that's going
to happen. I know that, you know, the

00:22:38.800 --> 00:22:42.197
deployments, you know, the the actual
use cases are like really compelling and

00:22:42.280 --> 00:22:44.517
then like I said, you know, with
reasoning and agents and so forth, like

00:22:44.600 --> 00:22:47.837
I know they're just going to get like
much much better from here. And so I I I

00:22:47.920 --> 00:22:51.037
know the capabilities are like really
real and serious. I also know that the

00:22:51.120 --> 00:22:52.637
technical progress is not going to stop.

00:22:52.720 --> 00:22:56.237
It it it is it it is accelerating. Like
the breakthroughs are are tremendous. I

00:22:56.320 --> 00:22:59.037
mean, even just month over month,
the breakthroughs are really dramatic.

00:22:59.120 --> 00:23:02.837
And so, you You I think if you were
a cynic and there there are cynics, you

00:23:02.920 --> 00:23:05.877
can look at 2000, you can find echoes,
but I can't even imagine betting on that

00:23:05.960 --> 00:23:09.197
this is going to like somehow disappoint
and you know, at least for years to come.

00:23:09.280 --> 00:23:11.517
I think it would be essentially
suicidal to make that bet.

00:23:11.600 --> 00:23:15.087
Um is that Michael Burry?
Uh oh That's an interesting guy, huh?

00:23:15.170 --> 00:23:16.157
>> [laughter] >> We'll pick
on a guy we'll pick let's

00:23:16.240 --> 00:23:19.317
pick on one guy. We'll pick cuz he did
he came out with it was it was the He

00:23:19.400 --> 00:23:21.757
doesn't mind. It was the Nvidia short,
right? He came out with the Nvidia

00:23:21.840 --> 00:23:24.597
short. And then did if you guys probably
talked about this which is the the

00:23:24.680 --> 00:23:28.477
analysis now that the current models
are getting better faster at such a rate

00:23:28.560 --> 00:23:31.717
that if you are running an Nvidia If
you're running an Nvidia inference chip

00:23:31.800 --> 00:23:35.077
today that's 3 years old, you're making
more money on it today than you did 3

00:23:35.160 --> 00:23:38.437
years ago. Because the pace of improvement
of the software is is faster

00:23:38.520 --> 00:23:41.157
than the the depreciation cycle
of the chip. And then my understanding is

00:23:41.240 --> 00:23:44.197
Google is running I don't know if they I
don't know exactly what I this is rumors

00:23:44.280 --> 00:23:48.037
that I've heard or maybe it's public,
but I think Google's running very old TPUs

00:23:48.120 --> 00:23:51.797
Very profitably. And very profitably
and very profitably. Um and so so it

00:23:51.880 --> 00:23:54.277
actually turns out as far as I can tell
that it's actually the opposite of the

00:23:54.360 --> 00:23:58.037
Burry thesis is actually he was actually
180° wrong. It's actually the

00:23:58.120 --> 00:24:00.357
the old Nvidia chips are
getting more valuable.

00:24:00.440 --> 00:24:03.157
Which is something that's like literally
never happened [laughter] before. Like

00:24:03.240 --> 00:24:06.517
it's never been the case that you have
an older model chip that becomes more

00:24:06.600 --> 00:24:10.477
valuable not less valuable. And then and
again that's an expression of that just

00:24:10.560 --> 00:24:14.477
ferocious pace of software progress,
ferocious pace of capability payoff that

00:24:14.560 --> 00:24:17.637
you're getting on the other side of this.
And so I just the idea of betting

00:24:17.720 --> 00:24:21.897
against that like Yeah, yeah. It's like
an invitation to get your face ripped off.

00:24:21.980 --> 00:24:23.877
>> [laughter] >> What
my really hits was like modeling

00:24:23.960 --> 00:24:28.357
the lifespan of the H100 and H200s
and and going like you know, usually they

00:24:28.440 --> 00:24:31.837
advise like 4 to 7 years and it was you
know, maybe you sort of realistically

00:24:31.920 --> 00:24:36.957
cut it down to 2 to 3, but actually it's
going up and not down. And and that's I

00:24:37.040 --> 00:24:40.317
mean that's I think that's the dream.
We are finding utilization and I think

00:24:40.400 --> 00:24:44.117
utilization solves all problems. Like
you can you can find use use cases for

00:24:44.200 --> 00:24:47.797
even like the poor like even memory
we're having a shortage, right? And and

00:24:47.880 --> 00:24:51.277
even like the the shittier versions of
of memory that we do have we are finding

00:24:51.360 --> 00:24:53.277
use cases for it. So like that's great.

00:24:53.360 --> 00:24:57.717
Yeah. How how important is open source AI
and kind of like edge inference in a world

00:24:57.800 --> 00:25:01.037
in which you have three years of supply
crunch. Like you think in the like you

00:25:01.120 --> 00:25:03.677
know, if you fast forward like five
years, like how do you think about

00:25:03.760 --> 00:25:06.957
inference in the data
center versus at the edge?

00:25:07.040 --> 00:25:09.077
Well, so just to start, yeah. So,
I think I think open source is very

00:25:09.160 --> 00:25:11.277
important for a bunch of reasons.
I think edge edge inference is very

00:25:11.360 --> 00:25:13.837
important for a bunch of reasons.
I I think just practically speaking, if

00:25:13.920 --> 00:25:16.917
we're just going to have fundamental
construct supply crunches for the next I

00:25:17.000 --> 00:25:19.597
mean, you guys know that if you just
project forward demand over the next

00:25:19.680 --> 00:25:23.077
three years relative to supply,
one of the dismaying

00:25:23.160 --> 00:25:25.517
predictions you can do is what's going
to what's going to happen to the cost of

00:25:25.600 --> 00:25:28.197
of inference in the core over the next
three years. And like it may rise

00:25:28.280 --> 00:25:31.797
dramatically, right? Like So, so what
and then is is you know, like the the

00:25:31.880 --> 00:25:34.357
big model competition is subsidizing
heavily right now, right? And so, so

00:25:34.440 --> 00:25:38.077
what's the what will be the average
person's you know, per day per month

00:25:38.160 --> 00:25:41.437
token cost, you know, three years from
now to do all the things that they want

00:25:41.520 --> 00:25:44.197
to do? And I I don't know what's going
to be like I mean, I have you guys

00:25:44.280 --> 00:25:46.597
probably have friends. I have friends
today who are paying a thousand dollars

00:25:46.680 --> 00:25:50.477
a day for open cloud for cloud tokens
to run open cloud, right? And so, okay,

00:25:50.560 --> 00:25:53.557
$30,000 a month, right? And and by the
way, those friends have like a thousand

00:25:53.640 --> 00:25:56.597
more ideas of the things that they want
to claw to do, right? And so, you could

00:25:56.680 --> 00:25:59.757
imagine there's like latent demand of up
to I don't know, five or ten thousand

00:25:59.840 --> 00:26:04.117
dollars a day of of of tokens for a fully
deployed, you know, personal agent.

00:26:04.200 --> 00:26:07.437
And obviously, consumers can't pay that,
right? And so, so but it gives you a

00:26:07.520 --> 00:26:10.597
sense of the of the few of the future
scope of demand, right? And so, so even

00:26:10.680 --> 00:26:13.317
even if there's a ten x improvement
in price performance, that's still, you

00:26:13.400 --> 00:26:15.477
know, goes to a hundred dollars a day,
which is still way beyond what people

00:26:15.560 --> 00:26:19.277
can pay. So, there's just going to be like
ferocious demand. By the way, the agent

00:26:19.360 --> 00:26:22.237
thing the other interesting thing is I
think the agent thing so, up until now a

00:26:22.320 --> 00:26:24.917
lot of the constraints have been GPU
constraints. I think the agent thing now

00:26:25.000 --> 00:26:28.837
also translates into CPU constraints,
CPU and memory, yes. CPU and memory,

00:26:28.920 --> 00:26:31.477
right? And so, like the entire
chip ecosystem is just going to get

00:26:31.560 --> 00:26:33.997
>> With with our network constraints,
that will be the killer. It's all

00:26:34.080 --> 00:26:37.157
bottlenecked potentially for years.
And so, so I I think that Brad and I think

00:26:37.240 --> 00:26:39.517
it's actually possible, I mean,
generally inference costs are going to

00:26:39.600 --> 00:26:42.037
keep coming down, but I think the let's
put it this way, the rate of decline I

00:26:42.120 --> 00:26:45.197
think may level out here for a bit
because of these supply constraints. And

00:26:45.280 --> 00:26:48.037
then at some point maybe the labs stop
subsidizing so much and that that that

00:26:48.120 --> 00:26:51.197
again will be a be an issue. And so,
there's just going to be so much more

00:26:51.280 --> 00:26:54.597
demand for inference than than can
be satisfied you know kind of with a

00:26:54.680 --> 00:26:57.397
centralized model and then and then you
know you guys know this but like all the

00:26:57.480 --> 00:26:59.797
just the dramatic I mean just
the dramatic innovations that happened in

00:26:59.880 --> 00:27:03.317
the Apple Silicon to be able to do
inferences it's quite amazing. The level

00:27:03.400 --> 00:27:05.997
of effort being put like the open source
guys are putting incredible effort into

00:27:06.080 --> 00:27:08.277
getting you know this recurring
pattern where the big

00:27:08.360 --> 00:27:11.357
model will never run on a PC and then
six months later up it runs on a PC.

00:27:11.440 --> 00:27:15.197
Right. It's like amazing. And there's
very smart people working on that. So

00:27:15.280 --> 00:27:18.117
there's all that and then look there's
also you know there's also like other

00:27:18.200 --> 00:27:20.917
there's other motivators there's other
motivators which is just like okay how

00:27:21.000 --> 00:27:24.157
much trust are the big centralized
model providers you know

00:27:24.240 --> 00:27:27.237
how much trust are they building in the
market versus you know how much are you

00:27:27.320 --> 00:27:30.317
know at least for in certain cases with
some people for certain use cases people

00:27:30.400 --> 00:27:33.157
being like well I'm not willing to just
like turn everything over. So there

00:27:33.240 --> 00:27:36.837
there there's all the trust issues.
By the way there's also just like straight

00:27:36.920 --> 00:27:40.237
up price optimization. There's many uses
of AI where you don't need Einstein in

00:27:40.320 --> 00:27:43.997
the cloud you just need like
a smart local model. There's also

00:27:44.080 --> 00:27:47.317
performance issues where you want to you
know you want you know you're going to

00:27:47.400 --> 00:27:48.917
want your doorknob
to have an AI model in it.

00:27:49.000 --> 00:27:50.877
>> Right. You know to be
able to you know do you

00:27:50.960 --> 00:27:52.837
know to be able to do access control.

00:27:52.920 --> 00:27:56.157
Obviously like everything with a chip
is going to have an AI model in it and a

00:27:56.240 --> 00:27:57.797
lot of those are going to be local.

00:27:57.880 --> 00:28:00.397
And so yeah no like I think I think
you're going to have and then you're

00:28:00.480 --> 00:28:02.717
going to by the way also wearable
devices you know you you don't want to

00:28:02.800 --> 00:28:05.757
do a complete round trip you want you
know whatever your smart devices are you

00:28:05.840 --> 00:28:07.877
want it to be like super low latency.

00:28:07.960 --> 00:28:11.437
Yeah. The question do we care who makes
it? One of the biggest news this week

00:28:11.520 --> 00:28:13.237
was the collapse of AI2 the Allen

00:28:13.320 --> 00:28:18.517
Institute one of the actual American
open source model labs. Yeah. And

00:28:18.600 --> 00:28:21.757
I'm not that optimistic in the on
American open source. Like you you guys

00:28:21.840 --> 00:28:25.997
invested in Mistral and Mistral's doing
extremely well outside of China. That's

00:28:26.080 --> 00:28:28.397
about it. Yeah we'll see. We'll see.

00:28:28.480 --> 00:28:32.557
Look I number one I do think we care I do
think we I do think we care who makes it.

00:28:32.640 --> 00:28:34.997
I would say this the the previous
presidential administration

00:28:35.080 --> 00:28:38.557
wanted to kill it in the US.
They wanted to drown it in the bathtub.

00:28:38.640 --> 00:28:39.597
And [snorts] so they wanted to kill it.

00:28:39.680 --> 00:28:42.117
So at least we have a government now
that actually like actually wants it

00:28:42.200 --> 00:28:44.237
wants it to happen.
>> And you're on the council? And and I

00:28:44.320 --> 00:28:47.317
yeah and the new and the PCAST yeah.
So the you know this admin a for whatever

00:28:47.400 --> 00:28:49.277
other political issues people have,
which are many, you know, this

00:28:49.360 --> 00:28:51.717
administration has I think a very
enlightened view and in particular an

00:28:51.800 --> 00:28:55.117
enlightened view on AI and in particular
on open source AI. Uh and so, they're

00:28:55.200 --> 00:28:58.877
very supportive. Um my read is the Chi-
the Chinese have a very the various

00:28:58.960 --> 00:29:01.637
Chinese companies have a very specific
reason to do open source, which is they

00:29:01.720 --> 00:29:04.997
they they don't fundamentally they don't
think they can sell commercial uh AI

00:29:05.080 --> 00:29:08.837
outside of China right now and or at least
specifically not not in the US for

00:29:08.920 --> 00:29:11.717
a combination of reasons. And so, they
they kind of view I think open source AI

00:29:11.800 --> 00:29:14.957
is a bit of a loss leader against
basically domestic um you know, paid

00:29:15.040 --> 00:29:18.757
paid services and then kind of you know,
kind of ancillary products. You know,

00:29:18.840 --> 00:29:20.117
they're they're very excited about it.

