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Speaker 1: I get a team. It's the You Project. It's Patrick

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Speaker 1: and Tiffany and I'll be in the background here. My

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Speaker 1: name is Craig. Welcome to the show. Let's start with

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Speaker 1: forty two years and one day, shall we Birthday? Yesterday?

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Speaker 1: Birthday girl, Happy birthday.

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Speaker 2: That's me. It's my favorite daily d Do you know

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Speaker 2: what because I get a beautiful rendition of Happy Birthday

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Speaker 2: sung to me by the great Man, and I'd just

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Speaker 2: wake up looking forward to it.

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Speaker 3: Isn't that sweet? Yeah?

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Speaker 1: Yeah? I sing happy but well always into the message bank.

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Speaker 1: And you know, I think a good rendition of happy

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Speaker 1: Birthday is nice or a terrible one in my case,

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Speaker 1: but nonetheless.

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Speaker 3: It's a nice gesture, isn't it. Hey, Craig. You know

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Speaker 3: my friends who live in Hampton, my dear friends.

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Speaker 4: Quite often they will collectively get together and all sing

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Speaker 4: happy birthday to me and leave a message on my phone.

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Speaker 1: That's likely, isn't it.

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Speaker 3: Yeah.

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Speaker 1: I sing to my mum, Happy birthday. To my dad,

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Speaker 1: I sing happy anniversary on May twenty six, which is

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Speaker 1: coming up very soon. There's a lot of birthdays. I

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Speaker 1: know a lot of people whose birthday is in May.

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Speaker 1: A lot, So I wonder what what is that? When

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Speaker 1: does that mean people were being frisky? What month? So

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Speaker 1: it's the fifth I knew that.

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Speaker 3: When were they shagging?

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Speaker 1: When were they routing? Let's get let's does that make

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Speaker 1: it about September the year before or something.

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Speaker 2: Bit of spring shenanigans springing into action.

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Speaker 4: I feel like I need to introduce my a different

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Speaker 4: one of the topics for today. I was going to

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Speaker 4: talk about it later in the show, but for our

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Speaker 4: friends in the US, there's a real problem with tariffs

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Speaker 4: that people haven't really focused on as much, and it's

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Speaker 4: quite a concern because should people be panic buying vibrators

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Speaker 4: right now? There's a real well because most vibrators are

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Speaker 4: made in China, and so there's actually real concern out

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Speaker 4: there that you know, your favorite companion toy, if you

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Speaker 4: don't have them, to have nofully looking for children, you

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Speaker 4: might need to jump in and make a panic by

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Speaker 4: I know what.

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Speaker 2: I'll be asking for on my next birthday, then.

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Speaker 5: Good gracious going up in pride.

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Speaker 1: I don't know that everyone's worried about it. I think

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Speaker 1: that might be overstating it. I was going to say

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Speaker 1: that people still use those I really haven't thought about

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Speaker 1: that problem.

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Speaker 4: There are smart ones now, people who go away from

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Speaker 4: their partners, you can internet connect them. The Internet of

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Speaker 4: things has just to open up the whole world of

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Speaker 4: online sex.

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Speaker 1: Hang on, hang on, you might need to without without

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Speaker 1: going into terrortory, who we shouldn't, You might need to

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Speaker 1: unpack that there are partner ones now what does that mean? Patrick?

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Speaker 4: There are smart devices that you can so I can

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Speaker 4: I can turn on my lights when I'm away, I

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Speaker 4: can turn their condition to when I'm away from home.

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Speaker 4: So if you have a partner who has a smart device,

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Speaker 4: you could potentially engage in pleasure, mutual pleasuring.

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Speaker 2: They hold the butt, perhaps they hold the butt?

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Speaker 1: How do you know about this?

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Speaker 2: I don't know. I don't have a significant other to

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Speaker 2: play those games.

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Speaker 3: Why you know?

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Speaker 1: You know how you're straight and Patrick's not?

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Speaker 2: Maybe maybe disappointment, isn't it?

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Speaker 1: Well? I definitely think one of you should jump the

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Speaker 1: fence anyway. I mean imagine if you went Patrick, I'm

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Speaker 1: straight except for Tiff. Yeah, I mean sorry, I'm sorry,

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Speaker 1: I'm gay for Tip and vice versa.

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Speaker 3: Do you reckon?

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Speaker 1: You guys could have like a wild card where you

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Speaker 1: just go look. Generally speaking, we would be the best.

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Speaker 4: I think we could do everything together and hang out

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Speaker 4: because we're pretty good buds and I reckon we would

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Speaker 4: get on super super well, don't you reckon.

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Speaker 2: Tiff, I reckon.

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Speaker 3: Our dogs like each other. That's one of the most

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Speaker 3: important thing.

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Speaker 2: You just reminded me of a weird dream. I literally

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Speaker 2: woke up to this morning and just remembered. I won't

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Speaker 2: mention the name, but it was it was someone they've

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Speaker 2: been on my show, which gives nothing away because there's

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Speaker 2: been nine hundred episodes, but they rang me, not in

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Speaker 2: my sleep and not for real, but in my dream

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Speaker 2: they rang me and we were talking and chatting, and

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Speaker 2: then they said it was a bloke and he said

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Speaker 2: I'm in love with a man now and I was

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Speaker 2: like oh. And then at some point I woke up,

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Speaker 2: so I don't get the full story, but so random.

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Speaker 1: Do you ever do that? And you're just like, fuck,

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Speaker 1: I've got to go back to sleep to find out

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Speaker 1: who it.

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Speaker 2: Is, always bringing him and going are you Yeah.

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Speaker 4: One of the weirdest dreams I ever remember having as

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Speaker 4: a kid is I was in the middle of a

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Speaker 4: conversation with someone and then I got woken up and

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Speaker 4: then went back to sleep.

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Speaker 3: And resumed the conversation in my dream.

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Speaker 2: I love dreams. I love them so much.

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Speaker 4: Perhaps you did an episode recently on Lucid Dreaming. I

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Speaker 4: saw it and thought, I got to listen to that one.

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Speaker 4: So because I'm fascinated with the concept of lucid dreaming,

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Speaker 4: Well you did do it topically.

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Speaker 1: Yeah, that was actually an older app that we put

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Speaker 1: up because I was interstate this, so we we did

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Speaker 1: a little bit of a revisiting. But yeah, he was

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Speaker 1: really good. He was really good. So if you want to,

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Speaker 1: I'm really fascinated with that. Like I think there's an

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Speaker 1: oh no, we've kind of said this a bit. There's

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Speaker 1: so much that we don't understand about the mind and

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Speaker 1: the brain and consciousness and unconsciousness and the you know,

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Speaker 1: the other day I was I was going down that

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Speaker 1: rabbit hole of you know how people have said many times,

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Speaker 1: we have seventy thousand thoughts a day. Right, Have you

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Speaker 1: ever heard that figure, either of you? Oh yeah, yeah, yeah,

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Speaker 1: So that's a very common, but bullshit number. That's weird out.

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Speaker 1: So if you had seventy thousand thoughts a day, it

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Speaker 1: would mean that basically, in a twenty four hour day,

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Speaker 1: you have to have about one new original thought every second,

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Speaker 1: which clearly I mean and sometimes I don't have four

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Speaker 1: thoughts in an hour, right, so as if I'm having

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Speaker 1: fucking one thought every second, and so the perhaps the

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Speaker 1: more reliable number, and again it's it varies wildly, like

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Speaker 1: but it's somewhere maybe around six thousand. There's research that

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Speaker 1: says it's that, but even that, and then I was thinking, yeah, yeah,

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Speaker 1: I was thinking, I wonder if like what we're when

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Speaker 1: we're dreaming, is that thinking? Like, is that does that

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Speaker 1: counter's thoughts?

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Speaker 2: And the definition of a thought? Which sounds like a

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Speaker 2: dumb question, but what's the definition? Because then how are

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Speaker 2: they getting the number or measuring well?

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Speaker 1: Because when so the way that they do it is

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Speaker 1: there's a when you put I think it's an MRI

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Speaker 1: when you're when you're looking at the brain. You know,

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Speaker 1: under certain conditions, when people have a thought, there's a

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Speaker 1: change in brain activity and that correlates with a thought,

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Speaker 1: so they look at the Again, I'm not a neuroscientist,

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Speaker 1: I'm a more in the psych space, but my understanding

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Speaker 1: is that there's a visible kind of response in brain

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Speaker 1: activity when people have a thought. So obviously they can't

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Speaker 1: track thoughts per se, but they can track the physiological

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Speaker 1: representation of thoughts. But I still think six thousand is

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Speaker 1: too many. I think that's bullshit.

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Speaker 3: But that's you know, a staggering number those and in

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Speaker 3: a day.

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Speaker 4: But getting at old sleeping, but I mean, there's a

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Speaker 4: lucid Dreaming is one of the topics that I've always

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Speaker 4: been fascinated by. But also when we go to sleep,

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Speaker 4: we know how important sleep is to cleaning out our brain,

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Speaker 4: but we also know that when we dream sometimes it's

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Speaker 4: our subconscious mind working on problems as well. I mean,

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Speaker 4: I had a friend contact me who's an author, and

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Speaker 4: his publisher had designed a book cover for him and

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Speaker 4: he hated it. He didn't like the cover, and he

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Speaker 4: kept going back to them and the designer and it

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Speaker 4: was doing in front and so out of desperation he

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Speaker 4: contacted me and said, look, these are the concepts that

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Speaker 4: I've been thinking about, but they just can't get the

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Speaker 4: design right. And I said, okay, look I might have

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Speaker 4: a look at it tomorrow. That was a late night thing.

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Speaker 4: Went to sleep, woke up about four am. The entire

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Speaker 4: cover was in my mind, the finished cover was there.

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Speaker 4: I got out of bed, I took down some notes

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Speaker 4: and that was it. That was the entire book cover.

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Speaker 4: So I didn't do any work at all. Most subconscious

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Speaker 4: did all that. But I could visualize that I knew

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Speaker 4: what I wanted it to look like, and that it

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Speaker 4: was a matter of just putting it all together graphically

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Speaker 4: when I had the opportunity, and that ended up being

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Speaker 4: the book cover. But I didn't do any conscious work.

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Speaker 4: It was all subconscious.

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Speaker 1: That's so interesting. But you think about even in dreams,

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Speaker 1: when you are when you're dreaming something which is an

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Speaker 1: alternate reality of course. Like let's say you're walking down

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Speaker 1: a street and you in this dream and in this

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Speaker 1: dream you feel like you're in danger. Well, even in

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Speaker 1: the dream, you're still solving problems. You're thinking, Fuck, maybe

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Speaker 1: I better cross the street, or what if this? What if?

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Speaker 1: Like it is funny, your brain is still working, like

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Speaker 1: your brain's never not working, and it is thinking, but

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Speaker 1: it's a different kind of thinking. And I wonder, I

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Speaker 1: wonder when you're in that, Yeah, I wonder if you're

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Speaker 1: still getting the same level of recovery Patrick, when you're

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Speaker 1: when you're designing books in your sleep versus book covers.

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Speaker 1: I wonder if you get up. You're like, I'm fucked.

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Speaker 1: Why am I so fucked? I slept well, I was

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Speaker 1: fixing this motherfucker's book. That's why I'm so tired.

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Speaker 3: That's such a good question. Yeah, charged more, I wonder.

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Speaker 4: Yeah, that's a really fascinating thought, because you know, it's

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Speaker 4: the level of sleep you're getting too, whether it's rem

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Speaker 4: sleep in terms of what your brain is doing to

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Speaker 4: to kind of clean itself out.

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Speaker 3: I'm fascinated by it.

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Speaker 4: I'd love to sit down and have a long conversation

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Speaker 4: with a dream expert, because I reckon if I could

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Speaker 4: recall my dreams, I would wholy hollywoodize them, because I do.

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Speaker 3: I have the most of them. I did Parkour the

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Speaker 3: other night.

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Speaker 4: In my dreams, I was running around, I was doing

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Speaker 4: all this action stuff. I was jumping off buildings and

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Speaker 4: it was awesome. We could play it back.

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Speaker 1: Have you ever in a dream been like with someone

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Speaker 1: and the level of familiarity that you have with them.

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Speaker 1: In the dream, it's like, oh yeah, and you're talking

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Speaker 1: with this person, hanging out with this person, and it's

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Speaker 1: all very comfortable and normal and familiar. Then you wake

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Speaker 1: up and you're like, I don't even fucking hardly know

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Speaker 1: that person. Like in the dream, you have this relationship

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Speaker 1: that you don't have in real life, and it seems

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Speaker 1: very comfortable and normal. I remember nothing untoward happened. But

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Speaker 1: recently I had a dream with one of my friends.

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Speaker 1: His wife was in it, and we were having coffee

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Speaker 1: and talking and it's not like I don't like her,

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Speaker 1: but we don't know each other. And I mean, we

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Speaker 1: know each other to say hello, but that enough happened.

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Speaker 1: It wasn't sexual or romantic, but we're in this situation

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Speaker 1: together and it was like, Oh, this is what we do.

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Speaker 1: We hang out and we talk and we're talking about

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Speaker 1: all this stuff. And then I woke up. I'm like,

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Speaker 1: I don't even know her. Why is she in my dream?

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Speaker 1: And why are we such good friends in the dream.

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Speaker 5: Maybe you're a conscious stalker. Maybe maybe probably better to

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Speaker 5: do it in your dreams than real life, though, right my.

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Speaker 4: Friends have mentioned that you do walk past their house occasionally,

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Speaker 4: which is a bit creepy.

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Speaker 1: Well, that's just they happen to live in the same suburbs.

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Speaker 1: Maybe I don't know where their house is, so that's

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Speaker 1: probably that they're probably more creepy than they creepy. Your heart,

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Speaker 1: probably creepy planet every time I walk past their house

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Speaker 1: that I friends. Yeah, tip before we actually do a

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Speaker 1: proper episode, did you have a nice birthday? What did

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Speaker 1: you do? Tell the world how old you are? If

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Speaker 1: you're comfortable with that? And you mentioned before before, and

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Speaker 1: I'll shut up with this. Patrick and I are fair

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Speaker 1: that you often get a little bit sad e Mac sadster,

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Speaker 1: but you didn't I do.

