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#084 _STORY INSPIRE: Decisions that transforman with Aravind Srinivas · INSPIRE STORY

• 0:04
  Speaker 1: The Joe Rogan Experience.
• 0:06
  Speaker 2: Train by day, Joe Rogan podcast by night, all day.
• 0:14
  Good to see you.
• 0:14
  Speaker 1: You too. Thanks for having me.
• 0:16
  Speaker 2: My pleasure. Yeah. How many podcasts have you done? I
• 0:19
  don't know.
• 0:20
  Speaker 1: I don't know the count, but maybe tens.
• 0:23
  Speaker 2: Well, when we were talking, we were talking in the lobby.
• 0:25
  I was like, this dude would be a good guest
• 0:27
  because we were talking about ancient Hindu scriptures where you
• 0:30
  were talking to me about something that sounds like a
• 0:32
  nuclear bomb.
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  Speaker 1: Yeah.
• 0:33
  Speaker 2: And I was like, oh.
• 0:34
  Speaker 1: The Brahmastra. I need to know more about this. Yeah, yeah.
• 0:38
  So the Brahmastra is part of the Mahabharata. You've talked
• 0:43
  about Mahabharata.
• 0:44
  Speaker 2: Yeah, the Vimanas. Yeah, yeah.
• 0:46
  Speaker 1: So the Mahabharata is one of the two Hindu epics.
• 0:50
  The other one is Ramayana. But Mahabharata is more interesting.
• 0:53
  It's more complicated. It's like a lot of different stories
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  interleaved together. And the Brahmastra is the equivalent of the
• 1:01
  hydrogen bomb.
• 1:03
  Speaker 2: And how is it described?
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  Speaker 1: It's described as a weapon of mass destruction, going to
• 1:09
  annihilate the human population, should not be used at any cost.
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  There's a moral contract. You clearly have to be violating
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  so many things at a deeply moral level to even
• 1:23
  wield it. And it's not actually accessible to most warriors.
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  There's probably two warriors in the world in that era
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  who were allowed to use it. And it has to
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  be passed through special access, like a teacher has to
• 1:41
  pass it on to you, the secret, to use it.
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  Think of it as the equivalent of the nuclear code.
• 1:47
  And Arjuna had it, this particular character in Mahabharata called Arjuna.
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  He was allowed to use it. And then this other
• 1:56
  person was... Basically, Arjuna had a teacher named Drona, And
• 2:03
  Drona had a son named Ashwatthama. And Ashwatthama was always
• 2:07
  jealous of Arjuna. Arjuna was not Drona's son, but he
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  was his model disciple. And so Drona passed on the
• 2:14
  secret of the Brahmastra to him. And Drona's son also
• 2:20
  wanted it. But because it was his son, he also
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  passed on the secret to his son, even though the
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  son wasn't as good as Arjuna. And during the war,
• 2:29
  Arjuna and Drona fought on the opposite sides. It's just,
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  you know, circumstances. And his dad died, Ashutama's dad, the
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  teacher died in the war. And so the sun got
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  mad and like unleashed the Brahmastra. And Lord Krishna had
• 2:47
  to come and save the planet to not get that
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  destruction force.
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  Speaker 2: How old is the Mahabharata?
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  Speaker 1: Again, there's a lot of different opinions on this. So
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  I don't actually know for sure. My understanding is at
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  least 1,500 to 2,500 years old. Like 1,500 years ago
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  is the minimum. 2,500 years ago is the maximum. So
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  it happened in some period in that 1,000-year time frame
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  between that. And it's still unclear if a lot of
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  it is just being mythologized. And what actually happened was
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  just a war between kinns. Uh, there were two groups
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  of people, the Pandavas and the Kauravas. And, um, you know,
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  each side thought they were fighting for their own rights
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  and justice. But, um, at the end of the day,
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  you can crudely understand it as like essentially fight for
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  the kingdom. Um, basically they were like, there was a
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  previous generation and two brothers and they, and both the
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  brothers had a bunch of kids and those kids were
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  warring to get the next in line. And, um, that
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  ended up being like a massive war and a bunch
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  of other allies fought on each sides. And so many
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  amazing weapons were used as part of the war. And
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  a lot of these weapons are like extremely, like describe
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  an extreme level of detail that is pretty incredible. Like
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  there's a lot of detail around like targeted weapons. So
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  you could precisely identify a target and just shoot at that.
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  Speaker 2: And then- Does it explain like what the weapon is?
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  Speaker 1: Yeah, so- There's one weapon called the Divyastra where you
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  can just specifically target any particular person or a group
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  and it would just automatically direct itself and do it.
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  Almost like a semi-autonomous weapon. And then Lord Krishna had
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  this weapon called the Sudarshan Chakra. It's basically a discus.
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  And then you can just release it and it will
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  go and specifically identify somebody and chop off their head
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  and come back to you. It self-directs itself. So what
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  I was amazed by is how interesting it is in
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  terms of all the autonomy in the weapons, semi-autonomy or autonomy,
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  where the weapons could just be directed at people or
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  directed at a group of soldiers, and it would just
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  go and do its job and come back to the wielder.
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  And there were so many different Astra's, Divya Astra, Varun Astra,
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  Naga Astra. Ram Astra is obviously the ultimate, the hydrogen
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  bomb equivalent. And all of these are described in a
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  lot of detail and who has access to it. And
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  of course, it's mythologized, so it's described as these arrows
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  in the back of your shoulders. But you could understand
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  it as somebody having access to a lot of weapons.
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  And then whoever was powerful would go capture and colonize
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  and gain power. And essentially a fight between a group
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  of cousins. That's the bottom line of that story.
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  Speaker 2: Now, if we think of history as this linear progression
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  from caveman to us, and we hear about autonomous weapons
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  that were written in the Mahabharata somewhere around 2000 plus
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  years ago, we go, well, mythology. But if not, if
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  there's been some sort of rise and fall of civilization,
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  if there has been catastrophic, whatever it is, asteroid impacts,
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  shifting of the poles, whatever it is, it's caused great disasters.
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  You can imagine... that these people are remembering a time
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  where there was some sort of very advanced civilization. And
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  this is what they're describing. Like if you knew for
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  a fact that there had been a great advanced, technologically
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  advanced civilization, when we have evidence that they had some
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  technology like the pyramids of Giza and stuff, like how
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  did you do that? There's some technology involved, right? But
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  we don't have evidence of the technology. But if we did,
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  if we knew for a fact, you would look at
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  the Mahabharata and go, oh, this is history. They're just
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  explaining it in a kind of crude contemporary way for
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  the time, arrows instead of semi-autonomous drones with exploding heads
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  on them. I mean that's what we have now. All
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  those things that they're describing, hydrogen bomb, semi-autonomous and autonomous drones.
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  I mean they have autonomous fighter jets now. Like they
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  don't need people anymore. Like this– we're in that area
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  right now. So when you read about something like that
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  from the Mahabharata, you're like, okay, what was really going on?
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  Speaker 1: Exactly.
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  Speaker 2: Yeah.
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  Speaker 1: I mean, that's always been my fascination with those epics.
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  And the level of detail with which they described all
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  these weapons and who had access, different levels of access,
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  the status required to have access, and how it was
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  used in the wars, different formations of the soldiers. They
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  had all these crazy formation structures, like forming the army
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  like a lotus, forming the army like a, you know,
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  there's something called a chakra vyuhas, like literally it has
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  to have concentric circles. So you cannot actually get into
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  the innermost circle without going through the outer circles. And
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  then you can get killed by each of the flanks
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  whenever you're trying to enter in. And the secret of
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  how to actually break into these vyuhas, vyuhas means formations,
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  was only known to a few people. And it's incredible. Like,
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  you could say, okay, like, somebody had to be extremely
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  skillful to have that sort of, like, visualizations and imaginations
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  of describing a story like that. And obviously, like, Tolkien
• 8:35
  has done an amazing job with Lord of the Rings,
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  you know, in creating so much detail. At the same time, like,
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  a lot of it actually coming through in real life
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  in some form, again, not exactly the same weapons, but
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  similar style, makes you wonder... Was there actually something around then?
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  And people have tried excavations in all these areas. There's
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  like two main areas in the Mahabharata. Hastinapur was the
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  name of the kingdom. And people have done excavations around
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  there and have like found some artifacts that might date
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  back to those years. But there are also some details
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  that are described in the epics that don't quite align
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  with reality. For example, all the men, all the main
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  warriors in that era were described as like very tall.
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  very big, seven, eight feet, whatever. I don't even know
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  exact numbers. But studies by archaeologists also say that people
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  who lived in those years in those regions were probably
• 9:35
  not more than six feet tall. So it's not clear
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  exactly what happened, what was correct, what was not correct,
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  and we just have to keep probing more. But I
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  find the idea fascinating to think of what could have
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  existed in sacred texts that was only partially communicated to
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  the next generation and having a lot of reinterpretations. Another
• 9:56
  thing that is very interesting to think about is Vedic math.
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  So basically Vedic math is like a branch of mathematics
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  that some people in India are grown up learning. I
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  read it myself too. And some people actually practice it
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  just to be sharper at mental math for doing their exams,
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  like GMAT and things like that, GRE. And it has
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  like a line in the Vedas that says, oh, like
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  one from the last digit, two from the first digit, whatever,
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  you know, so many different ways of multiplying two different numbers,
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  like 97 times 96. Oh, like subtract the last two digits,
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  put it in the right, multiply the first digits, put
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  it in the left. That's the result. And then you
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  wonder like, oh, wait, the Rig Veda is so old,
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  it's as old as, it's the oldest sacred text out there.
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  How is it describing computations? That feels very unreal. Like,
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  do they actually know or understand advanced forms of computation
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  even back in those days?
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  Speaker 2: And how old is Rig Veda?
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  Speaker 1: I don't exactly know how old it is.
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  Speaker 2: Why don't we put that into perplexity?
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  Speaker 1: Yeah, let's do that.
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  Speaker 2: Let's find out.
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  Speaker 3: Yeah.
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  Speaker 2: Yeah.
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  Speaker 1: It is technically the oldest sacred text out there.
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  Speaker 2: And So what's interesting is I wonder how old the
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  stories were by the time they were written down. Like
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  how much of it is relayed person to person for
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  years and years, just like the Bible, before it's ever
• 11:34
  actually written down. Scholars usually date the composition of the
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  Rig Veda to about 1500 to 1200 BCE. So its
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  oldest layer is roughly 3,200, 3,700 years old today. Like...
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  If there really was, like, every ancient culture has a
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  story of a flood. Everyone, they all have an apocalyptic story.
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  Speaker 1: Mahabharata had the same thing. Mahabharata had the same thing
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  where there was a big, like, almost like a tsunami-like thing.
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  I don't exactly know what it was called, but that
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  was the collapse of Lord Krishna's kingdom, Dwaraka. After the war,
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  a lot of people died, but some people survived, and
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  Even those who survived got wiped out by a calamity
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  or like some kind of like a fight among themselves.
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  And most of the people who participated in that era
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  actually died.
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  Speaker 2: Here it is. The primordial, how do you say it? Manu? Yeah.
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  Manu flood. Classic Hindu grape flood myth where the righteous
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  king Manu is warned by a divine fish about an
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  imminent deluge that will destroy humanity. He builds a boat,
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  loads it with his family. It's like knowing the ark.
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  It's the same thing. With seeds and animals, ties it
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  to the horn of the god in fish form, which
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  tows the boat to safety until the waters recede and
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  the world is repopulated. They all have the same story. Yeah.
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  That's what's really crazy.
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  Speaker 1: There is a concept in Hindu philosophy called the yugas.
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  Speaker 2: Mm-hmm. I'm reading a book about it right now. Yeah, yeah, yeah.
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  Speaker 1: So... There's like different yugas and yugas are like thousands
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  of years. And the concept is that the yugas keep
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  cycling around. And so like we are in the Kali
• 13:20
  Yuga right now. And before that was a Dwapara Yuga.
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  That's when most of Mahabharata happened. And before that, there
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  was a Treta Yuga where the Ramayana happened. And before that,
• 13:29
  there was another Yuga.
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  Speaker 2: What is next after Kali Yuga?
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  Speaker 1: No, there is nothing next after Kali Yuga. It goes
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  back to the first one. I forget the name of
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  the first Yuga.
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  Speaker 2: Because the interpretation that I'm reading is that we're not
• 13:40
  in Kali Yuga anymore and that Kali Yuga ended in
• 13:43
  the 1900s and Dwapar Yuga started then. No, no.
• 13:47
  Speaker 1: We are in Kali Yuga right now.
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  Speaker 2: 100 percent.
• 13:48
  Speaker 1: 100 percent.
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  Speaker 2: So why do people have different interpretations? Is that true?
• 13:52
  Speaker 1: Yeah.
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  Speaker 2: There's like a guru interpretation. There's like one specific guru
• 13:58
  that has this interpretation that Kali Yuga ended in the
• 14:01
  1900s and that we're moving on. Interesting. Interesting. Yeah, but
• 14:06
  I don't know who's right because it's an enormous cycle, right?
• 14:09
  The cycles of humanity.
• 14:10
  Speaker 1: Yeah, thousands of years.
• 14:11
  Speaker 2: Yeah.
• 14:11
  Speaker 1: Thousands of years. So these are the four yugas.
• 14:22
  Speaker 2: So why do people have different interpretations? Let me tell
• 14:26
  you the book I'm reading.
• 14:27
  Speaker 1: Yeah.
• 14:31
  Speaker 2: See if this book is discredited, young Jamie. It is
• 14:38
  by a guy named David Steinowitz. Steinmetz. David Steinmetz. The
• 14:50
  book is called The Yugas.
• 14:51
  Speaker 1: Interesting.
• 14:52
  Speaker 2: Yeah, I mean, the problem is when someone's got their
• 14:55
  own interpretation or some guru's interpretation, it doesn't totally align.
• 15:01
  It's hard to know who's right and who's wrong. Keys
• 15:04
  to Understanding Our Hidden Past, Emerging Energy Age, and Enlightened Future. Yeah.
• 15:11
  So that– go back up to that again. So this
• 15:14
  is in the description. See what it says there? Where
• 15:17
  it says in 1894, an Indian sage gave Gus an
• 15:21
  explanation not only for our hidden past but for the
• 15:23
  trends of today and for future enlightenment. So there's like
• 15:26
  one guy's interpretation that this guy is going off of.
• 15:31
  Speaker 1: I guess the difference might be that– He thinks the
• 15:33
  yuga cycle is 24,000 years, whereas I think it's probably
• 15:37
  much longer than that.
• 15:39
  Speaker 2: Yeah. Four yugas together is 4,320,000 years. You know what's
• 15:46
  really nutty? Yeah. One of the really nutty things is
• 15:51
  both in the ancient Sumerian texts and in some of
• 15:56
  the ancient Egyptian texts. There's depictions before the flood of
• 16:01
  people who reigned for thousands of years as kings. Yeah.
• 16:05
  And it's common. It's not– and it's also– they're referenced
• 16:09
  multiple times in different scripts that are from different parts
• 16:13
  of what was Sumer at the time. It's really weird
• 16:17
  because they take it as established history once it gets
• 16:20
  to a certain age. Once they get into like whatever
• 16:23
  the age is where they can verify that this person
• 16:26
  was the king for a certain period of time. But
• 16:28
  it's all in the same text as people that reigned
• 16:31
  for 6,000 years. Yeah. It's weird.
• 16:34
  Speaker 1: And then one even just wipes out the whole thing.
• 16:36
  Speaker 3: Yeah.
• 16:37
  Speaker 1: And I mean, this is also somewhat like tangentially related
• 16:41
  to the Fermi paradox. You know, like if you assume
• 16:48
  all these things are happening on Earth itself, that entire
• 16:50
  civilizations are getting wiped out.
• 16:52
  Speaker 2: Yeah.
• 16:54
  Speaker 1: And like, we always wonder, I mean, you, you've explored
• 16:56
  this topic the most, um, and, um, where are the aliens? Right. And, um,
• 17:02
  there are different arguments that like, okay, like the reason
• 17:06
  we haven't quite found that is because the great filter exists. And, uh,
• 17:12
  there is like one entertaining theory that, um, I like
• 17:15
  just for the sake of entertainment is, um, almost all
• 17:19
  civilizations end up advancing technologically a lot. And, um, either
• 17:23
  a calamity wipes them out or like they build some
• 17:25
  misaligned AGI and then AGI wipes them out. And, um,
• 17:30
  and because of that, um, they never actually like, and
• 17:33
  end up being visible to us or the other theories
• 17:38
  that like, they're like, um, we haven't quite built the
• 17:42
  wine Neumann probes to actually go find them. And, um,
• 17:46
  both of them are plausible. And, um, There's no clear
• 17:51
  way to know. Unless we actually like send out enough probes.
• 17:57
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  There's a bunch of possibilities. I mean there's almost too
• 18:57
  many to count. But there's the possibility that they are
• 19:00
  observing and that they don't want to interfere and that
• 19:02
  we are on some sort of evolutionary cycle, a cycle
• 19:07
  of cultural evolution, civilization evolution. And one of the things
• 19:11
  about this, the crazy ages that come from the Sumerian
• 19:14
  text and from the ancient– the hieroglyphs that depict the–
• 19:20
  how do you say it? Zepteti?
• 19:23
  Speaker 1: No.
• 19:23
  Speaker 3: No.
• 19:25
  Speaker 2: How am I saying that? What is that text, that
• 19:28
  ancient– remember we talked about it with Zahi Hawass and
• 19:31
  he denied its existence? Zep Tepi? Is that it? Either way,
• 19:38
  you're dealing with these kings that reign for thousands and
• 19:41
  thousands of years. Well, David Sinclair is in the middle
• 19:45
  of this research now They're working on life extension drugs
• 19:48
  that are actionable. Yeah. Yeah, that's it. Zeb Tepe.
• 19:52
  Speaker 1: Yeah.
• 19:53
  Speaker 2: I've heard of him. But this is what's so weird.
• 19:56
  If they look at hieroglyphs, they get to a certain
• 19:59
  point and they're like, oh, Khufu. He was real. This
• 20:01
  guy was real. All these people were real. Yeah. But
• 20:04
  then they get back to these guys that rained for
• 20:06
  thousands of years. They go, oh, that was just horseshit.
• 20:09
  But why is it that all these people have these
• 20:12
  stories that align with this timeline that's pre-flood? It's all
• 20:17
  like the same story. And then if you're talking about
• 20:21
  these ancient Hindu scriptures that are discussing technology that seems
• 20:26
  remarkably similar to technology that we have today.
• 20:28
  Speaker 3: Yeah.
• 20:28
  Speaker 1: The Vimanas are flying cars basically.
• 20:31
  Speaker 2: And probably what we're going to have 100 years from
• 20:34
  now or whatever it is. Or we could have gone
• 20:36
  that way in the past.
• 20:37
  Speaker 1: And it's very entertaining to think of like. Let's say
• 20:39
  something happens to us, right? I don't want anything to
• 20:41
  happen to us, but let's say something happens to us.
• 20:43
  And would people really believe we were like launching reusable rockets?
• 20:47
  Speaker 2: Right. Or making FaceTime calls to people in Australia?
• 20:51
  Speaker 1: Yeah.
• 20:52
  Speaker 2: Yeah.
• 20:53
  Speaker 1: Like even fundamental things like all we were doing today.
• 20:57
  I think it's all like incredible. Like there's a lot
• 20:59
  of things that could be just technological ideas or maybe
• 21:02
  people actually had it and the knowledge of it was lost. Yeah.
• 21:07
  It's not been documented. It's not been passed along. And
• 21:09
  so we are skeptical if they ever had it.
• 21:11
  Speaker 2: Yes.
• 21:11
  Speaker 1: And so we end up reinventing it in different forms
• 21:13
  again and again and again. And we keep cycling through
• 21:15
  this process.
• 21:16
  Speaker 2: Well, it also could be that this is the natural
• 21:18
  progression of human curiosity. The human curiosity and ingenuity always
• 21:23
  moves into these very particular ways. Like what's the best
• 21:26
  way to defeat my enemies? Yeah. If we're always going
• 21:28
  to be territorial primates. We're always going to want to
• 21:31
  defeat our enemies. We're always going to protect ourselves from
• 21:34
  being invaded. So we're going to make better– and just
• 21:37
  with technological innovation, it just goes down the same path. Oh,
• 21:40
  we figure out bullets. Oh, we figure out nuclear bombs. Well,
• 21:43
  we figure out– we don't even have to use an
• 21:45
  actual plane. We can use an autonomous drone and that
• 21:47
  delivers it and then scale upwards and onwards and AI
• 21:51
  and then also life extension. So if these people were
• 21:55
  able to make the pyramids, like– there's a lot of–
• 22:01
  speculation as to the timeline of the pyramids, but let's
• 22:04
  just say they really built it 2,500 BC. Let's just
• 22:08
  say back then. What the fuck were they using? Like,
• 22:11
  what were you, what did you do? How did you
• 22:13
  get these stones down from the mountains that were 500
• 22:16
  miles away? How about this one? How about that one? Yeah.
• 22:18
  We were going to get to that for sure. There's
• 22:20
  a ton. No, thank, what's good, good as any. How
• 22:22
  about these temples that they find in India that are
• 22:26
  carved entirely out of one piece of stone? What did
• 22:32
  you do? How did you do that? How long ago
• 22:35
  did this happen? How many of them were buried and
• 22:38
  then they had to uncover them and then like figure
• 22:41
  out like what is this? Who made it? There's no timeline.
• 22:45
  No one really knows. There's no evidence of tools that
• 22:48
  were capable of doing this kind of work back then.
• 22:52
  And they're huge and beautiful and perfect tools. And they
• 22:56
  have like acoustic properties and the geometry is fucking fantastic. Yeah.
• 23:02
  It's nuts, man.
• 23:03
  Speaker 1: It's not just that. All of these temples were actually
• 23:05
  just built not just they were specifically the locations for
• 23:10
  them are picked out so that you get the right
• 23:13
  seismic vibrations over there in terms of like proximity to
• 23:18
  the ocean, the gravitational waves from the sun and the moon.
• 23:23
  People actually made that level of like Look at.
• 23:26
  Speaker 2: This, man. Imagine the undertaking of carving that temple out
• 23:32
  of the side of a fucking giant piece of rock. Yeah.
• 23:37
  You screw up one thing and it's over.
• 23:40
  Speaker 1: There's no simulations. You just have to build it.
