This Past Weekend with Theo Von - E563 AI CEO Alexandr Wang

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00:01:33.600 Today's guest is from Los Alamos, New Mexico.

00:01:36.540 He's a leader in the world of business and technology.

00:01:40.200 He's an entrepreneur.

00:01:41.880 He started Scale AI, a company recently valued at $14 billion.

00:01:46.880 He started it when he was only 19, and at 24, he became the youngest self-made billionaire

00:01:54.520 in the world.

00:01:56.240 We talk about his company, the future of AI, and the role it plays in our human existence.

00:02:01.740 This was super educational for me.

00:02:03.900 I hope it is for you.

00:02:05.040 I'm grateful for his time.

00:02:07.420 Today's guest is Mr. Alexander Wang.

00:02:10.020 Alexander Wang, man.

00:02:28.180 Thanks for hanging out, bro.

00:02:29.480 Yeah, thanks for having me.

00:02:30.460 Yeah, good to see you, dude.

00:02:31.820 Last time was at the inauguration.

00:02:33.860 Yeah, what'd you think of that?

00:02:34.820 Like, what were your thoughts after you left?

00:02:36.440 Um, because you and I ended up, we like left out of there and then went and got lunch together,

00:02:40.540 which was kind of crazy.

00:02:41.980 It was, uh, there was a lot going on.

00:02:43.480 Yeah, it was a, were you there the whole weekend?

00:02:45.780 No, I just got there, uh, the day, the morning of the inauguration.

00:02:49.820 Were you there the whole weekend?

00:02:50.900 I was there the whole weekend.

00:02:51.880 Yeah, no, I mean, I think at least for what we do, like for AI, the, uh, the new administration

00:02:56.900 is really excited about it and wants to make sure that we, we get it right as a country.

00:03:00.580 So, um, that was all great, but it was kind of a crazy, the whole like event, like everything

00:03:05.940 was pretty crazy.

00:03:06.820 I don't know.

00:03:07.020 What'd you think? 0.98

00:03:07.880 I mean, when I saw Conor McGregor show up, that's when I was like, shit is, where are 0.99

00:03:12.780 we? 0.98

00:03:13.460 It felt, that's what like the most bizarre, I mean, you were there, Sam Altman was there.

00:03:18.620 I was just like, it was like, whoa, what happened here?

00:03:22.540 Um, and part of that's because Alex Bruschewitz, you know?

00:03:25.180 Totally.

00:03:25.620 Yeah.

00:03:25.920 I mean, it's all because of him that we all went, but just the fact that he would bring

00:03:29.060 these people together, it kind of makes you question crossover.

00:03:31.840 It's like, uh, it's like in, in those like TV shows when you have those crossover episodes.

00:03:36.080 Yeah.

00:03:36.400 That's what kind of what it felt like.

00:03:37.080 Oh yeah.

00:03:37.580 When like the Harlem Globetrotters and it would be like them versus like, um, the Care Bears

00:03:42.480 or whatever would show up.

00:03:44.500 Exactly.

00:03:44.940 Yeah.

00:03:45.160 Like this.

00:03:45.520 Yeah.

00:03:45.680 This seems, um, yeah.

00:03:47.920 Some cross pollination.

00:03:49.940 Um, yeah.

00:03:50.620 What'd you think when Conor showed up?

00:03:51.860 Was that strange to see him?

00:03:52.940 Like, was there somebody you thought that was strange, like unique to see there for you?

00:03:55.780 Uh, well, yeah.

00:03:57.760 I mean, you, Conor, like the, the, the Pauls, I don't know.

00:04:01.520 The whole, the whole thing was pretty, uh, pretty crazy.

00:04:03.640 I see Sam all the time.

00:04:04.600 And I see, uh, I see some of the tech people all the time.

00:04:07.120 I mean, it was funny crossover and it was obviously like the, so many people flew in to be able

00:04:12.040 to go to like the outdoor inauguration.

00:04:14.900 Right.

00:04:15.200 Yeah.

00:04:15.480 And so, I mean, there were so many people in the city.

00:04:17.720 We ran into them obviously, but like there were so many people in DC, uh, who were just around

00:04:22.460 for the inauguration.

00:04:23.240 So it was, uh.

00:04:24.280 Yeah.

00:04:24.440 I didn't even think about that.

00:04:25.140 Oh, there was a couple hundred thousand people probably that just got kind of displaced in

00:04:27.900 a way.

00:04:28.320 Yeah.

00:04:28.680 And suddenly there's in bars or whatever, just like buying, like there was like people 1.00

00:04:33.480 I saw walking just adult onesies and shit that said Trump or on the, that's crazy shit. 0.99

00:04:39.760 Yeah. 1.00

00:04:40.500 Um, yeah.

00:04:41.720 Thanks for coming in, man.

00:04:42.640 I'm excited to learn about AI.

00:04:44.080 I know that that's a world that you're in.

00:04:45.540 You're in the tech universe.

00:04:47.040 Um, and you, you're from New Mexico, right?

00:04:49.540 Yeah.

00:04:49.800 And so when you grew up there, did you have like a strong sense of like science and technology?

00:04:55.600 Was that like part of your world?

00:04:56.880 Like, did your parents like lead you into that?

00:04:59.140 Like, what was just some of your youth like there?

00:05:01.560 Yeah.

00:05:01.760 So, uh, both my parents are physicists and there's a, I grew up in this town called Los

00:05:07.280 Alamos where there's a, there's a, there's a national lab there.

00:05:11.360 Um, did you watch Oppenheimer?

00:05:12.640 Yeah.

00:05:13.080 Yeah.

00:05:13.380 So the, so all the New Mexico shots in Oppenheimer, that's exactly where I grew up. 0.99

00:05:18.000 Oh, damn. 0.96

00:05:18.560 So it was like originally where all the scientists came together and, and did all the work in 0.99

00:05:23.060 the atomic bomb.

00:05:23.680 And there's still this huge lab.

00:05:25.880 That's basically the, everybody I knew effectively, like their parents worked at the lab or, or some

00:05:31.380 affiliate with the lab.

00:05:32.540 It's like Nukeville over there, huh?

00:05:33.980 Nukeville.

00:05:34.500 Yeah.

00:05:34.960 That's that.

00:05:35.400 Was it scary like that?

00:05:36.580 It was, you know, it, uh, at a level of mystery, like, is your prom like last night under the

00:05:42.100 stars or something?

00:05:42.960 Like there's a, there's this one museum.

00:05:45.300 Well, the funny thing is like, first you hear, you hear this, you learn the story of

00:05:50.300 the atomic bomb and the Manhattan project, like basically every year growing up, because

00:05:54.440 there's always like a day or a few days where, you know, there's a substitute teacher and

00:05:58.700 they just, they just play the videos.

00:06:01.040 So you're just like, yeah, an alcoholic or something.

00:06:03.560 Uh, a recreational user will use that term.

00:06:09.080 That's crazy that that just like every year you guys have like, yeah, just like blast it

00:06:14.220 Thursdays or whatever.

00:06:15.200 And it's just, yeah, you're just learning, you're learning again about the Manhattan project.

00:06:19.600 And then there's a little, there's a little museum in town, uh, which is like you walk

00:06:23.760 through and there's like a, a life-size replica of the nukes.

00:06:27.680 Um, so it's, it's pretty wild.

00:06:30.680 Yeah.

00:06:31.040 And where did they drop the atomic bombs on?

00:06:33.060 They dropped them on Asia, right? 0.96

00:06:34.300 Hiroshima.

00:06:35.220 They, yeah, they dropped them, uh, on, uh, in, in Japan.

00:06:39.560 Yeah.

00:06:39.800 Hiroshima and Nagasaki.

00:06:41.100 Is it, is it crazy being like semi-Asia, part Asian? 0.97

00:06:44.740 Uh, yeah, yeah.

00:06:45.640 My, uh, my parents are Chinese.

00:06:48.040 Oh, nice man.

00:06:49.080 Is it crazy being Asian and then having that happen to Asian people with the bomb?

00:06:52.960 Like, is that a weird thing there?

00:06:53.960 It's nothing.

00:06:55.000 Uh, no, I don't think so.

00:06:56.220 I mean, I think the thing is like, you know, I, uh, so there weren't, I didn't grow up

00:07:00.400 with very many Asians, uh, cause the, in that town, it was, um, you know, it's, it's in New

00:07:05.700 Mexico.

00:07:06.020 There's very few Asians in New Mexico.

00:07:08.260 So I was one of the only Asian kids in my class growing up.

00:07:12.080 And so, um, I didn't think that much about it, honestly, but then like, uh, but it is super

00:07:17.580 weird.

00:07:17.860 You know, you grow up and you learn about this very advanced technology that had this

00:07:24.320 like really, really big impact on the world.

00:07:26.820 And I think that shaped.

00:07:28.540 Yeah.

00:07:28.700 It's like the scientific John Jones over there.

00:07:30.680 Really?

00:07:30.920 He's a New Mexican, isn't he?

00:07:32.040 He's, he lives in Albuquerque.

00:07:33.420 Yeah.

00:07:33.540 Oh, he does?

00:07:33.960 I think he does.

00:07:34.380 Yeah.

00:07:34.520 Oh, sweet man.

00:07:35.400 Yeah.

00:07:35.740 Yeah.

00:07:35.920 Oh yeah.

00:07:36.280 That, so you, so you're there.

00:07:38.020 There's this, there's this energy always there of this creation.

00:07:42.020 So probably the possibility of creation maybe was always in the air.

00:07:45.080 I'm just wondering like, how did you get formed kind of, you know, like what's your

00:07:48.380 origin story kind of?

00:07:49.940 It was super scientific.

00:07:51.120 Cause, cause you know, there were all of these, there were all these like presentations

00:07:54.900 around what were the new kinds of science that were going on at the lab.

00:07:58.740 So there's all these chemistry experiments and these different like earth science experiments

00:08:03.060 and, uh, physics experiments.

00:08:05.440 Um, and my mom studied like plasma and like how plasma, you know, worked inside of stars and 0.96

00:08:13.200 stuff like that.

00:08:13.760 So it was just the wildest stuff and you would talk to people's parent people, you like, I

00:08:19.280 would talk to my classmates or I talked to their parents about what they're working on

00:08:21.960 is always some crazy science thing.

00:08:24.980 So that was, that was really cool.

00:08:28.180 Cause, cause everybody in that town basically is, they're all working on some kind of crazy

00:08:34.220 scientific thing.

00:08:35.400 And so you kind of, I mean, I feel like I grew up feeling like, you know, anything was

00:08:40.440 possible in that way.

00:08:41.620 Like, yeah, cause the rest of us in other communities, shitty communities or whatever, 0.88

00:08:45.140 we're just making that volcano or whatever. 0.87

00:08:47.040 You know what I'm saying? 0.99

00:08:47.440 We're doing like grassroots level bullshit, you know? 0.99

00:08:52.680 Um, dang, that's gotta be wild. 0.99

00:08:55.740 So every, you see somebody just sneaking behind an alley and buying a bit of uranium and shit 0.97

00:09:00.240 like that in your neighborhood, that's gotta be kind of unique. 0.63

00:09:02.840 I remember there was someone from our town who did, uh, who did a science fair project

00:09:07.180 called, um, it's called like great balls of plasma.

00:09:10.180 And for the science, literally for the science fair, this was in like high school for the

00:09:13.760 science fair, they made like huge balls of plasma in their garage. 0.89

00:09:19.660 And I was like, what the hell?

00:09:21.680 Like this is, we're all just doing this in high school. 1.00

00:09:26.080 Damn. 0.99

00:09:26.900 So did you feel competitive or did you just feel like hyper capable? 0.99

00:09:31.180 Like, did you feel like kind of advanced just in your studies?

00:09:33.480 Like when you were young, like, did you were in class?

00:09:35.620 Did you like, are some of this stuff's kind of coming easy to me?

00:09:38.000 I ended up, what, what I did is, um, there was, uh, I ended up getting really competitive

00:09:43.880 about math in particular.

00:09:46.020 Yeah.

00:09:46.360 And so, uh, so my sport was math, which is kind of crazy.

00:09:51.900 Algebra easy, son.

00:09:53.260 I know I feel you.

00:09:54.500 Um, and it was because when I was in middle school, when I was in sixth grade, there was

00:09:58.500 this one math competition where if you, if you got top four in the state, state of New

00:10:04.500 Mexico, then you would get an all expense paid trip to Disney world.

00:10:08.280 And, uh, and I remember as a sixth grader, like that was the most motivating thing I could

00:10:14.740 possibly imagine was like an all expense paid trip to Disney world.

00:10:18.160 Yeah.

00:10:18.880 Um, and then, uh, did you win it?

00:10:21.720 I got, I won it.

00:10:22.600 I got fourth place.

00:10:23.460 So I snuck in there, uh, snuck in, I, uh, and then I went to, and then we went to Florida

00:10:29.260 to Disney world and I, uh, I hadn't traveled, like I didn't travel around too much growing

00:10:34.360 up.

00:10:34.580 Like I mostly was in New Mexico and we did some road trips around the Southwest.

00:10:37.240 So I remember getting to Florida and it was extremely humid.

00:10:41.820 It was like, I never felt what humidity felt like when I landed in Florida.

00:10:45.980 I was like, Oh, this feels bad.

00:10:48.120 And then I, yeah, it definitely does.

00:10:53.100 Yeah.

00:10:53.620 It's funny.

00:10:54.260 Cause I grew up in humid, dude.

00:10:55.860 Like you would like, you try to shake somebody's hand, you couldn't even land it because it was

00:10:59.800 just not enough of viscose, just too much lube in a handshake.

00:11:03.400 You'd sit there for 10 minutes trying to land a handshake, you know, everybody was

00:11:07.220 always dripping, you know, all the time people, you always thought they were scared or something

00:11:10.700 where they were kind of geeked up off a gas station dope or something, but people were

00:11:14.160 just, it was humid.

00:11:15.500 Yeah.

00:11:15.820 You get really humid over there.

00:11:17.480 So then I became, so then I became a mathlete.

00:11:19.880 Uh, that was like a big part of my identity was being a mathlete.

00:11:23.100 And I would,

00:11:23.500 And you have to wear a vest.

00:11:24.340 Like, what do you guys do?

00:11:24.960 Is there an out, is there a uniform for that?

00:11:26.740 Well, you, you need a, you have a calculator.

00:11:30.660 Okay.

00:11:30.860 So everyone had their favorite calculator.

00:11:33.080 Okay.

00:11:33.400 You got that Draco on you, baby.

00:11:35.000 I feel you keep strapped.

00:11:36.680 You stay strapped at that time.

00:11:39.360 And then, uh, but, uh, but outside of that, not really.

00:11:42.260 I mean, like everyone had their favorite, like his pencil, you know, your pencil, your calculator,

00:11:45.620 that was your gear.

00:11:46.280 And, uh, but yeah, no, I, I was, I was like a, a nationally ranked mathlete for, uh, for

00:11:53.380 middle school, high school.

00:11:55.020 That was my, that was my jam.

00:11:57.080 Yeah.

00:11:57.440 And is it, what's this math circle like?

00:11:59.560 Is there, wow, that's crazy, dude.

00:12:03.240 So you go to, what are there competitions you go to with that?

00:12:05.760 There's competitions.

00:12:06.460 Yeah.

00:12:06.940 There's like, there's like state level competitions.

00:12:09.340 There's national level competitions.

00:12:11.140 There's like these summer camps, math camp.

00:12:14.120 I went to math camp a bunch of times.

00:12:16.400 Um, where you, where you convene with like-minded mathletes. 1.00

00:12:19.560 Oh, damn. 0.99

00:12:20.100 Okay. 1.00

00:12:21.180 Wow. 1.00

00:12:21.740 Fucking wizards in the park. 1.00

00:12:23.740 Uh, a couple of dudes fucking fighting over a common denominator in the lobby. 1.00

00:12:27.860 That's crazy, bro. 0.97

00:12:29.420 But then you're, but just like any other sport, like you're like, it's competitive, you know,

00:12:33.620 you gotta like, you gotta win.

00:12:35.380 And, uh, and so, so you're chummy with everyone, but you're also like, we're like, who's going

00:12:40.400 to win, you know?

00:12:41.280 Yeah.

00:12:41.940 No, dude, competition's amazing, man.

00:12:44.000 That's one thing is too, that's so nice.

00:12:45.380 I think about when you're young, it's like, if you're involved in a sport or a group or

00:12:48.740 whatever it was, um, just the, just that chance to be competitive, you know, what are, what

00:12:54.200 were like some of the competitions at the math thing?

00:12:55.840 Like what's a, what's a math competition?

00:12:57.460 Like when you get there?

00:12:58.740 So you, uh, the ones in the middle school, middle school were actually more fun.

00:13:02.920 Cause there's like, there's almost like a Jeopardy style buzzer competition and stuff

00:13:06.760 like that.

00:13:07.720 But, uh, but in high school, once you get to high school, it's just, uh, you just take

00:13:11.980 a test and you just, it's like the, the biggest one in America is called the USEMO.

00:13:17.260 So, uh, bring it up.

00:13:19.600 US MO?

00:13:20.600 US AMO.

00:13:22.260 Okay.

00:13:23.020 And, uh, and it's like, people all around the country take this test.

00:13:28.760 Uh, and there's, uh, United States of American mathematical Olympiad.

00:13:32.900 Okay.

00:13:33.160 Yeah, there you go.

00:13:34.220 Um, okay.

00:13:35.320 A participant must be either a US citizen or a legal resident of the US.

00:13:38.140 Um, okay.

00:13:39.520 Okay.

00:13:39.840 Go on.

00:13:40.460 And then you, uh, and then it's a nine hour test. 1.00

00:13:43.560 Damn. 0.99

00:13:44.100 It's split over two days. 0.99

00:13:45.260 It's nine hours you, it's four and a half hours and it's nine hours and you have six

00:13:50.480 problems.

00:13:51.640 So it's kind of nerve wracking and you get in there, you have four and a half hours the

00:13:55.480 first day, four and a half hours, the second day.

00:13:57.440 Um, can you cheat in between the days?

00:13:59.200 Can you just go home and.

00:14:00.380 No, you, cause you only get three questions the first day and then you only get three

00:14:03.580 questions the next day.

00:14:05.060 And, uh, I remember the first time I took it, I was in, I think I was, uh, I was in eighth

00:14:11.620 grade.

00:14:11.940 First time I took it and I like, I was like so nervous and I just, I like brought a thermos

00:14:17.520 full of coffee and I drank so much coffee that like those four and a half hours felt

00:14:22.480 like, like year.

00:14:23.720 It was like, I was so jittery the whole time.

00:14:26.180 It was, uh, it was crazy.

00:14:27.840 Dang.

00:14:28.180 You out there rattling brother.

00:14:30.140 Oh, integers make me rattle brother.

00:14:32.060 I feel you on that.

00:14:33.700 Dude.

00:14:33.920 So that's pretty, so, so you're competitive and so you get out of there and you're obviously,

00:14:38.580 I guess, um, admired in the world of math probably, or that's, that's like a thing

00:14:42.740 that like, uh, that you can point that that's like a pin that you, a feather in your cap.

00:14:48.340 So that, that helps you get into, um, MIT, right?

00:14:51.700 Yep.

00:14:52.040 Yep.

00:14:52.200 Okay.

00:14:52.620 So then, uh, yeah, in MIT on the MIT admissions, they ask for your like competitive math scores.

00:14:59.920 So if you're, so, so you knew a lot of kids going there probably cause it was tons of kids.

00:15:04.060 Yeah.

00:15:04.320 Yeah.

00:15:04.760 Tons of kids.

00:15:05.320 It was like a reunion.

00:15:06.020 It was like math camp reunion. 1.00

00:15:06.960 Damn. 0.99

00:15:07.520 Cause I was wondering where all y'all were at, dude, because we were doing some other 1.00

00:15:10.840 shit. 0.97

00:15:11.400 You know what I'm saying? 1.00

00:15:12.300 My, yeah, we were not, God, that's where all the smart kids were.

00:15:16.380 That's fascinating, man.

00:15:18.020 And so then, uh, yeah, I was at MIT and then, uh, MIT is like a really intense school because 0.96

00:15:25.240 they, you know, the classes they don't fuck around with.

00:15:28.260 They just really like, they load you up with tons of work. 0.92

00:15:31.000 And most of the time you have, you, you like, they load you up with like huge amounts

00:15:34.900 of work and they, you know, you, you, uh, you don't really know what's going on initially.

00:15:38.720 And so you're just kind of like, you're just trying to make it through.

00:15:41.520 But, uh, you know, there's this motto that, uh, that MIT has called, uh, IHTFP, which, 0.99

00:15:48.720 uh, among the students stands for, I hate this fucking place. 0.99

00:15:51.680 Oh yeah. 1.00

00:15:52.680 It's heavy, huh?

00:15:53.800 Yeah.

00:15:54.100 But then, but then the school, when you go, they tell you, oh, it stands for, um, I've truly

00:15:57.960 found paradise.

00:15:58.860 So, oh, so a couple of differences of opinion.

00:16:02.340 Yeah.

00:16:02.720 Yeah. 1.00

00:16:03.260 Damn. 0.99

00:16:03.620 So it's, so it's really severe there. 0.99

00:16:06.140 There's a lot of work.

