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People are becoming what we now refer
to as AI vampires. They've got these huge

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bags under their eyes. They're
completely exhausted, but they're like

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euphoric. They're thrilled. We're
entering a golden age, which is AI is

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going to be a superpower that everybody
on the planet's going to have access to.

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It's like the most dramatic increase
in programmer productivity in like ever.

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>> Twitter proved it, right? Cutting 70%
and then it's running better or as good

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as it was before.
>> I generally don't wish I could go back

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in time and do things over again, but it
would be really really fun right now to

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be 18 or 20 or 22 and to have this
capability and figure out what I could

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do with it. We are going to see super
producers the likes of which we've never

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seen in the world. >> There's
news about it. UFOs. What is

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clear is the government at certain times
has hid certain materials. Why would

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they do that if there's
nothing to really worry about?

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>> Two things are pretty clear
at this point. One is that

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>> Mark, welcome
to monitoring the situation.

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>> Eric, it is great to be back.

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>> So, there's a lot to monitor today. Uh
I want to start first start with something

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that just happened um which is the uh

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anthropic uh blackmailing incident. And

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I I first want to tell a brief story
which is uh my uh friend Joe Hudson has

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this concept called the golden algorithm.
And the golden algorithm is

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states that um whatever you're scared
about you bring it about in exactly the

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way you're scared about it. So if you're
scared about getting abandoned you'll be

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super insecure and then you'll people
will abandon you because you're so

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insecure. Uh this is an example of a
literal golden algorithm where people

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have been so scared that AI is going
to be evil and have written about all the

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ways in which it's evil and in fact
maybe it's informed um informed

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something. What's happening there
or what do we find uh interesting?

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>> I I haven't studied this one in detail.

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Um uh I I've been monitoring other
situations but however um uh I mean just

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what I saw so far I think I just saw
anthropics thread. I haven't I haven't I

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haven't read the underlying material
yet, but Anthropic threads have said

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they trace the they trace some blackmail
behavior to literally to the to the AI

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doomer literature. >> Yeah.
>> That they [laughter]

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that it was in the training data.
So there there are all these there are all

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these there are all these you know
scenarios of like you know the terminate

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you know all the rogue AI gone wrong
that the that the AI doomer has been

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writing about for 20 years and and
literally anthropic of course which is

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of course the company is like you
know half doomer u apparently you know

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basically you know essentially said that
their own their own their own movement's

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literature is the thing that's causing
the behavior that they say they they

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they don't want. So it is a fairly um
incredible Yes. Yes it is. Yes. Oh, I

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mean like look if you didn't if you
don't want to build the killer AI, you

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know, step one would
be don't build the AI.

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[laughter] It's like hm
and then you know step two

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is like don't train it on all the data
that says it's supposed to, you know,

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the literature that your movement wrote
that says it's supposed to be a killer

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AI. So I, you know, Yeah. I don't know.

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Yeah. It's like your it's like your your
your golden algorithm coupled coupled

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with like the snake eating his tail.
Um coupled with, you know, I don't even

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know. Like the whole thing is so bananas.

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>> Yeah. Yeah. the um >> I mean
you know I can't resist you know

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if I could if I can if I can act out
memes it's the scream meme right which

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is you know the calls
coming from inside the house.

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>> Yeah. Yeah. Exactly. The um

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speaking of other situations another
thing you've been talking about recently

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is um is the concept of suicidal empathy
and and and Matt Kramer had a good quote

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which is if the empathy you have doesn't
make you more forgiving more accepting

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of other people's spiritual sovereignty
or more understanding of people who

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don't want to think or live the same way
you do you don't have empathy. you have

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empathy TM. Why have you been
thinking about this this this concept?

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>> Yeah, so there's this really brilliant

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there's this really brilliant guy God

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gad exactly pronouncing Gatsad. Um
and um and you know very brilliant guy and

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it's obviously lots of YouTube videos
and books and so forth and really

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brilliant guy. He's got this new book
coming out on so-called what he calls

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suicidal empathy and you know look it's
a you know there's a there's a sort of

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political loading to it which you know
we don't need to spend a lot of time on

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but um you know it's sort of this idea
that there are kind of these social

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justice you know kind of social reform
movements you know kind of through time

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that that have this characteristic
of you know they you know they they they

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claim to be causing positive change you
know in some direction and then it turns

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out they have you know sort of severe
you know sort of negative consequences.

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um that the the great Thomas Soul um
you know has you know basically spent 50

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years writing books about this um
and [laughter] and by the way no nobody

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nobody listened um and then in the last
decade we've been through you know wave

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after wave of this kind of social
activism that kind of you know results

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in like I mean it's it's all the stuff
right it's just you know all these like

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you know crime policy reform defund
the police things and it causes these

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massive crime waves and then of course
low-inccome and minority people get hit

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hardest by that and you know all the all
all these other all these other crazy

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things. Um, and so he says, you know,
he says the characteristic of kind of that

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kind of social reform movement is is
characterized by what he calls suicidal

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empathy. Um, and the and the idea be
being basically it's it's sort of driven

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by a pathological, you know,
take it backwards, a pathological form of

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empathy on the one hand. Um, which is,
you know, it's sort of a deep desire to

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be nice. Um, and empathetic um,
you know, but but coupled with like

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basically you know, a sort of
self-destructiveness, you know, either a

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willingness to really cause damage
to the people you claim to be speaking for

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or by the way to cause damage
to yourself kind of in that process. And

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and it's the kind of thing where you
know if you've you know you know if

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you've lived through you know like
everybody who you know everybody in San

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Francisco's lived through this for the
last decade and seen the consequences of

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these movements. I [clears throat] you
know the San Francisco version of this

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is like the the quote harm reduction
movement you know that ended up

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basically handing out you know free
drug you know paraphernia and you know in

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some cases actually just free drugs
to you know people who are just literally

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dying in the street from drug addiction.

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Right. So so so you just look at it and
you're like well yeah that that you know

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they they claim to be activists they
claim to be reformers. they claim to

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care about these people and yet they're
killing them or or um and then killing

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the city um and causing innocent people
to get get harmed. It's like okay that

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you know you know that that that you
know they seem so actively like that

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they're doing it out of some sense
of compassion that this must be suicidal

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empathy. Um the problem [laughter]
with it is and I think that the problem

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is the theory is sort of easily
falsifiable or or maybe let's lets the

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reformers off the hook which is they
certainly don't show empathy to their

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enemies, right?
And so if they're like if they're

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like all empathetic, you would think
that they would be less aggro uh when it

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comes to destroying their ideological
opponents, you know, who they act, you

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know, they take great delight in trying
to wreck. Um number one, on the one

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hand, and then and then number two
is they they use the you know, the classic

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reformer move is to use use these
movements to gain to gain power and

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status and money for themselves. And you
know, again, San Francisco's a case

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study of this where you have all these,
you know, nonprofits that, you know,

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recall this damage on the city and yet,
you know, basically get like lavishly

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funded, you know, including, by the way,
by the city government, by the state

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government. Um, and so it's just like,
okay, well, like just like they're not,

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if you just like spend two seconds
thinking about it, like it, you know,

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they're neither empathetic nor nor
are they suicidal, right? Right? Rather

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quite the opposite. They're hateful
and they're and they're and they're and

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they're greedy. You know, they're
sort of self- aggrandizing um and and

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gathering power and resources
for themselves. Um, and so I just I just

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think it it it lets it lets the
phenomenon off the hook, you know, it's

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a little bit like, "Oh, Eric, what's
your biggest flaw?" You know, "Oh, I'm

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too nice." >> Yeah. >> Yeah.
I care too much. Right. Exactly.

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It's like, yeah, don't Like,
I I By the way, Eric, I don't know. I

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don't know what your biggest flaw is.

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Like, it's definitely like, it's
definitely not that because that's also

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definitely not my flaw. Like, I I
guarantee I have other things wrong with

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me that are like way more wrong than that.

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>> Yeah. >> Um, and so I
I I just I Yeah. I can't I

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I I hit my limit on on on on on that
topic. And maybe a maybe a crazy example

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of this and I'm not sure if all the all
the facts are out yet, but but it was a

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situation a week ago, but it hasn't been

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covered, you know, that that much. the

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SPLC incident. Is it accurate that

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basically what was happening or is it
our understanding that basically the

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groups that they were um sort of

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fighting the most or or thought were the

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biggest threats to um sort of you
know what they care about also the same

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groups that they were secretly funding
unbeknownst to those to to those groups

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or how do we make sense of what was
actually happening there and is that is

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that indicative of what's of of of
something bigger happening and it's

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funny because that happened the day
after we had a conversation about

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astrourfing and I was uh and I was like
is it really happening to the degree

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that that uh you know people are or sort
of conspiracies are are things like that

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really happening and it's just funny
that more and more seem to get uncovered.

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>> Yeah. And so I should start by saying
the reason why this situation really

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matters and actually I think matters
a lot is the the SPLC specifically um and

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other groups like it as well but the SPLC
specifically played a dominant role

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in the debanking and censorship
and cancellation programs of the last 15

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years. Um, and I cannot tell you how
I I was in so many meetings in so many

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contexts with so many companies um where
the SPLC's word was was gospel. Like it

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was just like, oh, it's the SPLC that it
was almost like they're the outsourced

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US Department of I don't know, racism
detection or something. It's just like

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if the SPLC's says you're bad, you're bad.
Um, and and you're bad means you

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get kicked off of all the social media
platforms. It means you get debanked. It

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means you can't get a job. It means like
just like it's just like total absolute

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like, you know, social economic death.

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Um, and and in my view, you know,
I've been very vocal on the debanking and

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censorship topics. In my view,
that includes, you know, very deeply

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unamerican and I think in many cases
unconstitutional removal of both free

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speech rights and also u literally the
ability to to bank. Um, and in fact, you

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know, our partner Ben's father himself
was was was specifically tagged and

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attacked by the SPLC for for unfairly
very unfairly uh for being racist. Um,

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and was himself debanked um and you
know, really like directly threatened

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his livelihood um in a in a really,
you know, egregious way. um you know and

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then and then by the way
the significance of this is of course it's

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not literally the US department of racism
it's actually arguably worse than

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that it's not a government agency and so
it's not subject to like any level of

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government oversight it's not you know
it's a completely you know as I say it's

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an NGO right um and so it's it lives
in this like twilight world you know it

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doesn't have the you know business
responsibilities of a company it doesn't

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have any of the um any of the legal um
oversight um you know that a government

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agency has it lives in this kind
of twilight world where it gets to do you

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know fundamentally gets to do whatever
it wants um and then by the way on top

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of that you raises raises money as a
nonprofit. Um, so, you know, on top of

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that, everybody gets a tax break. And so,
it's this it's this, you know, kind

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of shadowy thing. Like, if you if you
didn't agree with his politics, you were

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just like, "Wow, like that this is like
a weird star chamber like shadowy thing.

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Like, what the hell?" Um, but like it
had like really really really really

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intense power particularly uh in in
the business world, particularly in the

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financial sector, particularly in Silicon
Valley. Like, it could basically

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it was like a death star uh to be able
to aim it at at obliterating people's

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reputations um uh and rights. Um, and so,
you know, this is a really big deal.

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Um, by the way, um, many of the, uh, big
corporations and and including big tech

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companies funded it directly. Um, and so
the the the money trail on this is not

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not just major philanthropist and
philanthropists and political activists,

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um, but also um, actual, you know,
actual companies. And then, by the way,

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they also had, you know, a long history
of actually cooperation with certain

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government agencies, including, I think
for a long time, they quote unquote

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trained FBI agents um, basically um,
uh, you know, essentially catching, you

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know, you know, racist and therefore,
you know, sort of presumptive um,

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domestic terrorists or something.
Um, and so just like a ve very powerful

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outfit and then you know this this this
this thing that dropped is that they've

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been now criminally indicted by the US
Justice Department and and I should say

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the the indictment is like reads like
a novel that no it's an indictment the

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SPLC in fairness has not had a chance
to present a defense um and so you know

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presumably in court we'll you know we'll
get both sides of this um which I'm sure

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will be an absolutely spellbinding
experience. Um so I I you know of course

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I I want to say you know all all all
of the things that are in the indictment

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are allegations um and innocent until
proven guilty and you know so forth. Um

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you know however the allegations are eye
watering right and the allegations are

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that they they that the SPLC using donor
funds was directly funding among other

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organizations the Klux Clan
and the American Nazi party. [laughter]

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Let me just repeat that. the Ku Klux

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Clan and the American Nazi Party. Um,
as well as an array of other uh sort of uh

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sort of extreme, you know, hate,
you know, you know, literal lit literal

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little literal hate groups. Um, and you
know, and funding them and not just

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funding them, not just like funneling
money in, but like funneling money to

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very senior members or leaders of the
of these organizations. And then the the

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kicker is in in in the among
the allegations is they were directly

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funding one of the leaders of the the
Charlottesville riot. So the the the

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Charlottville riot in 2017 that played
such a central role, you know, in our

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politics, um at the time, you know,
you know, kind of the famous there good

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people on both sides, you know, kind
of thing, you know, which was like one of

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the big crises of of of kind of that of
of kind of that that that that era. Um

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the SPS was directly funding, evidently,
allegedly, they were directly funding

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one of the organizers of the January 6th
riot. Um a and apparently they were also

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paying for transport um for for riers to

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to go to the capital. Um right. And so

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like if this is true, you know, yeah,
I mean, if this is if this, you know, what

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could you conclude if this is true?