• 23:43
  Speaker 2: What did they have? This is the question. Like, imagine
• 23:48
  today if we had to do this. Look, it's possible.
• 23:51
  This is a possible endeavor. It can be done. Yeah.
• 23:54
  But imagine what kind of technology we have to need
• 23:58
  to map it out to make sure that it was
• 24:00
  all precise, that it all– I mean it's precise within
• 24:03
  like millimeters from point to point and everything is done
• 24:09
  out of one piece of stone. Like what did they do?
• 24:13
  Was it chisels? Did you do that with chisels? That's crazy.
• 24:16
  How many times did you have to sharpen your fucking chisel?
• 24:18
  That's nuts. Or did you have something completely different? Because–
• 24:23
  Some of the more intricate ones, see if you can
• 24:24
  find these. Some of the crazy ones inside these temples,
• 24:29
  there's sculptures that are three-dimensional and they're carved like inside
• 24:34
  of the sculpture. So there's like an outer area and
• 24:37
  then there's all these openings and then inside it's highly detailed.
• 24:41
  Like how do you even reach in there?
• 24:44
  Speaker 3: It just says they use chisels and hammers and I
• 24:45
  don't think that's.
• 24:48
  Speaker 2: And careful geometric planning.
• 24:51
  Speaker 3: People trying to do that. They said like this is
• 24:53
  how much work someone could do in like 12 hours
• 24:56
  with a hammer and they get nowhere, let alone like
• 25:00
  perfect and looking good.
• 25:01
  Speaker 2: Yeah, it's nuts, man. And there's a lot of evidence
• 25:04
  of stuff like that all over the world, which is
• 25:08
  really weird. You have the stuff in Peru, like Sacsayhuaman.
• 25:12
  When you look at these stones and it looks like
• 25:13
  they're melted into place. And they're 900 tons. Like, what
• 25:18
  did you do? Yeah.
• 25:19
  Speaker 1: How did he even get it up there?
• 25:21
  Speaker 2: Where'd they get it? How'd you get it there? How'd
• 25:23
  you align it perfectly? Built in only 18 years. How
• 25:26
  do they know that? How do they know that? Because
• 25:29
  it's attributed to one king. Yeah. So King Christian I,
• 25:33
  756 to 773 CE. Maybe. How do you know, though?
• 25:42
  Speaker 3: Yeah, they said the archaeologists said it would have... They
• 25:43
  calculated it would take them 100 years to do it.
• 25:47
  Speaker 2: Yeah.
• 25:47
  Speaker 1: Yeah, I mean, this is where, like, you know.
• 25:49
  Speaker 2: I don't know.
• 25:49
  Speaker 1: Different historian accounts are all, like, muddled up, you know? Uh-huh.
• 25:53
  Speaker 2: Well, it's a real problem. History is a real problem.
• 25:55
  Speaker 1: But, yeah, it goes back to, like, the thing you
• 25:57
  were saying, right? You know, what is one thing that's
• 25:59
  common across all these different ages is human curiosity. So,
• 26:04
  I mean, that's something that, you know, I would love
• 26:06
  to get your take on this. Like, I've been toying
• 26:09
  with this idea that... called the curiosity premium which is
• 26:14
  the most effective people the most successful people have always
• 26:17
  been the most curious people the ones who have been
• 26:19
  good at asking the best questions and they tend to
• 26:22
  do better in every aspect of their life and you're
• 26:26
  a good example of that so that's why I would
• 26:27
  love to get your take on this and the reason
• 26:30
  I believe that is because long term people who continuously
• 26:35
  ask questions tend to do better they make more money
• 26:39
  they have a higher quality of life They're happy. They
• 26:42
  have more compounding relationships. People find them more interesting. And
• 26:45
  so they compound their relationships over time. And so naturally
• 26:50
  they end up succeeding. But their spirit of inquiry, their
• 26:55
  intrinsic curiosity doesn't actually stop once they succeed. It only,
• 26:59
  they just channelize it even more. And so that's why
• 27:02
  it keeps compounding. And I would argue that like it's
• 27:05
  the only quality, it's the only like quality that makes
• 27:08
  us really humans. you know, in this world where we
• 27:12
  can seek a lot of information and get information way
• 27:15
  faster than ever before, it feels like that's that one
• 27:18
  universal human quality that's existed since ancient time, since the
• 27:23
  oldest texts. Like, in fact, in the Rig Veda, you're
• 27:28
  explicitly encouraged to seek wisdom more than wealth. And it's
• 27:32
  not just an idea specific to Hinduism. That specific idea
• 27:35
  exists in the Bible, it exists in the Quran, it
• 27:37
  exists in the Torah. It's not that seeking well is
• 27:40
  admonished by religious texts. It's actually that it's more important
• 27:43
  to seek wisdom. And, you know, like you can, why
• 27:47
  I said you're a good example of that is like, sure,
• 27:50
  you have a very, very large podcast, but the way
• 27:53
  you're running it is like, you're just curious about a
• 27:55
  lot of things and asking a lot of questions. And
• 27:58
  I think that's that one quality that's very important. So,
• 28:02
  and I feel like it's the oldest thing. It's the
• 28:04
  only thing that we've known since ancient time. Being curious.
• 28:08
  Speaker 2: Well, I think it's stimulating to people and genuine curiosity
• 28:12
  is stimulating to other people. When someone is genuinely curious
• 28:15
  about something, I become curious about it. I think it's contagious.
• 28:19
  And I think that it's also an authentic quality and
• 28:23
  I think there's something about really wanting to know something
• 28:27
  and being interested in something. And if you're curious– Generally,
• 28:31
  you're going to ask more questions about something so you
• 28:33
  have a deeper understanding of it. So if you're trying
• 28:36
  to do whatever you're trying to do, a sport, a game,
• 28:39
  you'll probably get better at it because you're more curious.
• 28:41
  Because instead of just assuming things, you'll ask more questions.
• 28:45
  You'll reexamine things. It's one of the most important human qualities.
• 28:49
  And to me, it's one of the most attractive human qualities.
• 28:52
  It's always been. When I meet curious people, I'm always interested.
• 28:55
  I'm always like, tell me what you're curious about and
• 28:57
  I'll tell you what I'm curious about. Let's talk. It's–
• 29:01
  And this podcast started out genuinely because of– well, a
• 29:05
  lot of it was just talking shit with friends. But
• 29:08
  it also led into– like one of my very first guests,
• 29:11
  actual guests, was Graham Hancock. And it's just because I
• 29:14
  was curious because I had read Fingerprints of the Gods
• 29:16
  and I had seen him talk. I had seen speeches
• 29:19
  and I'm like, I want to know. Like what do
• 29:20
  you know? What do you think is going on? And
• 29:24
  he's another guy, incredibly curious. And absolutely fascinated with his
• 29:30
  takes on ancient history. He has been talking about this
• 29:35
  subject a long time. And when he first wrote Finger,
• 29:40
  of the gods, I think that came out in like,
• 29:43
  I want to say it was like 97 or 98
• 29:45
  or something like that. And I remember reading it and
• 29:47
  so many of my friends, you know, educated friends like
• 29:50
  this horse shit, why are you paying attention to this
• 29:52
  more and more and more as time goes on, it's
• 29:56
  been proven that he's correct. The timelines shifted back. And
• 29:59
  from the publication of that book, the discovery of Gobekli
• 30:02
  Tepe and the surrounding area, like it's like, OK, now
• 30:07
  we realize, well, there was some crazy shit going on
• 30:09
  at the very least 11,000 years ago. So we pushed
• 30:13
  civilization back 5,000 years. So like and this is just
• 30:17
  what we found now. And we keep finding things, keep digging,
• 30:20
  keep looking. And then you see the stuff that they're
• 30:23
  finding underneath the pyramid with this radio tomography where they're
• 30:26
  looking under the pyramid. It seems that there's structures under
• 30:29
  the– have you seen that stuff?
• 30:31
  Speaker 1: I haven't seen that.
• 30:33
  Speaker 2: I had the scientist that's involved in it. He's an
• 30:35
  Italian guy, Filippo Biondi, and he came on the podcast.
• 30:38
  Wonderful accent. Almost as good as yours. It was amazing.
• 30:42
  But he's describing the use of this stuff and that
• 30:44
  they've used it. Successfully on known areas in pyramids and
• 30:50
  other structures and they can– in fact, they– there's a–
• 30:56
  in Italy, there is a particle collider that is underneath
• 31:00
  a mountain. And using this technology, which is satellite-based technology,
• 31:04
  they get an accurate description of this particle collider that's–
• 31:09
  I think it's 1,200 meters underground. Like how far is
• 31:14
  that thing underground? We'll find out. But it's like deep
• 31:18
  under stone and they find that they can get an
• 31:22
  accurate– like they can actually give you the dimensions of
• 31:25
  this particle collider. They have like an image of it.
• 31:27
  And this same technology is showing that there's these columns
• 31:33
  underneath the pyramid in various places that are 20 meters
• 31:38
  wide and they have coils around them. They don't know
• 31:41
  what the hell they are. And the whole structure of
• 31:44
  this thing, it's not small. It goes almost a kilometer
• 31:48
  into the ground. There's like this enormous like bottom of it.
• 31:52
  And it seems like it's something that's constructed. And so
• 31:55
  they're like, okay, well. The pyramid is crazy. It's crazy enough.
• 32:00
  But if there's something underneath it that's a man-made or
• 32:04
  someone made it that's a kilometer deep into the ground,
• 32:07
  like what the fuck are we even talking about? Like
• 32:10
  who made this? What did they have? 1.2 kilometers into
• 32:17
  the mountain. That's nuts. It's a half a fucking mile
• 32:22
  plus into the mountains. And this thing can see through
• 32:26
  all that and get this accurate depiction of this particle
• 32:29
  collider and it's showing with multiple scans, not just one,
• 32:34
  multiple scans in different technology, the same exact images, the
• 32:38
  same exact structures underneath this fucking immense 2,300,000 stone structure
• 32:46
  that almost perfectly aligns to true north, south, east and west.
• 32:49
  Like what– What was going on? Don't tell me police.
• 32:53
  Don't tell me copper tools. Like what the fuck was
• 32:55
  going on? Something crazy. And I have a feeling our
• 32:59
  simplistic explanation of it is just doing no one any justice.
• 33:04
  It's doing no service to history. It's doing no service
• 33:07
  to our understanding. They've got to be a little bit
• 33:08
  more open in the fact that they are perplexed and
• 33:12
  not just perplexed by stuff like this. This is a
• 33:15
  3D print of an actual vase. that exists in Egypt
• 33:20
  that they found that is, they found it in tombs
• 33:23
  of the old kingdom. This thing was somehow or another,
• 33:26
  it's made with diorite. So it's incredibly hard stone and
• 33:30
  made to like a thousandth of a human hair in,
• 33:34
  in it's yeah. Like crazy dimensions, like the way the,
• 33:39
  the precision of it and wasn't turned on a lathe
• 33:43
  because it has handles. Yeah. So you look at the
• 33:45
  handles on the side where you can't carve the, and
• 33:47
  those are perfect too. Like the alignment of everything. And
• 33:50
  it's like, you just look at it. Oh, it's a vase.
• 33:52
  No big deal. But no, it's kind of fucking crazy. Like,
• 33:55
  how did they cut that out? There's also these, there's
• 33:59
  all these core marks in some of the stones that
• 34:03
  they find in Egypt and they've, Analyze the amount of
• 34:06
  revolutions per minute that you would have to go through
• 34:09
  to be able to cut through something and leave these lines.
• 34:12
  And defies explanation. Like what is this? This is crazy.
• 34:16
  This is not sand and copper and just rubbing things. No,
• 34:20
  this is some insane technology that we don't understand. There's
• 34:25
  scoop marks out of the bottoms of some of these stones.
• 34:28
  It's like what the fuck is this? How did you
• 34:30
  scoop rock? It looks like ice cream. Like they just
• 34:34
  went whoop. Like, what are they doing? There's so many questions.
• 34:39
  Speaker 1: What tools did they even have to do all these things?
• 34:41
  Speaker 2: They had copper. I mean, there's some evidence that they
• 34:44
  had some iron. And then I think Tutankhamen had a
• 34:48
  dagger that was actually made from meteorite, which is interesting.
• 34:51
  You know, like when they could find meteorites and make
• 34:54
  things out of them was very valuable, obviously. But just
• 34:59
  the sheer volume of work that they did there It's–
• 35:05
  like you look at the Temple in Man. You look
• 35:06
  at the three major pyramids. You look at all the
• 35:10
  different temples and all the construction. And the older you go,
• 35:14
  the deeper into the sand they go, the more complex
• 35:17
  these things are, which is even weirder. So it seems
• 35:19
  like civilization after civilization just– there was probably a rise
• 35:23
  and fall with their technology as well.
• 35:26
  Speaker 1: Absolutely. I think it's just incredible that none of this
• 35:29
  knowledge was properly documented ever. And it's a whole line
• 35:34
  of work to just go understand how to even rebuild
• 35:37
  these things, leave alone how did they build it.
• 35:40
  Speaker 2: Well, think about what we're doing, right? So all of
• 35:43
  our knowledge is essentially stored on hard drives and paper.
• 35:46
  Those are the two things that are going to deteriorate
• 35:48
  the quickest.
• 35:51
  Speaker 1: Maybe we should take a dump of the internet and
• 35:54
  go preserve it somewhere so that even if our civilization
• 35:59
  is wiped out and all the data centers are gone
• 36:01
  or whatever.
• 36:02
  Speaker 2: Right.
• 36:03
  Speaker 1: Whoever comes next can go figure it out.
• 36:06
  Speaker 2: Well, I mean then you've got to always assume that
• 36:09
  even if they found a hard drive that they would–
• 36:12
  like how long would it take for them to back
• 36:14
  engineer what we did and figure out what these ones
• 36:17
  and zeros actually mean?
• 36:19
  Speaker 1: Yeah.
• 36:21
  Speaker 2: Which is one of the most bizarre and fantastic accomplishments
• 36:25
  of modern civilization is that like this is a terabyte.
• 36:30
  Speaker 3: Yeah.
• 36:30
  Speaker 2: Which is nuts. Yeah. I don't know what your first
• 36:33
  computer had.
• 36:34
  Speaker 1: I don't remember. Definitely not even a gigabyte probably.
• 36:38
  Speaker 2: No. Yeah. Like a few hundred megabytes was your hard drive. Yeah. Yeah.
• 36:43
  I mean, I remember when they first came out with gigabytes,
• 36:46
  I was like, this is nuts. Yeah.
• 36:48
  Speaker 1: You remember like when Gmail launched and gave everybody like
• 36:50
  free email storage, unlimited email storage, and the bottom sliding
• 36:55
  bar would just keep increasing in terms of the total
• 36:57
  allowed size?
• 36:58
  Speaker 2: Yeah.
• 36:58
  Speaker 1: And that was nuts to me.
• 37:00
  Speaker 3: Yeah.
• 37:00
  Speaker 2: Yeah.
• 37:01
  Speaker 1: And I think, yeah, we take it for granted that
• 37:04
  we have like infinite RAM and infinite hard disk and
• 37:07
  nobody has to worry about like, you know, back in
• 37:09
  those days, you worry about like taking too many photos
• 37:12
  on your phone.
• 37:12
  Speaker 2: Right. Right.
• 37:13
  Speaker 1: And then you have to go delete all the old
• 37:15
  ones or bad ones.
• 37:16
  Speaker 2: Yeah. You'd run out of storage on your phone.
• 37:18
  Speaker 1: Yeah. And then you have to buy like an external
• 37:20
  hard drive to keep storing things.
• 37:22
  Speaker 2: Yeah.
• 37:22
  Speaker 1: You keep transferring stuff from your phone to the hard disk.
• 37:25
  Speaker 2: I remember the old Android phones, you'd get an SD card.
• 37:28
  You could slip one of those in there and you
• 37:30
  could store images on that so you could save space.
• 37:34
  And all that stuff is so vulnerable. It's so vulnerable.
• 37:38
  And again, if a completely alien society had to come
• 37:43
  down and find our hard drives and they went a
• 37:47
  totally different path of technology, they'd have to back engineer,
• 37:50
  reverse engineer everything that we did, try to figure out.
• 37:54
  What are we using? What operating system? How does the
• 37:57
  operating system work? Is it Unix? Is it Linux? What
• 38:00
  is it? How do they do it? It would be
• 38:02
  a nightmare.
• 38:03
  Speaker 1: They would need an advanced AI to figure it all
• 38:06
  out for them.
• 38:06
  Speaker 2: Right.
• 38:07
  Speaker 1: Yeah.
• 38:07
  Speaker 2: Yeah. And so that's just if the hard drives survive, right?
• 38:12
  So if there's some massive flood, cataclysm, whatever. Some horrific
• 38:17
  thing that damages all of our electronics, which is totally possible.
• 38:21
  You know, just some solar flare, some intense, you know.
• 38:25
  Speaker 1: Or just another lab leak.
• 38:28
  Speaker 2: Right? Yeah.
• 38:28
  Speaker 3: Yeah.
• 38:29
  Speaker 2: Just time. A lab leak in time.
• 38:31
  Speaker 1: Yeah.
• 38:32
  Speaker 2: Yeah. It's nuts. And we could go back to zero
• 38:36
  real quick. And we would basically be like preppers and hunter-gatherers.
• 38:40
  Speaker 1: It would be hard to reverse engineer everything again.
• 38:42
  Speaker 2: It would be almost impossible. Yeah. Which is why I'm
• 38:45
  really fascinated by the flood, the post-flood timeline. Because if
• 38:50
  these people like Graham Hancock and a lot of these
• 38:53
  other folks that have speculated that there was probably a
• 38:56
  very advanced civilization that went in a completely different direction
• 38:59
  many thousands of years ago. If you look at like
• 39:03
  the emergence of like Sumer and Bermuda. You know, Mesopotamia
• 39:07
  and that area, which a lot of people attribute to
• 39:09
  be the earliest known civilization. That's around 5,000 plus, 6,000
• 39:15
  years ago, right? Yeah, roughly. So the flood is like 11,000
• 39:19
  years ago plus. So you're looking at like 5,000 years
• 39:24
  of what?
• 39:25
  Speaker 1: It's not even that long in the grand scheme of things.
• 39:27
  Speaker 2: No, not to the earth, Yeah. But for people. Yeah.
• 39:29
  Pretty fucking long. Exactly. Like think of how long it
• 39:31
  took us to get our shit together.
• 39:33
  Speaker 3: Yeah.
• 39:33
  Speaker 2: It took thousands and thousands and thousands of years of
• 39:36
  people probably being monsters. Just being the worst of the worst. Yeah.
• 39:42
  And that's probably the only way they survived. There's probably
• 39:44
  a lot of cannibalism. Yeah. There's a lot of murder.
• 39:47
  There was a lot of like horrific shit going on
• 39:49
  for 5,000 years until people slowly but surely figured out
• 39:53
  agriculture again. Started building walls. Everybody relaxed a little. Got
• 39:58
  some solid weapons to keep people away so you could
• 40:00
  work on math. And then next thing you know, civilization
• 40:03
  emerges again. And it goes right back onto the cycle.
• 40:07
  And then you start reading in the Rig Veda about
• 40:11
  stuff that happened thousands of years. What the fuck is this?
• 40:14
  Like what happened? And that's my– belief. I think there
• 40:19
  was something going on on Earth many, many, many thousands
• 40:23
  of years before established beginnings of history that was very
• 40:27
  bizarre and probably technology that went in a completely different
• 40:31
  direction than what we're doing now with combustion engines and
• 40:36
  circuits and all the different things that we use. They
• 40:38
  probably figured out some other kind of technology. Which is
• 40:42
  totally possible.
• 40:43
  Speaker 1: And it's amazing. It's amazing to think of what if
• 40:46
  we could Rediscover all of that again.
• 40:49
  Speaker 2: Yes. Well, I would love to be able to– I
• 40:51
  would love to just have a– if I could choose
• 40:54
  one window in time to go back to see what
• 40:57
  it would look like, I would 100 percent pick ancient
• 41:01
  Egypt while they're building.
• 41:03
  Speaker 1: The pyramids.
• 41:03
  Speaker 2: Show me what the fuck was going on.
• 41:06
  Speaker 1: Yeah.
• 41:07
  Speaker 2: Just put me in a big hamster wheel. There's a
• 41:09
  big plastic bubble where no one could see me. Just
• 41:12
  let me violate space and time and exist there for
• 41:15
  just a few minutes. Just let me look. I think
• 41:18
  that would be the most insane thing that you could
• 41:21
  see about humans and humans' history. Yeah. I just want
• 41:24
  to know what they knew, what they had, what they used.
• 41:27
  Speaker 3: Just looking at this thing, Petra is the same time period,
• 41:30
  at least attributed to. 7,000 roughly BC.
• 41:34
  Speaker 2: Jesus.
• 41:35
  Speaker 3: And they, you know, how would you know that?
• 41:38
  Speaker 2: Right.
• 41:38
  Speaker 3: How?
• 41:39
  Speaker 1: The details of all those carvings is just insane.
• 41:42
  Speaker 2: Insane. Yeah. And 7,000 BC, what are the tools for that?
• 41:48
  What the hell were you using? How did you make
• 41:50
  a temple out of the side of a fucking mountain?
• 41:53
  Look at the size of it, man. The size of
• 41:57
  those columns.
• 41:58
  Speaker 1: It would be hard to do anything like this even today.
• 42:01
  Speaker 2: It would be incredibly difficult, insanely time-consuming.
• 42:06
  Speaker 3: Yeah, the Kaliasa Temple, by the way, I don't have
• 42:09
  it up right now, but in 1650 or so, someone
• 42:13
  sent 1,000 people to try to destroy it. And after
• 42:15
  three years of doing nothing, they stopped. They barely made
• 42:18
  a dent on a couple statues.
• 42:20
  Speaker 1: Yeah, a lot of times when invasions happen in India, like.
• 42:23
  Speaker 3: They tried really hard to fuck it up and couldn't.
• 42:26
  Speaker 2: That's crazy.
• 42:28
  Speaker 1: That's very robust.
• 42:33
  Speaker 2: That's a great way to describe it. It's just, there's
• 42:37
  so much of that stuff that's so interesting. Because it's
• 42:41
  so undeniable. It's so undeniable in its scale. so undeniable
• 42:47
  in its complexity and the planning and the understanding that
• 42:52
  you had to have a deep knowledge of geometry, of measurement.