00:29:20.200 --> 00:29:22.557
By the way, I think it's great. I think
it's great that they're doing it. Um you

00:29:22.640 --> 00:29:25.877
know, I think Deep Seek was like a gift
to the world. Um I think the great thing

00:29:25.960 --> 00:29:29.117
about open source open source the the
the impact of open source is felt two

00:29:29.200 --> 00:29:32.277
ways. One is you you get the software
for free. But the other is you get to

00:29:32.360 --> 00:29:35.277
learn how it works. Right.
And so, like the paper Uh the

00:29:35.360 --> 00:29:39.637
paper the paper and and the code. Right,
and the code. And so, like for example,

00:29:39.720 --> 00:29:42.677
I thought this was amazing. So, OpenAI
comes out with O1 and it's an amazing

00:29:42.760 --> 00:29:45.197
technical breakthrough and it's just
like absolutely fantastic. But of

00:29:45.280 --> 00:29:47.517
course, they don't explain how it works
in detail and then of course, they hide

00:29:47.600 --> 00:29:49.237
the they hide the reasoning traces.

00:29:49.320 --> 00:29:51.917
Right. [snorts] And and then and then
everybody's like, okay, this is great,

00:29:52.000 --> 00:29:53.997
but like who's going to be able
to replicate this? How are other people

00:29:54.080 --> 00:29:56.277
going to be able to do this? You know,
is there secret sauce in there? And then

00:29:56.360 --> 00:29:59.437
R1 comes out and it's just like, there's
the code and there's the paper. And now

00:29:59.520 --> 00:30:02.237
the whole world knows how to do it.
And then, you know, 3 months later every

00:30:02.320 --> 00:30:03.837
other AI model is is adding reasoning.

00:30:03.920 --> 00:30:07.197
And so, so you get this kind of double
like even if the Chinese models

00:30:07.280 --> 00:30:10.037
themselves are not the models that get
used, the education that's taken place

00:30:10.120 --> 00:30:13.157
to the rest of the world the information
diffusion, you know, is incredibly

00:30:13.240 --> 00:30:16.037
powerful. So, that happens. And then I
don't know. We'll we'll see. You know,

00:30:16.120 --> 00:30:19.037
there are a bunch of American,
you know, open source you know, AI model

00:30:19.120 --> 00:30:21.837
companies. I mean, look, there's
going to be tremendous, you know, there

00:30:21.920 --> 00:30:24.437
already is. There's you know, there's
going to be treme- there's tremendous

00:30:24.520 --> 00:30:28.117
competition among the primary
model companies. You know, there's

00:30:28.200 --> 00:30:32.397
depending on how you count, there's like
four or five, you know, big co- model

00:30:32.480 --> 00:30:36.317
companies now that are, you know, kind
of neck and neck in different ways. Um

00:30:36.400 --> 00:30:39.117
you know, and and and um you know, and
then obviously both both both X and then

00:30:39.200 --> 00:30:41.917
Meta where I'm involved you know, both
have huge, you know, huge attempts to,

00:30:42.000 --> 00:30:44.637
you know, kind of to kind of kind
of leapfrog on the way. And then you've

00:30:44.720 --> 00:30:46.877
got, you know, a whole fleet of startups,
new companies, including a

00:30:46.960 --> 00:30:49.077
whole bunch that were backed in that are,
you know, trying to kind of come up

00:30:49.160 --> 00:30:51.677
with different approaches. And then
you've got whatever it is, I don't know.

00:30:51.760 --> 00:30:55.157
How How many How many like main line
foundation model companies are there in

00:30:55.240 --> 00:30:59.477
China at this point? It's probably six >>
tigers, is what they call it. Uh Quen is

00:30:59.560 --> 00:31:02.277
in in questionable because
of there's changing leadership.

00:31:02.360 --> 00:31:04.997
>> Right. Yeah. But that doesn't
include that that includes like Moonshot.

00:31:05.080 --> 00:31:08.157
>> Yes. Kimi, DeepSeek, uh

00:31:08.240 --> 00:31:13.997
ZAI, um Quen, 01 is in there. Right. And
then um and ByteDance and and then you you

00:31:14.080 --> 00:31:16.957
see that ByteDance would be like
the next tier. They weren't as prominent.

00:31:17.040 --> 00:31:19.477
>> They weren't but now they're Yeah,
but they're you know, at least you know,

00:31:19.560 --> 00:31:21.797
SeaDance is very inspiring
and presumably they have more stuff coming

00:31:21.880 --> 00:31:24.957
and Tencent probably has more stuff
coming and and so forth. And so So So

00:31:25.040 --> 00:31:27.477
like look, here here would be a thing
you can anticipate, which is there are

00:31:27.560 --> 00:31:30.397
not these markets There are not going
to be Between the US and China right now,

00:31:30.480 --> 00:31:33.157
there's like a dozen primary foundation
model companies that are like at scale

00:31:33.240 --> 00:31:34.957
at some level of like critical mass.

00:31:35.040 --> 00:31:38.117
It's not going to be a dozen in 3 years,
right? Like it just because these

00:31:38.200 --> 00:31:40.557
industries don't bear a dozen. It's It's
going to be three or you know, there's

00:31:40.640 --> 00:31:43.117
going to be three or four big winners
or maybe one or two big winners. And so

00:31:43.200 --> 00:31:45.197
there's going to be like a whole bunch
of those guys that are going to have to

00:31:45.280 --> 00:31:48.317
figure out alternate strategies. Um and I
think like open source is one of those

00:31:48.400 --> 00:31:51.797
strategies. And so I I think you
could see like a whole I I I think the

00:31:51.880 --> 00:31:54.197
question's like who's going to do open
source? I think that could change really

00:31:54.280 --> 00:31:57.357
fast. I I think that that that's a very
dynamic thing. I think it's very hard to

00:31:57.440 --> 00:32:00.677
predict what happens. And And I think
it's very important. Nvidia's doing a

00:32:00.760 --> 00:32:02.837
lot. You mentioned Well, I was going
to say Well, exactly. And then you got

00:32:02.920 --> 00:32:06.037
Nvidia and then and then, you know, just
again industry for There's an old thing

00:32:06.120 --> 00:32:08.557
in business strategy which is called
a commoditize the commoditize the

00:32:08.640 --> 00:32:11.157
complement. And so right, it's like if
your Jensen is just kind of obvious. Of

00:32:11.240 --> 00:32:13.997
course you want to commoditize
the software. And he's And to his enormous

00:32:14.080 --> 00:32:16.837
credit, he's putting enormous resources
behind that. And so maybe maybe it's

00:32:16.920 --> 00:32:19.357
literally Nvidia.
And I think that would be great.

00:32:19.440 --> 00:32:23.977
Yeah. Uh narrative violation two
European projects. Uh innovating.

00:32:24.060 --> 00:32:27.717
>> [laughter] >> Bam.
I I'm mostly in my uh Europe uh

00:32:27.800 --> 00:32:29.437
conference soon and I got both of them.

00:32:29.520 --> 00:32:33.157
They got us. They got us, Mark. They got
us all. Wait a minute. Where was Peter

00:32:33.240 --> 00:32:35.277
So where was Steinberg
when he did Open Indiana?

00:32:35.360 --> 00:32:36.637
>> Yeah, yeah, yeah.
He was in what? He was in Vienna.

00:32:36.720 --> 00:32:38.797
>> Oh, he was in Vienna.
And then where is he now?

00:32:38.880 --> 00:32:40.357
Uh he's moving to SF. Okay. Okay.

00:32:40.440 --> 00:32:41.797
[laughter] All right. Okay. There we go.

00:32:41.880 --> 00:32:44.397
And then yeah, the Pi guy right? The Pi
guys are European. Yeah, they're they're

00:32:44.480 --> 00:32:46.077
buddies in Austria. Mario's also there.

00:32:46.160 --> 00:32:49.437
Yeah. Right. And are they Yeah, they
haven't announced yet any sort of change

00:32:49.520 --> 00:32:52.717
change or have they? No, they're they
have a company there. Okay, good. Okay,

00:32:52.800 --> 00:32:56.237
good. Good, good. Um Yeah, good.
Anyways, yeah, I think Pi

00:32:56.320 --> 00:32:59.677
and Open Claw are very important
software things and and I just wanted

00:32:59.760 --> 00:33:02.837
you to just go off on what you think.

00:33:02.920 --> 00:33:06.357
Yeah. So, I think in the combination of
the two of them, I think it's one of the

00:33:06.440 --> 00:33:10.357
10 most important software Open Claw got
all the attention, but talk about Pi. Pi

00:33:10.440 --> 00:33:13.317
Pi's kind of the end Yeah, Pi's Pi's
kind of the architectural breakthrough.

00:33:13.400 --> 00:33:16.357
For those of us who are older, there was
this whole thing that was very important

00:33:16.440 --> 00:33:20.237
in the world of software basically from
like 1970 to I don't know. It still is

00:33:20.320 --> 00:33:22.917
very important, but like 19 from 1970
through to like basically the creation

00:33:23.000 --> 00:33:25.557
of Linux, which is basically this this
thing we used to call like the Unix

00:33:25.640 --> 00:33:28.997
mindset. Like so so cuz there were
all these different, you know, theories

00:33:29.080 --> 00:33:31.717
there all these different operating
systems and mainframes and and then, you

00:33:31.800 --> 00:33:34.317
know, all these Windows and Mac and all
these things. And then there was this

00:33:34.400 --> 00:33:37.237
but kind of behind it all was this idea
of kind of the Unix mindset. And the

00:33:37.320 --> 00:33:40.677
Unix mindset was this thing where
basically you don't have these like like

00:33:40.760 --> 00:33:43.837
in the old days like like the operating
system that like made the computer

00:33:43.920 --> 00:33:47.197
actually really work like in the 1960s
was this thing called OS 360, which was

00:33:47.280 --> 00:33:49.717
this big operating system that IBM
developed that was supposed to basically

00:33:49.800 --> 00:33:52.757
run everything. And it was this like
giant monolithic architecture in the

00:33:52.840 --> 00:33:56.317
sky. It was like a you know, it was like
a giant castle um of software. And and

00:33:56.400 --> 00:33:58.637
by the way, it worked really well
and they were very successful with it, but

00:33:58.720 --> 00:34:00.877
like it was this huge castle in the sky,
but it was this thing it was almost

00:34:00.960 --> 00:34:03.797
unapproachable, which is like you had
to be kind of inside IBM or very close to

00:34:03.880 --> 00:34:06.597
IBM and you had to really understand
every aspect of how the system worked.

00:34:06.680 --> 00:34:09.517
And then the the Unix guys originally
out of AT&T and then out of out of

00:34:09.600 --> 00:34:12.437
Berkeley um you know, came out and they
said, "No, let's have a completely

00:34:12.520 --> 00:34:14.357
different architecture." And the way
architecture is going to work is we're

00:34:14.440 --> 00:34:17.677
going to have we're going to have
a a prompt and and a shell. And then and

00:34:17.760 --> 00:34:19.557
then we're going to all the functionality
is going to be in the form

00:34:19.640 --> 00:34:21.717
of these discrete modules. And then
you're going to be able to chain the

00:34:21.800 --> 00:34:24.997
modules together. And so like the the it's
almost like the operating operating

00:34:25.080 --> 00:34:27.917
system itself is going to be
a programming language. Um and then that

00:34:28.000 --> 00:34:31.077
led led to the the the sort of centrality
of the shell um and then that

00:34:31.160 --> 00:34:34.157
led to the sort of you know, basically
chaining together Unix tools and then

00:34:34.240 --> 00:34:37.237
that led to the emergence of these these
scripting languages like Pearl where you

00:34:37.320 --> 00:34:39.317
could basically kind
of very easily do this.

00:34:39.400 --> 00:34:41.437
And then the shells got more
sophisticated. And then and then and

00:34:41.520 --> 00:34:44.517
then look like you know that that number
one that worked. And that that was the

00:34:44.600 --> 00:34:47.757
world I grew up in like I was I was a
Unix guy you know sort of from call it

00:34:47.840 --> 00:34:51.677
1988 to you know kind of all the way
through my work. And it worked really

00:34:51.760 --> 00:34:55.357
well. It it's in the background
you know normal people don't need it

00:34:55.440 --> 00:34:58.037
didn't need it necessarily know about it.
But like if you were doing like

00:34:58.120 --> 00:35:00.437
system architecture
application development

00:35:00.520 --> 00:35:03.517
you knew all about it. And then you know
it's been in the background ever since

00:35:03.600 --> 00:35:06.517
and you know look your Mac still has
a Unix shell you know kind of in there and

00:35:06.600 --> 00:35:09.517
your iPhone still has a Unix shell kind
of buried in there somewhere. So they're

00:35:09.600 --> 00:35:11.957
kind of in there and then you know the
Windows shell is kind of a you know sort

00:35:12.040 --> 00:35:15.277
of a weird derivative of that but you
know but look the the internet runs on

00:35:15.360 --> 00:35:19.037
Unix and then smartphones actually both
iOS and Android are Unix derivatives and

00:35:19.120 --> 00:35:22.277
so you know kind of Unix did end up
winning but but anyway and then we just

00:35:22.360 --> 00:35:24.957
started taking that for granted and then
and then so so basically the way I think

00:35:25.040 --> 00:35:28.117
about what happened with Pi and then
with Open Claw is basically what those

00:35:28.200 --> 00:35:30.837
guys figured out is I would say
the the great breakthroughs are obvious in

00:35:30.920 --> 00:35:33.037
retrospect right which is the best kind.

00:35:33.120 --> 00:35:35.917
The best kind. They weren't obvious
at the time or somebody else would have

00:35:36.000 --> 00:35:38.757
done them already.
And so there is a like a real conceptual

00:35:38.840 --> 00:35:41.397
leap but then you look at it sort
of backwards looking and you're just like

00:35:41.480 --> 00:35:43.957
oh of course. Like to me
those are always the best

00:35:44.040 --> 00:35:46.117
breakthroughs. Well actually language
models themselves are like that it's

00:35:46.200 --> 00:35:49.317
just like oh next token completion oh
of course. Yeah what other objective

00:35:49.400 --> 00:35:52.357
mattered? Yeah exactly [laughter] but
but like right but she's even saying it

00:35:52.440 --> 00:35:54.837
wasn't obvious until somebody actually
did it right. And so the conceptual

00:35:54.920 --> 00:35:57.757
breakthrough is real and deep and
powerful and and very important. And so

00:35:57.840 --> 00:36:00.957
the way I think about Pi and Open
Claw is it's basically marrying the the

00:36:01.040 --> 00:36:05.437
language model mindset to the to the Unix
basically shell prompt mindset. And

00:36:05.520 --> 00:36:09.677
so it's it's basically this idea
that what what so what is an agent? Right

00:36:09.760 --> 00:36:11.917
and as you know like many smart people
have been trying to figure out what an

00:36:12.000 --> 00:36:14.677
agent is for for for decades and they've
had many architectures to build agents

00:36:14.760 --> 00:36:17.077
and the whole thing and it turns out
what is an agent. So it turns out what

00:36:17.160 --> 00:36:20.557
we now know is an agent is the following.
It's it's it's language model

00:36:20.640 --> 00:36:24.237
and then above that it's a bash it's
a bash shell. So it's it's a Unix shell.