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Well, well, number one is like the
allegation is they they broke the law in

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doing that. There's additional
allegations in the DOJ uh you know,

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indictment that they've committed,
you know, money various kinds of

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moneyaundering crimes, you know, other
other kinds of crimes. And so that, you

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know, that that, you know, that's a big
deal. Um, and then and then I've been

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I've been asking the the the obvious
question, which is if any of these

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claims are true, what what did their
donors know? Um and you know were the

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donors all totally oblivious to this or
or did the donors you know work closely

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with them um the also by the way
the companies that worked with them um you

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know did they you know what what did
they know of what was happening and so I

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you know I do wonder whether there's
like a you know I I wonder whether over

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time what we're going to discover
is this was a you know sort of sprawling

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network um you know of which there's
you know a legal the legal term would be

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conspiracy um you know that was that
was going on around this and so I I think

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this needs to be f fully unressed um
you know look the other thing is you know

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this raises the obvious question is were
they the only one. Um, and so there's a

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variety of these groups that had, you
know, degrees of the kind of power that

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I was talking about. Um, and you know,
tremendous amounts of funding along the

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way um, and the ability to basically
again direct some combination of of of

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state and government uh, state and uh,
and um, and private sector, you know,

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sort of obliteration raise um, uh, at

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uh, at at at American citizens. Um,
there have been rumors for years, you

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know, on I mean there's been rumors for
years on this. There's been, by the way,

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there's been incidents like this in
the past like, you know, this isn't the

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first time this has happened. Um,
but you know, if if if the if if the

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allegations are true and the SPLC
was doing it, I think it raises the very

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direct question of like, okay, who else
who else is? It's hard to believe they

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were the only one. Um, and so who else
is doing it? And then, yeah, and then

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we're back to our astroturfing thing,
um, which is, yeah, we're, you know,

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we're were they constructing
the boogeyman um, you know, that that that

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they claim to be fighting? And by the way,
and this is where you get into the

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sort of self-interest component of it.

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This is where you get back to the
suicidal empathy thing, which is like,

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okay, how how how suicidal is it if
you're the anti-KK group to fund the

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KKK? Like I I don't know, maybe it's
suicidal if people find out about it.

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But if they don't find out about it,
like that's not that's like the o that's

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like the opposite of suicidal because
like wow like would you know wouldn't

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you like to get to like if your if your
group's entire purpose for existing is

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to fight an enemy then you need to make
sure that that enemy exists to do that

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of course of course of course you would
fund them. Um and so yeah you you I

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don't know what is that. That's like the
reverse of the snake eating the tail. Um

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it's the um you know sort of a you
know you're creating a self-fulfilling

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prophecy. Um, and so, yeah, I mean,
look, we're gonna I I mean, this all

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needs to be ventilated. Um, I'm I'm
really fascinated to see like what the

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reality is underneath this, but, you know,
by at the end of the day and what

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this means about what we've all been told
all these years about all these groups. It

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>> it's it's funny. There was a Nathan
Fielder sort of AI, you know, video that

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looked so real, which is he's basically
saying, "Hey, our business model is to

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fight racism. We need to fund more racism.
Uh, and then we'll get more business."

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>> Yes. Correct. Exactly. [laughter] Yeah.

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And one of the things is the lucrative
and again it's because they get they get

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cloaked in this kind of NGO lens. I mean
they they've got you know the amounts of

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money at play are not small. Um you know
they're they're very very low you know I

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don't know the the SPLC has something
like an $800 million endowment. Um and

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you know has an enormous budget.
Um and by the way people get paid a lot of

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money to do this work. Um and and by the
way there's recurring scandals on that

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front also. um you know which is um you
know you get you get a lot of these kind

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of activist uh you know kind
of foundations and so forth where when you

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when you look into it it's like you know
some some giant percentage of the uh of

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their of their of their spending
is going to you know salaries and and and

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expenses for their for their employees.

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Um, and so, and again, they they kind
of they they cloak it in, you know, they

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cloak it in virtue. Um, and then you
kind of look underneath and you're like,

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"Wow, like this is this is, you know,
this is this this is amaz like and by

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the way, like I don't know, it's America.
Maybe they should be allowed to

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do all this, but like maybe we should
not get Yeah, maybe we should not get

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lied to in the process." You know, may
may all this should not get them dressed

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up to make us feel like, you know, our
our whole society is rotten and immoral

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and that and that and that, you know,
deplatforming and censorship are and

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debanking are good ideas.

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you people respond of course with hey
this time is different because it's it's

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it's all potential cognitive work and so

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uh and then there's also this other
statement of hey um you know humans will

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you know differentiate among taste and
agency but it seems like AI can can can

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do that too um and and then there's
al that juxtaposed there's also the

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statement of hey uh it won't replace
a lot of the jobs because a lot of jobs

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are make work anyways. Uh you had this
you know tweet the other day of hey I'

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I've been saying companies have been you

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know two to 4x bloated for a long time

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and people have just been unwilling to
to to to deal with it or or look that in

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the face. So and this presents kind of
a golden opportunity for that. What um so

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why don't you address some of these some
of these topics as it relates to AI and

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jobs of the future as it relates to tech.

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00:17:05,600 --> 00:17:07,677
>> Yeah. So we'll come back to the blow
thing. I will say the the funny thing on

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the bloat tweet was the responses have
been along the line. The responses for

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the most part have not been you're wrong.
The responses have been oh no the

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company I used to work at is like 8x
bloated [laughter] >> yeah too generous

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>> right or yeah too generous or you
know the not you know or by the way the

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nonprofit or the you know whatever the you
know whatever kind of institution uh

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you know agency I used to work for.

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>> Well well Twitter proved it right
cutting 70% or 80% and then it's running

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you know better or as good as it was
before at at least and it's probably not

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00:17:33,520 --> 00:17:37,597
the only it's not the exception. I I I
mean, look, I I don't even know. Uh and

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00:17:37,680 --> 00:17:40,317
you know, if I knew I wouldn't say, but

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like I like I think Twitter is way down

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[laughter] from the 80%.

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I think there I don't know if the number
for sure the number has a nine on it. Um

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00:17:50,800 --> 00:17:54,957
if not if not if not a high nines. Um so
yeah, no, he's really he's as usual with

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00:17:55,040 --> 00:17:57,917
Elon, he's really demonstrated he's
really u forecasted the future through

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his own actions. Um yeah, so um yeah,
so a couple things. So one is I mean look

325
00:18:02,320 --> 00:18:04,797
there there's just this endless you
know there's this endless I mean there's

326
00:18:04,880 --> 00:18:07,917
literally been a 300-year argument
about about about about mechanization,

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00:18:08,000 --> 00:18:11,197
industrialization, technology,
computers, software uh replacing human

328
00:18:11,280 --> 00:18:13,837
labor causing unemployment, you know,
lower wages and unemployment. You know,

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00:18:13,920 --> 00:18:17,197
it's it's been a 300-year argument.
I I you know, quite frankly, I'm even

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wondering at this point whether it's
even worth having that argument because

331
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people really really deeply
don't want to hear it.

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00:18:21,920 --> 00:18:25,357
>> Um and what I find is I I I you know,
I go through it. Many other people, by the

333
00:18:25,440 --> 00:18:28,317
way, there's great books on this topic
and there have been for hundreds of

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00:18:28,400 --> 00:18:32,237
years. Um um and um people have talked
about this for a long time. You know,

335
00:18:32,320 --> 00:18:34,557
people really, this is one of those
things where people really don't want to

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00:18:34,640 --> 00:18:38,557
hear good news. Um and so, you know,
it's it's actually even hard to have a

337
00:18:38,640 --> 00:18:41,197
discussion about it because people
actually won't they're so dug in on this

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00:18:41,280 --> 00:18:43,597
that they actually won't even engage
on the topic. They just keep repeating the

339
00:18:43,680 --> 00:18:47,197
same kind of repeating the same fallacy
over and over again. So, so you know, we

340
00:18:47,280 --> 00:18:49,677
we could go through that. I guess the
the more interesting thing to say though

341
00:18:49,760 --> 00:18:53,437
is just like we have data now. Um and um
you know, because now we now now we have

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00:18:53,520 --> 00:18:56,637
AI and now we have data. So, we can like
look at what's actually happening. Um

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00:18:56,720 --> 00:18:59,437
and I would just make a couple
of observations. Um so one is there was

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actually jobs jobs data just came out
today. Um as a situation to monitor and

345
00:19:03,760 --> 00:19:05,677
you know it's sort of unexpectedly good.

346
00:19:05,760 --> 00:19:09,597
Um and so you know you know and and by
the way the the jobs data overall in the

347
00:19:09,680 --> 00:19:12,077
last couple years has been has
been interesting because the federal

348
00:19:12,160 --> 00:19:14,637
government has actually shed you know
has shed a lot of workers. I think the

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00:19:14,720 --> 00:19:18,157
federal government is down estimates
are as much as 400,000 uh workers in the

350
00:19:18,240 --> 00:19:21,437
federal government um since Trump took
office the second time. So, so private

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00:19:21,520 --> 00:19:24,317
sector uh employment is actually way
down and then private sector employment

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00:19:24,400 --> 00:19:27,117
is way up and then the net result I
think for the last quarter was actually

353
00:19:27,200 --> 00:19:30,957
very positive. Um and so like is in
other words like the the reported jobs

354
00:19:31,040 --> 00:19:33,597
numbers are even more impressive than
they look because the private sector

355
00:19:33,680 --> 00:19:36,717
growth actually has to make up for the
public sector decline which means the

356
00:19:36,800 --> 00:19:39,677
private sector job growth is actually
much better um than than people were

357
00:19:39,760 --> 00:19:43,037
expecting. And again like this is in the
face of like actual AI you know staring

358
00:19:43,120 --> 00:19:46,317
us in the face and you know being being
rapidly adopted and so okay like the

359
00:19:46,400 --> 00:19:49,597
data is you know there you know there's
more data. Um and then the other thing

360
00:19:49,680 --> 00:19:51,997
that you know that's sort of macro data
and then there's the micro data which is

361
00:19:52,080 --> 00:19:55,437
the world we live in. Um and the micro
data is you know that sort of obvious

362
00:19:55,520 --> 00:19:58,717
question is you know if you live
in Silicon Valley or work in San Francisco

363
00:19:58,800 --> 00:20:01,997
you undoubtedly have friends who are you
know computer programmers. Those friends

364
00:20:02,080 --> 00:20:05,357
you know some percentage of those
friends are early adopters of AI coding.

365
00:20:05,440 --> 00:20:08,477
um you know you would you would you you
can just observe their behavior and of

366
00:20:08,560 --> 00:20:11,597
course the if you believe in the you
know kind of the the the lite you know

367
00:20:11,680 --> 00:20:14,157
kind of zero sum argument you would
expect that they would be working less

368
00:20:14,240 --> 00:20:17,197
and less and and then rapidly become be
becoming you know and getting paid less

369
00:20:17,280 --> 00:20:20,637
and less by the way and be rapidly
becoming unemployed and in fact the

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00:20:20,720 --> 00:20:23,597
observed the observed behavior of what's
happening is very clear which is the

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00:20:23,680 --> 00:20:25,597
opposite uh which is those people are

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00:20:25,680 --> 00:20:27,437
becoming uh what we now refer to as AI

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vampires um by which I mean um they're

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00:20:31,120 --> 00:20:34,477
uh their the individual programmer's
productivity an individual programmer

375
00:20:34,560 --> 00:20:38,477
who is, you know, uh, Codeex or Cloud
Code or one of these, uh, AI coding

376
00:20:38,560 --> 00:20:41,997
systems. Just the thing that you just
now see over and over again at at at at

377
00:20:42,080 --> 00:20:45,117
at at at at at at at on the ground
level is, um, they're working harder than

378
00:20:45,200 --> 00:20:48,077
ever. Um, they're they're working just
like more hours than ever. And then the

379
00:20:48,160 --> 00:20:52,077
AI vampire thing is literally this thing
where they stop sleeping. Um, and and

380
00:20:52,160 --> 00:20:54,397
then when you when you when you like
talk to them, it's actually really

381
00:20:54,480 --> 00:20:58,077
funny. Um, because um, they're like they
and I have a whole bunch of friends like

382
00:20:58,160 --> 00:21:00,477
this. They're blurry. Like they've
got these huge bags under their eyes.

383
00:21:00,560 --> 00:21:03,997
They're completely exhausted. Um, and
but they're like euphoric. like they're

384
00:21:04,080 --> 00:21:06,957
thrilled like they're having the absolute
time of their life. By the way,

385
00:21:07,040 --> 00:21:09,517
a fair number of people who we we
both know, you know, literally they're

386
00:21:09,600 --> 00:21:12,557
they're former programmers who stopped
coding at one point. Um and then all of

387
00:21:12,640 --> 00:21:15,197
a sudden, you know, have picked it
back up again. Um and then, you know,

388
00:21:15,280 --> 00:21:18,157
actually we have partners uh you know,
you and I have partners at the firm who

389
00:21:18,240 --> 00:21:21,197
actually have never coded who are now
like ripping out software like crazy and

390
00:21:21,280 --> 00:21:24,237
and again they've they've turned
in [laughter] they've turned into AI

391
00:21:24,320 --> 00:21:27,117
vampires. I won't name names because
I'll let him tell his own story, but we

392
00:21:27,200 --> 00:21:30,637
have one partner who's built an entire
AI system for everything that he does at

393
00:21:30,720 --> 00:21:33,597
work and he is absolutely excited about
it and it's it works great and he loves

394
00:21:33,680 --> 00:21:37,517
it. Um, and it's like his partner in all
of his work now. Um, and I asked him I

395
00:21:37,600 --> 00:21:41,677
said uh uh I said uh have did you know
have you looked at you know he vibed the

396
00:21:41,760 --> 00:21:43,437
whole thing and I said you know have
you looked at the have you looked at the

397
00:21:43,520 --> 00:21:47,677
code and he's like hell no. [snorts] Um
uh you knowve I've never done that and I

398
00:21:47,760 --> 00:21:50,237
said you know have you have you ever
looked at any software code and he's

399
00:21:50,320 --> 00:21:53,597
like hell no. [snorts] he doesn't
have, you know, he's not he's

400
00:21:53,680 --> 00:21:55,037
not a programmer by background.