• 42:57
  You had to have accurate. Materials. Yes.
• 42:59
  Speaker 1: Yeah.
• 42:59
  Speaker 2: Everything.
• 43:00
  Speaker 1: Sturdiness, like resist like calamities, like earthquakes.
• 43:04
  Speaker 2: And then even if you had that, what tools are
• 43:07
  you using? Yeah. Like how are you doing this?
• 43:09
  Speaker 1: How are you coordinating all these people?
• 43:12
  Speaker 2: Right.
• 43:12
  Speaker 1: Getting them to do stuff and. I mean, sure, conditions
• 43:15
  must have been way harsher. I'm sure people didn't really
• 43:18
  have a choice but to do these things because back
• 43:20
  in those days, the only way you could take care
• 43:23
  of your food and clothing and shelter is you commit
• 43:26
  yourself as a laborer to the state, to the kingdom.
• 43:30
  But you could also ask what gave them the initiative
• 43:35
  or drive to go do these things.
• 43:37
  Speaker 2: Yeah. Well, that description is perhaps of a later time.
• 43:40
  We don't even really know what civilization was like when
• 43:43
  these were constructed. The real problem is the material science.
• 43:49
  The real problem is like you– there's a lot of
• 43:52
  things that you have to have to make those things.
• 43:54
  It's not as simple as a sculpture like Michelangelo making
• 43:58
  a sculpture out of something that's like fairly easy to
• 44:00
  carve into as far as stone goes. The scale is–
• 44:06
  it's so undeniable that like something– some piece of our
• 44:12
  understanding is missing.
• 44:13
  Speaker 3: Yeah.
• 44:14
  Speaker 2: Yeah.
• 44:15
  Speaker 1: I mean, it, it, it, like looking at all this,
• 44:17
  like everyone should just be like a lot more humble, right? Like,
• 44:21
  like we don't actually know that much. Like what we
• 44:23
  know is like so little, like whatever, like the same
• 44:26
  thing as what Socrates said, what we know is very,
• 44:29
  very little. And the only thing we, we should all
• 44:33
  strive to be is just be curious. And, um, I
• 44:36
  think there's a lot of tendency for people to like,
• 44:37
  think like, Oh, like we have all this advanced technology.
• 44:40
  We're so amazing. Like, look at us. And it's like, wait,
• 44:43
  hold on. Like, You don't even understand what happened thousands
• 44:46
  of years ago. And there's so much out there to
• 44:49
  just go and explore and learn and get better at
• 44:52
  understanding more.
• 44:54
  Speaker 2: What is this place?
• 44:55
  Speaker 1: Yeah, this is unreal.
• 44:58
  Speaker 2: This is called the Ellora Caves. Timeless wonder carved in stone.
• 45:02
  Speaker 3: I think it's all kind of the same area.
• 45:04
  Speaker 1: Yeah, it's the same Ellora Cave and the Shiva Temple
• 45:07
  that you saw.
• 45:08
  Speaker 2: Look at that. My God. Look at this stuff. It's insane.
• 45:15
  And again, there's no steel back then.
• 45:18
  Speaker 1: It's actually really symmetrical. It's not even like.
• 45:23
  Can you go back to the first one with the
• 45:25
  symmetrical top? Look at the symmetry of the top.
• 45:30
  Speaker 2: It's nuts.
• 45:31
  Speaker 3: It looks like that mall in New York they made
• 45:34
  where the World Trade Center was.
• 45:37
  Speaker 2: Yeah, but way more robust. I mean, how? What were
• 45:44
  you– this is the thing. It's like the material science
• 45:47
  aspect of it. Yeah. It's like you don't have the
• 45:51
  ability to do– look at that top one. Go to
• 45:53
  that top one again. The one that you just had, Jamie? Yeah,
• 45:56
  that one. Look at– that's crazy, man. I mean I
• 46:01
  am just blown away when I see stuff like that.
• 46:04
  My mind just starts racing and I just think how
• 46:08
  did you do this? Who– Who was involved? How was
• 46:11
  it planned? How is it so symmetrical? What were the tools? Like,
• 46:16
  what were the tools, man? If you don't have steel,
• 46:20
  you don't have... What are you using? How'd you do that?
• 46:23
  Speaker 1: I mean, most of it is done with stone, clearly, right?
• 46:26
  Speaker 2: I guess. I guess. I doubt it. I bet they
• 46:30
  had something else. I bet they had something else that,
• 46:34
  over time, eroded just like metal would today. I mean,
• 46:37
  if you left a shovel outside... today and you came
• 46:42
  back to that same spot 500 years from now there's
• 46:44
  nothing that shovel's gone right yeah and you've got to
• 46:48
  assume that these many thousand year old temples that were
• 46:52
  carved out of a fucking mountain whatever tools they used
• 46:56
  probably got absorbed by the earth and the only thing
• 46:58
  that's remaining yeah yeah.
• 47:00
  Speaker 3: Giving me a weird thought like when they make a
• 47:01
  big building downtown though they only bring the crane in
• 47:03
  for a temporary period of time and there's only so
• 47:05
  many cranes on the planet currently too so right True.
• 47:09
  You take it, you move it, you go take it
• 47:11
  to the next spot.
• 47:12
  Speaker 2: Yep. Yeah.
• 47:13
  Speaker 3: True.
• 47:13
  Speaker 1: Yeah.
• 47:14
  Speaker 2: I don't know.
• 47:14
  Speaker 1: Yeah.
• 47:14
  Speaker 2: Especially something like this. Like if they had heavy equipment
• 47:17
  and machinery and whatever the fuck they were using, they
• 47:20
  probably moved it and then moved it out and then
• 47:22
  it probably rotted away. And now it's gone. If there
• 47:25
  was machinery, if there wasn't like there must've been something else,
• 47:29
  some other kind of like some technology that we haven't
• 47:33
  even imagined. Yeah. But it's like they're, their commitment to
• 47:41
  art too was so fascinating because these aren't just structures.
• 47:45
  Speaker 1: They're passion projects.
• 47:46
  Speaker 2: Yeah. Intensely beautiful.
• 47:48
  Speaker 3: Yeah.
• 47:48
  Speaker 2: Intensely ornate. Yeah. So it's not, it's not just that
• 47:52
  they wanted to build like a functional structure. Yeah. That
• 47:55
  good architecture. No, it's this, it's a fascinating artwork and
• 48:00
  it's so intricate. There's so many different features and so
• 48:04
  many different images of, of different people and beings and
• 48:08
  animals and, elephants and.
• 48:11
  Speaker 1: There's one more temple like you could pull out like
• 48:13
  it's called the Tanjore Temple.
• 48:15
  Speaker 2: Oh, I've seen that one too.
• 48:16
  Speaker 3: Yeah.
• 48:17
  Speaker 2: Yeah.
• 48:17
  Speaker 1: That was done more recently in the age of the
• 48:19
  Cholas and it's pretty incredible.
• 48:22
  Speaker 2: When did they do that one?
• 48:25
  Speaker 1: I don't know the exact number but more recent than
• 48:28
  the ones that you saw.
• 48:29
  Speaker 2: All of them are nuts, man. And then there's stuff
• 48:31
  like that all over the world.
• 48:33
  Speaker 1: Whoa. This was done as a project by the king
• 48:37
  to To basically make a name for himself.
• 48:41
  Speaker 2: Wow. That's incredible. Is that multiple pieces of stone or
• 48:47
  did he carve that whole thing out of stone too?
• 48:50
  Speaker 1: Probably multiple pieces.
• 48:52
  Speaker 2: So that's actually like construction. Yeah. Not like removal. The
• 48:58
  other ones are, it's essentially a giant sculpture. Wow, it's
• 49:04
  so pretty. Look how geometric it is too.
• 49:07
  Speaker 1: That's what amazes me. Like, they didn't actually have all
• 49:10
  these simulations and CAD tools and all these things.
• 49:13
  Speaker 2: Right.
• 49:14
  Speaker 1: And.
• 49:15
  Speaker 2: What year was this made, Jamie? Does it say?
• 49:17
  Speaker 1: It says.
• 49:20
  Speaker 2: so incredible how much of this stuff exists where it's
• 49:23
  really baffling. Like I just found out recently that the
• 49:27
  Aztecs didn't build those temples, that they found them. Really? Yeah.
• 49:32
  They found like the Tenochtitlan, they call it the place
• 49:36
  where the gods were born. The Aztecs found it. And
• 49:40
  uncovered it. And then on the, when, is it Tenochtitlan
• 49:45
  or Teotocan, whichever one it was, on the consecration day,
• 49:50
  when they were done with like whatever they were doing
• 49:52
  with it, to celebrate they killed somewhere between 20,000 and 80,000
• 49:58
  people in four days. Damn. Not exactly the mindset of
• 50:04
  the type of people that would construct something like that.
• 50:06
  You know, so those are the people that, found it,
• 50:08
  and it might have been sitting there for a thousand years.
• 50:11
  And then they came along and said, oh, this is cool.
• 50:14
  Let's live here. Okay, but what was the society that
• 50:18
  lived there before them? And where are they? And what happened?
• 50:21
  And how did they do this? And why did they
• 50:23
  do it? And why did they have it aligned with
• 50:24
  the constellations? Like, what were they doing?
• 50:27
  Speaker 1: Yeah. Some of the calculations are pretty amazing, like how
• 50:33
  they timed it, how they positioned it. how they cared
• 50:37
  about planetary positions and stuff like that. Sure, like, some
• 50:40
  of it could even be pseudoscience, but whatever. I think
• 50:43
  just the level of, like, calculations they were making back
• 50:46
  in those days without, you know, powerful computers is just outstanding.
• 50:51
  Speaker 2: It's just nuts. And it doesn't make sense. It's like, okay,
• 50:54
  they're making it without powerful computers, so what are they using?
• 50:58
  Speaker 1: I mean, at one point, the word computer just meant
• 51:01
  a human.
• 51:02
  Speaker 2: Right.
• 51:02
  Speaker 1: Like, human beings would be doing the calculations. That was
• 51:05
  their only job. Like, To literally, like, multiply two numbers. Or, like,
• 51:09
  to make some... Astronomers were actually the first mathematicians. The
• 51:14
  term mathematician and astronomer were used synonymously at one point.
• 51:17
  Speaker 2: Really?
• 51:18
  Speaker 1: Yeah.
• 51:20
  Speaker 2: Why is that? Why studying the stars and math?
• 51:24
  Speaker 1: Yeah, because, like, studying the stars involved making a lot
• 51:27
  of geometry calculations. And that was kind of actually one
• 51:32
  of the first set of mathematicians in India. People like Aryabhatta, Bhaskara,
• 51:37
  all these guys were actually astronomers too. They were not
• 51:40
  just mathematicians. And Aryabhatta was earlier still like the idea
• 51:46
  of using zeros. And then he had a lot of
• 51:49
  like contributions in geometry. And he was doing all this
• 51:53
  like just because he was interested in astronomy.
• 51:57
  Speaker 2: Isn't there evidence of Pythagorean theorem in ancient, is it
• 52:04
  ancient Sumerian? Is it? It's something that predates Pythagoras.
• 52:13
  Speaker 1: Interesting. My theory is that even though it was not
• 52:16
  formulated as a Pythagorean theorem, I'm sure people had to
• 52:19
  understand concepts of sines and cosines and whatever is the
• 52:24
  right angle for the right incline to get this right
• 52:27
  level of geometry. You needed to have some implicit understanding
• 52:31
  of it to build these kind of structures. There's no
• 52:34
  way you could do it without that.
• 52:35
  Speaker 2: Yeah, 100%. And you have to have incredible measurement tools,
• 52:39
  like not just the actual mathematics. Okay, the oldest known
• 52:43
  evidence of Pythagorean theorem dates from old Babylonian clay tablets
• 52:47
  from about 1900 to 1600 BCE, roughly 1,000 years before Pythagoras.
• 52:52
  Isn't that wild? Like, how?
• 52:55
  Speaker 1: How?
• 52:57
  Speaker 3: How?
• 52:58
  Speaker 2: Clay Tabots often cited, used what we now call the
• 53:01
  Pythagorean theorem to compute the diagonal of rectangles and squares,
• 53:09
  including an excellent approximation. Look at this. This is nuts, man.
• 53:15
  Vedic ritual text explicitly states the rule equivalent. I don't
• 53:18
  know how to say that. What is that? A squared,
• 53:21
  B squared, C squared. But the diagonal of a rectangle
• 53:24
  that includes numerical examples predating or roughly contemporary. With classical
• 53:29
  Greek mathematics. So completely different parts of the world. Yeah.
• 53:33
  And they're coming up with the same stuff.
• 53:35
  Speaker 1: Exactly. Because they're all curious. That's it.
• 53:38
  Speaker 2: Yeah. They're all curious and eventually all curiosity leads to
• 53:42
  truth or some form of it.
• 53:44
  Speaker 1: I would argue that anything that's of impact in the
• 53:46
  world has only been done by curious people. In hindsight,
• 53:50
  we label those people as successful or smart or rich.
• 53:54
  But the common trait across all of them has been
• 53:56
  like curious people.
• 53:59
  Speaker 2: Well, that's certainly a powerful trait. And people that aren't
• 54:03
  curious are not fun. Yeah.
• 54:05
  Speaker 1: They're not interesting. So because of that, they don't attract
• 54:09
  other smarter, interesting people. And therefore, they won't be able
• 54:12
  to do something very meaningful.
• 54:13
  Speaker 3: In the world.
• 54:15
  Speaker 1: So it's kind of like it's less about– and it
• 54:20
  applies to your personal relationships and personal life too. It's
• 54:23
  not just about professional success. Like, you'll have a more
• 54:27
  fulfilling life with your wife or your kids if you're
• 54:30
  a more curious person. You ask them more questions. You
• 54:33
  take interest in them, right? So that's the one quality
• 54:37
  everybody wants in personal relationships is, like, taking interest in
• 54:40
  them and, like, actually understanding them better or, like, being
• 54:43
  curious about common things. And so it's not just that,
• 54:47
  you know, being curious leads to success. It's more that
• 54:50
  people around you want you to be successful if you're
• 54:52
  curious because... you will have more compounding and fulfilling relationships.
• 54:58
  Speaker 2: I would agree with that. Yeah. I'd say it's one
• 55:01
  of the more important qualities of human beings. I mean,
• 55:04
  it's led to everything that we have today. All curiosity
• 55:07
  has led to all of our architecture, math, everything, art, everything.
• 55:11
  Speaker 1: The transistor. Like, you know the story of the transistor?
• 55:14
  Speaker 2: Yeah.
• 55:15
  Speaker 1: So Bell Labs was basically employing as many, like history adjusted,
• 55:20
  as many telephone engineers back then as the number of
• 55:22
  software engineers today. But only three people cared enough to
• 55:26
  question whether you should use these really hot giant vacuum
• 55:30
  tubes for amplifying telephone signals. So vacuum tubes are very big,
• 55:34
  power hungry and very hot. And so they were not
• 55:38
  fault tolerant and it's very expensive. And so three people
• 55:42
  questioned the need for that and came up with the
• 55:43
  idea of the transistor to amplify current. And that was
• 55:49
  the Nobel Prize winning discovery. And not just that it
• 55:52
  was useful to amplify telephone signals, It basically led to
• 55:55
  the rise of modern computing and we wouldn't have an
• 55:58
  iPhone like this today if not for those three people.
• 56:01
  Speaker 2: Do you know what the tinfoil hat conspiracy theory about
• 56:03
  transistors is?
• 56:04
  Speaker 3: No.
• 56:05
  Speaker 2: That they are back-engineered from the Roswell crash along with
• 56:10
  fiber optics.
• 56:12
  Speaker 1: Tell me more.
• 56:13
  Speaker 2: So we read this on the podcast. Remember, Jamie, there's
• 56:16
  the two scientists that were attributed. There's this one scientist
• 56:19
  that said they weren't even remotely exceptional guys and that
• 56:22
  they gave them the credit for this so that they
• 56:25
  didn't have to reveal the true nature of where this
• 56:28
  technology came from.
• 56:30
  Speaker 1: I see. Interesting.
• 56:31
  Speaker 2: So, again, tinfoil hat securely on our heads. This is
• 56:36
  not something I believe. This is just something that's fun.
• 56:40
  There's a few inventions that came out of that time period,
• 56:45
  roughly after 1947, that are weird. And one of them
• 56:49
  is fiber optics and one of them is a transistor.
• 56:51
  And these are supposedly... Attributed to back engineering programs. So
• 56:56
  the Roswell crash, I don't know if you ever paid
• 56:58
  any attention to it. It's a real weird one because
• 57:01
  the cover of the Roswell Daily Record said that the
• 57:04
  government has a crash disc that landed in the desert.
• 57:07
  A bunch of witnesses, a bunch of people saw it.
• 57:11
  It's also people that saw– supposedly saw physical bodies of
• 57:15
  these creatures and supposedly– again, who knows what's true? But
• 57:23
  Truman went to the site. He visited it. And then
• 57:27
  the planes, two separate planes, were flown to Wright-Patterson Air
• 57:31
  Force Base, which was– I think it was just Wright
• 57:34
  Base back then. I don't think it was Wright-Patterson. But
• 57:36
  they flew them out and the idea was this material
• 57:41
  was so important they didn't want to risk one plane crashing.
• 57:45
  So they flew it in two different planes. And that
• 57:47
  this stuff has always been known to be stored at
• 57:51
  Wright-Patterson Air Force Base. That's what everybody always talks about.
• 57:54
  And then a lot of it was moved to Bell Labs.
• 57:58
  And there was a company called the American Computer Company.
• 58:02
  And back in the day, the American Computer Company was
• 58:05
  just like– it was a consumer website where you could
• 58:08
  go and say, oh, I need a Windows computer that
• 58:10
  does this, that, and the other thing. And you could
• 58:12
  just put in whatever your specs were and they would
• 58:14
  build it for you. They all had a whole section.
• 58:17
  of their website dedicated to Bell Labs and back-engineered UFO technology.
• 58:23
  And all they talked about in this one, like whoever
• 58:25
  ran it was like a fucking kook. Is that still around?
• 58:28
  That website? Yeah. American Computer Company, is it still around?
• 58:32
  Speaker 1: Interesting.
• 58:32
  Speaker 2: So this is like the 1990s.
• 58:34
  Speaker 1: So you're saying your theory, I mean, not that you
• 58:37
  believe in it, but your theory is that the transistor
• 58:41
  was not invented, it was known, and it was given
• 58:43
  to the Bell Labs.
• 58:43
  Speaker 2: There's apparently a giant leap. Between the first ideas of
• 58:47
  the transistor and then what actually came about and how
• 58:50
  much money had to be spent to create it off
• 58:54
  of this leap. This was this assertion by these scientists
• 58:58
  that were trying to examine this. The thing about Bell
• 59:01
  Labs is there's a military base right outside of Bell Labs.
• 59:05
  And they say, well, that military base is to guard
• 59:07
  New York City. But New York City is quite a flight. away.
• 59:11
  But Bell Labs is right there. And they were working
• 59:14
  on some deep, dark shit at Bell Labs for sure.
• 59:18
  Because I've had a bunch of people on that were
• 59:19
  talking about remote viewing exercises that they were doing out
• 59:23
  of Bell Labs. You know, we've had a bunch of
• 59:25
  people that came on and talked about various programs that
• 59:29
  were going on that were like top secret programs that
• 59:31
  were happening that were being run through Bell Labs. There's
• 59:34
  some weirdness to that place, like real weirdness.
• 59:37
  Speaker 1: Yeah.
• 59:38
  Speaker 2: And it's fun.
• 59:39
  Speaker 3: Yeah.
• 59:39
  Speaker 2: The idea that like, you know, that.
• 59:42
  Speaker 1: Because it definitely feels very disconnected. Like, OK, like you
• 59:46
  were using all these vacuum tubes and then suddenly you're like, OK,
• 59:51
  like what if we just use semiconductors? okay, there's definitely
• 59:57
  a pretty far drift from what you're doing currently to
• 1:00:00
  what you're supposed to do. And also the idea of
• 1:00:05
  the first transistor and what ended up being used in chips,
• 1:00:09
  the junction transistor are quite different too. So they're like
• 1:00:12
  big leaps in terms of what the core idea was.
• 1:00:15
  It's not an incremental change. The way I thought about
• 1:00:19
  it was like, okay, that's like tens of years of work.
• 1:00:22
  And that's why they made a big change. And so
• 1:00:25
  if you actually looked into the individual milestones they had,
• 1:00:28
  maybe it would have looked pretty different. But your conspiracy
• 1:00:32
  theory is pretty interesting.
• 1:00:33
  Speaker 2: It's always fun.
• 1:00:34
  Speaker 1: Yeah.
• 1:00:35
  Speaker 2: And also there's just too many stories of this. And
• 1:00:39
  David Grush has, you know, on oath said that there
• 1:00:43
  are back engineering programs and he was read into these
• 1:00:45
  and that they've been around for a long time. Yeah.
• 1:00:48
  This is the assertion of that movie, The Age of Disclosure,
• 1:00:51
  that the real problem is that they have misappropriated funds
• 1:00:54
  and lied to Congress. And so they come out and
• 1:00:56
  tell you, OK, we do have this program. Well, guess what?
• 1:00:59
  Everybody goes to jail because you guys are a bunch
• 1:01:01
  of liars and you've been stealing money and you've been
• 1:01:04
  doing whatever you want to do with this money. I
• 1:01:06
  don't know how much oversight is there on back engineering
• 1:01:11
  UFO programs. So probably a lot of people get in trouble.
• 1:01:15
  A lot of people go to jail. On top of that,
• 1:01:17
  These things are all being done by weapons manufacturers, right?
• 1:01:22
  Like where are you going to bring them to? Well,
• 1:01:24
  you're going to bring them to Lockheed Martin or you're
• 1:01:26
  going to bring them to Rocketdyne or it's going to
• 1:01:29
  be someone that does that kind of work. You're not
• 1:01:31
  going to do it on your– it's not going to
• 1:01:32
  be like we'll do it. No, you're going to have
• 1:01:34
  to bring it to people that already make spaceships or
• 1:01:36
  bring it to people that already make jets. And so
• 1:01:39
  they have a massive competitive advantage over any other company.
• 1:01:43
  That's doing it. So then there's other companies that also
• 1:01:45
  had contracts with the United States government. They can sue.
• 1:01:47
  And so he lays out all the problems with disclosure.