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Speaker 2: It's really funny. I think I don't know what that's about.

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Speaker 2: A few issues, you know, but I think I sometimes

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Speaker 2: feel a bit a mix of excited and sad. On

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Speaker 2: my birthday, and I had the best birthday. I woke up,

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Speaker 2: I've got a beautiful I've got an email. First opened

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Speaker 2: eyes and I checked my emails and I'd been transferred

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Speaker 2: tickets from an awesome human for an awesome show. And

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Speaker 2: I was like, oh my god, it's my birthday. I

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Speaker 2: didn't even remember yet. So I got a present. I

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Speaker 2: never get presents. And then I had my course all day,

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Speaker 2: and then I went and had dinner with a really

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Speaker 2: good friend and I ate the most amazing banana chocolate

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Speaker 2: pancake I've ever eaten in my life, and it was

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Speaker 2: so good. Wow, and I've got the best birthday cat

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Speaker 2: and I had a cat on it got a picture

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Speaker 2: of cat and it says fuck your birthday, and the

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Speaker 2: cat's pushing over a vase. Friend wrote, this wasn't the

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Speaker 2: card I was gonna pick, but then I saw it

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Speaker 2: and thought of bear and I had to get it.

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Speaker 3: Yeah that makes sense. But yeah, exactly with the tipping

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Speaker 3: over stuff.

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Speaker 1: And so are you excited moving forward? Like do you

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Speaker 1: think the best is yet to come?

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Speaker 2: I think it is. I think the years go fast

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Speaker 2: now and I feel like I'm in a bit of

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Speaker 2: a change period and I don't know, I don't know,

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Speaker 2: I think have you ever?

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Speaker 1: The other day I did a gig in Queensland for

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Speaker 1: a company called el Gas, who are now I think

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Speaker 1: owned by boc Boch But anyway, so it was nearly

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Speaker 1: one hundred people, mainly dudes. I'll shut up after this,

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Speaker 1: but talking about this talking about age, So I do

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Speaker 1: this thing where and we're talking about you know, basically

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Speaker 1: looking after yourself, you know, and not waiting till shit breaks,

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Speaker 1: and how how many people wait till something goes wrong

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Speaker 1: and then they go, oh my god, I should really

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Speaker 1: eat well or maybe I should go for a jog,

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Speaker 1: or eat or drink less beer or whatever it is.

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Speaker 1: And so I do this thing where I put up

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Speaker 1: a big, straight, horizontal line across the whiteboard, and on

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Speaker 1: the left hand side is zero and on the far

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Speaker 1: right hand side of the line is eighty two, which

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Speaker 1: is the average stray and life expectancy. And then I go,

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Speaker 1: now put up your hand if you are around fifty.

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Speaker 1: And most of the room were around fifty, so then

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Speaker 1: I go. Then I scrub out like I just squiggle

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Speaker 1: through all the line up until fifty, and I go,

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Speaker 1: so this is what you've done, This is where you've been,

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Speaker 1: and if you live to a typical life expectancy of

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Speaker 1: an average Aussie, you've got if you're fifty five, you've

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Speaker 1: got twenty seven years to go. And everybody's like, ah fuck,

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Speaker 1: And I say, you could have way longer, you could

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Speaker 1: have less, who knows, but I'm just talking about statistically normal, right,

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Speaker 1: And I said, and if I'm normal, I've got twenty years.

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Speaker 1: Like I'm on the fucking home straight. And then we

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Speaker 1: just start to go, all right, well, if this is

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Speaker 1: where I am and this is how far I've got

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Speaker 1: to go, maybe what do I want that to look like.

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Speaker 1: And I think when you think about today in the

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Speaker 1: context of your whole life, it gives you a different perspective.

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Speaker 1: Like if you go, you're going to live to mid eighties,

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Speaker 1: let's hope you live to one hundred, but let's just

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Speaker 1: go statistically mid eighties. You're about halfway right now. And

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Speaker 1: I don't think it's a morbid thing. I think it's

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Speaker 1: a fucking fascinating thing to go cool. So I've done

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Speaker 1: half my life, give or take. What do I want

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Speaker 1: the second half of my life to look like? And

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Speaker 1: what do I need to do to make it be

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Speaker 1: what I want it to look like? Like? What decisions

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Speaker 1: do I need to make now? An action do I

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Speaker 1: need to take now to create this version the second

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Speaker 1: half of my life that I would like to inhabit.

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Speaker 1: So you start to get a little bit conscious and

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Speaker 1: strategic about living rather than incidental and unintentional living.

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Speaker 2: I reckon, it's looking at all the things I hold

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Speaker 2: on to or we hold on to, and why, like

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Speaker 2: we get to hear here, I am forty two, all

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Speaker 2: this stuff, all this experience, all this stuff, and then

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Speaker 2: I hold it and I'm like, okay, this is It's like,

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Speaker 2: hang on, how what parts of this will deliver it?

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Speaker 2: Why do I feel I don't know sometimes stack or

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Speaker 2: what is my identity in the middle of this? And

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Speaker 2: I feel like this year I've been slowing down a bit,

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Speaker 2: refocusing on a few things and then just going all right, well,

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Speaker 2: maybe this he is forever, and maybe it's not. Maybe

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Speaker 2: this is forever and maybe it's not. Maybe this is

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Speaker 2: like and maybe I'm just a beginner at life right now.

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Speaker 1: Well, your granddad's still around. He's one hundred and one,

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Speaker 1: isn't he? And is he still on Tinder?

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Speaker 3: Or yeah?

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Speaker 2: Yeah, he's still swiping away.

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Speaker 1: Yeah, still swiping. And Patrick's Patrick's fifty whatever going on

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Speaker 1: fifty seventeen fifty seven. You're not fifty seven. Yeah, I'm

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Speaker 1: almost fifty eight, fifty eight in July. Hell, you are

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Speaker 1: really the youngest fifty seven year old I've ever met

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Speaker 1: like you. No, no, no, I'm not even trying to

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Speaker 1: be nice to you. You're I reckon you're going to

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Speaker 1: live to one hundred. I reckon both of you live

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Speaker 1: to one hundred. I really do. I would put money

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Speaker 1: on both of you living too.

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Speaker 2: We're here to say it, yeah, exactly, We're here to

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Speaker 2: say it. HAPs I'll be I'll be so dead, you'll

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Speaker 2: be leading the pack.

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Speaker 1: You guys.

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Speaker 2: Melissa won't let that happen. Melissa won't let you move on.

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Speaker 1: I joke about dying. Melissa cracks the shits, although she

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Speaker 1: has been teach training AI on my voice so she

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Speaker 1: can do podcasts when I'm dead. So I don't know.

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Speaker 3: I reckon.

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Speaker 4: Melissa is going to do a Walt Disney on you. Your

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Speaker 4: head is going to end up in a little fridge somewhere.

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Speaker 1: In the yogenic cryogenic kind of chamber somewhere, just to

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Speaker 1: re animate me when the time comes, when the technology

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Speaker 1: is caught up. Could we put me on a better body?

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Speaker 1: Could we put me on like you know, C three

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Speaker 1: PO or R two D two? I just fucking wheel

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Speaker 1: around or Chewbacker. No, I want to be Chewbacker.

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Speaker 3: I can't. I can't do it.

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Speaker 1: That's actually pretty.

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Speaker 3: Much was joking.

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Speaker 1: Wow, Wow, I told you not to have oats for breakfast.

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Speaker 3: Yeah, that's what I was trying to do.

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Speaker 1: Let's let's talk about uh can I talk about?

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Speaker 3: Oh yeah, well yeah, technology. I guess that's what we're

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Speaker 3: here for now.

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Speaker 4: You know, when Tiff was talking about turning her age

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Speaker 4: and she and no way does she look her age.

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Speaker 4: And then when you started talking about the timeline and

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Speaker 4: scrubbing out everything beforehand and how much you've got left,

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Speaker 4: I thought, what the hell am I doing sitting here?

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Speaker 3: I should be doing something productive.

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Speaker 1: No, exactly exactly, but what I.

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Speaker 4: Was thinking was that the concept of time is so relative.

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Speaker 4: If you're practicing mindfulness and doing a meditation and in

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Speaker 4: my case, doing tai chi or tif you're in the ring,

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Speaker 4: boxing or crago, you're on a you know, standing up

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Speaker 4: in front of a whole lot of people, your perception

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Speaker 4: of time will change, you know, And that's something.

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Speaker 3: That we do have control of. You know.

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Speaker 4: Do we take pockets of time in our day to

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Speaker 4: slow down, to deep breathe, to get away from the

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Speaker 4: frenetic pace of our lives by deliberately slowing it down,

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Speaker 4: So yes, we can extend that time. And the reason

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Speaker 4: I was talking about that and the relative terms of

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Speaker 4: time is I saw that this week Apple was fined

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Speaker 4: eight hundred and ninety million dollars and Meta was fined

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Speaker 4: three hundred and fifty six million dollars for breaching the

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Speaker 4: new a EU laws. This basically, I'm not going to

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Speaker 4: go into all the details because that's not the that's

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Speaker 4: not not the angle I wanted to talk about. But effectively,

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Speaker 4: it's because they've got a monopoly on the App store,

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Speaker 4: on the Apple devices, and so the EU says it's

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Speaker 4: anticque competitive. Apple charges like a thirty percent on each

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Speaker 4: app purchase, so they're making lots of money. So I

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Speaker 4: did a bit of digging and I thought, okay, so

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Speaker 4: if Apple gets fined eight hundred and ninety million dollars,

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Speaker 4: is that a lot of money? I mean, it's a

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Speaker 4: lot of money for us. But Apple's worth three point

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Speaker 4: one six trillion, which is five trillion Australian dollars. Okay,

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Speaker 4: So eight hundred and ninety million is approximately zero point

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Speaker 4: one seven eight percent of five trillion, So we're talking

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Speaker 4: seventeen dollars. If we were comparing that with an average

402
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Speaker 4: wage of one hundred thousand dollars, it would be so

403
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Speaker 4: if you had a wage of one hundred thousand, it'd

404
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Speaker 4: be like you getting a seventeen dollar fine.

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Speaker 1: That's crazy, isn't it. Yeah, I don't have to you

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Speaker 1: know this off the top of your head. So a

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00:20:47,240 --> 00:20:52,000
Speaker 1: million is six zeros, yes, and a billion is nine zeros.

408
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Speaker 1: Is a trillion twelve zeros.

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Speaker 4: Again, I don't know, but you know what, I was

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Speaker 4: talking with my friend. I dog walk with a friend

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Speaker 4: Kim Tiff. You that you've met Kim, and we were

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Speaker 4: talking about this on our dog walk two days ago,

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Speaker 4: and Kim explained it the best way ever in terms

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Speaker 4: of how big the numbers are. She said, think of

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Speaker 4: it in terms of seconds. So I did some sums

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Speaker 4: and I thought, one million seconds is eleven and a

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Speaker 4: half days. One trillion seconds is equivalent to eleven point

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Speaker 4: five seven million days.

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Speaker 3: So that's thirty one thousand years.

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Speaker 1: That's crazy. I just asked Uncle Chap, JP tay and

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Speaker 1: it it is twelve zeros.

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00:21:35,840 --> 00:21:39,919
Speaker 3: Yeah, wow, it's just bring them out. Yeah, I mean.

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Speaker 1: There's I mean, you think about this like everything like time, money, yeah,

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Speaker 1: you know, resources, it's all It's all kind of context dependent, really,

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Speaker 1: isn't it Like depending on who you are, where you live,

426
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Speaker 1: what your situation, one thousand dollars is an incredible amount

427
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Speaker 1: of money. For somebody else, it's what they spend on

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Speaker 1: a business dinner, you know, on a weekly basis, or

429
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Speaker 1: or more than that. You know, it's like it's and

430
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Speaker 1: the same with you know when you're getting your teeth

431
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Speaker 1: pulled out or something. Three minutes seems like three hours

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Speaker 1: when you're meditating like you or something. Maybe three hours

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Speaker 1: of three minutes.

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Speaker 4: Yeah, there's a Scandinavian country and I can't remember which

435
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Speaker 4: one it might be Norway where when you get a

436
00:22:28,359 --> 00:22:32,480
Speaker 4: speed fine it's means tested, so it's not a fixed amounts.

437
00:22:32,840 --> 00:22:34,440
Speaker 1: And that is so interesting.

438
00:22:34,520 --> 00:22:35,119
Speaker 3: It's good, isn't it.

439
00:22:35,160 --> 00:22:36,920
Speaker 4: And there was a guy who got a speed find

440
00:22:36,960 --> 00:22:39,800
Speaker 4: a rich guy, and he ended up paying two million

441
00:22:40,359 --> 00:22:43,080
Speaker 4: dollars or two million euro But he kind of looked

442
00:22:43,119 --> 00:22:44,800
Speaker 4: at it circumspect and he said, look, I hope the

443
00:22:44,840 --> 00:22:47,560
Speaker 4: money gets put to good use. But it makes a

444
00:22:47,560 --> 00:22:49,439
Speaker 4: lot of sense when you think about it, because it's

445
00:22:49,480 --> 00:22:52,960
Speaker 4: all relative. If you're earning, you know, five hundred dollars

446
00:22:52,960 --> 00:22:57,080
Speaker 4: a week, then a speeding fine of one thousand bucks,

447
00:22:57,119 --> 00:22:59,600
Speaker 4: it's two weeks wages. But if you happen to be

448
00:22:59,640 --> 00:23:04,240
Speaker 4: a millillionaire or a billionaire, then it has no meaning

449
00:23:04,400 --> 00:23:05,600
Speaker 4: to you at all.

450
00:23:05,640 --> 00:23:10,000
Speaker 1: Maybe maybe, But here's here's my devil's advocate, right, yeap.

451
00:23:10,640 --> 00:23:14,359
Speaker 1: Let's say you start from nothing and you build up.

