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Speaker 1: Welcome to the Seize the Yay Podcast. Busy and happy

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Speaker 1: are not the same thing. We too rarely question what

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Speaker 1: makes the heart seeing. We work, then we rest, but

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Speaker 1: rarely we play and often don't realize there's more than

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Speaker 1: one way. So this is a platform to hear and

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Speaker 1: explore the stories of those who found lives they adore,

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Speaker 1: the good, bad and ugly, the best and worst day

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Speaker 1: will bear all the facets of Seizing your Yea. I'm

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Speaker 1: Sarah Davidson or a spoonful of Sarah, a lawyer turned

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Speaker 1: unentrepreneur who swapped the suits and heels to co found

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Speaker 1: matcha Maiden and matcha Milk. Bark Sees the Ya is

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Speaker 1: a series of conversations on finding a life you love

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Speaker 1: and exploring the self doubt, challenge, joy and fulfillment along

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Speaker 1: the way. Lovely yighborhood as promised, here is part two

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Speaker 1: of what could easily have snowballed into an eight hundred

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Speaker 1: and seventy five part series with my dear friend, doctor

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Speaker 1: Matt Agnew. I'm sure you guys can hear now why

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Speaker 1: we get along so well. He's just such an easy

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Speaker 1: conversationalist and we always go off on the most random

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Speaker 1: but enjoyable tangents. Even still with two parts, there is

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Speaker 1: so much we didn't get to about this fascinating human being,

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Speaker 1: and that's all without really touching on The Bachelor at all.

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Speaker 1: So I do hope that you have enjoyed getting to

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Speaker 1: know a different side of him for his proper intro

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Speaker 1: and bio. Go back to the first part. We pick

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Speaker 1: up in this episode right where we left off, which,

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Speaker 1: as it turns out, wasn't very far into his story

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Speaker 1: at all or his WATA. I think we made it

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Speaker 1: to the tender age of twelve in the timeline, and

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Speaker 1: there's still so much more ahead, so who knows. Part

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Speaker 1: three could still come at you in the future, But

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Speaker 1: in the meantime, I do hope you guys enjoy this

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Speaker 1: part two. Matthew Terrence agnew and because I'm just distinguishing

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Speaker 1: it from matt from The Bachelor, I feel like there's

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Speaker 1: to be a distinguisher, and that's Matthew Terrence, and that's

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Speaker 1: Matthew Terrence because I feel like that's a little lesser

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Speaker 1: known fact.

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Speaker 2: Anyway, my middle name or name, yeah, it is Lesson

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Speaker 2: and it's also spelt I think no, you're a singlar

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Speaker 2: and an E because I think the standard spelling of

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Speaker 2: Terrence I think is T A n C. Mine is

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Speaker 2: T E R E n C.

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Speaker 1: Yes, okay, which is yeah.

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Speaker 2: I'm not sure why my folks opted for that, but

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Speaker 2: they just fun yeah, And I feel my parents told

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Speaker 2: me this, and they deny this story to me now,

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Speaker 2: but they said that they gave me and my older brother.

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Speaker 2: So my eldest brother and my eldest I've only got

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Speaker 2: one sister, but they got the standard kind of like grandparents' names.

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Speaker 2: My oldest brother got Alan Thomas, and my sister got

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Speaker 2: Flora Charlotte's beautiful. Yeah. So me and then me and

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Speaker 2: my other brother was like, okay, well, what are we

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Speaker 2: giving them for their middle names?

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Speaker 1: You're just chucking to misspelled Terrence were miss spelled Terrence.

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Speaker 2: Yeah. So basically we got variations of my dad's name,

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Speaker 2: which is Terry. So I got Terrence and my brother

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Speaker 2: got Edward because there was at the time some of

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Speaker 2: the rallies called him.

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Speaker 1: Ted Oh my god, I know that was born. I

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Speaker 1: don't know. He's long yes on his passport.

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Speaker 2: Why did I think it was just I.

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Speaker 1: Think because we've always said Teddy, yeah, but Edwards his

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Speaker 1: passport names. Okay, there you go.

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Speaker 2: So we got Yeah, so Mark Edward and Matthew Terrence

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Speaker 2: and my parents I swear they told me this was

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Speaker 2: They wanted to give us the sort of regal sounding

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Speaker 2: name for when we eventually get knighted.

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Speaker 1: Nice. I love that. That is why.

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Speaker 2: Sir Matthew hasn't got the same ring as Sir Matthew Terrence,

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Speaker 2: just like Sir Mark or Sir Mark Edward's true. They

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Speaker 2: deny this story, and so now I'm second guessing I've

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Speaker 2: just made this. I don't know now because I'm like, hey, on,

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Speaker 2: have my parents they've told me this completely made up,

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Speaker 2: fabricated story and now are denying it, or have I

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Speaker 2: made this up in my head? I'm like, yeah, they

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Speaker 2: thought we're going to get knighted one day.

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Speaker 1: Are you gaslighting yourself? Gaslighting?

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Speaker 2: You don't know? The former?

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Speaker 1: Probably basically story though.

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Speaker 2: Yeah. So that's yeah, that's the origin of Terrence.

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Speaker 1: I mean, when you do go to space and get knighted, inevitable,

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Speaker 1: you'll laugh when you're like, I am Sir Matthew Terrence,

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Speaker 1: agne the astronaut, the astronaut direct, Yeah, and then you

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Speaker 1: can be so doctor actually, oh my God, what comes

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Speaker 1: first the hierarchy of titles? Is it doctor, sir? Or

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Speaker 1: would you just drop doctor drop.

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Speaker 2: Because you can do doctor or you can book PhD.

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Speaker 2: At the end new PhD. I slapped that out of

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Speaker 2: the end, slap sir at the start.

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Speaker 1: So you've got it's like bookmarks. Yeah, that is a

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Speaker 1: good bookmark your booking.

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Speaker 2: Yeah, you know what, Matthew Terrence agnew PhD.

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Speaker 1: That's nice. That's nice. And then you'd add like an

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Speaker 1: O I m at the end when you get your

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Speaker 1: Order of Australian Medal.

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Speaker 2: Correct. Yeah, So I've got a few things I need

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Speaker 2: to achieve from here, yeah, and not a ton of

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Speaker 2: time to do it.

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Speaker 1: Well. I'm like I started so strong on the first ever,

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Speaker 1: so on part one, being like I want to introduce

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Speaker 1: that not from a bachelora and blaha, warn't show who

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Speaker 1: you are, and then I made it to like, you know,

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Speaker 1: the age of twelve. I think that's as far as

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Speaker 1: I got.

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Speaker 2: So, yeah, I can't remember how deep we got because

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Speaker 2: you've had a number of these conversations offline.

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Speaker 1: I think maybe that's why. Because I was like, where

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Speaker 1: are we up to? And I just and then tangents

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Speaker 1: and it really is when you want to showcase every guest,

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Speaker 1: I want to showcase the best side of them that

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Speaker 1: I can. But when it's your friend, you know so

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Speaker 1: many more sides than you do about the average person

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Speaker 1: that then every tangent, Like normally I would like go

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Speaker 1: of a tangent and go that's not the best one.

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Speaker 1: Let's like focus on the one. Whereas with a friend

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Speaker 1: you're like, well, I need to pick up on that

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Speaker 1: because I know something about it. It's like you've done

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Speaker 1: too much research, Like I'm too well researched on Sir Matthew,

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Speaker 1: transact me pha I am. It's like a really different composition.

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Speaker 2: But you're saying it's like you find doing interviewing friends harder.

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Speaker 2: It's a challenge for you.

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Speaker 1: It's more because you put more pressure on yourself because

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Speaker 1: you know them better. So it's like your passion to

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Speaker 1: convey something is greater than like you always want to

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Speaker 1: convey someone in the best way you can, but when

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Speaker 1: you're invested in the person as well, you're like, I

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Speaker 1: want to show everything about this person that other people

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Speaker 1: might not know. And then yeah, halfway through you're like, wow,

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Speaker 1: I made it to like dot point one of four

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Speaker 1: hundred and twenty seven and like we're not even all

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Speaker 1: the way through your life yet, you know what I mean,

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Speaker 1: Like Jesus, we haven't even made it to the knighthood yet. No,

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Speaker 1: that was really sweat.

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Speaker 2: It was really sweet, because sweet and misplaced when you

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Speaker 2: were apologizing after our last recording because you felt you

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Speaker 2: didn't get deeper and it was Yeah, as I said,

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Speaker 2: it was a misplaced apology. It was no apology necessarily. Yeah,

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Speaker 2: it was really sweet to see this such a high

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Speaker 2: level of quality you hold yourself too, That's what it

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Speaker 2: was flattered that you wanted to have a second art

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Speaker 2: as well because it was so much fun last time. Yeah,

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Speaker 2: so yes, no, thank you bring back.

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Speaker 1: I really listened and I did enjoy that. I think

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Speaker 1: maybe like decide I wanted of you maybe did come

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Speaker 1: out more or because we weren't so structured, like maybe

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Speaker 1: it was more like this is who matters when he's

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Speaker 1: just chatting with a friend.

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Speaker 2: Yeah, yeah, definitely. I think that's because you don't necessarily

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Speaker 2: always see that. Certainly with social media, I've been able

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Speaker 2: to allow a lot of my personality to MOI a lot.

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Speaker 1: More of my personality.

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Speaker 2: Fun. Yeah, yeah, I've been able to allow more of

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Speaker 2: my personality out in social media and just in general,

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Speaker 2: just people seeing me more naturally and less curated as

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Speaker 2: they were first introduced to me. But you still don't

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Speaker 2: necessarily see these the tangents. Yeah, how does his friendship opera? Yeah,

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Speaker 2: I think it's It's like you said that, the unstructuredness

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Speaker 2: of it was actually quite nice. Yeah, well, I.

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Speaker 1: Really enjoyed it. But then when I was actually going

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Speaker 1: back to the questions afterwards, I was like, I literally

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Speaker 1: started your way toa around like taking time off school

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Speaker 1: and I think it was primary school. I don't even

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Speaker 1: know if I got to high school. But then didn't

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Speaker 1: even cover like any of your jobs, both of your

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Speaker 1: books AI, which is like the biggest topic. You've literally

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Speaker 1: gone and restudied it your business crash. Like, I was like,

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Speaker 1: what did I cover anything? So let's go back, let's

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Speaker 1: pick up where we left off. There are so many

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Speaker 1: things that I wanted to talk to you about, but

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Speaker 1: I think I need to start with dating purely because

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Speaker 1: the DM I mean, I can't even imagine what your

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Speaker 1: dms are, Like my dms before the episode even came out,

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Speaker 1: just saying I recorded with you were bananas, But one

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Speaker 1: thing that we did kind of give a like allude

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Speaker 1: to a little bit last time was the fact that

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Speaker 1: we had both had a big period and you would

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Speaker 1: continue to be in this big period of quitting alcohol,

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Speaker 1: and that certainly we bonded over something over and past.

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Speaker 1: Your business has been sort of one of the beautiful

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Speaker 1: developments out of that. But to bring it back to dating, firstly,

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Speaker 1: a lot of people were like easy single, such an

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Speaker 1: eligible guy, blah blah blah. Like I was like, I

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Speaker 1: know this, Like what what do you want me to

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Speaker 1: do with this information? But we didn't talk about it,

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Speaker 1: not yesterday but in the last episode. So I have

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Speaker 1: a couple of questions about it. Is dating post bachelor

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Speaker 1: easier or harder? Post alcohol easier or harder? Because I

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Speaker 1: think that's a really interesting question and post mental health

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Speaker 1: diagnoses easier or harder? Big questions they are?

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Speaker 2: They are big, They're meaty.

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Speaker 1: They're meaty. Why did I start with this? We're still

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Speaker 1: not going to get to astrophys exactly are dating episode?

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Speaker 2: Let's see if I can do this without so many tangents.

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Speaker 2: Is it harder or easier post batch? It's hard to

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Speaker 2: kind of go one way or the other. There is

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Speaker 2: a huge change in it. I would say I probably

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Speaker 2: get more matches on dating apps, and I imagine in

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Speaker 2: large part that is due to the fact that I

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Speaker 2: was on a TV show and I'm a known person.

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Speaker 2: I don't think anything materially has changed other than that

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Speaker 2: in regards to the change in number of matches. There's

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Speaker 2: a challenge in that it's harder to discern is someone

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Speaker 2: swiping on me because they're interested to get to know me,

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Speaker 2: or the kind of keener date the year, or if

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Speaker 2: it's a novelty, or trying to understand what's their motivation,

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Speaker 2: and that's a little harder to figure out. Sometimes I

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Speaker 2: think I'm much better at it. That's kind of been

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Speaker 2: a bit of a challenge. Another challenge related to it

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Speaker 2: is there's a lot of articles out there of which

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Speaker 2: I have no control over, that are typically completely inaccurate,

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Speaker 2: not amazing and no amazing, and that sometimes has impacted dating.

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Speaker 2: So as an example, I went on four or five

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Speaker 2: dates with this woman. We're getting on really well, it

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Speaker 2: was all really going great, and we'll teeing up date six,

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Speaker 2: and then she basically canceled. It's out. I don't think

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Speaker 2: we should meet up. You have a very public dating life.

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Speaker 1: Yeah, Okay.

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Speaker 2: The reality is if you look at my Instagram, if

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Speaker 2: you look at any of the articles, I've never shared

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Speaker 2: details about who I've dated. There's no one on my Instagram.

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Speaker 2: There's daily mail articles where there's been these made up

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Speaker 2: stories about who I'm dating. And I think we've joked

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Speaker 2: about this basically whenever I get a photo with someone

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Speaker 2: with compatible genitals, obviously, yeah, obviously it's like they're dating. Yeah.

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Speaker 2: So those articles, if someone's read them and thinks, oh,

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Speaker 2: he's look at all these people he's dating, it's always

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Speaker 2: in the news. I mean, none of them are true.

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Speaker 2: And I've always been very private with my dating life

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Speaker 2: and with basically anyone in my life, really my friends

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Speaker 2: and family as well. I made a very significant decision

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Speaker 2: when I went on TV to concede a lot of

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Speaker 2: my privacy. No one else in my life made that decision,

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Speaker 2: and so I really am deeply respectful of the fact

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Speaker 2: that they are entitled to that privacy. And so if

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Speaker 2: you look through my Instagram, there's not loads of my

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Speaker 2: family there, and that's not because I'm ashamed of them

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Speaker 2: or they're ashamed of me, but they value their privacy

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Speaker 2: my friends, they're there in bits and pieces. Again, whenever

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Speaker 2: I post something, they always make sure they're comfortable with that,

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Speaker 2: and there's no nothing, there's no shreds of the people

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Speaker 2: I've dated on there, because I've always been very private

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Speaker 2: about my dating life. So that was a frustration because

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Speaker 2: it was, you know, she said I had a very

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Speaker 2: public dating life, whereas I have quite the opposite. No

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Speaker 2: one could tell team. You couldn't say who have I

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Speaker 2: dated other than the person I dated on The Bachelor?

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Speaker 2: Tell me who I've dated in the last five years,

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Speaker 2: and none would be able to say that other than

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Speaker 2: my friends that I know in person. So that was

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Speaker 2: a frustration that's kind of come about. So is it

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Speaker 2: harder or easier post Bachelor? It's a different challenge. It's

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Speaker 2: a different challenge, I think I certainly, and I know

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Speaker 2: for a lot of men on dating apps, the biggest

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Speaker 2: challenge is just getting a match. So I'm in a

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Speaker 2: luxurious position where I don't have that as the challenging aspect,

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Speaker 2: because that's really hard to adjust. If you're just not

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Speaker 2: getting matches, it's really hard to figure out. It's not

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Speaker 2: a case of I need to do something different in

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Speaker 2: how I'm engaging or whatever's what's wrong with my profile

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Speaker 2: that's not resonating with people. So I don't have that

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Speaker 2: challenge as much.

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Speaker 1: That's because you're a data scientist and you crunch the data.

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Speaker 3: Well, finally enough some of the as now have insight

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Speaker 3: for it. Child you which photos people are slid to

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Speaker 3: go on more. So I'm like, okay, so this is

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Speaker 3: the good one. So there are data analytics behind the

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Speaker 3: scenes as well. So yeah, the game, I'm like, what

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Speaker 3: I know about this because I have periods where I'm like.

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Speaker 2: I'm not dating. It's too hard, you know, it takes

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Speaker 2: an emotional toll. And then I'll have periods I'm jumping

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Speaker 2: back in and usually after that period of something news happened.

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Speaker 1: Dates.

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Speaker 2: Yeah, analytics, now I can see the data, but it's

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Speaker 2: really I'll use it as an example. So the last

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Speaker 2: kind of this year, the last five people I've dated,

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Speaker 2: to give you an example of of I guess the

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Speaker 2: frustration I'm facing. One of them we probably dated for

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Speaker 2: a period of about three months. You know, a dozen

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Speaker 2: or so dates just ghosted me after three months. I

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Speaker 2: thought that's pretty brutal. Another one we on a few dates,

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Speaker 2: ghosted me a week and a half l artext and

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Speaker 2: I was like, hey, just just seeing what happened to you.

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Speaker 2: Found out she had someone else, it wasn't going anywhere,

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Speaker 2: so then started dating me, and then the other one

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Speaker 2: changed their mind. So I was kind of just like,

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Speaker 2: I don't know the standby anyway, the third was the

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Speaker 2: one who changed their mind because the dating the fourth

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Speaker 2: where it went on some dates and then kind of

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Speaker 2: just suddenly goes, oh, I want an open relationship, and

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Speaker 2: I'm like, that's fine, that's just not a compatibility for me.

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Speaker 2: Everyone's got preferences. And then the fifth one, and this

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Speaker 2: happens all the time. We get like foal six dates

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Speaker 2: in and suddenly it's you're perfect, You're amazing. But I'm

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Speaker 2: just not ready to date code for because I've had

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Speaker 2: probably in the last like two or three years, I

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Speaker 2: would have had a dozen women say this to me,

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Speaker 2: the same thing, the same thing, like, you know, significantly

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Speaker 2: into dating. It's not like half the first couple of dates.

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Speaker 2: It's date six, seven, eight, and it's always the same thing,

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Speaker 2: You're perfect, You're amazing, like Sharer with connglents, and I

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Speaker 2: even some of them are like, Okay, yeah, that's cool,

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Speaker 2: thanks letting me know. Probably half of them like, hey,

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Speaker 2: just checking, like you don't need a sugarcoat.

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Speaker 1: Like yeah, I want to exit into in the.

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Speaker 2: Most normal, casual ways, like hey, just let me know,

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Speaker 2: like you know, and they just double down on what

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Speaker 2: they're saying yea. And so people are like, you know,

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Speaker 2: why are you still single? What's what's wrong with you?

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Speaker 2: And I'm like, well, I don't think anything. I mean,

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Speaker 2: if there was something wrong, hopefully I would have figured

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Speaker 2: it out by now all my friends would have told me.