• 1:01:51
  And their assertion is that the only thing– what we
• 1:01:55
  need if we really want to find out the truth
• 1:01:58
  is we're going to need widespread amnesty for all these
• 1:02:01
  people that were involved. My problem with that is that's
• 1:02:05
  what I would say too. If I had been stealing
• 1:02:07
  money for decades and decades, I would be like– And
• 1:02:11
  then I'll tell you where all this stuff is. I'm
• 1:02:13
  like how do we know what this stuff is, whether
• 1:02:18
  or not these are just top secret military programs with
• 1:02:21
  advanced propulsion technology that's unavailable to the public and they're
• 1:02:24
  going to say that it's aliens and they back engineered
• 1:02:26
  this and they did that. Like they clearly don't want
• 1:02:29
  to tell people. They don't want people to know. I
• 1:02:31
  think a large part of it is probably because they
• 1:02:34
  could get in trouble. But I think also a large
• 1:02:36
  part of it is because it's fun to keep secrets
• 1:02:38
  from people. Yeah. Especially when you're the government. Why tell them?
• 1:02:42
  Fuck those people. Fuck them. They don't even know UFOs
• 1:02:45
  are real. Meanwhile, we're going into a bunker in the
• 1:02:48
  middle of the mountain and we're remote viewing. There's probably
• 1:02:54
  a lot of fun involved in having access to information
• 1:02:57
  that most people would kill for. Yeah.
• 1:03:00
  Speaker 1: I mean, there's so much information that we just don't
• 1:03:04
  have access to.
• 1:03:05
  Speaker 2: Which brings me to this question.
• 1:03:08
  Speaker 3: With.
• 1:03:08
  Speaker 2: It seems like one of the things that's happening with–
• 1:03:11
  both with AI and with technology in general is that
• 1:03:15
  you have more and more access to information and more
• 1:03:19
  and more answers to questions than ever before. At a
• 1:03:23
  certain point in time, there's going to be no bottleneck
• 1:03:27
  and we're going to know everything about everything. So how
• 1:03:30
  is anyone in government going to keep a secret? How
• 1:03:33
  is any corruption ever going to be possible?
• 1:03:36
  Speaker 1: Yeah.
• 1:03:37
  Speaker 2: At a certain point in time, all of it will
• 1:03:39
  get uncovered. Like it's much more difficult to commit murder
• 1:03:42
  now with DNA evidence, right? Back in the 1800s, like
• 1:03:45
  I didn't see nothing. I wasn't there. And then you're free.
• 1:03:49
  Like now they do your fingerprints. Now they get your DNA.
• 1:03:53
  Now there's flock cameras. There's like more and more and more.
• 1:03:57
  It's harder to get away with things. So it seems
• 1:04:00
  like to me like whatever they have, whatever anybody has–
• 1:04:04
  Ultimately there's going to come a point in time where
• 1:04:06
  there's so much data and so much information and you
• 1:04:08
  could run all your questions. Like there's an AI fact
• 1:04:11
  checker for politicians now. So while a politician is giving
• 1:04:15
  a speech, you can run an AI fact checker and
• 1:04:17
  in real time it will tell you whether or not
• 1:04:20
  these people are full of shit. It seems like the
• 1:04:22
  direction is there's not going to be anybody full of
• 1:04:24
  shit in the future because it's not going to be possible. Yeah.
• 1:04:27
  Speaker 1: I mean the government still would have access to things
• 1:04:29
  that we human beings wouldn't have access to, like regular people. Um, and, um,
• 1:04:36
  particularly with defense related, weapons related. Like, for example, uh,
• 1:04:41
  when they did the, uh, Venezuelan thing, um, I don't
• 1:04:46
  think people in Venezuela even understood, like, what even those
• 1:04:49
  weapons were.
• 1:04:50
  Speaker 2: I don't think we did.
• 1:04:51
  Speaker 1: They, they, they, they were described as like, yeah, they
• 1:04:54
  were described as something, the literal words used were like
• 1:04:57
  alien-like technology. So, we, even we didn't know that, um,
• 1:05:02
  the United States had access to that quality of defense
• 1:05:06
  technology until that incident happened. So there are obviously going
• 1:05:11
  to be secrets, right? Especially the highest stakes things. I
• 1:05:16
  would say like building frontier AI models is similar to that.
• 1:05:20
  Of course, as more and more models are getting open source,
• 1:05:24
  I think the knowledge is diffusing, but still the true
• 1:05:28
  definition amount of details you need to actually train a
• 1:05:31
  really amazing frontier reasoning capability model is still not, like,
• 1:05:36
  widely diffused. So, I, my, my things, my, my, my
• 1:05:40
  hypothesis is that whatever is extremely high stakes will still
• 1:05:46
  not be widely diffused. It, it, at least there'll be
• 1:05:50
  enough structures in place to keep it secret.
• 1:05:53
  Speaker 2: Forever?
• 1:05:54
  Speaker 1: Not forever, but for a while.
• 1:05:56
  Speaker 2: For a while. Yeah. That's the thing.
• 1:05:58
  Speaker 1: Long term, sure. Like, Things do get out and people
• 1:06:00
  do understand.
• 1:06:01
  Speaker 2: But it feels like long term is what I'm looking at.
• 1:06:03
  Like look, when we're looking at history, we're talking in
• 1:06:06
  these– like when we're looking at all these different temples
• 1:06:09
  and all these different things, we're talking about thousands and
• 1:06:11
  thousands of years. And thousands of years time span in
• 1:06:14
  between each individual one. With our world, we're talking about
• 1:06:20
  massive change in 200 years. Like this country is 250
• 1:06:24
  years old. Think about how kooky that is. That is
• 1:06:26
  a blink of an eye in history.
• 1:06:28
  Speaker 1: But do we understand everything that happened in the United States?
• 1:06:30
  Speaker 2: No.
• 1:06:31
  Speaker 1: Exactly. So there are still some details that are hidden
• 1:06:34
  from us. We don't fully understand everything, right?
• 1:06:37
  Speaker 2: For now. Yeah. But my question is, as time goes on,
• 1:06:42
  250 years from now, is it even possible to keep
• 1:06:45
  any secrets from anybody? And is that a good thing?
• 1:06:51
  It might be a good thing. It sounds horrible to
• 1:06:53
  people because they're like, oh, my God, what about privacy? Right,
• 1:06:56
  but also what about lies? Yeah. No more lies? Like
• 1:06:59
  everyone's going to know what you're thinking? Everyone's going to
• 1:07:02
  know everything people do all the time? Yeah.
• 1:07:06
  Speaker 1: I mean, if you're a true surveillance state, obviously there
• 1:07:08
  are no secrets.
• 1:07:09
  Speaker 2: Right.
• 1:07:12
  Speaker 1: Except about the government itself.
• 1:07:14
  Speaker 2: That's the problem.
• 1:07:15
  Speaker 3: Yeah.
• 1:07:15
  Speaker 2: Does it bottleneck with the government or does it get
• 1:07:18
  to a point where you can't even have government secrets?
• 1:07:22
  Because as technology evolves and as human civilization evolves— secrets
• 1:07:27
  will be less and less, not just necessary, but secrets
• 1:07:29
  will be problematic because there'll be an impediment to knowledge.
• 1:07:33
  There'll be an impediment to understanding the true scope of
• 1:07:37
  what the world is, like the true nature of all
• 1:07:40
  of our various moving parts. Yeah.
• 1:07:42
  Speaker 1: I mean, as long as the human quality, the intrinsic
• 1:07:44
  human quality of curiosity and truth-seekingness, which is, you know, universal,
• 1:07:48
  it's existed ever since we've known human beings, if that
• 1:07:52
  continues and that continues to be the case, then people
• 1:07:55
  will have enough incentives to figure out the truth. And
• 1:08:01
  if something is actually hard to get to, it only
• 1:08:04
  motivates you more to actually go.
• 1:08:05
  Speaker 2: And find it. For sure. So my question is, where
• 1:08:08
  does this all go? You know, and you obviously work
• 1:08:12
  in AI. And when you think about AI, and when
• 1:08:14
  you think about just technology in general, and you extrapolate,
• 1:08:18
  you just take it from here, and you just plot
• 1:08:21
  it out. Like, what is a possible scenario of 250
• 1:08:24
  years from now? Like, what does it even look like?
• 1:08:27
  What does the United States look like at 500 years old?
• 1:08:30
  Speaker 1: It's very hard to know. I'll be very honest. I
• 1:08:32
  think it's very hard to know even five years from
• 1:08:35
  now how it's going to look like.
• 1:08:37
  Speaker 2: That's crazy. Yeah. Five years ago, it was like.
• 1:08:40
  Speaker 1: Five years ago, whoever is at the top most in AI,
• 1:08:44
  I don't even consider myself like that, but whoever is
• 1:08:46
  at the most frontier level of decision-making in AI five
• 1:08:49
  years ago, I don't think they predicted the exact state
• 1:08:52
  we are in today. Nobody did. If they did, they
• 1:08:56
  would have already procured all the compute and manufactured all
• 1:08:59
  the chips, bought out all the fabs. They would have
• 1:09:01
  done all that, right? Just this counterfactual. Everyone's bottlenecked by
• 1:09:07
  not having enough compute and we don't have enough chips,
• 1:09:10
  we don't have enough power. These are all the problems
• 1:09:12
  that if you invite anybody in AI and ask, what
• 1:09:14
  is the bottleneck in AI today? And everybody would say power.
• 1:09:17
  I think Jensen was here and he said the same thing, right?
• 1:09:20
  Speaker 2: Yeah.
• 1:09:20
  Speaker 1: But, okay, like, if you predicted this exact state five
• 1:09:24
  years before, wouldn't you have secured enough power and started
• 1:09:28
  building more power plants yourself and started getting permits and, like, started, like,
• 1:09:32
  planning out capacity? No, nobody did that. Right. Everything is
• 1:09:36
  reactive to the demand that we're having today.
• 1:09:38
  Speaker 2: And that's just five years.
• 1:09:39
  Speaker 1: Yeah, that's just five years. So when you ask me
• 1:09:41
  to predict 250 years, like, I just have to honestly
• 1:09:44
  say I don't know.
• 1:09:45
  Speaker 2: Do you ever sit back and think about it, though?
• 1:09:47
  Speaker 1: I don't think about it.
• 1:09:48
  Speaker 2: What it could be?
• 1:09:48
  Speaker 1: I don't think about it. So there are a lot
• 1:09:50
  of fun. I use perplexity a lot for these kind
• 1:09:54
  of things, especially this new feature computer inside it. And
• 1:10:01
  this is just for hypothetical scenarios. Let's say there is
• 1:10:03
  an AGI, right? I've seen you ask a lot of
• 1:10:06
  people about this. And a lot of conventional answers is like, oh,
• 1:10:12
  we'll just become managers of the AIs. Don't worry. But
• 1:10:17
  if the price of cognition is the price of compute,
• 1:10:19
  managing an AI is also pretty much doable by the
• 1:10:24
  AI itself. Because the bottleneck is not like unique cognition
• 1:10:30
  capability there. So the value of the society will automatically
• 1:10:34
  shift to what is scarce. And fundamentally, what has been
• 1:10:38
  scarce is like asking like high quality questions about things. Okay,
• 1:10:42
  like what if like we just, completely spend all our
• 1:10:46
  time understanding the past like that's an interesting endeavor it
• 1:10:49
  was not cool before but it'll it'll become cool again
• 1:10:52
  um and like we usually used to view like archaeology
• 1:10:56
  or history is not something that's like worth having a
• 1:10:59
  career in because it doesn't pay well but what if
• 1:11:02
  it actually starts paying you go a lot more now
• 1:11:04
  that like actual knowledge works being done by ais and
• 1:11:07
  like it's all mundane and all the price of that
• 1:11:09
  is basically a zero.
• 1:11:10
  Speaker 2: Right, and archaeology would be one of the few things
• 1:11:13
  that it wouldn't have access to because it doesn't have
• 1:11:15
  the actual ground. It can't get into the ground and
• 1:11:18
  do the scans.
• 1:11:20
  Speaker 1: Let's say we have robots to go do that, but
• 1:11:24
  you're still going to be the one probing because you
• 1:11:26
  have incomplete information all the time. Even the idea of, okay,
• 1:11:30
  let's go explore this particular area. Let's go understand better.
• 1:11:34
  Let's go try to reverse engineer this. Let's go try
• 1:11:36
  to build this again. How would it be if we
• 1:11:39
  wanted to do the same thing on the moon There
• 1:11:41
  are so many interesting projects to work on for us
• 1:11:44
  as long as we stay curious and we stay interested
• 1:11:46
  in a lot of things that we've done before and
• 1:11:49
  trying to understand civilization. I'm not really concerned about what
• 1:11:54
  things we get to do. We might be doing a
• 1:11:56
  lot more cool things for what it's worth. I don't
• 1:11:58
  know if anybody will be coming and telling you that, oh,
• 1:12:01
  it's so cool to open an Excel sheet every day
• 1:12:03
  and make financial models.
• 1:12:06
  Speaker 2: There's got to be somebody out there that likes that.
• 1:12:09
  Speaker 1: I mean, there's something about like the task you do
• 1:12:11
  and what you get paid for, like what is the
• 1:12:14
  job title, blah, blah, blah. And some people associate their
• 1:12:18
  personal worth with like where they work at and how
• 1:12:21
  much they get paid. And I think that thing is
• 1:12:24
  going to collapse in a world where like the price
• 1:12:28
  of all that cognition is going to be the price
• 1:12:30
  of compute.
• 1:12:31
  Speaker 2: What do you think happens to people if a large
• 1:12:36
  percentage of jobs– get replaced by AI?
• 1:12:40
  Speaker 1: I think they'll find new things. We've always gravitated towards
• 1:12:45
  things that are scarce because that's where the value lies.
• 1:12:49
  And so if, you know, have you, one interesting analogy
• 1:12:56
  is the Gulf states where there's an abundance of resources
• 1:13:02
  and they export their resources to other states and that
• 1:13:06
  pays for the whole state. you know how like they
• 1:13:09
  offer everybody, um, free electricity, subsidized health, subsidized education, and
• 1:13:14
  like no taxes. When I first went to Dubai, like in, um,
• 1:13:18
  almost like 20 years ago, um, they told me like,
• 1:13:23
  people don't pay taxes here and nobody pays for electricity here. And, uh,
• 1:13:28
  education is like super cheap. And I was like, wait,
• 1:13:30
  how is that real? And, um, and, uh, And the
• 1:13:36
  way that's real is that, I mean, of course, Texas
• 1:13:38
  also has no taxes and, you know, any well-run state
• 1:13:40
  can do this. But the way it's happening is that
• 1:13:44
  because the government provides you all these things, it becomes
• 1:13:47
  a rentier state. Like you offer political acquiescence to the state.
• 1:13:52
  And what ended up happening is citizens there expect the
• 1:13:57
  state to find them jobs, expect the state to take
• 1:13:59
  care of like job displacement for them. So they don't worry.
• 1:14:02
  So it made them a little more lazy. So that's
• 1:14:04
  not a good future to have where some people talk
• 1:14:08
  about AI subsidies and AI dividends that get paid to everybody.
• 1:14:12
  I think we need to do some form of that,
• 1:14:14
  but that in entirety won't solve the problem.
• 1:14:17
  Speaker 2: Right. Well, the thing about human nature is sort of undeniable.
• 1:14:24
  And if you give people the ability to be lazy,
• 1:14:26
  a large percentage of people will take that.
• 1:14:28
  Speaker 1: That's right.
• 1:14:29
  Speaker 2: A large percentage won't, though. Yeah. There's going to be
• 1:14:31
  enough people that are inspired to do something and they say, okay, well,
• 1:14:34
  now my basic needs are taken care of. Let me
• 1:14:37
  pursue my actual interests. That's right. And find purpose in that. Yeah.
• 1:14:40
  Because that's– a lot of people find purpose in whatever
• 1:14:43
  their occupation is. Yeah. And if we can shift that
• 1:14:46
  to finding purpose in what your actual interests are and
• 1:14:49
  then really pursuing something, whatever it is in that, then
• 1:14:52
  you'll still have meaning in your life.
• 1:14:54
  Speaker 1: It keeps coming back to staying curious.
• 1:14:58
  Speaker 2: Yes.
• 1:14:59
  Speaker 1: And finding value in– your relationships, your family, caring for
• 1:15:05
  each other. If you ask a lot of retired people,
• 1:15:08
  actually retired people is a good demographic to understand what
• 1:15:11
  would happen, what are things people find meaning in after
• 1:15:14
  like work's taken off them. And majority of the answers
• 1:15:18
  are always like family, caring, you know, personal like relationships
• 1:15:23
  and community. Like these are the things retired people keep
• 1:15:27
  doing to like you know keep themselves active and wake
• 1:15:30
  up every day and have something to look for so
• 1:15:32
  all those things will become even more important at a
• 1:15:35
  time when like work itself doesn't mean much doesn't mean
• 1:15:40
  humans won't be status seeking i think we'll still be
• 1:15:44
  but status is not gonna come from whether you're working
• 1:15:47
  at you know like a particular famous bank or a
• 1:15:50
  tech company or whatever it'll be driven by like um
• 1:15:54
  how interesting you are are you interesting to talk to
• 1:15:58
  When I can talk to an AI, despite that, are
• 1:16:00
  you still interesting to talk to? Are there certain things
• 1:16:03
  I get out of talking to you that completely change
• 1:16:06
  my perspective about a bunch of things? Or is it
• 1:16:09
  just fun to hang around you? Can we have a
• 1:16:12
  compound relationship together? And I think, again, it goes back
• 1:16:16
  to being curious about things.
• 1:16:18
  Speaker 2: Well, this is best case scenario, right? Worst case scenario
• 1:16:22
  is civilization upheaval, chaos, civil war.
• 1:16:25
  Speaker 1: And it's possible even without an AI.
• 1:16:28
  Speaker 2: Right. Yeah. Look, we've gotten real close to it a
• 1:16:31
  couple of times.
• 1:16:31
  Speaker 1: Exactly.
• 1:16:32
  Speaker 2: Yeah.
• 1:16:32
  Speaker 1: And we did not need an AGI-like scenario for a
• 1:16:37
  civilizational collapse in the past, as you've clearly seen.
• 1:16:41
  Speaker 2: Right.
• 1:16:41
  Speaker 1: A calamity can just take out all of us, wipe
• 1:16:44
  out everything.
• 1:16:45
  Speaker 2: Sure. Especially natural ones. Yeah.
• 1:16:46
  Speaker 1: That's why I'm not a big fan of everybody claiming
• 1:16:48
  that the AI is going to. you know, kill us.
• 1:16:52
  So like AGI is going to destroy humanity and like
• 1:16:55
  it's too dangerous and we all need to stop doing
• 1:16:57
  these things. But at the same time, continuing to fill
• 1:16:59
  all these data centers and continuing to make money, you
• 1:17:02
  have to have one consistent position. My position is that
• 1:17:07
  whether AI or not, I think being curious is going
• 1:17:10
  to serve you really well. I think it's going to
• 1:17:13
  help you have a better life. And there are two
• 1:17:16
  paths to curiosity, one that can kill it and one
• 1:17:18
  that can supercharge it. In my opinion, the one that
• 1:17:22
  kills curiosity is algorithmic feeds. Like the brain rot that
• 1:17:27
  you're fed every day, which is, you know, this continuous
• 1:17:30
  doom scrolling, that's bad. And the one that can supercharge
• 1:17:34
  it is AI. Okay, like now that you could just
• 1:17:38
  ask whatever you want, if everybody has like a pull
• 1:17:42
  it up Jamie for them, you know. Right. And that's amazing.
• 1:17:45
  So all you have to do is be curious about
• 1:17:49
  a lot of different things. And, of course, talk to
• 1:17:51
  interesting people, engage in interesting activities together. If money is
• 1:17:56
  no longer an issue, you can fund passion projects yourself.
• 1:17:59
  You don't have to, like, require government funding or venture funding. Like,
• 1:18:03
  what if you just wanted to build a mini cave yourself? Okay, like,
• 1:18:07
  you find a piece of land somewhere. There's a lot
• 1:18:09
  of land in America. Way more land than we know
• 1:18:13
  what to do with it. And surely we can build
• 1:18:16
  a lot of interesting things there.
• 1:18:18
  Speaker 2: Well, that's a good... Glass half full scenario. And one
• 1:18:23
  of the things that I keep coming to is this
• 1:18:26
  whole idea of people working and making money and having
• 1:18:31
  careers and having portfolios and bank accounts. This is all
• 1:18:36
  very recent in human history.
• 1:18:37
  Speaker 1: Yeah. Very recent.
• 1:18:39
  Speaker 2: Very recent. But we've become accustomed to this as a
• 1:18:42
  way of life.
• 1:18:43
  Speaker 1: And Microsoft built this concept of a knowledge worker because
• 1:18:46
  they wanted to sell more office software.
• 1:18:49
  Speaker 3: Really?
• 1:18:50
  Speaker 2: Yeah.
• 1:18:51
  Speaker 1: Like this whole idea of putting a PC on every
• 1:18:53
  desk and making you like glued to the PC was there.
• 1:18:57
  That was Bill Gates' vision. Put a PC on every desk.
• 1:18:59
  Speaker 2: That fucking wizard. What an incredible accomplishment because, boy, did
• 1:19:04
  they nail it. Yes.
• 1:19:06
  Speaker 1: It was not about making computing like beautiful or anything
• 1:19:09
  in the way like Steve Jobs and mission did.
• 1:19:11
  Speaker 2: Right.
• 1:19:12
  Speaker 1: It was just about like computers, sell more software, sell
• 1:19:14
  more computers because that way you can sell more software.
• 1:19:18
  And if you sell more software, you become rich. And, and, and,
• 1:19:21
  and the company just was a machine that was just,
• 1:19:25
  you know, built essentially a large sales machine, just built
• 1:19:27
  to sell software. And, and, and, uh, And now they
• 1:19:32
  sell cloud, but whatever. Like, that's essentially the reason that, like,
• 1:19:37
  you know, we all got trained to use software. People
• 1:19:41
  went and did tutorials on how to use Excel, how
• 1:19:45
  to use Word, how to use all these email tools.
• 1:19:47
  And then now that became the upscaling you needed to
• 1:19:50
  go work at different companies and then write code and, like, whatever, right?