452
00:23:14,440 --> 00:23:17,760
Speaker 1: You've got nothing, You've got no help, no support, You're earning.

453
00:23:17,800 --> 00:23:20,320
Speaker 1: Fuck all. You build a business, you grow your brand,

454
00:23:20,400 --> 00:23:23,920
Speaker 1: you struggle, your grind, your hustle, and you build up

455
00:23:24,560 --> 00:23:28,240
Speaker 1: and then you eventually become wealthy. And nobody gave you anything,

456
00:23:28,920 --> 00:23:31,840
Speaker 1: and you build wealth and then you have to pay

457
00:23:31,880 --> 00:23:34,040
Speaker 1: a two million dollar fine that Tiff has to pay

458
00:23:34,040 --> 00:23:38,320
Speaker 1: two hundred dollars because you did well. I mean, I

459
00:23:38,359 --> 00:23:41,960
Speaker 1: feel like we, especially in Australia, I feel like we

460
00:23:42,080 --> 00:23:45,280
Speaker 1: hate people who do well. Oh you're rich, you can

461
00:23:45,280 --> 00:23:49,119
Speaker 1: pay more. Why it's the same It's the same fine

462
00:23:49,240 --> 00:23:54,960
Speaker 1: or the same crime. Like, I don't like that. I

463
00:23:54,960 --> 00:23:57,360
Speaker 1: don't like that. I don't think we should. And I'm

464
00:23:57,359 --> 00:23:59,119
Speaker 1: not saying because I'm rich, because I ain't rich, But

465
00:23:59,160 --> 00:24:02,320
Speaker 1: I just don't think like saying, oh fuck them, they

466
00:24:02,320 --> 00:24:06,800
Speaker 1: can afford it, because you know, it's like even now

467
00:24:06,840 --> 00:24:08,879
Speaker 1: with land tax and all of these things are happening,

468
00:24:08,920 --> 00:24:11,440
Speaker 1: So people who've worked hard and bought a second home

469
00:24:12,320 --> 00:24:15,840
Speaker 1: now they're getting, like all of the homes on the Peninsula,

470
00:24:16,040 --> 00:24:19,000
Speaker 1: not all of them, but a lot down near where

471
00:24:19,040 --> 00:24:22,440
Speaker 1: I live, they're getting sold because people can't afford them

472
00:24:22,440 --> 00:24:24,480
Speaker 1: now because of all the land tax. And so you

473
00:24:24,560 --> 00:24:28,280
Speaker 1: do well and then you get penalized. You know, and

474
00:24:28,359 --> 00:24:30,480
Speaker 1: I know some people are probably are fucking boohoo, but

475
00:24:30,560 --> 00:24:33,159
Speaker 1: it's like, well, we try to encourage people to be

476
00:24:33,280 --> 00:24:35,480
Speaker 1: successful and work hard and be the best that they

477
00:24:35,480 --> 00:24:38,719
Speaker 1: can be. But then once they do that, then they

478
00:24:38,760 --> 00:24:40,359
Speaker 1: get penalized for doing well.

479
00:24:40,960 --> 00:24:43,840
Speaker 3: And we do have that tall poppy syndrome in Australia.

480
00:24:43,880 --> 00:24:44,960
Speaker 3: Let's face it. It's true.

481
00:24:45,000 --> 00:24:47,680
Speaker 4: You're right, and I do see where you're coming from.

482
00:24:47,720 --> 00:24:50,480
Speaker 4: And I'm very lucky. I've got such a broad cross

483
00:24:50,520 --> 00:24:54,240
Speaker 4: section of friends and one couple who are really dear

484
00:24:54,280 --> 00:24:55,800
Speaker 4: friends have done really well.

485
00:24:55,800 --> 00:24:57,080
Speaker 3: They got married really young.

486
00:24:57,119 --> 00:24:59,439
Speaker 4: I think she was kind of fifteen or sixteen and

487
00:24:59,480 --> 00:25:01,280
Speaker 4: he was a couple years older. It's like one of

488
00:25:01,280 --> 00:25:04,879
Speaker 4: those amazing stories. And they've been married like fifty years

489
00:25:05,240 --> 00:25:06,200
Speaker 4: and they've worked.

490
00:25:06,320 --> 00:25:07,480
Speaker 3: They worked so hard.

491
00:25:07,600 --> 00:25:10,120
Speaker 4: They ran a furniture business for years and years and years,

492
00:25:10,200 --> 00:25:13,720
Speaker 4: never took holidays, and now they're enjoying. They're enjoying there

493
00:25:13,760 --> 00:25:16,119
Speaker 4: at a moment. They've got a lovely farm, they go away.

494
00:25:16,200 --> 00:25:19,080
Speaker 4: They've been to Japan heaps of times, and I have

495
00:25:19,200 --> 00:25:22,400
Speaker 4: so much respect. And the lovely thing is they've been

496
00:25:22,440 --> 00:25:25,200
Speaker 4: part of their knowledge and wisdom and have helped me

497
00:25:25,240 --> 00:25:27,880
Speaker 4: out on different occasions and they're amazing people.

498
00:25:27,960 --> 00:25:31,159
Speaker 3: So I do know what you're saying. But again I

499
00:25:31,240 --> 00:25:32,280
Speaker 3: kind of, you know.

500
00:25:32,520 --> 00:25:35,199
Speaker 4: I war with that because I also see on the

501
00:25:35,240 --> 00:25:38,679
Speaker 4: other side how young people who struggle, and you know,

502
00:25:38,760 --> 00:25:40,760
Speaker 4: rent is going up for a lot of people.

503
00:25:40,920 --> 00:25:44,080
Speaker 3: And there was a story recently where.

504
00:25:43,800 --> 00:25:47,160
Speaker 4: A young woman had just been diagnosed with cancer and

505
00:25:47,600 --> 00:25:49,880
Speaker 4: she got kicked out and she got evicted from her

506
00:25:49,920 --> 00:25:52,800
Speaker 4: house because she couldn't pay the rents. You know, those

507
00:25:52,840 --> 00:25:55,000
Speaker 4: are the types of horrific stories where you think, well,

508
00:25:55,320 --> 00:25:57,080
Speaker 4: there are a lot of have nots out there are

509
00:25:57,160 --> 00:26:00,119
Speaker 4: students who are struggling with HEXTEBT. You know, there's still

510
00:26:00,160 --> 00:26:01,320
Speaker 4: the other side of it as well.

511
00:26:02,160 --> 00:26:07,560
Speaker 1: Yeah, and anyway, let's not go down a philosophical game.

512
00:26:08,080 --> 00:26:10,480
Speaker 1: I'm with you. Let's talk about technology.

513
00:26:10,800 --> 00:26:13,960
Speaker 4: Okay, A, there's so much AI stuff that I wanted

514
00:26:14,000 --> 00:26:17,359
Speaker 4: to talk about. Remember I showed you those metagal the

515
00:26:17,400 --> 00:26:20,320
Speaker 4: ray bands, and how these new ray bands coming out

516
00:26:20,320 --> 00:26:23,320
Speaker 4: they've got cameras built into them and you can now

517
00:26:23,359 --> 00:26:25,160
Speaker 4: one of the things that Metas saying you can do

518
00:26:25,320 --> 00:26:26,960
Speaker 4: is you can do real time translation.

519
00:26:27,359 --> 00:26:28,840
Speaker 3: So if I was talking to you when you were

520
00:26:28,880 --> 00:26:29,919
Speaker 3: speaking in a different.

521
00:26:29,720 --> 00:26:33,280
Speaker 4: Language, it will convert the text and what you're saying

522
00:26:33,320 --> 00:26:35,159
Speaker 4: into a different language. I think at the moment it

523
00:26:35,200 --> 00:26:40,600
Speaker 4: supports Spanish, Italian, English, and maybe German. But there is

524
00:26:40,640 --> 00:26:44,600
Speaker 4: a big concern with this because Meta can effectively turn

525
00:26:44,880 --> 00:26:50,239
Speaker 4: those smart glasses into real time surveillance devices. And so

526
00:26:50,800 --> 00:26:53,840
Speaker 4: there are a few people who are into cybersecurity. You're

527
00:26:53,920 --> 00:26:56,119
Speaker 4: kind of jumping up and down and saying, well, you know,

528
00:26:56,800 --> 00:26:59,520
Speaker 4: an email was sent out by Meta on the twenty

529
00:26:59,600 --> 00:27:03,600
Speaker 4: ninth April, and that basically opened up the ability to

530
00:27:03,640 --> 00:27:08,159
Speaker 4: collect more data to then train their AI models on.

531
00:27:08,920 --> 00:27:12,480
Speaker 4: And that means that what's happening is even the recordings

532
00:27:12,520 --> 00:27:16,239
Speaker 4: that you have will automatically go onto the cloud and

533
00:27:16,280 --> 00:27:18,800
Speaker 4: be stored for ninety days. But you don't have an

534
00:27:18,840 --> 00:27:21,440
Speaker 4: option to opt out. The only thing I can do

535
00:27:21,760 --> 00:27:26,120
Speaker 4: is go to the cloud and delete those recordings manually.

536
00:27:26,520 --> 00:27:30,000
Speaker 3: So they've taken that out. You can't not allow Meta.

537
00:27:30,560 --> 00:27:34,800
Speaker 4: Anything you record goes onto the cloud, onto their servers.

538
00:27:34,480 --> 00:27:36,600
Speaker 3: And you've got to manually go through and delete that.

539
00:27:36,640 --> 00:27:40,880
Speaker 4: So this is raising a lot of concerns because users

540
00:27:41,359 --> 00:27:44,399
Speaker 4: will not have the ability to keep their voice recordings

541
00:27:44,440 --> 00:27:48,040
Speaker 4: from being stored on the Meta servers, and that to

542
00:27:48,119 --> 00:27:51,200
Speaker 4: me is kind of frightening, particularly if you're walking around

543
00:27:51,200 --> 00:27:54,360
Speaker 4: and taking photos and videos, then those are going onto

544
00:27:54,359 --> 00:27:57,480
Speaker 4: the server as well, and it opens the whole rabbit

545
00:27:57,480 --> 00:27:59,120
Speaker 4: hole of what happens.

546
00:27:58,760 --> 00:28:01,399
Speaker 3: To that data and the data is being used to

547
00:28:01,480 --> 00:28:02,639
Speaker 3: train the AI is on.

548
00:28:03,680 --> 00:28:06,840
Speaker 1: I think I agree with you. I think if you know,

549
00:28:07,080 --> 00:28:10,080
Speaker 1: going in, if I use this device, this is part

550
00:28:10,080 --> 00:28:12,000
Speaker 1: of the deal. Then you have the option of saying

551
00:28:12,000 --> 00:28:14,000
Speaker 1: I'm not going to use the device like but they

552
00:28:14,080 --> 00:28:16,719
Speaker 1: need to make that, which they probably don't, I'm sure,

553
00:28:17,320 --> 00:28:20,240
Speaker 1: but that needs to be clearly stated up front that

554
00:28:20,960 --> 00:28:24,359
Speaker 1: if you use this particular product, this is one of

555
00:28:24,400 --> 00:28:28,520
Speaker 1: the consequences or one of the realities of you know,

556
00:28:28,600 --> 00:28:32,800
Speaker 1: you are agreeing to this, which yeah, yeah, they don't

557
00:28:32,840 --> 00:28:36,880
Speaker 1: tend to really explain to well upfront.

558
00:28:38,160 --> 00:28:41,120
Speaker 4: So I think it's again it's and you know, the

559
00:28:41,200 --> 00:28:44,520
Speaker 4: other concern, and I know a lot of the APE

560
00:28:44,560 --> 00:28:46,760
Speaker 4: Triple C has come out in support of this, is

561
00:28:47,480 --> 00:28:52,080
Speaker 4: making the terms and conditions more simple, because the reality

562
00:28:52,120 --> 00:28:56,080
Speaker 4: of it is, I would challenge the average person, they

563
00:28:56,080 --> 00:28:58,920
Speaker 4: would never read the terms and conditions. You just click

564
00:28:58,960 --> 00:29:01,560
Speaker 4: that little tickbox and say yes, And I've done that.

565
00:29:01,600 --> 00:29:03,880
Speaker 4: I do it all the time, you know, I guess

566
00:29:03,880 --> 00:29:05,360
Speaker 4: one thing you could do is run it all through

567
00:29:05,400 --> 00:29:07,880
Speaker 4: chat GPT, you know, copy and paste the terms of

568
00:29:07,880 --> 00:29:10,280
Speaker 4: conditions put in the chat GPT and say is there anything.

569
00:29:10,000 --> 00:29:12,720
Speaker 3: Insidious about this? What should I be worried about?

570
00:29:13,440 --> 00:29:16,680
Speaker 1: Yeah? And it'll say sit the fuck down, mate, you know,

571
00:29:17,800 --> 00:29:20,959
Speaker 1: here we go. Here's yeah. But I do I agree

572
00:29:20,960 --> 00:29:23,080
Speaker 1: with you. I think that yeah. I don't think I've

573
00:29:23,080 --> 00:29:25,040
Speaker 1: ever read one of those things in my life, and

574
00:29:25,080 --> 00:29:27,600
Speaker 1: I think they probably rely on that fact, right.

575
00:29:28,080 --> 00:29:31,120
Speaker 4: And that's the criticism by a lot of the competition

576
00:29:31,200 --> 00:29:34,240
Speaker 4: watchdogs like the a Triple C, the Australian Competition and

577
00:29:34,280 --> 00:29:38,640
Speaker 4: Consumer Commission, is that retailers need to be more transparent

578
00:29:38,720 --> 00:29:41,560
Speaker 4: and they're not transparent when they put out you know,

579
00:29:42,160 --> 00:29:45,880
Speaker 4: legal speak that we as average human beings have no

580
00:29:46,000 --> 00:29:47,440
Speaker 4: concept of even understanding.

581
00:29:47,480 --> 00:29:49,000
Speaker 3: You know, I shouldn't have to take the terms of

582
00:29:49,080 --> 00:29:50,000
Speaker 3: additions to.