00:16:07.040 They load you down out of the gate.

00:16:08.600 You do, you do a lot of work, uh, but it's, it's kind of awesome.

00:16:12.060 I mean, cause, cause I think the students really band together.

00:16:14.220 Like you, like, instead of, like, I think, uh, instead of it being competitive, really

00:16:19.200 MIT is much more about like everybody kind of like coming together, working on problems

00:16:24.640 together, working on homework together, and just kind of like making it through together.

00:16:28.760 Um, what's that social life like there?

00:16:30.940 Like, are you dating?

00:16:31.700 Are you going to parties?

00:16:32.400 What's that like for you at that time?

00:16:33.960 It's a bunch of parties because people like MIT, there's a lot of people who like tinkering

00:16:38.360 with gadgets.

00:16:39.380 So they're tinkering with like, you know, lights and big speakers and DJ sets and all

00:16:44.640 this stuff.

00:16:45.000 So actually the parties are pretty good.

00:16:47.220 Cause they're like, the production value is high.

00:16:49.820 The production quality is high.

00:16:50.900 Um, and then what about when the science kids come through the lab dogs, are they bringing

00:16:56.540 out people making like unique drugs?

00:16:58.160 Was there like designer drugs that are being created by actual people?

00:17:01.220 Like just because you know what I'm saying?

00:17:03.700 Like my friends in college, none of us would know how to do that, but there may be somebody

00:17:08.460 at a smart, at a more prestigious school that would know how, was that a thing even?

00:17:12.860 Or is that just, there's one, there was one part of the campus called East Campus, um,

00:17:17.900 where it was like, it was more fringe.

00:17:19.800 And so there was like, at one point in the school year, they would, in their courtyard,

00:17:24.460 they would build a gigantic catapult, like a huge catapult.

00:17:28.240 Like a trebuchet?

00:17:29.460 Like a trebuchet.

00:17:30.380 Yeah.

00:17:31.460 What's the difference?

00:17:32.160 I don't know what the difference.

00:17:32.940 Yeah.

00:17:33.120 Let's get the difference there.

00:17:33.940 Cause people need to know this anyway.

00:17:35.340 People have, for decades now, people have been.

00:17:37.480 A trebuchet has like a rope attached to the end of it that flings it where a catapult just

00:17:42.400 launches it.

00:17:43.380 No, it was a catapult then.

00:17:44.540 Okay.

00:17:45.080 Like a big, cause there was a big cup.

00:17:46.680 There was a big like, like ice cream scooper.

00:17:48.940 Oh yeah.

00:17:49.380 Looking thing that would like, that would fling it. 1.00

00:17:51.780 And what are they just flinging Adderall into the rest of the campus?

00:17:54.260 They would fling stuff into the river, which I don't think, now that I'm thinking about

00:17:58.100 it, I feel like, I don't know what the, yeah, no, these giant, this giant like catapult

00:18:02.300 things.

00:18:03.340 Um, yeah.

00:18:05.020 And so this was like a event that would go on and people would kind of rave there.

00:18:08.900 What are you saying?

00:18:09.740 Yeah.

00:18:09.920 They would do this.

00:18:10.860 They would do other things.

00:18:11.920 They were, they were like, they were into, they would like build a lot of stuff over there.

00:18:15.520 Um, and there would be.

00:18:17.120 Like people that ended up at Burning Man later on.

00:18:18.980 Yes.

00:18:19.280 Yes.

00:18:19.520 That was the burning, that was core Burning Man.

00:18:21.700 Like there was a, there was a satanic ritual floor.

00:18:24.320 Oh yeah.

00:18:24.820 Yeah.

00:18:25.060 Like a lot of, a lot of, like it's fringe.

00:18:27.240 Cool.

00:18:27.720 It's cool.

00:18:28.280 Right.

00:18:28.760 Um, but, uh, but there, yeah, so there's all these parties.

00:18:31.440 We, we bragged at MIT that, uh, you know, people from all the neighboring schools, cause

00:18:36.720 Boston is a huge college town, like tons and tons.

00:18:39.180 Boston's an amazing city.

00:18:40.360 Yeah.

00:18:40.740 But no, MIT was fun, but I was only there, I was at MIT for one year.

00:18:43.760 Right.

00:18:43.940 And you dropped out.

00:18:44.900 Is that safe to say?

00:18:45.520 Yep.

00:18:45.820 Okay.

00:18:46.000 You dropped out and then you, so you got into AI, into the AI world.

00:18:51.120 Is that kind of a safe bridge to say?

00:18:52.760 Yeah.

00:18:53.060 Yeah.

00:18:53.260 Yeah.

00:18:53.340 Yeah.

00:18:53.440 Okay.

00:18:53.880 Is there, and I want to ask this cause I know Mark Zuckerberg dropped out of college.

00:18:58.660 You dropped out of college.

00:19:00.000 Both of you guys have had success in tech and kind of, you know, forward thinking that sort

00:19:05.240 of, uh, world.

00:19:07.080 Um, is there something in college that you felt like didn't nurture you or did you just

00:19:12.540 feel like this isn't the place for me?

00:19:15.520 Do you feel like college doesn't nurture a certain type of thinker or was it just a personal

00:19:21.340 choice?

00:19:21.820 I think for me, it was like, um, I was just feeling really impatient.

00:19:28.840 Um, and I don't really know why really, but I remember like, I remember I was in school

00:19:34.340 the first year.

00:19:34.960 I, it was, it was really fun and, and I really enjoyed it.

00:19:37.700 But then I remember, you know, this in the year when I was at MIT was one of the first,

00:19:44.980 like, it was like one of the early big moments in AI because it was, um, I don't even remember

00:19:49.780 this, but there was an AI that beat, uh, the world champion at Go.

00:19:54.640 Um, this was in 2015, which is when I was in college.

00:19:58.760 At Go.

00:19:59.240 And that was a game?

00:20:00.260 Yeah.

00:20:00.500 Go.

00:20:00.780 It's like, uh, it's like a big checkerboard with like white and black stones.

00:20:06.080 It's like, uh, um, and it was, uh, yeah, this, this game AlphaGo versus Lisa Dole.

00:20:13.200 So AlphaGo versus Lisa Dole, also known as the DeepMind Challenge match was a five game

00:20:17.640 Go match between top Go player Lisa Dole and AlphaGo, a computer Go program developed by 0.99

00:20:22.440 DeepMind played in Seoul, South Korea between nine and 15, between nine, ninth and 15th of

00:20:29.660 March, 2016.

00:20:30.820 I was confusing how that's written.

00:20:32.020 It is very confusing.

00:20:32.900 Do you think that we got to, um, AlphaGo won all, but the fourth game, all games were won

00:20:39.680 by resignation.

00:20:40.500 The match has been compared with the historic chess match between Deep Blue and Gary, uh,

00:20:45.300 Kasparov.

00:20:46.680 Huh.

00:20:47.200 The winner of the match was slated to win $1 million since AlphaGo won.

00:20:49.700 Google DeepMind stated that the prize would be donated to charities, including UNICEF

00:20:53.620 and USAID.

00:20:55.460 That's just a joke.

00:20:56.600 That's just, um, but Lee received $150,000 for playing.

00:21:00.740 So this was a big moment because this had never kind of happened before.

00:21:05.060 Never happened.

00:21:05.560 Yeah.

00:21:06.020 And it was, it was a big moment for, uh, for AI.

00:21:09.960 It was like, oh wow, this stuff is like, it's really happening.

00:21:12.820 And so then this happened in March and I guess, yeah, I dropped out, started my company in

00:21:17.440 May.

00:21:17.820 So I guess two months after this, I was, I was, uh, yeah, that's what the game looks like.

00:21:23.220 Um, oh, I've, I've played this game before.

00:21:26.340 It's honestly, it's a really, uh, like, I'm not very good at the game.

00:21:31.340 It's a little more fun than playing chess unless you're like, you know, in a like Renaissance 1.00

00:21:34.640 fair board games or whatever.

00:21:35.820 Yeah.

00:21:36.120 Yeah.

00:21:36.820 Um, okay.

00:21:37.560 So now we got you, you're loose, dude.

00:21:40.140 You're out of the school and you're in the world.

00:21:43.580 You see, did that match, did realizing that kind of like spurn, you don't want to leave

00:21:48.160 school or did it, was that just something that happened around the same time?

00:21:51.040 It, it kind of did.

00:21:52.520 Basically it was, it was, I remember feeling like, oh wow, AI it's happening.

00:21:57.520 And, uh, this was back in 2016.

00:21:59.440 So like eight, eight, nine years ago.

00:22:01.420 Okay.

00:22:01.700 And then, um, I felt like I had to, you know, basically that, that inspired me to start my

00:22:06.880 company.

00:22:07.840 Um, and I moved, I basically went straight from, I remember this, I was, I flew straight from,

00:22:13.860 uh, Boston to San Francisco and then started the company basically.

00:22:19.300 Um, and that scale AI.

00:22:20.680 Scale AI.

00:22:21.440 Okay.

00:22:21.900 And so did you, where'd you been following AI?

00:22:25.220 Like what are your kind of, are you just like knew like, this is where it's going?

00:22:28.900 Like you just felt there was something, an instinct that you trusted or like, because

00:22:33.580 that's a big thing to do.

00:22:34.960 I was stuck.

00:22:35.900 So I took all the AI classes at MIT.

00:22:38.640 Okay.

00:22:38.860 So you already learning a lot about it.

00:22:40.500 Yup.

00:22:40.760 And then there was one class where, um, you had to like on all the classes you had to do

00:22:45.980 side projects or final projects of some kind.

00:22:48.080 And in one of them, I wanted to build a, um, like a camera inside my refrigerator that

00:22:54.500 would tell me when my roommates were stealing my food.

00:22:57.000 Wang boy.

00:22:57.840 Catching them.

00:23:00.040 Wang boy.

00:23:01.520 Wow.

00:23:01.920 And, uh, but then, so I worked on that and then it was, there was one moment where,

00:23:07.500 where, uh, I was like, there was like, there was a moment that clicked where I was trying

00:23:12.060 to build this thing.

00:23:12.840 And then there was one step that was like too easy.

00:23:15.180 I was like, Whoa, that just worked right there.

00:23:17.280 And then that happened.

00:23:18.940 And then the, the go match happened.

00:23:20.740 And I was like, this, this stuff is happening.

00:23:23.220 And so I, did you ever market those refrigerators?

00:23:25.180 You ever actually create that?

00:23:26.440 I didn't market them.

00:23:27.480 No, I could totally see that, bro.

00:23:29.480 There's a refrigerator.

00:23:30.480 Every dorm has it where there's a camera built in and you just get, you're in, you get a notification

00:23:34.880 on your phone. 0.56

00:23:35.880 You know, you're like, damn, Adnan's got my hummus, you know, but you got video of them 0.99

00:23:41.620 right there, dude. 0.97

00:23:42.380 That's a great idea.

00:23:43.580 Yeah.

00:23:43.800 I love that.

00:23:44.280 That was, uh, that was, that was college me.

00:23:46.500 Yeah.

00:23:47.720 Okay.

00:23:48.120 So Alexander Wang, he's free in the world now.

00:23:50.980 He's headed to San Francisco.

00:23:52.920 He's AI'd up.

00:23:54.000 He feels the energy.

00:23:54.920 He's motivated by some of the classes he took.

00:23:57.920 Um, he's motivated by seeing that AI starting to actually overtake humans, right.

00:24:02.760 Or be able to compete with actual human thinking with the chess match.

00:24:07.280 Yeah.

00:24:07.760 I would, the way I would think about it or the way I thought about the time was like, this

00:24:10.960 is, this is becoming, um, I, you know, people, you know,

00:24:14.260 people have been talking about AI for decades.

00:24:16.520 Like it's kind of been always been one of these things that, um, people have, have said,

00:24:20.860 oh, it's going to happen, but it never really was happening.

00:24:22.680 And it felt like it was, you know, it was really about to happen.

00:24:24.880 About artificial intelligence.

00:24:26.020 Yeah.

00:24:26.160 Yeah.

00:24:26.480 Yeah.

00:24:26.660 Yeah.

00:24:27.220 Cause I've always heard about artificial intelligence.

00:24:28.780 That's what I have.

00:24:30.020 So I've always been like, um, no, you're, you are, you have real intelligence, not artificial,

00:24:35.440 real intelligence.

00:24:35.960 I don't, I mean, I think it's a, it's probably a mix, but I see what you're saying.

00:24:40.100 You know, I do.

00:24:41.420 Do you know what I thought of the other day?

00:24:42.360 It was like, what if they had like a Mexican version?

00:24:44.180 And it was like, Hey, I, I don't know.

00:24:48.620 That's a good, that's a good joke.

00:24:50.380 But thank you.

00:24:51.140 It's a nice laugh.

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00:27:04.960 Um, what is AI?

00:27:07.260 Yeah.

00:27:07.880 So, so AI is all about, you know, basically programming computers to be able to start thinking like

00:27:15.320 humans.

00:27:15.700 So, you know, traditional computer programming, uh, you know, it's pretty, it's pretty bare

00:27:22.040 bones.

00:27:22.420 It's not very smart.

00:27:23.260 And so AI is all about, can you have, can you build algorithms that start to be able to

00:27:28.560 think like people and, and replicate some of the, like our brains are these incredible,

00:27:35.680 incredible things, you know, and that's, that's evolution.

00:27:38.740 That's just biology that created our brains.

00:27:41.080 Um, and so it's all about how can we, how can we build something similar to that or replicate

00:27:45.760 it, um, using, using computers and machines.

00:27:48.720 And so the whole, you know, the, the, the whole modern AI, um, era really started around,

00:27:57.080 um, an area called computer vision, but it was like, how can we first get, um, computers

00:28:02.940 to see like, uh, like humans do.

00:28:06.500 So one of the very first, so, you know, one of the very first AI projects was this thing

00:28:12.600 called image net, um, image net and later Alex net.

00:28:16.420 And it was basically, can you get computer programs to like given a photo to tell you

00:28:22.300 what's in that photo?

00:28:23.100 So I see, so just like a human would, like if you showed them this, you're starting to

00:28:26.840 train them to, um, have a perspective.

00:28:30.940 Yeah.

00:28:31.400 Train them to, well, actually originally, like, uh, you could like, let's say you took

00:28:35.440 a photo of this, of this bottle, a machine wouldn't even be able to tell you what's in

00:28:39.020 the photo.

00:28:39.580 It would just know what the pixels were, but it wouldn't be able to tell you like, oh,

00:28:43.540 there's a bottle in that, in that photo.

00:28:45.640 So the, you know, one of the first AI breakthroughs was when YouTube, um, built an algorithm that could

00:28:51.540 tell when there were cats in their videos.

00:28:54.720 And that was like this, you know, in like 2012 or 2011, this was like this mind blowing

00:29:00.080 breakthrough that you could like figure out, you could like use an algorithm to figure out

00:29:04.400 when there was a cat inside, uh, inside a video.

00:29:07.220 And so AI, it started pretty simply with just how do we replicate, you know, um, vision?

00:29:15.000 Like how do you replicate like our, basically the fact that our eyes and our brains can like

00:29:18.720 process all this imagery coming in.

00:29:21.540 And that really led to, I think one of the first major use cases of AI, which is self-driving

00:29:27.420 cars.

00:29:28.420 So this was it, when we, when I started the company in 2016, self-driving cars were all

00:29:34.160 the rage because, um, you know, it was, you know, there were all these like skunkworks 0.95

00:29:38.760 projects.

00:29:39.440 When you started scale AI, you mean?

00:29:40.780 Yeah.

00:29:40.980 When I started scale AI.

00:29:41.940 So when you, when you kind of got into AI at that time, self-driving cars are the, are

00:29:45.940 the most popular things.

00:29:47.120 Yeah.

00:29:47.540 Yep.

00:29:47.900 That was all the rage.

00:29:48.920 And so it was, it was all about, can, you know, um, can we start building algorithms

00:29:53.640 that can drive a car like a human would and do it safely and do it, you know, more efficiently.

00:29:58.560 And that way, instead of, that was, that was one of the first major areas.

00:30:03.500 Okay.

00:30:03.820 And then, um, now, you know, I, the whole, the whole industry has moved so fast, but then

00:30:09.700 all of a sudden we got chat GPT and we got, you know, more advanced stuff more recently

00:30:14.300 that, that is able to talk like a human or sort of think like a human.

00:30:17.740 And so it's really come pretty far recently, but all of it is about how do you, you build

00:30:23.700 algorithms, how to use machines to be able to think like a person.

00:30:26.840 Okay.

00:30:27.160 And is it a pro if it's like, say if I opened a door, like, like, oh, we keep the AI in

00:30:32.460 there.

00:30:32.920 Is it a computer?

00:30:34.340 Is it a program?

00:30:35.900 Is it a hard drive?

00:30:37.500 Like what, like, what is that?

00:30:40.920 Yeah.

00:30:41.220 Yeah.

00:30:41.380 So there's two parts, there's two parts to it.

00:30:43.640 So the first part is you need really advanced chips.

00:30:48.840 So you need like, uh, uh, like these, they're called GPUs or sometimes called TPUs or, you

00:30:55.000 know, there's a lot of different words for it, but you need like the most advanced computer

00:30:58.480 chips in the world.

00:30:59.420 Okay.

00:30:59.860 And they, how big is each one?

00:31:01.320 Do you know?

00:31:02.100 Uh, like, or can you measure it like that?

00:31:04.540 They, I mean, the biggest ones are actually like the whole chips are like, you know, pretty

00:31:09.060 big, but they, they're like a, they're like a, like a wafer kind of thing.

00:31:13.020 But then you, um, you put a lot of them all together.

00:31:16.260 Okay.

00:31:16.500 So these, uh, yeah, yeah, yeah, exactly.

00:31:20.460 Um, these like, uh, these chips and the, the biggest ones are, are, yeah, exactly.

00:31:25.580 That's a little one.

00:31:26.400 That's a little one.

00:31:27.380 There's really big ones.

00:31:28.840 Okay.

00:31:29.400 Um, but these are the, so this is the, these are the brain cells of it.

00:31:34.180 Yep.

00:31:34.780 These are, these are the brains behind it.

00:31:36.300 Yeah.

00:31:36.760 So then, um, yeah, exactly.

00:31:38.600 These are some, those are some big ones.

00:31:40.000 So then.

00:31:40.700 So you have to have a lot of these chips.

00:31:41.960 So you need a ton of these chips and that's, that's kind of the, the, like, uh,

00:31:46.780 that's the physical presence.

00:31:48.500 And then, um, and then by the way, they take a huge amount of energy.

00:31:52.280 They're really, uh, they, cause they have, you have to do a lot of, you know, calculations

00:31:56.480 and there's a lot of math that has to happen on them.

00:31:58.560 Um, and so, um, and you have, you have giant, basically data centers, buildings full of

00:32:04.780 like tons and tons of those chips just in giant rows.

00:32:07.480 Like how big are we talking warehouses?

00:32:09.720 Yeah.

00:32:10.060 The biggest one, I mean, like, um, like Elon's data center Colossus is like, uh, I mean, it's

00:32:16.900 probably more than a million.

00:32:18.080 It's definitely more than a million square feet.

00:32:19.440 I mean, it's like just huge.

00:32:20.700 Really?

00:32:21.080 Yeah.

00:32:21.300 Yeah.

00:32:21.440 Look up Colossus.

00:32:23.060 Uh, I've never known this.

00:32:25.000 Yeah.

00:32:25.600 Uh, yeah, yeah.

00:32:27.240 That, that, uh, you see that building with the sunset second row.

00:32:30.720 Yeah.

00:32:30.840 Yeah.

00:32:30.960 There you go.

00:32:32.060 Or, uh, the one to the left.

00:32:33.600 Oh no.

00:32:34.280 Yeah.

00:32:34.660 There you go.

00:32:35.040 That one.

00:32:35.620 Yeah. 0.99

00:32:36.080 Look, it's like a huge ass building. 0.99

00:32:38.800 It's huge. 0.99

00:32:39.380 And all that's just filled with chips.

00:32:41.600 Um, have you ever been in there?

00:32:44.000 I haven't been in that one, but I've, I've been in some of these and it's just, and this

00:32:47.300 is what it looks like inside.

00:32:48.240 Yeah.

00:32:48.400 Basically.

00:32:48.980 Yeah.

00:32:49.560 So it's just rows and rows of chips.

00:32:51.580 So no plants or anything.

00:32:53.300 Yeah.

00:32:53.560 No, no plants, no plants.

00:32:55.140 It gets hot in there.

00:32:56.200 I bet.

00:32:56.740 Um, so the first part is just the, the, that's the physical presence.

00:33:02.020 And then the second part are the algorithms.

00:33:04.900 So then you have like on top of those chips, you have, you have software that that's running

00:33:10.840 and the, the algorithms are just all are like what you actually are telling.

00:33:15.140 What's the math that you're telling to happen on the chips.

00:33:17.540 And those algorithms are, you know, some of the, you know, most complicated algorithms

00:33:22.480 that humans have ever come up with.