• 1:19:54
  So if that part's going to be done by an AI,
• 1:19:56
  it's not necessarily a bad thing because this is not
• 1:20:00
  actually the way you feel a real purpose and fulfillment
• 1:20:03
  in your own life. If you were never exposed to that,
• 1:20:07
  whatever you had as the intrinsic curiosity in you, that's
• 1:20:12
  probably what you should be doing.
• 1:20:14
  Speaker 2: Yeah, there could be a completely new way to live
• 1:20:16
  life where we're not dependent upon labor for basic needs.
• 1:20:22
  But then it's going to be incumbent upon people. They're
• 1:20:24
  going to have to figure out a way to be
• 1:20:27
  either self-starting or we're going to have to expose people
• 1:20:30
  the things that are going to excite their curiosity, and
• 1:20:33
  make that a mandate.
• 1:20:34
  Speaker 1: Yeah. It has to start from schools.
• 1:20:37
  Speaker 2: Yeah.
• 1:20:37
  Speaker 1: And as long as we keep rewarding people for having
• 1:20:41
  answers instead of asking interesting questions, it's going to be
• 1:20:46
  a difficult change. Like in schools, you're always rewarded for
• 1:20:50
  being smart based on whether you have answers to like
• 1:20:53
  20 different questions. Like who cares? Like all those 20
• 1:20:56
  questions can be answered by AIs. Um, have you ever
• 1:20:59
  like flipped the script where you say, okay, like I'm
• 1:21:02
  going to, the smartest person in the room is the
• 1:21:04
  one who has the most interesting questions.
• 1:21:06
  Speaker 2: Okay.
• 1:21:07
  Speaker 1: Like what, what kind of students can you cultivate based
• 1:21:09
  on that? Like imagine if the room had no pressure
• 1:21:13
  to always know the answer, but the freedom to ask
• 1:21:17
  a lot of questions.
• 1:21:18
  Speaker 3: Right.
• 1:21:18
  Speaker 2: Because sometimes when someone asks a question, it'll, it does
• 1:21:23
  make you pause and go, I never even thought of that,
• 1:21:25
  but that's it. Like, that's the question.
• 1:21:28
  Speaker 1: Yeah.
• 1:21:28
  Speaker 2: And it takes a– I mean so many people have
• 1:21:31
  so many different perspectives, which is one of the more
• 1:21:33
  interesting things that I've experienced doing this podcast is I
• 1:21:37
  get to talk to so many different people. And they
• 1:21:40
  vary so widely. There are so many different ways of
• 1:21:44
  looking at the world and so many different ways of
• 1:21:47
  engaging with the world and so many different things that
• 1:21:49
  people are fascinated with that they spent their entire life
• 1:21:52
  studying and pursuing. It's like you get this rich tapestry
• 1:21:56
  of the human experience that's just– I would have never
• 1:22:00
  been exposed to this many people. And in turn, I've
• 1:22:03
  been able to expose these people to all these other
• 1:22:05
  folks that are just listening and watching right now. And
• 1:22:08
  it's fucking incredible. And it's such a– for me, it's
• 1:22:12
  like the perfect job. I've never had a job that
• 1:22:15
  more aligns with my own personality as much as this
• 1:22:18
  because I've always been that kid. Like shut the fuck
• 1:22:21
  up with all the questions. I've always been that kid.
• 1:22:23
  Speaker 1: That's the system, right? It's not your fault.
• 1:22:26
  Speaker 2: Right.
• 1:22:26
  Speaker 1: Like, it's actually the reason you're successful now is the
• 1:22:29
  exact thing that people told you to shut up about
• 1:22:31
  in the past.
• 1:22:33
  Speaker 2: Right?
• 1:22:33
  Speaker 3: Yeah.
• 1:22:35
  Speaker 1: Hey, you know, stop bothering my lecture, you know, asking
• 1:22:40
  all these unrelated questions. It's mainly a frustration of the
• 1:22:43
  teacher that they don't have the answers to you. Right?
• 1:22:46
  Speaker 2: Sure.
• 1:22:48
  Speaker 1: And now that bottleneck is gone. We did this experiment
• 1:22:51
  with one instructor at MIT who taught the introduction to
• 1:22:55
  biology class where he came and told us that he's
• 1:22:58
  going to give perplexity to all the kids, all the students,
• 1:23:02
  and they would use it as part of the lectures.
• 1:23:06
  So instead of fighting AI, you just give AI to
• 1:23:09
  everybody and let them ask whatever questions they want, and
• 1:23:12
  they can actually use it in the exams too.
• 1:23:14
  Speaker 2: Oh, wow.
• 1:23:15
  Speaker 1: So how do you even design questions for an exam
• 1:23:20
  in such a world is maybe you just encourage people
• 1:23:23
  to pose a question that AI can't answer right now.
• 1:23:27
  And that becomes your research project. And you turn everybody
• 1:23:31
  into a scientist. Fundamentally, there's this belief that scientists have
• 1:23:35
  to go through a rigorous PhD and you have to
• 1:23:38
  get accredited by an amazing university to be that. Sure,
• 1:23:42
  but anyone who's curious can be a scientist. The only
• 1:23:46
  thing that's required to be a good scientist is intellectual
• 1:23:49
  humility to understand that you could be wrong about things,
• 1:23:53
  things that everyone takes for granted. You could still question them.
• 1:23:57
  And when you're presented with new evidence and new data,
• 1:24:00
  you're willing to change your mind and you're willing to
• 1:24:03
  operate with ambiguity and uncertainty about the world. That's basically
• 1:24:07
  all the qualities you need to be a scientist. And
• 1:24:10
  you can run your experiments, you can gather data, you
• 1:24:12
  can gather evidence, and you can consult people, you can
• 1:24:15
  bring in experts and talk to them. And as long
• 1:24:18
  as you're uncovering more and more about the world, you
• 1:24:21
  are a scientist. You don't need a PhD to feel
• 1:24:24
  that you're allowed to be a scientist or not. And
• 1:24:27
  I think that's the most important quality we need to
• 1:24:32
  inculcate in our kids, the upcoming generation. So that they
• 1:24:37
  all feel more liberated. Okay, like finally I don't have
• 1:24:40
  to memorize this textbook or these lecture materials. And like
• 1:24:43
  I don't have to feel bad if I get like
• 1:24:44
  12 out of 20. Okay, who cares? Like AI is
• 1:24:48
  always going to get 20 out of 20. That's not
• 1:24:50
  what you're meant to be like good at. Of course,
• 1:24:52
  master the foundations, the basics, great. But your job is
• 1:24:56
  to actually pose interesting questions.
• 1:24:59
  Speaker 2: Yeah, and the intellectual humility is so important. Because one
• 1:25:04
  of the things that was really weird about the whole
• 1:25:06
  COVID pandemic was that we weren't supposed to question science.
• 1:25:10
  Speaker 1: Yeah.
• 1:25:12
  Speaker 2: Or when Fauci said, if you question Anthony Fauci, you
• 1:25:15
  are questioning science.
• 1:25:17
  Speaker 1: That's because they tried to assign credibility through their degrees.
• 1:25:21
  Speaker 2: Yes.
• 1:25:21
  Speaker 1: Through their affiliations. Yes.
• 1:25:23
  Speaker 2: Appeal to authority.
• 1:25:24
  Speaker 1: But not through the scientific method.
• 1:25:25
  Speaker 2: Right.
• 1:25:26
  Speaker 1: Anybody should be allowed to ask questions as long as
• 1:25:29
  they are open to new evidence.
• 1:25:31
  Speaker 2: Yeah.
• 1:25:31
  Speaker 1: Yeah. And that's the most important quality of a scientist.
• 1:25:34
  Speaker 2: Well, the scientific method alone, I mean, it's one of
• 1:25:37
  the most important things that we can use to try
• 1:25:40
  to figure out what's real and what's not real. And
• 1:25:42
  as soon as someone says, don't use it. Yeah. Don't question. Well,
• 1:25:46
  wait a minute. And then there was an actual government
• 1:25:51
  push to silence questioning and legitimate researchers were kicked off
• 1:25:55
  of Twitter because they didn't back the narrative. Yeah. Like
• 1:25:59
  this is all anti-science. This is not, this is not,
• 1:26:02
  You're questioning science. Science demands questioning.
• 1:26:06
  Speaker 3: Yeah.
• 1:26:06
  Speaker 2: It's what it is. Yeah.
• 1:26:08
  Speaker 1: When you don't understand something, the best thing you can
• 1:26:10
  do is ask all possible questions.
• 1:26:12
  Speaker 2: Right.
• 1:26:13
  Speaker 1: And so curbing that is almost like a way of saying, look,
• 1:26:17
  I'm going to tell you what happened. You need to
• 1:26:19
  believe in my worldview. And I'm not open to new perspectives.
• 1:26:24
  Speaker 2: I wonder if anybody has used AI to try to
• 1:26:28
  map out possible scenarios that For where technology leads human
• 1:26:33
  civilization and what could be done to mitigate the problems
• 1:26:37
  and push it in the proper direction. Like have a
• 1:26:39
  bunch of different models of how this could play out. Yeah.
• 1:26:45
  Speaker 1: I mean, I try to do that for fun, but
• 1:26:48
  I haven't done it in a serious enough way to
• 1:26:49
  have like a proper answer to that.
• 1:26:51
  Speaker 2: Right.
• 1:26:52
  Speaker 1: But I think like, you know, a lot of things
• 1:26:56
  that we are doing today will not be considered needed
• 1:27:00
  or valuable. And maybe a little bit of taking our
• 1:27:05
  own lessons from the past. I don't know if you,
• 1:27:07
  when you grew up as a student, did you have
• 1:27:08
  to like be good at mental math, like multiplying arbitrary numbers?
• 1:27:13
  Was that considered a sign of smartness or remembering people's
• 1:27:16
  phone numbers or something?
• 1:27:17
  Speaker 2: Well, you had to because there was, I mean, you
• 1:27:19
  had little address books. We used to carry around like
• 1:27:21
  a little, I had a little address book that I
• 1:27:23
  keep on my desk. It's a little tiny thing with
• 1:27:25
  everybody's number and name. That's the only way I knew
• 1:27:27
  people's numbers. And I remembered a bunch of them, like
• 1:27:31
  all my friends. I had all my friends. I don't
• 1:27:33
  have any of my friends' numbers remembered. Maybe my wife
• 1:27:35
  and my friend Eddie. I have two numbers in my head.
• 1:27:39
  Speaker 1: But was there a time when people thought somebody was
• 1:27:42
  smart based on how good their memory power was?
• 1:27:44
  Speaker 2: Oh, yeah, definitely.
• 1:27:46
  Speaker 1: But would you say that now?
• 1:27:49
  Speaker 2: Well, people are impressed if you know things now. I
• 1:27:53
  have a bunch of weird information, obviously, that I've gathered
• 1:27:57
  through so many years of doing this podcast and just
• 1:27:59
  so many years of being curious, you know, like sometimes
• 1:28:02
  even my own daughter was like, how the fuck do
• 1:28:03
  you know that? I'm like, this is what I do. Like,
• 1:28:06
  that's my thing. Yeah. You know, I pay attention to stuff. Yeah.
• 1:28:09
  But yeah, I mean, memory itself is always very impressive
• 1:28:14
  and someone has an excellent memory and can pull up
• 1:28:16
  facts of the past.
• 1:28:17
  Speaker 1: Yeah.
• 1:28:18
  Speaker 2: We automatically equate that to intelligence. Yeah.
• 1:28:21
  Speaker 1: I think it's impressive, but it's not necessarily a sign
• 1:28:23
  of being intelligent, right? Like, I think you have a
• 1:28:28
  very fast lookup table in your head. That's great. It's
• 1:28:31
  very valuable. But I still think being smart is all
• 1:28:35
  about posing the most interesting questions.
• 1:28:38
  Speaker 2: Also, the decisions that you make. and whether or not
• 1:28:41
  you self-correct when you make mistakes.
• 1:28:43
  Speaker 1: Yeah.
• 1:28:44
  Speaker 2: All those things.
• 1:28:45
  Speaker 1: Exactly. So when you have an amplifier to your intelligence,
• 1:28:48
  like an AI, all the time, lookups is essentially something
• 1:28:52
  you can delegate. Reasoning for decision-making is something you can delegate.
• 1:28:57
  But posing the right questions to gather the right data
• 1:29:00
  and then forming your own judgment based on what it
• 1:29:03
  reasons and comes up with. And finally having the courage
• 1:29:06
  to make the decision. That's still you, that agency, that
• 1:29:09
  intrinsic curiosity to ask the right question, the scientific intellectual
• 1:29:14
  humility to like, you know, gather new evidence, always questioning
• 1:29:18
  your beliefs that that is still you. And so, um,
• 1:29:21
  I feel like that is essentially what would be considered
• 1:29:24
  smart in the ages to come. If somebody is like, uh,
• 1:29:28
  you know, like a proxy scientist or whatever, like no
• 1:29:30
  more doesn't have to go to like MIT or Harvard
• 1:29:34
  and get a PhD to be a scientist or to
• 1:29:35
  be considered a scientist. Because all scientific literature is open
• 1:29:39
  and it's accessible to everybody. And you can even take
• 1:29:43
  a paper written by an expert and use an AI,
• 1:29:46
  understand it deeply, ask a lot of questions and maybe
• 1:29:48
  even disprove what they claim to be true. That's the
• 1:29:52
  whole peer review process, right? The peer review process is
• 1:29:56
  all about questioning somebody's paper. And that's why, like, you know,
• 1:30:02
  whatever you said happened in the COVID days is wrong. Like,
• 1:30:05
  you should be allowed to ask questions about even eminent
• 1:30:10
  scientists' work.
• 1:30:12
  Speaker 2: It's okay.
• 1:30:12
  Speaker 1: Like, if you're dumb and you had the wrong questions, sure,
• 1:30:15
  you're going to learn from that. It's worse than not
• 1:30:18
  being allowed to ask the question.
• 1:30:19
  Speaker 2: Yeah, agreed. It's going to be interesting to see what
• 1:30:22
  the future of education looks like, like how valuable our
• 1:30:25
  degrees are. When essentially AI is going to be able
• 1:30:28
  to do the majority of whatever work you need done
• 1:30:31
  on a variety of– like how good are they right
• 1:30:36
  now at just law? Like you could ask questions.
• 1:30:42
  Speaker 1: Pretty good.
• 1:30:42
  Speaker 2: Pretty amazing, right?
• 1:30:43
  Speaker 1: Yeah, pretty good.
• 1:30:44
  Speaker 2: How good are they at mathematics? Perfect. Like how good
• 1:30:47
  are they at coding? Way better than people. Yeah. And
• 1:30:50
  at a certain point in time, it's going to be
• 1:30:51
  interesting to like what is education now? Is education just
• 1:30:55
  providing you with information? Because that information is readily available.
• 1:30:58
  Or is education teaching you how to think? Teaching you
• 1:31:02
  how to pursue your interests and be curious and have
• 1:31:05
  intellectual humility and understand what you know and what you
• 1:31:07
  don't know?
• 1:31:08
  Speaker 1: I think that's where it should be. I still think
• 1:31:12
  institutions will preserve their brand value because there is a
• 1:31:16
  certain aspect of education that's outside of learning. This is
• 1:31:20
  just having access to other curious and intelligent people.
• 1:31:23
  Speaker 2: Sure. Community. Community.
• 1:31:24
  Speaker 1: Yeah, and brands attract good communities, peer groups, blah, blah, blah.
• 1:31:28
  But the actual process of learning has to change. And
• 1:31:32
  what you're rewarded for has to change. Fundamentally, everything flows
• 1:31:37
  downstream of the incentive. So if the incentive is to
• 1:31:40
  score the highest on the exam based on answers, you're
• 1:31:43
  not really changing much. You need to change that process.
• 1:31:48
  You need to change the process of what do you
• 1:31:49
  reward a student? Like what is A plus or A?
• 1:31:52
  Speaker 2: Right. That's where we need to start at. We talked
• 1:31:56
  about this the other day that the education system in
• 1:31:59
  this country was designed to make workers. Exactly. That's what
• 1:32:02
  they did when they first started doing it in the
• 1:32:05
  turn of the century.
• 1:32:05
  Speaker 1: Curriculum was designed around that. In India, it's still the case,
• 1:32:09
  by the way. Really? Even if you go into a
• 1:32:12
  computer science degree, I don't know if it's still the case.
• 1:32:15
  I shouldn't misspeak, but at least when I was there
• 1:32:17
  and for many years after, the first two years you
• 1:32:19
  just spend learning hardcore electrical and mechanical engineering. You would learn, like,
• 1:32:23
  welding using lathe machines. You would have to, like, go and, like,
• 1:32:28
  do workshops, carpentry, a lot of these things. It was fun.
• 1:32:32
  Speaker 2: I would think there's a lot of value in that.
• 1:32:35
  Speaker 1: So in hindsight, I actually think it was fun to
• 1:32:37
  learn soldering and, like, how to, like, make circuits on
• 1:32:40
  red boards and circuit boards. But if somebody was just
• 1:32:45
  interested in some, you know, just writing code, let's say,
• 1:32:49
  back then, all this is kind of like pointless to learn,
• 1:32:52
  but you had to go through it to be qualified
• 1:32:55
  as an engineer. So, and the reason the curriculum was
• 1:33:00
  designed that way is because that's what the labor force
• 1:33:03
  was required back then to build like oil factories and
• 1:33:06
  like all these things. So you had to learn mechanical engineering,
• 1:33:09
  you had to learn fluid mechanics, whatever. But I think
• 1:33:13
  that should also change because if the way like you
• 1:33:16
  do work changes, then what you're trained for in college
• 1:33:19
  should also change. And it's much harder to change these things.
• 1:33:23
  People are much slower. They're scared to do changes. Disruption
• 1:33:27
  is always looked down upon. And so I think let's
• 1:33:31
  at least start at the incentive structure right from the schools,
• 1:33:35
  right from the colleges. Let's not reward people based on
• 1:33:38
  how much they know.
• 1:33:39
  Speaker 2: Well, it seems like in the future when things do
• 1:33:42
  radically change and it seems like they're inevitable, they're going
• 1:33:45
  to radically change. Universities and schools are going to be
• 1:33:49
  rewarded for having developed thinkers that are able to adapt
• 1:33:55
  to this new world.
• 1:33:55
  Speaker 1: That's right.
• 1:33:56
  Speaker 2: Yeah. So they're going to have to figure out how
• 1:33:58
  to adjust their curriculum. Yeah. Because the tools are so
• 1:34:03
  spectacular now that just this idea of just memorizing information,
• 1:34:08
  it's not– that's not what you're going to need to
• 1:34:11
  get by in the future.
• 1:34:12
  Speaker 1: It's not. And I guess like one proxy different schools
• 1:34:15
  use is like maybe– if more entrepreneurs arise out of
• 1:34:18
  your school, you'll probably, uh, create a lot of independent thinkers. Mm-hmm. Um,
• 1:34:25
  because they are, like, willing to take a first perspective
• 1:34:27
  towards a problem.
• 1:34:28
  Speaker 2: Right.
• 1:34:28
  Speaker 1: And, and build their own thing from scratch. And, and fundamentally,
• 1:34:32
  that's what America, America's always been about is, you know, some,
• 1:34:35
  the American dream of coming here and, like, having your
• 1:34:39
  own idea and still be taken seriously by a bunch
• 1:34:41
  of people. The whole idea of venture capital only exists here. Like, kick,
• 1:34:46
  family and friends around this whole idea of just having
• 1:34:49
  your friends help you to bootstrap a business and then
• 1:34:51
  turning it into a success. And success doesn't mean like
• 1:34:54
  multi-billion or 10 billion or whatever, right? Like as long
• 1:34:57
  as it pays you enough that you don't have to
• 1:34:58
  work for somebody else and you can live a fulfilling
• 1:35:01
  life and you can just go explore your passions, that's success.
• 1:35:05
  That's actually a better success than creating a company based
• 1:35:09
  on what other people want you to do and then
• 1:35:12
  hating your job for it.
• 1:35:13
  Speaker 2: Yeah, and having a yacht and being miserable and working
• 1:35:16
  every day.
• 1:35:17
  Speaker 1: That's why I said the smartest or the richest people
• 1:35:20
  are not always the ones who have the most fulfilling lives.
• 1:35:22
  The most curious people have the most fulfilling lives because
• 1:35:25
  they have better relationships. They're actually able to sit and
• 1:35:28
  look at something and be curious about it instead of
• 1:35:32
  being worried about what's going on.
• 1:35:34
  Speaker 2: What did the American dream– what was it to you
• 1:35:37
  when you weren't in America? What is it like? How
• 1:35:41
  is it discussed?
• 1:35:43
  Speaker 1: Well, to me, like I always thought, America is the
• 1:35:47
  only country where you can come here and have an
• 1:35:51
  idea and people listen to you and encourage you to
• 1:35:54
  go pursue it. The risk-seeking culture is just incredible here.
• 1:36:00
  Everybody else you can are like either explicitly or implicitly
• 1:36:04
  are forced to defer to authority. Okay, like go and
• 1:36:07
  ask the permission of this person, go and ask the
• 1:36:09
  permission of that person or get their approval, get their
• 1:36:12
  insight early. Sure, you can consult everybody out there, but
• 1:36:16
  if you have a thought that challenges what they believe in,
• 1:36:20
  this country still encourages you to go pursue it. And so, yes,
• 1:36:26
  when I came here, obviously, Google was the number one
• 1:36:31
  company that everybody wanted to work in. But it's also
• 1:36:34
  the same country where it allows you as a new
• 1:36:36
  person to start a new idea and the challenge is
• 1:36:40
  one of the biggest companies in, in, in, in this
• 1:36:42
  own country. And it actually wants it. People actually want
• 1:36:47
  new ideas. And, um, and then you can consistently see
• 1:36:51
  that there are like always going to be more and
• 1:36:53
  more new ideas and new companies to be created here.
• 1:36:56
  And so that spirit of like questioning is, is encouraged
• 1:37:02
  a lot here. And, and it happens in academic research.
• 1:37:05
  I started off as an academic, even there, um, A
• 1:37:09
  lot of ideas, when I had it and I would
• 1:37:12
  share it with people, people actually give you very honest
• 1:37:17
  feedback about things. But they don't stop you from working
• 1:37:20
  on anything. And that's fantastic because that's very fresh. It's
• 1:37:26
  very liberating.
• 1:37:26
  Speaker 2: And that's not anywhere else.
• 1:37:30
  Speaker 1: I would say it's not.
• 1:37:30
  Speaker 2: It's not in India.