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Speaker 2: And I don't know if I'm just tremendously unlucky that

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Speaker 2: I keep going on these dates to people and ending

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Speaker 2: up in that situation. But it's yeah, I just I'm

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Speaker 2: confused about why that seems to be the case.

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Speaker 1: That's so interesting. As a data guy, that would be

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Speaker 1: really hard.

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Speaker 2: Yeah, And that's why half the time I'll be like, hey,

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Speaker 2: just checking, like like let me know, you know, was

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Speaker 2: there anything in particular that and they're like, no, no, genuinely,

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Speaker 2: you are exactly who I'm looking for. I just yeah,

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Speaker 2: it's time date.

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Speaker 1: Yeah, that's so interesting.

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Speaker 2: And I just apparently I'm just You're just a timing

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Speaker 2: in the world. So I mean it gets Yeah, like

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Speaker 2: I said, it does take an emotional toll because obviously

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Speaker 2: after six seven eight dates, you're starting to get some feels. Yeah,

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Speaker 2: And so it's not just like a one or two

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Speaker 2: day and you're like yep, sweet, all right, move on.

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Speaker 2: It's you're starting to kind of make it a deeper connection.

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Speaker 2: And certainly, I think in your thirties day six, seven eight,

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Speaker 2: you're starting to kind of be like, yeah, it's not

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Speaker 2: like dating year twenties.

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Speaker 1: You know, it's so different. It's like whatever, man having

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Speaker 1: a good time time.

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Speaker 2: Yeah, I think in your thirties you're kind of thinking

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Speaker 2: a bit more, Okay, what's how do I really feel here?

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Speaker 2: You know more about yourself and who you are, you

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Speaker 2: know what you're looking for. They also have the same

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Speaker 2: kind of idea about who they are and who they're

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Speaker 2: looking for, so you can kind of figure out compatibilities

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Speaker 2: much quicker. So yeah, I've kind of just that wasn't

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Speaker 2: even your question.

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Speaker 1: I've just kind of but that was a really interesting

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Speaker 1: That was a really interesting insight because I do think

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Speaker 1: things like The Bachelor, and I don't I don't mean

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Speaker 1: to keep coming back to that because I'm trying to

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Speaker 1: show the side of you that isn't that, which is

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Speaker 1: part of this conversation. I think it sometimes trivializes. I mean,

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Speaker 1: some people have come out and had particularly in the

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Speaker 1: early seasons. I've interviewed a lot of people on the

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Speaker 1: show and have good friends who have gotten married have

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Speaker 1: kids out of the relationship that they had on the show.

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Speaker 1: I think sometimes it can trivialize the concept of day

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Speaker 1: to be, like to forget that, like by date eight

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Speaker 1: you have court feels. In your thirties, it is like

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Speaker 1: you are trying to find a partner for life and

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Speaker 1: that it is like timing is a really big thing. Yeah,

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Speaker 1: and it's cosmic, like funny talking to an astrophysicist. It

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Speaker 1: is cosmic that anyone actually ends up finding the right

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Speaker 1: timing with the person they're compatible with, Like that's actually

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Speaker 1: the stats are actually quite like unlikely. Yeah, yeah, you know,

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Speaker 1: so it's magic that it does happen.

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Speaker 2: For sure. And I think that timing piece in your

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Speaker 2: thirties becomes even harder because you have so much more

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Speaker 2: going on and rightfully, you're starting to craft a life

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Speaker 2: for yourself that you're really happy with and content with,

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Speaker 2: and the partner is to enrich that further not to

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Speaker 2: complete it, so to spay, and so you've got inherently

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Speaker 2: a more busier lifestyle, and the timing element is sometimes

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Speaker 2: even more challenging than so. Yeah, to your point that

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Speaker 2: timing is already hard, and then I think in your

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Speaker 2: thirties it can sometimes be a little harder again, but yeah,

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Speaker 2: I think it does trivialize. You're right. The dating, I

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Speaker 2: think a little bit. It's still it's still quite challenging.

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Speaker 2: I think for everyone. And I think, yeah, it's it's

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Speaker 2: like just everyone, like, don't.

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Speaker 1: Ghost, like the common courtesy.

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Speaker 2: You're mature enough, if you're old enough to date, you're

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Speaker 2: old enough to give someone you know, be respectful and

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Speaker 2: say thanks, but no thanks. You don't need to go

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Speaker 2: into detail.

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Speaker 1: You just call it.

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Speaker 2: Or whatever. Like ghosting is like, you know, gutter behavior,

385
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Speaker 2: really low and men and women do it. It's indifferent

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Speaker 2: whether you're a guy, a gal, non binary, power whatever.

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Speaker 1: You know.

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Speaker 2: Ship people ghosts. Yeah, that's the line.

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Speaker 1: Mates, don't let mates ghost. Also your friends accountable. There's

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Speaker 1: anything you.

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Speaker 2: Take out of let's let's you know, rather than getting

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Speaker 2: angry and dating, let's be respectful, let's improve. I actually

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Speaker 2: saw this view again being the data nerd I am.

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Speaker 2: It was a data visualization. It was one of those

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Speaker 2: ones you know where it starts like a year and

396
00:20:05,640 --> 00:20:06,760
Speaker 2: it's got the graph.

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Speaker 1: And did you just say graph graph?

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Speaker 2: Yeah, jo graph South Australia.

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Speaker 1: Oh my god, you're only born there.

400
00:20:17,960 --> 00:20:21,960
Speaker 2: Twelve but like plant and oh my god, you.

401
00:20:22,040 --> 00:20:28,240
Speaker 1: Do it is such as Australian things. Data scientist that

402
00:20:28,320 --> 00:20:29,000
Speaker 1: says graph.

403
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Speaker 2: I also say data, which I think most will say data.

404
00:20:33,240 --> 00:20:35,680
Speaker 1: Good Americans say data, Yeah all right, yeah, let's spent.

405
00:20:35,760 --> 00:20:37,160
Speaker 1: Normal people say graphs.

406
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Speaker 2: Graph, graphing, calculator graph, there's a bar graph, bar graph, yeah, barograph,

407
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Speaker 2: and you know it's I don't know if I'm gonna

408
00:20:47,240 --> 00:20:49,399
Speaker 2: be able to get this out, sorry, but you know

409
00:20:49,520 --> 00:20:52,120
Speaker 2: they it's like the years ticking over and they grow

410
00:20:52,240 --> 00:20:55,080
Speaker 2: and like change in order about you know, it will

411
00:20:55,119 --> 00:20:58,440
Speaker 2: be something the most common name, for example, and so

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00:20:58,560 --> 00:21:01,000
Speaker 2: it's like jack and then as the things go that's

413
00:21:01,000 --> 00:21:03,159
Speaker 2: small gets smaller and another one grows and then it

414
00:21:03,520 --> 00:21:06,400
Speaker 2: takes Have you seen that visualization? It was one of those,

415
00:21:06,480 --> 00:21:09,560
Speaker 2: but for how people met their partner from ninety twenty

416
00:21:09,600 --> 00:21:11,760
Speaker 2: four to twenty twenty four, which I will send you

417
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Speaker 2: the link as well it's really cool, but it has

418
00:21:14,040 --> 00:21:16,080
Speaker 2: it changing. And obviously at the start it's like family

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00:21:16,160 --> 00:21:18,399
Speaker 2: and friends and work or something like that, and then

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Speaker 2: bars kind of come in there and stuff. Anyway, it

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Speaker 2: gets to twenty twenty four. By twenty twenty four, sixty

422
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Speaker 2: percent is dating apps. Bars had dropped eight percent.

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Speaker 1: Oh my god.

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Speaker 2: And then I can't remember the other the other data

425
00:21:32,080 --> 00:21:39,280
Speaker 2: in there, but yeah, it was it was run close exactly,

426
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Speaker 2: but yeah, it was startingpiation. Club groups poor groups for

427
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Speaker 2: South Australians said I'm shocked. I think people like I

428
00:21:53,680 --> 00:21:55,879
Speaker 2: don't notice that. So I was saying once I need

429
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Speaker 2: to go buy some more plants.

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Speaker 1: Well plant, it's definitely is how Australian. That's really interesting.

431
00:22:03,320 --> 00:22:09,440
Speaker 1: So fancy, I don't say really transport okay in a

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Speaker 1: club do you say trance music?

433
00:22:11,920 --> 00:22:15,160
Speaker 2: Trance? Yeah? What do you say trance?

434
00:22:17,920 --> 00:22:26,760
Speaker 3: I've always said trance is let's dance to some I can.

435
00:22:26,760 --> 00:22:29,600
Speaker 1: See dance because dances formal, but trance is so casual

436
00:22:30,200 --> 00:22:39,600
Speaker 1: transfer doing a pole about this, it's definitely not trust.

437
00:22:39,720 --> 00:22:42,480
Speaker 2: Yeah. Well, if I did this pole on my Instagram,

438
00:22:42,600 --> 00:22:47,120
Speaker 2: I imagine the ratio of people saying trance would match

439
00:22:47,280 --> 00:22:54,920
Speaker 2: my Adelaide too smart. I've got no tiny UK audience,

440
00:22:54,960 --> 00:23:00,639
Speaker 2: so you know that I'm always in the insights speaking of.

441
00:23:01,000 --> 00:23:04,879
Speaker 1: So one thing I did not do was continue to

442
00:23:04,920 --> 00:23:06,320
Speaker 1: go through the fact that one thing I think is

443
00:23:06,359 --> 00:23:08,359
Speaker 1: amazing about you and I kind of started to hammer

444
00:23:08,480 --> 00:23:09,880
Speaker 1: home the point and then I don't even know where

445
00:23:09,880 --> 00:23:15,280
Speaker 1: my brain went. But you went from very vulnerably and

446
00:23:15,359 --> 00:23:19,280
Speaker 1: generously sharing how much your schooling was interrupted by mental

447
00:23:19,320 --> 00:23:23,439
Speaker 1: health breaks, and yet you went on to do five degrees,

448
00:23:23,480 --> 00:23:26,560
Speaker 1: which I've been at pains to remind everyone about, and

449
00:23:26,800 --> 00:23:31,040
Speaker 1: incredibly intellectually rigorous degrees, and then have had a very

450
00:23:31,119 --> 00:23:35,680
Speaker 1: successful time across various roles in the workforce at big

451
00:23:35,760 --> 00:23:40,280
Speaker 1: companies like Chevron at Lassian. So again intellectual rigor which

452
00:23:40,400 --> 00:23:43,879
Speaker 1: seems incompatible and having a dating life as well and

453
00:23:43,920 --> 00:23:47,639
Speaker 1: a social life which seems often incompatible with a mental

454
00:23:47,680 --> 00:23:50,480
Speaker 1: illness to the point that it interrupted your schooling. So

455
00:23:50,640 --> 00:23:53,000
Speaker 1: one thing that was really reassuring was hearing that you

456
00:23:53,160 --> 00:23:57,560
Speaker 1: can still go on to have a really high functioning

457
00:23:58,359 --> 00:24:02,840
Speaker 1: career and life. What was the change from the big

458
00:24:02,920 --> 00:24:04,960
Speaker 1: breaks from school to then being able to do UNI

459
00:24:05,560 --> 00:24:09,240
Speaker 1: and then being able to hold down really high performing jobs. Sorry,

460
00:24:09,280 --> 00:24:14,960
Speaker 1: that was such a pivot, the fact you can hold

461
00:24:15,000 --> 00:24:15,920
Speaker 1: the data in your head.

462
00:24:16,040 --> 00:24:19,200
Speaker 2: Yeah, it's I bring up in a similar vein to this.

463
00:24:20,080 --> 00:24:22,280
Speaker 2: When I was in primary school, I couldn't read very well,

464
00:24:22,359 --> 00:24:24,960
Speaker 2: and so I had to Yeah, I had to go

465
00:24:25,160 --> 00:24:30,440
Speaker 2: into this kind of intensive assistive extracurricular reading thing that

466
00:24:30,560 --> 00:24:33,280
Speaker 2: the school held called reading recovery, and so I used

467
00:24:33,280 --> 00:24:35,840
Speaker 2: to go to these additional reading classes to learn to read,

468
00:24:36,320 --> 00:24:39,639
Speaker 2: which is I guess I'd bring that up often with

469
00:24:40,240 --> 00:24:43,200
Speaker 2: in particular students in that. Eventually I went on and

470
00:24:43,240 --> 00:24:46,440
Speaker 2: wrote a book which going from someone two books, so

471
00:24:46,640 --> 00:24:49,760
Speaker 2: for going from someone who was so poor at reading

472
00:24:49,880 --> 00:24:53,280
Speaker 2: he needed to utilize the assistive facilities that the primary

473
00:24:53,280 --> 00:24:56,280
Speaker 2: school provided for reading to being able to do that.

474
00:24:56,359 --> 00:25:00,320
Speaker 2: I used that to kind of just remind, yeah, children

475
00:25:00,400 --> 00:25:02,840
Speaker 2: and students in particular, that if you're finding things hard,

476
00:25:02,880 --> 00:25:06,399
Speaker 2: that's okay. We all face different challenges and it doesn't

477
00:25:06,440 --> 00:25:08,440
Speaker 2: define you. And it's also not something that you can

478
00:25:08,640 --> 00:25:11,720
Speaker 2: still work at and get to a point where you're

479
00:25:11,760 --> 00:25:14,640
Speaker 2: comfortable and feeling more confident about whatever it is you're

480
00:25:14,640 --> 00:25:17,600
Speaker 2: finding challenging. And so I think it's not quite what

481
00:25:17,760 --> 00:25:20,240
Speaker 2: you're saying. By in a similar fashion, I think it

482
00:25:20,520 --> 00:25:25,359
Speaker 2: just helps people contextualize that the battles you face, whether

483
00:25:25,440 --> 00:25:32,000
Speaker 2: that's academically or psychologically, you can overcome and still lead

484
00:25:32,080 --> 00:25:35,000
Speaker 2: a very fulfilling life and achieve things that you can

485
00:25:35,040 --> 00:25:37,680
Speaker 2: be proud of, whatever that happens to be. So to

486
00:25:37,800 --> 00:25:41,919
Speaker 2: your question about how, was that it.

487
00:25:42,040 --> 00:25:44,520
Speaker 1: Was the how or that it was just really making

488
00:25:44,600 --> 00:25:47,720
Speaker 1: the point generally, I think that exactly what you said.

489
00:25:48,600 --> 00:25:53,680
Speaker 1: I think silo ourselves very heavily into I'm academic, I'm creative,

490
00:25:53,800 --> 00:25:56,680
Speaker 1: I'm sporty, I'm artie, and then you kind of think

491
00:25:56,720 --> 00:25:59,560
Speaker 1: you're in that silo forever. And I think people really

492
00:25:59,680 --> 00:26:03,520
Speaker 1: limit their dreams or the things that they will their

493
00:26:03,560 --> 00:26:06,720
Speaker 1: goals because they think, well, I couldn't do business because

494
00:26:06,760 --> 00:26:09,359
Speaker 1: I'm an academic person, or I couldn't do academia because

495
00:26:09,359 --> 00:26:12,160
Speaker 1: I'm not smart. And if you look at your school life,

496
00:26:12,200 --> 00:26:15,879
Speaker 1: you wouldn't think astrophysicist. And I think the jump. What

497
00:26:16,080 --> 00:26:18,480
Speaker 1: I love to do with this show is connect jumps

498
00:26:18,520 --> 00:26:21,320
Speaker 1: that you wouldn't expect and remind people listening, either for

499
00:26:21,359 --> 00:26:25,400
Speaker 1: themselves or their children or friends or whatever, that there

500
00:26:25,440 --> 00:26:27,840
Speaker 1: are so many people who have made jumps that you

501
00:26:27,880 --> 00:26:29,879
Speaker 1: wouldn't think. A child in read and recovery, would go

502
00:26:30,000 --> 00:26:32,600
Speaker 1: on to do five not just five normal degrees, but

503
00:26:32,760 --> 00:26:37,159
Speaker 1: five scientific degrees and then work in like heavily academic

504
00:26:37,640 --> 00:26:41,119
Speaker 1: intellectual industry. And that in itself is just really reassuring.

505
00:26:41,240 --> 00:26:45,360
Speaker 1: But also from your perspective, were you ever dissuaded from

506
00:26:45,400 --> 00:26:47,880
Speaker 1: thinking you could go into like did you not think

507
00:26:47,920 --> 00:26:50,200
Speaker 1: of yourself as the smart guy? But now you're branded

508
00:26:50,240 --> 00:26:51,800
Speaker 1: a nerd? That's so interesting to me.

509
00:26:52,000 --> 00:26:55,760
Speaker 2: I'd always been very academically focused. My parents are both

510
00:26:55,920 --> 00:26:59,359
Speaker 2: very analytical and scientific. My mum's got a degree in

511
00:26:59,400 --> 00:27:04,040
Speaker 2: pure mathematic which is the worst thing, and my dad, yeah,

512
00:27:04,119 --> 00:27:06,720
Speaker 2: maths and engineering and computer science, a double degree. And

513
00:27:06,800 --> 00:27:09,560
Speaker 2: so you know, I've inherited already a little bit of

514
00:27:09,640 --> 00:27:13,920
Speaker 2: natural ability, I think, or natural kind of propensity to

515
00:27:14,880 --> 00:27:18,360
Speaker 2: mathematics and science. So I've got, I guess, a privilege

516
00:27:18,400 --> 00:27:22,200
Speaker 2: there that not everyone has. But my parents, I think

517
00:27:22,240 --> 00:27:26,199
Speaker 2: they really emphasized the importance of education, and so they

518
00:27:26,280 --> 00:27:29,680
Speaker 2: weren't you know, cracking the whip or anything or like

519
00:27:29,840 --> 00:27:32,560
Speaker 2: locking me in rooms and stuff, But they were always

520
00:27:32,640 --> 00:27:37,040
Speaker 2: really encouraging to focus and apply, and if something was hard,

521
00:27:37,119 --> 00:27:39,520
Speaker 2: they would sit down and try and work through, and

522
00:27:40,160 --> 00:27:45,159
Speaker 2: they fostered a really really positive learning situation for me

523
00:27:45,280 --> 00:27:48,040
Speaker 2: and my siblings, such that we always wanted to do

524
00:27:48,160 --> 00:27:51,879
Speaker 2: really well academically because they had encouraged us, supported us,

525
00:27:51,960 --> 00:27:55,000
Speaker 2: and said, why it's really important to just kind of

526
00:27:55,000 --> 00:27:57,640
Speaker 2: setting yourself up. And I think that's still true now.