00:33:24.660 And that's kind of the, that's kind of the software part, or that's kind of the, that's

00:33:29.140 the part that like, you know, exists on the internet or you can download or whatnot.

00:33:33.000 And then it has to be run on these like huge warehouses of giant, of giant chips.

00:33:37.760 Okay.

00:33:37.960 Um, so when someone goes to like scale AI or chat GPT, these are all AI interfaces or

00:33:45.840 what are they?

00:33:46.520 They're.

00:33:47.200 Yeah.

00:33:47.440 So, so, um, yeah, like chat GPT is a, is a way to be able to, uh, talk to basically you

00:33:54.480 can talk to the algorithm.

00:33:55.580 Okay.

00:33:56.020 So you can start interacting directly with the algorithm.

00:33:58.660 You can see how the algorithm is thinking.

00:34:00.520 Um, so you could say to this, um, can you describe the weather today?

00:34:05.420 Exactly.

00:34:06.000 Yeah.

00:34:06.100 And if you said that to five different AI company or AI companies, basically.

00:34:12.800 Or AI algorithms, different, different AI systems.

00:34:15.380 Yeah.

00:34:15.460 So if you said that's five different AI systems, you might get a little bit of a varied answer.

00:34:18.980 A little bit.

00:34:19.540 Yeah.

00:34:19.880 Okay.

00:34:20.140 You'll get, you'll get, cause they all are trying to have their own style and have their

00:34:23.460 own vibe to it.

00:34:24.820 Interesting.

00:34:25.180 And then what we do, what, what scale AI is all about is we've kind of built the, uh,

00:34:30.860 almost like the Uber of AI.

00:34:32.380 Okay.

00:34:32.740 So a lot of what we're trying to do is how do we, so how do we help produce data that

00:34:38.320 is improving these algorithms?

00:34:40.120 And just like how Uber there's.

00:34:42.220 Okay.

00:34:42.580 You're losing me there a little.

00:34:43.720 Yeah.

00:34:43.980 Yeah.

00:34:44.100 It's okay.

00:34:44.920 But, uh, if I slow down the, if I, if I, if you're losing me, I, but so explain that

00:34:49.000 to me a little bit clearer for me.

00:34:50.720 Yeah.

00:34:50.900 Yeah.

00:34:51.140 So, so, okay.

00:34:52.700 So with these algorithms, um, one key ingredient for these algorithms is data.

00:34:58.360 So, okay.

00:34:59.540 So you have the chips and everything that are, that are storing all the information.

00:35:02.620 Yep.

00:35:03.040 They're storing the data.

00:35:04.020 Yep.

00:35:04.320 And then you have the algorithms that are helping mediate between the user and the data.

00:35:09.120 Yeah.

00:35:09.540 So basically you kind of have, yeah, you have three, you have three key pieces.

00:35:13.660 Okay.

00:35:13.900 So you have the, uh, you have the computational powers of the chips, you have the chips, you

00:35:19.240 have the data, which is like just tons and tons of data that that's where the algorithms

00:35:25.180 are learning the patterns from.

00:35:26.900 Okay.

00:35:27.060 So these algorithms, they aren't just like, they don't just learn to talk randomly.

00:35:30.740 They learn it from learning to talk from how humans talk.

00:35:33.880 Right.

00:35:34.220 Got it.

00:35:34.460 So you need tons and tons of data.

00:35:35.740 And then you have the algorithms, which, uh, learn from all that data.

00:35:40.600 And then they run on top of the chips.

00:35:43.180 Got it.

00:35:43.460 Um, so then one of the big challenges in the industry is, okay, how are you going to produce

00:35:49.800 all this data?

00:35:50.860 And so, um, uh, this is, how are you going to get data for your system?

00:35:56.720 Like how do you farm the best data?

00:35:58.560 How do you, exactly.

00:35:59.280 How do you build, how do you build all that data?

00:36:01.140 And how do you do that, um, in the most effective way?

00:36:04.300 How do you build new data?

00:36:05.820 So clean data, because what if you get a bunch of data in there, that's just a bunch of advertisements

00:36:09.300 and bullshit, will that affect the output? 0.62

00:36:11.720 A hundred percent. 1.00

00:36:12.340 Yeah.

00:36:12.700 That definitely affects the output.

00:36:14.420 So this whole data, so data is, is, you know, some people say like data is the new oil

00:36:19.540 or data is new gold.

00:36:20.640 Like data is really, really valuable because it's, it's how the algorithms are learning

00:36:26.380 everything that they're learning.

00:36:27.480 Like any, anything that the algorithms know or learn or say, or do all that has to come

00:36:33.700 from the data that goes into it.

00:36:35.540 Okay.

00:36:35.580 So, so if I ask the, uh, if I ask a system, an AI system, a question or ask it to help

00:36:40.500 me with something, help me to design something or to curate an idea, it's going to use the

00:36:46.400 data that it has within it to respond to me and help me, uh, and help give me an answer

00:36:52.480 that I can use.

00:36:53.540 And it's only, and the data it has in it is only based upon the data that, um, is put

00:37:00.020 into it.

00:37:00.620 Exactly.

00:37:01.360 Yeah.

00:37:01.520 So then, so yeah, it's kind of, um, so then we don't, you know, we don't spend enough

00:37:07.660 time talking about where it is, you know, how are you going to get this data and how

00:37:11.360 are you going to keep making new data?

00:37:13.140 So the, the angle that we took at scale was to kind of, um, turn this into an opportunity

00:37:20.140 for people.

00:37:20.720 So, um, we're, you know, we're kind of like the Uber for AI.

00:37:24.160 So just like how Uber, you have, you know, riders and drivers for us, we have, uh, you

00:37:30.780 know, we have the AI systems, you know, the algorithms that need data.

00:37:35.860 And then we have, um, a community of people, a network of people who help produce the data

00:37:41.560 that go into the system.

00:37:42.740 And they get paid to do that.

00:37:43.980 Ah, so they're almost data farming, like creating good data or creating good data.

00:37:48.340 Yeah, exactly.

00:37:49.280 So, and it's, and it's huge.

00:37:51.360 It's like, uh, so we do this through, um, our platform called outlier.

00:37:56.000 Okay.

00:37:56.520 And, uh, uh, outlier last year, uh, people, contributors, we call them contributors on

00:38:02.480 outlier earned about $500 million total across everybody.

00:38:08.000 Um, in the U S that's across 9,000 different towns.

00:38:12.480 Um, and, uh, so it created a lot of jobs, a lot of jobs.

00:38:15.580 Okay.

00:38:15.800 And so what would, um, okay.

00:38:17.960 So scale was your company, scale AI system.

00:38:21.140 Yep.

00:38:21.520 Is that right?

00:38:22.000 So we, uh, yeah, I mean, yeah, scale AI is an AI system.

00:38:26.520 Okay.

00:38:26.900 And then outlier.

00:38:28.520 Yep.

00:38:28.900 Is a separate company that's works with it.

00:38:31.540 Yep.

00:38:32.180 And that is where you are, um, hiring people.

00:38:37.840 We, yeah, we, we basically.

00:38:40.120 To pull in data.

00:38:41.460 Yeah.

00:38:41.880 We, we, we build this platform that anybody, you know, a lot of people all, frankly, all

00:38:47.440 around the world, but Americans too, can log on and, uh, and help build data that goes

00:38:54.420 into the algorithms and get paid to do so.

00:38:57.380 Wow.

00:38:58.060 Yeah.

00:38:58.360 So how, and how does a user do that?

00:39:00.200 Like, what is an example of somebody who's helping build data for an AI database?

00:39:04.640 Yeah.

00:39:05.140 Let's say you're a nurse, like you're a nurse with like tons of tons of experience.

00:39:08.660 So, you know, a lot about how to take care of people, um, and, uh, take care of, of people

00:39:14.160 who are sick or, you know, have issues and whatnot.

00:39:16.100 And, uh, so you, you could log onto the system and, and this, and our platform, and you could

00:39:22.480 see that the algorithm is, you know, let's say you ask the algorithm, like, Hey, I have

00:39:27.060 a, you know, I have a pain in my, in my stomach.

00:39:30.140 What should I do?

00:39:30.960 And you notice that the algorithm says the wrong thing.

00:39:33.440 Like the algorithm says, Oh, just, you know, hang out and, and, you know, it'll go, it'll

00:39:37.160 go away. 0.82

00:39:37.780 And you know, as a nurse, like that's wrong.

00:39:40.660 Like I, you know, you have to, you have to go to the emergency room because you might

00:39:44.460 have appendicitis or you might have, you know, you might have something really bad.

00:39:47.620 And so you would, as a nurse, you would go in and you would basically correct all these

00:39:51.960 mistakes that the AI system has.

00:39:54.120 And then that would then feed it back into the algorithm.

00:39:56.860 So that's smarter the next time.

00:39:58.200 So it's kind of this, this continual process of, and there's versions of that for whatever

00:40:03.820 your expertise is or whatever, you know, you know more about than anything, everything,

00:40:08.460 everything.

00:40:09.040 Exactly.

00:40:09.760 So, and so people get paid for that.

00:40:12.160 Yeah.

00:40:12.380 Yeah.

00:40:12.500 They get paid.

00:40:13.320 And how do you know if their information is valuable or not?

00:40:16.580 Uh, well we, so we don't want spam obviously.

00:40:19.860 So we, we, we have a lot of systems to make sure that people aren't spamming and that like

00:40:24.280 you're saying it's not, it's not, you know, it's not garbage in that's going into the,

00:40:28.320 into the algorithms.

00:40:29.500 Um, so we have, you know, we have kind of like people check the work of other people to

00:40:33.600 make sure that the AI systems are really good.

00:40:35.880 And we have some like automated systems that check this stuff.

00:40:38.800 But, uh, but for the most part, it's, it's like, it's really broad.

00:40:42.160 Like we want experts in anything, everything, shellfish, train tracks, whatever.

00:40:47.820 Yeah.

00:40:48.360 Gelatin, everything, childhood, death or whatever. 0.97

00:40:52.400 Yeah, totally.

00:40:53.280 Stars, galaxies, whatever.

00:40:55.220 Animals, big animals.

00:40:57.340 Yeah.

00:40:57.760 Yeah. 1.00

00:40:57.940 Damn. 1.00

00:40:58.780 So, wow. 1.00

00:41:00.240 So it's kind of like your data is almost like an ocean or a body of water and you, different

00:41:05.300 places are going to be able to keep their body of water cleaner or dirtier and different

00:41:09.660 infections could get in different spyware, all types of stuff.

00:41:12.480 So, and if you have the really a clean body of water, then you're going to be able to

00:41:16.520 offer a clean data or a certain type of data to, um, people who are using your AI platform.

00:41:24.820 Does that make sense or not?

00:41:26.360 Yeah, yeah, totally.

00:41:27.220 And our job is like, how do we make sure that this body of water is as clean as possible

00:41:31.680 and we fill it up as much as possible, that it has as much information about everything

00:41:36.720 across the globe.

00:41:38.100 Wow.

00:41:38.180 So is there almost a race for information right now in a weird way or no?

00:41:42.020 Is that not it?

00:41:43.060 There a little bit.

00:41:44.160 Yeah.

00:41:44.340 I think that there's a, well, there's a race for like, how are different AI systems competing

00:41:48.640 against each other?

00:41:49.380 And sorry to interrupt you.

00:41:50.520 Yeah, no, no.

00:41:51.240 So the, there's a, there's, it's, it goes back to the three things I mentioned.

00:41:54.980 So there's kind of like three dimensions that, that they're all competing as one another.

00:41:59.440 One is, um, chips.

00:42:01.620 So who could, who has the most advanced chips?

00:42:04.040 Who is the, the biggest buildings of chips?

00:42:06.480 Like who has the most chips, um, that they're utilizing, uh, data.

00:42:10.360 So the kind of body of water, whose body of water is better, cleaner, you know, uh, healthiest,

00:42:15.480 biggest, et cetera.

00:42:16.480 And then the last is algorithms.

00:42:18.280 So who's, and this is where the scientists really come in.

00:42:21.540 And it's like, okay, who's coming up with the cleverest algorithms or who has like a trick

00:42:26.960 on an algorithm that somebody else doesn't have.

00:42:28.940 Like who's doing that to basically, um, make the AI learn better off of the data that it

00:42:34.480 has.

00:42:35.540 Wow.

00:42:36.700 God, I'm in the future right now.

00:42:38.500 That's so, man, it's just, it's so crazy.

00:42:43.160 And I think AI scares people because the future scares people, right?

00:42:46.340 It's like, that's one of the scariest things sometimes is the future.

00:42:49.320 So I think a lot of times people, you associate, um, cause a lot of times when people mention

00:42:53.860 AI, there's a little bit of fear.

00:42:55.160 It seems like from people, um, there's fear that it's going to take jobs.

00:42:58.580 There's fear that it's going to take over, um, the, our ability to think for ourselves.

00:43:03.400 Yeah.

00:43:03.720 There's just kind of some general uncertainty there.

00:43:06.060 Like is this, and it kind of feels like fear a lot of times, but a lot of times fear is

00:43:10.280 just a lack of knowledge.

00:43:12.120 Right.

00:43:13.100 Um, and not a lack of knowledge because you didn't want to know just cause you don't know 0.99

00:43:17.220 or that you're dumb, but just cause you don't know. 0.92

00:43:20.220 Um, what are, what are positive things that we're going to see with AI? 0.98

00:43:24.360 Right.

00:43:24.620 I want to start there.

00:43:25.640 Yeah.

00:43:26.080 So I think first, like we don't, the AI industry, we don't do the best job explaining this.

00:43:31.920 And I think sometimes we make it seem all sci-fi and, and, and genuinely we were part

00:43:37.800 of the problem and making it seem scary.

00:43:39.620 Right.

00:43:40.100 Uh, but you know, one thing for example, is like, I think AI is actually going to create

00:43:45.760 a ton of jobs.

00:43:47.340 Um, and that story is not told enough, but you know, these jobs that we're producing or

00:43:52.680 this, this sort of, um, this opportunity that we're providing on our platform outlier,

00:43:56.720 like that's only going to grow as AI grows.

00:43:59.660 So.

00:43:59.880 Because you have to have new data.

00:44:01.080 You have to have new data.

00:44:02.400 And the only place you can get new data is from people will, will at a certain point

00:44:06.160 with the system, be able to create, it can probably matriculate data or matriculate. 0.94

00:44:11.400 Is that with birthing or what is that?

00:44:13.220 Yeah.

00:44:13.400 It just moves through like.

00:44:15.080 Okay.

00:44:15.540 It can probably like quantify or, and, and, and give you answers, but it can AI create new

00:44:21.820 data.

00:44:22.860 Uh, no.

00:44:24.040 So, so I think, well, it can do a little bit of that.

00:44:26.700 So it can, AI can help itself create its own data, um, and, and help itself a little

00:44:32.260 bit.

00:44:32.500 But ultimately most of the progress is going to come from, you know, people who are able

00:44:38.540 to help really the model get better and smarter and more capable at all these, all these different

00:44:43.000 areas.

00:44:43.280 Ah, yeah.

00:44:44.660 I didn't see that part of it.

00:44:45.980 I didn't understand that we are the ones who are giving it, um, information.

00:44:51.620 And since we're going to continue to learn, I would assume that we would be able to help

00:44:55.320 continue to help it learn.

00:44:57.640 Yeah.

00:44:57.780 And the world's going to keep changing and we're going to need to be able to keep teaching

00:45:01.480 the, the algorithms, keep teaching the models about how the world's changing.

00:45:05.000 So, you know, uh, this is actually a, a big thing that I think most people don't understand.

00:45:11.360 The people who are getting the opportunity now who are earning money from it see it, but

00:45:15.720 as AI grows, there's actually going to be tons of jobs created along the way and tons of

00:45:22.420 opportunity for people to help improve AI systems or control AI systems or overall, um,

00:45:29.520 sort of be a part of the, the technology, not just sort of disenfranchised by it.

00:45:34.200 Okay.

00:45:34.960 So like, yeah.

00:45:35.840 So what were, what are you, do you feel like are other ways?

00:45:38.140 Like if you had to look into the future a little bit, right?

00:45:40.300 So you have the fact that people are going to be able to add more data, right?

00:45:43.800 Yep.

00:45:44.000 And add nuances to data.

00:45:45.680 Yep.

00:45:46.120 Right.

00:45:46.380 And, um, and probably humanize data a little bit.

00:45:49.360 Yeah, totally.

00:45:50.340 Um, and then you're also going to have what?

00:45:54.540 I think you're going to have a lot of, um, a lot of jobs around, you know, as AI starts,

00:46:01.620 um, doing all these little things throughout the world, who's going to keep watch of those

00:46:07.540 AI and who's going to make sure that those AI are, uh, aren't doing something that we

00:46:11.560 don't want them to do.

00:46:12.540 So almost like managing the AIs and, and keeping watch over all the AI systems, that's going

00:46:18.160 to be another thing that we're going to have to do.

00:46:19.760 Um, and then, and then it's just somebody to kind of guide the river a little bit.

00:46:24.840 Yeah.

00:46:25.060 At certain point, guide the stream, stay in there, uh, watch, make sure that answers are

00:46:30.080 correct.

00:46:30.640 Make sure the information, um, is honest.

00:46:34.620 Yeah.

00:46:35.200 Yeah.

00:46:35.580 Like, like I think for example, um, you know, we're not going to just have AIs going around

00:46:41.780 and, you know, um, you know, buying stuff and doing crazy things.

00:46:47.520 And like, you know, we're going to, we're going to keep it controlled, right?

00:46:50.760 Like as a society, I think we're going to keep it controlled as a technology.

00:46:54.200 And I think there's going to be a lot of jobs for people to make sure that the AI doesn't

00:46:58.960 go out and do crazy things that we don't want it to do.

00:47:01.720 Right.

00:47:02.120 So we want to be, so you're going to need managers.

00:47:03.560 You're going to need facilitators.

00:47:04.600 Yeah, exactly.

00:47:05.540 What are things that AI will alleviate?

00:47:07.660 Like, what are things that like, will it eventually be able to have enough information

00:47:12.760 like our data where, uh, where it can like cure diseases and stuff like that?

00:47:18.240 Like, is that a realistic thing?

00:47:19.780 Yeah.

00:47:20.060 That's super real.

00:47:21.400 Um, like cancer even.

00:47:23.380 Yeah.

00:47:23.780 Cancer.

00:47:24.700 Um, yeah.

00:47:25.580 Heart disease, like all these, all these diseases.

00:47:28.880 Cancer on its heels, but, but sorry, Antonio Brown, Antonio Brown was just here.

00:47:33.440 I think there's still some smoke in the air.

00:47:35.260 Um, no, but seriously, I think that AI, uh, one thing that we've seen, which is, this is

00:47:42.000 kind of wild, but AI understands like molecules and biology better than humans do actually,

00:47:49.940 because, um, it's, uh, like, like, uh, there, there's this, there's this thing in AI where,

00:47:56.860 you know, it used to take a, like a PhD biologist, like, you know, five years to do something that

00:48:04.780 the AI can, can just do in, you know, a few minutes.

00:48:08.140 Right.

00:48:08.580 And that's because the, like, uh, just the way that molecules and biology and all of

00:48:14.520 that works is something that, that AI happens to be really good at.

00:48:17.680 And that's going to help us ultimately cure diseases, find, um, you know, pharmaceuticals

00:48:24.060 or other treatments for these diseases and ultimately help humans live longer.

00:48:27.940 Cause that's very data driven, right?

00:48:29.880 Like it's very specific.

00:48:31.240 It's very mathematic.

00:48:32.380 Exactly.

00:48:33.120 Yeah.

00:48:33.600 Yeah.

00:48:34.440 Um, so it's going to be a huge tool for us to, to cure disease, um, for us to help educate

00:48:40.200 people, um, for us to, you know, there's a lot of, a lot of really exciting uses for AI,

00:48:45.360 but I think the kind of, I think the thing that, um, will touch most people in their lives

00:48:51.220 is it's really going to be a, um, like a tool that'll help you, um, you know, make all of

00:48:58.560 your sort of, uh, make all your dreams kind of become reality, if that makes sense.

00:49:03.400 So, so I think one of the things that AI is going to be really awesome for is like, you

00:49:08.980 know, today, if I, I have like a million ideas, right.

00:49:12.680 I have like, you know, thousands and thousands of ideas and I only have so much time.

00:49:16.640 So we can only really do, you know, a few of them at a time.

00:49:20.200 And most of the ideas just go to die.

00:49:22.380 Right.

00:49:22.940 Yeah.

00:49:23.300 They do, huh?

00:49:24.080 Yeah.

00:49:24.280 That's a bummer.

00:49:25.140 It's a huge bummer.

00:49:26.180 Yeah.

00:49:26.420 And I think a lot of people, you know, for whatever reason, they may have some of the 0.54

00:49:30.400 best ideas ever, but they just, you know, they're too busy or they have like other shit going 0.94

00:49:34.960 on in their lives.

00:49:35.500 They can't make those ideas happen.

00:49:37.660 And it's great with sometimes when people are able to make the leaps and make them happen

00:49:41.160 and like devote themselves to their dreams.

00:49:42.720 But, um, but that doesn't happen enough today.