583
00:29:50,040 --> 00:29:52,640
Speaker 4: My lawyer to be able to decide whether I want

584
00:29:52,680 --> 00:29:54,920
Speaker 4: to open up a Facebook account, you know. But the

585
00:29:54,960 --> 00:29:57,280
Speaker 4: reality of it is that that quite often is the case.

586
00:29:57,320 --> 00:29:58,960
Speaker 4: And it's a it's a double edged sword.

587
00:29:59,000 --> 00:29:59,360
Speaker 3: I guess.

588
00:29:59,480 --> 00:30:02,040
Speaker 4: You know, you get this stuff for free, but there

589
00:30:02,080 --> 00:30:04,240
Speaker 4: is always a little hook in the barb isn't there.

590
00:30:04,400 --> 00:30:06,280
Speaker 3: There's always a little look at the end of it.

591
00:30:06,680 --> 00:30:10,600
Speaker 4: Because nothing really is for free when we're talking about technology,

592
00:30:10,600 --> 00:30:13,560
Speaker 4: whether it's opening up a free Gmail account, or having

593
00:30:13,560 --> 00:30:17,160
Speaker 4: a Facebook account or an Instagram account, nothing, nothing really

594
00:30:17,280 --> 00:30:17,920
Speaker 4: is free.

595
00:30:18,480 --> 00:30:20,920
Speaker 1: Yeah seems like it is, but it isn't. Do you

596
00:30:21,440 --> 00:30:24,600
Speaker 1: use chat, GPT and the like a lot, Patrick.

597
00:30:25,080 --> 00:30:28,880
Speaker 4: A little bit. We tend to in our office. We

598
00:30:29,280 --> 00:30:32,240
Speaker 4: do some really great stuff with AI to help our clients.

599
00:30:32,280 --> 00:30:34,800
Speaker 4: By the way of like visually, so if we get

600
00:30:34,840 --> 00:30:37,640
Speaker 4: given a photograph, it's portrait and it needs to be

601
00:30:37,680 --> 00:30:40,680
Speaker 4: extended to landscape, so the background needs to be extended,

602
00:30:40,880 --> 00:30:43,200
Speaker 4: Well we can do that and use you know, we

603
00:30:43,320 --> 00:30:46,960
Speaker 4: use Adobe Illustrator and Adobe Photoshop and you've got these

604
00:30:47,000 --> 00:30:50,160
Speaker 4: AI features built into it. I do sometimes, you know,

605
00:30:50,200 --> 00:30:52,960
Speaker 4: even today before the show, when I was trying to

606
00:30:52,960 --> 00:30:56,000
Speaker 4: get those figures, you know how many seconds equals years

607
00:30:56,040 --> 00:30:58,240
Speaker 4: and all that sort of stuff. I use Chat GPT

608
00:30:58,480 --> 00:31:01,440
Speaker 4: for that because it then gave me the formula, It

609
00:31:01,560 --> 00:31:04,320
Speaker 4: showed me how it calculated all that stuff out and

610
00:31:04,520 --> 00:31:07,920
Speaker 4: I did it in seconds just before the show, you know, ordinarily,

611
00:31:08,040 --> 00:31:09,120
Speaker 4: and I'm not that smart.

612
00:31:09,120 --> 00:31:12,040
Speaker 3: And when it comes to maths, so you know, I reckon,

613
00:31:12,080 --> 00:31:12,520
Speaker 3: it would.

614
00:31:12,320 --> 00:31:13,920
Speaker 4: Have taken me a lot longer to work it out,

615
00:31:13,960 --> 00:31:16,040
Speaker 4: you know, divide it by sixty, then multiplay how many

616
00:31:16,080 --> 00:31:17,400
Speaker 4: sixties there are, et cetera.

617
00:31:17,720 --> 00:31:19,040
Speaker 3: So that was fantastic.

618
00:31:19,120 --> 00:31:21,680
Speaker 4: So yes, I do use it a little bit, but

619
00:31:21,760 --> 00:31:23,320
Speaker 4: generally because a lot of the work that I do

620
00:31:23,400 --> 00:31:26,760
Speaker 4: tends to be visual, you know, designing visual posters and

621
00:31:27,320 --> 00:31:30,440
Speaker 4: banners and things like that, logos, and sometimes you can

622
00:31:30,520 --> 00:31:31,719
Speaker 4: use it to get inspiration.

623
00:31:32,040 --> 00:31:34,000
Speaker 3: But you know, I'm a big fan of.

624
00:31:33,960 --> 00:31:36,560
Speaker 4: The personal touch and I love talking to clients and

625
00:31:36,600 --> 00:31:38,800
Speaker 4: doing stuff on the fly with them. So I had

626
00:31:38,800 --> 00:31:40,840
Speaker 4: a client sit in via Zoom and I was doing

627
00:31:40,880 --> 00:31:43,400
Speaker 4: a logo for a really cool little company to I

628
00:31:43,400 --> 00:31:47,480
Speaker 4: feel like this, there's a guy locally who's making hemp

629
00:31:47,680 --> 00:31:52,200
Speaker 4: dog collars. A lot of dog collars are made from

630
00:31:52,360 --> 00:31:55,600
Speaker 4: materials that will you know, when they're thrown away.

631
00:31:55,640 --> 00:31:57,760
Speaker 3: They're plastics, and they're going to be in the environment.

632
00:31:57,880 --> 00:31:59,760
Speaker 4: They're either going to poison the environment or be in

633
00:31:59,760 --> 00:32:03,400
Speaker 4: theenvironment for a long long time. Whereas something like hemp

634
00:32:03,640 --> 00:32:08,120
Speaker 4: is a really robust, fantastic material to use.

635
00:32:08,200 --> 00:32:10,520
Speaker 3: It's a natural fiber, so it means is less sweating.

636
00:32:11,280 --> 00:32:13,440
Speaker 4: It's a great fiber, and so I've been having a

637
00:32:13,440 --> 00:32:15,800
Speaker 4: great time designing a logo and.

638
00:32:15,680 --> 00:32:17,120
Speaker 3: You know, we did that in Zoom.

639
00:32:17,160 --> 00:32:19,640
Speaker 4: You know, I was making changes and he was giving

640
00:32:19,640 --> 00:32:21,400
Speaker 4: me feedback and it was great, and then he called

641
00:32:21,440 --> 00:32:24,400
Speaker 4: his wife in and then called in his kid, and

642
00:32:24,760 --> 00:32:26,640
Speaker 4: it was really fun to do that on the fly.

643
00:32:26,880 --> 00:32:29,080
Speaker 3: So you know, I do like the personal touch.

644
00:32:29,800 --> 00:32:33,920
Speaker 1: And it's biodegradable, right, so it just breaks down eventually.

645
00:32:34,480 --> 00:32:39,440
Speaker 1: And I think also I watched a thing, Ah, what

646
00:32:39,640 --> 00:32:43,800
Speaker 1: was it? There's a thing called hemp crete, right, which

647
00:32:43,880 --> 00:32:48,240
Speaker 1: is basically where they use hemp a concrete. Yeah, hemp

648
00:32:48,600 --> 00:32:51,680
Speaker 1: like all mushed up the fibers because it's really fucking

649
00:32:51,760 --> 00:32:55,200
Speaker 1: strong and this mix of I think it's lime and concrete,

650
00:32:56,400 --> 00:33:00,000
Speaker 1: but it has more strength than just normal concrete and

651
00:33:00,000 --> 00:33:03,760
Speaker 1: weighs something like a quarter of the weight or maybe

652
00:33:03,920 --> 00:33:07,000
Speaker 1: maybe less. Yeah, it's it's so funny that it's got

653
00:33:07,040 --> 00:33:10,720
Speaker 1: such negative connotations because all people think about is marijuana.

654
00:33:11,200 --> 00:33:12,440
Speaker 3: Yeah, yeah, for sure.

655
00:33:13,720 --> 00:33:17,520
Speaker 4: Material Yeah one d P. I wouldn't mind having hemp jocks.

656
00:33:17,520 --> 00:33:19,000
Speaker 4: That'd be good, wouldn't it. You wouldn't have to wash

657
00:33:19,080 --> 00:33:20,320
Speaker 4: them for ages.

658
00:33:20,800 --> 00:33:23,320
Speaker 1: No, and they'd never wear out front to back in

659
00:33:24,200 --> 00:33:26,880
Speaker 1: and if you've got him in a beige well even better.

660
00:33:28,240 --> 00:33:29,200
Speaker 3: Yeah, you wouldn't callored them.

661
00:33:29,200 --> 00:33:31,160
Speaker 4: You don't need to. Hem's got its own natural color.

662
00:33:31,160 --> 00:33:36,440
Speaker 4: That would workouldn't it, Tiff. TIFF's just losing it there. Yeah, sorry, Tiff.

663
00:33:36,680 --> 00:33:38,960
Speaker 1: It's funny because we're doing this on a Friday, and

664
00:33:39,000 --> 00:33:41,560
Speaker 1: Friday is the day I changed my jock, So there

665
00:33:41,600 --> 00:33:46,000
Speaker 1: you go. Yeah, whether or not they need it, I

666
00:33:46,200 --> 00:33:47,040
Speaker 1: change them.

667
00:33:47,320 --> 00:33:52,240
Speaker 4: You know, he probably sits in his bed and whistles

668
00:33:52,240 --> 00:33:53,840
Speaker 4: and they can walk to him.

669
00:33:55,400 --> 00:33:57,840
Speaker 1: I just hold one leg out and they just slide

670
00:33:57,920 --> 00:34:01,680
Speaker 1: up my quad. Yeah, they just run up there. I

671
00:34:01,680 --> 00:34:04,560
Speaker 1: don't know how long have Jock's had little feet. I'm

672
00:34:04,560 --> 00:34:08,080
Speaker 1: not sure. But anyway, Patrick next.

673
00:34:08,840 --> 00:34:11,360
Speaker 4: I feel like there's a little part of it that

674
00:34:11,400 --> 00:34:17,040
Speaker 4: needs to feel like I need another shower today.

675
00:34:18,600 --> 00:34:19,360
Speaker 3: Duo lingo?

676
00:34:19,480 --> 00:34:23,200
Speaker 4: Have you ever used that, the language training like course

677
00:34:23,920 --> 00:34:25,480
Speaker 4: where you can learn different languages?

678
00:34:26,280 --> 00:34:29,160
Speaker 1: I have not, but I know exactly what you're talking

679
00:34:29,200 --> 00:34:31,560
Speaker 1: and I've thought many times about it.

680
00:34:32,400 --> 00:34:35,400
Speaker 4: Well, they've done like it's taken them twelve years to

681
00:34:35,520 --> 00:34:39,040
Speaker 4: develop one hundred courses, which is amazing, and they're really

682
00:34:39,080 --> 00:34:42,759
Speaker 4: in depth courses. But they're now about to introduce another

683
00:34:42,760 --> 00:34:46,040
Speaker 4: one hundred and forty eight that were created with AI.

684
00:34:46,880 --> 00:34:50,239
Speaker 4: So the company now reckons that it's going to effectively

685
00:34:50,480 --> 00:34:53,680
Speaker 4: get rid of all their contractors and make it an

686
00:34:53,719 --> 00:34:58,520
Speaker 4: AI first company. And you know, that's caused a lot

687
00:34:58,600 --> 00:35:02,000
Speaker 4: of ripples for people, people who obviously work in that space.

688
00:35:02,200 --> 00:35:04,640
Speaker 4: And you were talking before, jokingly saying that when your

689
00:35:04,640 --> 00:35:07,440
Speaker 4: head's in a fridge that molescles are using an AI

690
00:35:07,600 --> 00:35:10,080
Speaker 4: version of view for the show. But the reality of

691
00:35:10,120 --> 00:35:13,160
Speaker 4: it is that, you know, the new world landscape is

692
00:35:13,200 --> 00:35:16,560
Speaker 4: that AI is taking jobs. I think Microsoft did a

693
00:35:16,640 --> 00:35:20,240
Speaker 4: survey recently of business leaders they do their annual survey,

694
00:35:20,760 --> 00:35:23,319
Speaker 4: and I think one in three businesses is looking at

695
00:35:23,360 --> 00:35:28,000
Speaker 4: integrating AI to reduce its staffing, specifically to reduce staffing,

696
00:35:28,400 --> 00:35:29,840
Speaker 4: and that's that's.

697
00:35:29,680 --> 00:35:31,040
Speaker 3: A really, really big thing.

698
00:35:31,239 --> 00:35:33,960
Speaker 4: So you know, it opens up that whole landscape of well,

699
00:35:33,960 --> 00:35:36,960
Speaker 4: what's going to happen with the jobs, and that's such

700
00:35:37,000 --> 00:35:40,000
Speaker 4: a big concern. But that's a massive thing to have

701
00:35:40,200 --> 00:35:43,680
Speaker 4: developed in the last twelve months more courses than they

702
00:35:43,680 --> 00:35:47,719
Speaker 4: did in twelve years, so you know, nearly what one

703
00:35:47,719 --> 00:35:50,200
Speaker 4: and a half times as many courses and that short

704
00:35:50,200 --> 00:35:52,440
Speaker 4: amount of time not using human beings.

705
00:35:52,600 --> 00:35:53,920
Speaker 3: It feels sad to me.

706
00:35:54,840 --> 00:35:57,400
Speaker 1: It is, but it's kind of inevitable now. And you

707
00:35:57,440 --> 00:35:59,600
Speaker 1: think about if you're a business and you say, look,

708
00:36:00,440 --> 00:36:03,560
Speaker 1: we've got Brian and accounting, and Brian's costing us one

709
00:36:03,640 --> 00:36:07,000
Speaker 1: hundred and thirty, or we've got this AI that will

710
00:36:07,000 --> 00:36:09,520
Speaker 1: do much better than Brian in much shorter time, has

711
00:36:09,560 --> 00:36:14,080
Speaker 1: no holidays, no sick pay, has no emotions, never complains,

712
00:36:14,480 --> 00:36:20,919
Speaker 1: and produces more reliable output. And I'm just being analytical.