527
00:27:57,920 --> 00:28:00,440
Speaker 2: I think it's probably the way they did, I think

528
00:28:00,560 --> 00:28:03,760
Speaker 2: is the right way compared to the current I guess

529
00:28:03,920 --> 00:28:08,520
Speaker 2: education system where the pressure placed on year twelves. This

530
00:28:08,760 --> 00:28:12,000
Speaker 2: is like door die, you know, like if you if

531
00:28:12,040 --> 00:28:14,360
Speaker 2: you don't do well in your exams, that's you're done,

532
00:28:15,320 --> 00:28:18,639
Speaker 2: which is completely bullshed. And I try to kind of

533
00:28:18,720 --> 00:28:20,399
Speaker 2: do a bit of a post each year around this

534
00:28:20,520 --> 00:28:22,440
Speaker 2: time of year when the exams are in full flight,

535
00:28:22,640 --> 00:28:25,600
Speaker 2: just to help try and remind students that this isn't

536
00:28:25,640 --> 00:28:29,919
Speaker 2: you know. You do your best, but don't grind yourself

537
00:28:29,960 --> 00:28:32,280
Speaker 2: into the ground doing it or beat yourself up. I

538
00:28:33,040 --> 00:28:35,240
Speaker 2: remember I was. I threw up before most of my

539
00:28:35,359 --> 00:28:38,000
Speaker 2: exams in a year to a half because I was

540
00:28:38,080 --> 00:28:39,920
Speaker 2: so stressed about performing.

541
00:28:40,240 --> 00:28:42,880
Speaker 1: Yeah, and again throwing up in your school life.

542
00:28:43,200 --> 00:28:47,640
Speaker 2: Yeah yeah, unfortunately and that, yeah, that pressure never came

543
00:28:47,680 --> 00:28:51,240
Speaker 2: from my parents. It was putting on myself, and largely

544
00:28:51,320 --> 00:28:54,040
Speaker 2: because of that external pressure from this system that you know,

545
00:28:54,160 --> 00:28:55,720
Speaker 2: if you don't do well, you won't get into the

546
00:28:55,760 --> 00:28:58,520
Speaker 2: degree you want, and then you know you've ruined your life.

547
00:28:59,360 --> 00:29:02,120
Speaker 2: And we know that is not true at all, it's

548
00:29:02,240 --> 00:29:04,880
Speaker 2: never been true, but that I think is still pervasive

549
00:29:05,240 --> 00:29:07,520
Speaker 2: among students, and so I think it's really important to

550
00:29:07,640 --> 00:29:12,400
Speaker 2: kind of, yeah, just help people students and kids realize that, Yeah,

551
00:29:12,760 --> 00:29:15,200
Speaker 2: definitely try your best, and if there's a particular degree

552
00:29:15,240 --> 00:29:17,520
Speaker 2: you really want to get into, try and do that.

553
00:29:18,280 --> 00:29:20,360
Speaker 2: But if you don't, there's so many other ways to

554
00:29:20,480 --> 00:29:23,080
Speaker 2: still get into that degree you want, or maybe you

555
00:29:23,200 --> 00:29:25,920
Speaker 2: end up getting gaining an interest in a different thing altogether,

556
00:29:26,280 --> 00:29:29,760
Speaker 2: or I mean us in particular have had such random,

557
00:29:30,280 --> 00:29:34,360
Speaker 2: random career paths that you never have picked. So I

558
00:29:34,440 --> 00:29:39,160
Speaker 2: think it's yeah, I think it's really important for yeah,

559
00:29:39,200 --> 00:29:41,240
Speaker 2: I think I think for students to be aware of that,

560
00:29:42,040 --> 00:29:44,040
Speaker 2: just to take a bit of that pressure off. And

561
00:29:44,120 --> 00:29:46,360
Speaker 2: that's not to say like be lazy and don't try,

562
00:29:47,040 --> 00:29:50,640
Speaker 2: but it's to the point of, yeah, apply yourself, work hard,

563
00:29:51,280 --> 00:29:54,560
Speaker 2: but certainly don't beat yourself up. Whatever the results are,

564
00:29:54,880 --> 00:29:58,920
Speaker 2: you will have a fulfilling life and career and it's

565
00:29:58,960 --> 00:30:01,440
Speaker 2: going to be a windy part that Avyn is going

566
00:30:01,480 --> 00:30:03,800
Speaker 2: to be on a windy path. You never know that.

567
00:30:03,840 --> 00:30:05,480
Speaker 2: I think when you're going through year twelve, no.

568
00:30:05,600 --> 00:30:07,680
Speaker 1: One hundred percent or most of the dog points. Like

569
00:30:07,720 --> 00:30:09,320
Speaker 1: I was even looking at your LinkedIn, it's sort of

570
00:30:09,360 --> 00:30:13,040
Speaker 1: like mechanical engineer at Chevron, which is not necessarily where

571
00:30:13,120 --> 00:30:16,640
Speaker 1: you expect. Now it's like you know two times author

572
00:30:16,920 --> 00:30:19,960
Speaker 1: on AI that doesn't necessarily match up with like a

573
00:30:20,080 --> 00:30:22,760
Speaker 1: facilities engineer in like a mining company you know in

574
00:30:22,920 --> 00:30:25,920
Speaker 1: Perth in Wa. It's like such a big I think

575
00:30:25,960 --> 00:30:28,840
Speaker 1: the dots often make sense looking backwards, but going forwards

576
00:30:28,880 --> 00:30:30,280
Speaker 1: you just got to put one foot in front of

577
00:30:30,280 --> 00:30:33,040
Speaker 1: the other. And so another thing that I think is

578
00:30:33,080 --> 00:30:35,120
Speaker 1: really difficult is when you are going forward and trying

579
00:30:35,160 --> 00:30:38,680
Speaker 1: to find putting this either society or yourself putting pressure

580
00:30:38,720 --> 00:30:40,920
Speaker 1: on you to find your ya or like seize your

581
00:30:41,000 --> 00:30:44,240
Speaker 1: ya and find your joy. Is if you can't see

582
00:30:44,360 --> 00:30:47,640
Speaker 1: that pathway that someone else has done, you don't know

583
00:30:47,720 --> 00:30:49,160
Speaker 1: to aim for it because you don't know that that's

584
00:30:49,160 --> 00:30:52,239
Speaker 1: a job. So can you talk to us about now

585
00:30:52,320 --> 00:30:56,040
Speaker 1: having written two books, we did touch briefly on astrophysics,

586
00:30:56,520 --> 00:30:58,760
Speaker 1: but I want to know, like what, how does that

587
00:30:58,840 --> 00:31:01,560
Speaker 1: translate into a for you, like you wrote your first

588
00:31:01,640 --> 00:31:05,640
Speaker 1: book on Doctor Matt's Guide to Space, Like how did

589
00:31:05,680 --> 00:31:08,760
Speaker 1: you even learn all that stuff? And if you wanted

590
00:31:08,800 --> 00:31:11,880
Speaker 1: to get someone excited about astrophysics, I'll take one thing

591
00:31:11,920 --> 00:31:13,360
Speaker 1: out of the first book. What would it be?

592
00:31:14,840 --> 00:31:16,960
Speaker 2: One thing out of the first book or something?

593
00:31:17,040 --> 00:31:20,120
Speaker 1: You're like, you know because you didn't work in astrophysics first.

594
00:31:20,480 --> 00:31:22,160
Speaker 2: No, No, it started in engineering.

595
00:31:22,320 --> 00:31:23,880
Speaker 1: Yeah, so that's interesting to me as well because it

596
00:31:23,960 --> 00:31:25,120
Speaker 1: wasn't space focused at all.

597
00:31:25,400 --> 00:31:29,600
Speaker 2: No, No, And I always liked space. I did as

598
00:31:29,680 --> 00:31:32,080
Speaker 2: part of the degree, my bachelor degree, I had to

599
00:31:32,120 --> 00:31:33,840
Speaker 2: do physics one on one on one O two, which

600
00:31:33,920 --> 00:31:37,240
Speaker 2: had a portion of the physics component was astronomy, and

601
00:31:37,360 --> 00:31:40,040
Speaker 2: I loved it. But I think I had no idea

602
00:31:40,040 --> 00:31:43,040
Speaker 2: what I wanted to do. I remember talking to my

603
00:31:43,400 --> 00:31:45,520
Speaker 2: folks a lot, being like what do I pick? I

604
00:31:45,560 --> 00:31:48,479
Speaker 2: don't know anything, and Dad suggests engineering, largely because it's

605
00:31:48,520 --> 00:31:52,440
Speaker 2: such a strong degree. There's always demand for engineers and

606
00:31:52,480 --> 00:31:54,480
Speaker 2: I only see that changing for the next fifty one

607
00:31:54,560 --> 00:31:57,240
Speaker 2: hundred years, and so I kind of picked out. I

608
00:31:57,280 --> 00:31:59,080
Speaker 2: thought an engineer was like a mechanic. I thought you

609
00:31:59,160 --> 00:32:02,280
Speaker 2: were on the tools and stuff That's what I.

610
00:32:02,280 --> 00:32:03,840
Speaker 1: Thought of when I saw Chevron. I was like, were

611
00:32:03,840 --> 00:32:05,240
Speaker 1: you like building like I.

612
00:32:05,320 --> 00:32:07,360
Speaker 2: Did, actually not building, but I did get to go

613
00:32:07,480 --> 00:32:09,880
Speaker 2: out there, and like I was changing some spools on

614
00:32:10,040 --> 00:32:12,400
Speaker 2: this stuff. So spools are like, you know, it's like

615
00:32:12,480 --> 00:32:14,120
Speaker 2: a pipe with flanges.

616
00:32:13,800 --> 00:32:16,000
Speaker 1: Each, Like what's a spool even a flange.

617
00:32:16,000 --> 00:32:19,480
Speaker 2: I'm like, oh my god, imagine a pipe that kind

618
00:32:19,480 --> 00:32:22,160
Speaker 2: of like flares out and so to you attached to

619
00:32:22,280 --> 00:32:25,360
Speaker 2: them together that flare you kind of bolt between. So

620
00:32:25,720 --> 00:32:28,680
Speaker 2: spool is the pipe with the flange dams. So I

621
00:32:28,760 --> 00:32:30,400
Speaker 2: was changing spools. So I got to go out there,

622
00:32:30,400 --> 00:32:32,240
Speaker 2: you know, in the hard hat, the high veers, the

623
00:32:32,480 --> 00:32:35,960
Speaker 2: steel caps, got this giant talk ranch unt screwing things

624
00:32:36,080 --> 00:32:39,080
Speaker 2: taking them out. Yeah, it was really fun. So a

625
00:32:39,120 --> 00:32:41,160
Speaker 2: lot of the stuff I wasn't on the tools. I'd

626
00:32:41,160 --> 00:32:42,560
Speaker 2: been going out and it had been doing a lot

627
00:32:42,600 --> 00:32:45,520
Speaker 2: of inspections and yeah, kind of going through data sheets,

628
00:32:45,600 --> 00:32:48,120
Speaker 2: always data, yeah.

629
00:32:48,080 --> 00:32:52,920
Speaker 1: But dating data, data, spool data the ground.

630
00:32:53,040 --> 00:32:56,520
Speaker 2: Yeah. But yeah, I got to do a little bit

631
00:32:56,560 --> 00:33:00,760
Speaker 2: on the tools, which was really fun. And yeah, so

632
00:33:00,880 --> 00:33:03,480
Speaker 2: how did I how did I end up in astrophysics?

633
00:33:04,960 --> 00:33:06,000
Speaker 1: Make the dots connect?

634
00:33:07,200 --> 00:33:12,720
Speaker 2: So yeah, exactly, let me extrapolate interpolate in this case. Sorry,

635
00:33:13,120 --> 00:33:13,360
Speaker 2: I was.

636
00:33:13,360 --> 00:33:17,760
Speaker 1: Sorry, guys. Extravolate and interpolate are different words. Most people

637
00:33:17,800 --> 00:33:19,080
Speaker 1: don't use both in their vernacular.

638
00:33:19,760 --> 00:33:23,880
Speaker 2: Continue all right, So yeah, kind of after it about

639
00:33:23,880 --> 00:33:25,840
Speaker 2: three or four years of engineering, and i'd done a

640
00:33:25,880 --> 00:33:30,000
Speaker 2: couple of different engineering companies, both a client and a EPCM,

641
00:33:30,080 --> 00:33:32,959
Speaker 2: which is engineering procurement construction management.

642
00:33:33,040 --> 00:33:35,320
Speaker 1: I think so much of your LinkedIn does not make

643
00:33:35,360 --> 00:33:35,960
Speaker 1: sense anyway.

644
00:33:36,560 --> 00:33:40,360
Speaker 2: Yeah, I mean that's part of writing a CV, right,

645
00:33:40,480 --> 00:33:42,680
Speaker 2: make it sound really impressive, and it's like, what did

646
00:33:42,680 --> 00:33:44,960
Speaker 2: you do change a light bulb? You know that?

647
00:33:45,440 --> 00:33:46,560
Speaker 1: But it's actually a spool.

648
00:33:46,840 --> 00:33:49,640
Speaker 2: Yeah, it's a spool, it's got flangers. So I'd work

649
00:33:49,640 --> 00:33:51,760
Speaker 2: for a couple of engineering companies. I'd done office based,

650
00:33:51,760 --> 00:33:54,120
Speaker 2: I'd done site based, so I'd try a real kind

651
00:33:54,160 --> 00:33:57,560
Speaker 2: of mix of engineering, and it just wasn't gelling for me.

652
00:33:58,280 --> 00:34:00,720
Speaker 2: And one of the advantages I had from working site

653
00:34:00,760 --> 00:34:03,560
Speaker 2: based in oil and gas was it's well paid, and

654
00:34:03,640 --> 00:34:06,720
Speaker 2: so I had a nice kind of pool of savings

655
00:34:06,840 --> 00:34:09,840
Speaker 2: that I could think, all right, well, a lot of

656
00:34:09,880 --> 00:34:11,600
Speaker 2: people that's the time where you know, putting money into

657
00:34:11,800 --> 00:34:13,400
Speaker 2: their homes. And I was like, I don't know if

658
00:34:13,400 --> 00:34:14,880
Speaker 2: I'm ready for that. I kind of don't know what

659
00:34:14,960 --> 00:34:16,640
Speaker 2: I want to do. I might just you know, do

660
00:34:16,680 --> 00:34:19,200
Speaker 2: a bit of travel, live abroad, you know, that whole

661
00:34:19,280 --> 00:34:20,360
Speaker 2: kind of discovery yourself.

662
00:34:20,440 --> 00:34:23,279
Speaker 1: Yeah, yeah, just going to take a Yeah.

663
00:34:23,480 --> 00:34:25,400
Speaker 2: And I was in a luxurious position because I had

664
00:34:25,480 --> 00:34:29,120
Speaker 2: worked in quite a lucrative profession and so I was

665
00:34:29,160 --> 00:34:30,600
Speaker 2: trying to you know, I was traveling at beer, I

666
00:34:30,719 --> 00:34:32,880
Speaker 2: was living, you know, I visited my brother in Europe.

667
00:34:32,920 --> 00:34:35,160
Speaker 2: I was I lived in Buenos Aires for four months.

668
00:34:35,520 --> 00:34:40,920
Speaker 2: Really yeah, yeah, it's beautiful. I loved it. And then

669
00:34:40,920 --> 00:34:42,680
Speaker 2: I was like, what do I want to do? And

670
00:34:42,960 --> 00:34:46,360
Speaker 2: I remember my parents saying, well, think about what what

671
00:34:46,480 --> 00:34:49,040
Speaker 2: did you used to really like as a kid, And

672
00:34:49,760 --> 00:34:54,279
Speaker 2: I said, I like space, Yeah, the Mars Rover and

673
00:34:54,440 --> 00:34:57,120
Speaker 2: so you know, astrophysics is just space for adults. And

674
00:34:57,280 --> 00:35:01,759
Speaker 2: so I applied for a mass is in astro at

675
00:35:02,400 --> 00:35:06,040
Speaker 2: lund University in Sweden and it ended up getting accepted

676
00:35:06,239 --> 00:35:10,000
Speaker 2: and went there and at the time, so so you

677
00:35:10,160 --> 00:35:13,680
Speaker 2: like this story. So I initially got rejected for the

678
00:35:13,719 --> 00:35:17,240
Speaker 2: application because they were, oh, you don't have the physics background,

679
00:35:17,239 --> 00:35:18,719
Speaker 2: because I've only done physics one at one and one

680
00:35:18,800 --> 00:35:21,239
Speaker 2: two as part of the engineering degree, so despite the

681
00:35:21,280 --> 00:35:23,080
Speaker 2: fact they had a Bachelor of Science and applied maths

682
00:35:23,200 --> 00:35:25,799
Speaker 2: a Bachelor of Engineering at MECH and they were.

683
00:35:25,760 --> 00:35:28,200
Speaker 1: Like, no, it's insufficient, as in you would have needed

684
00:35:28,280 --> 00:35:29,480
Speaker 1: a pure physics story.

685
00:35:29,760 --> 00:35:32,120
Speaker 2: Yeah, like third year physics, okay, And so I got

686
00:35:32,160 --> 00:35:34,040
Speaker 2: there resilt. I was like, ah, that sucks, and I

687
00:35:34,080 --> 00:35:36,520
Speaker 2: thought I'll submit an appeal. There's an appeal process. I

688
00:35:36,560 --> 00:35:38,399
Speaker 2: submitted it and I was like, oh, look, it's based

689
00:35:38,440 --> 00:35:42,960
Speaker 2: on what's written on the application. I need all the requirements.

690
00:35:43,680 --> 00:35:47,200
Speaker 2: I got this email months later and they go, and

691
00:35:47,320 --> 00:35:51,040
Speaker 2: this is so Swedish. They go, oh, so we've looked

692
00:35:51,239 --> 00:35:55,360
Speaker 2: at your application and the requirements and what was written

693
00:35:55,520 --> 00:35:58,880
Speaker 2: on the requirements that was listed on the website. You

694
00:35:59,000 --> 00:36:02,480
Speaker 2: did fulfill them incorrect but you did fulfill them.

695
00:36:02,680 --> 00:36:03,359
Speaker 3: So we need.

696
00:36:07,080 --> 00:36:10,840
Speaker 2: On the website. Yeah, and it's after living in sweet

697
00:36:10,840 --> 00:36:14,759
Speaker 2: I'm like, this is very sweet to my advantage. Yes,

698
00:36:15,040 --> 00:36:18,279
Speaker 2: So I ended up getting in not necessarily the requirements

699
00:36:18,320 --> 00:36:21,200
Speaker 2: they whoever had put them up mustn't have, you know,

700
00:36:21,400 --> 00:36:24,080
Speaker 2: quite written them correctly. And so what was written now

701
00:36:24,080 --> 00:36:25,840
Speaker 2: I did fulfill and so I got accepted. And so

702
00:36:26,400 --> 00:36:31,120
Speaker 2: when I did the Masters, they I remember talking to

703
00:36:31,200 --> 00:36:33,920
Speaker 2: the astro coordinator and I was like, look, you know

704
00:36:33,960 --> 00:36:36,160
Speaker 2: I haven't done I've only done first year physics. And

705
00:36:36,200 --> 00:36:37,480
Speaker 2: they're like, all right, what we can do is get

706
00:36:37,480 --> 00:36:39,719
Speaker 2: you to fill in. Because you had like four electives

707
00:36:39,760 --> 00:36:43,880
Speaker 2: that you could specialize in astro units, we'll use a

708
00:36:43,920 --> 00:36:48,080
Speaker 2: couple of them to basically like so you can do

709
00:36:48,239 --> 00:36:51,520
Speaker 2: like yeah, because it was the physics. So I was like, oh,

710
00:36:52,320 --> 00:36:54,920
Speaker 2: it was atomic and molecular physics and nuclear physics I

711
00:36:55,000 --> 00:36:58,000
Speaker 2: had to do. So I jumped straight from first year

712
00:36:58,040 --> 00:37:02,720
Speaker 2: to third year physics was like eight years earlier. Essentially,

713
00:37:02,760 --> 00:37:05,640
Speaker 2: I was going straight into thirty year physics in the

714
00:37:05,719 --> 00:37:09,520
Speaker 2: class and everything above the bachelor level at Swedish unions

715
00:37:09,640 --> 00:37:10,240
Speaker 2: is done in English.