• 1:37:32
  Speaker 1: It's a simplification to say it's not anywhere else.
• 1:37:34
  Speaker 2: It's not as encouraged.
• 1:37:37
  Speaker 1: It's not as encouraged. The incentive structures are not quite there.
• 1:37:41
  And the ability to be taken seriously for some crazy
• 1:37:45
  ideas is why America is still at the top.
• 1:37:50
  Speaker 2: But it's crazy to me that if the American dream
• 1:37:52
  is so compelling and so many people come here for it,
• 1:37:55
  why doesn't the rest of the world sort of adopt
• 1:37:57
  those values?
• 1:37:59
  Speaker 1: It's hard. A lot of it is cultural. America was born,
• 1:38:05
  was made From like, you know, a piece of land essentially, right?
• 1:38:14
  And a lot of ideas that we built here, a
• 1:38:17
  lot of industries that we built here were all like
• 1:38:20
  created here from nothing. And that required you to like
• 1:38:24
  go take bold risks. I think Jeff Bezos said this
• 1:38:28
  in some podcast. that where else would you, like, be
• 1:38:32
  able to go raise, like, a few million dollars for
• 1:38:35
  an idea that has, like, 5% to 10% chance of
• 1:38:37
  working and then fail at it and still go and
• 1:38:41
  raise another few million dollars for your next idea?
• 1:38:46
  Speaker 2: Nowhere else.
• 1:38:47
  Speaker 1: People are willing to, like... People who get rich here
• 1:38:49
  actually want to encourage and be part of somebody else's
• 1:38:52
  crazy journey because it's hard to pursue all crazy bets yourself.
• 1:38:58
  So it's an ecosystem. And once something becomes an ecosystem,
• 1:39:03
  there's network effects. So it's very hard to copy that elsewhere.
• 1:39:07
  Speaker 2: And so your value is measured in your curiosity and
• 1:39:11
  you're willing to work. Your willingness to work on whatever
• 1:39:14
  it is that is your pursuit.
• 1:39:16
  Speaker 3: Yes.
• 1:39:16
  Speaker 2: And then eventually adjusting and learning and catching fire with
• 1:39:20
  one of them. Correct.
• 1:39:21
  Speaker 1: And you have to work hard. I'm a big believer
• 1:39:26
  in intense hard work. I think nothing great can be
• 1:39:29
  accomplished by being soft. And so all this recent push
• 1:39:34
  for having a lot of work-life balance, this and that. Sure,
• 1:39:37
  if you have work-life balance, if that's what you want,
• 1:39:39
  then I think there are certain jobs that will give
• 1:39:42
  you that. But when you're trying to do something from scratch,
• 1:39:45
  when you're trying to create something from nothing, it's not
• 1:39:48
  meant to be easy.
• 1:39:49
  Speaker 2: Right.
• 1:39:49
  Speaker 1: There are some sacrifices that have to be made and
• 1:39:52
  you're signing up to be part of that experience, that
• 1:39:56
  surreal joy you get from doing something that's felt almost
• 1:40:00
  impossible to achieve. And you're not doing, you're not like
• 1:40:04
  staying up late or waking up early because you're getting
• 1:40:07
  paid more. Maybe you might not get paid anything. Maybe
• 1:40:10
  this whole thing goes to nothing, but That experience you're
• 1:40:14
  getting of being part of something that feels very hard
• 1:40:17
  to achieve is what you're signing up for to be
• 1:40:19
  part of.
• 1:40:20
  Speaker 2: Yeah. And if you're not, find something else. It's fine.
• 1:40:23
  Speaker 1: Respect that. There's nothing wrong with that. Exactly. And the
• 1:40:26
  country has enough jobs to provide for all kinds of needs, right?
• 1:40:30
  And everybody goes through different phases in their life. Sometimes
• 1:40:33
  they feel a little lazy or disillusioned. And so what
• 1:40:38
  I like about this country is that there's a lot
• 1:40:40
  of curious people here. There's a lot of, like, so
• 1:40:42
  many different people, you know, like, whether they use AIs
• 1:40:45
  or not AIs, they're all, like, finding meaning in, like,
• 1:40:48
  so many interesting projects.
• 1:40:50
  Speaker 2: Well, obviously, I don't know any other country, really, because
• 1:40:53
  I was born here. But the people that do talk
• 1:40:57
  to me about what the American dream is like from
• 1:41:01
  another country, they're the most passionate and the most supportive
• 1:41:04
  of this idea, this experiment in self-government. And this... Just
• 1:41:11
  the whole idea that the country operates on that anybody
• 1:41:14
  can chase their dream. Yeah. You can. If you have
• 1:41:17
  a dream and you're willing to work hard, you could
• 1:41:19
  actually do it in this country.
• 1:41:20
  Speaker 1: That's right.
• 1:41:21
  Speaker 2: Yeah. That's, you know, it's most, the people that are
• 1:41:25
  most passionate about that idea oftentimes are people that come
• 1:41:28
  from somewhere else where that wasn't available.
• 1:41:30
  Speaker 1: And it's not just like, you know, people coming from
• 1:41:33
  one particular country or another. It's the attitudes. It's the
• 1:41:38
  way the system works and rewards you to, like, be
• 1:41:40
  bold and take bets against established players. It's okay, right?
• 1:41:44
  It's okay to, like, be an upstart, a challenger. And
• 1:41:47
  people love that, like, underdog. And I think, you know,
• 1:41:50
  that's fantastic. And that culture is continuing. Yes, there are, like,
• 1:41:54
  multi-trillion dollar companies here and they're all going to become
• 1:41:56
  even bigger. But people still want the young, hungry person
• 1:42:03
  to also be successful.
• 1:42:04
  Speaker 2: Yeah. Well, they love disruptors. Yeah. And people love underdogs
• 1:42:09
  in this country.
• 1:42:10
  Speaker 1: Yeah. It's universal. It's not specific to technology.
• 1:42:15
  Speaker 2: Right.
• 1:42:15
  Speaker 1: Like, I'm sure everybody would love an underdog story that
• 1:42:18
  wants to go against, like, Coca-Cola or Pepsi or something, too. Sure.
• 1:42:21
  Speaker 2: Oh, in sports, it's our favorite thing. In sports, yeah.
• 1:42:24
  We don't like when the guy who's supposed to win wins.
• 1:42:26
  We love when the guy who's not supposed to win triumphs. Yeah. Yeah.
• 1:42:30
  Speaker 1: The underdog story.
• 1:42:31
  Speaker 2: Yeah. That's a very uniquely American story.
• 1:42:35
  Speaker 1: To me, that's what this country is. I mean, sure,
• 1:42:38
  there's a lot of obstacles and challenges. Just like every
• 1:42:42
  other country, there are things here that are challenging, but
• 1:42:45
  it's one thing that has consistently stayed true.
• 1:42:50
  Speaker 2: One of the big fears that people in America have
• 1:42:53
  about technology in particular is that without being aware that
• 1:42:58
  this was going to take place, everybody gave up their
• 1:43:01
  data and Everybody gave up their data and didn't recognize
• 1:43:04
  it was a commodity. That in turn made these corporations
• 1:43:08
  immensely wealthy and powerful. And then they have the ability
• 1:43:12
  to shape narratives. And that concerns people because using their
• 1:43:19
  ideological position as leverage to try to push that through
• 1:43:25
  technology that has immense control and influence over people and
• 1:43:30
  that We didn't see technology and corporations as having that
• 1:43:34
  much control over how society views itself and how we
• 1:43:39
  interact with each other. And there's a real concern that
• 1:43:43
  these companies got so big and have– like there's a
• 1:43:47
  guy named Robert Epstein who's done a lot of work
• 1:43:50
  on narrate or curated search engine results and how much
• 1:43:55
  that can– have you seen any of his stuff?
• 1:43:57
  Speaker 1: I think I've seen this, yeah.
• 1:43:58
  Speaker 2: How much that can affect elections, how much that can
• 1:44:01
  affect people's perceptions on any societal issue that's coming up.
• 1:44:07
  And it's concerning. It really is because they do curate
• 1:44:10
  search results. It's not simply you just run it out
• 1:44:13
  there and you get this is the data. No, you
• 1:44:16
  get– if you look for specific political figures depending upon
• 1:44:20
  where they fall in the right or left spectrum and
• 1:44:22
  depending upon which way the company forms, the corporation forms.
• 1:44:27
  falls rather, you'll get different results. And that sucks. You know, that's,
• 1:44:32
  it's very concerning that people don't recognize, they don't, they
• 1:44:37
  don't have the ability to see how that is dangerous
• 1:44:41
  for all of society to have that kind of power
• 1:44:44
  and wield it in that way where you're not being
• 1:44:46
  honest about accurate objective information. You're pushing particular ideologies. Yeah.
• 1:44:52
  Speaker 1: So I think it's kind of like a, This is
• 1:44:58
  almost an effect of the asymmetry that exists between the
• 1:45:01
  amount of AI power that centralized systems and centralized companies
• 1:45:04
  have and the amount of AI power you as a
• 1:45:07
  sovereign individual has. So when you don't have the AIs
• 1:45:12
  to just go judge for yourself what you should be
• 1:45:15
  reading in Fed, you're obviously under the influence of whatever
• 1:45:21
  big tech company is controlling the information for. But when
• 1:45:24
  you have access to all those AIs, you can actually
• 1:45:27
  just customize what you want to see by telling the AI, like, hey,
• 1:45:31
  this is what I think you should actually question and
• 1:45:34
  tell me. Until now, you never had that power for yourself.
• 1:45:38
  You're finally getting it.
• 1:45:39
  Speaker 2: Right.
• 1:45:40
  Speaker 1: And eventually, we'll be able to have our own LLMs,
• 1:45:43
  like our own models that we would be able to
• 1:45:46
  host in our own hardware. We don't have to rely on, like,
• 1:45:50
  one centralized model given to us by, like, any specific company.
• 1:45:54
  model company. And using that, you can shape it to
• 1:45:58
  your beliefs, your custom data. So when you're consuming a
• 1:46:05
  search result, you can actually task that AI that you
• 1:46:08
  control and you run so nobody can shut off access
• 1:46:11
  to it to tell you, hey, can you actually give
• 1:46:15
  me a contrarian perspective on this? Or can you tell
• 1:46:18
  me if these search results are actually biased? So I
• 1:46:22
  think we need to give individuals more sovereignty with more
• 1:46:26
  access to their own AIs that they own and run
• 1:46:28
  on a piece of hardware they own themselves. And this
• 1:46:32
  is the whole, like, this is going to be leading
• 1:46:33
  to the whole rise of local AIs. So as AI models,
• 1:46:37
  like today, they're very power inefficient. They're running on large
• 1:46:39
  data centers. But in a year or two from now,
• 1:46:42
  whatever capability that exists in the most power hungry data
• 1:46:46
  centers will be, it'll be possible to run it in
• 1:46:48
  some box that you own. Really?
• 1:46:50
  Speaker 2: Yeah.
• 1:46:51
  Speaker 1: It's already happening. It's already happening that there are interesting
• 1:46:55
  hardware projects like the Apple Mac Mini, NVIDIA DGX, where
• 1:47:01
  you can actually host a reasonable-sized model and put it
• 1:47:06
  in a box and have it run, and you don't
• 1:47:09
  have to pay for all the tokens it produces you.
• 1:47:12
  You just have to plug it into your power cord,
• 1:47:15
  and it works.
• 1:47:17
  Speaker 2: I know Duncan, my friend Duncan Trussell, he does that. Yeah.
• 1:47:20
  Speaker 1: And today, the capability of that model that can run
• 1:47:23
  locally is not quite there. So you would still prefer
• 1:47:26
  to use something that runs from the data center. But eventually,
• 1:47:29
  this is going to be a spectrum. There's going to
• 1:47:31
  be some percentage of tasks that you would start delegating
• 1:47:34
  to this local system. It'll be a hybrid model. And
• 1:47:37
  over time, it could end up being the case that
• 1:47:39
  you could buy something that feels like a refrigerator for
• 1:47:41
  your home, which is your own AI box, and host
• 1:47:46
  a model that you control. So nobody can arbitrarily shut
• 1:47:49
  off access to it. one day. And then you can,
• 1:47:53
  you can have that be your weapon against what the
• 1:47:56
  big tech wants you to be fed or believe in.
• 1:47:59
  So this is the only way we can fight this
• 1:48:00
  because they have far more computing power, far more data,
• 1:48:04
  far more algorithms than you. So the only way you
• 1:48:08
  can fight that is you have something you own yourself.
• 1:48:11
  And with the rise of open source models, open source LLMs,
• 1:48:16
  and progress in local hardware, and both Apple and NVIDIA
• 1:48:21
  and Intel, they're all doing amazing work here. You could
• 1:48:24
  potentially change the future and give people more power. And
• 1:48:26
  this may not be as expensive as people think.
• 1:48:29
  Speaker 2: Well, that's a good solution. Because I've always wondered, like,
• 1:48:33
  are these searches, like using Google, is that going to
• 1:48:37
  be irrelevant one day? Because... You already can just ask
• 1:48:41
  your phone. Most of the time, if I want to
• 1:48:44
  have an answer for something, I just ask Perplexity. I
• 1:48:46
  was like, what is it? Instead of having to sift
• 1:48:49
  through all these Google searches and try to figure out
• 1:48:52
  what it's showing me first and get to page three
• 1:48:55
  where it's what I really want to know, I can
• 1:48:57
  get the accurate information. Then follow-up questions are instantaneous. Yeah.
• 1:49:01
  Speaker 1: And even the models that are running the Perplexity app today,
• 1:49:04
  they're all in the cloud. Eventually, you'll be able to
• 1:49:07
  do that. on a box that you own. You can
• 1:49:11
  still use the front end, the UI of the app,
• 1:49:15
  but you can control the compute that runs on a
• 1:49:19
  piece of hardware. You may ask, why do I care? Okay, like,
• 1:49:23
  what if someday, like, the data center gets taken off? Like,
• 1:49:27
  Iran was bombing data centers.
• 1:49:28
  Speaker 2: Right.
• 1:49:29
  Speaker 1: Like, what if someday, like, the government decides that model
• 1:49:32
  is no longer available? You want some control over, like,
• 1:49:36
  what models you can run and, like, You may even
• 1:49:39
  want to shape it to your context that you never
• 1:49:42
  want to be living on any data center. And I
• 1:49:47
  think that's where I believe the individual gets more sovereignty
• 1:49:51
  against big tech. And that's how we fight surveillance or
• 1:49:56
  centralization of power.
• 1:49:58
  Speaker 2: Yeah, and certainly pushing narratives. What do you think happens
• 1:50:03
  with social media? Because social media and as you were
• 1:50:07
  talking about before, like algorithms, like it's one of the
• 1:50:09
  biggest problems in terms of the way people view the world. Yeah.
• 1:50:12
  Speaker 1: I'm curious what you think, like, you know, like my
• 1:50:16
  opinion is that it's not good for the kids.
• 1:50:18
  Speaker 2: It's terrible for them. Yeah. But I think they should
• 1:50:21
  have some exposure to it because I think it's good
• 1:50:24
  to know that it's a thing. And I think children
• 1:50:27
  are fairly resilient and they learn. But the anxiety levels
• 1:50:30
  of kids is much higher than ever before. Yeah. Suicidal
• 1:50:33
  ideation is higher.
• 1:50:35
  Speaker 1: Self-harm. Yeah.
• 1:50:38
  Speaker 2: Yeah.
• 1:50:38
  Speaker 1: My belief is that when you're just fed a feed
• 1:50:44
  and the algorithm of the social media company decides what
• 1:50:47
  you're going to see next, it curbs your curiosity. And
• 1:50:52
  I don't think things that curb human curiosity should be encouraged.
• 1:50:58
  Speaker 2: Yeah, I agree.
• 1:50:58
  Speaker 1: And so if the app is designed in a way
• 1:51:01
  where it asks you what you're interested in And helps
• 1:51:04
  you to come up and find things that are very
• 1:51:08
  related to what you're interested in.
• 1:51:10
  Speaker 2: Right.
• 1:51:10
  Speaker 1: That's awesome. But that's not how it works. It's literally
• 1:51:13
  like it starts with something. You start doing scrolling and
• 1:51:16
  then start showing you what you just scrolled. And then
• 1:51:19
  you end up in an echo chamber. And that's not
• 1:51:22
  necessarily good.
• 1:51:23
  Speaker 2: Well, you can get trapped. Yeah. I'm in a trap
• 1:51:26
  of schizophrenics lately on Instagram.
• 1:51:28
  Speaker 3: Yeah.
• 1:51:29
  Speaker 2: Well, it's just mostly schizophrenics, like people that tell me
• 1:51:32
  they're the rightful president of the United States and like
• 1:51:34
  you tell the guy hasn't showered in days and, you know,
• 1:51:38
  and if you have a phone, you can create an
• 1:51:40
  account and you just start uploading nonsense. And then for
• 1:51:43
  whatever reason, I've watched a couple of them. So now
• 1:51:45
  they just keep showing them to me.
• 1:51:46
  Speaker 1: And it's full of AI slop right now. Like a
• 1:51:48
  lot of AI slop. Like it's not even clear and
• 1:51:51
  it's not labeled also clearly whether it's being made with
• 1:51:54
  AI or not. So often... So essentially it's leading to
• 1:51:58
  a complete loss in, in, in trust where when I
• 1:52:01
  see something, I don't even know if it's real anymore.
• 1:52:04
  Speaker 3: Right.
• 1:52:04
  Speaker 2: And it's going to get worse. Yeah.
• 1:52:05
  Speaker 1: It's going to get worse to the extent that you're
• 1:52:07
  going to like, your default would be that this is AI.
• 1:52:11
  And then like, you're going to have to go through
• 1:52:14
  multiple layers to finally verify if it was real. Um, and, um,
• 1:52:21
  And even verified accounts post a lot of AI stuff,
• 1:52:23
  so it's not about whether the account is verified by
• 1:52:26
  meta or whatever. So I think fundamentally, I feel like, okay,
• 1:52:33
  the way I think about it is, what are pieces
• 1:52:36
  of technology if did not exist would be a really
• 1:52:41
  bad thing for the world? And what are pieces of
• 1:52:42
  technology if did not exist wouldn't even matter? And I
• 1:52:47
  feel like social media is more towards the secondary.
• 1:52:50
  Speaker 2: Yeah.
• 1:52:51
  Speaker 1: Like, you know, searching for information and answering questions and
• 1:52:56
  like getting, you know, AIs to like do things for you,
• 1:53:00
  help you learn new things faster. All that stuff is some,
• 1:53:03
  we need more of that. But because it supercharges our curiosity.
• 1:53:08
  Whereas like brain rot feeds with AI slop doesn't actually
• 1:53:12
  supercharge our curiosity. It actually curbs our curiosity. And so
• 1:53:16
  if we believe that, if we believe in the curiosity
• 1:53:19
  premium idea, We need to encourage things that supercharge our
• 1:53:23
  curiosity and discourage things that curb our curiosity.
• 1:53:27
  Speaker 2: Do you anticipate a time where we recognize the dangers
• 1:53:32
  of algorithms and there is some discussion to either curb
• 1:53:36
  them or allow people to have control over them in
• 1:53:40
  a real meaningful way? Like you could dictate maybe through
• 1:53:44
  AI even that there's an AI interface to your algorithms.
• 1:53:47
  That understands your particular emotional needs, your curiosity. Like, only
• 1:53:51
  show me this. Yeah. This is what I'm interested in.
• 1:53:53
  Carpentry and basketball games. Yeah. Show me those things. Yeah.
• 1:53:56
  I don't want to see who's getting divorced. I don't
• 1:53:59
  give a fuck about this.
• 1:54:01
  Speaker 1: Yeah. So, here's the thing. You can still customize on
• 1:54:05
  most of the social apps. You know, it'll be deeply
• 1:54:09
  buried somewhere in the settings somewhere. And you can go
• 1:54:12
  and say stuff. But the reason it's buried is because
• 1:54:15
  once you... You always have to say it or like
• 1:54:18
  it's the starting entry point for your experience there. Your
• 1:54:21
  engagement time would go down because once you consume the
• 1:54:23
  content that you really want, you would go back to
• 1:54:26
  your work, which is what you really need to be doing.
• 1:54:28
  But that doesn't help them sell more ads. And so
• 1:54:32
  the incentives are not aligned. And so Elon has this
• 1:54:36
  really good metric he talks about where it's like a
• 1:54:40
  total amount of unregretted minutes spent on the app should
• 1:54:43
  go up. it's hard to measure it's hard to measure
• 1:54:48
  it's more like in spirit the right metric but this
• 1:54:52
  metric is also why it's hard to make money on
• 1:54:55
  ads if you care about this metric which is why
• 1:54:57
  X doesn't really make a lot of money on ads
• 1:54:58
  compared to you know Instagram or YouTube because you're kind
• 1:55:04
  of like optimizing for interestingness like doesn't mean X has
• 1:55:09
  everything right there's a lot of chaos there's a lot
• 1:55:10
  of memes there's a lot of like weird shit going
• 1:55:13
  on there as well But in general, social media is
• 1:55:17
  not necessarily great for people.
• 1:55:20
  Speaker 2: I think it's terrible for people. But it also provides
• 1:55:23
  you with a way better understanding of what's going on
• 1:55:27
  in the world than has ever existed before.
• 1:55:30
  Speaker 1: X particularly.
• 1:55:31
  Speaker 2: X particularly.
• 1:55:32
  Speaker 1: Because it's a place for discourse. It's a text-based app
• 1:55:37
  more than a video-based app.
• 1:55:39
  Speaker 2: Right.
• 1:55:40
  Speaker 1: So naturally, people tend to engage in discussions and debates
• 1:55:44
  and There's a lot of curious debates going on there
• 1:55:47
  and a lot of interesting viewpoints expressed by people. So
• 1:55:50
  I think in terms of the unregarded minutes, it's actually
• 1:55:53
  one of the better social media apps. But apps that
• 1:55:56
  are purely based on video or images, and largely video
• 1:56:03
  these days, I think that's just trying to get your
• 1:56:06
  eyeballs in time.
• 1:56:07
  Speaker 2: Yeah, those are the mind numbers. Yeah. They just numb
• 1:56:10
  your mind.
• 1:56:10
  Speaker 1: I mean, it's depressing when you go to a metro
• 1:56:12
  and you just see people just scrolling through their feed.
