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Speaker 1: The dumbest people can make a lot of money. If

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Speaker 1: you have a good network. You can have a billion

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Speaker 1: dollars in net worth but have zero dollars in a

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Speaker 1: bank account. It was just a number on paper. It

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Speaker 1: could go to zero at any point in time. Lucywoll

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Speaker 1: is a billionaire entrepreneur and scale AI code found them.

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Speaker 2: She shares unfiltered views on AI wealth, ambition, and the

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Speaker 2: trade offs behind success.

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Speaker 3: Everybody wants to be a billionaire, but is there a

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Speaker 3: side that people don't think about.

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Speaker 2: It becomes a lot more lonely.

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Speaker 1: It's harder to trust new people nowadays, and that makes sense.

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Speaker 3: You and your friends brought the forefront of what a

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Speaker 3: lot of people are calling the AI revolution. I'm curious, like,

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Speaker 3: what do you guys argue about.

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Speaker 1: It's probably like fifty to fifty on AGI happening, and

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Speaker 1: I just don't think we're even close to getting there.

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Speaker 2: Loms are not human intelligence.

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Speaker 1: It takes data and it summarizes it and it kind

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Speaker 1: of predicts the next word.

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Speaker 3: When people say that they're scared AI is going to

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Speaker 3: take their jobs, what do you say to them.

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Speaker 1: Yes, if you're like an entry level engineer of AI,

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Speaker 1: it's probably going to replace you. The ones that learn

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Speaker 1: how to supplement that are going to be extremely successful.

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Speaker 3: Hi, guys, Kate here, thank you so much for tuning

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Speaker 3: into today's episode. Lucy Guo is a founder, investor, and

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Speaker 3: one of the youngest self made women in tech. If

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Speaker 3: you've been enjoying post run high, please be sure to

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Speaker 3: follow the show wherever you're listening, and we will be

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Speaker 3: right back with our conversation after this short break. Today

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Speaker 3: we are joined by Lucy Guo. She's an entrepreneur, founder,

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Speaker 3: and at just thirty years old, she's the youngest self

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Speaker 3: made female billionaire on the planet. Lucy recently unseated Taylor

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Speaker 3: Swift for this title, but we just found out on

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Speaker 3: our run that Lucy has recently been unseated as well,

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Speaker 3: so both Taylor and Lucy now are no longer Ye

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Speaker 3: cheers too.

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Speaker 2: Than you and know the founder of Calshi. She's really amazing.

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Speaker 1: I think she immigrated from Brazil and if I'm wrong

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Speaker 1: about that, please don't cancel me.

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Speaker 3: Okay, but how long did you hold the title for?

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Speaker 2: I don't even know months.

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Speaker 1: I actually I think it's gonna get unseated like every

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Speaker 1: few months, especially as evaluations are climbing with tech companies.

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Speaker 3: I could see that. Yeah, I could see that. And

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Speaker 3: I feel like also when it comes to this AI wave,

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Speaker 3: it's like younger and younger people are kind of at

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Speaker 3: the forefront of it naturally.

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Speaker 2: Mm hmm exactly, yep.

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Speaker 1: So it is interesting, yeah, and it just like costs

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Speaker 1: a lot less money to reach higher valuations now because

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Speaker 1: AI is like ten xting every employee. So I think

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Speaker 1: we're just gonna see like an unsea every few months,

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Speaker 1: which is awesome, right, because like we're seeing females win.

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Speaker 3: We're seeing females win. But I do have to just

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Speaker 3: call out, like the fact that you unseated Taylor Swift

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Speaker 3: is so crazy, right, It's such a wild thing to

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Speaker 3: think about. What did your parents think when that happened?

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Speaker 1: Yeah, I mean I don't think they thought anything of it,

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Speaker 1: because at the end of the day, it's like you

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Speaker 1: can have a billion dollars in net worth but have

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Speaker 1: zero dollars in a bank account, which I mentioned on

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Speaker 1: our run and at that point in time, like you know,

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Speaker 1: I wasn't liquid, so it was just a number on paper.

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Speaker 1: It could go to zero at any point in time.

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Speaker 1: My parents are more so just worried about my safety.

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Speaker 1: And I think they were a little annoyed too, because,

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Speaker 1: like you know, with the headlines, people start coming to

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Speaker 1: them asking them for money, to try to get money

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Speaker 1: from me, and they're just like, dude, like we don't

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Speaker 1: have it.

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Speaker 3: Did you expect the headlines?

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Speaker 1: I didn't expect the headlines. It kind of happened by accident.

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Speaker 1: The Forbes reporter texted me going like, oh, like, wouldn't

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Speaker 1: this make you a billionaire now? And I was like,

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Speaker 1: I responded back, lol, only on paper, or like I

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Speaker 1: wish I were like like I literally sounded like lol.

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Speaker 1: And then the next thing I know, she's like, oh,

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Speaker 1: by the way, im running a story on I'm like,

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Speaker 1: you're what, can I at least change my quote here?

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Speaker 3: Right? It is so funny too, because it's like, obviously

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Speaker 3: now you're more so in the public eye, right, and

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Speaker 3: like you're on so many podcasts and you do a

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Speaker 3: lot of press. But I feel like when you are

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Speaker 3: younger and you're you know, just starting out and you're

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Speaker 3: building this company, it's like the top thing on your mind,

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Speaker 3: especially building a company like Scalai, isn't oh I want

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Speaker 3: to get all this press? You know what? I mean,

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Speaker 3: so it is kind of a foreign concept, and then

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Speaker 3: all of a sudden, Yeah, You're on all these headlines

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Speaker 3: and Forbes is talking.

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Speaker 1: About you, and yeah, literally had no idea was coming

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Speaker 1: out like I had. I probably should have assumed if

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Speaker 1: a Foures reporter is texting me at this question, she's

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Speaker 1: writing a story.

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Speaker 2: I just didn't think she was.

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Speaker 3: Yeah, what was the emotional reaction in that moment when

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Speaker 3: you saw those articles drop?

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Speaker 1: I really didn't think much of it. I think like

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Speaker 1: my high, like the emotional high probably came when like

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Speaker 1: my celebrity crushes started sliding to my DMS.

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Speaker 2: And I was like, oh my god, like this is incredible.

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Speaker 2: But outside of that, like life was normal.

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Speaker 3: Dowie. Are we allowed to say any of the slip

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Speaker 3: crushes that slid?

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Speaker 2: Nope, I can't say it. I'm friends with them now,

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Speaker 2: so I really can't.

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Speaker 3: Okay, that's just like, yeah, isn't that crazy. It's like

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Speaker 3: when you achieve a certain level of success and then

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Speaker 3: all of a sudden, some of the people that you've

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Speaker 3: idolized throughout your life become your peers.

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Speaker 1: Kind of Yeah, I mean I wouldn't say I necessarily

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Speaker 1: idolize them. But it'd be like, you know, growing up,

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Speaker 1: I was like I recognize the face, or like I

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Speaker 1: thought they were really hot and I was like, oh

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Speaker 1: my god, like they just slid in my DMS and yeah,

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Speaker 1: like now we're all homies, which is great.

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Speaker 3: Yeah, the world gets so much smaller. I feel like

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Speaker 3: the more you grow and the more you just meet

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Speaker 3: new people and just like network, I know.

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Speaker 1: I realized the world is just really small. Actually, I

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Speaker 1: mean like I feel like I'm probably like three degrees

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Speaker 1: away from anyone, at least it feels that way sometimes.

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Speaker 3: For the purpose of this episode, I would love people

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Speaker 3: to get to know you, because you have truly lived

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Speaker 3: such an interesting life and you're so young. So I

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Speaker 3: want you to kind of take us back and talk

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Speaker 3: to us about the environment that shaped you growing up

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Speaker 3: in Fremont, California. Paint the picture for us.

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Speaker 2: Yeah for sure.

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Speaker 1: I mean Freemant is a very small, boring city. So

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Speaker 1: my parents were immigrant parents a long story shore. They

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Speaker 1: were super strict, you know, I think traditional tiger parents.

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Speaker 1: They put keyloggers on my computer. They like really emphasize

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Speaker 1: the importance of like education and money, hence why they

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Speaker 1: had the keyloggers. They wanted to make sure that I

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Speaker 1: was constantly studying and like not playing video games. Unfortunately

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Speaker 1: I got rid of the keyloggers. But yeah, I grew

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Speaker 1: up in Fremont and then very quickly wanted to figure

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Speaker 1: out how to like earn money on the internet because

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Speaker 1: my parents took away money from me as like punishment.

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Speaker 1: So I discovered PayPal, and that's when you know, I

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Speaker 1: got a visa debit card and I like signed up

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Speaker 1: for PayPal and started getting into like the online gaming world. Uh,

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Speaker 1: and learned that there's a whole black market where you

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Speaker 1: can you know, like buy and sell rare items, et cetera.

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Speaker 1: So I got into like that's kind of how like,

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Speaker 1: I guess how I learned how to code. I started

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Speaker 1: building bots on the internet and started building my own.

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Speaker 2: Websites, et cetera.

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Speaker 1: But freedom was also just like I would say, like

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Speaker 1: I didn't really have a lot of friends growing up,

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Speaker 1: and then like the few friends I did have ended

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Speaker 1: up moving out of Fremont. So I was just like

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Speaker 1: a little lonely kid on the internet.

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Speaker 3: M M. And when you say on the internet, like,

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Speaker 3: what were you doing on the internet?

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Speaker 1: Yeah, so it's played a lot of games, so like neopets,

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Speaker 1: RuneScape battle on et cetera. And then I was on

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Speaker 1: these like gaming forums where the black markets were happening,

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Speaker 1: and I was like, you know, literally trading up pets

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Speaker 1: and like items. I was selling whole accounts, and I

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Speaker 1: was learning to code. Also, like I both went to

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Speaker 1: Fries Electronics and just up a bunch of books or

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Speaker 1: would like sit and read there. And then I would

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Speaker 1: also like, there are a lot of sources on the

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Speaker 1: internet obviously teaching you how to do things.

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Speaker 2: So I was just learning a lot.

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Speaker 3: And what would you say, like, sparked your interest in

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Speaker 3: that so young?

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Speaker 2: You know, yeah, I would say definitely my parents.

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Speaker 1: So I was like bullied in school for not wearing

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Speaker 1: the coolest clothes or like I wanted to have certain things.

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Speaker 1: My parents were very protective of me. So like one

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Speaker 1: example is, like I think one of the first things

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Speaker 1: I bought with the money I was earninge was a

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Speaker 1: skateboard because I really wanted to skateboard, and like also

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Speaker 1: those like you.

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Speaker 2: Know, ripsticks.

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Speaker 1: It was called a waveboard, but I loved y like ripsticks, waveboards,

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Speaker 1: et cetera. And these are all things that like I

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Speaker 1: was not allowed to have, so I wanted to find

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Speaker 1: a way to make money and have them, and I

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Speaker 1: ended up finding a lot of things from just like

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Speaker 1: the money I made about like an iPhone three.

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Speaker 2: Amongst other items.

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Speaker 3: That's pretty bad ass to be able to buy an

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Speaker 3: iPhone three when you're younger, you know, Like I mean,

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Speaker 3: that's a huge flex to have that kind of money.

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Speaker 3: Think about that, right, and then also be able to

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Speaker 3: pay the phone bills, like get the payments right? Why

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Speaker 3: were your parents anti you getting things like a rip stick?

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Speaker 1: They are much more conservative than me, so I would say,

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Speaker 1: like they just viewed it as dangerous, right, And they

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Speaker 1: really wanted me to like be inside studying all day long,

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Speaker 1: which I think makes sense because they're Asian immigrants, so

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Speaker 1: education gave them everything they had in life, Like they

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Speaker 1: grew up really poor. They immigrated from to America to

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Speaker 1: like get a better education, but also to have hope.

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Speaker 1: So I think they wanted that for me too, Like

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Speaker 1: they were like, if you become a doctor or an engineer,

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Speaker 1: your step for life. And if not, well, actually, I

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Speaker 1: don't even think they want to be an engineer. They

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Speaker 1: just wanted to be a doctor. Their life be as surgeon.

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Speaker 1: So yeah, like they were just anti everything else.

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Speaker 3: Yeah, And I you know, I've interviewed a lot of

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Speaker 3: people that come from immigrant households, and I mean, I

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Speaker 3: think the way your families value education and you know,

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Speaker 3: getting good grades and going the traditional career paths is

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Speaker 3: uh like it it is, So it's something to look

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Speaker 3: up to a way, like I think it is a

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Speaker 3: really good way to like raise kids. So I'm curious,

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Speaker 3: you know, from your but there's also there's pros and

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Speaker 3: cons to everything.

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Speaker 1: I mean, like if you look get like Asians make

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Speaker 1: more money than like everyone else, I'm pretty sure.

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Speaker 3: So, but I'm curious, like growing up in a household

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Speaker 3: with Chinese parents that were immigrants in the US that

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Speaker 3: really valued education, what would you say are the things

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Speaker 3: that you took as pros from your up upbringing and

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Speaker 3: then maybe things that you could have done without.

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Speaker 1: Yeah, I mean I think the main pro is that

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Speaker 1: you really learned discipline, right, Like, for example, they threw

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Speaker 1: me in avocust competitions, and like you really had to

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Speaker 1: be disciplined to learn the advocusts really well, the memorize

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Speaker 1: in your head. Then competing in these competitions. I think

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Speaker 1: that the value of education is really important. It's just

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Speaker 1: like what kind of education. I think education spans more

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Speaker 1: than getting good grades. Like I think I would rather

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Speaker 1: my kid be like a prodigy in one thing and

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Speaker 1: fail everything else than be like get a's and everything.

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Speaker 2: And that is like how I viewed education when I

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Speaker 2: was younger.

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Speaker 1: So like they taught me to work hard, they taught

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Speaker 1: me discipline, like I would see my parents wake up

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Speaker 1: at like five am every day and work until midnight

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Speaker 1: one am, like they didn't really sleep much, and that

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Speaker 1: was something like that's something that I like value a

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Speaker 1: lot in my life. So these are all good things

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Speaker 1: I think things I could have done without, Like I

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Speaker 1: do wish I was allowed a little bit more of

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Speaker 1: a social life.

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Speaker 2: I wasn't allowed to have sleepovers.

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Speaker 1: I wasn't really allowed to like have that much fun.

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Speaker 1: I was really good at sports, but my parents didn't

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Speaker 1: let me play sports growing up, Like I remember, like

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Speaker 1: I was heartbroken one day I had to quit swim

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Speaker 1: and then I made the basketball team and they forced

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Speaker 1: me to quit, amongst like every other sport. And I

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Speaker 1: do think that sports also teach discipline and hard work

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Speaker 1: and camaraderie and team like like teamwork, and I didn't

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Speaker 1: really get that experience growing up.

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Speaker 3: Why do you think there was like the anti sports mindset.

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Speaker 1: I think I just thought I wasn't important, right, Like

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Speaker 1: I think that they're like, okay, cool, like you're not fat,

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Speaker 1: so like you don't need to play sports. And I

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Speaker 1: know that's how like that because I remember complaining like

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Speaker 1: how come my little brother gets to play sports?

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Speaker 2: And they're like, well he's fat. Yeah, he's really skinny.

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Speaker 2: Now he's actually skinnier than me. Now.

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Speaker 3: No, it is so interesting, but I mean it is true,

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Speaker 3: and I like what you said too. It's like people

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Speaker 3: that play on sports teams do end up making great

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Speaker 3: work hires because they are good team members and the

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Speaker 3: camaraderie part is important in your work life, you know, and.

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Speaker 1: Like to be excellent sports, it requires a lot of

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Speaker 1: hard work and discipline. Yeah, so I actually really value

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Speaker 1: hiring athletes because I know they have that hard work.

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Speaker 2: And discipline in them.

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Speaker 1: And usually, like if you are hard working discipline in

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Speaker 1: one thing, it can it translates to something else.

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Speaker 3: Yeah, And it's clear that athletics is something that you

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Speaker 3: always valued because guys, when right before our run, I

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Speaker 3: found out that this morning Lucy ran seven miles because

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Speaker 3: she did two back to back Berries classes, and the

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Speaker 3: audience that listened to this podcast is familiar with Berries.