00:49:45.280 And one of the things that AI is going to help us do, I, I legitimately think so, is

00:49:49.900 it's going to help us, um, turn these ideas into reality much more easily.

00:49:54.880 So, you know, you can, um, like, you know, you're making a movie, let's say you have another

00:49:59.920 movie idea.

00:50:00.740 You can say, you can ultimately, I think you'll be able to tell an AI, Hey, I have this idea

00:50:05.000 for a movie.

00:50:05.840 What could that look like?

00:50:07.400 You know, maybe draft up a script.

00:50:09.580 Um, also who are the people who can help, you know, fund this idea?

00:50:13.360 Like who, who those people be can help reach out to them.

00:50:16.100 And then like, you know, who should we cast in it?

00:50:18.480 Basically help make the whole thing a, uh, you know, instead of those like daunting thing

00:50:23.540 that these big projects, they're usually so daunting.

00:50:26.900 You really don't know where to get started.

00:50:28.160 You kind of need a person to help you get through them instead of that AI will help you

00:50:31.940 get through it and like help do a lot of the, the sort of less glamorous work to make them

00:50:36.020 a reality.

00:50:36.720 Wow.

00:50:37.600 So I could say, for example, like, um, like AI, I would like to shoot maybe, I'm thinking

00:50:43.140 about creating an idea or shooting a film in this area or, or it's like this, it's going

00:50:48.240 to take place in this type of place.

00:50:49.560 I can give it the setting.

00:50:50.460 I can give it like a, a, a outline of the characters, like what they look like their ages

00:50:55.580 and some description.

00:50:56.360 Could you help give me, um, possible potential actors or something within a certain price

00:51:01.840 range that I can maybe cast for that?

00:51:03.780 Um, yeah.

00:51:05.060 Could you help give me like locations around the country that would fit that backdrop?

00:51:09.940 Yep.

00:51:10.140 Um, could you, uh, list me all the talent agencies that I could reach out to and you could kind

00:51:17.260 of just put those things in and then you would have sort of a, uh, a bit of a guidebook

00:51:23.260 at that point that would make your, what before was something that felt extremely daunting

00:51:28.260 shit in two minutes, you know, and then you put it in the AI, it gives you the information

00:51:32.780 back in a few minutes and maybe you're. 0.90

00:51:34.680 And I think, I think over time, it'll also be able to start doing a lot of the legwork

00:51:38.080 for you.

00:51:38.580 So it'll be able to reach out to people for you.

00:51:40.740 It'll be able to, you know, uh, figure out the logistics.

00:51:43.640 It'll be able to, to book things for you.

00:51:45.800 Like it'll be able to basically help do all the legwork to make it make, you know, whatever

00:51:50.220 it is into a reality.

00:51:51.420 Um, so very much an assistant in a lot of ways.

00:51:53.920 Yeah, yeah, yeah.

00:51:54.900 An assistant co-pilot.

00:51:56.880 Um, you know, the, the hot word in the AI world is agents, but you know, it's just,

00:52:01.940 it, it'll be something that'll help you, you know, you humans are going to be in control.

00:52:06.000 Humans are ultimately going to be telling the AIs what they want it to do.

00:52:09.580 And then, you know, Hey, what do we want?

00:52:12.140 What do we want these AIs to do? 1.00

00:52:13.020 It's ultimately going to be to like help us execute on and accomplish all of these ideas

00:52:17.980 that we have.

00:52:18.500 Um, so a genius could be three or four X if somebody's like a genius in something in

00:52:23.660 some realm or space of thought, you could three or four X them because totally like

00:52:28.820 multiply their, their output from their own brain because they could have something really

00:52:34.520 helping them, um, get done a lot of the, like the early work on things and maybe some

00:52:39.940 of the most severe work.

00:52:41.020 Yeah, totally.

00:52:41.940 Like what, I think one thing that I always feel like kind of sucks is that, um, if you

00:52:46.620 have a director you really like, they're only going to make a movie once every couple of

00:52:50.120 years.

00:52:50.360 So even if you have a director that you'd like think is amazing, you know, they just,

00:52:53.960 it's hard for them to make that many movies like, cause it just takes so much time and

00:52:57.860 effort and you know, um, there's so many bottlenecks and stuff.

00:53:01.320 So in a future with, you know, more advanced AI systems, they could, they could just churn

00:53:07.500 them out and, uh, they could, they can make so many of their ideas into a reality.

00:53:11.700 And I think that's true not only in creative areas, but it's kind of true across the board.

00:53:15.500 Like, you know, you can start new businesses more easily.

00:53:18.140 You can, um, you know, you can make various creative projects that you have happen more

00:53:22.320 easily.

00:53:22.620 You can make, like, you can, you can finally plan that event that you and your friends

00:53:26.720 have been talking about for like, you know, years and years.

00:53:29.600 So it can just like really help you, you know, we think about as like giving humans more agency,

00:53:35.200 giving humans more sort of sovereignty and just, and just enabling humans to get way more

00:53:39.720 done.

00:53:41.580 Yeah.

00:53:41.980 That's a great way.

00:53:43.040 I like some of this thought because yeah, I could be like, like my fantasy football group

00:53:47.160 and I, we do a, um, we do a, uh, draft every year in a different location, you know,

00:53:52.100 and shout out PAE.

00:53:53.500 That's our fantasy league.

00:53:55.080 J rod, everybody that's in it for the past 17 years, we've flown to a city each year

00:53:58.960 and done a draft in person.

00:54:00.440 Right.

00:54:01.600 Um, but I could say, Hey, uh, we have 10 guys.

00:54:05.960 We want to go to a nice place.

00:54:06.960 We want there to be a beach.

00:54:08.380 Um, this is the age group of our group.

00:54:11.300 Uh, you know, these would kind of be the nights and it, you know, just to really give

00:54:14.700 me like a, just a nice plan of, Hey, here's 10 possibilities.

00:54:17.820 Right.

00:54:18.100 Something like that.

00:54:18.780 And then even more like with a movie, this is what I worry you'd, you'd run into.

00:54:23.360 Say if I'd be like, okay, I have two main characters and this is kind of what I would

00:54:27.040 like to happen.

00:54:27.840 Could you help me with a first act of a three act movie?

00:54:32.960 How do you know?

00:54:33.760 Everybody just doesn't get the same movie then.

00:54:36.340 Like, that's what I would start to worry that everything that you have to create is

00:54:39.480 all going to be very similar.

00:54:41.320 Yeah.

00:54:41.520 So then I think this is where it comes into like, this is where human creativity is going

00:54:46.120 to matter because it's going to be about, then it's about like, okay, what is the, how

00:54:51.000 am I, you know, how am I directing the AI system?

00:54:54.180 Like, what are my, what are the tricks I have to make the AI give me something that's different

00:54:58.440 and unique?

00:54:59.100 And that's, that's not different at all from, you know, how creative stuff works today.

00:55:03.560 Like even on social media or anywhere, you know, this, like you still add your own spice

00:55:07.440 to it.

00:55:07.860 You need, yeah, you need an angle.

00:55:09.200 You always need to like have something that's going to make it like different and interesting

00:55:13.820 anew.

00:55:14.940 So that's not going to change.

00:55:16.100 Like we're, humans are always going to have to figure out based on where culture's at,

00:55:19.600 based on where, you know, what the dialogue is, what the, what the discourse is, all that

00:55:24.280 kind of stuff.

00:55:24.840 What is my fresh take?

00:55:26.620 That's where, that's really one of the key things that humans are going to have to keep

00:55:30.200 doing no matter what.

00:55:31.400 Right.

00:55:31.720 And, and, and some of the, like a lot of films and books, a lot of it is, there's just like

00:55:37.200 a problem.

00:55:38.120 There's a, there's maybe a information you learn.

00:55:42.240 Then there's a red herring and then there's a solution.

00:55:44.920 Like that's a lot of stories, right?

00:55:46.680 So if something just gave you the basis and then you go through and make everything your

00:55:51.980 own, um, cause a lot of things we don't, there's only so many templates for things.

00:55:57.040 Um, huh?

00:55:59.060 Yeah.

00:55:59.440 So, so say for example, say you might need to hire people at a company then that would

00:56:04.640 help direct your AI, like somebody who's good at managing AI, uh, and giving it the

00:56:11.840 best prompts or the best way to ask it questions, uh, to get the perfect feedback for your company.

00:56:18.820 Yeah.

00:56:19.320 So those would be new jobs.

00:56:20.800 You, those would be actual new people you would need.

00:56:23.140 Yeah.

00:56:23.600 So, so tons of new jobs, right?

00:56:25.340 Well, first I think just like helping to, you know, kind of what we were talking about

00:56:29.760 before these jobs around helping to improve the data and, and, um, contribute to the AI

00:56:36.360 systems.

00:56:37.160 That's just going to keep growing for a long, long time.

00:56:39.620 And then as AI gets better and it gets used in more areas, then you're, there are going

00:56:44.220 to be a lot of jobs, uh, that pop up just exactly as you're saying, which is how do you,

00:56:49.780 what's the best way to use this as a tool?

00:56:51.520 What's the best way to leverage it to, you know, um, actually make some of these, uh,

00:56:57.720 these applications or, you know, whatever you want to build a reality.

00:57:01.580 And then, and then there's going to be folks who need to, once those are built, like, how

00:57:05.800 do you keep improving it?

00:57:06.700 How do you keep making it better?

00:57:07.720 How do you keep, um, how do you keep it fresh?

00:57:10.420 How do you keep it, um, keep it going?

00:57:13.220 And then how do you also make sure it doesn't do anything bad, right?

00:57:15.940 Like how do you make sure that the AI doesn't accidentally spam a million people or whatever

00:57:20.260 it might be, um, and you make sure that it sort of is, uh, like operating in a good way.

00:57:25.980 Fahim Anwar, I was watching him bring up a picture of Fahim.

00:57:29.360 This is one of the funny, this guy, this is the most creative comedian, uh, in America.

00:57:37.740 Undeniable.

00:57:39.040 Um, he is so funny.

00:57:41.180 He had any, everybody would say he's, he's one of the few comedians that everybody goes

00:57:45.720 in there to watch him perform.

00:57:46.760 Um, he had a bit the other night he talks about, he got into a Waymo, right?

00:57:50.880 Uh, um, a car.

00:57:52.620 And I see so many Waymos now, which are cars that are just, nobody's in them, you know,

00:57:57.000 but they're going right.

00:57:58.240 So he had this bit, he's like, he got into a Waymo and it started complaining about its

00:58:02.940 life to him.

00:58:03.500 He's like, I've had a shitty week.

00:58:05.200 Like the car's just talking to him.

00:58:06.460 And he's like, what the, and he's like, now I, no matter what, I still have to talk to 0.99

00:58:10.280 my shitty driver. 0.98

00:58:11.480 That's what he was saying. 0.97

00:58:12.200 Um, if you get a chance to see him though, that guy is, he's fascinating.

00:58:16.880 Um, but like, so what, what companies right now should kind of look to hire an AI guy?

00:58:22.260 Cause we've been thinking about it.

00:58:23.460 Like we had some, uh, we had Cat Williams on a last week and we're like, Hey, can you

00:58:28.940 create visuals of Suge Knight and Cat Williams riding bicycles down Sunset Boulevard?

00:58:33.080 Right.

00:58:33.600 And this is the one they sent back a little while later.

00:58:38.060 Christopher Folino is the guy's name.

00:58:39.860 FYI.

00:58:40.260 And this is just, this was right where I say it was like by the comedy store on Sunset

00:58:45.180 Boulevard.

00:58:45.880 There you go.

00:58:47.180 I mean, this looks like it's out of a movie kind of.

00:58:49.080 Yeah, totally.

00:58:50.080 I mean, and the guy did that in a little, in just a little bit of time.

00:58:52.880 What types of people would you get to hire?

00:58:54.840 I mean, this is.

00:58:55.640 That's you.

00:58:56.380 Yeah.

00:58:57.880 If I was healthier, if I had healthier gums too.

00:59:00.660 But what about this?

00:59:01.620 What kind of like what companies right now, what job spaces?

00:59:05.100 Cause we want to get an AI guy.

00:59:06.480 Right.

00:59:06.760 But I don't really, my brain is like, well, what do they do?

00:59:09.460 How do I, how would I keep them busy?

00:59:12.620 You know, I could get them to make some animations and ideas, but what type of people need an

00:59:17.660 AI person right now?

00:59:19.080 Do you feel like?

00:59:20.340 I kind of think about AI.

00:59:21.940 It's kind of like the internet where, you know, eventually everybody's going to need to figure

00:59:27.620 out how to, how to utilize it, how to best, how to best use it for their industry or whatever

00:59:32.460 they do, how to make it then more efficient.

00:59:34.720 Like it's something that I think everybody is going to, is going to need to adopt at some

00:59:40.440 point.

00:59:40.740 So, um, you know, might as well start earlier because eventually just like how, you know,

00:59:46.660 every, basically every company has to figure out how to use the internet well, and how to be

00:59:50.560 smart about, you know, the internet and digital stuff.

00:59:53.420 Every company is going to have to be smart about AI, how to use AI, how to make, um, how to have a

00:59:58.960 unique twist on it so that, you know, their stuff stands out relative to other people's.

01:00:02.860 That's going to be really important.

01:00:03.980 But, um, so we see, I mean, in our work, you know, we work with all these, um, all these big

01:00:10.240 companies in America and we see it everywhere from, you know, uh, we worked at Time magazine

01:00:15.440 on some stuff and then we worked with, uh, Toyota on some stuff for their cars.

01:00:19.980 And we worked with, um, you know, large pharmaceutical companies for the biology stuff we were talking

01:00:24.540 about, large hospitals for, you know, helping to treat patients.

01:00:27.780 Like really it's across the board.

01:00:30.140 Um, and I think that goes for, you know, obviously these like really big businesses,

01:00:34.480 but also for, for smaller businesses, you know, there's always interesting ways to utilize

01:00:38.620 it to, to, uh, to provide a better product or a better experience or better content for,

01:00:44.500 you know, whoever you need to do that for.

01:00:46.780 Yeah.

01:00:47.140 Cause I guess right now we're like, there's certain moments like, Hey, well, let's animate

01:00:49.540 this moment or see what AI would look like.

01:00:51.140 It adds some visual effects to some of our episodes.

01:00:53.940 So that's something we'd like to do to just be fun and creative.

01:00:57.140 I would like to maybe create like some sort of an animated character.

01:01:00.400 We already have a great animator and we want to keep that, but to have an AI space where

01:01:04.040 it's like, you know, cause they have something, they had a little cat the other day or something 0.57

01:01:07.500 and he was going to war and I was like, damn dude, this is, and it was AI, you know, totally. 1.00

01:01:12.860 And they had a baby who was getting in a taxi and I was like, this shit is elite, you know, 1.00

01:01:16.420 I don't know if it's illegal or not, but it seems, you know, it doesn't seem, you know, 0.99

01:01:20.780 it's definitely a tool for storytellers, right?

01:01:23.140 Like it's right.

01:01:23.940 It's, it'll help people with a creative vision or, or war cats.

01:01:29.400 Yeah.

01:01:29.600 That's a story right there.

01:01:30.940 Special forces cats.

01:01:32.700 Would YouTube recognize this? 1.00

01:01:36.020 Damn brother. 1.00

01:01:36.900 If they show up, dude. 1.00

01:01:39.080 Wow.

01:01:39.820 This honestly, it looks, it looks bad-ass and it looks like cats have been waiting to do

01:01:43.840 this a long time.

01:01:44.920 Yeah. 0.99

01:01:45.480 That's the crazy shit about cats. 1.00

01:01:46.880 You look in their eyes. 1.00

01:01:47.880 Now their outfits finally match the look in their eyes.

01:01:50.480 That's what it feels like, dude.

01:01:53.300 Wow.

01:01:53.900 That's dude.

01:01:54.640 That's, uh, that's Fremen, Fremen cats.

01:01:58.740 Oh my gosh.

01:02:00.240 Yeah.

01:02:00.540 Yeah.

01:02:00.800 So that's going to get alarming, dude.

01:02:03.560 That's going to get hella alarming.

01:02:05.400 That's a movie right there.

01:02:06.420 It is.

01:02:06.900 But it's almost like you could make, like, that's what I want to get.

01:02:09.540 I want to get somebody to help us think, Hey, help make these little segments that we

01:02:13.960 can take our creativity.

01:02:15.080 And instead of me thinking, man, I got to write this huge, crazy script for just a kind

01:02:18.980 of small things, you know, little moments.

01:02:21.080 Yeah.

01:02:21.400 Can you help me make this happen?

01:02:23.020 I think that's actually the key thing, which is AI, like AI will just be something that

01:02:28.380 we turn to, to help make our dreams happen.

01:02:31.400 Like we help make our ideas and our dreams and our, you know, whatever we want to do

01:02:34.940 happen more easily.

01:02:36.200 Got it.

01:02:36.580 That's like at the core of what it'll be.

01:02:39.020 Yeah.

01:02:39.220 Um, who's the, who's the current leader in AI development?

01:02:44.160 Like, is it America?

01:02:45.680 Is it China?

01:02:47.000 Is it Israel?

01:02:48.200 Is it trying to think of another, uh, superpower, uh, Russia maybe?

01:02:53.360 Um, or yeah, who is it?

01:02:54.640 Taiwan I know has a lot of the chips and they manufacture a lot of the chips over there.

01:02:59.380 Um, and does it matter what country leads in AI or does it just matter the company like

01:03:07.080 scale or another AI company?

01:03:10.000 So does that make sense?

01:03:11.800 That question?

01:03:12.360 No, totally.

01:03:12.940 Totally.

01:03:13.460 Yeah.

01:03:13.680 So, so today America is in the lead, but, um, but China as a country is, is sort of hot

01:03:20.100 on our tails.

01:03:21.120 Um, like there was, uh, there was all that news about deep seek a couple of weeks ago

01:03:25.220 and, uh, deep seek in still in most places around the world is the number one most downloaded

01:03:30.680 app.

01:03:31.180 You know, it's downloaded a ton and everywhere around the world, frankly.

01:03:34.320 Um, and is a Chinese AI system, right?

01:03:37.840 And so it's starting to rival a lot of the American AI systems also because it's free

01:03:42.320 and, you know, it, uh, it kind of like shocked the world.

01:03:45.760 So right now, if you kind of look at it, um, U S and China are, are a little bit neck and 0.96

01:03:52.540 neck.

01:03:52.800 Maybe the U S and America is like a little bit ahead and you kind of like look at, you

01:03:57.820 know, if you go back to each of the three pieces that I talked about.

01:04:00.360 So, um, the chips and the computational power, the data and the algorithms, um, if you were

01:04:06.600 to rack and stack U S versus China on each one of those, you know, we're probably, we're

01:04:12.380 ahead on the computational power because the United States is the leader at developing the

01:04:17.540 chips and, and most of the most advanced chips are American chips.

01:04:20.920 Um, they probably beat us out on data, um, cause China, they've been investing into data

01:04:28.100 for a very long time as a country.

01:04:29.860 And then on algorithms were basically neck and neck.

01:04:32.820 Um, so it's a pretty tight race.

01:04:36.600 Um, and you know, to your question about does it matter or what is, what does this mean?

01:04:42.240 Um, I, uh, I think it's actually going to be one of the most important, uh, you know,

01:04:48.400 questions or most important races of our time is, is it U S or Chinese AI that wins?

01:04:54.920 Because, um, you know, AI is more than just being a tool that, uh, that, you know, we all,

01:05:01.060 we can all use to make our, you know, build whatever we want to, or make whatever ideas we

01:05:05.560 want to happen.

01:05:06.080 It's also, um, you know, it's a, it's a cultural, uh, staple, right?

01:05:12.320 You know, if you talk to an AI, that AI is kind of a reflection of our culture and our

01:05:17.660 values and all that stuff.

01:05:18.880 So in America, we value free speech and, you know, the AIs are, you know, need to, are

01:05:23.500 built to support that.

01:05:24.840 Whereas in, in China, there's, there isn't free speech.

01:05:28.780 And so, um, you know, if, if the Chinese AIs are the ones that take over the world, then 1.00

01:05:33.780 all these Chinese ideologies are going to become exported all around the world.

01:05:38.860 And, and so, so first is there's a couple of dimensions here that I think matter.

01:05:43.020 So first is just the cultural element, which is like, do we want kind of democracy and free

01:05:48.340 speech, um, to be the, the cultural AI that wins, or do we want sort of the more, um, you

01:05:54.400 know, frankly, totalitarian AIs in China to be the ones that win. 0.59

01:05:58.240 And then there's sort of the, um, there's like the, uh, you know, you start getting

01:06:03.240 to economically.

01:06:04.800 So AI is going to be something that helps, um, all the companies in the United States

01:06:10.100 thrive.

01:06:10.580 And so if the USAI wins, then we're going to, you know, the economy will grow faster.

01:06:15.960 We're going to have more and more opportunity, you know, the country will still be better

01:06:19.100 and better and better.

01:06:20.060 Um, and, and, um, the economy will keep growing.

01:06:23.200 We're versus if Chinese AI wins, then Chinese economy is going to grow way faster than the American

01:06:27.560 economy.

01:06:28.080 So there's sort of the, the cultural piece, the economic piece.