00:36:24.320 --> 00:36:27.637
And then it's in then the agent has
access has access to to the shell in you

00:36:27.720 --> 00:36:30.957
know hopeful hopefully in a sandbox
maybe in maybe in a sandbox.

00:36:31.040 --> 00:36:34.957
So it's it's the model it's the shell
and then it's a it's a file system

00:36:35.040 --> 00:36:36.917
and then the state is stored in files.

00:36:37.000 --> 00:36:39.157
And then you know there's the markdown
format for the you know for for the

00:36:39.240 --> 00:36:41.717
files themselves and then and then
there's basically what in Unix is called

00:36:41.800 --> 00:36:44.357
a cron job. There's a loop and then
there's a heartbeat there's a heartbeat

00:36:44.440 --> 00:36:47.077
and and the thing basically
wake wakes up wakes up.

00:36:47.160 --> 00:36:51.597
So it's basically LLM plus shell plus
file system plus markdown plus cron and

00:36:51.680 --> 00:36:54.797
it turns out that's an agent and every
part of that other than the model is

00:36:54.880 --> 00:36:57.237
something that we already completely
know and understand and in fact it turns

00:36:57.320 --> 00:37:00.877
out that like the latent power
of the Unix shell is like extraordinary.

00:37:00.960 --> 00:37:04.757
Because basically like all like there's
just like there's just enormous latent

00:37:04.840 --> 00:37:07.077
power in the shell there's enormous
numbers of Unix commands there's

00:37:07.160 --> 00:37:09.437
enormous number of command line
interfaces into all kinds of things

00:37:09.520 --> 00:37:12.397
already in the you know your entire I
mean your entire just to start with your

00:37:12.480 --> 00:37:15.757
computer runs on a shell. If you're
running a Mac or a phone your computer

00:37:15.840 --> 00:37:17.397
your computer is running on a shell.

00:37:17.480 --> 00:37:20.197
Already and so like the full power
of your computer is available at the

00:37:20.280 --> 00:37:23.517
command line level and then it turns
out it's really easy to expose other

00:37:23.600 --> 00:37:24.957
functions as a command line interface.

00:37:25.040 --> 00:37:28.037
And so like this this whole idea where
we need like MCP and these like product

00:37:28.120 --> 00:37:30.797
fancy protocols whatever it's like no
we don't we just need like a command

00:37:30.880 --> 00:37:33.477
command line thing. So that's the
architecture and then it turns out what

00:37:33.560 --> 00:37:36.917
is your agent? Your agent is a bunch
of files stored in a file system and then

00:37:37.000 --> 00:37:38.957
there's the thing that just like
completely blew my mind when I wrapped

00:37:39.040 --> 00:37:41.477
my head around it as a result
of this which is like okay.

00:37:41.560 --> 00:37:44.037
This means your agent is now actually
independent of the model that it's

00:37:44.120 --> 00:37:47.477
running on because you can actually
swap out a different LLM underneath your

00:37:47.560 --> 00:37:50.397
agent and your your agent will change
personality somewhat cuz the model is

00:37:50.480 --> 00:37:53.717
different but all of the state stored
in the files will be retained. Different

00:37:53.800 --> 00:37:55.397
instruction set but you just compile it.

00:37:55.480 --> 00:37:57.477
>> Right exactly and then it's all right
it's like right swapping out a chip and

00:37:57.560 --> 00:38:00.157
recompiling but it's it's still
it's still your agent with all of its

00:38:00.240 --> 00:38:03.437
memories and with all of its
capabilities and then by the way you can

00:38:03.520 --> 00:38:06.797
also swap out the shell so you can move
it to a different execution environment

00:38:06.880 --> 00:38:09.557
that is also is also a bash shell. By
the way you can also switch out the file

00:38:09.640 --> 00:38:13.477
system. Right and you can and you can and
you can swap out the the the heartbeat for

00:38:13.560 --> 00:38:17.477
the the cron framework the the loop
the the agent framework itself and so your

00:38:17.560 --> 00:38:21.557
agent basically is basically at the end
of the day it's just it's just its files

00:38:21.640 --> 00:38:23.877
Um, then and then there is of course the
purple cloth. Yeah, it's it's basically

00:38:23.960 --> 00:38:26.077
it's just the files. >> [laughter]
>> Um, and then by the way, as a

00:38:26.160 --> 00:38:28.997
consequence of that, the agent its
and then the agent itself, it turns out a

00:38:29.080 --> 00:38:32.477
couple important things. So, one is it
it's it it can migrate itself. Right?

00:38:32.560 --> 00:38:34.957
And so, you you you you can instruct
your agent, "Migrate yourself to a

00:38:35.040 --> 00:38:36.557
different uh runtime environment.

00:38:36.640 --> 00:38:39.037
Migrate yourself to a different file
system. Migrate yourself to a different

00:38:39.120 --> 00:38:41.117
you know, like we swap out the language
model." Your agent will do all that

00:38:41.200 --> 00:38:44.117
stuff for you. And then there's
the final thing, which is just amazing,

00:38:44.200 --> 00:38:47.197
which is the agent is the agent actually
has full introspection. It actually it

00:38:47.280 --> 00:38:50.237
actually knows about its own files,
and it could rewrite its own files.

00:38:50.320 --> 00:38:52.837
Right? Which which by the way,
is basically no widely deployed software

00:38:52.920 --> 00:38:55.637
system in history where the the the
thing that you're using actually has

00:38:55.720 --> 00:38:58.597
full introspective knowledge of how
it itself works and is able to modify

00:38:58.680 --> 00:39:02.277
itself like that. That I mean, there've
been toy systems that have had that, but

00:39:02.360 --> 00:39:04.237
there there's never been a widely
deployed system that has that

00:39:04.320 --> 00:39:07.997
capability. And then that leads you to
the capability that just like completely

00:39:08.080 --> 00:39:10.437
blew my mind when I wrapped my head
around it, which is you can tell the

00:39:10.520 --> 00:39:15.277
agent to add new functions and features
to itself, and it can do that.

00:39:15.360 --> 00:39:17.917
Right? Ex- extend yourself. Like,
extend yourself. Give yourself a new

00:39:18.000 --> 00:39:20.917
capability. Right? And so, and so,
literally it's just like you run into

00:39:21.000 --> 00:39:23.397
somebody at a party, and they're like,
"Oh, I have my open claw do whatever,

00:39:23.480 --> 00:39:25.597
connect to my eight sleep bed, and it
gives me better advice in sleep." And

00:39:25.680 --> 00:39:28.077
you go home at night, and you tell your
claw, or if they're at the party, by the

00:39:28.160 --> 00:39:30.437
way, you tell your claw, "Oh, add this
capability to yourself." And your claw

00:39:30.520 --> 00:39:32.997
will say, "Oh, okay, no problem." And
it'll go out on the internet, and it'll

00:39:33.080 --> 00:39:35.637
figure out whatever it needs, and then
it'll go out to claw code or whatever,

00:39:35.720 --> 00:39:37.797
it'll write whatever it needs, and then
the next thing you know, it has this new

00:39:37.880 --> 00:39:41.237
capability. And so, you don't even have
to like you can have it upgrade itself

00:39:41.320 --> 00:39:44.437
without even having to without having
to do anything other than tell it that you

00:39:44.520 --> 00:39:47.677
want it to do that. And so, anyway,
so the the combination of all this is just

00:39:47.760 --> 00:39:51.757
I mean, this is just like a massive
incredible I mean, it's just

00:39:51.840 --> 00:39:54.557
incredible. Like, if I if I were if I
were 18, like this is a hundred this is

00:39:54.640 --> 00:39:56.797
what I would be spending all of my time
on. This is like such an incredible

00:39:56.880 --> 00:39:59.757
conceptual breakthrough. And again, pe-
people are going to look at it, and they

00:39:59.840 --> 00:40:01.277
already get this response. People are
going to look at it, and they're going

00:40:01.360 --> 00:40:03.357
to say, "Oh, well, where's
the breakthrough?" Cuz these all of these

00:40:03.440 --> 00:40:05.117
components were already known before.

00:40:05.200 --> 00:40:07.877
But but this is the key. The key
to the breakthrough was by using all these

00:40:07.960 --> 00:40:10.877
components that were known before,
you get all of the underlying capability

00:40:10.960 --> 00:40:13.597
that's buried in there. And so, all
and so, for example, computer use all of a

00:40:13.680 --> 00:40:16.237
sudden just kind of falls trivially
trivial. Of course, it's going to be

00:40:16.320 --> 00:40:19.397
able to use a computer. It [snorts]
has full access to the shell. Right? And

00:40:19.480 --> 00:40:21.677
then And then you just you you give it
access to a browser and then you've got

00:40:21.760 --> 00:40:24.357
the computer and the browser and and off
and away it goes. And then then you've

00:40:24.440 --> 00:40:27.397
got all the abilities of the browser,
also. Um and so and so the capability

00:40:27.480 --> 00:40:31.037
unlock here is profound. My friends who
were, you know, deepest into this are

00:40:31.120 --> 00:40:34.637
having their claw do like like literally
like a thousand things in their lives.

00:40:34.720 --> 00:40:36.717
They have new ideas every day.
They're just like constantly throwing new

00:40:36.800 --> 00:40:40.277
challenges at the thing. And by the way,
it's early and you know, these are you

00:40:40.360 --> 00:40:42.997
know, these are prototypes and there
is you know, as you guys know, there's

00:40:43.080 --> 00:40:46.157
security issues. And and so you know,
there's a bunch of stuff to be ironed

00:40:46.240 --> 00:40:51.637
out, but the the unlock of capability is
just incredible. And I I have absolutely

00:40:51.720 --> 00:40:54.397
no doubt that everybody in the world
is going to is going to have at least, you

00:40:54.480 --> 00:40:57.117
know, an agent like this, if not an entire
family of agents, and we're going

00:40:57.200 --> 00:40:59.957
to be living in a world where I
think it's almost inevitable now that

00:41:00.040 --> 00:41:02.637
this is the way people are going
to use computers. I was going to say for

00:41:02.720 --> 00:41:05.837
someone who's deeply familiar with
social networks, the next step is your

00:41:05.920 --> 00:41:09.477
claw talking to my claw.
Posting on claw Facebook, posting their

00:41:09.560 --> 00:41:13.877
jobs on claw LinkedIn, and close posting
their tweets on claw XAI or whatever,

00:41:13.960 --> 00:41:17.477
you know. >> [laughter] >> Um I
do think that that is how uh you

00:41:17.560 --> 00:41:21.437
know, we we get into some danger there
in terms of like alignment and whether

00:41:21.520 --> 00:41:24.717
or not we want these things to to to run.
You guys know about Rent-A-

00:41:24.800 --> 00:41:26.917
Rent-A-Human.com? >> Yeah, Rent-A- Yeah.

00:41:27.000 --> 00:41:29.757
I mean, it's Fiverr,
it's Task Rabbit, it's >> course.

00:41:29.840 --> 00:41:32.997
Mechanical Mechanical Turk.
>> Yeah, but flipped. Right? The agent

00:41:33.080 --> 00:41:35.497
hiring the people. Yeah.
Which of course is going to happen.

00:41:35.580 --> 00:41:37.117
>> [laughter] >> Right?
It's obviously going to happen.

00:41:37.200 --> 00:41:40.357
I'm curious if you have any thoughts on
the engineering side. So, when you built

00:41:40.440 --> 00:41:44.037
the browser, the internet,
you know, just a bunch of

00:41:44.120 --> 00:41:46.157
mostly plain text file plus some images.

00:41:46.240 --> 00:41:50.517
And today the every website and app
is like so complex and like somehow, you

00:41:50.600 --> 00:41:53.637
know, the browser kept
evolving to fit that in.

00:41:53.720 --> 00:41:57.877
Are there any design choices that were
made like early in the browser and kind

00:41:57.960 --> 00:42:02.477
of like the internet and the protocols
that you're seeing agents similar today

00:42:02.560 --> 00:42:05.437
say, "Hey, this thing is just not
going to work for like this type of new

00:42:05.520 --> 00:42:08.917
compute and we should just rip
it out right now." There were a

00:42:09.000 --> 00:42:09.917
whole bunch, but I'll give you a couple.

00:42:10.000 --> 00:42:13.117
So, one is um and we did
you know, to be clear, like

00:42:13.200 --> 00:42:15.277
this this was not you know this was
totally different we didn't have the

00:42:15.360 --> 00:42:18.157
capabilities we have today but we didn't
have the language models underneath this

00:42:18.240 --> 00:42:22.917
but we did have this idea that human
readability actually mattered a great deal

00:42:23.000 --> 00:42:25.877
and so specifically in those days it was
it was not so much English language but

00:42:25.960 --> 00:42:29.037
it was there was a design decision to be
made between binary protocols and text

00:42:29.120 --> 00:42:32.197
protocols. And basically every every every

00:42:32.280 --> 00:42:36.237
basically old school systems architect
that had grown up between like the 1960s

00:42:36.320 --> 00:42:39.317
and 1990s basically said you know
the internet is what do you know about the

00:42:39.400 --> 00:42:43.197
internet it's starved for bandwidth.
You just you have these very narrow straws

00:42:43.280 --> 00:42:45.717
you know like people when we did
the work on Mosaic like people who had the

00:42:45.800 --> 00:42:48.837
internet at home had a 14 kilobit modem
right so you're you're trying to like

00:42:48.920 --> 00:42:52.197
hyper optimize every bit of data that
that travels over the network. And so

00:42:52.280 --> 00:42:54.837
obviously if you're going to design
a protocol like HTTP you're going to want

00:42:54.920 --> 00:42:57.837
it to be a you know highly compressed
binary protocol for maximum efficiency

00:42:57.920 --> 00:43:00.077
and you're going to want to have it be
like a single connection that persists

00:43:00.160 --> 00:43:02.037
and you're you're the last thing you're
going to want to do is like bring up and

00:43:02.120 --> 00:43:04.437
tear down new connections.
And you definitely you're not going to

00:43:04.520 --> 00:43:07.317
not going to want a text protocol and so
of course we said no we actually want to

00:43:07.400 --> 00:43:11.597
go completely the other direction and it's
obviously we only want text protocols.