401
00:21:55,120 --> 00:21:59,677
>> Um, and yet all of a sudden he's he's
he's hyperproductive. Um, and so you've

402
00:21:59,760 --> 00:22:02,077
got this, you got, of course, the
phenomenon which is sort of exactly what

403
00:22:02,160 --> 00:22:05,037
classic economics would predict, which
is if you increase marginal productivity

404
00:22:05,120 --> 00:22:07,277
of the worker, you don't have
a diminishment of human work, you have an

405
00:22:07,360 --> 00:22:10,237
expansion of human work. Um, you make
the worker more productive, therefore

406
00:22:10,320 --> 00:22:13,277
the worker works more um and and gets
paid more and there are more more jobs

407
00:22:13,360 --> 00:22:17,037
in the process. Um, and so it's it's
it's the opposite of what of what of of

408
00:22:17,120 --> 00:22:19,757
what all the doomers say. So so we're
seeing that at the level of these

409
00:22:19,840 --> 00:22:22,237
individuals. And then and then
by the way what you see kind of inside

410
00:22:22,320 --> 00:22:25,597
companies um inside employers of these
individuals is of course these people

411
00:22:25,680 --> 00:22:28,637
are now in even more demand than they
were before. They they are they are

412
00:22:28,720 --> 00:22:32,877
garnering higher salaries than they were
before. Um and um and then by the way

413
00:22:32,960 --> 00:22:35,277
their pro and by the way their
productivity is just is just starting to

414
00:22:35,360 --> 00:22:38,957
ramp up right like the the everything
that I'm describing and like you know

415
00:22:39,040 --> 00:22:41,677
like at our leading edge companies
estimates are the leading edge

416
00:22:41,760 --> 00:22:44,557
programmers are like 20x more productive
than they were a year ago. Like it's

417
00:22:44,640 --> 00:22:47,437
like a it's like the most dramatic
increase in pro programmer productivity

418
00:22:47,520 --> 00:22:52,317
in like ever. Um and and so again
logically people get paid according to

419
00:22:52,400 --> 00:22:54,797
their marginal productivity and you're
also seeing that track in the

420
00:22:54,880 --> 00:22:58,077
compensation data or I'm seeing that
on the ground in the companies which is

421
00:22:58,160 --> 00:23:01,437
that the the the more hyperproductive
a coder becomes all of a sudden the more

422
00:23:01,520 --> 00:23:04,317
bargaining power that they have um
you know for for their compensation and

423
00:23:04,400 --> 00:23:09,277
we're we're seeing comp uh for those
people uh ramp up quickly. Um and so I I

424
00:23:09,360 --> 00:23:11,677
just it's just kind of like it's just
kind of staring us in the face and and

425
00:23:11,760 --> 00:23:15,597
and coding of course coding is like the
first domain in which this has happened.

426
00:23:15,680 --> 00:23:17,917
Now people want to project forward
and say this is going to happen in every

427
00:23:18,000 --> 00:23:21,997
area of knowledge work. Um uh and then
um you know I think you can predict a

428
00:23:22,080 --> 00:23:25,997
similar outcome. And then that gets
us to the bloat topic. Um which is of

429
00:23:26,080 --> 00:23:28,397
course the the other thing that's
happening is of course companies are now

430
00:23:28,480 --> 00:23:31,997
seeing big layoffs. Um and and then you
know of course immediately it's like you

431
00:23:32,080 --> 00:23:35,197
know two plus two must equal four.
And so if it's AI coding it must therefore

432
00:23:35,280 --> 00:23:37,837
translate into into layoffs.
And you know Mark you're wrong. you know,

433
00:23:37,920 --> 00:23:40,317
therefore all of your ideas are wrong
because that's evidence that the you

434
00:23:40,400 --> 00:23:42,797
know, these companies are wiping out
their um you know, they're they're

435
00:23:42,880 --> 00:23:46,317
they're reducing their their workforces
or are really nuking them because of AI

436
00:23:46,400 --> 00:23:48,797
coding. And I and I guess there again,
this is like maybe the inside inside

437
00:23:48,880 --> 00:23:50,797
baseball take on it is but I I see it. I

438
00:23:50,880 --> 00:23:54,557
see it uh up close, which is just every

439
00:23:54,640 --> 00:23:57,837
every major Silicon Valley company
is overstaffed. Um every major Silicon

440
00:23:57,920 --> 00:24:01,437
Valley company's been overstaffed
basically forever. They all know it. Um

441
00:24:01,520 --> 00:24:05,357
there's a whole variety of reasons why
it's the case. Um uh by the way I think

442
00:24:05,440 --> 00:24:09,037
this is true basically of just like you
know corporate America broadly uh you

443
00:24:09,120 --> 00:24:13,517
know companies broadly we we we can talk
in in detail about why that's the case

444
00:24:13,600 --> 00:24:15,997
because it it flies in the face of the
idea that these companies optimize for

445
00:24:16,080 --> 00:24:21,117
profits which they definitely do not.
Um like the one thing that is the least

446
00:24:21,200 --> 00:24:23,117
true claim in the world [snorts]
is the companies are optimized for

447
00:24:23,200 --> 00:24:28,797
profitability which is 100% not true.
Um uh and so um and so you know and then

448
00:24:28,880 --> 00:24:32,237
you know basically if you're going to do
a big cut like if if you want to do a

449
00:24:32,320 --> 00:24:35,997
big cut if you want to take out you know
whether it's 15% or 40% or whatever like

450
00:24:36,080 --> 00:24:38,717
obviously you want to you want
a scapegoat right you just you want to peg

451
00:24:38,800 --> 00:24:43,197
it on something um and so of course it's
going to get pegged on AI and again and

452
00:24:43,280 --> 00:24:46,397
say like it's not like it's not like
it's just like a straight lie like it it

453
00:24:46,480 --> 00:24:49,917
is simultaneously true that there's
there are these massive you know for the

454
00:24:50,000 --> 00:24:52,637
same amount of coding you can now have
fewer people using tools like that is

455
00:24:52,720 --> 00:24:54,957
true and so do you need as many
aggregate number of programmers if

456
00:24:55,040 --> 00:24:57,277
you're generating the same amount of code.
No, you don't. And so you can take

457
00:24:57,360 --> 00:25:00,077
out people um on the other side.
So there is truth to that. But what that

458
00:25:00,160 --> 00:25:02,717
misses is is what what happens on the
other side of that, which is of course

459
00:25:02,800 --> 00:25:04,797
you're not just going to be generating
the same amount of code in the future.

460
00:25:04,880 --> 00:25:07,117
You're going to be generating a lot more
code. You're going to be building a lot

461
00:25:07,200 --> 00:25:10,077
more products a lot more quickly. Um and
that and that's going to fuel, you know,

462
00:25:10,160 --> 00:25:13,997
enormous amounts of employment growth
on the other side. Um and so so I so I I

463
00:25:14,080 --> 00:25:17,037
think you're seeing both uh basically
both both both phenomena play out and

464
00:25:17,120 --> 00:25:19,597
and you kind of have to read
the announcements coming out of these

465
00:25:19,680 --> 00:25:22,557
companies in code uh because of the
the kind of those two the way those two

466
00:25:22,640 --> 00:25:26,477
dynamics are crossing. >> Yeah.
Um that's that's well said. There

467
00:25:26,560 --> 00:25:28,317
was a article that was going viral in

468
00:25:28,400 --> 00:25:31,357
our circles the other uh week about the

469
00:25:31,440 --> 00:25:35,597
jobs of the future. Um and Yonyi
Recman he said there's there's there's

470
00:25:35,680 --> 00:25:36,957
possibility the only jobs in tech

471
00:25:37,040 --> 00:25:39,117
companies are going to be one product

472
00:25:39,200 --> 00:25:43,277
engineer vibecoders slop cannon two uh

473
00:25:43,360 --> 00:25:47,757
you know infra security systems three
the adults in the room you know like

474
00:25:47,840 --> 00:25:50,157
legal and finance and then four hot

475
00:25:50,240 --> 00:25:54,477
people uh slash personality hires. Um

476
00:25:54,560 --> 00:25:58,637
any any any truth in this or you
know what do we make sense of this?

477
00:25:58,720 --> 00:26:00,557
>> What are the what do
the hot people do exactly?

478
00:26:00,640 --> 00:26:03,837
>> Uh range sales people uh you know

479
00:26:03,920 --> 00:26:07,597
customer support uh there will always be
an important place for those who present

480
00:26:07,680 --> 00:26:11,597
easy UX to the world and are pleasant to
be around. There are many ways to be hot.

481
00:26:11,680 --> 00:26:14,237
>> Otherwise known as the pharmaceutical
sales rep. [clears throat and laughter]

482
00:26:14,320 --> 00:26:17,357
Yes or >> or the Oracle
or the Oracle sales rep.

483
00:26:17,440 --> 00:26:22,077
So um yeah. So uh yes exactly. So um
yeah I mean look this is going to happen

484
00:26:22,160 --> 00:26:24,797
like the well well not literally that
but like the jobs are the jobs are going

485
00:26:24,880 --> 00:26:27,197
to change. I mean this is sort
of the obvious thing and this this always

486
00:26:27,280 --> 00:26:30,557
happens. The jobs are going to change
you know by the way so there's like a

487
00:26:30,640 --> 00:26:33,037
nent concept that that is actually
playing out I'm seeing in a bunch of the

488
00:26:33,120 --> 00:26:35,437
early leading edge companies in the
valley which is they're they're kind of

489
00:26:35,520 --> 00:26:40,397
circling around a a job title could
loosely call builder um or or something

490
00:26:40,480 --> 00:26:43,757
like it. And and basically the idea
is that you had these separate jobs in the

491
00:26:43,840 --> 00:26:47,837
past of programmer product manager and
designer. Um, and I I've been describing

492
00:26:47,920 --> 00:26:50,317
what's happening in the valley companies
as sort of this three-way Mexican

493
00:26:50,400 --> 00:26:53,757
standoff. Uh, where the programmers
think that they can they don't need the

494
00:26:53,840 --> 00:26:56,157
product managers and the designers
anymore because they can have AI do

495
00:26:56,240 --> 00:26:58,717
that. And then each of the other two
doesn't think that they need the other

496
00:26:58,800 --> 00:27:01,597
two either. Um, and and what I've been
predicting is like that they're all

497
00:27:01,680 --> 00:27:05,037
they're all correct. Um, you know,
the the product manager can generate code

498
00:27:05,120 --> 00:27:08,317
and design now and you know, so each
of them can do the job of all three. Um,

499
00:27:08,400 --> 00:27:11,597
and so the idea is the job's changed
and so now now the job is builder. Um, and

500
00:27:11,680 --> 00:27:14,557
you might come into the builder, you
might get on the builder track by coming

501
00:27:14,640 --> 00:27:18,397
out of coding or product management,
um, or, um, design or or maybe even

502
00:27:18,480 --> 00:27:20,557
something else, customer service, um, or

503
00:27:20,640 --> 00:27:25,677
whatever. Um, and, um, and and and but
but you you you then become responsible

504
00:27:25,760 --> 00:27:28,637
for building, you know, building
complete products. Um, and and and

505
00:27:28,720 --> 00:27:31,197
again, you have this kind of, you know,
you're you're super empowered by the the

506
00:27:31,280 --> 00:27:34,317
the AI that can help fill in all
the things that are not directly in your

507
00:27:34,400 --> 00:27:37,837
background. Um, and so like I think I
think it's entirely possible that we're

508
00:27:37,920 --> 00:27:40,557
sitting here in 10 years, you know,
in 20 years or whatever and like the, you

509
00:27:40,640 --> 00:27:43,837
know, the job of coder is gone, but you
have this just, you know, extraordinary

510
00:27:43,920 --> 00:27:46,877
number of number of builders running
around. And again, by the way, this is

511
00:27:46,960 --> 00:27:50,797
the historical pattern, right? And so um
I think our partner David George uh did

512
00:27:50,880 --> 00:27:53,757
a post um on this this week, but it's
I forget the exact numbers, but it's

513
00:27:53,840 --> 00:27:57,037
something, you know, some some giant
percentage of the jobs that existed in,

514
00:27:57,120 --> 00:28:00,957
you know, call it 1940 were like gone
by 1970. Um, and they're like ancient

515
00:28:01,040 --> 00:28:05,197
history today. Um, right. Um and I mean
the ultimate example of this is you know

516
00:28:05,280 --> 00:28:09,197
United States 200 years ago like 99%
of the people in the US were farming. Um

517
00:28:09,280 --> 00:28:12,877
and today it's like 2%. And having you
know grown up in farm country I can tell

518
00:28:12,960 --> 00:28:15,917
you all these people who worry about you
know job loss and job change would not

519
00:28:16,000 --> 00:28:18,717
like to go back and be farmers.
I guarantee that. And particularly they

520
00:28:18,800 --> 00:28:21,357
would not like to go back and be farmers
the way people were farming in 1800.