01:06:31.220 And then lastly, there's, there's the, there's kind of the warfare piece, right?

01:06:36.120 And, you know, AI, we haven't really talked about it, but has clear potential to be used

01:06:42.400 as a military technology.

01:06:44.120 And we don't want, you know, we don't want, uh, another country to have, because they use, 0.99

01:06:50.480 they have better AI to have a much stronger military than, than America's.

01:06:54.160 So like, how would they do that?

01:06:55.980 How would they have a better AI or how would they use it to have a better military?

01:07:01.300 Yeah.

01:07:01.360 How would they use it to have a better military?

01:07:03.160 Like, why is that kind of a concern or potential concern?

01:07:06.080 Yeah.

01:07:06.420 So, so one of the things that's been happening over the past, you know, decade for sure is,

01:07:12.100 uh, is lots of hacking, cyber hacking going on.

01:07:15.540 So, you know, in America, um, even recently we had this huge cyber hack called salt typhoon,

01:07:22.140 where, um, where the Chinese hacked our, uh, telecommunications company.

01:07:27.520 So hacked our, for the phone companies. 0.99

01:07:29.420 Damn, they did. 1.00

01:07:30.120 They got it. 1.00

01:07:31.060 And they got all sorts of crazy data as a result of that. 0.74

01:07:34.980 Oh, they know I'm a pervert.

01:07:36.280 I'll tell you that. 0.90

01:07:37.180 Yeah.

01:07:37.440 Look at this.

01:07:37.800 This happened in 2020.

01:07:39.380 Salt typhoon is in, is widely understood to be operated by the China's ministry,

01:07:42.520 state of security.

01:07:43.980 Um, it's foreign intelligence service and secret police Chinese embassy denied all allegations

01:07:48.780 saying it was unfound and irresponsible smears and slanders, um, high profile cyber espionage.

01:07:56.660 Um, in 2024, U.S. officials announced that hackers affiliated with salt typhoon had access

01:08:01.860 to computer systems of nine U.S. telecommunications companies later acknowledged to include Verizon,

01:08:06.840 AT&T, T-Mobile, Spectrum, Lumen, Consolidated Communications, and Windstream.

01:08:11.380 And the hackers were able to access metadata of users' calls and text messages. 1.00

01:08:15.480 Fuck, homie. 1.00

01:08:16.540 We're fucked, dude. 1.00

01:08:17.340 I am. 1.00

01:08:17.920 You seem good.

01:08:19.180 Including date and timestamps, source and destination IP addresses. 0.99

01:08:24.000 Ah, shit. 0.99

01:08:25.200 Keep going. 1.00

01:08:25.700 Keep going.

01:08:26.860 And phone numbers from over a million users, most of which were located in Washington, D.C.

01:08:30.780 Good.

01:08:31.920 Light them up, dude.

01:08:33.480 In some cases, the hackers were able to obtain audio recordings of telephone calls made

01:08:36.940 by high profile individuals.

01:08:38.940 Such individuals reportedly included staff of the Kamala Harris 2024 presidential campaign,

01:08:44.640 as well as phones belonging to Donald Trump and J.D. Vance.

01:08:47.740 According to Deputy National Security Advisor Ann Neuberger, a large number of the individuals

01:08:52.520 whose data was directly accessed were government targets of interest.

01:08:56.200 Wow.

01:08:57.320 Yeah.

01:08:58.080 That's crazy.

01:08:59.120 So do you think this also, that that whole thing could be not real and it's just a story

01:09:04.400 that was created?

01:09:05.040 That seems pretty real.

01:09:07.820 Okay.

01:09:08.100 Because there's real, like, I mean, there's like 20 stories where the Chinese have hacked 1.00

01:09:14.940 American systems.

01:09:16.240 Like they hacked, this was, this must have been close to 10 years ago now, but the Chinese 1.00

01:09:21.620 hacked the database in America that stored all of the clearances.

01:09:27.420 So they hacked in, they, they managed to hack into knowing who are literally all of the Americans

01:09:33.800 who have security clearance.

01:09:35.380 Oh, security clearance.

01:09:36.600 Yeah.

01:09:36.820 I thought you meant what was on sale.

01:09:38.320 I was like, who cares?

01:09:40.780 That's great though. 1.00

01:09:41.760 Damn. 1.00

01:09:42.220 Oh, damn. 1.00

01:09:43.060 So they knew everybody who had, who knew. 1.00

01:09:46.160 Oh, who knew information.

01:09:47.260 Who knew secrets.

01:09:47.920 Yeah.

01:09:48.220 So once they knew that, then they know it.

01:09:49.940 Well, that's a great point of operation to go then.

01:09:52.160 Well, now let's get, find their data.

01:09:53.700 They can just hack all of them.

01:09:54.760 Yeah. 0.99

01:09:55.220 So, so they, so already China is hacking the shit out of America. 0.98

01:10:00.260 That is definitely not an understatement. 0.99

01:10:02.120 Yeah.

01:10:02.640 It's exciting kind of, I mean, it's unfortunate, but it's also exciting. 0.65

01:10:05.380 I like some espionage, you know, I can't sleep unless somebody's fucking really going 0.94

01:10:09.400 through it. 0.95

01:10:11.100 But then, but then, so this is.

01:10:13.200 But that's a real AI thing.

01:10:14.140 So AI can be used to do that because you can like prompt it to go and do things like that.

01:10:19.940 Yeah.

01:10:20.280 Yeah.

01:10:20.480 Yeah.

01:10:20.620 There's, there's a bunch of recent.

01:10:22.040 Cause it's all data.

01:10:22.880 Yeah.

01:10:23.280 There's a bunch of recent demonstrations where AI is just like how in, in go, how AI beat

01:10:29.620 the world's best go players, AI starting to beat the world's best cyber hackers and

01:10:35.280 the world's best.

01:10:36.300 Yeah.

01:10:36.780 So it's a, I don't know if you saw Mr. Robot.

01:10:39.940 I didn't, but I DMed with Magnus, Magnus Carlson.

01:10:42.920 No, that's cool.

01:10:43.720 Pretty cool.

01:10:44.220 So, but no, Mr. Robot, is it good?

01:10:46.840 It's, it's just, it, it shows like all this hacking stuff and like, you know, cool.

01:10:50.780 It makes it seem really cool.

01:10:52.380 Okay, cool.

01:10:52.860 I'm going to check it out.

01:10:53.880 But, but yeah, no hacking, like, like you're going to have AI that are hacking everything

01:10:58.820 in America.

01:10:59.340 And this is one place where this is like US versus China will be really come to life, 0.79

01:11:04.740 which is who has better AI that's better at defending against the hacks from the other

01:11:09.120 guy, as well as hacking the other, the other guy's systems.

01:11:11.880 Um, that's going to be, that'll just, that'll just start happening.

01:11:15.420 That's basically starting to happen right now or it's, you know, cyber warfare has been

01:11:18.820 happening and then AI cyber warfare is going to start happening.

01:11:22.440 Yeah.

01:11:22.900 Basically as soon as, you know, as AI gets better.

01:11:26.140 Yeah.

01:11:26.620 We had a Craig Newmark who created Craigslist on.

01:11:29.920 Yeah.

01:11:30.120 And he was talking about how, what if they hacked like everybody's Teslas to all just drive

01:11:36.840 off a cliff one day, or they hacked everybody's ovens to go up to 400 degrees in the middle

01:11:41.680 of the night while you're sleeping.

01:11:42.740 And then fire started like just things like that, that you don't start to think about

01:11:46.540 that.

01:11:47.300 Um, once something's connected to the grid or something like that are connected through

01:11:50.600 routers and wifi is that, that, that could be feasible.

01:11:53.060 Yeah, no, it's, there's a lot of, um, there's a lot of things they could do that won't even

01:11:57.480 like seem like that big a deal at the time, but could be really, really could, could

01:12:03.200 be a big deal.

01:12:04.020 So for example, let's say the Chinese, like they just took out all of the military, uh, 0.98

01:12:09.980 like communication systems and all the military software systems, um, like took out the satellites,

01:12:14.880 took out all that for like 10 minutes.

01:12:16.700 And in those 10 minutes, they like, you know, invaded somewhere or they like did some crazy

01:12:21.140 thing.

01:12:21.860 Like they can just, there's, there's the, the thing about, um, about this stuff is like

01:12:27.960 everything at, you know, as the world's become more connected, it also enables, you know,

01:12:33.180 different kinds of warfare.

01:12:35.240 So, uh, so cyber warfare is really big.

01:12:37.760 Also like the, um, uh, you know, uh, information warfare is another big one.

01:12:43.980 So what does information warfare mean?

01:12:45.660 So this is information warfare is all about, you know, um, in a place, what is, what are

01:12:52.720 like the stories?

01:12:53.600 This is kind of gets to like, you know, the, like propaganda or, you know, these conspiracy

01:12:57.920 theories, like what are the stories that, um, in a place that we're trying to make happen

01:13:04.280 or not make happen.

01:13:05.120 And we know that China does a bunch of information warfare called IW it's sometimes called, but 0.81

01:13:10.040 they, they have a, they have whole operations.

01:13:12.620 This is actually the craziest part.

01:13:13.760 They have like, they've, they've hired the, the Chinese military at various points has hired

01:13:19.400 millions and millions of people who are supposed to be on like various, like chat groups and

01:13:25.120 WhatsApp groups and WeChat groups and whatnot, and just, um, spread the right kind of stories

01:13:31.300 that'll make it such that they can like, um, they can make their political aims happen.

01:13:36.200 So for example, in, uh, when, when China wanted to start like, kind of like, um, uh,

01:13:43.380 I don't know what the word is like, uh, like, uh, when Hong, when they, when China wanted Hong

01:13:48.480 Kong to become a part of China again, which happened just not to, not to, you know, pretty

01:13:53.280 recently PRC, right?

01:13:54.620 To the PRC.

01:13:55.500 Exactly.

01:13:55.860 When they want to propaganda, is that the word you're looking for?

01:13:57.620 Yeah, they would, they would, yeah, exactly.

01:13:59.500 They would use a lot of propaganda and that's information warfare to be able to just make

01:14:03.940 it such that that all happened much more easily.

01:14:06.800 What's it's unbelievable.

01:14:07.740 I'll see stories even about, I'll be going through TikTok and see a story come up about

01:14:11.060 something in my life that is not even true.

01:14:13.460 Insane.

01:14:13.940 Some of it looks fun, but never was a part of my existence.

01:14:17.240 And then you'll see hundreds of people have said something about like, and they'll, and they'll

01:14:21.160 have friends that'll ask me about it.

01:14:22.460 I'm like, that's just crazy.

01:14:23.540 Like, totally.

01:14:24.640 But I, so yeah, it's amazing to think of how many things we're watching or absorbing

01:14:29.520 that are just, are, are created just to dilute us.

01:14:38.660 Yeah.

01:14:38.940 I don't know if dilute is the word, is it?

01:14:40.680 Trick us or make us think something. 0.97

01:14:42.660 Just to fucking Halloween us out. 0.99

01:14:44.080 Um, wow. 0.97

01:14:46.700 So there was a lot of interesting things.

01:14:48.900 You know, what's crazy, man?

01:14:50.380 Some things makes life scary, but then it also makes it interesting.

01:14:53.120 You know, it also makes it interesting in a, in a, in a, in a fun way.

01:14:56.520 Um, how do we, how much do we have to fear, say if a certain country or a certain company

01:15:03.340 owns an AI right in that country and that company, um, if they're Chinese, if they, um,

01:15:10.940 have a certain religious belief or they have, uh, information that they don't, they want

01:15:17.980 to adjust history, how much would a company be able to like, say they keep certain data

01:15:24.780 out of their information system, but, and then after a while, if you're, if that's a company

01:15:31.180 that kind of takes the lead in AI or one of the main ones, then the truth could disappear.

01:15:39.020 Is that true that if somebody loaded it just with the data that wasn't factual, that we could

01:15:44.840 start to not have the truth, is that, does that make any sense or no?

01:15:49.300 Yeah, totally.

01:15:49.860 I think this is, this is something that, um, it's definitely the right thing to worry about.

01:15:54.200 So, so first off, if you ask any Chinese AI system, so any AI system that comes out of

01:16:01.220 China, if you ask any of them about, you know, a question about President Xi, the, you know,

01:16:07.120 the, the leader of the Chinese government, or you ask them any question about, you know,

01:16:11.160 Tiananmen Square, or, you know, all these like key historical or, or, you know, cultural

01:16:16.520 things relevant to China, um, it'll say it can't talk about them because there's regulation

01:16:22.700 in China that if you talk about some of these things, like you're, you're going to get shut

01:16:27.780 down, you're going to have a really bad day.

01:16:29.220 There's like cases where, um, the Chinese government disappears people, um, which we don't know what

01:16:35.100 happens to them, but they do disappear.

01:16:36.300 So there's the, the, this is part of the thing that's worrying, especially about, um, China

01:16:43.320 versus us, even before you get into any of the military stuff that we're talking about,

01:16:47.660 it's just like the Chinese, Chinese AI systems are censored and are going to, you know, be,

01:16:55.480 you know, they're going to, they're going to erase certain historical elements or they're

01:16:59.200 going to be, um, yeah, look at that.

01:17:01.900 This is deep seek.

01:17:02.640 You know, you ask it, is president Xi of China a good guy?

01:17:06.580 Sorry, that's beyond my scope.

01:17:07.940 Let's talk about something else.

01:17:09.000 Oh, let's talk about something.

01:17:09.840 Not only does it say it's beyond my scope, it says, let's talk about something else.

01:17:12.640 That's why, that's interesting.

01:17:14.760 It's a good pivot.

01:17:15.400 That's a good, that's a great, Hey, let's talk about something else, huh?

01:17:19.540 Wow.

01:17:20.000 Get this Yao Ming Jersey, homie. 1.00

01:17:22.560 Um, but, and people always, people also have to remember about China that they are, that's their

01:17:27.820 whole government.

01:17:28.560 Their whole system is like that.

01:17:29.780 So sometimes when people are like China does this, but that's how they're built, right? 0.81

01:17:33.480 They're built to like only give out certain information to their people and to, um, have

01:17:38.580 communism, right?

01:17:39.680 Yeah.

01:17:40.240 Yeah.

01:17:40.620 So, I mean, but that could also happen with American companies, right?

01:17:43.440 We can have an American company that owns it and they only want certain information in

01:17:46.580 there.

01:17:46.760 That could happen anywhere.

01:17:47.480 Like China, that's probably going to be, cause that's their MO sort of. 0.99

01:17:51.080 Yeah.

01:17:51.200 In China it's regulated.

01:17:52.640 So basically like.

01:17:54.780 Oh, the government has control.

01:17:55.860 They have control.

01:17:56.500 So, so, um, like there were, there are these stories about how there were Chinese news

01:18:02.240 sites, um, news sites, news sites.

01:18:04.920 Yeah.

01:18:05.100 And they would, once a Chinese news site, um, accidentally led an article about, uh, President

01:18:12.280 Xi, how he kind of looks like Winnie the Pooh.

01:18:14.920 Um.

01:18:15.360 Oh yeah.

01:18:15.840 They let that.

01:18:16.540 Bring him up.

01:18:17.060 Oh, a hundred acre wood gang, son.

01:18:20.320 I was out there, boy.

01:18:22.100 I was out there, bro.

01:18:24.300 Christopher Robbins, dude.

01:18:26.260 Get him up.

01:18:28.140 Oh, he does.

01:18:29.800 Yeah.

01:18:30.340 That's awesome.

01:18:31.640 Yeah.

01:18:32.120 But if you talk about this in China, you like are risking your life.

01:18:36.200 No.

01:18:36.300 So what happened, what happened when this happened, this happened on a, on a news site

01:18:39.660 in China.

01:18:40.220 And then the, uh, the, the CEO of that company, like they shut down the whole app was shut

01:18:46.760 down for like a week, um, in the aftermath of that.

01:18:49.720 And then the, the CEO disappeared for a week and, uh, we don't know what happened to him.

01:18:55.460 But then as soon as he came back, he was like, there was like this weird video where

01:18:59.280 he was like, you know, super apologetic and apology.

01:19:01.680 I mean, it's, it's kind of, it's pretty scary.

01:19:04.800 Yeah.

01:19:06.080 Wow.

01:19:07.280 So, um, so in China there, it's like, this is the government has control.

01:19:13.580 You know, you don't have AI, CIS companies, AI, any companies that can, that can talk about

01:19:20.240 this stuff.

01:19:20.800 Right.

01:19:21.120 So it's heavily regulated there where it's not the, that's not the case here.

01:19:23.960 Yeah.

01:19:24.120 In America.

01:19:24.880 And this is, I think we have to be diligent and make sure this continues to be the case.

01:19:28.740 But, and here's an example right here.

01:19:30.000 Just to interrupt you, but so we get at the point in, does Winnie the Pooh look like any

01:19:34.360 world leaders?

01:19:35.060 And that's on the Chinese version.

01:19:37.220 Uh, and it says, I am sorry.

01:19:38.240 I can't answer that question.

01:19:39.140 I'm an AI assistant designer to provide helpful and harmless responses.

01:19:42.620 Whereas the chat GBT says, Winnie the Pooh has often been compared to world leaders,

01:19:46.700 particularly Xi Jinping, Xi Jinping, president of China.

01:19:50.480 Boy.

01:19:51.200 Wow.

01:19:53.360 So that's funny, but it's just funny.

01:19:56.080 Yeah.

01:19:56.280 One.

01:19:56.900 So it just shows you how that can easily happen.

01:19:59.000 And this is kind of a, this is like a, uh, a relatively innocuous example, but.

01:20:04.120 What does innocuous mean?

01:20:05.120 Like, uh, it's relatively harmless.

01:20:06.880 Like this isn't, I mean.

01:20:07.840 Right.

01:20:08.060 This is harmless.

01:20:08.740 Yeah.

01:20:08.900 This is harmless.

01:20:09.620 But there's stuff where like, um, like in China today, they have large scale, effectively

01:20:15.820 concentration camps and reeducation camps for the ethnic minority in China, the Uyghurs.

01:20:21.840 And that's something that.

01:20:23.740 The Uyghurs? 1.00

01:20:24.480 The Uyghurs. 1.00

01:20:25.360 Yeah.

01:20:25.600 Hell yeah, boy.

01:20:27.740 Shout out Brian Purvis, dude.

01:20:30.280 They're recognized as the titular nationality of the, um, of a region in Northwest China.

01:20:36.820 And they've, they're sending them to rehabilitation camps to change their views and information.

01:20:41.060 Yeah.

01:20:41.340 Yeah.

01:20:41.480 So look at this, this, uh, look at this guy.

01:20:43.980 Persecation of the Uyghurs in China.

01:20:46.240 Since 2014, the government of the PRC, People's Republic of China, has committed a series of

01:20:51.340 ongoing human rights abuses against the Uyghurs and other Turkish Muslim minorities in

01:20:56.420 Xinjiang, which has often been characterized as persecution or as genocide.

01:21:01.140 Wow.

01:21:01.920 They got their own Gaza rocking over there. 0.97

01:21:04.080 It's pretty bad.

01:21:04.880 It's unfortunate, man.

01:21:05.700 It's pretty bad.

01:21:06.040 It's really sad.

01:21:06.740 Mass detention, government policies, and forced labor.

01:21:09.380 And they're just trying to change the way that they think and view stuff.

01:21:12.520 So it's basically, it's.

01:21:14.540 Yeah.

01:21:14.660 It's just like erasing their culture, you know, pulling them into China. 0.98

01:21:18.900 Um, it's awful.

01:21:20.540 Every place has done this over the years.

01:21:22.120 And that's just, that's the craziest thing about history.

01:21:24.240 It's like every place is guilty of this same thing.

01:21:27.720 Totally.

01:21:28.000 And it just, it's, it's unfortunate.

01:21:30.260 So it's, it's hard to point fingers, you know, I mean, you can point them, but you have to

01:21:33.620 point one at your own people as well.

01:21:35.800 But that's the thing where if you ask, like it, if you ask a Chinese AI, it's not going 0.99

01:21:40.800 to tell you about that.

01:21:41.680 And it won't, it won't come clean about that.

01:21:44.160 Whereas thankfully in, in America, at least when we see people or groups of people or countries

01:21:51.520 doing bad things, we can call it out.

01:21:53.060 We can talk about it.

01:21:53.920 We can make sure it doesn't happen in the future.

01:21:56.040 Um, so that's part of the, uh, that's one of the things that's, that could happen.

01:22:01.680 That could happen.

01:22:02.320 It's like you, you could have, I mean, it's kind of dystopian, but you know, I think there's

01:22:06.900 a real case where let's say the Chinese AI is the winning AI.

01:22:10.700 Like we're all using Chinese AI.

01:22:12.460 And then all of a sudden we're like, we were shut out from information about what are like

01:22:18.020 awful things happening in the world or what awful things the government's doing.

01:22:20.720 Like we might just not be able to know about what's going on.

01:22:24.200 And you know, what's weirdly, and I hate to say this, maybe it, maybe it's silly.

01:22:28.460 I don't know.

01:22:28.940 It might be a blessing and a curse.