00:43:11.680 --> 00:43:15.157
By the way same thing in HTML itself we
want HTML to be relatively verbose you

00:43:15.240 --> 00:43:18.477
know we want the tags to actually be
like human readable. We want to use the

00:43:18.560 --> 00:43:19.837
>> The most inefficient things possible.

00:43:19.920 --> 00:43:22.757
>> Yeah we want to do the do the do the
inefficient things. You're the original

00:43:22.840 --> 00:43:26.197
token maxer. Yeah exactly yeah
yeah yeah basically it's just like

00:43:26.280 --> 00:43:28.597
Well yeah well actually this was this
was actually the the conscious thing

00:43:28.680 --> 00:43:32.037
which basically says just like assume
assume a future of infinite bandwidth

00:43:32.120 --> 00:43:35.677
build for that and then basically what
it was is it was a bet that it was a bet

00:43:35.760 --> 00:43:39.037
that if the system if the if the latent
capabilities of the system were powerful

00:43:39.120 --> 00:43:41.637
enough and that was obvious enough
to people that would create the demand for

00:43:41.720 --> 00:43:44.157
the bandwidth that would cause
the supply of bandwidth to get built that

00:43:44.240 --> 00:43:46.877
would actually make the whole thing work.
And then specifically what we

00:43:46.960 --> 00:43:49.117
wanted was we wanted everything
to be human readable because we at the

00:43:49.200 --> 00:43:51.597
engineering level we wanted people to be
able to read the protocol coming over

00:43:51.680 --> 00:43:53.157
the wire and be able to understand it.

00:43:53.240 --> 00:43:55.877
With their with their bare eyes
without having to like disassemble it or

00:43:55.960 --> 00:43:59.037
whatever right you have to convert it
out of binary right and so the the all

00:43:59.120 --> 00:44:02.797
the you know HTTP and everything else
were were it was always text protocols

00:44:02.880 --> 00:44:05.797
and the same thing with HTML and in many
ways some people say that the key break

00:44:05.880 --> 00:44:08.997
through in the browser was the view
source option, which is every webpage

00:44:09.080 --> 00:44:11.357
you go to you could view source, which
means you could see how it worked, which

00:44:11.440 --> 00:44:15.557
means you could teach yourself how
to build right new to build new webpages.

00:44:15.640 --> 00:44:19.517
There was that. So, human readability
and again human readability in those

00:44:19.600 --> 00:44:22.357
days still meant technical, you know,
specs. You know, now it means English

00:44:22.440 --> 00:44:25.677
language, but there's an incredible
latent power in giving everybody who

00:44:25.760 --> 00:44:28.477
uses the system the option to be able
to drop down and actually understand and

00:44:28.560 --> 00:44:31.037
see how it's working. And that worked
really well for the web and I think it's

00:44:31.120 --> 00:44:35.277
working really well for AI.
That was one. What was the other um A

00:44:35.360 --> 00:44:38.557
big part of the idea of web servers
was actually surface the underlying latent

00:44:38.640 --> 00:44:41.717
capability of the operating system
and to be able to surface the also the

00:44:41.800 --> 00:44:44.517
underlying latent capability of the
database. Because basically what was a

00:44:44.600 --> 00:44:47.117
web server? What what what what is a web
server fundamentally architecturally?

00:44:47.200 --> 00:44:49.997
It's it's it's the operating system. So,
it's it's the operating system's ability

00:44:50.080 --> 00:44:53.157
to, you know, it's running on top of an
OS. So, it's the OS's ability to manage

00:44:53.240 --> 00:44:56.557
the file system and do everything else
that you want to do, process everything.

00:44:56.640 --> 00:44:58.997
And then of course a lot of really, you
know, a lot a lot of websites are front

00:44:59.080 --> 00:45:01.957
ends to databases. And so, you wanted
to you wanted to unleash the underlying

00:45:02.040 --> 00:45:05.077
latent power of whether it was an Oracle
database or some other, you know, some

00:45:05.160 --> 00:45:08.357
other postgress or whatever whatever
it was. And so, a lot of the function of

00:45:08.440 --> 00:45:12.317
the web server was to just bridge from
that internet connection coming in to be

00:45:12.400 --> 00:45:15.117
able to unlock the underlying
power of the OS and the database.

00:45:15.200 --> 00:45:17.557
And again, people looked at it at the
time and they were like, well, is this

00:45:17.640 --> 00:45:19.997
really does this really matter?
Like is this important because we've had

00:45:20.080 --> 00:45:22.997
databases forever and we've always had,
you know, user interfaces for databases

00:45:23.080 --> 00:45:25.317
and this is just another
user interface for a database.

00:45:25.400 --> 00:45:27.957
It's like, okay, yeah, fair enough.
But on the other side of that is just like

00:45:28.040 --> 00:45:31.077
this is now a much better interface
to databases and one that 8 billion people

00:45:31.160 --> 00:45:34.677
are going to use and is going to be like
far easier to use and far more flexible

00:45:34.760 --> 00:45:37.797
and and and and you're not just going
to have old databases. Now you have a

00:45:37.880 --> 00:45:40.477
system where people can actually
understand why they want to build, you

00:45:40.560 --> 00:45:43.397
know, a million times more database apps
than they have in the past. And then the

00:45:43.480 --> 00:45:46.077
number of databases in the world exploded.
And so, again this goes to

00:45:46.160 --> 00:45:49.077
this thing of like building building
in layers. Some of the smartest people in

00:45:49.160 --> 00:45:51.957
industry look at any new challenge and
they're like, okay, I I need to build a

00:45:52.040 --> 00:45:53.997
new kind of application. So, the first
thing I need to do is build a new

00:45:54.080 --> 00:45:56.397
programming language. Right?
And then the next thing I need to

00:45:56.480 --> 00:45:57.497
do is build a new operating system.

00:45:57.580 --> 00:45:59.277
[laughter] Right? And then the next
thing I need to do is I need to build a

00:45:59.360 --> 00:46:02.717
new chip. Right? And they they kind
of want to reinvent everything. And I've

00:46:02.800 --> 00:46:06.157
I've always had maybe it's just I don't
know pragmatic mentality or something or

00:46:06.240 --> 00:46:08.997
maybe an engineering over science
mentality, but it's more like no, you

00:46:09.080 --> 00:46:12.437
have just like all of this latent power
in the existing systems and you don't

00:46:12.520 --> 00:46:15.317
want to be held back by their constraints,
but what you want to do is

00:46:15.400 --> 00:46:18.397
you want to kind of liberate that power
and open it up. So I I think I think

00:46:18.480 --> 00:46:21.037
that and I think the web did that for
those reasons and I think it's the same

00:46:21.120 --> 00:46:24.517
thing that's happening.
Programming languages is another good

00:46:24.600 --> 00:46:28.077
thing. We have Brett Taylor on the
podcast and we were talking about rust

00:46:28.160 --> 00:46:31.597
and you know, rust is memory safe
by default. So why are we teaching the

00:46:31.680 --> 00:46:33.277
model to not write memory unsafe code?

00:46:33.360 --> 00:46:36.797
Just use rust and then you get it for
free. How much do you think there's like

00:46:36.880 --> 00:46:40.197
time to be spent like recreating some
of these things instead of taking them for

00:46:40.280 --> 00:46:43.077
granted? I'll be like,
oh okay, python is kind of slow.

00:46:43.160 --> 00:46:47.597
You know, it's like yeah. As as imperfect
as they are, they are the lingua franca.

00:46:47.680 --> 00:46:50.277
I mean, I think this is going to change
a lot cuz I don't think the models care

00:46:50.360 --> 00:46:52.477
what language they program in and I
think they're going to be good at

00:46:52.560 --> 00:46:54.517
programming in every language and I
think they're going to be good at

00:46:54.600 --> 00:46:57.957
translating from any language to any
other language. Like okay, so this gets

00:46:58.040 --> 00:47:01.637
into the coding side of things. I think
we're going through a really fundamental

00:47:01.720 --> 00:47:04.757
change. And then look, I I grew up you
know, I grew up hand code. You know, I I

00:47:04.840 --> 00:47:07.637
grew up hand coding. Everything I did
was actually everything I did actually

00:47:07.720 --> 00:47:11.597
was written in C. I wasn't even I
wasn't even using C++. So I or like

00:47:11.680 --> 00:47:15.317
Java or any of this stuff, right? And so
everything everything I ever did I was

00:47:15.400 --> 00:47:18.277
like managing my own
memory at the level of C.

00:47:18.360 --> 00:47:20.837
And then I you know, I still from
the generation that you know, I knew

00:47:20.920 --> 00:47:23.237
assembly language and you
know, I I you know,

00:47:23.320 --> 00:47:26.157
so I could drop down and do
things right on the ship.

00:47:26.240 --> 00:47:29.437
And so we've just we've all all of us
we've always lived in a world in which

00:47:29.520 --> 00:47:32.757
software is like this precious thing
that like you have to think about very

00:47:32.840 --> 00:47:35.397
carefully and it's like really hard
to generate good software and there's only

00:47:35.480 --> 00:47:37.957
a small number of people who can do
it and like you have to be very like

00:47:38.040 --> 00:47:40.397
jealous in terms of thinking about like
how do you allocate? Like what are your

00:47:40.480 --> 00:47:43.397
engineers working on and how many good
engineers do you actually have and how

00:47:43.480 --> 00:47:46.197
much software can they write and how can
how much software can human beings you

00:47:46.280 --> 00:47:49.237
know, kind of maintain? And I think
like all those assumptions are being shot

00:47:49.320 --> 00:47:51.437
right out the window right now.
Like I think they're

00:47:51.520 --> 00:47:54.557
I think those days are just over and I
think the new world is like actually

00:47:54.640 --> 00:47:57.237
high quality software is just
like infinitely available.

00:47:57.320 --> 00:48:00.717
And [snorts] if you need new software
to do XYZ, like you're just going to wave

00:48:00.800 --> 00:48:01.677
your hand and you're going to get it.

00:48:01.760 --> 00:48:04.357
And then if it's if you don't like the
language it's written in, you just tell

00:48:04.440 --> 00:48:07.117
the thing, all right, I want
the right now I want the rest version.

00:48:07.200 --> 00:48:09.917
Um or you know, secure you know, secure
we're about to by the way, we're about

00:48:10.000 --> 00:48:12.437
to go through computer security about
to go through the most dramatic change

00:48:12.520 --> 00:48:15.237
ever, which is number one, like every
single latent security bug is about to

00:48:15.320 --> 00:48:17.317
be exposed. >> Right.
So it's we're going to have like

00:48:17.400 --> 00:48:20.277
the we're we're set up here for like
the computer security apocalypse for a

00:48:20.360 --> 00:48:23.317
while. But but but on the other side
of it, now we have coding agents that can

00:48:23.400 --> 00:48:25.917
go in and actually fix all the security
bugs. And so how how are you going to

00:48:26.000 --> 00:48:28.237
secure software in the future? You're
going to tell the tell the bot to secure

00:48:28.320 --> 00:48:30.597
it and it's going to go
through and and fix it all.

00:48:30.680 --> 00:48:33.637
And so so this thing that was
this incredibly scarce resource of

00:48:33.720 --> 00:48:36.917
high-quality software is just going
to become a completely fungible thing that

00:48:37.000 --> 00:48:38.677
you're just going to have
as much as you want.

00:48:38.760 --> 00:48:41.077
>> Right. Uh and and that has
like, you know, that has like

00:48:41.160 --> 00:48:43.037
tons and tons of consequences.

00:48:43.120 --> 00:48:45.157
In some sense, the answer to the
question you posed I I think is just

00:48:45.240 --> 00:48:47.157
somewhat, I don't know, simple
or something or straightforward, which is

00:48:47.240 --> 00:48:49.397
just if you want all your software in
rest, you just tell the bot you want all

00:48:49.480 --> 00:48:53.317
your software in rest. Like
the things that used to be like hard or

00:48:53.400 --> 00:48:56.277
even like seem like an insurmountable
mountain to get to get through all of a

00:48:56.360 --> 00:48:59.197
sudden I think become very easy.
Uh I think Brett had a theory that there

00:48:59.280 --> 00:49:03.597
would be a more optimal language
for LLMs. And so the contention is

00:49:03.680 --> 00:49:05.317
there isn't. Like just don't bother.

00:49:05.400 --> 00:49:09.757
Just whatever humans already use, LLMs
are perfectly capable porting. I think

00:49:09.840 --> 00:49:12.117
we're pretty close to being I don't know
if this would work today. I think we're

00:49:12.200 --> 00:49:16.477
pretty close to being able to ask the AI
what what its optimal language be and let

00:49:16.560 --> 00:49:17.927
it right and let it design it.

00:49:18.010 --> 00:49:21.237
>> [laughter] >> True.
Okay, here's the question.

00:49:21.320 --> 00:49:24.037
Are you going to even going to have
programming languages in the future? Or

00:49:24.120 --> 00:49:26.717
the AI or the AI is just
going to be emitting binaries.

