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Speaker 1: You're listening to a podcast from News Talk sed be

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Speaker 1: follow this and our Wide Ranger podcasts now on iHeartRadio.

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Speaker 2: Hello, great you Zilla's welcome to Matton Tyler Full Show

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Speaker 2: Podcast number two seven seven Wow, two hundred and seventy

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Speaker 2: seven shows for Thursday, the twenty ninth January. Boy, boy,

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Speaker 2: do we get people angry today?

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Speaker 3: Oh my god.

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Speaker 2: Fizzin at the Bug postulates that maybe Bruce Springsteen's new

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Speaker 2: song was a bit cringe compared to some of his

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Speaker 2: old works, as lack of metaphorical content and the lyrics

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Speaker 2: just read a little bit by a like sounds a

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Speaker 2: little bit like a cable newsticker.

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Speaker 3: Where did that go? Death threats?

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Speaker 2: Abuse turns out the thing that Kiwi's most care about,

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Speaker 2: and you'll hear this in the show. It's not you know,

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Speaker 2: their families, not their jobs. It's not there the inclement

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Speaker 2: weather we've had, the terrible weather we've had. It's what's

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Speaker 2: happening with immigration in a state fourteen thousand.

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Speaker 3: Killum is Yep, that's what fizzes people in New Zealand

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Speaker 3: at the moment.

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Speaker 2: Who would have thought, yep, yeah, threatening to kill people

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Speaker 2: in New Zealand for what's happening on the other side

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Speaker 2: of the world.

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Speaker 3: We're going to be all right, folks, So download, subscribe,

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Speaker 3: give us a review, give a taste to keep you

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Speaker 3: all right. You seen Basile, you go love you.

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Speaker 4: The big stories, the big issues, the big trends, and

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Speaker 4: everything in between.

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Speaker 1: Matt Heath and Taylor Adams Afternoons News.

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Speaker 3: Talk said, the very good afternoon to you. Welcome to

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Speaker 3: the show. Really really good to have your company as always.

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Speaker 3: Hope you're doing pretty well. Get a Mets, get a

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Speaker 3: Tyler gd Evan. I just got an amusing phone call

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Speaker 3: that I thought.

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Speaker 2: I'd share with everyone please. So we've got someone coming

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Speaker 2: around to the house tomorrow to do a bit of

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Speaker 2: work and they were like, can you make sure you

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Speaker 2: secure your dog?

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Speaker 3: And I see it.

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Speaker 2: Wow, there's no need And he said it's a health

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Speaker 2: and safety issue. You need to secure the dog before

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Speaker 2: I came around. And I said, I can assure you

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Speaker 2: you don't need to worry about me securing my dog

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Speaker 2: before you come around.

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Speaker 5: Yeah.

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Speaker 3: I could hear this conversation, folks. So Matt was heaving

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Speaker 3: it here before we're going to go on here. I

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Speaker 3: think the exact words you use where when you see Colin.

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Speaker 3: You know what I'm trying to say here, because Colin,

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Speaker 3: he's a good boy, but he's not an aggressive boy.

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Speaker 3: He's not that big. You might as well get us

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Speaker 3: to secure the slugs in our bathroom in terms of

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Speaker 3: the danger that is going to before you with that dog.

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Speaker 2: But I mean, I'm not laughing at the guy, because

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Speaker 2: you know, if you're coming around to someone house, there

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Speaker 2: must be so many times when you've come around there's

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Speaker 2: a big, terrifying, angry dog.

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Speaker 3: You got to ask. But knowing what my dog is,

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Speaker 3: which is the I love him to death. And he's

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Speaker 3: probably listening right now. Colin, you're a good boy.

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Speaker 2: You're good boy, but one of the most pathetic creatures

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Speaker 2: that has ever been come into existence on this planet.

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Speaker 3: Yeah, there's no terrifying prisons there, no, Yeah, But to.

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Speaker 2: Be fair, he's an absolute mockery of his wolve and ancestors.

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Speaker 3: But there would be a lot of people that would

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Speaker 3: say that, don't worry about my dog. My dog's lovely,

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Speaker 3: and then they get around there and it's a ginormous

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Speaker 3: crank Dane or a Rottweiler. But in this case, I

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Speaker 3: think you just need to send the photo of Colin

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Speaker 3: to the sky and say, hey, lock here's Colin. Yeah,

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Speaker 3: you got nothing to worry about.

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Speaker 2: I mean even after that he said at the end,

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Speaker 2: just please lock them in another room. And I was like, well,

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Speaker 2: how are we going to do business if you won't

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Speaker 2: even trust me on this one point. I'm not looking

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Speaker 2: at spend quite a lot of money with you. Yep,

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Speaker 2: and you won't trust me that you don't need to

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Speaker 2: worry about my dog.

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Speaker 3: That's a good technic. After I've told you three or

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Speaker 3: four times. Yeah, so he's coming around, Colin's going to

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Speaker 3: be out and he's got to deal with it. Yeah, yeah, good.

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Speaker 2: What I might do is I might go and borrow

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Speaker 2: my neighbors German shepherds. They're angry does just for a

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Speaker 2: practical judgeyh fantastic, Right on to the show today after

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Speaker 2: three o'clock. The traditional ki we dream of buying a

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Speaker 2: door upper may be fading. This is according to new research.

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Speaker 3: So a December survey, it found more than half of

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Speaker 3: active buyers are now seeking homes that feel new or updated.

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Speaker 3: Only six of those surveyed said they were actively searching

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Speaker 3: for a fixer upper. So really keen to have a

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Speaker 3: chat with you on this one. Dey is at that tradition,

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Speaker 3: old Keiwi tradition long gone. Is it too much of

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Speaker 3: a fath to look at a house? It's going to

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Speaker 3: need a lot of work, and you think, I just

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Speaker 3: can't be asked with this. I just want something that's

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Speaker 3: a little bit newer than this.

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Speaker 2: Well, any kind of major things, a massive compliance expenditure.

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Speaker 2: I mean, I'm going through that right now, and it

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Speaker 2: is a yeah. I mean cause you look at the

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Speaker 2: house and go, I could do that, we could do this,

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Speaker 2: We do that, we'll do that, just remove that. Yep,

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Speaker 2: we'll do this, and then the house will be perfect.

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Speaker 2: And then next thing you know, you've spent three hundred

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Speaker 2: thousand dollars and you might as well have just bought

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Speaker 2: a house that was two hundred thousand dollars more than

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Speaker 2: the one that you were going to buy.

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Speaker 3: Exactly looking forward to that after three o'clock, after two o'clock,

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Speaker 3: where we worried a little bit too much about AI

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Speaker 3: taking our jobs. We're all freaking out about it last

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Speaker 3: year that AI was coming in fast and we're all

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Speaker 3: going to lose our jobs. And if you don't upskill

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Speaker 3: in human connection jobs, then you're not going to have

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Speaker 3: anything to do. According to new research out of the Economist,

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Speaker 3: that's not true. White collar workers are still doing very well.

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Speaker 3: Since late twenty and twenty two, America has added another

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Speaker 3: roughly three million white collar jobs, which includes management, professional, sales,

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Speaker 3: and office roles. White blue collar employment has remained flat,

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Speaker 3: but many occupations are actually increasing. Despite that alarm over

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Speaker 3: the future of AI.

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Speaker 2: Yes, so was twenty twenty five, Well, twenty twenty five,

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Speaker 2: no doubt was the year.

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Speaker 3: Of freaking out about AI.

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Speaker 2: Everyone was freaking out saying it was going to eliminate everything.

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Speaker 2: But this just hasn't panned out like that. I was

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Speaker 2: looking at unders saying that a use of AI is

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Speaker 2: flattening out. So is it going to be like I mean,

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Speaker 2: are you freaking out less about it?

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Speaker 5: Now?

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Speaker 2: As you've seen it being embedded, as you've been using

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Speaker 2: it more, have you realized its limitations And you're starting

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Speaker 2: to think that maybe the panic the chicken lickening was

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Speaker 2: unnecessary and all it's going to do is help people

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Speaker 2: do their job more efficiently. Because there's one thing everyone

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Speaker 2: knows when it comes to AI and needs a human

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Speaker 2: looking at it does and needs human oversight that you

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Speaker 2: could never do anything with AI and just let it

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Speaker 2: go out there without checking.

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Speaker 3: It needs a watch man.

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Speaker 2: It lies, it just reproduces stuff before it. Calculations will

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Speaker 2: be wrong, facts will be wrong. The whole thing is.

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Speaker 2: And there's all these you know, these jokes online that

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Speaker 2: you can see now where people are pretending to be

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Speaker 2: AI and just you ask the person to do something,

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Speaker 2: they just stand there.

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Speaker 3: I'll find the audio of it. Actually it's pretty funny.

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Speaker 3: I'll pay it afterwards.

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Speaker 2: But just the responses that the vacuous responses that AI gives.

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Speaker 3: You, and how readily it admits it's just been lying

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Speaker 3: to you. Yeah, it's going to be good after two o'clock.

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Speaker 3: But right now, let's have a chat about celebrity opinions.

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Speaker 3: This is after Bruce Springsteen. He has dedicated his new

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Speaker 3: song to the people of Minneapolis. This song criticizes the

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Speaker 3: US President Donald Trump's ongoing immigration enforcement operations in the city. So,

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Speaker 3: in a statement, Bruce Springstein City wrote and recorded the

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Speaker 3: song over the weekend and released it in response to

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Speaker 3: a second shooting by federal immigration agents in Minneapolis. So

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Speaker 3: it raises the question, I would say Minneapolis, but you

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Speaker 3: say Minneapolis. Minneapolis Minneapolis, Tomato Tomato, No, I think it's Minneapolis. Okay,

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Speaker 3: that's a mere culprit that I can take right here,

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Speaker 3: right now. So are you swayed by celebrity opinions like

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Speaker 3: Bruce Springsteen obviously has in this situation or do you

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Speaker 3: think many of them are so out of touch that

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Speaker 3: their thoughts are just meaningless? Yeah?

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Speaker 2: When I heard the song, and irrespective of the issue,

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Speaker 2: whatever side people are on the issue, because people just

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Speaker 2: want to draw this down into this US immigration issue,

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Speaker 2: which couldn't have less to do with us in New Zealand,

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Speaker 2: how America wants to enforce its immigration policies is so

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Speaker 2: far from anything to do with us.

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Speaker 3: Yeah, it does. It affect your data, So we want.

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Speaker 2: To enforce our immigration policies now borders, that's something we

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Speaker 2: could be interested in, but theirs is not. But I

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Speaker 2: just as soon as I heard this Bruce Springsteen song,

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Speaker 2: and as soon as I heard what he was saying

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Speaker 2: on stage it and you can't decide Sometimes the emotions

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Speaker 2: you have for things, you just a reaction to it,

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Speaker 2: and your action sometimes says more about you. Then it

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Speaker 2: kind of reveals something about you you didn't even know.

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Speaker 3: And as soon as I heard this, I just felt cringe.

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Speaker 2: He just came across as the most out of touch

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Speaker 2: sitting at home. The lyrics sound like the ticker a

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Speaker 2: ticker from CNN. So, for example, if you take the

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Speaker 2: lyrics from I Know, one of his one of his

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Speaker 2: most incredible songs, like born down in a dead Man's Town,

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Speaker 2: The first kick I took was when I hit the ground,

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Speaker 2: end up like a dog that's been beat too much

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Speaker 2: till you spend half your life just to cover it up.

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Speaker 2: Got in a little hometown jam, so they put a

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Speaker 2: rifle in my hands. Sent off to a foreign land

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Speaker 2: to go and kill the yellow man born in the USA.

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Speaker 2: It's a political statement from him, and it's talking about

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Speaker 2: but it's sort of a worry about the plight of

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Speaker 2: working class people in America and being sent off to

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Speaker 2: Vietnam Vietnam war that wasn't necessarily related to them. But

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Speaker 2: there's nothing cringe about that. It's so carefully crafted. But

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Speaker 2: the lyrics to this song are as follows. Here we

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Speaker 2: go and just bring this up. Trump's federal thugs beat

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Speaker 2: up on his face and his chest. Then we heard

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Speaker 2: the gunshots and Alex pretty lay in the snow, dead.

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Speaker 2: Their claim was self defense, sir, just don't believe your eyes.

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Speaker 2: It's our blood on the streets and these whistles and

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Speaker 2: phones against Stephen Miller and gnomes dirty lives. I mean,

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Speaker 2: you're just reading the ticket from cnnbody, Yeah, you're just

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Speaker 2: reading some news out.

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Speaker 3: Feels a little bit like that's run through chet GPT. Maybe,

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Speaker 3: but it does. But maybe that's it.

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Speaker 2: It feels a little bit of an opinion that he

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Speaker 2: has been given and then he has just re I'm

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Speaker 2: sure he cares about it. I'm sure he's passionate about it,

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Speaker 2: but it just feels lazier than what it used to be,

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Speaker 2: less meaningful, more just taking what he's told and repeating it.

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Speaker 3: Kind of do you feel like it's been forced down

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Speaker 3: your throat? That is previous work was very poetic and

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Speaker 3: clearly it was political, but there was an artistry to it,

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Speaker 3: whereas this you've well, it's more contrived.

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Speaker 2: Well just you know, it's always weird an art when

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Speaker 2: you just use the exact names of the people that

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Speaker 2: you would. I mean, take this from my hometown. What

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Speaker 2: a great song that was. I was eight years old

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Speaker 2: and running with a dime in my hand and to

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Speaker 2: the bus stop to pick up a paper for my

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Speaker 2: old man. I sit on his lap and that big

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Speaker 2: old buick and stare as we drove through the town.

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Speaker 2: He'd tussle my hair and say, son, take a good

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Speaker 2: look around. This is your hometown. And then the song

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Speaker 2: goes through and then the town's going through a bad

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Speaker 2: thing with all the steel mills and everything shutting down,

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Speaker 2: and you know, the gutting of the industrial heart of

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Speaker 2: a mirror. And then it ends with him driving out

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Speaker 2: of town saying this is this is my hometown.

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Speaker 3: Yeah.

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Speaker 2: It's deeply touching and brilliant and tells the story, maybe

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Speaker 2: a political story, from from the perspective of a person experiencing.

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Speaker 2: And that's why I think one of the reasons why

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Speaker 2: Bruce Springsteen was so loved, you know, originally just considered

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Speaker 2: a Bob Dylan ripoff, and interestingly, this later song is

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Speaker 2: a ripoff of a Bob Dylan vocal June. But then

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Speaker 2: he just told the stories of the working class in

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Speaker 2: such a way and now he just seems like an

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Speaker 2: elitist in his bubble multi multi hundred millions of dollars,

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Speaker 2: just so distant from the people that he's lecturing too.

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Speaker 3: Yeah, Yeah, this is going to be a great chat.

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Speaker 3: Love to hear your thoughts. So what one hundred and

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Speaker 3: eighty ten eighty So are you swayed by a celebrity

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Speaker 3: opinion like Bruce clearly has with what's going on in

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Speaker 3: the US? Or do you think they are so out

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Speaker 3: of touch that these thoughts are meaningless, that they should

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Speaker 3: stay out of politics, or if they are talking about politics,

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Speaker 3: that they need to accept that most people don't care.

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Speaker 3: Love to hear your thoughts. Ninety two. Ninety two is

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Speaker 3: the text number as well, Becktory. Shortly, it is seventeen

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Speaker 3: past one.

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Speaker 2: Strangely, people in New Zealand just looking at these texts

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Speaker 2: seem to really care about American immigration.

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Speaker 3: Enforcement. They certainly do. Maybe ask yourself, why do you

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Speaker 3: care about that? Yeah? Why is that in your feed?

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Speaker 3: And not what's happening in Iran? For example? It's going

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Speaker 3: to be a great chat. Seventeen past one.

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Speaker 4: The big stories, the big issues, the big trends, and

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Speaker 4: everything in between.

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Speaker 1: Matt Heath and Tyler Adams afternoons excused talks, that'd.

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Speaker 3: Be afternoon twenty past one. Do you take on board

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Speaker 3: what celebrities have to say when it comes to things

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Speaker 3: like politics, or do you think many of them are

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Speaker 3: out of touch and you just do not care. Love

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Speaker 3: to hear your thoughts. I e one hundred and eighty

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Speaker 3: ten eighty is that number?

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Speaker 2: Matt and Tyler Springsten is a nuberwealthy? Is zuber wealthy

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Speaker 2: in am opion, totally out of touch with the common

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Speaker 2: people he reaches out to in his music.

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Speaker 3: Cheers Craig.

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Speaker 2: I feel like he wasn't out of touch in the past.

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Speaker 2: He seemed to be so in touch with people.

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Speaker 6: You know.

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Speaker 2: That was one of the great things I was saying

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Speaker 2: before about his music. It seemed to just embody you know,

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Speaker 2: born to run, embodies that relationship.

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Speaker 3: You know that was a success right that he was

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Speaker 3: a working class man. He could understand those experiences. But

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Speaker 3: maybe when you get to a level of wealth that

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Speaker 3: he's got, that becomes harder to understand what's actually going

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Speaker 3: on on the ground.

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Speaker 2: The stick says mister free speech man. Heath wants to

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Speaker 2: silence the most powerful voice in a generation because he

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Speaker 2: thinks he has become Cringe. No, I definitely don't want

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Speaker 2: to silence him, and I definitely couldn't even if I

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Speaker 2: wanted to. If I tried to silence Bruce Springsteen. No,

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Speaker 2: I think he should say it. I'm just I just

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Speaker 2: felt I just felt sorry for him a little bit.

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Speaker 2: It's felt a bit cringe when I heard the song.

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Speaker 2: And sometimes I feel that across political opinions from artists,

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Speaker 2: when they're put like from really really wealthy artists that

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Speaker 2: are cloistered from the world, I just feel like they're

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Speaker 2: not talking from personal experience. But but and I think

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Speaker 2: I'm just not sure. I mean, all we're asking is,

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Speaker 2: do you do you value the opinions of celebrities?

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Speaker 7: Yeah?

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Speaker 3: Oh, one hundred and eighty ten eighty love to hear

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Speaker 3: your thoughts.

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Speaker 8: Get a John there was the mattress said he was privileged.

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Speaker 3: M Bruce Springsteen's privileged.

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Speaker 9: Well, mattress said, that isn't it?

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Speaker 2: Well, what Bruce Springsteen's a mult time I think he's

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Speaker 2: worth is pushing He just sold his songwriting royalties. I

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Speaker 2: think he's pushing five hundred million dollars in worth.

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Speaker 9: What's the problem with that?

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Speaker 3: There's no problem with it. Who said there was a

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Speaker 3: problem with it? There's no privilege? Yeah, yeah, no, I

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Speaker 3: said heat from he He is almost certainly out of touch,

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Speaker 3: is what I was meaning. So he is he is.

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Speaker 9: What crons would you say that he's born and bred

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Speaker 9: in that country. He's got to say what he says.

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Speaker 3: It was in this country.

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Speaker 2: Absolutely, No one's arguing that he hasn't got a right

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Speaker 2: to say about it. We were quite clear that he's

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Speaker 2: got every right to say whatever he wants. We're just

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Speaker 2: saying whether whether whether you value opinions of celebrities because

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Speaker 2: of the world that they live, and as the question

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Speaker 2: we're asking John, Yeah, he just.

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Speaker 9: Wanted one celebrity. Well, well celebrity, that's your broad sweet,

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Speaker 9: that's that's movie stars and whatever.

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Speaker 3: The hell do you do you value what they say?

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Speaker 7: John?

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Speaker 3: Do you value what Bruce Springsteen sees in this?

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Speaker 9: And so you're moving on to me now, But I'm

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Speaker 9: just stating what he said though he was a heat

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Speaker 9: to preach to her for a while, no mats, and

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Speaker 9: he was saying all these things like, you know, no.

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Speaker 3: What can you say that again?

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Speaker 10: Sorry?

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Speaker 2: I love your accent, I love your accent, but I'm

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Speaker 2: struggling to just understand it.

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Speaker 3: Can you can you say that?

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Speaker 5: Last?

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Speaker 9: I was just picking up what you said, was Bruce Springsteen,

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Speaker 9: you know you're a platform there for a few minutes

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Speaker 9: and you know you're going on about being privileged and

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Speaker 9: money came into an a list.

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Speaker 2: Well, yeah, yeah, he's he's isolated from the He started

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Speaker 2: off being very in touch with the working classes of

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Speaker 2: America and a lot of his songs was about the

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Speaker 2: shutting down of small town because you know, industry was

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Speaker 2: being moved out and moved overseas.

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Speaker 3: And now he is in.

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Speaker 2: A totally different world old And the question I'm asking is,

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Speaker 2: is his opinion a little bit out of time.

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Speaker 9: He's in a totally different world in America is in

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Speaker 9: a totally different world. Let me put this, If you're

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Speaker 9: living in America right now, what.

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Speaker 3: World was Jubian America? Of course on the earth.

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Speaker 9: No, that's if you're in America.

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Speaker 8: That's that's that.

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Speaker 9: Don't answer if you're an American. Now what society you're

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Speaker 9: living in?

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Speaker 5: Mm hmm.

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Speaker 3: But I think you're right.

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Speaker 2: I mean, so that's that's what What kind of question

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Speaker 2: is that? I mean, there's ask me why you're asking

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Speaker 2: that question? Of course i'd be in the American society you.

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Speaker 8: Just referred there.

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Speaker 9: About the world, he's it's about his own country. He's

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Speaker 9: living pembles polically and he's making a comment about it

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Speaker 9: in songs.

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Speaker 3: But again, are you influenced by that? John, That's the question.

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Speaker 3: I think there's a bit of confusion here.

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Speaker 2: That he's making a comment about his own world. And

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Speaker 2: it's not particularly just Bruce Springsteen, but there's been a

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Speaker 2: number of celebrities that come out and said recently that

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Speaker 2: what we say doesn't help our causes because of the

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Speaker 2: position that we are in, and if anything, it makes

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Speaker 2: things more divisive. But his his there's no doubt that

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Speaker 2: he's living in America and commentat in America, and he's

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Speaker 2: absolutely one hundred percent allowed to do that. The question is,

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Speaker 2: do you value his opinion or do you develop? Do

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Speaker 2: you do you think someone's fame and celebrity gives them

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Speaker 2: more insight into the world.

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Speaker 9: No, no, No, he's in.

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Speaker 7: He's in.

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Speaker 9: He's first of all, he's outside the political world to

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Speaker 9: comment on the bullshit that's going on in America and the

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Speaker 9: political system.

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Speaker 3: Correct.

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Speaker 2: I don't know because he because he he's he's very

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Speaker 2: heavily involved in the Democratic Party. He donates the Democratic Party, yep,

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Speaker 2: he Well, I don't know if he financially support gets

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Speaker 2: money from them. I don't think he does that, but

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Speaker 2: he tours, he has concerts at rallies for the Democratic Party,

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Speaker 2: he has supported Democratic candidates.

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Speaker 3: He he played a concert for Camilla, he played concerts

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Speaker 3: for Obama. He's been a huge part of the Democratic

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Speaker 3: Party in America for a very long time. So that

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Speaker 3: probably gives.

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Speaker 2: Them more credibility in a way because he has he

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Speaker 2: has said, and it is I can kind of see

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Speaker 2: what you're saying, John, when it comes to Bruce Springsteen.

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Speaker 2: It's probably not the best example of this because he's

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Speaker 2: obviously been been expressing politics for a very very long time.

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Speaker 2: And sometimes you just get an actor on a red

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Speaker 2: carpet saying something and it seems more vacuous, and so

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Speaker 2: it's kind of a little bit of a we're kind

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Speaker 2: of I think you said it at the start.

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Speaker 3: It's kind of a broad broad stroke.

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Speaker 2: We're saying celebrities, Bruce Springsteen, right through to the like

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Speaker 2: of I don't know, Ariana Grande.

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Speaker 3: Tom Hanks. You know, you could just take your peck

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Speaker 3: on celebrity. But yeah, the question of value rock. Yeah,

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Speaker 3: And it's by the sounds of it, John, that you

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Speaker 3: started off defending Bruce Springsteen. You can say what he

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Speaker 3: likes absolutely he can, but then it sounds like you say, well,

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Speaker 3: you don't really value what he's got to say about politics,

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Speaker 3: your lover's music politics. You can't kill you.

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Speaker 9: No, the only you do it was just your mad

413
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Speaker 9: was bringing like the financial thing into the topics that

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Speaker 9: but he's made his money for nothing, so that was

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Speaker 9: just the cross approos of it really. But moving on

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Speaker 9: in rigars to having a view absolutely because he bought

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Speaker 9: and breda.

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Speaker 3: Yeah, thank you so much for you cool John. I

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Speaker 3: appreciate it. Yeah, fair enough, love the accent as well,

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Speaker 3: beautiful and look.

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Speaker 2: Absolutely can have a view, But do you give celebrity

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Speaker 2: views any credence?

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Speaker 3: Yeah, come on through. Oh one hundred and eighty ten eighty,

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Speaker 3: it's twenty seven past one.

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Speaker 1: The headlines and the hard questions, it's the mic asking breakfast.

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Speaker 3: Election, you're thrown up. Its biggest retirement today so far,

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Speaker 3: jud Colins, of course is calling times Judith Colins, as

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Speaker 3: well as the moment you decided is there a story

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Speaker 3: behind any of that or not?

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Speaker 11: No?

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Speaker 12: I just really start the last yours. So I just

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Speaker 12: so enjoyed these portfolios are there. This has been my

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Speaker 12: best term of parliament and I thought I really want

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Speaker 12: to go out on the high. Twenty four years is

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Speaker 12: quite a long time in Parliament, and you know, it's

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Speaker 12: a good of Saint Mike. I'm now the mother of

437
00:20:17,333 --> 00:20:19,373
Speaker 12: the House and it's like, you know, anything I had

438
00:20:19,373 --> 00:20:23,693
Speaker 12: to be twenty something haughty children. It's really difficult. So

439
00:20:24,053 --> 00:20:26,133
Speaker 12: I just really felt that it was time to go twelve

440
00:20:26,213 --> 00:20:29,013
Speaker 12: years in opposition twelve years in government. It's a pretty

441
00:20:29,013 --> 00:20:29,693
Speaker 12: good symmetry.

442
00:20:29,973 --> 00:20:32,653
Speaker 1: Back tomorrow at six am the Mike Hosking Breakfast with

443
00:20:32,813 --> 00:20:34,573
Speaker 1: the Defendant News Talk zb.

444
00:20:34,573 --> 00:20:37,173
Speaker 3: HAP pass one. So do you listen to what celebrities

445
00:20:37,253 --> 00:20:39,973
Speaker 3: think or do their views feel completely removed from every

446
00:20:40,053 --> 00:20:40,893
Speaker 3: day reality.

447
00:20:41,333 --> 00:20:44,693
Speaker 2: I think the whole Bruce Springs Bruce Springsteen thing and

448
00:20:44,773 --> 00:20:47,572
Speaker 2: the issue that he's singing about is slightly taking us

449
00:20:47,573 --> 00:20:50,253
Speaker 2: off track because it's so in everyone's face. You know,

450
00:20:50,293 --> 00:20:54,413
Speaker 2: the algorithm is feeding up this Minnesota stuff hard towards us.

451
00:20:54,453 --> 00:20:57,413
Speaker 2: As you said before, ask yourself, why what's happening Iran

452
00:20:57,453 --> 00:20:59,773
Speaker 2: hasn't been fed up at the same level. That's a

453
00:20:59,813 --> 00:21:04,213
Speaker 2: totally different discussion. It is, but I think it's maybe

454
00:21:04,213 --> 00:21:06,532
Speaker 2: because of who Bruce Springsteen is, you know, and he

455
00:21:06,613 --> 00:21:09,533
Speaker 2: is a political activisty is part of the Democratic Party.

456
00:21:09,773 --> 00:21:10,213
Speaker 3: Et cetera.