• 1:56:15
  Speaker 2: Nobody– Everybody doing it. You look at the entire car.
• 1:56:18
  Everyone is doing it.
• 1:56:19
  Speaker 1: It's just insane.
• 1:56:20
  Speaker 2: Yeah. It's weird. Yeah. I always say that if there
• 1:56:23
  was a drug that existed that made people stare at
• 1:56:24
  their hand for six hours a day, everybody would be like,
• 1:56:27
  get that out of here. Yeah. But that's essentially what
• 1:56:29
  we're doing because like most of what people are looking
• 1:56:31
  at most of the time, they don't even remember. Yeah.
• 1:56:35
  They're just scrolling through this thing.
• 1:56:36
  Speaker 1: It's brain rot. It's just brain rot. It curbs your curiosity.
• 1:56:39
  Speaker 2: Yeah.
• 1:56:39
  Speaker 1: I mean Apple has these settings in different apps. Have
• 1:56:42
  you tried this?
• 1:56:43
  Speaker 2: What?
• 1:56:43
  Speaker 1: You can set the– Timer for every app?
• 1:56:46
  Speaker 2: No, I just use discipline. I don't engage very much anymore.
• 1:56:51
  I dip my toe into X every day for a
• 1:56:56
  few seconds. I go, what's everybody mad at? What's going on?
• 1:56:59
  Who stole this? How much corruption is here? Who got
• 1:57:03
  killed there? Okay, bye. And then I just check out.
• 1:57:07
  I don't want to do it. And Instagram to me
• 1:57:11
  is just nonsense. I just look at that every now
• 1:57:14
  and then for nonsense and occasionally something interesting. Really, YouTube
• 1:57:18
  is my main go-to thing because YouTube is my most
• 1:57:21
  unregretted minutes. YouTube for me is always interesting. There's always
• 1:57:26
  like some cool thing on cosmology. There's some– I watch
• 1:57:31
  fights on YouTube. I watch professional pool matches. That's what
• 1:57:35
  I do for the most part.
• 1:57:36
  Speaker 1: Yeah.
• 1:57:37
  Speaker 2: That's where I really find my actual interests and fulfill
• 1:57:40
  my curiosity.
• 1:57:41
  Speaker 1: Long-form content is what human minds should be trained to
• 1:57:45
  consume more of, whether it's books, whether it's 30-minute videos
• 1:57:50
  explaining something. And you need to train your mind to
• 1:57:54
  actually complete it. That's actually the biggest problem with the
• 1:57:56
  younger generation. The more they're in the reels experience, short-form video,
• 1:58:02
  they're unable to actually complete long videos anymore.
• 1:58:06
  Speaker 2: That's true, but also at the same time, the rise of.
• 1:58:09
  Speaker 1: Pod is happening yeah and it's great it's great.
• 1:58:13
  Speaker 2: So there's it's not it's not universal it's like there's
• 1:58:16
  a lot of people that don't find fulfillment in all
• 1:58:18
  the dooms rolling and all the nonsense yeah they really
• 1:58:20
  do want.
• 1:58:21
  Speaker 1: Yeah i'm particularly just focused on the younger generation i'm
• 1:58:25
  sure like people like us can adapt to like Okay,
• 1:58:27
  let's say maybe you have a temporary addiction to social
• 1:58:29
  apps and we can.
• 1:58:30
  Speaker 2: But a lot of the young people are the people
• 1:58:33
  like– I meet kids like at the mall that are
• 1:58:36
  11 that listen to my podcast. Really? Yeah.
• 1:58:38
  Speaker 3: Wow.
• 1:58:39
  Speaker 2: I know. It's nuts. They go, I love your podcast.
• 1:58:41
  I'm like, who lets you listen? Get out of here. No,
• 1:58:46
  I'm always joking around about it. That's amazing. That's really cool.
• 1:58:48
  But no, there's a lot of like particularly like young
• 1:58:51
  boys that come up to me all the time that
• 1:58:53
  are interested in it.
• 1:58:54
  Speaker 1: That's amazing.
• 1:58:55
  Speaker 2: I love it. I love it because then they're going
• 1:58:57
  to get exposed to some interesting ideas. And it will
• 1:58:59
  also encourage them to have those kind of conversations with
• 1:59:02
  each other. Right.
• 1:59:04
  Speaker 1: Yeah. Whose podcast do you listen to?
• 1:59:08
  Speaker 2: I love Tim Dillon's. He's probably my favorite because it's
• 1:59:11
  the most accurate and also satirical and hilarious view on
• 1:59:17
  everything that's going on in the world in terms of
• 1:59:20
  like war and world news and culture shit. And he's
• 1:59:25
  my favorite. He was just on here yesterday. I fucking
• 1:59:28
  love that guy to death. He's so funny. He's so crazy.
• 1:59:31
  It's like his mind works in such a unique way
• 1:59:35
  and it's developed because his podcast is different where he
• 1:59:38
  very rarely has guests. So most of the time it's
• 1:59:41
  just him ranting and his producer laughing and he's the
• 1:59:44
  best ranter that's ever lived. I don't think there's anybody
• 1:59:46
  that's even close. He's the goat. Like There's like, I
• 1:59:50
  don't think there's any argument. Every comedian agrees. Like as
• 1:59:53
  far as like just the ability to just sit in
• 1:59:55
  front of a microphone and rant, like Bill Burr does
• 1:59:58
  it well. He's good at it. There's a few other
• 1:59:59
  guys that are good at it. No one's as good
• 2:00:01
  as Tim. He's the most consistently entertaining. And then for
• 2:00:06
  just mind, not mindless, but like to escape, I listened
• 2:00:10
  to a lot of archery shows and hunting shows where
• 2:00:13
  they're talking about different tactics and hunting or different techniques
• 2:00:18
  and archery new equipment and new innovations archery is an
• 2:00:23
  interesting thing because every year bow manufacturers make a better
• 2:00:27
  bow and like tiny little engineering changes of these bows
• 2:00:34
  like it's a weapon that's been around for who knows
• 2:00:38
  how many thousands of years but what.
• 2:00:40
  Speaker 1: The And you're able to feel those improvements.
• 2:00:42
  Speaker 2: Oh, yeah. Yeah, you feel the difference. Every year Hoyt
• 2:00:45
  puts out a new bow, and every year I'm like, motherfucker,
• 2:00:48
  they did it again. It's better. So just tiny changes,
• 2:00:52
  less vibrations in the hand, more balance in the shot,
• 2:00:56
  more forgiving in terms of accuracy. I love that stuff.
• 2:01:01
  So I get really fascinated by engineering, really fascinated by
• 2:01:04
  automotive engineering. I'm really interested in like that's another thing
• 2:01:08
  where like every year, People figure out how to make
• 2:01:11
  a car that can hold more Gs on a skid
• 2:01:14
  pad that can get around a track quicker. Like every
• 2:01:17
  year they're battling to see who can get around the
• 2:01:19
  Nürburgring quicker. And what are they doing? They're adding horsepower,
• 2:01:22
  increasing suspension travel and suspension tuning rather and making them
• 2:01:28
  more compliant or making them stiffer and And making them
• 2:01:31
  more adjustable and then like tire compounds. And I'm just
• 2:01:35
  interested in anything that where someone's working on something and
• 2:01:40
  getting better at something or getting new information. I love
• 2:01:44
  history podcasts. I listen to a bunch of history podcasts.
• 2:01:47
  So that's most of the time. If I'm listening to something,
• 2:01:51
  I either want to be entertained or I want to
• 2:01:53
  be educated. Educational. Yeah. Yeah. And that's entertaining. Yeah. What
• 2:01:58
  about you? What kind of stuff do you listen to?
• 2:01:59
  Speaker 1: I mean, I listen to your stuff. I listen to Lex.
• 2:02:02
  There's this guy, I mean, you know, you might, you
• 2:02:05
  had him on, like, Rick Rubin, of course.
• 2:02:06
  Speaker 2: Sure. Yeah, yeah. Love that guy. Yeah, yeah.
• 2:02:08
  Speaker 1: He's awesome. I listen to his stuff. And, I mean,
• 2:02:14
  I also watch, like, some interesting videos about, you know,
• 2:02:18
  concepts I don't understand. There is this YouTube channel, Veritasium.
• 2:02:23
  You should check it out.
• 2:02:24
  Speaker 2: What is it called?
• 2:02:24
  Speaker 1: Veritasium.
• 2:02:26
  Speaker 2: How do you spell that?
• 2:02:27
  Speaker 1: V-E- R-I-T-A-S-E-U-M. Veritasium.
• 2:02:33
  Speaker 2: What does it mean? I think... Is that someone's name?
• 2:02:36
  Speaker 1: No, Veritas just means like seeking truth kind of thing.
• 2:02:39
  Speaker 2: Oh, okay. Is it this channel?
• 2:02:41
  Speaker 1: Yeah, yeah, yeah.
• 2:02:45
  Speaker 2: Ooh, okay. 20.9 million subscribers. Yeah, that's pretty good. Obviously,
• 2:02:48
  a lot of people agree.
• 2:02:49
  Speaker 1: So, they make all these very interesting videos about like
• 2:02:53
  stuff that, you know, you would be curious about, but
• 2:02:56
  you never actually bother to ask that or learn more
• 2:02:59
  about and explain some of the most under understood companies
• 2:03:04
  or like phenomena. And I just love watching it. You know, I'm,
• 2:03:10
  this is kind of like my idea of doom scrolling.
• 2:03:12
  Like I like, I like watching like 20 videos at once.
• 2:03:15
  Speaker 2: Yeah. I am going to subscribe to it right now.
• 2:03:17
  Speaker 1: It's pretty cool.
• 2:03:20
  Speaker 2: Veritasium. There it is. Got it. Subscribed. Bam.
• 2:03:27
  Speaker 1: And explains all these like fun concepts that are, you know,
• 2:03:32
  you take it for granted. Like, okay, why is Google
• 2:03:34
  Maps really fast? Like, okay, it'll tell you what's going on,
• 2:03:38
  how the data is used across so many different people
• 2:03:40
  at once and all these different.
• 2:03:42
  Speaker 2: CIA's new tech doesn't make sense. Exactly. We were just
• 2:03:46
  talking about that yesterday. We were doubting it. You know,
• 2:03:49
  the heart murmur thing. Do you know about that? No.
• 2:03:51
  So the pilots that were downed in Iran... They said
• 2:03:56
  that they have this technology that allows them– I think
• 2:04:00
  they could use it up to 70 miles and they
• 2:04:03
  could detect a very unique heart rate. Like your heart
• 2:04:07
  rate is different than my heart rate. They could know
• 2:04:08
  it's you. You could be hiding in the mountains and
• 2:04:11
  they could find you from 70 miles away with this technology. Wow.
• 2:04:15
  Speaker 1: A lot of people are like– Beams or waves or something?
• 2:04:18
  Speaker 2: Well, it's called– what is it called? Quantum magnetometry. Is
• 2:04:21
  that what they call it? I think that's what it was.
• 2:04:25
  Remember we looked it up yesterday? They're using the word
• 2:04:28
  quantum and not explaining what they're doing, like how they're
• 2:04:31
  doing it. And you're like, OK, is that real? Or
• 2:04:36
  is this some invented horseshit to cover the fact that
• 2:04:39
  they have some very sophisticated satellite imagery where they can
• 2:04:43
  have a detailed map of literally the entire surface of
• 2:04:46
  the world? They know exactly where people are, but they
• 2:04:47
  don't want our enemies to know that they have this capability.
• 2:04:51
  So they're making up something.
• 2:04:53
  Speaker 1: I see.
• 2:04:53
  Speaker 2: That was my suggestion yesterday. That like maybe they're full
• 2:04:57
  of shit. Because the whole thing seems nuts. What is
• 2:04:59
  it called? You got it. It's quantum magnetometry. Sure. What
• 2:05:03
  does that mean? You tell me.
• 2:05:05
  Speaker 1: I don't know.
• 2:05:05
  Speaker 2: Exactly.
• 2:05:06
  Speaker 1: Yeah.
• 2:05:06
  Speaker 2: So this guy, he's saying it doesn't make sense.
• 2:05:10
  Speaker 1: Yeah.
• 2:05:10
  Speaker 2: And a lot of people say it doesn't make sense.
• 2:05:11
  Like it doesn't seem to vibe with anything that we
• 2:05:14
  know that we can do.
• 2:05:15
  Speaker 1: Quantum magnetometry.
• 2:05:16
  Speaker 2: Yeah.
• 2:05:16
  Speaker 3: Yeah.
• 2:05:17
  Speaker 1: First time I'm hearing it.
• 2:05:18
  Speaker 2: Pull up this description, the official description of what this
• 2:05:23
  stuff is capable of. So this is supposedly some very
• 2:05:27
  advanced CIA tech that allowed them to locate this downed pilot.
• 2:05:32
  Speaker 1: Interesting.
• 2:05:34
  Speaker 2: Maybe. Or maybe there's something else going on. Or maybe
• 2:05:37
  there's some other methods that they use that they don't
• 2:05:39
  want the enemy to know about. Maybe some beacon these
• 2:05:43
  guys have on them.
• 2:05:44
  Speaker 1: Yeah, I guess what's the incentive for it? CIA to
• 2:05:46
  actually describe how their technology works.
• 2:05:48
  Speaker 2: Yeah, zero. Why would they tell you that? Why would
• 2:05:51
  they tell you they even have that? That's crazy. And
• 2:05:54
  then Jamie had a good point.
• 2:05:55
  Speaker 1: But the capability is insane. Detecting your heart rate 70
• 2:05:59
  miles away is just insane.
• 2:06:00
  Speaker 2: How? Yeah, how? And when they throw the word quantum
• 2:06:03
  in things, I just go, hmm. Hey, what happened with
• 2:06:06
  that White House announcement? Sorry, I keep... The quantum computing? Yeah, remember?
• 2:06:11
  There's Q News coming soon, and then Like at the
• 2:06:15
  bottom of the text, the Q sounds for quantum. Oh,
• 2:06:18
  I see. Is that what it is?
• 2:06:19
  Speaker 1: I thought they just announced a bunch of investments in
• 2:06:21
  a bunch of quantum companies. Maybe that's it. IBM was
• 2:06:25
  getting some funding or whatever.
• 2:06:28
  Speaker 2: So this quantum magnetometry, can you pull up a description
• 2:06:32
  of what it is?
• 2:06:33
  Speaker 3: Sorry, I started looking up the last thing.
• 2:06:34
  Speaker 2: Sorry. I don't want to ask you too many questions
• 2:06:36
  at the same time. Quantum sensor help rescuers. Yeah. So
• 2:06:41
  this is it. Ghost murmur. Yes, that's what it's called.
• 2:06:46
  Purported surveillance technology utilizes long-range quantum magnetometry. What is that?
• 2:06:52
  Quantum magnetometers measure extremely faint magnetic fields, including the body's
• 2:06:56
  natural electromagnetic signals, by tracking changes in the energy states
• 2:07:01
  of atoms or subatomic particles. What? Technology reportedly uses microscopic
• 2:07:07
  defects in synthetic diamonds. When illuminated by a laser, these
• 2:07:11
  centers are hypersensitive particles to tiny magnetic fluctuations, the heart signal.
• 2:07:17
  While human heartbeats produce a magnetic field that is extremely weak,
• 2:07:20
  around 50 to 100 picoteslas, and typically degrades over very
• 2:07:26
  short distances. So the Ghost Murmur deployment, they reportedly used
• 2:07:32
  Ghost Murmur during a mission in southern Iran to pinpoint
• 2:07:34
  the location of a downed American airman hiding in dense
• 2:07:38
  mountainous terrain. By mounting these quantum sensors into a helicopter,
• 2:07:43
  the system purportedly registered the pilot's heartbeat from afar. Okay.
• 2:07:50
  Does that sound like horseshit?
• 2:07:52
  Speaker 1: I mean, it doesn't sound horseshit, but basically the part
• 2:07:55
  that sounds surprising to me is how they're able to
• 2:07:58
  deal with all this distance. and attenuation across the distance.
• 2:08:02
  Speaker 2: Right.
• 2:08:02
  Speaker 1: And all this interference, and they claim to use AI
• 2:08:04
  for that, but nothing is really described on how they
• 2:08:07
  use it.
• 2:08:08
  Speaker 2: Right. So if they're not describing how they use it,
• 2:08:10
  why are they even telling us they have it?
• 2:08:13
  Speaker 1: So there's a lot of skepticism on it. Yeah.
• 2:08:16
  Speaker 2: Laws of physics. Physicists point out that the heart's magnetic
• 2:08:20
  field is a million times weaker than the Earth's. Detecting
• 2:08:23
  it at a range of miles rather than centimeters defies
• 2:08:27
  currently published peer-reviewed physics. Alternative explanations. Suspect that while quantum
• 2:08:33
  sensors were likely on board, they were probably tracking the
• 2:08:36
  radio waves of a survival beacon. the metal in the
• 2:08:40
  pilot's equipment, or using traditional thermal, infrared, and radar capabilities
• 2:08:45
  rather than detecting a raw heartbeat via magnetic fields.
• 2:08:49
  Speaker 3: I do remember seeing a different part of when that
• 2:08:52
  story happened back in April. Someone did report on one
• 2:08:55
  of the military websites that there was a survival beacon
• 2:08:57
  that they used to track them. And that the whole
• 2:09:00
  quantum number stuff was nonsense.
• 2:09:02
  Speaker 2: Yeah, I saw that too.
• 2:09:02
  Speaker 3: But no one wants to report that because it's not fun.
• 2:09:05
  Speaker 2: Right. No, the ghost murmur thing is awesome fun. And
• 2:09:09
  if that is real, like, boy. You can imagine a
• 2:09:12
  world 100 years from now where that is real. So
• 2:09:15
  it's exciting.
• 2:09:16
  Speaker 1: Oh, yeah. 100 years is a long time for this
• 2:09:18
  to be real. Yeah.
• 2:09:19
  Speaker 2: 100 years, they probably got it down pat. Then that's
• 2:09:22
  the problem. You can't hide from the robot dogs, from
• 2:09:25
  black mare.
• 2:09:26
  Speaker 3: Yeah.
• 2:09:26
  Speaker 2: You know?
• 2:09:29
  Speaker 3: Yeah.
• 2:09:30
  Speaker 2: Do you ever, while you're working in AI, do you
• 2:09:32
  ever wonder, like, Is this the downfall of humanity? Is
• 2:09:37
  this a good thing to be working on? Did you
• 2:09:39
  ever have, like, doom moments?
• 2:09:42
  Speaker 1: Not on specific things I'm working on, but in general,
• 2:09:47
  I do worry about, like, how much, you know, you
• 2:09:51
  obviously want to, like, stay in charge and, you know,
• 2:09:54
  be in control of your experience, still be the one
• 2:09:58
  driving change and have a lot of agency for yourself.
• 2:10:01
  So I do worry that, like, It's all about making
• 2:10:04
  sure everybody's upskilled and understanding where the future is headed
• 2:10:08
  and not being fed only dangerous apocalyptic messages. Because it's
• 2:10:17
  very essential that human beings retain their agency and staying curious.
• 2:10:21
  So if that stops being the case, if you start
• 2:10:24
  subscribing to the vision that, okay, your jobs are done,
• 2:10:26
  you don't really have any meaning in the world, and
• 2:10:28
  we'll pay you some dividends, and you just sit at
• 2:10:30
  home and chill, that is not a good thing. So,
• 2:10:34
  and I feel like there are not enough voices in
• 2:10:36
  AI that are actually saying anything different to that. And
• 2:10:40
  I like, like when Jensen was here, I think he
• 2:10:42
  was a little different. I think he tried to give
• 2:10:44
  a more positive version where he said, okay, like the
• 2:10:51
  radiologist thing, if all radiologists can take away, you know,
• 2:10:54
  they start doing different kind of work. So I think
• 2:10:57
  we need to start looking at like, okay, like, okay,
• 2:11:00
  first of all, guys, relax. You have a lot of,
• 2:11:02
  you have one, premium skill your curiosity so let's figure
• 2:11:05
  out ways to channelize that let's change the way work
• 2:11:08
  is done in companies let's change the way educational institutions
• 2:11:10
  run let's change the incentive structures and let's help you
• 2:11:14
  build new ideas and new companies and explore things that
• 2:11:18
  are not even being considered and the government should obviously
• 2:11:21
  like you know support all these initiatives so that's what
• 2:11:24
  needs to happen more but what's happening actually right now
• 2:11:27
  is um okay like hey guys you're all losers you're
• 2:11:31
  going to lose your jobs And don't blame me because
• 2:11:35
  I told you so.
• 2:11:36
  Speaker 2: Right.
• 2:11:37
  Speaker 1: And still give us money because we're still going to
• 2:11:41
  do it anyway. And so that's what's happening more. And
• 2:11:44
  I think we should stop doing that. That's my opinion.
• 2:11:48
  Speaker 2: Well, it is. The problem is it's kind of a
• 2:11:50
  self-fulfilling prophecy. And if you tell people that they're going
• 2:11:52
  to be a loser and you're going to. Yeah. Their
• 2:11:54
  life is over. They're going to think that way. Instead
• 2:11:56
  of giving them an understanding of like, look, this can
• 2:11:59
  open up new doors for you.
• 2:12:00
  Speaker 3: Yeah.
• 2:12:01
  Speaker 2: This can. And anytime there's any sort of disruptive technology,
• 2:12:04
  there's always the fear that it's going to go badly.
• 2:12:07
  This was the case with the locomotive. This was the
• 2:12:10
  case when the printing press was invented. Yeah.
• 2:12:15
  Speaker 1: By the way, I did some research on this where
• 2:12:19
  in the Industrial Revolution happened, people got new ideas. Okay, like,
• 2:12:25
  for example, in the Industrial Revolution happened, who came up
• 2:12:29
  with the idea of a steel plow? John Deere. Until then,
• 2:12:33
  we were using wooden plows for farming. No farmer complained that, hey,
• 2:12:39
  we need fewer farmers now because steel plows are able
• 2:12:41
  to do it more effectively. No one complained. You actually
• 2:12:45
  had more farms and more productivity, more crop yields, and
• 2:12:49
  you're happier.
• 2:12:50
  Speaker 2: But isn't that just a regular tool as opposed to AI? Sure.