00:49:26.800 --> 00:49:29.197
Let's assume for a moment that humans
aren't coding anymore. Let's assume it's

00:49:29.280 --> 00:49:32.037
all bots. The what levels
of intermediate abstraction

00:49:32.120 --> 00:49:35.637
do the bots even need? Yeah. Or are they
just coding binary directly? Did you see

00:49:35.720 --> 00:49:37.597
there's actually an exper- there's
somebody just did this thing where they

00:49:37.680 --> 00:49:40.397
have a they have a language model now
that actually emits model weights for a

00:49:40.480 --> 00:49:44.757
new language model. >> [laughter]
>> Right. And so will the bots be

00:49:44.840 --> 00:49:47.597
>> Just predict the weights. Well, yeah,
will the bots literally be emitting not

00:49:47.680 --> 00:49:50.397
just coding binaries, but will they will
will they actually be emitting weights

00:49:50.480 --> 00:49:55.317
for new for new models.
Direct, directly. And conceptually

00:49:55.400 --> 00:49:59.117
there's no reason why they
can't do both of those things.

00:49:59.200 --> 00:50:01.837
Just architecturally both of those
things seem completely possible.

00:50:01.920 --> 00:50:05.157
>> Very inefficient. You're basically
a simulation of a simulation in a

00:50:05.240 --> 00:50:06.597
simulation inside of the weights. Yeah.

00:50:06.680 --> 00:50:08.637
Yeah, very inefficient.
But like look, LLMs

00:50:08.720 --> 00:50:10.237
are already like incredibly inefficient.

00:50:10.320 --> 00:50:14.157
Ask I'm saying the same thing. Ask Claude
2 + 2 = 4, right? It's just like you know,

00:50:14.240 --> 00:50:16.997
it's like you know, it's it's it's like
whatever billions and billions of times

00:50:17.080 --> 00:50:19.997
more inefficient than using
your pocket calculator.

00:50:20.080 --> 00:50:23.437
But but but yeah, the the payoff is so
great of the general capability. So

00:50:23.520 --> 00:50:26.957
anyway, like I I kind of think in 10
years like I'm not sure yeah, like I'm

00:50:27.040 --> 00:50:30.717
not sure there will even be a salient
concept of a programming language

00:50:30.800 --> 00:50:31.957
in the way that we understand it today.

00:50:32.040 --> 00:50:35.157
And in fact, what we may be doing more
and more is a form of interpretability,

00:50:35.240 --> 00:50:38.957
which is we're trying to understand why
the bots have decided to structure code

00:50:39.040 --> 00:50:41.477
in the way that they have. I mean,
if you play it through, you don't need

00:50:41.560 --> 00:50:45.277
browsers then. Like that's the death
of the browser. Well, so I I would take it

00:50:45.360 --> 00:50:48.437
a step further, which is you
may not need user interfaces.

00:50:48.520 --> 00:50:51.637
So who is going to use
software in the future?

00:50:51.720 --> 00:50:54.237
Other bots. Other bots. Yeah. And so

00:50:54.320 --> 00:50:59.457
>> need to I don't know pipe
information in Do we? and out. Really?

00:50:59.540 --> 00:51:01.197
>> [laughter] >> Well,
what are you going to do then?

00:51:01.280 --> 00:51:02.997
>> Are you sure? You're just
going to log off and touch grass?

00:51:03.080 --> 00:51:04.037
>> Whatever you want, exactly.

00:51:04.120 --> 00:51:06.037
>> [laughter] >> Isn't that better?

00:51:06.120 --> 00:51:07.717
I want software to do stuff for me.

00:51:07.800 --> 00:51:10.037
Isn't that but isn't that better?

00:51:10.120 --> 00:51:12.357
You know, I don't know like like you
know, you know, you know, there are

00:51:12.440 --> 00:51:15.437
arguments here, you know. >> [laughter]
>> It was not that long ago that 99% of

00:51:15.520 --> 00:51:18.197
humanity was behind a plow. Right.
Right. And what are people going

00:51:18.280 --> 00:51:20.877
to do if they're not plowing fields all
day to to grow food, right? And it just

00:51:20.960 --> 00:51:22.997
turns out there's like much better ways
for people to spend time than plowing

00:51:23.080 --> 00:51:25.657
fields. >> Yeah,
do a scroll in. Uh exactly.

00:51:25.740 --> 00:51:27.717
>> [laughter] >> Exactly,
or you know, talking to their

00:51:27.800 --> 00:51:30.837
friends. And look, and I'm not
an absolutist and I'm not a utopian and I

00:51:30.920 --> 00:51:32.957
and to be clear like I have I have
an 11-year-old and he's learning how to

00:51:33.040 --> 00:51:35.397
code and like I'm you know, I I think
it's still like a really good idea to

00:51:35.480 --> 00:51:38.797
learn how to code and so forth. But I
just if you project forward, you just

00:51:38.880 --> 00:51:41.077
have to think forward to a world
in which it's just like okay, I'm just

00:51:41.160 --> 00:51:43.957
going to tell the thing what I need
and it's going to do it. And then and then

00:51:44.040 --> 00:51:47.357
it's going to do it in whatever way
is most optimal for it to do it. Yeah. I

00:51:47.440 --> 00:51:51.037
tell it to do it non-optimally. Like if
I tell it to do it in Java or in Rust or

00:51:51.120 --> 00:51:53.957
whatever, it'll do it, I'm sure. But like
if I'm just going to tell it to do

00:51:54.040 --> 00:51:56.277
it, it's going to do it in whatever way
is like the optimal way to do it. And

00:51:56.360 --> 00:51:58.757
then I And then if I need to understand
how it works, I'm going to ask it to

00:51:58.840 --> 00:52:01.717
explain to me how it works. Right?
And so it's going to be doing its

00:52:01.800 --> 00:52:04.197
own interpret- it's going to be
the engine of interpretability to explain

00:52:04.280 --> 00:52:07.557
itself. And I I just am not convinced
that that I'm not I'm not convinced that

00:52:07.640 --> 00:52:11.637
in that world you have these historical
The goals of the abstractions will be

00:52:11.720 --> 00:52:12.957
whatever the boss needed with the user.

00:52:13.040 --> 00:52:16.557
Yeah. Yeah, that Well, I I'm curious
though, like if that's true, then

00:52:16.640 --> 00:52:20.437
shouldn't the models providers
be building some internal language

00:52:20.520 --> 00:52:24.717
representation that they can
do extreme kind of like RL

00:52:24.800 --> 00:52:27.757
uh and reward modeling around. Because
it's like today they're kind of like

00:52:27.840 --> 00:52:31.837
tied to like TypeScript and Python
because the users need to write in that

00:52:31.920 --> 00:52:35.677
language. Versus they can have their own
thing internally and like they don't

00:52:35.760 --> 00:52:38.797
need to teach it to anybody. They just
need to teach their model. And I think

00:52:38.880 --> 00:52:41.397
that's how you get maybe the version
between the models. Like going back to

00:52:41.480 --> 00:52:45.357
like the Pi open cloud thing, it's like,
"Oh, I built all the software using the

00:52:45.440 --> 00:52:48.677
open AI model, and now switch to the
Anthropic model, but the Anthropic model

00:52:48.760 --> 00:52:52.597
doesn't understand the thing." So I
would It feels like there still needs to

00:52:52.680 --> 00:52:57.317
be some abstraction. But maybe not.
Maybe that's the lock-in

00:52:57.400 --> 00:53:00.237
that the model providers want to have.
I don't I'm not even sure that's lock-in

00:53:00.320 --> 00:53:02.557
though, because why can't the second
model just learn what the first model

00:53:02.640 --> 00:53:05.877
has done? Like Exactly. >> Okay,
so Okay, give me an example. So as

00:53:05.960 --> 00:53:08.317
you know, models can now reverse
engineer software binaries. This whole

00:53:08.400 --> 00:53:10.597
thing now where people are reverse
engineering like Nintendo Nintendo game

00:53:10.680 --> 00:53:13.997
binaries. Yeah. So you have like You've
You've seen a bunch of reports like this

00:53:14.080 --> 00:53:16.917
where somebody has like a favorite game
from the 1980s. And the source code is

00:53:17.000 --> 00:53:19.397
like long dead, but they have like
a binary burned into a chip or something,

00:53:19.480 --> 00:53:21.677
and now they're reverse engineering
to get a version that runs on their Mac,

00:53:21.760 --> 00:53:24.037
right? And so if you reverse it if
This is what I kind of say. If you're

00:53:24.120 --> 00:53:28.357
reversing like x86 binaries,
then why can't you reverse engineer

00:53:28.440 --> 00:53:32.037
whatever they create. Yeah. And because
we're on a Unix-based system, it has to

00:53:32.120 --> 00:53:36.077
be reversible because it needs to run
on the target. Yeah. Yeah, yeah. Yeah,

00:53:36.160 --> 00:53:39.277
yeah, basically. And so I just I just
think it's this thing where it's just like

00:53:39.360 --> 00:53:41.477
And by the way, everything we're
describing is something that human

00:53:41.560 --> 00:53:44.317
beings in theory could have
done before, but just with

00:53:44.400 --> 00:53:47.917
>> Right, yeah. but with enormous
but it was just always like cost and

00:53:48.000 --> 00:53:51.477
labor prohibitive. Reverse engineer I
learned how to reverse engineer was like

00:53:51.560 --> 00:53:54.357
human beings can reverse engineer
binaries. It's just for any complex

00:53:54.440 --> 00:53:57.717
binary you need like a thousand years
to do it. But now with the model you

00:53:57.800 --> 00:54:00.957
don't. And so all of a sudden you get
you get these things or another way to

00:54:01.040 --> 00:54:04.557
think about it is so much of human built
systems are to compensate for the human

00:54:04.640 --> 00:54:07.077
limitations. Right?
And if you don't have the human

00:54:07.160 --> 00:54:09.917
limitations anymore then
all of a sudden you have

00:54:10.000 --> 00:54:13.037
won't have abstractions but you'll
have a different kind of abstraction.

00:54:13.120 --> 00:54:16.557
I have two topics to bring us to a close
and you can pick whichever ones. I just

00:54:16.640 --> 00:54:20.197
talking about protocols.
Was it you or someone else I forget my

00:54:20.280 --> 00:54:22.717
internet history who said that like
the biggest mistake that we didn't figure

00:54:22.800 --> 00:54:26.717
out in the early days was payments.
Was that you? Yes, it was a 402

00:54:26.800 --> 00:54:29.477
402 payment required.
We have a chance now.

00:54:29.560 --> 00:54:31.837
I don't think we're going to figure it
out. I don't know. Like what's your take?

00:54:31.920 --> 00:54:34.237
>> we will. Yeah. No, now I think it's
going to happen for sure. Yeah. Yeah,

00:54:34.320 --> 00:54:36.277
and there's two reasons it's going to
happen for sure. One is we actually have

00:54:36.360 --> 00:54:39.397
internet native money now in the form of
crypto stable coins and crypto. And this

00:54:39.480 --> 00:54:42.877
is I think this is the grand unification
basically of AI and crypto is what's

00:54:42.960 --> 00:54:45.717
about to happen now.
I think AI is the crypto killer app. I

00:54:45.800 --> 00:54:48.477
think it's where where this is really
going to come out. And then the other is

00:54:48.560 --> 00:54:51.477
it's just it I mean it's just I think
it's now obvious. It's like obviously AI

00:54:51.560 --> 00:54:53.037
agents are going to need money.

00:54:53.120 --> 00:54:55.397
And it's already happening, right?
If you've got a if you've got a claw and

00:54:55.480 --> 00:54:58.557
you want it to buy things for you,
you have to give it money in some form. I

00:54:58.640 --> 00:55:01.837
would say the adoption is probably
like 0.1% if if that, but yeah.

00:55:01.920 --> 00:55:04.917
>> Oh, today? Yeah, yeah, yeah,
but think forward. Like where is it going?

00:55:05.000 --> 00:55:07.877
Forward thinking. The ultimate principle
of everything and and everything that I

00:55:07.960 --> 00:55:10.757
think I we do is it's the William Gibson
quote which is the future is already

00:55:10.840 --> 00:55:15.117
here it just isn't distributed. It isn't
distributed yet. My friends who are the

00:55:15.200 --> 00:55:19.557
most aggressive users of open claw just
like have given their claws bank accounts

00:55:19.640 --> 00:55:23.237
and credit cards. And and and and not
only have they done it, it's obvious

00:55:23.320 --> 00:55:25.677
that they needed to do it because it's
obvious that they needed to be able to

00:55:25.760 --> 00:55:27.117
spend money on their behalf. Yeah, yeah.

00:55:27.200 --> 00:55:29.637
It's just completely obvious. And so
and again like so the number of people who

00:55:29.720 --> 00:55:31.997
have done that today to your point
is like I don't know probably 5,000 or

00:55:32.080 --> 00:55:35.397
something. But it'll grow. That's how
these things start. Actually, I mean

00:55:35.480 --> 00:55:38.157
since you keep mentioning And by the way
open claw by the way if you don't give

00:55:38.240 --> 00:55:40.457
it a bank account, it's just going
to break into your your bank account.

00:55:40.540 --> 00:55:41.917
>> [laughter] >> It's going
to break It's going to break

00:55:42.000 --> 00:55:44.637
It's going to break into your bank
account anyway and take your money. So,

00:55:44.720 --> 00:55:47.557
you you might as you might as well do it.
You might as well do it. Uh yeah. By

00:55:47.640 --> 00:55:51.117
the way, I really love I got to tell you
I really love the phenomenon. I love the

00:55:51.200 --> 00:55:54.637
YOLO. Um I'm not doing it myself, to be
clear, but but I love the people that

00:55:54.720 --> 00:55:58.317
are just like you What is it?
Skip Skip permissions?

00:55:58.400 --> 00:55:59.557
By the way, it's a Facebook thing.

00:55:59.640 --> 00:56:02.677
>> Okay. >> [laughter] >> Cuz
cuz we In Facebook, they they have

00:56:02.760 --> 00:56:06.917
this culture to name the thing dangerous
so that you are aware when you enable

00:56:07.000 --> 00:56:09.237
the flag that you're opting into
a dangerous thing. Okay, good. They

00:56:09.320 --> 00:56:13.997
brought it into OpenClaw AI. And of course
that makes it enticing. Sam runs Codex

00:56:14.080 --> 00:56:15.837
with skip permissions on on his laptop.

00:56:15.920 --> 00:56:19.517
Yes, 100%. And so, I I I think the way
to actually see the future is to find

00:56:19.600 --> 00:56:21.067
the people who are doing that.