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Speaker 3: They know how intense those workouts are. Yeah, so that

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Speaker 3: is bad. So when did you start doing Berries?

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Speaker 2: I feel like a decade ago.

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Speaker 1: Like what happened was I broke my jaw and I

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Speaker 1: just couldn't well, I couldn't talk and I couldn't eat.

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Speaker 1: So in my head, I was like, this is the

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Speaker 1: perfect time to get hot, right, So I said class

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Speaker 1: pass discovered Berries as like this is the most amazing

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Speaker 1: workout because I really like intensity, Like I don't like

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Speaker 1: feeling like I wasted my time, and when I do

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Speaker 1: other workouts and I'm not like completely dead and out

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Speaker 1: of breath, I do feel like I wasted my time.

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Speaker 2: Even at Berries.

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Speaker 1: If a class is too easy, I will like try

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Speaker 1: to make it harder, like I'll double tread or something.

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Speaker 1: So yeah, I uh discovered berries and just want every

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Speaker 1: single day since then.

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Speaker 2: Wow.

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Speaker 3: So you talked about how your parents were strict with

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Speaker 3: money and how as a punishment they would take money

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Speaker 3: away from you, right and you know, maybe not give

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Speaker 3: you an allowance or not pay for certain things. So

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Speaker 3: I'm curious like growing up with parents that also you know,

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Speaker 3: we're frugal with money and maybe saved them every you know,

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Speaker 3: every dollar they made kind of thing. How did that

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Speaker 3: shape your relationship with money now?

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Speaker 1: I mean it's probably the reason why I am like

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Speaker 1: so chill, right, Like I don't see the necessity in

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Speaker 1: going to like nice restaurants like I do.

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Speaker 3: Think, like, for example, would you describe yourself as chill?

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Speaker 2: I mean, okay, let me rephrase. I want to describe myself.

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Speaker 2: I'm no, no, I'm not that chill, but okay, I have.

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Speaker 3: To say it because I just chill. Oh no, no,

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Speaker 3: because I am not chill at all and so like

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Speaker 3: but and like just knowing you, I mean the fact

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Speaker 3: that you're doing two back to back Berry's classes like

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Speaker 3: six am, seven am, and then like you're going to

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Speaker 3: raves every week.

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Speaker 2: Like, yeah, I'm I'm intense. I'm definitely an intense person.

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Speaker 1: Okay, I have what was the astrology? I think five

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Speaker 1: scorpios in me, I'm very intense. But I think like

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Speaker 1: I yeah, like I I like a good deal, and

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Speaker 1: I think that like I used to see my mom

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Speaker 1: stack coupons and get like ninety percent off at Macy's

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Speaker 1: or something so like, for example, like I discovered a

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Speaker 1: new restaurants by going on Uber Eats and door Dash

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Speaker 1: looking at the buyo and get one free offers. Then

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Speaker 1: I cross research it with Yelp and see what it

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Speaker 1: looks like into reviews and if it's good, then I'll

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Speaker 1: try one item and if I like the item, then

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Speaker 1: I go to the restaurant itself and try the other items.

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Speaker 2: So I found like all the best hole in the walls.

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Speaker 1: This way because it's usually like you know, good restaurants

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Speaker 1: that just want people to try their food and realize like, oh,

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Speaker 1: like this is worthwhile to get. Yeah, So I would say,

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Speaker 1: like I'm chill in that, like I'm not I'm not needy.

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Speaker 1: I'm impatient and I need stimulation, but I'm not needy.

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Speaker 3: Yeah.

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Speaker 2: I like that.

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Speaker 3: And it's kind of like money hacks as a kid

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Speaker 3: are ingrained with you when you become an adult, like

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Speaker 3: I do certain things like that. So I love that.

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Speaker 3: Was there a certain like pressure in your household to

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Speaker 3: achieve a certain level of success. I think there's.

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Speaker 1: Definitely pressure to achieve a certain level of success, but

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Speaker 1: I don't think it was like that high like coming

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Speaker 1: out of high school. My parents actually just thought it

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Speaker 1: was a failure because my brother is like ten times

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Speaker 1: smater than me. So they were just like, we want

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Speaker 1: you to be a pharmacist, and I'm like, I would

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Speaker 1: literally die of.

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Speaker 2: Boredom being a pharmacist.

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Speaker 1: Not to mention, I would fail pharmacy school because the

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Speaker 1: one thing I can't do is memorize things, Like I

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Speaker 1: can't memorize like facts, I can't memorize people's faces.

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Speaker 2: I certainly won't be.

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Speaker 1: Able to memorize medications. I would probably accidentally get the

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Speaker 1: wrong medication to someone. So I was like, I will

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Speaker 1: actually like go to jail if you make me go

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Speaker 1: to pharmacy school because I would fail or I would

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Speaker 1: make a really severe mistake. But yeah, like I think

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Speaker 1: they were just like, yeah, but you only have to

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Speaker 1: work three days a week, and like you make six figures,

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Speaker 1: et cetera, and then you can find a good person

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Speaker 1: to marry.

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Speaker 2: So that was there, like all they really cared about, even.

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Speaker 3: Though they knew that in high school you were interested

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Speaker 3: in programming and computer science.

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Speaker 1: So it's interesting, like I didn't even realize to like

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Speaker 1: major in computer science until this like random person I

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Speaker 1: met in college confidential told me to I was really

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Speaker 1: good at chemistry, so I would say, like I was

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Speaker 1: probably the best in the entire school in ap chemistry,

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Speaker 1: like ruined a curve on tests, et cetera. So I thought, oh,

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Speaker 1: I'm going to be a chemical engineer because I'm good

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Speaker 1: at math, I'm good at chemistry, I'm good at physics.

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Speaker 1: But yeah, they were like, you can't study engineering because

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Speaker 1: you're a woman. And I'm like, mom, you were an

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Speaker 1: electrical engineer and you're a woman. Very outdated ideas, but

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Speaker 1: I think that like their comeback was just like mom

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Speaker 1: was the only person in her class that was a woman,

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Speaker 1: and that being said, she was the best, like she

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Speaker 1: was actually the smartest person in class, but because she

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Speaker 1: was the only one, they're like, your mom's just special,

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Speaker 1: like women can't be engineers.

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Speaker 2: I was like, what hell?

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Speaker 1: So I almost got like like I was going to argue,

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Speaker 1: but then like some random person on Internet was like,

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Speaker 1: you should have had for computer science because your entire

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Speaker 1: background makes a perfect application for CS.

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Speaker 2: That's like good points.

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Speaker 3: So yeah, that's what I did, Okay, And was it

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Speaker 3: true that in high school you started teaching yourself how

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Speaker 3: to program and code?

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Speaker 1: It was in like second grade, so like back when

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Speaker 1: I was like hacking these neopets, I was learning to

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Speaker 1: make bots, and I started learning like other programming languages afterwards,

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Speaker 1: and in high school I did take a computer science class.

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Speaker 1: I forgot what language they taught, Java maybe, which I

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Speaker 1: guess I didn't know at that point in time, but yeah.

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Speaker 3: Okay, So it started in second grade as a way

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Speaker 3: to kind of fuel your like fun activity like yeah exautline, Yeah, okay,

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Speaker 3: that's really interesting. And then why the decision to go

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Speaker 3: to Carnegie Mellon.

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Speaker 2: Yeah, so I took summer classes at Carnegie Mellon.

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Speaker 1: So I took a CS class intellectual engineering class, and

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Speaker 1: then my professor wrote my letter for electrical engineering, so

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Speaker 1: I like wrote my recommenation letter for the college, which

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Speaker 1: this class was like known as a weader class, so

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Speaker 1: most people failed it. So I already knew I was

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Speaker 1: like one hundred percent getting in because the professor wrote

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Speaker 1: me a recommendation letter. And then it was just wayne,

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Speaker 1: like did I really want to like go to this college?

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Speaker 1: And I really enjoyed my summer and I knew that,

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Speaker 1: like I was going to apply for commuter science. I

413
00:17:23,480 --> 00:17:25,720
Speaker 1: had like an interest in design because that's why I was, like,

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Speaker 1: I was learning photoshopping a bunch of things for like,

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Speaker 1: you know, my own personal projects in high school. And

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Speaker 1: they were the I think they are the only college

417
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Speaker 1: in the US right now ranked like number one for

418
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Speaker 1: commuter science and number one for like design and human

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Speaker 1: computer interactions. So I knew I wanted to be an

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Speaker 1: HCI major as well. So it's no brainer, Like I

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Speaker 1: didn't have to really apply to other other colleges and

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Speaker 1: I was going to go to the number one program

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Speaker 1: in the country.

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Speaker 2: So yeah, right, And.

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Speaker 3: So how did your parents feel about that transition from

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Speaker 3: pharmacists to then going to Cardie.

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Speaker 2: Oh, they were so happy. They're like a butter science

428
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Speaker 2: is so easy.

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Speaker 1: It's a girl major because they didn't realize computer science was.

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Speaker 2: And then like once I got there and I.

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Speaker 1: Was like, like I would show them my homework assignmentsider like,

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Speaker 1: oh wow, this is really hard.

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Speaker 3: No, really hard. And I'm curious, you know when you

434
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Speaker 3: were in you know, classes with other kids that I mean,

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Speaker 3: these are like the top students are at carnegimil and

436
00:18:18,080 --> 00:18:20,520
Speaker 3: studying the same thing that you're studying, right, what was

437
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Speaker 3: it like being in that environment?

438
00:18:23,000 --> 00:18:23,520
Speaker 2: It was great.

439
00:18:23,560 --> 00:18:27,600
Speaker 1: I mean I think that everyone was really friendly at CMU, and.

440
00:18:27,880 --> 00:18:30,040
Speaker 2: I was like, I'm gonna be honest, I was pretty average.

441
00:18:30,080 --> 00:18:33,840
Speaker 1: I would say, like within computer science, I excelled in

442
00:18:33,920 --> 00:18:37,440
Speaker 1: like human computer interactions. So but it's the difference human

443
00:18:37,520 --> 00:18:40,719
Speaker 1: human interactions, so like, uh, it's more design, right, So

444
00:18:40,760 --> 00:18:42,800
Speaker 1: I would call it like ux design.

445
00:18:43,440 --> 00:18:44,240
Speaker 2: So we would learn.

446
00:18:44,119 --> 00:18:47,040
Speaker 1: Things on like why like Apple created like the bounce logo,

447
00:18:47,200 --> 00:18:50,800
Speaker 1: et cetera, and like we designed random things like weather

448
00:18:50,840 --> 00:18:54,040
Speaker 1: apps and other apps. I think it taught you, like

449
00:18:54,800 --> 00:18:57,119
Speaker 1: really how to think about products, and obviously I was

450
00:18:57,119 --> 00:19:00,040
Speaker 1: interested in building a company and building apps, so I

451
00:19:00,040 --> 00:19:02,000
Speaker 1: thought it was like some of my most fun classes.

452
00:19:02,400 --> 00:19:03,120
Speaker 2: Yeah.

453
00:19:03,320 --> 00:19:06,960
Speaker 1: But going back, like I really enjoyed my environment. I

454
00:19:06,960 --> 00:19:09,000
Speaker 1: guess like I made a lot of friends at Carnegie Mellon.

455
00:19:09,680 --> 00:19:11,920
Speaker 1: I think it was like a big change from high

456
00:19:11,920 --> 00:19:13,879
Speaker 1: school because it's very lame in call it like in

457
00:19:13,960 --> 00:19:16,639
Speaker 1: high school, like you know, didn't have a lot of friends,

458
00:19:16,720 --> 00:19:17,800
Speaker 1: wasn't a cool kid at all.

459
00:19:18,480 --> 00:19:19,600
Speaker 2: Go to Carnegie Mellon.

460
00:19:19,440 --> 00:19:21,439
Speaker 1: And because I think, I'm like the there's not a

461
00:19:21,440 --> 00:19:24,240
Speaker 1: lot of women in the program. So suddenly, like from

462
00:19:24,320 --> 00:19:27,080
Speaker 1: day one, I was popular and I was like it

463
00:19:27,800 --> 00:19:29,840
Speaker 1: was so strange to me, Like I didn't know how

464
00:19:29,840 --> 00:19:30,480
Speaker 1: to feel about it.

465
00:19:30,880 --> 00:19:33,720
Speaker 3: What was the breakdown male versus female, like percentage wise?

466
00:19:33,840 --> 00:19:35,080
Speaker 2: I don't know, if I had to give a guess,

467
00:19:35,119 --> 00:19:36,120
Speaker 2: I was like probably.

468
00:19:37,720 --> 00:19:43,520
Speaker 1: Somewhere between eighty twenty to like, yeah, maybe it was

469
00:19:43,560 --> 00:19:44,160
Speaker 1: like eighty twenty.

470
00:19:44,240 --> 00:19:46,479
Speaker 3: Oh my god, yeah that's that's small.

471
00:19:46,520 --> 00:19:49,840
Speaker 2: I could be really wrong, but yeah it was. There

472
00:19:49,840 --> 00:19:50,800
Speaker 2: were not a lot of women.

473
00:20:00,640 --> 00:20:04,520
Speaker 3: I come from an advertising background, and so we would

474
00:20:04,520 --> 00:20:06,359
Speaker 3: do a lot of like designs of websites, you know,

475
00:20:06,400 --> 00:20:08,479
Speaker 3: so very much so more focused on like the design

476
00:20:08,520 --> 00:20:10,560
Speaker 3: aspect of it. But what's cool about you is it's

477
00:20:10,560 --> 00:20:13,199
Speaker 3: interesting knowing that you had both that UI background with

478
00:20:13,280 --> 00:20:15,080
Speaker 3: then that like love for ux.

479
00:20:15,320 --> 00:20:18,000
Speaker 1: Yeah. Now, I would say, like a product was why

480
00:20:18,080 --> 00:20:20,040
Speaker 1: I ended up even at Carnegie melon right, because I

481
00:20:20,080 --> 00:20:21,719
Speaker 1: was building all these apps, Like I built my own

482
00:20:21,760 --> 00:20:24,480
Speaker 1: virtual pet site, I built a Twitter bot, I built

483
00:20:24,480 --> 00:20:27,560
Speaker 1: like random internet marketing tools. But like I was always

484
00:20:27,600 --> 00:20:29,840
Speaker 1: thinking about like how these things would work. And then

485
00:20:30,600 --> 00:20:32,680
Speaker 1: even my freshman year Carnegie Mellon, I was going to

486
00:20:32,680 --> 00:20:34,439
Speaker 1: a lot of hack thoughts and really learning about like

487
00:20:34,760 --> 00:20:38,160
Speaker 1: actual apps, and I found out about all these startups

488
00:20:38,200 --> 00:20:43,000
Speaker 1: et cetera. So like, and my dream was always I think,

489
00:20:43,240 --> 00:20:44,040
Speaker 1: like I was.

490
00:20:43,960 --> 00:20:44,760
Speaker 2: Always an entrepreneur.

491
00:20:44,800 --> 00:20:46,399
Speaker 1: I should have known my dream was to build a

492
00:20:46,440 --> 00:20:50,600
Speaker 1: company and not be a chemical engineer. So it was

493
00:20:50,680 --> 00:20:53,360
Speaker 1: just something that like I gravitated very heavily towards when

494
00:20:53,359 --> 00:20:55,159
Speaker 1: I realized, like, oh, I could double major in this.

495
00:20:55,720 --> 00:20:59,520
Speaker 3: At Carnegie Mellon, was there a culture of being an

496
00:20:59,640 --> 00:21:01,600
Speaker 3: entrepreneur and creating startups?

497
00:21:01,720 --> 00:21:02,359
Speaker 2: Absolutely not.

498
00:21:02,960 --> 00:21:06,480
Speaker 1: I think it exists at like, you know, Stamford, Harvard, Mit,

499
00:21:06,760 --> 00:21:09,240
Speaker 1: But we didn't really have it there at Carnegie Mellon because,

500
00:21:09,440 --> 00:21:12,600
Speaker 1: like I think we people might fight, but I think

501
00:21:12,640 --> 00:21:15,600
Speaker 1: we objectively had the hardest computer science program and it's

502
00:21:15,640 --> 00:21:21,639
Speaker 1: super theoretical and people are pretty much just like trained

503
00:21:21,680 --> 00:21:23,400
Speaker 1: to go work at a top company.