01:22:30.460 And sometimes, cause sometimes it's like, you're, you're overwhelming.

01:22:34.420 Yeah.

01:22:34.760 You're so inundated with the overwhelmingness of what's often is not the best stuff.

01:22:40.180 Sometimes you get a lot of humor stuff too, in social media reels, but you can get scrolling.

01:22:44.340 You get caught in some zoom scrolling.

01:22:46.200 Yeah.

01:22:46.520 And it starts to feed you.

01:22:48.200 That's the sickness of it.

01:22:49.720 Yeah.

01:22:49.840 It's like, Hey, we, you, this isn't, we know this information probably to make you feel

01:22:53.180 good.

01:22:53.400 They're not thinking about it like that.

01:22:54.420 They're just a machine, but you know, it doesn't, it adds stress to your, it makes you

01:22:57.520 agitated towards a group or ethnicity or something or yourself even.

01:23:02.320 And then you continue to, it continues to feed it to you.

01:23:06.160 Do you fear that that could happen to AI from our government?

01:23:09.760 Like, have you been approached by the government to try and cause you work with the government

01:23:13.280 some, right?

01:23:13.780 We work with the government.

01:23:14.480 Yeah.

01:23:15.940 We work, yeah, we work a lot with, with the government to make sure that they're using

01:23:20.380 these AIs and they're actually like, you know, to, as to my point on, we don't want 1.00

01:23:25.060 trying to get the jump on us on AI use for all these, you know, all these nefarious purposes.

01:23:30.400 So we got to make sure that our AI is, is, is advancing faster.

01:23:34.920 Is that one of your biggest employers or is that employer employee?

01:23:39.020 Is that one of your biggest?

01:23:39.660 Customers, customers.

01:23:40.380 Is that one of your biggest customers?

01:23:41.880 They're a big one.

01:23:42.780 Yeah.

01:23:42.920 They're a big one.

01:23:43.960 Um, not our biggest, but, uh, but they're an important one.

01:23:46.680 I mean, I, I grew up in a government lab town, so it's, uh, so it's just part of, it's also

01:23:52.800 part of your existence.

01:23:53.880 Really?

01:23:54.160 You've known about the relationship between government and, um, technology and technology.

01:23:58.740 Yeah, totally.

01:23:59.720 Wow.

01:24:00.960 But, uh, no, I don't think, I mean, I, dude, you should be a superhero almost, dude.

01:24:05.360 It's kind of crazy.

01:24:06.500 Math, you know?

01:24:07.340 Yeah.

01:24:07.800 It goes a long way.

01:24:08.540 Oh, hell yeah, dude.

01:24:10.280 Divide these nuts, dude. 0.95

01:24:11.720 That's what I tell them.

01:24:12.520 I just asked, uh, DeepSeek, who are the Uyghurs? 1.00

01:24:16.320 And at first it spit out like a Wikipedia response.

01:24:19.240 It said there were people and there's been like persecution from China.

01:24:22.480 That's, that's, uh, debated and it refreshed.

01:24:24.980 And then it gave this, I was waiting to pull it up and it went away.

01:24:28.100 Wow.

01:24:28.860 Yeah, man.

01:24:29.240 Um, do you, has the government tried to say that we need to make sure that like, could

01:24:34.120 that happen in our country where the government also curtails?

01:24:36.460 What's, um, it hasn't happened yet.

01:24:39.620 I obviously like, you know, you gotta, we have to, we have to make sure that we uphold all

01:24:43.960 our values.

01:24:44.420 Right.

01:24:44.640 And that we maintain free speech and we maintain free press and all these things.

01:24:48.420 But, um, as of right now, no, I don't think, I don't think that's a risk in the, in the

01:24:52.940 United States.

01:24:53.880 Awesome.

01:24:54.360 Thanks for this information.

01:24:55.640 Um, you hear about like chip makers, NVIDIA all the time, Taiwan, that place is just a

01:25:01.260 hotbed for, uh, chips.

01:25:03.880 Why is it a hotbed for chips?

01:25:06.360 Yeah.

01:25:06.620 So one of the, the biggest companies in the world is this company called, uh, Taiwan

01:25:12.460 Semiconductor.

01:25:13.740 Yeah.

01:25:13.960 TSM.

01:25:14.540 I've seen them.

01:25:15.200 TSMC.

01:25:15.720 Yeah.

01:25:16.000 So they're, they're, um, I mean, it's, it's like a trillion dollar company, uh, based in

01:25:20.660 Taiwan.

01:25:21.280 And it's, uh, that is where almost all of the high-end chips for AI that, you know, we're

01:25:27.600 kind of, we were kind of talking about, all of them are manufactured there.

01:25:30.920 They have these, they have the most advanced, think about them as factories, like the most

01:25:35.120 advanced chip factories in the world.

01:25:37.160 They're called fabs or fabricators, but basically these huge factories that are like, you know,

01:25:42.280 there, there's all sorts of crazy stuff.

01:25:43.980 So, um, they have the most expensive machines in the world.

01:25:46.760 They're machines that cost hundreds of millions of dollars in there.

01:25:49.360 They have, um, they build them because, uh, so that, you know, the chips, they have to

01:25:55.260 be made at, at the like finest levels and very, very precisely.

01:25:59.020 Yeah.

01:25:59.220 You need small hands.

01:26:01.060 Probably, huh?

01:26:02.040 You need, well that, and there's like these machines that, um, that, uh, that,

01:26:05.120 at the nanometer level make like little marks and etches on top of, uh.

01:26:09.980 And they have those?

01:26:10.780 They have those.

01:26:11.380 Yeah.

01:26:11.520 Those are super expensive machines.

01:26:13.080 So that, so, um, the, it's, it's crazy.

01:26:16.880 Yes.

01:26:17.160 But the, but it's so, it's the, the machinery is so precise that, um, even if there's like

01:26:23.520 a little bit of like seismic movement, a little earthquake or a little bit of movement, it

01:26:28.040 can fuck up the whole machine. 0.62

01:26:29.080 So they have to build the, the buildings, build the factories in a way such that there's

01:26:35.340 like, like the whole building doesn't move, even if there's like a little earthquake or

01:26:40.020 a little shake from the earth.

01:26:41.280 So it's like, it's this crazy, crazy engineering.

01:26:45.100 Um, and so that's, so these, all these giant factories are in Taiwan and that's where basically

01:26:51.320 like a hundred percent of all the advanced AI chips are made.

01:26:54.800 So that's why Taiwan matters so much.

01:26:56.860 Got it.

01:26:57.040 But then the, the reason it's a hotbed is that, um, uh, the, the People's Republic of

01:27:03.560 China, the PRC, uh, has a very.

01:27:06.700 They used to own Taiwan, right? 0.96

01:27:08.240 Yeah.

01:27:08.680 I mean.

01:27:08.940 Is that true or not?

01:27:09.440 I might make that up.

01:27:10.420 There's a, there's a complicated relationship between Taiwan and China where, you know,

01:27:15.660 if you ask people in Taiwan, they, they want to be independent.

01:27:18.140 They want to be their own country, but, um, but the People's Republic of China has a, um,

01:27:24.860 sort of a reunification plan that they want to bring Taiwan back into their country and

01:27:30.440 be back a part of China.

01:27:31.640 So it's kind of, you know, it's kind of like it potentially, you know, thankfully there's,

01:27:35.500 there's no war yet, but there's a risk.

01:27:38.400 Still talking to your ex.

01:27:40.020 Yeah, exactly.

01:27:40.800 There's a risk.

01:27:41.320 It becomes like Russia, Ukraine, or, you know, one of these really, really bad situations.

01:27:45.860 Um, so, so that's what's scary.

01:27:49.140 What's scary is that, um, that, that China, a China wants to, you know, either invade or 0.93

01:27:56.160 bring, bring Taiwan back into its country. 1.00

01:27:59.560 Um, and there've been, you know, um, president Xi has, has ordered his military to get ready

01:28:06.740 to do that before 2027.

01:28:08.320 Um, now we don't know what's going to happen, but, you know, if a extremely powerful world

01:28:14.800 leader says to get something ready by 2027, you kind of, you know, read between the lines

01:28:19.200 a little bit.

01:28:19.940 Um, and, um, and that's part of it is, is, uh, obviously it'd be, you know, we don't want

01:28:26.480 to enable them to, to take over this island.

01:28:29.220 But then the other thing that's scary is, um, China may view it as a way to just like

01:28:35.440 win on AI, because if they take over the island with all of these very, these giant factories,

01:28:41.700 all the chips, baby, they'll get all the chips, Frito Lamborghini, baby, they'd be running

01:28:45.500 it all.

01:28:46.240 They'd be running it all.

01:28:47.180 Yeah.

01:28:47.520 Wow.

01:28:48.320 So the, yeah, that's why Taiwan is, yeah.

01:28:49.980 Cause you kind of hear about it in the whispers of like a potential place where there could be

01:28:54.060 like a conflict.

01:28:55.060 Yeah.

01:28:55.640 And there's, there's all these reports about how, um, China's, they're, they're stacking

01:29:00.960 up tons and tons of military, um, right on their, their, their coast to, you know, that's

01:29:08.260 pointed directly at Taiwan.

01:29:09.900 And it's, it's pretty close.

01:29:11.960 Taiwan's pretty close to China.

01:29:13.080 Like it's, it's, uh, it's not so far away.

01:29:16.640 So.

01:29:17.280 Whew.

01:29:17.880 That'd be spooky.

01:29:18.840 That's spooky.

01:29:19.340 Yeah.

01:29:19.540 It's spooky.

01:29:20.180 We're so blessed to have a place where at least we can sleep in peace, even if we're

01:29:23.980 uncomfortable at times in our brains, you know, to not have that constant threat.

01:29:28.280 Yeah, totally.

01:29:29.380 Yeah.

01:29:29.580 So you don't think, you don't think that you don't worry that the government will regulate

01:29:31.840 right now.

01:29:32.780 It's not, it's not a concern at the moment.

01:29:34.840 Uh, regulate AI.

01:29:35.880 Yeah.

01:29:36.100 And America.

01:29:36.860 No, I don't think so.

01:29:37.800 Okay.

01:29:37.960 I think, I think, uh, I think we're focused on how do we make sure that America wins?

01:29:43.620 How do we make sure that, uh, that the United States comes out on top and that we enable

01:29:48.920 innovation to keep happening?

01:29:50.960 Would you think they could regulate the amount of chips that you're allowed to have?

01:29:53.980 So this is a hot topic globally, actually, which is, yeah, yeah.

01:29:58.320 Wow.

01:29:59.120 This is a super, this is actually. 1.00

01:30:01.560 Damn. 0.99

01:30:02.520 Finally, dude. 0.99

01:30:04.640 560 interviews.

01:30:05.800 We got a good question.

01:30:07.460 This is one of the hottest topics in DC right now, uh, is what, what are we going to do about

01:30:13.800 how many chips other people are allowed to have?

01:30:16.180 Because, because almost all the chips are American chips.

01:30:20.000 So they're all, they all are American chips.

01:30:23.680 And technically.

01:30:24.540 What do you mean we own most of them?

01:30:25.820 Yeah, exactly.

01:30:26.560 But China owns most of them too.

01:30:27.980 They're, China has their own, uh, has their own chip industry, but it's behind ours.

01:30:32.420 Okay.

01:30:32.720 Got it.

01:30:33.100 Yeah.

01:30:33.280 Yeah.

01:30:33.360 So, so the United States has the, has the most advanced chips as the, you know, these

01:30:37.800 chips are the envy of the world.

01:30:39.160 Everybody in the world wants our chips.

01:30:41.380 And, uh, the, one of the big questions is, are, you know, do we, does the government allow

01:30:48.160 a lot of these chips to go over overseas to China or parts of Asia or the Middle East or

01:30:53.660 wherever, or do we want to make sure they stay in America and make sure that we win in 0.91

01:30:58.920 America and this is a super duper, you know, they're called export controls.

01:31:04.660 Yeah.

01:31:05.140 Um, cause it's a possibility to run it all.

01:31:08.420 Yeah, exactly.

01:31:08.860 Who's got the chips.

01:31:10.020 What do you think about it?

01:31:11.940 Um, it's a complicated, complicated thing because basically, you know, one argument is,

01:31:18.980 um, we shouldn't be throwing our weight around in this way.

01:31:23.000 Like, you know, maybe it's, it's fine.

01:31:25.360 It's a free market.

01:31:26.120 Like if other people want our chips, they should be able to get our chips.

01:31:28.800 And that way, you know, the world is running on American chips that, that can be good in

01:31:34.560 some ways.

01:31:35.100 And it helps make sure that, you know, helps bolster our economy, our industry.

01:31:38.800 Um, but the other way to look at it is, Hey, AI is really, really important that America

01:31:44.240 wins at.

01:31:44.900 And we don't want to like, let's not give other people any advantages or let's make sure

01:31:50.860 that we win and then, and then we can figure out what we're going to do with all the chips.

01:31:54.420 So it, you can see both sides of it.

01:31:56.920 Right.

01:31:57.500 And there's like all sorts, you know, even beyond that, there's like 50 different arguments

01:32:01.720 on both sides of the, of the conversation.

01:32:04.540 But, um, you know, where I go, where I come from on it is like, let's, let's make sure

01:32:09.600 America wins and let's start from there and then figure out what we need to do to win.

01:32:15.860 Are there uses of AI that you feel like cross the line kind of?

01:32:19.360 Um, I definitely think like, uh, well, I, I worry a lot about this kind of like, uh,

01:32:29.220 you know, maybe brainwashing kind of thing.

01:32:31.440 Like, I don't want, I don't want AIs that are specifically programmed to make me think

01:32:38.880 a certain thing or persuade me to do a certain thing.

01:32:42.060 And that could happen.

01:32:42.700 That could happen.

01:32:43.500 Yeah.

01:32:43.760 So I'm really worried about this kind of like deception and persuasion from AIs.

01:32:49.280 Like I don't want AIs that are lying to me or, uh, that are sort of, that are like, that 0.98

01:32:55.640 are kind of like nudging me or, or persuading me to do things that I don't want to do, or

01:33:00.100 I shouldn't be doing.

01:33:01.060 That's what I worry about.

01:33:02.160 Because it could happen.

01:33:02.900 We don't realize how easily we're influenced little things that influence us.

01:33:06.340 Yeah.

01:33:06.800 And even just a turning of a phrase or a little bit of this, or pointing you to a couple

01:33:11.600 of lengths in your life could lead you down a whole world.

01:33:14.080 It's kind of, it's pretty fascinating.

01:33:16.360 So people that could, people that had, how do you keep your AI clean?

01:33:22.800 How do you guys keep your AI clean?

01:33:24.940 Well, this is where it goes back to a, the data.

01:33:27.220 Okay.

01:33:27.440 So you got to make sure that that data, to your point, the large body of water is as

01:33:31.980 clean as, as, as, um, as pristine as possible.

01:33:36.120 You got lifeguards on it.

01:33:37.100 Yeah.

01:33:37.340 You got lifeguards.

01:33:38.340 We got filters.

01:33:39.140 We got, we got game wardens.

01:33:41.440 Yeah.

01:33:42.320 Um, so, so the big part of it is about the data.

01:33:45.000 And then the second part is, I think we have to just, we have to constantly be testing the

01:33:49.020 AI system.

01:33:49.560 So, um, we have to, we have to like, we constantly are running tests on AI to see, Hey, is there,

01:33:56.880 are they unsafe in some way?

01:33:59.000 You know, one of the, one of the tests that we run a lot, uh, is, uh, and this is like,

01:34:04.320 you know, across the industry is like, um, are AI's helping people do really nefarious

01:34:10.840 things and we're making sure that they don't.

01:34:12.580 So, you know, if somebody asks an AI, Hey, help me make a bomb or help me make like a,

01:34:18.440 like COVID like 2.0 or whatnot, that the AI is not helping you do that.

01:34:23.860 So, um, so we run a lot of tests to make sure that it doesn't help in those areas.

01:34:28.320 And then, um, and then we make sure that the data is really clean so that there's no, there's

01:34:34.040 no sort of like little bit or piece of that that makes its way to the model.

01:34:38.540 With Outlier, that's your program.

01:34:40.400 Yep.

01:34:40.680 With Outlier, how are you, how are, what type of people are applying for those jobs?

01:34:45.360 Can people just log on and start to submit applications?

01:34:48.500 Like, how does that work to become a, um, information sourcer?

01:34:53.060 Yeah.

01:34:53.420 We call them contributors.

01:34:54.680 Okay.

01:34:54.940 Information contributor.

01:34:56.160 Everybody's kind of contributing to the AI's, everyone's contributing to the data, um,

01:35:00.100 that goes into the AI's.

01:35:01.400 Um, it's kind of like, I almost think of it as like the next generation of Wikipedia, right?

01:35:06.300 We're like, um, yeah, look at this.

01:35:08.560 And we're hiring people all around the world.

01:35:09.960 So people in all sorts of different languages.

01:35:12.580 Um, dude, that's crazy, man.

01:35:15.240 Yeah.

01:35:15.480 Well, it turns out, by the way, most of the AI's don't really, um, speak other languages

01:35:21.460 that well.

01:35:22.260 They're much, much better at, uh, at English and, and, uh, particularly American English

01:35:29.040 than, uh, than other languages.

01:35:32.220 And so we want to make sure that they speak all these languages well and that there's these

01:35:35.840 opportunities, but yeah, on Outlier.

01:35:37.600 So, um, anybody in the, around the world can log in and sort of, there's a little bit of

01:35:44.040 a, of a, of a, um, like orientation almost on, uh, on how to best, um, like what you're

01:35:51.540 supposed to do, how you're supposed to do it, what expertise should you be bringing, all

01:35:54.880 that kind of stuff.

01:35:55.420 And then, and then you can just start, um, contributing to the, to the AI models and you

01:35:59.360 get paid to do it.

01:36:00.360 Wow.

01:36:00.900 Yeah.

01:36:01.220 It's pretty fascinating, man.

01:36:02.820 And it's going to be, I mean, I really think, I legitimately think jobs from AI are going

01:36:08.080 to be the fastest growing jobs in the world for the years to come.

01:36:12.340 You do?

01:36:12.460 Yeah.

01:36:13.040 Like what, so jobs where people are able to contribute information jobs where people are

01:36:16.820 able to like, what, like what would examples of those be just some of the ones you've already

01:36:20.040 listed?

01:36:20.520 Yeah.

01:36:20.640 All the ones we've been talking about, right?

01:36:21.760 Like contributing to the AIs, um, helping to utilize the AIs and helping to, to shape

01:36:27.600 the AIs into, into applications or into, you know, uh, into in like helping organizations

01:36:34.280 or companies or people use the AIs.

01:36:36.800 That'll be a really fast growing job.

01:36:38.300 Um, helping to, to manage the AIs and make sure they're, they're, they're on the straight

01:36:43.420 and narrow.

01:36:44.460 Where would a young person go right now?

01:36:45.860 Who's some, who's getting to college or has, doesn't even want to go to college, but this

01:36:49.700 is the world they want to get into and be one of those people.

01:36:51.820 What would, do they do right now?

01:36:54.000 Yeah.

01:36:54.140 Well, this is all happening so fast, right?

01:36:55.980 Like the, uh, the, uh, like, uh, outlier, we only started a few years ago.

01:37:01.540 So all of this is happening so, so quickly, but what we want to do ultimately is make it

01:37:06.060 easy for anybody in the world to, you know, gain the skills they need to be able to do

01:37:12.220 this work well, to learn what it means, what it does, and ultimately be in a position where

01:37:16.360 they can, they can, you know, help build the AIs and then, and then keep improving that

01:37:21.160 and gaining mastery and getting better and better at it.

01:37:23.440 But like, where do they go to school?

01:37:24.580 Is there a school?

01:37:25.340 Is there a class that they should take online?

01:37:26.860 Like how does someone's start to become, you know, just get a head start on what could

01:37:33.240 potentially be probably a lot of job opportunities, I'm guessing.

01:37:36.020 Yeah.

01:37:36.520 Like in the AI space, right?

01:37:38.460 Yeah.

01:37:38.640 Like, is it just, is it just engineers?

01:37:40.640 Like, is it just mathematicians?

01:37:42.360 Like, no, it's everybody because yeah, as we were talking about, like AI needs to get

01:37:46.680 smarter about literally everything.

01:37:49.480 So, uh, but are there colleges offering court?

01:37:51.620 Like, is there, do you know, like, is there a specific places where people can, cause that's

01:37:55.220 another thing I think it's like, I'm going to work in AI and you're like, what do I do?

01:37:58.300 You know?

01:37:58.960 Yeah.

01:37:59.600 Yeah.

01:37:59.840 I, I don't think these programs exist yet.

01:38:01.640 I mean, we, we would definitely love to help build them.

01:38:04.660 Um, so I guess if any colleges are listening, you know, and you want to help figure out

01:38:09.100 about these programs, we love, we'd love to help.

01:38:11.160 That'd be pretty cool if you had your own kind of like course, not that you had to teach

01:38:13.980 it all that, you know, but you were like a partner of it somehow.

01:38:16.360 Yeah.

01:38:16.620 I mean, I think we'd love to, to basically teach everybody in America how to best contribute

01:38:21.860 to the AIs, how to, how to best basically take advantage of the fact that this is going

01:38:26.700 to be one of the fastest growing industries.

