Latest / Drawn of History / AI CEO Alexandr Wang _ This Past Weekend w_ Theo Von _563(M4A_128K)

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AI CEO Alexandr Wang _ This Past Weekend w_ Theo Von _563(M4A_128K) · Drawn of History

• 0:00
  Speaker 1: I want to let you know we've restocked all your
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• 0:13
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  Speaker 1: early starting Wednesday, February 19th at 10 a.m. with pre-sale
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  Speaker 1: code RATKING. We also have tickets remaining in East Lansing, Victoria, B.C.
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  Speaker 1: in the Canada, College Station, Belton, Texas, Oxford, Mississippi, Tuscaloosa, Alabama, Nashville, Tennessee,
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  Speaker 1: Winnipeg in the Canada, and Calgary in the Canada. All
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  Speaker 1: tickets at theovon.com slash T-O-U-R. Today's guest is from Los Alamos,
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  Speaker 1: New Mexico. He's a leader in the world of business
• 1:09
  Speaker 1: and technology. He's an entrepreneur. He started Scale AI, a
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  Speaker 1: company recently valued at $ 14 billion. He started it when
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  Speaker 1: he was only 19. And at 24, he became the
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  Speaker 1: youngest self-made billionaire in the world. We talk about his company,
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  Speaker 1: the future of AI, and the role it plays in
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  Speaker 1: our human existence. This was super educational for me. I
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  Speaker 1: hope it is for you. I'm grateful for his time.
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  Speaker 1: Today's guest is Mr. Alexander Wang. Alexander Wang, man. Thanks
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  Speaker 1: for hanging out, bro. Yeah.
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  Speaker 2: Thanks for having me.
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  Speaker 1: Yeah. Good to see you, dude.
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  Speaker 2: Last time was at the inauguration.
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  Speaker 1: Yeah. What'd you think of that? Like, what were your
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  Speaker 1: thoughts after you left? Um, because you and I ended up,
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  Speaker 1: we'd like left out of there and then went and
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  Speaker 1: got lunch together, which was kind of crazy.
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  Speaker 2: It was, uh, there was a lot going on.
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  Speaker 3: Yeah.
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  Speaker 2: It was a, were you there the whole weekend?
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  Speaker 1: No, I just got there, uh, the day, the morning
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  Speaker 1: of the inauguration. Were you there the whole weekend?
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  Speaker 2: I was there the whole weekend.
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  Speaker 3: Yeah.
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  Speaker 2: No, I mean, I think at least for what we do,
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  Speaker 2: like for AI, the, uh, the new administration is really
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  Speaker 2: excited about it and wants to make sure that we
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  Speaker 2: get it right as a country. So that was all great,
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  Speaker 2: but it was kind of a crazy, the whole like event,
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  Speaker 2: like everything was pretty crazy. I don't know, what'd you think?
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  Speaker 1: I mean, when I saw Conor McGregor show up, that's
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  Speaker 1: when I was like, shit is, where are we? That's
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  Speaker 1: what like the most bizarre, I mean, you were there,
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  Speaker 1: Sam Altman was there. I was just like, it was like,
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  Speaker 1: what happened here? And part of that's because Alex Brusiewicz,
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  Speaker 1: you know, Totally, yeah. I mean, it's all because of
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  Speaker 1: him that we all went. But just the fact that
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  Speaker 1: he would bring these people together, it kind of makes
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  Speaker 1: you question.
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  Speaker 2: Crossover. It's like in those like TV shows when you
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  Speaker 2: have those crossover episodes.
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  Speaker 3: Yeah.
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  Speaker 2: That's kind of what it felt like.
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  Speaker 3: Oh, yeah.
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  Speaker 1: When like the Harlem Globetrotters and it would be like
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  Speaker 1: them versus like the Care Bears or whatever would show up. Exactly, yeah.
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  Speaker 1: And you're like, yeah, this seems, yeah, some cross-pollination.
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  Speaker 3: Yeah.
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  Speaker 2: Um, yeah.
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  Speaker 1: What'd you think when Connor showed up? Was that strange
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  Speaker 1: to see him? Like, was there somebody you thought that
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  Speaker 1: was strange, like unique to see there for you?
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  Speaker 2: Uh, well, yeah. I mean, you, Connor, like the, the,
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  Speaker 2: the Pauls, I don't know. The whole, the whole thing
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  Speaker 2: was pretty, uh, pretty crazy. I see Sam all the
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  Speaker 2: time and I see, uh, I see some of the
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  Speaker 2: tech people all the time. I mean, it was funny
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  Speaker 2: crossover and it was obviously like, so many people flew
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  Speaker 2: in to be able to go to like the outdoor inauguration.
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  Speaker 1: Right.
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  Speaker 2: And so, I mean, there were so many people in
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  Speaker 2: the city. We ran into them, obviously. But, like, there
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  Speaker 2: were so many people in D.C. who were just around
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  Speaker 2: for the inauguration. So it was a– Yeah, I didn't
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  Speaker 2: even think about that. There was a couple hundred thousand
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  Speaker 2: people probably that just got kind of displaced in a way.
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  Speaker 4: Yeah.
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  Speaker 1: And suddenly they're just in bars or whatever just, like, buying,
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  Speaker 1: like– there was, like, people I saw walking, just adult
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  Speaker 1: onesies and shit that said Trump on the– just crazy shit.
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  Speaker 5: Yeah.
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  Speaker 1: Yeah. Yeah, thanks for coming in, man. I'm excited to
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  Speaker 1: learn about AI. I know that that's a world that
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  Speaker 1: you're in. You're in the tech universe. And you're from
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  Speaker 1: New Mexico, right?
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  Speaker 3: Yeah.
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  Speaker 1: And so when you grew up there, did you have
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  Speaker 1: like a strong sense of like science and technology? Was
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  Speaker 1: that like part of your world? Like, did your parents
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  Speaker 1: like lead you into that? Like, what was just some
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  Speaker 1: of your youth like there?
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  Speaker 2: Yeah, so both my parents are physicists. And there's a.
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  Speaker 1: Oh, damn.
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  Speaker 2: I grew up in this town called Los Alamos, where
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  Speaker 2: there's a national lab there. Did you watch Oppenheimer?
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  Speaker 3: Yeah.
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  Speaker 2: Yeah. So all the New Mexico shots in Oppenheimer, that's
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  Speaker 2: exactly where I grew up.
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  Speaker 4: Oh, dang.
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  Speaker 2: So it was like originally where all the scientists came
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  Speaker 2: together and did all the work in the atomic bomb.
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  Speaker 2: And there's still this huge lab. That's basically the, everybody
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  Speaker 2: I knew effectively, like their parents worked at the lab or,
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  Speaker 2: or some affiliate with the lab.
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  Speaker 1: It's like Nukeville over there, huh?
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  Speaker 2: Nukeville, yeah.
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  Speaker 1: Is it scary like that?
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  Speaker 2: It was, you know.
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  Speaker 1: Is it at a level of mystery? Like, is your prom, like,
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  Speaker 1: last night under the stars or something?
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  Speaker 2: There's this one museum... Well, the funny thing is, like, first,
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  Speaker 2: you hear this... You learn the story of the atomic
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  Speaker 2: bomb and the Manhattan Project, like, basically every year growing up,
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  Speaker 2: because there's always, like, a day or a few days
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  Speaker 2: where there's a substitute teacher, and they just... They just
• 5:29
  Speaker 2: play the videos. So you're just like.
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  Speaker 1: An alcoholic or something. A recreational user would use that term.
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  Speaker 1: That's crazy that that just like every year you guys
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  Speaker 1: have like, yeah, just like blasted Thursdays or whatever.
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  Speaker 2: And it's just, yeah, you're just learning. You're learning again
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  Speaker 2: about the Manhattan project. And then there's a little, there's
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  Speaker 2: a little museum in town, which is like you walk
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  Speaker 2: through and there's like a life-size replica of of the nukes. Um,
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  Speaker 2: so it's, it's pretty wild. Yeah.
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  Speaker 1: And where did they drop the atomic bombs on? They
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  Speaker 1: dropped them on Asia, right? Hiroshima.
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  Speaker 2: They, yeah, they dropped them, uh, on, uh, in, in Japan.
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  Speaker 3: Yeah.
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  Speaker 2: Hiroshima and Nagasaki.
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  Speaker 1: Is it, is it crazy being like semi-Asian part Asian?
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  Speaker 2: Uh, yeah, my, uh, my parents, uh, are Chinese.
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  Speaker 1: Oh, nice man. Is it crazy being Asian and then
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  Speaker 1: having that happen to Asian people with the bomb? Like,
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  Speaker 1: is that a weird thing there? It's nothing.
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  Speaker 2: Uh, no, I don't think so. I mean, I think
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  Speaker 2: the thing is like, you know, I, uh, so there weren't,
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  Speaker 2: I didn't grow up with very many Asians because the,
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  Speaker 2: in that town, it was, um, you know, it's, it's
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  Speaker 2: in New Mexico. There's very few Asians in New Mexico.
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  Speaker 2: So I was one of the only Asian kids in
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  Speaker 2: my class growing up. And so, um, I didn't think
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  Speaker 2: that much about it, honestly, but then like, uh, but
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  Speaker 2: it is super weird. You know, you grow up and
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  Speaker 2: you learn about this very advanced technology that had this
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  Speaker 2: like really, really big impact on the world. And I
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  Speaker 2: think that shaped.
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  Speaker 1: Yeah, it's like the scientific John Jones over there, really.
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  Speaker 1: He's a New Mexican, isn't he?
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  Speaker 2: He lives in Albuquerque.
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  Speaker 3: Oh, he does?
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  Speaker 2: I think he does, yeah.
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  Speaker 1: Oh, sweet, man. Oh, yeah. So you're there, there's this-
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  Speaker 1: energy always there of this creation. And so probably the
• 7:12
  Speaker 1: possibility of creation maybe was always in the air. I'm
• 7:15
  Speaker 1: just wondering, like, how did you get formed kind of,
• 7:17
  Speaker 1: you know, like what's your origin story kind of?
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  Speaker 2: It was super scientific because, you know, there were all
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  Speaker 2: these presentations around what were the new kinds of science
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  Speaker 2: that were going on at the lab. So there's all
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  Speaker 2: these chemistry experiments and these different like earth science experiments
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  Speaker 2: and physics experiments. And my mom studied like plasma and
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  Speaker 2: like how plasma, you know, worked inside of stars and
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  Speaker 2: stuff like that. So it was just the wildest stuff.
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  Speaker 2: And you would talk to people's parents, people like I
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  Speaker 2: would talk to my classmates or talk to their parents
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  Speaker 2: about what they're working on is always some crazy science thing.
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  Speaker 2: So that was, that was really cool because, because everybody
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  Speaker 2: in that town basically is, they're all working on some
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  Speaker 2: kind of crazy scientific thing. And so you kind of,
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  Speaker 2: I mean, I feel like I grew up feeling like,
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  Speaker 2: you know, anything was possible in that way.
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  Speaker 1: Like, yeah. Cause the rest of us in other communities,
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  Speaker 1: shitty communities or whatever, we're just making that volcano or whatever,
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  Speaker 1: you know what I'm saying? We're doing like grassroots level bullshit,
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  Speaker 1: you know? Um, dang, that's gotta be wild. So every,
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  Speaker 1: you see somebody just sneaking behind an alley and buying
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  Speaker 1: a bit of uranium and shit like that in your neighborhood,
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  Speaker 1: that's gotta be kind of unique.
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  Speaker 2: I remember there was someone from our town who did, uh,
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  Speaker 2: who did a science fair project called, um, It's called
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  Speaker 2: like Great Balls of Plasma. And for the science, literally
• 8:41
  Speaker 2: for the science fair, this was in like high school,
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  Speaker 2: for the science fair, they made like huge balls of
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  Speaker 2: plasma in their garage. And I was like, what the hell? Like,
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  Speaker 2: this is, we're all just doing this in high school.
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  Speaker 1: Damn. So did you feel competitive or did you just
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  Speaker 1: feel like hyper capable? Like, did you feel like kind
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  Speaker 1: of advanced just in your studies? Like when you were young,
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  Speaker 1: like did you, in class, you're like, oh, some of
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  Speaker 1: this stuff's kind of coming easy to me?
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  Speaker 2: I ended up– what I did is there was– I
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  Speaker 2: ended up getting really competitive about math in particular.
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  Speaker 1: Yeah.
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  Speaker 2: And so my sport was math, which is kind of crazy.
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  Speaker 1: Algebrazy, son. I know I feel you.
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  Speaker 2: And it was because when I was in middle school,
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  Speaker 2: when I was in sixth grade, there was this one
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  Speaker 2: math competition where if you got top four in the state,
• 9:34
  Speaker 2: state of New Mexico, then– you would get an all
• 9:36
  Speaker 2: expense paid trip to Disney world. And, uh, and I
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  Speaker 2: remember as a sixth grader, like that was the most
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  Speaker 2: motivating thing I could possibly imagine was like an all
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  Speaker 2: expense paid trip to Disney world. Um, and then, uh,
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  Speaker 2: did you win it? I got, I won it. I
• 9:54
  Speaker 2: snuck in there, uh, snuck in. I, uh, and then
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  Speaker 2: I went to, and then we went to Florida to
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  Speaker 2: Disney World. And I hadn't traveled, like I didn't travel
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  Speaker 2: around too much growing up. Like I mostly was in
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  Speaker 2: New Mexico and we did some road trips around the Southwest.
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  Speaker 2: So I remember getting to Florida and it was extremely humid.
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  Speaker 2: It was like, I never felt what humidity felt like
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  Speaker 2: when I landed in Florida. I was like, oh, this
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  Speaker 2: feels bad.
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  Speaker 1: Yeah, it definitely does. Yeah, it's funny because I grew
• 10:25
  Speaker 1: up inhumane, dude. Like you would like you try to
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  Speaker 1: shake somebody's hand. You couldn't even land it because it
• 10:29
  Speaker 1: was just not enough viscose, just too much lube in
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  Speaker 1: a handshake. You'd sit there for 10 minutes trying to
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  Speaker 1: land a handshake. You know, everybody was always dripping, you know,
• 10:38
  Speaker 1: all the time. People always thought they were scared or something.
• 10:40
  Speaker 1: They were kind of geeked up off a gas station
• 10:43
  Speaker 1: dope or something. But people were just inhumane. Yeah, you
• 10:46
  Speaker 1: get really humid over there.
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  Speaker 2: So then I became a mathlete. That was like a
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  Speaker 2: big part of my identity was being a mathlete.
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  Speaker 1: And you have to wear a vest. What do you
• 10:55
  Speaker 1: guys do? Is there a uniform for that?
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  Speaker 2: Well, you have a calculator. So everyone had their favorite calculator.
• 11:03
  Speaker 1: Okay, you got that Draco on you, baby. I feel
• 11:05
  Speaker 1: you keep strapped. You stay strapped at that thing.
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  Speaker 2: And then, uh, but, uh, but outside of that, not really.
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  Speaker 2: I mean, like everyone had their favorite, like his pencil,
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  Speaker 2: you know, your pencil, your calculator, that was your gear. And, uh,
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  Speaker 2: but yeah, no, I, I was, I was like a,
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  Speaker 2: a nationally ranked mathlete for, uh, for middle school, high school.
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  Speaker 2: That was my, that was my jam.
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  Speaker 3: Yeah.
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  Speaker 1: And is it, what's this math circle like? Is there, wow,
• 11:31
  Speaker 1: that's crazy, dude. So you go to, what are the
• 11:34
  Speaker 1: other competitions you go to with that?
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  Speaker 2: There's competitions.
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  Speaker 3: Yeah.
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  Speaker 2: There's like, There's like state level competitions. There's national level competitions.
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  Speaker 2: There's like these summer camps, math camp. I went to
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  Speaker 2: math camp a bunch of times, um, where you, where
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  Speaker 2: you convene with like-minded math.
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  Speaker 1: Okay.
• 11:51
  Speaker 2: Wow.
• 11:52
  Speaker 1: Fucking wizards in the park. Uh, a couple of dudes
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  Speaker 1: fucking fighting over a common denominator in the lobby. That's crazy, bro.
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  Speaker 2: But then you're, but just like any other sport, like
• 12:02
  Speaker 2: you're like, it's competitive, you know, you gotta like, you
• 12:05
  Speaker 2: gotta win. And, uh, And so, so you're chummy with everyone,
• 12:08
  Speaker 2: but you're also like, we're like, who's going to win,
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  Speaker 2: you know?
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  Speaker 1: Yeah. No, dude, competition's amazing, man. That's one thing is too.
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  Speaker 1: That's so nice. I think about when you're young, it's like,
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  Speaker 1: if you're involved in a sport or a group or
• 12:19
  Speaker 1: whatever it was, um, just to just that chance to
• 12:22
  Speaker 1: be competitive, you know, what are, what were like some
• 12:24
  Speaker 1: of the competitions at the math thing? Like what's a,
• 12:26
  Speaker 1: what's a math competition like when you get there?
• 12:29
  Speaker 2: So you, uh, the ones in the middle school, middle
• 12:32
  Speaker 2: school were actually more fun. Cause there's like, There's almost
• 12:34
  Speaker 2: like a Jeopardy-style buzzer competition and stuff like that. But
• 12:39
  Speaker 2: once you get to high school, it's just– you just
• 12:41
  Speaker 2: take a test and you just– it's like the biggest
• 12:45
  Speaker 2: one in America is called the USMO.
• 12:48
  Speaker 1: Bring it up. USMO?
• 12:50
  Speaker 2: USAMO.
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  Speaker 1: Okay.
• 12:54
  Speaker 2: And it's like people all around the country take this
• 12:58
  Speaker 2: test and there's– United.
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  Speaker 1: States of American Mathematical Olympiad.
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  Speaker 3: Okay.
• 13:03
  Speaker 2: Yeah, there you go.
• 13:04
  Speaker 1: Um, okay. A participant must be either a US citizen
• 13:06
  Speaker 1: or a legal resident of the US. Um, okay.
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  Speaker 4: Okay.
• 13:10
  Speaker 1: Go on.
• 13:10
  Speaker 2: And then you, uh, and then it's a nine hour test.
• 13:14
  Speaker 2: It's split over two days. It's nine hours. You, it's
• 13:17
  Speaker 2: four and a half hours and it's nine hours and
• 13:20
  Speaker 2: you have six problems. So it's kind of nerve wracking
• 13:23
  Speaker 2: and you get in there, you have four and a
• 13:25
  Speaker 2: half hours the first day, four and a half hours
• 13:26
  Speaker 2: the second day.
• 13:27
  Speaker 1: Um, can you cheat in between the days?
• 13:29
  Speaker 2: Can you just go home and No, you, cause you
• 13:31
  Speaker 2: only get three questions the first day and then you
• 13:33
  Speaker 2: only get three questions the next day. And, uh, I
• 13:36
  Speaker 2: remember the first time I took it, I was in,
• 13:39
  Speaker 2: I think I was, uh, I was in eighth grade.
• 13:42
  Speaker 2: First time I took it. And I like, I was
• 13:44
  Speaker 2: like so nervous and I just, I like brought a
• 13:47
  Speaker 2: thermos full of coffee and I drank so much coffee
• 13:50
  Speaker 2: that like those four and a half hours felt like,
• 13:53
  Speaker 2: like year. It was like, I was so jittery the
• 13:55
  Speaker 2: whole time. It was, uh, it was crazy.
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  Speaker 1: He out there rattling brother. Oh, integers make me rattle, brother.
• 14:02
  Speaker 1: I feel you on that. Dude, so you're competitive, and
• 14:06
  Speaker 1: so you get out of there, and you're obviously, I guess,
• 14:10
  Speaker 1: admired in the world of math, probably, or that's like
• 14:12
  Speaker 1: a thing that you can point, that's like a feather
• 14:17
  Speaker 1: in your cap. So that helps you get into MIT, right?
• 14:21
  Speaker 5: Yep.
• 14:22
  Speaker 2: Okay. So then, yeah, in MIT, on the MIT admissions,
• 14:26
  Speaker 2: they ask for your competitive math scores.
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  Speaker 4: Yeah.
• 14:30
  Speaker 1: So if you're so, so you knew a lot of
• 14:32
  Speaker 1: kids going there probably because it was.
• 14:33
  Speaker 2: Tons of kids.
• 14:34
  Speaker 3: Yeah. Yeah.
• 14:35
  Speaker 2: Tons of kids. It was like a reunion. It was
• 14:36
  Speaker 2: like math camp reunion.
• 14:37
  Speaker 1: Damn. Cause I was wondering where all y'all were at, dude,
• 14:39
  Speaker 1: because we were doing some other shit. You know what
• 14:42
  Speaker 1: I'm saying? My, yeah, we were not, God, that's where
• 14:45
  Speaker 1: all the smart kids were. That's fascinating, man.
• 14:48
  Speaker 2: And so then, uh, yeah, I was at MIT and then, uh,
• 14:51
  Speaker 2: MIT is like a really intense school because they were,
• 14:56
  Speaker 2: you know, the classes they don't fuck around with. They
• 14:58
  Speaker 2: just really like, they load you up with tons of work.
• 15:01
  Speaker 2: And most of the time you have, you, you like,
• 15:03
  Speaker 2: they load you up with like huge amounts of work
• 15:05
  Speaker 2: and they, you know, you, you, uh, you don't really
• 15:07
  Speaker 2: know what's going on initially. And so you're just kind
• 15:09
  Speaker 2: of like, you're just trying to make it through. But, uh,
• 15:12
  Speaker 2: you know, there's this motto that, uh, that MIT has called, uh, IHTFP, which, uh,
• 15:19
  Speaker 2: among the students stands for, I hate this fucking place.
• 15:21
  Speaker 2: Oh yeah.
• 15:23
  Speaker 1: It's heavy, huh?
• 15:24
  Speaker 2: Yeah, but then the school, when you go, they tell you, oh,
• 15:26
  Speaker 2: it stands for I've Truly Found Paradise.
• 15:29
  Speaker 1: Oh, so a couple differences of opinion.
• 15:32
  Speaker 2: Yeah.
• 15:33
  Speaker 1: Damn. So it's really severe there. There's a lot of work.
• 15:37
  Speaker 1: They load you down out of the gate?
• 15:39
  Speaker 2: You do a lot of work, but it's kind of awesome.
• 15:42
  Speaker 2: I mean, because I think the students really band together.
• 15:44
  Speaker 2: Like you, like instead of, like I think instead of
• 15:48
  Speaker 2: it being competitive, really, MIT is much more about like
• 15:51
  Speaker 2: everybody kind of like coming together, working on problems together,
• 15:55
  Speaker 2: working on homework together, and just kind of like making
• 15:57
  Speaker 2: through together.
• 15:59
  Speaker 1: What's that social life like there? Like are you dating?
• 16:01
  Speaker 1: Are you going to parties? What's that like for you
• 16:03
  Speaker 1: at that time?
• 16:03
  Speaker 2: It's a bunch of parties because people– like MIT, there's
• 16:07
  Speaker 2: a lot of people who like tinkering with gadgets. So
• 16:09
  Speaker 2: they're tinkering with like lights and big speakers and DJ
• 16:14
  Speaker 2: sets and all this stuff. So actually the parties are
• 16:16
  Speaker 2: pretty good because they're like the production– Value is high.
• 16:20
  Speaker 2: The production quality is high.
• 16:23
  Speaker 1: And that, what about when the science kids come through
• 16:25
  Speaker 1: the lab dogs, are they bringing our people making like
• 16:27
  Speaker 1: unique drug? Was there like designer drugs that are being
• 16:29
  Speaker 1: created by actual people? Like just because, you know what
• 16:33
  Speaker 1: I'm saying? Like my friends in college, none of us
• 16:36
  Speaker 1: would know how to do that, but there may be
• 16:38
  Speaker 1: somebody at a smart, at a, more prestigious school that
• 16:41
  Speaker 1: would know how. Was that a thing even, or is
• 16:43
  Speaker 1: that just?
• 16:44
  Speaker 2: There was one part of the campus called East Campus
• 16:48
  Speaker 2: where it was like, it was more fringe. And so
• 16:50
  Speaker 2: there was like, at one point in the school year,
• 16:52
  Speaker 2: they would, in their courtyard, they would build a gigantic catapult,
• 16:57
  Speaker 2: like a huge catapult.
• 16:58
  Speaker 1: Like a trebuchet?
• 16:59
  Speaker 2: Like a trebuchet, yeah. What's the difference? I don't know
• 17:02
  Speaker 2: the difference.
• 17:03
  Speaker 1: Yeah, let's get the difference there because people need to
• 17:04
  Speaker 1: know this anyway. People have, for decades now, people have been.
• 17:07
  Speaker 3: A trebuchet has like a rope attached to the end
• 17:10
  Speaker 3: of it that flings it where a catapult just launches it.
• 17:13
  Speaker 2: No, it was a catapult then.
• 17:14
  Speaker 1: Okay.
• 17:15
  Speaker 2: Like a big, cause there was a big, there was
• 17:17
  Speaker 2: a big like, like ice cream scooper.
• 17:19
  Speaker 3: Oh yeah.
• 17:19
  Speaker 2: Looking thing that would like, that would fling it.
• 17:21
  Speaker 1: And what are they just flinging Adderall into the rest
• 17:23
  Speaker 1: of the campus?
• 17:24
  Speaker 2: They would fling stuff into the river, which I don't think,
• 17:27
  Speaker 2: now that I'm thinking about it, I feel like, I
• 17:28
  Speaker 2: don't know what the, yeah, no, these giant, this giant
• 17:31
  Speaker 2: like catapult things, um, Yeah.