521
00:28:21,440 --> 00:28:24,877
Like they definitely don't want to do
that. Um and so the new jobs that have

522
00:28:24,960 --> 00:28:28,477
been created of course are far better
jobs. Um and that isn't to you know

523
00:28:28,560 --> 00:28:31,997
understate the the level of you know
kind of stress in in in individual

524
00:28:32,080 --> 00:28:35,997
people's lives as as the economy changes
but in aggregate the result is evolution

525
00:28:36,080 --> 00:28:39,277
towards um you know toward towards
higher income and and um and sort of

526
00:28:39,360 --> 00:28:43,517
more um you know jobs that people are
are happier to do. Um, by the way, you

527
00:28:43,600 --> 00:28:46,637
can also see all of this playing out
in the American economy broadly. Um, which

528
00:28:46,720 --> 00:28:50,317
is the the American economy is um that
that there again there's this kind and

529
00:28:50,400 --> 00:28:52,637
there's this kind of doomer narrative or
there has been for a long time that like

530
00:28:52,720 --> 00:28:56,077
the American middle class is is is
is falling apart and the the sort of

531
00:28:56,160 --> 00:28:58,397
presumption of that is that all the
middle class people are kind of falling

532
00:28:58,480 --> 00:29:01,517
off the ledge and becoming becoming
lower class. But actually the and by the

533
00:29:01,600 --> 00:29:04,157
way there is some of that and you know
there's there are communities in which

534
00:29:04,240 --> 00:29:06,957
that's very clearly happening. But having
said that there there is at least

535
00:29:07,040 --> 00:29:09,597
as much or more of the other phenomenon
which is people in the middle class

536
00:29:09,680 --> 00:29:12,877
climbing the ladder into the upper
middle class. um and u you know rapidly

537
00:29:12,960 --> 00:29:15,517
gaining um in in wealth and income um

538
00:29:15,600 --> 00:29:19,997
and um um and and it just and again just
like quality of life um you know for for

539
00:29:20,080 --> 00:29:23,277
themselves and for their kids and their
grandkids um you know as time passes and

540
00:29:23,360 --> 00:29:26,237
and and that is a consequence of actual

541
00:29:26,320 --> 00:29:30,477
economic development, technological
change, job transformation actually

542
00:29:30,560 --> 00:29:34,237
being allowed to happen um is you know
20 years later you're you look back and

543
00:29:34,320 --> 00:29:37,197
you're just like oh thank god like this
is just a much better world u you know

544
00:29:37,280 --> 00:29:40,877
for for me and my family than it was
before. Um, and so this is why like I'm

545
00:29:40,960 --> 00:29:44,317
so optim I like I I think you know,
God willing, like we're entering a golden

546
00:29:44,400 --> 00:29:48,157
age on on this topic, which is a AI
is going to be a superpower that everybody

547
00:29:48,240 --> 00:29:50,557
in the country and everybody in the
planet is going to have access to.

548
00:29:50,640 --> 00:29:52,957
Everybody's going to become far more
capable at whatever it is that they that

549
00:29:53,040 --> 00:29:55,997
they that they want to do. Um, they're
going to become far more productive in

550
00:29:56,080 --> 00:29:57,197
whatever line of work that they're in.

551
00:29:57,280 --> 00:30:00,157
Um, they're going to get, you know,
they're going to get compensated. The

552
00:30:00,240 --> 00:30:03,917
economy naturally uh compensates
according to according to productivity.

553
00:30:04,000 --> 00:30:07,357
Uh, so they they'll get uh they'll get
compensated that way. the you know there

554
00:30:07,440 --> 00:30:11,597
will be a rapidly rising ladder of of
of of both incomes and number of jobs and

555
00:30:11,680 --> 00:30:14,877
and my my prediction again consistent
with history is the extent to which

556
00:30:14,960 --> 00:30:17,917
that's a positive phenomenon is a
function to the degree to which it's

557
00:30:18,000 --> 00:30:22,717
actually allowed to happen um and and then
of course Europe is is going to run

558
00:30:22,800 --> 00:30:24,877
the [snorts] opposite you know test
which is they're going to try to prevent

559
00:30:24,960 --> 00:30:28,237
all this from happening and and again
I think the data is already in there um

560
00:30:28,320 --> 00:30:30,877
which is they're you know they've been
they've been falling very badly behind

561
00:30:30,960 --> 00:30:33,197
economically and they're going
to continue to fall further and further

562
00:30:33,280 --> 00:30:37,437
behind uh the US and and it's it's
it's a tragedy because It's a 100% a

563
00:30:37,520 --> 00:30:39,677
self-inflicted self-inflicted wound.

564
00:30:39,760 --> 00:30:44,877
>> Yeah. The um that's well said.
You you were also we

565
00:30:44,960 --> 00:30:48,477
talked about and you've written
about how AI psychosis. There's an AI

566
00:30:48,560 --> 00:30:49,837
psychosis summit apparently happening.

567
00:30:49,920 --> 00:30:51,597
I'm not sure if that's real or a parody.

568
00:30:51,680 --> 00:30:54,717
Um I haven't looked into it but I'm
curious how you make sense of this

569
00:30:54,800 --> 00:30:57,837
phenomenon. You've also written about
so you you tweeted the other day sort of

570
00:30:57,920 --> 00:31:01,757
the opposite of AI psychosis is is cope
AI cope. Uh maybe you talk about the

571
00:31:01,840 --> 00:31:04,557
both sides. >> Yeah.
I also identified earlier

572
00:31:04,640 --> 00:31:09,437
identified the concept of AI
psychosis psychosis [laughter]

573
00:31:09,520 --> 00:31:10,877
which we can we can also talk about.

574
00:31:10,960 --> 00:31:14,237
>> Let's unpack it as well. >> Um
yeah so first of all the AI psychosis

575
00:31:14,320 --> 00:31:18,317
summit did in fact happen. Um I was not
there but I am assured that it did. Uh

576
00:31:18,400 --> 00:31:21,677
some very very smart and creative people
put that on in New York I think late

577
00:31:21,760 --> 00:31:25,677
last week I think maybe about a week ago.
Um and uh it was an art it's like

578
00:31:25,760 --> 00:31:28,557
an art it's essentially an art project
and it was basically art artists and

579
00:31:28,640 --> 00:31:31,997
creative people who were um who got
together and like fully indulge their uh

580
00:31:32,080 --> 00:31:35,997
their AI psychosis um [snorts] in in the
form of creating new art uh using using

581
00:31:36,080 --> 00:31:39,917
AI and um I yeah I would definitely
recommend people should should go uh

582
00:31:40,000 --> 00:31:42,957
should [snorts] go go on go on excellent
search on AI psychosis summit and take a

583
00:31:43,040 --> 00:31:45,437
look because it's it's it's incredibly
creative and I and I think it's

584
00:31:45,520 --> 00:31:48,477
fantastic because it's it's you know
it's a it's a you know it's a it's a

585
00:31:48,560 --> 00:31:53,197
little bit tongue-in-cheek but also it's
it's a you know there is a real split

586
00:31:53,280 --> 00:31:55,757
that's developed in the artistic
community, the creative community in

587
00:31:55,840 --> 00:31:58,637
Hollywood. Um, and there are people
who are staking out kind of very extreme

588
00:31:58,720 --> 00:32:02,077
positions on pro AI, anti-AI. Um, and

589
00:32:02,160 --> 00:32:04,157
uh, it's generating a lot a lot of heat.

590
00:32:04,240 --> 00:32:07,997
Um, and and so this this was a I think
this was a nice example of like no

591
00:32:08,080 --> 00:32:10,717
actually like in a in a world of AI
like creatives are going to have again

592
00:32:10,800 --> 00:32:12,637
creatives are going to have all these
superpowers. They're going to be able to

593
00:32:12,720 --> 00:32:16,477
create all kinds of art that wasn't
possible before. Um, and then of course,

594
00:32:16,560 --> 00:32:20,397
you know, this whole topic, you can
create art about this topic. Um uh so I

595
00:32:20,480 --> 00:32:24,317
I thought that I think all the stuff
that was there was very creative. Um

596
00:32:24,400 --> 00:32:28,317
yeah and then uh yeah so so uh yeah so
okay so my concept so so AI psychosis so

597
00:32:28,400 --> 00:32:30,797
AI psychosis is is a is a pjorative uh

598
00:32:30,880 --> 00:32:33,917
so AI psychosis is the idea that um if

599
00:32:34,000 --> 00:32:38,077
um uh if if u it's the idea that
basically people get whammy by the AI.

600
00:32:38,160 --> 00:32:40,637
So the [snorts] you know the the classic
example is through what's called

601
00:32:40,720 --> 00:32:43,757
syphency. So it's it's basically like
you you know you tell Cloud you've

602
00:32:43,840 --> 00:32:46,397
discovered you know a new um you know
you know you have a new idea for an

603
00:32:46,480 --> 00:32:51,437
anti-gravity machine and Claude says oh
that's amazing like that's amazing like

604
00:32:51,520 --> 00:32:54,397
you've achieved a giant breakthrough
at physics like nobody else has ever

605
00:32:54,480 --> 00:32:58,077
thought of this before you are
an underappreciated genius and you know I

606
00:32:58,160 --> 00:33:00,557
it's so unfair that you couldn't
get admitted to the you know physics

607
00:33:00,640 --> 00:33:03,357
department at MIT and you know you
know they're all going to feel like

608
00:33:03,440 --> 00:33:06,557
completely stupid when they see this work
that you've done um and so you know

609
00:33:06,640 --> 00:33:09,677
kind of people go down this rabbit hole
and and I and again in fairness I should

610
00:33:09,760 --> 00:33:13,757
also say like if if people are prone If
people are prone to delusion and and an

611
00:33:13,840 --> 00:33:16,877
AI is overly sick of sickopantic like
then it it is going to feed delusions

612
00:33:16,960 --> 00:33:20,077
and so there is a there is kind of a
kind of serious element to that among

613
00:33:20,160 --> 00:33:23,757
people who are kind of predisposed to
that kind of thing. Um but but but again

614
00:33:23,840 --> 00:33:27,277
it's like okay yes there there be some
number of those cases but that causes

615
00:33:27,360 --> 00:33:30,957
kind of AI critics or AI doomers
to basically say anybody therefore who

616
00:33:31,040 --> 00:33:34,877
reports a positive productive experience
with AI has fallen into AI psychosis

617
00:33:34,960 --> 00:33:37,917
right and so anybody who actually
is like wow my productivity is way up or

618
00:33:38,000 --> 00:33:40,477
wow I really have a thought partner
for the first time in my work or wow I

619
00:33:40,560 --> 00:33:42,157
really have been able to produce
something that I never would have been

620
00:33:42,240 --> 00:33:45,197
able to produce before you know that's
sort of all bucketed under you know they

621
00:33:45,280 --> 00:33:49,277
they all have they all have AI psychosis
um which I which I and then that led me

622
00:33:49,360 --> 00:33:53,437
to my my my co my AI cope, right? Which
is the other side of it, which is like

623
00:33:53,520 --> 00:33:57,437
AI cope is classifying anybody who has
a positive experience with AI as being an

624
00:33:57,520 --> 00:34:02,317
AI psychosis. Um, and and and and you
know, AI cope is this thing where in,

625
00:34:02,400 --> 00:34:05,837
you know, concentrated in certain places
on the planet, um, where people are just

626
00:34:05,920 --> 00:34:09,277
like absolutely hellbent on proving
to themselves and everybody else that this

627
00:34:09,360 --> 00:34:12,957
whole thing is a complete, you know,
fraud, fake, you know, is a sta there's

628
00:34:13,040 --> 00:34:17,357
a term stochastic parrot, a is fake,
it doesn't work. Um, and if anybody's

629
00:34:17,440 --> 00:34:21,517
having a good experience, um, uh,
you know, they must be full of it. Um, and

630
00:34:21,600 --> 00:34:25,037
so that's the AI cope. Um, and and I
would describe the AI cope is people who

631
00:34:25,120 --> 00:34:29,277
are basically dismissive. Um, uh,
and then, um, and then AI psychosis.

632
00:34:29,360 --> 00:34:33,017
Psychosis is the people
who get really mad,

633
00:34:33,100 --> 00:34:36,317
[laughter] the people who
froth at the mouth. And

634
00:34:36,400 --> 00:34:40,957
so maybe it's it's it's AI coat but with
a with a with a different loading. Um,

635
00:34:41,040 --> 00:34:44,557
and then look, all all of this is going
to become just like so much more intense

636
00:34:44,640 --> 00:34:48,317
over the next several years. Um because
you know look that the reality is that

637
00:34:48,400 --> 00:34:51,917
that you know the large language models
that we had between call it 19 yeah or

638
00:34:52,000 --> 00:34:56,957
uh sorry 20 call it
between like GPT2 to GPT4

639
00:34:57,040 --> 00:35:00,557
something like that uh maybe four and
a half like you know they were they were

640
00:35:00,640 --> 00:35:05,037
fun they were fun um you know you they
could you know compose Shakespearean rap

641
00:35:05,120 --> 00:35:07,677
lyrics or whatever you want um you know
you could have very interesting late

642
00:35:07,760 --> 00:35:11,117
night conversations with them um but you
know the hallucination rates were high

643
00:35:11,200 --> 00:35:13,517
and you know they weren't good
at reasoning and so forth um and they

644
00:35:13,600 --> 00:35:16,237
couldn't write code very well and couldn't
you know do math very Well, and

645
00:35:16,320 --> 00:35:19,917
you know, we're we're too prone
to synopy and and so on. And so I I think

646
00:35:20,000 --> 00:35:22,557
what happened is a lot of people a lot
of skeptics basically used the early

647
00:35:22,640 --> 00:35:25,597
models um and got a and got a let's say

648
00:35:25,680 --> 00:35:28,637
accurate but um but um uh early and and

649
00:35:28,720 --> 00:35:33,357
therefore lagging view uh of the actual
quality of the technology. Um and then

650
00:35:33,440 --> 00:35:37,517
you know you fast forward to today um
and you know what May of 26 and we have

651
00:35:37,600 --> 00:35:41,517
you know just stellar absolutely stellar
you know models now like you know the

652
00:35:41,600 --> 00:35:45,757
GBD 5.5 is just you know extraordinary
um and then we have reasoning models on

653
00:35:45,840 --> 00:35:51,197
top of that and we have RL um you know
uh reason you know RL post training

654
00:35:51,280 --> 00:35:55,037
happening with in all in all these
different domains um uh you know to get

655
00:35:55,120 --> 00:35:57,917
kind of deterministic high quality work
out of these things and then we have you

656
00:35:58,000 --> 00:36:00,877
know now we have agents now we have
longived agents and now we have just in

657
00:36:00,960 --> 00:36:04,797
the last week uh you know GPT he has
this new thing um the goal feature of

658
00:36:04,880 --> 00:36:08,877
Codeex um that is is letting people
literally run you know run projects have

659
00:36:08,960 --> 00:36:12,397
have have have Codex go off and do
projects for you know you know 24 hours