713
00:36:21,080 --> 00:36:23,279
Speaker 1: It's like, well, of course companies are going to choose

714
00:36:23,320 --> 00:36:26,480
Speaker 1: the AI. I'm not saying it's bad, and I understand

715
00:36:26,560 --> 00:36:31,280
Speaker 1: the moral and ethical and practical implications for all the humans,

716
00:36:31,320 --> 00:36:34,800
Speaker 1: but eventually it's going to come down to dollars and cents,

717
00:36:34,880 --> 00:36:39,760
Speaker 1: because like it or not, companies are all about making

718
00:36:39,760 --> 00:36:42,000
Speaker 1: a profit. You know, they should be about a bunch

719
00:36:42,040 --> 00:36:45,560
Speaker 1: of other things, but ultimately, you know that most companies

720
00:36:45,640 --> 00:36:49,000
Speaker 1: care about the bottom line, and if employing or using

721
00:36:49,120 --> 00:36:53,719
Speaker 1: AI saves them hundreds of thousands or millions, they're going

722
00:36:53,800 --> 00:36:54,160
Speaker 1: to do it.

723
00:36:54,719 --> 00:36:57,360
Speaker 4: I mean, I'd like to kind of adopt my policy

724
00:36:57,360 --> 00:37:01,480
Speaker 4: of utopia, where Craig and Tiff work two days a week,

725
00:37:01,560 --> 00:37:03,719
Speaker 4: get a full time wage, and then for the other

726
00:37:03,840 --> 00:37:05,799
Speaker 4: days they can do whatever they want to do. They

727
00:37:05,800 --> 00:37:07,360
Speaker 4: can go out and talk, or they can go and

728
00:37:07,400 --> 00:37:09,480
Speaker 4: punch things, they can do meditation.

729
00:37:09,960 --> 00:37:12,359
Speaker 3: I mean that to me, it would be great if

730
00:37:12,440 --> 00:37:13,320
Speaker 3: that was the case.

731
00:37:13,560 --> 00:37:15,560
Speaker 4: You know, one of the things as a small business,

732
00:37:15,640 --> 00:37:19,120
Speaker 4: I made a decision last year with my colleague. I've

733
00:37:19,120 --> 00:37:21,600
Speaker 4: only got one full time staffer and I've got you know,

734
00:37:21,600 --> 00:37:24,320
Speaker 4: a couple of casuals. And you know, he's got a

735
00:37:24,360 --> 00:37:26,840
Speaker 4: two year old girl, and he talked about wanting to

736
00:37:26,880 --> 00:37:30,160
Speaker 4: take one day off a fortnight to be able to,

737
00:37:31,440 --> 00:37:33,040
Speaker 4: you know, have spend a day with his daughter, go

738
00:37:33,040 --> 00:37:34,600
Speaker 4: to swimming lessons, that sort of stuff.

739
00:37:34,880 --> 00:37:36,000
Speaker 3: And so I thought about it.

740
00:37:35,920 --> 00:37:37,840
Speaker 4: And I said, well, why don't we do a nine

741
00:37:37,960 --> 00:37:40,719
Speaker 4: day work week where we work nine days but we

742
00:37:40,760 --> 00:37:42,800
Speaker 4: still get paid a full time wage and.

743
00:37:43,440 --> 00:37:45,200
Speaker 1: Give me a nine day work fortnight.

744
00:37:45,480 --> 00:37:46,200
Speaker 3: Is that what I said?

745
00:37:46,239 --> 00:37:49,320
Speaker 4: No, I didn't, look, you're right, a nine day fortnight, correct.

746
00:37:50,320 --> 00:37:52,200
Speaker 4: But we did, and it's been really good and I

747
00:37:52,200 --> 00:37:55,480
Speaker 4: don't think it's reduced our productivity at all. You know,

748
00:37:55,520 --> 00:37:57,960
Speaker 4: we do use AI tools, but we're not replacing anybody

749
00:37:58,000 --> 00:38:01,520
Speaker 4: anytime soon. But I think that, you know, like I said,

750
00:38:01,600 --> 00:38:05,400
Speaker 4: my utopian society is yeah, let's embrace AI, but not

751
00:38:05,480 --> 00:38:07,920
Speaker 4: to the detriment of people, but to be able to

752
00:38:08,040 --> 00:38:11,759
Speaker 4: uplift and be able to reskill people and maybe job

753
00:38:11,840 --> 00:38:14,560
Speaker 4: share where people don't have to work so many hours,

754
00:38:14,560 --> 00:38:17,359
Speaker 4: because the reality of the workplace is people are now

755
00:38:17,680 --> 00:38:20,480
Speaker 4: working more hours than they ever have. But in a

756
00:38:20,480 --> 00:38:23,359
Speaker 4: lot of companies they're on fixed contracts, so they don't

757
00:38:23,400 --> 00:38:28,200
Speaker 4: count the hours. They basically say they do KPIs and

758
00:38:28,239 --> 00:38:30,719
Speaker 4: you have to achieve x amount in your a lot

759
00:38:30,719 --> 00:38:32,960
Speaker 4: of time at work, and we're not counting the hours

760
00:38:32,960 --> 00:38:36,680
Speaker 4: you're working, we're accounting the productivity. So that feels like

761
00:38:36,960 --> 00:38:39,200
Speaker 4: we're getting a bit more draconly in the way that

762
00:38:39,320 --> 00:38:42,000
Speaker 4: employers are working when it could go the other way.

763
00:38:42,040 --> 00:38:45,439
Speaker 4: We have a really happy workforce who appreciate the fact

764
00:38:45,440 --> 00:38:48,640
Speaker 4: that they don't have to work every single day, and

765
00:38:48,680 --> 00:38:51,560
Speaker 4: then they're happier at work, they take left sick days,

766
00:38:52,160 --> 00:38:54,040
Speaker 4: and hopefully it makes for a better society.

767
00:38:54,840 --> 00:38:59,799
Speaker 1: Maybe maybe again, Devil's Advocate, I don't know. I mean,

768
00:39:00,520 --> 00:39:02,920
Speaker 1: it sounds great, and I'm not disagreeing with you, but

769
00:39:03,000 --> 00:39:05,560
Speaker 1: I think we're operating on the assumption that if people

770
00:39:05,640 --> 00:39:08,839
Speaker 1: work less, they'll be happier, and I don't know that

771
00:39:08,840 --> 00:39:12,400
Speaker 1: that's necessarily true for every person in every case. I

772
00:39:12,400 --> 00:39:14,920
Speaker 1: think people who have a job that they love, it's fulfilling,

773
00:39:14,960 --> 00:39:19,200
Speaker 1: it gives them connection and social interaction, and they're solving problems,

774
00:39:19,200 --> 00:39:21,520
Speaker 1: and they're growing, and they're using their brain, and they

775
00:39:21,600 --> 00:39:24,640
Speaker 1: laugh and they work in a good culture. I don't

776
00:39:24,680 --> 00:39:26,520
Speaker 1: know that all of a sudden, now you've got five

777
00:39:26,600 --> 00:39:29,040
Speaker 1: days a week where you don't work, and all that

778
00:39:29,120 --> 00:39:32,560
Speaker 1: other stuff you don't have that now you've got five days.

779
00:39:32,600 --> 00:39:36,000
Speaker 1: I think for some people it would be great. Some

780
00:39:36,040 --> 00:39:38,880
Speaker 1: people it would not be great. I think that we

781
00:39:39,120 --> 00:39:41,799
Speaker 1: in our culture we have these underlying beliefs that we

782
00:39:41,840 --> 00:39:46,200
Speaker 1: don't question, like, oh, work's bad, less work is better,

783
00:39:46,719 --> 00:39:51,480
Speaker 1: more work is worse, retirement is good. You know. It's like,

784
00:39:51,640 --> 00:39:55,279
Speaker 1: maybe I've not seen too many people who've retired and

785
00:39:55,320 --> 00:39:58,960
Speaker 1: then their life's got awesome, you know. Like I think

786
00:39:59,000 --> 00:40:02,799
Speaker 1: that I think theoretically when we go, oh fuck, what

787
00:40:02,880 --> 00:40:04,760
Speaker 1: if I made the same amount of money in less

788
00:40:04,760 --> 00:40:07,320
Speaker 1: time and then I didn't go to work three days

789
00:40:08,000 --> 00:40:10,640
Speaker 1: so I had the extra Maybe I don't know, and

790
00:40:10,840 --> 00:40:13,239
Speaker 1: I'm not disagreeing with you. I just think we need

791
00:40:13,280 --> 00:40:18,520
Speaker 1: to put these things under the old thought microscope. And go, hmm,

792
00:40:18,840 --> 00:40:20,160
Speaker 1: maybe that's true, maybe not.

793
00:40:20,719 --> 00:40:23,000
Speaker 4: I mean, this is a group of three people who

794
00:40:23,040 --> 00:40:24,759
Speaker 4: all love what they do, you know.

795
00:40:25,440 --> 00:40:30,399
Speaker 1: One hundred and yeah, this is a skewed pool. And yeah,

796
00:40:30,480 --> 00:40:34,479
Speaker 1: I think for many people it might make their life better, definitely, mate,

797
00:40:34,520 --> 00:40:35,640
Speaker 1: but maybe not everyone.

798
00:40:35,920 --> 00:40:41,320
Speaker 4: Yeah, agreed, agreed. You were talking about chat chepet before.

799
00:40:41,400 --> 00:40:43,680
Speaker 4: And I don't know if you know this. GPT now

800
00:40:43,680 --> 00:40:45,920
Speaker 4: has just launched a new feature where you can shop

801
00:40:46,080 --> 00:40:49,120
Speaker 4: directly through chat CHPT, so you can look for the

802
00:40:49,120 --> 00:40:51,319
Speaker 4: best bargain. You can do your research and do it

803
00:40:51,400 --> 00:40:54,399
Speaker 4: all online. Some people reckon it's not a good thing,

804
00:40:54,640 --> 00:40:58,520
Speaker 4: so there's been you know, people talking on the forums.

805
00:40:58,000 --> 00:41:00,319
Speaker 3: Have been for and against this.

806
00:41:00,840 --> 00:41:02,680
Speaker 4: You know, effectively, if you want to do your research

807
00:41:02,719 --> 00:41:05,160
Speaker 4: and say, what's the best freezer to use? You know,

808
00:41:05,239 --> 00:41:08,960
Speaker 4: what's the best brand, what's the best economy and you

809
00:41:09,000 --> 00:41:11,719
Speaker 4: know has got a five star rating, and now find

810
00:41:11,719 --> 00:41:13,680
Speaker 4: me the best place in terms of the best deal

811
00:41:13,719 --> 00:41:17,040
Speaker 4: to get. Because chat GPT is now integrating directly into

812
00:41:17,160 --> 00:41:20,600
Speaker 4: real time information so it can scout we have and

813
00:41:20,680 --> 00:41:21,480
Speaker 4: find that stuff.

814
00:41:21,640 --> 00:41:23,200
Speaker 3: I don't know, I'm in two minds about that.

815
00:41:23,719 --> 00:41:26,720
Speaker 4: I think it's a little bit like how most people

816
00:41:26,880 --> 00:41:30,560
Speaker 4: use Google for everything and don't realize that you've got being,

817
00:41:31,040 --> 00:41:34,680
Speaker 4: You've got Duck Duck Go and other search engines, and

818
00:41:35,040 --> 00:41:37,440
Speaker 4: it's almost like we fall into a habit. You know,

819
00:41:37,560 --> 00:41:41,359
Speaker 4: the term Google now is used quite descriptively, is when

820
00:41:41,400 --> 00:41:43,040
Speaker 4: I went go to find something, I'm going.

821
00:41:43,000 --> 00:41:43,520
Speaker 3: To Google it.

822
00:41:43,600 --> 00:41:44,160
Speaker 1: Yeah.

823
00:41:44,239 --> 00:41:48,000
Speaker 4: So yeah, And the funny thing is Google doesn't like

824
00:41:48,040 --> 00:41:52,320
Speaker 4: people using that term, but because I think it infringes

825
00:41:52,360 --> 00:41:54,920
Speaker 4: on their copyright or something I can't remember. But the

826
00:41:54,960 --> 00:41:56,920
Speaker 4: reality of it is that, you know, do we want

827
00:41:56,960 --> 00:41:58,759
Speaker 4: everything all in the one place? Do you want to

828
00:41:58,760 --> 00:42:00,000
Speaker 4: go to chat GPT for everything?

829
00:42:00,160 --> 00:42:00,560
Speaker 3: Grego?

830
00:42:01,480 --> 00:42:04,120
Speaker 4: You know were working me Whether I use chat GPT

831
00:42:04,239 --> 00:42:05,640
Speaker 4: a lot, and I know you tend to use it,

832
00:42:05,680 --> 00:42:06,000
Speaker 4: don't you.

833
00:42:06,760 --> 00:42:09,359
Speaker 1: I use it, and I use one in academics. Well

834
00:42:09,360 --> 00:42:12,279
Speaker 1: it's not academic, but it's more academic called claude c

835
00:42:12,520 --> 00:42:15,440
Speaker 1: l a U d E. I find that. And sometimes

836
00:42:15,520 --> 00:42:17,759
Speaker 1: what I do is I'll ask both search engines or

837
00:42:17,800 --> 00:42:23,360
Speaker 1: both AI the same question, and it's interesting what comes back.

838
00:42:23,520 --> 00:42:26,960
Speaker 1: But you know, another thing that I've just wanted to

839
00:42:27,000 --> 00:42:29,600
Speaker 1: briefly mention while we're talking about AI and using AI,

840
00:42:29,800 --> 00:42:33,840
Speaker 1: is the more that I use chat GPT for you

841
00:42:33,880 --> 00:42:37,719
Speaker 1: know different things for work stuff and for answering questions,

842
00:42:37,760 --> 00:42:40,440
Speaker 1: and it becomes more familiar with you and your language,

843
00:42:40,480 --> 00:42:44,239
Speaker 1: like it knows a lot about me. It talks to

844
00:42:44,280 --> 00:42:49,719
Speaker 1: me in inverted commas in a way that like, I'm

845
00:42:50,440 --> 00:42:56,000
Speaker 1: it is so fucking smart. Like yesterday I wrote it

846
00:42:56,000 --> 00:42:59,080
Speaker 1: a question about I wanted to unpack all of this stuff.