716
00:37:10,360 --> 00:37:14,600
Speaker 1: There's so much bachelor in your life. I'm like, what bachelor? Oh? Sorry,

717
00:37:16,760 --> 00:37:21,959
Speaker 1: funny everything above the bachelor undergraduate level undergrad.

718
00:37:21,719 --> 00:37:24,880
Speaker 2: Level is taught in English. But I remember when they're like,

719
00:37:24,920 --> 00:37:27,400
Speaker 2: all right, yeah, so you remember this from second year physics.

720
00:37:27,480 --> 00:37:31,280
Speaker 2: And I was like, if we don't remember physics.

721
00:37:31,400 --> 00:37:35,240
Speaker 1: And put it in English, remind me just second year physics.

722
00:37:35,320 --> 00:37:36,520
Speaker 2: I've just I can't quite place.

723
00:37:36,600 --> 00:37:38,000
Speaker 1: I've got a blanket, can quite place.

724
00:37:38,600 --> 00:37:41,480
Speaker 2: Remind me again? I think actually a couple of people. Yeah,

725
00:37:41,600 --> 00:37:44,320
Speaker 2: just remind us. So they were the two of the

726
00:37:44,400 --> 00:37:48,040
Speaker 2: hardest units. Was basically getting dropped in third year physics

727
00:37:48,840 --> 00:37:51,920
Speaker 2: and trying to, you know, fumble my way through and

728
00:37:52,400 --> 00:37:56,000
Speaker 2: learning from scratch and yeah, it's but yeah, it is

729
00:37:56,080 --> 00:37:59,000
Speaker 2: all in English. And it was funny because I remember

730
00:37:59,040 --> 00:38:01,840
Speaker 2: talking to some of the sweet and they're like, I

731
00:38:01,960 --> 00:38:03,840
Speaker 2: don't know how to say this word in Swedish. I

732
00:38:03,880 --> 00:38:07,680
Speaker 2: only know this physics technical physics term in English because

733
00:38:07,719 --> 00:38:08,680
Speaker 2: we learned it in England.

734
00:38:08,800 --> 00:38:09,000
Speaker 3: Yeah.

735
00:38:09,560 --> 00:38:13,000
Speaker 2: So it's a really interesting, yeah, kind of culture in

736
00:38:13,160 --> 00:38:16,200
Speaker 2: terms of the education system because it is this in particular,

737
00:38:16,280 --> 00:38:18,759
Speaker 2: lunit is a very international university, and so you've got

738
00:38:18,800 --> 00:38:22,080
Speaker 2: this melting pot of cultures. And I remember the way

739
00:38:22,120 --> 00:38:24,239
Speaker 2: a couple of my Swedish friends explained it. They're like, yeah,

740
00:38:24,640 --> 00:38:27,920
Speaker 2: we basically we speak English and Sweden, but we then

741
00:38:27,920 --> 00:38:30,439
Speaker 2: also get this secret language that no one else really knows.

742
00:38:31,120 --> 00:38:32,600
Speaker 1: Yeah. Nice, so we can talk about it.

743
00:38:32,760 --> 00:38:34,960
Speaker 2: Yeah you guys, Yeah, that's kind of true. Like you

744
00:38:35,040 --> 00:38:38,080
Speaker 2: speak to Swedes and you wouldn't even know, like because

745
00:38:38,080 --> 00:38:43,200
Speaker 2: they imitate perfect English or American accents, so you can't

746
00:38:43,200 --> 00:38:45,920
Speaker 2: even tell half the time. And then they also get

747
00:38:45,920 --> 00:38:48,000
Speaker 2: this secret language that nine million people know in the

748
00:38:48,040 --> 00:38:51,520
Speaker 2: world else no one else. I was like, Okay, that's

749
00:38:51,640 --> 00:38:53,920
Speaker 2: an interesting that I put it. So I did. Yeah,

750
00:38:53,920 --> 00:38:56,560
Speaker 2: I did the masters there. It was fifty percent courts work,

751
00:38:56,600 --> 00:39:00,640
Speaker 2: fift percent research, and just fell in with the research

752
00:39:00,840 --> 00:39:03,760
Speaker 2: aspect of it. Just really loved it. I was modeling

753
00:39:04,040 --> 00:39:08,560
Speaker 2: exoplanet atmospheres, so I was basically modeling, yeah, and I

754
00:39:08,640 --> 00:39:12,160
Speaker 2: was trying to understand if there were there's these types

755
00:39:12,160 --> 00:39:13,680
Speaker 2: of planets. Do you want me to get in the

756
00:39:13,719 --> 00:39:15,959
Speaker 2: weeds to weak? I love this. So there's these types

757
00:39:16,000 --> 00:39:21,160
Speaker 2: of planets, exoplanets called hot Jupiter's good name, and they're

758
00:39:21,200 --> 00:39:25,719
Speaker 2: basically giant gas planets like Jupiter, but they're they're hot,

759
00:39:26,360 --> 00:39:28,440
Speaker 2: and they're hot because they are so close to the

760
00:39:28,560 --> 00:39:31,359
Speaker 2: star we're talking instead of orbiting. You know, the Earth

761
00:39:31,360 --> 00:39:34,040
Speaker 2: takes three sixty five days, Jupiter's like eleven years or

762
00:39:34,080 --> 00:39:37,960
Speaker 2: something like that. They take three days. Whoa, so three

763
00:39:38,040 --> 00:39:40,000
Speaker 2: days to all but they're that close, which means they're

764
00:39:40,520 --> 00:39:45,080
Speaker 2: super hot several thousand degrees. And what that does is

765
00:39:45,160 --> 00:39:48,640
Speaker 2: it puffs up the atmosphere because it's so hot and energetic,

766
00:39:49,320 --> 00:39:53,239
Speaker 2: and the starlight can shine through the atmosphere, and we

767
00:39:53,320 --> 00:39:56,600
Speaker 2: can observe that starlight and what's interacting with the atmosphere.

768
00:39:56,640 --> 00:39:59,280
Speaker 2: We can understand kind of things about the chemical composition

769
00:39:59,360 --> 00:40:01,040
Speaker 2: of the atmosphere, temperature of the atmosphere.

770
00:40:01,160 --> 00:40:03,479
Speaker 1: How excited, He's got a totally different look on your face.

771
00:40:05,160 --> 00:40:05,600
Speaker 2: Excited.

772
00:40:05,840 --> 00:40:08,400
Speaker 1: This is the definition of yay when someone's face does that.

773
00:40:08,680 --> 00:40:12,319
Speaker 2: Okay, Yeah, So this really really cool project. I had

774
00:40:12,360 --> 00:40:16,360
Speaker 2: this really cool research element, had this computational element modeling

775
00:40:16,400 --> 00:40:19,480
Speaker 2: these atmospheres, and just really liked it. And that's what

776
00:40:19,680 --> 00:40:23,000
Speaker 2: sent me down the academic route for for the next

777
00:40:23,040 --> 00:40:27,800
Speaker 2: four years going into research to my PhD and had applied.

778
00:40:27,840 --> 00:40:30,279
Speaker 2: I had a couple of PhD interviews in Europe. I

779
00:40:30,360 --> 00:40:34,000
Speaker 2: had one in Munich, one in Amstam, and one in Leiden,

780
00:40:34,080 --> 00:40:37,359
Speaker 2: which is a smaller international UNI about forty minute train

781
00:40:37,440 --> 00:40:42,120
Speaker 2: from Amsterdam. Did really strongly in interviews, but I was

782
00:40:42,280 --> 00:40:45,800
Speaker 2: the second choice for all three and only had one spot.

783
00:40:46,239 --> 00:40:47,600
Speaker 2: And all of them came back and they said you

784
00:40:47,840 --> 00:40:51,319
Speaker 2: were amazing. There was just some slightly more aligned. If

785
00:40:51,360 --> 00:40:55,480
Speaker 2: they turn it down though, you're you're in none to town.

786
00:40:55,960 --> 00:40:57,719
Speaker 2: So I was like, Okay, that sucks. So then I

787
00:40:57,800 --> 00:41:01,040
Speaker 2: started looking Australian unis and for a few in New

788
00:41:01,080 --> 00:41:04,400
Speaker 2: South Wales and Melbourne, and I ended up the project

789
00:41:04,480 --> 00:41:08,560
Speaker 2: at Swinburn just I really liked it. It was searching

790
00:41:08,680 --> 00:41:11,960
Speaker 2: for Earth two point zero basically planets like Earth Solar

791
00:41:12,000 --> 00:41:16,359
Speaker 2: system analogus cool and again largely it was a computational

792
00:41:16,800 --> 00:41:20,319
Speaker 2: and modeling project, which is you've kind of got two

793
00:41:20,360 --> 00:41:23,759
Speaker 2: streams in astrophysics. You've got the observational astronomers who they're

794
00:41:24,239 --> 00:41:27,000
Speaker 2: using the telescopes to get data and then trying to

795
00:41:27,520 --> 00:41:28,600
Speaker 2: identify what they're.

796
00:41:28,440 --> 00:41:31,320
Speaker 1: Looking at, looking and discovering it.

797
00:41:31,520 --> 00:41:33,320
Speaker 2: And then you've got the kind of the theory and

798
00:41:33,360 --> 00:41:36,239
Speaker 2: computational astrophysicists, and so they're the ones who are kind

799
00:41:36,280 --> 00:41:39,479
Speaker 2: of they're coming up with theoretical predictions in my case,

800
00:41:39,600 --> 00:41:42,840
Speaker 2: modeling and computationally predicting things, and then that kind of

801
00:41:42,880 --> 00:41:46,200
Speaker 2: gets fed to the observational astronomers and then they'll be

802
00:41:46,239 --> 00:41:49,279
Speaker 2: looking to say, okay, this is what's being predicted. Can

803
00:41:49,360 --> 00:41:51,200
Speaker 2: we find something that kind of can verify this or not?

804
00:41:51,320 --> 00:41:53,160
Speaker 2: And so you've got this kind of synergy between those

805
00:41:53,200 --> 00:41:57,640
Speaker 2: two streams of astrophysicists that is so cool.

806
00:41:57,719 --> 00:42:00,200
Speaker 1: I mean, like the average lay person probably has not

807
00:42:00,440 --> 00:42:04,960
Speaker 1: ever heard that description before of like what astrophysicists are

808
00:42:05,080 --> 00:42:08,879
Speaker 1: actually doing, and then like how that could impact our

809
00:42:09,000 --> 00:42:11,680
Speaker 1: daily life, ie, we run out of space on Earth,

810
00:42:11,760 --> 00:42:13,720
Speaker 1: there may be is Earth two point zero, like someone

811
00:42:13,880 --> 00:42:16,279
Speaker 1: out there. And part of CZA that I get so

812
00:42:16,400 --> 00:42:21,239
Speaker 1: excited about is showing people that, Like one day, you're

813
00:42:21,360 --> 00:42:23,040
Speaker 1: like sitting in the shower and you're probably thinking, who

814
00:42:23,160 --> 00:42:25,240
Speaker 1: is the person who's going to find the other planet

815
00:42:25,480 --> 00:42:27,319
Speaker 1: that we're going to? You know, like that there are

816
00:42:27,400 --> 00:42:31,440
Speaker 1: people whose job is to answer the equations and make

817
00:42:31,480 --> 00:42:33,400
Speaker 1: the theories that figure out where we could live like

818
00:42:33,520 --> 00:42:35,040
Speaker 1: that kind of stuff. I don't think you think about

819
00:42:35,040 --> 00:42:36,440
Speaker 1: that on a day to day basis, but there's some

820
00:42:36,560 --> 00:42:38,640
Speaker 1: kid who might be listening who goes, I'm going to

821
00:42:38,640 --> 00:42:42,320
Speaker 1: be the person who answered that question. But I do

822
00:42:42,440 --> 00:42:45,160
Speaker 1: think a lot of people do do PhDs on extremely

823
00:42:45,280 --> 00:42:48,400
Speaker 1: specific theoretical questions and then don't really know what to

824
00:42:48,520 --> 00:42:52,239
Speaker 1: do with it afterwards in the workforce. And you have

825
00:42:52,360 --> 00:42:54,600
Speaker 1: not only then gone on to like write a book

826
00:42:54,680 --> 00:42:57,600
Speaker 1: and educate people about space, but then have also requalified

827
00:42:57,640 --> 00:42:59,520
Speaker 1: in like something completely different back on Earth.

828
00:43:00,400 --> 00:43:00,720
Speaker 2: AI.

829
00:43:01,760 --> 00:43:05,520
Speaker 1: So you've done a PhD in astrophysics you've moved away

830
00:43:05,560 --> 00:43:08,759
Speaker 1: from like the nuts and bolts and spools and flannels

831
00:43:09,040 --> 00:43:13,719
Speaker 1: of engineering and then written this book on space are

832
00:43:13,800 --> 00:43:16,160
Speaker 1: you working? Like, do you consider that as work as

833
00:43:16,160 --> 00:43:19,840
Speaker 1: an astrophysicist? And then why AI and going back to

834
00:43:19,880 --> 00:43:22,040
Speaker 1: do an entire master's on that and then a whole

835
00:43:22,080 --> 00:43:22,439
Speaker 1: new book.

836
00:43:22,680 --> 00:43:25,680
Speaker 2: Yes, yes, all right, this is another big question.

837
00:43:26,360 --> 00:43:27,000
Speaker 1: We haven't got it.

838
00:43:27,280 --> 00:43:28,879
Speaker 2: They're all kind of multi.

839
00:43:28,680 --> 00:43:30,560
Speaker 1: Part yeah, because I'm like, oh my god, how do

840
00:43:30,640 --> 00:43:31,360
Speaker 1: I get all it? Like?

841
00:43:31,400 --> 00:43:36,800
Speaker 2: And I still like Jesus Okay, So do I consider

842
00:43:36,840 --> 00:43:40,360
Speaker 2: it's I think in the purest sense, a scientist is

843
00:43:40,400 --> 00:43:44,520
Speaker 2: someone who's publishing with some level of frequency. You're writing papers,

844
00:43:44,520 --> 00:43:47,719
Speaker 2: you're writing papers, you're publishing in scientific journals. So during

845
00:43:47,760 --> 00:43:51,000
Speaker 2: my PhD, I was working as an astrophysicist. I was

846
00:43:51,040 --> 00:43:53,399
Speaker 2: publishing papers. I think by the end of my degree,

847
00:43:53,560 --> 00:43:55,600
Speaker 2: I think there was a bit of an overhang. I

848
00:43:55,680 --> 00:43:57,640
Speaker 2: had some papers in the work. So the last paper

849
00:43:57,680 --> 00:44:00,960
Speaker 2: I published was in twenty twenty and it wasn't a

850
00:44:01,000 --> 00:44:03,759
Speaker 2: first author. I was like fourth or fifth author or

851
00:44:03,800 --> 00:44:08,200
Speaker 2: something like that. So I ended up I think over

852
00:44:08,239 --> 00:44:11,400
Speaker 2: the whole court, I had about eight papers for first author.

853
00:44:11,800 --> 00:44:14,920
Speaker 2: That's quite Yeah, that's that's good. Typically for a PhD,

854
00:44:15,000 --> 00:44:16,960
Speaker 2: you need to have at least one first author. The

855
00:44:17,040 --> 00:44:19,960
Speaker 2: idea of PhD is you're adding new ideas to the

856
00:44:20,000 --> 00:44:24,480
Speaker 2: body of knowledge, and a paper's sort of evidence of that. Yeah,

857
00:44:24,719 --> 00:44:27,560
Speaker 2: you typically need at least one, but you know, three

858
00:44:27,640 --> 00:44:29,880
Speaker 2: or four is really great result. I was thrilled to

859
00:44:29,960 --> 00:44:32,120
Speaker 2: get four out of the door for that. So in

860
00:44:32,200 --> 00:44:36,640
Speaker 2: the purest sense, I'm not actively working as an astrophysicist

861
00:44:36,640 --> 00:44:39,919
Speaker 2: because I'm not actively researching and I'm not actively publishing. Okay,

862
00:44:40,000 --> 00:44:42,319
Speaker 2: I would say in the same way, though, I am

863
00:44:42,400 --> 00:44:44,760
Speaker 2: an astrophysicist in the same art. So I'm an engineer.

864
00:44:44,800 --> 00:44:47,880
Speaker 2: I've got the qualification, I've worked in the field. I

865
00:44:48,040 --> 00:44:50,560
Speaker 2: just would say I'm not actively working as an engineer

866
00:44:50,600 --> 00:44:51,480
Speaker 2: an astrophysicist.

867
00:44:51,640 --> 00:44:54,400
Speaker 1: Yeah, okay, that was my question, Like what is working

868
00:44:54,440 --> 00:44:56,680
Speaker 1: as an astrophysicist? Is it a research role or are

869
00:44:56,719 --> 00:44:58,680
Speaker 1: you working for NASA? Like I think people don't actually

870
00:44:58,760 --> 00:45:03,000
Speaker 1: understand what you're doing, and I mean I imagine.

871
00:45:02,719 --> 00:45:04,160
Speaker 2: It the same as you you're still a lawyer.

872
00:45:04,440 --> 00:45:07,600
Speaker 1: Yeah, I'm just lawyer. In Yeah, you're not actively.

873
00:45:07,320 --> 00:45:09,319
Speaker 2: Working and so I think, yeah, if if you've got

874
00:45:09,360 --> 00:45:11,279
Speaker 2: the credentials and you've worked in that space, and I

875
00:45:11,440 --> 00:45:15,120
Speaker 2: think you just lose that because you're no longer practicing. Yeah, totally,

876
00:45:15,440 --> 00:45:20,600
Speaker 2: it's just you're not practicing. So I think that's one definition,

877
00:45:20,719 --> 00:45:22,879
Speaker 2: probably the one I use or lean on the most

878
00:45:22,960 --> 00:45:24,719
Speaker 2: in terms of the book. So I think you've got

879
00:45:24,760 --> 00:45:26,840
Speaker 2: a few And I'm going to answer this because it

880
00:45:26,960 --> 00:45:29,040
Speaker 2: kind of ties in with what you're saying. Maybe people

881
00:45:29,040 --> 00:45:30,960
Speaker 2: aren't sure where you can go with a with a PhD.