457
00:21:10,733 --> 00:21:13,493
Speaker 2: But so I think maybe a bitter way to look

458
00:21:13,533 --> 00:21:15,693
Speaker 2: at it would be take someone like Jennifer Lawrence, who

459
00:21:15,773 --> 00:21:19,733
Speaker 2: was really really politically outspoken. But you know, she's the

460
00:21:20,013 --> 00:21:20,813
Speaker 2: Oscar winning.

461
00:21:20,613 --> 00:21:22,813
Speaker 3: Actress yep, gright know, a lot of great movies.

462
00:21:22,853 --> 00:21:25,493
Speaker 2: Yeah, fantastic, but this is what she recently seen in

463
00:21:25,533 --> 00:21:27,453
Speaker 2: an interview. It will start with the interviewer.

464
00:21:28,333 --> 00:21:31,532
Speaker 10: You have been politically outspoken in the past. In the

465
00:21:31,533 --> 00:21:35,493
Speaker 10: first Trump administration, you know, you had a lot to say.

466
00:21:35,813 --> 00:21:39,533
Speaker 10: I'm curious how you feel about talking out now.

467
00:21:41,253 --> 00:21:44,093
Speaker 13: I don't really know if I should. I think like

468
00:21:45,573 --> 00:21:50,893
Speaker 13: the first Trump administration was so wild and just how

469
00:21:50,973 --> 00:21:54,812
Speaker 13: can we let this stand? Like I felt like I

470
00:21:54,893 --> 00:21:57,773
Speaker 13: was running around like a chicken with my head cut off.

471
00:21:58,533 --> 00:22:05,013
Speaker 13: But as we've learned, election after elections, celebrities do not

472
00:22:05,853 --> 00:22:10,653
Speaker 13: make a difference whatsoever and who people vote for. And

473
00:22:10,733 --> 00:22:14,293
Speaker 13: so then what am I doing. I'm just sharing my

474
00:22:14,573 --> 00:22:18,853
Speaker 13: opinion on something that's going to just add fuel to

475
00:22:19,013 --> 00:22:21,453
Speaker 13: a fire that's ripping the country apart. I mean, we

476
00:22:21,493 --> 00:22:28,533
Speaker 13: are so divided. I think I'm in a complicated recalibration

477
00:22:30,093 --> 00:22:35,453
Speaker 13: because I'm also an artist and I with this temperature

478
00:22:35,653 --> 00:22:39,733
Speaker 13: and the way that things can turn out. I don't

479
00:22:39,773 --> 00:22:44,693
Speaker 13: want to start turning people off to films into art

480
00:22:44,853 --> 00:22:48,813
Speaker 13: that could change consciousness or change the world because they

481
00:22:48,853 --> 00:22:57,093
Speaker 13: don't like my political opinions. I want to protect my

482
00:22:57,893 --> 00:23:03,013
Speaker 13: craft so that you can still get lost in what

483
00:23:03,053 --> 00:23:05,213
Speaker 13: I'm doing, what I'm showing.

484
00:23:06,853 --> 00:23:08,293
Speaker 2: I mean, you could look at that in a self

485
00:23:08,413 --> 00:23:11,093
Speaker 2: interested way, you know, I want to keep making money.

486
00:23:11,493 --> 00:23:14,133
Speaker 2: Over half of America supports Trump. So she was slapping

487
00:23:14,133 --> 00:23:15,453
Speaker 2: off Trump and it was getting in the way of

488
00:23:15,453 --> 00:23:17,133
Speaker 2: her making money and making movies.

489
00:23:17,253 --> 00:23:18,053
Speaker 3: Yeah, but you.

490
00:23:18,133 --> 00:23:21,373
Speaker 2: Get you get the point she's she went hard. She

491
00:23:21,453 --> 00:23:23,493
Speaker 2: realized that no one really gared what she had to say,

492
00:23:23,933 --> 00:23:26,813
Speaker 2: and she stopped. Yeah, to a certain extent, And yeah

493
00:23:26,853 --> 00:23:28,173
Speaker 2: that I mean.

494
00:23:28,213 --> 00:23:30,533
Speaker 3: I can resonate with that because, and this will sound

495
00:23:30,573 --> 00:23:33,333
Speaker 3: a bit trite, but it's genuinely how I feel at

496
00:23:33,333 --> 00:23:34,973
Speaker 3: the moment. When it comes to celebrities, I want to

497
00:23:35,013 --> 00:23:37,733
Speaker 3: hear about acting from Jennifer Lawrence. She's a phenomenal actress,

498
00:23:37,813 --> 00:23:39,733
Speaker 3: so I want to hear about her films and what

499
00:23:39,773 --> 00:23:42,893
Speaker 3: she thinks about that element. When someone like her talks

500
00:23:42,893 --> 00:23:45,453
Speaker 3: about politics, I feel it's clumsy and heavy handed and

501
00:23:45,453 --> 00:23:47,893
Speaker 3: they're smacking people over the head with their thoughts and

502
00:23:48,573 --> 00:23:52,093
Speaker 3: opinions that they may think should be valued about the

503
00:23:52,173 --> 00:23:53,453
Speaker 3: other people's thoughts and thinking.

504
00:23:53,733 --> 00:23:56,173
Speaker 2: Yeah, I mean, the best way artists if they want

505
00:23:56,173 --> 00:24:00,293
Speaker 2: to change minds is through great art worth you know,

506
00:24:00,693 --> 00:24:03,853
Speaker 2: meaning in it, right, Yeah, And that's what I believe,

507
00:24:04,253 --> 00:24:08,173
Speaker 2: and perhaps this is why I found it cringy. The

508
00:24:08,173 --> 00:24:14,053
Speaker 2: the the Bruce Springsteen song is that it's it didn't

509
00:24:14,053 --> 00:24:16,893
Speaker 2: do that. It was just read like a ticker Trump's

510
00:24:16,893 --> 00:24:19,613
Speaker 2: Federal thugs beat up on his face and his jest.

511
00:24:19,693 --> 00:24:22,093
Speaker 2: These are the actual lyrics from the song. Then we

512
00:24:22,133 --> 00:24:24,893
Speaker 2: heard the gunshot and Alex Peretti lay in the snow dead.

513
00:24:25,813 --> 00:24:28,613
Speaker 2: Their claim was self defense. You don't believe your eyes.

514
00:24:29,093 --> 00:24:33,493
Speaker 2: It's our blood and bones. And these whistles and phones

515
00:24:33,653 --> 00:24:35,893
Speaker 2: against Stephen Miller and gnomes dirty lies.

516
00:24:36,373 --> 00:24:38,573
Speaker 3: It's there's not much subtlety there.

517
00:24:38,653 --> 00:24:42,133
Speaker 2: It's not changing anyone's opinion who doesn't already followed that opinion.

518
00:24:42,413 --> 00:24:45,653
Speaker 2: Whereas great art, and I would say that he has

519
00:24:45,733 --> 00:24:48,293
Speaker 2: created great art in his life, and songs like Born

520
00:24:48,373 --> 00:24:51,693
Speaker 2: to Run may because they show you the other side

521
00:24:51,733 --> 00:24:54,813
Speaker 2: and lead you in the journey emotionally on the other

522
00:24:54,853 --> 00:24:59,413
Speaker 2: side of a discussion. Yeah, that's that's cringe to.

523
00:24:59,413 --> 00:25:02,373
Speaker 3: Me, but heavy handed. Oh one hundred and eighty ten

524
00:25:02,413 --> 00:25:04,293
Speaker 3: eighty is the number to cool. Love to hear your thoughts.

525
00:25:04,333 --> 00:25:06,533
Speaker 3: We've got the headlines coming up. Then we're taking more

526
00:25:06,533 --> 00:25:08,933
Speaker 3: of your calls. We've got a truckload techs coming in

527
00:25:08,973 --> 00:25:10,213
Speaker 3: as well, so we'll get to a couple of those

528
00:25:10,213 --> 00:25:12,133
Speaker 3: if you want to seend one nineteen ninety two is

529
00:25:12,173 --> 00:25:14,133
Speaker 3: that number? It is twenty six to two.

530
00:25:15,573 --> 00:25:18,973
Speaker 1: You've talk sab headlines with blue bubble taxis.

531
00:25:19,053 --> 00:25:22,293
Speaker 14: It's no trouble with a blue bubble. Health New Zealand's

532
00:25:22,333 --> 00:25:26,653
Speaker 14: confirmed an IT outage hit to Upper North Island hospitals

533
00:25:26,653 --> 00:25:31,533
Speaker 14: in Tettai, Tokudo, Waitamata, Auckland and counties Monuco last night,

534
00:25:32,013 --> 00:25:35,413
Speaker 14: taking twelve hours to resolve. Hospitals in the South Island

535
00:25:35,453 --> 00:25:38,933
Speaker 14: and Lower North have also had major outages this month.

536
00:25:39,813 --> 00:25:44,293
Speaker 14: Wellington City Council's apologized after a second rate payer bill blunder,

537
00:25:44,653 --> 00:25:47,813
Speaker 14: this time an overcharge for the Regional Greater Wellington Levee

538
00:25:48,253 --> 00:25:52,293
Speaker 14: last month that undercharged for its sludge levy. AA insurance

539
00:25:52,333 --> 00:25:56,253
Speaker 14: is pausing signing up new property policies in flood prone

540
00:25:56,293 --> 00:26:01,253
Speaker 14: Westport but promises existing home and landlord policyholders can renew.

541
00:26:02,333 --> 00:26:06,373
Speaker 14: A geopolitical analyst told the BBC he thinks the tax

542
00:26:06,413 --> 00:26:09,693
Speaker 14: are likely in Iran is to similar with the US,

543
00:26:09,853 --> 00:26:14,173
Speaker 14: which is building its regional military forces. North Canterbury State

544
00:26:14,293 --> 00:26:17,653
Speaker 14: Highway seven is blocked south of the Hannah Springs turnoff

545
00:26:18,013 --> 00:26:20,733
Speaker 14: after a serious crash between a motorcycle and a car.

546
00:26:21,493 --> 00:26:25,213
Speaker 14: A boil water notice is lifted for Eketahuna after testing

547
00:26:25,493 --> 00:26:30,173
Speaker 14: drinking water standards for three consecutive days. Crane Brothers to

548
00:26:30,213 --> 00:26:34,453
Speaker 14: grow high Street flagship with major store expansion. You can

549
00:26:34,493 --> 00:26:37,213
Speaker 14: read more at Ensen Herell Premium. Back to matt Ethan

550
00:26:37,293 --> 00:26:38,133
Speaker 14: Tyler Adam.

551
00:26:37,893 --> 00:26:40,893
Speaker 3: Thanks you very much, Raelian. So are celebrity opinions influential

552
00:26:41,053 --> 00:26:43,133
Speaker 3: or just noise from people out of touch with the

553
00:26:43,173 --> 00:26:45,533
Speaker 3: real world. We are talking about this because Bruce Springsteen

554
00:26:45,813 --> 00:26:49,773
Speaker 3: has just released released a new track called Streets of Minneapolis,

555
00:26:50,413 --> 00:26:52,773
Speaker 3: which is clearly political about what is going on on

556
00:26:52,813 --> 00:26:56,413
Speaker 3: the ground. Clearly clearly it's basically a news article. It

557
00:26:56,453 --> 00:26:59,333
Speaker 3: certainly is. So here is a little bit, actually it's

558
00:26:59,333 --> 00:27:01,533
Speaker 3: basically an opinion, a piece in the newspaper. Here's a

559
00:27:01,573 --> 00:27:02,853
Speaker 3: little bit of that soul.

560
00:27:02,933 --> 00:27:11,373
Speaker 15: Now from still Thos Peter bob his face and his chest.

561
00:27:12,813 --> 00:27:18,493
Speaker 15: Then heard the gunshots and Alex pretty ly in the

562
00:27:18,733 --> 00:27:19,853
Speaker 15: snow dead.

563
00:27:21,133 --> 00:27:24,213
Speaker 3: Their clean self defense suit.

564
00:27:25,253 --> 00:27:31,533
Speaker 15: Just don't be leave your eyes. It's are playing bones

565
00:27:31,653 --> 00:27:36,493
Speaker 15: and these whistles and phones, A can'st Miller and Norm's dirty.

566
00:27:38,213 --> 00:27:43,093
Speaker 3: Here you go. That chat GPT has been used. It

567
00:27:43,133 --> 00:27:45,613
Speaker 3: feels like it. Yeah, But also what is the Bob

568
00:27:45,693 --> 00:27:46,213
Speaker 3: Dylan song?

569
00:27:46,613 --> 00:27:48,693
Speaker 2: And he's obviously putting on a Bob Dylan voice, but

570
00:27:48,773 --> 00:27:50,653
Speaker 2: you know, his whole career, he's been accused of ripping

571
00:27:50,693 --> 00:27:52,133
Speaker 2: off Bob Dylan sort.

572
00:27:51,933 --> 00:27:53,773
Speaker 3: Of moved away from that a little bit later by the.

573
00:27:53,693 --> 00:27:55,053
Speaker 2: Time he got to the Born to Run album, But

574
00:27:55,893 --> 00:27:58,573
Speaker 2: he's imitating Bob Dylan's voice there more than he has

575
00:27:58,613 --> 00:28:00,413
Speaker 2: in a number of years. But also that is the

576
00:28:00,453 --> 00:28:02,333
Speaker 2: vocal tune to a very famous Bob Dylan song.

577
00:28:02,373 --> 00:28:02,733
Speaker 3: What is it?

578
00:28:02,773 --> 00:28:05,933
Speaker 2: So he's stolen the Bob Dylan voice and vocal chair.

579
00:28:06,093 --> 00:28:08,533
Speaker 2: It's just it's just on the outside of my memory.

580
00:28:08,333 --> 00:28:10,733
Speaker 3: If you know, nine two nine two, but very pop

581
00:28:10,813 --> 00:28:13,373
Speaker 3: villani ish that song. So taking your calls on eight

582
00:28:13,493 --> 00:28:15,373
Speaker 3: hundred and eighty ten eighty and your texts on nine

583
00:28:15,413 --> 00:28:16,253
Speaker 3: two niney two.

584
00:28:16,573 --> 00:28:18,773
Speaker 2: Out of Chuch Springsting songs will strike a chord with

585
00:28:18,773 --> 00:28:19,773
Speaker 2: a majority of Americans.

586
00:28:19,773 --> 00:28:20,573
Speaker 3: He still relates.

587
00:28:20,693 --> 00:28:23,413
Speaker 2: As for CNN Crawler at seventy six, do you think

588
00:28:23,453 --> 00:28:25,813
Speaker 2: he possibly could have formed his own opinions? I think

589
00:28:26,053 --> 00:28:28,013
Speaker 2: you're way off the mark with this one, Matt. Maybe

590
00:28:28,053 --> 00:28:33,653
Speaker 2: you just don't rate the song. Yeah, No, it's actually

591
00:28:34,253 --> 00:28:39,333
Speaker 2: the polls say that most of Americans support the the

592
00:28:39,373 --> 00:28:42,333
Speaker 2: immigration enforcement that's going on now. And I don't want

593
00:28:42,373 --> 00:28:44,213
Speaker 2: to focus on that, but you know, latest polls is

594
00:28:44,333 --> 00:28:48,693
Speaker 2: sixty six percent of Americans support removing illegal immigrants from.

595
00:28:48,573 --> 00:28:51,613
Speaker 3: From the country. Yep. So so it's not true.

596
00:28:51,813 --> 00:28:54,813
Speaker 2: But I mean that shouldn't that shouldn't change with what

597
00:28:54,853 --> 00:28:57,533
Speaker 2: you're what you sing about just because it's you know,

598
00:28:57,773 --> 00:28:58,893
Speaker 2: even a five percent.

599
00:28:58,653 --> 00:29:00,653
Speaker 3: Agree with you, then you write to think about it.

600
00:29:00,693 --> 00:29:02,293
Speaker 2: You can sing about whatever you want, you can say

601
00:29:02,293 --> 00:29:03,893
Speaker 2: whatever whatever you want.

602
00:29:04,053 --> 00:29:06,613
Speaker 3: And again that's the age just I mean, in fact.

603
00:29:06,493 --> 00:29:10,373
Speaker 2: Just because he's saying something that's not as popular in

604
00:29:10,413 --> 00:29:12,493
Speaker 2: the United States across the whole of my United States

605
00:29:12,613 --> 00:29:15,493
Speaker 2: is very popular in his circle and his elitist bubbles

606
00:29:15,813 --> 00:29:19,533
Speaker 2: and pretty pretty popular and probably even Minnesota, but not

607
00:29:19,613 --> 00:29:20,413
Speaker 2: across the whole country.

608
00:29:20,493 --> 00:29:22,013
Speaker 3: Yeah, but again, he can say what he likes, but

609
00:29:22,093 --> 00:29:24,293
Speaker 3: that seems irrelevant. Yeah, it's whether you take that on

610
00:29:24,333 --> 00:29:26,853
Speaker 3: board or you value it. Does it change your political leaning?

611
00:29:26,893 --> 00:29:28,293
Speaker 3: So eight hundred and eighty ten eighty is and I

612
00:29:28,373 --> 00:29:29,573
Speaker 3: but a call right.

613
00:29:29,493 --> 00:29:34,053
Speaker 5: Welcome to the show, he leads technicians of the best talkback.

614
00:29:34,213 --> 00:29:40,653
Speaker 5: You guys, Hey, Look, regarding Springsteen, he is cringe. I

615
00:29:40,653 --> 00:29:43,133
Speaker 5: mean I used to really, you know, enjoy his albums,

616
00:29:43,213 --> 00:29:46,493
Speaker 5: especially Nebraska was just a piece of American poetry, you

617
00:29:46,533 --> 00:29:48,213
Speaker 5: know that, you know.

618
00:29:48,293 --> 00:29:49,493
Speaker 3: Just great stuff.

619
00:29:49,533 --> 00:29:51,533
Speaker 5: But you know, over the end, I've seen him live,

620
00:29:51,653 --> 00:29:54,253
Speaker 5: you know, and he did a great maze. But he's

621
00:29:55,693 --> 00:29:58,093
Speaker 5: he's just so cringe now. I mean, who kid is?

622
00:29:58,493 --> 00:30:01,893
Speaker 5: I mean, he's using this sort of the political political

623
00:30:01,973 --> 00:30:07,093
Speaker 5: situation to sort of keep himself in the limelight. I

624
00:30:07,133 --> 00:30:09,293
Speaker 5: mean that sounds cynical, but it's probably true. It's a

625
00:30:09,333 --> 00:30:11,653
Speaker 5: bit like I'll tell you who he reminds me of

626
00:30:11,693 --> 00:30:15,333
Speaker 5: in the celebrity world is Robert de Nierre. He's another

627
00:30:15,373 --> 00:30:18,893
Speaker 5: one that's sort of been clattering on. I mean, I'm

628
00:30:18,893 --> 00:30:23,093
Speaker 5: no fan of Donald Trump or Stephen Miller. I think they,

629
00:30:23,653 --> 00:30:25,453
Speaker 5: you know, should be shot for the ball of their

630
00:30:25,493 --> 00:30:30,213
Speaker 5: own turd. But you know, the fact is fan Springsteen

631
00:30:31,173 --> 00:30:34,573
Speaker 5: is I think he's losing the plot. I think he

632
00:30:34,613 --> 00:30:38,013
Speaker 5: takes himself too seriously and with all the you know,

633
00:30:38,133 --> 00:30:41,093
Speaker 5: accolades over the years and as fans and you know,

634
00:30:41,133 --> 00:30:42,733
Speaker 5: I mean these people that go to his shows and

635
00:30:43,133 --> 00:30:45,893
Speaker 5: been to twenty of his shows, you know, things like that,

636
00:30:45,973 --> 00:30:47,493
Speaker 5: and he's well known for that.

637
00:30:48,293 --> 00:30:50,173
Speaker 6: I think he just thinks, you.

638
00:30:50,133 --> 00:30:51,573
Speaker 5: Know, what he says is.

639
00:30:53,013 --> 00:30:54,133
Speaker 6: Important.

640
00:30:54,373 --> 00:30:56,733
Speaker 5: And you know, most of us say a lot. I mean,

641
00:30:56,733 --> 00:30:59,173
Speaker 5: if we if that's our political bandy, you know, the

642
00:30:59,173 --> 00:31:02,733
Speaker 5: way we feel about things in America, we'll say the

643
00:31:02,773 --> 00:31:05,453
Speaker 5: same things. We don't have to sing about it, you know,

644
00:31:05,493 --> 00:31:06,053
Speaker 5: in a song.

645
00:31:06,853 --> 00:31:09,573
Speaker 2: It's interesting because I think think if I had been

646
00:31:09,653 --> 00:31:12,253
Speaker 2: as successful as Bruce Springsteen and was playing two huge

647
00:31:12,293 --> 00:31:14,693
Speaker 2: crowds like he plays too, had been called the Boss,

648
00:31:14,733 --> 00:31:16,253
Speaker 2: I mean, his nickname is the Boss, and.

649
00:31:16,293 --> 00:31:19,413
Speaker 3: His incredible for a working man, the working man.

650
00:31:19,573 --> 00:31:23,613
Speaker 2: I think my point right is that I would is

651
00:31:23,613 --> 00:31:25,693
Speaker 2: that I would probably be full of myself and think

652
00:31:25,733 --> 00:31:27,653
Speaker 2: that I knew more than other people. If I was

653
00:31:27,693 --> 00:31:29,653
Speaker 2: his age, I've been lauded as long as It'd be

654
00:31:29,773 --> 00:31:33,053
Speaker 2: so hard to stay humble if you were in his position,

655
00:31:33,493 --> 00:31:38,493
Speaker 2: and it would be so hard to say in touch impossible, you.

656
00:31:38,453 --> 00:31:41,773
Speaker 5: Know, I mean you read, I mean I read their biography.

657
00:31:41,773 --> 00:31:42,693
Speaker 3: I mean, yeah, he.

658
00:31:42,693 --> 00:31:45,933
Speaker 5: Lives in a world that's and I'm not knocking him

659
00:31:45,933 --> 00:31:48,493
Speaker 5: for it. You know, he's done some fantastics and he's

660
00:31:48,533 --> 00:31:52,933
Speaker 5: an American curcler, you know, like Dylan. But what's that

661
00:31:53,133 --> 00:31:57,173
Speaker 5: song Stuck in Somewhere bar blah with something or other

662
00:31:57,293 --> 00:31:58,053
Speaker 5: blues by.

663
00:31:59,573 --> 00:31:59,933
Speaker 3: Dylan?

664
00:32:00,213 --> 00:32:01,293
Speaker 16: And I'm a big Dylan fan.

665
00:32:01,333 --> 00:32:04,093
Speaker 5: I've seen him a few times, but I can't remember

666
00:32:04,173 --> 00:32:06,173
Speaker 5: the name of the song, but it's stuck in.

667
00:32:06,733 --> 00:32:08,093
Speaker 1: Some kind the.

668
00:32:09,533 --> 00:32:11,973
Speaker 16: Sort of the blues.

669
00:32:11,813 --> 00:32:14,493
Speaker 3: And that sounds like that song that is that home?

670
00:32:14,853 --> 00:32:15,813
Speaker 3: Is that homesick blues?

671
00:32:16,093 --> 00:32:16,253
Speaker 7: Rod?

672
00:32:17,213 --> 00:32:17,453
Speaker 17: Ah?

673
00:32:17,533 --> 00:32:20,573
Speaker 3: It probably is? Yeah, Yeah, Yeah, it's a beautiful sing

674
00:32:20,853 --> 00:32:23,533
Speaker 3: really got it? You nailed it. I got it from

675
00:32:23,533 --> 00:32:24,373
Speaker 3: your beautiful singing.

676
00:32:25,173 --> 00:32:27,093
Speaker 6: Ah Ah, I got a terrible voice.

677
00:32:27,773 --> 00:32:29,453
Speaker 5: Hey, look, you guys are doing a great show.

678
00:32:29,493 --> 00:32:29,973
Speaker 7: You always do.

679
00:32:30,053 --> 00:32:31,613
Speaker 9: I've always looked forward to it after work.

680
00:32:31,653 --> 00:32:34,813
Speaker 5: I sort of work early in the morning, treadled about midday,

681
00:32:34,853 --> 00:32:35,813
Speaker 5: and you know.

682
00:32:36,053 --> 00:32:37,493
Speaker 8: Yeah, I always look forward to you.

683
00:32:37,533 --> 00:32:40,413
Speaker 5: I mean, anything you do is pretty poignant.

684
00:32:41,533 --> 00:32:44,533
Speaker 2: What a lovely thing to say, Rod, subterranean homesick Blues

685
00:32:44,613 --> 00:32:47,493
Speaker 2: is the name of the song and the song that that.

686
00:32:47,733 --> 00:32:49,253
Speaker 2: I don't know if he's purposely doing it. He might

687
00:32:49,293 --> 00:32:52,973
Speaker 2: have acknowledged it, but it's Desolation Row is the Bob

688
00:32:53,053 --> 00:32:57,413
Speaker 2: Dylan song that that according to fifty eight thousand texts,

689
00:32:57,653 --> 00:33:01,133
Speaker 2: Bruce Springsteen is ripping for that. That that new song

690
00:33:01,173 --> 00:33:02,653
Speaker 2: of his, the anti Trump song.

691
00:33:02,733 --> 00:33:03,853
Speaker 3: We might try and do a bit of a mash

692
00:33:03,933 --> 00:33:06,053
Speaker 3: up of the two. See how how close they are. Oh,

693
00:33:06,093 --> 00:33:08,653
Speaker 3: eight hundred and eighty ten eighty is the number of call?

694
00:33:09,133 --> 00:33:11,573
Speaker 3: Nine two ninety two. Do you care or take on

695
00:33:11,613 --> 00:33:14,133
Speaker 3: board what celebrities have to say about politics or are

696
00:33:14,173 --> 00:33:17,213
Speaker 3: they so out of touch that it just doesn't matter?

697
00:33:17,253 --> 00:33:18,813
Speaker 3: It is a quarter to two begvery so.

698
00:33:19,853 --> 00:33:22,253
Speaker 1: The issues that affect you and a bit of fun

699
00:33:22,293 --> 00:33:26,013
Speaker 1: along the way. Matt Heath and Taylor Adams Afternoons.

700
00:33:25,573 --> 00:33:30,413
Speaker 3: News talk sai'd be fourteen to two, Steve, you reckon

701
00:33:30,853 --> 00:33:33,573
Speaker 3: Bruce Springsteen is irrelevant to your life in New Zealand?

702
00:33:34,573 --> 00:33:36,853
Speaker 18: Oh very much so, mate. He's just stay in his lane.

703
00:33:36,933 --> 00:33:39,973
Speaker 18: Really pretty much has nothing to do with me. And

704
00:33:41,053 --> 00:33:42,853
Speaker 18: basically what it boils down to, really the end of

705
00:33:42,893 --> 00:33:46,213
Speaker 18: the day is right versus left and liberal versus conservative.

706
00:33:47,453 --> 00:33:49,013
Speaker 18: You know, all I can see is there's a lot

707
00:33:49,013 --> 00:33:52,613
Speaker 18: of Darwin Awards being issued in America, especially in minnaculous

708
00:33:52,653 --> 00:33:56,253
Speaker 18: at the moment. But yeah, you know what what a

709
00:33:56,293 --> 00:34:01,973
Speaker 18: celebrity says about the world, who's you know, as rightly said,

710
00:34:02,013 --> 00:34:03,493
Speaker 18: you guys are saying. You know, he's come from a

711
00:34:03,533 --> 00:34:06,893
Speaker 18: place where he's growing up, you know, a working classman.