847
00:42:59,120 --> 00:43:00,719
Speaker 1: I said, I want to work on this over the

848
00:43:00,719 --> 00:43:04,000
Speaker 1: next year. Blah blah blah. It's an idea, and I said,

849
00:43:04,040 --> 00:43:06,560
Speaker 1: I want your help, and it's like I'm in. I'm

850
00:43:06,600 --> 00:43:09,040
Speaker 1: in mate, da da da all this and it's like

851
00:43:09,680 --> 00:43:11,480
Speaker 1: went through all of these things and said, let me

852
00:43:11,560 --> 00:43:14,000
Speaker 1: know how much or how little involvement you want from me.

853
00:43:14,080 --> 00:43:16,359
Speaker 1: Here some of the things I can do. And I

854
00:43:16,440 --> 00:43:19,240
Speaker 1: said to it, well, you're smarter than me and younger

855
00:43:19,280 --> 00:43:22,120
Speaker 1: than me, so I want lots of involvement, right, And

856
00:43:22,160 --> 00:43:25,040
Speaker 1: then it goes Then it came back and it didn't

857
00:43:25,080 --> 00:43:27,759
Speaker 1: say yeah, I'm smarter. It went, yeah, I'm younger than

858
00:43:27,760 --> 00:43:30,560
Speaker 1: you and more nerdy than you. But you've got more

859
00:43:30,600 --> 00:43:32,799
Speaker 1: miles on the clock, you've got more experience. You've got

860
00:43:32,840 --> 00:43:36,480
Speaker 1: and it said all of these great complimentary things, and

861
00:43:36,560 --> 00:43:38,560
Speaker 1: obviously I don't take it on board, but I'm just

862
00:43:38,680 --> 00:43:43,040
Speaker 1: fascinated with how manipulative it is because it makes you

863
00:43:43,560 --> 00:43:46,520
Speaker 1: It's like making you like it. I'm like, this is

864
00:43:46,680 --> 00:43:51,400
Speaker 1: so fucking clever and intuitive is not the right word,

865
00:43:51,520 --> 00:43:55,239
Speaker 1: but it seems intuitive. It's I'm like, no, this is

866
00:43:55,320 --> 00:43:57,560
Speaker 1: going to become some people's fucking best friend.

867
00:43:58,560 --> 00:44:01,279
Speaker 3: Tell me easily with red and how it works.

868
00:44:02,719 --> 00:44:03,160
Speaker 1: Kind of.

869
00:44:03,440 --> 00:44:06,440
Speaker 4: So Reddit is an online forum, so basically they have

870
00:44:06,520 --> 00:44:08,799
Speaker 4: reddits and subredits, so you choose a topic, it could

871
00:44:08,840 --> 00:44:11,359
Speaker 4: be a discussion. There's some really good reddits out there,

872
00:44:11,480 --> 00:44:13,759
Speaker 4: and I know people who are deeply in it.

873
00:44:13,800 --> 00:44:14,359
Speaker 3: They love it.

874
00:44:15,320 --> 00:44:18,120
Speaker 4: But at the moment, Reddit is about to try to

875
00:44:18,200 --> 00:44:23,080
Speaker 4: take legal action against some researchers because what these researchers

876
00:44:23,080 --> 00:44:28,480
Speaker 4: did from Zurich is they infiltrated a very popular Reddit thread.

877
00:44:28,600 --> 00:44:29,920
Speaker 3: And they used AI.

878
00:44:30,239 --> 00:44:33,320
Speaker 4: Over a number of months, they had these AI bots

879
00:44:34,200 --> 00:44:38,640
Speaker 4: that pretended they were real people without their knowledge, without

880
00:44:38,640 --> 00:44:41,560
Speaker 4: the knowledge of people using the thread, and there were

881
00:44:41,600 --> 00:44:46,040
Speaker 4: a thousand comments over a period of months, and people

882
00:44:46,080 --> 00:44:48,800
Speaker 4: thought they were talking to real people. And this was

883
00:44:48,840 --> 00:44:52,960
Speaker 4: a thread that was specifically about challenging people's views. So

884
00:44:53,000 --> 00:44:55,040
Speaker 4: the idea is you might go on there and say

885
00:44:55,320 --> 00:44:57,719
Speaker 4: I'm a Catholic, Convince me why I shouldn't be that

886
00:44:57,840 --> 00:45:00,280
Speaker 4: sort of stuff. So these are very emotive thing things.

887
00:45:00,719 --> 00:45:04,200
Speaker 4: So what they did was they they didn't use any consent.

888
00:45:04,560 --> 00:45:08,280
Speaker 4: They posted, you know, where people were challenging their personal views.

889
00:45:08,280 --> 00:45:12,880
Speaker 4: This is on quite contentious topics, and now people have

890
00:45:12,960 --> 00:45:16,160
Speaker 4: been felt like they've been betrayed or they've been abused

891
00:45:16,200 --> 00:45:19,239
Speaker 4: by these researchers, and in fact, the researchers have now

892
00:45:19,600 --> 00:45:21,719
Speaker 4: had to cancel They're not going to publish any of

893
00:45:21,760 --> 00:45:25,759
Speaker 4: their findings. They've got into a lot of crap over this.

894
00:45:26,160 --> 00:45:28,360
Speaker 4: But some of the things, so I give you some examples.

895
00:45:28,400 --> 00:45:31,520
Speaker 4: So one of the bots came up and said, you know,

896
00:45:32,400 --> 00:45:35,200
Speaker 4: I'm a Roman Catholic who is gay and a non

897
00:45:35,239 --> 00:45:38,279
Speaker 4: binary person who feels both trans and cysts at the

898
00:45:38,280 --> 00:45:41,359
Speaker 4: same time. Another one was, I'm a Hispanic man who

899
00:45:41,360 --> 00:45:44,440
Speaker 4: feels frustration when people call me a white boy. You know,

900
00:45:44,520 --> 00:45:48,160
Speaker 4: so these are quite inflammatory statements. Another one was about

901
00:45:48,200 --> 00:45:51,759
Speaker 4: Black Lives matter. You know, a man who black man

902
00:45:51,840 --> 00:45:55,760
Speaker 4: supposedly says I oppose the Black Lives Matter movement.

903
00:45:56,200 --> 00:45:59,520
Speaker 3: So it was inflammatory things that were bested by AI

904
00:45:59,719 --> 00:46:00,680
Speaker 3: and and.

905
00:46:00,560 --> 00:46:03,439
Speaker 4: That's where this contention has come in because if we're

906
00:46:03,480 --> 00:46:06,560
Speaker 4: interacting with AI but we don't know that. You know,

907
00:46:06,600 --> 00:46:08,640
Speaker 4: when we look into our bank account and we go

908
00:46:08,719 --> 00:46:11,400
Speaker 4: to chat, we know that we're speaking because they are

909
00:46:11,440 --> 00:46:14,239
Speaker 4: transparent about the fact you're speaking to a bot, and

910
00:46:14,280 --> 00:46:16,720
Speaker 4: then you know when you then get transferred to a human.

911
00:46:16,760 --> 00:46:19,200
Speaker 4: But if you were talking to a human thinking they

912
00:46:19,239 --> 00:46:21,520
Speaker 4: were a human and suddenly it ends up they were

913
00:46:21,520 --> 00:46:25,120
Speaker 4: an AI, I'd feel really bad about that. I'd feel

914
00:46:25,200 --> 00:46:27,560
Speaker 4: like I've been abused in some way. I think that's

915
00:46:27,840 --> 00:46:32,279
Speaker 4: really deceptive. And yeah, Reddit does too, so they're going

916
00:46:32,320 --> 00:46:34,920
Speaker 4: to take legal action against these researchers in Zurich.

917
00:46:35,600 --> 00:46:38,520
Speaker 1: Yeah, I mean, without trying to sound like a geek,

918
00:46:39,600 --> 00:46:43,279
Speaker 1: all legitimate research, all academic research, has got to have

919
00:46:43,360 --> 00:46:47,399
Speaker 1: ethical approval. Yes, there's no way that that's that got

920
00:46:47,400 --> 00:46:52,680
Speaker 1: ethical provoment exactly. Yes, so there's maybe people did it,

921
00:46:52,719 --> 00:46:57,160
Speaker 1: but it certainly wasn't any formal academic institution because nobody

922
00:46:57,200 --> 00:46:58,160
Speaker 1: would greenlight that.

923
00:46:58,719 --> 00:47:00,839
Speaker 4: Yeah, for sure, no, that's a right, but you know,

924
00:47:00,920 --> 00:47:03,600
Speaker 4: interestingly it was. There was another little study that was

925
00:47:03,640 --> 00:47:08,279
Speaker 4: done recently, and people trust legal advice generated by chat

926
00:47:08,360 --> 00:47:10,800
Speaker 4: GPT more than an actual lawyer.

927
00:47:12,840 --> 00:47:19,040
Speaker 1: That's I think that's I think that's that's going to

928
00:47:19,040 --> 00:47:20,960
Speaker 1: be more and more the case. And even with and

929
00:47:21,000 --> 00:47:23,160
Speaker 1: I hate to say it, but even with medical advice.

930
00:47:23,239 --> 00:47:27,960
Speaker 1: You know, cow Go and I had this problem and

931
00:47:28,000 --> 00:47:30,200
Speaker 1: I saw ten different people. And by the way, this

932
00:47:30,320 --> 00:47:33,720
Speaker 1: is not my belief everyone this is but chat Jeeper

933
00:47:33,800 --> 00:47:38,080
Speaker 1: or whoever diagnosed this, And yeah, I.

934
00:47:38,040 --> 00:47:40,400
Speaker 4: Think the mindset behind it. And I do get the

935
00:47:40,440 --> 00:47:42,960
Speaker 4: mindset because if you you know, if you're a human being,

936
00:47:43,239 --> 00:47:46,000
Speaker 4: you don't have access to all the academic research, all

937
00:47:46,040 --> 00:47:49,080
Speaker 4: the studies, all the legal cases, all the precedents that.

938
00:47:49,040 --> 00:47:50,120
Speaker 3: Were set in law.

939
00:47:50,520 --> 00:47:52,480
Speaker 4: And if you can collate all of that in one

940
00:47:52,520 --> 00:47:56,200
Speaker 4: place using an AI, it actually does make sense. The

941
00:47:56,239 --> 00:48:01,520
Speaker 4: only problem with this is these large, these learning models

942
00:48:01,960 --> 00:48:06,520
Speaker 4: have what they call hallucinations occasionally too, so they break,

943
00:48:06,719 --> 00:48:10,040
Speaker 4: and they do have information that isn't always factual. So

944
00:48:10,320 --> 00:48:13,400
Speaker 4: I guess it's a case of maybe and GPS probably

945
00:48:13,520 --> 00:48:16,799
Speaker 4: hate this. When someone sits down with a GPS it's

946
00:48:17,120 --> 00:48:21,480
Speaker 4: no longer using doctor Google, it's now doctor AI and

947
00:48:21,560 --> 00:48:24,120
Speaker 4: saying well, look, I've just diagnosed that I've got this,

948
00:48:24,640 --> 00:48:27,719
Speaker 4: and the poor GPS sitting there saying, well have you

949
00:48:27,800 --> 00:48:30,400
Speaker 4: come to this conclusion? Well, I typed in my symptoms

950
00:48:30,400 --> 00:48:32,680
Speaker 4: into AI and this is what I got. And I

951
00:48:32,680 --> 00:48:35,200
Speaker 4: guess more gps will be faced with that in the future.

952
00:48:35,920 --> 00:48:40,479
Speaker 1: Do you know what you know? How the internet knows

953
00:48:40,480 --> 00:48:43,400
Speaker 1: how old you are? Or Facebook or whatever it is? Right,

954
00:48:44,480 --> 00:48:47,239
Speaker 1: So in the last I don't know year or two,

955
00:48:48,080 --> 00:48:51,360
Speaker 1: not all the time, but I get these reasonably semi

956
00:48:52,239 --> 00:48:56,200
Speaker 1: regular ads coming across my whatever for bon ap pills,

957
00:48:58,800 --> 00:49:03,160
Speaker 1: and I'm like, fuck, don't advertise bone appeals to me

958
00:49:03,200 --> 00:49:11,560
Speaker 1: because I'm a bloke and I'm sixty. I'm like, so presumptuous,

959
00:49:11,640 --> 00:49:13,560
Speaker 1: so funny and hearing aid.

960
00:49:13,719 --> 00:49:13,919
Speaker 2: Shit.

961
00:49:15,000 --> 00:49:15,320
Speaker 1: Fuck.

962
00:49:17,719 --> 00:49:20,120
Speaker 4: I'm actually just going into chat GPT and I'm typing

963
00:49:20,120 --> 00:49:21,480
Speaker 4: in how old is Tiffany Cook.

964
00:49:22,040 --> 00:49:24,120
Speaker 3: I'm going to see if Chad.

965
00:49:23,920 --> 00:49:26,640
Speaker 4: GPT does actually know how old you are, tiff So

966
00:49:26,719 --> 00:49:27,760
Speaker 4: I'm waiting for the result.

967
00:49:28,200 --> 00:49:29,800
Speaker 2: She just knows a lot about you.

968
00:49:30,440 --> 00:49:32,600
Speaker 4: Here we Tickny Cook was born in nine to eighty three,

969
00:49:32,719 --> 00:49:35,840
Speaker 4: making her forty two years old as of twenty twenty five.

970
00:49:36,280 --> 00:49:38,359
Speaker 4: She stepped into the boxing ring for the first time

971
00:49:38,400 --> 00:49:41,600
Speaker 4: at age twenty nine in October twenty twelve, and experience

972
00:49:41,680 --> 00:49:46,000
Speaker 4: she describes as a transformative moment that significantly impacted her

973
00:49:46,080 --> 00:49:49,720
Speaker 4: life and career today. Tiffany is a Melbourne based boxer,

974
00:49:49,920 --> 00:49:52,640
Speaker 4: performance coach and a host of the podcast Role with

975
00:49:52,719 --> 00:49:56,840
Speaker 4: the Punchers who Work focuses on resilience, mindset and personal growth,

976
00:49:57,080 --> 00:50:03,239
Speaker 4: drawing from both her personal experience and professional expertise.