882
00:45:31,600 --> 00:45:36,200
Speaker 2: You've got a few options, and there's three options and

883
00:45:36,320 --> 00:45:39,120
Speaker 2: I did two of them. One is going down the

884
00:45:39,200 --> 00:45:44,400
Speaker 2: full academic route, just becoming doing a post university continuing

885
00:45:44,520 --> 00:45:47,640
Speaker 2: down the research path. That's really really great. I love

886
00:45:47,719 --> 00:45:50,759
Speaker 2: the research. If I didn't end up doing what I

887
00:45:50,880 --> 00:45:55,279
Speaker 2: did in terms of, you know, moving into I guess

888
00:45:55,320 --> 00:45:57,319
Speaker 2: the public space a bit more and become a more

889
00:45:57,360 --> 00:46:00,480
Speaker 2: active science communicator, I think I probably would have pursued

890
00:46:00,760 --> 00:46:02,839
Speaker 2: at least a few more years of research going down

891
00:46:02,880 --> 00:46:05,600
Speaker 2: the post dot path because it is really research is

892
00:46:05,719 --> 00:46:11,799
Speaker 2: really fun because you're doing something you're really excited about. Yeah,

893
00:46:11,800 --> 00:46:12,120
Speaker 2: I love it.

894
00:46:12,239 --> 00:46:12,680
Speaker 1: I love it.

895
00:46:13,640 --> 00:46:16,920
Speaker 2: Research is fun. And I think you definitely don't even

896
00:46:17,040 --> 00:46:20,839
Speaker 2: know that is a career path when you're selecting your

897
00:46:21,040 --> 00:46:24,319
Speaker 2: degrees or when you're finishing high school, you don't even

898
00:46:24,320 --> 00:46:26,520
Speaker 2: know that's a thing you can do. So that's one

899
00:46:26,560 --> 00:46:28,120
Speaker 2: of the passing it down. The second one is going

900
00:46:28,160 --> 00:46:31,160
Speaker 2: into industry, which is what I did first. At the time,

901
00:46:31,640 --> 00:46:33,960
Speaker 2: data science was still I wouldn't say that was new,

902
00:46:34,040 --> 00:46:38,200
Speaker 2: but it was becoming more trendy and more common. The

903
00:46:38,320 --> 00:46:43,000
Speaker 2: degrees in data science were still quite new, so most firms,

904
00:46:43,520 --> 00:46:46,880
Speaker 2: data science consultancies or just places looking for data scientists.

905
00:46:47,719 --> 00:46:52,680
Speaker 2: We're looking for typically PhDs, like highly analytical PhDs that

906
00:46:52,800 --> 00:46:55,680
Speaker 2: they could then fill the gaps and unskill them as

907
00:46:55,719 --> 00:46:59,040
Speaker 2: a data scientist because the Bachelor of Data Science or

908
00:46:59,080 --> 00:47:01,839
Speaker 2: the Masters of Data Sciences weren't quite a mature degree yet.

909
00:47:01,840 --> 00:47:04,400
Speaker 2: That were still it was still gaining a bit of momentum.

910
00:47:05,120 --> 00:47:07,279
Speaker 2: So that was good for me because it meant, you know,

911
00:47:07,320 --> 00:47:09,360
Speaker 2: if I had have been competing with this cohort of

912
00:47:09,440 --> 00:47:12,040
Speaker 2: new data scientists coming out, I probably would have had

913
00:47:12,080 --> 00:47:16,480
Speaker 2: a real hard time getting a data science position. Fortunately

914
00:47:16,520 --> 00:47:19,239
Speaker 2: for me, timing wise, they were still quite new, so

915
00:47:19,280 --> 00:47:21,880
Speaker 2: they're like, yeah, we'll take the PhD, you know, the nerd.

916
00:47:22,440 --> 00:47:24,080
Speaker 1: The nerdy will take the nerd.

917
00:47:23,920 --> 00:47:25,840
Speaker 2: Who knows how to program and it's worked, you know,

918
00:47:25,920 --> 00:47:29,120
Speaker 2: with spools and flangs. Yeah, so that's the other one industry.

919
00:47:29,160 --> 00:47:30,920
Speaker 2: The third one is and that's kind of where I've

920
00:47:31,000 --> 00:47:33,080
Speaker 2: ended up now, which is going into science communication. And

921
00:47:33,160 --> 00:47:35,560
Speaker 2: so that's what I do now very actively through social

922
00:47:35,600 --> 00:47:39,880
Speaker 2: media but also through my books speaking all of that

923
00:47:40,000 --> 00:47:43,360
Speaker 2: fun stuff. And I love that and a lot of

924
00:47:43,400 --> 00:47:45,440
Speaker 2: my friends also have science communicators and they love it

925
00:47:45,480 --> 00:47:48,920
Speaker 2: as well. And it's definitely a different skill set, and

926
00:47:49,040 --> 00:47:52,440
Speaker 2: it's one like any skill or any muscle, you need

927
00:47:52,560 --> 00:47:55,759
Speaker 2: to train it. And I'm sure it's the same as

928
00:47:55,840 --> 00:47:59,880
Speaker 2: a MC and keynote speaker that you are. It's a

929
00:48:00,040 --> 00:48:02,600
Speaker 2: muscle that you've had to train over time. It's not

930
00:48:02,719 --> 00:48:04,560
Speaker 2: something you just suddenly can do. And I think a

931
00:48:04,560 --> 00:48:07,280
Speaker 2: lot of people see someone like yourself is really polished

932
00:48:07,280 --> 00:48:09,440
Speaker 2: at doing that, and they're like, oh, she's just really talented,

933
00:48:09,440 --> 00:48:12,600
Speaker 2: and it's like she's talented, but there's thousands of hours

934
00:48:12,760 --> 00:48:13,600
Speaker 2: high as well.

935
00:48:14,080 --> 00:48:16,080
Speaker 1: Yeah look at the graph, Yeah, look at the graph.

936
00:48:16,239 --> 00:48:19,000
Speaker 2: Right, she didn't just wake up and suddenly became this

937
00:48:19,200 --> 00:48:22,759
Speaker 2: dominant kenot speaker an MC. Right, it's like anything, and

938
00:48:22,880 --> 00:48:26,040
Speaker 2: science communication is the same. To become quite effective at it,

939
00:48:26,600 --> 00:48:30,360
Speaker 2: you need to yeah, sing hundreds thousands of ours into it.

940
00:48:30,400 --> 00:48:34,360
Speaker 2: And I was doing that before The Bachelor. The Bachelor,

941
00:48:34,440 --> 00:48:38,040
Speaker 2: certainly it was an accelerant, and it's a bit of

942
00:48:38,080 --> 00:48:41,840
Speaker 2: the peanut butter as well for the dog medicine. But

943
00:48:41,960 --> 00:48:44,040
Speaker 2: I was before that. I was already starting to do

944
00:48:44,320 --> 00:48:46,359
Speaker 2: more of it. I was doing some radio interviews here

945
00:48:46,440 --> 00:48:48,600
Speaker 2: and there. I've done some public lectures. I've done a

946
00:48:48,680 --> 00:48:49,279
Speaker 2: fringe show.

947
00:48:50,840 --> 00:48:51,040
Speaker 1: Yeah.

948
00:48:51,280 --> 00:48:54,120
Speaker 2: So, and I did some fringe shows after Bachelor as well,

949
00:48:54,160 --> 00:48:56,239
Speaker 2: but I'd done one before. So I was starting to

950
00:48:56,280 --> 00:49:02,200
Speaker 2: push into the science communication space and this accelerated tremendously.

951
00:49:02,440 --> 00:49:05,560
Speaker 1: Okay, that makes more sense now because in the dots

952
00:49:05,600 --> 00:49:09,440
Speaker 1: connecting thing, I was like, why be so passionate about astrophysics,

953
00:49:09,680 --> 00:49:11,600
Speaker 1: write a book about space, but then go back and

954
00:49:11,640 --> 00:49:15,040
Speaker 1: do another master's in AI to then have data scientists roles,

955
00:49:15,080 --> 00:49:17,759
Speaker 1: which seemed like this like unrelated pivot. But now I

956
00:49:17,800 --> 00:49:22,160
Speaker 1: can kind of see how connecting Yes, Yeah, like, oh,

957
00:49:22,239 --> 00:49:26,399
Speaker 1: it's like a transferable skill. That's like the logical logical jump.

958
00:49:26,440 --> 00:49:29,520
Speaker 1: Even though you've come like it's not necessarily space focused.

959
00:49:30,400 --> 00:49:33,719
Speaker 1: Now it's like and talking about science communication doing it

960
00:49:33,760 --> 00:49:35,560
Speaker 1: for children as well. So your latest book is a

961
00:49:35,719 --> 00:49:38,920
Speaker 1: children's book, which sounds easier, but I actually think distilling

962
00:49:39,719 --> 00:49:43,600
Speaker 1: complicated things into child friendly explanations is harder.

963
00:49:44,040 --> 00:49:46,560
Speaker 2: Yeah, it was actually just before we churned it out,

964
00:49:46,560 --> 00:49:48,040
Speaker 2: I thought, because it'd be remiss with me. Not so

965
00:49:48,040 --> 00:49:54,160
Speaker 2: at least because the biggest scandal I had was that

966
00:49:54,280 --> 00:49:56,640
Speaker 2: I worked at NAB. I don't know if you recall

967
00:49:56,800 --> 00:50:00,759
Speaker 2: this story. So I've never worked at NAB. I worked

968
00:50:00,800 --> 00:50:04,400
Speaker 2: at a data science consultancy called Quantium, and they have

969
00:50:04,600 --> 00:50:07,400
Speaker 2: a number of different departments. They've got like a health department,

970
00:50:07,440 --> 00:50:10,799
Speaker 2: They've got like an FMCG so fast moving consumer goods

971
00:50:11,040 --> 00:50:13,480
Speaker 2: were worse goals all that, and they had a banking

972
00:50:13,560 --> 00:50:15,840
Speaker 2: and finance department which I was in at. One of

973
00:50:15,880 --> 00:50:19,800
Speaker 2: their clients was NAB. And so as a consultant, you

974
00:50:20,320 --> 00:50:23,200
Speaker 2: often will they'll have to create a NAB account for

975
00:50:23,239 --> 00:50:26,600
Speaker 2: you because you need to access their systems. Oh my god,

976
00:50:27,320 --> 00:50:30,160
Speaker 2: someone I don't even know how it. I don't know

977
00:50:30,200 --> 00:50:32,239
Speaker 2: if Science saw that I had a NAB email and

978
00:50:32,280 --> 00:50:37,200
Speaker 2: they're like, oh, he's a banker. What And I remember

979
00:50:37,239 --> 00:50:39,200
Speaker 2: this thing coming up and I got an email from

980
00:50:39,280 --> 00:50:42,920
Speaker 2: someone at NAB and they're like, hey, someone, a media

981
00:50:42,960 --> 00:50:44,640
Speaker 2: person just called us and asked if you worked at

982
00:50:44,760 --> 00:50:48,120
Speaker 2: NAB and I'm like, well, I mean I work, you know,

983
00:50:48,200 --> 00:50:50,279
Speaker 2: I don't worry. What what do you mean? I'm like,

984
00:50:50,360 --> 00:50:51,480
Speaker 2: what do you want us to say? I'm like, well,

985
00:50:51,480 --> 00:50:54,200
Speaker 2: I don't care. Like I was like, this is just

986
00:50:54,280 --> 00:50:58,640
Speaker 2: a weird question. Are you a banker? Well? No, Anyway,

987
00:50:58,800 --> 00:51:00,600
Speaker 2: it became this big thing that like, oh, he's not

988
00:51:00,600 --> 00:51:03,319
Speaker 2: an astrophysically, he's a banker. And I was like, oh, well,

989
00:51:03,360 --> 00:51:05,759
Speaker 2: I mean as far as scandals go, this is pretty tame,

990
00:51:06,760 --> 00:51:09,880
Speaker 2: but also like my biggest I'm not until apparently I

991
00:51:09,960 --> 00:51:11,840
Speaker 2: didn't know this because I'm like working at the Quantum

992
00:51:11,920 --> 00:51:13,920
Speaker 2: office and I had, you know, some of the more

993
00:51:13,960 --> 00:51:15,640
Speaker 2: senior guys would have to go to the NAB building

994
00:51:15,719 --> 00:51:18,120
Speaker 2: regularly to talk to some of the senior execs there,

995
00:51:18,520 --> 00:51:19,680
Speaker 2: and I remember one of them came in and they

996
00:51:19,719 --> 00:51:23,080
Speaker 2: said that there's like several paps waiting there. I'm parently

997
00:51:23,200 --> 00:51:25,759
Speaker 2: hoping they could get a far off me. Yeah, trying

998
00:51:25,760 --> 00:51:27,680
Speaker 2: to They I guess wanted a photo me walking into

999
00:51:27,680 --> 00:51:28,920
Speaker 2: the NAB building or something.

1000
00:51:29,080 --> 00:51:29,400
Speaker 1: Interest.

1001
00:51:29,680 --> 00:51:31,880
Speaker 2: I was like, all right, this is the weird and

1002
00:51:31,960 --> 00:51:33,960
Speaker 2: I was like, what is going on? And then I

1003
00:51:34,000 --> 00:51:35,920
Speaker 2: had people like, oh, you work at NAB doing and

1004
00:51:35,920 --> 00:51:38,240
Speaker 2: I'm like, I've never worked at a bank in my life,

1005
00:51:38,960 --> 00:51:42,200
Speaker 2: so it's such a big thing. I'm modeling. I'm doing

1006
00:51:42,360 --> 00:51:47,000
Speaker 2: like predictive models on transaction data to try and build

1007
00:51:47,000 --> 00:51:50,080
Speaker 2: out like approval processes for certain things. And it was

1008
00:51:50,160 --> 00:51:52,200
Speaker 2: just anyway, it was really amusing to me because I

1009
00:51:52,239 --> 00:51:53,720
Speaker 2: was like, this is the weirdest scandal.

1010
00:51:53,880 --> 00:51:55,800
Speaker 1: Yeah, I can't believe this is even a story.

1011
00:51:55,920 --> 00:51:58,480
Speaker 2: Yeah, but I was like whatever, Anyway, I thought that

1012
00:51:58,480 --> 00:52:00,600
Speaker 2: would be an amusing anecdote because it was something at

1013
00:52:00,640 --> 00:52:02,680
Speaker 2: the time that was just like whole Matt, He's just

1014
00:52:02,800 --> 00:52:04,839
Speaker 2: deceived everyone, and I'm like, well, I haven't. And also

1015
00:52:05,000 --> 00:52:05,600
Speaker 2: and also why.

1016
00:52:05,520 --> 00:52:08,440
Speaker 1: Would being a banker be like such a secret a

1017
00:52:08,600 --> 00:52:09,200
Speaker 1: secret bag.

1018
00:52:09,480 --> 00:52:11,360
Speaker 2: It's like why would that be seen? It's like is

1019
00:52:11,440 --> 00:52:12,279
Speaker 2: that a bad thing?

1020
00:52:12,960 --> 00:52:14,200
Speaker 1: I mean in some schools of.

1021
00:52:14,239 --> 00:52:19,920
Speaker 2: Thought anyway, So I was quite using. Anyway, What was

1022
00:52:20,000 --> 00:52:20,680
Speaker 2: your actual.

1023
00:52:20,480 --> 00:52:25,360
Speaker 1: Question was this is problem the children? So why do

1024
00:52:25,440 --> 00:52:27,960
Speaker 1: you go from I am talking about space in these

1025
00:52:28,160 --> 00:52:32,800
Speaker 1: huge like science education black holes like big questions to

1026
00:52:34,200 --> 00:52:36,920
Speaker 1: is my phone reading my mind? Yes? So for children?

1027
00:52:37,040 --> 00:52:40,320
Speaker 2: Yes, big pivot, big pivot. So data science is essentially

1028
00:52:40,400 --> 00:52:43,879
Speaker 2: applied artificial intelligence. So you're going through data and you're

1029
00:52:44,000 --> 00:52:48,719
Speaker 2: using typically machine learning algorithms, which are the lifeblood of

1030
00:52:48,840 --> 00:52:52,680
Speaker 2: artificial intelligence. Everything AI is powered by machine learning algorithms,

1031
00:52:53,280 --> 00:52:57,960
Speaker 2: and you're applying them to different problems, usually with a

1032
00:52:58,120 --> 00:53:01,320
Speaker 2: ton of data to off. And it can be to

1033
00:53:01,400 --> 00:53:07,239
Speaker 2: make decisioning decisions or to inform decisions. Sometimes it can

1034
00:53:07,280 --> 00:53:09,960
Speaker 2: be predictive modeling to try and predict certain things about

1035
00:53:10,000 --> 00:53:13,799
Speaker 2: what's happening within certain spaces. Sometimes it can be really clever. Idea,

1036
00:53:13,800 --> 00:53:15,320
Speaker 2: I was working on an algorithm that was trying to

1037
00:53:15,400 --> 00:53:18,080
Speaker 2: discern how people were using a piece of software, what

1038
00:53:18,200 --> 00:53:20,120
Speaker 2: they were actually doing with it, based on the actions

1039
00:53:20,160 --> 00:53:23,320
Speaker 2: and behaviors they were doing, what is their use case?

1040
00:53:23,840 --> 00:53:26,080
Speaker 2: So that was really cool. So data science is applied AI.

1041
00:53:26,160 --> 00:53:30,640
Speaker 2: So I was already doing AI. But the more exotic

1042
00:53:30,840 --> 00:53:36,560
Speaker 2: and exciting algorithms aren't really commonplace in enterprise. They're more

1043
00:53:36,640 --> 00:53:40,080
Speaker 2: in research and big tech companies like your facebooks and

1044
00:53:40,680 --> 00:53:43,239
Speaker 2: your Googles and stuff like that. And I really wanted

1045
00:53:43,239 --> 00:53:45,320
Speaker 2: to learn more about the exotic algorithms.

1046
00:53:45,360 --> 00:53:47,680
Speaker 1: Of course you did, so you were like, fuck it,

1047
00:53:47,680 --> 00:53:48,840
Speaker 1: I'll do a fifth degree.

1048
00:53:48,920 --> 00:53:50,839
Speaker 2: So I did. Yeah, So I went back and did

1049
00:53:51,120 --> 00:53:54,719
Speaker 2: the AI degree and that was fantastic. Got to do

1050
00:53:54,800 --> 00:53:57,319
Speaker 2: all the really cool stuff the neural networks, which are

1051
00:53:57,360 --> 00:54:01,040
Speaker 2: the things they power a lot of, like image recognition

1052
00:54:01,120 --> 00:54:05,080
Speaker 2: and computer vision generative or transformers, which are the.

1053
00:54:06,760 --> 00:54:12,279
Speaker 1: Stop laughing, the transformers. I'm just translating for everyone in

1054
00:54:12,400 --> 00:54:13,560
Speaker 1: other states of Australia.