504
00:21:24,280 --> 00:21:27,840
Speaker 2: There was nothing that like, I don't.

505
00:21:27,560 --> 00:21:29,600
Speaker 1: Even know if I knew someone else in college that

506
00:21:29,680 --> 00:21:31,760
Speaker 1: like wanted to be an entrepreneur. I remember when I

507
00:21:31,800 --> 00:21:33,199
Speaker 1: dropped out of college, like it would have like when

508
00:21:33,240 --> 00:21:34,880
Speaker 1: I got the deal fellowship, it would have been celebrated

509
00:21:34,880 --> 00:21:37,280
Speaker 1: at any of these other colleges I was like laughed

510
00:21:37,320 --> 00:21:38,240
Speaker 1: at at Carnegie Mellon.

511
00:21:38,359 --> 00:21:40,080
Speaker 3: Yeah, I would imagine at the time too, when you

512
00:21:40,119 --> 00:21:41,879
Speaker 3: were in college, it was like everybody wanted the top

513
00:21:41,960 --> 00:21:44,639
Speaker 3: drop at like Dropbox, Yes, right popular.

514
00:21:44,720 --> 00:21:46,520
Speaker 2: Dropbox and Palentarer were popular there in my time.

515
00:21:46,600 --> 00:21:49,200
Speaker 3: Right. Yeah, let's set some context. What were those big

516
00:21:49,240 --> 00:21:51,920
Speaker 3: companies at at the time when you were in college

517
00:21:51,960 --> 00:21:54,600
Speaker 3: that kids out of Carnegie Mellon wanted to get placed at.

518
00:21:54,920 --> 00:21:57,400
Speaker 1: Yes, I would say the number one at the point

519
00:21:57,400 --> 00:21:59,800
Speaker 1: in time I was in college was paland here, especially

520
00:21:59,800 --> 00:22:02,080
Speaker 1: because Palenteer would come and do like these crazy like

521
00:22:02,920 --> 00:22:07,320
Speaker 1: challenges and the smartest kids would win prizes. So Palanteer

522
00:22:07,320 --> 00:22:10,120
Speaker 1: and Dropbox, what I would say, we're the number We're

523
00:22:10,160 --> 00:22:10,920
Speaker 1: the top two.

524
00:22:10,720 --> 00:22:11,800
Speaker 2: While I was at Carneia Mellon.

525
00:22:12,119 --> 00:22:16,040
Speaker 3: Okay, you mentioned the fellowship, So this fellowship basically changed

526
00:22:16,080 --> 00:22:18,679
Speaker 3: your life, right, I would say, so yeah, okay, so

527
00:22:18,760 --> 00:22:20,720
Speaker 3: can you explain to us what the fellowship is and

528
00:22:20,760 --> 00:22:21,840
Speaker 3: how you found out about it.

529
00:22:22,160 --> 00:22:24,439
Speaker 1: So I actually found out about the fellowship back in

530
00:22:24,520 --> 00:22:27,640
Speaker 1: high school. Someone mentioned that I should visit this hackathon

531
00:22:27,680 --> 00:22:30,320
Speaker 1: house called Rainbow Mansions, So I did, and they mentioned

532
00:22:30,359 --> 00:22:31,960
Speaker 1: a teal fellowship and I kind of, you know, forgot

533
00:22:32,000 --> 00:22:35,119
Speaker 1: about it, but kept in touch with them very lightly.

534
00:22:36,000 --> 00:22:38,199
Speaker 1: So I remember, I believe it was my sophomore year

535
00:22:38,200 --> 00:22:39,359
Speaker 1: at Carnelie mellen or junior.

536
00:22:39,400 --> 00:22:41,560
Speaker 2: I was graduating early, so and I know what you would.

537
00:22:41,359 --> 00:22:42,520
Speaker 3: Call so you did graduate early.

538
00:22:42,600 --> 00:22:43,680
Speaker 2: Now I was supposed to graduate early.

539
00:22:43,800 --> 00:22:45,960
Speaker 1: So I have like four CS classes left and four

540
00:22:46,080 --> 00:22:50,239
Speaker 1: HCI classes left to get my double major. So my

541
00:22:50,680 --> 00:22:52,840
Speaker 1: sophomore slash junior year, whatever you want to call it.

542
00:22:53,040 --> 00:22:54,800
Speaker 1: They were like, hey, you should bought for the Teal Fellowship,

543
00:22:55,040 --> 00:22:57,800
Speaker 1: and I kind of just thought like why not because

544
00:22:57,800 --> 00:22:59,720
Speaker 1: I felt like I was learning more at these hackathons

545
00:22:59,760 --> 00:23:01,600
Speaker 1: and I was in college at this point in time.

546
00:23:01,640 --> 00:23:03,639
Speaker 1: Because in college, I was learning a lot of theoretical stuff,

547
00:23:03,680 --> 00:23:05,359
Speaker 1: but like at these hackathons was actually like, you know,

548
00:23:05,400 --> 00:23:09,120
Speaker 1: building maps and learning languages that were applicable to doing

549
00:23:09,160 --> 00:23:09,880
Speaker 1: what I wanted to do.

550
00:23:10,240 --> 00:23:13,320
Speaker 2: So I ended up applying.

551
00:23:13,440 --> 00:23:15,399
Speaker 1: And what the tel Fellowship is for those that don't know,

552
00:23:15,840 --> 00:23:18,119
Speaker 1: They were giving twenty kids under twenty years old one

553
00:23:18,160 --> 00:23:20,240
Speaker 1: hundred thousand dollars to drop out of school because Peter

554
00:23:20,359 --> 00:23:22,199
Speaker 1: wanted to make a point that you don't need college

555
00:23:22,200 --> 00:23:23,000
Speaker 1: to be successful.

556
00:23:23,800 --> 00:23:25,040
Speaker 2: And I think there weren't.

557
00:23:24,920 --> 00:23:26,680
Speaker 1: Enough results at that point in time, to be honest,

558
00:23:26,680 --> 00:23:28,240
Speaker 1: So it was kind of laughed at.

559
00:23:29,080 --> 00:23:30,080
Speaker 2: By a lot of people.

560
00:23:30,720 --> 00:23:33,760
Speaker 1: Now looking back, everyone's like Wow, the Teal fellowship really worked.

561
00:23:34,280 --> 00:23:38,600
Speaker 1: I think like everyone in my class is super successful. Yeah,

562
00:23:38,640 --> 00:23:40,359
Speaker 1: I just used the like hackthon project I worked on

563
00:23:40,400 --> 00:23:42,400
Speaker 1: I think like a week before I applied and ended

564
00:23:42,440 --> 00:23:45,280
Speaker 1: up getting it, So made decision to drop out of college.

565
00:23:45,359 --> 00:23:48,760
Speaker 3: Just to back up, describe to us what a hackathon is, Like,

566
00:23:48,800 --> 00:23:52,000
Speaker 3: what is the atmosphere like when you're walking into a hackathon?

567
00:23:52,040 --> 00:23:53,959
Speaker 3: Who are the people that are there? What's going on?

568
00:23:54,520 --> 00:23:57,160
Speaker 1: Well, every hackelon is different, but like these were university hackathons,

569
00:23:57,280 --> 00:24:00,600
Speaker 1: So think like university kids from all different college. It

570
00:24:00,640 --> 00:24:03,439
Speaker 1: would literally like bust people in from you know, like

571
00:24:03,480 --> 00:24:05,840
Speaker 1: Carnegiamil into MIT for example for hack and my tea.

572
00:24:06,640 --> 00:24:09,680
Speaker 1: So I think like thousands and thousands of students and

573
00:24:09,720 --> 00:24:12,320
Speaker 1: they're like twenty four to forty eight hour coding competitions

574
00:24:12,400 --> 00:24:15,200
Speaker 1: where you're judge based off of like what app you make,

575
00:24:15,400 --> 00:24:17,880
Speaker 1: Like is there a business use case? Like how does

576
00:24:17,920 --> 00:24:20,760
Speaker 1: the design look like, how technical is it, et cetera.

577
00:24:22,160 --> 00:24:24,359
Speaker 2: So I basically discovered back then.

578
00:24:24,359 --> 00:24:26,680
Speaker 1: The formula for winning, which was created an iOS app

579
00:24:26,720 --> 00:24:30,160
Speaker 1: that looked pretty because most of these people attending hacklones

580
00:24:30,160 --> 00:24:32,360
Speaker 1: were just pure CS majors. So I didn't really know

581
00:24:32,400 --> 00:24:33,800
Speaker 1: how to design an app, and I think because I

582
00:24:33,840 --> 00:24:36,480
Speaker 1: already had like such product focused thinking, I could score

583
00:24:36,520 --> 00:24:38,240
Speaker 1: on the business aspect of things as well.

584
00:24:38,640 --> 00:24:40,040
Speaker 2: So I was just.

585
00:24:39,920 --> 00:24:43,800
Speaker 1: Making these like iOS apps that looked pretty and winning

586
00:24:43,960 --> 00:24:46,240
Speaker 1: a prize every single hackathon. I don't think I left

587
00:24:46,280 --> 00:24:48,399
Speaker 1: a hackthon without winning like something. So I ended up

588
00:24:48,400 --> 00:24:51,399
Speaker 1: collecting like iPads, like a lot of iPads about these

589
00:24:51,400 --> 00:24:52,520
Speaker 1: hackthons are just amazing.

590
00:24:52,960 --> 00:24:55,439
Speaker 3: Yeah, okay, got it. And so at the hackathon, it

591
00:24:55,480 --> 00:24:57,320
Speaker 3: was basically like you would go and kind of present

592
00:24:57,359 --> 00:24:58,760
Speaker 3: on stage to everybody that was there.

593
00:25:00,320 --> 00:25:02,800
Speaker 1: There wasn't really stage presentation, so like you would basically

594
00:25:02,800 --> 00:25:04,760
Speaker 1: it's almost like a science for like a booth, so

595
00:25:05,040 --> 00:25:07,560
Speaker 1: because there's thousands of students, right, and then it's teams

596
00:25:07,560 --> 00:25:11,280
Speaker 1: of up to four usually, So you would just have

597
00:25:11,480 --> 00:25:14,240
Speaker 1: your little booth and then people would come by see

598
00:25:14,280 --> 00:25:15,800
Speaker 1: the app, and you would also submit it on a

599
00:25:15,800 --> 00:25:19,240
Speaker 1: website and it would score points and then they would

600
00:25:19,240 --> 00:25:20,080
Speaker 1: announce winners.

601
00:25:20,160 --> 00:25:21,960
Speaker 3: So did you go solo or did you team up? With?

602
00:25:22,000 --> 00:25:24,040
Speaker 2: People always teamed up. It's so fun as a team.

603
00:25:24,160 --> 00:25:26,720
Speaker 1: But I was like the like I would literally get

604
00:25:26,760 --> 00:25:28,720
Speaker 1: so upset if a team member would fall asleep.

605
00:25:28,800 --> 00:25:32,240
Speaker 2: I'm like, guys, like we're we're competing.

606
00:25:32,359 --> 00:25:34,080
Speaker 1: Wake up, and you're like, we need a nap, And

607
00:25:34,119 --> 00:25:36,440
Speaker 1: I'm like, I don't care you need a nap.

608
00:25:36,920 --> 00:25:38,520
Speaker 3: Okay, I love this. So you have a very like

609
00:25:38,560 --> 00:25:39,720
Speaker 3: competitive spirit too.

610
00:25:40,680 --> 00:25:41,320
Speaker 2: I love Barries.

611
00:25:41,400 --> 00:25:44,400
Speaker 3: Yeah, that's funny. I feel like you need to get

612
00:25:44,400 --> 00:25:46,480
Speaker 3: into like a class though that has like scores, you

613
00:25:46,520 --> 00:25:47,960
Speaker 3: know what I mean. Like there used to be like

614
00:25:48,000 --> 00:25:49,280
Speaker 3: Flywheel back in the day. I don't know if you

615
00:25:49,320 --> 00:25:50,879
Speaker 3: know if it's still a thing, but Flywheel used to

616
00:25:50,920 --> 00:25:52,359
Speaker 3: have like a leaderboard when you would go and do

617
00:25:52,400 --> 00:25:53,160
Speaker 3: a spin class.

618
00:25:53,440 --> 00:25:55,040
Speaker 1: Oh no way, yeah, I mean it'd be kind of

619
00:25:55,040 --> 00:25:56,560
Speaker 1: harder to do a Barries, but this one you can

620
00:25:56,680 --> 00:25:58,560
Speaker 1: just like you could just eyeball berries. Like I'm like, okay,

621
00:25:58,600 --> 00:26:01,080
Speaker 1: I'm lifting heavier weights than everyone in class. Yeah, Like

622
00:26:01,119 --> 00:26:03,880
Speaker 1: I'm doing more reps than everyone in class, so therefore

623
00:26:03,920 --> 00:26:04,240
Speaker 1: I win.

624
00:26:04,480 --> 00:26:06,680
Speaker 3: Do you do the floor or you also do the

625
00:26:06,720 --> 00:26:09,760
Speaker 3: treadmill sec Yeah? Okay, So when people are sprinting on

626
00:26:09,760 --> 00:26:10,480
Speaker 3: the treadmills next.

627
00:26:10,400 --> 00:26:11,880
Speaker 2: To you, I got twenty miles power. No one's fast

628
00:26:11,960 --> 00:26:12,159
Speaker 2: at me.

629
00:26:13,480 --> 00:26:16,120
Speaker 3: I am dead. When Jeremy's going fifteen miles per hour,

630
00:26:16,160 --> 00:26:18,080
Speaker 3: I get nervous for him, like I will not go

631
00:26:18,080 --> 00:26:20,520
Speaker 3: above a nine on a Barri's treadmill. I'm also pregnant,

632
00:26:20,560 --> 00:26:21,240
Speaker 3: but they should.

633
00:26:21,040 --> 00:26:21,439
Speaker 2: Run with me.

634
00:26:21,520 --> 00:26:24,960
Speaker 1: I get on fifteen for like a minute nowadays, I

635
00:26:25,040 --> 00:26:29,440
Speaker 1: used to be faster. Yeah he should, Like, I like accompetition.

636
00:26:29,000 --> 00:26:30,280
Speaker 3: Going out of twenty is insane.

637
00:26:30,640 --> 00:26:33,479
Speaker 1: Yeah, but like it's just like it's for thirty second sprints.

638
00:26:33,480 --> 00:26:35,480
Speaker 1: But I don't think it's a true twenty mile power

639
00:26:35,520 --> 00:26:37,520
Speaker 1: for thirty seconds because it takes like fifteen seconds for

640
00:26:37,560 --> 00:26:40,040
Speaker 1: a treadmill to speed up. And then also like I

641
00:26:40,080 --> 00:26:43,480
Speaker 1: actually think I'm so fast at berries because I'm lighter,

642
00:26:43,760 --> 00:26:47,320
Speaker 1: so I bounce off the treadmills more. At least that's

643
00:26:47,359 --> 00:26:49,840
Speaker 1: my hypothesis, because like I've had track stars and like

644
00:26:49,880 --> 00:26:52,240
Speaker 1: Olympic athletes run with me and they're just like, how

645
00:26:52,280 --> 00:26:54,440
Speaker 1: the hell do you do that? And my answer is literally,

646
00:26:54,440 --> 00:26:56,640
Speaker 1: just like I think it's just because I'm tiny.

647
00:26:58,040 --> 00:27:00,480
Speaker 3: No, but you're still going that same speed, Like you're

648
00:27:00,520 --> 00:27:02,080
Speaker 3: still going to speed that it says you're going.

649
00:27:02,119 --> 00:27:03,520
Speaker 2: I am still going to the speed I'm going.

650
00:27:03,400 --> 00:27:05,840
Speaker 3: Yeah, which it's, guys, a very freaking fast speed. If

651
00:27:05,840 --> 00:27:07,600
Speaker 3: you've been to a Barri's class and somebody's going twenty

652
00:27:07,640 --> 00:27:22,320
Speaker 3: you know they're going they're sprinting. Teal Fellowship. You have

653
00:27:22,400 --> 00:27:24,760
Speaker 3: to start with a company idea in mind, right, So

654
00:27:25,040 --> 00:27:27,000
Speaker 3: what did you use your grant for?