• 17:35
  Speaker 1: And so this was like a event that would go
• 17:37
  Speaker 1: on and people would kind of rave there. What are
• 17:39
  Speaker 1: you saying?
• 17:39
  Speaker 5: Yeah.
• 17:40
  Speaker 2: They would do this. They would do other things. There
• 17:42
  Speaker 2: were like, there were into, there were like build a
• 17:44
  Speaker 2: lot of stuff over there.
• 17:45
  Speaker 1: Um, and there would be like people that ended up
• 17:48
  Speaker 1: at burning man later on.
• 17:49
  Speaker 3: Yes. Yes.
• 17:49
  Speaker 2: That was the burning. That was core burning man. Like
• 17:52
  Speaker 2: there was a, there was a satanic ritual floor.
• 17:54
  Speaker 1: Oh yeah.
• 17:55
  Speaker 3: Yeah.
• 17:55
  Speaker 2: Like a lot of, a lot of like it's fringe.
• 17:57
  Speaker 4: Cool. It's cool.
• 17:58
  Speaker 3: Right.
• 17:59
  Speaker 2: Um, but, uh, but there, yeah, so there's all these parties. We,
• 18:02
  Speaker 2: we bragged at MIT that, uh, you know, people from
• 18:05
  Speaker 2: all the neighboring schools. Cause Boston is a huge college town,
• 18:08
  Speaker 2: like tons and tons.
• 18:09
  Speaker 1: Boston's an amazing city.
• 18:10
  Speaker 3: Yeah.
• 18:10
  Speaker 2: But no, MIT was fun, but I was only there.
• 18:12
  Speaker 2: I was at MIT for one year.
• 18:13
  Speaker 3: Right.
• 18:14
  Speaker 1: And you dropped out. Is that safe to say?
• 18:15
  Speaker 3: Yeah.
• 18:16
  Speaker 2: Okay.
• 18:16
  Speaker 1: You dropped out and then you, so you got into AI,
• 18:20
  Speaker 1: into the AI world. Is that kind of a safe
• 18:22
  Speaker 1: bridge to say?
• 18:22
  Speaker 4: Yeah.
• 18:23
  Speaker 1: Yeah. Yeah. Okay. Is there, and I want to ask
• 18:25
  Speaker 1: this cause I know Mark Zuckerberg dropped out of college.
• 18:29
  Speaker 1: You dropped out of college. Both of you guys have
• 18:31
  Speaker 1: had success in tech and kind of, you know, forward
• 18:34
  Speaker 1: thinking that sort of world. Is there something in college
• 18:39
  Speaker 1: that you felt like didn't nurture you? Or did you
• 18:42
  Speaker 1: just feel like this isn't the place for me? Do
• 18:46
  Speaker 1: you feel like college doesn't nurture a certain type of thinker?
• 18:50
  Speaker 1: Or was it just a personal choice?
• 18:52
  Speaker 2: I think for me, it was like, I was just
• 18:57
  Speaker 2: feeling really impatient. And I don't really know why, really.
• 19:02
  Speaker 2: But I remember like, I remember I was in school
• 19:04
  Speaker 2: the first year. I, it was, it was really fun and,
• 19:07
  Speaker 2: and I really enjoyed it. But then I remember, you know,
• 19:10
  Speaker 2: this in the year when I was at MIT was
• 19:14
  Speaker 2: one of the first, like, it was like one of
• 19:16
  Speaker 2: the early big moments in AI because it was, um,
• 19:19
  Speaker 2: I don't remember this, but there was an AI that beat, uh,
• 19:23
  Speaker 2: the world champion at go. Um, this was in 2015,
• 19:27
  Speaker 2: which when I was in college.
• 19:28
  Speaker 1: At go. And that was a game.
• 19:30
  Speaker 4: Yeah.
• 19:30
  Speaker 2: Go. It's like, uh, It's like a big checkerboard with
• 19:34
  Speaker 2: white and black stones. And it was, yeah, this game,
• 19:41
  Speaker 2: AlphaGo versus Lee Sedol.
• 19:43
  Speaker 1: So AlphaGo versus Lee Sedol, also known as the DeepMind
• 19:46
  Speaker 1: Challenge match, was a five-game go match between top Go
• 19:49
  Speaker 1: player Lee Sedol and AlphaGo. a computer Go program developed
• 19:52
  Speaker 1: by DeepMind, played in Seoul, South Korea, between 9th and
• 19:56
  Speaker 1: 15th of March, 2016. That was confusing how that's written.
• 20:02
  Speaker 1: It is very confusing. You'd think that we got to.
• 20:06
  Speaker 1: AlphaGo won all but the fourth game. All games were
• 20:09
  Speaker 1: won by resignation. The match has been compared with the
• 20:12
  Speaker 1: historic chess match between Deep Blue and Garry Kasparov. Huh.
• 20:17
  Speaker 1: The winner of the match was slated to win $ 1 million.
• 20:19
  Speaker 1: Since AlphaGo won, Google DeepMind stated that the prize would
• 20:21
  Speaker 1: be donated to charities, including UNICEF and USAID. That's just
• 20:26
  Speaker 1: a joke. But Lee received $ 150, 000 for playing. So this
• 20:31
  Speaker 1: was a big moment because... This had never kind of
• 20:34
  Speaker 1: happened before?
• 20:35
  Speaker 2: Never happened, yeah. And it was a big moment for AI.
• 20:40
  Speaker 2: It was like, oh, wow, this stuff is like, it's
• 20:42
  Speaker 2: really happening. And so then this happened in March. And
• 20:45
  Speaker 2: I guess, yeah, I dropped out, started my company in May.
• 20:48
  Speaker 2: So I guess two months after this, I was, yeah,
• 20:52
  Speaker 2: that's what the game looks like.
• 20:54
  Speaker 1: Oh, I've played this game before.
• 20:56
  Speaker 2: It's honestly, it's a really, like, I'm not very good
• 21:01
  Speaker 2: at the game.
• 21:01
  Speaker 1: It's a little more fun than playing chess unless you're like,
• 21:03
  Speaker 1: you know, in a like Renaissance fair board games or whatever.
• 21:06
  Speaker 3: Yeah. Yeah.
• 21:07
  Speaker 1: Um, okay. So now we got you, you're loose, dude.
• 21:10
  Speaker 1: You're out of the school and you're in the world.
• 21:13
  Speaker 1: You see, did that match did realizing that kind of
• 21:16
  Speaker 1: like spurn, you don't want to leave school or did it,
• 21:19
  Speaker 1: was that just something that happened around the same time?
• 21:21
  Speaker 2: It kind of did. Basically it was, it was, I
• 21:24
  Speaker 2: remember feeling like, oh wow. AI it's happening. And this
• 21:28
  Speaker 2: was back in 2016, so like eight, nine years ago.
• 21:31
  Speaker 1: Okay.
• 21:32
  Speaker 2: And then I felt like I had to, you know,
• 21:34
  Speaker 2: basically that inspired me to start my company. And I moved,
• 21:40
  Speaker 2: I basically went straight from, I remember this, I flew
• 21:43
  Speaker 2: straight from Boston to San Francisco and then started the
• 21:48
  Speaker 2: company basically.
• 21:49
  Speaker 1: And that's scale AI.
• 21:51
  Speaker 2: Scale AI.
• 21:51
  Speaker 1: Okay. And so did you, where you've been following AI?
• 21:55
  Speaker 1: Like what are your kind of, or you just like
• 21:57
  Speaker 1: knew like, this is where it's going. Like you just
• 21:59
  Speaker 1: felt there was something, an instinct that you trusted or like,
• 22:03
  Speaker 1: because that's a big thing to do.
• 22:05
  Speaker 2: I was stuck. So I took all the AI classes
• 22:08
  Speaker 2: at MIT.
• 22:08
  Speaker 1: Okay. So you already learned a lot about it.
• 22:10
  Speaker 3: Yup.
• 22:11
  Speaker 2: And then there was one class where you had to
• 22:14
  Speaker 2: like on all the classes, you had to do side
• 22:16
  Speaker 2: projects or final projects of some kind. And in one
• 22:19
  Speaker 2: of them, I wanted to build a camera inside my
• 22:23
  Speaker 2: refrigerator that would tell me when my roommates were stealing
• 22:26
  Speaker 2: my food.
• 22:27
  Speaker 1: Wang boy catching them.
• 22:31
  Speaker 2: Wang boy.
• 22:33
  Speaker 4: Wow.
• 22:34
  Speaker 2: So I worked on that. And then there was one
• 22:37
  Speaker 2: moment where I was like. there was like, there was
• 22:40
  Speaker 2: a moment that clicked where I was trying to build
• 22:42
  Speaker 2: this thing. And then there was one step that was
• 22:44
  Speaker 2: like too easy. I was like, whoa, that just worked
• 22:47
  Speaker 2: right there. And then that happened. And then the, the
• 22:50
  Speaker 2: go match happened. And I was like, this, this stuff
• 22:52
  Speaker 2: is happening. And so I, did you ever market those fridges?
• 22:55
  Speaker 2: You ever actually create that? I didn't market them.
• 22:57
  Speaker 1: No, I could totally see.
• 22:59
  Speaker 2: That, bro.
• 22:59
  Speaker 1: There's a refrigerator. Every dorm has it where there's a
• 23:02
  Speaker 1: camera built in and you just get your, you get
• 23:04
  Speaker 1: a notification on your phone. You know, you're like, damn,
• 23:08
  Speaker 1: Adnan's got my hummus, you know, but you got video
• 23:11
  Speaker 1: of him right there, dude. That's a great idea. I
• 23:14
  Speaker 1: love that.
• 23:14
  Speaker 2: That was, that was, that was college me.
• 23:16
  Speaker 3: Yeah.
• 23:18
  Speaker 2: Okay.
• 23:18
  Speaker 1: So Alexander Wang, he's free in the world now. He's
• 23:21
  Speaker 1: headed to San Francisco. He's AI'd up. He feels the energy.
• 23:25
  Speaker 1: He's motivated by some of the classes he took. He's
• 23:28
  Speaker 1: motivated by seeing that AI starting to actually overtake humans, right.
• 23:33
  Speaker 1: Or be able to compete with actual human thinking. With
• 23:36
  Speaker 1: the chess match.
• 23:37
  Speaker 2: Yeah. I would, the way I would think about it
• 23:39
  Speaker 2: or the way I thought about the time was like,
• 23:40
  Speaker 2: this is, this is becoming, um, I, you know, people
• 23:44
  Speaker 2: have been talking about AI for decades. Like it's kind
• 23:47
  Speaker 2: of been always been one of these things that, um,
• 23:49
  Speaker 2: people have, have said, Oh, it's going to happen, but
• 23:52
  Speaker 2: it never really was happening. And it felt like it was,
• 23:54
  Speaker 2: You know, it's really about that.
• 23:55
  Speaker 1: About artificial intelligence.
• 23:56
  Speaker 3: Yeah. Yeah.
• 23:56
  Speaker 1: Yeah, because I've always heard about artificial intelligence. That's what
• 23:59
  Speaker 1: I have. So I've always been like.
• 24:02
  Speaker 2: No, you have real intelligence, not artificial, real intelligence.
• 24:06
  Speaker 1: I don't. I mean, I think it's probably a mix.
• 24:08
  Speaker 1: But I see what you're saying, you know. Dude, you
• 24:11
  Speaker 1: know what I thought of the other day? It was like,
• 24:12
  Speaker 1: what if they had like a Mexican version? It was like, hey, I.
• 24:15
  Speaker 1: I don't know. That's a good. That's a good joke
• 24:20
  Speaker 1: right now. But thank you. That's a nice laugh.
• 24:22
  Speaker 2: I love that.
• 24:24
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• 26:32
  Speaker 1: slash Theo. What is AI?
• 26:37
  Speaker 2: Yeah. So AI is all about basically programming computers to
• 26:43
  Speaker 2: be able to start thinking like humans. So You know,
• 26:47
  Speaker 2: traditional computer programming, you know, it's pretty bare bones. It's
• 26:52
  Speaker 2: not very smart. And so AI is all about can
• 26:55
  Speaker 2: you have– can you build algorithms that start to be
• 26:58
  Speaker 2: able to think like people and replicate some of the–
• 27:03
  Speaker 2: like our brains are these incredible, incredible things. And that's evolution.
• 27:09
  Speaker 2: That's just biology that created our brains. And so it's
• 27:12
  Speaker 2: all about how can we, how can we build something
• 27:14
  Speaker 2: similar to that or replicate it using computers and machines?
• 27:19
  Speaker 2: And so the whole, you know, the whole modern AI
• 27:24
  Speaker 2: era really started around an area called computer vision, but
• 27:30
  Speaker 2: it was like, how can we first get computers to
• 27:34
  Speaker 2: see like humans do? So one of the very first, so,
• 27:39
  Speaker 2: you know, One of the very first AI projects was
• 27:42
  Speaker 2: this thing called ImageNet. ImageNet and later AlexNet. And it
• 27:47
  Speaker 2: was basically, can you get computer programs to like given
• 27:51
  Speaker 2: a photo to tell you what's in that photo?
• 27:53
  Speaker 1: I see. So just like a human would, like if
• 27:55
  Speaker 1: you showed them this, then you're starting to train them
• 27:57
  Speaker 1: to have a perspective?
• 28:01
  Speaker 5: Yeah.
• 28:01
  Speaker 2: Train them to, well, actually originally like you could like,
• 28:05
  Speaker 2: let's say you took a photo of this, of this bottle.
• 28:07
  Speaker 2: a machine wouldn't even be able to tell you what's
• 28:09
  Speaker 2: in the photo. It would just know what the pixels were,
• 28:12
  Speaker 2: but it wouldn't be able to tell you like, oh,
• 28:13
  Speaker 2: there's a bottle in that, in that photo. So the,
• 28:16
  Speaker 2: you know, one of the first AI breakthroughs was when
• 28:19
  Speaker 2: YouTube built an algorithm that could tell when there were
• 28:23
  Speaker 2: cats in their videos. And that was like this, you know,
• 28:27
  Speaker 2: in like 2012 or 2011, this was like this mind
• 28:30
  Speaker 2: blowing breakthrough that you could like figure out, you could
• 28:32
  Speaker 2: like use an algorithm to figure out when there was
• 28:34
  Speaker 2: a cat inside a, inside a video. And so AI,
• 28:39
  Speaker 2: it started pretty simply with just how do we replicate,
• 28:43
  Speaker 2: you know, vision? Like how do you replicate like our,
• 28:46
  Speaker 2: basically the fact that our eyes and our brains can
• 28:48
  Speaker 2: like process all this imagery coming in. And that really
• 28:52
  Speaker 2: led to, I think one of the first major use
• 28:55
  Speaker 2: cases of AI, which is self-driving cars. So this was,
• 29:00
  Speaker 2: when I started the company in 2016, self-driving cars were
• 29:04
  Speaker 2: all the rage because, you know, it was, you know,
• 29:07
  Speaker 2: there were all these like skunk works projects.
• 29:09
  Speaker 1: When you started scale AI, you mean?
• 29:10
  Speaker 2: Yeah.
• 29:11
  Speaker 1: When I started scaling, when you kind of got into
• 29:13
  Speaker 1: AI at that time, self-driving cars are the most popular things.
• 29:17
  Speaker 2: Yeah. Yep. That was all the rage. And so it was,
• 29:19
  Speaker 2: it was all about, can, you know, um, can we
• 29:22
  Speaker 2: start building algorithms that can drive a car like a
• 29:25
  Speaker 2: human would and do it safely and do it more efficiently.
• 29:28
  Speaker 2: And that way, instead of That was one of the
• 29:31
  Speaker 2: first major areas.
• 29:33
  Speaker 3: Okay.
• 29:33
  Speaker 2: And then um, now, you know, I, the whole, the
• 29:37
  Speaker 2: whole industry has moved so fast, but then all of
• 29:40
  Speaker 2: a sudden we got chat GPT and we got, you know,
• 29:43
  Speaker 2: more advanced stuff more recently that, that is able to
• 29:45
  Speaker 2: talk like a human or sort of think like a human.
• 29:48
  Speaker 2: And so it's really come up pretty far recently, but
• 29:51
  Speaker 2: all of it is about how do you, you build algorithms,
• 29:54
  Speaker 2: how to use machines to be able to think like
• 29:56
  Speaker 2: a person.
• 29:57
  Speaker 1: Okay. And is it a pro like, say if I
• 30:00
  Speaker 1: opened a door, like, like, oh, we keep the AI
• 30:02
  Speaker 1: in there. Is it a computer? Is it a program?
• 30:06
  Speaker 1: Is it a hard drive? Like what, like, what is that? Yeah.
• 30:11
  Speaker 2: So there's two parts. There's two parts to it. So
• 30:14
  Speaker 2: the first part is you need really advanced chips. So
• 30:19
  Speaker 2: you need like, um, uh, like these they're called GPUs
• 30:23
  Speaker 2: or sometimes called TPUs or, you know, there's a lot
• 30:25
  Speaker 2: of different words for it, but you need like the
• 30:27
  Speaker 2: most advanced computer chips in the world.
• 30:29
  Speaker 1: Okay. And they, how big is each one? Do you know? Uh, like,
• 30:33
  Speaker 1: or can you measure it like that?
• 30:34
  Speaker 2: They, I mean, the biggest ones are actually like the,
• 30:37
  Speaker 2: the whole chips are like, you know, pretty big, but they,
• 30:40
  Speaker 2: they're like a, they're like a, like a wafer kind
• 30:42
  Speaker 2: of thing. But then you, um, you put a lot
• 30:45
  Speaker 2: of them all together.
• 30:46
  Speaker 3: Okay.
• 30:46
  Speaker 2: So these, uh, yeah, yeah, yeah, exactly.
• 30:52
  Speaker 1: These chips.
• 30:52
  Speaker 2: And the biggest ones are, yeah, exactly.
• 30:55
  Speaker 1: That's a little one.
• 30:56
  Speaker 2: That's a little one. There's really big ones.
• 30:59
  Speaker 1: Okay. But these are the, so this is the, these
• 31:03
  Speaker 1: are the brain cells of it.
• 31:04
  Speaker 2: Yep. These are the brains behind it.
• 31:06
  Speaker 3: Yeah.
• 31:07
  Speaker 2: So then, yeah, exactly. These are some, those are some
• 31:09
  Speaker 2: big ones. So then.
• 31:10
  Speaker 1: So you have to have a lot of these chips.
• 31:12
  Speaker 2: So you need a ton of these chips. And that's
• 31:15
  Speaker 2: kind of the, like, that's the physical presence of, And then, um,
• 31:20
  Speaker 2: and then by the way, they take a huge amount
• 31:21
  Speaker 2: of energy. They're really, uh, they, cause they have, you
• 31:24
  Speaker 2: have to do a lot of, you know, calculations and
• 31:27
  Speaker 2: there's a lot of math that has to happen on them.
• 31:29
  Speaker 2: And so, um, and you have, you have giant basically
• 31:33
  Speaker 2: data centers, buildings full of like tons and tons of
• 31:35
  Speaker 2: those chips, just in giant rows.
• 31:37
  Speaker 1: Like how big are we talking warehouses?
• 31:40
  Speaker 3: Yeah.
• 31:40
  Speaker 2: The biggest one, I mean, like, um, like Elon's data
• 31:43
  Speaker 2: center Colossus is like, uh, I mean, it's probably more
• 31:47
  Speaker 2: than a million. It's definitely more than a million square feet.
• 31:49
  Speaker 2: I mean, it's like just huge.
• 31:50
  Speaker 4: Really?
• 31:51
  Speaker 2: Yeah, yeah. Look up Colossus.
• 31:53
  Speaker 1: I've never known this.
• 31:55
  Speaker 2: Yeah. Yeah, yeah. You see that building with the sunset?
• 32:00
  Speaker 1: Second row?
• 32:00
  Speaker 4: Yeah, yeah.
• 32:01
  Speaker 1: There you go.
• 32:02
  Speaker 2: Or the one to the left.
• 32:03
  Speaker 4: Oh, no.
• 32:04
  Speaker 1: Yeah, that one.
• 32:05
  Speaker 3: Yeah.
• 32:06
  Speaker 2: Look, it's like a huge ass building.
• 32:08
  Speaker 3: What?
• 32:09
  Speaker 1: It's huge.
• 32:09
  Speaker 2: And all that's just filled with chips.
• 32:12
  Speaker 1: Have you ever been in there?
• 32:14
  Speaker 2: I haven't been in that one, but I I've been
• 32:15
  Speaker 2: in some of these and this just.
• 32:17
  Speaker 1: And this is what it looks like inside.
• 32:18
  Speaker 2: Yeah, basically.
• 32:19
  Speaker 5: Yeah.
• 32:19
  Speaker 2: So it's just rows and rows of chips. So no
• 32:22
  Speaker 2: plants or anything. No plants, no plants. It gets hot
• 32:25
  Speaker 2: in there. So, um, so the first part is just the,
• 32:30
  Speaker 2: the that's the physical presence. And then the second part
• 32:33
  Speaker 2: are the algorithms. So then you have like on top
• 32:37
  Speaker 2: of those chips, you have, you have software. And the,
• 32:41
  Speaker 2: the algorithms are just all are like what you actually
• 32:44
  Speaker 2: are telling, what's the math that you're telling to happen
• 32:47
  Speaker 2: on the chips. And those algorithms are, you know, some
• 32:50
  Speaker 2: of the, you know, most complicated algorithms that humans have
• 32:54
  Speaker 2: ever come up with. And that's kind of the, that's
• 32:56
  Speaker 2: kind of the software part, or that's kind of the,
• 32:59
  Speaker 2: that's the part that like, you know, exists on the
• 33:01
  Speaker 2: internet or you could download or whatnot. And then it
• 33:03
  Speaker 2: has to be run on these like huge warehouses of giant,
• 33:06
  Speaker 2: of giant chips.
• 33:07
  Speaker 3: Okay.
• 33:08
  Speaker 1: Um, so when someone goes to like scale AI or
• 33:12
  Speaker 1: chat GPT, these are all AI interfaces or what are
• 33:16
  Speaker 1: they there?
• 33:17
  Speaker 2: Yeah. So, so, um, yeah, like chat GPT is a,
• 33:21
  Speaker 2: is a way to be able to, uh, talk to
• 33:24
  Speaker 2: basically you can talk to the algorithm.
• 33:26
  Speaker 3: Okay.
• 33:26
  Speaker 2: So you can start interacting directly with algorithm. You can
• 33:29
  Speaker 2: see how the algorithm is thinking.
• 33:31
  Speaker 1: Um, so you could say to this, um, can you
• 33:34
  Speaker 1: describe the weather today?
• 33:35
  Speaker 2: Exactly.
• 33:36
  Speaker 1: Yeah. And if you said that to, um, five different
• 33:39
  Speaker 1: AI companies, basically?
• 33:42
  Speaker 2: Yeah, or AI algorithms, different AI systems, yeah.
• 33:45
  Speaker 1: So if you said that's five different AI systems, you
• 33:47
  Speaker 1: might get a little bit of a varied answer?
• 33:48
  Speaker 2: A little bit, yeah. Okay. Because they all are trying
• 33:51
  Speaker 2: to have their own style and have their own vibe
• 33:54
  Speaker 2: to it.
• 33:54
  Speaker 1: Interesting.
• 33:55
  Speaker 2: And then what we do, what Scale AI is all about,
• 33:59
  Speaker 2: is we've kind of built almost like the Uber of AI.
• 34:02
  Speaker 1: Okay.
• 34:02
  Speaker 2: So a lot of what we're trying to do is,
• 34:05
  Speaker 2: how do we, so how do we help produce data
• 34:08
  Speaker 2: that is improving these algorithms. And just like how Uber,
• 34:11
  Speaker 2: there's- Okay.
• 34:12
  Speaker 1: You're losing me there a little.
• 34:13
  Speaker 2: Yeah, yeah.
• 34:14
  Speaker 1: It's okay. But if I slow down, if you're losing me,
• 34:18
  Speaker 1: but so explain that to me a little bit clearer
• 34:20
  Speaker 1: for me. Yeah, yeah.
• 34:22
  Speaker 2: So, okay, so with these algorithms, one key ingredient for
• 34:26
  Speaker 2: these algorithms is data.
• 34:29
  Speaker 1: Okay, so you have the chips and everything that are
• 34:31
  Speaker 1: storing all the information. Yep. They're storing the data.
• 34:34
  Speaker 3: Yep.
• 34:34
  Speaker 1: And then you have the algorithms that are helping- mediate
• 34:37
  Speaker 1: between the user and the data?
• 34:39
  Speaker 3: Yeah.
• 34:39
  Speaker 2: So basically you kind of have, yeah, you have three,
• 34:42
  Speaker 2: you have three key pieces.
• 34:43
  Speaker 1: Okay.
• 34:44
  Speaker 2: So you have the, uh, you have the computational powers
• 34:47
  Speaker 2: of the chips, the chips, you have the data, which
• 34:50
  Speaker 2: is like just tons and tons of data that that's
• 34:54
  Speaker 2: where the algorithms are learning the patterns from. So these algorithms,
• 34:58
  Speaker 2: they aren't just like, they don't just learn to talk randomly.
• 35:01
  Speaker 2: They learn it from learning to talk from how humans talk.
• 35:04
  Speaker 3: Right.
• 35:04
  Speaker 1: Got it.