1055
00:54:13,680 --> 00:54:16,279
Speaker 2: Yeah, the transformers which power that's what the T and

1056
00:54:16,360 --> 00:54:20,440
Speaker 2: GBT stands for, is based on neural networks. So I

1057
00:54:20,520 --> 00:54:22,880
Speaker 2: got to do some of them. Reinforcement learning, which is

1058
00:54:22,920 --> 00:54:26,120
Speaker 2: again a training mechanism. If you remember Alpha Go. Alpha

1059
00:54:26,160 --> 00:54:30,400
Speaker 2: Go was a AI they built to play the game Go,

1060
00:54:30,920 --> 00:54:34,080
Speaker 2: which is I don't even know. I feel like it's

1061
00:54:34,120 --> 00:54:37,319
Speaker 2: a Chinese game originally, and it's like something like you're

1062
00:54:37,360 --> 00:54:40,840
Speaker 2: putting balls down and you like jump them, and I

1063
00:54:40,920 --> 00:54:42,279
Speaker 2: can't remember how it works. It's a sort of like

1064
00:54:42,400 --> 00:54:45,160
Speaker 2: chess kind of stuff. But they build an algorithm called

1065
00:54:45,200 --> 00:54:49,360
Speaker 2: Alpha Go and they basically trained it using reinforcement learning,

1066
00:54:49,400 --> 00:54:51,000
Speaker 2: and so they didn't teach it the rules of it.

1067
00:54:51,440 --> 00:54:54,160
Speaker 2: They just had an algorithm that sort of rewarded what

1068
00:54:54,320 --> 00:54:57,440
Speaker 2: it did based on like a few different kind of mechanisms,

1069
00:54:57,880 --> 00:55:00,880
Speaker 2: and then got it to learn from existing games, but

1070
00:55:00,960 --> 00:55:04,400
Speaker 2: also just playing like a billion games, and based on

1071
00:55:04,480 --> 00:55:06,799
Speaker 2: what it did, it kind of got rewards to incentivize

1072
00:55:06,840 --> 00:55:09,640
Speaker 2: it to do certain things. And so they trained this

1073
00:55:09,920 --> 00:55:13,000
Speaker 2: algorithm from scratch without teaching it the game. It learnt

1074
00:55:13,040 --> 00:55:16,640
Speaker 2: it itself, and it ended up and it now beats

1075
00:55:16,719 --> 00:55:20,400
Speaker 2: humans the time. It just dominates. So that was a

1076
00:55:20,440 --> 00:55:23,920
Speaker 2: really cool application because I think with sort of the

1077
00:55:24,080 --> 00:55:28,200
Speaker 2: chess algorithms, they were trained on existing chess games, so

1078
00:55:28,239 --> 00:55:30,600
Speaker 2: it was like, you know, he's a million games of

1079
00:55:30,719 --> 00:55:34,000
Speaker 2: chess to try and teach it, whereas Alpha go was

1080
00:55:34,320 --> 00:55:36,239
Speaker 2: we're not going to tell you the games, which is

1081
00:55:36,239 --> 00:55:38,520
Speaker 2: going to get you to play it a trillion figure now,

1082
00:55:39,000 --> 00:55:40,560
Speaker 2: and we're going to reward you when you do good

1083
00:55:40,600 --> 00:55:42,320
Speaker 2: stuff and punish you when you do bad stuff. And

1084
00:55:42,400 --> 00:55:44,680
Speaker 2: it just figured out and it started doing strategies that

1085
00:55:44,760 --> 00:55:48,320
Speaker 2: humans hadn't done before because it could because that's the

1086
00:55:49,040 --> 00:55:52,879
Speaker 2: humans over all the Alpha games ever might have played.

1087
00:55:52,920 --> 00:55:55,520
Speaker 2: You know, I don't know a billion games. You know,

1088
00:55:55,600 --> 00:55:58,600
Speaker 2: they made this algorithm play ten billion. Of course, if

1089
00:55:58,600 --> 00:56:00,600
Speaker 2: you played ten times and many games might come up

1090
00:56:00,600 --> 00:56:02,160
Speaker 2: with a strategy that you wouldn't have come up with

1091
00:56:02,239 --> 00:56:04,880
Speaker 2: in the first billion games. So I had these strategies

1092
00:56:04,880 --> 00:56:06,800
Speaker 2: that developed. They were like, no humans figured out that

1093
00:56:06,840 --> 00:56:09,359
Speaker 2: strategy yet, and so it was just a really cool

1094
00:56:09,600 --> 00:56:12,560
Speaker 2: demonstration of reinforcement learning. So that's why I went back

1095
00:56:12,600 --> 00:56:16,200
Speaker 2: to do the Masters, because I liked doing applied AI

1096
00:56:16,719 --> 00:56:19,560
Speaker 2: in industry, but I wanted to know more about the

1097
00:56:19,719 --> 00:56:25,960
Speaker 2: really exotic, powerful, interesting algorithms that aren't commonplace in industry yet. Yeah,

1098
00:56:26,360 --> 00:56:29,279
Speaker 2: so that was the motivation. In terms of the book. Yeah,

1099
00:56:29,280 --> 00:56:31,239
Speaker 2: I agree with you. Writing for a younger age group

1100
00:56:31,440 --> 00:56:35,759
Speaker 2: is more challenging. You're limited by number of words as well.

1101
00:56:36,320 --> 00:56:38,560
Speaker 2: I think the upper limit at the time when I

1102
00:56:38,640 --> 00:56:41,000
Speaker 2: first started that children's book, I think it was only

1103
00:56:41,120 --> 00:56:43,160
Speaker 2: like twelve thousand words.

1104
00:56:43,200 --> 00:56:45,719
Speaker 1: Oh my god to even discuss AI at all.

1105
00:56:46,200 --> 00:56:49,000
Speaker 2: But I went well over that. I think I landed

1106
00:56:49,040 --> 00:56:50,640
Speaker 2: on about seventeen or eighteen thousand.

1107
00:56:51,000 --> 00:56:53,880
Speaker 1: I remember episodes. Amount of words, yeah.

1108
00:56:53,960 --> 00:56:56,480
Speaker 2: And I remember telling the past. Look, I can trim

1109
00:56:56,560 --> 00:56:58,440
Speaker 2: it down, but I think I'm going to and I

1110
00:56:58,680 --> 00:57:00,960
Speaker 2: tried done a really hard acts cunt. I'm like, this

1111
00:57:01,040 --> 00:57:04,440
Speaker 2: one's like fourteen thousand, and I'm like it's missing, it's

1112
00:57:04,520 --> 00:57:06,680
Speaker 2: losing a lot of stuff, and they're like, nah, if

1113
00:57:06,719 --> 00:57:08,600
Speaker 2: you reckon the seventeen k is the one. Let's do that.

1114
00:57:08,719 --> 00:57:11,440
Speaker 2: Then they've been a wonderful publisher, Alan and ant One,

1115
00:57:11,680 --> 00:57:14,000
Speaker 2: And yeah, it was really challenging keeping the word limit

1116
00:57:14,120 --> 00:57:18,360
Speaker 2: down and then also to your point, using easily accessible

1117
00:57:18,440 --> 00:57:25,040
Speaker 2: words and explaining complex concepts and often abstract concepts in

1118
00:57:25,160 --> 00:57:28,720
Speaker 2: a way that's engaging and accessible for a younger age group,

1119
00:57:28,760 --> 00:57:34,440
Speaker 2: but also not so childlike that adults won't enjoy it. Yeah,

1120
00:57:34,480 --> 00:57:37,880
Speaker 2: because I'd still wanted it's targeted for eight to twelve

1121
00:57:37,920 --> 00:57:40,200
Speaker 2: year olds, but I wanted people, you know, high schoolers

1122
00:57:40,280 --> 00:57:41,840
Speaker 2: or adults to also be able to pick it up

1123
00:57:41,880 --> 00:57:42,760
Speaker 2: and enjoy reading it.

1124
00:57:43,400 --> 00:57:47,560
Speaker 1: And my dad loves it, and he's's so sweet and.

1125
00:57:47,680 --> 00:57:51,400
Speaker 2: He used to work as a CEO in WA and

1126
00:57:51,600 --> 00:57:54,360
Speaker 2: he obviously through his work there met a lot of

1127
00:57:54,440 --> 00:57:58,200
Speaker 2: other C suite execs and he buys loads of my

1128
00:57:58,240 --> 00:58:02,600
Speaker 2: book and gives them copies. Say, you know this is

1129
00:58:02,840 --> 00:58:04,720
Speaker 2: it's a short book because you can it's a target

1130
00:58:04,720 --> 00:58:06,760
Speaker 2: a young age. You can build out a couple of hours.

1131
00:58:07,200 --> 00:58:09,440
Speaker 2: I have some fundamental understanding artificial intelligence.

1132
00:58:09,480 --> 00:58:10,200
Speaker 1: Oh my god.

1133
00:58:10,400 --> 00:58:12,960
Speaker 2: So it was a challenge, but I really loved it

1134
00:58:13,080 --> 00:58:16,560
Speaker 2: because it's ended up as a book that children can enjoy,

1135
00:58:16,920 --> 00:58:19,680
Speaker 2: but the amount of adults and in particular parents who

1136
00:58:19,720 --> 00:58:21,760
Speaker 2: have told me that they've really enjoyed it and found

1137
00:58:21,760 --> 00:58:24,640
Speaker 2: it really handy because yeah, they can read it quickly

1138
00:58:24,640 --> 00:58:28,200
Speaker 2: and now they feel more empowered around AI. I'm really

1139
00:58:28,240 --> 00:58:28,720
Speaker 2: happy with it.

1140
00:58:30,400 --> 00:58:31,840
Speaker 1: I'm so glad with that we had a chance to

1141
00:58:31,880 --> 00:58:33,320
Speaker 1: talk more about it, because I feel like we didn't

1142
00:58:33,320 --> 00:58:36,439
Speaker 1: have time last time, and it is like I think.

1143
00:58:36,960 --> 00:58:38,640
Speaker 1: One of the things I love on this show is

1144
00:58:38,720 --> 00:58:42,440
Speaker 1: how random people's careers can end up being, and how

1145
00:58:42,520 --> 00:58:44,640
Speaker 1: you can we're all multi passionate. Not many people wake

1146
00:58:44,720 --> 00:58:45,760
Speaker 1: up and go, I want to be a doctor. I'm

1147
00:58:45,760 --> 00:58:47,960
Speaker 1: going to just do medicine. Even doctors are like, which

1148
00:58:48,040 --> 00:58:49,640
Speaker 1: kind of medicine? What kind of direction do you want

1149
00:58:49,680 --> 00:58:51,280
Speaker 1: to go? Do you want to go private practice hospitals?

1150
00:58:51,320 --> 00:58:53,720
Speaker 1: Like what part of the body? I think we yeah, again,

1151
00:58:53,800 --> 00:58:56,920
Speaker 1: go back to that like siloing of the simplicity of categorization,

1152
00:58:58,000 --> 00:59:00,120
Speaker 1: but and think our scientist has to look like this.

1153
00:59:00,240 --> 00:59:02,680
Speaker 1: But you've done books, You've done adult books, children's books,

1154
00:59:02,720 --> 00:59:05,680
Speaker 1: space AI And then on top of that, like to

1155
00:59:05,880 --> 00:59:09,480
Speaker 1: full circle back to the beginning. The question about dating

1156
00:59:09,800 --> 00:59:13,360
Speaker 1: post quitting alcohol and whether that was easy or hard

1157
00:59:13,520 --> 00:59:17,600
Speaker 1: or socializing post alcohol solved the problem of the non

1158
00:59:17,680 --> 00:59:21,640
Speaker 1: OLC landscape being like pretty poor. When we both first

1159
00:59:21,680 --> 00:59:24,800
Speaker 1: stopped drinking alcohol, you decided, look, I've done five degrees,

1160
00:59:25,000 --> 00:59:28,680
Speaker 1: I've got two books, I understand a business. Yeah, so

1161
00:59:28,880 --> 00:59:32,600
Speaker 1: you are now a founder and ceosh.

1162
00:59:32,760 --> 00:59:34,800
Speaker 2: I still feel like a wanker putting founder.

1163
00:59:34,520 --> 00:59:38,520
Speaker 1: In my type, but like talk about multi passionate and

1164
00:59:38,680 --> 00:59:44,240
Speaker 1: like multifaceted careers that all have they all it's like

1165
00:59:44,320 --> 00:59:45,800
Speaker 1: cohesive in its own weird way.

1166
00:59:46,120 --> 00:59:48,760
Speaker 2: Yeah, no, thank you. So, yeah, it was I stopped

1167
00:59:48,800 --> 00:59:51,840
Speaker 2: drinking at start at twenty twenty, which, yeah, obviously we

1168
00:59:51,960 --> 00:59:54,560
Speaker 2: met after that period, so we were both obviously teattlers,

1169
00:59:55,040 --> 00:59:58,000
Speaker 2: and yeah, we touched on it before we started recording this,

1170
00:59:58,080 --> 01:00:00,080
Speaker 2: But obviously during the era when you stopp drink it

1171
01:00:00,160 --> 01:00:04,520
Speaker 2: was even harder twenty like mid twenty tens is zero. Yeah,

1172
01:00:04,600 --> 01:00:05,600
Speaker 2: Beryl was your option.

1173
01:00:06,040 --> 01:00:08,919
Speaker 1: I think I was just like lemonon bitters, lemon age.

1174
01:00:09,040 --> 01:00:12,360
Speaker 2: Yeah, that was it. Sparkling Water twenty twenty. It was

1175
01:00:12,760 --> 01:00:16,160
Speaker 2: getting better. I think there was starting to have some more.

1176
01:00:16,240 --> 01:00:18,160
Speaker 2: I think Kalan zero and Heineken zero was starting to

1177
01:00:18,760 --> 01:00:22,000
Speaker 2: ramp up in quantity, and you started seeing some of

1178
01:00:22,080 --> 01:00:24,880
Speaker 2: the craft beers were coming through. So your heaps normal

1179
01:00:24,920 --> 01:00:28,480
Speaker 2: you're sober, not things like that. So they suddenly was

1180
01:00:28,720 --> 01:00:35,800
Speaker 2: some some more options and socializing and dating post giving

1181
01:00:35,880 --> 01:00:40,240
Speaker 2: up alcohol. Socializing was very hard initially.

1182
01:00:41,280 --> 01:00:42,120
Speaker 1: It's such a crutch.

1183
01:00:42,200 --> 01:00:45,360
Speaker 2: For such a crutch, you go through this period of

1184
01:00:46,000 --> 01:00:47,400
Speaker 2: without alcohol, Am I boring?

1185
01:00:49,760 --> 01:00:51,080
Speaker 1: What is my personality? It's fun?

1186
01:00:51,120 --> 01:00:54,200
Speaker 2: So yeah, exactly, And you've got this idea that yeah

1187
01:00:54,280 --> 01:00:58,960
Speaker 2: you've if you've used it so frequently when you're socializing,

1188
01:00:59,560 --> 01:01:01,640
Speaker 2: you're how people actually want to talk to me if

1189
01:01:01,640 --> 01:01:03,480
Speaker 2: I'm not not drinking. I mean, it doesn't take you

1190
01:01:03,560 --> 01:01:05,200
Speaker 2: long to realize, well, yeah, actually they're talking to you,

1191
01:01:05,280 --> 01:01:06,680
Speaker 2: They're not like talking to the alcohol.

1192
01:01:07,120 --> 01:01:08,919
Speaker 1: Well sometimes, but that's fine.

1193
01:01:09,000 --> 01:01:14,720
Speaker 2: Yeah, yeah, I love you mate, Yes, but yeah, I

1194
01:01:14,800 --> 01:01:18,400
Speaker 2: think initially, and I imagine it was probably similar for yourself.

1195
01:01:19,240 --> 01:01:20,800
Speaker 2: You kind of for the first six months, I reckon

1196
01:01:20,840 --> 01:01:23,600
Speaker 2: I needed an excuse. I need I'm doing fair Fast

1197
01:01:23,640 --> 01:01:26,080
Speaker 2: and doing drudge. Youly, oh really enjoyed drudge a life.

1198
01:01:26,080 --> 01:01:29,640
Speaker 2: I'm doing dry August, so training for something, just having

1199
01:01:30,480 --> 01:01:33,560
Speaker 2: an excuse. And I tell people all the time that

1200
01:01:33,840 --> 01:01:36,320
Speaker 2: you know, that's initially, that's that's that's fine if that's

1201
01:01:36,320 --> 01:01:38,600
Speaker 2: what you need initially, because it is really hard when

1202
01:01:38,640 --> 01:01:41,320
Speaker 2: you first stop drinking to have the commonness to tell

1203
01:01:41,400 --> 01:01:43,920
Speaker 2: people you're not drinking. And so initially, if you need

1204
01:01:44,040 --> 01:01:46,280
Speaker 2: to just roll out an excuse, then then do that

1205
01:01:46,640 --> 01:01:49,880
Speaker 2: until you feel comfortable and confident in saying it. I

1206
01:01:49,960 --> 01:01:52,200
Speaker 2: also joke a lot. It's the only substance you need

1207
01:01:52,280 --> 01:01:55,800
Speaker 2: to justify why you're not doing it. Isn't so interesting

1208
01:01:56,160 --> 01:01:58,280
Speaker 2: no one else you know, you don't rock up to

1209
01:01:58,360 --> 01:02:01,040
Speaker 2: a party and nine's going, oh, not doing heroin? To

1210
01:02:02,200 --> 01:02:03,360
Speaker 2: do you have to You've got to get up and

1211
01:02:03,720 --> 01:02:08,800
Speaker 2: tomorrow right, yeah, no ketamine?

1212
01:02:09,200 --> 01:02:11,600
Speaker 1: Oh you're driving okay, fair, fair fad.

1213
01:02:12,080 --> 01:02:15,840
Speaker 3: Otherwise like yeah, it's like otherwise people are like, they're like,

1214
01:02:15,960 --> 01:02:19,040
Speaker 3: why are you not drinking this poison? What is it?

1215
01:02:21,520 --> 01:02:24,320
Speaker 2: Yeah, you could to get up here tomorrow.

1216
01:02:24,560 --> 01:02:28,120
Speaker 1: Yeah, it's not heroin day to day maybe later. Yeah.

1217
01:02:28,560 --> 01:02:31,840
Speaker 2: So it's this bizarre substance that for some reason you

1218
01:02:31,960 --> 01:02:35,720
Speaker 2: need to justify while you're not doing it. And so yeah,

1219
01:02:35,800 --> 01:02:37,520
Speaker 2: initially it took me a while to get there. Eventually

1220
01:02:37,560 --> 01:02:39,080
Speaker 2: I got to the place where I was very confident

1221
01:02:39,160 --> 01:02:43,240
Speaker 2: and also much more comfortable about my own mental illness

1222
01:02:43,280 --> 01:02:46,120
Speaker 2: as well, so I could tell people, yeah, I don't drink,

1223
01:02:46,480 --> 01:02:48,960
Speaker 2: and if they pride. I could say I don't drink

1224
01:02:49,000 --> 01:02:51,000
Speaker 2: because I have mental illness and it shouldn't take a

1225
01:02:51,000 --> 01:02:54,800
Speaker 2: sign as to you know, add a depressant to depression

1226
01:02:55,320 --> 01:02:55,600
Speaker 2: and go.