655
00:27:27,520 --> 00:27:30,000
Speaker 1: You can apply with anything, and I ended up playing

656
00:27:30,040 --> 00:27:30,960
Speaker 1: with the Hackathon project.

657
00:27:31,160 --> 00:27:33,280
Speaker 2: It was like a multiplayer.

658
00:27:32,800 --> 00:27:36,920
Speaker 1: Like games thing where you could create like real time

659
00:27:37,000 --> 00:27:40,760
Speaker 1: games with friends and for example, like the quiz questions

660
00:27:40,760 --> 00:27:43,640
Speaker 1: in it, so you could like study off of these games, so.

661
00:27:43,640 --> 00:27:46,280
Speaker 2: Like custom educational games, let's call it that.

662
00:27:47,160 --> 00:27:49,159
Speaker 1: And then got the Teal Fellowship. What I ended up

663
00:27:49,160 --> 00:27:52,919
Speaker 1: doing with the Teal Fellowship was this app that it

664
00:27:53,000 --> 00:27:55,399
Speaker 1: was another concept of the app I had created a hackathon,

665
00:27:55,520 --> 00:27:57,560
Speaker 1: so like at another hacklon, I basically created door Dash

666
00:27:57,600 --> 00:28:00,440
Speaker 1: before door Dash existed, but then DoorDash had come out

667
00:28:00,480 --> 00:28:03,440
Speaker 1: by the time that I got the TL Fellowship, so

668
00:28:04,800 --> 00:28:06,919
Speaker 1: it made a little bit of a change where instead

669
00:28:06,920 --> 00:28:10,040
Speaker 1: of restaurants, it was like regular people cooking for other people.

670
00:28:10,880 --> 00:28:13,560
Speaker 1: Launched it at my campus and people were making like

671
00:28:13,600 --> 00:28:17,480
Speaker 1: one hundred dollars an hour selling different things to students.

672
00:28:18,080 --> 00:28:20,560
Speaker 1: And the idea was that because campuses are so like

673
00:28:20,640 --> 00:28:23,600
Speaker 1: high density, you can make a lot of deliveries in

674
00:28:23,600 --> 00:28:25,719
Speaker 1: a very little time, so you can optimize them out

675
00:28:25,800 --> 00:28:30,000
Speaker 1: you're making per hour. AP did well, and then we

676
00:28:30,000 --> 00:28:34,359
Speaker 1: were like, let's go expand long story short, this is

677
00:28:34,400 --> 00:28:37,480
Speaker 1: actually very illegal for many reasons, like you actually need

678
00:28:37,520 --> 00:28:41,560
Speaker 1: like a centralized food kitchen to cook things. I think

679
00:28:41,560 --> 00:28:44,360
Speaker 1: they actually they did change through laws over COVID. From

680
00:28:44,400 --> 00:28:47,480
Speaker 1: what I hear, the fact that very illegal, so and

681
00:28:47,600 --> 00:28:50,000
Speaker 1: it was dropping it and then decided I wanted to

682
00:28:50,000 --> 00:28:53,960
Speaker 1: go work at companies and like learn more. I guess

683
00:28:54,160 --> 00:28:57,400
Speaker 1: about like launching features and products and seeing that long

684
00:28:57,440 --> 00:29:00,840
Speaker 1: term impact and building culture, et cetera, and company. I

685
00:29:00,840 --> 00:29:02,719
Speaker 1: felt like I had more learning to do working at places.

686
00:29:02,760 --> 00:29:05,080
Speaker 1: So I ended up dropping APP and going to be

687
00:29:05,200 --> 00:29:05,960
Speaker 1: a normal person.

688
00:29:06,520 --> 00:29:09,480
Speaker 3: Okay, when you decided to take the Teal fellowship, so

689
00:29:09,600 --> 00:29:11,840
Speaker 3: you have to you had to leave school in order

690
00:29:11,920 --> 00:29:14,160
Speaker 3: to do it right? Yes, what did your parents think

691
00:29:14,160 --> 00:29:14,960
Speaker 3: about that decision?

692
00:29:15,160 --> 00:29:16,680
Speaker 1: Oh, they stopped talking to me for a little bit.

693
00:29:16,760 --> 00:29:19,800
Speaker 1: They were not happy, which again, education gave them everything

694
00:29:19,800 --> 00:29:21,320
Speaker 1: that they had in life, so I think for them

695
00:29:21,440 --> 00:29:24,320
Speaker 1: it was like a really big sign of disrespect and

696
00:29:24,360 --> 00:29:26,880
Speaker 1: they felt like they had sacrificed everything, you know, immigrating

697
00:29:26,920 --> 00:29:29,960
Speaker 1: to America, leaving their family behind. Et cetera, just for

698
00:29:30,040 --> 00:29:33,320
Speaker 1: their kids to be like, Haha, never mind, I'm not

699
00:29:33,360 --> 00:29:34,080
Speaker 1: going to college.

700
00:29:34,120 --> 00:29:35,520
Speaker 2: Like I think it was probably the biggest slap in

701
00:29:35,520 --> 00:29:37,360
Speaker 2: the face for them.

702
00:29:37,440 --> 00:29:39,840
Speaker 3: What were those conversations like when you were like, well,

703
00:29:39,880 --> 00:29:40,720
Speaker 3: this is what I'm doing.

704
00:29:41,480 --> 00:29:44,520
Speaker 1: They definitely were disappointed. They're like, we thought we would

705
00:29:44,560 --> 00:29:50,080
Speaker 1: raise a child that had like risk analysis and I

706
00:29:50,120 --> 00:29:53,320
Speaker 1: They were just like, this makes no sense. Why in

707
00:29:53,360 --> 00:29:56,520
Speaker 1: the world would you leave college to have a ninety

708
00:29:56,640 --> 00:30:00,320
Speaker 1: nine point nine nine nine percent chance of failure? And

709
00:30:00,360 --> 00:30:03,040
Speaker 1: then the conversations kind of just stopped because I marked

710
00:30:03,080 --> 00:30:07,000
Speaker 1: my parents as spam and then I guess they emailed

711
00:30:07,000 --> 00:30:08,920
Speaker 1: me or actually I didn't mark my parents a spam.

712
00:30:08,960 --> 00:30:10,360
Speaker 1: What happened with my dad was sending me a lot

713
00:30:10,400 --> 00:30:12,520
Speaker 1: of emails that look the same, and then Gmail marked

714
00:30:12,520 --> 00:30:15,600
Speaker 1: them as spam. So then I discovered these emails like

715
00:30:15,760 --> 00:30:18,320
Speaker 1: years later, but yeah, he was just in my spam

716
00:30:18,320 --> 00:30:19,920
Speaker 1: in box. But he was pretty much saying the same

717
00:30:19,920 --> 00:30:22,040
Speaker 1: thing every single day, which is, you're an idiot.

718
00:30:22,080 --> 00:30:25,520
Speaker 3: Go back to school, and how do you deal with

719
00:30:25,560 --> 00:30:27,760
Speaker 3: that when you know the people that raised you, that

720
00:30:28,080 --> 00:30:30,120
Speaker 3: you know you've taken advice from your whole life are

721
00:30:30,160 --> 00:30:31,520
Speaker 3: telling you what you're doing is wrong.

722
00:30:31,720 --> 00:30:34,920
Speaker 1: Yeah, I mean I know that, Like I just knew

723
00:30:34,960 --> 00:30:37,160
Speaker 1: culturally we were so different, right, Like America is all

724
00:30:37,160 --> 00:30:39,720
Speaker 1: about taking risks and Chinese culture is about paying, like

725
00:30:39,760 --> 00:30:42,960
Speaker 1: what's the most optimal safe path, which is why everyone's

726
00:30:43,000 --> 00:30:45,160
Speaker 1: pushed to be a doctor, engineer, or lawyer because those

727
00:30:45,200 --> 00:30:47,880
Speaker 1: are three fields where you know, traditionally you'll make a

728
00:30:47,880 --> 00:30:49,960
Speaker 1: decent amount of money, right, like you're making at least

729
00:30:49,960 --> 00:30:52,360
Speaker 1: six figures, and then adie like upper level if you

730
00:30:52,400 --> 00:30:53,120
Speaker 1: do really well, you.

731
00:30:53,040 --> 00:30:54,160
Speaker 2: Could be making eight figures.

732
00:30:54,560 --> 00:30:59,360
Speaker 1: So I think that they just had a different idea

733
00:30:59,560 --> 00:31:00,760
Speaker 1: of what made sense.

734
00:31:01,400 --> 00:31:03,200
Speaker 2: And again, education gave them everything.

735
00:31:02,920 --> 00:31:06,080
Speaker 1: They had, So I just I knew they loved me,

736
00:31:06,480 --> 00:31:08,560
Speaker 1: but this was like the path that I was going

737
00:31:08,600 --> 00:31:09,120
Speaker 1: to choose.

738
00:31:08,960 --> 00:31:12,400
Speaker 3: To go on. Yeah, And it also seems like you,

739
00:31:12,800 --> 00:31:15,880
Speaker 3: from a young age weren't afraid of taking risks and

740
00:31:15,920 --> 00:31:17,800
Speaker 3: trying new things and putting yourself out there.

741
00:31:17,840 --> 00:31:20,440
Speaker 2: I would say, like, I'm pretty risk tolerant.

742
00:31:20,720 --> 00:31:23,200
Speaker 1: I remember, like, I think I was seventeen years old,

743
00:31:23,480 --> 00:31:26,560
Speaker 1: maybe seventeen, maybe eighteen, and I went just like I

744
00:31:26,680 --> 00:31:28,720
Speaker 1: stole my parents' car and went skydiving, and I'm in

745
00:31:28,720 --> 00:31:31,360
Speaker 1: my sky diving license but like I have like no

746
00:31:31,880 --> 00:31:35,360
Speaker 1: sense of danger. I went like I was solo traveling,

747
00:31:35,440 --> 00:31:38,040
Speaker 1: and like I decided to like climb this mountain and

748
00:31:38,080 --> 00:31:41,200
Speaker 1: stay there until after sunset, not thinking about what would

749
00:31:41,240 --> 00:31:45,400
Speaker 1: happen when the sunset. Anyways, I ended up going into

750
00:31:45,560 --> 00:31:48,160
Speaker 1: a strangerous car like these three guys who didn't speak

751
00:31:48,160 --> 00:31:50,120
Speaker 1: English with a dead phone, and just like kind of

752
00:31:50,160 --> 00:31:52,200
Speaker 1: tried to go like choot choo choo to get them

753
00:31:52,200 --> 00:31:54,080
Speaker 1: to get me to like the train center. But obviously,

754
00:31:54,120 --> 00:31:57,360
Speaker 1: like that could have gone really wrong. So yeah, just

755
00:31:57,480 --> 00:31:58,400
Speaker 1: zero sense of danger.

756
00:31:58,720 --> 00:32:01,800
Speaker 3: That makes sense. And it's so fun because what I

757
00:32:01,840 --> 00:32:04,160
Speaker 3: love about you is you were obviously a serious academic

758
00:32:04,240 --> 00:32:05,840
Speaker 3: and you were very smart obviously to get into a

759
00:32:05,840 --> 00:32:07,920
Speaker 3: school like Carnegie Mellon and to do what you were doing.

760
00:32:07,960 --> 00:32:10,920
Speaker 3: It's hard to learn programming and be able to even

761
00:32:10,960 --> 00:32:14,560
Speaker 3: just understand the basics of things like within computer science.

762
00:32:15,040 --> 00:32:17,320
Speaker 3: So it's just so cool knowing that you have these

763
00:32:17,400 --> 00:32:20,240
Speaker 3: you know, multiple sides too, where you have this like

764
00:32:20,360 --> 00:32:22,840
Speaker 3: wild side where you know you love raves and you

765
00:32:23,080 --> 00:32:26,360
Speaker 3: love skydiving and things that are adrenaline rushing, right, but

766
00:32:26,360 --> 00:32:29,640
Speaker 3: then you're also can be very serious and obviously have

767
00:32:30,080 --> 00:32:31,200
Speaker 3: had this incredible career.

768
00:32:31,320 --> 00:32:32,640
Speaker 2: Yeah, I think life is all that fun.

769
00:32:32,760 --> 00:32:35,520
Speaker 3: So, yeah, you know a lot of people in the

770
00:32:35,560 --> 00:32:39,160
Speaker 3: audience that are listening to us, they maybe are children

771
00:32:39,160 --> 00:32:42,720
Speaker 3: of immigrants or have parents that sacrificed enormously for a

772
00:32:42,840 --> 00:32:45,440
Speaker 3: very specific version of success that we're kind of talking about,

773
00:32:45,840 --> 00:32:48,680
Speaker 3: and those people often feel like pursuing their own path

774
00:32:48,800 --> 00:32:51,520
Speaker 3: is betrayal. So I'm curious, like, how do you carry

775
00:32:51,520 --> 00:32:54,240
Speaker 3: the weight of someone else's sacrifice without letting it crush

776
00:32:54,280 --> 00:32:54,920
Speaker 3: your own ambition.

777
00:32:55,400 --> 00:32:56,800
Speaker 1: I mean, I think at the end of the day,

778
00:32:57,760 --> 00:33:01,240
Speaker 1: things aren't really as risky as people make it out

779
00:33:01,240 --> 00:33:04,960
Speaker 1: to be. So for example, quitting college, right, like you

780
00:33:04,960 --> 00:33:08,080
Speaker 1: always have the option to go back to college. Quitting

781
00:33:08,080 --> 00:33:10,240
Speaker 1: your job, Like, if you are actually good at your job,

782
00:33:10,400 --> 00:33:13,800
Speaker 1: you'll have that job offer again after you attempt to

783
00:33:13,800 --> 00:33:17,640
Speaker 1: do like whatever you want to do. So I think

784
00:33:17,680 --> 00:33:21,720
Speaker 1: that like when you can get your parents to understand like, hey,

785
00:33:21,760 --> 00:33:24,760
Speaker 1: it's not that much of a risk, like they'll be

786
00:33:24,800 --> 00:33:28,520
Speaker 1: accepting and they never get there, like you yourself know that

787
00:33:28,600 --> 00:33:33,360
Speaker 1: it might be temporary disappointment to them, but it like

788
00:33:33,560 --> 00:33:35,440
Speaker 1: you need to do what makes you happy at the

789
00:33:35,480 --> 00:33:38,160
Speaker 1: end of the day, and if it's temporary disappointment. Like,

790
00:33:38,360 --> 00:33:40,360
Speaker 1: let's say you try it and in five years you

791
00:33:40,400 --> 00:33:42,640
Speaker 1: decide like it doesn't work out, you fail, you can

792
00:33:42,680 --> 00:33:44,440
Speaker 1: go back to that college, you can go back to

793
00:33:44,480 --> 00:33:48,560
Speaker 1: that job, and then they won't be disappointed anymore. Things

794
00:33:48,560 --> 00:33:51,880
Speaker 1: are always reversible in general, So like, as long as

795
00:33:51,880 --> 00:33:54,800
Speaker 1: the the decision is a reversible decision at the end

796
00:33:54,800 --> 00:33:58,080
Speaker 1: of the day, like you're not going to disappoint your

797
00:33:58,120 --> 00:33:58,680
Speaker 1: parents forever.

798
00:33:59,360 --> 00:34:01,120
Speaker 3: I love that person. And I actually just had a

799
00:34:01,120 --> 00:34:04,320
Speaker 3: really similar conversation with being chenned from gold House when

800
00:34:04,360 --> 00:34:06,920
Speaker 3: he was leaving Facebook or sorry, when he was leaving

801
00:34:06,960 --> 00:34:10,120
Speaker 3: YouTube to start gold House. It was that exact same

802
00:34:10,160 --> 00:34:12,200
Speaker 3: framework of thinking. He was like, well, I could try

803
00:34:12,239 --> 00:34:15,080
Speaker 3: to do this and you know, take on this risk

804
00:34:15,080 --> 00:34:16,960
Speaker 3: that I think could work out, or I could just

805
00:34:17,000 --> 00:34:18,399
Speaker 3: go back to YouTube, you know.