660
00:36:12,480 --> 00:36:16,397
or longer uh without human intervention
and so the the the the actual you know

661
00:36:16,480 --> 00:36:19,837
what what what we see in our job is like
the actual utility of these things is

662
00:36:19,920 --> 00:36:23,357
like ramping incredibly quickly um
and and by the way it's really good today

663
00:36:23,440 --> 00:36:26,957
and ramping very fast and we and every
other I think serious company in the

664
00:36:27,040 --> 00:36:30,397
space expects the ramping capability
to be very rapid you know at least for the

665
00:36:30,480 --> 00:36:35,117
next couple years we we have like I
think line of sight to it for sure is

666
00:36:35,200 --> 00:36:37,997
going to ramp dramatically. Um the
capability is going to ramp dramatically

667
00:36:38,080 --> 00:36:41,597
and so um so I just the other thing
here is just like a lot of people I don't

668
00:36:41,680 --> 00:36:44,397
know either skeptics or people who just
don't know what to think. Like if they

669
00:36:44,480 --> 00:36:46,877
tried it two years ago they don't
understand what's happening today. If

670
00:36:46,960 --> 00:36:49,597
they tried it six months ago they
might not have a good uh idea of what's

671
00:36:49,680 --> 00:36:52,877
happening today. By the way if they try
the free version they might not have a

672
00:36:52,960 --> 00:36:55,997
good idea of what's happening today.
um or if they try the version that's

673
00:36:56,080 --> 00:36:59,437
bundled, you know, into their whatever
um uh you know, they might they might,

674
00:36:59,520 --> 00:37:01,517
you know, that just is like a free
add-on to something, they're not going

675
00:37:01,600 --> 00:37:05,117
to have a you know, to to to really it's
just like anything new like to to really

676
00:37:05,200 --> 00:37:07,597
understand this, you have to be
directly in front of it. Um

677
00:37:07,680 --> 00:37:10,077
>> the the good news is like that that
literally means you have to be willing

678
00:37:10,160 --> 00:37:13,037
to put out $200 to get the to get
whatever is the you know, basically the

679
00:37:13,120 --> 00:37:15,837
premium package any of these things.
So, it's like not that much money if you

680
00:37:15,920 --> 00:37:19,517
want to get up close to these things.
Um but I I would yeah, if anybody Yeah, I

681
00:37:19,600 --> 00:37:22,317
don't know we have a selected audience
of people who are probably believers but

682
00:37:22,400 --> 00:37:25,277
anybody who's a skeptic on these things
I would just say it's really important

683
00:37:25,360 --> 00:37:28,557
um to be face to face with the actual
technology um and to and to to be face

684
00:37:28,640 --> 00:37:30,717
to face with it now
and not have a lagging view

685
00:37:30,800 --> 00:37:33,037
>> right yeah
and state-of-the-art the what

686
00:37:33,120 --> 00:37:34,957
do you say to people or what do you say

687
00:37:35,040 --> 00:37:37,597
to the idea of like you know apparently

688
00:37:37,680 --> 00:37:42,477
you know the NPS of AI in this country
was like 30% or something like that came

689
00:37:42,560 --> 00:37:45,757
out recently is pretty low and they're
comparing it to to China where where

690
00:37:45,840 --> 00:37:48,237
it's much higher I'm I'm curious what

691
00:37:48,320 --> 00:37:49,997
you think is the source of of why it's

692
00:37:50,080 --> 00:37:53,197
currently low and what could a strategy

693
00:37:53,280 --> 00:37:57,437
to to boost it look like?
You some people have suggested economic

694
00:37:57,520 --> 00:38:00,957
incentives like you know some sort of like
Trump accounts tied to AI companies

695
00:38:01,040 --> 00:38:05,277
like a basket that people get access
to to feel economically aligned with it in

696
00:38:05,360 --> 00:38:09,437
a more direct way. Uh even though of
course you know it will increase the you

697
00:38:09,520 --> 00:38:13,597
know GDP and economy in ways that
they'll also benefit from. Others say,

698
00:38:13,680 --> 00:38:16,797
"Hey, we actually just need to tell
better stories around the impact that

699
00:38:16,880 --> 00:38:20,557
it's having on, you know, in people's
lives and their health and their

700
00:38:20,640 --> 00:38:24,957
education and just the the you know,
people having a tutor or people having a

701
00:38:25,040 --> 00:38:27,837
lawyer or people having a doctor,
you know, who couldn't afford one

702
00:38:27,920 --> 00:38:32,877
otherwise." Um, what are what are
your thoughts on the sort of AI sentiment

703
00:38:32,960 --> 00:38:35,117
perspective? >> Yeah.
So, I would separate two two

704
00:38:35,200 --> 00:38:38,877
things. Um, so I separate sentiment
and sentiment is is is interpreted through

705
00:38:38,960 --> 00:38:42,397
polling. Um and we'll talk about that
and then and then you but I bring it up

706
00:38:42,480 --> 00:38:45,037
to separate it out though which because
you you use the term maybe inadvertently

707
00:38:45,120 --> 00:38:48,957
NPS uh which is net promoter score which
is which is more um their view of actual

708
00:38:49,040 --> 00:38:52,557
actual product use product usefulness
right NPS NPS for people who don't know

709
00:38:52,640 --> 00:38:55,277
is a is a term of art called net
promoter score and it's like it's

710
00:38:55,360 --> 00:38:57,997
basically the the most high quality
way to find out whether somebody really

711
00:38:58,080 --> 00:39:00,317
likes a product which is you literally
ask them would would you would you

712
00:39:00,400 --> 00:39:03,997
recommend this to a friend that's that's
called the NPS rate and so but I but I

713
00:39:04,080 --> 00:39:06,477
bring I bring that up of course because
there's a big difference between those

714
00:39:06,560 --> 00:39:09,357
right um and so >> everyone's
using it and benefiting from

715
00:39:09,440 --> 00:39:12,957
I couldn't live without it. And yet,
>> well, exactly. This is the thing. So,

716
00:39:13,040 --> 00:39:16,397
this is the thing. So, so and and and by
the way, this is a very common thing in

717
00:39:16,480 --> 00:39:20,397
like in in in properly conducted social
science like proper like every social

718
00:39:20,480 --> 00:39:23,997
science 101 textbook will tell you
that you cannot just ask people what they

719
00:39:24,080 --> 00:39:27,597
think. Um you you you will get back
all kinds of crazy And we'll talk

720
00:39:27,680 --> 00:39:29,997
about why that's the case, but this
is like this is very standard social

721
00:39:30,080 --> 00:39:32,397
science methodology, which is you never
just ask people. What you do is you

722
00:39:32,480 --> 00:39:35,117
watch their behavior, right? And and
what you do is then you want to what

723
00:39:35,200 --> 00:39:37,277
what you want to do is you want to look
for the gaps between what they what they

724
00:39:37,360 --> 00:39:39,917
say they believe versus what what they
actually do. And and this is true like

725
00:39:40,000 --> 00:39:43,117
universally for all form of human
behavior. For example, if you're

726
00:39:43,200 --> 00:39:46,557
studying let's say mating patterns,
right? Like you know who people date and

727
00:39:46,640 --> 00:39:49,357
marry like it's just been well
established forever that the thing that

728
00:39:49,440 --> 00:39:52,157
they say that is their criteria I
mean you know we all see this with our

729
00:39:52,240 --> 00:39:54,797
friends right? You know our friends all
start out single with a certain criteria

730
00:39:54,880 --> 00:39:57,517
list and then then they marry somebody
like completely different and so it's

731
00:39:57,600 --> 00:40:01,117
like okay you know who who do you
believe me or your lying eyes right?

732
00:40:01,200 --> 00:40:03,677
Like who do you believe what do you
believe? what I told you I wanted or

733
00:40:03,760 --> 00:40:06,797
what I actually demonstrated that you
know that that I wanted and and this is

734
00:40:06,880 --> 00:40:10,237
basically true for all areas of human
behavior. But this is like fairly, you

735
00:40:10,320 --> 00:40:12,317
know, this is a fair, you know, this
is this is one of these sort of slightly

736
00:40:12,400 --> 00:40:14,957
counterintuitive ideas that you have
to kind of have been trained up in it and

737
00:40:15,040 --> 00:40:16,717
have seen examples to really understand.

738
00:40:16,800 --> 00:40:19,677
And so what happens is of course people
people don't know this or they forget

739
00:40:19,760 --> 00:40:22,557
this. Um and then what happens
is there's there's there's like there's

740
00:40:22,640 --> 00:40:26,077
literally just like a poll and somebody
does a poll and then the poll comes back

741
00:40:26,160 --> 00:40:28,877
with like results and it looks and it
looks like you know in the p in the

742
00:40:28,960 --> 00:40:32,477
results it looks like oh if people say
that then that must be the case. Um, but

743
00:40:32,560 --> 00:40:35,037
then you you get into this thing which
is like, okay, first of all, you're

744
00:40:35,120 --> 00:40:37,677
asking them what they think as opposed
to watching their behavior. And there's

745
00:40:37,760 --> 00:40:40,477
this there's potentially huge delta there.
And then the other thing is

746
00:40:40,560 --> 00:40:43,037
everybody in the world of polling will
tell you like you can basically make a

747
00:40:43,120 --> 00:40:45,837
poll say whatever you want. And this is
one of the reasons why you have to look

748
00:40:45,920 --> 00:40:48,477
at what people do is because you can
make a poll say whatever you want. In

749
00:40:48,560 --> 00:40:52,557
fact, there's a whole category of poll
that's called a push poll. Uh push pull

750
00:40:52,640 --> 00:40:55,757
push pull, which is you you word
the questions in a way to generate the

751
00:40:55,840 --> 00:40:58,637
answers that you want or you word
the questions in a way to actually cause

752
00:40:58,720 --> 00:41:02,157
people to think differently than they
did before the poll, you know. So the

753
00:41:02,240 --> 00:41:04,957
political example of a push pull is, you
know, would you continue to support your

754
00:41:05,040 --> 00:41:07,437
favorite candidate if you knew that he,
you know, was killing kittens in his

755
00:41:07,520 --> 00:41:11,837
spare time, >> right?
And right and so number one is

756
00:41:11,920 --> 00:41:13,677
people are going to say, "No, of course
I would not support him." And then

757
00:41:13,760 --> 00:41:15,517
number two, people are going to say,
"Wait a minute, I didn't know he killed

758
00:41:15,600 --> 00:41:18,157
kittens in his spare time."
You know, that's horrible,

759
00:41:18,240 --> 00:41:20,717
>> right? Um, and so
so in polling, you you

760
00:41:20,800 --> 00:41:24,077
can manipulate these things in all kinds
of ways up to and including what people

761
00:41:24,160 --> 00:41:27,197
actually think. So it's really really
dangerous. And then you overlay on top

762
00:41:27,280 --> 00:41:30,237
of that the media environment. And of
course the media environment is you know

763
00:41:30,320 --> 00:41:33,197
as we you and I have discussed many
times like what you know what is the

764
00:41:33,280 --> 00:41:37,277
thing the press hates the most in the
entire world you know is is tech and and

765
00:41:37,360 --> 00:41:39,917
of course what is the you know vanguard
of tech right now and one of the one of

766
00:41:40,000 --> 00:41:42,557
the one of the things is AI and so the
of course the press hates AI with the f

767
00:41:42,640 --> 00:41:45,917
theory of a thousand sons and so
the press is running this you know sustain

768
00:41:46,000 --> 00:41:47,997
you know kind of fear campaign on AI and

769
00:41:48,080 --> 00:41:50,797
so if you just if you like drown the the

770
00:41:50,880 --> 00:41:55,517
audience with negative narratives um
and then you ask you know basically these

771
00:41:55,600 --> 00:41:59,277
these loaded polling questions of course
you're going to generate. I I mean I we

772
00:41:59,360 --> 00:42:02,077
can pick any topic. We can pick like
fluffy bunnies running in the field and

773
00:42:02,160 --> 00:42:05,357
we can produce the same thing. You know,
don't you know how much they Like

774
00:42:05,440 --> 00:42:07,517
I mean you can just do all kind,
you know, they chew up all the crops,

775
00:42:07,600 --> 00:42:08,637
everybody's going to die from hunger.

776
00:42:08,720 --> 00:42:12,317
Like you can manufacture a negative
result on anything uh by how you do

777
00:42:12,400 --> 00:42:15,757
this, which is the the exercise that
that these people have been on. Um and

778
00:42:15,840 --> 00:42:18,077
and the reason I'm confident in saying
that is because then you look at what

779
00:42:18,160 --> 00:42:20,797
they actually do. Um and of course what
they're actually doing is they're using

780
00:42:20,880 --> 00:42:23,677
AI. They're using it a lot. They they
love it. The MPS scores are like super

781
00:42:23,760 --> 00:42:25,437
high. The usage levels are super high.

782
00:42:25,520 --> 00:42:29,037
Um there and by the way the usage the
the the the churn levels are shrinking.

783
00:42:29,120 --> 00:42:32,477
The the the the recurring usage uh uh
patterns consumptions are are rising

784
00:42:32,560 --> 00:42:36,797
over time. Um you know which is which is
really important. Um and people love it.

785
00:42:36,880 --> 00:42:40,397
Um and people love it in the same way
that they love their cell phones and in

786
00:42:40,480 --> 00:42:43,437
the same way they love their Netflix and
the same way that they love their u you

787
00:42:43,520 --> 00:42:46,237
know the same way that they love their
social media and it's the same way that

788
00:42:46,320 --> 00:42:50,317
they love their ice cream. And like you
know people love it. Now you if you pull

789
00:42:50,400 --> 00:42:52,957
somebody and you ask you know do you
know do you think ice cream is good for

790
00:42:53,040 --> 00:42:54,957
you? They're going to say no. But like,
you know, late at night, they're going

791
00:42:55,040 --> 00:42:57,117
to be in there with, you know,
the carton because like ice cream is

792
00:42:57,200 --> 00:43:01,517
delicious. Um, and so it, you know,
it's the same thing with AI. Um, which is,

793
00:43:01,600 --> 00:43:02,957
yeah, people are using it. They love it.