712
00:34:07,453 --> 00:34:09,813
Speaker 18: He's done the hard but now he's living a life

713
00:34:09,813 --> 00:34:13,133
Speaker 18: of I don't know if you say luxury, but definitely

714
00:34:13,172 --> 00:34:15,573
Speaker 18: he's living a life what he wants to live. But

715
00:34:15,692 --> 00:34:17,973
Speaker 18: that doesn't give him the right to sort of preach

716
00:34:18,013 --> 00:34:21,533
Speaker 18: to everyone else his opinion, and we should all listen

717
00:34:21,573 --> 00:34:24,573
Speaker 18: to his opinion because you know, his opinion means nothing.

718
00:34:24,773 --> 00:34:28,333
Speaker 3: So well, I mean, I guess he is allowed to

719
00:34:28,373 --> 00:34:30,053
Speaker 3: say whatever he wants as loud as he wants and

720
00:34:30,493 --> 00:34:31,773
Speaker 3: get it out there. Obviously, in terms of.

721
00:34:32,013 --> 00:34:35,212
Speaker 18: Speech, absolutely he can sing out, well, he's done a song,

722
00:34:35,253 --> 00:34:37,413
Speaker 18: he can sing to the raptors. But you know, it's

723
00:34:37,533 --> 00:34:41,133
Speaker 18: up to it's up to someone who, you know, whoever,

724
00:34:41,133 --> 00:34:43,933
Speaker 18: who wants to listen to what he's saying. But I

725
00:34:43,973 --> 00:34:46,373
Speaker 18: know what, I'll this large to be turning my back

726
00:34:46,373 --> 00:34:46,652
Speaker 18: on him.

727
00:34:46,653 --> 00:34:49,973
Speaker 3: Really, thanks for your cool. Steve appreciate it. Yep, ohight

728
00:34:50,093 --> 00:34:51,692
Speaker 3: undred and eighty ten eighties and number to call. Plenty

729
00:34:51,693 --> 00:34:54,653
Speaker 3: of teachs coming through. On nine two ninety two as well.

730
00:34:55,413 --> 00:34:58,453
Speaker 2: Obama campaigned on a crackdown on legal migrants to the

731
00:34:58,533 --> 00:35:03,813
Speaker 2: States and then deported millions. He shamed Bush for not

732
00:35:03,933 --> 00:35:07,413
Speaker 2: doing enough as a part of his election campaign, and

733
00:35:07,493 --> 00:35:12,693
Speaker 2: Bruce performed at Obama's rallies and inauguration. Seems like Bruce's

734
00:35:12,773 --> 00:35:15,573
Speaker 2: changed his opinion over the recent years. Yeah, is this Texter,

735
00:35:15,773 --> 00:35:17,653
Speaker 2: But I don't want to really talk about the immigration.

736
00:35:17,773 --> 00:35:20,413
Speaker 2: This is a classic thing, the immigration issue, and for

737
00:35:20,453 --> 00:35:24,013
Speaker 2: some reason beyond my understanding, is a very huge.

738
00:35:23,813 --> 00:35:25,732
Speaker 3: Issue in New Zealand right now. What America is doing

739
00:35:25,733 --> 00:35:27,013
Speaker 3: with its immigration.

740
00:35:27,013 --> 00:35:30,692
Speaker 2: Despite horror's happening all around the world, much bigger. It's

741
00:35:30,693 --> 00:35:32,212
Speaker 2: like what's happening in Iran, but where That's what we're

742
00:35:32,213 --> 00:35:32,573
Speaker 2: focused on.

743
00:35:33,213 --> 00:35:34,053
Speaker 3: Line after headline.

744
00:35:34,133 --> 00:35:36,533
Speaker 2: Yeah, so we're struggling to get to the general chat

745
00:35:36,533 --> 00:35:41,733
Speaker 2: whether the cloistered nature of and the bubble that celebrities

746
00:35:41,773 --> 00:35:44,453
Speaker 2: live and how removed they are from the rest ruffs,

747
00:35:44,453 --> 00:35:47,373
Speaker 2: whether their opinions should be given any credits at all.

748
00:35:47,693 --> 00:35:49,652
Speaker 3: It's hard to get to that at the topic. Isn't

749
00:35:49,653 --> 00:35:52,413
Speaker 3: it through? There's a side sidetrack here which is very

750
00:35:52,573 --> 00:35:54,373
Speaker 3: tempting for a lot of people. But keep those texts

751
00:35:54,413 --> 00:35:56,893
Speaker 3: coming through and love to hear from you. Whether celebrities,

752
00:35:56,933 --> 00:36:00,053
Speaker 3: whether it's Bruce Springsteen or an actor or whoever, can

753
00:36:00,093 --> 00:36:03,173
Speaker 3: they influence you with their political chat you say the

754
00:36:03,253 --> 00:36:05,613
Speaker 3: likes of Mark Rufflo. I actually like Mark Rufflo as

755
00:36:05,613 --> 00:36:07,613
Speaker 3: an actor. He's done some terrible acting work, but he's

756
00:36:07,613 --> 00:36:10,413
Speaker 3: done some very good stuf, great actor, Zodiac, Shutter Island.

757
00:36:10,493 --> 00:36:12,413
Speaker 3: He's been a phenomenal actor. But he is one that

758
00:36:12,533 --> 00:36:15,773
Speaker 3: does my hidden because again, it feels so heavy handed,

759
00:36:16,053 --> 00:36:18,373
Speaker 3: and it feels like he's got such a high opinion

760
00:36:18,453 --> 00:36:21,772
Speaker 3: of his value as an actor that what he says

761
00:36:21,813 --> 00:36:24,653
Speaker 3: about politics, and he's incredibly political and on the left,

762
00:36:25,053 --> 00:36:27,812
Speaker 3: that I should I'm a bad person if I don't

763
00:36:27,813 --> 00:36:30,293
Speaker 3: agree with Mark Ruffalo. Love him as an actor, but

764
00:36:30,373 --> 00:36:32,733
Speaker 3: I hate when he does that because it feels like

765
00:36:33,173 --> 00:36:35,692
Speaker 3: he is on a holier than now crusade to say

766
00:36:35,733 --> 00:36:36,252
Speaker 3: you're all bad.

767
00:36:36,333 --> 00:36:39,093
Speaker 2: He seems more performative than nearly any other because he's

768
00:36:39,093 --> 00:36:40,533
Speaker 2: talking about when he's on the red carpet.

769
00:36:40,533 --> 00:36:42,933
Speaker 3: Recently, the golden globes, and he was talking about.

770
00:36:43,053 --> 00:36:47,172
Speaker 2: One person that had died, and he was so said

771
00:36:47,173 --> 00:36:49,133
Speaker 2: about it here Jimmy Kimmel, crying about this one person

772
00:36:49,133 --> 00:36:53,533
Speaker 2: that had died and some kind of ice.

773
00:36:53,333 --> 00:36:56,013
Speaker 3: Initiative just to bring it back to that. Unfortunately, but.

774
00:36:58,093 --> 00:37:01,053
Speaker 2: Meanwhile you'll see you've never met the person you're crying

775
00:37:01,093 --> 00:37:03,293
Speaker 2: about that one person. That's because it's the bright shiny

776
00:37:03,333 --> 00:37:05,772
Speaker 2: object that has been put in front of your vacuous eyes. Mate,

777
00:37:06,173 --> 00:37:09,613
Speaker 2: read around, and you would just be shedding is constantly

778
00:37:10,133 --> 00:37:14,653
Speaker 2: for people who are dying in horrible situations all around

779
00:37:14,813 --> 00:37:17,253
Speaker 2: the world. I mean, he's not there crying about the

780
00:37:17,293 --> 00:37:22,253
Speaker 2: people that protesting against, to repeat myself, the horrific regime

781
00:37:22,373 --> 00:37:26,252
Speaker 2: in Iran. People are just protesting because they've been under

782
00:37:26,293 --> 00:37:32,533
Speaker 2: the absolute boot of some of the most evil authoritarian.

783
00:37:34,093 --> 00:37:38,013
Speaker 3: Government. And you know since the Middle Ages, yep, on

784
00:37:38,373 --> 00:37:41,373
Speaker 3: this planet, selective for empathy. This is exactly what it is,

785
00:37:41,373 --> 00:37:44,493
Speaker 3: selective empathy. Yeah, because it's this bright, shiny object that's

786
00:37:44,533 --> 00:37:46,773
Speaker 3: put in front of everyone's eyes. Yeah, back very shortly

787
00:37:46,773 --> 00:37:48,493
Speaker 3: taking your calls, Oh, eight hundred and eighty ten eighty,

788
00:37:48,533 --> 00:37:50,413
Speaker 3: it is eight to two.

789
00:37:50,893 --> 00:37:54,453
Speaker 1: Matd Heath Tyler Adams taking your calls on eight hundred

790
00:37:54,453 --> 00:37:57,853
Speaker 1: and eighty ten eighty. It's Matt Heathen, Tyler Adams. Afternoons

791
00:37:57,973 --> 00:37:58,813
Speaker 1: news talks be.

792
00:37:59,493 --> 00:38:03,813
Speaker 3: Very good afternoons you it is six minutes to two, Trevor,

793
00:38:03,973 --> 00:38:08,973
Speaker 3: you reckon. These entertainers make fools of themselves.

794
00:38:09,253 --> 00:38:11,213
Speaker 7: Not even you two would actually coming up elections.

795
00:38:11,253 --> 00:38:12,333
Speaker 19: I don't try, but.

796
00:38:14,013 --> 00:38:15,333
Speaker 7: You know, it just daggers me.

797
00:38:15,373 --> 00:38:15,893
Speaker 18: Why they do it.

798
00:38:15,933 --> 00:38:17,893
Speaker 7: I mean, I just remember the last election of America,

799
00:38:18,173 --> 00:38:22,253
Speaker 7: Lebron James Michelle Obama. We had offer Oprah wimfree. She

800
00:38:22,333 --> 00:38:24,693
Speaker 7: done it, and but we know why she done it,

801
00:38:24,693 --> 00:38:26,693
Speaker 7: because she made millions of dollars out of doing the

802
00:38:26,693 --> 00:38:29,533
Speaker 7: PULLI promotion for Kamala Harris and the one that made

803
00:38:29,533 --> 00:38:31,893
Speaker 7: me laugh in America is I think Matt might have

804
00:38:31,973 --> 00:38:37,293
Speaker 7: mentioned it Taylor Swift. I remember CNN they do vomit inducing,

805
00:38:37,493 --> 00:38:41,732
Speaker 7: chunder inducing little bites. When will Taylor Swift come out

806
00:38:41,733 --> 00:38:45,573
Speaker 7: and back Kamala Harris, you know, like fromting like that,

807
00:38:45,733 --> 00:38:47,732
Speaker 7: and if those people put any video on it, don't.

808
00:38:47,853 --> 00:38:50,093
Speaker 7: Donald Trump got ninety nine percent of his votes because

809
00:38:50,093 --> 00:38:53,292
Speaker 7: of who he was. You know, he didn't need these

810
00:38:53,493 --> 00:38:56,573
Speaker 7: superstars thinking that he was going to get involved if

811
00:38:56,573 --> 00:38:59,093
Speaker 7: he did, if it works, Crump, he had the.

812
00:38:59,133 --> 00:39:03,093
Speaker 2: Whole Do you do you feel equally cringe towards people

813
00:39:03,133 --> 00:39:07,053
Speaker 2: like kid Rock and the celebrities. I guess Hulk Hogan

814
00:39:07,373 --> 00:39:09,493
Speaker 2: that that mountain support of Trump.

815
00:39:10,333 --> 00:39:13,813
Speaker 7: Yeah, it was absolutely stupid, Holkogrom getting on the stage,

816
00:39:13,853 --> 00:39:19,613
Speaker 7: and yeah I do. But you think they whole Cogan

817
00:39:19,693 --> 00:39:22,213
Speaker 7: was a bit of humor about him. I mean, these

818
00:39:22,293 --> 00:39:24,533
Speaker 7: other fours that I mentioned, they come across as like

819
00:39:24,653 --> 00:39:28,093
Speaker 7: seriously intelligent. This is all that matters. And as I say,

820
00:39:28,293 --> 00:39:30,413
Speaker 7: I just find it amazing that people, I mean, they

821
00:39:30,453 --> 00:39:33,053
Speaker 7: should learn that makes no difference. Trump got his votes

822
00:39:33,133 --> 00:39:36,053
Speaker 7: last year because of who he was and what he

823
00:39:36,173 --> 00:39:38,733
Speaker 7: said and the way he acted. Whether he deserved the

824
00:39:38,813 --> 00:39:40,973
Speaker 7: votes or not, he got it because here's himself.

825
00:39:41,413 --> 00:39:42,813
Speaker 3: Yeah, thank you so much for us. So we've got

826
00:39:42,853 --> 00:39:44,733
Speaker 3: to let you go because we've got to other the news.

827
00:39:44,893 --> 00:39:48,733
Speaker 3: This is a very good point, though, don't forget says

828
00:39:48,733 --> 00:39:50,253
Speaker 3: this text now one second. It was great, but the

829
00:39:50,253 --> 00:39:51,013
Speaker 3: point was, don't.

830
00:39:50,813 --> 00:39:54,493
Speaker 2: Forget how celebrity and politics are tied up in America.

831
00:39:54,533 --> 00:39:56,773
Speaker 2: It's the point I hadn't thought of, you know, Ronald Reagan,

832
00:39:56,973 --> 00:39:59,453
Speaker 2: I don't forget in America politics is intrinsically tied up

833
00:39:59,453 --> 00:40:02,653
Speaker 2: with celebrity pool Trump, The Apprentice, Ronald Reagan, et cetera,

834
00:40:04,373 --> 00:40:07,373
Speaker 2: Arnold Schwarznig, Americans appelled in by a shallow celebrity growth.

835
00:40:07,373 --> 00:40:09,933
Speaker 3: That is true. That is a great points. Yeah, hey,

836
00:40:10,253 --> 00:40:12,013
Speaker 3: let's carry this on because so many people want to

837
00:40:12,013 --> 00:40:13,173
Speaker 3: have a chat. So can you hear from you? Oh,

838
00:40:13,213 --> 00:40:16,693
Speaker 3: eight hundred and eighty ten eighty News is coming.

839
00:40:16,533 --> 00:40:22,693
Speaker 1: Up talking with you all afternoon. It's Matt Heathen, Taylor

840
00:40:22,733 --> 00:40:25,013
Speaker 1: Adams Afternoons News Talks.

841
00:40:25,013 --> 00:40:28,493
Speaker 3: They'd be a very good afternoon to you. Welcome back

842
00:40:28,573 --> 00:40:31,533
Speaker 3: into the program. We are carrying on our discussion about

843
00:40:31,613 --> 00:40:34,293
Speaker 3: celebrity influence when it comes to things like politics. Do

844
00:40:34,373 --> 00:40:37,773
Speaker 3: you take on board what people in a privileged position

845
00:40:37,933 --> 00:40:39,933
Speaker 3: have to say about the state of the world, like

846
00:40:40,013 --> 00:40:42,413
Speaker 3: Bruce Springsteen at the moment just released a new song

847
00:40:42,413 --> 00:40:46,133
Speaker 3: about what's happening in Minneapolis. Or do you think they're

848
00:40:46,213 --> 00:40:49,013
Speaker 3: so out of touch with everyday life and everyday people

849
00:40:49,413 --> 00:40:51,293
Speaker 3: that you look at them and think you just cringe

850
00:40:51,293 --> 00:40:52,613
Speaker 3: and I don't care what you got to say.

851
00:40:52,693 --> 00:40:55,013
Speaker 2: Yeah, and I think, actually, and this is what we've

852
00:40:55,013 --> 00:40:57,093
Speaker 2: discovered the last hour. This Bruce Springsteen is probably not

853
00:40:57,093 --> 00:40:59,293
Speaker 2: the best example of it. I am a big Bruce

854
00:40:59,333 --> 00:41:02,253
Speaker 2: Springsteen fan, but I found the song cringe because it

855
00:41:02,453 --> 00:41:06,333
Speaker 2: just felt like he was talking about the latest shiny objecks.

856
00:41:06,413 --> 00:41:09,533
Speaker 2: It felt like a man sitting in front.

857
00:41:09,413 --> 00:41:11,013
Speaker 3: Of cable news.

858
00:41:11,933 --> 00:41:14,373
Speaker 2: Just it didn't feel as in touch as he has

859
00:41:14,453 --> 00:41:17,652
Speaker 2: been in the past with the actual emotions on the

860
00:41:17,693 --> 00:41:21,173
Speaker 2: ground of people like you take this lyric from Darkness

861
00:41:21,213 --> 00:41:24,853
Speaker 2: at the edge of town, Darkness on the edge of town. Now,

862
00:41:24,893 --> 00:41:27,172
Speaker 2: some folks are born into a good life and other

863
00:41:27,213 --> 00:41:30,413
Speaker 2: folks get it anyway. Anyhow, Well, now I lost my

864
00:41:30,493 --> 00:41:33,013
Speaker 2: money and I lost my wife, then things don't seem

865
00:41:33,093 --> 00:41:34,013
Speaker 2: to matter much to me.

866
00:41:34,133 --> 00:41:34,413
Speaker 3: Now.

867
00:41:34,893 --> 00:41:38,973
Speaker 2: It's just that that song is so sad and deeply

868
00:41:39,293 --> 00:41:44,133
Speaker 2: moving about the plight of powerless people against the huge

869
00:41:44,173 --> 00:41:47,813
Speaker 2: forces of You know what that was was factories closing

870
00:41:47,813 --> 00:41:49,853
Speaker 2: down in small towns with the main thing that Bruce

871
00:41:49,853 --> 00:41:53,333
Speaker 2: Springsteen was singing about for a long time, and you know,

872
00:41:53,653 --> 00:41:58,533
Speaker 2: people being shipped off to Vietnam because they were petty criminals, etc.

873
00:41:59,733 --> 00:42:02,813
Speaker 2: You know, well, not shipped off, but given that very

874
00:42:02,813 --> 00:42:05,253
Speaker 2: few options in life and ending up in Vietnam. Just

875
00:42:05,253 --> 00:42:06,773
Speaker 2: to talk about born in the.

876
00:42:06,733 --> 00:42:09,973
Speaker 3: USA, that's one. Just cringe when I heard it. I

877
00:42:10,133 --> 00:42:12,692
Speaker 3: just the way he just uses.

878
00:42:13,893 --> 00:42:17,413
Speaker 2: Basically the ticker from a cable news channel as his lyrics.

879
00:42:17,533 --> 00:42:19,973
Speaker 2: Maybe he's rushed it out, yea, you know, maybe he didn't.

880
00:42:20,013 --> 00:42:22,652
Speaker 2: Sure he crafted darkness on the edge of Town.

881
00:42:23,333 --> 00:42:26,253
Speaker 3: He probably spent a long time crafting. Yeah, you couldn't

882
00:42:26,253 --> 00:42:28,853
Speaker 3: call it subtle. And what you just read before in

883
00:42:28,893 --> 00:42:32,053
Speaker 3: his earlier work is poetic subtleness. That was beautiful and

884
00:42:32,093 --> 00:42:34,733
Speaker 3: told his story and it obviously came from his own experiences.

885
00:42:35,013 --> 00:42:37,133
Speaker 3: But this does feel ham fisted, I think is the

886
00:42:37,133 --> 00:42:39,093
Speaker 3: word on thinking. Yeah, ham fisted is the word.

887
00:42:39,533 --> 00:42:41,573
Speaker 2: And I think maybe why Bruce Springsteen is a bad

888
00:42:41,573 --> 00:42:44,133
Speaker 2: example for this discussion is because Bruce Springsteen has always

889
00:42:44,133 --> 00:42:47,013
Speaker 2: been political. He is a member of the Democratic Party.

890
00:42:47,613 --> 00:42:54,172
Speaker 2: He has campaigned for Obama and Biden and Kamala and

891
00:42:54,373 --> 00:42:57,413
Speaker 2: he I mean he campaigned hard for Obama when one

892
00:42:57,453 --> 00:43:02,333
Speaker 2: of Obama's main campaign things was being harsh on immigration

893
00:43:02,773 --> 00:43:04,373
Speaker 2: and deporting as many people as you can. I mean,

894
00:43:04,413 --> 00:43:07,093
Speaker 2: listen to what Obama said back in the day. You

895
00:43:07,133 --> 00:43:10,333
Speaker 2: would think it's what Trump has saying now. And Bruce

896
00:43:10,373 --> 00:43:12,652
Speaker 2: Springsteen did campaign for them. But there's a different issue. Yes,

897
00:43:12,813 --> 00:43:14,453
Speaker 2: there's a different issue, but I just think if you

898
00:43:14,533 --> 00:43:17,933
Speaker 2: talk about someone like Mark Ruffalo maybe or Taylor Swift, Yeah,

899
00:43:18,213 --> 00:43:21,613
Speaker 2: their political views are probably what more we're talking about

900
00:43:21,613 --> 00:43:23,213
Speaker 2: when it comes to the vacuous nature.

901
00:43:23,333 --> 00:43:27,413
Speaker 3: Yeah, nature or professional athletes. And one case that came

902
00:43:27,493 --> 00:43:30,573
Speaker 3: to the four this week is American tennis player. He's

903
00:43:30,573 --> 00:43:33,493
Speaker 3: an up and come superstar. His name is Lerner Tin.

904
00:43:33,573 --> 00:43:35,853
Speaker 3: He's been playing at the Australian Open and he had

905
00:43:35,853 --> 00:43:39,333
Speaker 3: a fantastic when early on. He's out of the competition now,

906
00:43:39,333 --> 00:43:41,212
Speaker 3: but he did incredibly well at the age of twenty

907
00:43:41,333 --> 00:43:43,853
Speaker 3: years old. So during his media conference, he was asked

908
00:43:43,853 --> 00:43:45,973
Speaker 3: a range of questions about the game, but one in

909
00:43:46,013 --> 00:43:48,013
Speaker 3: particular was incredibly political.

910
00:43:49,093 --> 00:43:51,813
Speaker 20: Hi, then I a great match. It's been a big

911
00:43:51,893 --> 00:43:54,692
Speaker 20: day for both, Yuan Ivoyovitch. You really sort of feel

912
00:43:54,733 --> 00:43:57,853
Speaker 20: like the future for American tennis between you. It's also

913
00:43:57,893 --> 00:44:00,533
Speaker 20: interesting that you're both second gen immigrants in the US,

914
00:44:00,693 --> 00:44:02,493
Speaker 20: Like in the context of everything that's happening at the

915
00:44:02,493 --> 00:44:05,413
Speaker 20: moment with Trump and Ice, like what does your heritage

916
00:44:05,413 --> 00:44:07,533
Speaker 20: mean to you and how important are immigrants to America

917
00:44:07,533 --> 00:44:08,613
Speaker 20: and American sports today?

918
00:44:14,013 --> 00:44:17,133
Speaker 10: Sorry, I don't I really want to talk about that

919
00:44:17,253 --> 00:44:17,573
Speaker 10: right now.

920
00:44:18,893 --> 00:44:22,413
Speaker 2: He is an expert on tennis. He's playing in a

921
00:44:22,413 --> 00:44:27,213
Speaker 2: tennis tournament. He's on the stage to talk about tennis.

922
00:44:27,973 --> 00:44:30,813
Speaker 2: That reporter asking that question for their own game. They're

923
00:44:30,853 --> 00:44:34,973
Speaker 2: only asking that question to try and get some copy.

924
00:44:34,693 --> 00:44:37,333
Speaker 3: Right, get a headline, go viral.

925
00:44:37,413 --> 00:44:39,893
Speaker 2: This person's either for or against Trump is really what

926
00:44:39,933 --> 00:44:42,133
Speaker 2: they're doing because they know that gets clicks, a little

927
00:44:42,133 --> 00:44:43,813
Speaker 2: bit of entrentment, and you can also hear it in

928
00:44:43,853 --> 00:44:46,893
Speaker 2: the reporter's voice. He knows that he's being I don't

929
00:44:46,893 --> 00:44:50,493
Speaker 2: know what would be the word for it, tricky, yeah,

930
00:44:50,933 --> 00:44:54,293
Speaker 2: Or he knows he's being a bit dirty, a bit scummy. Yeah,

931
00:44:55,133 --> 00:44:59,333
Speaker 2: But you know you're a tennis player. He just wants

932
00:44:59,373 --> 00:45:01,413
Speaker 2: to talk about tennis, yep. And that's great, And I

933
00:45:01,493 --> 00:45:03,453
Speaker 2: want to hear everything here to say about tennis, Yes,

934
00:45:03,453 --> 00:45:05,613
Speaker 2: because he knows a lot more about tennis than nearly

935
00:45:05,653 --> 00:45:06,453
Speaker 2: anyone on the planet.

936
00:45:06,533 --> 00:45:06,692
Speaker 7: Yep.

937
00:45:06,733 --> 00:45:08,652
Speaker 3: And you've got to watch that video because after that

938
00:45:08,773 --> 00:45:11,013
Speaker 3: question and you just see him pot his hands into

939
00:45:11,053 --> 00:45:13,373
Speaker 3: his faces and like, are you kidding me? I just

940
00:45:13,453 --> 00:45:16,133
Speaker 3: had a fantastic win and you're asking me about this

941
00:45:16,213 --> 00:45:18,613
Speaker 3: political nonsense when I'm here to play tennis and do

942
00:45:18,653 --> 00:45:22,813
Speaker 3: the best I can. But that arguably is how celebrities

943
00:45:22,813 --> 00:45:26,333
Speaker 3: may wish to comment about politics. If they are asked

944
00:45:26,493 --> 00:45:28,493
Speaker 3: about what is happening in the wider will say, I

945
00:45:28,493 --> 00:45:30,493
Speaker 3: don't know. I'm an actor. That's what I'll do. Ask

946
00:45:30,493 --> 00:45:32,573
Speaker 3: me about acting, and I'll give you any ancey you want.

947
00:45:32,693 --> 00:45:34,373
Speaker 3: But what do you say? O eight hundred and eighty

948
00:45:34,413 --> 00:45:35,732
Speaker 3: ten eighty is the number to call?

949
00:45:35,893 --> 00:45:37,933
Speaker 2: The sex says, well, you too want to be celebs,

950
00:45:38,253 --> 00:45:40,173
Speaker 2: think your views are more important than your listeners, and

951
00:45:40,213 --> 00:45:42,693
Speaker 2: continue to try and impart your views on others without

952
00:45:42,733 --> 00:45:44,173
Speaker 2: truly listening to others.

953
00:45:44,333 --> 00:45:44,812
Speaker 3: See Greg.

954
00:45:44,853 --> 00:45:46,613
Speaker 2: I pushed back on that, and I'd say that Tyler

955
00:45:46,653 --> 00:45:49,453
Speaker 2: and I are only here really to facilitate a conversation.

956
00:45:49,533 --> 00:45:51,453
Speaker 3: Yep, on eight hundred and eighty ten eighty.

957
00:45:51,613 --> 00:45:54,973
Speaker 2: And call through and we'll put you on air and

958
00:45:55,013 --> 00:45:57,133
Speaker 2: we'll discuss what your opinion is.

959
00:45:57,533 --> 00:45:59,693
Speaker 3: Want to be celebs? I mean, Tyler was in Woman's

960
00:45:59,693 --> 00:46:02,013
Speaker 3: Weekly recently. Yeah, I'm getting there. I'm pretty close to

961
00:46:02,053 --> 00:46:04,133
Speaker 3: being a wannabe. I'm not quite there yet. I don't

962
00:46:04,133 --> 00:46:06,573
Speaker 3: know what that is on the alphabet, Probably about an

963
00:46:06,693 --> 00:46:11,453
Speaker 3: F Dallas. Always good to touch you, mate, How are you?

964
00:46:12,493 --> 00:46:12,692
Speaker 21: Yeah?

965
00:46:12,733 --> 00:46:12,973
Speaker 6: Good?

966
00:46:13,093 --> 00:46:13,613
Speaker 8: Good, guys.