806
00:34:18,520 --> 00:34:19,200
Speaker 2: Yeah exactly.

807
00:34:19,680 --> 00:34:21,400
Speaker 1: So yeah, and if you're good at your job, like

808
00:34:21,400 --> 00:34:23,279
Speaker 1: you're probably going to be get paid. You're going to

809
00:34:23,320 --> 00:34:25,120
Speaker 1: get paid more when you go back to your job.

810
00:34:25,440 --> 00:34:27,680
Speaker 1: You might lose a few years, but the like, the

811
00:34:27,800 --> 00:34:29,799
Speaker 1: risk is losing a few years of your life, but

812
00:34:29,920 --> 00:34:32,759
Speaker 1: it's not a permanent thing. Most decisions are reversible, so

813
00:34:33,080 --> 00:34:35,040
Speaker 1: like your parents will be proud one way or the

814
00:34:35,040 --> 00:34:36,719
Speaker 1: other at the end of the day.

815
00:34:36,840 --> 00:34:39,120
Speaker 3: Okay, so you go from the Teel Fellowship to then

816
00:34:39,320 --> 00:34:43,439
Speaker 3: working at companies like Facebook, Snapchat, and Quorra. Right when

817
00:34:43,520 --> 00:34:46,040
Speaker 3: did you start Scale AI? And like, how did the

818
00:34:46,040 --> 00:34:46,960
Speaker 3: idea come to you guys?

819
00:34:47,120 --> 00:34:48,680
Speaker 2: Yeah, so Scale is actually a pivot.

820
00:34:49,080 --> 00:34:52,000
Speaker 1: We were working on projects during snap so started off

821
00:34:52,040 --> 00:34:55,240
Speaker 1: with class pass for clubbing. Terrible idea, and then pivoted

822
00:34:55,320 --> 00:34:58,360
Speaker 1: into a healthcare app, which the idea was like, we

823
00:34:58,360 --> 00:35:00,520
Speaker 1: would find you the best doctors for procedure. You needed

824
00:35:00,560 --> 00:35:02,760
Speaker 1: to think like the best doctor for a jost surgery.

825
00:35:03,600 --> 00:35:06,640
Speaker 1: And then terrible idea again. But we were calling doctors

826
00:35:06,640 --> 00:35:09,200
Speaker 1: all the time and we were literally like, man, we

827
00:35:09,200 --> 00:35:11,600
Speaker 1: wish there was like some way that whenever someone pressed

828
00:35:11,600 --> 00:35:13,840
Speaker 1: this button it would call a doctor for us. And

829
00:35:14,000 --> 00:35:17,400
Speaker 1: our yc roommates p Nash and Kingston they were like, oh, se,

830
00:35:17,440 --> 00:35:19,320
Speaker 1: you mean like an API for humans. And at this

831
00:35:19,360 --> 00:35:20,880
Speaker 1: point I had built a bunch of projects and I

832
00:35:20,920 --> 00:35:23,600
Speaker 1: knew that that would go viral. So I remember looking

833
00:35:23,640 --> 00:35:25,040
Speaker 1: at them and I was like, do you guys want

834
00:35:25,040 --> 00:35:27,879
Speaker 1: to work on that because that's a fair thing to do, right,

835
00:35:27,960 --> 00:35:29,640
Speaker 1: And then he was just like, no, you can work

836
00:35:29,680 --> 00:35:32,759
Speaker 1: on it. I was like, okay, great, So we over

837
00:35:32,800 --> 00:35:36,040
Speaker 1: the weekend during YC like made an API for humans.

838
00:35:36,239 --> 00:35:38,560
Speaker 1: It was very bare bones or API docs and then

839
00:35:38,600 --> 00:35:41,799
Speaker 1: whenever the API was hit, it would manually go to

840
00:35:41,880 --> 00:35:43,840
Speaker 1: me and like Twilio would wake me up and be

841
00:35:43,920 --> 00:35:45,360
Speaker 1: like hey, like go send a call back, and I

842
00:35:45,360 --> 00:35:47,640
Speaker 1: would like manually send a callback so the results back

843
00:35:47,680 --> 00:35:51,840
Speaker 1: to the person that sent the API call. So launched

844
00:35:51,880 --> 00:35:53,880
Speaker 1: it on product Hunt and I had a lot of

845
00:35:53,880 --> 00:35:55,759
Speaker 1: friends from hackathons at this point in time, so I

846
00:35:55,800 --> 00:35:58,840
Speaker 1: literally just DMed every single person i'd met hackthons. I

847
00:35:58,840 --> 00:36:00,840
Speaker 1: was like, hey, can you upvote it us on product Hunts.

848
00:36:01,160 --> 00:36:02,799
Speaker 1: So that's how it reached a top of product Hunt

849
00:36:02,800 --> 00:36:05,520
Speaker 1: to like literally DMed hundreds of people. And when it

850
00:36:05,560 --> 00:36:07,720
Speaker 1: reaches the top, like it was like the one website.

851
00:36:07,800 --> 00:36:10,360
Speaker 1: Vcs were looking at the scope out and new startups

852
00:36:10,400 --> 00:36:13,080
Speaker 1: at that point in time, so I had a bunch

853
00:36:13,120 --> 00:36:14,839
Speaker 1: of vcs reach out after Wow.

854
00:36:15,040 --> 00:36:17,120
Speaker 3: And I love that you were able to tack to

855
00:36:17,160 --> 00:36:19,600
Speaker 3: tap into your network, you know, and that's kind of like,

856
00:36:19,640 --> 00:36:21,640
Speaker 3: you know, people use that quote your network is your

857
00:36:21,680 --> 00:36:22,160
Speaker 3: net worth.

858
00:36:22,760 --> 00:36:26,560
Speaker 1: It's absolutely true. Like I think the dumbest people can

859
00:36:26,600 --> 00:36:28,760
Speaker 1: make a lot of money. If you have a good network.

860
00:36:29,400 --> 00:36:32,280
Speaker 3: Yeah, and it's almost like, you know, I even feel

861
00:36:32,280 --> 00:36:35,520
Speaker 3: like with the Teal Fellowship, like you're around so many

862
00:36:35,560 --> 00:36:38,200
Speaker 3: people that even though you know, that business that you

863
00:36:38,239 --> 00:36:40,680
Speaker 3: were working on with the Teal Fellowship didn't end up

864
00:36:40,719 --> 00:36:44,879
Speaker 3: panning into you know, maybe what you would, I mean,

865
00:36:44,920 --> 00:36:48,560
Speaker 3: maybe what you wanted it to, but it obviously led

866
00:36:48,640 --> 00:36:50,960
Speaker 3: you to maybe meeting incredible people through that as well.

867
00:36:51,040 --> 00:36:53,760
Speaker 1: Yeah, I mean I think that when you get thrown

868
00:36:53,960 --> 00:36:56,759
Speaker 1: into this environment where everyone thinks you're a prodigy, you

869
00:36:56,760 --> 00:36:58,799
Speaker 1: get a lot of more doors that open up for you.

870
00:36:58,920 --> 00:37:01,600
Speaker 1: So everyone's willing to spend more time mentoring you, meeting you,

871
00:37:01,680 --> 00:37:04,279
Speaker 1: et cetera. So you meet like the best engineers, the

872
00:37:04,320 --> 00:37:07,480
Speaker 1: best founders, and obviously you learn from those people and

873
00:37:07,520 --> 00:37:08,960
Speaker 1: you become the average of the five people that you

874
00:37:09,000 --> 00:37:11,040
Speaker 1: hang around with most, and the five people you're gonna

875
00:37:11,000 --> 00:37:12,920
Speaker 1: be hanging out with most are going to be just

876
00:37:12,920 --> 00:37:17,279
Speaker 1: like the smartest, most intelligent people, the most ambitious, some

877
00:37:17,320 --> 00:37:19,640
Speaker 1: of the hardest working onest planet.

878
00:37:19,719 --> 00:37:23,320
Speaker 2: I think when you're in a Teel fellowship, so everyone

879
00:37:23,360 --> 00:37:24,200
Speaker 2: just rubs off each.

880
00:37:24,040 --> 00:37:26,480
Speaker 3: Other, right, you're almost lucky if you're the dumbest person

881
00:37:26,480 --> 00:37:26,879
Speaker 3: in the room.

882
00:37:26,920 --> 00:37:27,680
Speaker 2: Oh you're Yeah.

883
00:37:27,719 --> 00:37:29,879
Speaker 1: I definitely felt like the dumbest verson in room several times,

884
00:37:29,880 --> 00:37:31,680
Speaker 1: which is great. I loving the dumbest vers in room.

885
00:37:31,800 --> 00:37:33,520
Speaker 3: Yeah, no, because it means there's so much that you

886
00:37:33,520 --> 00:37:35,880
Speaker 3: can learn from people around you, you know. I also

887
00:37:35,920 --> 00:37:39,680
Speaker 3: read that at SCALI, you were responsible for recruiting some

888
00:37:39,800 --> 00:37:42,400
Speaker 3: of the company's first employees, which really sent set the

889
00:37:42,440 --> 00:37:45,000
Speaker 3: foundation for the company. Right, So I'm curious. You know,

890
00:37:45,680 --> 00:37:47,680
Speaker 3: there's a lot of entrepreneurs and founders that listen to

891
00:37:47,760 --> 00:37:50,000
Speaker 3: this show. What would you say makes a good hire?

892
00:37:50,640 --> 00:37:52,799
Speaker 1: Yeah, so this goes back to, like your network, here's

893
00:37:52,840 --> 00:37:57,960
Speaker 1: net worth. My advice for people is, if you're in college,

894
00:37:58,160 --> 00:38:01,120
Speaker 1: go meet every single person in college that you consider

895
00:38:01,520 --> 00:38:03,920
Speaker 1: super intelligent and be friends with them, because they're going

896
00:38:03,960 --> 00:38:05,520
Speaker 1: to be the easiest people to recruit.

897
00:38:06,040 --> 00:38:07,399
Speaker 2: I think that college.

898
00:38:07,120 --> 00:38:10,040
Speaker 1: Kids, when they're looking for new jobs, they have higher

899
00:38:10,120 --> 00:38:13,239
Speaker 1: risk tolerance than someone with the family, and a lot

900
00:38:13,239 --> 00:38:14,840
Speaker 1: of it is emotional, right, Like, there could be a

901
00:38:14,840 --> 00:38:17,279
Speaker 1: company willing to pay them millions, but if they are

902
00:38:17,680 --> 00:38:21,719
Speaker 1: good friends with you and they believe in you, they

903
00:38:21,719 --> 00:38:23,839
Speaker 1: will be willing to give up millions of dollars.

904
00:38:23,520 --> 00:38:24,359
Speaker 2: To work with you.

905
00:38:26,080 --> 00:38:28,520
Speaker 1: If you are not in college, I would highly suggest

906
00:38:28,680 --> 00:38:31,480
Speaker 1: just trying to find networks very similar to your college

907
00:38:31,680 --> 00:38:35,280
Speaker 1: with the high density of intelligent people. And I think

908
00:38:35,600 --> 00:38:37,680
Speaker 1: I just the college is like the best opportunity to that,

909
00:38:37,760 --> 00:38:39,239
Speaker 1: right because if you went to like a Harvard and

910
00:38:39,360 --> 00:38:43,680
Speaker 1: MIT at Stanford, like everyone around is smart, and there's

911
00:38:43,840 --> 00:38:45,520
Speaker 1: very few places in your life where you're going to

912
00:38:45,520 --> 00:38:47,919
Speaker 1: be around such a high density of intelligent people. They're

913
00:38:47,920 --> 00:38:50,520
Speaker 1: like the same intelligence level as you afterwards, but there

914
00:38:50,560 --> 00:38:51,440
Speaker 1: are still communities.

915
00:38:51,719 --> 00:38:53,879
Speaker 2: So because like hiring is all about.

916
00:38:53,880 --> 00:38:57,839
Speaker 1: I think hiring and keeping people is all emotions, right,

917
00:38:57,880 --> 00:39:00,279
Speaker 1: Like it's emotional sales and emotional retention, because the best

918
00:39:00,320 --> 00:39:02,399
Speaker 1: people are always going to have better offers out there,

919
00:39:02,800 --> 00:39:04,880
Speaker 1: and they will choose you because they believe in you,

920
00:39:04,920 --> 00:39:16,440
Speaker 1: and they will stay there because they believe in you.

921
00:39:16,440 --> 00:39:19,000
Speaker 3: You ended up leaving SCALEI in twenty eighteen, but you

922
00:39:19,080 --> 00:39:21,560
Speaker 3: retained five percent of your steak in the company, and

923
00:39:21,640 --> 00:39:23,960
Speaker 3: from twenty eighteen to twenty twenty five, you know, you

924
00:39:24,080 --> 00:39:26,400
Speaker 3: kept that steak. You talk to a lot of people

925
00:39:26,440 --> 00:39:30,160
Speaker 3: that maybe were co founders. It ended up liquidating, but

926
00:39:30,239 --> 00:39:32,040
Speaker 3: you had the patience to say, I want to keep

927
00:39:32,120 --> 00:39:32,959
Speaker 3: my five percent stake.

928
00:39:33,080 --> 00:39:35,759
Speaker 1: I actually did some liquidations and that was the worst

929
00:39:35,760 --> 00:39:38,520
Speaker 1: mistake of my life, so I would have more than that.

930
00:39:39,560 --> 00:39:42,239
Speaker 2: But yeah, I guess I was kind of patient you were.

931
00:39:42,600 --> 00:39:45,719
Speaker 3: So what made you a what made you liquidate some?

932
00:39:46,360 --> 00:39:48,920
Speaker 3: And then be why did you decide to keep that

933
00:39:48,960 --> 00:39:50,320
Speaker 3: five percent steak in the business.

934
00:39:50,640 --> 00:39:53,120
Speaker 1: Yeah, so I would say I liquidated enough that I

935
00:39:53,120 --> 00:39:55,759
Speaker 1: would be able to be like somewhat comfortable. So I

936
00:39:55,760 --> 00:40:00,040
Speaker 1: was reading you know, like fat fire on Reddit, and

937
00:40:00,120 --> 00:40:02,800
Speaker 1: I was just like, Okay, this isn't necessarily fat fire,

938
00:40:03,040 --> 00:40:06,400
Speaker 1: but I would be able to technically live off this

939
00:40:06,440 --> 00:40:09,040
Speaker 1: for the rest of my life. So I liquidated a

940
00:40:09,040 --> 00:40:12,520
Speaker 1: certain amount and then immediately I bought like the seventy

941
00:40:12,600 --> 00:40:17,320
Speaker 1: thousand dollars apartment in Vegas, which is just like I

942
00:40:17,520 --> 00:40:22,279
Speaker 1: could live off that, right, And I just wanted to

943
00:40:22,360 --> 00:40:25,600
Speaker 1: have like some cushion so I wouldn't be CouchSurfing again.

944
00:40:26,160 --> 00:40:29,040
Speaker 1: But yeah, after that, like I just I didn't feel

945
00:40:29,080 --> 00:40:30,879
Speaker 1: like a need to upgrade my life, Like my life

946
00:40:30,960 --> 00:40:31,600
Speaker 1: was phenomenal.

947
00:40:32,560 --> 00:40:35,359
Speaker 3: When you say CouchSurfing again, when were you CouchSurfing? Oh?

948
00:40:35,440 --> 00:40:37,600
Speaker 1: Yeah, like while I was building right, like during a

949
00:40:37,600 --> 00:40:40,120
Speaker 1: teal fellowship, et cetera, I just le find.

950
00:40:39,880 --> 00:40:42,040
Speaker 3: Couches really yeah, with friends.

951
00:40:42,800 --> 00:40:44,799
Speaker 1: Yeah, I mean we would find like, like you know,

952
00:40:44,960 --> 00:40:48,960
Speaker 1: my co founder's brother, for examples, couch but we would

953
00:40:49,000 --> 00:40:49,880
Speaker 1: just find couches.