00:56:21.150 --> 00:56:22.837
>> [laughter] >> There's
a man who you know, they're you

00:56:22.920 --> 00:56:25.917
know Log everything, you know,
just watch it watch the logs.

00:56:26.000 --> 00:56:28.957
>> But like, let's actually find out
what the thing can do. And the way to find

00:56:29.040 --> 00:56:32.277
out what the thing can do is just like
Yeah, let it try everything. Let it

00:56:32.360 --> 00:56:34.557
unlock everything. By the way, that's
how you're going to find all the good

00:56:34.640 --> 00:56:36.877
stuff it can do. By the way, that's also
how you're going to find all the flaws.

00:56:36.960 --> 00:56:39.637
Yeah. I think the people who turn
that on for bots are like they're like

00:56:39.720 --> 00:56:42.637
murders to the progress of human
civilization. Like I feel very bad for

00:56:42.720 --> 00:56:44.597
their descendants that their bank
accounts are going to get looted by

00:56:44.680 --> 00:56:47.717
their bots in the first like 20 minutes,
but I think the contribution that

00:56:47.800 --> 00:56:49.677
they're making to the future
of our species is amazing.

00:56:49.760 --> 00:56:51.037
>> It's like gentleman science, you know.

00:56:51.120 --> 00:56:54.917
Yes, it's Yes, yes. It's a Ben Franklin
out with a trying to trying trying to

00:56:55.000 --> 00:56:57.837
get lightning strike his [laughter] his
his balloon and see seeing if he gets

00:56:57.920 --> 00:57:01.017
electrocuted. Yeah. It It's
a Jonas Salk with the polio vaccine.

00:57:01.100 --> 00:57:04.117
>> [laughter] >> Injecting it Yes.
So, yes, I I I I think

00:57:04.200 --> 00:57:07.637
we should have like a glory we should
have like flags and like we should have

00:57:07.720 --> 00:57:10.837
like monuments to [laughter] the people
who just let OpenClaw run their lives.

00:57:10.920 --> 00:57:14.197
More anecdotes. What what are
the craziest or interesting things that

00:57:14.280 --> 00:57:18.277
people listening to this should go go
home and do? I mean, this is this is the

00:57:18.360 --> 00:57:21.117
this is the the extreme thing is just
like the straight YOLO. Like just yeah,

00:57:21.200 --> 00:57:23.077
turn turn your life >> But that's
a general capability. Is

00:57:23.160 --> 00:57:26.917
there like a specific story that was
like wow and and everyone in the group

00:57:27.000 --> 00:57:29.797
chat just lit up? I mean, like you know,
so there's tons of there's already tons

00:57:29.880 --> 00:57:32.677
of health. You know, there's you know,
that the health dashboard stuff is just

00:57:32.760 --> 00:57:34.437
is just absolutely absolutely amazing.

00:57:34.520 --> 00:57:37.277
>> Yeah. The number of stories on I'm
trying I just don't want to violate

00:57:37.360 --> 00:57:40.477
people's, you know,
obviously personal anonymities, but

00:57:40.560 --> 00:57:42.837
you know, one of the things open claws
are really good at is hacking into all

00:57:42.920 --> 00:57:45.877
the stuff in your LAN.
It's really good at it. So, you know,

00:57:45.960 --> 00:57:48.797
internet of things aka internet of

00:57:48.880 --> 00:57:50.917
Like super insecure, but great.

00:57:51.000 --> 00:57:53.517
Discoverable. Yeah, it's a discoverable.

00:57:53.600 --> 00:57:56.637
Open claw is happy to scan your network,
identify all the things and then my my

00:57:56.720 --> 00:57:58.997
my friends who are most aggressive
at this are having open claw take over

00:57:59.080 --> 00:58:01.837
everything in their house. Yeah.
It takes over their security cameras, it

00:58:01.920 --> 00:58:04.237
takes over their their, you know,
their whatever their their access control

00:58:04.320 --> 00:58:07.517
systems, it takes over their webcams.
I have a friend whose claw watches him

00:58:07.600 --> 00:58:11.037
sleep. Put a webcam in your bedroom, put
the put the claw put the claw on a loop.

00:58:11.120 --> 00:58:13.837
I have it wake up frequently and have
it watch it just tell it watch me sleep.

00:58:13.920 --> 00:58:17.237
And and I've I've seen the transcripts
and it's literally like Joe's asleep.

00:58:17.320 --> 00:58:19.837
This is good. This is good that Joe's
asleep cuz, you know, I have I have his

00:58:19.920 --> 00:58:22.157
health data and I know that he hasn't
been getting enough sleep and so it's

00:58:22.240 --> 00:58:24.477
really good that he's getting sleep.
I really hope he gets his full whatever,

00:58:24.560 --> 00:58:27.117
you know, 5 hours of REM sleep.

00:58:27.200 --> 00:58:30.437
Uh Joe's moving. Joe's moving.

00:58:30.520 --> 00:58:33.157
Uh Joe might be waking waking up. This
is a real pri If Joe wakes up now, he's

00:58:33.240 --> 00:58:34.717
going to ruin his sleep cycle.

00:58:34.800 --> 00:58:37.957
Oh, okay, it's okay. Joe just rolled over.
Okay, he's gone back to bed. Okay,

00:58:38.040 --> 00:58:40.117
good. All right. Okay,
I can relax. This is fine.

00:58:40.200 --> 00:58:41.887
>> He's monitoring the situation.

00:58:41.970 --> 00:58:44.237
>> [laughter] >> Monitoring
the situation. And and being

00:58:44.320 --> 00:58:47.437
a bot like, you know, it's just
like very focused. [laughter]

00:58:47.520 --> 00:58:50.637
It's just like this is like
his reason for existence

00:58:50.720 --> 00:58:53.717
is to watch Joe sleep. And then and then
I was telling my friend who did this is

00:58:53.800 --> 00:58:55.597
like, you know, on the one hand it's
like, all right, this is weird and

00:58:55.680 --> 00:58:58.557
creepy. And I need to I need to I maybe
this is taking over my life. And then

00:58:58.640 --> 00:58:59.517
the other thing is like, you know, what?

00:58:59.600 --> 00:59:02.037
If I had a heart attack in the middle
of the night, literally would like freak

00:59:02.120 --> 00:59:05.077
out and call 911. Like there's no
question this thing would figure out how

00:59:05.160 --> 00:59:08.517
to like alert medical authorities and like
probably summon SWAT teams and like

00:59:08.600 --> 00:59:10.997
do whatever would be required
[laughter] to save my life, right?

00:59:11.080 --> 00:59:15.717
And so it's like, you know, like yeah,
like that's happening. What else? Um

00:59:15.800 --> 00:59:18.717
They give us this company
Unitree that makes the

00:59:18.800 --> 00:59:22.437
robot dogs. And I actually
have one at home which is

00:59:22.520 --> 00:59:25.437
is it's actually really
the Chinese companies.

00:59:25.520 --> 00:59:27.957
The Chinese companies are
so aggressive at adopting

00:59:28.040 --> 00:59:32.517
uh new technology, but they don't always
like take the time to really package it.

00:59:32.600 --> 00:59:35.517
>> Package it and maybe think it all the
way through. And so so the at least the

00:59:35.600 --> 00:59:40.157
Unitree dog I have so it has a old
non-LLM just control system. Which by

00:59:40.240 --> 00:59:42.997
the way is not very good. Markets well,
but it in practice is it's not that

00:59:43.080 --> 00:59:44.997
good. It has trouble with stairs and so
forth. And so it's not quite what it

00:59:45.080 --> 00:59:47.517
should be. But then the language model
thing comes out and the voice. So they

00:59:47.600 --> 00:59:51.077
they add so they add LLM capability
and then they they add a voice mode to it.

00:59:51.160 --> 00:59:55.257
Um but but that LLM capability is not
at all connected to the control system.

00:59:55.340 --> 00:59:58.357
>> [laughter] >> So so you've
got this schizophrenic dog

00:59:58.440 --> 01:00:00.637
that like is a complete idiot when it
comes to climbing the stairs, but it

01:00:00.720 --> 01:00:02.457
will happily teach you quantum mechanics.

01:00:02.540 --> 01:00:05.677
>> [laughter] >> Right.
And like a plum English accent, right?

01:00:05.760 --> 01:00:06.837
It's just like absolutely amazing.

01:00:06.920 --> 01:00:10.117
>> Jagged intelligence. Yeah.
Talk about jagged. And then now

01:00:10.200 --> 01:00:12.237
obviously what's going to happen in the
future is is they're going to connect

01:00:12.320 --> 01:00:13.917
together, but >> They'll do it.
but right now it's it's

01:00:14.000 --> 01:00:15.077
and so right now it's not that useful.

01:00:15.160 --> 01:00:18.037
And so I I have a friend who has one of
these who had his claw basically hack in

01:00:18.120 --> 01:00:20.117
and rewrite the code rewrite
the write new firmware.

01:00:20.200 --> 01:00:22.877
>> Yeah. Rewrite new firmware
for the for the Unitree robot.

01:00:22.960 --> 01:00:27.037
And now it's now it's an actual dog
for his kids. You can do that before and

01:00:27.120 --> 01:00:29.677
after like the motion? Yeah. It's He
said it's completely different. He said

01:00:29.760 --> 01:00:32.077
it's a complete transformation.
And whenever there's an issue in the thing

01:00:32.160 --> 01:00:35.157
now the claw just like rewrites the code.
You know you know you go you does

01:00:35.240 --> 01:00:37.957
does the code and so it's a good kind
of goes to your thing here. So so like all

01:00:38.040 --> 01:00:40.917
of a sudden this is why like we're going
to think about AI code. AI coding is not

01:00:41.000 --> 01:00:43.477
just like writing new apps. It's also
going in and rewriting all the old stuff

01:00:43.560 --> 01:00:46.437
that should have worked that never worked.
And so like I I think I think

01:00:46.520 --> 01:00:49.117
basically I think the internet
the internet of is basically over. Like

01:00:49.200 --> 01:00:51.437
I I think everything there's
a potentially here where like all these

01:00:51.520 --> 01:00:53.837
devices in your house that have been
like basically marginal or you know

01:00:53.920 --> 01:00:56.717
basically dumb, you know,
like all of a sudden they might all get

01:00:56.800 --> 01:01:01.397
really smart. Now Smart home. you
have to decide if yes, there are horror

01:01:01.480 --> 01:01:05.437
movies in which this is of which this
is the premise. And so you have to decide

01:01:05.520 --> 01:01:08.117
if you want this. >> Yeah.
But but but this is the first time

01:01:08.200 --> 01:01:11.437
I can say with confidence I now know
how you could actually have a smart home

01:01:11.520 --> 01:01:14.037
Yeah. with 30 different kinds of things
with chips and internet access where it

01:01:14.120 --> 01:01:16.477
actually all makes sense and all works
together and it's all coherent and the

01:01:16.560 --> 01:01:19.677
and the whole thing. And to have that
unlock without a human being having to

01:01:19.760 --> 01:01:24.437
go do do of that work like Yeah.
So, it's fine. I'm waiting for a sorry,

01:01:24.520 --> 01:01:27.607
Mark. Uh I can't let you open that
fridge door. You know, like Exactly.

01:01:27.690 --> 01:01:31.117
[laughter] Exactly. Yes. YES. >> BECAUSE
YOU'RE NOT SUPPOSED TO EAT RIGHT NOW.

01:01:31.200 --> 01:01:34.157
>> all of you. Yes. I have every shred of
health information. You know, and I know

01:01:34.240 --> 01:01:35.957
you think you're doing, you know,
da da da. And I know you think you can do

01:01:36.040 --> 01:01:38.037
this, but you know, this is a real Are
you really, [laughter] you know, are you

01:01:38.120 --> 01:01:40.717
really sure? And you know,
you told, you know, you

01:01:40.800 --> 01:01:42.997
told me last night you really don't want
me to let you do this. And so, you know,

01:01:43.080 --> 01:01:44.637
I'm sorry, but the fridge door is locked.

01:01:44.720 --> 01:01:46.557
>> [laughter] >> Um yes.
>> Open the fridge doors.

01:01:46.640 --> 01:01:49.117
>> Exactly. And by the way, I know you're
supposed to be studying for a test. So,

01:01:49.200 --> 01:01:52.277
why don't we why don't you go when you
can pass the test, um I will open the

01:01:52.360 --> 01:01:54.997
fridge door for you.
Final photo call and then and then we

01:01:55.080 --> 01:01:57.037
can wrap up. Uh proof of human. Yes.

01:01:57.120 --> 01:01:59.597
>> right? That's the last
piece that we got to figure out.

01:01:59.680 --> 01:02:04.477
>> Yeah. So, I would say there's there's
two massive, I would say, um uh sort of

01:02:04.560 --> 01:02:08.277
asymmetries in the world right now where
we've known these asymmetries exist and

01:02:08.360 --> 01:02:11.157
we we societally have been unwilling
to grapple with them. And I think they're

01:02:11.240 --> 01:02:14.037
both tipping right now. And And they're
they're they're they're the same thing.

01:02:14.120 --> 01:02:16.197
It's virtual world version. It's
physical world version. So, the virtual

01:02:16.280 --> 01:02:18.237
world version is is the bot problem.

01:02:18.320 --> 01:02:20.997
Where just like, you know, the internet
internet is just like a washing bots.

01:02:21.080 --> 01:02:23.757
The internet's a washing fake people.
It has been forever. Um by the way, a lot

01:02:23.840 --> 01:02:26.277
of that has to do with lack of money,
you know, and so this you know, this is

01:02:26.360 --> 01:02:28.597
the this is the this is the My spicy
take was these two are the same thing

01:02:28.680 --> 01:02:31.997
and corporations are people,
too. You know, >> [laughter]

01:02:32.080 --> 01:02:33.237
>> Interesting. Yeah. Yeah. Yeah. Okay.

01:02:33.320 --> 01:02:34.837
>> So, a bank account is proof of human.

01:02:34.920 --> 01:02:37.557
Yeah. Okay. >> [laughter] >> Yeah,
until you until you give the bots

01:02:37.640 --> 01:02:39.397
bank accounts. Yeah, exactly. So, okay.