967
00:46:13,893 --> 00:46:16,293
Speaker 21: Well, the difference with you guys is you're paid. You're

968
00:46:16,333 --> 00:46:19,733
Speaker 21: paid to have an opinion, so that puts you in

969
00:46:19,773 --> 00:46:20,773
Speaker 21: a different category.

970
00:46:20,893 --> 00:46:22,893
Speaker 2: I mean, you'd be silly to tune into New Storks.

971
00:46:22,893 --> 00:46:25,333
Speaker 2: You'd be and not expect to have some people on

972
00:46:25,453 --> 00:46:27,973
Speaker 2: Mike's giving their opinions. I mean that's what it says

973
00:46:28,013 --> 00:46:29,733
Speaker 2: on the tin, doesn't it Dallas.

974
00:46:30,053 --> 00:46:34,573
Speaker 21: Yeah, And people might say, well, watch why your political

975
00:46:34,693 --> 00:46:38,053
Speaker 21: views are not worth any more than anyone else's. But again,

976
00:46:38,733 --> 00:46:44,333
Speaker 21: you know you're paid to give your opinions, including in politics,

977
00:46:45,093 --> 00:46:47,813
Speaker 21: so that you could say some of your hosts might

978
00:46:47,813 --> 00:46:50,533
Speaker 21: be out of touch they're so wealthy. But that's another discussion.

979
00:46:51,133 --> 00:46:56,453
Speaker 21: But when it comes to songwright, songwriters are different from actors.

980
00:46:56,533 --> 00:47:03,653
Speaker 21: Actors are just paid to acting is by vere nature.

981
00:47:04,813 --> 00:47:07,733
Speaker 21: It's not authentic being an actor. It's just someone who's

982
00:47:07,773 --> 00:47:09,973
Speaker 21: paid I basically on the screen.

983
00:47:10,173 --> 00:47:12,013
Speaker 2: This is a very good point, Dallas. That you're making

984
00:47:12,053 --> 00:47:15,813
Speaker 2: a very good point because those are Bruce Springsteen's words,

985
00:47:16,133 --> 00:47:18,732
Speaker 2: and however he's getting them, whether he's out of touch

986
00:47:18,853 --> 00:47:22,933
Speaker 2: or not, they are what he believes and he's saying them,

987
00:47:23,333 --> 00:47:26,293
Speaker 2: You're exactly right. Whereas an actor they read a script

988
00:47:26,613 --> 00:47:30,453
Speaker 2: and then they're being interviewed, So what they're famous for

989
00:47:30,573 --> 00:47:32,813
Speaker 2: is separate from them what they say in the interview.

990
00:47:34,133 --> 00:47:35,613
Speaker 2: That's a very good point, Dallas.

991
00:47:36,013 --> 00:47:38,773
Speaker 21: We give far too much credence to actors. Actors, We

992
00:47:39,013 --> 00:47:42,212
Speaker 21: just raise them up far. This celebrity thing, ah yet

993
00:47:42,293 --> 00:47:45,573
Speaker 21: drives and it doesn't work. We saw that with the

994
00:47:45,653 --> 00:47:49,652
Speaker 21: Kamala Harris. It didn't work. Celebrities came out for her.

995
00:47:50,333 --> 00:47:56,013
Speaker 21: But Solows it's different. I expect solowters to express their views,

996
00:47:56,093 --> 00:47:59,453
Speaker 21: you know, and like the Rising? Do you guys like

997
00:47:59,493 --> 00:48:01,373
Speaker 21: The Rising after nine to eleven?

998
00:48:01,933 --> 00:48:02,253
Speaker 8: Bruce?

999
00:48:03,213 --> 00:48:05,213
Speaker 3: Yeah, yeah, that was beautiful.

1000
00:48:05,533 --> 00:48:07,773
Speaker 2: It was Yeah, that's that's so true. I love so

1001
00:48:07,853 --> 00:48:10,253
Speaker 2: much of his his music, but that was that was

1002
00:48:10,773 --> 00:48:14,413
Speaker 2: so And you know, I'm not from New York's but

1003
00:48:14,573 --> 00:48:16,533
Speaker 2: spent bit of time there, but I know how incredibly

1004
00:48:16,573 --> 00:48:19,533
Speaker 2: important that song was for people from New York. And

1005
00:48:20,933 --> 00:48:23,772
Speaker 2: it's just a beautiful poetry and a beautiful point. Yeah,

1006
00:48:24,053 --> 00:48:25,773
Speaker 2: you're so right, Dallas is incredible.

1007
00:48:26,573 --> 00:48:28,733
Speaker 21: I think the thing is the difference is that he

1008
00:48:28,813 --> 00:48:31,933
Speaker 21: had time to reflect. But it's very difficult to write

1009
00:48:31,973 --> 00:48:37,613
Speaker 21: a song when something's just happened like this Alex pretty thing.

1010
00:48:37,653 --> 00:48:41,413
Speaker 21: It's just happened. It's very hard to write, as you say,

1011
00:48:41,653 --> 00:48:45,693
Speaker 21: a poetic song just in the moment, and so he

1012
00:48:45,813 --> 00:48:50,973
Speaker 21: read for a news for headline, whereas if he had

1013
00:48:51,013 --> 00:48:54,733
Speaker 21: more time to reflect, he might come up with something

1014
00:48:54,773 --> 00:48:59,933
Speaker 21: more artistic and poetic. Yeah, it's really tricky because you

1015
00:49:00,613 --> 00:49:02,613
Speaker 21: want to be relevant, you want to as a song

1016
00:49:02,853 --> 00:49:05,093
Speaker 21: to get stuff out there, but it's really hard on

1017
00:49:05,133 --> 00:49:08,013
Speaker 21: the spur of the moment come up with a classic,

1018
00:49:08,053 --> 00:49:08,253
Speaker 21: you know.

1019
00:49:08,893 --> 00:49:11,093
Speaker 2: Yeah, do you think do you think metaphor is a

1020
00:49:11,093 --> 00:49:14,692
Speaker 2: better way to go than just listing? Because I guess

1021
00:49:14,693 --> 00:49:17,093
Speaker 2: one of the reasons and reason why I'm talking about

1022
00:49:17,093 --> 00:49:19,812
Speaker 2: this is because you can't decide how you feel about something.

1023
00:49:19,813 --> 00:49:21,293
Speaker 2: But I heard that song and I cringed. And I'm

1024
00:49:21,333 --> 00:49:23,813
Speaker 2: a Bruce Springsteen fan. It just cringed at it. And

1025
00:49:24,373 --> 00:49:27,373
Speaker 2: that might be because he seems to be listening stuff

1026
00:49:27,413 --> 00:49:29,253
Speaker 2: out of a news article or after a ticker on

1027
00:49:29,293 --> 00:49:32,172
Speaker 2: a cable news channel, as opposed to what you're talking

1028
00:49:32,173 --> 00:49:35,533
Speaker 2: about with the you know, previous songs have been beautiful

1029
00:49:35,573 --> 00:49:37,973
Speaker 2: metaphors that that have deeper meaning.

1030
00:49:39,013 --> 00:49:42,533
Speaker 21: Yeah, and it's poetry. The difference between poetry and propaganda,

1031
00:49:42,933 --> 00:49:43,133
Speaker 21: you know.

1032
00:49:43,413 --> 00:49:46,413
Speaker 3: Yeah. Yeah, I'm just trying to think. I mean, punk

1033
00:49:46,493 --> 00:49:48,333
Speaker 3: music has always been very good at it, but even

1034
00:49:48,373 --> 00:49:50,373
Speaker 3: modern punk music, and whether you like them or not,

1035
00:49:50,453 --> 00:49:52,693
Speaker 3: Green Day and the release the album American Idiot that

1036
00:49:52,733 --> 00:49:55,133
Speaker 3: did very well, and you might not agree with their politics,

1037
00:49:55,573 --> 00:49:58,133
Speaker 3: but it didn't feel I mean, clearly it was commercial,

1038
00:49:58,213 --> 00:50:01,413
Speaker 3: but they took their time to write that whole album

1039
00:50:01,493 --> 00:50:04,293
Speaker 3: and to have that subtle artistry woven in between. And

1040
00:50:04,293 --> 00:50:06,573
Speaker 3: there's another punk band called Rise Against. It's been very

1041
00:50:06,573 --> 00:50:09,453
Speaker 3: successful and they've got incredible lyrics about the state of

1042
00:50:09,733 --> 00:50:13,453
Speaker 3: America at the time. But this operation with Pruce Springsteen,

1043
00:50:13,453 --> 00:50:15,693
Speaker 3: as you say, Dallas, to me, it just feels a

1044
00:50:15,733 --> 00:50:18,333
Speaker 3: little bit too commercial, a little bit too fast, a

1045
00:50:18,333 --> 00:50:20,653
Speaker 3: little bit too ham fisted and blunt.

1046
00:50:21,493 --> 00:50:26,933
Speaker 21: M remember Paul Simon that was often criticized during apartheid,

1047
00:50:27,293 --> 00:50:31,692
Speaker 21: you know, for not coming out in support of South

1048
00:50:31,733 --> 00:50:35,212
Speaker 21: African blacks, and his answer was, we said, I went

1049
00:50:35,293 --> 00:50:40,013
Speaker 21: there and promoted their musicians in my album Graceland, and

1050
00:50:40,413 --> 00:50:44,053
Speaker 21: I promoted African musicians around the world. That was my

1051
00:50:44,213 --> 00:50:48,733
Speaker 21: way of supporting the movement rather than writing an anti

1052
00:50:48,773 --> 00:50:49,692
Speaker 21: apartheid song.

1053
00:50:49,773 --> 00:50:49,973
Speaker 1: You know.

1054
00:50:50,333 --> 00:50:53,573
Speaker 9: Yeah, yes, again, there's different ways of.

1055
00:50:54,133 --> 00:50:59,093
Speaker 21: But it comes back to I think what you're abjecting

1056
00:50:59,173 --> 00:51:01,973
Speaker 21: to you guys is the fact that it's not a

1057
00:51:02,013 --> 00:51:02,613
Speaker 21: great song.

1058
00:51:04,173 --> 00:51:05,773
Speaker 3: It would be funny if it came down after us

1059
00:51:05,813 --> 00:51:08,013
Speaker 3: accusing him of being evacuous. It was just that we

1060
00:51:08,013 --> 00:51:11,613
Speaker 3: didn't there might be some truth in that.

1061
00:51:11,693 --> 00:51:13,733
Speaker 2: Yeah, that's dirty, Bob Donner, but it's funny. I was

1062
00:51:13,733 --> 00:51:15,573
Speaker 2: talking about that metaphor thing. But a song that I

1063
00:51:15,653 --> 00:51:21,652
Speaker 2: really like is special aka Free Nelson Mandela, And you

1064
00:51:21,653 --> 00:51:23,772
Speaker 2: couldn't get a song that's more just.

1065
00:51:23,733 --> 00:51:25,853
Speaker 3: Straight down the line. There's no metaphor in that, is it.

1066
00:51:25,973 --> 00:51:30,013
Speaker 21: Yeah, Paul McCartney, you wrote a terrible song after nine

1067
00:51:30,053 --> 00:51:33,652
Speaker 21: to eleven freedom, you know, and again it was just

1068
00:51:33,733 --> 00:51:36,613
Speaker 21: strung together at the last minute moment. Yeah, it's just

1069
00:51:36,773 --> 00:51:42,333
Speaker 21: freedom Free, it's all the time, the shock.

1070
00:51:42,653 --> 00:51:42,893
Speaker 6: Yeah.

1071
00:51:42,933 --> 00:51:44,493
Speaker 3: Hey, thank you so much for your call, Dallas. Yeah,

1072
00:51:44,493 --> 00:51:45,893
Speaker 3: thank you very much. You got to take a break,

1073
00:51:45,893 --> 00:51:47,813
Speaker 3: but taking your calls on eight hundred and eighty ten

1074
00:51:47,853 --> 00:51:49,533
Speaker 3: eighty and plenty of texts to get to as well.

1075
00:51:49,533 --> 00:51:51,253
Speaker 3: If you want to send one nine nine two is

1076
00:51:51,293 --> 00:51:52,373
Speaker 3: that number eighteen bus.

1077
00:51:52,213 --> 00:51:57,333
Speaker 1: Two your home of afternoon Talk Matt Heathen, Taylor Adams

1078
00:51:57,373 --> 00:52:01,053
Speaker 1: Afternoons call eight hundred eighty ten eighty used Talk.

1079
00:52:00,933 --> 00:52:06,893
Speaker 3: Said be afternoon. It is twenty past two. As success, Matt,

1080
00:52:06,933 --> 00:52:09,813
Speaker 3: you say you'll listen to musicians more than actors. What

1081
00:52:09,933 --> 00:52:12,613
Speaker 3: about tech bros. What do you think about Musk talking

1082
00:52:12,653 --> 00:52:14,813
Speaker 3: about politics? Personally, I prefer.

1083
00:52:14,533 --> 00:52:18,813
Speaker 2: When Musk talks about space I think, and you know,

1084
00:52:19,333 --> 00:52:19,973
Speaker 2: tech stuff.

1085
00:52:20,253 --> 00:52:21,453
Speaker 3: He knows a lot about that stuff.

1086
00:52:21,533 --> 00:52:24,933
Speaker 2: Yeah, that I prefer when he talks about his area

1087
00:52:24,973 --> 00:52:25,533
Speaker 2: of expertise.

1088
00:52:25,613 --> 00:52:28,293
Speaker 3: Yeah, that's his wheelhouse. Yeah, I mean I'll listen to

1089
00:52:28,373 --> 00:52:31,853
Speaker 3: him talking about space travel forever.

1090
00:52:32,373 --> 00:52:34,333
Speaker 2: You know, in the tech that they've got going with

1091
00:52:34,373 --> 00:52:35,933
Speaker 2: Space X is absolutely phenomenal.

1092
00:52:36,053 --> 00:52:39,013
Speaker 3: It is fascinating. Just a wider question on that because

1093
00:52:39,013 --> 00:52:42,893
Speaker 3: someone did text and said, Jimmy Carr, I'd prefer to

1094
00:52:42,893 --> 00:52:44,693
Speaker 3: listen to what Jimmy Carr has saying. Jimmy Carr is

1095
00:52:44,773 --> 00:52:47,933
Speaker 3: very philosophical about life. But I think there's a difference there.

1096
00:52:47,973 --> 00:52:52,653
Speaker 3: If Bruce wanted to articulate his view of the wider

1097
00:52:52,693 --> 00:52:55,093
Speaker 3: world and his experience has grown up in philosophy and

1098
00:52:55,133 --> 00:52:58,293
Speaker 3: whether you know stoicism works for him or how he

1099
00:52:58,813 --> 00:53:03,053
Speaker 3: deals with with failure, that's very different to a blunt,

1100
00:53:03,173 --> 00:53:05,813
Speaker 3: black and white approach to politics. I think that's what

1101
00:53:06,333 --> 00:53:10,773
Speaker 3: I feel aggrieved by. When celebrities do that that, I'm

1102
00:53:10,893 --> 00:53:13,773
Speaker 3: very interested in their world experience. Absolutely, they've got to

1103
00:53:13,813 --> 00:53:16,213
Speaker 3: where they've got to because the phenomenal human beings like

1104
00:53:16,253 --> 00:53:20,693
Speaker 3: Bruce Springsteen. He's clearly a phenomenal musician and very successful.

1105
00:53:20,933 --> 00:53:23,693
Speaker 3: But this blunt approach to what is a very nuanced

1106
00:53:23,693 --> 00:53:27,253
Speaker 3: situation across the world, not just in America, just feels

1107
00:53:27,293 --> 00:53:30,133
Speaker 3: a bit on the nose. It's just unless it's cringe

1108
00:53:31,853 --> 00:53:33,693
Speaker 3: for some reason. Yes, and that's why we're trying to

1109
00:53:33,693 --> 00:53:36,653
Speaker 3: get to the heart of it. I think anna welcome

1110
00:53:36,653 --> 00:53:37,013
Speaker 3: to show.

1111
00:53:38,093 --> 00:53:38,333
Speaker 8: Hello.

1112
00:53:39,093 --> 00:53:43,493
Speaker 22: Yes, Well, I actually don't cringe. I'm actually really happy

1113
00:53:43,573 --> 00:53:47,333
Speaker 22: he's doing that because somebody's got to say it. I

1114
00:53:47,373 --> 00:53:50,133
Speaker 22: think the climate in the United States has changed a

1115
00:53:50,173 --> 00:53:52,533
Speaker 22: lot over one years, turned into a terror state for

1116
00:53:52,653 --> 00:53:56,413
Speaker 22: many many people, and people that oppose the current regime

1117
00:53:56,533 --> 00:53:59,933
Speaker 22: are possibly being silenced in.

1118
00:54:00,893 --> 00:54:03,213
Speaker 2: How are people being silenced? Because all I hear is

1119
00:54:03,253 --> 00:54:05,772
Speaker 2: people that are posed there. All I hear is people

1120
00:54:05,813 --> 00:54:07,652
Speaker 2: that oppose the regime. So you know, people say that

1121
00:54:07,693 --> 00:54:10,733
Speaker 2: they've been silent, but the voices are very clear of

1122
00:54:11,253 --> 00:54:12,573
Speaker 2: people that oppose the Rajame.

1123
00:54:13,413 --> 00:54:19,013
Speaker 22: I think they're like Facebook, They're tracing people down if

1124
00:54:19,013 --> 00:54:21,933
Speaker 22: they have a different opinion in the United States, and

1125
00:54:21,973 --> 00:54:23,893
Speaker 22: then they're putting them down the list, so you can't.

1126
00:54:23,893 --> 00:54:25,093
Speaker 22: Actually that's.

1127
00:54:27,013 --> 00:54:30,413
Speaker 2: I don't think they're taken over by Trumpatalky hasn't been

1128
00:54:30,413 --> 00:54:33,453
Speaker 2: taken over by Trump, and Facebook isn't putting messages down.

1129
00:54:34,973 --> 00:54:39,973
Speaker 22: But it's I'm pleased tell me exactly saying what's going on,

1130
00:54:40,053 --> 00:54:44,493
Speaker 22: because it's it's a very serious situation. What's happening. It's

1131
00:54:44,493 --> 00:54:45,692
Speaker 22: turning into a terror state.

1132
00:54:46,613 --> 00:54:48,373
Speaker 2: Can you can you say how it's turning into it?

1133
00:54:48,453 --> 00:54:51,692
Speaker 2: Just to describe for me, because you know how it's

1134
00:54:51,733 --> 00:54:56,133
Speaker 2: turning into a tiarrat state for normal people in the States.

1135
00:54:56,733 --> 00:54:59,212
Speaker 22: You can be picked up for no reason at all

1136
00:54:59,493 --> 00:55:03,053
Speaker 22: by ice throwing event and then from there and you

1137
00:55:03,053 --> 00:55:04,453
Speaker 22: don't know what's going to happen to you. There will

1138
00:55:04,493 --> 00:55:09,133
Speaker 22: not be any process or anything. They don't through warrants.

1139
00:55:11,493 --> 00:55:13,773
Speaker 3: Were you getting this information from n or if you

1140
00:55:13,773 --> 00:55:14,053
Speaker 3: don't know.

1141
00:55:14,693 --> 00:55:16,213
Speaker 22: All out there, I don't know how you would have

1142
00:55:16,293 --> 00:55:18,213
Speaker 22: run out of touch. You're not getting this information or

1143
00:55:18,253 --> 00:55:18,973
Speaker 22: if you get it, you know.

1144
00:55:19,333 --> 00:55:21,652
Speaker 2: People people have been what you say, they're just being

1145
00:55:21,693 --> 00:55:24,053
Speaker 2: packed up off the street for no reason.

1146
00:55:24,893 --> 00:55:29,413
Speaker 22: Yeah, they come to people's houses. They think usually it's

1147
00:55:29,453 --> 00:55:31,853
Speaker 22: people of color, but now it's also going to be

1148
00:55:31,853 --> 00:55:35,373
Speaker 22: the white people as well, if they think that you

1149
00:55:35,413 --> 00:55:39,013
Speaker 22: maybe might not vote for Trump because he's now saying

1150
00:55:39,053 --> 00:55:42,533
Speaker 22: he will withdraw ICE from Minnesota is the major.

1151
00:55:44,133 --> 00:55:44,373
Speaker 3: People.

1152
00:55:45,093 --> 00:55:48,413
Speaker 2: So you're saying that people are going that the immigration

1153
00:55:48,813 --> 00:55:52,213
Speaker 2: enforcement in the United States is going around to people's

1154
00:55:52,253 --> 00:55:54,613
Speaker 2: houses that don't agree with Trump and arresting them.

1155
00:55:54,773 --> 00:55:56,653
Speaker 22: No, no, they don't like the look of these people.

1156
00:55:56,693 --> 00:56:00,653
Speaker 22: They might be Latinos, so they might be darker collar skin.

1157
00:56:01,693 --> 00:56:03,613
Speaker 22: If they just feel like picking up someone, they just

1158
00:56:03,613 --> 00:56:06,413
Speaker 22: goes to people's houses, not a restaurants. They don't need

1159
00:56:06,413 --> 00:56:09,893
Speaker 22: it anymore. And it's really serious.

1160
00:56:09,573 --> 00:56:12,933
Speaker 3: That there hasn't been any There hasn't been any law

1161
00:56:13,053 --> 00:56:16,013
Speaker 3: change on that. I mean, I am there.

1162
00:56:15,853 --> 00:56:19,653
Speaker 22: Has been actually changes done these any more search warrants.

1163
00:56:19,653 --> 00:56:22,013
Speaker 22: They can just come in and make their own search warrants.

1164
00:56:22,373 --> 00:56:24,773
Speaker 3: Where have you read that from an I don't think

1165
00:56:25,973 --> 00:56:28,652
Speaker 3: on social media. I don't think that's true. I mean,

1166
00:56:28,693 --> 00:56:29,973
Speaker 3: what what if you think about.

1167
00:56:30,013 --> 00:56:32,772
Speaker 22: What if you think about but I think it is true.

1168
00:56:32,853 --> 00:56:35,133
Speaker 2: What if you think about the enforcement from ICE in

1169
00:56:35,133 --> 00:56:36,573
Speaker 2: the United States and it's not really the issue you

1170
00:56:36,573 --> 00:56:39,533
Speaker 2: want to talk to because of absolutely with New Zealand and.

1171
00:56:39,613 --> 00:56:41,773
Speaker 22: That's been killed by ice. And it's the second person,

1172
00:56:42,133 --> 00:56:46,652
Speaker 22: a white person. You don't even hear both what I

1173
00:56:46,653 --> 00:56:47,213
Speaker 22: am happy?

1174
00:56:48,053 --> 00:56:48,773
Speaker 3: Now, hang on a minute.

1175
00:56:49,133 --> 00:56:51,173
Speaker 2: So you've made these these quite big claims, and so

1176
00:56:51,333 --> 00:56:53,613
Speaker 2: just let me answer you here for a second.

1177
00:56:54,253 --> 00:56:54,973
Speaker 3: What if you think.

1178
00:56:54,813 --> 00:56:59,533
Speaker 2: About the the the intensity of the forcement of ice.

1179
00:57:00,253 --> 00:57:03,973
Speaker 2: You know that that they are following federal law, which

1180
00:57:04,693 --> 00:57:07,893
Speaker 2: if you if you're if you're in America, and you

1181
00:57:07,893 --> 00:57:11,893
Speaker 2: you know if you in America illegally, because because if

1182
00:57:11,933 --> 00:57:14,373
Speaker 2: I went to America, if I went to America right,

1183
00:57:14,613 --> 00:57:17,573
Speaker 2: I wouldn't think I could just live there without I'd

1184
00:57:17,613 --> 00:57:20,093
Speaker 2: imagine how to get deported at some point.

1185
00:57:19,973 --> 00:57:21,493
Speaker 22: They need to be legal, but they're also picking up

1186
00:57:21,573 --> 00:57:23,773
Speaker 22: legal people. They don't check anything, they don't like the

1187
00:57:23,813 --> 00:57:24,853
Speaker 22: look of you, they's picture.

1188
00:57:25,013 --> 00:57:25,733
Speaker 3: Are you sure about that?

1189
00:57:25,893 --> 00:57:29,013
Speaker 22: And it's intensified in the last year, and I just

1190
00:57:29,093 --> 00:57:31,013
Speaker 22: feel so sorry for the people of the United States

1191
00:57:31,093 --> 00:57:34,693
Speaker 22: because it's turning into a terrorist state, and if it's

1192
00:57:34,733 --> 00:57:37,453
Speaker 22: not stopped and nobody points it out, it can easily

1193
00:57:37,493 --> 00:57:40,653
Speaker 22: spread to anywhere else in the world because it's already

1194
00:57:40,693 --> 00:57:42,653
Speaker 22: spreading behavior.

1195
00:57:42,733 --> 00:57:44,933
Speaker 3: Do you ever worry that you may be being played

1196
00:57:44,973 --> 00:57:45,773
Speaker 3: by the algorithms.

1197
00:57:47,133 --> 00:57:47,373
Speaker 10: I know.

1198
00:57:47,613 --> 00:57:50,333
Speaker 22: I'm not worried about that. I'm all worried about people

1199
00:57:50,373 --> 00:57:53,373
Speaker 22: can't see it. What's going on as money, what's being

1200
00:57:53,453 --> 00:57:55,173
Speaker 22: started as well, I don't know. I'm not making it

1201
00:57:55,293 --> 00:57:55,613
Speaker 22: up either.

1202
00:57:55,733 --> 00:58:00,293
Speaker 3: So do you understanding that that social media is feeding

1203
00:58:00,533 --> 00:58:03,453
Speaker 3: more extreme things to you because of your very interest

1204
00:58:03,493 --> 00:58:05,413
Speaker 3: in it, that you've seen something that you believe is

1205
00:58:05,493 --> 00:58:08,053
Speaker 3: true and high likelihood that it's not so, then it

1206
00:58:08,133 --> 00:58:11,093
Speaker 3: cantinues to pump more extreme versions of what is a

1207
00:58:11,333 --> 00:58:14,693
Speaker 3: very nuanced and issue with a lot of gray in it.

1208
00:58:15,453 --> 00:58:17,493
Speaker 3: You can understand that if you're getting your information from

1209
00:58:17,533 --> 00:58:18,853
Speaker 3: social media, that is happening to you.

1210
00:58:18,933 --> 00:58:21,133
Speaker 22: Right, Yeah, I have to get my information on social

1211
00:58:21,173 --> 00:58:24,293
Speaker 22: media because the normal news media don't actually pour the

1212
00:58:24,373 --> 00:58:27,813
Speaker 22: news properly. You do you agree that this man, Alex

1213
00:58:27,853 --> 00:58:32,813
Speaker 22: Pretti was murdered on the streets, well, handle them, protect

1214
00:58:32,893 --> 00:58:37,853
Speaker 22: someone and he's not shooting anyone. He was and as

1215
00:58:37,973 --> 00:58:42,533
Speaker 22: the other one was the woman before that name Nicole good.

1216
00:58:42,573 --> 00:58:45,493
Speaker 3: Well, okay, okay, they're very they're very, very complicated issues,

1217
00:58:45,533 --> 00:58:50,813
Speaker 3: and they they are complicated. I mean, I don't have

1218
00:58:50,893 --> 00:58:52,613
Speaker 3: it any any skin in the game at all because

1219
00:58:52,613 --> 00:58:54,853
Speaker 3: I'm in New Zealand. By looking at it, there's no doubt.

1220
00:58:55,093 --> 00:58:57,133
Speaker 3: There's no doubt that it's complicated.

1221
00:58:56,973 --> 00:58:58,933
Speaker 22: To New Zealand because there's already some voices.