954
00:40:50,239 --> 00:40:52,719
Speaker 3: That's so interesting because within the Teal Fellowship, I'm sure

955
00:40:52,760 --> 00:40:55,560
Speaker 3: there were some students that used as students, I guess,

956
00:40:55,640 --> 00:40:58,200
Speaker 3: or some entrepreneurs that use some of that money to

957
00:40:59,120 --> 00:41:00,960
Speaker 3: at least accommodate living, right, I.

958
00:41:00,880 --> 00:41:04,400
Speaker 1: Think some people, Yeah, But I wanted to just invest it, like,

959
00:41:04,440 --> 00:41:06,000
Speaker 1: I didn't want to use it in places where it

960
00:41:06,000 --> 00:41:08,760
Speaker 1: didn't need to be used. So it's like when people

961
00:41:08,800 --> 00:41:11,320
Speaker 1: like founders pay themselves a salary versus not paying themselves

962
00:41:11,320 --> 00:41:13,520
Speaker 1: a salary, right, Like some founders decide to pay themselves

963
00:41:13,600 --> 00:41:17,280
Speaker 1: high salaries, others pay themselves nothing. And I was definitely

964
00:41:17,320 --> 00:41:20,920
Speaker 1: the like of the mentality pay nothing and live as

965
00:41:21,000 --> 00:41:23,200
Speaker 1: cheaply as possible so we can reinvest in a business.

966
00:41:23,520 --> 00:41:24,480
Speaker 3: What do you think that taught you?

967
00:41:25,760 --> 00:41:26,920
Speaker 2: I mean, I think.

968
00:41:28,719 --> 00:41:31,440
Speaker 1: I think the one lesson I learned from paying yourself

969
00:41:31,560 --> 00:41:34,120
Speaker 1: less or paying yourself nothing versus your employees is that

970
00:41:34,400 --> 00:41:36,839
Speaker 1: they really respect you when you do that, and they're

971
00:41:36,840 --> 00:41:41,279
Speaker 1: willing to take lower salaries when you can pitch as

972
00:41:41,280 --> 00:41:43,640
Speaker 1: a founder, Like, hey, like I'm only paying myself X

973
00:41:43,680 --> 00:41:47,239
Speaker 1: amount or I'm not paying myself because that's when like

974
00:41:47,280 --> 00:41:48,920
Speaker 1: the negotiats have come in, and like when a lot

975
00:41:48,920 --> 00:41:51,359
Speaker 1: of I think bitterness comes in, even in like America, right,

976
00:41:51,400 --> 00:41:53,439
Speaker 1: Like people look up ceo salaries and be like, wow,

977
00:41:53,520 --> 00:41:55,759
Speaker 1: like I'm only getting paid minimum wage, but CEO is

978
00:41:55,800 --> 00:41:59,680
Speaker 1: paying themselves X, Y and z, and like for them

979
00:41:59,719 --> 00:42:02,759
Speaker 1: to see that your CEO setting an example by not

980
00:42:02,760 --> 00:42:05,120
Speaker 1: taking anything because they want to reinvest everything into the company,

981
00:42:06,040 --> 00:42:08,120
Speaker 1: it makes your employees believe in a company more.

982
00:42:08,280 --> 00:42:10,279
Speaker 3: Yeah, I think it's good advice to founders out there too,

983
00:42:10,400 --> 00:42:13,000
Speaker 3: especially when you have small teams yep, exactly, and you're

984
00:42:13,040 --> 00:42:14,719
Speaker 3: asking a lot from the people that work for you.

985
00:42:14,880 --> 00:42:17,520
Speaker 3: During those seven years from twenty eighteen to twenty twenty five,

986
00:42:17,800 --> 00:42:20,120
Speaker 3: what was going through your psyche watching the company evolve.

987
00:42:21,360 --> 00:42:23,279
Speaker 1: I'm not gonna I wasn't really paying attention at that

988
00:42:23,320 --> 00:42:26,640
Speaker 1: point in time. I was like I was in Ventures,

989
00:42:26,719 --> 00:42:29,120
Speaker 1: I was investing in a lot of companies, and I

990
00:42:29,160 --> 00:42:31,560
Speaker 1: think because I'm very like all in on what I'm

991
00:42:31,560 --> 00:42:33,680
Speaker 1: doing at the moment, so like at the moment Ventures,

992
00:42:33,680 --> 00:42:35,280
Speaker 1: I was more all in on like watching the companies

993
00:42:35,320 --> 00:42:38,279
Speaker 1: I was investing and grow and it always makes you

994
00:42:38,600 --> 00:42:41,200
Speaker 1: so happy when they raise any round or like you know,

995
00:42:41,200 --> 00:42:43,320
Speaker 1: they have a win that like I can celebrate with them.

996
00:42:43,680 --> 00:42:46,960
Speaker 1: So that was really my focus, to be honest, I

997
00:42:46,960 --> 00:42:49,719
Speaker 1: don't think I really like it was like one of

998
00:42:49,840 --> 00:42:52,359
Speaker 1: like Scale was one of those companies and for those

999
00:42:52,440 --> 00:42:54,719
Speaker 1: audience that doesn't know, like SURGI was a big competitor

1000
00:42:54,760 --> 00:43:01,040
Speaker 1: that was doing more revenue and also profitable sou even

1001
00:43:01,080 --> 00:43:02,759
Speaker 1: though we had like all these government contracts and it

1002
00:43:02,800 --> 00:43:05,600
Speaker 1: was growing, like I was never really certain like was

1003
00:43:05,640 --> 00:43:07,560
Speaker 1: it going to IPO? When is it going to IPO?

1004
00:43:08,200 --> 00:43:10,240
Speaker 1: Am I ever gonna be liquid? Will Scale go to zero?

1005
00:43:10,680 --> 00:43:12,360
Speaker 1: Like it was like kind of a saying that like

1006
00:43:12,719 --> 00:43:14,600
Speaker 1: Scales one of the fastest growing companies, I can go

1007
00:43:14,640 --> 00:43:19,400
Speaker 1: to zero tomorrow. So like going back to like you know, liquidity,

1008
00:43:19,440 --> 00:43:22,480
Speaker 1: it was just like is this money even real? And

1009
00:43:22,640 --> 00:43:25,960
Speaker 1: I never had high hopes, so I knew there was

1010
00:43:25,960 --> 00:43:27,640
Speaker 1: always a risk that it would go to zero and

1011
00:43:27,760 --> 00:43:30,000
Speaker 1: I'm so happy that Meta bought it. But that was

1012
00:43:30,000 --> 00:43:32,120
Speaker 1: like probably the first time I was like, oh great,

1013
00:43:32,200 --> 00:43:33,920
Speaker 1: like really the Ferston I've paid attention.

1014
00:43:34,640 --> 00:43:36,680
Speaker 3: Yeah, just goes back to your entrepreneurial spirit. It's like

1015
00:43:36,680 --> 00:43:38,680
Speaker 3: while you're building a company. It's not like you're not

1016
00:43:38,719 --> 00:43:40,640
Speaker 3: thinking about your other options.

1017
00:43:40,239 --> 00:43:41,680
Speaker 2: Right exactly.

1018
00:43:41,880 --> 00:43:45,200
Speaker 3: Yeah, in twenty twenty five, when Meta acquired it, your

1019
00:43:45,280 --> 00:43:47,359
Speaker 3: five percent ended up making you one point two five

1020
00:43:47,400 --> 00:43:50,719
Speaker 3: billion dollars. Everybody wants to be a billionaire or a multimillionaire, right,

1021
00:43:50,760 --> 00:43:53,440
Speaker 3: whatever it may be. But is there a side that

1022
00:43:53,640 --> 00:43:57,000
Speaker 3: people don't think about when you actually get to those

1023
00:43:57,080 --> 00:43:58,160
Speaker 3: numbers and become that.

1024
00:43:59,280 --> 00:44:01,040
Speaker 1: I mean, I would say the main side is that,

1025
00:44:01,080 --> 00:44:04,280
Speaker 1: like it becomes a lot more lonely, right, Like, I'm

1026
00:44:04,360 --> 00:44:07,000
Speaker 1: so grateful that I had such a good core group

1027
00:44:07,040 --> 00:44:10,759
Speaker 1: of friends, but it's harder to trust new people nowadays.

1028
00:44:10,760 --> 00:44:11,600
Speaker 2: And that makes sense.

1029
00:44:12,360 --> 00:44:14,120
Speaker 1: I think there's a reason why a lot of billionaires

1030
00:44:14,160 --> 00:44:16,279
Speaker 1: are only friends with other billionaires, and it's because it's

1031
00:44:16,280 --> 00:44:19,000
Speaker 1: easy to trust other billionaires because they don't have something

1032
00:44:19,040 --> 00:44:22,200
Speaker 1: to use from you. But I think that, like, if

1033
00:44:22,239 --> 00:44:25,279
Speaker 1: you're entering that status without like a core, solidive group

1034
00:44:25,320 --> 00:44:27,400
Speaker 1: of friends initially that you know would never ask of

1035
00:44:27,440 --> 00:44:30,760
Speaker 1: anything from you or use you, like, you're kind of screwed.

1036
00:44:32,600 --> 00:44:34,800
Speaker 2: Yeah, that's like the best way could put it.

1037
00:44:34,960 --> 00:44:38,400
Speaker 3: Was there something simpler about your life before achieving a

1038
00:44:38,440 --> 00:44:41,000
Speaker 3: certain levels of success and making a certain amount of money.

1039
00:44:42,320 --> 00:44:45,239
Speaker 1: I mean, like I have to hire security guards now,

1040
00:44:45,280 --> 00:44:48,279
Speaker 1: which sucks, Like there's like extra expenses, right, Like I

1041
00:44:48,280 --> 00:44:50,480
Speaker 1: don't want to hire security, but I kind of have to.

1042
00:44:50,840 --> 00:44:52,560
Speaker 1: I think about things like I almost want the DC

1043
00:44:52,760 --> 00:44:54,920
Speaker 1: Mexico City this weekend. I still think I should have.

1044
00:44:55,120 --> 00:44:56,840
Speaker 1: I'm actually really annoyed none of my friends would go

1045
00:44:56,920 --> 00:44:58,560
Speaker 1: with me, but they're like being protected. They're like, Lucy,

1046
00:44:58,560 --> 00:45:01,440
Speaker 1: if we go to DC Mexico City, like the cartels there,

1047
00:45:01,480 --> 00:45:03,759
Speaker 1: Like what if you get kidnapped? Like you are like

1048
00:45:03,880 --> 00:45:06,400
Speaker 1: potentially a target And it's like, guys, I'm not a target.

1049
00:45:06,440 --> 00:45:08,040
Speaker 1: I'm not famous, Like no knows who I am.

1050
00:45:08,120 --> 00:45:08,719
Speaker 2: Like let's go.

1051
00:45:09,160 --> 00:45:11,719
Speaker 1: But like you have like these people around you now

1052
00:45:11,719 --> 00:45:13,600
Speaker 1: that are suddenly scared to do things with you. Like

1053
00:45:13,600 --> 00:45:15,439
Speaker 1: if I didn't have this title, I would have raved

1054
00:45:15,480 --> 00:45:19,360
Speaker 1: this weekend in Mexico City. But I just had protective friends,

1055
00:45:19,360 --> 00:45:22,640
Speaker 1: And like I would feel a little scared going alone

1056
00:45:22,680 --> 00:45:24,800
Speaker 1: now too, versus like before I would have no issue

1057
00:45:24,800 --> 00:45:27,279
Speaker 1: going alone. So I do think like in some ways it.

1058
00:45:27,239 --> 00:45:28,359
Speaker 2: Limits your freedom.

1059
00:45:28,960 --> 00:45:31,759
Speaker 3: Yeah, are you dating anybody? I'm not dating anyone, okay,

1060
00:45:31,760 --> 00:45:33,640
Speaker 3: because I would imagine that was one of my follow

1061
00:45:33,680 --> 00:45:35,520
Speaker 3: up questions is like, I feel like that gets hard

1062
00:45:35,640 --> 00:45:37,399
Speaker 3: right when it comes to dating, because that's a whole

1063
00:45:37,400 --> 00:45:40,120
Speaker 3: other level of trust. You have to like really scope

1064
00:45:40,120 --> 00:45:42,279
Speaker 3: out the prospects. But you can also just date somebody

1065
00:45:42,320 --> 00:45:43,560
Speaker 3: that great breakup story.

1066
00:45:43,640 --> 00:45:47,399
Speaker 2: But I'm not gonna say anything, Oh my god, Okay, but.

1067
00:45:47,400 --> 00:45:51,440
Speaker 1: Yes, you're right, you will get used and abused. I

1068
00:45:51,480 --> 00:45:53,680
Speaker 1: think it's like I'm definitely I have to think more

1069
00:45:53,680 --> 00:45:56,600
Speaker 1: about like, like I'm a very big energy person, so

1070
00:45:57,120 --> 00:46:00,840
Speaker 1: I can generally tell if someone is using me for

1071
00:46:00,960 --> 00:46:03,759
Speaker 1: money or not. But I do think that, like you know,

1072
00:46:03,800 --> 00:46:06,600
Speaker 1: when you're in love that can blind you and you

1073
00:46:06,640 --> 00:46:08,399
Speaker 1: can like make excuses.

1074
00:46:07,880 --> 00:46:09,479
Speaker 2: When they ask for like more and more and more.

1075
00:46:09,960 --> 00:46:11,600
Speaker 1: I think like my new dating role would just be

1076
00:46:11,640 --> 00:46:13,399
Speaker 1: like and I would need someone that's like self made

1077
00:46:13,440 --> 00:46:17,400
Speaker 1: and like made their own money, or someone that you know,

1078
00:46:18,080 --> 00:46:21,239
Speaker 1: just didn't care about nice things at all. Because it's

1079
00:46:21,239 --> 00:46:23,319
Speaker 1: like when you find someone that like didn't make their

1080
00:46:23,320 --> 00:46:26,840
Speaker 1: own money, but they have expectations of a lifestyle that

1081
00:46:26,880 --> 00:46:29,640
Speaker 1: they can't afford, that's when issues come in.

1082
00:46:29,880 --> 00:46:33,280
Speaker 3: Topic of AI, So you've been around AI for a while,

1083
00:46:33,880 --> 00:46:36,200
Speaker 3: when you started talking about it back in the day,

1084
00:46:36,200 --> 00:46:38,080
Speaker 3: maybe when you were at Carnegie Mellon or when you

1085
00:46:38,120 --> 00:46:40,920
Speaker 3: guys were starting scale AI, did you guys have any

1086
00:46:41,000 --> 00:46:43,040
Speaker 3: idea how big of a deal it would be and

1087
00:46:43,120 --> 00:46:44,320
Speaker 3: how big it would turn into?

1088
00:46:45,200 --> 00:46:47,839
Speaker 1: Not at all, Like I said, so, we've started out

1089
00:46:47,840 --> 00:46:51,640
Speaker 1: with scale API API for humans, and we weren't even

1090
00:46:51,640 --> 00:46:54,520
Speaker 1: thinking about AI, Like we thought we were gonna compete

1091
00:46:54,560 --> 00:46:57,920
Speaker 1: against the BPO industry with phone calls until a like

1092
00:46:57,960 --> 00:47:00,000
Speaker 1: phone calls are really hard to quality control. But Crew

1093
00:47:00,120 --> 00:47:02,080
Speaker 1: ended up becoming one of our first customers and we're like,

1094
00:47:02,080 --> 00:47:04,240
Speaker 1: oh wow, like we can actually take all this data

1095
00:47:04,480 --> 00:47:07,320
Speaker 1: and use AI and then reduce like the humans and

1096
00:47:07,360 --> 00:47:09,600
Speaker 1: the loop over time and then that you know, would

1097
00:47:09,640 --> 00:47:13,719
Speaker 1: increase margins, et cetera. And it was only then we're like, oh, okay, cool,

1098
00:47:13,760 --> 00:47:15,960
Speaker 1: Like this is going to be a huge company because

1099
00:47:16,040 --> 00:47:18,440
Speaker 1: self driving is just one market and there's tons of

1100
00:47:18,440 --> 00:47:21,200
Speaker 1: other markets we can enter, but self driving alone has

1101
00:47:21,440 --> 00:47:25,000
Speaker 1: billions of dollars to spend in investing in good data.