1227
01:02:56,360 --> 01:03:01,720
Speaker 1: No, that graph is is. Yeah, it's I'm really laboring

1228
01:03:01,760 --> 01:03:08,080
Speaker 1: the graft and get off your case. Really really just

1229
01:03:08,520 --> 01:03:10,760
Speaker 1: laying it on you. I'm sorry, it's just too funny.

1230
01:03:14,200 --> 01:03:14,840
Speaker 2: Have a part.

1231
01:03:15,120 --> 01:03:16,280
Speaker 1: Why is it not called park?

1232
01:03:17,520 --> 01:03:18,080
Speaker 2: Don't it?

1233
01:03:19,800 --> 01:03:21,040
Speaker 1: Why is it not called park?

1234
01:03:21,880 --> 01:03:23,040
Speaker 2: The rest of this episode?

1235
01:03:24,240 --> 01:03:25,520
Speaker 1: Please do consistent.

1236
01:03:26,360 --> 01:03:29,360
Speaker 2: But yeah, so I'm very comfortable being able to say, look,

1237
01:03:29,800 --> 01:03:33,040
Speaker 2: I've got mental illness, and it just ruins that. Even further,

1238
01:03:33,440 --> 01:03:36,280
Speaker 2: it shouldn't have taken me that long to figure out

1239
01:03:36,360 --> 01:03:40,680
Speaker 2: how bad this was to my existing mental illness illnesses.

1240
01:03:41,280 --> 01:03:43,840
Speaker 2: So yeah, it took me a while initially to get

1241
01:03:43,840 --> 01:03:47,560
Speaker 2: to that place of being comfortable saying that socializing. Now

1242
01:03:47,640 --> 01:03:51,320
Speaker 2: it's it's easy, especially because I guess I've been quite

1243
01:03:51,400 --> 01:03:53,480
Speaker 2: public about the fact I don't drink and I've got

1244
01:03:53,520 --> 01:03:56,280
Speaker 2: mental illness. It's you know, everyone kind of already is

1245
01:03:56,320 --> 01:03:58,919
Speaker 2: aware of that. I have been at times where people

1246
01:03:59,000 --> 01:04:00,880
Speaker 2: buy like around the kill, and I go, I don't drink.

1247
01:04:01,200 --> 01:04:02,840
Speaker 2: I just saw you're drinking a beer I'm like, it's

1248
01:04:02,840 --> 01:04:03,960
Speaker 2: a calm zero. Yeah.

1249
01:04:04,960 --> 01:04:07,680
Speaker 1: Yeah, get with the program. It's twenty twenty four.

1250
01:04:07,880 --> 01:04:10,880
Speaker 2: They think like I'm trying to get out of a shot.

1251
01:04:10,920 --> 01:04:13,720
Speaker 2: I'm like, mate, I have done a shot for you know, five.

1252
01:04:13,640 --> 01:04:16,680
Speaker 1: Years, maybe when I was having mob child. When I

1253
01:04:16,720 --> 01:04:18,720
Speaker 1: was pregnant, people would you med a side I I'm like, okay,

1254
01:04:18,880 --> 01:04:21,280
Speaker 1: it's pre juice, calm down.

1255
01:04:22,000 --> 01:04:24,080
Speaker 2: I had a friend who when she was pregnant, I

1256
01:04:24,440 --> 01:04:26,120
Speaker 2: would rock up to a picnic and I'm but I've

1257
01:04:26,120 --> 01:04:27,760
Speaker 2: got some heidaken zeros if you want one. This is

1258
01:04:27,880 --> 01:04:31,240
Speaker 2: before parsh obviously, it's past now obviously, but that some

1259
01:04:31,280 --> 01:04:34,120
Speaker 2: heindn zeros goes No, I can't because like people just look,

1260
01:04:34,200 --> 01:04:37,840
Speaker 2: they can't see that it's a zero. Can You're just

1261
01:04:37,960 --> 01:04:41,400
Speaker 2: like smashing a bu well pregnant And I was like okay, yeah,

1262
01:04:41,480 --> 01:04:41,880
Speaker 2: fair enough.

1263
01:04:41,880 --> 01:04:45,160
Speaker 1: I mean, well if you want to, they're there to

1264
01:04:45,240 --> 01:04:46,560
Speaker 1: the party, I understand.

1265
01:04:46,680 --> 01:04:49,080
Speaker 2: So yeah, it's funny. There's that kind of extra level

1266
01:04:49,320 --> 01:04:51,960
Speaker 2: or extra thing that women have to face when not

1267
01:04:52,080 --> 01:04:57,560
Speaker 2: drinking as well. But yeah, so and and dating again,

1268
01:04:57,640 --> 01:05:00,040
Speaker 2: the same thing I had with am I interesting. I

1269
01:05:00,120 --> 01:05:02,960
Speaker 2: was like, what if I rock up and it's like, gee,

1270
01:05:03,000 --> 01:05:03,840
Speaker 2: this guy's a dud.

1271
01:05:03,960 --> 01:05:08,960
Speaker 1: This yes, boring on the flipping. So, oh my god.

1272
01:05:09,520 --> 01:05:14,240
Speaker 1: He ordered a green tea with his spaghetti.

1273
01:05:20,560 --> 01:05:22,360
Speaker 2: Yeah, man, this guy he was all right. And then

1274
01:05:22,400 --> 01:05:24,960
Speaker 2: he just like had a pub whole spaghetti and the

1275
01:05:25,000 --> 01:05:27,760
Speaker 2: green tea. He poured the green tea in the spaghetti

1276
01:05:27,880 --> 01:05:28,400
Speaker 2: like he.

1277
01:05:28,520 --> 01:05:30,080
Speaker 1: Just got really weird, freaking psycho.

1278
01:05:30,960 --> 01:05:33,240
Speaker 2: I didn't have a date. One's actually with a woman

1279
01:05:33,320 --> 01:05:35,920
Speaker 2: who she picked the venue. It was like a rooftop bar.

1280
01:05:36,600 --> 01:05:38,080
Speaker 2: We got there. I was like, I'm going to grab

1281
01:05:38,120 --> 01:05:39,640
Speaker 2: a beer and you can I get you a drink,

1282
01:05:39,880 --> 01:05:41,919
Speaker 2: which is back when I was still drinking. He goes, oh, yeah,

1283
01:05:41,960 --> 01:05:46,120
Speaker 2: you're just a glass of warm water like a tea

1284
01:05:46,200 --> 01:05:47,520
Speaker 2: and just no, just warm water.

1285
01:05:48,200 --> 01:05:50,400
Speaker 1: Which is actually do you know better for your inside?

1286
01:05:50,600 --> 01:05:53,400
Speaker 2: Yes, yes, I do not know that. So I went

1287
01:05:53,480 --> 01:05:55,640
Speaker 2: to the bar and I was like, oh, yeah, just

1288
01:05:55,680 --> 01:05:57,520
Speaker 2: a pint of can't remember. And I go, look, this

1289
01:05:57,600 --> 01:05:59,520
Speaker 2: is gonna be a bit of an odd order. But

1290
01:06:00,000 --> 01:06:01,480
Speaker 2: and he goes, now I've heard them all before. I go, oh,

1291
01:06:01,680 --> 01:06:07,520
Speaker 2: just a glass of warm water. He goes. He goes like,

1292
01:06:08,240 --> 01:06:11,040
Speaker 2: like tap water. I think, so, I don't know.

1293
01:06:11,400 --> 01:06:13,920
Speaker 1: So and he never went back to the team again.

1294
01:06:14,040 --> 01:06:18,040
Speaker 2: Yeah, it was a nice date, but yeah, it was

1295
01:06:18,160 --> 01:06:20,400
Speaker 2: just that was a yeah, so that was not green

1296
01:06:20,480 --> 01:06:25,800
Speaker 2: tea but close spaghetty. But yeah, I think initially the

1297
01:06:25,840 --> 01:06:28,160
Speaker 2: same thing the dates, and especially I think it's very

1298
01:06:28,200 --> 01:06:30,880
Speaker 2: commonplace for people you're a bit nervous for a day,

1299
01:06:31,000 --> 01:06:33,360
Speaker 2: maybe you have a settler. Yeah, and so not having

1300
01:06:33,400 --> 01:06:38,360
Speaker 2: that as well, now I'm so comfortable with who I

1301
01:06:38,520 --> 01:06:41,040
Speaker 2: am and with might not drink that I rock up

1302
01:06:41,080 --> 01:06:44,000
Speaker 2: and it's completely fine, and it's you know, you remember,

1303
01:06:44,080 --> 01:06:47,240
Speaker 2: and everyone I know who stops drinking goes through a

1304
01:06:47,280 --> 01:06:50,360
Speaker 2: similar phase and then they're like, oh yeah, no, you

1305
01:06:50,480 --> 01:06:54,640
Speaker 2: kind of rediscover yourself a bit in that. Oh yeah

1306
01:06:54,720 --> 01:06:58,360
Speaker 2: that the alcohol wasn't my personality, Like my personality was

1307
01:06:58,440 --> 01:07:01,120
Speaker 2: my personality? Yeah yeah, yeah, kind of had started relying

1308
01:07:01,160 --> 01:07:04,160
Speaker 2: on this thing to draw that out, where ITAs reality,

1309
01:07:04,160 --> 01:07:05,320
Speaker 2: you can draw it out whenever you like.

1310
01:07:05,560 --> 01:07:06,240
Speaker 1: That's so true.

1311
01:07:06,840 --> 01:07:08,600
Speaker 2: You just kind of forget a little bit if you've,

1312
01:07:08,800 --> 01:07:11,280
Speaker 2: if you've relied on alcohol a bit for it. So yeah,

1313
01:07:11,360 --> 01:07:13,800
Speaker 2: dating now is fine. I get some really sweet things

1314
01:07:13,840 --> 01:07:15,920
Speaker 2: that whether you know, we'll be able to at a

1315
01:07:16,000 --> 01:07:18,840
Speaker 2: bar and the lady will ask is okay if I

1316
01:07:18,920 --> 01:07:21,680
Speaker 2: have a dream? Yeah, of course, I'm like, it's like,

1317
01:07:21,840 --> 01:07:28,600
Speaker 2: I'm not a gremlin, la. I mean, but it's sweet

1318
01:07:28,600 --> 01:07:30,400
Speaker 2: because that is a thing for some people where it

1319
01:07:30,480 --> 01:07:33,600
Speaker 2: can be a triggering thing. Typically they will avoid a

1320
01:07:33,640 --> 01:07:36,200
Speaker 2: bar altogether. So by virtue of the fact that I'm

1321
01:07:36,280 --> 01:07:39,440
Speaker 2: organizing dates at bars, they can feel relaxed, you know.

1322
01:07:40,400 --> 01:07:41,880
Speaker 1: But it's a really sweet thing to.

1323
01:07:41,960 --> 01:07:45,600
Speaker 2: Ask, because depending on someone's relationship, that can be just

1324
01:07:45,680 --> 01:07:47,200
Speaker 2: by having a drink in front of someone can be

1325
01:07:47,280 --> 01:07:50,440
Speaker 2: really challenging. So I'm lucky that that's not the case

1326
01:07:50,480 --> 01:07:52,840
Speaker 2: for me. So yeah, I always say, you know, I

1327
01:07:52,920 --> 01:07:55,040
Speaker 2: don't drink, but if you want to go for it,

1328
01:07:55,520 --> 01:07:59,120
Speaker 2: there's yeah, just drink whatever you want. Like, I literally

1329
01:07:59,160 --> 01:08:02,520
Speaker 2: don't care. I love they've got stunkered on the day. Yeah,

1330
01:08:03,680 --> 01:08:05,600
Speaker 2: we're probably not compatible.

1331
01:08:06,240 --> 01:08:07,920
Speaker 1: Social calendar activity, isn't it.

1332
01:08:09,600 --> 01:08:11,520
Speaker 2: Yeah, So yeah, I was getting up tomorrow morning for.

1333
01:08:11,560 --> 01:08:13,800
Speaker 1: A run, but they're on the way then.

1334
01:08:14,280 --> 01:08:17,639
Speaker 2: Yeah, so I think I think, you know, no one's

1335
01:08:17,720 --> 01:08:21,080
Speaker 2: doing that really, So sorry. As dating goes, I'm so

1336
01:08:21,240 --> 01:08:24,720
Speaker 2: comfortable with myself and yeah, with whoever I want to

1337
01:08:24,800 --> 01:08:27,280
Speaker 2: date with, I'm like, yeah, if you want to drink, drink.

1338
01:08:27,320 --> 01:08:28,960
Speaker 2: If you don't want to drink, don't drink like whatever

1339
01:08:29,280 --> 01:08:30,320
Speaker 2: you feel comfortable doing.

1340
01:08:30,520 --> 01:08:31,320
Speaker 1: She's your an adventure.

1341
01:08:31,360 --> 01:08:32,240
Speaker 2: Choose your own adventure.

1342
01:08:32,560 --> 01:08:34,320
Speaker 1: Well, I love that your adventure that you choose is

1343
01:08:34,360 --> 01:08:36,880
Speaker 1: always like one hundred and fifty million out of ten.

1344
01:08:37,880 --> 01:08:39,800
Speaker 1: Like I'm going to quit drinking, so I'm going to

1345
01:08:39,840 --> 01:08:41,240
Speaker 1: start a business. And it's like I'm going to be

1346
01:08:41,320 --> 01:08:43,559
Speaker 1: interested in AI. I'm going to do another masters. It's

1347
01:08:43,600 --> 01:08:46,599
Speaker 1: like you just don't do anything like in a chill,

1348
01:08:47,560 --> 01:08:49,439
Speaker 1: lazy way. You're just like, I'm going to take it

1349
01:08:49,880 --> 01:08:51,680
Speaker 1: like to the max that I can. It's a very

1350
01:08:51,760 --> 01:08:54,679
Speaker 1: high achiever trade, which I like is like full dedication.

1351
01:08:55,280 --> 01:08:59,040
Speaker 1: So you've started your own alcoholic this is very pot kettle.

1352
01:08:59,640 --> 01:09:05,479
Speaker 2: Well make my own brand though, but you didn't, Well,

1353
01:09:05,560 --> 01:09:12,120
Speaker 2: hang on, but your green tea brand. Yeah, you're the one.

1354
01:09:12,720 --> 01:09:17,479
Speaker 2: This is true. You know it was he called me like, mate,

1355
01:09:17,520 --> 01:09:19,880
Speaker 2: she just got the pastor and green tea out and

1356
01:09:20,200 --> 01:09:21,080
Speaker 2: started the business.

1357
01:09:22,479 --> 01:09:25,080
Speaker 1: That's so true. Oh my god, take a long, hard

1358
01:09:25,080 --> 01:09:25,599
Speaker 1: look in the mirror.

1359
01:09:26,600 --> 01:09:30,280
Speaker 2: So yeah, he started started pash with a mate or

1360
01:09:30,280 --> 01:09:33,040
Speaker 2: a couple of mates and the friend that I did

1361
01:09:33,080 --> 01:09:37,479
Speaker 2: it with. We'd bounced around startup ideas for years. I

1362
01:09:37,560 --> 01:09:39,280
Speaker 2: had one that I actually got to an m v

1363
01:09:39,400 --> 01:09:42,720
Speaker 2: P minimum viable product. For those who aren't familiar with

1364
01:09:42,760 --> 01:09:46,720
Speaker 2: the term, it was an app called Socrates Way. It

1365
01:09:46,920 --> 01:09:49,720
Speaker 2: was built the website and the web app and the

1366
01:09:50,520 --> 01:09:53,160
Speaker 2: iPhone app, and it was basically it was like a

1367
01:09:53,360 --> 01:09:58,200
Speaker 2: marketplace for high school tutors. So your your parent, your

1368
01:09:58,240 --> 01:10:00,800
Speaker 2: child needs help for maths. You pull up this app,

1369
01:10:00,880 --> 01:10:04,000
Speaker 2: you type in your postcode, Maths comes up with a

1370
01:10:04,040 --> 01:10:06,519
Speaker 2: bunch of people. There's a review system, there's a booking system,

1371
01:10:06,600 --> 01:10:09,280
Speaker 2: all of that. Jazz got it to an MVP stage

1372
01:10:09,320 --> 01:10:11,439
Speaker 2: and then got really busy with my PhD, dropped it

1373
01:10:11,439 --> 01:10:13,840
Speaker 2: and never picked it up again. So we'd bounced around that,

1374
01:10:13,920 --> 01:10:17,000
Speaker 2: We bounced around a ton over the same person. And

1375
01:10:17,080 --> 01:10:20,200
Speaker 2: then eventually he it was about two years, I think,

1376
01:10:20,280 --> 01:10:22,439
Speaker 2: all a year and a half into my sobriety that

1377
01:10:22,560 --> 01:10:25,360
Speaker 2: he goes, would you, you know, would your fancy doing

1378
01:10:25,360 --> 01:10:27,160
Speaker 2: a bit of a we try a non olk startup.

1379
01:10:27,760 --> 01:10:29,439
Speaker 2: And I was like, well, yeah, you know, I've wanted

1380
01:10:29,479 --> 01:10:30,880
Speaker 2: to try a start up for a long time, and

1381
01:10:31,000 --> 01:10:34,000
Speaker 2: this is something that I'm authentically involved in and really

1382
01:10:34,040 --> 01:10:37,519
Speaker 2: passionate about. And so away we went and built it

1383
01:10:37,640 --> 01:10:40,160
Speaker 2: for probably twelve or eighteen months till we finally got

1384
01:10:40,200 --> 01:10:42,880
Speaker 2: to launch, and then now we've been trading for kind

1385
01:10:42,880 --> 01:10:44,519
Speaker 2: of coming up eighteen months at the end of the job.

1386
01:10:45,800 --> 01:10:48,719
Speaker 2: So it's yeah, it's been a it's been a really

1387
01:10:48,800 --> 01:10:54,200
Speaker 2: steep learning curve and having learning curve, and it's it's

1388
01:10:54,240 --> 01:10:57,240
Speaker 2: been really it was. It was a hard decision when

1389
01:10:57,280 --> 01:11:00,280
Speaker 2: I finally quit the nine to five, and one of

1390
01:11:00,320 --> 01:11:04,360
Speaker 2: the things I think that helped me make the decision was,

1391
01:11:05,200 --> 01:11:10,000
Speaker 2: even if this doesn't work out, the skills and professional

1392
01:11:10,080 --> 01:11:13,120
Speaker 2: development I'll have over the period of running a business

1393
01:11:13,920 --> 01:11:18,200
Speaker 2: will probably outweigh the professional development from just doing data

1394
01:11:18,240 --> 01:11:21,800
Speaker 2: science for the same period of time. I can kind

1395
01:11:21,840 --> 01:11:24,680
Speaker 2: of always jump back into data science the skill set

1396
01:11:24,720 --> 01:11:27,120
Speaker 2: that I've developed from running a business and not having

1397
01:11:27,240 --> 01:11:30,000
Speaker 2: kind of really you know, been involved in the bus.