1222
00:59:00,413 --> 00:59:04,093
Speaker 2: You've got a protester that's going to a protest with

1223
00:59:04,493 --> 00:59:07,813
Speaker 2: carrying a loaded weapon, then it's complicated no matter.

1224
00:59:07,893 --> 00:59:10,813
Speaker 3: I'm not saying that he that that. I think it's

1225
00:59:10,853 --> 00:59:13,133
Speaker 3: tragic that he died. It's tragic that he died.

1226
00:59:13,173 --> 00:59:15,613
Speaker 2: But you can't say it's not complicated when there is

1227
00:59:15,653 --> 00:59:18,173
Speaker 2: at least that complication in the situation. I would just

1228
00:59:18,253 --> 00:59:20,173
Speaker 2: I would just I would just think if I was you,

1229
00:59:20,413 --> 00:59:24,813
Speaker 2: I just just maybe maybe just consider that you're being

1230
00:59:25,133 --> 00:59:27,653
Speaker 2: you're being feared a lot of the most terrifying stuff

1231
00:59:27,653 --> 00:59:28,453
Speaker 2: in the world to keep you.

1232
00:59:29,453 --> 00:59:31,893
Speaker 22: I see. But if the people cannot see it's here,

1233
00:59:31,973 --> 00:59:33,093
Speaker 22: this could easily happen here.

1234
00:59:33,373 --> 00:59:33,653
Speaker 3: Okay.

1235
00:59:34,093 --> 00:59:36,613
Speaker 22: Thank you for in the United States to turn it

1236
00:59:36,693 --> 00:59:38,133
Speaker 22: into an awful place.

1237
00:59:37,973 --> 00:59:41,413
Speaker 3: For most I was just talking to I was just

1238
00:59:41,493 --> 00:59:45,053
Speaker 3: talking there the other day. Thank you very much.

1239
00:59:45,133 --> 00:59:46,093
Speaker 22: Enno. Can I read you one?

1240
00:59:49,733 --> 00:59:54,693
Speaker 1: Mad Heathen Tyler Adams afternoons call eighty on Youth Talk

1241
00:59:54,773 --> 00:59:56,093
Speaker 1: ZV for a good afternoon.

1242
00:59:56,133 --> 00:59:58,613
Speaker 3: Tuitors had passed too. Now we were up against the

1243
00:59:58,693 --> 01:00:02,013
Speaker 3: clock there. Yeah, sorry, Enna, we appreciate you calling up,

1244
01:00:02,013 --> 01:00:04,733
Speaker 3: but we gave you a very good chance to have

1245
01:00:04,853 --> 01:00:05,253
Speaker 3: your say.

1246
01:00:05,653 --> 01:00:08,453
Speaker 2: But we just have to we're we're beholden to the

1247
01:00:08,493 --> 01:00:11,293
Speaker 2: clock here. Yeah, we've got to get to news and

1248
01:00:11,573 --> 01:00:13,773
Speaker 2: ads and such. But I think if you call Anna

1249
01:00:14,253 --> 01:00:18,093
Speaker 2: and it is getting a whole lot of feedback her call.

1250
01:00:19,733 --> 01:00:24,053
Speaker 3: One second? Here where boy, there's so many texts coming through.

1251
01:00:24,093 --> 01:00:25,933
Speaker 3: They are flooding through a nine two niney two.

1252
01:00:27,093 --> 01:00:29,053
Speaker 2: Here we go, here we go, one second? Where is

1253
01:00:29,093 --> 01:00:31,813
Speaker 2: it Obama to pour to five million people? Not a

1254
01:00:31,893 --> 01:00:35,773
Speaker 2: peep from these morons guys. To be clear, your caller

1255
01:00:35,893 --> 01:00:39,613
Speaker 2: Anna was one hundred percent accurate. Read BBC and CNN.

1256
01:00:39,973 --> 01:00:43,053
Speaker 2: They have given themselves authority to pick up people without

1257
01:00:43,133 --> 01:00:45,893
Speaker 2: a warrant. You guys need to actually inform yourselves rather

1258
01:00:45,933 --> 01:00:49,653
Speaker 2: than patronizing callers who don't hold your views. I don't

1259
01:00:49,933 --> 01:00:52,373
Speaker 2: see how we patronized. And we just asked her a

1260
01:00:52,493 --> 01:00:55,693
Speaker 2: series of questions of where she got her information from. Yeah,

1261
01:00:56,013 --> 01:00:58,293
Speaker 2: and it sounds like you get your information from CNN

1262
01:00:58,373 --> 01:00:58,853
Speaker 2: and BBC.

1263
01:00:59,213 --> 01:00:59,413
Speaker 3: Yep.

1264
01:00:59,533 --> 01:01:01,173
Speaker 2: You should always try and get your information from it

1265
01:01:01,173 --> 01:01:02,693
Speaker 2: as many sources as you can if you're getting it

1266
01:01:02,693 --> 01:01:03,973
Speaker 2: from TikTok, there's.

1267
01:01:03,773 --> 01:01:06,453
Speaker 3: A good chance that you're being played by the algorithm. Yeah,

1268
01:01:06,653 --> 01:01:09,213
Speaker 3: very good chance. But I appreciate your call, and we're

1269
01:01:09,213 --> 01:01:10,933
Speaker 3: going to carry this on. I eight one hundred eighty

1270
01:01:11,133 --> 01:01:13,933
Speaker 3: ten eighty. Can I just say quickly, I don't think

1271
01:01:13,973 --> 01:01:16,333
Speaker 3: it matters what Bruce Springsteen said with Anna, And it

1272
01:01:16,453 --> 01:01:18,773
Speaker 3: was right in there, no matter what, didn't matter what

1273
01:01:18,853 --> 01:01:20,973
Speaker 3: old Bruce he had to say, And it was there

1274
01:01:21,093 --> 01:01:22,013
Speaker 3: well and truly, And.

1275
01:01:22,053 --> 01:01:23,973
Speaker 2: How naive met naive of me to think that we

1276
01:01:24,053 --> 01:01:28,293
Speaker 2: could have this chat about the opinions of celebrities when

1277
01:01:28,333 --> 01:01:32,933
Speaker 2: you consider how removed they are from everyday reality without

1278
01:01:32,973 --> 01:01:35,253
Speaker 2: it going down an ice chat. I mean, as I

1279
01:01:35,333 --> 01:01:39,893
Speaker 2: said to start at the start, why do New Zealanders

1280
01:01:40,093 --> 01:01:45,213
Speaker 2: care about immigration in America? Why do we care about

1281
01:01:45,293 --> 01:01:50,333
Speaker 2: immigration and interference, you know, enforcement in America, particularly with

1282
01:01:50,413 --> 01:01:51,253
Speaker 2: what's happening in Iran.

1283
01:01:51,413 --> 01:01:54,173
Speaker 3: Yeah, do yourself a favor. Worry about your actual life,

1284
01:01:54,213 --> 01:01:56,493
Speaker 3: what is happening in you every day? Oh, one hundred

1285
01:01:56,533 --> 01:01:58,653
Speaker 3: and eighty ten eighty is unable to call back very shortly.

1286
01:01:58,733 --> 01:02:00,053
Speaker 3: Headlines with Raylene coming.

1287
01:01:59,973 --> 01:02:04,733
Speaker 1: Up, You's talk said be Headlines with Blue Bubble taxi.

1288
01:02:05,053 --> 01:02:08,293
Speaker 14: It's no trouble with a blue bubble, but registrict Counts

1289
01:02:08,533 --> 01:02:12,733
Speaker 14: says AA Insurances pause on new policies in Westport shouldn't

1290
01:02:12,773 --> 01:02:17,333
Speaker 14: send immediate major shock waves because existing customers can renew.

1291
01:02:18,173 --> 01:02:19,373
Speaker 3: Statsun Zed says.

1292
01:02:19,293 --> 01:02:22,933
Speaker 14: Annual reports reached a record almost eighty point one billion

1293
01:02:23,013 --> 01:02:26,373
Speaker 14: dollars last year. That's up fourteen percent annually.

1294
01:02:27,253 --> 01:02:28,813
Speaker 3: The US President is warning.

1295
01:02:28,613 --> 01:02:33,173
Speaker 14: A naval armada's moving towards Iran with tensions rising. US

1296
01:02:33,293 --> 01:02:36,693
Speaker 14: military forces are reportedly steadily building in the region, and

1297
01:02:36,853 --> 01:02:40,053
Speaker 14: Iran is threatening to respond in force to any attack.

1298
01:02:41,213 --> 01:02:41,813
Speaker 6: The MP for.

1299
01:02:41,933 --> 01:02:45,453
Speaker 14: Ykathl is criticizing a blanket ban on fire fighters using

1300
01:02:45,573 --> 01:02:49,013
Speaker 14: boats and jet skis and water emergencies since last year,

1301
01:02:49,573 --> 01:02:52,853
Speaker 14: Fire and Emergency says it had to prioritize other capabilities.

1302
01:02:53,853 --> 01:02:58,653
Speaker 14: The greyhound racing industries wielding declining euthanasia and proposing safer

1303
01:02:58,773 --> 01:03:01,773
Speaker 14: tracks as it tries to convince Parliament not to bring

1304
01:03:01,853 --> 01:03:05,173
Speaker 14: in a ban proposed for August, and a boil water

1305
01:03:05,333 --> 01:03:09,573
Speaker 14: notice has lifted for Eketahuna after testing met drinking water

1306
01:03:09,693 --> 01:03:14,093
Speaker 14: standards explained theF one loophole that Red Bull and Liam

1307
01:03:14,173 --> 01:03:17,413
Speaker 14: Lawson could exploit in twenty twenty six. See more at

1308
01:03:17,453 --> 01:03:19,773
Speaker 14: inzid here or premium. I'm back to Matt Eath and

1309
01:03:19,853 --> 01:03:20,453
Speaker 14: Tyna Adam.

1310
01:03:20,533 --> 01:03:23,413
Speaker 3: Thank you very much. Ray Lean, it's twenty four to three.

1311
01:03:23,533 --> 01:03:26,053
Speaker 3: So are you swayed by celebrity opinions or do you

1312
01:03:26,133 --> 01:03:28,493
Speaker 3: think they are out of touch and their thoughts don't

1313
01:03:28,533 --> 01:03:30,493
Speaker 3: mean much. It's amazing to me.

1314
01:03:30,653 --> 01:03:33,213
Speaker 2: How incredibly angry people are getting on the text between

1315
01:03:33,213 --> 01:03:36,333
Speaker 2: on nine two nine two about American immigration. Do you

1316
01:03:36,453 --> 01:03:38,973
Speaker 2: not ever stop and think why that shiny object is

1317
01:03:38,973 --> 01:03:41,973
Speaker 2: put in front of you, why you are spending I mean,

1318
01:03:42,013 --> 01:03:43,853
Speaker 2: I was at a barbecue over the break and someone

1319
01:03:43,893 --> 01:03:48,133
Speaker 2: came out and was talking about ice over a barbecue

1320
01:03:48,173 --> 01:03:51,373
Speaker 2: in New Zealand. Do you not think that this might

1321
01:03:51,453 --> 01:03:55,093
Speaker 2: be something to do with all the big social media

1322
01:03:55,173 --> 01:03:58,053
Speaker 2: tech companies being based in America, all that content coming

1323
01:03:58,093 --> 01:04:01,973
Speaker 2: out of America, that you might just be colateral damage

1324
01:04:02,133 --> 01:04:04,733
Speaker 2: on trying to get eyeballs in the United States of America.

1325
01:04:05,013 --> 01:04:08,893
Speaker 2: That why would you suddenly be caring so very much

1326
01:04:08,933 --> 01:04:11,853
Speaker 2: about that particular issue with all the other millions of

1327
01:04:11,893 --> 01:04:17,893
Speaker 2: issues in the world where people are absolutely frothing abusively

1328
01:04:18,693 --> 01:04:23,893
Speaker 2: threateningly angry at hosts on a new Zealand afternoon radio

1329
01:04:24,053 --> 01:04:29,293
Speaker 2: show about immigration enforcement in the United States of America.

1330
01:04:29,733 --> 01:04:31,653
Speaker 2: Do people ever stop just for a second? I mean

1331
01:04:31,693 --> 01:04:33,293
Speaker 2: I try and do it all the time. And actually

1332
01:04:33,333 --> 01:04:35,413
Speaker 2: this is sounding condescending because I get sucked into it

1333
01:04:35,453 --> 01:04:36,173
Speaker 2: all the time as well.

1334
01:04:36,653 --> 01:04:37,413
Speaker 3: Social media will.

1335
01:04:37,293 --> 01:04:41,173
Speaker 2: Put up these shiny object issues and you suddenly find

1336
01:04:41,213 --> 01:04:42,453
Speaker 2: yourself watching videos on them.

1337
01:04:42,573 --> 01:04:44,413
Speaker 3: Yep, but you've got to stop.

1338
01:04:44,533 --> 01:04:48,053
Speaker 2: Well, I think you should stop and go. Why is

1339
01:04:48,173 --> 01:04:51,013
Speaker 2: this particular issue being put in front of me? Do

1340
01:04:51,213 --> 01:04:53,373
Speaker 2: I need to have an opinion on this particular issue?

1341
01:04:53,733 --> 01:04:56,893
Speaker 2: Does it make the life of me, my friends, my

1342
01:04:57,093 --> 01:05:02,773
Speaker 2: family better if I am frothingly, abusively angry about this

1343
01:05:02,973 --> 01:05:07,573
Speaker 2: particular piece of law enforcement or whatever you want to

1344
01:05:07,653 --> 01:05:10,653
Speaker 2: call it, whether you agree with it or disagree with

1345
01:05:10,773 --> 01:05:13,933
Speaker 2: it in a state of America that you probably haven't

1346
01:05:14,053 --> 01:05:15,493
Speaker 2: mentioned in the last twenty years.

1347
01:05:16,133 --> 01:05:18,813
Speaker 3: Yeah, like you, I have to remind myself a lot

1348
01:05:19,013 --> 01:05:21,253
Speaker 3: because it is very shiny and it's very tempting to

1349
01:05:21,293 --> 01:05:23,693
Speaker 3: get involved in it. But you've got to always think

1350
01:05:24,093 --> 01:05:26,493
Speaker 3: what things can I control in my life? How is

1351
01:05:26,533 --> 01:05:28,653
Speaker 3: this going to impact my everyday life. I've got enough

1352
01:05:28,973 --> 01:05:32,093
Speaker 3: issues to worry about without worrying about immigration policy. In America.

1353
01:05:32,333 --> 01:05:34,573
Speaker 3: I've got to worry about things I can control. I

1354
01:05:34,653 --> 01:05:36,853
Speaker 3: can't control any of those sort of things. But what

1355
01:05:36,973 --> 01:05:39,773
Speaker 3: I can control is my thoughts and feelings about things

1356
01:05:39,813 --> 01:05:41,533
Speaker 3: that actually happening in my life. But to get so

1357
01:05:41,813 --> 01:05:45,613
Speaker 3: fizzed up and angry and fire and brimstone that you've

1358
01:05:45,653 --> 01:05:50,013
Speaker 3: got to care about this, Why why don't you care

1359
01:05:50,093 --> 01:05:53,973
Speaker 3: about immigration enforcement on the other side of the planet.

1360
01:05:54,253 --> 01:05:57,453
Speaker 2: It's fourteen thousand kilometers away, it's very close.

1361
01:05:57,573 --> 01:05:59,253
Speaker 3: Yeah, I've got too much going on in my life

1362
01:05:59,253 --> 01:06:01,893
Speaker 3: to worry about American immigration at the moment. But thank

1363
01:06:01,933 --> 01:06:03,093
Speaker 3: you very much for those decks.

1364
01:06:03,213 --> 01:06:06,853
Speaker 2: But you both should be sacked. You completely bullied that

1365
01:06:06,973 --> 01:06:10,853
Speaker 2: young woman who was just defending Trump, and you're just

1366
01:06:10,933 --> 01:06:13,413
Speaker 2: defending Trump on your goddamn radio says this text that

1367
01:06:13,493 --> 01:06:15,773
Speaker 2: we should be sacked. How are we bullying that woman?

1368
01:06:16,093 --> 01:06:18,493
Speaker 2: We're just asking you questions as she was saying things

1369
01:06:18,533 --> 01:06:21,013
Speaker 2: that she didn't necessarily have evidence to back up.

1370
01:06:22,053 --> 01:06:25,613
Speaker 3: Guys, I are private paid thugs, you numpties.

1371
01:06:25,773 --> 01:06:26,053
Speaker 8: They're not.

1372
01:06:27,053 --> 01:06:29,053
Speaker 3: But whether you hate them or not, they're not private

1373
01:06:29,133 --> 01:06:32,013
Speaker 3: paid thugs. Question is why you care that? Quite come

1374
01:06:32,053 --> 01:06:32,573
Speaker 3: to show.

1375
01:06:34,373 --> 01:06:37,493
Speaker 19: You know, Nick, Let me start off first by saying

1376
01:06:37,493 --> 01:06:38,733
Speaker 19: I'm not going to be frothing at you.

1377
01:06:39,973 --> 01:06:41,813
Speaker 3: You can froth as you if you like Nick, You'll

1378
01:06:41,853 --> 01:06:43,653
Speaker 3: be careful with that. There's a couple of meanings there.

1379
01:06:43,933 --> 01:06:45,853
Speaker 16: Let me secondly say that I hope and it's doing

1380
01:06:45,893 --> 01:06:49,653
Speaker 16: all right after that call, Yeah, she was pretty wound up.

1381
01:06:50,453 --> 01:06:52,653
Speaker 16: What I want to say, basically, guys, is you know,

1382
01:06:53,293 --> 01:06:56,613
Speaker 16: also I don't understand how people get so fixated on

1383
01:06:57,093 --> 01:06:58,813
Speaker 16: things that happen on the other side of the world

1384
01:06:59,013 --> 01:07:03,413
Speaker 16: like this, when we've got our own concerns and priorities.

1385
01:07:03,133 --> 01:07:03,773
Speaker 17: On New Zealand.

1386
01:07:03,853 --> 01:07:06,493
Speaker 16: You know, we've got our fair share of worries that

1387
01:07:06,613 --> 01:07:09,093
Speaker 16: we can actually make a change because in our own country.

1388
01:07:10,373 --> 01:07:13,213
Speaker 19: But to answer your question you asked earlier, why do

1389
01:07:13,453 --> 01:07:17,773
Speaker 19: people get so wound up or involved or whatever about

1390
01:07:17,933 --> 01:07:20,893
Speaker 19: an immigration policy in the United States or a different country.

1391
01:07:21,093 --> 01:07:23,093
Speaker 19: I don't think it's actually got anything to do with

1392
01:07:23,213 --> 01:07:26,133
Speaker 19: the immigration policy on the other side of the world.

1393
01:07:26,453 --> 01:07:28,813
Speaker 16: I think that all boils down to left wing politics

1394
01:07:28,853 --> 01:07:29,813
Speaker 16: and right being politics.

1395
01:07:29,853 --> 01:07:31,213
Speaker 19: Which side do you affiliate to?

1396
01:07:32,813 --> 01:07:35,693
Speaker 16: For example, I wish that every person that ever listens

1397
01:07:35,733 --> 01:07:37,693
Speaker 16: to news Talk ZB could hear what I'm going to

1398
01:07:37,773 --> 01:07:41,413
Speaker 16: say now and instead of always frothing like this, making

1399
01:07:41,453 --> 01:07:43,893
Speaker 16: an opinion of something like that and frothing and getting

1400
01:07:43,973 --> 01:07:46,893
Speaker 16: so livid about it. And I've many people have tried

1401
01:07:46,933 --> 01:07:50,333
Speaker 16: to initiate this conversation with me in recent weeks. Why

1402
01:07:50,413 --> 01:07:53,653
Speaker 16: don't people just take off their right leaning glasses or

1403
01:07:53,733 --> 01:07:57,093
Speaker 16: left leaning glasses and look at it at a neutral

1404
01:07:57,133 --> 01:08:00,053
Speaker 16: sort of standpoint and look at the facts. She quotes

1405
01:08:00,053 --> 01:08:03,413
Speaker 16: it over there that she you know, get informal. Sorry,

1406
01:08:03,493 --> 01:08:06,133
Speaker 16: one of your text has said they're reporting it on

1407
01:08:06,253 --> 01:08:10,533
Speaker 16: CNN and BBC. What what side of the political spectrum

1408
01:08:10,573 --> 01:08:11,813
Speaker 16: of those two news outlets?

1409
01:08:14,013 --> 01:08:15,093
Speaker 3: Yeah, I think it's pretty clear.

1410
01:08:15,213 --> 01:08:17,253
Speaker 8: Yeah, you know, like you also say.

1411
01:08:17,333 --> 01:08:19,373
Speaker 16: And I had a conversation with one of my colleagues

1412
01:08:19,452 --> 01:08:22,773
Speaker 16: last Saturday and I asked her, I said, so this

1413
01:08:23,053 --> 01:08:25,253
Speaker 16: was a conversation that was about I said, it was

1414
01:08:25,293 --> 01:08:28,293
Speaker 16: about Gaza, it was about everything. And I said, so,

1415
01:08:28,412 --> 01:08:30,893
Speaker 16: where do you get the information that you're telling me?

1416
01:08:31,013 --> 01:08:31,133
Speaker 12: Now?

1417
01:08:31,213 --> 01:08:34,412
Speaker 16: Oh, there's this guy. He's a podcast on YouTube. He's

1418
01:08:34,452 --> 01:08:35,412
Speaker 16: a left wing podcast.

1419
01:08:35,492 --> 01:08:36,293
Speaker 8: I said, well, there you go.

1420
01:08:37,572 --> 01:08:41,052
Speaker 16: Don't just listen to, you know, these single sided sources

1421
01:08:41,093 --> 01:08:43,293
Speaker 16: on social media, because that's all you're going to be

1422
01:08:43,372 --> 01:08:46,213
Speaker 16: knowing that, that's all you can form an opinion on it.

1423
01:08:47,333 --> 01:08:50,412
Speaker 16: You know, I know that everybody is staruting this. Oh,

1424
01:08:50,532 --> 01:08:53,133
Speaker 16: it's brown people getting rounded up in American it's only

1425
01:08:53,253 --> 01:08:55,652
Speaker 16: brown people and brown people. Listen, brown people that I've

1426
01:08:55,692 --> 01:08:59,412
Speaker 16: got a friend in Kansas, lovely lovely lady. She's half

1427
01:08:59,612 --> 01:09:04,253
Speaker 16: Native American Indian and she's half Honduran, and she has

1428
01:09:04,333 --> 01:09:08,652
Speaker 16: even told me that seasonal workers that overstay the vis

1429
01:09:08,772 --> 01:09:13,933
Speaker 16: is over there from European countries, White European countries like Germans, Lithuanians.

1430
01:09:14,133 --> 01:09:14,812
Speaker 14: The list goes on.

1431
01:09:15,213 --> 01:09:19,013
Speaker 16: They're also getting rounded up. But unfortunately that doesn't make

1432
01:09:19,133 --> 01:09:22,893
Speaker 16: the news because that doesn't sell. You can't play the

1433
01:09:23,013 --> 01:09:26,093
Speaker 16: racism card when it comes to that. So obviously these

1434
01:09:26,173 --> 01:09:29,612
Speaker 16: news outlets will only be spouting about the unit unfortunate

1435
01:09:29,732 --> 01:09:32,333
Speaker 16: South Americans or other brown people that get rounded up.

1436
01:09:32,692 --> 01:09:34,973
Speaker 16: And then people just get so fixated on this lot.

1437
01:09:35,213 --> 01:09:38,213
Speaker 16: Look at the abhorrent racesm happening over there, But people

1438
01:09:38,452 --> 01:09:40,852
Speaker 16: just don't make the time. If they really want to

1439
01:09:40,893 --> 01:09:44,293
Speaker 16: get so invested in it's fine do that, but make

1440
01:09:44,372 --> 01:09:48,093
Speaker 16: a little bit of effort, use your gray matter and

1441
01:09:48,372 --> 01:09:50,732
Speaker 16: just think out of the box and look at all

1442
01:09:50,812 --> 01:09:51,973
Speaker 16: the different aspects of it.

1443
01:09:52,532 --> 01:09:54,372
Speaker 3: Yeah, and I think we'll see you want to do

1444
01:09:54,452 --> 01:09:55,093
Speaker 3: that these days.

1445
01:09:55,692 --> 01:09:57,572
Speaker 2: I think another thing people can do is just ask

1446
01:09:57,973 --> 01:10:00,453
Speaker 2: why that has been put in front of them so repeatedly.

1447
01:10:01,293 --> 01:10:04,492
Speaker 2: If there is an f there is a you know,

1448
01:10:04,692 --> 01:10:10,092
Speaker 2: motivation by say, for example, you know a news company

1449
01:10:10,572 --> 01:10:14,173
Speaker 2: or a or a platform that you're on. Could there

1450
01:10:14,253 --> 01:10:18,412
Speaker 2: be a motivation that's apart from just getting pure truth

1451
01:10:18,492 --> 01:10:18,652
Speaker 2: to you?

1452
01:10:19,293 --> 01:10:20,933
Speaker 3: Could there be a medical.

1453
01:10:22,213 --> 01:10:22,572
Speaker 18: Cycle?

1454
01:10:22,612 --> 01:10:27,812
Speaker 16: Ideology is literally separating us further from each other than

1455
01:10:27,853 --> 01:10:30,133
Speaker 16: it ever has, and it continues to do so, and

1456
01:10:30,372 --> 01:10:31,532
Speaker 16: people just need to.

1457
01:10:31,572 --> 01:10:33,172
Speaker 3: Wake up and let it stop.

1458
01:10:33,893 --> 01:10:37,213
Speaker 16: You look at America today, it is more divided than

1459
01:10:37,253 --> 01:10:39,772
Speaker 16: it has ever been in its history. And I've got

1460
01:10:39,853 --> 01:10:44,093
Speaker 16: multiple American born and raised friends over there, even immigrants

1461
01:10:44,133 --> 01:10:47,093
Speaker 16: from Puerto Rico and places like that, that have said,

1462
01:10:47,893 --> 01:10:51,452
Speaker 16: this place, you just cannot talk politics or anything like

1463
01:10:51,532 --> 01:10:56,213
Speaker 16: that to anyone anymore these days. Otherwise it's fire and brimstone.

1464
01:10:57,372 --> 01:10:57,572
Speaker 6: Yeah.

1465
01:10:57,772 --> 01:11:00,052
Speaker 3: Thank If you call Nick Good call, Oh, one hundred

1466
01:11:00,053 --> 01:11:01,852
Speaker 3: and eighty ten eighty is the number. Call Can you

1467
01:11:01,893 --> 01:11:04,732
Speaker 3: get your thoughts on this discussion? Also, nine two ninety

1468
01:11:04,732 --> 01:11:07,532
Speaker 3: two is the text as well. We'll get to a

1469
01:11:07,572 --> 01:11:09,812
Speaker 3: few more of those who is going through this text

1470
01:11:09,812 --> 01:11:12,333
Speaker 3: two Big A Dicks on Newstalk ZB. We're allowed to

1471
01:11:12,372 --> 01:11:14,333
Speaker 3: think about anything we want to, and not just what

1472
01:11:14,492 --> 01:11:17,013
Speaker 3: meth Matt thinks we should. Tyler, you need to grow

1473
01:11:17,093 --> 01:11:20,133
Speaker 3: up here. Okay, but who said that you were allowed

1474
01:11:20,173 --> 01:11:21,532
Speaker 3: to think about whatever you want? Yeah?

1475
01:11:21,572 --> 01:11:24,932
Speaker 2: So what's wrong with asking you? You know, I mean

1476
01:11:25,333 --> 01:11:27,492
Speaker 2: think whatever you want, say whatever you want. That's how

1477
01:11:27,532 --> 01:11:30,093
Speaker 2: we started the whole thing. Yep, but you might just

1478
01:11:30,213 --> 01:11:32,692
Speaker 2: want to spend some time thinking if you want to,

1479
01:11:32,732 --> 01:11:33,333
Speaker 2: you don't have to.