1102
00:47:26,000 --> 00:47:28,359
Speaker 1: So I would say like, it wasn't really until that

1103
00:47:28,440 --> 00:47:31,719
Speaker 1: point of the pivot from scale API to scale AI,

1104
00:47:31,760 --> 00:47:34,120
Speaker 1: which did happen early in the business, that we were like, okay,

1105
00:47:34,239 --> 00:47:36,040
Speaker 1: like we have a big company on our hands.

1106
00:47:36,200 --> 00:47:38,160
Speaker 3: And then when it comes to AI itself, you know,

1107
00:47:38,520 --> 00:47:42,359
Speaker 3: you're a lot of people would consider you an AI

1108
00:47:42,400 --> 00:47:44,360
Speaker 3: expert or somebody that was kind of at the forefront

1109
00:47:44,480 --> 00:47:47,239
Speaker 3: of what's now going on. So I'm curious. You know,

1110
00:47:47,400 --> 00:47:51,040
Speaker 3: when people say that they're they're scared AI is going

1111
00:47:51,080 --> 00:47:52,960
Speaker 3: to take their jobs, what do you say to them?

1112
00:47:53,880 --> 00:47:55,440
Speaker 1: I think in general, AI is going to be our

1113
00:47:55,440 --> 00:47:57,920
Speaker 1: best co pilot. Think of it as like, you know,

1114
00:47:57,960 --> 00:48:00,880
Speaker 1: a second you that is going to help you not

1115
00:48:01,000 --> 00:48:03,560
Speaker 1: do the mundane, repeatable tasks that you don't want to do,

1116
00:48:03,600 --> 00:48:05,919
Speaker 1: because AI is very good at like repeatable tasks, right,

1117
00:48:06,800 --> 00:48:09,080
Speaker 1: But and then you can focus on the more important things.

1118
00:48:09,680 --> 00:48:11,359
Speaker 1: So I think the hardest problems to solve, I think

1119
00:48:11,400 --> 00:48:13,480
Speaker 1: relationship building, et cetera. Like when you think about it,

1120
00:48:13,520 --> 00:48:15,120
Speaker 1: is AI going to go out and close a deal

1121
00:48:15,160 --> 00:48:15,560
Speaker 1: for you?

1122
00:48:15,600 --> 00:48:16,360
Speaker 2: Like, no, it's not.

1123
00:48:17,320 --> 00:48:19,120
Speaker 1: A lot of deals are closed because you wine and

1124
00:48:19,200 --> 00:48:21,560
Speaker 1: dine people. And I think that there's gonna be new

1125
00:48:21,640 --> 00:48:23,880
Speaker 1: jobs that are created, Like yes, if you're like an

1126
00:48:24,040 --> 00:48:27,040
Speaker 1: entry level engineer, like AI is probably going to replace you,

1127
00:48:27,480 --> 00:48:28,399
Speaker 1: but that.

1128
00:48:28,440 --> 00:48:29,479
Speaker 2: Means just get better.

1129
00:48:30,080 --> 00:48:33,080
Speaker 1: And like the ones that learn how to supplement their

1130
00:48:33,160 --> 00:48:34,920
Speaker 1: job with AI are the ones that are going to

1131
00:48:34,960 --> 00:48:36,160
Speaker 1: be extremely successful.

1132
00:48:36,400 --> 00:48:38,759
Speaker 3: And you know, even going back to like when you

1133
00:48:38,800 --> 00:48:42,680
Speaker 3: were at hackathons and learning how to build like iOS apps,

1134
00:48:42,719 --> 00:48:44,880
Speaker 3: you know, now we were my husband and I were

1135
00:48:44,880 --> 00:48:47,600
Speaker 3: playing around with Claude the other day and I think

1136
00:48:47,600 --> 00:48:49,319
Speaker 3: it was Claude. I forget what platform we were on,

1137
00:48:49,440 --> 00:48:51,960
Speaker 3: but we so quickly were able to just build an app.

1138
00:48:52,360 --> 00:48:55,200
Speaker 1: Yeah, you probably ruple it, but you could so quickly

1139
00:48:55,200 --> 00:48:57,200
Speaker 1: build an app nowadays, like you just prompt it and

1140
00:48:57,239 --> 00:48:59,640
Speaker 1: it'll create an MVP, which has made it to that

1141
00:48:59,680 --> 00:49:02,560
Speaker 1: I think before you really needed like an engineer to

1142
00:49:02,560 --> 00:49:06,000
Speaker 1: build a company, and now or like to even like

1143
00:49:06,200 --> 00:49:10,080
Speaker 1: fundraise for a company, right because investors don't like investing

1144
00:49:10,160 --> 00:49:13,239
Speaker 1: in projects that don't have an MVP. Like even my

1145
00:49:13,440 --> 00:49:15,719
Speaker 1: hy thesis as an investor was I only invest in

1146
00:49:15,760 --> 00:49:18,799
Speaker 1: the best engineers because they can continue building until they

1147
00:49:18,800 --> 00:49:22,560
Speaker 1: get customers, so their runway just extends a lot longer

1148
00:49:22,600 --> 00:49:24,440
Speaker 1: than anyone else, because like if you need to hire

1149
00:49:24,440 --> 00:49:27,520
Speaker 1: a team, like your runway just automatically shortens. But with

1150
00:49:27,640 --> 00:49:29,360
Speaker 1: all these new AI tools, like I think you can

1151
00:49:29,440 --> 00:49:33,640
Speaker 1: get to an MVP and get customers without knowing any

1152
00:49:33,680 --> 00:49:37,560
Speaker 1: engineering honestly, and then to scale that's when you do

1153
00:49:37,600 --> 00:49:39,239
Speaker 1: need to hire good engineers, but you can now do

1154
00:49:39,280 --> 00:49:41,680
Speaker 1: that later on. So it's really democratized, I think even

1155
00:49:41,719 --> 00:49:42,960
Speaker 1: just start up access for people.

1156
00:49:43,239 --> 00:49:45,480
Speaker 3: Right, So, you and your friends brought the forefront of

1157
00:49:45,600 --> 00:49:47,680
Speaker 3: what a lot of people are calling the AI revolution.

1158
00:49:48,200 --> 00:49:51,040
Speaker 3: So I'm curious, like what do you guys argue about

1159
00:49:51,080 --> 00:49:53,319
Speaker 3: as it relates to AI, Like where do some people

1160
00:49:53,400 --> 00:49:55,360
Speaker 3: see it going? Where do other people see it not?

1161
00:49:55,800 --> 00:49:57,520
Speaker 1: I would say it's probably like fifty to fifty on

1162
00:49:57,760 --> 00:50:01,799
Speaker 1: AGI happening and like soon, and I just don't think

1163
00:50:01,840 --> 00:50:03,160
Speaker 1: we're even close to getting there.

1164
00:50:04,360 --> 00:50:06,480
Speaker 2: So that's my take on it.

1165
00:50:06,719 --> 00:50:08,440
Speaker 1: I think that a lot of people that are, you know,

1166
00:50:08,719 --> 00:50:10,680
Speaker 1: more positive it's going to happen next five to ten

1167
00:50:10,760 --> 00:50:14,600
Speaker 1: years are very incentivized to say that because they own

1168
00:50:14,680 --> 00:50:15,920
Speaker 1: AI companies.

1169
00:50:15,960 --> 00:50:17,880
Speaker 3: And break that down for us, what is AGI?

1170
00:50:20,200 --> 00:50:23,920
Speaker 2: I would most simply break it down on like human intelligence.

1171
00:50:24,400 --> 00:50:27,799
Speaker 3: Yeah, and so like unpacked that statement though, like literally

1172
00:50:27,880 --> 00:50:29,359
Speaker 3: explain to me what you just said as if I'm

1173
00:50:29,400 --> 00:50:31,919
Speaker 3: like a five like a fifth grader, because it's it's

1174
00:50:31,960 --> 00:50:32,960
Speaker 3: over my head a little bit.

1175
00:50:35,160 --> 00:50:41,040
Speaker 1: So right now, like obviously lms are not human intelligence.

1176
00:50:41,120 --> 00:50:44,360
Speaker 1: It's like you can like it takes data and it

1177
00:50:44,400 --> 00:50:47,120
Speaker 1: summarizes it and it kind of predicts the next word, right,

1178
00:50:47,880 --> 00:50:50,440
Speaker 1: whereas is human intelligence is like it thinks like a human.

1179
00:50:51,840 --> 00:50:55,080
Speaker 1: There's like, you know, the Turing test, where like can

1180
00:50:55,200 --> 00:51:01,239
Speaker 1: you tell if it's AI or a human? And I

1181
00:51:01,400 --> 00:51:05,239
Speaker 1: just like, right now, AI doesn't even like if you

1182
00:51:05,840 --> 00:51:08,960
Speaker 1: were to trying to think of a good examp, sorry,

1183
00:51:09,200 --> 00:51:12,920
Speaker 1: like if you were to give AI like like it

1184
00:51:12,960 --> 00:51:15,480
Speaker 1: can't really tell like what's important what's not unless you

1185
00:51:15,520 --> 00:51:17,800
Speaker 1: like kind of label like this is important, it's not important,

1186
00:51:17,800 --> 00:51:20,960
Speaker 1: et cetera. So it doesn't really have reasoning. It doesn't

1187
00:51:21,000 --> 00:51:23,400
Speaker 1: really have memory. Like there is like a certain amount

1188
00:51:23,440 --> 00:51:26,560
Speaker 1: of memory that AI can have, but it doesn't know

1189
00:51:26,600 --> 00:51:29,080
Speaker 1: what to keep and what not to keep. There's like

1190
00:51:29,400 --> 00:51:31,680
Speaker 1: labeling that's kind of helping with that. Like, for example,

1191
00:51:31,719 --> 00:51:34,000
Speaker 1: it could be like, oh, this is trauma related, we

1192
00:51:34,040 --> 00:51:36,920
Speaker 1: should probably remember this person that like went through trauma.

1193
00:51:37,360 --> 00:51:43,000
Speaker 2: But I just don't think we're getting there anytime.

1194
00:51:42,680 --> 00:51:44,960
Speaker 3: Soon, okay, because I feel like the crazy thing with

1195
00:51:45,000 --> 00:51:48,279
Speaker 3: AI too is even like eight months ago, we were

1196
00:51:48,320 --> 00:51:52,120
Speaker 3: talking about how everybody was like, oh, you can become

1197
00:51:52,320 --> 00:51:54,440
Speaker 3: a Oh my gosh, what it's the term. I'm like

1198
00:51:54,480 --> 00:51:57,560
Speaker 3: Havin like tip of the tongue syndrome. Prompt engineering was

1199
00:51:57,600 --> 00:51:59,359
Speaker 3: like a big thing, right, people were talking about eight

1200
00:51:59,360 --> 00:52:01,440
Speaker 3: months ago, and now I feel like prompt engineering is

1201
00:52:01,440 --> 00:52:04,240
Speaker 3: still definitely a thing in like an industry that's start

1202
00:52:04,360 --> 00:52:06,319
Speaker 3: like becoming a thing, but maybe even less so just

1203
00:52:06,320 --> 00:52:08,800
Speaker 3: because of how much AI is adopting and growing, right,

1204
00:52:08,960 --> 00:52:10,719
Speaker 3: So it's like it's it's My point is that it's

1205
00:52:10,760 --> 00:52:14,560
Speaker 3: like it's changing so quickly and so rapidly, but like,

1206
00:52:14,800 --> 00:52:17,400
Speaker 3: when when does that evolution stop.

1207
00:52:18,280 --> 00:52:20,200
Speaker 2: I don't actually know when the evolution will stop.

1208
00:52:20,760 --> 00:52:24,399
Speaker 3: Yeah, Like I guess it is when the what you're

1209
00:52:24,480 --> 00:52:27,000
Speaker 3: talking about kind of comes into realitymore.

1210
00:52:27,040 --> 00:52:29,280
Speaker 2: I don't know, yeah, but I think that we're pretty

1211
00:52:29,320 --> 00:52:29,920
Speaker 2: far off from that.

1212
00:52:30,120 --> 00:52:32,920
Speaker 3: So have you looked into the SaaS apocalypse at all?

1213
00:52:33,960 --> 00:52:35,640
Speaker 2: Oh, you mean with all the SaaS stocks down?

1214
00:52:35,840 --> 00:52:36,120
Speaker 3: M hm.

1215
00:52:36,680 --> 00:52:40,319
Speaker 1: I mean I know that Clawbot caused that I had

1216
00:52:40,360 --> 00:52:45,239
Speaker 1: some stocks affected, but overall, I mean I think that

1217
00:52:45,320 --> 00:52:48,600
Speaker 1: things will improve. I think there's like a fear right

1218
00:52:48,600 --> 00:52:51,239
Speaker 1: people are like, oh my god, like this AI is

1219
00:52:51,280 --> 00:52:54,880
Speaker 1: gonna take over software stocks because it's just gonna be

1220
00:52:54,920 --> 00:52:57,040
Speaker 1: able to do everything by the end. They like all

1221
00:52:57,080 --> 00:53:00,200
Speaker 1: these companies are very specialized in what they do, and

1222
00:53:00,239 --> 00:53:03,080
Speaker 1: they probably have proprietary data that make it so that

1223
00:53:03,360 --> 00:53:05,680
Speaker 1: another AI bot can't do it as well as if

1224
00:53:05,719 --> 00:53:08,480
Speaker 1: they applied AI to it, because like, your model is

1225
00:53:08,520 --> 00:53:10,359
Speaker 1: only as good as a data that you provide it,

1226
00:53:11,040 --> 00:53:14,359
Speaker 1: and AI is just going to make every company more

1227
00:53:14,400 --> 00:53:18,160
Speaker 1: optimized because every single person can be a ten x employee,

1228
00:53:18,200 --> 00:53:20,120
Speaker 1: which then means that, like, you know, your margin's are

1229
00:53:20,160 --> 00:53:22,000
Speaker 1: going to be a higher and profit's going to be higher.

1230
00:53:22,239 --> 00:53:24,040
Speaker 1: So I hope we met, Like you know, all the

1231
00:53:24,040 --> 00:53:27,759
Speaker 1: SaaS stocks go back up. I've seen some rebounds, but yeah,

1232
00:53:27,800 --> 00:53:30,000
Speaker 1: I haven't really checked all my money's invest in hardware now.

1233
00:53:31,560 --> 00:53:31,759
Speaker 2: Yeah.

1234
00:53:31,920 --> 00:53:34,480
Speaker 3: No, it is crazy, and it didn't it. I mean,

1235
00:53:34,640 --> 00:53:37,320
Speaker 3: it was like this narrative sweeping Wall Street this past week.

1236
00:53:37,840 --> 00:53:39,239
Speaker 3: It was like all over the news.

1237
00:53:39,160 --> 00:53:41,680
Speaker 1: Yep, yep, yeah, and now I was down quite a lot.

1238
00:53:41,680 --> 00:53:43,000
Speaker 1: I had a heart attack when I locked and I

1239
00:53:43,040 --> 00:53:44,880
Speaker 1: was like, okay, nope, hardware only now.

1240
00:53:45,200 --> 00:53:48,240
Speaker 3: Also, I read another headline that said that what's happening

1241
00:53:48,280 --> 00:53:50,720
Speaker 3: right now with AI is the biggest thing that's happened

1242
00:53:50,760 --> 00:53:53,360
Speaker 3: since the Industrial Revolution. What is your take on that.

1243
00:53:54,680 --> 00:53:57,040
Speaker 1: I think it's bigger than Industrial Revolution. I think it's

1244
00:53:57,080 --> 00:53:59,480
Speaker 1: going to touch everything that we do. And because AI

1245
00:53:59,719 --> 00:54:05,080
Speaker 1: does scale, I would say faster and better, we're going

1246
00:54:05,160 --> 00:54:08,560
Speaker 1: to be seeing changes more rapidly than Industrial Revolution.

1247
00:54:09,000 --> 00:54:11,960
Speaker 3: You know, your company now is called Passes, and I

1248
00:54:12,000 --> 00:54:13,239
Speaker 3: want you to kind of touch on a little bit

1249
00:54:13,280 --> 00:54:17,880
Speaker 3: about what you guys do. How is AI affecting your business?