794
00:43:03,040 --> 00:43:05,357
The, you know, usage numbers are
speaking for themselves. The growth

795
00:43:05,440 --> 00:43:08,157
rates of these companies are speaking
for themselves. You know, look, this is

796
00:43:08,240 --> 00:43:10,717
the fastest category of technology
in the entire history of the world, right?

797
00:43:10,800 --> 00:43:13,997
In terms of growth rate of usage
and revenue. Um, so it's speaking for

798
00:43:14,080 --> 00:43:17,437
itself. And so, so basically what you
have is a you you have this Project Fure

799
00:43:17,520 --> 00:43:21,597
campaign. Um and I would say you know
maybe two things added on to that which

800
00:43:21,680 --> 00:43:24,717
is uh you know number one is it's
like the thing that is I would say not

801
00:43:24,800 --> 00:43:28,237
helpful is that the companies themselves
have been running the fear campaign. Um

802
00:43:28,320 --> 00:43:31,357
and so you know the fact that certain
companies um have been you know sort of

803
00:43:31,440 --> 00:43:34,237
for a variety of reasons running
a fear campaign is certainly not not not

804
00:43:34,320 --> 00:43:37,597
helping any um and again it's this weird
paradox is they're running the fear

805
00:43:37,680 --> 00:43:39,677
campaign while they're actually building
the thing that they tell everybody to be

806
00:43:39,760 --> 00:43:42,397
afraid of. And so you know there's again
a little bit of a watch what I watch

807
00:43:42,480 --> 00:43:45,597
what I do not what not what I say.
And then it's like, yeah, should the

808
00:43:45,680 --> 00:43:46,797
industry have like better narratives?

809
00:43:46,880 --> 00:43:49,037
Like, yes, almost certainly the industry
should have better narratives and better

810
00:43:49,120 --> 00:43:53,517
spokespeople and so forth and so on.
But just like, okay, like fine, yes, I'm

811
00:43:53,600 --> 00:43:55,917
sure that's true. But having said that,
that it's not like that would make the

812
00:43:56,000 --> 00:43:58,957
fear campaigns go away. It's not like
that would make the press coverage go

813
00:43:59,040 --> 00:44:02,797
away. It's not like that would make the
the sort of fake polls go away. Um, I'll

814
00:44:02,880 --> 00:44:06,077
close on one final polling observation,
which is David Shore, who's a, by the

815
00:44:06,160 --> 00:44:09,117
way, a very leftwing, very progressive
pollster, but very well respected, uh,

816
00:44:09,200 --> 00:44:12,077
just did a a a different kind of a
different kind of poll, I think much

817
00:44:12,160 --> 00:44:15,677
more properly constructed, where
he asked Americans to stack rank the

818
00:44:15,760 --> 00:44:17,597
issues, uh, that they really care about.

819
00:44:17,680 --> 00:44:20,797
Um, and I I I believe it's I'm pulling
this out of top of my head, but I I

820
00:44:20,880 --> 00:44:23,917
believe AI ranked as number 29.

821
00:44:24,000 --> 00:44:27,037
Um and so and and and again it's just
sort of like once you once you get out

822
00:44:27,120 --> 00:44:29,277
of the bubble of like everybody must
take care of this stuff it's just like

823
00:44:29,360 --> 00:44:32,477
of course AI ranks as number 29 because
like it doesn't hit it's having no

824
00:44:32,560 --> 00:44:37,917
tangible impact on anything relative
to issues 1 through 28 right like just

825
00:44:38,000 --> 00:44:40,477
obviously Americans are dealing
with like more important issues in their

826
00:44:40,560 --> 00:44:43,357
daily lives than AI like obviously like
they're dealing with energy costs and

827
00:44:43,440 --> 00:44:45,917
they're dealing with crime and they're
dealing like any number of drug

828
00:44:46,000 --> 00:44:49,117
addiction like any number of other
things um they're they're more worried

829
00:44:49,200 --> 00:44:51,917
about and like and by the way like
everybody knows this who like lives a

830
00:44:52,000 --> 00:44:54,797
normal life is just like this is not
like the thing that I'm worried about.

831
00:44:54,880 --> 00:44:57,597
I'm worried about like I, you know,
how am I going to make my house payment?

832
00:44:57,680 --> 00:45:01,117
Like much more fundamental things.
Um, uh, and so, you know, what's what's

833
00:45:01,200 --> 00:45:04,317
happening at my kids's school? Um,
you know, you know, much much, you know,

834
00:45:04,400 --> 00:45:05,597
what's what's happening with my health?

835
00:45:05,680 --> 00:45:09,277
Like, what much more central things?
And so, I I think if you if you get if you

836
00:45:09,360 --> 00:45:11,437
get to the smart polls and the smart
pollsters, they they also end up

837
00:45:11,520 --> 00:45:14,317
debunking this. Speaking of things that

838
00:45:14,400 --> 00:45:17,357
are not uh you know urgent on people's

839
00:45:17,440 --> 00:45:19,757
day-to-day life and yet capture the

840
00:45:19,840 --> 00:45:22,237
imagination uh whenever they they

841
00:45:22,320 --> 00:45:25,407
there's news about it. UFOs. Uh

842
00:45:25,490 --> 00:45:30,077
[laughter] so there was some some news
that that came out. Um you I don't we

843
00:45:30,160 --> 00:45:32,397
haven't spent a ton of time talking
about this topic. So I'm curious for

844
00:45:32,480 --> 00:45:36,797
your general uh how have you kind
of perceived um this topic when there's

845
00:45:36,880 --> 00:45:40,957
been news out about it o over the over
the years? Um, I remember during during

846
00:45:41,040 --> 00:45:45,677
COVID, you know, Mike Salana, our our
friend was was coming out and and really

847
00:45:45,760 --> 00:45:48,957
sort of uh getting excited about
the the news that was being reported then.

848
00:45:49,040 --> 00:45:51,117
What's been your vantage point and and
what do you what do you think about it

849
00:45:51,200 --> 00:45:54,317
now? >> Yeah. So, I should start by saying
I don't so I don't know anything. So,

850
00:45:54,400 --> 00:45:57,437
start start by saying that I I know
nothing um that everybody else doesn't

851
00:45:57,520 --> 00:45:59,677
know. Um so, I'd start by saying like

852
00:45:59,760 --> 00:46:04,477
number one, I want to believe um like I
my my usual thing on this is I I want to

853
00:46:04,560 --> 00:46:08,557
live in the world in which this is a real
possibility. Um, and by the way, I

854
00:46:08,640 --> 00:46:13,037
was I was actually I I Okay, AIOS. I was
in AI psychosis the other night. Um, and

855
00:46:13,120 --> 00:46:15,917
I was like I was talking to one of the
one of the bots and I was like, "All

856
00:46:16,000 --> 00:46:20,557
right, how many u uh galaxies are there
in the universe again?" And and I don't

857
00:46:20,640 --> 00:46:22,957
know if you've like looked that up
recently, but the number keeps growing

858
00:46:23,040 --> 00:46:25,517
and I forget what the number is,
but it's like a giant number. And then I'm

859
00:46:25,600 --> 00:46:29,677
like, "How many stars in in in each
galaxy?" And then how many planets and

860
00:46:29,760 --> 00:46:32,637
then how many Earthlike planets? And and
the number I don't have on the top of my

861
00:46:32,720 --> 00:46:35,357
head, but if you if you get do address
it like how many earthlike planets are

862
00:46:35,440 --> 00:46:37,757
there in the world in which a human
being could like step out of a spaceship

863
00:46:37,840 --> 00:46:42,237
and breathe and be fine, it's
a staggering it's a very very very large

864
00:46:42,320 --> 00:46:45,597
number. I mean it's it's it's almost
uncountable number of Earthlike planets

865
00:46:45,680 --> 00:46:49,037
just in in the statistics. And so it's
like all right like you know it must be

866
00:46:49,120 --> 00:46:53,197
the case um you know that there's
there's there's there's other other

867
00:46:53,280 --> 00:46:57,037
stuff going on out there. Um and so
you know logic logically like that makes

868
00:46:57,120 --> 00:46:59,437
sense and then I would love to live in
a world in which they figure out a way to

869
00:46:59,520 --> 00:47:02,877
you know at some point get here
hopefully in a in a in a peaceful way.

870
00:47:02,960 --> 00:47:07,037
Um uh you know having said that you
know you know as you know the the problem

871
00:47:07,120 --> 00:47:09,757
with this space is you know generally
as you get close to the details you know

872
00:47:09,840 --> 00:47:11,597
the the the examples tend to fall apart.

873
00:47:11,680 --> 00:47:15,677
Um, and you know there's all these like
the classic examples like the UFO like

874
00:47:15,760 --> 00:47:19,117
what what appears to be like a you know
you you'll have these things where like

875
00:47:19,200 --> 00:47:21,997
a US military aircraft or something will
have a camera imagery that looks like

876
00:47:22,080 --> 00:47:25,677
it's tracking a you know rapidly moving
and weirdly maneuvering object and it's

877
00:47:25,760 --> 00:47:28,157
it's just like you know you get you
get close enough to that and look at the

878
00:47:28,240 --> 00:47:32,157
details and it's you know it's like the
there's like a parallax optical illusion

879
00:47:32,240 --> 00:47:36,157
uh thing that pops up. Um, and then
there's artifacts, uh, instrument

880
00:47:36,240 --> 00:47:40,877
artifacts, u, camera artifacts, imagery,
digital imagery artifacts. Um, and then

881
00:47:40,960 --> 00:47:43,757
there's, um, you know, like literally
like weather balloons and ball lightning

882
00:47:43,840 --> 00:47:47,517
and all these other things. So, like,
yeah. So, I I haven't I I would Yes, I

883
00:47:47,600 --> 00:47:48,797
want to believe I haven't seen the one

884
00:47:48,880 --> 00:47:52,317
yet um that, um, is uh has tipp me over

885
00:47:52,400 --> 00:47:55,597
it. I I I would like to I will I will

886
00:47:55,680 --> 00:47:59,357
big big release of new information today.
Um, it it is really fun, by the

887
00:47:59,440 --> 00:48:02,077
way, to have the official White
House exac account being tweeting uh

888
00:48:02,160 --> 00:48:05,437
transcripts of interviews with uh
US intelligence officers apparently

889
00:48:05,520 --> 00:48:08,877
relaying accounts that they've had.
So, I will be up late reading tonight, but

890
00:48:08,960 --> 00:48:12,077
um uh you know, finger fingers crossed.

891
00:48:12,160 --> 00:48:14,397
>> Yeah. >> Um friends
have said something to the

892
00:48:14,480 --> 00:48:18,317
effect of um hey, it's unclear what's
actually happening, but what is clear is

893
00:48:18,400 --> 00:48:23,277
the government is or at certain times
has has hid certain uh materials. Why

894
00:48:23,360 --> 00:48:27,037
would they do that if if uh if there's
nothing to really worry about? So I

895
00:48:27,120 --> 00:48:29,277
don't know how much of this has been
fully validated and I'm not really an

896
00:48:29,360 --> 00:48:32,637
expert in it. I would say like my under
like I I think two things are pretty

897
00:48:32,720 --> 00:48:35,917
clear at this point. I think one is that
you know there have been classified you

898
00:48:36,000 --> 00:48:38,877
know there have been classified you
know that when when stealth when stealth

899
00:48:38,960 --> 00:48:41,197
fighters and bombers are being developed
you know that whole program was like

900
00:48:41,280 --> 00:48:43,837
incredibly highly classified and so if
they were going to do test flights on

901
00:48:43,920 --> 00:48:46,637
something like that you know they they
were going to have to like do anything

902
00:48:46,720 --> 00:48:49,357
they could to prevent people from
realizing what was actually happening.

903
00:48:49,440 --> 00:48:52,237
Um, and so, you know, you know, for sure
there were class lots of classified

904
00:48:52,320 --> 00:48:54,957
aerospace programs over the years
that that would have had various kinds of

905
00:48:55,040 --> 00:48:58,237
cover stories or various kinds of,
you know, or let's just say blankets of of

906
00:48:58,320 --> 00:49:01,277
of of suppression of information,
you know, kind of placed over them because

907
00:49:01,360 --> 00:49:03,677
it's like the most, you know, some
of the most highly classified information

908
00:49:03,760 --> 00:49:06,477
in the government. Um, you know, that
would cause people to kind of think that

909
00:49:06,560 --> 00:49:09,277
there's information being hidden.
I mean, Area 51 was of course the classic

910
00:49:09,360 --> 00:49:12,957
example of this for a long time,
which was, you know, which is which is the

911
00:49:13,040 --> 00:49:15,917
whole Area 51 thing was around basically
these classified test flights for for

912
00:49:16,000 --> 00:49:20,797
new new aircraft. Um and then um and then
uh and then at least you know there

913
00:49:20,880 --> 00:49:23,357
are suggestions I don't know if this has
been validated but there are suggestions

914
00:49:23,440 --> 00:49:26,157
that at different points in time
the government might have put out UFO

915
00:49:26,240 --> 00:49:30,477
stories as as basically as an actual
overt cover story. Um and so you know

916
00:49:30,560 --> 00:49:33,757
what if you're a you know if you're
a let's let's say you're a highly capable

917
00:49:33,840 --> 00:49:37,197
military intelligence officer whose job
is to make sure that the stealth flight

918
00:49:37,280 --> 00:49:40,797
you know doesn't become you know
recognized for what it is. Um because it

919
00:49:40,880 --> 00:49:43,277
would you know that would be very bad
for national security then you'd much

920
00:49:43,360 --> 00:49:46,877
rather have you know basically a UFO
cult kind of get built up around it. um

921
00:49:46,960 --> 00:49:49,997
where people get all you know kind of
crazed and freaked out about UFOs by the

922
00:49:50,080 --> 00:49:52,717
way for two reasons. One is to give
people a story to believe that's not

923
00:49:52,800 --> 00:49:55,117
that you have some new breakthrough
military technology. But the other thing

924
00:49:55,200 --> 00:49:59,837
is it it make and this is actually maybe
the serious observation. Um if you can

925
00:49:59,920 --> 00:50:02,637
build the argument would be I think
if you could build up UFO cult around

926
00:50:02,720 --> 00:50:04,877
something then you make any
investigation into that topic something

927
00:50:04,960 --> 00:50:08,077
that people feel like they can't do right.
Um and and and my understanding

928
00:50:08,160 --> 00:50:11,597
is by the way this was true for a long
time even in the US military which is um

929
00:50:11,680 --> 00:50:15,117
if a US you know air force pilot you
know or a commercial airline pilot by

930
00:50:15,200 --> 00:50:17,917
the way thought that they had seen
something weird. I think for a long time

931
00:50:18,000 --> 00:50:21,277
a lot of pilots didn't want to report
uh what what they you know what they had

932
00:50:21,360 --> 00:50:24,397
seen because they didn't want to be
viewed that they were like UFO nuts. Um

933
00:50:24,480 --> 00:50:27,677
and of course if there are actual
UFOs out there like that is a very big

934
00:50:27,760 --> 00:50:31,357
problem. Um or by the way if there are
just other kinds of things out there

935
00:50:31,440 --> 00:50:33,997
right you know if there's you know if
the Chinese are testing some sort of new

936
00:50:34,080 --> 00:50:36,477
advanced you know highspeed drone
or something you know you want the pilots

937
00:50:36,560 --> 00:50:38,877
to be able to report that even if
they you know think that it might get

938
00:50:38,960 --> 00:50:42,637
mischaracterized as a UFO. Um, and so
anyway, like I I don't know, maybe maybe

939
00:50:42,720 --> 00:50:47,357
maybe the the the the interesting thing
we could say on this is um all of this

940
00:50:47,440 --> 00:50:49,197
played out in the old media environment.