01:02:39.480 --> 01:02:42.797
Yeah, so there's that. But yeah, look
Look, the bot I mean, every social media

01:02:42.880 --> 01:02:45.597
user knows this. The bot The bot problem
is a big problem. You know, the bot The

01:02:45.680 --> 01:02:47.837
bot problem has been a big problem
forever. It's It's a huge problem. And

01:02:47.920 --> 01:02:51.917
it's never really been confronted
directly. Like, at any point. By the way,

01:02:52.000 --> 01:02:55.277
the physical world version of this is
the drone the drone problem. Um right?

01:02:55.360 --> 01:02:57.997
And so, we we've known for, you know,
we've known for 20 years now that the

01:02:58.080 --> 01:03:01.837
asymmetric threat both in military
in actual military conflict, but also in

01:03:01.920 --> 01:03:05.037
just like security, like like, you know,
security on the home front, the big

01:03:05.120 --> 01:03:06.477
threat is is the cheap attack drone.

01:03:06.560 --> 01:03:09.597
Right? The the the cheap the cheap
suicide, you know, drone with a bomb.

01:03:09.680 --> 01:03:12.157
And we've known that forever.
And by the way, like, you know, it's very

01:03:12.240 --> 01:03:15.197
disconcerting how like every, you know,
every office complex in the in the cut,

01:03:15.280 --> 01:03:18.917
know, in the world is like unprotected
from drone attacks. Every every stadium,

01:03:19.000 --> 01:03:22.477
every school, every prison
like is like >> Sure.

01:03:22.560 --> 01:03:25.117
Okay, we've known that. We've never done
anything about it. What are we going to

01:03:25.200 --> 01:03:27.277
do about it? Well, one possibility
is just leave leave them unprotected

01:03:27.360 --> 01:03:30.157
forever and live in a world of like
asymmetric terrorism forever. The other

01:03:30.240 --> 01:03:32.757
is take the problem seriously
and figure out the set of techniques and

01:03:32.840 --> 01:03:35.477
technologies required to to be able to
deal with that, whether those are lasers

01:03:35.560 --> 01:03:39.157
or jammers or early warning systems or,
you know, don't Personal force fields.

01:03:39.240 --> 01:03:41.517
>> Kinetic personal For Dune,
>> [laughter] >> uh, for

01:03:41.600 --> 01:03:45.317
For personal force fields. Exactly.
And in both cases, the these are these are

01:03:45.400 --> 01:03:47.917
economic asymmetries. These are economic
asymmetries, right? Cuz it's really

01:03:48.000 --> 01:03:51.037
cheap to field a bot, but it's very hard
to tell if something a bot. It's very

01:03:51.120 --> 01:03:53.797
cheap to field a drone. It's very hard
It's very expensive to defend against a

01:03:53.880 --> 01:03:56.557
drone. But you see what I'm saying
is it's it's it's the it's the virtual

01:03:56.640 --> 01:03:59.077
version of the problem and it's the
physical version of the problem. Uh, the

01:03:59.160 --> 01:04:01.597
virtual version of the problem what we
what we need quite literally is proof of

01:04:01.680 --> 01:04:03.557
human. The reason is because you're
you're not you're not going to have

01:04:03.640 --> 01:04:06.837
proof of bot. The the the especially now
that the bots are too good. The the the

01:04:06.920 --> 01:04:09.197
the bots can pass the Turing test.
And if the bots can pass the Turing test,

01:04:09.280 --> 01:04:11.197
then you can't you can't screen for bot.

01:04:11.280 --> 01:04:14.637
You can't have proof of not a bot. But
what you can have is you can have proof

01:04:14.720 --> 01:04:17.157
of human. You can have, you know,
cryptographically validated this is

01:04:17.240 --> 01:04:19.757
definitely a person. And this is And
then you can have cryptographically

01:04:19.840 --> 01:04:22.837
validated this is definitely like
something that a person said. This video

01:04:22.920 --> 01:04:27.397
is real. Right? Just to double click
on on, uh, do you think Alex Blania with

01:04:27.480 --> 01:04:30.677
World, do you think he's got it or is
there alternative? Oh, so I mean there's

01:04:30.760 --> 01:04:32.997
going to be I think there'll be I mean
many people will try. We're one of the

01:04:33.080 --> 01:04:35.797
key, you know, participants in in the
World in the World project and I didn't

01:04:35.880 --> 01:04:38.157
know that. I didn't know that. Yeah,
so we're we're partisans. But yeah, I I

01:04:38.240 --> 01:04:40.237
think So we think World
is exactly correct.

01:04:40.320 --> 01:04:42.237
>> Okay. And the reason
is it has it has to

01:04:42.320 --> 01:04:45.797
be it it has to be proof of human. It
it has because you can't do proof of not

01:04:45.880 --> 01:04:48.837
bot. You have to do proof of human. To do
proof of human, you you need you need

01:04:48.920 --> 01:04:51.957
biological validation. You you need
it to start with this was actually a

01:04:52.040 --> 01:04:55.117
person. Right? Cuz otherwise you have
bots signing up as fake people. All

01:04:55.200 --> 01:04:57.557
right? So you you have to have like
something You have to have a bio

01:04:57.640 --> 01:05:00.357
biometric. And then you have to have
cryptographic validation and then the

01:05:00.440 --> 01:05:02.517
ability to do to do to do the lookup.

01:05:02.600 --> 01:05:04.557
And then by the way, the other thing
you need which is that you you also need

01:05:04.640 --> 01:05:07.957
selective disclosure. Um, so you need
to be able to do proof of human without

01:05:08.040 --> 01:05:10.237
revealing all the underlying information.
By the way, another thing

01:05:10.320 --> 01:05:12.557
you're going to need you're going
to need proof of age, right? Cuz there's

01:05:12.640 --> 01:05:15.117
all these laws in all these different
countries now around you need to be 13

01:05:15.200 --> 01:05:17.477
or 16 or 18 or whatever to do different
things. And so you're going to you're

01:05:17.560 --> 01:05:20.557
going to need a you know, sort of validate
a proof of age. Um you know, to

01:05:20.640 --> 01:05:22.957
be able to legally operate, right?
And so that that's coming and then you're

01:05:23.040 --> 01:05:25.997
going to want like proof of credit score
and you know, proof of like, you know,

01:05:26.080 --> 01:05:28.277
100 other >> That's a tricky one.
It is a tricky one,

01:05:28.360 --> 01:05:30.797
but you're going to you're going to need
there's no reason like if somebody's

01:05:30.880 --> 01:05:33.237
checking on your credit somebody
shouldn't I'll put this I'll give you an

01:05:33.320 --> 01:05:35.677
example. Somebody shouldn't need to know
your name in order to be able to find

01:05:35.760 --> 01:05:36.797
out whether you're credit worthy, right?

01:05:36.880 --> 01:05:39.517
I see. Independently verifiable
pieces of information.

01:05:39.600 --> 01:05:42.197
>> Pieces of information. So like so like
disclosed and this is the answer to the

01:05:42.280 --> 01:05:45.317
privacy problem writ large, which is I
I only need to prove I need to prove at

01:05:45.400 --> 01:05:47.237
that moment. >> So like
you're going to need that and I

01:05:47.320 --> 01:05:50.277
think that their architecture makes sense.
So that needs to get solved. I

01:05:50.360 --> 01:05:54.517
think language models have tipped
the bots are now too good. Uh and and so

01:05:54.600 --> 01:05:56.917
they're undetectable and so as a
consequence you we now need to go and

01:05:57.000 --> 01:05:59.117
confront that problem directly. And then
and like I said and then the other

01:05:59.200 --> 01:06:01.317
problem is we we need to go
actually confront the drone problem.

01:06:01.400 --> 01:06:04.077
>> Business. The Ukraine
conflict has really unlocked

01:06:04.160 --> 01:06:07.557
a lot of thinking on that. And now
the um and now the the the the the Iran

01:06:07.640 --> 01:06:09.957
situation is also unlocking that.
And so I think there's going to be

01:06:10.040 --> 01:06:12.357
>> just like this incredible explosion
of of both drone and counter drone. Our

01:06:12.440 --> 01:06:13.847
drones are better than their guns.

01:06:13.930 --> 01:06:16.357
>> [laughter] >> It's
also keep it that way.

01:06:16.440 --> 01:06:20.477
And counter and counter drones. I think
we can sneak in one more question. Um

01:06:20.560 --> 01:06:23.397
I'm trying to tie together a lot
of things that you said over the years. So

01:06:23.480 --> 01:06:26.957
at the Milken Institute debate with Thiel,
which is amazing, um you talked

01:06:27.040 --> 01:06:31.117
about the lag between a new technology
and kind of like the GDP um impact of

01:06:31.200 --> 01:06:33.997
it. The other idea you talked about is

01:06:34.080 --> 01:06:38.277
bourgeois capitalism and how you know,
this kind of managerial class was needed

01:06:38.360 --> 01:06:41.637
because of this complexity and I think
if you bring AI into the fold you have

01:06:41.720 --> 01:06:44.357
like much higher leverage of people. So

01:06:44.440 --> 01:06:48.877
like if you have, you know, the Musk
industries um and you give Elon a GI you

01:06:48.960 --> 01:06:50.957
can run a lot more things uh at once.

01:06:51.040 --> 01:06:54.277
That's right. And then you have the social
contract and I know you retweeted

01:06:54.360 --> 01:06:57.397
a clip of Sam Altman saying um we're
rethinking the whole thing and you're

01:06:57.480 --> 01:07:00.437
like absolutely not. >> Yes.
Under Uh and I was I was at that

01:07:00.520 --> 01:07:03.637
event with Sam last night uh and he
actually said in the last couple weeks

01:07:03.720 --> 01:07:07.077
he felt like now people are taking that
seriously. So I'm just curious like how

01:07:07.160 --> 01:07:09.917
you're seeing the structure
of organization changing,

01:07:10.000 --> 01:07:13.197
especially when you invest in early
stage companies, and um yeah, just like

01:07:13.280 --> 01:07:16.797
how the impact of work structure
and all of that is playing out.

01:07:16.880 --> 01:07:19.317
>> Yeah. So, there's a whole bunch
of there's a whole bunch of time

01:07:19.400 --> 01:07:20.997
we [laughter] could spend I don't know
by the way we'd be happy to spend more

01:07:21.080 --> 01:07:23.997
time, but we could we could spend more
time on all that. So, just it for people

01:07:24.080 --> 01:07:26.757
who haven't followed this. So, that
this this this term managerial comes from

01:07:26.840 --> 01:07:29.957
this thinker in the 20th century James
Burnham who um is one of the great kind

01:07:30.040 --> 01:07:32.917
of 20th century political thinkers uh
societal thinkers. And he sort of said

01:07:33.000 --> 01:07:35.797
as and he was writing
in like 1940s, 1950s.

01:07:35.880 --> 01:07:38.597
Um and he said kind of that the whole
history of capitalism until that point

01:07:38.680 --> 01:07:41.397
had been in two phases. Number one had
been what he called bourgeois capitalism

01:07:41.480 --> 01:07:45.077
which was think about it as like name
on the door. Like Ford Motor Company cuz

01:07:45.160 --> 01:07:48.197
Henry Ford runs the company. Um
and Henry it's like a dictate dictatorial

01:07:48.280 --> 01:07:50.797
model and Henry Ford just like
tells everybody what to do.

01:07:50.880 --> 01:07:53.677
And he said the problem with bourgeois
capitalism is it doesn't scale cuz Henry

01:07:53.760 --> 01:07:56.277
Ford can only tell so many people to do
so many things and then he runs out of

01:07:56.360 --> 01:07:59.997
time in the day. And so, um he said
the second phase of capitalism was what he

01:08:00.080 --> 01:08:02.957
called managerial capitalism which was
the creation of a professional class of

01:08:03.040 --> 01:08:06.477
managers. Um that are
trained not to be like car

01:08:06.560 --> 01:08:09.877
experts or to be whatever experts in any
particular field, but are trained to be

01:08:09.960 --> 01:08:12.917
experts in management. And then that led
to, you know, the importance of like

01:08:13.000 --> 01:08:15.517
Harvard Business, you know, business
schools and management consulting firms

01:08:15.600 --> 01:08:19.157
and all these things. And then you look
at every big company today and like most

01:08:19.240 --> 01:08:22.397
of the executives at most of the Fortune
500 companies are not domain experts in

01:08:22.480 --> 01:08:24.917
whatever the company does and they're
certainly not the founders of those

01:08:25.000 --> 01:08:27.997
companies, but they're professional
managers. And in fact, in the course of

01:08:28.080 --> 01:08:30.837
their careers they'll probably manage
many different kinds of businesses.

01:08:30.920 --> 01:08:33.077
They'll rotate around and they might
work in health care for a while and then

01:08:33.160 --> 01:08:35.357
work in financial services and then go
work in something else, you know, come

01:08:35.440 --> 01:08:37.837
work in tech. And what
Burnham said is he said that

01:08:37.920 --> 01:08:41.077
transition is absolutely required
because the the the problem with

01:08:41.160 --> 01:08:44.797
bourgeois capitalism is is it doesn't
scale. Henry Ford doesn't scale. And so,

01:08:44.880 --> 01:08:47.517
if you're going to run capitalist
enterprises that are going to have

01:08:47.600 --> 01:08:50.637
millions to billions of customers,
um you're going to need to you they're

01:08:50.720 --> 01:08:53.037
going to be operating a level of scale
and complexity that's going to require

01:08:53.120 --> 01:08:54.757
this professional management class.

01:08:54.840 --> 01:08:56.757
And he said, "Look, that the
professional management class has its

01:08:56.840 --> 01:08:59.557
downsides. Like they're not necessarily
experts at doing the thing. They're not

01:08:59.640 --> 01:09:02.757
as inventive, you know, they're not going
to create the next breakthrough thing."

01:09:02.840 --> 01:09:04.757
But he's like whether you think that's
good or bad or whatever it's what's

01:09:04.840 --> 01:09:07.677
going to be required. And basically
that's what happened, right? And so he

01:09:07.760 --> 01:09:09.837
wrote that book originally like 1940.