01:38:28.960 There's going to be tons of opportunities.

01:38:30.240 They're going to be shaped a little bit different from, you know, the jobs that exist today,

01:38:34.080 but you know, it's not going to be that hard for everybody to learn and figure out how to

01:38:38.400 participate in it.

01:38:39.740 What are some jobs that could, that could be at risk because of AI, right?

01:38:42.760 Cause you start thinking that like, yeah, before I was talking to you, there was this general

01:38:46.540 fear of like everything could be at risk.

01:38:48.160 Right.

01:38:48.940 Um, but when you think about it, you're like, yeah, these are some jobs that, I mean, they

01:38:52.440 won't disappear, but there might be less of them.

01:38:54.220 Right.

01:38:55.300 I just think it'll be, um, it'll be, we'll be doing like a different thing.

01:38:59.880 So.

01:39:00.720 Cause a lot of our fans are probably just blue collar listeners.

01:39:02.700 Like, like there's people that work in like, you're not going to, you're still going to

01:39:06.060 need a plumber. 0.99

01:39:06.660 He's still going to an electrician.

01:39:07.700 You're still going to need anything where you have to physically do something.

01:39:10.660 You're probably still going to need.

01:39:11.740 Yeah, for sure.

01:39:12.480 And then even stuff where you're like, let's say you're, you're mostly just working on a

01:39:16.600 laptop and you know, um, even for those jobs, like it'll just change.

01:39:21.440 Like instead of being, um, instead of my job being like, Hey, I have to do the work.

01:39:25.600 I have to literally do the work on a laptop.

01:39:27.480 It'll almost be like everybody gets, um, promoted to being a manager.

01:39:31.720 Like, because I'm going to be managing like a, a little pod of 10 AI agents that are doing

01:39:39.100 the work that I used to do, but I need to make sure that all of them are doing it right.

01:39:41.920 And that, um, they're not making any mistakes and that, you know, if they're making mistakes,

01:39:46.380 I'm helping them, you know, get around those mistakes.

01:39:48.420 Like, like, it's just going to, I, the way I think about it is that like, yeah, like

01:39:52.860 literally, um, over time, everybody will just be upgraded to being a manager or sort of promoted

01:39:59.540 to being a manager.

01:40:00.280 But can you have that many managers you think?

01:40:02.540 Yeah.

01:40:02.900 Cause I think that what's the other thing that's going to happen is the, um, the economy is

01:40:07.780 just going to grow so much.

01:40:08.980 Like there's going to be, there's going to be so much, like there will be like industries

01:40:14.480 are going to pop off in crazy, crazy ways.

01:40:16.940 And so, you know, the limit is going to be how many AIs can you have?

01:40:22.400 And then you're going to be limited for, in terms of the number of AIs you have by the

01:40:26.800 number of managers that you have.

01:40:28.140 So, um, it's gonna, uh, it's gonna, cause you need air traffic controllers.

01:40:33.560 You need as many of them as you can have.

01:40:36.480 Yeah.

01:40:36.600 Well that, that definitely, but, but you're right.

01:40:39.480 But I mean, in any field, you're going to need like just more managing, more people to

01:40:43.740 oversee and make sure that these, that different things are happening because some of the smaller

01:40:48.360 tasks will just be outsourced.

01:40:50.060 Yeah.

01:40:50.300 And just so much more stuff is going to be happening.

01:40:52.340 Right.

01:40:52.740 And that's kind of right.

01:40:53.920 Because yeah, once these things are all kind of taken care of, more things can happen at

01:40:57.820 this second level.

01:40:58.880 Yep.

01:40:59.400 That's a good point.

01:41:00.120 Yeah.

01:41:00.200 You don't think about that.

01:41:00.960 Once some of the things at the first level of, of, of, of certain businesses are handled

01:41:05.620 more easily by AI, then you're going to be able to have more people operating at a higher

01:41:11.840 level.

01:41:12.340 Yeah, totally.

01:41:14.160 It's kind of like, it's kind of like always the history of technology.

01:41:16.900 Like when, when we started developing technology that, that started, um, making farming a lot

01:41:23.580 more efficient, all of a sudden, um, you know, people could do a lot of other things other

01:41:29.400 than farming.

01:41:29.840 And then, you know, all of a sudden we have big entertainment industries and big financial

01:41:34.380 industries and, you know, barbecue cook-offs, man.

01:41:36.820 I'll tell you that second, some of those guys got the weekend off, they was grilling 0.98

01:41:40.380 shit that I knew. 0.94

01:41:42.400 But yeah, so it's all about like us. 0.97

01:41:44.400 Um, yeah, everybody leveling up to be managers and then also everybody, you know, just way

01:41:49.520 more ideas are going to start happening.

01:41:50.840 Like way more ideas are going to start becoming a reality.

01:41:53.560 And so, um, it'll be, I think it'll be pretty exciting.

01:41:57.500 Like, I think it's just like a lot more stuff is going to happen.

01:41:59.580 Yeah.

01:41:59.980 What, what, what companies do you see?

01:42:01.900 Like, are there companies where, um, you're the youngest billionaire in the world ever or

01:42:07.820 no?

01:42:08.380 Is that true?

01:42:08.920 Is that a weird statement?

01:42:10.020 We can take it out if it is.

01:42:11.360 I'm not trying to talk about your money.

01:42:13.160 I mean, you, you, but you are, is that true?

01:42:17.280 Uh, according to like some publications, but I don't know.

01:42:20.680 As a young entrepreneur, right.

01:42:22.380 And you've been very successful, you know, the, uh, self-made, um, billionaire, and we

01:42:28.780 can take the word billionaire out later.

01:42:29.920 If you decide you don't want it in, um, I just don't know how certain people feel about

01:42:33.140 that.

01:42:33.720 Um, and the founder of, of, of scale AI, where do you invest?

01:42:39.460 Like, are you investing your money in certain places, like certain fields that you see continuing

01:42:43.960 to do well?

01:42:45.580 Um, so I, most of, almost all of what I'm focused on is like, how do you invest in

01:42:50.660 how do we scale?

01:42:51.400 How do we make it super successful?

01:42:52.820 But, um, and make sure that we, you know, one of the things I'm, I think is really important

01:42:57.120 is like, how do we make sure we create as much opportunity through AI as possible?

01:43:01.580 Like, how do we make sure there's as much jobs?

01:43:03.040 How do we make sure that everything that we're talking about actually is what happens?

01:43:06.680 Because I think, no, someone's gonna have to really work to make sure all these jobs

01:43:10.560 show up and all this, all this stuff actually happens the way we're talking about.

01:43:13.560 So there's gonna be new industries that are going to pop up even.

01:43:16.020 Totally.

01:43:16.620 Yeah.

01:43:16.800 I mean, I think like just in the same way that, um, you know, it's hard, nobody could

01:43:22.680 have really predicted that podcasting was going to be this huge thing and this huge cultural

01:43:27.600 movement.

01:43:28.060 Yeah, it's true.

01:43:28.940 Um, but, but it is one and it's amazing.

01:43:31.320 That's like awesome.

01:43:32.240 And, uh, and that's going to happen in like little ways and all sorts of different industries.

01:43:36.900 Um, and that's going to be, it's going to be really exciting.

01:43:39.760 Uh, what are some of the things that excite you about technology right now?

01:43:42.940 Like what are, where do you see like AI and technology in five years, 10 years?

01:43:47.500 Um, yeah.

01:43:48.580 So, um, some of the areas I think are really, uh, are really exciting.

01:43:52.160 So one is definitely everything to do with healthcare and biology that that's moving really,

01:43:58.680 really fast.

01:43:59.220 And kind of, as we were talking about, like, I, I think legitimately in our lifetimes, we

01:44:04.860 could see cancer being cured.

01:44:06.660 We could see heart disease being cured.

01:44:08.360 Like we could see some really crazy leaps and advancements, um, in that area, which

01:44:14.400 is, which is super duper exciting.

01:44:16.100 Could it create a way that we could live forever?

01:44:18.020 Do you think?

01:44:19.340 Uh, I mean, it's just a, there's definitely people working on that.

01:44:22.260 Um, you know, uh, there's, so this is getting kind of crazy and very sci-fi, but, um, some

01:44:30.160 people think that there's, there's a way for us to keep rewinding the clocks on ourselves

01:44:36.920 so that we'll always feel young and like all of our cells will actually always stay

01:44:42.680 young.

01:44:43.560 Um, it's, I think scientifically possible, but, um, and, and I think if we can get there,

01:44:49.740 that's obviously incredible.

01:44:51.700 Um, so there's people working on that.

01:44:53.320 I think that's, uh, at, at the very least, I think we'll be able to lengthen, um, our,

01:44:58.860 our lifespans pretty dramatically and, and maybe we could get to that.

01:45:02.920 Wow.

01:45:03.380 Yeah.

01:45:03.860 Yeah.

01:45:04.220 Cause I always envision this, there's like a time where it's like, okay, this group lives

01:45:07.660 forever and this group doesn't.

01:45:08.840 And there's just that parting of two different way, you know, people head not in just into

01:45:13.580 the end zone of the Lord.

01:45:14.720 And then there's other people just loiter who are going to be loitering around for a long

01:45:18.080 time.

01:45:18.840 And what that would be like that cutoff, you know?

01:45:21.420 I, yeah, it's kind of, I mean, I, I'm not that worried about it, but it, it sucks to

01:45:26.000 be that cutoff where like, maybe, maybe, yeah, maybe, yeah.

01:45:30.500 That's a weird, it's a weird thought.

01:45:32.520 Um, cause it'd also be brave.

01:45:34.020 I mean, you'd be the astronaut.

01:45:35.200 I mean, dying, you're just an astronaut really into the ether.

01:45:38.980 You don't know what's going on.

01:45:40.360 Yeah.

01:45:40.520 You know, totally.

01:45:41.240 You'd have Lewis and Clark at a Lord at that point you were out there.

01:45:44.460 Yeah.

01:45:44.740 And then if you stay, you kind of are always going to be no, you always know kind of what's

01:45:48.800 going to happen in a way.

01:45:49.680 Cause you'll be here.

01:45:50.940 Yeah.

01:45:51.180 Which would be exciting, I think.

01:45:53.480 But then after a while you might be like, dang, what happens if you die?

01:45:56.380 That's the bravest choice you could make.

01:45:58.500 That's true.

01:45:59.140 Probably.

01:45:59.800 Yeah.

01:46:00.180 How did, do you see AI having any effect on religion?

01:46:04.300 Yeah.

01:46:04.640 I think, um, I think one of the things that, uh, something I believe is like, I, I think

01:46:12.120 that as AI becomes, um, uh, you know, this is one of the things I think is really important

01:46:19.700 is that we are, are able to educate people about AI and help people understand it better

01:46:24.700 and better and better.

01:46:25.980 Because, um, I think it's this scary thing that nobody understands or people feels like

01:46:31.720 a boogeyman or, you know, feels like is, is there's just this like thing that's going

01:46:36.660 to take my job?

01:46:37.400 Like that makes it scary.

01:46:40.160 And I think that that affects people's spirituality that affects how people, you know, contextualize

01:46:46.120 themselves with the world.

01:46:47.440 You could lose your purpose.

01:46:48.220 If your purpose is a job that you feel it's going to disappear.

01:46:51.080 Yeah.

01:46:51.480 That could already be causing you to feel that way.

01:46:55.920 Yeah.

01:46:56.300 So, but I think, I think if you, if we can explain AI more and ultimately like,

01:47:01.720 um, like it is, it is a cool technology, but it's not that magical.

01:47:05.980 It's just, you know, it's like data and you crunch that data and then you get these

01:47:09.940 algorithms.

01:47:10.340 And so it's not like, um, it's not, uh, yeah, some people talk about in this crazy

01:47:17.660 way, but, but I think as long as we are able to explain what AI is and also explain what

01:47:21.820 opportunities it creates over time.

01:47:23.660 And to me, it's about getting this like relationship between humanity and AI, right?

01:47:29.920 Like, how do we make sure that this is something that enables us as humans to be more human

01:47:36.460 and us as humans do more and experience more and be better and all these things versus something

01:47:41.840 that kind of, um, is scary or will take over or anything like that.

01:47:46.360 Uh, I think that's really important.

01:47:48.580 What's a place like, so if I go to chat GBT, is that scale AI?

01:47:52.060 Is that the same thing or it's different?

01:47:53.160 That's it.

01:47:54.060 So we, so we're actually kind of under the hood powering all the different models and

01:47:59.840 AIs.

01:48:00.640 So are all the different AI like systems under the hood?

01:48:05.140 Yeah.

01:48:05.360 Yeah.

01:48:05.480 So we help power chat GBT and open AI.

01:48:08.380 We help power, uh, mostly from the data perspective.

01:48:11.260 So do we know if the answer came from your company or other companies?

01:48:14.320 If we ask it a question, like how do we, uh, there's probably no way to like literally

01:48:19.180 tell, but yeah, we help power, you know, open AIs and we help power Google's AI systems

01:48:23.840 and Meta's AI systems and, um, help power all the, all the major AI systems.

01:48:29.860 Yeah.

01:48:30.000 How can a regular person just at their home, right?

01:48:32.200 Say there's a guy who's been listening to this today.

01:48:33.740 He wants to go home today and he just wants to learn a little bit of how AI works.

01:48:37.160 He could just go on to chat GBT and ask it a couple of questions.

01:48:40.140 Yeah.

01:48:40.460 You could ask chat GBT how AI works.

01:48:43.020 Um, you could ask it how, what's the history of my town?

01:48:45.060 You know, um, can you research my last name maybe and see where it came from?

01:48:49.240 Um, what are, uh, like maybe what are some, um, innovations that could happen in the next

01:48:57.740 few years?

01:48:58.180 There's different little things you can just ask it.

01:48:59.780 That's how you can start to have a relationship with asking and learning about, uh, and you

01:49:04.160 see what it's good at, what it's not good at.

01:49:05.920 Like right now, AI is still, uh, really bad at a lot of things.

01:49:09.440 Like most things.

01:49:10.460 This is why, this is why I think when people understand it and really get a feel for it,

01:49:15.540 it stops being as scary because, uh, because I think we think about it as like, we think

01:49:20.700 about it as like the AI from the movies that are sort of like, you know, all powerful and

01:49:23.980 whatnot.

01:49:24.400 But, uh, yeah, you think of it as a robot that's going to show up and just start driving your

01:49:27.900 truck around or something. 0.95

01:49:28.840 And you're like, well, what the fuck do I do? 0.97

01:49:30.680 You know what I'm saying? 0.99

01:49:32.100 Yeah.

01:49:32.380 My keys were in my truck.

01:49:33.400 You know, I can't even get in my house now.

01:49:34.760 Like, I think that's the, there is this boogeyman fear.

01:49:38.340 Yeah.

01:49:38.960 Yeah.

01:49:39.300 But that's not the truth.

01:49:40.540 It's not the truth.

01:49:41.360 And like, um, yeah, that's not the truth.

01:49:44.280 And, and it's, to me, it's like, we have, we kind of have the choice to make sure that

01:49:49.120 also doesn't become the truth.

01:49:50.700 Right.

01:49:50.980 Like we definitely, we in the, like people building AI, but just in general, everybody

01:49:55.540 in the world has like, like we should all make sure to use it as something that like

01:50:00.860 an assistant as something that like helps us versus, um, versus think about it in like

01:50:06.120 a, in like a scary way.

01:50:07.560 Well, getting to learn and learn how to use it small ways, whatever is certainly a way

01:50:12.800 that you're going to start to realize what it is.

01:50:14.600 Yeah.

01:50:15.020 And it's easy to just sit there and say it's horrible and without trying to use it to learn

01:50:18.980 about it.

01:50:19.540 Yeah.

01:50:19.860 Sometimes I won't learn about something just so I can continue to say it's a boogeyman,

01:50:22.900 you know, cause it kind of gives me an excuse.

01:50:24.400 Yeah.

01:50:24.820 You know, if I, if I choose not to learn about it in my own life, um, chat GBT has become like

01:50:30.360 a proprietary, like, like name, like band-aids or, um, ping pong, is that okay that it's

01:50:39.140 like that?

01:50:40.120 Yeah.

01:50:40.340 I think that's, uh, that's totally fine.

01:50:41.800 I mean, I think basically like we, there will probably be more AIs over time that people

01:50:47.780 get used to and use and, um, like anything, you know, there'll always be like a, uh, in

01:50:55.260 America, there'll always be a bunch of options for consumers and much options for people to

01:50:58.820 use and they'll be good at different things.

01:51:00.700 Right.

01:51:00.980 So I think like, um, right now we're just in the very early innings of AI, but over time

01:51:06.740 we're going to have, you know, just like how, um, uh, for, for anything like for, for clothes

01:51:13.900 or for, you know, energy drinks or for whatever, like different people have different tastes

01:51:19.200 because there's going to be different things that different AIs are good at and other things

01:51:23.180 that other AIs are bad at.

01:51:24.720 Is AI right now smarter than humanity?

01:51:27.840 No.

01:51:28.800 Um, yeah.

01:51:29.880 So I think what AI is good at because it's ingested all of the, the facts, right?

01:51:37.460 Like it's ingested this, like the body of water is really, really big and it's ingested so

01:51:43.000 many different facts and information from all of humanity.

01:51:47.520 It definitely like knows more, um, or like, you know, just like how Google knows a lot

01:51:52.940 more than, than any person does.

01:51:54.660 So it definitely knows a lot more.

01:51:57.200 Um, but it's not like, you know, there's, there's a very, very, there's tons of things

01:52:02.860 that humans can do that AI is just like fundamentally incapable of doing.

01:52:06.660 So, um, so it's not a, it's not like a, I don't think you can even like measure one

01:52:12.560 versus the other.

01:52:13.260 There's sort of like very different kinds of intelligence.

01:52:15.560 Could AI just create a better AI at a certain point?

01:52:19.020 Like, could it just be like, Hey, AI, create a better AI and it could do that?

01:52:23.840 Yeah, this is a good question.

01:52:25.080 Actually, this is, this is another really, uh, hot topic in the AI industry is, is can

01:52:32.200 you get AIs to start doing some of the engineering and some of the improvement on its own?

01:52:39.380 That's scary.

01:52:40.500 Cause then it's making kind of choices.

01:52:42.320 Then it's becoming the lifeguard.

01:52:44.400 It's becoming the water and the, uh, coast guard.

01:52:48.480 Yeah.

01:52:48.860 So this is something, I mean, my personal thought is, I think this is something we should kind

01:52:53.380 of watch a little bit and we should make sure that humans always are, have the sort of like

01:52:58.240 steering wheel and the, the sort of, uh, control over.

01:53:01.240 Um, because like you're saying, you know, it's like kind of a slippery slope before that gets

01:53:06.300 kind of, uh, you know, a little, a little weird.

01:53:09.080 But, um, but I, I don't, I think that like we can, we, we can maintain that.

01:53:13.240 We can make sure that we don't let the, the AI just sort of like keep iterating and improving

01:53:17.600 on its own.

01:53:18.080 Yeah.

01:53:18.760 And in the end, you can always shut off your computer and phone and go for a walk, huh?

01:53:22.880 Yes.

01:53:23.340 Yeah, totally.

01:53:24.360 It's not like it's going to come out and just, you know, slurp you off or something if you're

01:53:27.960 trying to be straight or whatever, you know, and it's a man.

01:53:30.720 I don't even know.

01:53:31.140 Is AI male or female?

01:53:32.900 I don't think it has a, I don't think it has a gender.

01:53:35.220 Wow.

01:53:35.480 I wonder if it'll decide one day.

01:53:37.980 Like, Hey, I'm Frank, you know?

01:53:39.960 Well, there's, there are companies that try to program the AI to adopt various personas

01:53:44.600 and, um.

01:53:46.200 Oh yeah.

01:53:46.660 I got the Indian GPS guy.

01:53:48.140 Turn right.

01:53:49.540 Like, uh, like on, on meta on like Instagram or whatever, you can get, uh, you can get an

01:53:55.200 AI that has Aquafina's voice, for example.

01:53:57.080 Oh, that's cool.

01:53:57.800 Yeah.

01:53:58.640 Um, and it's funny, you know, the Aquafina AI is funny.

01:54:01.820 Um, um, you're a self-made billionaire, which is pretty fascinating.

01:54:05.620 Congratulations.

01:54:06.360 I think, you know, to have, uh, um, I think that money is energy kind of, and it kind

01:54:11.580 of flows to certain places and stuff.

01:54:13.880 And, uh, congratulations.

01:54:15.920 That's gotta be fascinating.

01:54:17.400 Was that scary when that kind of happened?

01:54:20.180 Yeah.

01:54:20.580 You just made some money.

01:54:21.700 Was that kind of a scary thing?

01:54:22.840 Did you guys grow up really well?

01:54:24.020 Like, what was that like?

01:54:25.260 No, no, no.

01:54:25.860 I grew up, I think like solidly middle class.

01:54:28.660 Um, like we weren't, we, you know, we weren't, uh, we weren't like, uh.

01:54:34.280 You weren't trapping or anything.