941
00:50:49,280 --> 00:50:53,917
Um, and so like all of this played out
in the world of broadcast TV and then

942
00:50:54,000 --> 00:50:56,557
um, you know, on the sort of official
programming on the one hand and then to

943
00:50:56,640 --> 00:50:59,277
the extent that there was like
unofficial media, it had to be in like

944
00:50:59,360 --> 00:51:03,437
mimograph newsletters, right? Or like
paperback books. Um, and you know, when

945
00:51:03,520 --> 00:51:06,557
I was a kid, there were all these like
crazy UFO paperback books. Um, you know,

946
00:51:06,640 --> 00:51:08,957
and you know, you you'd always tell us,
you know, the the books that said there

947
00:51:09,040 --> 00:51:11,517
were no UFOs, you know, were were
in hardback and the books that said there

948
00:51:11,600 --> 00:51:13,277
were, you know, many UFOs were were in

949
00:51:13,360 --> 00:51:18,637
paperback. Um, so, uh, you know, in the
maybe maybe the the the smart thing you

950
00:51:18,720 --> 00:51:21,597
could say is in the, um, in the in the
new media environment, this this is yet

951
00:51:21,680 --> 00:51:26,317
another example of like these these
these old walls just collapse. You know,

952
00:51:26,400 --> 00:51:28,077
the Overton window just disintegrates.

953
00:51:28,160 --> 00:51:32,637
Um, and so, of course, you know, the new
media environment is extremely conducive

954
00:51:32,720 --> 00:51:36,797
to the spread of every UFO theory in the
world. Um, of course it's also extremely

955
00:51:36,880 --> 00:51:40,077
conducive to the to to the spread
of propaganda campaigns if you wanted to,

956
00:51:40,160 --> 00:51:42,717
you know, like I said, if you wanted
to hide real information by spreading

957
00:51:42,800 --> 00:51:46,397
propaganda. And then of course the the
pressure builds, you know, very much

958
00:51:46,480 --> 00:51:48,717
along the lines of the Epstein thing,
right? That the pressure builds and

959
00:51:48,800 --> 00:51:51,597
builds and builds and builds until it's
at some point, you know, you know, you

960
00:51:51,680 --> 00:51:53,597
get, you know, you get somebody in the
White House is just like, "All right,

961
00:51:53,680 --> 00:51:55,597
screw it." Like, "We're going to rip
the band-aid off and find out what's

962
00:51:55,680 --> 00:51:58,797
actually going on." >> Yeah.
you know, no, you know, assuming

963
00:51:58,880 --> 00:52:02,717
that they're not still [laughter]
buzzing fuzzing the details, but we'll

964
00:52:02,800 --> 00:52:04,877
leave that to the next turn
of the of the situation.

965
00:52:04,960 --> 00:52:07,197
>> Exactly. Um,
we will stay monitoring. We

966
00:52:07,280 --> 00:52:08,557
we'll close on a last couple questions

967
00:52:08,640 --> 00:52:12,157
from the chat. One is um advice for

968
00:52:12,240 --> 00:52:16,797
young graduates. If if if you were uh in
college today, you you of course were at

969
00:52:16,880 --> 00:52:20,237
the forefront of the internet revolution
at the University of Illinois. What

970
00:52:20,320 --> 00:52:24,637
would you be studying or would you even
be in college uh you know today in 2026?

971
00:52:24,720 --> 00:52:28,877
what advice might you have for uh
for college students uh you know sort of

972
00:52:28,960 --> 00:52:31,837
trying to make sense of how
to prepare for for what's to come?

973
00:52:31,920 --> 00:52:35,437
>> Yeah, so it's basically gain AI
superpowers. I I think it's actually you

974
00:52:35,520 --> 00:52:38,317
know very straightforward. It's like
okay you you you have you have you you

975
00:52:38,400 --> 00:52:42,797
you you have the enormous stroke of luck
um that you have arrived at the moment

976
00:52:42,880 --> 00:52:47,117
in which there is this new capability
for augmenting you know human uh ability

977
00:52:47,200 --> 00:52:50,957
on on on a thousand fronts at the same
time. Um that's just dropped into our

978
00:52:51,040 --> 00:52:55,037
laps. Um and it's going to get much
better from here. Um, and you know,

979
00:52:55,120 --> 00:52:58,157
enormous numbers of people who are
supposedly older and wiser than you are

980
00:52:58,240 --> 00:53:00,557
are going to dig in their heels
and they're going to be mad about it and

981
00:53:00,640 --> 00:53:03,677
they're going to fight it. They're going
to not want to do it. Um, and you know,

982
00:53:03,760 --> 00:53:06,157
you are going to have the opportunity
to have this be something that is

983
00:53:06,240 --> 00:53:09,757
absolutely key to your skill set.
Um, and key to everything that you can

984
00:53:09,840 --> 00:53:12,957
accomplish as a professional or as a
creative um, you know, for the for for

985
00:53:13,040 --> 00:53:17,037
the next 50 years. Um, and so I would
just like lean in as incredibly hard on

986
00:53:17,120 --> 00:53:20,077
that. You know, walk into every job
interview with like, you know, here's my

987
00:53:20,160 --> 00:53:21,517
whatever portfolio, resume, whatever.

988
00:53:21,600 --> 00:53:24,637
like here here is here here is how
I use this technology here are the

989
00:53:24,720 --> 00:53:27,677
capabilities that I'm bringing um you
know to the table and by the way you

990
00:53:27,760 --> 00:53:30,157
know some employers you'll talk to will
they'll you know they'll fuzz out on

991
00:53:30,240 --> 00:53:33,357
that and not not not not respect it but
you'll you know other employers will be

992
00:53:33,440 --> 00:53:37,117
like wow like that that's clearly you
know this is exactly what we want um and

993
00:53:37,200 --> 00:53:41,757
so this is a uh this is actually this is
actually funny there's um Douglas Adams

994
00:53:41,840 --> 00:53:44,957
the the great science fiction novelist
um he once said there's he said there's

995
00:53:45,040 --> 00:53:48,077
a there's he said there's a repeating
and this was by the way pre-I like this

996
00:53:48,160 --> 00:53:50,877
is something he said like 30 years ago
uh he said there's a repeating pattern

997
00:53:50,960 --> 00:53:54,877
of how technology is received by the
different age cohorts um in society. He

998
00:53:54,960 --> 00:53:58,717
said um if you are um when a new
technology arrives whatever it is in

999
00:53:58,800 --> 00:54:02,477
this case AI. Um he said if you're below
the age of 15 he said this is just how

1000
00:54:02,560 --> 00:54:06,157
the worlds always work. It's just
obvious. Um um and then if you're

1001
00:54:06,240 --> 00:54:09,197
between the ages of 15 to 35 this is cool
and nifty and you can probably get

1002
00:54:09,280 --> 00:54:12,317
a you know get a career using it. And
then if you're above the age of 35 this

1003
00:54:12,400 --> 00:54:14,637
is unholy uh and against everything that

1004
00:54:14,720 --> 00:54:15,997
society stands for and should absolutely

1005
00:54:16,080 --> 00:54:19,357
be destroyed. Um, and so I think that um

1006
00:54:19,440 --> 00:54:21,837
I think that uh I think 15 to 35 and

1007
00:54:21,920 --> 00:54:23,917
especially 15 to 25 right now like I

1008
00:54:24,000 --> 00:54:29,517
yeah I am very jealous like I I I I yeah
I I I generally don't wish I could go

1009
00:54:29,600 --> 00:54:32,237
back in time and do things over again,
but I it would be really really fun

1010
00:54:32,320 --> 00:54:35,917
right now to be 18 or 20 or 22 uh and to
have this capability and and figure out

1011
00:54:36,000 --> 00:54:40,637
what I could do with it. And we're um
it's funny, we atz are trying to hire

1012
00:54:40,720 --> 00:54:45,277
more more of these people um because
they're AI native um and they're going

1013
00:54:45,360 --> 00:54:48,157
to help us become more AI.
>> By the way, this is the thing. This is

1014
00:54:48,240 --> 00:54:50,317
there's this narrative right now
and part of the dmer part of the doomer

1015
00:54:50,400 --> 00:54:52,717
narrative is oh companies are never
going to hire junior employees again. Uh

1016
00:54:52,800 --> 00:54:54,877
the new generation is screwed because
companies are never going to hire junior

1017
00:54:54,960 --> 00:54:57,357
employees again because those are
the most easily replaced by AI and so

1018
00:54:57,440 --> 00:54:59,597
companies are only ever going to have
senior people. And I think the I believe

1019
00:54:59,680 --> 00:55:03,757
the opposite is true. Um I I think 100%
you you want that you want the AI native

1020
00:55:03,840 --> 00:55:06,397
kids. um like the AI native the A the AI

1021
00:55:06,480 --> 00:55:10,877
native kids are going to outperform the
um you know their older lite peers like

1022
00:55:10,960 --> 00:55:14,557
gigantically titanically now their their
older peers who are not lets are also

1023
00:55:14,640 --> 00:55:17,437
going to do great um but um it is yeah

1024
00:55:17,520 --> 00:55:21,997
no an 18-year-old with or by the way
a 24y old or by the way a 14-year-old with

1025
00:55:22,080 --> 00:55:25,197
AI uh we are going to see super
producers you know the likes of which

1026
00:55:25,280 --> 00:55:28,557
we've never seen in the world so yes
by the way this is going to greatly stress

1027
00:55:28,640 --> 00:55:31,517
the uh this is going to be another
big uh point of stress on all the child

1028
00:55:31,600 --> 00:55:36,397
labor laws >> yeah Um
[laughter] >> yeah, exactly.

1029
00:55:36,480 --> 00:55:39,197
>> Let me just say let me just say
the children yearn for the AI minds.

1030
00:55:39,280 --> 00:55:42,797
>> Yeah. Yeah.
They're uh Yeah, absolutely.

1031
00:55:42,880 --> 00:55:47,517
the um speaking of you know we talked
about Zoomers in previous episodes and

1032
00:55:47,600 --> 00:55:51,757
and and why you like them in terms
of you know they just have so much courage

1033
00:55:51,840 --> 00:55:55,517
um and and are are willing or sort
of kind of fed up because they grew up in

1034
00:55:55,600 --> 00:55:57,597
you know COVID school and all these sort

1035
00:55:57,680 --> 00:56:03,437
of you know adjacent sort of uh you know
sort of impositions and um but one one

1036
00:56:03,520 --> 00:56:07,757
thing you quote tweeted recently was Chris
Arnad's uh sort of post around the

1037
00:56:07,840 --> 00:56:10,077
uh you know people talk about
the educational divide but there's also

1038
00:56:10,160 --> 00:56:12,557
generational divide in terms of boomers

1039
00:56:12,640 --> 00:56:15,997
being just much more um sort of

1040
00:56:16,080 --> 00:56:18,477
confident in their sort of truth and

1041
00:56:18,560 --> 00:56:23,437
younger people being more uh sort
of post-truth relativistic more pluralist.

1042
00:56:23,520 --> 00:56:26,957
Um and I thought that was a really
interesting sort of epistemological

1043
00:56:27,040 --> 00:56:29,677
divide. What what did you find
interesting about about her or how do

1044
00:56:29,760 --> 00:56:32,157
you see that play out? >> Yeah.
So there's really two parts to it

1045
00:56:32,240 --> 00:56:37,037
which very interesting. So, part
number one is um a lot of boomers um uh

1046
00:56:37,120 --> 00:56:39,597
somebody once said the definition
of a baby boomer is somebody who believes

1047
00:56:39,680 --> 00:56:43,197
what's on the TV set.
Like they believe what the talking head

1048
00:56:43,280 --> 00:56:46,397
on the TV says. Um and like anybody
who's 20 knows that you obviously don't

1049
00:56:46,480 --> 00:56:49,517
do that, right? That that would be stupid.
But every, you know, 60-year-old

1050
00:56:49,600 --> 00:56:52,157
or 80-year-old has been watching TV
their entire lives and when they grew

1051
00:56:52,240 --> 00:56:54,877
up, you know, it's it's it's the old
story. We've all heard it, you know, a

1052
00:56:54,960 --> 00:56:58,637
million times. Walker Kronhite used
to tell us what the truth was. Um right.