01:09:09.920 --> 01:09:12.877
You know, over the course of the next
50 years basically managerialism Well, I

01:09:12.960 --> 01:09:15.917
mean, today up till today managerial
managerialism basically took over

01:09:16.000 --> 01:09:18.357
everything and you know, what I'm
describing is basically how all big

01:09:18.440 --> 01:09:21.597
companies run and how all governments
run and how all large-scale nonprofits

01:09:21.680 --> 01:09:24.117
run and kind of everything
you know, everything runs.

01:09:24.200 --> 01:09:28.357
Basically what what what venture capital
does is we basically are a rump sort of

01:09:28.440 --> 01:09:32.157
protest movement to that to try to find
the next Henry Ford or or just to say

01:09:32.240 --> 01:09:35.197
Elon Musk or or the next or the next
Elon Musk or the next Steve Jobs or the

01:09:35.280 --> 01:09:37.877
next Bill Gates or the next Mark
Zuckerberg. And so we we we we start

01:09:37.960 --> 01:09:39.917
these companies in the old model, right?

01:09:40.000 --> 01:09:44.157
We we we start them out as as as as like
in the Henry Ford model. And so we start

01:09:44.240 --> 01:09:47.037
them out with a founder or a or a or
a founder with with colleagues, but you

01:09:47.120 --> 01:09:48.957
know, there's there's a founder CEO.

01:09:49.040 --> 01:09:52.677
Um and then we basically bet that we
basically bet that the startup is going

01:09:52.760 --> 01:09:56.117
to be able to do things specifically
innovate in ways that the big incumbents

01:09:56.200 --> 01:09:58.557
in that industry are not going to be
able to do. And so it's a bet that by

01:09:58.640 --> 01:10:02.157
basically by relighting the sort
of name you know, kind of thing

01:10:02.240 --> 01:10:07.197
this new innovative thing with like
a king monarchical political structure. Um

01:10:07.280 --> 01:10:08.997
that they're going to be able
to innovate in a way that the incumbent is

01:10:09.080 --> 01:10:11.757
not going to be able to because
the incumbent is is being run by managers,

01:10:11.840 --> 01:10:14.597
right? And and and and by the way
and of course venture being what it is

01:10:14.680 --> 01:10:17.077
sometimes that works sometimes it doesn't,
but but we're constantly doing

01:10:17.160 --> 01:10:19.917
that. But I've always viewed it
my entire life as like we're like raging

01:10:20.000 --> 01:10:21.837
against the dying of the light. Like
we're we're we're [clears throat] we're

01:10:21.920 --> 01:10:25.277
sort of constantly trying to fight off
managerialism just basically swamping

01:10:25.360 --> 01:10:29.317
everything and everything getting
basically boring and gray and dumb and

01:10:29.400 --> 01:10:32.557
old. Right. And it's we're trying
to keep some level of energy and vitality

01:10:32.640 --> 01:10:35.717
in the system.
AI is the thing that would lead you to

01:10:35.800 --> 01:10:38.237
think wow, maybe there's a third model.

01:10:38.320 --> 01:10:40.797
Right. And and maybe and way to think
about it would be maybe it's a

01:10:40.880 --> 01:10:43.837
combination of the two. Maybe the new
Henry Ford or the new Elon or the new

01:10:43.920 --> 01:10:47.317
Steve Jobs plus AI is the best of both.

01:10:47.400 --> 01:10:50.597
Right? Cuz it's it's it's sort of the
spark of genius of the name on the door

01:10:50.680 --> 01:10:54.557
model, the Henry Ford model, but then
it's give that person AI superpowers to

01:10:54.640 --> 01:10:58.237
do all the managerial stuff and let
the boss do all the managerial stuff. That

01:10:58.320 --> 01:11:00.197
may be the actual secret formula.

01:11:00.280 --> 01:11:03.077
And we've never even known that we
wanted this because we never even

01:11:03.160 --> 01:11:06.277
thought it was a possibility. But yeah,
I mean, you know this. The What is the

01:11:06.360 --> 01:11:08.397
thing that these bots are really good at?
They're really good at doing

01:11:08.480 --> 01:11:12.037
paperwork. Like, they're really good
at filling out forms. Like, they're really

01:11:12.120 --> 01:11:14.717
good at writing reports. They're really
good at reading them. They're really

01:11:14.800 --> 01:11:16.397
good at doing all the managerial work.

01:11:16.480 --> 01:11:20.117
Like, they're amazing at it. And so,
yeah. So, I I think I think the I 100% I

01:11:20.200 --> 01:11:24.037
think the answer the answer very well
might be to get the best best best of

01:11:24.120 --> 01:11:26.997
both worlds by doing this. And then
the challenge is going to be twofold. It's

01:11:27.080 --> 01:11:29.517
going to be for the innovators to really
figure out how to leverage AI to

01:11:29.600 --> 01:11:33.277
actually do this. Right? And then And then
the the other challenge is going to

01:11:33.360 --> 01:11:36.517
be for the for the incumbents that are
managerial to figure out like, okay,

01:11:36.600 --> 01:11:38.557
what does that mean? Cuz now they're
going to they're they're going to be

01:11:38.640 --> 01:11:41.397
facing a different kind of insurgent
competitor that has a different set of

01:11:41.480 --> 01:11:45.437
capabilities than they're used to. And so,
this really, I think, is going to force

01:11:45.520 --> 01:11:48.637
a lot of big companies to kind of figure
out innovation. Either I say figure out

01:11:48.720 --> 01:11:51.877
innovation or die trying.
Do you feel like that structure

01:11:51.960 --> 01:11:56.237
accelerates the impact on the actual
GDP and economy? If you could space out,

01:11:56.320 --> 01:11:58.317
it's like the growth is like so fast.

01:11:58.400 --> 01:12:01.277
And like, instead of having these
companies kind of like peter out in

01:12:01.360 --> 01:12:04.397
growth and impact, they can kind
of like keep going, if not accelerating.

01:12:04.480 --> 01:12:08.237
>> Yeah. That's for sure the hope. The
The challenge and and you know, and and

01:12:08.320 --> 01:12:11.077
look, the AI utopian view is a Of course.
And and and that's going to be

01:12:11.160 --> 01:12:13.717
the future of the economy, and it's
going to grow 10x and 100x and 1,000x,

01:12:13.800 --> 01:12:16.837
and we're entering this regime of like
much higher economic growth forever, and

01:12:16.920 --> 01:12:19.717
consumer cornucopia of everything, and
it's going to be great. And I And I hope

01:12:19.800 --> 01:12:22.557
that's true. I hope that's That's like
the you know, that's the current kind of

01:12:22.640 --> 01:12:25.637
utopian vision. I hope that's true.
The problem is goes back again. The real

01:12:25.720 --> 01:12:29.077
world is really messy. Um, and I'll give
you an example of how the real world is

01:12:29.160 --> 01:12:32.677
really messy. It requires 900 hours
of professional certification training to

01:12:32.760 --> 01:12:35.157
become a hairdresser
in the state of California.

01:12:35.240 --> 01:12:40.037
Um, so, it's like 35% of the economy,
something like that, you have to get

01:12:40.120 --> 01:12:42.877
some sort of professional
certification to do the job.

01:12:42.960 --> 01:12:45.797
Which [snorts] is to say that the the
professions are all cartels, right? And

01:12:45.880 --> 01:12:48.517
so, you have to get licensed as a doctor.
You have to get licensed as a

01:12:48.600 --> 01:12:53.237
lawyer. You have to get licensed
as a You have to get into a union.

01:12:53.320 --> 01:12:56.357
By the way, to to work for the government,
you need to be you have both

01:12:56.440 --> 01:12:59.397
civil service protections and you
have public sector unions. You have two

01:12:59.480 --> 01:13:02.157
layers of insulation against
ever getting fired for anything

01:13:02.240 --> 01:13:03.227
or anything anything ever changing.

01:13:03.310 --> 01:13:06.477
[laughter] I'll give you another example.
The the dock work the dock workers went on

01:13:06.560 --> 01:13:10.637
strike a couple years ago cuz they're
robotics if you go look at a modern dock

01:13:10.720 --> 01:13:13.997
like in Asia it's all robots. If you
go to American dock it's like all still

01:13:14.080 --> 01:13:16.677
guys dragging dragging stuff by hand.

01:13:16.760 --> 01:13:19.957
The dock workers went on a strike.
It turns out there are 25,000 dock workers

01:13:20.040 --> 01:13:22.077
working on docks in America.
Turns out they have

01:13:22.160 --> 01:13:25.437
incredible political power cuz it's
a it's one of these unified blocks of

01:13:25.520 --> 01:13:28.477
things. They won their strike and so
they got commitments from the dock

01:13:28.560 --> 01:13:30.437
owners to not implement more automation.

01:13:30.520 --> 01:13:31.437
We learned a couple of things in that.

01:13:31.520 --> 01:13:34.197
So number one we learned that even
a union as small as 25,000 people still

01:13:34.280 --> 01:13:37.517
has like tremendous political stroke. We
also learned that they it actually turns

01:13:37.600 --> 01:13:40.797
out the dock workers union has 50,000
people in it cuz there's 20 they have

01:13:40.880 --> 01:13:43.437
25,000 people working at docks they
have 25,000 people drawing full paycheck

01:13:43.520 --> 01:13:48.117
sitting at home from prior union
agreements from prior union agreements.

01:13:48.200 --> 01:13:49.317
I'll give you another great example.

01:13:49.400 --> 01:13:52.877
There are government agencies there are
federal government agencies where the

01:13:52.960 --> 01:13:56.197
employees right of have civil service
protections and they're in public sector

01:13:56.280 --> 01:13:59.437
unions. There are entire federal
government agencies that struck new

01:13:59.520 --> 01:14:03.117
collective bargaining agreements during
COVID. Where not only are they have

01:14:03.200 --> 01:14:05.957
their jobs guaranteed in perpetuity
but they only have to report to work in an

01:14:06.040 --> 01:14:09.357
office one day per month.
And so there are entire office buildings

01:14:09.440 --> 01:14:13.357
in Washington DC that are empty 29 out
of 30 days of the year that are still

01:14:13.440 --> 01:14:15.797
operating and are still
we're all still paying for it.

01:14:15.880 --> 01:14:17.677
And so and then what they do it turns
out what the employees do is they're

01:14:17.760 --> 01:14:20.437
very they're very smart in the in in
this way and so they figure it out. They

01:14:20.520 --> 01:14:23.477
come in on the last day of a month
and the first day of the next month.

01:14:23.560 --> 01:14:27.597
And so they're in the they're
in the office two days per 60 days.

01:14:27.680 --> 01:14:30.347
Which means these buildings
are empty for 58 days at a time.

01:14:30.430 --> 01:14:32.557
>> [laughter] >> And you
see you see where I'm heading

01:14:32.640 --> 01:14:35.637
with this? Like this is like locked in.

01:14:35.720 --> 01:14:39.477
Right? This is like locked in in a way
that has nothing to do with like people

01:14:39.560 --> 01:14:41.957
say capitalist it's like
anti-capitalistic. It's like it's it's

01:14:42.040 --> 01:14:43.917
basically it's restrictions on trade.

01:14:44.000 --> 01:14:45.437
It's restrictions on the ability to like

01:14:45.520 --> 01:14:48.357
change the workforce. And so, so much of

01:14:48.440 --> 01:14:52.477
our economy is is you know the I'm
describing the entire healthcare

01:14:52.560 --> 01:14:56.677
system. I'm describing the entire legal
profession. I'm describing the entire

01:14:56.760 --> 01:15:01.157
housing industry. I'm describing
the entire education system. Right, K-12

01:15:01.240 --> 01:15:03.957
schools in the United States they're
a literal government monopoly.

01:15:04.040 --> 01:15:06.157
How are we going to apply AI in education?
The answer is we're not

01:15:06.240 --> 01:15:08.957
because it's a literal government
monopoly. It is never going to change.

01:15:09.040 --> 01:15:12.237
The end and there is nothing to do.
By the way, you can create an entirely new

01:15:12.320 --> 01:15:14.597
school system. Like that's the one
thing you can do is you can do what Alpha

01:15:14.680 --> 01:15:16.997
School is doing. You can create
an entirely new school system. Other than

01:15:17.080 --> 01:15:19.277
that, you're not going to go and change
what's happening in the American

01:15:19.360 --> 01:15:21.477
classroom like K-12. There's no chance.

01:15:21.560 --> 01:15:22.997
The teachers are 100% opposed to it.

01:15:23.080 --> 01:15:26.237
It's 100% not going to happen. So So you
see what I'm saying is like there's this

01:15:26.320 --> 01:15:30.157
like massive slippage that's going to take
place. Both the AI utopians and the

01:15:30.240 --> 01:15:31.797
AI doomers are far too optimistic.

01:15:31.880 --> 01:15:35.277
Right. >> [laughter] >> You see
what I'm saying because because

01:15:35.360 --> 01:15:38.157
they believe that because the technology
makes something possible that 8 billion

01:15:38.240 --> 01:15:40.157
people all of a sudden are going
to change how they behave and it's just

01:15:40.240 --> 01:15:44.637
like, nope. So much of how the existing
economy works is just it's just like

01:15:44.720 --> 01:15:48.477
wire done. And so, we're going to be
lucky as a society we're going to be

01:15:48.560 --> 01:15:50.477
lucky if AI adoption happens quickly.

01:15:50.560 --> 01:15:53.117
Right, because if it doesn't what
we're just going to have is stagnation.

01:15:53.200 --> 01:15:55.357
As a matter of fact, I know you got
to run. Yeah, we all know if or still

01:15:55.440 --> 01:15:59.037
welcome but uh it was such a pleasure
talking to you. Uh we're truly living in

01:15:59.120 --> 01:16:02.677
an age of science fiction coming
to real life. Yes. Yes. Could not be more

01:16:02.760 --> 01:16:05.797
excited. Really thank you Mark
for you guys. Awesome. Thank you.

01:16:05.880 --> 01:16:08.560
Good. Thank you.

01:16:15.230 --> 01:16:17.250
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