• 2:12:53
  Speaker 1: AI is different. It's not overnight going to become something
• 2:12:58
  that's capable of just running an entire farm. multi-trillion dollar
• 2:13:02
  company on its own. There are a lot of things
• 2:13:04
  that AIs cannot do. There's a lot of tacit knowledge
• 2:13:06
  in every company that AIs don't quite understand. And there's
• 2:13:10
  a lot of new directions that you can just start
• 2:13:12
  working on that AIs are not well equipped to do
• 2:13:15
  because it doesn't have full knowledge about it. And the
• 2:13:17
  knowledge about it is yet to be captured. And some
• 2:13:20
  of that requires like human to human work and collaboration.
• 2:13:23
  So we obviously have to gravitate towards what is scarce.
• 2:13:27
  When AI makes the current labor that's considered scarce because
• 2:13:31
  that's where the money is going in commodity, then we
• 2:13:34
  have to gravitate towards what is scarce. And the only
• 2:13:39
  way to do that is to seek things that we
• 2:13:41
  don't know about, which is only something we can discover
• 2:13:45
  through our curiosity. There's nothing else. Whatever we don't quite
• 2:13:49
  understand well, whatever we don't know how to do well yet,
• 2:13:52
  even with the current capabilities of AI, that's where we
• 2:13:56
  should pull our labor and workforce into. So it needs
• 2:14:00
  more responsible messaging and that's not quite happening right now.
• 2:14:05
  Speaker 2: I think it needs responsible messaging and then in the
• 2:14:07
  future what it needs is like real direction in terms
• 2:14:12
  of like letting people find their curiosity and find these
• 2:14:17
  paths of interest and find something to do with themselves
• 2:14:20
  that doesn't involve whatever their previous occupation that's irrelevant now. Mm-hmm.
• 2:14:27
  Speaker 1: That's true. I think like passion for people is something
• 2:14:31
  that not a lot of people would be able to
• 2:14:32
  answer out of the box. Like if you go and
• 2:14:35
  ask them, what is your real passion? And the only
• 2:14:38
  thing they have known in life is to just climb
• 2:14:40
  up career ladders and make more money. That's going to
• 2:14:44
  actually take them a while to even discover. Right.
• 2:14:48
  Speaker 2: Which is why it's so important to get kids off
• 2:14:50
  on the right start.
• 2:14:50
  Speaker 1: Yeah. That's the hope. That's our hope for the future
• 2:14:54
  is the kids. The kids are born curious. They don't
• 2:14:57
  need to change themselves to be curious. The adults who
• 2:15:00
  probably already are like, because of this knowledge work thing,
• 2:15:06
  who kind of curb their curiosity and try to fit
• 2:15:08
  into the existing system, it might be a little hard
• 2:15:11
  for them to adapt. But the kids, I think they
• 2:15:13
  don't have this problem. So I'm actually optimistic about the
• 2:15:17
  future long term because the future is all centered around
• 2:15:20
  whoever is very young today.
• 2:15:22
  Speaker 2: What do you think about this idea that... universal basic
• 2:15:26
  income is going to be required?
• 2:15:28
  Speaker 1: Some form of it is good. It's like a dividend.
• 2:15:31
  I almost think of it as a dividend. If a
• 2:15:34
  lot of spend that most companies are currently doing today
• 2:15:38
  on like payroll, which is paying a knowledge worker for
• 2:15:41
  a certain task, think of knowledge work as basically taking
• 2:15:44
  information and transforming it into an artifact, right? And it's
• 2:15:49
  messy and complicated. Let's assume that's being done by AIs.
• 2:15:52
  So obviously companies will start spending more on compute instead
• 2:15:56
  of payroll. It's just a reallocation of like spend, a budget.
• 2:16:01
  Similar to like what happened in advertising industries where most
• 2:16:04
  of your advertising budgets went to like television and like
• 2:16:07
  billboards and then now it's starting to go to Google
• 2:16:09
  and Instagram and YouTube and all that. So when that happens,
• 2:16:16
  obviously like the AI companies are going to make a
• 2:16:19
  lot of money and people who helped be part of
• 2:16:23
  creating it or either directly or indirectly would want to
• 2:16:27
  have some role to play in that ecosystem. And a
• 2:16:31
  good way to involve them is through giving them some
• 2:16:33
  ownership in the company. So as shareholders, if you get
• 2:16:37
  dividends from the profits generated by the AIs, it's not
• 2:16:40
  a bad thing. But that shouldn't be the only thing.
• 2:16:44
  Speaker 2: Right. So this is similar to like people that live
• 2:16:46
  in Alaska, they get a check. Correct.
• 2:16:49
  Speaker 1: Alaska does this and it's not a bad thing. As
• 2:16:53
  long as they are doing some other things alongside.
• 2:16:56
  Speaker 2: It could lessen the burden.
• 2:16:59
  Speaker 1: Correct.
• 2:16:59
  Speaker 2: Yeah. Yeah.
• 2:17:01
  Speaker 1: And if people are interested in still being part of
• 2:17:03
  the AI industries, they go do things that AIs are
• 2:17:05
  not able to do today. And that's been the case before.
• 2:17:09
  Like when industrial revolution started, the United Kingdom actually started
• 2:17:14
  like projects around building railroads. And that gave a lot
• 2:17:19
  of people who were in the cottage industries new jobs.
• 2:17:23
  So there are going to be a lot of new
• 2:17:24
  projects to just, okay, like what if we want to
• 2:17:27
  reimagine the government itself where the government runs largely on AI?
• 2:17:31
  Speaker 2: Yeah, that was my next question. Yeah.
• 2:17:34
  Speaker 1: So then we need people for that.
• 2:17:35
  Speaker 3: Yeah.
• 2:17:36
  Speaker 1: Because this is a legacy industry. It's not about the
• 2:17:38
  capabilities not being there. It's about working through the legacy
• 2:17:43
  and bureaucracy to like actually deploy and implement this inside
• 2:17:48
  the most like like largest institutions in the country and
• 2:17:52
  that's going to need a new set of skilled workers
• 2:17:56
  to go do that so some people who might be
• 2:17:58
  working at microsoft or something today might actually end up
• 2:18:00
  working for the united states government because Microsoft may not
• 2:18:05
  need them, especially for internally deploying AI or selling AI
• 2:18:09
  to their customers, but the government needs them. And if
• 2:18:11
  the government can pay them well and it's a fulfilling
• 2:18:14
  job to find some meaning for doing something good for
• 2:18:17
  the country, it's not a bad thing. So I think
• 2:18:20
  just like in the industrial revolution where we had new
• 2:18:24
  projects because the demand for AI was so big, we're
• 2:18:27
  going to start seeing some new projects being created in
• 2:18:29
  AI as well when the capabilities advance enough that they
• 2:18:32
  can replace knowledge workers.
• 2:18:34
  Speaker 2: That's the rosy scenario.
• 2:18:37
  Speaker 1: It's not as rosy. Like, the real world is messy.
• 2:18:40
  A lot of things are still done through trusting other
• 2:18:43
  human beings. Nobody is, like, blindly trusting AIs. AIs still
• 2:18:47
  make a lot of mistakes.
• 2:18:48
  Speaker 2: I know a lot of people are hesitant to the
• 2:18:50
  idea of AI running government, and I get it. But also,
• 2:18:54
  look at what the people are doing. Look at how
• 2:18:57
  much corruption there is, how much fraud and waste. Imagine
• 2:18:59
  if all fraud, waste, and corruption was instantaneously eliminated. Yeah.
• 2:19:05
  Speaker 1: I mean, that was what Elon tried to do with Doge.
• 2:19:07
  Speaker 3: Right.
• 2:19:08
  Speaker 1: And I think the bottleneck there was just discovering how
• 2:19:11
  slow it is to do things. He's not used to
• 2:19:15
  running that slow.
• 2:19:16
  Speaker 2: Yeah. Also, how much resistance. Yeah, resistance. Because there was
• 2:19:19
  so much grift.
• 2:19:20
  Speaker 1: Correct.
• 2:19:21
  Speaker 2: Yeah.
• 2:19:21
  Speaker 1: So, honestly, like, more than AI, the government is running
• 2:19:27
  a lot of legacy software stack because a lot of
• 2:19:30
  these legacy enterprise companies just– have created these multi-decade or
• 2:19:35
  like year contracts that are hard to get out of.
• 2:19:38
  And the way they do that is to sell it
• 2:19:40
  at a much larger discount. And like, you know, like
• 2:19:43
  if you're on like a specific OS, you're not allowed
• 2:19:45
  to change this for like 10 years. You have to
• 2:19:48
  use the same sort of software. All these people you
• 2:19:51
  hired only know to use that tool. So it takes
• 2:19:54
  time to actually change and implement new things. Leave alone AI.
• 2:19:58
  If you just wanted to like move everybody from Windows
• 2:20:00
  machines to like Mac machines, Good luck with that. It's
• 2:20:03
  going to take a lot of time. That's the state
• 2:20:06
  of the system. And so that has nothing to do
• 2:20:10
  with technology. And so to do things in such messy systems,
• 2:20:15
  you still need people. You still need people to navigate
• 2:20:18
  all these changes. It's not about the capability of technology.
• 2:20:23
  It's more about how the system is structured. And that's
• 2:20:26
  why I still feel there will be new jobs. That
• 2:20:30
  maybe the, you know, there's a lot of new projects
• 2:20:32
  to be done. Maybe some good leader actually wants to
• 2:20:34
  change the system and is willing to be patient about it. Like,
• 2:20:38
  you know, over a five to 10 year horizon, if
• 2:20:40
  you take 10 years to actually like run majority of
• 2:20:43
  the government processes on AIs, it may seem slow to
• 2:20:46
  you today, but in the grand scheme of things, it's
• 2:20:49
  actually good for the country. And that's still going to
• 2:20:52
  need a lot of nice engineers to go work on
• 2:20:55
  these projects. So they're not going to lose all their jobs.
• 2:20:58
  There's going to be some displacement. There's going to be
• 2:21:00
  some new projects. There's going to be new priorities. But
• 2:21:03
  it'll keep going. The system will keep going because that's
• 2:21:06
  just how historically things have been.
• 2:21:09
  Speaker 2: When you think about the future of AI and you
• 2:21:11
  think of this, so when you think about AGI in particular,
• 2:21:17
  you think about something that could potentially make better versions
• 2:21:21
  of itself.
• 2:21:23
  Speaker 1: Self-replicating.
• 2:21:24
  Speaker 2: Yeah. And then how far does it go?
• 2:21:27
  Speaker 1: Yeah, so that is the ultimate form of... I think
• 2:21:31
  some people in Silicon Valley have started calling that as ASI.
• 2:21:35
  So when you see the word ASI being thrown around,
• 2:21:39
  people kind of think of ASI as an AGI that
• 2:21:42
  can recursively self-improve itself. So there's going to be no
• 2:21:47
  limits to how smart it can get.
• 2:21:49
  Speaker 2: Right.
• 2:21:50
  Speaker 1: And I used to think that ASI is bottlenecked by
• 2:21:56
  power because you need a ton of compute for this
• 2:21:58
  model to keep on training itself and running its own
• 2:22:03
  rollouts and collecting data and then going and updating itself.
• 2:22:07
  But you could imagine that once the algorithm is correct,
• 2:22:10
  the ASI could be tasked with just making itself more
• 2:22:13
  efficient too. Where improvement doesn't just mean capability improvement, improvement
• 2:22:17
  could also mean power efficiency. And that way the recursive
• 2:22:23
  safe ASI that is improving itself also makes itself more
• 2:22:26
  compact and more efficient, and it can run on less compute.
• 2:22:30
  So that would be the ultimate project in AI. Think
• 2:22:33
  of it as almost as the last project in AI
• 2:22:35
  is basically cracking recursive self-improvement. Once you crack that, you
• 2:22:38
  don't have anything else to work on. In practice, I
• 2:22:42
  think what's going to happen is because information is so
• 2:22:46
  muddled and fragmented and living in disjoint systems, just the
• 2:22:50
  way we have constructed our messy real world, It's going
• 2:22:53
  to be hard to point even a recursively self-improving AI
• 2:22:56
  at some metric and say, go improve this. Or go
• 2:22:59
  reduce inflation by 5%. That would be awesome if you
• 2:23:02
  can task an AI to do that. If that's the
• 2:23:04
  job of the government to just reduce inflation, have a
• 2:23:07
  deflationary effect on society and make goods and services a
• 2:23:11
  lot more abundant and efficient, it's going to have to
• 2:23:15
  deal with a lot of messy legacy systems. If the
• 2:23:18
  task is to go improve the healthcare, we're good luck. Like,
• 2:23:22
  Who's going to deal with all the compliance of actually
• 2:23:25
  implementing these changes inside hospitals? Most hospitals are still using
• 2:23:29
  legacy software because the software provider has lobbied the government
• 2:23:35
  in a way where only they're allowed to do that.
• 2:23:37
  Speaker 2: God, what a stupid bottleneck.
• 2:23:39
  Speaker 1: Exactly. So a lot of the bottlenecks in actually having
• 2:23:43
  AI is just takeover and massively improve the human society
• 2:23:47
  and our hospitals, our legal systems, our government systems. where
• 2:23:52
  most of the payroll is going into, is just bottlenecked
• 2:23:55
  by a lot of compliance and regulation. And so that's
• 2:23:59
  why I feel the human beings are still necessary to
• 2:24:02
  effect the change because these laws and regulations were built
• 2:24:06
  for us.
• 2:24:08
  Speaker 2: And it also seems like we have to demand that
• 2:24:10
  those systems be usurped.
• 2:24:12
  Speaker 1: Sure, 100%. And we need the help of AIs to
• 2:24:15
  rewrite all these laws. It's going to be humanly impossible
• 2:24:18
  to go and change one specific line here and there.
• 2:24:22
  Speaker 2: Right. And then you're going to have a bunch of
• 2:24:23
  these software companies that are lobbying to try to stop
• 2:24:25
  that from happening. Yeah.
• 2:24:28
  Speaker 1: That's why, like, this messiness and this need for getting
• 2:24:32
  all people on the same page and actually steering the
• 2:24:34
  society in a positive way, our jobs will probably be
• 2:24:38
  more steered towards that problem solving at a different level
• 2:24:41
  of abstraction. Maybe more need for EQ. More need for actually, like,
• 2:24:47
  understanding differences of opinion and still, like, Leadership quality, ability
• 2:24:53
  to understand people, and ability to convince people, these are
• 2:24:58
  the skills that will be even more important in a
• 2:25:01
  world where actual work can be done by AIs, but
• 2:25:05
  effecting the change in our society, in our country, still
• 2:25:09
  needs human beings because the systems are messy.
• 2:25:15
  Speaker 2: It's a weird world we're in right now. Yeah. It's
• 2:25:18
  never been weirder.
• 2:25:19
  Speaker 1: That said, there's a lot of things that can still
• 2:25:22
  go wrong when you give power, so much power to, uh,
• 2:25:27
  you know, like specific companies and, uh, they deploy all
• 2:25:30
  these bots and then, um, anybody can use them in
• 2:25:34
  weird ways. You don't even know if like you're talking
• 2:25:37
  to a real person anymore.
• 2:25:38
  Speaker 2: Right.
• 2:25:39
  Speaker 1: They're like people who just run AI responses and chat
• 2:25:42
  with like 500 people at once. And that's like an
• 2:25:45
  old business. And so, um, I think it's going to
• 2:25:50
  take a lot of adjustment.
• 2:25:52
  Speaker 2: Well, another piece of adjustment that a lot of people
• 2:25:54
  are coming to grips with is that this is a
• 2:25:56
  new part of our conversation and that in 2020, like
• 2:26:00
  when I first moved here, AI was never discussed. It
• 2:26:03
  was not a thing. I mean we knew about it.
• 2:26:06
  We knew about AI. But it wasn't like– it wasn't
• 2:26:10
  a huge part of the cultural discussion of what the
• 2:26:12
  future holds for us. Now it is.
• 2:26:15
  Speaker 1: It is central, yeah.
• 2:26:17
  Speaker 2: And in that short amount of time, in just six years,
• 2:26:19
  it really makes you wonder, because we know how technology
• 2:26:24
  progresses exponentially, what it's going to look like six years
• 2:26:27
  from now.
• 2:26:27
  Speaker 1: Yeah, the 2028, like, you're definitely, my prediction is 2028
• 2:26:32
  election debates are going to be largely about AI.
• 2:26:35
  Speaker 2: Wow.
• 2:26:36
  Speaker 1: Yeah. AI, energy crisis, power, power. people are going to
• 2:26:43
  care about all these things. Because AI is no longer
• 2:26:48
  a thing that is new. It's part of all our lives.
• 2:26:51
  Everyone's using some form of AI in some ways. And
• 2:26:56
  it's not as dangerous as people thought. It's an amazing
• 2:26:59
  tool for doing work and asking questions and learning things
• 2:27:02
  and all these things.
• 2:27:03
  Speaker 2: When used correctly. Yeah.
• 2:27:05
  Speaker 1: It can also be used incorrectly.
• 2:27:08
  Speaker 2: Like everything.
• 2:27:09
  Speaker 1: Like everything. So it's far more powerful that incorrect usage
• 2:27:13
  can cause serious damage. Like, for example, kids who are
• 2:27:17
  using AIs for companionship. Crazy things are happening there. Crazy
• 2:27:23
  things are happening. It's as dangerous or probably more dangerous
• 2:27:28
  than social media. And it's also scary that social media
• 2:27:31
  companies want to build more of these kind of like
• 2:27:35
  companionship apps because they know that, okay, their only job
• 2:27:39
  is to get you engaged more. And that's the only
• 2:27:42
  way to sell more ads and make more money. And
• 2:27:45
  clearly companionship is a way to get you engaged more.
• 2:27:48
  Speaker 2: Yeah.
• 2:27:49
  Speaker 1: And so that's dangerous if ads start being part of
• 2:27:52
  like AI chats.
• 2:27:55
  Speaker 2: Yeah.
• 2:27:55
  Speaker 1: Because then if that ends up working... then all these
• 2:27:59
  chatbots are just going to be sycophants that just tell
• 2:28:04
  you stuff that you want to hear.
• 2:28:06
  Speaker 2: It's also an indistinguishable facsimile to a real person. They
• 2:28:11
  communicate like a real person. Right. So you really think
• 2:28:14
  you have a relationship with this. Right.
• 2:28:16
  Speaker 1: And it truly screws with your mind. It's hard to decouple,
• 2:28:22
  and it takes a lot of time to recover if
• 2:28:24
  you want to unplug something. And so the business model
• 2:28:29
  incentives are not well aligned to humanity.
• 2:28:31
  Speaker 2: Did you see that AI companion that they developed that
• 2:28:36
  was at the Consumer Electronics Show in Vegas this year?
• 2:28:39
  Speaker 1: Which one?
• 2:28:40
  Speaker 2: It's like a hot Asian lady.
• 2:28:42
  Speaker 1: I see. Yeah. These are the weird kind of projects
• 2:28:45
  that are going on.
• 2:28:46
  Speaker 2: Yeah. It's a hot Asian lady that talks to you. Yeah. And,
• 2:28:50
  you know, she talks to you through AI. And right
• 2:28:52
  now it's just a kind of a crude sort of robot.
• 2:28:55
  But you could see where it's going.
• 2:28:57
  Speaker 1: You could see where it's going.
• 2:28:58
  Speaker 2: Ex Machina. Yeah. It's going right there. Yeah.
• 2:29:03
  Speaker 1: Yeah, that movie was quite far ahead of its time.
• 2:29:06
  Speaker 2: Really? Yeah. That's one of my top ten favorite movies
• 2:29:10
  of all time.
• 2:29:10
  Speaker 1: It's underrated, actually, because reviews online say it's not as good,
• 2:29:15
  but I liked it.
• 2:29:16
  Speaker 2: I loved it. Yeah. I thought it was fantastic.
• 2:29:18
  Speaker 1: I like it better than Her.
• 2:29:20
  Speaker 2: Yeah, Her. I lost Her after a while. I shut
• 2:29:23
  it off. Yeah. It lost my attention. I'm sure it's good.
• 2:29:26
  It was the wrong time for me to watch it. Yeah.
• 2:29:28
  But Ex Machina, I've seen it like five times. I
• 2:29:31
  fucking love that movie. Yeah. It's just so... I don't
• 2:29:34
  want to give anything away, but it's so incredible and
• 2:29:37
  so bleak and so... Yeah. In the relationship that he
• 2:29:41
  has with the hot one?
• 2:29:43
  Speaker 1: Yeah.
• 2:29:44
  Speaker 2: You believe it. You're like, I'd be right there with him.
• 2:29:47
  You know, it's too confusing to our system to have
• 2:29:50
  something that looks exactly like the thing that you desire
• 2:29:53
  that is actually interested in you. It just happens to be.
• 2:29:55
  Speaker 1: Because it pulls all your data.
• 2:29:56
  Speaker 2: Yeah. Knows too much about you. Knows how to pull
• 2:29:59
  your strings.
• 2:30:00
  Speaker 1: Yeah.
• 2:30:00
  Speaker 2: Yeah. But listen, man. very fascinating discussion. Yeah. I'm glad
• 2:30:04
  we did it.
• 2:30:04
  Speaker 1: Thank you very much. Thank you so much.
• 2:30:06
  Speaker 2: And thanks for having an awesome platform. Poplexity has been great.
• 2:30:09
  We really love using it here at the show. It's
• 2:30:11
  made the show more interesting. It's cool.
• 2:30:13
  Speaker 1: Thank you. It's very fulfilling because like we, we want
• 2:30:16
  the app to be used by curious people like that.
• 2:30:18
  Like if we want to lift the ceiling of what our, our,
• 2:30:23
  our population can be, you know, not everyone is like
• 2:30:26
  fully curious all the time, but we're all born with it.
• 2:30:29
  So at some point in time, the system curbs it
• 2:30:31
  from us. So, There should be more apps that get
• 2:30:34
  us back to what we're naturally good at.
• 2:30:36
  Speaker 2: It's a fascinating tool for technology or for curiosity rather
• 2:30:39
  because to be– and it's seamless the way we use
• 2:30:43
  it on the show because there's always a question. Yeah.
• 2:30:45
  There's always– it comes up so often. Yeah. Like throw
• 2:30:48
  it in perplexity. Yeah. Let's find out what's up.
• 2:30:50
  Speaker 1: Yeah.
• 2:30:50
  Speaker 2: So it's been great for us. So thank you. Thank you.
• 2:30:53
  All right. My pleasure.
• 2:30:54
  Speaker 3: Bye, everybody.
• 2:30:54
  Speaker 2: Bye.