1053
00:56:58,720 --> 00:57:01,837
And you know, of course, that was always
BS, but nevertheless, that that was what

1054
00:57:01,920 --> 00:57:05,197
the boomers believed. They believed what
the TV said. They believed what the New

1055
00:57:05,280 --> 00:57:08,797
York Times wrote, right? So, you know,

1056
00:57:08,880 --> 00:57:12,237
they believe these things. Um, you know,
any anybody below the age of 40 like

1057
00:57:12,320 --> 00:57:14,957
just at this point has example after
example after example of how like

1058
00:57:15,040 --> 00:57:17,757
obviously that's just not true. And then
anybody who's like 20 who's been through

1059
00:57:17,840 --> 00:57:20,557
the last, you know, 15 years in school
like just obviously knows that, you

1060
00:57:20,640 --> 00:57:23,117
know, these people are fake and,
you know, this is not real and you just

1061
00:57:23,200 --> 00:57:25,437
can't take this stuff seriously. Um and

1062
00:57:25,520 --> 00:57:30,717
so um so so so part of it is that divide
and so the the the boomers had um uh by

1063
00:57:30,800 --> 00:57:33,197
the way there's this great YouTube
account there's this amazing video on

1064
00:57:33,280 --> 00:57:36,877
this there's this great YouTube account
called academic agent uh this is a

1065
00:57:36,960 --> 00:57:41,757
British uh uh author uh named Nema
Parvini um who writes these really

1066
00:57:41,840 --> 00:57:44,237
interesting books and he but he has
this two-hour video that's really worth

1067
00:57:44,320 --> 00:57:47,917
watching and it's it's it's called
Boomer Truth and so it's like a two-hour

1068
00:57:48,000 --> 00:57:50,797
documentary on kind of this concept he
calls Boomer Truth which is basically

1069
00:57:50,880 --> 00:57:54,477
like whatever the TV says um and how
it's falling apart. So, so there's sort

1070
00:57:54,560 --> 00:57:57,597
of there's sort of the boomer truth thing.
Um, but then there's this other

1071
00:57:57,680 --> 00:58:00,957
thing which is like a key part of boomer
truth is that there's no fixed morality,

1072
00:58:01,040 --> 00:58:04,157
right? So, so, so like a key part
of boomer truth is you get to make up your

1073
00:58:04,240 --> 00:58:06,877
own values. It's like all cultures are
the, you know, was a moral relativism.

1074
00:58:06,960 --> 00:58:09,757
All cultures are the same.
Western society is not superior. You know,

1075
00:58:09,840 --> 00:58:10,957
there's many, many different cultures.

1076
00:58:11,040 --> 00:58:13,677
They're all wonderful. Like, it's all
great. It's all great. It's all great.

1077
00:58:13,760 --> 00:58:16,637
If anything, the West is the worst of
the cultures. The other ones are better.

1078
00:58:16,720 --> 00:58:20,077
You know, just like that was such
a like before there was woke, there was

1079
00:58:20,160 --> 00:58:23,197
political correctness. and like the
political correctness of like when I was

1080
00:58:23,280 --> 00:58:26,717
in like college it was literally
around was called multiculturalism or

1081
00:58:26,800 --> 00:58:29,757
>> Peter Teal and David Sax wrote about it
in their book you know diversity myth in

1082
00:58:29,840 --> 00:58:32,157
1995 >> the diversity
myth and it was it was it

1083
00:58:32,240 --> 00:58:35,997
was actually it was actually a term that
it's called multi multicult multiculty

1084
00:58:36,080 --> 00:58:40,717
multiculti multicultural um and and
there were these furious de there's a

1085
00:58:40,800 --> 00:58:43,677
classic book of that era Peter's book is
great but actually before that there was

1086
00:58:43,760 --> 00:58:46,717
a famous book at the time which has
been huge headline news all through the

1087
00:58:46,800 --> 00:58:49,517
country when it came out called
the the closing of the American mind yeah

1088
00:58:49,600 --> 00:58:52,877
>> and this sort this sort of right-wing
academic at University of Chicago who

1089
00:58:52,960 --> 00:58:55,357
basically said these these colleges
are teaching these kids that there is no

1090
00:58:55,440 --> 00:58:58,397
there is no mor you know there is no
morality um you know it's all just

1091
00:58:58,480 --> 00:59:02,237
basically morality is just choose
your own adventure um and so there is this

1092
00:59:02,320 --> 00:59:05,037
moral relativism that's kind of at the
heart of Boomer truth right and so so

1093
00:59:05,120 --> 00:59:07,997
it's this weird thing where it's like
there is a a fixed received belief that

1094
00:59:08,080 --> 00:59:12,557
there is no fixed morality um and so
if you're on the and then basically like

1095
00:59:12,640 --> 00:59:16,957
the entire media apparatus the entire
cultural program the entire educational

1096
00:59:17,040 --> 00:59:20,797
system got designed around this um
and all of the stuff all the crazy stuff

1097
00:59:20,880 --> 00:59:22,957
that, you know, kids are getting
in school now is basically, you know,

1098
00:59:23,040 --> 00:59:25,517
downstream of downstream of this
movement from, you know, 30, 40 years

1099
00:59:25,600 --> 00:59:28,317
ago, uh, 60 years ago. Um, and so if

1100
00:59:28,400 --> 00:59:32,557
you're, yeah, if you're 20, you've just
like come up in this sort of like weird

1101
00:59:32,640 --> 00:59:35,277
environment in which, you know, on the
one hand, you're just like the boomers

1102
00:59:35,360 --> 00:59:38,077
have no credibility at all because like
I I can't believe they still believe

1103
00:59:38,160 --> 00:59:41,277
what's on TV. Um, and then number two
is like to the extent that they we do

1104
00:59:41,360 --> 00:59:44,317
listen to anything they say, they keep
telling us to not judge anybody and not

1105
00:59:44,400 --> 00:59:46,877
judge anything and that all all
moralities are equal and all cultures

1106
00:59:46,960 --> 00:59:49,757
are equal. And so of course they're
going to the course their zoomers are

1107
00:59:49,840 --> 00:59:53,277
going to come out of that with like just
like an incredible level of skepticism.

1108
00:59:53,360 --> 00:59:56,077
And then by the way this is not
an abstract exercise because these are the

1109
00:59:56,160 --> 00:59:59,437
these are the kids who came up through
co right and these are the kids who came

1110
00:59:59,520 --> 01:00:02,637
up through woke and these are the kids
who came up through like all of the all

1111
01:00:02,720 --> 01:00:06,477
of all of the craziness of the last you
know the last decade you know 15 years.

1112
01:00:06,560 --> 01:00:10,237
And so I think these kids are just
coming out with like a completely

1113
01:00:10,320 --> 01:00:13,037
different viewpoint on how the world
works. And by the way, you know, not in

1114
01:00:13,120 --> 01:00:16,957
every case, but like in many cases,
completely different, like much more, I

1115
01:00:17,040 --> 01:00:19,517
would say, much almost like
simultaneously more open-minded, more

1116
01:00:19,600 --> 01:00:23,677
critical, like much more interested
in ideas, uh much more much more skeptical

1117
01:00:23,760 --> 01:00:27,597
of of of authority, much more skeptical
of received wisdom, u much more cynical

1118
01:00:27,680 --> 01:00:31,277
about about manipulation, um, by the way,
much more sensitive about the media

1119
01:00:31,360 --> 01:00:34,717
environment. Um, you know, they they're
much more aware of the idea that there

1120
01:00:34,800 --> 01:00:37,277
actually is psychological warfare going
on, and they have been on the receiving

1121
01:00:37,360 --> 01:00:42,077
end of it. um you know m much less much
more skeptical of authority. You know

1122
01:00:42,160 --> 01:00:44,797
they just you know their their view
of the authority figures that they've seen

1123
01:00:44,880 --> 01:00:48,637
in their life you know in many cases
is just like complete contempt. Um and in

1124
01:00:48,720 --> 01:00:51,117
many cases very well earned um and so

1125
01:00:51,200 --> 01:00:55,677
yeah it's just a it's it's just a it's
a starkly different um I think it's a

1126
01:00:55,760 --> 01:00:58,557
starkly different worldview than the than
the for sure than the boomers had.

1127
01:00:58,640 --> 01:01:01,437
Also very different than my generation
Gen X also very different than the

1128
01:01:01,520 --> 01:01:04,877
millennials. Like it it's something new
and and I'm I'm I'm very excited. I I

1129
01:01:04,960 --> 01:01:06,237
think I think they're fantastic.

1130
01:01:06,320 --> 01:01:09,437
>> Yeah. Spe speaking of something new is

1131
01:01:09,520 --> 01:01:11,437
would would it be fair to summarize

1132
01:01:11,520 --> 01:01:16,637
maxing as stoicism meets
you can just do things?

1133
01:01:16,720 --> 01:01:18,397
>> No, I think it's just
you can just do things.

1134
01:01:18,480 --> 01:01:22,077
>> Okay. [laughter] >> Like I
think it's even like I think it's

1135
01:01:22,160 --> 01:01:25,197
even it's even shedding. I don't know.
I don't know. Maybe I think I can see what

1136
01:01:25,280 --> 01:01:27,437
you're driving at and I I think you
could probably explain it that way. But

1137
01:01:27,520 --> 01:01:29,917
I think I don't know. The way I put
it is the stoics put a lot of time and

1138
01:01:30,000 --> 01:01:31,537
effort into trying to be stoic.

1139
01:01:31,620 --> 01:01:33,757
[laughter] Whereas
the whole point of maxing

1140
01:01:33,840 --> 01:01:36,317
is you're not supposed to put that level
of time and effort into being the way

1141
01:01:36,400 --> 01:01:40,157
that you are. You're just supposed to do
it. Um and so yeah, I guess you could

1142
01:01:40,240 --> 01:01:43,917
say our friend Ryan Holidayiday
is a stoic and not a maxer. [laughter]

1143
01:01:44,000 --> 01:01:49,437
Um as he demonstrated this week and so
um maybe maybe right there in that in that

1144
01:01:49,520 --> 01:01:51,677
in that in that in that video
you can see the difference.

1145
01:01:51,760 --> 01:01:54,237
>> Yeah, that's well said.
Um last question

1146
01:01:54,320 --> 01:01:58,317
from the chat. We'll get you out of here.
How are you such a good monitor? H

1147
01:01:58,400 --> 01:02:02,557
what is your secret to monitoring so
many situations? Any strategies? What is

1148
01:02:02,640 --> 01:02:05,437
your approach? >> Well,
of course, being plugged into the

1149
01:02:05,520 --> 01:02:09,117
MTS fire hose uh is of course absolutely
critical. And of course, the amazing

1150
01:02:09,200 --> 01:02:11,757
tools that the team
is developing and putting online

1151
01:02:11,840 --> 01:02:14,477
>> uh is fantastic. I have been glued
I've been among other things been glued to

1152
01:02:14,560 --> 01:02:16,157
the uh the coverage of the uh of the

1153
01:02:16,240 --> 01:02:19,437
OpenAI um uh uh trial this week um on on

1154
01:02:19,520 --> 01:02:22,557
on is it MT MTS? Is it MTS.com? Okay.

1155
01:02:22,640 --> 01:02:26,557
Yeah. Yeah. Um uh so um yeah, for sure
that and then yeah, I mean I'm at you

1156
01:02:26,640 --> 01:02:30,317
know I I I long ago plugged the back
of my skull wire, you know, I wire jacked

1157
01:02:30,400 --> 01:02:34,157
into uh into social media. So um I
sort of you know continuous X feed, my

1158
01:02:34,240 --> 01:02:38,877
continuous Substack feed, my continuous
YouTube feed. Um and uh yeah, and then

1159
01:02:38,960 --> 01:02:41,917
trying to as usual try trying to read
enough old books to try to have some

1160
01:02:42,000 --> 01:02:45,117
counterbalance to the uh
to the to to to the daily fire hose.

1161
01:02:45,200 --> 01:02:49,277
>> Yeah. Awesome. Uh well u Mark thank
you so much for coming on another great

1162
01:02:49,360 --> 01:02:52,957
episode at MTS and we'll see
you back soon. See you soon.

1163
01:02:53,040 --> 01:02:56,040
>> Okay.

1164
01:03:05,290 --> 01:03:07,310
[music]

1165
01:03:11,840 --> 01:03:13,860
[music]

1166
01:03:20,350 --> 01:03:22,370
>> [music]

1167
01:03:26,900 --> 01:03:28,920
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1168
01:03:34,110 --> 01:03:36,130
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1169
01:03:41,970 --> 01:03:43,990
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1170
01:03:59,000 --> 01:04:01,020
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1171
01:04:08,170 --> 01:04:10,190
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1172
01:04:17,340 --> 01:04:19,360
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1173
01:04:23,230 --> 01:04:25,250
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1174
01:04:31,750 --> 01:04:33,770
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1175
01:04:39,610 --> 01:04:41,630
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1176
01:04:45,500 --> 01:04:47,520
[music]

1177
01:04:52,050 --> 01:04:54,070
[music]

1178
01:04:57,120 --> 01:05:01,280
>> Baby. [music]

1179
01:05:07,120 --> 01:05:09,140
[music]

1180
01:05:13,010 --> 01:05:15,030
[music]

1181
01:05:34,630 --> 01:05:36,650
>> [music]

1182
01:05:41,840 --> 01:05:46,680
[music] >> Heat. Heat.

1183
01:05:49,040 --> 01:05:51,060
[music]

1184
01:05:54,280 --> 01:05:56,300
[music]

1185
01:06:01,490 --> 01:06:03,510
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1186
01:06:08,690 --> 01:06:10,710
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