1398
01:11:30,120 --> 01:11:32,680
Speaker 2: I didn't do economics or commerce at UNI. I was

1399
01:11:32,800 --> 01:11:34,680
Speaker 2: missing a lot of that skill set and so this

1400
01:11:34,840 --> 01:11:37,120
Speaker 2: was a way for me to kind of fill those gaps.

1401
01:11:37,400 --> 01:11:39,200
Speaker 1: I love you're trying to just be the ultimate human

1402
01:11:39,240 --> 01:11:41,000
Speaker 1: with skills in like every single.

1403
01:11:41,200 --> 01:11:47,320
Speaker 2: Because I want to, like, you know, they're like it's

1404
01:11:47,400 --> 01:11:49,160
Speaker 2: like one are your Degrees're like, well, I'm a doctor,

1405
01:11:49,280 --> 01:11:52,760
Speaker 2: like a physician doctor, but then also a PhD. Yeah,

1406
01:11:52,800 --> 01:11:55,360
Speaker 2: I also did an expedition in Antarctica for eight eight

1407
01:11:55,439 --> 01:11:57,400
Speaker 2: months and oh yeah what Else're like, Oh, I got

1408
01:11:57,439 --> 01:11:59,840
Speaker 2: ten thousand hours flying a chop and I'm like, yeah, okay,

1409
01:12:00,080 --> 01:12:04,080
Speaker 2: so yeah, cheers. I made a non aarcoholic beer, which

1410
01:12:04,120 --> 01:12:04,639
Speaker 2: is pretty good.

1411
01:12:05,080 --> 01:12:06,000
Speaker 1: Well, that's pretty great.

1412
01:12:07,080 --> 01:12:09,960
Speaker 2: It's just unfortunately it's not like in the application process

1413
01:12:10,000 --> 01:12:12,400
Speaker 2: to like, how many beverages have you invented?

1414
01:12:12,600 --> 01:12:14,360
Speaker 1: Well, you don't know, look on the website and see

1415
01:12:14,360 --> 01:12:16,960
Speaker 1: if the criteria that's listed on the website is not

1416
01:12:17,080 --> 01:12:18,080
Speaker 1: the criteria that you need.

1417
01:12:18,160 --> 01:12:20,559
Speaker 2: Put the pressure on the Swedes, get them.

1418
01:12:20,439 --> 01:12:21,280
Speaker 1: To write the website.

1419
01:12:21,760 --> 01:12:25,320
Speaker 2: So no, and look, I marginally joke there, but I

1420
01:12:25,479 --> 01:12:29,040
Speaker 2: do genuinely want to have an I applied to be

1421
01:12:29,080 --> 01:12:31,639
Speaker 2: an astronaut a couple of years back when European Space

1422
01:12:31,680 --> 01:12:32,799
Speaker 2: Agency opened its application.

1423
01:12:33,040 --> 01:12:36,200
Speaker 1: Really so you and Catherine you did mention this, were

1424
01:12:36,240 --> 01:12:39,120
Speaker 1: in the same ination application, coh.

1425
01:12:41,840 --> 01:12:45,880
Speaker 2: So, between the two of us we passed six out

1426
01:12:45,920 --> 01:12:49,400
Speaker 2: of seven rounds. Yes, six out of six were hers.

1427
01:12:50,640 --> 01:12:53,880
Speaker 2: But let's not get into that. Let's get into the

1428
01:12:53,960 --> 01:12:55,600
Speaker 2: data there average.

1429
01:12:57,320 --> 01:12:58,160
Speaker 1: As a cohort.

1430
01:13:00,040 --> 01:13:01,720
Speaker 2: Yeah, so I applied. There was I think it was

1431
01:13:01,720 --> 01:13:05,040
Speaker 2: about twenty seven thousand applicants in the process, and the

1432
01:13:05,200 --> 01:13:07,960
Speaker 2: first cut is ninety percent get cut. So I got

1433
01:13:08,000 --> 01:13:12,400
Speaker 2: cut then. But they usually open their doors every ten

1434
01:13:12,560 --> 01:13:14,720
Speaker 2: or eleven years, but I think they're trying to ramp

1435
01:13:14,760 --> 01:13:16,120
Speaker 2: it back up, so it might be as short as

1436
01:13:16,160 --> 01:13:16,679
Speaker 2: six to eight.

1437
01:13:17,000 --> 01:13:18,400
Speaker 1: So you do want to get to spac I want

1438
01:13:18,400 --> 01:13:19,200
Speaker 1: to have another crack.

1439
01:13:19,080 --> 01:13:21,320
Speaker 2: And so I've got you know, between that, I've got

1440
01:13:21,360 --> 01:13:23,839
Speaker 2: another kind of six years. So what are the skills

1441
01:13:23,840 --> 01:13:26,360
Speaker 2: I need to try and develop from here until then

1442
01:13:27,120 --> 01:13:30,360
Speaker 2: to make for a more compelling applicant.

1443
01:13:30,600 --> 01:13:33,240
Speaker 1: I love how logical you are about, like I want

1444
01:13:33,240 --> 01:13:36,000
Speaker 1: to get from A to B? What in my part chart?

1445
01:13:36,080 --> 01:13:37,920
Speaker 1: Am I missing from A to B? And you're the same.

1446
01:13:38,000 --> 01:13:40,360
Speaker 1: With dating, You're the same with upskilling, you're the same.

1447
01:13:40,400 --> 01:13:43,519
Speaker 1: But it's really interesting. It's like such a logical way

1448
01:13:43,520 --> 01:13:45,040
Speaker 1: to look at it, because most people go, I want

1449
01:13:45,040 --> 01:13:47,760
Speaker 1: to go from A to B and instra gratuity and like,

1450
01:13:48,160 --> 01:13:49,680
Speaker 1: I just what's the quickest way I can get there?

1451
01:13:49,760 --> 01:13:52,760
Speaker 1: And you're like, no, what am I missing? It's such

1452
01:13:52,800 --> 01:13:55,160
Speaker 1: a d driven way to look at it. But it's

1453
01:13:55,200 --> 01:13:57,880
Speaker 1: also like I just had an interview this morning where

1454
01:13:58,000 --> 01:14:00,080
Speaker 1: the question that you said is like, what was the

1455
01:14:00,120 --> 01:14:02,120
Speaker 1: worst case scenario? You go back to what you're doing before.

1456
01:14:02,280 --> 01:14:04,599
Speaker 1: And the thing I always try and like remind people

1457
01:14:04,600 --> 01:14:07,600
Speaker 1: when they have a big goal, especially one that seems impossible,

1458
01:14:07,720 --> 01:14:10,960
Speaker 1: like space A. Someone has to go. So someone is

1459
01:14:11,040 --> 01:14:13,479
Speaker 1: going to go the data is someone will why not you?

1460
01:14:14,200 --> 01:14:17,320
Speaker 1: And b the time is going to pass anyway. So

1461
01:14:17,479 --> 01:14:19,960
Speaker 1: when you said I'm going to be a data scientist

1462
01:14:20,160 --> 01:14:22,600
Speaker 1: and I'll learn X, or I'm going to get you know,

1463
01:14:22,720 --> 01:14:24,880
Speaker 1: do a business and I'll learn why, which is a

1464
01:14:24,960 --> 01:14:28,160
Speaker 1: million times X, It's like you're going to spend eighteen

1465
01:14:28,200 --> 01:14:30,320
Speaker 1: months either way, you might as well spend eighteen months

1466
01:14:30,400 --> 01:14:31,080
Speaker 1: learning the stuff.

1467
01:14:31,400 --> 01:14:35,280
Speaker 2: I say this exact thing. Oh my god, people are like, ah,

1468
01:14:36,000 --> 01:14:37,760
Speaker 2: should I You know, I'm thinking of going back to

1469
01:14:37,840 --> 01:14:39,960
Speaker 2: do medicine. But you know, by the time I finished,

1470
01:14:39,960 --> 01:14:43,000
Speaker 2: I'll be thirty six. I'm like, you can six anyway,

1471
01:14:43,160 --> 01:14:46,160
Speaker 2: Oh my god. Right, Like you turn thirty six and

1472
01:14:46,280 --> 01:14:47,400
Speaker 2: you've got a degree or you don't.

1473
01:14:47,560 --> 01:14:49,639
Speaker 1: Like, yeah, either you turn thirty six with the medical

1474
01:14:49,680 --> 01:14:52,000
Speaker 1: degree or you turn thirty six without it, doing exactly

1475
01:14:52,040 --> 01:14:53,479
Speaker 1: the same thing as you're doing a twenty.

1476
01:14:53,320 --> 01:14:55,760
Speaker 3: Six Yeah, Oh my god, it's like you did an

1477
01:14:55,760 --> 01:14:58,080
Speaker 3: agree and it's like, oh, well, you just delete it

1478
01:14:58,160 --> 01:14:58,919
Speaker 3: four years.

1479
01:14:59,200 --> 01:15:02,280
Speaker 2: You're forty now wake you skipped on. That's not how

1480
01:15:02,320 --> 01:15:05,040
Speaker 2: it works. The time is the time goes right, it's

1481
01:15:05,040 --> 01:15:07,519
Speaker 2: going to You're gonna eventually get there. So there's something

1482
01:15:07,600 --> 01:15:10,280
Speaker 2: you want to do, just just do it, like why not?

1483
01:15:10,560 --> 01:15:12,639
Speaker 2: And even if you start it and you decide actually

1484
01:15:12,640 --> 01:15:15,560
Speaker 2: it wasn't for me, Like, that's still the like the

1485
01:15:15,720 --> 01:15:17,599
Speaker 2: period when you stop, you're still going to get there.

1486
01:15:17,760 --> 01:15:24,040
Speaker 1: Yeah, I know, the time keeps going correct, just continuous

1487
01:15:24,200 --> 01:15:29,439
Speaker 1: to continuum science. We've solved We've solved the universe.

1488
01:15:29,560 --> 01:15:32,600
Speaker 2: We've solved life, which I thought we were based on

1489
01:15:32,680 --> 01:15:34,519
Speaker 2: our tangents. I thought we would solve life.

1490
01:15:34,560 --> 01:15:36,960
Speaker 1: I thought we had solved life. Turns out we hadn't

1491
01:15:38,479 --> 01:15:42,519
Speaker 1: there And I also have realized it is you are

1492
01:15:42,560 --> 01:15:45,920
Speaker 1: so multifaceted. It is impossible even as a two parter,

1493
01:15:46,400 --> 01:15:48,680
Speaker 1: Like I feel like we need a ten part for

1494
01:15:48,840 --> 01:15:50,920
Speaker 1: all the changes that we need. But I've already run

1495
01:15:51,000 --> 01:15:53,320
Speaker 1: us over time and solving life is a really great

1496
01:15:53,360 --> 01:15:57,160
Speaker 1: place y place. But I'm so glad that we got

1497
01:15:57,200 --> 01:15:59,360
Speaker 1: to visit some of the other facets of what you're

1498
01:15:59,360 --> 01:16:01,519
Speaker 1: doing and the fact that you do want to get

1499
01:16:01,560 --> 01:16:03,840
Speaker 1: to space because in six to eight years we'll do

1500
01:16:04,000 --> 01:16:08,600
Speaker 1: the anniversary episode, and I think the time will have

1501
01:16:08,640 --> 01:16:10,720
Speaker 1: passed anyway, so we'll be fascinating to hear what you

1502
01:16:10,880 --> 01:16:14,360
Speaker 1: have done in that pie chart. And like you said,

1503
01:16:14,479 --> 01:16:17,800
Speaker 1: worst case scenario is you've tried to position yourself for

1504
01:16:18,720 --> 01:16:21,160
Speaker 1: the next astronaut intake, you don't make it. You've got

1505
01:16:21,200 --> 01:16:23,600
Speaker 1: all these extra skills. It's not a waste, that's it.

1506
01:16:24,080 --> 01:16:24,240
Speaker 3: You know.

1507
01:16:24,640 --> 01:16:26,240
Speaker 2: And one of the ones I want to start doing

1508
01:16:26,320 --> 01:16:30,920
Speaker 2: is getting some pilot time chopper or plane. But that's like,

1509
01:16:31,439 --> 01:16:33,680
Speaker 2: you know, I don't get into the national programs. I

1510
01:16:33,760 --> 01:16:36,640
Speaker 2: got to learn how to fly a plane creationally.

1511
01:16:36,760 --> 01:16:39,600
Speaker 1: You know, I can drink and drive because pash not

1512
01:16:39,920 --> 01:16:40,880
Speaker 1: exactly exactly.

1513
01:16:41,240 --> 01:16:43,639
Speaker 2: Yeah, so you know, get up in the plane, crack

1514
01:16:43,720 --> 01:16:45,560
Speaker 2: at tinny like you can't do it. Actually on the

1515
01:16:45,600 --> 01:16:48,080
Speaker 2: way I can. On the way here. I was walking

1516
01:16:48,120 --> 01:16:49,639
Speaker 2: in with this and a guy goes a bit early

1517
01:16:49,680 --> 01:16:49,840
Speaker 2: for that.

1518
01:16:52,000 --> 01:16:55,120
Speaker 1: Actually, no, it's never too early for a part, never too.

1519
01:16:55,040 --> 01:16:56,920
Speaker 2: Early for a past, never too early for a past.

1520
01:16:57,000 --> 01:17:00,720
Speaker 2: So yeah, it's a great. It's Actually we we won

1521
01:17:01,000 --> 01:17:04,080
Speaker 2: as you did. We were in the World Beer Awards

1522
01:17:04,120 --> 01:17:07,240
Speaker 2: and we won Best Non Alcoholic beer in Australia that

1523
01:17:07,400 --> 01:17:10,520
Speaker 2: is insane. That some of the some of the big incumbents,

1524
01:17:10,840 --> 01:17:13,200
Speaker 2: that is amazing. So if you want to taste the

1525
01:17:13,280 --> 01:17:17,320
Speaker 2: best non alcoholic beer in Australia as determined by the

1526
01:17:17,400 --> 01:17:18,160
Speaker 2: World Beer.

1527
01:17:18,040 --> 01:17:24,720
Speaker 1: Awards, drink and also look out for Pash on Short Tank. Yes,

1528
01:17:26,080 --> 01:17:27,479
Speaker 1: I don't know if the episode will have come out

1529
01:17:27,479 --> 01:17:28,640
Speaker 1: by the time this does. Maybe not.

1530
01:17:28,880 --> 01:17:31,320
Speaker 2: Oh yeah, actually it sure depends. Yeah, you may have

1531
01:17:31,400 --> 01:17:33,720
Speaker 2: already seen me. You may you may not have.

1532
01:17:33,800 --> 01:17:35,600
Speaker 1: If you have not tune in.

1533
01:17:37,880 --> 01:17:46,640
Speaker 2: Shark Tank next chapter, the next chapter. Yes, you're going

1534
01:17:46,680 --> 01:17:52,439
Speaker 2: to Marthew Argue.

1535
01:17:52,479 --> 01:17:55,880
Speaker 1: Your name is spelled correctly to be Terrances, but unfortunately

1536
01:17:56,200 --> 01:17:56,439
Speaker 1: it's not.

1537
01:17:57,760 --> 01:18:02,080
Speaker 2: That's why they the folks wrote it down the light Terrans.

1538
01:18:07,640 --> 01:18:12,960
Speaker 1: Well, thanks for an amata, Thank you for joining me,

1539
01:18:13,800 --> 01:18:18,320
Speaker 1: Thank you, thank you well. As I said again, we

1540
01:18:18,400 --> 01:18:21,280
Speaker 1: could have gone so much further into all of these

1541
01:18:21,360 --> 01:18:23,760
Speaker 1: topics and many others, but I'm so glad that we

1542
01:18:23,880 --> 01:18:26,760
Speaker 1: at least came back for part two, and I hope

1543
01:18:26,760 --> 01:18:29,680
Speaker 1: that you guys enjoyed listening. All the links are in

1544
01:18:29,800 --> 01:18:33,080
Speaker 1: the show notes to his books to Pash to the

1545
01:18:33,160 --> 01:18:35,880
Speaker 1: other episodes that we mentioned in part one, where you

1546
01:18:35,920 --> 01:18:38,120
Speaker 1: can hear more about the behind the scenes from The

1547
01:18:38,160 --> 01:18:40,800
Speaker 1: Bachelor if that's what you're interested in, or much more

1548
01:18:40,880 --> 01:18:43,920
Speaker 1: detail about his mental health journey on Jess Rose podcast.

1549
01:18:44,200 --> 01:18:47,360
Speaker 1: And please, as always, do share the episode on socials

1550
01:18:47,400 --> 01:18:49,439
Speaker 1: do it now and take a screenshot and share it,

1551
01:18:49,560 --> 01:18:52,880
Speaker 1: tagging and doctor Matt Agnew to thank him for his time,

1552
01:18:53,439 --> 01:18:55,920
Speaker 1: generous vulnerability, and also just to let him know what

1553
01:18:56,040 --> 01:18:59,040
Speaker 1: you thought if there was anything that really resonated. I

1554
01:18:59,240 --> 01:19:03,280
Speaker 1: cannot believe we are in November already. Gosh, time is flying. Yes,

1555
01:19:03,439 --> 01:19:05,800
Speaker 1: I am back to saying that every single episode, but truly,

1556
01:19:05,960 --> 01:19:08,280
Speaker 1: I mean, it's just speeding up at such a rapid rate.

1557
01:19:08,320 --> 01:19:10,240
Speaker 1: In fact, I should have asked the astrophysicists about that

1558
01:19:10,640 --> 01:19:14,160
Speaker 1: the whole time space continuum thing, but it really does

1559
01:19:14,240 --> 01:19:17,200
Speaker 1: seem to just be escalating. I hope that you guys

1560
01:19:17,400 --> 01:19:19,960
Speaker 1: are enjoying having some yeay a little bit more regularly

1561
01:19:20,040 --> 01:19:22,360
Speaker 1: in your ears, and as always, please do let me

1562
01:19:22,400 --> 01:19:25,000
Speaker 1: know if you have any feedback, suggestions, or requests. We

1563
01:19:25,080 --> 01:19:27,280
Speaker 1: will take a short break over Christmas, but then we'll

1564
01:19:27,280 --> 01:19:29,760
Speaker 1: be back at this much more regular cadence next year

1565
01:19:29,840 --> 01:19:34,040
Speaker 1: and have some incredible other guests either already recorded or

1566
01:19:34,200 --> 01:19:36,720
Speaker 1: in the pipeline. It's going to be such a big

1567
01:19:36,800 --> 01:19:39,479
Speaker 1: twenty twenty five. It just is so lovely to be back.

1568
01:19:39,880 --> 01:19:42,479
Speaker 1: But in the meantime, I hope you guys are having

1569
01:19:42,600 --> 01:20:05,599
Speaker 1: a wonderful week and are seizing your yay assass