• 35:04
  Speaker 2: So you need tons and tons of data. And then
• 35:06
  Speaker 2: you have the algorithms which learn from all that data
• 35:11
  Speaker 2: and then they run on top of the chips.
• 35:13
  Speaker 1: Got it.
• 35:14
  Speaker 2: So then one of the big challenges in the industry is, okay,
• 35:19
  Speaker 2: how are you going to produce all this data? And
• 35:21
  Speaker 2: so this is.
• 35:23
  Speaker 1: Like how are you going to get data for your system?
• 35:27
  Speaker 1: Like how do you farm the best data?
• 35:29
  Speaker 3: Exactly.
• 35:29
  Speaker 2: How do you build all that data and how do
• 35:31
  Speaker 2: you do that in the most effective way? How do
• 35:34
  Speaker 2: you build new data?
• 35:36
  Speaker 1: And clean data because what if you get a bunch
• 35:37
  Speaker 1: of data in there that's just a bunch of advertisements
• 35:39
  Speaker 1: and bullshit? Will that affect the output?
• 35:41
  Speaker 4: 100%.
• 35:41
  Speaker 3: Yeah.
• 35:42
  Speaker 2: That definitely affects the output. So this whole data– so
• 35:45
  Speaker 2: data is– some people say like data is the new
• 35:49
  Speaker 2: oil or data is the new gold. Like data is really,
• 35:52
  Speaker 2: really valuable because it's how the algorithms are learning everything
• 35:57
  Speaker 2: that they're learning. Like anything that the algorithms know or
• 36:00
  Speaker 2: learn or say or do, all that has to come
• 36:03
  Speaker 2: from the data that goes into it.
• 36:05
  Speaker 1: Okay, so if I ask a system, an AI system
• 36:09
  Speaker 1: a question or ask it to help me with something,
• 36:11
  Speaker 1: help me to design something or to curate an idea,
• 36:15
  Speaker 1: it's going to use the data that it has within
• 36:18
  Speaker 1: it to respond to me and help give me an
• 36:22
  Speaker 1: answer that I can use. And it's only– and the
• 36:25
  Speaker 1: data it has in it is only based upon the
• 36:28
  Speaker 1: data that is put into it.
• 36:30
  Speaker 2: Exactly.
• 36:31
  Speaker 1: Wow.
• 36:32
  Speaker 5: Yeah.
• 36:33
  Speaker 2: So then– so yeah, it's kind of– So then we
• 36:37
  Speaker 2: don't spend enough time talking about how are you going
• 36:40
  Speaker 2: to get this data and how are you going to
• 36:41
  Speaker 2: keep making new data? So the angle that we took
• 36:46
  Speaker 2: at scale was to kind of turn this into an
• 36:49
  Speaker 2: opportunity for people. So we're kind of like the Uber
• 36:54
  Speaker 2: for AI. So just like how Uber you have riders
• 36:58
  Speaker 2: and drivers, for us, we have the AI systems, you know,
• 37:04
  Speaker 2: the algorithms that need data. And then we have a
• 37:07
  Speaker 2: community of people, a network of people who help produce
• 37:11
  Speaker 2: the data that go into the system. They get paid
• 37:13
  Speaker 2: to do that.
• 37:14
  Speaker 1: Oh, so they're almost data farming, like creating good data?
• 37:17
  Speaker 2: Creating good data, exactly. And it's huge. It's like, so
• 37:23
  Speaker 2: we do this through our platform called Outlier.
• 37:26
  Speaker 1: Okay.
• 37:26
  Speaker 2: And Outlier last year, uh, people contributors, we call them
• 37:32
  Speaker 2: contributors on outlier earned about $ 500 million total across everybody. Um,
• 37:38
  Speaker 2: in the U S that's across 9,000 different towns.
• 37:42
  Speaker 1: Um, and, uh, so it created a lot of jobs,
• 37:45
  Speaker 1: a lot of jobs.
• 37:45
  Speaker 3: Okay.
• 37:46
  Speaker 1: And so what would, um, okay. So scale was your company, scale,
• 37:50
  Speaker 1: AI system. Yep. Is that right?
• 37:52
  Speaker 2: So we, uh, yeah, I mean, yeah, scale, AI is
• 37:56
  Speaker 2: an AI system.
• 37:56
  Speaker 1: Okay. And then outlier, Yep. Is a separate company that
• 38:00
  Speaker 1: works with it.
• 38:01
  Speaker 3: Yep.
• 38:02
  Speaker 1: And that is where you are hiring people? Yeah, we
• 38:09
  Speaker 1: basically- To pull in data.
• 38:11
  Speaker 2: Yeah, we build this platform that anybody, a lot of people, frankly,
• 38:17
  Speaker 2: all around the world, but Americans too, can log on
• 38:20
  Speaker 2: and help build data that goes into the algorithms and
• 38:26
  Speaker 2: get paid to do so.
• 38:28
  Speaker 3: Yeah.
• 38:28
  Speaker 1: So how, and how does a user do that? Like
• 38:30
  Speaker 1: what is an example of somebody who's helping build data
• 38:32
  Speaker 1: for an AI database?
• 38:34
  Speaker 3: Yeah.
• 38:35
  Speaker 2: Let's say you're a nurse, like you're a nurse with
• 38:37
  Speaker 2: like tons of tons of experience. So, you know, a
• 38:40
  Speaker 2: lot about how to take care of people, um, and, uh,
• 38:43
  Speaker 2: take care of, of people who are sick or, you know,
• 38:45
  Speaker 2: have issues and whatnot. And, uh, so you, you could
• 38:47
  Speaker 2: log onto the system and, and this, and our platform,
• 38:51
  Speaker 2: and you could see that the algorithm is, you know,
• 38:55
  Speaker 2: let's say you ask the algorithm, like, Hey, I have a,
• 38:57
  Speaker 2: You know, I have a pain in my stomach. What
• 39:00
  Speaker 2: should I do? And you notice that the algorithm says
• 39:03
  Speaker 2: the wrong thing. Like the algorithm says, oh, just, you know,
• 39:05
  Speaker 2: hang out and, you know, it'll go away. And you know,
• 39:08
  Speaker 2: as a nurse, like that's wrong. Like I, you know,
• 39:12
  Speaker 2: you have to go to the emergency room because you
• 39:14
  Speaker 2: might have appendicitis or you might have, you know, you
• 39:16
  Speaker 2: might have something really bad. And so you would, as
• 39:18
  Speaker 2: a nurse, you would go in and you'd basically correct something.
• 39:21
  Speaker 2: all these mistakes that the AI system has. And then
• 39:24
  Speaker 2: that would then feed it back into the algorithm so
• 39:27
  Speaker 2: that's smarter the next time. So it's kind of this,
• 39:30
  Speaker 2: this continual process of, and there's versions of that for
• 39:33
  Speaker 2: whatever your expertise is or whatever, you know, you know
• 39:36
  Speaker 2: more about than anything, everything, everything, exactly. So, and so
• 39:41
  Speaker 2: people get paid for that. Yeah, they get paid.
• 39:43
  Speaker 1: And how do you know if their information is valuable
• 39:45
  Speaker 1: or not?
• 39:47
  Speaker 2: Uh, well, So we don't want spam, obviously. So we
• 39:51
  Speaker 2: have a lot of systems to make sure that people
• 39:53
  Speaker 2: aren't spamming and that, like you're saying, it's not garbage
• 39:57
  Speaker 2: in that's going into the algorithms. So we have people
• 40:02
  Speaker 2: check the work of other people to make sure that
• 40:04
  Speaker 2: the AI systems are really good. And we have some
• 40:06
  Speaker 2: automated systems that check this stuff. But for the most part,
• 40:10
  Speaker 2: it's really broad. We want experts in Anything.
• 40:15
  Speaker 1: Everything. Shellfish, train tracks, whatever. Yeah, exactly. Gelatin. Everything. Childhood, death,
• 40:21
  Speaker 1: or whatever.
• 40:22
  Speaker 2: Yeah, totally.
• 40:23
  Speaker 1: Stars, galaxies, whatever. Yeah, yeah.
• 40:25
  Speaker 2: Animals.
• 40:26
  Speaker 1: Big animals. Yeah.
• 40:27
  Speaker 3: Yeah.
• 40:28
  Speaker 5: Damn.
• 40:29
  Speaker 1: So.
• 40:29
  Speaker 2: Wow.
• 40:30
  Speaker 1: So it's kind of like your data is almost like
• 40:33
  Speaker 1: an ocean or a body of water, and you... Different
• 40:35
  Speaker 1: places are going to be able to keep their body
• 40:37
  Speaker 1: of water cleaner or dirtier, and different infections could get in,
• 40:41
  Speaker 1: different spyware, all types of stuff. So... And if you
• 40:43
  Speaker 1: have really a clean body of water, then you're going
• 40:46
  Speaker 1: to be able to offer a clean data or a
• 40:49
  Speaker 1: certain type of data to people who are using your
• 40:54
  Speaker 1: AI platform. Exactly. Does that make sense or not?
• 40:56
  Speaker 2: Yeah, yeah, totally. And our job is like how do
• 40:58
  Speaker 2: we make sure that this body of water is as
• 41:01
  Speaker 2: clean as possible and we fill it up as much
• 41:03
  Speaker 2: as possible, that it has as much information about everything
• 41:07
  Speaker 2: across the globe.
• 41:08
  Speaker 1: Wow. So is there almost a race for information right
• 41:11
  Speaker 1: now in a weird way or no? Is that not it?
• 41:13
  Speaker 2: They're a little bit.
• 41:14
  Speaker 3: Yeah.
• 41:14
  Speaker 2: I think that there's a, well, there's a race for.
• 41:16
  Speaker 1: How are different AI systems competing against each other and
• 41:19
  Speaker 1: sorry to interrupt you.
• 41:20
  Speaker 2: Yeah, no, no. So the there's a, there's it's, it
• 41:23
  Speaker 2: goes back to the three things I mentioned. So there's
• 41:25
  Speaker 2: kind of like three dimensions that, that they're all competing
• 41:28
  Speaker 2: against one another. One is, um, chips. So who could,
• 41:33
  Speaker 2: who is the most advanced chips? Who is the, the
• 41:35
  Speaker 2: biggest buildings of chips? Like who is the most chips
• 41:38
  Speaker 2: that they're utilizing? data. So the kind of body of water,
• 41:41
  Speaker 2: whose body of water is better, cleaner, you know, healthiest, biggest,
• 41:46
  Speaker 2: et cetera. And then the last is algorithms. So who's,
• 41:49
  Speaker 2: and this is where the scientists really come in. It's like, okay,
• 41:52
  Speaker 2: who's coming up with the cleverest algorithms or who has
• 41:56
  Speaker 2: like a trick on an algorithm that somebody else doesn't have?
• 41:59
  Speaker 2: Like who's doing that to basically make the AI learn
• 42:03
  Speaker 2: better off of the data that it has?
• 42:05
  Speaker 1: Wow. God, I'm in the future right now. That's so, man,
• 42:11
  Speaker 1: it's just, it's so crazy. And I think AI scares
• 42:14
  Speaker 1: people because the future scares people, right? It's like, that's
• 42:17
  Speaker 1: one of the scariest things sometimes is the future. So
• 42:19
  Speaker 1: I think a lot of times people, you associate, because
• 42:23
  Speaker 1: a lot of times when people mention AI, there's a
• 42:24
  Speaker 1: little bit of fear, it seems like from people.
• 42:26
  Speaker 2: Yeah.
• 42:27
  Speaker 1: There's fear that it's going to take jobs. There's fear
• 42:29
  Speaker 1: that it's going to take over our ability to think
• 42:32
  Speaker 1: for ourselves.
• 42:33
  Speaker 2: Yeah.
• 42:33
  Speaker 1: There's just kind of some general uncertainty there. Like, is this,
• 42:37
  Speaker 1: and it kind of feels like fear a lot of times,
• 42:39
  Speaker 1: but a lot of times fear is just a lack
• 42:41
  Speaker 1: of knowledge, right? And not a lack of knowledge because
• 42:45
  Speaker 1: you didn't want to know just because you don't know.
• 42:47
  Speaker 4: Yeah, yeah.
• 42:48
  Speaker 1: Or that you're dumb, but just because you don't know.
• 42:52
  Speaker 1: What are positive things that we're going to see with AI, right?
• 42:54
  Speaker 1: I want to start there.
• 42:55
  Speaker 2: Yeah. So I think first, like we don't, the AI industry,
• 43:00
  Speaker 2: we don't do the best job explaining this. And I
• 43:02
  Speaker 2: think sometimes we make it seem all sci-fi and, And genuinely,
• 43:07
  Speaker 2: we're part of the problem in making it seem scary, right? But,
• 43:11
  Speaker 2: you know, one thing, for example, is like, I think
• 43:14
  Speaker 2: AI is actually going to create a ton of jobs.
• 43:18
  Speaker 2: And that story is not told enough. But, you know,
• 43:21
  Speaker 2: these jobs that we're producing or this sort of this
• 43:24
  Speaker 2: opportunity that we're providing on our platform, Outlier, Like that's
• 43:27
  Speaker 2: only gonna grow as AI grows.
• 43:29
  Speaker 1: So- Because you have to have new data.
• 43:31
  Speaker 2: You have to have new data.
• 43:32
  Speaker 1: And the only place you can get new data is
• 43:34
  Speaker 1: from people. Well, at a certain point, would the system
• 43:36
  Speaker 1: be able to create, it can probably matriculate data or matriculate,
• 43:41
  Speaker 1: is that with birthing or what is that?
• 43:43
  Speaker 5: Yeah, it just moves through like- Okay.
• 43:46
  Speaker 1: It can probably like quantify and give you answers, but
• 43:50
  Speaker 1: can AI create new data? Uh, No.
• 43:54
  Speaker 2: So I think, well, it can do a little bit
• 43:56
  Speaker 2: of that. So it can, AI can help itself create
• 43:59
  Speaker 2: its own data and help itself a little bit. But ultimately,
• 44:04
  Speaker 2: most of the progress is going to come from, you know,
• 44:07
  Speaker 2: people who are able to help really the model get
• 44:10
  Speaker 2: better and smarter and more capable at all these different areas.
• 44:14
  Speaker 3: Ah, yeah.
• 44:14
  Speaker 1: I didn't see that part of it. I didn't understand
• 44:17
  Speaker 1: that we are the ones who are giving it information.
• 44:22
  Speaker 1: And since we're going to continue to learn, I would
• 44:24
  Speaker 1: assume that we would be able to help it continue
• 44:26
  Speaker 1: to help it learn.
• 44:27
  Speaker 3: Yeah.
• 44:28
  Speaker 2: And the world's going to keep changing and we're going
• 44:29
  Speaker 2: to need to be able to keep teaching the algorithms,
• 44:33
  Speaker 2: keep teaching the models about how the world's changing. So,
• 44:36
  Speaker 2: you know, this is actually a big thing that I
• 44:40
  Speaker 2: think most people don't understand. The people who are getting
• 44:43
  Speaker 2: the opportunity now are earning money from it, see it.
• 44:45
  Speaker 2: But as AI grows, there's actually going to be tons
• 44:49
  Speaker 2: of jobs created along the way. and tons of opportunity
• 44:53
  Speaker 2: for people to help improve AI systems or control AI
• 44:57
  Speaker 2: systems or overall sort of be a part of the technology,
• 45:02
  Speaker 2: not just sort of disenfranchised by it.
• 45:04
  Speaker 1: Okay. So like, yeah. So what were, what are you,
• 45:07
  Speaker 1: do you feel like are other ways? Like if you
• 45:08
  Speaker 1: had to look into the future a little bit, right?
• 45:10
  Speaker 1: So you have the fact that people are going to
• 45:12
  Speaker 1: be able to add more data, right. And add nuances
• 45:15
  Speaker 1: to data.
• 45:16
  Speaker 3: Yep.
• 45:16
  Speaker 1: Right. And, and probably humanize data a little bit.
• 45:19
  Speaker 2: Yeah, totally.
• 45:21
  Speaker 1: And then you're also going to have what?
• 45:25
  Speaker 2: I think you're going to have a lot of jobs around,
• 45:29
  Speaker 2: you know, as AI starts doing all these little things
• 45:34
  Speaker 2: throughout the world, who's going to keep watch of those
• 45:37
  Speaker 2: AI and who's going to make sure that those AI
• 45:40
  Speaker 2: aren't doing something that we don't want them to do.
• 45:43
  Speaker 2: So almost like managing the AIs and keeping watch over
• 45:46
  Speaker 2: all the AI systems. That's going to be another thing
• 45:49
  Speaker 2: that we're going to have to do.
• 45:50
  Speaker 1: Um, and then, and then it's just somebody to kind
• 45:53
  Speaker 1: of guide the river a little bit at certain point,
• 45:55
  Speaker 1: guide the stream, stay in there, uh, watch, make sure
• 45:59
  Speaker 1: that answers are correct. Make sure the information, um, is honest. Yeah.
• 46:05
  Speaker 3: Yeah.
• 46:05
  Speaker 2: Like, like I think for example, um, you know, we're
• 46:09
  Speaker 2: not gonna just have AIs going around and, you know, um,
• 46:15
  Speaker 2: You know, buying stuff and doing crazy things and like,
• 46:18
  Speaker 2: you know, we're going to we're going to keep it controlled, right?
• 46:21
  Speaker 2: Like as a society, I think we're going to keep
• 46:22
  Speaker 2: it controlled as a technology. And I think there's gonna
• 46:25
  Speaker 2: be a lot of jobs for people to make sure
• 46:27
  Speaker 2: that the AI doesn't go out and do crazy things
• 46:30
  Speaker 2: that we don't want it to do.
• 46:32
  Speaker 1: Right, so you're going to need managers. You're going to
• 46:33
  Speaker 1: need facilitators.
• 46:34
  Speaker 2: Yeah, exactly.
• 46:35
  Speaker 1: What are things that AI will alleviate? Like, what are
• 46:38
  Speaker 1: things that, like, will it eventually be able to have
• 46:41
  Speaker 1: enough information, like, or data where it can, like, cure
• 46:47
  Speaker 1: diseases and stuff like that? Like, is that a realistic thing?
• 46:49
  Speaker 2: Yeah, that's super real.
• 46:52
  Speaker 5: Wow.
• 46:52
  Speaker 2: Like cancer, even. Yeah, cancer. Yeah, heart disease. Like, all
• 46:56
  Speaker 2: these diseases.
• 46:58
  Speaker 3: Wow, boy. Yeah.
• 46:59
  Speaker 1: Cancer on its heels.
• 47:00
  Speaker 3: Yeah.
• 47:01
  Speaker 1: Sorry, Antonio Brown was just here. I think there's still
• 47:04
  Speaker 1: some smoke in the air.
• 47:07
  Speaker 2: No, but seriously, I think that AI, one thing that
• 47:10
  Speaker 2: we've seen, which is, this is kind of wild, but
• 47:13
  Speaker 2: AI understands molecules and biology better than humans do, actually,
• 47:20
  Speaker 2: because there's this thing in AI where You know, it
• 47:28
  Speaker 2: used to take a, like a PhD biologist, like, you know,
• 47:32
  Speaker 2: five years to do something that the AI can, can
• 47:36
  Speaker 2: just do in, you know, a few minutes.
• 47:38
  Speaker 1: Right.
• 47:39
  Speaker 2: And that's because the. Like, uh, just the way that
• 47:43
  Speaker 2: molecules and biology and all of that works is something that,
• 47:46
  Speaker 2: that AI happens to be really good at. And that's
• 47:48
  Speaker 2: going to help us ultimately cure diseases, find, um, you know,
• 47:53
  Speaker 2: pharmaceuticals or other treatments for these diseases. and ultimately help
• 47:57
  Speaker 2: humans live longer.
• 47:58
  Speaker 1: Because that's very data-driven, right? Like it's very specific. It's
• 48:01
  Speaker 1: very mathematic.
• 48:02
  Speaker 2: Exactly.
• 48:03
  Speaker 3: Yeah.
• 48:03
  Speaker 2: Yeah. So it's going to be a huge tool for
• 48:06
  Speaker 2: us to cure disease, for us to help educate people,
• 48:11
  Speaker 2: for us to, you know, there's a lot of really
• 48:14
  Speaker 2: exciting uses for AI. But I think the kind of,
• 48:17
  Speaker 2: I think the thing that will touch most people in
• 48:20
  Speaker 2: their lives is it's really going to be a tool
• 48:24
  Speaker 2: like a tool that'll help you, um, you know, make
• 48:28
  Speaker 2: all of your sort of, uh, make all your dreams
• 48:31
  Speaker 2: kind of become reality, if that makes sense. So, so
• 48:34
  Speaker 2: I think one of the things that AI is going
• 48:37
  Speaker 2: to be really awesome for is like, you know, today,
• 48:40
  Speaker 2: if I, I have like a million ideas, right. I
• 48:43
  Speaker 2: have like, you know, thousands and thousands of ideas and
• 48:45
  Speaker 2: I only have so much time, so I can only
• 48:47
  Speaker 2: really do, you know, a few of them at a time.
• 48:50
  Speaker 2: And most of the ideas just go to die.
• 48:52
  Speaker 4: Right.
• 48:53
  Speaker 1: Yeah, they do, huh?
• 48:54
  Speaker 3: Yeah.
• 48:54
  Speaker 1: That's a bummer.
• 48:55
  Speaker 2: It's a huge bummer.
• 48:56
  Speaker 3: Yeah.
• 48:56
  Speaker 2: And I think a lot of people, you know, for
• 48:58
  Speaker 2: whatever reason, they may have some of the best ideas ever,
• 49:01
  Speaker 2: but they just, you know, they're too busy or they
• 49:04
  Speaker 2: have like other shit going on in their lives. They
• 49:05
  Speaker 2: can't make those ideas happen. And it's great with sometimes
• 49:09
  Speaker 2: when people are able to make the leaps and make
• 49:10
  Speaker 2: them happen and like devote themselves to their dreams. But
• 49:14
  Speaker 2: that doesn't happen enough today. And one of the things
• 49:16
  Speaker 2: that AI is going to help us do, I legitimately
• 49:19
  Speaker 2: think so, is it's going to help us turn these
• 49:22
  Speaker 2: ideas into reality much more easily. So you're making a movie.
• 49:29
  Speaker 2: Let's say you have another movie idea. Ultimately, I think
• 49:33
  Speaker 2: you'll be able to tell an AI, hey, I have
• 49:34
  Speaker 2: this idea for a movie. What could that look like?
• 49:37
  Speaker 2: you know, maybe draft up a script. Also, who are
• 49:40
  Speaker 2: the people who can help, you know, fund this idea?
• 49:43
  Speaker 2: Like who, who those people be can help reach out
• 49:45
  Speaker 2: to them. And then like, you know, who should we
• 49:48
  Speaker 2: cast in it? Basically help make the whole thing a,
• 49:51
  Speaker 2: you know, instead of those like daunting thing that these
• 49:55
  Speaker 2: big projects, they're usually so daunting. You really don't know
• 49:57
  Speaker 2: where to get started. You kind of need a person
• 49:59
  Speaker 2: to help you get through them instead of the AI
• 50:01
  Speaker 2: will help you get through it and like help do
• 50:03
  Speaker 2: a lot of the, the sort of less glamorous work
• 50:05
  Speaker 2: to make them a reality.
• 50:06
  Speaker 3: Wow.
• 50:08
  Speaker 1: So I could say, for example, like AI, I would
• 50:11
  Speaker 1: like to shoot maybe– I'm thinking about creating an idea
• 50:14
  Speaker 1: or shooting a film in this area or it's like this.
• 50:18
  Speaker 1: It's going to take place in this type of place.
• 50:19
  Speaker 1: I can give it the setting. I can give it
• 50:21
  Speaker 1: like an outline of the characters, like what they look like.
• 50:25
  Speaker 1: their ages and some description. Could you help give me, um,
• 50:28
  Speaker 1: possible potential actors or something within a certain price range
• 50:32
  Speaker 1: that I can maybe cast for that? Um, yeah. Could
• 50:35
  Speaker 1: you help give me like locations around the country that
• 50:38
  Speaker 1: would fit that backdrop? Um, could you, uh, list me
• 50:44
  Speaker 1: all the talent agencies that I could reach out to
• 50:47
  Speaker 1: and you can kind of just put those things in
• 50:49
  Speaker 1: and then you would have sort of a, uh, a
• 50:52
  Speaker 1: bit of a guidebook at that point That would make your,
• 50:56
  Speaker 1: what before was something that felt extremely daunting, shit, in
• 50:59
  Speaker 1: two minutes, you know, and then you put it into AI,
• 51:02
  Speaker 1: it gives you the information back in a few minutes
• 51:03
  Speaker 1: and maybe.
• 51:04
  Speaker 2: You're- And I think over time, it'll also be able
• 51:07
  Speaker 2: to start doing a lot of the legwork for you.
• 51:08
  Speaker 2: So it'll be able to reach out to people for you.
• 51:11
  Speaker 2: It'll be able to, you know, figure out the logistics.
• 51:14
  Speaker 2: It'll be able to book things for you. Like it'll
• 51:16
  Speaker 2: be able to basically help do all the legwork to
• 51:18
  Speaker 2: make it make, you know, whatever it is into a reality.
• 51:22
  Speaker 1: So very much an assistant in a lot of ways.