1480
01:11:33,812 --> 01:11:35,812
Speaker 3: You know why you're getting the stuff fed to you.

1481
01:11:35,973 --> 01:11:37,933
Speaker 3: That's that's all I'm saying. Yep, good call. It is

1482
01:11:38,053 --> 01:11:39,812
Speaker 3: a quarter to three. Back very shortly.

1483
01:11:41,013 --> 01:11:44,333
Speaker 1: Matt Heath, Taylor Adams with you is your afternoon rolls

1484
01:11:44,412 --> 01:11:47,692
Speaker 1: on Matt Heath and Taylor Adams Afternoons news Talk.

1485
01:11:47,772 --> 01:11:51,333
Speaker 3: Say'd be very good afternoon due It is thirteen to three.

1486
01:11:51,492 --> 01:11:53,492
Speaker 3: Taking your calls of course on oh, eight hundred and

1487
01:11:53,572 --> 01:11:55,612
Speaker 3: eighty ten eighty. If you want to see into text

1488
01:11:55,692 --> 01:11:59,333
Speaker 3: ninety two ninety two is that number? Quick text here

1489
01:11:59,412 --> 01:12:02,652
Speaker 3: get our guys. Great quote I heard from Denzel Washington say,

1490
01:12:02,853 --> 01:12:05,093
Speaker 3: if you don't read the newspaper, you're uninformed. If you

1491
01:12:05,173 --> 01:12:06,812
Speaker 3: do read it, you're misinformed. You've got to find a

1492
01:12:06,853 --> 01:12:10,452
Speaker 3: balance between and for multiple sources. Don't just trust the

1493
01:12:10,532 --> 01:12:13,412
Speaker 3: pulpit of truth, and certainly not just social media.

1494
01:12:14,253 --> 01:12:16,213
Speaker 2: The six says, I'm an American living in New Zealand.

1495
01:12:16,213 --> 01:12:18,213
Speaker 2: I'm so pleased Kei's care about what's happening in my

1496
01:12:18,253 --> 01:12:22,293
Speaker 2: home country. How can our world leaders, including New Zealand Leeds,

1497
01:12:22,333 --> 01:12:25,452
Speaker 2: not call Trump out on so much of what he's

1498
01:12:25,492 --> 01:12:26,892
Speaker 2: doing because they're scared of him?

1499
01:12:26,973 --> 01:12:27,452
Speaker 3: Simple as that.

1500
01:12:27,812 --> 01:12:30,253
Speaker 2: But my fellow Keyi's along with my fellow Americans, are

1501
01:12:30,333 --> 01:12:32,412
Speaker 2: not afraid to call Trump out. Not the best song

1502
01:12:32,452 --> 01:12:36,412
Speaker 2: in the world, but I totally resonate with Bruce Springsteen's message.

1503
01:12:36,492 --> 01:12:38,812
Speaker 2: Maybe you think differently if you had loved one over

1504
01:12:38,853 --> 01:12:41,093
Speaker 2: the eggs guys. Thanks great show, Lizzie. I do have

1505
01:12:41,213 --> 01:12:43,532
Speaker 2: loved ones in America, and I get quite a different

1506
01:12:43,612 --> 01:12:46,692
Speaker 2: message out of America than what I see on social media.

1507
01:12:47,093 --> 01:12:49,692
Speaker 2: But you know, she says, I'm so pleased Kiwi's care

1508
01:12:49,732 --> 01:12:51,692
Speaker 2: about what's happening in my home country. I mean, that's

1509
01:12:51,732 --> 01:12:54,492
Speaker 2: great for you that people Keys care about what's happening

1510
01:12:54,492 --> 01:12:56,452
Speaker 2: in home country. I'm not sure if it's great for

1511
01:12:56,612 --> 01:13:00,213
Speaker 2: New Zealanders to spend so much of their time and

1512
01:13:00,532 --> 01:13:05,253
Speaker 2: energy and live in such a state of anxiety about

1513
01:13:05,372 --> 01:13:08,333
Speaker 2: what's happening in Minnesota on the other side of the place.

1514
01:13:08,853 --> 01:13:10,052
Speaker 3: Fourteen thousand kilime.

1515
01:13:09,973 --> 01:13:13,852
Speaker 2: This way, I'm pretty sure there's some reasonably pressing issues

1516
01:13:14,492 --> 01:13:17,013
Speaker 2: here in New Zealand that we could expend some of

1517
01:13:17,093 --> 01:13:21,492
Speaker 2: that energy on. Maybe spend zero point zero five percent

1518
01:13:22,372 --> 01:13:25,333
Speaker 2: of your time thinking about that. But judging from these texts,

1519
01:13:25,452 --> 01:13:27,772
Speaker 2: a lot of people seem to be I wouldn't be

1520
01:13:27,772 --> 01:13:31,732
Speaker 2: surprised if they're pushing seventy five percent of their thinking

1521
01:13:31,812 --> 01:13:34,213
Speaker 2: time on issues that have been flashed in front of

1522
01:13:34,253 --> 01:13:35,492
Speaker 2: them by social media.

1523
01:13:35,572 --> 01:13:37,052
Speaker 3: But thank you so much for your text.

1524
01:13:37,692 --> 01:13:37,972
Speaker 5: Text.

1525
01:13:38,812 --> 01:13:41,892
Speaker 3: I think they're called text, aren't they. Lizzie, Yeah, there's

1526
01:13:41,893 --> 01:13:43,612
Speaker 3: a great text, Clary, how are your mates?

1527
01:13:44,093 --> 01:13:46,412
Speaker 6: Yes, Hello, I'm not going to waste my time. I

1528
01:13:46,492 --> 01:13:49,372
Speaker 6: pre us two minutes, so I'll just say cure to

1529
01:13:49,572 --> 01:13:53,652
Speaker 6: everyone in New Zealand. I'm a Bruce Rifle. I'm a musician,

1530
01:13:53,732 --> 01:13:57,532
Speaker 6: and I'm a painter, and I have an opinion.

1531
01:13:57,253 --> 01:13:58,452
Speaker 3: Of my own good.

1532
01:13:59,412 --> 01:14:03,572
Speaker 6: The thing is, the last caller was touching very close

1533
01:14:03,612 --> 01:14:08,652
Speaker 6: to my point. Now, as a musician, we didn't want

1534
01:14:08,732 --> 01:14:12,213
Speaker 6: to be celebrities. That was the first thing. Because we

1535
01:14:12,372 --> 01:14:14,692
Speaker 6: love music so much, we just thought we'd get out

1536
01:14:14,732 --> 01:14:18,532
Speaker 6: there and do it. It's the people that makes people celebrities,

1537
01:14:18,732 --> 01:14:22,133
Speaker 6: whether we like it, whether we don't. They are all

1538
01:14:22,452 --> 01:14:26,452
Speaker 6: selected by human beings, a crowd of populace, a country,

1539
01:14:26,572 --> 01:14:32,052
Speaker 6: a nation. Now, musicians had the ability and the gift

1540
01:14:32,612 --> 01:14:36,333
Speaker 6: of each one to deliver a message of how they

1541
01:14:36,572 --> 01:14:40,933
Speaker 6: feel and a reflection on the world as it is.

1542
01:14:42,093 --> 01:14:45,452
Speaker 6: If we go back in music history, I'm pretty sure

1543
01:14:45,532 --> 01:14:49,133
Speaker 6: that Mozart who entertained the queen or the king, etc.

1544
01:14:49,492 --> 01:14:49,732
Speaker 12: Etc.

1545
01:14:52,412 --> 01:14:58,572
Speaker 6: Different movements had different own algorithms, and that was sending

1546
01:14:58,612 --> 01:15:00,933
Speaker 6: out the message to the king or the queen. It

1547
01:15:01,013 --> 01:15:04,732
Speaker 6: might be Popper's glory. Now if we go further into

1548
01:15:04,812 --> 01:15:11,693
Speaker 6: the generations, the music changed dramastically. As everything changes everything

1549
01:15:12,253 --> 01:15:17,492
Speaker 6: we think always. And the thing is, I've taken up stoicism,

1550
01:15:17,973 --> 01:15:23,173
Speaker 6: and Stoicism helps me with my Christianity, and Christianity helps

1551
01:15:23,253 --> 01:15:27,012
Speaker 6: me with my neighbor in the way I see people

1552
01:15:27,372 --> 01:15:30,333
Speaker 6: and how I conduct my life as a musician and

1553
01:15:30,372 --> 01:15:37,852
Speaker 6: an artist. I don't like singing two radical songs. Now,

1554
01:15:38,013 --> 01:15:41,293
Speaker 6: I've got a favorite band in my life, and I'm

1555
01:15:41,372 --> 01:15:46,972
Speaker 6: influenced by the artists, and it's David Gilmore and Water

1556
01:15:47,572 --> 01:15:52,612
Speaker 6: well you know Waters, Rogie Waters Nason and right, yes,

1557
01:15:52,772 --> 01:15:57,892
Speaker 6: Pink Floyd. Yes, they've influenced Thought. They are a fantastic band.

1558
01:15:58,412 --> 01:16:01,812
Speaker 6: And back to the conversation we're having at the kickoff

1559
01:16:01,853 --> 01:16:08,333
Speaker 6: about the topic. Yes, musicians, music and artists can influence.

1560
01:16:08,412 --> 01:16:12,452
Speaker 6: It's the mind, however it does. It's not out there

1561
01:16:12,572 --> 01:16:16,692
Speaker 6: to influence you to change yourself, to be a radical

1562
01:16:17,253 --> 01:16:20,492
Speaker 6: or to be a saintly person. It's out there to

1563
01:16:20,652 --> 01:16:24,852
Speaker 6: share that. Hey, our opinion and music is exactly the

1564
01:16:25,013 --> 01:16:29,013
Speaker 6: same reflection that you're giving us. So you give us

1565
01:16:29,093 --> 01:16:30,973
Speaker 6: a lot of love, we show you a lot of

1566
01:16:31,053 --> 01:16:35,133
Speaker 6: love back. The human race is based on anarchy and

1567
01:16:35,412 --> 01:16:39,812
Speaker 6: anger and negativity because we're born that way. The thing is,

1568
01:16:39,933 --> 01:16:44,492
Speaker 6: we have this non natural love. We had to develop

1569
01:16:44,572 --> 01:16:47,652
Speaker 6: this and it comes through music. It comes through good,

1570
01:16:47,933 --> 01:16:49,013
Speaker 6: healthy discussion.

1571
01:16:49,372 --> 01:16:51,253
Speaker 3: Good on you, Clary. Yeah, and thank you for that

1572
01:16:51,532 --> 01:16:54,412
Speaker 3: great philosophy. I love that. I love what you're saying.

1573
01:16:54,973 --> 01:16:56,812
Speaker 3: But we have to go to an ad break. Yeah,

1574
01:16:56,893 --> 01:16:59,053
Speaker 3: what a great call. And who can't back up the

1575
01:16:59,133 --> 01:17:01,732
Speaker 3: stoke philosophy as well? Thank you very much, Clary. Back

1576
01:17:01,772 --> 01:17:03,972
Speaker 3: in a mow it is eight minutes to three.

1577
01:17:04,772 --> 01:17:07,133
Speaker 1: The issues that affect you and a bit of fun

1578
01:17:07,213 --> 01:17:12,093
Speaker 1: along the way. Heathan Tyler Adams Afternoons NEWSTALKSB.

1579
01:17:12,812 --> 01:17:16,412
Speaker 3: News Talks b it is five to three. We wanted

1580
01:17:16,452 --> 01:17:16,892
Speaker 3: to talk.

1581
01:17:16,772 --> 01:17:20,772
Speaker 2: About the value of celebrity opinions on the show today

1582
01:17:20,853 --> 01:17:24,213
Speaker 2: and whether we think they are celebrities are out of

1583
01:17:24,293 --> 01:17:26,972
Speaker 2: touch or not right yes, which is pretty forage top topic.

1584
01:17:27,013 --> 01:17:27,732
Speaker 3: Get trulyd befare.

1585
01:17:28,133 --> 01:17:30,492
Speaker 2: Before the show, I thought, well, that's pretty banal. It's

1586
01:17:30,492 --> 01:17:34,012
Speaker 2: a banal observation. It's been observed a lot that celebrities

1587
01:17:34,093 --> 01:17:36,013
Speaker 2: might be out of touch. And the only reason why

1588
01:17:36,053 --> 01:17:37,772
Speaker 2: to bring it up because I cringed at a song

1589
01:17:38,213 --> 01:17:40,293
Speaker 2: from one of my favorite artists, and it surprised me

1590
01:17:40,372 --> 01:17:42,812
Speaker 2: that I cringed at the song because I'm a big fan.

1591
01:17:42,933 --> 01:17:44,772
Speaker 2: So I was just asking question, why am I cringing

1592
01:17:44,933 --> 01:17:48,053
Speaker 2: at Bruce Springsteen? And it was something to do with

1593
01:17:48,372 --> 01:17:54,852
Speaker 2: the separation between his beautifully poetic, metaphorical lyrics of old

1594
01:17:55,173 --> 01:17:58,932
Speaker 2: and just the ticker that read like a cable news network.

1595
01:17:59,692 --> 01:18:02,293
Speaker 2: But for some reason, most of our callers Texas and

1596
01:18:02,452 --> 01:18:06,813
Speaker 2: Texas are so rapidly concerned about immigration enforcement in Minnesota

1597
01:18:06,893 --> 01:18:10,852
Speaker 2: that they we never got around to discussing.

1598
01:18:11,013 --> 01:18:12,173
Speaker 3: Pal topic we wanted to.

1599
01:18:12,933 --> 01:18:16,492
Speaker 2: What we got was a lot of throthing abuse and

1600
01:18:16,732 --> 01:18:20,452
Speaker 2: threats from people who think it is an incredibly pressing

1601
01:18:20,933 --> 01:18:27,772
Speaker 2: issue for Kiwi's what is up to? And look, I disagree,

1602
01:18:27,893 --> 01:18:30,093
Speaker 2: and I would just ask you to ask yourself why

1603
01:18:30,333 --> 01:18:34,093
Speaker 2: you are getting fed this stuff? And you know who

1604
01:18:34,253 --> 01:18:37,452
Speaker 2: gains from this passion that you have for this particular topic.

1605
01:18:37,732 --> 01:18:38,933
Speaker 2: I mean, if you want it, you can ask that

1606
01:18:39,013 --> 01:18:41,293
Speaker 2: question or just carry on being angry about it.

1607
01:18:41,572 --> 01:18:42,692
Speaker 3: If that's what you want to do, you can do

1608
01:18:42,772 --> 01:18:44,852
Speaker 3: that as well. Of course good advice. Anyone can say

1609
01:18:44,853 --> 01:18:46,213
Speaker 3: what they want and do what they want, and think

1610
01:18:46,253 --> 01:18:48,213
Speaker 3: what they want and look at what they want. Yep, exactly,

1611
01:18:48,333 --> 01:18:50,852
Speaker 3: they go for it. You do you spicy couple of hours,

1612
01:18:50,933 --> 01:18:53,772
Speaker 3: Thank you very much. Coming up after three o'clock we're

1613
01:18:53,812 --> 01:18:56,532
Speaker 3: going to change tech. We want to talk about AI.

1614
01:18:56,853 --> 01:18:59,172
Speaker 3: That is coming up next. Stay tuned.

1615
01:19:01,692 --> 01:19:05,452
Speaker 1: You're on new home in Stateful and Entertaining Talk. It's

1616
01:19:05,612 --> 01:19:09,772
Speaker 1: Mattie and Taylor Adams afternoons on news Talk Sebby.

1617
01:19:10,372 --> 01:19:12,572
Speaker 3: Good day to you, Welcome back and to the show.

1618
01:19:12,933 --> 01:19:14,893
Speaker 2: That's really funny. We'll move on to this topic we

1619
01:19:14,933 --> 01:19:17,173
Speaker 2: want to talk about next AI. But it's incredible to

1620
01:19:17,333 --> 01:19:19,852
Speaker 2: be how people just hear what they want to hear.

1621
01:19:19,973 --> 01:19:23,213
Speaker 2: So equal amounts of people have texted and abusing us

1622
01:19:23,293 --> 01:19:26,892
Speaker 2: for being pro Trump and anti Trump?

1623
01:19:27,412 --> 01:19:28,812
Speaker 3: Why do you hate Trump so much? Why do you

1624
01:19:28,853 --> 01:19:29,612
Speaker 3: love Trump so much?

1625
01:19:29,692 --> 01:19:32,612
Speaker 2: When we didn't mention Trump once in an entire hour,

1626
01:19:32,933 --> 01:19:36,133
Speaker 2: we were talking about celebrities and whether it was so.

1627
01:19:36,173 --> 01:19:37,572
Speaker 3: Hell of it tell them to be both at once.

1628
01:19:37,772 --> 01:19:39,372
Speaker 3: I mean, that's the state of the world right now,

1629
01:19:39,492 --> 01:19:41,812
Speaker 3: isn't it. People will hear what they want to hear,

1630
01:19:42,532 --> 01:19:45,812
Speaker 3: to be both at once on something you weren't talking about.

1631
01:19:48,093 --> 01:19:51,092
Speaker 3: Thank you very much for those tics, though, right, let's

1632
01:19:51,452 --> 01:19:53,372
Speaker 3: get into this topic. This is going to be a doozy.

1633
01:19:53,452 --> 01:19:55,333
Speaker 3: We do want to talk about AI. So last year,

1634
01:19:55,372 --> 01:19:57,572
Speaker 3: as we remember, there was a heck of a lot

1635
01:19:57,612 --> 01:19:59,972
Speaker 3: of talk about AI decimating jobs left, right and center.

1636
01:19:59,973 --> 01:20:02,492
Speaker 3: It was coming for our occupations hard and fast, and

1637
01:20:02,572 --> 01:20:05,052
Speaker 3: we needed to pivot or be out of work. However,

1638
01:20:05,492 --> 01:20:08,412
Speaker 3: an article in The Economist argues that despite fears of

1639
01:20:08,532 --> 01:20:13,213
Speaker 3: AI causing mass unemployments among professionals, white collar work isn't

1640
01:20:13,253 --> 01:20:16,893
Speaker 3: being a raised. It's been reshaped and expanded. AI boost productivity,

1641
01:20:16,933 --> 01:20:19,492
Speaker 3: but it doesn't eliminate the need for human judgment and

1642
01:20:19,652 --> 01:20:24,253
Speaker 3: complex coordination that many white collar roles require. It goes

1643
01:20:24,293 --> 01:20:27,013
Speaker 3: into some of the numbers. So from twenty twenty two,

1644
01:20:27,812 --> 01:20:31,213
Speaker 3: America added roughly three million white collar jobs.

1645
01:20:31,412 --> 01:20:35,333
Speaker 2: So the jobs that were supposed to disappear with AI

1646
01:20:35,652 --> 01:20:37,012
Speaker 2: have actually been increasing.

1647
01:20:37,093 --> 01:20:40,732
Speaker 3: They have been increasing and expanding into different roles, and

1648
01:20:40,812 --> 01:20:44,933
Speaker 3: as It mentions many times in this particular article, the

1649
01:20:45,173 --> 01:20:47,572
Speaker 3: AI as it stands right now, and there's no evidence

1650
01:20:47,612 --> 01:20:50,933
Speaker 3: that it is going to increase to the level of

1651
01:20:51,492 --> 01:20:52,213
Speaker 3: general AI.

1652
01:20:53,173 --> 01:20:54,732
Speaker 2: Yeah, I mean, I just read the stat in this

1653
01:20:55,093 --> 01:20:57,732
Speaker 2: article in the Economists that cracks me up because I'm

1654
01:20:57,732 --> 01:21:01,732
Speaker 2: not sure what it's meaning. As you know, stats are stats,

1655
01:21:01,772 --> 01:21:05,853
Speaker 2: always open for interpretation. Only about four percent of occupations

1656
01:21:05,973 --> 01:21:08,852
Speaker 2: use AI for three quarters and more of the tasks.

1657
01:21:09,333 --> 01:21:12,173
Speaker 2: So only four percent of occupations use AI for three

1658
01:21:12,293 --> 01:21:14,572
Speaker 2: quarters or more of their tasks. Just trying to work

1659
01:21:14,612 --> 01:21:18,452
Speaker 2: out what that actually means. But yeah, So if twenty

1660
01:21:18,532 --> 01:21:23,972
Speaker 2: twenty five was the year of freaking out about AI,

1661
01:21:24,053 --> 01:21:25,892
Speaker 2: it definitely was, wasn't it. The rubber hit the road

1662
01:21:25,933 --> 01:21:27,133
Speaker 2: and everyone was like, it is here.

1663
01:21:27,452 --> 01:21:29,772
Speaker 3: It is hardcore. YEP. I always worried about it.

1664
01:21:29,853 --> 01:21:33,692
Speaker 2: And it's going to erase all of us. It doesn't

1665
01:21:33,692 --> 01:21:36,732
Speaker 2: seem to have played out like that. No, And also

1666
01:21:36,973 --> 01:21:40,173
Speaker 2: AI usage seems to be plateauing to the point they're

1667
01:21:40,173 --> 01:21:44,452
Speaker 2: talking about an AI bubble bursting potentially just because a

1668
01:21:44,492 --> 01:21:46,412
Speaker 2: lot of the promises that have been made around AI.

1669
01:21:46,893 --> 01:21:48,933
Speaker 2: I mean, you can't do anything without them saying you

1670
01:21:49,013 --> 01:21:51,812
Speaker 2: want the AI, you want AI involved, right, Yes, most

1671
01:21:51,853 --> 01:21:54,572
Speaker 2: of the time people are saying no, thanks, or rather

1672
01:21:54,612 --> 01:21:58,133
Speaker 2: I would rather copilot passed off. If you don't mind,

1673
01:21:58,213 --> 01:22:00,532
Speaker 2: it is a pain in the ars sometimes. Yeah, I

1674
01:22:00,612 --> 01:22:02,652
Speaker 2: don't believe the search at the top of the page

1675
01:22:02,732 --> 01:22:03,173
Speaker 2: on Google.

1676
01:22:03,412 --> 01:22:05,652
Speaker 3: You get rid of that. That is a awful the

1677
01:22:05,732 --> 01:22:07,892
Speaker 3: AI search which you've got no idea whether it's true

1678
01:22:07,973 --> 01:22:11,692
Speaker 3: or not. Yeah, good idea. So how are you feeling

1679
01:22:11,692 --> 01:22:13,492
Speaker 3: about it? Are you feeling? Coming in? Twenty twenty six?

1680
01:22:13,652 --> 01:22:14,812
Speaker 3: Is twenty twenty six the.

1681
01:22:15,053 --> 01:22:17,492
Speaker 2: Year of being realistic about AI and seeing it is

1682
01:22:17,572 --> 01:22:19,933
Speaker 2: something that can help you but won't replace you.

1683
01:22:20,253 --> 01:22:22,892
Speaker 3: Yeah. Oh, eight hundred eighty ten eighty is the number

1684
01:22:22,893 --> 01:22:24,852
Speaker 3: to call if you still use AI, and many of

1685
01:22:24,973 --> 01:22:27,532
Speaker 3: us do. What are you using it for? And have

1686
01:22:27,652 --> 01:22:30,452
Speaker 3: you reduced your alliance on AI if you are part

1687
01:22:30,492 --> 01:22:32,652
Speaker 3: of a business, Is it true what is happening in

1688
01:22:32,732 --> 01:22:35,852
Speaker 3: America that the freak out over AI taking all our

1689
01:22:35,933 --> 01:22:38,492
Speaker 3: jobs just hasn't come to pass. In fact, it is

1690
01:22:38,572 --> 01:22:42,532
Speaker 3: increasing the number of jobs in some sectors and changing

1691
01:22:42,652 --> 01:22:44,412
Speaker 3: for the better. And others love to hear from you. Oh,

1692
01:22:44,412 --> 01:22:46,253
Speaker 3: eight hundred eighty ten eighty is that number to call?

1693
01:22:46,333 --> 01:22:48,053
Speaker 3: Nine two? Nine to two is a text. It's one

1694
01:22:48,093 --> 01:22:49,492
Speaker 3: of those things, you know.

1695
01:22:49,772 --> 01:22:53,213
Speaker 2: The best case scenario for AI would be that it

1696
01:22:53,333 --> 01:22:55,333
Speaker 2: removes a lot of the mundane tasks that we have

1697
01:22:55,492 --> 01:23:00,572
Speaker 2: to do, and each person becomes more productive, but not

1698
01:23:00,933 --> 01:23:03,932
Speaker 2: more productive to the point that you make another human

1699
01:23:04,013 --> 01:23:09,572
Speaker 2: beings job, you know, remove someone out another person's job, Yes,

1700
01:23:09,692 --> 01:23:12,973
Speaker 2: but more productive than that that you grow, you know,

1701
01:23:13,093 --> 01:23:14,852
Speaker 2: you bring in more.

1702
01:23:15,612 --> 01:23:16,612
Speaker 3: You've become more productive.

1703
01:23:16,612 --> 01:23:19,612
Speaker 2: As it was, I'm looking for the word, you create

1704
01:23:19,692 --> 01:23:22,933
Speaker 2: more such that there's more to go around, if you

1705
01:23:22,973 --> 01:23:23,572
Speaker 2: see what I'm saying.

1706
01:23:23,893 --> 01:23:25,852
Speaker 3: Really struggling for the words here. Well, what appears to

1707
01:23:25,933 --> 01:23:28,173
Speaker 3: be happening in America is AI is coming into the

1708
01:23:28,293 --> 01:23:31,293
Speaker 3: four But as you mentioned before, Maat, there's always going

1709
01:23:31,333 --> 01:23:33,892
Speaker 3: to be a need for human judgment. So these jobs

1710
01:23:34,572 --> 01:23:37,692
Speaker 3: are not going they are just being reshaped so that

1711
01:23:38,213 --> 01:23:40,972
Speaker 3: humans are still looking after the AIS that are doing

1712
01:23:41,333 --> 01:23:44,093
Speaker 3: the admin side of things. The beer, basic jobs and

1713
01:23:44,213 --> 01:23:45,932
Speaker 3: new jobs are being created. But'd love to hear your

1714
01:23:45,973 --> 01:23:48,253
Speaker 3: thoughts on this. Is this what you're seeing in your

1715
01:23:48,333 --> 01:23:50,093
Speaker 3: place of work? Oh, eight one hundred and eighty ten

1716
01:23:50,173 --> 01:23:52,652
Speaker 3: eighty is that number to call and nine two ninety

1717
01:23:52,652 --> 01:23:54,892
Speaker 3: two is the text. We will be back very shortly.

1718
01:23:55,173 --> 01:23:58,612
Speaker 3: It is eleven past three US talks. It'd be fourteen

1719
01:23:58,652 --> 01:24:00,692
Speaker 3: past three, so we're talking about AI. There was a

1720
01:24:00,732 --> 01:24:03,253
Speaker 3: big freak out last year about AI coming for most

1721
01:24:03,333 --> 01:24:06,412
Speaker 3: of our jobs, but in a well researched article in

1722
01:24:06,452 --> 01:24:10,173
Speaker 3: The Economist, it actually shows that that hasn't come to pass.

1723
01:24:10,333 --> 01:24:14,773
Speaker 3: In fact, it is more white collar jobs being created

1724
01:24:14,933 --> 01:24:19,253
Speaker 3: and changing. So AI is tending to upgrade jobs rather

1725
01:24:19,293 --> 01:24:20,692
Speaker 3: than raise them. Is that what you're seeing?

1726
01:24:20,973 --> 01:24:21,173
Speaker 13: Wow?

1727
01:24:21,572 --> 01:24:23,532
Speaker 2: And says my job is totally changing because of AI.