1250
00:54:18,640 --> 00:54:21,600
Speaker 1: Yeah, so we're in a creator economy. We've created infrastructure

1251
00:54:21,600 --> 00:54:24,800
Speaker 1: to have creators monetize their brand through different revenue streams,

1252
00:54:24,800 --> 00:54:28,439
Speaker 1: whether that's merchandise, live streaming, one on one call, subscription.

1253
00:54:28,000 --> 00:54:28,480
Speaker 2: Et cetera.

1254
00:54:29,640 --> 00:54:31,640
Speaker 1: Right now, I would say AI is helping us and

1255
00:54:31,680 --> 00:54:35,719
Speaker 1: that it's like really helping creators workflows. So we, for example,

1256
00:54:36,360 --> 00:54:39,919
Speaker 1: have a smart pricing model where we help automatically price

1257
00:54:40,000 --> 00:54:43,080
Speaker 1: content for creators so they can drive more revenue. When

1258
00:54:43,120 --> 00:54:45,680
Speaker 1: this is turned on, creators actually make an on average

1259
00:54:45,719 --> 00:54:47,360
Speaker 1: three x more than they would if they were like

1260
00:54:47,400 --> 00:54:49,160
Speaker 1: to guess the price of how much like fans would

1261
00:54:49,160 --> 00:54:52,359
Speaker 1: pay for certain things. So that's just one example we're

1262
00:54:52,360 --> 00:54:55,400
Speaker 1: working on, like agentic tools. So I think the dream

1263
00:54:55,400 --> 00:54:57,120
Speaker 1: world for a lot of creators is that all they

1264
00:54:57,120 --> 00:54:59,560
Speaker 1: have to do is create content and AI helps run

1265
00:54:59,560 --> 00:55:00,600
Speaker 1: their business for them.

1266
00:55:01,120 --> 00:55:03,640
Speaker 2: So that's kind of like where our vision is there?

1267
00:55:04,320 --> 00:55:06,600
Speaker 3: Why the interest in the creator economy? Like how did

1268
00:55:06,600 --> 00:55:07,880
Speaker 3: you start tabbling in it?

1269
00:55:08,320 --> 00:55:08,600
Speaker 2: Yeah?

1270
00:55:08,640 --> 00:55:10,560
Speaker 1: I think I became friends with a lot of creators

1271
00:55:10,560 --> 00:55:13,799
Speaker 1: over COVID, and I realized that creators are entrepreneurs. And

1272
00:55:13,800 --> 00:55:16,480
Speaker 1: what's interesting about creators is that, like their fans are

1273
00:55:16,520 --> 00:55:20,240
Speaker 1: like rabbit fans, they don't have the customer acquisition costs

1274
00:55:20,239 --> 00:55:22,960
Speaker 1: that like I would have if I were launching a product,

1275
00:55:22,960 --> 00:55:24,879
Speaker 1: like if they just put it on people buy it, right.

1276
00:55:25,719 --> 00:55:28,879
Speaker 1: So I think that like they really are like these

1277
00:55:29,920 --> 00:55:34,279
Speaker 1: cmos that cost nothing almost. So I was just like, okay, wow,

1278
00:55:34,320 --> 00:55:37,279
Speaker 1: like you guys are not thinking about yourself as a

1279
00:55:37,280 --> 00:55:40,359
Speaker 1: business as much as you should be. I think that

1280
00:55:40,440 --> 00:55:42,719
Speaker 1: the entertainment industry in general, with like how agents and

1281
00:55:42,760 --> 00:55:45,880
Speaker 1: managers make money, don't incentivize that either, because you know,

1282
00:55:45,920 --> 00:55:48,279
Speaker 1: it's usually like a twenty thirty percent take on like

1283
00:55:48,360 --> 00:55:50,359
Speaker 1: you know brand neals for example, But if a brand

1284
00:55:50,440 --> 00:55:53,360
Speaker 1: gave you equity, like there's not a contract to get equity,

1285
00:55:53,640 --> 00:55:56,560
Speaker 1: And these agents and managers don't really understand equity as well,

1286
00:55:56,800 --> 00:55:59,200
Speaker 1: so they're losing out on like this long term wealth generation,

1287
00:55:59,400 --> 00:56:02,759
Speaker 1: but like they should owning equity in companies that they're promoting,

1288
00:56:03,040 --> 00:56:05,520
Speaker 1: they're putting on themselves, and like they're viewing entire marketing

1289
00:56:05,640 --> 00:56:07,759
Speaker 1: for a brand and blow up. So like around that time,

1290
00:56:07,800 --> 00:56:10,279
Speaker 1: you know, like Logan Poul had Prime, Jake Paul with

1291
00:56:10,400 --> 00:56:14,839
Speaker 1: Like Better, and then a Kylie with her Lipstick, et cetera.

1292
00:56:14,920 --> 00:56:16,440
Speaker 1: Like I saw all these brands pop up, and I

1293
00:56:16,440 --> 00:56:19,360
Speaker 1: was like, creators really, like there's an opportunity for creators

1294
00:56:19,440 --> 00:56:22,520
Speaker 1: to like build these large brands. And we've seen, like

1295
00:56:22,560 --> 00:56:25,120
Speaker 1: you know, we we're literally seen unicorn companies be built,

1296
00:56:25,320 --> 00:56:27,960
Speaker 1: but not all creators like need unicorn companies. Like I

1297
00:56:28,000 --> 00:56:30,360
Speaker 1: have friends who you know, have undred million followers, but

1298
00:56:30,360 --> 00:56:33,040
Speaker 1: they make millions a year off of like hoodies.

1299
00:56:33,200 --> 00:56:35,319
Speaker 3: And I think that is an interesting point to make

1300
00:56:35,400 --> 00:56:38,280
Speaker 3: because like obviously Logan Paul's, Jake Poul's of the World,

1301
00:56:38,360 --> 00:56:41,319
Speaker 3: Kylie Jenner, like those are such extreme examples of like

1302
00:56:41,360 --> 00:56:45,160
Speaker 3: the one percent of like celebrity creator. But then yeah,

1303
00:56:45,200 --> 00:56:48,720
Speaker 3: there's people like I. There's even random examples that somebody

1304
00:56:48,760 --> 00:56:50,520
Speaker 3: has given me in the past where it's like an

1305
00:56:50,520 --> 00:56:53,879
Speaker 3: agricultural creator that created like a seed company and it's

1306
00:56:53,920 --> 00:56:56,640
Speaker 3: so niche, but it creates so much success for their

1307
00:56:56,719 --> 00:56:59,080
Speaker 3: like distinct audience exactly, Like not everyone.

1308
00:56:58,719 --> 00:57:01,040
Speaker 1: Needs billions of dollars, but like with like under a

1309
00:57:01,040 --> 00:57:02,360
Speaker 1: million followers, like you can.

1310
00:57:02,320 --> 00:57:04,399
Speaker 2: Make millions a year, and that is life changing money

1311
00:57:04,440 --> 00:57:05,759
Speaker 2: for a lot of people. Absolutely.

1312
00:57:06,360 --> 00:57:08,279
Speaker 3: The thing that I find so interesting about you is

1313
00:57:08,560 --> 00:57:11,400
Speaker 3: you have always been entrepreneurial. You've gone from building your

1314
00:57:11,440 --> 00:57:16,680
Speaker 3: own businesses to scale AI to now passes. So what

1315
00:57:16,840 --> 00:57:19,800
Speaker 3: is it that makes you want to keep your skin

1316
00:57:19,840 --> 00:57:21,840
Speaker 3: in the game when it comes to building businesses? Because

1317
00:57:21,840 --> 00:57:23,240
Speaker 3: I feel like a lot of people in your situation

1318
00:57:23,280 --> 00:57:24,840
Speaker 3: would be like, I'm just gonna sit back and do

1319
00:57:24,920 --> 00:57:27,200
Speaker 3: my thing, and I have enough money now to you know,

1320
00:57:27,200 --> 00:57:28,840
Speaker 3: get me through the rest of my life comfortably.

1321
00:57:29,480 --> 00:57:32,600
Speaker 1: Yeah, I mean I think it's uh, well, hey, my brain,

1322
00:57:32,800 --> 00:57:34,840
Speaker 1: I get very very bored, so I always need to

1323
00:57:34,880 --> 00:57:38,240
Speaker 1: be doing something. I'd actually tried to retire earlier. I

1324
00:57:38,280 --> 00:57:41,720
Speaker 1: traveled around the world, et cetera. But I just got

1325
00:57:42,240 --> 00:57:43,960
Speaker 1: like I felt like my brain wasn't being used. Like

1326
00:57:43,960 --> 00:57:45,960
Speaker 1: I was happy for sure, like meeting all these new people,

1327
00:57:45,960 --> 00:57:48,520
Speaker 1: having all these experiences, but I'm someone that like likes

1328
00:57:48,560 --> 00:57:51,840
Speaker 1: solving problems and it's the best way to use my brain,

1329
00:57:51,920 --> 00:57:54,680
Speaker 1: I think, and I think I have this passion for

1330
00:57:54,800 --> 00:57:57,480
Speaker 1: like having people like be entrepreneurs. Like I even went

1331
00:57:57,520 --> 00:57:59,400
Speaker 1: the Microsoft and did a whole talk, and like my

1332
00:57:59,440 --> 00:58:01,640
Speaker 1: talk was like Microsoft, go be an entrepreneur.

1333
00:58:03,000 --> 00:58:04,360
Speaker 3: Microsoft is like get her out of here.

1334
00:58:05,560 --> 00:58:07,240
Speaker 1: Yeah, And it's been really nice, like having a lot

1335
00:58:07,240 --> 00:58:08,760
Speaker 1: of people come up to me, like you inspired me

1336
00:58:08,760 --> 00:58:11,160
Speaker 1: being an entrepreneur, and like it's changed my life. So

1337
00:58:11,200 --> 00:58:13,960
Speaker 1: that's something that I really enjoy and I think that,

1338
00:58:14,000 --> 00:58:17,480
Speaker 1: like that's part of the reason why I continue just building.

1339
00:58:18,040 --> 00:58:19,760
Speaker 2: I'd be very bored sitting alone.

1340
00:58:19,920 --> 00:58:22,240
Speaker 1: Like if you're able to like have fun and make

1341
00:58:22,320 --> 00:58:24,200
Speaker 1: impact in people's lives, like why not?

1342
00:58:24,600 --> 00:58:28,720
Speaker 3: Yeah, I mean I agree, And yeah, what is uh

1343
00:58:28,800 --> 00:58:30,520
Speaker 3: if you could go back and tell your you know,

1344
00:58:31,640 --> 00:58:33,919
Speaker 3: fourteen year old self, twenty year old self one piece

1345
00:58:33,920 --> 00:58:35,120
Speaker 3: of advice, But what would it.

1346
00:58:35,040 --> 00:58:36,960
Speaker 1: Be If I could tell my fourteen or twenty year

1347
00:58:37,000 --> 00:58:38,880
Speaker 1: old stuff one piece of advice, it would probably be

1348
00:58:39,120 --> 00:58:42,280
Speaker 1: to like make friends with more people. Again, like your

1349
00:58:42,320 --> 00:58:45,640
Speaker 1: network is your net worth. And even though I made

1350
00:58:45,720 --> 00:58:49,840
Speaker 1: amazing friends in like school and ended up hiring them,

1351
00:58:50,240 --> 00:58:52,720
Speaker 1: I do think I should have stayed in touch with

1352
00:58:52,760 --> 00:58:54,760
Speaker 1: them more. But like I could have expanded my network

1353
00:58:54,840 --> 00:58:59,160
Speaker 1: much larger than I did. And that's a regret I have.

1354
00:58:59,480 --> 00:59:01,600
Speaker 3: Yeah, I get that, But I also do think there's

1355
00:59:01,640 --> 00:59:04,680
Speaker 3: benefits to when you're in high school laying a little

1356
00:59:04,680 --> 00:59:07,080
Speaker 3: bit more low, you know, and doing your thing and

1357
00:59:07,120 --> 00:59:09,240
Speaker 3: focusing on your interest, because I feel like people can

1358
00:59:09,280 --> 00:59:10,920
Speaker 3: get up to the wrong things in high school. So

1359
00:59:10,920 --> 00:59:11,320
Speaker 3: it's cool.

1360
00:59:11,360 --> 00:59:11,760
Speaker 2: I agree.

1361
00:59:11,760 --> 00:59:13,200
Speaker 1: Like, I think that's probably more of a college thing,

1362
00:59:13,880 --> 00:59:16,200
Speaker 1: like you know, staying in touch with the hackathon community,

1363
00:59:16,240 --> 00:59:17,840
Speaker 1: et cetera, because I think at that point, I like

1364
00:59:17,840 --> 00:59:21,440
Speaker 1: stop going hackathons at twenty years old, but I wish

1365
00:59:21,480 --> 00:59:24,320
Speaker 1: I didn't stop, and I continue, you know, I'm still involved,

1366
00:59:24,360 --> 00:59:27,200
Speaker 1: Like I will go judge for example, actually.

1367
00:59:27,200 --> 00:59:28,720
Speaker 2: Ask to judge a lot of hackathons. I did start

1368
00:59:28,720 --> 00:59:30,200
Speaker 2: a hackathon I guess over COVID.

1369
00:59:30,520 --> 00:59:35,120
Speaker 1: But like, I like, like if I continue going, like

1370
00:59:35,280 --> 00:59:37,680
Speaker 1: you really want to be at the pulse of young people,

1371
00:59:38,760 --> 00:59:42,360
Speaker 1: and it would be nice to like still be there.

1372
00:59:43,480 --> 00:59:45,360
Speaker 3: It's cool to know that hackathons are still going on.

1373
00:59:45,520 --> 00:59:46,320
Speaker 2: They are still going on.

1374
00:59:46,400 --> 00:59:47,960
Speaker 3: Yeah, I kind of want to check one out and

1375
00:59:48,000 --> 00:59:49,360
Speaker 3: just to see what these kids are coming up with.

1376
00:59:49,440 --> 00:59:51,360
Speaker 1: Yeah, I mean I think like the strongest one here

1377
00:59:51,440 --> 00:59:53,240
Speaker 1: is probably like, uh la hacks.

1378
00:59:53,280 --> 00:59:54,800
Speaker 2: I think that's by UCLA.

1379
00:59:54,680 --> 00:59:59,240
Speaker 1: And in cow Hacks because UCLA and Caltech are great

1380
00:59:59,240 --> 01:00:02,840
Speaker 1: colleges and they get people from both those universities.

1381
01:00:03,160 --> 01:00:04,800
Speaker 3: Yeah, oh I left that us.

1382
01:00:05,000 --> 01:00:08,160
Speaker 2: Sorry, Yessie, They're great too well.

1383
01:00:08,280 --> 01:00:09,919
Speaker 3: Lucy, thank you so much for being with us today

1384
01:00:09,920 --> 01:00:11,720
Speaker 3: on post Rien. Hi. Thank you for running with us

1385
01:00:11,720 --> 01:00:12,720
Speaker 3: and then sitting down to chat.

1386
01:00:12,760 --> 01:00:14,400
Speaker 2: Yeah, of course, thank you for having me. Thank you.

1387
01:00:19,040 --> 01:00:22,240
Speaker 3: Hi. Guys, Kate here, thank you so much for tuning in.

1388
01:00:22,320 --> 01:00:23,640
Speaker 3: If you made it all the way to the end

1389
01:00:23,640 --> 01:00:26,880
Speaker 3: of this conversation, you are a real one. Your support

1390
01:00:26,960 --> 01:00:30,800
Speaker 3: truly helps us continue bringing you inspiring conversations. So if

1391
01:00:30,800 --> 01:00:33,360
Speaker 3: you learn something new from this episode, please share it

1392
01:00:33,400 --> 01:00:35,360
Speaker 3: with a friend and make sure you're following post friend

1393
01:00:35,400 --> 01:00:38,600
Speaker 3: high wherever you're listening. We're posting every Monday, so I

1394
01:00:38,640 --> 01:00:40,160
Speaker 3: will see you guys next week.