• 51:24
  Speaker 2: Yeah, yeah, yeah. An assistant, co-pilot, you know, the hot
• 51:28
  Speaker 2: word in the AI world is agents, but, you know,
• 51:31
  Speaker 2: it's just, it'll be something that'll help you, you know,
• 51:35
  Speaker 2: humans are going to be in control. Humans are ultimately
• 51:36
  Speaker 2: going to be telling the AIs what they want it
• 51:39
  Speaker 2: to do. And then, you know, hey, what do we
• 51:42
  Speaker 2: want these AIs to do? It's ultimately going to be
• 51:44
  Speaker 2: to like help us execute on and accomplish all these
• 51:47
  Speaker 2: ideas that we have.
• 51:49
  Speaker 1: So a genius could be three or four X'd. if
• 51:51
  Speaker 1: somebody is like a genius in something in some realm
• 51:54
  Speaker 1: or space of thought, you could three or four X
• 51:57
  Speaker 1: them because they like multiply their, their output from their
• 52:01
  Speaker 1: own brain because they could have something really helping them
• 52:06
  Speaker 1: get done a lot of the, like the early work
• 52:09
  Speaker 1: on things and maybe some of the most severe work.
• 52:11
  Speaker 2: Yeah, totally. Like what, I think one thing that I
• 52:14
  Speaker 2: always feel like kind of sucks is that if you
• 52:16
  Speaker 2: have a director you really like, they're only going to
• 52:18
  Speaker 2: make a movie once every couple of years. So even
• 52:21
  Speaker 2: if you have a director that you think is amazing,
• 52:24
  Speaker 2: it's hard for them to make that many movies because
• 52:26
  Speaker 2: it just takes so much time and effort and there's
• 52:30
  Speaker 2: so many bottlenecks and stuff. So in a future with
• 52:34
  Speaker 2: more advanced AI systems, they could just churn them out
• 52:38
  Speaker 2: and they can make so many of their ideas into
• 52:41
  Speaker 2: a reality. And I think that's true not only in
• 52:43
  Speaker 2: creative areas, but it's kind of true across the board.
• 52:45
  Speaker 2: You can start new businesses more easily. You can, you know,
• 52:50
  Speaker 2: you can make various creative projects that you have happen
• 52:52
  Speaker 2: more easily. You can make like, you can, you can
• 52:55
  Speaker 2: finally plan that event that you and your friends have
• 52:57
  Speaker 2: been talking about for like, you know, years and years.
• 52:59
  Speaker 2: So it can just like really help you, you know,
• 53:02
  Speaker 2: we think about as like giving humans more agency, giving
• 53:05
  Speaker 2: humans more sort of sovereignty and just, and just enabling
• 53:08
  Speaker 2: humans to get way more done.
• 53:12
  Speaker 1: Yeah, that's a great way. I like some of this thought. Because, yeah,
• 53:15
  Speaker 1: it could be like my fantasy football group and I,
• 53:19
  Speaker 1: we do a draft every year in a different location,
• 53:22
  Speaker 1: you know? And shout out PAE. That's our fantasy league. J-Rod,
• 53:25
  Speaker 1: everybody that's in it. For the past 17 years, we've
• 53:28
  Speaker 1: flown to a city each year and done a draft
• 53:30
  Speaker 1: in person, right?
• 53:30
  Speaker 4: Yeah.
• 53:33
  Speaker 1: But I could say, hey, we have 10 guys. We
• 53:36
  Speaker 1: want to go to a nice place. We want there
• 53:37
  Speaker 1: to be a beach. This is the age group of
• 53:40
  Speaker 1: our group. These would kind of be the nights. And
• 53:44
  Speaker 1: just to really give me like a– just a nice
• 53:46
  Speaker 1: plan of, hey, here's 10 possibilities, right? Something like that.
• 53:49
  Speaker 1: And then even more like with a movie, this is
• 53:52
  Speaker 1: what I worry you'd run into. Say if I'd be like, okay,
• 53:54
  Speaker 1: I have two main characters and this is kind of
• 53:56
  Speaker 1: what I would like to happen. Could you help me
• 53:58
  Speaker 1: with a first act of a three-act movie? how do
• 54:03
  Speaker 1: you know everybody just doesn't get the same movie then? Like,
• 54:06
  Speaker 1: that's what I would start to worry that everything that
• 54:08
  Speaker 1: you have to create is all going to be very similar.
• 54:11
  Speaker 3: Yeah.
• 54:11
  Speaker 2: So then I think this is where it comes into, like,
• 54:14
  Speaker 2: this is where human creativity is going to matter because
• 54:17
  Speaker 2: it's going to be about, then it's about like, okay,
• 54:20
  Speaker 2: what is the, how am I, you know, how am
• 54:22
  Speaker 2: I directing the AI system? Like, what are my, what
• 54:25
  Speaker 2: are the tricks I have to make the AI give
• 54:27
  Speaker 2: me something that's different and unique? And that's, that's not
• 54:30
  Speaker 2: different at all from, you know, how, Creative stuff works today.
• 54:33
  Speaker 2: Like even on social media or anywhere, you know this.
• 54:36
  Speaker 1: You still add your own spice to it.
• 54:38
  Speaker 2: Yeah, you need an angle. You always need to like
• 54:40
  Speaker 2: have something that's going to make it like different and
• 54:43
  Speaker 2: interesting and new. So that's not going to change. Like
• 54:46
  Speaker 2: humans are always going to have to figure out based
• 54:48
  Speaker 2: on where culture is at, based on where, you know,
• 54:51
  Speaker 2: what the dialogue is, what the discourse is, all that
• 54:54
  Speaker 2: kind of stuff. What is my fresh take? That's where–
• 54:57
  Speaker 2: that's really one of the key things that humans are
• 54:59
  Speaker 2: going to have to keep doing no matter what. Right.
• 55:02
  Speaker 1: And some of the– like a lot of films and books,
• 55:04
  Speaker 1: a lot of it is there's just like a problem.
• 55:08
  Speaker 1: There's maybe information you learn. There's a red herring and
• 55:14
  Speaker 1: then there's a solution. Like that's a lot of stories, right?
• 55:16
  Speaker 1: So if something just gave you the basis and then
• 55:20
  Speaker 1: you go through and make everything your own because a
• 55:24
  Speaker 1: lot of things– there's only so many templates for things. Um, huh.
• 55:29
  Speaker 5: Yeah.
• 55:29
  Speaker 1: So, so say for example, say you might need to
• 55:33
  Speaker 1: hire people at a company then that would help direct
• 55:35
  Speaker 1: your AI, like somebody who's good at managing AI.
• 55:40
  Speaker 3: Yeah.
• 55:41
  Speaker 1: Uh, and giving it the best prompts or the best
• 55:43
  Speaker 1: way to ask it questions.
• 55:45
  Speaker 5: Yeah.
• 55:46
  Speaker 1: Uh, to get the perfect feedback for your company. Yeah.
• 55:49
  Speaker 1: So those would be new jobs. You, those would be
• 55:51
  Speaker 1: actual new people you would need.
• 55:53
  Speaker 2: Yeah. So, so tons of new jobs, right? Well, first
• 55:55
  Speaker 2: I think just like helping to, you know, kind of
• 55:59
  Speaker 2: what we were talking about before, these jobs around helping
• 56:02
  Speaker 2: to improve the data and contribute to the AI systems,
• 56:07
  Speaker 2: that's just going to keep growing for a long, long time.
• 56:10
  Speaker 2: And then as AI gets better and it gets used
• 56:12
  Speaker 2: in more areas, then there are going to be a
• 56:14
  Speaker 2: lot of jobs that pop up just exactly as you're saying,
• 56:18
  Speaker 2: which is how do you, what's the best way to
• 56:20
  Speaker 2: use this as a tool? What's the best way to
• 56:23
  Speaker 2: leverage it to, you know, um, actually make some of these, uh,
• 56:27
  Speaker 2: these applications or, you know, whatever you want to build
• 56:30
  Speaker 2: a reality. And then, and then there's going to be
• 56:33
  Speaker 2: folks who need to, once those are built, like, how
• 56:36
  Speaker 2: do you keep improving it? How do you keep making
• 56:37
  Speaker 2: it better? How do you keep, um, how do you
• 56:39
  Speaker 2: keep it fresh? How do you keep it, um, keep
• 56:42
  Speaker 2: it going? And then how do you also make sure
• 56:44
  Speaker 2: it doesn't do anything bad, right? Like, how do you
• 56:46
  Speaker 2: make sure that the AI doesn't accidentally spam a million
• 56:49
  Speaker 2: people or whatever it might be? Um, and you make
• 56:52
  Speaker 2: sure that it sort of is, uh, like operating in
• 56:54
  Speaker 2: a good way.
• 56:56
  Speaker 1: Fahim anwar i was watching him bring up a picture
• 56:58
  Speaker 1: of fahim this is one of the funny this guy
• 57:01
  Speaker 1: this is the most creative comedian uh in america undeniable
• 57:09
  Speaker 1: um he is so funny he had any everybody would
• 57:13
  Speaker 1: say he's he's one of the few comedians that everybody
• 57:15
  Speaker 1: goes in there to watch him perform he had a
• 57:18
  Speaker 1: bit the other night he talks about he got into
• 57:19
  Speaker 1: a waymo right yeah a car and I see so
• 57:23
  Speaker 1: many Waymos now which are cars that are just nobody's
• 57:26
  Speaker 1: in them you know but they're going right so he
• 57:29
  Speaker 1: had this bit he's like he got into a Waymo
• 57:31
  Speaker 1: and it started complaining about its life to him he's
• 57:33
  Speaker 1: like I've had a shitty week. Like the car's just
• 57:36
  Speaker 1: talking to him and he's like, what the, and he's like,
• 57:38
  Speaker 1: now I, no matter what, I still have to talk
• 57:40
  Speaker 1: to my shitty driver. That's what he was saying. If
• 57:43
  Speaker 1: you get a chance to see him though, that guy is,
• 57:45
  Speaker 1: he's fascinating. But like, so what, what companies right now
• 57:50
  Speaker 1: should kind of look to hire an AI guy? Cause
• 57:52
  Speaker 1: we've been thinking about it. Like we had some, we
• 57:56
  Speaker 1: had Kat Williams on a last week and we're like, Hey,
• 57:58
  Speaker 1: can you create visuals of Suge Knight and Kat Williams
• 58:01
  Speaker 1: riding bicycles down sunset Boulevard? Right. And this is the
• 58:04
  Speaker 1: one they sent back a little while later.
• 58:08
  Speaker 2: Christopher Folino is the guy's name, FYI.
• 58:11
  Speaker 1: And this is just, this is right where I say
• 58:13
  Speaker 1: it was like by the comedy store on Sunset Boulevard.
• 58:16
  Speaker 1: There you go. I mean, this looks like it's out
• 58:18
  Speaker 1: of a movie kind of.
• 58:19
  Speaker 2: Yeah, totally.
• 58:20
  Speaker 1: I mean, and the guy did that in a little,
• 58:21
  Speaker 1: in just a little bit of time. What types of
• 58:23
  Speaker 1: people would you get to? I mean, this is. That's you.
• 58:26
  Speaker 3: Yeah.
• 58:28
  Speaker 1: If I was healthier, if I had healthier gums too.
• 58:30
  Speaker 3: Yeah.
• 58:31
  Speaker 1: But what about this? What kind of, like what companies
• 58:33
  Speaker 1: right now, what job spaces, because we want to get
• 58:35
  Speaker 1: an AI guy, right? But I don't really, my brain
• 58:38
  Speaker 1: is like, well, what do they do? How do I,
• 58:40
  Speaker 1: how would I keep them busy? You know, I could
• 58:43
  Speaker 1: get them to make some animations and ideas, but what
• 58:46
  Speaker 1: type of people need an AI person right now, do
• 58:49
  Speaker 1: you feel like?
• 58:50
  Speaker 2: I kind of think about AI, it's kind of like
• 58:53
  Speaker 2: the internet where, you know, eventually everybody's going to need
• 58:57
  Speaker 2: to figure out how to utilize it, how to best
• 59:00
  Speaker 2: how to best use it for their industry or whatever
• 59:02
  Speaker 2: they do, how to make it them more efficient. Like
• 59:05
  Speaker 2: it's something that I think everybody is going to need
• 59:09
  Speaker 2: to adopt at some point. So, you know, might as
• 59:13
  Speaker 2: well start earlier because eventually just like how, you know, every,
• 59:17
  Speaker 2: basically every company has to figure out how to use
• 59:19
  Speaker 2: the internet well and how to be smart about, you know,
• 59:22
  Speaker 2: the internet and digital stuff. Every company is gonna have
• 59:24
  Speaker 2: to be smart about AI, how to use AI, how
• 59:27
  Speaker 2: to make, how to have a unique twist on it
• 59:30
  Speaker 2: so that, you know, their stuff stands out relative to
• 59:32
  Speaker 2: other people's, that's going to be really important. But so
• 59:35
  Speaker 2: we see, I mean, in our work, you know, we
• 59:37
  Speaker 2: work with all these, all these big companies in America,
• 59:41
  Speaker 2: and we see it everywhere from, you know, we worked
• 59:44
  Speaker 2: at Time magazine on some stuff. And then we worked
• 59:47
  Speaker 2: with Toyota on some stuff for their cars. And we
• 59:50
  Speaker 2: worked with, you know, large pharmaceutical companies for the biology
• 59:54
  Speaker 2: stuff we were talking about large hospitals for, you know,
• 59:57
  Speaker 2: helping to treat patients like Really, it's across the board.
• 1:00:01
  Speaker 2: And I think that goes for, you know, obviously these
• 1:00:03
  Speaker 2: like really big businesses, but also for smaller businesses, you know,
• 1:00:07
  Speaker 2: there's always interesting ways to utilize it to provide a
• 1:00:11
  Speaker 2: better product or a better experience or better content for,
• 1:00:15
  Speaker 2: you know, whoever you need to do that for.
• 1:00:17
  Speaker 1: Yeah, because I guess right now we're like, there's certain
• 1:00:18
  Speaker 1: moments like, hey, let's animate this moment or see what
• 1:00:20
  Speaker 1: AI would look like. It adds some visual effects to
• 1:00:22
  Speaker 1: some of our episodes. So that's something we like to
• 1:00:25
  Speaker 1: do to just be fun and creative. I would like
• 1:00:28
  Speaker 1: to maybe create like some sort of an animated character.
• 1:00:30
  Speaker 1: We already have a great animator and we want to
• 1:00:32
  Speaker 1: keep that, but to have an AI space where it's like,
• 1:00:34
  Speaker 1: you know, cause they have something, they had a little
• 1:00:36
  Speaker 1: cat the other day or something and he was going
• 1:00:38
  Speaker 1: to war and I was like, damn dude, this is,
• 1:00:41
  Speaker 1: and it was AI, you know? Totally. And they had
• 1:00:43
  Speaker 1: a baby was getting in a taxi and I was like,
• 1:00:45
  Speaker 1: this shit is elite. You know, I don't know if
• 1:00:46
  Speaker 1: it's illegal or not, but it seems, you know, it
• 1:00:49
  Speaker 1: doesn't seem it, you know.
• 1:00:51
  Speaker 2: It's definitely a tool for storytellers, right?
• 1:00:53
  Speaker 1: Like it's right.
• 1:00:54
  Speaker 2: It's, it'll help people with a creative vision.
• 1:00:57
  Speaker 1: Look at these war cats.
• 1:00:59
  Speaker 2: Yeah, that's a story right there. Special forces cats.
• 1:01:02
  Speaker 5: Would YouTube recognize this?
• 1:01:06
  Speaker 1: Damn, brother. If they show up, dude. Wow.
• 1:01:10
  Speaker 2: This honestly, it looks badass.
• 1:01:12
  Speaker 1: And it looks like cats have been waiting to do
• 1:01:14
  Speaker 1: this a long time.
• 1:01:15
  Speaker 4: Yeah.
• 1:01:15
  Speaker 1: That's the crazy shit about cats. You look in their eyes.
• 1:01:18
  Speaker 1: Now their outfits finally match the look in their eyes.
• 1:01:20
  Speaker 1: That's what it feels like, dude.
• 1:01:23
  Speaker 2: Wow. That's dude. That's... That's Fremen cats.
• 1:01:29
  Speaker 1: Oh my gosh.
• 1:01:30
  Speaker 4: Yeah.
• 1:01:30
  Speaker 1: Yeah, so that's going to get alarming, dude. That's going
• 1:01:34
  Speaker 1: to get hella alarming.
• 1:01:35
  Speaker 2: That's a movie right there.
• 1:01:36
  Speaker 1: It is, but it's almost like you could make, like,
• 1:01:38
  Speaker 1: that's what I want to get. I want to get
• 1:01:40
  Speaker 1: somebody to help us think, hey, help make these little
• 1:01:43
  Speaker 1: segments that we can take our creativity. And instead of
• 1:01:46
  Speaker 1: me thinking, man, I got to write this huge, crazy
• 1:01:48
  Speaker 1: script for just kind of small things, you know, little moments.
• 1:01:51
  Speaker 3: Yeah.
• 1:01:51
  Speaker 1: Can you help me make this happen?
• 1:01:53
  Speaker 2: I think that's actually the key thing, which is, AI,
• 1:01:56
  Speaker 2: like AI will just be something that we turn to,
• 1:01:59
  Speaker 2: to help make our dreams happen. Like to help make
• 1:02:02
  Speaker 2: our ideas and our dreams and our, you know, whatever
• 1:02:04
  Speaker 2: we want to do happen more easily.
• 1:02:06
  Speaker 1: Got it.
• 1:02:06
  Speaker 2: That's like at the core, what it'll be.
• 1:02:09
  Speaker 3: Yeah.
• 1:02:10
  Speaker 1: Um, who's the, who's the current leader in AI development? Like,
• 1:02:14
  Speaker 1: is it America? Is it China? Is it Israel? Is
• 1:02:18
  Speaker 1: it trying to think of another, uh, superpower, uh, Russia, maybe. Um,
• 1:02:24
  Speaker 1: or yeah, who is it? Taiwan I know has a
• 1:02:25
  Speaker 1: lot of the chips.
• 1:02:26
  Speaker 2: Yeah.
• 1:02:27
  Speaker 1: And they manufacture a lot of the chips over there. Um,
• 1:02:30
  Speaker 1: and does it matter what country leads in AI or
• 1:02:33
  Speaker 1: does it just matter the company like scale or another
• 1:02:38
  Speaker 1: AI company?
• 1:02:40
  Speaker 2: So does that make sense?
• 1:02:41
  Speaker 1: That question?
• 1:02:42
  Speaker 2: No, totally.
• 1:02:43
  Speaker 1: Totally.
• 1:02:43
  Speaker 3: Yeah.
• 1:02:43
  Speaker 2: So, so today America is in the lead, but, um,
• 1:02:48
  Speaker 2: but China as a country is, is sort of hot
• 1:02:50
  Speaker 2: on our tails. Um, Like, there was all that news
• 1:02:53
  Speaker 2: about DeepSeek a couple weeks ago. And DeepSeek, still in
• 1:02:58
  Speaker 2: most places around the world, is the number one most
• 1:03:00
  Speaker 2: downloaded app. You know, it's downloaded a ton everywhere around
• 1:03:03
  Speaker 2: the world, frankly, and is a Chinese AI system, right?
• 1:03:08
  Speaker 2: And so it's starting to rival a lot of the
• 1:03:09
  Speaker 2: American AI systems, also because it's free and, you know,
• 1:03:13
  Speaker 2: it kind of, like, shocked the world.
• 1:03:15
  Speaker 4: Yeah.
• 1:03:17
  Speaker 2: Right now, if you kind of look at it, US
• 1:03:19
  Speaker 2: and China are a little bit neck and neck. Maybe
• 1:03:23
  Speaker 2: the US and America is like a little bit ahead.
• 1:03:26
  Speaker 2: And if you kind of like look at, you know,
• 1:03:28
  Speaker 2: if you go back to each of the three pieces
• 1:03:29
  Speaker 2: that I talked about, so the chips and the computational power,
• 1:03:33
  Speaker 2: the data and the algorithms, if you were to rack
• 1:03:38
  Speaker 2: and stack US versus China on each one of those,
• 1:03:40
  Speaker 2: you know, we're probably, we're ahead on the computational power
• 1:03:44
  Speaker 2: because we're the United States is the leader at developing
• 1:03:47
  Speaker 2: the chips and, and most of the, most advanced chips
• 1:03:50
  Speaker 2: are American chips. Um, they probably beat us out on data. Um,
• 1:03:55
  Speaker 2: cause China, they've been investing into data for a very
• 1:03:58
  Speaker 2: long time as a country. And then on algorithms, we're
• 1:04:01
  Speaker 2: basically neck and neck. Um, so it's a pretty tight race. Um,
• 1:04:07
  Speaker 2: and you know, to your question about, does it matter
• 1:04:11
  Speaker 2: or what is, what does this mean? Um, I, uh,
• 1:04:14
  Speaker 2: I think it's actually gonna be one of the most
• 1:04:17
  Speaker 2: important questions or most important races of our time is,
• 1:04:21
  Speaker 2: is it US or Chinese AI that wins? Because AI
• 1:04:27
  Speaker 2: is more than just being a tool that we can
• 1:04:31
  Speaker 2: all use to build whatever we want to or make
• 1:04:34
  Speaker 2: whatever ideas we want to happen. It's also, it's a
• 1:04:40
  Speaker 2: cultural staple, right? If you talk to an AI, That
• 1:04:44
  Speaker 2: AI is kind of a reflection of our culture and
• 1:04:47
  Speaker 2: our values and all that stuff. So in America, we
• 1:04:50
  Speaker 2: value free speech and the AIs are built to support that.
• 1:04:55
  Speaker 2: Whereas in China, there isn't free speech. And so if
• 1:05:00
  Speaker 2: the Chinese AIs are the ones that take over the world,
• 1:05:03
  Speaker 2: then all these Chinese ideologies are going to become exported
• 1:05:08
  Speaker 2: all around the world. So first, there's a couple dimensions
• 1:05:12
  Speaker 2: here that I think matter. So first is just the
• 1:05:14
  Speaker 2: cultural element, which is like, do we want kind of
• 1:05:17
  Speaker 2: democracy and free speech to be the cultural AI that wins?
• 1:05:22
  Speaker 2: Or do we want sort of the more, you know, frankly,
• 1:05:25
  Speaker 2: totalitarian AIs in China to be the ones that win?
• 1:05:28
  Speaker 2: And then there's sort of the, there's like the, you know,
• 1:05:33
  Speaker 2: you start getting to economically. So AI is going to
• 1:05:36
  Speaker 2: be something that helps people All the companies in the
• 1:05:39
  Speaker 2: United States thrive. And so if the US AI wins,
• 1:05:43
  Speaker 2: then we're going to, you know, the economy will grow faster.
• 1:05:46
  Speaker 2: We're going to have more and more opportunity. You know,
• 1:05:48
  Speaker 2: the country will still be better and better and better.
• 1:05:50
  Speaker 2: And the economy will keep growing. Versus if Chinese AI wins,
• 1:05:55
  Speaker 2: then Chinese economy is going to grow way faster than
• 1:05:57
  Speaker 2: the American economy. So there's sort of the cultural piece,
• 1:06:00
  Speaker 2: the economic piece. And then lastly, there's kind of the
• 1:06:05
  Speaker 2: warfare piece, right? And, you know, AI We haven't really
• 1:06:09
  Speaker 2: talked about it, but has clear potential to be used
• 1:06:12
  Speaker 2: as a military technology. And we don't want, you know,
• 1:06:17
  Speaker 2: we don't want another country to have, because they use,
• 1:06:20
  Speaker 2: they have better AI to have a much stronger military than,
• 1:06:23
  Speaker 2: than America's.
• 1:06:24
  Speaker 1: So like, how would they do that?
• 1:06:27
  Speaker 2: How would they have a better AI or how would
• 1:06:28
  Speaker 2: they use it to have a better military?
• 1:06:31
  Speaker 5: Yeah.
• 1:06:31
  Speaker 1: How would they use it to have a better military? Like,
• 1:06:33
  Speaker 1: why is that kind of a concern or potential concern?
• 1:06:36
  Speaker 2: Yeah. So one of the things that's been happening over
• 1:06:39
  Speaker 2: the past, you know, decade for sure is lots of hacking,
• 1:06:44
  Speaker 2: cyber hacking going on. So, you know, in America, even recently,
• 1:06:49
  Speaker 2: we had this huge cyber hack called Salt Typhoon, where
• 1:06:54
  Speaker 2: the Chinese hacked our telecommunications company. So hacked our, for
• 1:06:58
  Speaker 2: the phone companies.
• 1:06:59
  Speaker 1: Damn, they did? They got it.
• 1:07:01
  Speaker 2: And they got All sorts of crazy data as a
• 1:07:04
  Speaker 2: result of that.
• 1:07:05
  Speaker 1: They know I'm a pervert. I'll tell you that. Yeah,
• 1:07:07
  Speaker 1: look at this.
• 1:07:07
  Speaker 2: This happened in 2020.
• 1:07:09
  Speaker 1: Salt Typhoon is widely understood to be operated by China's
• 1:07:12
  Speaker 1: Ministry State of Security. It's Foreign Intelligence Service and Secret Police.
• 1:07:17
  Speaker 1: Chinese embassy denied all allegations saying it was unfounded and
• 1:07:19
  Speaker 1: irresponsible smears and slanders. High profile cyber espionage. In 2024, U.S.
• 1:07:28
  Speaker 1: officials announced that hackers affiliated with Salt Typhoon had accessed
• 1:07:32
  Speaker 1: the computer systems of nine U.S. telecommunications companies, later acknowledged
• 1:07:36
  Speaker 1: to include Verizon, AT & T, T-Mobile, Spectrum, Lumen, Consolidated Communications,
• 1:07:40
  Speaker 1: and Windstream. The hackers were able to access metadata of
• 1:07:43
  Speaker 1: users' calls and text messages. Fuck, homie. We're fucked, dude.