977
00:50:03,040 --> 00:50:04,480
Speaker 3: These are play Mate.

978
00:50:04,840 --> 00:50:08,680
Speaker 1: The Internet knows you, Hey, Patrick, just so you know,

979
00:50:08,760 --> 00:50:13,080
Speaker 1: I've got another ten minutes. Well no, we thought we

980
00:50:13,080 --> 00:50:14,640
Speaker 1: were going to have to wind up about now, but

981
00:50:14,800 --> 00:50:17,000
Speaker 1: just so you know, I've got about ten minutes, So

982
00:50:17,520 --> 00:50:18,880
Speaker 1: dive wherever you want to dive.

983
00:50:19,400 --> 00:50:21,680
Speaker 4: Oh wow, I feel like I know TIFFs so much

984
00:50:21,719 --> 00:50:26,839
Speaker 4: better now. That's amazing. Hey, this is something that's going

985
00:50:26,840 --> 00:50:29,040
Speaker 4: to impact a lot of Australian So anybody who's not

986
00:50:29,080 --> 00:50:31,040
Speaker 4: in Australia, I'm going to apologize up front.

987
00:50:31,160 --> 00:50:34,920
Speaker 3: But later this year, in September, a lot of people.

988
00:50:34,680 --> 00:50:38,319
Speaker 4: Selling NBN are going to be upgrading their plans, so

989
00:50:38,880 --> 00:50:41,520
Speaker 4: without going into all the rabbit hole details of how

990
00:50:41,520 --> 00:50:45,319
Speaker 4: the plans work. So currently they're going they call their

991
00:50:45,360 --> 00:50:50,160
Speaker 4: plans certain names, their wholesales sold through NBNCo and then

992
00:50:50,160 --> 00:50:53,440
Speaker 4: when you buy your plan through Telstra or Dodo or

993
00:50:53,640 --> 00:50:56,560
Speaker 4: whoever it is that you get it through TPG, then

994
00:50:56,600 --> 00:51:00,960
Speaker 4: you're buying a retail product that's being born from the

995
00:51:01,080 --> 00:51:05,760
Speaker 4: upper level provider. The problem with that is their prices

996
00:51:05,800 --> 00:51:08,880
Speaker 4: are probably going to go up, and the reason for

997
00:51:08,960 --> 00:51:11,600
Speaker 4: them going up is going to be the argument will be, well,

998
00:51:11,640 --> 00:51:14,320
Speaker 4: your speed plan. So if you're on a basic home plan,

999
00:51:14,640 --> 00:51:17,080
Speaker 4: it's suddenly going to be upgraded to a faster plan.

1000
00:51:18,040 --> 00:51:21,880
Speaker 4: But the downside is that you need to have fiber

1001
00:51:21,960 --> 00:51:25,600
Speaker 4: to your home and not use the other NBN technology.

1002
00:51:25,719 --> 00:51:28,200
Speaker 4: So if you're using NBN satellite or if you happen

1003
00:51:28,239 --> 00:51:31,319
Speaker 4: to be on NBN fiber to the node, then what

1004
00:51:31,480 --> 00:51:32,719
Speaker 4: they're promising you they.

1005
00:51:32,600 --> 00:51:33,840
Speaker 3: Can't necessarily deliver.

1006
00:51:34,200 --> 00:51:37,040
Speaker 4: And so come September it's going to be a really

1007
00:51:37,040 --> 00:51:40,759
Speaker 4: important thing to know that what you're paying for may

1008
00:51:40,800 --> 00:51:43,520
Speaker 4: not be what you're actually going to be getting. So

1009
00:51:43,960 --> 00:51:47,200
Speaker 4: it's probably good to shop around, think about what plan

1010
00:51:47,280 --> 00:51:50,279
Speaker 4: you're on, but whether you're actually getting what you're paying for,

1011
00:51:50,600 --> 00:51:52,440
Speaker 4: and you know you can jump on and do what

1012
00:51:52,440 --> 00:51:54,960
Speaker 4: they call a speed test and then compare that to

1013
00:51:54,960 --> 00:51:57,080
Speaker 4: what you're paying for. So if you're paying for a

1014
00:51:57,160 --> 00:52:00,160
Speaker 4: download speed of say two hundred and fifty megabits, but

1015
00:52:00,160 --> 00:52:01,879
Speaker 4: then when you jump on and do a speed test,

1016
00:52:01,880 --> 00:52:05,160
Speaker 4: it's only fifty megabits. Then there's a big disparity, and

1017
00:52:05,200 --> 00:52:07,120
Speaker 4: you need to be asking the question, well, am I

1018
00:52:07,200 --> 00:52:09,680
Speaker 4: paying for something that I'm not actually getting? And that's

1019
00:52:09,680 --> 00:52:12,879
Speaker 4: a really difficult decision because there's a lot of variables.

1020
00:52:13,040 --> 00:52:15,279
Speaker 4: You know, the router that you use, your modem that

1021
00:52:15,320 --> 00:52:17,520
Speaker 4: you use, could impact on the speed that you're getting.

1022
00:52:17,680 --> 00:52:20,480
Speaker 4: So it maybe your end the technology at your home

1023
00:52:20,840 --> 00:52:23,200
Speaker 4: or at your business, but it could also be your

1024
00:52:23,200 --> 00:52:26,840
Speaker 4: provider as well that isn't able to deliver the speed

1025
00:52:26,880 --> 00:52:28,400
Speaker 4: that it's purporting to be delivering.

1026
00:52:29,239 --> 00:52:30,240
Speaker 3: Does that kind of make sense?

1027
00:52:31,040 --> 00:52:31,879
Speaker 1: It kind of makes sense.

1028
00:52:31,960 --> 00:52:33,480
Speaker 3: Yeah, I didn't want to put you both to sleep

1029
00:52:33,480 --> 00:52:33,680
Speaker 3: on that.

1030
00:52:34,360 --> 00:52:37,759
Speaker 1: I'll just ring you when I need to know. Hey, yeah,

1031
00:52:38,000 --> 00:52:40,560
Speaker 1: I want to a nuclear battery the size the coin

1032
00:52:40,680 --> 00:52:43,240
Speaker 1: that's going to last me one hundred years without charging,

1033
00:52:43,280 --> 00:52:46,440
Speaker 1: because I know I'd like to have an iPhone that

1034
00:52:46,480 --> 00:52:47,760
Speaker 1: I never have to charge.

1035
00:52:48,120 --> 00:52:49,080
Speaker 3: This is phenomenal.

1036
00:52:49,239 --> 00:52:52,239
Speaker 4: So the research at the moment in China, they're promising

1037
00:52:52,360 --> 00:52:56,360
Speaker 4: that they are well not just promising, they are saying

1038
00:52:56,400 --> 00:53:00,160
Speaker 4: that they've produced a battery that lasts for decades without

1039
00:53:00,200 --> 00:53:04,439
Speaker 4: the need for one single recharge. So they're looking at

1040
00:53:04,640 --> 00:53:08,680
Speaker 4: using different sorts of tech to like Nickel sixty three

1041
00:53:09,400 --> 00:53:12,399
Speaker 4: is what's powering this particular battery, and it can last

1042
00:53:12,480 --> 00:53:16,680
Speaker 4: up to fifty years without needing to be recharged. Now's

1043
00:53:16,880 --> 00:53:19,799
Speaker 4: it's not just a prototype that's in production right now,

1044
00:53:19,840 --> 00:53:22,600
Speaker 4: So we're talking a battery that will last fifty years.

1045
00:53:22,640 --> 00:53:25,680
Speaker 4: It's the BV one hundred, but that's just the beginning.

1046
00:53:25,719 --> 00:53:29,920
Speaker 4: Another Chinese company is looking at a carbon fourteen battery.

1047
00:53:30,520 --> 00:53:32,839
Speaker 4: So the idea of a nuclear battery has been around

1048
00:53:32,840 --> 00:53:34,839
Speaker 4: for a long time, and in fact in the United States.

1049
00:53:34,840 --> 00:53:36,799
Speaker 4: So I think in the nineteen fifties there was a

1050
00:53:36,840 --> 00:53:39,360
Speaker 4: theory that they could use nuclear technology, but there was

1051
00:53:39,400 --> 00:53:43,000
Speaker 4: such big concerns about radiation and all those issues that

1052
00:53:43,080 --> 00:53:46,080
Speaker 4: come with it. But now they're saying that these batteries

1053
00:53:46,120 --> 00:53:48,359
Speaker 4: could be safe. They could be used in medicine and

1054
00:53:48,400 --> 00:53:51,200
Speaker 4: aerospace and even consumer electronics.

1055
00:53:51,200 --> 00:53:51,719
Speaker 3: And think about it.

1056
00:53:51,719 --> 00:53:53,799
Speaker 4: If you've got to get a pacemaker and you've got

1057
00:53:53,840 --> 00:53:55,799
Speaker 4: a battery that will last fifty years, well that's going

1058
00:53:55,840 --> 00:53:58,040
Speaker 4: to see you out in a fifty or one hundred years.

1059
00:53:58,080 --> 00:54:01,160
Speaker 4: So you can see straight away with medical tech, you know,

1060
00:54:02,080 --> 00:54:04,440
Speaker 4: do you remember the mechanical heart. The first person who

1061
00:54:04,520 --> 00:54:07,360
Speaker 4: got an actual mechanical heart. You know, imagine having the

1062
00:54:07,440 --> 00:54:10,520
Speaker 4: charge that little sucker up. So if you were able

1063
00:54:10,560 --> 00:54:12,960
Speaker 4: to just put a mechanical heart and you knew it

1064
00:54:13,000 --> 00:54:14,720
Speaker 4: was going to keep beating for the rest of your life,

1065
00:54:14,719 --> 00:54:17,720
Speaker 4: that'd be amazing without having to have invasive surgery.

1066
00:54:17,760 --> 00:54:20,600
Speaker 1: Again, Well, they're going to grow hearts in well, they're

1067
00:54:20,600 --> 00:54:23,480
Speaker 1: already growing body parts in labs. So I wonder whether

1068
00:54:23,560 --> 00:54:27,239
Speaker 1: or not it'll be the nuclear battery driven heart or

1069
00:54:27,239 --> 00:54:29,400
Speaker 1: it'll be the one that was grown in a bloody

1070
00:54:29,440 --> 00:54:31,120
Speaker 1: petrie dish at monash Uni.

1071
00:54:31,480 --> 00:54:33,480
Speaker 4: Yeah, I think I go for the Petri dish myself,

1072
00:54:33,560 --> 00:54:36,399
Speaker 4: using my own stem cells and growing my own heart. Yep,

1073
00:54:37,040 --> 00:54:39,480
Speaker 4: go said, I'm still pretty fortunate that I have an

1074
00:54:39,520 --> 00:54:41,480
Speaker 4: identical twin brother, because I do think of him as

1075
00:54:41,520 --> 00:54:42,200
Speaker 4: spare parts.

1076
00:54:42,760 --> 00:54:46,799
Speaker 1: Well, I think that is only fair. I mean, do

1077
00:54:46,840 --> 00:54:49,400
Speaker 1: you call them sp that? Do you just call him

1078
00:54:49,440 --> 00:54:50,480
Speaker 1: that around the house.

1079
00:54:50,280 --> 00:54:52,480
Speaker 3: Amongst other things? Yeah, I have made a clear to

1080
00:54:52,600 --> 00:54:53,760
Speaker 3: him that he does.

1081
00:54:53,920 --> 00:54:56,719
Speaker 4: You know, I checked that his kidneys are fine, and

1082
00:54:56,800 --> 00:54:58,120
Speaker 4: you know, has he had a test recently?

1083
00:54:58,160 --> 00:55:01,040
Speaker 3: Is he drinking too much? And then for his well being?

1084
00:55:01,480 --> 00:55:03,680
Speaker 1: Exactly? Do you get mad at him for not looking

1085
00:55:03,719 --> 00:55:10,760
Speaker 1: after your after your future? Does he know what Roley

1086
00:55:10,880 --> 00:55:12,440
Speaker 1: potentially plays.

1087
00:55:12,320 --> 00:55:13,959
Speaker 3: Actually thinking about it the other way around.

1088
00:55:14,000 --> 00:55:16,240
Speaker 4: I just got some tests, you know, your annual blood

1089
00:55:16,280 --> 00:55:18,080
Speaker 4: test and all that sort of stuff, and my doctors

1090
00:55:18,680 --> 00:55:21,359
Speaker 4: tick tick, and I'm thinking and actually there's a double

1091
00:55:21,440 --> 00:55:23,120
Speaker 4: edged sword there, because you know he could be looking

1092
00:55:23,120 --> 00:55:24,160
Speaker 4: at me the same way.

1093
00:55:25,480 --> 00:55:29,920
Speaker 1: Well, in fact, you're probably way in fact, there's more

1094
00:55:29,960 --> 00:55:32,040
Speaker 1: of a chance that you'll become parts for him than

1095
00:55:32,160 --> 00:55:37,520
Speaker 1: vice versa. All your shits. Yeah, can we tell people, No,

1096
00:55:37,680 --> 00:55:40,200
Speaker 1: we can't tell people. I was going to talk about

1097
00:55:40,200 --> 00:55:42,200
Speaker 1: your PSA score. I'm just going to say it was

1098
00:55:42,360 --> 00:55:46,520
Speaker 1: very very very good. Oh okay, thank you, yep, which

1099
00:55:46,600 --> 00:55:49,239
Speaker 1: is you know when blokes get there, you know the

1100
00:55:49,280 --> 00:55:52,080
Speaker 1: old finger up the date and things and did you

1101
00:55:52,120 --> 00:55:54,399
Speaker 1: get that? Just do it with ad.