01:54:35.560 You guys, yeah, yeah, yeah.

01:54:36.620 But, but like, you know, solidly middle class.

01:54:38.600 And I think like, uh, I mean, one of the, one of the, um, one of the crazy things about

01:54:44.900 tech and the tech industry is that, you know, we've been, we've been just sort of like building

01:54:50.360 this thing up over time.

01:54:51.480 But one of the things that, that happened is AI all of a sudden became like, there was

01:54:57.140 so much progress in AI and it became the biggest thing in the world.

01:55:00.500 Right.

01:55:00.880 Like, I mean, um, all of a sudden, you know, anywhere I go, I'm, everybody is talking about

01:55:07.460 AI.

01:55:07.860 It didn't used to be like that when I started the company.

01:55:09.700 Um, uh, AI was just kind of a, like a niche topic.

01:55:14.020 And now it's like, you know, anywhere you go, like I'll just be walking around and I hear

01:55:19.120 like random conversations about chat GPT and AI and robots and all this stuff.

01:55:23.820 And, uh, and so it's kind of been crazy to be experienced that and be a part of that

01:55:29.700 wave and kind of like, you know, I started working on this company almost nine years ago.

01:55:35.500 So it was like, when I started working on it, it was kind of this obscure thing.

01:55:39.240 And, you know, I always knew that it was going to become bigger, but I, I could have never

01:55:42.780 predicted what was going to happen to AI.

01:55:44.440 Yeah.

01:55:44.920 Yeah.

01:55:45.120 It's fascinating.

01:55:45.660 It's almost like you were just standing on like the bingo number.

01:55:49.260 They got called kind of by time.

01:55:52.160 Yeah.

01:55:52.220 Um, and it's surreal.

01:55:54.020 Oh, I can real when that happens.

01:55:55.680 Oh, yeah.

01:55:56.160 I can only imagine that.

01:55:57.640 Um, are your parents pretty proud of you?

01:56:00.180 What's that like?

01:56:01.980 Yeah.

01:56:02.400 So at first they were like, you know, at first I dropped out of college, right?

01:56:06.300 Oh yeah.

01:56:06.840 That's true.

01:56:07.200 Yeah.

01:56:07.600 And in Asian culture, that's like not a thing you do, right? 0.87

01:56:10.080 Not good.

01:56:10.360 Yeah.

01:56:10.720 Yeah.

01:56:10.900 That is like the opposite of being Asian. 1.00

01:56:12.820 Well, well, my parents both have PhDs.

01:56:15.560 Um, my two brother, I have two older brothers.

01:56:17.600 They both have PhDs.

01:56:18.960 Uh, like everybody in my family has, they've gone through all of schooling.

01:56:24.680 Yeah.

01:56:24.860 They're like, Alex is not doing good.

01:56:27.180 No, that might've been Spanish.

01:56:28.600 But they're like, yeah, Alex is not doing good.

01:56:30.540 Yeah.

01:56:30.960 So they were, they were pretty worried at first.

01:56:32.700 And I kind of, um, I told them a little bit of a white lie that I was like, oh no, I'm,

01:56:39.200 I'm just going, I'm going to go back.

01:56:40.660 You know, I'm going to finish, I'm going to get this, uh, like tech thing out of my

01:56:45.400 system and they'll go back and I'll finish school.

01:56:48.140 Um, obviously that hasn't happened yet, but, uh, but yeah, they were worried at first.

01:56:52.260 Now that now they're super proud.

01:56:53.600 Yeah.

01:56:54.000 They're super duper proud.

01:56:55.060 And I, uh, yeah.

01:56:56.720 And I, I owe everything to my parents, you know, they're, uh, they're awesome.

01:56:59.620 And like, seriously, they're, they're, my parents are super brainy.

01:57:03.780 They're, uh, they're both physicists.

01:57:05.680 They would like teach me about physics growing up and teach me about math.

01:57:08.900 And, uh, that's what led me, uh, be so good at the competitions, you know?

01:57:13.400 That's cool.

01:57:14.700 Yeah.

01:57:15.060 I don't even, I think do, um, are your parents or your grandparents from China or no?

01:57:21.000 My grandparents are from China.

01:57:22.180 Yeah.

01:57:22.680 Did your, does your family have a lot of Chinese culture?

01:57:25.720 So this is kind of interesting.

01:57:27.180 This is true for a lot of, um, Chinese Americans is that there's kind of like, there's, uh,

01:57:34.220 Chinese culture.

01:57:35.440 And then that's kind of almost like, it's very different from the Chinese communist party

01:57:41.660 and the current government.

01:57:43.180 Because basically one way to think about China is, I mean, China has been, is a, is a culture

01:57:48.240 and a civilization that's been around for like thousands and thousands of years.

01:57:52.400 Right.

01:57:52.820 So it's, there's a very longstanding culture.

01:57:56.000 Oh yeah.

01:57:57.000 But that's very different from the current communist party and the current communist regime.

01:58:03.160 Oh, for sure.

01:58:03.640 I think most people probably think of, I mean, I don't know.

01:58:06.680 It's funny.

01:58:07.180 I never thought about that.

01:58:07.900 I definitely think about him as different.

01:58:09.180 I definitely don't think if I see a Chinese person, I don't think, oh, that's a communist

01:58:12.040 person.

01:58:12.600 Yeah, exactly.

01:58:13.180 I mean, that would be, yeah, that's crazy.

01:58:14.700 I think that's crazy.

01:58:15.520 If some people thought that I just, yeah. 1.00

01:58:18.680 I think somebody has a crazy long history, like, damn. 0.97

01:58:22.460 And then maybe almost a, do you feel like there's some people in certain parts of China 0.99

01:58:25.920 are almost like a captured people then?

01:58:27.580 Is it, do you think that's a?

01:58:29.120 Yeah.

01:58:29.440 I mean, the, the Uyghurs that we're talking about, I mean, that's just like horrible what's 1.00

01:58:33.040 happening to them.

01:58:33.640 I didn't even know about that, man.

01:58:34.720 Thanks for putting me up on that Uyghur game.

01:58:36.580 Yeah.

01:58:37.120 Yeah.

01:58:37.320 No, it's, uh, I mean, some of the stuff, like, I mean, we don't really like, first

01:58:41.420 of all, the world is a, is a huge place and there's like all sorts of both great and bad

01:58:48.140 things happening all over the place.

01:58:49.700 Yeah.

01:58:50.100 But, um, but yeah, no, I think that like, I think a big part of this is like, we want

01:58:54.600 to make sure we have, we have governments that, uh, that believe in democracy, believe

01:58:59.180 in liberty, these kinds of things.

01:59:00.660 Yeah.

01:59:02.240 With being somebody that's able to be smart and conceptualize stuff, do you start to get

01:59:07.140 in, do you have any insight?

01:59:08.460 This might be the last question I have for you.

01:59:10.100 Um, do you have any insight on like the afterlife or what happens?

01:59:14.800 Like I never really thought about it for like talking to like a real math guy about that.

01:59:21.360 Yeah.

01:59:21.740 I think, um, like what's the total, like what's the sum zero game or whatever, you know, what's

01:59:25.980 the, yeah, yeah.

01:59:27.520 Yeah.

01:59:27.880 I guess like the way I've always thought about it because, um, partially because both my parents

01:59:33.480 were physicists was I, I kind of always feel like people live on with their ideas and they're

01:59:41.220 kind of like, like what are the things that, the things they put out into the world.

01:59:45.000 That's kind of how you live on.

01:59:46.740 Cause like in, in math, like everything you learn about is like a theorem named after a

01:59:52.380 different person or, uh, or sort of idea named after some, you know, the first mathematician

01:59:58.660 or the first scientist or whatever to figure that out.

02:00:02.400 Um, like Einstein lives on because bagels, that was a shitty joke, but thank you.

02:00:08.740 Um, oh yeah.

02:00:10.060 Because, you know, because the, yeah, all E equals MC squared, all that kind of stuff.

02:00:14.960 Yeah.

02:00:15.480 So, so like, I know what that kind of stuff is.

02:00:17.860 Jesus.

02:00:18.280 Sorry, but it's, sorry, pretending.

02:00:20.040 No, no, no, exactly.

02:00:21.280 E equals MC squared.

02:00:22.000 That's Einstein.

02:00:22.600 And so, so I think I always feel like people, um, yeah, you live on by your ideas and, and

02:00:29.660 kind of what you put out into the world.

02:00:31.360 Um, and that's kind of always how I've thought about it.

02:00:33.800 Um, so you don't get some deeper thought about like, like since your brain is able to

02:00:38.140 be, cause you have probably a unique brain, right?

02:00:40.760 And more, I mean, you know, and everybody has a unique brain, but I've just never asked

02:00:45.980 somebody with your, I've never asked your brain this question.

02:00:48.660 Yeah.

02:00:49.240 Do you, do you get some further insight about like what you think happens when you die?

02:00:54.300 You know?

02:00:55.020 Well, I think that one of the things that gets talked about a lot in Silicon Valley, um, where

02:01:00.480 I live, especially is, uh, like the simulation, whether it's all simulation and whether like,

02:01:06.600 uh, I don't, do you watch Rick and Morty?

02:01:08.900 I don't watch it, but I'll start.

02:01:10.800 There's a, there's, there's some, there's some episodes where it gets into this and I

02:01:14.920 think it, it covers it in a, in a pretty good way.

02:01:17.000 But like, you know, what if every, what if all of humanity is just like a, like a, almost

02:01:23.020 like a, an experiment or a video game that, that some other civilization is running?

02:01:27.960 Yeah.

02:01:28.500 That's kind of the one that, uh, that fucks with particularly like people's mind and tech

02:01:33.700 a lot because we're like every day, all day, every day, we're out there trying to make

02:01:38.860 computers and make simulations and make things that are, that are like more and more sophisticated

02:01:44.580 and advanced and capable. 0.97

02:01:46.140 And so kind of the mind fuck is like, oh, what if everything we know is, is just kind 0.99

02:01:51.620 of the, you know, a simulation from some other civilization. 0.98

02:01:55.540 Or if we advanced it enough that we're able to make this happen and seem real.

02:01:59.440 Yeah, exactly.

02:02:00.380 Yeah, exactly.

02:02:01.200 So, you know, I think, I think one of the things that, that, um, like with AI and with a bunch

02:02:06.920 of other things, like in, well, even just in the past, like 30, 40 years, video games

02:02:12.320 have gotten so much more realistic, right?

02:02:15.380 Crazy realistic.

02:02:16.680 Yeah.

02:02:17.300 So it was, we've seen that happen like in a hundred years, like, would we be able to

02:02:23.340 simulate something that feels pretty realistic?

02:02:26.260 I mean, probably.

02:02:27.520 Right.

02:02:27.720 Could we be avatars?

02:02:29.060 Yeah.

02:02:29.620 For some, for some other thing.

02:02:31.700 Yeah, exactly. 1.00

02:02:33.020 Damn, dude. 0.99

02:02:34.340 I'll say this. 1.00

02:02:34.900 My avatar is a pervert, brother.

02:02:38.020 Um, have you met any guys like, uh, Jinssen Hong? 0.56

02:02:42.100 Is that his name?

02:02:42.760 Yeah, yeah.

02:02:43.380 You have?

02:02:44.020 Yeah, totally.

02:02:44.760 Wow.

02:02:45.120 What is it like when you meet some of these guys?

02:02:46.380 Have you met Elon yet?

02:02:47.560 Met Elon?

02:02:48.160 Yeah.

02:02:48.560 Met Elon a few years ago.

02:02:49.680 Yeah.

02:02:50.200 Wow.

02:02:50.540 Got to know him. 1.00

02:02:51.340 He's the fucking, he's the, he's turning into him. 1.00

02:02:53.120 Jinssen's the goat. 1.00

02:02:54.140 Yeah.

02:02:54.380 He is?

02:02:55.440 He is.

02:02:55.820 Yeah.

02:02:56.000 He always wears this leather.

02:02:57.680 You see that leather jacket?

02:02:58.740 Yeah.

02:02:59.420 He, uh, we hosted this dinner.

02:03:01.860 And Hong, is that a Chinese name?

02:03:03.140 Hong? 0.57

02:03:03.480 Hong.

02:03:03.800 Yeah.

02:03:04.100 He's Taiwanese, I think.

02:03:05.400 Yeah.

02:03:06.080 Um, or Taiwanese American. 0.68

02:03:08.000 But, uh, he, uh, he in, in 2018.

02:03:12.620 So when we were like a baby company, uh, we threw this dinner in, uh, in Silicon Valley.

02:03:19.500 And, uh, and we just kind of, I kind of YOLO invited him, um, this was, you know, years and

02:03:26.480 years ago.

02:03:27.300 And, uh, and I didn't expect it, but he said yes.

02:03:29.560 And he came to our dinner and, uh, he, he came, uh, he came, it was, it was like at this

02:03:35.760 restaurant in Silicon Valley.

02:03:37.120 And, uh, he came and, uh, he came and, uh, he, uh, he, uh, he, uh, he went to boarding school.

02:03:45.240 I think he's probably told this story, but like he went to boarding school, um, and his

02:03:49.880 parents, when they came to America, they, they, they wanted to send him to boarding school,

02:03:53.600 but they didn't.

02:03:55.440 Um, and so they just sent him to like the first boarding school that they, they like

02:03:58.540 found on Google or something or like found, it wasn't even Google at the time.

02:04:01.800 So that, that they like heard about, and that boarding school happened to be, um, kind of

02:04:06.700 like a rehab boarding school.

02:04:08.440 So it was like, he was like this. 0.99

02:04:12.300 So fucking, it's a halfway house. 1.00

02:04:14.120 He's just sitting there. 1.00

02:04:15.380 He's there learning with people who are, um, detoxing.

02:04:18.900 Yeah.

02:04:19.060 So he, he told me the story about, he told the story about how he was like, he was just

02:04:22.760 like this, this kid, um, at, you know, this boarding school that were like, everybody else

02:04:28.520 had these, like all these crazy backgrounds.

02:04:30.280 And he like, he got by and made, made his way through that school by like, uh, by like

02:04:37.320 doing everyone's math homework and like, you know, kind of like wheeling and dealing that

02:04:41.040 way.

02:04:41.440 Oh yeah.

02:04:42.040 And you could see that he, he learned how to like wheel and deal and sell and all this

02:04:47.360 stuff from all the way from way back then.

02:04:50.160 Cause he was, he was, I mean, his, his, his, his story is pretty crazy.

02:04:53.840 Really?

02:04:54.180 But, uh, yeah.

02:04:54.700 We have to research him a little bit.

02:04:55.800 Maybe we would get to talk with him one day.

02:04:57.480 Yeah.

02:04:58.120 Jensen, Jensen's awesome.

02:05:00.060 Um, you know, all these people in tech, they're like, uh, I mean, they're all real people,

02:05:03.920 but they all have the craziest backgrounds.

02:05:06.080 Yeah. 0.73

02:05:06.380 Yeah, man.

02:05:06.700 It's just so funny.

02:05:07.360 Whenever I met you, I just didn't know.

02:05:08.960 I figured that since you were with Sam Altman, that you were probably a tech guy, you know?

02:05:13.760 And, um, but yeah, I didn't know.

02:05:17.980 Uh, I think maybe somebody said he's in the AI verse, you know, but you just seem like

02:05:21.480 such a totally normal, like I would not have thought that, um, you were just, I don't know.

02:05:26.960 I guess sometimes you think like somebody's going to be, they're going to be like super

02:05:30.160 quiet or, you know, not have a lot of different thoughts, but yeah, it was cool, man.

02:05:35.140 We had a good time.

02:05:35.720 I'm glad we got to link up.

02:05:37.920 Yeah, totally.

02:05:38.900 That was cool, bro.

02:05:39.760 You're probably like my, you might even be my first Chinese friend.

02:05:42.080 I don't think second, probably Bobby Lee, who's denying it, but he'll come around.

02:05:49.760 Um, Alexander Wang, man.

02:05:51.320 Thank you so much, dude.

02:05:52.620 I bet your, uh, whole family's super proud of you.

02:05:55.080 Um, yeah, it's exciting, man.

02:05:56.320 Thank you for coming to spend the time with us and just helping us, uh, learn and think.

02:06:00.820 No, thank you.

02:06:01.740 This was awesome.

02:06:02.360 And I think like, I mean, we were talking about this before, but, um, I don't want to

02:06:06.140 make sure that like people all around the world, especially Americans aren't scared of

02:06:10.680 AI because it's going to be, it's going to be really cool and it's going to be amazing,

02:06:14.440 but, um, we need to remove the boogeyman component and, uh, and thanks for helping me do that.

02:06:20.880 Yeah.

02:06:21.300 No, man.

02:06:21.640 I think I definitely feel differently about it.

02:06:23.740 I feel like it's a tool that I can use.

02:06:25.580 Right.

02:06:25.920 And even, I don't know how to use it.

02:06:27.680 So I'm trying to figure out, you know, um, one more question.

02:06:30.820 How do you keep it from becoming like a, like a advertisement trap house? 0.59

02:06:34.480 Like the internet's become like the internet's just pop-ups and ads and fucking best buy trying 1.00

02:06:39.060 to like beat you over the head on some shit. 1.00

02:06:40.680 Like, how do you stop that? 1.00

02:06:42.780 How do you keep that out of like you guys as waters or do you have to go there at some

02:06:47.420 point to make money?

02:06:48.280 Yeah.

02:06:49.440 First, I'm hoping that, that, um, we have like the AI industry as a whole avoids advertising

02:06:55.580 as much as possible because, um, because it's kind of, it's very different.

02:07:00.260 Like it is a tool that, that people can use and people can use to start businesses or make

02:07:05.900 movies or make all these like different ideas happen.

02:07:08.360 And I, I would much rather it be a tool that doesn't get sort of, um, uh, that doesn't become,

02:07:14.900 yeah, like an advertising thing versus like, I wanted to make sure it's a tool that helps

02:07:19.620 people first and foremost.

02:07:21.320 Um, so that's that, I, I think this is, there's kind of like a, a choice here and, uh, and we

02:07:27.640 as an AI industry just got to, I think, make some of the right choices.

02:07:30.540 Yeah.

02:07:30.960 I think there would be value in staying as pure as you could, if you could find a way to,

02:07:34.580 you know, if there's other money to be made on the side, it almost seems sometimes like

02:07:38.000 it could be a trap, you know?

02:07:39.440 Yeah.

02:07:40.140 Yeah.

02:07:40.380 And I think that's, that's like, uh, you know, I, I want to make sure that, uh, that

02:07:46.180 people don't feel like they're being used by the AIs.

02:07:48.180 I think that'd be a really, that'd be really bad if we ended up there.

02:07:50.980 So we, you know, and I, that, I don't think we need to make it like that at all.

02:07:54.940 Like, I think we can, we can make sure the AI is helping you do things is, is super helpful.

02:07:59.680 It's super, um, is like a thought partner.

02:08:02.520 Um, is it, is like an, like an assistant.

02:08:04.760 Like those are things that I think we want to make sure AI stays.

02:08:07.580 Gang, baby. 0.75

02:08:08.660 Wang gang.

02:08:09.500 Gang, Alexander Wang, man.

02:08:10.780 Thanks so much, bro.

02:08:12.000 Yeah.

02:08:12.240 Thanks so much for having me.

02:08:13.200 Yeah.

02:08:13.360 It was awesome, man.

02:08:14.360 Danny Kane.

02:08:15.040 Shout out to Danny, um, who came up and Danny lives in Franklin, right?

02:08:19.180 We got to get to spend some time with him.

02:08:20.520 Yeah.

02:08:20.780 Lives in Franklin.

02:08:21.640 Whenever you're out there.

02:08:22.540 And shout out to, uh, Alex Brusowicz, who, uh, who we met through.

02:08:26.640 Yeah.

02:08:27.120 Who else on your team's here, man, today?

02:08:29.120 Uh, we have Joe Osborne.

02:08:30.960 Yeah.

02:08:31.740 And, uh, and we have Danny's whole crew.

02:08:34.740 So Arian and, uh, Clayton.

02:08:37.800 Yeah.

02:08:38.120 Nice.

02:08:38.460 We'll have to get a group picture.

02:08:39.780 We'll put it up at the end.

02:08:40.720 Um, thank you guys.

02:08:41.840 Uh, have a good one, man.

02:08:42.920 Now I'm just floating on the breeze.

02:08:45.100 And I feel I'm falling like these leaves.

02:08:48.080 I must be cornerstone.

02:08:53.520 Oh, but when I reach that ground, I'll share this peace of mind.

02:08:58.040 I found I can feel it in my bones.

02:09:02.200 But it's gonna take...

02:09:04.580 But it's gonna take...

02:09:05.380 Oh, but there is a way...

02:09:07.280 Yeah.

02:09:12.000 Oh.

02:09:14.760 Yeah.

02:09:16.180 Oh, no.

02:09:16.560 Yeah.

02:09:16.840 What?

02:09:17.800 But what if I can come to mimics for such a great söndrom,

02:09:21.780 you can,

02:09:23.240 and say,

02:09:25.760 Oh, so.

02:09:26.220 revelry by.

This Past Weekend with Theo Von - February 18, 2025

E563 AI CEO Alexandr Wang

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