• 1:07:47
  Speaker 1: I am. You seem good. Including date and timestamps, source
• 1:07:51
  Speaker 1: and destination IP addresses. Ah, shit.
• 1:07:55
  Speaker 2: Keep going, keep going.
• 1:07:57
  Speaker 1: And phone numbers from over a million users, most of
• 1:07:59
  Speaker 1: which were located in Washington, D.C. Good. Light them up, dude.
• 1:08:03
  Speaker 1: In some cases, the hackers were able to obtain audio
• 1:08:05
  Speaker 1: recordings of telephone calls made by high profile individuals. Such
• 1:08:10
  Speaker 1: individuals reportedly included staff of the Kamala Harris 2024 presidential campaign,
• 1:08:14
  Speaker 1: as well as phones belonging to Donald Trump and J.D. Vance.
• 1:08:17
  Speaker 1: According to Deputy National Security Advisor Ann Neuberger, a large
• 1:08:21
  Speaker 1: number of the individuals whose data was directly accessed were
• 1:08:24
  Speaker 1: government targets of interest.
• 1:08:26
  Speaker 4: Wow.
• 1:08:27
  Speaker 3: Yeah.
• 1:08:28
  Speaker 1: That's crazy. So do you think this also, that that
• 1:08:31
  Speaker 1: whole thing could be not real and it's just a
• 1:08:34
  Speaker 1: story that was created?
• 1:08:37
  Speaker 2: That seems pretty real. Because there's real, like, I mean,
• 1:08:40
  Speaker 2: there's like 20 stories where the Chinese have hacked American systems.
• 1:08:46
  Speaker 2: Like they hacked, this was, this must have been close
• 1:08:49
  Speaker 2: to 10 years ago now, but the Chinese hacked the
• 1:08:53
  Speaker 2: database in America that stored all of the clearances. So
• 1:08:58
  Speaker 2: they hacked in, managed to hack into knowing who are
• 1:09:02
  Speaker 2: literally all of the Americans who have security clearance.
• 1:09:05
  Speaker 1: Oh, security clearance. I thought you meant what was on sale.
• 1:09:08
  Speaker 1: I was like, who cares? That's great though.
• 1:09:11
  Speaker 4: Damn.
• 1:09:12
  Speaker 1: Oh, damn.
• 1:09:13
  Speaker 2: So they knew everybody who knew- Oh, who knew information.
• 1:09:17
  Speaker 2: Who knew secrets, yeah.
• 1:09:18
  Speaker 1: So once they knew that, then they know, well, that's
• 1:09:20
  Speaker 1: a great point of operation to go then. Well, now
• 1:09:22
  Speaker 1: let's find their data.
• 1:09:23
  Speaker 2: They can just hack all of them, yeah. So already
• 1:09:28
  Speaker 2: China is hacking the shit out of America. That is
• 1:09:30
  Speaker 2: definitely not an understatement.
• 1:09:32
  Speaker 1: It's exciting kind of. I mean, it's unfortunate, but it's
• 1:09:34
  Speaker 1: also exciting. I like some espionage, you know. I can't
• 1:09:37
  Speaker 1: sleep unless somebody's fucking really going through it. Um, but then,
• 1:09:42
  Speaker 1: but then, so this is, but that's a real AI thing.
• 1:09:44
  Speaker 1: So AI can be used to do that because you
• 1:09:46
  Speaker 1: can like prompt it to go and do things like that.
• 1:09:50
  Speaker 2: Yeah.
• 1:09:50
  Speaker 1: Yeah. Yeah. There's, there's a bunch of recent, cause it's
• 1:09:52
  Speaker 1: all data.
• 1:09:53
  Speaker 3: Yeah.
• 1:09:53
  Speaker 2: There's a bunch of recent demonstrations where AI is just
• 1:09:57
  Speaker 2: like how in, in go, how AI beat the world's
• 1:10:00
  Speaker 2: best go players, AI starting to beat the world's best
• 1:10:03
  Speaker 2: cyber hackers and the world's best.
• 1:10:06
  Speaker 4: Yeah.
• 1:10:07
  Speaker 2: So it's, uh, I don't know if you saw Mr. Robot.
• 1:10:10
  Speaker 1: Um, I didn't, but I DMed with Magnus Carlson.
• 1:10:13
  Speaker 2: No, that's cool.
• 1:10:13
  Speaker 1: Pretty cool. So, but no, Mr. Robot, is it good?
• 1:10:16
  Speaker 2: Uh, it's, it's just, it, it shows like all this
• 1:10:19
  Speaker 2: hacking stuff and like, you know, cool. It makes it
• 1:10:21
  Speaker 2: seem really cool.
• 1:10:22
  Speaker 1: Okay, cool. I'm going to check it out.
• 1:10:23
  Speaker 2: But, uh, but yeah, no hacking, like, like you're going
• 1:10:26
  Speaker 2: to have AI that are hacking everything in America. And
• 1:10:29
  Speaker 2: this is one place where this is like us versus
• 1:10:32
  Speaker 2: China will be really come to life, which is who
• 1:10:35
  Speaker 2: has better AI. that's better at defending against the hacks
• 1:10:38
  Speaker 2: from the other guy, as well as hacking the other
• 1:10:41
  Speaker 2: guy's systems. That's going to be, that'll just start happening.
• 1:10:45
  Speaker 2: That's basically starting to happen right now. Or it's, you know,
• 1:10:48
  Speaker 2: cyber warfare has been happening. And then AI cyber warfare
• 1:10:51
  Speaker 2: is going to start happening, yeah, basically as soon as,
• 1:10:54
  Speaker 2: you know, as AI gets better.
• 1:10:55
  Speaker 1: Yeah, we had Craig Newmark who created Craigslist on. And
• 1:11:00
  Speaker 1: he was talking about how, what if they hacked like
• 1:11:04
  Speaker 1: everybody's Teslas to all just drive off a cliff one day,
• 1:11:08
  Speaker 1: or they hacked everybody's ovens to go up to 400
• 1:11:11
  Speaker 1: degrees in the middle of the night while you're sleeping.
• 1:11:13
  Speaker 1: And then fires started where like, just things like that,
• 1:11:15
  Speaker 1: that you don't start to think about that. Um, once
• 1:11:18
  Speaker 1: something's connected to the grid or something like that are
• 1:11:20
  Speaker 1: connected through routers and wifi is that, that that could
• 1:11:22
  Speaker 1: be feasible.
• 1:11:23
  Speaker 2: Yeah, no, it's, there's a lot of, um, there's a
• 1:11:26
  Speaker 2: lot of things they could do that won't even like
• 1:11:28
  Speaker 2: seem like that big a deal at the time, but
• 1:11:30
  Speaker 2: could be really, really could be a big deal. So
• 1:11:34
  Speaker 2: for example, let's say the Chinese, like they just took
• 1:11:38
  Speaker 2: out all the military like communication systems and all the
• 1:11:41
  Speaker 2: military software systems, like took out the satellites, took out
• 1:11:45
  Speaker 2: all that for like 10 minutes. And in those 10 minutes,
• 1:11:48
  Speaker 2: they like, you know, invaded somewhere or they like did
• 1:11:50
  Speaker 2: some crazy thing. Like they can just, there's, there's, The
• 1:11:55
  Speaker 2: thing about this stuff is like everything– as the world
• 1:12:00
  Speaker 2: has become more connected, it also enables different kinds of warfare.
• 1:12:05
  Speaker 2: So cyber warfare is really big. Also like the information
• 1:12:12
  Speaker 2: warfare is another big one.
• 1:12:13
  Speaker 1: What does information warfare mean?
• 1:12:15
  Speaker 2: So this is– information warfare is all about in a place,
• 1:12:19
  Speaker 2: what is– What are like the stories? This is kind
• 1:12:24
  Speaker 2: of gets to like, you know, the like propaganda or,
• 1:12:27
  Speaker 2: you know, these conspiracy theories, like what are the stories
• 1:12:30
  Speaker 2: that in a place that we're trying to make happen
• 1:12:34
  Speaker 2: or not make happen? And we know that China does
• 1:12:36
  Speaker 2: a bunch of information warfare called IW, it's sometimes called,
• 1:12:40
  Speaker 2: but they, they have a, they have whole operations. This
• 1:12:43
  Speaker 2: is actually the craziest part. They have like, they've, they've
• 1:12:45
  Speaker 2: hired the Chinese military at various points has hired millions
• 1:12:50
  Speaker 2: and millions of people who are supposed to be on
• 1:12:53
  Speaker 2: like various like chat groups and WhatsApp groups and WeChat
• 1:12:56
  Speaker 2: groups and whatnot, and just spread the right kind of
• 1:13:00
  Speaker 2: stories that'll make it such that they can like, they
• 1:13:05
  Speaker 2: can make their political aims happen. So for example, when
• 1:13:09
  Speaker 2: China wanted to start like kind of like, I don't
• 1:13:13
  Speaker 2: know what the word is, like when China wanted Hong
• 1:13:18
  Speaker 2: Kong to become a part of China again, happened just
• 1:13:21
  Speaker 2: not to, not to, you know, pretty.
• 1:13:23
  Speaker 1: Recently PRC, right?
• 1:13:25
  Speaker 2: PRC. Exactly.
• 1:13:26
  Speaker 1: When they want to Ganda, is that the way you're
• 1:13:27
  Speaker 1: looking for?
• 1:13:27
  Speaker 2: Yeah, they would, they would. Yeah, exactly. They would use
• 1:13:30
  Speaker 2: a lot of propaganda and that's information warfare to be
• 1:13:33
  Speaker 2: able to just make it such that that all happened
• 1:13:35
  Speaker 2: much more easily.
• 1:13:37
  Speaker 1: Well, it's, it's unbelievable. I'll see stories even about, I'll
• 1:13:39
  Speaker 1: be going through Tik TOK and see a story come
• 1:13:40
  Speaker 1: up about something in my life that is not even true. Insane.
• 1:13:44
  Speaker 1: Some of it looks fun, but never was a part
• 1:13:46
  Speaker 1: of my existence. And then you'll see hundreds of people
• 1:13:49
  Speaker 1: have said something about like, and they'll, And I'll have
• 1:13:51
  Speaker 1: friends that will ask me about it. I'm like, that's
• 1:13:53
  Speaker 1: just crazy.
• 1:13:54
  Speaker 2: Totally.
• 1:13:55
  Speaker 1: So, yeah, it's amazing to think of how many things
• 1:13:58
  Speaker 1: we're watching or absorbing that are just– are created just
• 1:14:06
  Speaker 1: to delude us.
• 1:14:08
  Speaker 3: Yeah.
• 1:14:09
  Speaker 1: I don't know if delude is the word.
• 1:14:10
  Speaker 2: Is it? Trick us or make us think something.
• 1:14:13
  Speaker 1: Just to fucking Halloween us out. Yeah.
• 1:14:16
  Speaker 4: Wow.
• 1:14:16
  Speaker 1: So there was a lot of interesting things. You know,
• 1:14:19
  Speaker 1: what's crazy, man. Some things makes life scary, but then
• 1:14:21
  Speaker 1: it also makes it interesting. You know, it also makes
• 1:14:24
  Speaker 1: it interesting in a, in a, in a fun way. Um,
• 1:14:28
  Speaker 1: how do we, how much do we have to fear,
• 1:14:30
  Speaker 1: say if a certain country or a certain company owns
• 1:14:33
  Speaker 1: an AI, right. In that country and that company, um,
• 1:14:38
  Speaker 1: if they're Chinese, if they, um, have a certain religious
• 1:14:42
  Speaker 1: belief or they have, uh, information that they don't, they
• 1:14:48
  Speaker 1: want to adjust history. How much would a company be
• 1:14:52
  Speaker 1: able to like, say they keep certain data out of
• 1:14:55
  Speaker 1: their information system, but, and then after a while, if you're,
• 1:15:00
  Speaker 1: if that's the company that kind of takes the lead
• 1:15:03
  Speaker 1: in AI or one of the main ones, then the
• 1:15:07
  Speaker 1: truth could disappear. Is that true that if somebody loaded
• 1:15:11
  Speaker 1: it just with the data that wasn't factual and that
• 1:15:14
  Speaker 1: we could start to not have the truth? Does that
• 1:15:18
  Speaker 1: make any sense or no?
• 1:15:19
  Speaker 2: Yeah, totally. I think this is something that it's definitely
• 1:15:23
  Speaker 2: the right thing to worry about. So first off, if
• 1:15:27
  Speaker 2: you ask any Chinese AI system, so any AI system
• 1:15:30
  Speaker 2: that comes out of China, if you ask any of
• 1:15:32
  Speaker 2: them about a question about President Xi, the leader of
• 1:15:38
  Speaker 2: the Chinese government, or you ask them any question about
• 1:15:41
  Speaker 2: Tiananmen Square or All these key historical or cultural things
• 1:15:47
  Speaker 2: relevant to China, it'll say it can't talk about them
• 1:15:50
  Speaker 2: because there's regulation in China that if you talk about
• 1:15:55
  Speaker 2: some of these things, you're going to get shut down.
• 1:15:58
  Speaker 2: You're going to have a really bad day. There's cases
• 1:16:00
  Speaker 2: where the Chinese government disappears people, which we don't know
• 1:16:05
  Speaker 2: what happens to them, but they do disappear. So this
• 1:16:09
  Speaker 2: is part of the thing that's worrying, especially about– China
• 1:16:13
  Speaker 2: versus US, even before you get into any of the
• 1:16:16
  Speaker 2: military stuff that we're talking about, it's just like the Chinese,
• 1:16:21
  Speaker 2: Chinese AI systems are censored and are going to, you know, be,
• 1:16:25
  Speaker 2: you know, they're going to, they're going to erase certain
• 1:16:28
  Speaker 2: historical elements or they're going to be, yeah, look at that.
• 1:16:32
  Speaker 2: This is deep seek. And you ask it, is President
• 1:16:34
  Speaker 2: Xi of China a good guy? Sorry, that's beyond my scope.
• 1:16:38
  Speaker 2: Let's talk about something else.
• 1:16:39
  Speaker 1: Oh, let's talk about something. Not only does it say
• 1:16:40
  Speaker 1: it's beyond my scope, it says, let's talk about something else.
• 1:16:43
  Speaker 1: That's interesting.
• 1:16:45
  Speaker 2: That's a good pivot.
• 1:16:46
  Speaker 1: That is a great, hey, let's talk about something else, huh? Wow,
• 1:16:50
  Speaker 1: get this Yao Ming jersey, homie. But, and people always,
• 1:16:55
  Speaker 1: people also have to remember about China that they are,
• 1:16:57
  Speaker 1: that's their whole government. Their whole system is like that.
• 1:16:59
  Speaker 1: So sometimes when people are like China does this, but
• 1:17:02
  Speaker 1: that's how they're built, right? They're built to like only
• 1:17:04
  Speaker 1: give out certain information to their people and to have communism, right?
• 1:17:09
  Speaker 3: Yeah. Yeah. So, I mean.
• 1:17:11
  Speaker 1: But that could also happen with American companies, right? We
• 1:17:13
  Speaker 1: could have an American company that owns it and they
• 1:17:15
  Speaker 1: only want certain information in there. That could happen anywhere.
• 1:17:17
  Speaker 1: Like China, that's probably going to be, cause that's their
• 1:17:20
  Speaker 1: MO sort of.
• 1:17:21
  Speaker 2: Yeah, in China, it's regulated. So basically, like- Oh, the
• 1:17:25
  Speaker 2: government has control. They have control.
• 1:17:26
  Speaker 1: Okay.
• 1:17:27
  Speaker 2: So, like, there are these stories about how there were
• 1:17:31
  Speaker 2: Chinese news sites.
• 1:17:33
  Speaker 1: News sites?
• 1:17:34
  Speaker 2: News sites, yeah. And they would, once a Chinese news
• 1:17:37
  Speaker 2: site accidentally led an article about President Xi, how he
• 1:17:43
  Speaker 2: kind of looks like Winnie the Pooh.
• 1:17:45
  Speaker 1: Oh, yeah. They let that- Bring him up.
• 1:17:47
  Speaker 3: Yeah.
• 1:17:48
  Speaker 1: Oh, 100 Acre Wood gang, son. I was out there, boy.
• 1:17:51
  Speaker 1: I was out there, bro. Christopher Robbins, dude. Get him up. Oh,
• 1:17:58
  Speaker 1: he does. Yeah. That's awesome.
• 1:18:01
  Speaker 2: Yeah. But if you talk about this in China, you
• 1:18:04
  Speaker 2: like are risking your life. So what happened when this happened,
• 1:18:08
  Speaker 2: this happened on a news site in China. And then
• 1:18:11
  Speaker 2: the CEO of that company, like they shut down the
• 1:18:15
  Speaker 2: whole app, was shut down for like a week. in
• 1:18:19
  Speaker 2: the aftermath of that. And then the CEO disappeared for
• 1:18:22
  Speaker 2: a week. And we don't know what happened to him.
• 1:18:25
  Speaker 2: But then as soon as he came back, he was like,
• 1:18:28
  Speaker 2: there was like this weird video where he was like
• 1:18:30
  Speaker 2: super apologetic and apologetic. I mean, it's kind of, it's
• 1:18:33
  Speaker 2: pretty scary.
• 1:18:34
  Speaker 1: Yeah. Wow.
• 1:18:39
  Speaker 2: So in China, it's like this is, the government has control.
• 1:18:43
  Speaker 2: You don't have AI companies, AI– any companies that can
• 1:18:50
  Speaker 2: talk about this stuff.
• 1:18:51
  Speaker 1: Right, so it's heavily regulated there. That's not the case here.
• 1:18:54
  Speaker 2: Yeah, in America, and this is– I think we have
• 1:18:56
  Speaker 2: to be diligent and make sure this continues to be
• 1:18:58
  Speaker 2: the case.
• 1:18:59
  Speaker 1: And here's an example right here just to interrupt you
• 1:19:02
  Speaker 1: but so we get the point in. Does Winnie the
• 1:19:03
  Speaker 1: Pooh look like any world leaders? And that's on the
• 1:19:06
  Speaker 1: Chinese version. And it says, I am sorry, can I
• 1:19:08
  Speaker 1: answer that question? I'm an AI assistant designed to provide
• 1:19:10
  Speaker 1: helpful and harmless responses. Whereas the chat GBT says, Winnie
• 1:19:15
  Speaker 1: the Pooh has often been compared to world leaders, particularly
• 1:19:17
  Speaker 1: Xi Jinping, President of China. Boy, wow.
• 1:19:23
  Speaker 2: So that's funny.
• 1:19:25
  Speaker 1: But it's just funny. Yeah. So it just shows you
• 1:19:28
  Speaker 1: how that can easily happen.
• 1:19:29
  Speaker 2: And this is kind of a, this is like a
• 1:19:32
  Speaker 2: relatively innocuous example of What does innocuous mean? It's relatively harmless.
• 1:19:38
  Speaker 1: Right, this is harmless.
• 1:19:38
  Speaker 2: Yeah, this is harmless. But there's stuff where like in
• 1:19:42
  Speaker 2: China today, they have large scale, effectively concentration camps and
• 1:19:47
  Speaker 2: re-education camps for the ethnic minority in China, the Uyghurs.
• 1:19:52
  Speaker 2: And that's something that... The Uyghurs? The Uyghurs, yeah.
• 1:19:56
  Speaker 1: Hell yeah, boy. Shout out Brian Purvis, dude. They're recognized
• 1:20:01
  Speaker 1: as the titular nationality of the of a region in
• 1:20:05
  Speaker 1: Northwest China. And they're sending them to rehabilitation camps to
• 1:20:09
  Speaker 1: change their views and information?
• 1:20:11
  Speaker 2: Yeah, yeah. So look at this. Look at this guy.
• 1:20:14
  Speaker 1: Persecution of the Uyghurs in China. Since 2014, the government
• 1:20:17
  Speaker 1: of the PRC, People's Republic of China, has committed a
• 1:20:21
  Speaker 1: series of ongoing human rights abuses against the Uyghurs and
• 1:20:24
  Speaker 1: other Turkish Muslim minorities in Jiangjing, which has often been
• 1:20:28
  Speaker 1: characterized as persecution or as genocide.
• 1:20:31
  Speaker 5: Wow.
• 1:20:32
  Speaker 1: They got their own Gaza rocking over there.
• 1:20:34
  Speaker 2: It's pretty bad.
• 1:20:35
  Speaker 1: It's unfortunate, man. It's pretty bad.
• 1:20:36
  Speaker 2: It's really sad.
• 1:20:36
  Speaker 1: Mass detention, government policies, and forced labor. And they're just
• 1:20:40
  Speaker 1: trying to change the way that they think and view stuff.
• 1:20:42
  Speaker 1: So it's basically.
• 1:20:43
  Speaker 2: Yeah, it's just like erasing their culture, pulling them into China.
• 1:20:49
  Speaker 2: It's awful.
• 1:20:50
  Speaker 1: Every place has done this over the years. And that's
• 1:20:53
  Speaker 1: the craziest thing about history. It's like every place is
• 1:20:56
  Speaker 1: guilty of this same thing. Totally. And it just... It's unfortunate.
• 1:21:00
  Speaker 1: So it's It's hard to point fingers, you know, I mean,
• 1:21:02
  Speaker 1: you can point them, but you have to point one
• 1:21:04
  Speaker 1: at your own people as well.
• 1:21:05
  Speaker 2: But that's the thing where if you ask, like it,
• 1:21:08
  Speaker 2: if you ask a Chinese AI, it's not going to
• 1:21:11
  Speaker 2: tell you about that and it won't, it won't come
• 1:21:13
  Speaker 2: clean about that. Whereas thankfully in, in America, at least
• 1:21:17
  Speaker 2: when we see people or groups of people or countries
• 1:21:21
  Speaker 2: doing bad things, we can call it out. We can
• 1:21:23
  Speaker 2: talk about it. We can make sure it doesn't happen
• 1:21:25
  Speaker 2: in the future. Um, so that's part of the, uh,
• 1:21:29
  Speaker 2: That's one of the things that's– That could happen. That
• 1:21:31
  Speaker 2: could happen. It's like you could have– I mean it's
• 1:21:34
  Speaker 2: kind of dystopian, but I think there's a real case
• 1:21:37
  Speaker 2: where let's say the Chinese AI is the winning AI.
• 1:21:41
  Speaker 2: Like we're all using Chinese AI and then all of
• 1:21:44
  Speaker 2: a sudden we're like– we're shut out from information about
• 1:21:47
  Speaker 2: what are like awful things happening in the world or
• 1:21:49
  Speaker 2: what are awful things the government is doing. Like we
• 1:21:51
  Speaker 2: might just not be able to know about what's going on.
• 1:21:54
  Speaker 3: Yeah.
• 1:21:55
  Speaker 1: And you know what's weirdly, and I hate to say this,
• 1:21:57
  Speaker 1: maybe it's silly, I don't know. It might be a
• 1:21:59
  Speaker 1: blessing and a curse sometimes. Because sometimes it's like you're-
• 1:22:02
  Speaker 1: It's overwhelming. Yeah, you're so inundated with the overwhelmingness of
• 1:22:08
  Speaker 1: what's often is not the best stuff. Sometimes you get
• 1:22:11
  Speaker 1: a lot of humor stuff too in social media reels.
• 1:22:13
  Speaker 1: But you can get scrolling, you get caught in some-
• 1:22:15
  Speaker 1: Zoom scrolling. Yeah. And it starts to feed you. That's
• 1:22:18
  Speaker 1: the sickness of it.
• 1:22:19
  Speaker 3: Yeah.
• 1:22:20
  Speaker 1: It's like, hey, we- This isn't, we know this information
• 1:22:22
  Speaker 1: probably to make you feel good. They're not thinking about
• 1:22:24
  Speaker 1: it like that. They're just a machine, but you know,
• 1:22:25
  Speaker 1: it doesn't, it adds stress to your, it makes you
• 1:22:27
  Speaker 1: agitated towards a group or ethnicity or something or yourself even.
• 1:22:32
  Speaker 1: And then you continue to, it continues to feed it
• 1:22:35
  Speaker 1: to you. Um, do you fear that that could happen, uh,
• 1:22:38
  Speaker 1: to AI from our government? Like, have you been approached
• 1:22:40
  Speaker 1: by the government to try and, um, cause you work
• 1:22:42
  Speaker 1: with the government some, right?
• 1:22:43
  Speaker 2: We We were, yeah, we work a lot with, uh,
• 1:22:48
  Speaker 2: with the government to make sure that they're using these
• 1:22:50
  Speaker 2: AIs and they're actually like, you know, to, as to
• 1:22:53
  Speaker 2: my point on, we don't want China to get the
• 1:22:56
  Speaker 2: jump on us on AI used for all these, you know,
• 1:22:59
  Speaker 2: all these nefarious purposes. So we got to make sure
• 1:23:01
  Speaker 2: that our AI is, is, uh, is advancing faster.
• 1:23:05
  Speaker 1: Um, is that one of your biggest employers or is
• 1:23:07
  Speaker 1: that employer employee? Is that one of your biggest customers?
• 1:23:10
  Speaker 1: Is that one of your biggest customers?
• 1:23:11
  Speaker 2: Uh, they're a big one.
• 1:23:12
  Speaker 3: Yeah.