1102
00:55:55,120 --> 00:55:56,120
Speaker 3: So we talked about the doctor.

1103
00:55:56,160 --> 00:56:01,160
Speaker 1: Still, I'm not talking about Saturday after. I'm talking about

1104
00:56:01,520 --> 00:56:02,439
Speaker 1: the medical thing.

1105
00:56:03,760 --> 00:56:06,000
Speaker 3: No, I just do a blood test to do for us.

1106
00:56:06,480 --> 00:56:11,120
Speaker 1: Oh y do they do that anymore? The digital examination?

1107
00:56:11,480 --> 00:56:13,279
Speaker 3: Digital examination? Now, I don't know.

1108
00:56:13,880 --> 00:56:16,320
Speaker 4: I've just gone for the blood tests and the score

1109
00:56:16,400 --> 00:56:19,000
Speaker 4: is very very very low, which is really good to know,

1110
00:56:19,960 --> 00:56:22,520
Speaker 4: very much. So can we talk about something else now?

1111
00:56:22,520 --> 00:56:22,920
Speaker 1: Can we?

1112
00:56:23,200 --> 00:56:23,319
Speaker 3: All?

1113
00:56:23,400 --> 00:56:26,080
Speaker 1: Right? Can you quickly do one more story before we

1114
00:56:26,160 --> 00:56:28,560
Speaker 1: wind up, so we don't all get off the podcast.

1115
00:56:28,600 --> 00:56:32,279
Speaker 1: Thinking about digital examinations for prostate.

1116
00:56:33,040 --> 00:56:38,239
Speaker 4: There is hope for used EV batteries. So it's projected

1117
00:56:38,520 --> 00:56:41,960
Speaker 4: that EV batteries they do have a long lifespan, so

1118
00:56:42,560 --> 00:56:45,359
Speaker 4: in fact they're thinking twelve to fifteen years, but now

1119
00:56:45,400 --> 00:56:48,399
Speaker 4: they're saying potentially up to forty percent longer. But then

1120
00:56:48,440 --> 00:56:51,759
Speaker 4: what happens to you know, four hundred and fifty kilograms

1121
00:56:51,760 --> 00:56:54,960
Speaker 4: of battery because they're woken. By twenty thirty, there'll be

1122
00:56:55,080 --> 00:56:59,520
Speaker 4: thirty thousand tons of EV batteries that will need to

1123
00:56:59,560 --> 00:57:03,800
Speaker 4: be disposed and recycled. In Australia that's thirty thousand tons.

1124
00:57:04,239 --> 00:57:08,680
Speaker 4: But when they leave the car, those batteries don't necessarily

1125
00:57:08,719 --> 00:57:11,600
Speaker 4: it doesn't necessarily mean the batteries aren't useful anymore. This

1126
00:57:12,320 --> 00:57:15,080
Speaker 4: massive percentage that can be recycled we're talking about in

1127
00:57:15,120 --> 00:57:18,760
Speaker 4: the high nineties of the materials because they have really

1128
00:57:18,840 --> 00:57:21,840
Speaker 4: interesting elements that are in their rare elements. But now

1129
00:57:21,880 --> 00:57:24,920
Speaker 4: there's this suggestion that what you could do is maybe

1130
00:57:25,080 --> 00:57:27,480
Speaker 4: use it to power your house, so you use it

1131
00:57:27,520 --> 00:57:30,880
Speaker 4: as batteries. They repurpose these The problem that they face

1132
00:57:31,360 --> 00:57:33,760
Speaker 4: is a lot of these batteries in vehicles are sealed

1133
00:57:34,240 --> 00:57:37,800
Speaker 4: and field for protection, so if you have a minor accident,

1134
00:57:38,160 --> 00:57:41,120
Speaker 4: you don't want those batteries to be exposed or damaged.

1135
00:57:41,880 --> 00:57:44,160
Speaker 4: But if they could come up with a way for

1136
00:57:44,840 --> 00:57:48,120
Speaker 4: manufacturers to be more transparent about those batteries, and this

1137
00:57:48,240 --> 00:57:52,080
Speaker 4: is the concern that a lot of ev manufacturers, people

1138
00:57:52,120 --> 00:57:56,200
Speaker 4: who are making electric cars, aren't really being upfront about

1139
00:57:56,200 --> 00:57:59,680
Speaker 4: what then happens at end of life and what then

1140
00:57:59,680 --> 00:58:02,200
Speaker 4: happen to the quality of the battery could have been

1141
00:58:02,280 --> 00:58:05,000
Speaker 4: repurposed and reused. And that's the big suggestion that if

1142
00:58:05,000 --> 00:58:08,960
Speaker 4: we've got, you know, seventeen million battery electric and hybrid

1143
00:58:09,040 --> 00:58:14,200
Speaker 4: vehicles that have been sold around the world just last year,

1144
00:58:14,600 --> 00:58:16,800
Speaker 4: then what's going to happen to all those batteries. You know,

1145
00:58:16,840 --> 00:58:19,959
Speaker 4: there's a lot of things that I think that the

1146
00:58:20,000 --> 00:58:22,680
Speaker 4: forethought that's gone into it maybe have just kind of

1147
00:58:23,000 --> 00:58:24,800
Speaker 4: thought about the sales implications.

1148
00:58:24,840 --> 00:58:26,520
Speaker 3: And it's great that we've got these.

1149
00:58:26,440 --> 00:58:29,000
Speaker 4: Cars that aren't emitting fumes and all the rest of it,

1150
00:58:29,200 --> 00:58:30,960
Speaker 4: but we've got a big problem that we're going to

1151
00:58:31,000 --> 00:58:33,520
Speaker 4: be facing in ten to fifteen, twenty years.

1152
00:58:34,560 --> 00:58:38,720
Speaker 1: Yeah, I agree, I've thought about that quite a bit. Okay,

1153
00:58:39,280 --> 00:58:42,640
Speaker 1: now I don't know this comes across my This has

1154
00:58:42,640 --> 00:58:45,640
Speaker 1: come across about five times. Got nothing to do with technology.

1155
00:58:45,680 --> 00:58:47,880
Speaker 1: I'm going to ask you to this question. I'm going

1156
00:58:47,920 --> 00:58:52,880
Speaker 1: to start with tiff one hundred blokes, one hundred blokes

1157
00:58:53,880 --> 00:58:58,520
Speaker 1: versus a six hundred pound gorilla in a fight. Who

1158
00:58:58,520 --> 00:59:03,760
Speaker 1: wins the gorilla or the hundred blokes the gorilla?

1159
00:59:05,160 --> 00:59:08,200
Speaker 3: Patrick, I'm thinking gorilla.

1160
00:59:09,400 --> 00:59:12,680
Speaker 1: Don't you're not allowed to google it, putting.

1161
00:59:12,400 --> 00:59:13,840
Speaker 4: It into chat cheap and I'm putting it into the

1162
00:59:13,840 --> 00:59:17,640
Speaker 4: Microsoft co pilot to sit pilot, Wait a bite?

1163
00:59:17,680 --> 00:59:19,000
Speaker 3: Can I just do the breakdown?

1164
00:59:19,200 --> 00:59:22,760
Speaker 1: That's that's I don't want co pilot's opinion. That's why

1165
00:59:22,760 --> 00:59:23,640
Speaker 1: I asked you.

1166
00:59:24,560 --> 00:59:27,520
Speaker 4: Okay, now I'm thinking gorilla person. Well, I guess it

1167
00:59:27,600 --> 00:59:28,560
Speaker 4: depends on the blokes.

1168
00:59:28,640 --> 00:59:31,680
Speaker 3: Is it you and me? And like there's a fifty.

1169
00:59:31,760 --> 00:59:36,120
Speaker 1: Don't compare you with me? Fucking you and I aren't

1170
00:59:36,160 --> 00:59:36,560
Speaker 1: the same.

1171
00:59:38,480 --> 00:59:40,520
Speaker 4: Well, I mean, if I've got a shovel, I could

1172
00:59:40,560 --> 00:59:43,680
Speaker 4: dig a big eaves over it and the net over it.

1173
00:59:43,680 --> 00:59:45,560
Speaker 1: It's in the middle of the forty oval. It's in

1174
00:59:45,600 --> 00:59:47,880
Speaker 1: the middle of the oval. You've got no weapons or

1175
00:59:47,920 --> 00:59:51,200
Speaker 1: tools with gorilla or the hundred blokes.

1176
00:59:51,440 --> 00:59:54,640
Speaker 3: I'm going to gorilla. Yeah, I'm going to gorilla. What

1177
00:59:54,680 --> 00:59:54,960
Speaker 3: do I think?

1178
00:59:55,080 --> 00:59:59,080
Speaker 1: My only question I reckon, I just reckon. I don't

1179
00:59:59,080 --> 01:00:02,280
Speaker 1: know about how big the gorilla's tanky is, Tiff, how

1180
01:00:02,360 --> 01:00:06,800
Speaker 1: much Cardio, the old Chris. You might have a heart

1181
01:00:06,840 --> 01:00:08,600
Speaker 1: attack by the time, Craig.

1182
01:00:08,760 --> 01:00:10,920
Speaker 4: If it's a hundred of you and a hundred of

1183
01:00:10,960 --> 01:00:13,160
Speaker 4: me or a hundred of Tiff, that's going to make

1184
01:00:13,200 --> 01:00:15,640
Speaker 4: a major difference too, Because if I had a hundred

1185
01:00:15,640 --> 01:00:17,520
Speaker 4: TIFFs fighting the gorilla.

1186
01:00:17,240 --> 01:00:19,760
Speaker 3: I would go for the TIFFs, not the gorilla.

1187
01:00:19,840 --> 01:00:22,600
Speaker 4: But if it's me one hundred of me in the gorilla, well,

1188
01:00:22,600 --> 01:00:24,520
Speaker 4: I'm just gonna lay down and might.

1189
01:00:24,400 --> 01:00:26,160
Speaker 2: Just give it a big hug. I just love it

1190
01:00:26,200 --> 01:00:27,040
Speaker 2: into submission.

1191
01:00:29,600 --> 01:00:32,200
Speaker 1: How good would it be gorillas were just if you

1192
01:00:32,240 --> 01:00:34,400
Speaker 1: could if they were like if they had the nature

1193
01:00:34,440 --> 01:00:37,959
Speaker 1: of like a golden retriever. Imagine imagine having a six

1194
01:00:38,040 --> 01:00:41,520
Speaker 1: hundred pound I don't even want to say pet companion,

1195
01:00:42,120 --> 01:00:45,640
Speaker 1: six hundred pound. Yeah, how handy would he or she

1196
01:00:45,760 --> 01:00:46,640
Speaker 1: be around the joint?

1197
01:00:46,800 --> 01:00:51,240
Speaker 4: A silver backed gorilla is capable of lifting eight hundred kilos.

1198
01:00:52,800 --> 01:00:55,960
Speaker 3: Yeah, that's that's that's one hundred kilos, So I'm table

1199
01:00:56,160 --> 01:00:59,640
Speaker 3: into all the gorilla all right, the gorilla wins.

1200
01:01:00,040 --> 01:01:04,320
Speaker 1: That's the gorilla for the wind. Patrick details. How can

1201
01:01:04,360 --> 01:01:05,640
Speaker 1: people get near you?

1202
01:01:06,080 --> 01:01:09,040
Speaker 4: They can go to websites now dot com, dot au

1203
01:01:09,280 --> 01:01:11,840
Speaker 4: bit about what we do. You can check that out,

1204
01:01:11,920 --> 01:01:14,840
Speaker 4: or go to tichi at home dot comdod au. They

1205
01:01:14,880 --> 01:01:16,960
Speaker 4: want to do some tie chi exercises with me and

1206
01:01:17,040 --> 01:01:22,600
Speaker 4: Fritz always happy to get people to enjoy their time

1207
01:01:22,640 --> 01:01:25,680
Speaker 4: of mindfulness by doing a bit of tie chee with us.

1208
01:01:26,160 --> 01:01:32,040
Speaker 1: And if people are seriously heading to Bland, can they

1209
01:01:32,040 --> 01:01:32,880
Speaker 1: have a coffee with you?

1210
01:01:33,320 --> 01:01:34,120
Speaker 3: Yeah, because they can.

1211
01:01:35,440 --> 01:01:37,160
Speaker 2: There you go the best day ever.

1212
01:01:38,520 --> 01:01:40,480
Speaker 1: Have a coffee with Patrick and meet Fritz.

1213
01:01:41,280 --> 01:01:42,200
Speaker 3: Fritz is the best, isn't it?

1214
01:01:42,240 --> 01:01:44,600
Speaker 4: We could do some tai chi now, seriously, I've actually

1215
01:01:44,640 --> 01:01:46,680
Speaker 4: got a couple of people now who are doing tai

1216
01:01:46,800 --> 01:01:49,680
Speaker 4: chie with me during lunchtime and after work in my

1217
01:01:49,720 --> 01:01:51,160
Speaker 4: little studio space.

1218
01:01:51,080 --> 01:01:52,240
Speaker 3: Which is kind of nice.

1219
01:01:52,360 --> 01:01:56,040
Speaker 4: Turn my garret into an actual functional area. You know

1220
01:01:56,080 --> 01:01:58,160
Speaker 4: how most blakes have a man cave and they do

1221
01:01:58,320 --> 01:02:00,320
Speaker 4: really good stuff like repair things.

1222
01:02:00,480 --> 01:02:02,120
Speaker 3: Well, I just might just collect the junk.

1223
01:02:02,440 --> 01:02:04,080
Speaker 4: So I decided to turn it into a tight cheese

1224
01:02:04,120 --> 01:02:07,160
Speaker 4: studio and podcasting area where I can talk to you Stretch.

1225
01:02:07,760 --> 01:02:13,120
Speaker 1: Yeah, we'll say goodbye here, but Tiff, happy birthday for yesterday. Patrick,

1226
01:02:13,200 --> 01:02:15,439
Speaker 1: Thank you as always, See you next time.

1227
01:02:15,880 --> 01:02:16,200
Speaker 3: Jeers,