1728
01:24:23,652 --> 01:24:25,972
Speaker 2: Instead of typing the doctor's letters, we copy and paste

1729
01:24:26,013 --> 01:24:27,892
Speaker 2: what the consultation has produced and.

1730
01:24:27,933 --> 01:24:30,732
Speaker 3: Format the letter and proofread it. It's so boring, I'm

1731
01:24:30,812 --> 01:24:33,173
Speaker 3: leaving hair right. Okay, that is a big change.

1732
01:24:33,893 --> 01:24:35,173
Speaker 2: Well, I mean they're going to be changes like that,

1733
01:24:35,293 --> 01:24:36,852
Speaker 2: aren't they. You know when it says it's going to

1734
01:24:36,893 --> 01:24:39,492
Speaker 2: get rid of jobs or keep them. I mean, there

1735
01:24:39,572 --> 01:24:41,372
Speaker 2: used to be typing pools and they've totally gone. And

1736
01:24:41,452 --> 01:24:43,452
Speaker 2: before typing pools, they used to just you know, at

1737
01:24:43,492 --> 01:24:45,412
Speaker 2: the back of a legal office, there were just people

1738
01:24:45,692 --> 01:24:48,493
Speaker 2: clerks who just had to write out all the documents

1739
01:24:48,572 --> 01:24:49,772
Speaker 2: with quill.

1740
01:24:50,213 --> 01:24:53,732
Speaker 3: Yeah, fit with feather and ink. I mean, it does

1741
01:24:53,812 --> 01:24:56,333
Speaker 3: go on to say, historically technology has cut out those

1742
01:24:56,412 --> 01:24:59,372
Speaker 3: monotonous tasks within jobs while leaving humans to handle the

1743
01:24:59,452 --> 01:25:01,972
Speaker 3: more complex parts. And this is what AI is doing.

1744
01:25:02,013 --> 01:25:06,492
Speaker 3: It's taking out more of those admin the monotonous type jobs,

1745
01:25:06,612 --> 01:25:09,572
Speaker 3: and then it's moving humans to be the judges of

1746
01:25:09,612 --> 01:25:11,692
Speaker 3: the work that the AI are doing. And I think

1747
01:25:11,732 --> 01:25:14,053
Speaker 3: that is I mean, arguably that's what it should be

1748
01:25:14,133 --> 01:25:15,652
Speaker 3: there for I've been before.

1749
01:25:15,692 --> 01:25:17,572
Speaker 2: I failed to come up with a consent sentence on

1750
01:25:17,652 --> 01:25:19,732
Speaker 2: the fly because I'm too dumb, but I worked on

1751
01:25:19,812 --> 01:25:22,253
Speaker 2: it during the air break. So the idea would be

1752
01:25:22,333 --> 01:25:27,412
Speaker 2: that AI doesn't replace workers, does the admin and clears

1753
01:25:27,412 --> 01:25:31,133
Speaker 2: the admins so they can create more value, more customers,

1754
01:25:31,372 --> 01:25:35,612
Speaker 2: and more jobs. Right, that's the that's the rose colored

1755
01:25:35,812 --> 01:25:37,052
Speaker 2: spectacles view.

1756
01:25:36,893 --> 01:25:40,133
Speaker 3: Of what AI grows a business. Yeah, absolutely, Ray, how

1757
01:25:40,173 --> 01:25:40,333
Speaker 3: are you.

1758
01:25:41,253 --> 01:25:42,892
Speaker 8: Yeah, I'm good, thank you. How are you, Fellas?

1759
01:25:43,173 --> 01:25:45,333
Speaker 3: Very good? So what's happening in the world of AI

1760
01:25:45,572 --> 01:25:46,093
Speaker 3: where you work?

1761
01:25:46,853 --> 01:25:49,053
Speaker 8: Yeah, well, I'm in an education my wife and I

1762
01:25:49,133 --> 01:25:52,253
Speaker 8: and then I've been in education for twenty years. But look,

1763
01:25:52,253 --> 01:25:55,213
Speaker 8: when AI started coming about and I recognized some of

1764
01:25:55,253 --> 01:25:58,532
Speaker 8: the problems that AI could solve, we just sat about

1765
01:25:58,572 --> 01:26:03,052
Speaker 8: building something and some as you're exactly right, like, it's

1766
01:26:03,093 --> 01:26:06,213
Speaker 8: going to remove all the tedious tasks that I hated

1767
01:26:06,253 --> 01:26:10,333
Speaker 8: as an educator and to allow us to do just

1768
01:26:10,492 --> 01:26:13,933
Speaker 8: greater things with kids. What we've managed to built that

1769
01:26:14,053 --> 01:26:19,173
Speaker 8: absolutely experites all of the compliance processes to the point

1770
01:26:19,173 --> 01:26:22,293
Speaker 8: where now our AI will almost do all the planning

1771
01:26:22,333 --> 01:26:24,572
Speaker 8: and evaluation so that we can just focus on building

1772
01:26:24,652 --> 01:26:29,013
Speaker 8: relationships and getting great outcomes. But it's entirely reliant on

1773
01:26:29,293 --> 01:26:29,932
Speaker 8: the educator.

1774
01:26:31,013 --> 01:26:33,692
Speaker 3: So does that mean that anyone loses their job?

1775
01:26:33,812 --> 01:26:33,972
Speaker 6: Right?

1776
01:26:35,093 --> 01:26:35,133
Speaker 5: No?

1777
01:26:35,612 --> 01:26:35,893
Speaker 11: No, No.

1778
01:26:36,412 --> 01:26:39,092
Speaker 8: In fact, the way that I'm seeing it is because

1779
01:26:39,133 --> 01:26:41,213
Speaker 8: it's replacing the things that I never signed up to

1780
01:26:41,293 --> 01:26:44,333
Speaker 8: do at university, which was sit in the dark corner

1781
01:26:44,372 --> 01:26:47,492
Speaker 8: trying to figure out and makes sense of reading samples

1782
01:26:47,532 --> 01:26:50,933
Speaker 8: and writing samples and all that sort of thing. It

1783
01:26:51,173 --> 01:26:54,333
Speaker 8: just speeds that process up so much and means I

1784
01:26:54,452 --> 01:26:55,893
Speaker 8: get to do what I actually sign up will just

1785
01:26:55,973 --> 01:26:59,492
Speaker 8: go and change kids' lives. But I think fundamentally what

1786
01:26:59,612 --> 01:27:02,933
Speaker 8: we've built was called OURO. What we've built couldn't have

1787
01:27:03,013 --> 01:27:07,612
Speaker 8: come from an external support or external system have been

1788
01:27:08,612 --> 01:27:12,652
Speaker 8: au are Oh so you could. You had to have

1789
01:27:12,732 --> 01:27:14,612
Speaker 8: been a bit peeveed off with the system and been

1790
01:27:14,652 --> 01:27:17,812
Speaker 8: frustrated by it over so many years. And then this

1791
01:27:18,093 --> 01:27:21,053
Speaker 8: obviously arrived and my business partner Russell and I we

1792
01:27:21,213 --> 01:27:25,053
Speaker 8: just started really digging into it and honestly, what we're

1793
01:27:25,333 --> 01:27:28,492
Speaker 8: it's on pilot down here in Brent Turtinger and the

1794
01:27:28,692 --> 01:27:31,772
Speaker 8: time savings have been phenomenal, but let alone the reporting

1795
01:27:31,853 --> 01:27:35,053
Speaker 8: and compliance burdens are just disappearing.

1796
01:27:36,053 --> 01:27:37,692
Speaker 3: And that's a great thing, though, isn't it ray that

1797
01:27:38,213 --> 01:27:41,852
Speaker 3: you can assign those more menial tasks, but an important

1798
01:27:41,893 --> 01:27:44,652
Speaker 3: tasks is something like AI that you've created and those

1799
01:27:44,732 --> 01:27:46,933
Speaker 3: human elements which is why you want to do the

1800
01:27:47,013 --> 01:27:49,812
Speaker 3: job that you do, and AI hopefully will never be

1801
01:27:49,853 --> 01:27:52,452
Speaker 3: able to do those things. Means that you can build

1802
01:27:52,532 --> 01:27:54,412
Speaker 3: your business and focus on what you're best at.

1803
01:27:55,173 --> 01:27:58,093
Speaker 8: One hundred percent. I think we don't believe that documentation

1804
01:27:58,213 --> 01:28:00,333
Speaker 8: should disappear. We just don't. We just don't believe a

1805
01:28:00,412 --> 01:28:03,852
Speaker 8: human needs to do it anymore. And with the ability

1806
01:28:03,893 --> 01:28:07,732
Speaker 8: to taking for example like working with children with you know,

1807
01:28:07,812 --> 01:28:12,692
Speaker 8: neurodivers city or anxiety, trauma, learning behavioral needs, we take

1808
01:28:12,772 --> 01:28:15,372
Speaker 8: non clinical information and we and we run it through

1809
01:28:15,372 --> 01:28:19,692
Speaker 8: our system and it builds real time, literal reports that

1810
01:28:19,772 --> 01:28:22,692
Speaker 8: we can use right now instead of waiting like tell

1811
01:28:22,772 --> 01:28:24,612
Speaker 8: us if you don't know, some of the waitless for

1812
01:28:24,732 --> 01:28:27,692
Speaker 8: some of the for support into the into these schools

1813
01:28:27,812 --> 01:28:31,333
Speaker 8: is two to four years, and now we are doing

1814
01:28:31,452 --> 01:28:34,372
Speaker 8: what those what though, because all the all the external

1815
01:28:34,412 --> 01:28:37,012
Speaker 8: support systems have to come in and utilize New Zealand

1816
01:28:37,053 --> 01:28:40,173
Speaker 8: curriculums because that's who they come under unless it's a

1817
01:28:40,412 --> 01:28:43,892
Speaker 8: real high health need. Our system now does the whole

1818
01:28:43,933 --> 01:28:47,692
Speaker 8: lot by itself. It gives back power to schools because

1819
01:28:47,732 --> 01:28:49,732
Speaker 8: when you sit there waiting for support for children who

1820
01:28:49,772 --> 01:28:53,973
Speaker 8: clearly need help, you get frustrated and ultimately a lot

1821
01:28:54,013 --> 01:28:56,452
Speaker 8: of the protests, yes, have been about money, but the

1822
01:28:56,492 --> 01:28:59,012
Speaker 8: second thing is we need help in class. We need help,

1823
01:28:59,333 --> 01:29:01,652
Speaker 8: and just throwing more humans at it is never going

1824
01:29:01,692 --> 01:29:04,093
Speaker 8: to work. But if we equip them with something like

1825
01:29:04,173 --> 01:29:08,293
Speaker 8: what we've built with Row, we're just seeing it absolut

1826
01:29:08,612 --> 01:29:13,532
Speaker 8: revolutionize those things that when we just don't enjoy doing.

1827
01:29:13,692 --> 01:29:17,173
Speaker 8: And also I'm capturing multiple voices and angles when it

1828
01:29:17,253 --> 01:29:19,132
Speaker 8: comes to helping kids and outcomes.

1829
01:29:20,013 --> 01:29:23,572
Speaker 2: Now, I mean, this is an example of an area

1830
01:29:23,772 --> 01:29:28,772
Speaker 2: where there is a shortage of humans. So you've got

1831
01:29:28,933 --> 01:29:31,253
Speaker 2: you've got you know, it's not a matter of losing humans.

1832
01:29:31,492 --> 01:29:33,692
Speaker 2: There's more than there's more work than the humans can do.

1833
01:29:34,652 --> 01:29:39,133
Speaker 2: So then you employ AI such that the humans are

1834
01:29:39,173 --> 01:29:42,133
Speaker 2: there can can actually get the work done that needs

1835
01:29:42,173 --> 01:29:42,532
Speaker 2: to be done.

1836
01:29:43,532 --> 01:29:47,293
Speaker 8: Yes, and it's an entire partnership built off what they

1837
01:29:47,492 --> 01:29:50,372
Speaker 8: see in real time. You know, the systems that we

1838
01:29:50,492 --> 01:29:55,333
Speaker 8: have to upload support networks for children or support outcomes

1839
01:29:55,372 --> 01:29:57,933
Speaker 8: for children, they're so clunky and slow because they're all

1840
01:29:57,973 --> 01:30:02,133
Speaker 8: built on just compliance, and no government can They can't

1841
01:30:02,173 --> 01:30:04,893
Speaker 8: come in and measure love care respect us. See that

1842
01:30:05,013 --> 01:30:09,852
Speaker 8: Techer's roll. This The only way to measure us is compliance. However,

1843
01:30:10,372 --> 01:30:12,652
Speaker 8: if we can build systems like we have built like Orro,

1844
01:30:13,173 --> 01:30:17,412
Speaker 8: which then takes real time observations from the multiple adults

1845
01:30:17,452 --> 01:30:20,093
Speaker 8: that are dealing with children in the day, we just

1846
01:30:20,173 --> 01:30:23,172
Speaker 8: drop it into our system through a messaging type scenario.

1847
01:30:23,572 --> 01:30:25,852
Speaker 8: And it's just doing all the reporting. And I can

1848
01:30:25,933 --> 01:30:28,452
Speaker 8: tell you right now, when we first started, we thought, shit,

1849
01:30:28,532 --> 01:30:30,253
Speaker 8: do we have something here? Sorry, do we have something?

1850
01:30:31,532 --> 01:30:35,652
Speaker 8: But honestly, when we've seen it, when we put it

1851
01:30:35,732 --> 01:30:38,253
Speaker 8: to market and taken it out to schools and seeing

1852
01:30:38,293 --> 01:30:40,173
Speaker 8: the relief on their face that they now don't have

1853
01:30:40,333 --> 01:30:44,372
Speaker 8: to have the report with the reporting burden consume hours

1854
01:30:44,412 --> 01:30:46,972
Speaker 8: and some of these reports are five hours per child.

1855
01:30:47,333 --> 01:30:49,892
Speaker 3: Wow, yeah, her term per term.

1856
01:30:50,572 --> 01:30:53,293
Speaker 8: If you've got twenty of those children per term, you've

1857
01:30:53,333 --> 01:30:55,772
Speaker 8: got to go through and one hundred hours are reported, right.

1858
01:30:55,812 --> 01:30:57,572
Speaker 2: I suppose the amount of energy that you put into that.

1859
01:30:57,612 --> 01:30:59,812
Speaker 2: There's only so much energy that a human can can

1860
01:31:01,013 --> 01:31:03,932
Speaker 2: put into a particular project. And if they're already coming

1861
01:31:03,973 --> 01:31:05,732
Speaker 2: out when they're coming to the you know, face to

1862
01:31:05,812 --> 01:31:08,693
Speaker 2: face you know dealings with the students, if they're already

1863
01:31:09,492 --> 01:31:12,692
Speaker 2: exhausted from what they're doing before that, then then they're

1864
01:31:12,692 --> 01:31:15,573
Speaker 2: not going to do what's best and what's most important. Ironically,

1865
01:31:15,732 --> 01:31:17,813
Speaker 2: using AI for a more human.

1866
01:31:18,093 --> 01:31:22,372
Speaker 8: Approach correct And the the reality also is is that

1867
01:31:22,452 --> 01:31:24,213
Speaker 8: you tend not to start a report for the end

1868
01:31:24,213 --> 01:31:27,093
Speaker 8: of termine week one. There's always a very real big

1869
01:31:27,213 --> 01:31:31,732
Speaker 8: peak that our burden comes on at those times between

1870
01:31:31,772 --> 01:31:33,652
Speaker 8: week eight and ten. You've got to get it all done.

1871
01:31:35,013 --> 01:31:38,253
Speaker 8: So because we're capturing live information, we can literally dial

1872
01:31:38,293 --> 01:31:40,532
Speaker 8: into a system and see how our child's going today

1873
01:31:41,572 --> 01:31:45,452
Speaker 8: without having to you know, create more meeting times and

1874
01:31:46,093 --> 01:31:49,212
Speaker 8: ultimately take teachers away from kids and their own families

1875
01:31:50,492 --> 01:31:52,572
Speaker 8: where they're sitting there trying to figure out how they

1876
01:31:52,692 --> 01:31:53,612
Speaker 8: help this child.

1877
01:31:55,333 --> 01:31:56,012
Speaker 3: With this product.

1878
01:31:56,093 --> 01:31:59,412
Speaker 2: So is this product being formed as a company or

1879
01:31:59,652 --> 01:32:03,453
Speaker 2: is it as and is it being bought by schools?

1880
01:32:03,732 --> 01:32:04,972
Speaker 3: Has it been brought by the education.

1881
01:32:05,732 --> 01:32:08,612
Speaker 8: What we've been doing was we literally conception. They came

1882
01:32:08,692 --> 01:32:10,412
Speaker 8: up with the idea in March. We tested it in

1883
01:32:10,532 --> 01:32:12,892
Speaker 8: our own business. We've got an early learning business down

1884
01:32:12,933 --> 01:32:16,173
Speaker 8: here and tod On a future focus. We tried it

1885
01:32:16,293 --> 01:32:19,293
Speaker 8: in there. We realized, actually, man, I think we have something.

1886
01:32:19,372 --> 01:32:21,173
Speaker 8: But we thought, right, let's test it in the market.

1887
01:32:21,652 --> 01:32:24,572
Speaker 8: We got it into schools locally with my kids go.

1888
01:32:25,253 --> 01:32:27,372
Speaker 8: They loved it. Then we got it into other schools

1889
01:32:27,412 --> 01:32:30,973
Speaker 8: and we really started to realize this this was, and

1890
01:32:31,133 --> 01:32:33,452
Speaker 8: this was this is just in its infancy of what

1891
01:32:33,572 --> 01:32:36,812
Speaker 8: it was doing. We started to realize the real time

1892
01:32:36,933 --> 01:32:39,892
Speaker 8: games that people were getting because teachers, we're time poor.

1893
01:32:40,372 --> 01:32:42,492
Speaker 8: We only have four and a half hours really of

1894
01:32:42,572 --> 01:32:44,173
Speaker 8: contact in front of a kid, and we're trying to

1895
01:32:44,772 --> 01:32:47,693
Speaker 8: change change what's going on, you know, for their outcomes.

1896
01:32:48,293 --> 01:32:52,532
Speaker 8: So once we realized that the the ease of user.

1897
01:32:53,013 --> 01:32:54,973
Speaker 8: So if the user's got to be able to you know,

1898
01:32:54,973 --> 01:32:57,012
Speaker 8: they've got to be able to utilize it easily, otherwise

1899
01:32:57,013 --> 01:32:59,612
Speaker 8: they won't use it either. We built that as simply

1900
01:32:59,612 --> 01:33:02,812
Speaker 8: as possible, tested it. We're now in multiple schools in

1901
01:33:02,853 --> 01:33:05,173
Speaker 8: tot On and we're we meet we've met with the

1902
01:33:05,213 --> 01:33:07,452
Speaker 8: ministry a few times and we're really keen to get

1903
01:33:07,532 --> 01:33:10,612
Speaker 8: some support them because what we know we will save

1904
01:33:10,732 --> 01:33:12,933
Speaker 8: and teach ours is just a phenomenal.

1905
01:33:12,532 --> 01:33:15,253
Speaker 2: And well, thank you so much, Ray, I appreciate your call.

1906
01:33:16,053 --> 01:33:18,492
Speaker 2: So that's AI there, what Ray Ray's talking about, Then

1907
01:33:18,532 --> 01:33:19,852
Speaker 2: that's AI. You don't need to be scared of.

1908
01:33:20,013 --> 01:33:22,572
Speaker 3: Yeah, that's a compelling argument that AI. You use that

1909
01:33:22,732 --> 01:33:24,852
Speaker 3: for the tasks that would take up a lot of

1910
01:33:24,893 --> 01:33:29,293
Speaker 3: your mental power and instead divert that to actually dealing

1911
01:33:29,372 --> 01:33:31,772
Speaker 3: face to face with who you need to fantastic.

1912
01:33:31,333 --> 01:33:34,532
Speaker 2: As that true across the industries that you're working in

1913
01:33:34,772 --> 01:33:36,253
Speaker 2: eight hundred and eighty ten eighty.

1914
01:33:36,093 --> 01:33:42,053
Speaker 3: Back in the twenty four past three, Matt Heath and

1915
01:33:42,133 --> 01:33:45,612
Speaker 3: Tyler Adams afternoons call oh eight hundred eighty ten eighty

1916
01:33:45,732 --> 01:33:48,812
Speaker 3: on US Talk ZB. It is twenty six past three.

1917
01:33:48,812 --> 01:33:50,572
Speaker 3: So we're talking about AI. Last year, as we know,

1918
01:33:50,652 --> 01:33:52,732
Speaker 3: a lot of concern about the number of jobs AI

1919
01:33:53,053 --> 01:33:55,652
Speaker 3: was going to take, But it looks like, according to

1920
01:33:55,853 --> 01:33:59,773
Speaker 3: deep research by the economists, that AI instead is transforming workplaces,

1921
01:33:59,853 --> 01:34:02,492
Speaker 3: making them more productive and actually creating new jobs. Are

1922
01:34:02,532 --> 01:34:03,932
Speaker 3: you finding that in your place of work?

1923
01:34:04,253 --> 01:34:07,892
Speaker 2: SARSA texts? COULDAI fixed the failing IT system and health?

1924
01:34:08,333 --> 01:34:11,132
Speaker 2: That's an interesting idea because that's the kind of similar

1925
01:34:11,213 --> 01:34:13,333
Speaker 2: to education. Right, so we've got house system. I was

1926
01:34:13,372 --> 01:34:15,612
Speaker 2: in hospital recently and it just seemed that the.

1927
01:34:15,692 --> 01:34:18,772
Speaker 3: Doctors were incredible, the nurses were incredible, everyone was incredible,

1928
01:34:19,173 --> 01:34:23,053
Speaker 3: but they could see they were just stymied by how

1929
01:34:23,253 --> 01:34:26,933
Speaker 3: the system worked. Surrounding You've got people just staying in

1930
01:34:27,053 --> 01:34:29,852
Speaker 3: beds because they can't move them on to the next thing.

1931
01:34:29,893 --> 01:34:32,892
Speaker 3: They're just drowning and admin. Yeah, the paperwork that they've

1932
01:34:32,893 --> 01:34:34,452
Speaker 3: got to do. I mean that is a task that

1933
01:34:34,532 --> 01:34:38,652
Speaker 3: AI arguably could complete to allow nurses and doctors to

1934
01:34:38,772 --> 01:34:41,492
Speaker 3: spend more FaceTime the bedside manner, and that's what they're

1935
01:34:41,492 --> 01:34:44,412
Speaker 3: there for. One hundred percent efficient health system would still

1936
01:34:44,933 --> 01:34:50,093
Speaker 3: employ all the nurses, doctors, and I mean hopefully less

1937
01:34:50,732 --> 01:34:53,532
Speaker 3: you know, upper management. Yeah. Oh one hundred and eighteen

1938
01:34:53,572 --> 01:34:58,052
Speaker 3: eighty is the number to call. Hi, Yeah, Katrina. Oh sorry,

1939
01:34:58,133 --> 01:35:00,093
Speaker 3: so Katrina, welcome to the show. I hadn't turned your

1940
01:35:00,133 --> 01:35:03,772
Speaker 3: phone on for a second. Their apologies, right, okay, time

1941
01:35:03,893 --> 01:35:04,213
Speaker 3: just try and.

1942
01:35:04,213 --> 01:35:06,612
Speaker 23: Find a place to park up in Kingston and lovely

1943
01:35:06,732 --> 01:35:12,532
Speaker 23: Kingston beautiful bottom of the lake. Yes, yes, yes it is,

1944
01:35:12,572 --> 01:35:15,253
Speaker 23: so then I don't get interrupted. You don't want to

1945
01:35:15,253 --> 01:35:16,333
Speaker 23: be driving around the lake.

1946
01:35:17,333 --> 01:35:21,132
Speaker 3: You're yarning up Devil staircase in a lot of trouble.

1947
01:35:22,532 --> 01:35:24,652
Speaker 23: Yeah, yes, grandfather built part of that.

1948
01:35:24,853 --> 01:35:26,732
Speaker 17: Yes, you don't want to be.

1949
01:35:29,213 --> 01:35:30,172
Speaker 3: Amazing road.

1950
01:35:33,333 --> 01:35:36,052
Speaker 23: Granddad? Yes, yes, there'd be about one hundred and hundred

1951
01:35:36,053 --> 01:35:37,412
Speaker 23: and twenty by now if you're still.

1952
01:35:37,293 --> 01:35:37,612
Speaker 6: A lot.

1953
01:35:40,612 --> 01:35:44,732
Speaker 23: Anyway, anyway, Andrew, Andrew said you liked my text all

1954
01:35:44,812 --> 01:35:46,853
Speaker 23: yet AI in regards to dairy farming.

1955
01:35:46,732 --> 01:35:49,612
Speaker 3: Yeah, it's fascinating so I spotted it before. So you're

1956
01:35:49,612 --> 01:35:52,372
Speaker 3: a Southland dairy farmer of course. But you guys have

1957
01:35:52,492 --> 01:35:56,253
Speaker 3: started investing more in AI and it wouldn't be typically

1958
01:35:56,333 --> 01:35:58,452
Speaker 3: when you think about AI and you know, I'm not

1959
01:35:58,492 --> 01:36:00,852
Speaker 3: being disparaging here, but typically you wouldn't think a dairy

1960
01:36:00,933 --> 01:36:02,772
Speaker 3: farm would be one to take it on board.

1961
01:36:04,053 --> 01:36:06,133
Speaker 23: Okay, so you so you guys have missed a few

1962
01:36:06,213 --> 01:36:09,652
Speaker 23: Coronation Street episodes. Then obviously what's happening in the dairy world.

1963
01:36:11,412 --> 01:36:14,093
Speaker 23: It's called wearables. So we call them wearables, and so

1964
01:36:15,572 --> 01:36:19,972
Speaker 23: they are products that have come out that monitor your cows.

1965
01:36:20,093 --> 01:36:22,652
Speaker 23: And so that could be you can have a collar

1966
01:36:23,133 --> 01:36:25,492
Speaker 23: that's on your cow, or it could be an ear tag,

1967
01:36:25,933 --> 01:36:28,013
Speaker 23: or it could be what's called a bowlus where they

1968
01:36:28,492 --> 01:36:31,452
Speaker 23: where they put like a big tablets, you know type

1969
01:36:31,492 --> 01:36:34,813
Speaker 23: thing into the gut and you've got all this monitoring

1970
01:36:34,933 --> 01:36:38,812
Speaker 23: of cows were being So it's been out probably and

1971
01:36:38,973 --> 01:36:41,812
Speaker 23: I'm thinking about six years or so, but just over

1972
01:36:41,853 --> 01:36:43,812
Speaker 23: the past few years, especially in the past two or

1973
01:36:43,853 --> 01:36:48,093
Speaker 23: three years, it's rarely really taken taken off. Now, one

1974
01:36:48,772 --> 01:36:50,973
Speaker 23: one product that you may have heard of is and

1975
01:36:51,053 --> 01:36:54,572
Speaker 23: it's associated with rocket Lab and Central government have invested

1976
01:36:54,612 --> 01:36:59,132
Speaker 23: in an outfit could halt it and that's a different

1977
01:36:59,213 --> 01:37:02,253
Speaker 23: lot of another once again another wearable, but having the

1978
01:37:02,372 --> 01:37:06,093
Speaker 23: ability to not have to have fences, so it's a

1979
01:37:06,133 --> 01:37:08,532
Speaker 23: bit like a I suppose, but like a mule up.

1980
01:37:08,973 --> 01:37:12,852
Speaker 23: You know, he doesn't go beyond a certain Yeah. Yeah,

1981
01:37:13,053 --> 01:37:15,412
Speaker 23: so you may have heard of that, that young young guy.