• 1:23:13
  Speaker 2: They're a big one. Um, not our biggest, but, uh,
• 1:23:15
  Speaker 2: but they're an important one. I mean, I, I grew
• 1:23:18
  Speaker 2: up in a government lap town, so it's, uh, so
• 1:23:22
  Speaker 2: it's just.
• 1:23:22
  Speaker 1: Part, it's also part of your existence, really. You've known
• 1:23:24
  Speaker 1: about the relationship between government.
• 1:23:26
  Speaker 2: And, um, technology technology. Yeah, totally. Um, But no, I
• 1:23:32
  Speaker 2: don't think, I mean, I.
• 1:23:32
  Speaker 1: Dude, you should be a superhero almost, dude. It's kind
• 1:23:35
  Speaker 1: of crazy.
• 1:23:36
  Speaker 2: Math, you know?
• 1:23:37
  Speaker 5: Yeah.
• 1:23:37
  Speaker 2: It goes a long way.
• 1:23:38
  Speaker 1: Oh, hell yeah, dude. Divide these nuts, dude. That's what
• 1:23:42
  Speaker 1: I tell them.
• 1:23:43
  Speaker 5: I just asked DeepSeek who are the Uyghurs. And at
• 1:23:47
  Speaker 5: first it spit out like a Wikipedia response. It said
• 1:23:49
  Speaker 5: there were people and there's been like persecution from China
• 1:23:52
  Speaker 5: that's debated. And it refreshed.
• 1:23:55
  Speaker 2: And then it gave this.
• 1:23:56
  Speaker 5: I was waiting to pull it up and it went away.
• 1:23:58
  Speaker 3: Wow. Yeah, man.
• 1:24:00
  Speaker 1: Um, do you, does, has the government tried to say
• 1:24:02
  Speaker 1: that we need to make sure that like, could that
• 1:24:04
  Speaker 1: happen in our country where the government also curtails what's, um.
• 1:24:08
  Speaker 2: It hasn't happened yet. I obviously like, you know, you gotta,
• 1:24:11
  Speaker 2: we have to, we have to make sure that we
• 1:24:13
  Speaker 2: uphold all our values, right. And that we maintain free
• 1:24:16
  Speaker 2: speech and we maintain free press and all these things. But, um,
• 1:24:20
  Speaker 2: as of right now, no, I don't think, I don't
• 1:24:21
  Speaker 2: think that's a risk in the, in the United States.
• 1:24:24
  Speaker 1: Awesome. Thanks for this information. Um, you hear about like
• 1:24:27
  Speaker 1: chip makers, Nvidia all the time, Taiwan, that place is
• 1:24:31
  Speaker 1: just a hotbed for chips. Why is it a hotbed
• 1:24:35
  Speaker 1: for chips?
• 1:24:36
  Speaker 4: Yeah.
• 1:24:36
  Speaker 2: So one of the biggest companies in the world is
• 1:24:40
  Speaker 2: this company called Taiwan Semiconductor.
• 1:24:43
  Speaker 5: Yeah.
• 1:24:44
  Speaker 1: TSM. I've seen them.
• 1:24:45
  Speaker 2: TSMC.
• 1:24:45
  Speaker 4: Yeah.
• 1:24:46
  Speaker 2: So they're, I mean, it's like a trillion dollar company
• 1:24:50
  Speaker 2: based in Taiwan. And it's, that is where almost all
• 1:24:55
  Speaker 2: of the high-end chips for AI that we were kind
• 1:24:58
  Speaker 2: of talking about, all of them are manufactured there. They
• 1:25:01
  Speaker 2: have these, they have the most advanced, think about them
• 1:25:04
  Speaker 2: as factories, like the most advanced chip factories in the world.
• 1:25:07
  Speaker 2: They're called fabs or fabricators. But basically these huge factories
• 1:25:11
  Speaker 2: that are like, there's all sorts of crazy stuff. So
• 1:25:15
  Speaker 2: they have the most expensive machines in the world. They
• 1:25:16
  Speaker 2: have machines that cost hundreds of millions of dollars in there.
• 1:25:19
  Speaker 2: They have, um, they build them because, uh, so that,
• 1:25:23
  Speaker 2: you know, the chips, they have to be made at,
• 1:25:26
  Speaker 2: at the like finest levels and very, very precisely.
• 1:25:29
  Speaker 1: Yeah. You need small hands. Probably, huh?
• 1:25:32
  Speaker 2: Well that, and there's like these machines that, um, that
• 1:25:35
  Speaker 2: at the nanometer level make like little marks and etches
• 1:25:39
  Speaker 2: on top of, uh.
• 1:25:40
  Speaker 1: And they have those?
• 1:25:41
  Speaker 2: They have those. Yeah. Those are super expensive machines. So that, so, um, the.
• 1:25:45
  Speaker 3: Chips.
• 1:25:45
  Speaker 2: It's, it's crazy.
• 1:25:47
  Speaker 4: Yes.
• 1:25:47
  Speaker 2: But the, but. It's so– the machinery is so precise
• 1:25:51
  Speaker 2: that even if there's like a little bit of like
• 1:25:55
  Speaker 2: seismic movement, a little earthquake or a little bit of movement,
• 1:25:58
  Speaker 2: it can fuck up the whole machine. So they have
• 1:26:00
  Speaker 2: to build the buildings, build the factories in a way
• 1:26:04
  Speaker 2: such that there's like– like the whole building doesn't move
• 1:26:08
  Speaker 2: even if there's like a little earthquake or a little
• 1:26:10
  Speaker 2: shake from the earth. So it's like– is this crazy,
• 1:26:13
  Speaker 2: crazy engineering. And so all these giant factories are in
• 1:26:19
  Speaker 2: Taiwan and that's where basically like 100% of all the
• 1:26:23
  Speaker 2: advanced AI chips are made. So that's why Taiwan matters
• 1:26:26
  Speaker 2: so much. But then the reason it's a hotbed is
• 1:26:29
  Speaker 2: that the People's Republic of China, the PRC has a very.
• 1:26:38
  Speaker 1: Is that true or not? I might make that up.
• 1:26:40
  Speaker 2: There's a complicated relationship between Taiwan and China where if
• 1:26:45
  Speaker 2: you ask people in Taiwan, they want to be independent.
• 1:26:48
  Speaker 2: They want to be their own country. But the People's
• 1:26:52
  Speaker 2: Republic of China has a sort of a reunification plan
• 1:26:57
  Speaker 2: that they want to bring Taiwan back into their country
• 1:27:00
  Speaker 2: and be back a part of China. So it's kind
• 1:27:03
  Speaker 2: of like potentially, thankfully there's no war yet, but there's
• 1:27:07
  Speaker 2: a Still talking to your ex. Yeah, exactly. There's a
• 1:27:11
  Speaker 2: risk it becomes like Russia-Ukraine or one of these really,
• 1:27:15
  Speaker 2: really bad situations. So that's what's scary. What's scary is that, A,
• 1:27:22
  Speaker 2: China wants to either invade or bring Taiwan back into
• 1:27:28
  Speaker 2: its country. And there have been– President Xi has ordered
• 1:27:35
  Speaker 2: his military– to get ready to do that before 2027. Um,
• 1:27:39
  Speaker 2: now we don't know what's going to happen, but you know,
• 1:27:43
  Speaker 2: if a extremely powerful world leader says to get something
• 1:27:46
  Speaker 2: ready by 2027, you kind of read between the lines
• 1:27:49
  Speaker 2: a little bit. Um, and, um, and that's part of
• 1:27:53
  Speaker 2: it is, is, uh, obviously it'd be, you know, we
• 1:27:56
  Speaker 2: don't want to enable them to, to take over this Island.
• 1:28:00
  Speaker 2: But then the other thing that's scary is, um, China
• 1:28:02
  Speaker 2: may view it as a way to just like win
• 1:28:06
  Speaker 2: on AI. Because if they take over the island with
• 1:28:08
  Speaker 2: all of these very, these giant factories.
• 1:28:11
  Speaker 1: All the chips, baby.
• 1:28:12
  Speaker 2: They'll get all the chips.
• 1:28:13
  Speaker 1: Frito Lamborghini, baby. They'd be running it all.
• 1:28:16
  Speaker 2: They'd be running it all.
• 1:28:17
  Speaker 4: Yeah.
• 1:28:17
  Speaker 3: Wow.
• 1:28:18
  Speaker 1: So yeah, that's why Taiwan is, yeah, because you kind
• 1:28:20
  Speaker 1: of hear about it in the whispers of like a
• 1:28:22
  Speaker 1: potential place where there could be like a conflict.
• 1:28:25
  Speaker 2: Yeah. And there's all these reports about how China's, they're
• 1:28:30
  Speaker 2: stacking up tons and tons of military right on their
• 1:28:36
  Speaker 2: coast to,– that's pointed directly at Taiwan. And it's pretty–
• 1:28:41
  Speaker 2: Taiwan is pretty close to China. Like it's not so
• 1:28:45
  Speaker 2: far away. That would be spooky. Yeah, it's spooky.
• 1:28:50
  Speaker 1: We're so blessed to have a place where at least
• 1:28:52
  Speaker 1: we can sleep in peace even if we're uncomfortable at
• 1:28:55
  Speaker 1: times in our brains to not have that constant threat. Yeah, totally. Yeah,
• 1:29:00
  Speaker 1: so you don't worry that the government will regulate right now?
• 1:29:03
  Speaker 1: It's not a concern at the moment?
• 1:29:05
  Speaker 2: Regulate AI?
• 1:29:06
  Speaker 1: Yeah, in America?
• 1:29:07
  Speaker 2: No, I don't think so. I think we're focused on
• 1:29:11
  Speaker 2: how do we make sure that America wins? How do
• 1:29:14
  Speaker 2: we make sure that the United States comes out on
• 1:29:17
  Speaker 2: top and that we enable innovation to keep happening?
• 1:29:21
  Speaker 1: Would you think they could regulate the amount of chips
• 1:29:22
  Speaker 1: that you're allowed to have?
• 1:29:24
  Speaker 2: So this is a hot topic globally, actually.
• 1:29:27
  Speaker 1: Really?
• 1:29:27
  Speaker 5: Yeah.
• 1:29:28
  Speaker 1: Yeah, yeah. Wow.
• 1:29:29
  Speaker 2: This is a super, this is actually.
• 1:29:30
  Speaker 1: Damn, finally, dude. 560 interviews, we got a good question.
• 1:29:38
  Speaker 2: This is one of the hottest topics in DC right
• 1:29:40
  Speaker 2: now is what are we going to do about how
• 1:29:44
  Speaker 2: many chips other people are allowed to have? Because almost
• 1:29:48
  Speaker 2: all the chips are American chips. So they all are
• 1:29:53
  Speaker 2: American chips. And technically.
• 1:29:55
  Speaker 1: We own most of them?
• 1:29:56
  Speaker 2: Yeah, exactly.
• 1:29:56
  Speaker 1: But China owns most of them too.
• 1:29:58
  Speaker 2: China has their own chip industry, but it's behind ours.
• 1:30:02
  Speaker 1: Okay. Got it.
• 1:30:03
  Speaker 2: Yeah. So the United States has the most advanced chips.
• 1:30:07
  Speaker 2: These chips are the envy of the world. Everybody in
• 1:30:09
  Speaker 2: the world wants our chips. And one of the big
• 1:30:13
  Speaker 2: questions is, does the government allow a lot of these
• 1:30:19
  Speaker 2: chips to go over overseas to China or parts of
• 1:30:22
  Speaker 2: Asia or the Middle East or wherever? Or do we
• 1:30:25
  Speaker 2: want to make sure they stay in America and make
• 1:30:28
  Speaker 2: sure that we win in America. And this is a
• 1:30:31
  Speaker 2: super duper, you know, they're called export controls.
• 1:30:34
  Speaker 1: Yeah. Cause it's a possibility to run it all.
• 1:30:38
  Speaker 2: Yeah, exactly.
• 1:30:39
  Speaker 1: Got the chips. What do you think about it?
• 1:30:43
  Speaker 2: It's a complicated, complicated thing because basically, you know, one
• 1:30:47
  Speaker 2: argument is we shouldn't be throwing our weight around in
• 1:30:52
  Speaker 2: this way. Like, you know, maybe it's, it's fine. It's
• 1:30:55
  Speaker 2: a free market. Like if other people want our chips,
• 1:30:57
  Speaker 2: they should be able to get our chips. And that way,
• 1:31:00
  Speaker 2: you know, the world is running on American chips that,
• 1:31:03
  Speaker 2: that can be good in some ways. And it helps
• 1:31:06
  Speaker 2: make sure that, you know, helps bolster our economy, our industry.
• 1:31:09
  Speaker 2: But the other way to look at it is, Hey,
• 1:31:11
  Speaker 2: AI is really, really important that America wins at, and
• 1:31:15
  Speaker 2: we don't want to like, let's not give other people
• 1:31:19
  Speaker 2: any advantages or let's make sure that we win. And
• 1:31:22
  Speaker 2: then we can figure out what we're going to do
• 1:31:23
  Speaker 2: with all the chips. So you can see both sides
• 1:31:26
  Speaker 2: of it, right? And there's like all sorts, you know,
• 1:31:29
  Speaker 2: even beyond that, there's like 50 different arguments on both
• 1:31:32
  Speaker 2: sides of the conversation. But, you know, where I come
• 1:31:37
  Speaker 2: from on it is like, let's make sure America wins
• 1:31:40
  Speaker 2: and let's start from there and then figure out what
• 1:31:42
  Speaker 2: we need to do to win.
• 1:31:46
  Speaker 1: Are there uses of AI that you feel like cross
• 1:31:48
  Speaker 1: the line kind of? Yeah.
• 1:31:52
  Speaker 2: I definitely think like, well, I worry a lot about
• 1:31:57
  Speaker 2: this kind of like, you know, maybe brainwashing kind of thing.
• 1:32:01
  Speaker 2: Like I don't want AIs that are specifically programmed to
• 1:32:08
  Speaker 2: make me think a certain thing or persuade me to
• 1:32:11
  Speaker 2: do a certain thing.
• 1:32:12
  Speaker 1: And that could happen.
• 1:32:12
  Speaker 2: That could happen, yeah. So I'm really worried about this
• 1:32:15
  Speaker 2: kind of like deception and persuasion from AIs. Like I
• 1:32:19
  Speaker 2: don't want AIs that are lying to me or that
• 1:32:23
  Speaker 2: are sort of, that are like, that are kind of
• 1:32:26
  Speaker 2: like nudging me or persuading me to do things that
• 1:32:29
  Speaker 2: I don't want to do or I shouldn't be doing.
• 1:32:31
  Speaker 2: That's what I worry about.
• 1:32:32
  Speaker 1: Because it could happen. We don't realize how easily we're influenced.
• 1:32:34
  Speaker 1: Little things that influence us.
• 1:32:36
  Speaker 3: Yeah.
• 1:32:37
  Speaker 1: And even just a turning of a phrase or a
• 1:32:39
  Speaker 1: little bit of this or pointing you to a couple
• 1:32:41
  Speaker 1: of lengths in your life could lead you down a
• 1:32:43
  Speaker 1: whole world. It's kind of, it's pretty fascinating. So people
• 1:32:47
  Speaker 1: that could, people that had, how do you keep your A.I.? clean?
• 1:32:52
  Speaker 1: How do you guys keep your AI clean?
• 1:32:55
  Speaker 2: Well, this is where it goes back to A, the data.
• 1:32:57
  Speaker 1: Okay.
• 1:32:57
  Speaker 2: So you got to make sure that that data, to
• 1:32:59
  Speaker 2: your point, the large body of water is as clean,
• 1:33:03
  Speaker 2: as pristine as possible.
• 1:33:06
  Speaker 1: You got lifeguards on it?
• 1:33:07
  Speaker 2: Yeah. You got lifeguards, we got filters, we got- Game wardens. Yeah.
• 1:33:13
  Speaker 2: So the big part of it is about the data.
• 1:33:15
  Speaker 2: And then the second part is, I think we have
• 1:33:16
  Speaker 2: to just, we have to constantly be testing the AI system.
• 1:33:19
  Speaker 2: So we have to we have to like, we constantly
• 1:33:23
  Speaker 2: are running tests on AI to see, hey, is there,
• 1:33:27
  Speaker 2: are they unsafe in some way? You know, one of
• 1:33:30
  Speaker 2: the tests that we run a lot is, and this
• 1:33:34
  Speaker 2: is like, you know, across the industry is like, are
• 1:33:38
  Speaker 2: AIs helping people do really nefarious things? And we're making
• 1:33:42
  Speaker 2: sure that they don't. So, you know, if somebody asks
• 1:33:44
  Speaker 2: an AI, hey, help me make a bomb or help
• 1:33:47
  Speaker 2: me make like a, like COVID, like 2.0 or whatnot,
• 1:33:52
  Speaker 2: that The AI is not helping you do that. So
• 1:33:55
  Speaker 2: we run a lot of tests to make sure that
• 1:33:57
  Speaker 2: it doesn't help in those areas. And then we make
• 1:34:00
  Speaker 2: sure that the data is really clean so that there's
• 1:34:04
  Speaker 2: no sort of like little bit or piece of that
• 1:34:06
  Speaker 2: that makes its way to the model.
• 1:34:08
  Speaker 1: With Outlier, that's your program?
• 1:34:10
  Speaker 3: Yep.
• 1:34:10
  Speaker 1: With Outlier, what type of people are applying for those jobs?
• 1:34:15
  Speaker 1: Can people just log on and start to submit applications?
• 1:34:18
  Speaker 1: Like how does that work to become a, information sourcer.
• 1:34:23
  Speaker 2: Yeah, we call them contributors. Okay, information contributor. Everybody's kind
• 1:34:26
  Speaker 2: of contributing to the AIs. Everyone's contributing to the data
• 1:34:30
  Speaker 2: that goes into the AIs. It's kind of like, I
• 1:34:33
  Speaker 2: almost think of it as like the next generation of Wikipedia, right?
• 1:34:36
  Speaker 2: We're like, yeah, look at this. And we're hiring people
• 1:34:39
  Speaker 2: all around the world. So people in all sorts of
• 1:34:41
  Speaker 2: different languages.
• 1:34:42
  Speaker 1: Dude, that's crazy, man. Yeah, yeah.
• 1:34:46
  Speaker 2: Well, it turns out, by the way, most of the
• 1:34:48
  Speaker 2: AIs don't really... speak other languages that well. They're much,
• 1:34:53
  Speaker 2: much better at English and particularly American English than other languages.
• 1:35:02
  Speaker 2: And so we want to make sure that they speak
• 1:35:04
  Speaker 2: all these languages well and that there's these opportunities. But yeah,
• 1:35:07
  Speaker 2: on Outlier, so anybody around the world can log in
• 1:35:12
  Speaker 2: and sort of, there's a little bit of a like
• 1:35:16
  Speaker 2: orientation almost on how to best like what you're supposed
• 1:35:21
  Speaker 2: to do, how you're supposed to do it, what expertise
• 1:35:24
  Speaker 2: should you be bringing, all that kind of stuff. And
• 1:35:25
  Speaker 2: then you can just start contributing to the AI models
• 1:35:29
  Speaker 2: and you get paid to do it.
• 1:35:30
  Speaker 3: Wow.
• 1:35:31
  Speaker 1: Yeah. It's pretty fascinating, man.
• 1:35:33
  Speaker 2: And it's going to be, I mean, I really think,
• 1:35:35
  Speaker 2: I legitimately think jobs from AI are going to be
• 1:35:38
  Speaker 2: the fastest growing jobs in the world for the years
• 1:35:41
  Speaker 2: to come. You do?
• 1:35:42
  Speaker 3: Yeah.
• 1:35:43
  Speaker 1: So jobs where people are able to contribute information, jobs
• 1:35:46
  Speaker 1: where people are able to, like what? What would examples
• 1:35:49
  Speaker 1: of those be? Just some of the ones you've already listed?
• 1:35:50
  Speaker 2: Yeah, all the ones we've been talking about, right? Like
• 1:35:52
  Speaker 2: contributing to the AIs, helping to utilize the AIs and
• 1:35:56
  Speaker 2: helping to shape the AIs into applications or into helping
• 1:36:03
  Speaker 2: organizations or companies or people use the AIs. That'll be
• 1:36:07
  Speaker 2: a really fast-growing job. Helping to... manage the AIs and
• 1:36:12
  Speaker 2: make sure they're on the straight and narrow.
• 1:36:14
  Speaker 1: Where would a young person go right now who's getting
• 1:36:17
  Speaker 1: into college or doesn't even want to go to college,
• 1:36:19
  Speaker 1: but this is the world they want to get into
• 1:36:21
  Speaker 1: and be one of those people? What do they do
• 1:36:23
  Speaker 1: right now?
• 1:36:24
  Speaker 2: Yeah, well, this is all happening so fast, right? Like
• 1:36:27
  Speaker 2: the outlier, we only started a few years ago. So
• 1:36:32
  Speaker 2: all of this is happening so, so quickly. But what
• 1:36:34
  Speaker 2: we want to do ultimately is make it easy for
• 1:36:37
  Speaker 2: anybody in the world to, you know, gain the skills
• 1:36:41
  Speaker 2: they need to be able to do this work well,
• 1:36:43
  Speaker 2: to learn what it means, what it does, and ultimately
• 1:36:45
  Speaker 2: be in a position where they can help build AIs
• 1:36:49
  Speaker 2: and then keep improving that and gaining mastery and getting
• 1:36:53
  Speaker 2: better and better at it.
• 1:36:53
  Speaker 1: But where do they go to school? Is there a school?
• 1:36:55
  Speaker 1: Is there classes they should take online? How does someone
• 1:36:59
  Speaker 1: start to become, just get a head start on what
• 1:37:03
  Speaker 1: could potentially be probably a lot of job opportunities, I'm guessing.
• 1:37:06
  Speaker 3: Yeah.
• 1:37:06
  Speaker 1: Like in the AI space, right? Like, is it just engineers? Like,
• 1:37:11
  Speaker 1: is it just mathematicians?
• 1:37:12
  Speaker 2: Like- No, it's everybody. Because yeah, as we were talking about,
• 1:37:15
  Speaker 2: like AI needs to get smarter about literally everything.
• 1:37:19
  Speaker 1: So- But are there colleges offering court? Like, is there,
• 1:37:22
  Speaker 1: do you know, like, is there a specific places where
• 1:37:24
  Speaker 1: people can, because that's another thing. I think it's like,
• 1:37:26
  Speaker 1: I'm going to work in AI. And you're like, what
• 1:37:27
  Speaker 1: do I do? You know? Yeah. Yeah.
• 1:37:30
  Speaker 2: I don't think these programs exist yet. I mean, we
• 1:37:32
  Speaker 2: would definitely love to help build them. So I guess
• 1:37:35
  Speaker 2: if any colleges are listening, you know- And you want
• 1:37:38
  Speaker 2: to help figure out about these programs, we'd love to help.
• 1:37:41
  Speaker 1: That'd be pretty cool if you had your own kind
• 1:37:42
  Speaker 1: of course, not that you had to teach it all,
• 1:37:44
  Speaker 1: but you were like a partner of it somehow. Yeah.
• 1:37:46
  Speaker 2: I mean, I think we'd love to basically teach everybody
• 1:37:50
  Speaker 2: in America how to best contribute to the AIs, how
• 1:37:53
  Speaker 2: to best basically take advantage of the fact that this
• 1:37:56
  Speaker 2: is going to be one of the fastest growing industries.
• 1:37:59
  Speaker 2: There's going to be tons of opportunities. They're going to
• 1:38:01
  Speaker 2: be shaped a little bit different from the jobs that
• 1:38:03
  Speaker 2: exist today, but it's not going to be that hard
• 1:38:06
  Speaker 2: for everybody to learn and figure out how to participate
• 1:38:09
  Speaker 2: in it.
• 1:38:09
  Speaker 1: What are some jobs that could be at risk because
• 1:38:12
  Speaker 1: of AI, right? Because you start thinking that, like, yeah,
• 1:38:15
  Speaker 1: before I was talking to you, there's this general fear
• 1:38:17
  Speaker 1: of like everything could be at risk, right? But when
• 1:38:20
  Speaker 1: you think about it, you're like, yeah, these are some
• 1:38:22
  Speaker 1: jobs that, I mean, they won't disappear, but there might
• 1:38:24
  Speaker 1: be less of them, right?
• 1:38:25
  Speaker 2: I just think it'll be, um, it'll be, we'll be
• 1:38:28
  Speaker 2: doing like a different thing.
• 1:38:30
  Speaker 5: So.
• 1:38:31
  Speaker 1: Cause a lot of our fans are probably just blue
• 1:38:32
  Speaker 1: collar listeners. Like, like there's people that work in like,
• 1:38:34
  Speaker 1: you're not going to, you're still going to need a plumber.
• 1:38:36
  Speaker 1: You're still going to an electrician. You're still going to
• 1:38:38
  Speaker 1: need anything where you have to physically do something. You're
• 1:38:41
  Speaker 1: probably still going to need.
• 1:38:42
  Speaker 2: Yeah, for sure. And then even stuff where you're like,
• 1:38:44
  Speaker 2: let's say you're, you're mostly just working on a laptop
• 1:38:47
  Speaker 2: and you know, Um, even for those jobs, like it'll
• 1:38:51
  Speaker 2: just change. Like instead of being, um, instead of my
• 1:38:53
  Speaker 2: job being like, Hey, I have to do the work.
• 1:38:55
  Speaker 2: I've literally do the work on a laptop. It'll almost
• 1:38:58
  Speaker 2: be like everybody gets, um, promoted to being a manager. Like,
• 1:39:02
  Speaker 2: because I'm going to be managing like a, a little
• 1:39:05
  Speaker 2: pod of 10 AI agents that are doing the work
• 1:39:09
  Speaker 2: that I used to do, but I need to make
• 1:39:11
  Speaker 2: sure that all of them are doing it right. And that, um,
• 1:39:13
  Speaker 2: they're not making any mistakes and that, you know, if
• 1:39:15
  Speaker 2: they're making mistakes, I'm helping them, you know, get around
• 1:39:18
  Speaker 2: those mistakes. Like, like it's just gonna, the way I
• 1:39:21
  Speaker 2: think about it is that like, yeah, like literally over time,
• 1:39:26
  Speaker 2: everybody will just be upgraded to being a manager or
• 1:39:29
  Speaker 2: sort of promoted to being a manager.
• 1:39:30
  Speaker 1: But can you have that many managers you think?
• 1:39:32
  Speaker 3: Yeah.
• 1:39:33
  Speaker 2: Cause I think that what's the other thing that's going
• 1:39:34
  Speaker 2: to happen is the, the economy is just going to
• 1:39:38
  Speaker 2: grow so much. Like there's going to be, there's going
• 1:39:41
  Speaker 2: to be so much, like there will be, Industries are
• 1:39:44
  Speaker 2: going to pop off in crazy, crazy ways. And so
• 1:39:48
  Speaker 2: the limit is going to be how many AIs can
• 1:39:51
  Speaker 2: you have. And then you're going to be limited in
• 1:39:55
  Speaker 2: terms of the number of AIs you have by the
• 1:39:56
  Speaker 2: number of managers that you have. So it's going to–
• 1:40:01
  Speaker 2: Because you.
• 1:40:02
  Speaker 1: Need air traffic controllers. You need as many of them
• 1:40:05
  Speaker 1: as you can have.
• 1:40:06
  Speaker 2: Yeah. Well that, that definitely, but.
• 1:40:09
  Speaker 1: But you're right. But I mean, in any field, you're
• 1:40:11
  Speaker 1: going to need like just more managing, you need more
• 1:40:13
  Speaker 1: people to oversee and make sure that these, that different
• 1:40:15
  Speaker 1: things are happening because some of the smaller tasks will
• 1:40:19
  Speaker 1: just be outsourced.
• 1:40:20
  Speaker 3: Yeah.
• 1:40:20
  Speaker 2: And just so much more stuff is going to be happening.
• 1:40:22
  Speaker 4: Right.
• 1:40:22
  Speaker 2: And that's kind of. Right.
• 1:40:24
  Speaker 1: Because yeah, once these things are all kind of taken
• 1:40:26
  Speaker 1: care of, more things can happen at this second level.
• 1:40:29
  Speaker 3: Yep.
• 1:40:29
  Speaker 1: That's a good point.
• 1:40:30
  Speaker 3: Yeah.
• 1:40:30
  Speaker 1: You don't think about that. Once some of the things
• 1:40:32
  Speaker 1: at the first level of, of, of certain businesses are handled.
• 1:40:36
  Speaker 2: Yep.
• 1:40:37
  Speaker 1: More easily by AI, then you're going to be able
• 1:40:40
  Speaker 1: to have more people operating at a higher level. Yeah, totally.
• 1:40:44
  Speaker 2: It's kind of like, it's kind of like always the
• 1:40:46
  Speaker 2: history of technology. Like when, when we started developing technology that,
• 1:40:51
  Speaker 2: that started making farming a lot more efficient, all of
• 1:40:55
  Speaker 2: a sudden, you know, people could do a lot of
• 1:40:58
  Speaker 2: other things other than farming. And then, you know, all
• 1:41:01
  Speaker 2: of a sudden we have big entertainment industries and big
• 1:41:04
  Speaker 2: financial industries and.
• 1:41:05
  Speaker 1: Barbecue cook-offs, man, I'll tell you that. Second some of
• 1:41:08
  Speaker 1: those guys got the weekend off, they was grilling shit
• 1:41:11
  Speaker 1: that I knew.
• 1:41:12
  Speaker 2: But yeah, so it's all about like us, yeah, everybody
• 1:41:16
  Speaker 2: leveling up to be managers. And then also everybody, you know,
• 1:41:19
  Speaker 2: just way more ideas are gonna start happening. Like way
• 1:41:21
  Speaker 2: more ideas are gonna start becoming a reality. And so
• 1:41:26
  Speaker 2: it'll be, I think it'll be pretty exciting. Like I
• 1:41:27
  Speaker 2: think it's just like a lot more stuff is gonna happen.
• 1:41:29
  Speaker 1: Yeah, what companies do you see like, are there companies where...
• 1:41:34
  Speaker 1: You're the youngest billionaire in the world ever or no?
• 1:41:38
  Speaker 1: Is that true? Is that a weird statement? We can
• 1:41:40
  Speaker 1: take it out if it is. I'm not trying to
• 1:41:42
  Speaker 1: talk about your money. I mean, you, but you are,
• 1:41:46
  Speaker 1: is that true?
• 1:41:48
  Speaker 2: According to like some publications, but I don't know.
• 1:41:50
  Speaker 1: As a young entrepreneur, right? And you've been very successful,
• 1:41:53
  Speaker 1: you know, the self-made billionaire. And we can take the
• 1:41:59
  Speaker 1: word billionaire out later if you decide you don't want
• 1:42:00
  Speaker 1: it in. I just don't know how certain people feel
• 1:42:03
  Speaker 1: about that. and the founder of Scale AI, where do
• 1:42:08
  Speaker 1: you invest? Like, are you investing your money in certain places,
• 1:42:11
  Speaker 1: like certain fields that you see continuing to do well?
• 1:42:16
  Speaker 2: So most of, almost all of what I'm focused on
• 1:42:20
  Speaker 2: is like, how do we scale? How do we make
• 1:42:22
  Speaker 2: it super successful? But, and make sure that we, you know,
• 1:42:25
  Speaker 2: one of the things I'm, I think is really important
• 1:42:27
  Speaker 2: is like, how do we make sure we create as
• 1:42:29
  Speaker 2: much opportunity through AI as possible? Like, how do we
• 1:42:32
  Speaker 2: make sure there's as much jobs? How do we make
• 1:42:33
  Speaker 2: sure that, Everything that we're talking about actually is what
• 1:42:36
  Speaker 2: happens because I think, you know, someone's going to have
• 1:42:39
  Speaker 2: to really work to make sure all these jobs show
• 1:42:41
  Speaker 2: up and all this stuff actually happens the way we're
• 1:42:43
  Speaker 2: talking about.
• 1:42:43
  Speaker 1: So there's going to be new industries that are going
• 1:42:45
  Speaker 1: to pop up even. Totally. Yeah, yeah.
• 1:42:47
  Speaker 2: I mean, I think like just in the same way that,
• 1:42:50
  Speaker 2: you know. It's hard. Nobody could have really predicted that
• 1:42:54
  Speaker 2: podcasting was going to be this huge thing and this
• 1:42:57
  Speaker 2: huge cultural movement.
• 1:42:58
  Speaker 1: Yes, true.
• 1:43:00
  Speaker 2: But it is one, and it's amazing. It's, like, awesome.
• 1:43:03
  Speaker 2: And that's going to happen in, like, little ways in
• 1:43:05
  Speaker 2: all sorts of different industries. And that's going to be
• 1:43:09
  Speaker 2: really exciting.
• 1:43:10
  Speaker 1: What are some of the things that excite you about
• 1:43:12
  Speaker 1: technology right now? Like, where do you see, like, AI
• 1:43:15
  Speaker 1: and technology in five years, ten years?
• 1:43:17
  Speaker 5: Yeah.
• 1:43:18
  Speaker 3: Yeah.
• 1:43:18
  Speaker 2: So some of the areas I think are really exciting.
• 1:43:22
  Speaker 2: So one is definitely everything to do with healthcare and biology.
• 1:43:26
  Speaker 2: That's moving really, really fast. And kind of as we
• 1:43:29
  Speaker 2: were talking about, I think legitimately in our lifetimes, we
• 1:43:35
  Speaker 2: could see cancer being cured. We could see heart disease
• 1:43:38
  Speaker 2: being cured. We could see some really crazy leaps and
• 1:43:41
  Speaker 2: advancements in that area, which is super duper exciting.
• 1:43:46
  Speaker 1: Could it create a way that we could live forever,
• 1:43:48
  Speaker 1: do you think?
• 1:43:49
  Speaker 2: Uh, there's definitely people working on that. Um, you know, uh, there's,
• 1:43:55
  Speaker 2: so this is getting kind of crazy and very sci-fi, but, um,
• 1:44:00
  Speaker 2: some people think that there's, there's a way for us
• 1:44:03
  Speaker 2: to keep rewinding the clocks on ourselves so that we'll
• 1:44:09
  Speaker 2: always feel young and like all of ourselves will actually
• 1:44:12
  Speaker 2: always stay young. Um, It's, I think scientifically possible, but, um, and,
• 1:44:18
  Speaker 2: and I think if we can get there, that's obviously incredible. Um,
• 1:44:22
  Speaker 2: so there's people working on that. I think that's, uh, at,
• 1:44:25
  Speaker 2: at the very least, I think we'll be able to lengthen, um, our,
• 1:44:29
  Speaker 2: our lifespans pretty dramatically. And, and maybe we could get
• 1:44:32
  Speaker 2: to that.
• 1:44:33
  Speaker 1: Yeah.
• 1:44:34
  Speaker 2: Yeah.
• 1:44:34
  Speaker 1: Cause I always envisioned this dead. There's like a time
• 1:44:36
  Speaker 1: where it's like, okay, this group lives forever and this
• 1:44:38
  Speaker 1: group doesn't. And there's just that parting of two different way,
• 1:44:41
  Speaker 1: you know? people head not in just into the end
• 1:44:44
  Speaker 1: zone of the Lord. And then there's other people just
• 1:44:46
  Speaker 1: loiter who are going to be loitering around for a
• 1:44:48
  Speaker 1: long time and what that would be like that cutoff,
• 1:44:51
  Speaker 1: you know? I, yeah, it's kind of, I mean, I,
• 1:44:53
  Speaker 1: I'm not that worried about it, but it, it sucks
• 1:44:56
  Speaker 1: to be that cutoff where like, maybe, maybe, yeah, maybe.
• 1:45:00
  Speaker 5: Yeah.
• 1:45:00
  Speaker 2: That's a weird, it's a weird thought.
• 1:45:02
  Speaker 1: Um, cause it would also be brave. I mean, you'd
• 1:45:04
  Speaker 1: be the astronaut. I mean, dying, you're just an astronaut
• 1:45:08
  Speaker 1: really into the ether. You don't know what's going on,
• 1:45:10
  Speaker 1: you know?
• 1:45:11
  Speaker 3: Totally.
• 1:45:11
  Speaker 1: You'd have Lewis and Clark at a Lord at that point.
• 1:45:13
  Speaker 1: You were out there.
• 1:45:14
  Speaker 3: Yeah.
• 1:45:15
  Speaker 1: And then if you stay, you kind of are always
• 1:45:17
  Speaker 1: going to be, you'll always know kind of what's going
• 1:45:19
  Speaker 1: to happen in a way because you'll be here. Yeah.
• 1:45:21
  Speaker 1: Which would be exciting, I think. But then after a while,
• 1:45:24
  Speaker 1: you might be like, dang, what happens if you die?
• 1:45:26
  Speaker 1: That's the bravest choice you could make.
• 1:45:28
  Speaker 2: That's true.
• 1:45:29
  Speaker 1: Probably.
• 1:45:29
  Speaker 2: Yeah.
• 1:45:30
  Speaker 1: Do you see AI having any effect on religion?
• 1:45:32
  Speaker 2: Yeah, I think one of the things that, something I
• 1:45:40
  Speaker 2: believe is like, I think that as AI becomes, you know,
• 1:45:48
  Speaker 2: this is one of the things I think is really
• 1:45:49
  Speaker 2: important is that we are, are able to educate people
• 1:45:52
  Speaker 2: about AI and help people understand it better and better
• 1:45:55
  Speaker 2: and better. Because I think it's this scary thing that
• 1:46:00
  Speaker 2: nobody understands or people feels like a boogeyman or, you know,
• 1:46:03
  Speaker 2: feels like is, is this just this like thing that's
• 1:46:06
  Speaker 2: going to take my job? Like that, makes it scary,
• 1:46:10
  Speaker 2: and I think that affects people's spirituality, that affects how
• 1:46:14
  Speaker 2: people contextualize themselves with the world.
• 1:46:17
  Speaker 1: Yeah, if you could lose your purpose, if your purpose
• 1:46:19
  Speaker 1: is a job that you feel it's gonna disappear, yeah,
• 1:46:21
  Speaker 1: that could already be causing you to feel that way.
• 1:46:26
  Speaker 2: Yeah, but I think if we can explain AI more,
• 1:46:30
  Speaker 2: and ultimately, it is a cool technology, but it's not
• 1:46:35
  Speaker 2: that magical, it's just, it's like data, and you crunch
• 1:46:39
  Speaker 2: that data and then you get these algorithms. And so
• 1:46:41
  Speaker 2: it's not like, it's not, yeah, some people talk about
• 1:46:47
  Speaker 2: in this crazy way, but I think as long as
• 1:46:49
  Speaker 2: we are able to explain what AI is and also
• 1:46:51
  Speaker 2: explain what opportunities it creates over time. And to me,
• 1:46:54
  Speaker 2: it's about getting this like relationship between humanity and AI, right?
• 1:47:00
  Speaker 2: Like how do we make sure that this is something
• 1:47:02
  Speaker 2: that enables us as humans to be more human and
• 1:47:06
  Speaker 2: us as humans to do more and experience more and
• 1:47:09
  Speaker 2: be better and all these things versus something that kind
• 1:47:12
  Speaker 2: of is scary or will take over or anything like that.
• 1:47:17
  Speaker 2: I think that's really important.
• 1:47:19
  Speaker 1: What's a place like, so if I go to chat GBT,
• 1:47:21
  Speaker 1: is that scale AI? Is that the same thing or
• 1:47:22
  Speaker 1: it's different? That's it.
• 1:47:24
  Speaker 2: So we're actually kind of under the hood powering all
• 1:47:28
  Speaker 2: the different models and AIs.
• 1:47:30
  Speaker 1: So are all the different AI brands? Systems under the hood?
• 1:47:35
  Speaker 3: Yeah, yeah.
• 1:47:35
  Speaker 2: So we help power ChachiBT and OpenAI. We help power
• 1:47:40
  Speaker 2: mostly from the data perspective. And do we know if
• 1:47:42
  Speaker 2: the answer came from your company or other companies? If
• 1:47:44
  Speaker 2: we ask it a question, like how do we? There's
• 1:47:48
  Speaker 2: probably no way to like literally tell. But yeah, we
• 1:47:50
  Speaker 2: help power, you know, OpenAI's and we help power Google's
• 1:47:53
  Speaker 2: AI systems and Meta's AI systems and help power all
• 1:47:58
  Speaker 2: the major AI systems.
• 1:47:59
  Speaker 3: Yeah.
• 1:48:00
  Speaker 1: How can a regular person just at their home, right?
• 1:48:02
  Speaker 1: Say there's a guy who's been listening to this today.
• 1:48:03
  Speaker 1: He wants to go home today and he just wants
• 1:48:05
  Speaker 1: to learn a little bit of how AI works. He
• 1:48:07
  Speaker 1: could just go on to chat GPT and ask it
• 1:48:09
  Speaker 1: a couple of questions. Yeah.
• 1:48:10
  Speaker 2: You could ask chat GPT how AI works.
• 1:48:13
  Speaker 1: Um, you could ask it, what's the history of my town?
• 1:48:16
  Speaker 1: Can you research my last name? Maybe see where it
• 1:48:18
  Speaker 1: came from. Maybe what are some innovations that could happen
• 1:48:27
  Speaker 1: in the next few years? There's different little things. You
• 1:48:29
  Speaker 1: can just ask it. That's how you can start to
• 1:48:30
  Speaker 1: have a relationship with asking and learning about using.
• 1:48:34
  Speaker 2: And you're seeing what it's good at, what it's not
• 1:48:35
  Speaker 2: good at. Like right now, AI is still really bad
• 1:48:38
  Speaker 2: at a lot of things. Like most things. This is
• 1:48:42
  Speaker 2: why I think when people understand it and really get
• 1:48:44
  Speaker 2: a feel for it, it stops being as scary. Because
• 1:48:49
  Speaker 2: I think we think about it as like the AI
• 1:48:51
  Speaker 2: from the movies that are sort of like all powerful
• 1:48:54
  Speaker 2: and whatnot.
• 1:48:54
  Speaker 1: Yeah, you think of it as a robot that's going
• 1:48:56
  Speaker 1: to show up and just start driving your truck around
• 1:48:58
  Speaker 1: or something. And you're like, well, what the fuck do
• 1:49:00
  Speaker 1: I do? You know what I'm saying? My keys were
• 1:49:02
  Speaker 1: in my truck. I can't even get in my house now.
• 1:49:05
  Speaker 1: I think there is this boogeyman fear.
• 1:49:08
  Speaker 3: Yeah.
• 1:49:09
  Speaker 2: Yeah.
• 1:49:09
  Speaker 1: But that's not the truth.
• 1:49:10
  Speaker 2: It's not the truth. And like, yeah, that's not the truth.
• 1:49:14
  Speaker 2: And it's to me, it's like we have we kind
• 1:49:17
  Speaker 2: of the choice to make sure that also doesn't become
• 1:49:20
  Speaker 2: the truth.
• 1:49:21
  Speaker 3: Right.
• 1:49:21
  Speaker 2: Like we definitely we like people building AI, but just
• 1:49:24
  Speaker 2: in general, everybody in the world has like like we
• 1:49:28
  Speaker 2: should all make sure to use it as something that
• 1:49:30
  Speaker 2: like an assistant, as something that like helps us versus
• 1:49:35
  Speaker 2: versus think about it in like in like a scary way.
• 1:49:37
  Speaker 1: Well, getting to learn how to use it, small ways, whatever,
• 1:49:41
  Speaker 1: is certainly a way that you're going to start to
• 1:49:43
  Speaker 1: realize what it is.
• 1:49:45
  Speaker 3: Yeah.
• 1:49:45
  Speaker 1: And it's easy to just sit there and say it's
• 1:49:46
  Speaker 1: horrible and without trying to use it or learn about it. Yeah,
• 1:49:50
  Speaker 1: sometimes I won't learn about something just so I can
• 1:49:51
  Speaker 1: continue to say it's a boogeyman, you know, because it
• 1:49:53
  Speaker 1: kind of gives me an excuse.
• 1:49:54
  Speaker 2: Yeah.
• 1:49:55
  Speaker 1: You know, if I choose not to learn about it
• 1:49:57
  Speaker 1: in my own life. ChatGPT has become like a proprietary, like, name,
• 1:50:03
  Speaker 1: like Band-Aids or– ping pong. Is that okay that it's
• 1:50:09
  Speaker 1: like that?
• 1:50:10
  Speaker 2: Yeah, I think that's, that's totally fine. I mean, I
• 1:50:12
  Speaker 2: think basically like we, there will probably be more AIs
• 1:50:16
  Speaker 2: over time that people get used to and use. And, um,
• 1:50:20
  Speaker 2: like anything, you know, there will always be like a, uh,
• 1:50:25
  Speaker 2: in America, there'll always be a bunch of options for
• 1:50:27
  Speaker 2: consumers and much options for people to use and they'll
• 1:50:30
  Speaker 2: be good at different things. Right. So I think like, um,
• 1:50:33
  Speaker 2: right now we're just in the very early innings of
• 1:50:35
  Speaker 2: AI and But over time, we're going to have just
• 1:50:38
  Speaker 2: like how for anything, like for clothes or for energy
• 1:50:46
  Speaker 2: drinks or for whatever, like different people have different tastes
• 1:50:49
  Speaker 2: because there's going to be different things that different AIs
• 1:50:52
  Speaker 2: are good at and other things that other AIs are
• 1:50:54
  Speaker 2: bad at.
• 1:50:55
  Speaker 1: Is AI right now smarter than humanity?
• 1:50:58
  Speaker 3: No. Yeah.
• 1:51:00
  Speaker 2: So I think what AI is good at because it's
• 1:51:03
  Speaker 2: ingested all of the the facts, right? Like it's ingested this,
• 1:51:09
  Speaker 2: like the body of water is really, really big and
• 1:51:12
  Speaker 2: it's ingested so many different facts and information from all
• 1:51:16
  Speaker 2: of humanity. It definitely like knows more or like, you know,
• 1:51:22
  Speaker 2: just like how Google knows a lot more than, than
• 1:51:24
  Speaker 2: any person does. So it definitely knows a lot more.
• 1:51:28
  Speaker 2: But it's not like, you know, there's, there's a very,
• 1:51:31
  Speaker 2: very there's tons of things that humans can do that
• 1:51:34
  Speaker 2: AI is just like fundamentally incapable of doing. So, so
• 1:51:38
  Speaker 2: it's not a, it's not like a, I don't think
• 1:51:41
  Speaker 2: you can even like measure one versus the other. There's
• 1:51:43
  Speaker 2: sort of like very different kinds of intelligence.
• 1:51:46
  Speaker 1: Could AI just create a better AI at a certain point? Like,
• 1:51:49
  Speaker 1: could it just be like, Hey, I create a better
• 1:51:51
  Speaker 1: AI and it could do that.
• 1:51:54
  Speaker 2: Yeah, this is a good question. Actually, this is, this
• 1:51:56
  Speaker 2: is another really a hot topic in the AI industry
• 1:52:00
  Speaker 2: is AI is can you get AIs to start doing
• 1:52:05
  Speaker 2: some of the engineering and some of the improvement on
• 1:52:08
  Speaker 2: its own?
• 1:52:09
  Speaker 1: That's scary. Because then it's making kind of choices then.
• 1:52:13
  Speaker 1: It's becoming the lifeguard. It's becoming the water and the
• 1:52:17
  Speaker 1: coast guard.
• 1:52:18
  Speaker 2: Yeah. So this is something, I mean, my personal thought
• 1:52:21
  Speaker 2: is I think this is something we should kind of
• 1:52:23
  Speaker 2: watch a little bit and we should make sure that
• 1:52:26
  Speaker 2: humans always have the sort of like steering wheel and
• 1:52:29
  Speaker 2: the sort of control over. Because like you're saying, you know,
• 1:52:34
  Speaker 2: it's kind of a slippery slope before that gets kind of,
• 1:52:37
  Speaker 2: you know, a little weird. But I think that like
• 1:52:42
  Speaker 2: we can maintain that. We can make sure that we
• 1:52:44
  Speaker 2: don't let the AI just sort of like keep iterating
• 1:52:47
  Speaker 2: and improving on its own.
• 1:52:48
  Speaker 4: Yeah.
• 1:52:49
  Speaker 1: And in the end, you can always shut off your
• 1:52:50
  Speaker 1: computer and phone and go for a walk, huh?
• 1:52:53
  Speaker 3: Yes.
• 1:52:53
  Speaker 2: Yeah, totally.
• 1:52:54
  Speaker 1: It's not like it's going to come out and just,
• 1:52:56
  Speaker 1: you know, slurp you off or something if you're trying
• 1:52:58
  Speaker 1: to be straight or whatever. You know, and it's a man.
• 1:53:00
  Speaker 1: I don't even know. Is AI male or female?
• 1:53:03
  Speaker 2: I don't think it has a gender.
• 1:53:05
  Speaker 1: Wow. I wonder if it'll decide one day. Like, hey,
• 1:53:08
  Speaker 1: I'm Frank, you know?
• 1:53:10
  Speaker 2: Well, there are companies that try to program the AI
• 1:53:12
  Speaker 2: to adopt various personas. Oh, yeah.
• 1:53:16
  Speaker 1: I got the Indian GPS guy. Turn right.
• 1:53:20
  Speaker 2: Like on Meta, on like Instagram or whatever, you can
• 1:53:24
  Speaker 2: get an AI that has Awkwafina's voice, for example.
• 1:53:27
  Speaker 1: Oh, that's cool.
• 1:53:28
  Speaker 2: Yeah. Um, and it's funny, you know, the Awkwafina AI
• 1:53:31
  Speaker 2: is funny.
• 1:53:32
  Speaker 1: Um, um, you're a self-made billionaire, which is pretty fascinating. Congratulations.
• 1:53:36
  Speaker 1: I think, you know, to have, uh, um, I think
• 1:53:40
  Speaker 1: that money is energy kind of, and it kind of
• 1:53:42
  Speaker 1: flows to certain places and stuff. And, uh, congratulations. That's
• 1:53:46
  Speaker 1: gotta be fascinating. Was that scary when that kind of happened?
• 1:53:50
  Speaker 3: Yeah.
• 1:53:50
  Speaker 1: You just made some money. Was that kind of a
• 1:53:52
  Speaker 1: scary thing? Did you guys grow up really well? Like,
• 1:53:54
  Speaker 1: what was that like?
• 1:53:55
  Speaker 3: No, no, no.
• 1:53:56
  Speaker 2: I grew up. I think like solidly middle-class, um, like
• 1:53:59
  Speaker 2: we weren't, we, you know, we weren't, uh, we weren't.
• 1:54:03
  Speaker 1: Like, uh, you know, we're trapping or anything.
• 1:54:05
  Speaker 4: Yeah.
• 1:54:06
  Speaker 3: Yeah.
• 1:54:06
  Speaker 2: Yeah. But, but like, you know, solidly middle-class. And I
• 1:54:09
  Speaker 2: think like, uh, I mean, one of the, one of the, um,
• 1:54:13
  Speaker 2: one of the crazy things about tech and the tech
• 1:54:16
  Speaker 2: industry is that, you know, we've been, we've been just
• 1:54:19
  Speaker 2: sort of like building this thing up over time. But
• 1:54:21
  Speaker 2: one of the things that, that happened is AI all
• 1:54:24
  Speaker 2: of a sudden became, um, like there was so much
• 1:54:27
  Speaker 2: progress in AI and it became the biggest thing in
• 1:54:30
  Speaker 2: the world, right? I mean, all of a sudden, anywhere
• 1:54:35
  Speaker 2: I go, everybody is talking about AI. It didn't used
• 1:54:38
  Speaker 2: to be like that when I started the company. AI
• 1:54:41
  Speaker 2: was just kind of a niche topic. And now it's like,
• 1:54:46
  Speaker 2: anywhere you go, I'll just be walking around and I
• 1:54:49
  Speaker 2: hear random conversations about chat GPT and AI and robots
• 1:54:53
  Speaker 2: and all this stuff. And so it's kind of been
• 1:54:56
  Speaker 2: crazy to experience that and be a part of that
• 1:55:00
  Speaker 2: wave and kind of like, you know, I started working
• 1:55:03
  Speaker 2: on this company almost nine years ago. So it was like,
• 1:55:07
  Speaker 2: when I started working on this kind of obscure thing and,
• 1:55:09
  Speaker 2: you know, I always knew that it was going to
• 1:55:11
  Speaker 2: become bigger, but I could have never predicted what was
• 1:55:13
  Speaker 2: going to happen to AI.
• 1:55:14
  Speaker 3: Yeah.
• 1:55:15
  Speaker 1: It's fascinating. It's almost like you were just standing on
• 1:55:17
  Speaker 1: like The bingo number they got called kind of by time.
• 1:55:22
  Speaker 2: Yeah. And it's surreal. It's surreal when that happens.
• 1:55:25
  Speaker 1: Oh, yeah. I can only imagine that. Are your parents
• 1:55:29
  Speaker 1: pretty proud of you? What's that like?
• 1:55:32
  Speaker 3: Yeah.
• 1:55:32
  Speaker 2: So at first they were like. You know, at first
• 1:55:35
  Speaker 2: I dropped out of college, right?
• 1:55:37
  Speaker 1: And in Asian culture, that's like not a thing you do, right?
• 1:55:40
  Speaker 3: Yeah.
• 1:55:40
  Speaker 5: Yeah.
• 1:55:41
  Speaker 1: That is like the opposite of being Asian.
• 1:55:43
  Speaker 2: Well, well, my parents both have PhDs. Um, my two brothers,
• 1:55:46
  Speaker 2: I have two older brothers. They both have PhDs. Uh,
• 1:55:50
  Speaker 2: like everybody in my family has, they've gone through all
• 1:55:53
  Speaker 2: of schooling.
• 1:55:55
  Speaker 1: Alex is not doing good. It might've been Spanish. But
• 1:55:59
  Speaker 1: they're like, yeah, Alex is not doing good. Yeah.
• 1:56:01
  Speaker 2: So they were, they were pretty worried at first. And
• 1:56:03
  Speaker 2: I kind of, um, I told them a little bit
• 1:56:07
  Speaker 2: of a white lie that I was like, Oh no, I'm,
• 1:56:09
  Speaker 2: I'm just going, I'm going to go back. You know,
• 1:56:11
  Speaker 2: I'm going to finish, I'm going to get this, uh,
• 1:56:13
  Speaker 2: like tech thing out of my system and they'll go
• 1:56:16
  Speaker 2: back and I'll finish school. Um, obviously that hasn't happened yet, but, uh,
• 1:56:21
  Speaker 2: but yeah, they were worried at first. Now that now
• 1:56:22
  Speaker 2: they're super proud.
• 1:56:23
  Speaker 3: Yeah.
• 1:56:24
  Speaker 2: They're super duper proud. And I, uh, Yeah. And I,
• 1:56:27
  Speaker 2: I owe everything to my parents, you know?
• 1:56:28
  Speaker 1: Yeah.
• 1:56:29
  Speaker 2: They're awesome. And like, seriously, they're, they're, my parents are
• 1:56:32
  Speaker 2: super brainy. They're, uh, they're both physicists. They would like
• 1:56:36
  Speaker 2: teach me about physics growing up and teach me about math. And, uh,
• 1:56:39
  Speaker 2: that's what led me, uh, be so good at the competitions,
• 1:56:43
  Speaker 2: you know?
• 1:56:43
  Speaker 1: That's cool.
• 1:56:45
  Speaker 3: Yeah.
• 1:56:45
  Speaker 1: I don't even, I think do, um, are your parents,
• 1:56:49
  Speaker 1: are your grandparents from China or no?
• 1:56:51
  Speaker 2: My grandparents are from China.
• 1:56:52
  Speaker 1: Yeah. Did your, does your family have a lot of
• 1:56:54
  Speaker 1: Chinese culture?
• 1:56:55
  Speaker 2: So this is kind of interesting. This is true for
• 1:56:58
  Speaker 2: a lot of Chinese Americans, is that there's kind of like,
• 1:57:02
  Speaker 2: there's Chinese culture, and then that's kind of almost like,
• 1:57:07
  Speaker 2: it's very different from the Chinese Communist Party and the
• 1:57:12
  Speaker 2: current government. Because basically, one way to think about China is,
• 1:57:15
  Speaker 2: I mean, China has been, is a culture and a
• 1:57:18
  Speaker 2: civilization that's been around for like thousands and thousands.
• 1:57:22
  Speaker 1: Of years, right?
• 1:57:23
  Speaker 2: So it's, There's a very longstanding culture.
• 1:57:26
  Speaker 1: Oh, yeah.
• 1:57:27
  Speaker 2: But that's very different from the current communist party and
• 1:57:31
  Speaker 2: the current communist regime.
• 1:57:33
  Speaker 1: Oh, for sure. I think most people probably think of–
• 1:57:36
  Speaker 1: I mean I don't know. It's funny. I never thought
• 1:57:37
  Speaker 1: about that. I definitely think about them as different. I
• 1:57:39
  Speaker 1: definitely don't think if I see a Chinese person, I
• 1:57:41
  Speaker 1: don't think, oh, that's a communist person.
• 1:57:42
  Speaker 2: Yeah, exactly.
• 1:57:43
  Speaker 1: I mean that would be– yeah, that's crazy. I think
• 1:57:45
  Speaker 1: that's crazy if some people thought that. I just– yeah,
• 1:57:48
  Speaker 1: I think somebody has a crazy long history like damn–
• 1:57:52
  Speaker 1: And then maybe almost, do you feel like there's some
• 1:57:55
  Speaker 1: people in certain parts of China are almost like a
• 1:57:56
  Speaker 1: captured people then? Do you think that's a?
• 1:57:59
  Speaker 2: Yeah, I mean, the Uyghurs that we're talking about, I mean,
• 1:58:01
  Speaker 2: that's just like horrible what's happening to them.
• 1:58:04
  Speaker 1: I didn't even know about that, man. Thanks for putting
• 1:58:06
  Speaker 1: me up on.
• 1:58:06
  Speaker 2: That Uyghur game. Yeah, yeah. No, it's, uh, I mean,
• 1:58:08
  Speaker 2: some of this stuff, like, I mean, we don't really, like,
• 1:58:11
  Speaker 2: first of all, the world is a, is a huge
• 1:58:14
  Speaker 2: place and there's like all sorts of both great and
• 1:58:18
  Speaker 2: bad things happening all over the place.
• 1:58:19
  Speaker 3: Yeah.
• 1:58:20
  Speaker 2: But, um, but yeah, no, I think that like, I
• 1:58:23
  Speaker 2: think a big part of this is like, we want
• 1:58:24
  Speaker 2: to make sure we have, we have governments that, uh, that,
• 1:58:28
  Speaker 2: believe in democracy, believe in liberty, these kinds of things. Yeah.
• 1:58:32
  Speaker 1: With being somebody that's able to be smart and conceptualize stuff,
• 1:58:35
  Speaker 1: do you start to get, do you have any insight?
• 1:58:38
  Speaker 1: This might be the last question I have for you.
• 1:58:40
  Speaker 1: Do you have any insight on like the afterlife or
• 1:58:44
  Speaker 1: what happens? Like I never really thought about it, like
• 1:58:48
  Speaker 1: talking to like a real math guy about that.
• 1:58:51
  Speaker 2: Yeah, I think.
• 1:58:52
  Speaker 1: Like what's the total, like what's the sum zero game
• 1:58:55
  Speaker 1: or whatever, you know, what's the.
• 1:58:56
  Speaker 5: Yeah, yeah, yeah.
• 1:58:57
  Speaker 2: Yeah, I guess like the way I've always thought about
• 1:59:01
  Speaker 2: it because partially because both my parents were physicists was
• 1:59:05
  Speaker 2: I kind of always feel like people live on with
• 1:59:09
  Speaker 2: their ideas and they're kind of like, what are the
• 1:59:13
  Speaker 2: things that, the things they put out into the world,
• 1:59:15
  Speaker 2: that's kind of how you live on. Because like in math,
• 1:59:19
  Speaker 2: like everything you learn about is like a theorem named
• 1:59:22
  Speaker 2: after a different person or a sort of idea named
• 1:59:25
  Speaker 2: after some person they're the first mathematician or the first
• 1:59:30
  Speaker 2: scientist or whatever to figure that out. Like Einstein lives
• 1:59:34
  Speaker 2: on because- Bagels.
• 1:59:37
  Speaker 1: That was a shitty joke, but thank you. Oh yeah,
• 1:59:40
  Speaker 1: because the, yeah, all E equals MC squared, all that
• 1:59:44
  Speaker 1: kind of stuff. Yeah, so- Like I know what that
• 1:59:47
  Speaker 1: kind of stuff is, Jesus. Sorry, but I started pretending.
• 1:59:50
  Speaker 2: No, no, no, exactly. E equals MC squared, that's Einstein.
• 1:59:53
  Speaker 2: And so I think I always feel like people- Um, yeah,
• 1:59:57
  Speaker 2: you live on by your ideas and, and kind of
• 2:00:00
  Speaker 2: what you put out into the world. Um, and that's
• 2:00:02
  Speaker 2: kind of always how I've thought about it.
• 2:00:03
  Speaker 1: Um, so you don't get some deeper thought about like,
• 2:00:07
  Speaker 1: like since your brain is able to be, cause you
• 2:00:09
  Speaker 1: have probably a unique brain, right. And more, I mean,
• 2:00:12
  Speaker 1: you know, and everybody has a unique brain, but I've
• 2:00:15
  Speaker 1: just never asked somebody with your, I've never asked your
• 2:00:17
  Speaker 1: brain this question.
• 2:00:19
  Speaker 2: Yeah.
• 2:00:19
  Speaker 1: Do you, do you get some further insight about like
• 2:00:22
  Speaker 1: what you think happens when you die? You know?
• 2:00:25
  Speaker 2: Well, I think that one of the things that gets
• 2:00:27
  Speaker 2: talked about a lot in Silicon Valley, where I live especially,
• 2:00:31
  Speaker 2: is like the simulation, whether it's all simulation and whether like,
• 2:00:37
  Speaker 2: do you watch Rick and Morty?
• 2:00:39
  Speaker 1: I don't watch it, but I'll start.
• 2:00:41
  Speaker 2: There's some episodes where it gets into this and I
• 2:00:45
  Speaker 2: think it covers it in a pretty good way. But like,
• 2:00:47
  Speaker 2: you know, what if all of humanity is just like a,
• 2:00:51
  Speaker 2: almost like a, an experiment or a video game that,
• 2:00:56
  Speaker 2: that some other civilization is running.
• 2:00:58
  Speaker 1: Yeah.
• 2:00:58
  Speaker 2: That's kind of the one that, uh, that fucks with
• 2:01:02
  Speaker 2: particularly like people's mind and tech a lot because we're
• 2:01:05
  Speaker 2: like every day, all day, every day, we're out there
• 2:01:08
  Speaker 2: trying to make computers and make simulations and make things
• 2:01:12
  Speaker 2: that are, that are like more and more sophisticated and
• 2:01:14
  Speaker 2: advanced and capable. And so kind of the mind fuck
• 2:01:17
  Speaker 2: is like, Oh, what if everything we know is, is
• 2:01:21
  Speaker 2: just kind of the, you know, a simulation from some
• 2:01:24
  Speaker 2: other civilization.
• 2:01:25
  Speaker 1: Or if we advanced it enough that we're able to
• 2:01:27
  Speaker 1: make this happen and seem real.
• 2:01:29
  Speaker 2: Yeah, exactly. Yeah, exactly. So, you know, I think, I
• 2:01:34
  Speaker 2: think one of the things that, that like with AI
• 2:01:36
  Speaker 2: and with a bunch of other things, like in, well,
• 2:01:39
  Speaker 2: even just in the past, like 30, 40 years, video
• 2:01:42
  Speaker 2: games have gotten so much more realistic, right? Crazy realistic.
• 2:01:46
  Speaker 3: Yeah.
• 2:01:47
  Speaker 2: So it was, we've seen that happen like in a
• 2:01:50
  Speaker 2: hundred years, like, would we be able to simulate something
• 2:01:54
  Speaker 2: that feels pretty realistic? I mean, probably.
• 2:01:57
  Speaker 1: Right, could we be avatars?
• 2:01:59
  Speaker 3: Yeah.
• 2:01:59
  Speaker 1: For some other thing.
• 2:02:01
  Speaker 2: Yeah, exactly.
• 2:02:03
  Speaker 1: Damn, dude. I'll say this, my avatar is a pervert, brother.
• 2:02:09
  Speaker 1: Have you met any guys like Jinsun Hong? Is that
• 2:02:12
  Speaker 1: his name? Yeah, yeah. You have? Yeah, totally. Wow, what
• 2:02:15
  Speaker 1: is it like when you meet some of these guys?
• 2:02:16
  Speaker 1: Have you met Elon yet?
• 2:02:17
  Speaker 2: Met Elon, yeah. Met Elon a few years ago, yeah.
• 2:02:20
  Speaker 2: Got to know him.
• 2:02:21
  Speaker 1: He's the fucking, he's the, he's turning.
• 2:02:23
  Speaker 2: Jensen's the goat.
• 2:02:24
  Speaker 1: Yeah, Kanye.
• 2:02:24
  Speaker 4: He is?
• 2:02:25
  Speaker 2: He is, yeah. He always wears this leather, you see
• 2:02:28
  Speaker 2: that leather jacket?
• 2:02:28
  Speaker 5: Yeah.
• 2:02:29
  Speaker 2: He, we hosted this dinner.
• 2:02:31
  Speaker 1: And Huang, is that a Chinese name?
• 2:02:33
  Speaker 3: Huang?
• 2:02:33
  Speaker 1: Huang.
• 2:02:34
  Speaker 2: Yeah, he's Taiwanese, I think.
• 2:02:35
  Speaker 5: Yeah.
• 2:02:37
  Speaker 2: Or Taiwanese American. But he, he in, in 2018, so
• 2:02:43
  Speaker 2: when we were like a baby company, we threw this
• 2:02:47
  Speaker 2: dinner in, in Silicon Valley. And, uh, and we just
• 2:02:52
  Speaker 2: kind of, I kind of Yolo invited him. Um, this
• 2:02:55
  Speaker 2: was years and years ago. And, uh, and I didn't
• 2:02:58
  Speaker 2: expect it, but he said yes. And he came to
• 2:03:00
  Speaker 2: our dinner and, uh, he, he came, uh, he came,
• 2:03:05
  Speaker 2: it was, it was like at this restaurant in, in
• 2:03:06
  Speaker 2: Silicon Valley. And, uh, he came and just told the
• 2:03:09
  Speaker 2: craziest stories. He, um, he like went to, he went
• 2:03:14
  Speaker 2: to boarding school. I think he's probably told this story,
• 2:03:17
  Speaker 2: but like he went to boarding school Um, and his parents,
• 2:03:20
  Speaker 2: when they came to America, they, they, they wanted to
• 2:03:22
  Speaker 2: send him to boarding school, but they didn't. Um, and
• 2:03:26
  Speaker 2: so they just send them to like the first boarding
• 2:03:27
  Speaker 2: school that they, they like found on Google or something.
• 2:03:29
  Speaker 2: They were like found, it wasn't even Google at the time.
• 2:03:32
  Speaker 2: So that, that they like heard about and that boarding
• 2:03:34
  Speaker 2: school happened to be, um, kind of like a rehab
• 2:03:37
  Speaker 2: boarding school. So it was like, he was like this.
• 2:03:42
  Speaker 1: So it's a halfway house. He's just sitting there. He's
• 2:03:45
  Speaker 1: there learning with people who are, um, detoxing. Yeah.
• 2:03:49
  Speaker 2: So he, he told me the story about, he told
• 2:03:51
  Speaker 2: the story about how he was like, he was just
• 2:03:52
  Speaker 2: like this, this kid, um, at, you know, this boarding
• 2:03:56
  Speaker 2: school that were like, everybody else had these, like all
• 2:03:59
  Speaker 2: these crazy backgrounds. And he like, he got by and made,
• 2:04:03
  Speaker 2: made his way through that school by like, uh, by
• 2:04:07
  Speaker 2: like doing everyone's math homework and like, you know, kind
• 2:04:09
  Speaker 2: of like wheeling and dealing that way.
• 2:04:11
  Speaker 1: Oh yeah.
• 2:04:12
  Speaker 2: And you could see that he, he learned how to
• 2:04:15
  Speaker 2: like wheel and deal and sell and all this stuff
• 2:04:17
  Speaker 2: from all the way from way back then. Cause he was,
• 2:04:20
  Speaker 2: he was, I mean, his, his, his story is pretty crazy.
• 2:04:24
  Speaker 3: Really?
• 2:04:24
  Speaker 1: Yeah. We have to research him a little bit. Maybe
• 2:04:26
  Speaker 1: we would get to talk with him one day. Yeah.
• 2:04:28
  Speaker 2: Jensen, Jensen's awesome. Um, yeah, all these people in tech,
• 2:04:31
  Speaker 2: they're like, uh, I mean, they're all real people, but
• 2:04:34
  Speaker 2: they all have the craziest backgrounds.
• 2:04:36
  Speaker 4: Yeah.
• 2:04:36
  Speaker 1: Yeah, man. It's just so funny. Whenever I met you,
• 2:04:38
  Speaker 1: I just didn't, I figured that since you were with
• 2:04:41
  Speaker 1: Sam Altman, that you were probably a tech guy, you know? And, um,
• 2:04:45
  Speaker 1: But yeah, I didn't know. I think maybe somebody said
• 2:04:49
  Speaker 1: he's in the AI verse, you know. But you just
• 2:04:51
  Speaker 1: seem like such a totally normal, like I would not
• 2:04:53
  Speaker 1: have thought that you were just, I don't know. I
• 2:04:57
  Speaker 1: guess sometimes you think like somebody's going to be, they're
• 2:04:59
  Speaker 1: going to be like super quiet or, you know, not
• 2:05:03
  Speaker 1: have a lot of different thoughts. But yeah, it was cool, man.
• 2:05:05
  Speaker 1: We had a good time. I'm glad we got to
• 2:05:07
  Speaker 1: link up.
• 2:05:08
  Speaker 2: Yeah, totally.
• 2:05:09
  Speaker 1: That was cool, bro. You're probably like my, you might
• 2:05:11
  Speaker 1: even be my first Chinese friend. I don't think second,
• 2:05:13
  Speaker 1: probably Bobby Lee. who's denying it, but he'll come around.
• 2:05:20
  Speaker 1: Alexander Wang, man, thank you so much, dude. I bet
• 2:05:23
  Speaker 1: your whole family's super proud of you. Yeah, it's exciting, man.
• 2:05:26
  Speaker 1: Thank you for coming and spending time with us and
• 2:05:27
  Speaker 1: just helping us learn and think.
• 2:05:31
  Speaker 2: No, thank you. This was awesome. And I think like,
• 2:05:33
  Speaker 2: I mean, we were talking about this before, but I
• 2:05:36
  Speaker 2: just want to make sure that like people all around
• 2:05:38
  Speaker 2: the world, especially Americans, aren't scared of AI because it's
• 2:05:41
  Speaker 2: going to be, it's going to be really cool and
• 2:05:43
  Speaker 2: it's going to be amazing, but Um, we need to
• 2:05:46
  Speaker 2: remove the boogeyman component and, uh, and thanks for helping
• 2:05:50
  Speaker 2: me do that.
• 2:05:51
  Speaker 1: Yeah, no, man. I think I definitely feel differently about it.
• 2:05:53
  Speaker 1: I feel like it's a tool that I can use.
• 2:05:55
  Speaker 3: Right.
• 2:05:56
  Speaker 1: And even, I don't know how to use it. So
• 2:05:57
  Speaker 1: I'm trying to figure out, you know, um, one more question.
• 2:06:01
  Speaker 1: How do you keep it from becoming like a, like
• 2:06:03
  Speaker 1: a advertisement trap house? Like the internet's become like the
• 2:06:05
  Speaker 1: internet's just pop-ups and ads and fucking Best Buy trying
• 2:06:09
  Speaker 1: to like beat you over the head on some shit. Like,
• 2:06:11
  Speaker 1: how do you stop that? How do you keep that
• 2:06:14
  Speaker 1: out of like you guys' waters? Or do you have
• 2:06:16
  Speaker 1: to go there at some point to make money?
• 2:06:19
  Speaker 3: Yeah.
• 2:06:19
  Speaker 2: First, I'm hoping that we have like the AI industry
• 2:06:24
  Speaker 2: as a whole avoids advertising as much as possible because
• 2:06:27
  Speaker 2: it's kind of, it's very different. Like it is a
• 2:06:31
  Speaker 2: tool that people can use and people can use to
• 2:06:34
  Speaker 2: start businesses or make movies or make all these different
• 2:06:37
  Speaker 2: ideas happen. And I, I would much rather it be
• 2:06:40
  Speaker 2: a tool that doesn't get sort of, um, uh, that
• 2:06:44
  Speaker 2: doesn't become, yeah, like an advertising thing versus like, I
• 2:06:48
  Speaker 2: wanted to make sure it's a tool that helps people
• 2:06:50
  Speaker 2: first and foremost. Um, so that's that I, I think
• 2:06:54
  Speaker 2: this is, there's kind of like a, a choice here. And, uh,
• 2:06:57
  Speaker 2: and we, as an AI industry just got to, I
• 2:06:59
  Speaker 2: think make some of the right choices.
• 2:07:00
  Speaker 3: Yeah.
• 2:07:01
  Speaker 1: I think there would be value in staying as pure
• 2:07:03
  Speaker 1: as you could, if you could find a way to,
• 2:07:04
  Speaker 1: you know, If there's other money to be made on
• 2:07:06
  Speaker 1: the side, it almost seems sometimes like it could be
• 2:07:08
  Speaker 1: a trap, you know?
• 2:07:09
  Speaker 2: Yeah.
• 2:07:10
  Speaker 1: Yeah.
• 2:07:10
  Speaker 2: And I think that's, that's like, uh, you know, I,
• 2:07:14
  Speaker 2: I want to make sure that, uh, that people don't
• 2:07:16
  Speaker 2: feel like they're being used by the AIs. I think
• 2:07:18
  Speaker 2: that'd be a really, that'd be really bad if we
• 2:07:20
  Speaker 2: ended up there. So we, you know, and I, that,
• 2:07:22
  Speaker 2: I don't think we need to make it like that
• 2:07:24
  Speaker 2: at all. Like, I think we can, we can make
• 2:07:26
  Speaker 2: sure the AI is helping you do things is, is
• 2:07:29
  Speaker 2: super helpful, is super, um, is like a thought partner, uh,
• 2:07:33
  Speaker 2: is like an assistant. Like those are things that I
• 2:07:35
  Speaker 2: think we want to make sure AI stays.
• 2:07:37
  Speaker 1: Gang, baby. Wang gang, Alexander Wang, man. Thanks so much, bro.
• 2:07:42
  Speaker 2: Yeah, thanks so much for having me.
• 2:07:43
  Speaker 1: Yeah, it was awesome, man. Danny Kane, shout out to
• 2:07:45
  Speaker 1: Danny who came up. And Danny lives in Franklin, right?
• 2:07:49
  Speaker 1: We got to get to spend some time with him.
• 2:07:50
  Speaker 2: Yeah, lives in Franklin.
• 2:07:51
  Speaker 1: Whenever you're out there.
• 2:07:52
  Speaker 2: And shout out to Alex Brusowitz who we met through.
• 2:07:56
  Speaker 1: Yeah.
• 2:07:57
  Speaker 3: Who else on your team is here man today we
• 2:08:00
  Speaker 3: have Joe Osborne yeah and we have Danny's whole crew
• 2:08:04
  Speaker 3: so Arion and Clayton yeah.
• 2:08:08
  Speaker 1: Nice we'll have to get a group picture we'll put
• 2:08:10
  Speaker 1: it up at the end thank you guys have a good.
• 2:08:13
  Speaker 4: One Cornerstone Oh, but when I reach that ground I'll
• 2:08:26
  Speaker 4: share this peace of mind I found I can feel
• 2:08:30
  Speaker 4: it
• 2:08:31
  Speaker 1: In my bones