1982
01:37:15,692 --> 01:37:17,772
Speaker 23: Not that I want to give him lots of time,

1983
01:37:18,372 --> 01:37:21,333
Speaker 23: that's right, but another product, but wearable. This is something

1984
01:37:21,412 --> 01:37:24,893
Speaker 23: we're using in cow sheds to monitor their wellbeing, to

1985
01:37:25,013 --> 01:37:27,492
Speaker 23: identify especially when they're on heat. So when you can

1986
01:37:27,612 --> 01:37:32,573
Speaker 23: go to mating, which we call AI because it's artificial insemination,

1987
01:37:35,093 --> 01:37:37,892
Speaker 23: very confusing and and there's lots of things when we're

1988
01:37:37,893 --> 01:37:41,253
Speaker 23: dealing with animals, it's that humans animals what he's talking about.

1989
01:37:41,652 --> 01:37:44,732
Speaker 23: So this this past season for mating, but in the

1990
01:37:44,853 --> 01:37:48,133
Speaker 23: spring or the end of the spring, we're taking that

1991
01:37:48,253 --> 01:37:50,932
Speaker 23: we're going because we've invested so much in that capital

1992
01:37:51,093 --> 01:37:54,452
Speaker 23: of those colors. We've decided not to get the big boys,

1993
01:37:54,532 --> 01:37:56,933
Speaker 23: to get the big Freezians to come and at the

1994
01:37:57,213 --> 01:38:00,532
Speaker 23: end to do their rompy bompy stuff. At the very end,

1995
01:38:00,572 --> 01:38:03,532
Speaker 23: to get those girls that weren't actually pregnant. So what

1996
01:38:03,652 --> 01:38:08,013
Speaker 23: we're doing is we're relying one hundred percent on that.

1997
01:38:09,732 --> 01:38:10,092
Speaker 17: Data.

1998
01:38:10,173 --> 01:38:12,932
Speaker 23: When they come, she'd get milked if they're on heat.

1999
01:38:13,372 --> 01:38:14,692
Speaker 23: So we're having to catch it, so I hopefully that

2000
01:38:14,772 --> 01:38:18,812
Speaker 23: should save us this huge amount of money. Yeah, okay

2001
01:38:18,853 --> 01:38:23,253
Speaker 23: to downsloads. Those big boys won't become McDonald's burgers, so

2002
01:38:23,612 --> 01:38:25,812
Speaker 23: you know that that'll come out of the food chart.

2003
01:38:30,732 --> 01:38:31,132
Speaker 3: Disaster.

2004
01:38:32,253 --> 01:38:37,532
Speaker 23: But from a capital investment perspective, there's a huge amount

2005
01:38:37,572 --> 01:38:39,933
Speaker 23: of us. Now we've got the Southern field Day that

2006
01:38:40,053 --> 01:38:43,612
Speaker 23: Wymma just out of Gore coming up in the middle

2007
01:38:43,652 --> 01:38:48,013
Speaker 23: of February, and there will be a wearable war going on.

2008
01:38:48,333 --> 01:38:50,013
Speaker 23: I mean you'll if you tune up to you if

2009
01:38:50,013 --> 01:38:53,652
Speaker 23: you're shopping lands and they're improving stuff all the time.

2010
01:38:53,732 --> 01:38:57,252
Speaker 23: Like I said to my text, it's on the physical wellbeing.

2011
01:38:57,412 --> 01:38:59,772
Speaker 23: I'm sure at some point we'll get into the mental

2012
01:39:00,612 --> 01:39:06,372
Speaker 23: psycho sort of side of cow. I'm sure and analyzing

2013
01:39:06,412 --> 01:39:09,853
Speaker 23: that because I tell you what, cowside, golog calf psychology

2014
01:39:10,333 --> 01:39:15,173
Speaker 23: is amazing to watch. But yeah, there's a lot going

2015
01:39:15,253 --> 01:39:17,412
Speaker 23: on now. We haven't eliminated any staff.

2016
01:39:17,853 --> 01:39:18,692
Speaker 3: Yeah, there's thing I was.

2017
01:39:18,652 --> 01:39:21,572
Speaker 2: Thinking about that with Katrina, because that's another thing, like

2018
01:39:21,732 --> 01:39:25,532
Speaker 2: another industry where you're actually having trouble getting staff. It's

2019
01:39:25,572 --> 01:39:28,013
Speaker 2: not so much the idea that people are going to

2020
01:39:28,093 --> 01:39:30,452
Speaker 2: lose jobs as there is work to do that people

2021
01:39:30,532 --> 01:39:31,412
Speaker 2: don't want to do am I.

2022
01:39:31,492 --> 01:39:35,412
Speaker 23: Correct, Oh, absolutely, Like one of the guys that we've

2023
01:39:35,452 --> 01:39:37,892
Speaker 23: got on farm who in the spare time just wants

2024
01:39:37,933 --> 01:39:39,852
Speaker 23: to do gaming. So he just you know, he comes in,

2025
01:39:40,173 --> 01:39:42,412
Speaker 23: he does his job and then there's a time of gaming.

2026
01:39:42,893 --> 01:39:45,013
Speaker 23: He was so excited to show me the other day

2027
01:39:45,492 --> 01:39:48,053
Speaker 23: about the cows coming on and how they can analyze

2028
01:39:48,133 --> 01:39:51,213
Speaker 23: so much more about them to the point they can

2029
01:39:51,293 --> 01:39:53,213
Speaker 23: make it. We have a thing called pro track, so

2030
01:39:53,213 --> 01:39:56,732
Speaker 23: it does automatic drafting, it makes we announcements when they

2031
01:39:56,853 --> 01:39:57,133
Speaker 23: come on.

2032
01:39:57,452 --> 01:39:58,212
Speaker 16: Onto the platform.

2033
01:39:58,812 --> 01:40:00,693
Speaker 23: Just a princess.

2034
01:40:03,253 --> 01:40:06,492
Speaker 3: It's the gamification of dairy. Fantastic it is.

2035
01:40:06,692 --> 01:40:08,732
Speaker 23: Or he was telling me, you know, like he was

2036
01:40:08,772 --> 01:40:11,253
Speaker 23: giving one of the other staff a hard time. Alex,

2037
01:40:11,492 --> 01:40:17,612
Speaker 23: your mother has arrived. So there's lots and lots of things.

2038
01:40:17,652 --> 01:40:20,692
Speaker 23: And I've never seen Rob take so much interest. My

2039
01:40:20,812 --> 01:40:24,812
Speaker 23: dear is interested in gaming, but he can see the

2040
01:40:25,053 --> 01:40:29,293
Speaker 23: value of monitoring the cows until we can pick up

2041
01:40:29,412 --> 01:40:31,612
Speaker 23: you know, if you've got mess titus and you may

2042
01:40:31,652 --> 01:40:33,173
Speaker 23: look at them and you may not think so, or

2043
01:40:33,213 --> 01:40:35,932
Speaker 23: they've got a lot more. The lameness is happening earlier

2044
01:40:35,973 --> 01:40:40,733
Speaker 23: than before, and what we physically could see the technology

2045
01:40:41,133 --> 01:40:43,492
Speaker 23: is amazing. But it's quite competitive out there, but that's

2046
01:40:43,532 --> 01:40:44,053
Speaker 23: good for us.

2047
01:40:44,253 --> 01:40:46,532
Speaker 3: I bet, is there a potential here, Katrina, because you

2048
01:40:46,572 --> 01:40:48,812
Speaker 3: mentioned you haven't laid anyone off, But is the potential

2049
01:40:48,853 --> 01:40:51,172
Speaker 3: with this sort of technology. It makes you more efficient,

2050
01:40:51,293 --> 01:40:54,412
Speaker 3: you have better breeding seasons, you're producing more milk. There

2051
01:40:54,572 --> 01:40:56,532
Speaker 3: seems to me a potential there that you might need

2052
01:40:56,652 --> 01:40:58,652
Speaker 3: more people to help out because you're more productive.

2053
01:40:58,933 --> 01:41:01,812
Speaker 23: No, no, no, no, I don't want any more.

2054
01:41:01,933 --> 01:41:02,652
Speaker 6: I don't want anything.

2055
01:41:03,652 --> 01:41:07,732
Speaker 3: No fair enough. Princess Foa seems like, you know, she

2056
01:41:07,853 --> 01:41:09,172
Speaker 3: might be a bit hard work sometimes.

2057
01:41:09,732 --> 01:41:13,612
Speaker 23: Oh she's a revery bit of a br sometimes. And

2058
01:41:13,732 --> 01:41:15,412
Speaker 23: she wasn't even a peet when she was a calf

2059
01:41:15,452 --> 01:41:17,772
Speaker 23: and a raised her as a calf. But I want

2060
01:41:17,812 --> 01:41:19,173
Speaker 23: to put you would have gone. But you know, because

2061
01:41:19,173 --> 01:41:22,532
Speaker 23: we meeted things by their production worth and their breeding worth,

2062
01:41:22,973 --> 01:41:25,372
Speaker 23: it means we can cull them out, you know, just

2063
01:41:25,532 --> 01:41:30,652
Speaker 23: identify them earlier and then progressively cull them out, you know,

2064
01:41:31,213 --> 01:41:33,492
Speaker 23: and you're not wasting money. You can pick up if

2065
01:41:33,492 --> 01:41:36,173
Speaker 23: they've got you know, a thing called yonees or something

2066
01:41:36,372 --> 01:41:39,692
Speaker 23: like that. So it gives you a lot more a

2067
01:41:39,772 --> 01:41:42,812
Speaker 23: lot more data and the stuff, I means the stuff

2068
01:41:42,893 --> 01:41:45,412
Speaker 23: can go and do other things like feeding out, you know,

2069
01:41:45,572 --> 01:41:49,412
Speaker 23: moving senses, doing stuff on farm and all that sort

2070
01:41:49,452 --> 01:41:49,812
Speaker 23: of stuff.

2071
01:41:50,053 --> 01:41:51,572
Speaker 2: Well, thanks so much for corkatre And I'm going to

2072
01:41:51,612 --> 01:41:55,692
Speaker 2: go to the news headlines, but look three incredibly positive

2073
01:41:55,692 --> 01:41:58,253
Speaker 2: stories about AI. Loving so I was twenty twenty six

2074
01:41:58,372 --> 01:42:01,572
Speaker 2: the year of celebrating AI. Of twenty twenty five is

2075
01:42:01,612 --> 01:42:04,732
Speaker 2: the year of freaking out. Well, spoiler, there's a bunch

2076
01:42:04,772 --> 01:42:06,732
Speaker 2: of people freaking out that we'll have next still coming through.

2077
01:42:06,772 --> 01:42:08,692
Speaker 3: Oh, one hundred and eighty ten eighty is number of

2078
01:42:08,732 --> 01:42:09,772
Speaker 3: call headlines coming up.

2079
01:42:11,013 --> 01:42:14,732
Speaker 14: You've talk said, be headlines with blue bubble taxis it's

2080
01:42:14,853 --> 01:42:18,052
Speaker 14: no trouble with a blue bubble. The government is building

2081
01:42:18,213 --> 01:42:22,293
Speaker 14: specialist schools for children with complex learning needs in Palmerston

2082
01:42:22,372 --> 01:42:25,412
Speaker 14: North and South Auckland, opening in twenty twenty seven and

2083
01:42:25,572 --> 01:42:29,213
Speaker 14: twenty eight. The PSA says Health Ends needs to look

2084
01:42:29,253 --> 01:42:33,133
Speaker 14: at all hospital systems after a widespread twelve hour IT

2085
01:42:33,612 --> 01:42:37,812
Speaker 14: outage yesterday and Upper North Island hospitals. Hospitals in the

2086
01:42:37,933 --> 01:42:41,093
Speaker 14: South Island and Lower North also had outages this month.

2087
01:42:42,173 --> 01:42:45,372
Speaker 14: Two landlords have been fined sixty thousand dollars for tenancy

2088
01:42:45,452 --> 01:42:51,253
Speaker 14: breaches across thirty four properties, including cockroach infestations, sewage overflows

2089
01:42:51,372 --> 01:42:54,973
Speaker 14: and holes in the walls. One person's died in a

2090
01:42:55,013 --> 01:42:57,772
Speaker 14: collision between a car and a motorbike on State Highway

2091
01:42:57,853 --> 01:43:01,572
Speaker 14: seven near Hannah Springs and North Canterbury. The roads closed

2092
01:43:01,652 --> 01:43:06,173
Speaker 14: between Hannah Springs and Culverdin. Kiwi Rail says it's summer

2093
01:43:06,253 --> 01:43:09,132
Speaker 14: of rail closures has let it finish. It's all physical

2094
01:43:09,213 --> 01:43:12,492
Speaker 14: work on Auckland's rail network before the City rail Link

2095
01:43:12,532 --> 01:43:16,452
Speaker 14: opens this year. All lines closed since December reopen today.

2096
01:43:17,452 --> 01:43:21,133
Speaker 14: The failure of Labour's first year fees free scheme, here's

2097
01:43:21,253 --> 01:43:23,812
Speaker 14: why it didn't work. You can read more at it

2098
01:43:23,893 --> 01:43:26,293
Speaker 14: and said Herald Premium. Now back to Matt Heath and

2099
01:43:26,372 --> 01:43:27,052
Speaker 14: Tyler Adams.

2100
01:43:29,532 --> 01:43:32,533
Speaker 3: Thank you very much, Rayleen. We are talking about artificial

2101
01:43:32,612 --> 01:43:35,372
Speaker 3: intelligence in twenty twenty six. Is this the year maybe

2102
01:43:35,452 --> 01:43:37,932
Speaker 3: we start celebrating it as last year we did get

2103
01:43:37,933 --> 01:43:38,652
Speaker 3: a little bit worried.

2104
01:43:38,933 --> 01:43:41,293
Speaker 2: Karen says. They never said AI was coming for your jobs.

2105
01:43:41,333 --> 01:43:43,452
Speaker 2: In twenty twenty six, they said it would come for

2106
01:43:43,572 --> 01:43:46,652
Speaker 2: your jobs. Eventually, it will start with processing jobs such

2107
01:43:46,652 --> 01:43:48,732
Speaker 2: as lawyers and accountants, and then move on to the

2108
01:43:48,853 --> 01:43:53,253
Speaker 2: speaking jobs like judges and US talk ZB hosts Let's

2109
01:43:53,293 --> 01:43:55,452
Speaker 2: be honest. A monkey with a tambourine could do your job,

2110
01:43:55,572 --> 01:43:57,772
Speaker 2: so eventually AI will be able to do it.

2111
01:43:58,652 --> 01:44:01,133
Speaker 3: Two, I would love a monkey with a tambourine in

2112
01:44:01,213 --> 01:44:03,412
Speaker 3: the studio. Well how much they would cost?

2113
01:44:03,732 --> 01:44:03,892
Speaker 17: Well?

2114
01:44:03,973 --> 01:44:06,053
Speaker 3: Can't we just get a monkey with a tambourine?

2115
01:44:06,372 --> 01:44:06,532
Speaker 7: You know?

2116
01:44:06,692 --> 01:44:09,173
Speaker 2: I mean agree with you totally. Karen will be gone

2117
01:44:09,213 --> 01:44:11,932
Speaker 2: within three or four months. Yep, replaced by It's coming

2118
01:44:12,013 --> 01:44:14,973
Speaker 2: an art. Yeah, there's no doubt about that. But why

2119
01:44:15,093 --> 01:44:16,572
Speaker 2: wait to get the monkey with the tambourine?

2120
01:44:16,732 --> 01:44:16,932
Speaker 3: Yeah?

2121
01:44:17,053 --> 01:44:19,532
Speaker 2: Why can't it be the Matt Heath and Tyler Adams

2122
01:44:19,612 --> 01:44:21,452
Speaker 2: and Monkey with a tambourine show and Zibet.

2123
01:44:21,532 --> 01:44:23,812
Speaker 3: Yeah, we'll phone some people and see if we can

2124
01:44:23,812 --> 01:44:26,093
Speaker 3: get that monkey with a tambourine. But for you tomorrow, Karen,

2125
01:44:26,293 --> 01:44:29,772
Speaker 3: let's do it now. Marlene, welcome to the show. Hey guys,

2126
01:44:29,853 --> 01:44:31,893
Speaker 3: how are you very good? I think you? Thank you

2127
01:44:31,973 --> 01:44:32,333
Speaker 3: for calling?

2128
01:44:33,053 --> 01:44:33,812
Speaker 1: Yeah, no worries.

2129
01:44:33,933 --> 01:44:39,972
Speaker 11: Okay. So I'm South African proudly and English second language.

2130
01:44:40,772 --> 01:44:45,013
Speaker 11: So we've had a change in management recently at my workplace,

2131
01:44:45,173 --> 01:44:48,492
Speaker 11: and the manager is really encouraging us to use the

2132
01:44:48,612 --> 01:44:52,653
Speaker 11: tools that's available. And I tell you what, I'm absolutely

2133
01:44:52,893 --> 01:44:58,972
Speaker 11: hooked on AI where I previously so co pilot on Outlook,

2134
01:44:59,013 --> 01:45:02,933
Speaker 11: for instance, where you could previously so. For me, I

2135
01:45:03,133 --> 01:45:06,213
Speaker 11: really have to think about my words, and sometimes it

2136
01:45:06,333 --> 01:45:10,812
Speaker 11: feels like I don't immediately know the English word to use.

2137
01:45:11,732 --> 01:45:14,772
Speaker 11: And even though I can speak English and I understand it,

2138
01:45:14,973 --> 01:45:18,012
Speaker 11: I really have to think about the words, my grammar.

2139
01:45:18,652 --> 01:45:22,812
Speaker 11: And so I tell you what. I now type my email.

2140
01:45:23,333 --> 01:45:26,333
Speaker 11: I dump it into co Pilot and I write the

2141
01:45:26,572 --> 01:45:31,412
Speaker 11: most professional email in pure English I've seen.

2142
01:45:31,973 --> 01:45:34,292
Speaker 3: Well, it sounds like you've got a pretty good mastering

2143
01:45:34,372 --> 01:45:37,652
Speaker 3: of the English language. They're talking to you, Malan, Yeah, well,

2144
01:45:37,853 --> 01:45:38,492
Speaker 3: I'm not sure.

2145
01:45:38,692 --> 01:45:43,372
Speaker 11: I still struggle with my denses most days, but now

2146
01:45:43,652 --> 01:45:48,732
Speaker 11: I find the words through AI, and I can diype

2147
01:45:48,973 --> 01:45:53,692
Speaker 11: really good professional emails, and even my work, I work faster.

2148
01:45:53,933 --> 01:45:56,133
Speaker 11: I don't have to sit and think about things in

2149
01:45:56,253 --> 01:46:00,133
Speaker 11: oh that I use the right denses. It is amazing.

2150
01:46:01,093 --> 01:46:04,492
Speaker 11: You dump it in Copilot and then it will say

2151
01:46:04,572 --> 01:46:08,492
Speaker 11: to you here's the more professional version. It takes the

2152
01:46:08,612 --> 01:46:10,692
Speaker 11: emotion out of it, and then.

2153
01:46:12,093 --> 01:46:12,692
Speaker 3: Do you have to check?

2154
01:46:12,732 --> 01:46:14,333
Speaker 2: You have to check it before it goes out. Though

2155
01:46:14,372 --> 01:46:16,173
Speaker 2: you can't trust AI just to send it, so you're

2156
01:46:16,213 --> 01:46:18,652
Speaker 2: going through reading it again and making sure it's right Marlene.

2157
01:46:19,253 --> 01:46:23,333
Speaker 11: You know what, I've never had to do that depending

2158
01:46:23,412 --> 01:46:26,692
Speaker 11: on this situation. I then copy that over no, and

2159
01:46:26,853 --> 01:46:29,852
Speaker 11: it even gives me. It will say Okay in your email,

2160
01:46:29,973 --> 01:46:33,492
Speaker 11: I see you are referring to this issue. Do you

2161
01:46:33,612 --> 01:46:36,213
Speaker 11: want me to do a checklist for future use that

2162
01:46:36,372 --> 01:46:39,892
Speaker 11: you can actually check off things? So it goes further

2163
01:46:40,053 --> 01:46:42,652
Speaker 11: than just are you worried?

2164
01:46:42,732 --> 01:46:43,092
Speaker 3: Marlene?

2165
01:46:43,173 --> 01:46:47,052
Speaker 2: That will the the emails, because they're all being created

2166
01:46:47,093 --> 01:46:51,932
Speaker 2: by copilot, will lack emotion, lack the human the human

2167
01:46:52,213 --> 01:46:54,772
Speaker 2: touch that you might need. I mean, people talk about

2168
01:46:55,093 --> 01:46:58,772
Speaker 2: LinkedIn posts, and LinkedIn is the social media. So it's

2169
01:46:58,853 --> 01:47:02,412
Speaker 2: so rifle with social media that the blandness of the

2170
01:47:02,572 --> 01:47:06,412
Speaker 2: posts is Yes, it means that it almost becomes slop,

2171
01:47:06,452 --> 01:47:07,173
Speaker 2: it becomes nothing.

2172
01:47:08,053 --> 01:47:08,253
Speaker 1: Yeah.

2173
01:47:08,372 --> 01:47:11,412
Speaker 11: And that's the one thing I do change is because

2174
01:47:11,452 --> 01:47:15,572
Speaker 11: with our customers, I've got a really personal relationship and

2175
01:47:16,652 --> 01:47:19,612
Speaker 11: they are used to my soul of writing. So I

2176
01:47:19,732 --> 01:47:22,732
Speaker 11: would start the email with hey, how you going, how's

2177
01:47:22,772 --> 01:47:25,372
Speaker 11: your weak? That kind of thing, and then I sort

2178
01:47:25,412 --> 01:47:28,532
Speaker 11: of change that don't what AI is giving me, and

2179
01:47:28,652 --> 01:47:32,532
Speaker 11: then I ended again in my version, and I tell

2180
01:47:32,572 --> 01:47:36,133
Speaker 11: you what, it saves a lot of time with not

2181
01:47:36,412 --> 01:47:39,572
Speaker 11: these no plans to let any staff go because the

2182
01:47:39,933 --> 01:47:43,652
Speaker 11: company I work for is pretty job specific, so and

2183
01:47:44,133 --> 01:47:46,812
Speaker 11: we just talk to each other better and I'm learning

2184
01:47:46,893 --> 01:47:52,333
Speaker 11: new words. That's amazing. I absolutely love it. I absolutely

2185
01:47:52,412 --> 01:47:52,652
Speaker 11: love it.

2186
01:47:52,853 --> 01:47:55,772
Speaker 3: Is it part of Arlene that I understand You've only

2187
01:47:55,812 --> 01:47:57,732
Speaker 3: got so much, you know, sort of bandwidth for a

2188
01:47:57,772 --> 01:48:00,532
Speaker 3: particular day, and that helps you out so you can

2189
01:48:00,572 --> 01:48:03,852
Speaker 3: actually focus your mental prowess on on other jobs. But

2190
01:48:05,213 --> 01:48:07,133
Speaker 3: is there a worry for you the more you use it,

2191
01:48:07,293 --> 01:48:09,932
Speaker 3: the more you lean on it and use it as

2192
01:48:09,973 --> 01:48:12,173
Speaker 3: a crutch. Does that sort of plow on your mind

2193
01:48:12,253 --> 01:48:13,812
Speaker 3: that is using it too much?

2194
01:48:13,893 --> 01:48:14,133
Speaker 15: Maybe?

2195
01:48:15,053 --> 01:48:15,093
Speaker 5: No.

2196
01:48:15,893 --> 01:48:20,572
Speaker 11: I actually, because English is my second language, I actually

2197
01:48:20,692 --> 01:48:22,772
Speaker 11: look at the wording and I go, oh, my gosh.

2198
01:48:22,893 --> 01:48:26,213
Speaker 11: I never would have thought to use those words because

2199
01:48:26,293 --> 01:48:30,293
Speaker 11: my conversational English is pretty much different to my writing

2200
01:48:30,412 --> 01:48:36,732
Speaker 11: English because I'm English second language. So yeah, so I

2201
01:48:36,973 --> 01:48:40,293
Speaker 11: use in conversations. I always joke about it that I

2202
01:48:40,492 --> 01:48:44,572
Speaker 11: use normal words that don't keep me into trouble so

2203
01:48:44,772 --> 01:48:48,973
Speaker 11: when I die, but when I dipe, I need to

2204
01:48:49,173 --> 01:48:53,652
Speaker 11: sound professional because people don't always know me so and

2205
01:48:53,732 --> 01:48:55,732
Speaker 11: I don't want them to look at my email and

2206
01:48:55,853 --> 01:48:58,293
Speaker 11: go oh, Bladdy oll. This one is so South African.

2207
01:48:58,372 --> 01:48:59,932
Speaker 11: She's got no idea what she's doing.

2208
01:49:00,492 --> 01:49:02,972
Speaker 2: Well, I'll just talk to you for three or four minutes.

2209
01:49:03,013 --> 01:49:06,372
Speaker 2: And you were very articulate. Yeah, with what you were saying.

2210
01:49:06,732 --> 01:49:08,692
Speaker 3: Marlene, thank you very much for giving us azo. Eight

2211
01:49:08,772 --> 01:49:10,652
Speaker 3: hundred and eighty ten eighty is the number to call.

2212
01:49:10,732 --> 01:49:12,973
Speaker 3: How are you using AI in your workplace? Is it

2213
01:49:13,053 --> 01:49:15,812
Speaker 3: transforming it rather than taking jobs? Love to hear from you.

2214
01:49:15,893 --> 01:49:17,973
Speaker 3: Nine to nine two is a text. It's sixteen to

2215
01:49:18,093 --> 01:49:20,932
Speaker 3: four up next an opinion from a text to Ellen.

2216
01:49:21,053 --> 01:49:23,053
Speaker 3: He says AI doesn't exist. Oh, this is going to

2217
01:49:23,053 --> 01:49:23,333
Speaker 3: be good.

2218
01:49:23,732 --> 01:49:27,772
Speaker 1: Okay, have a chat with the lads on eight hundred

2219
01:49:27,893 --> 01:49:31,892
Speaker 1: eighty ten eighty Matt Heathen, Tyler Adams afternoons used talk z.

2220
01:49:31,933 --> 01:49:36,693
Speaker 3: EDB News talk ZEDB. We are talking about artificial intelligence,

2221
01:49:36,732 --> 01:49:38,812
Speaker 3: a lot of concern last year it was going to

2222
01:49:38,853 --> 01:49:40,852
Speaker 3: take jobs left for right and center. But it appears

2223
01:49:40,893 --> 01:49:41,612
Speaker 3: that's not the case.

2224
01:49:41,933 --> 01:49:42,133
Speaker 8: Yeah.

2225
01:49:42,532 --> 01:49:45,532
Speaker 3: Well, according to the economists, they never get anything wrong.

2226
01:49:45,692 --> 01:49:47,213
Speaker 3: That publication. Hallo.

2227
01:49:47,333 --> 01:49:49,452
Speaker 2: In my opinion, there is no such thing as AI.

2228
01:49:49,893 --> 01:49:51,973
Speaker 2: This is this is just flipping the conversation. It's here

2229
01:49:52,053 --> 01:49:54,973
Speaker 2: allan here, There's no such thing I was wondering, what

2230
01:49:55,213 --> 01:49:57,452
Speaker 2: is this thing that people call AI. It seems that

2231
01:49:57,532 --> 01:50:00,052
Speaker 2: it is the opposite of a flow chart called chart flow.

2232
01:50:00,133 --> 01:50:02,572
Speaker 2: That is, it appears to be a way of programming

2233
01:50:02,612 --> 01:50:05,452
Speaker 2: and commuter so that it so called recognizes the so

2234
01:50:05,612 --> 01:50:08,412
Speaker 2: called priorities it has been programmed to so called well

2235
01:50:08,572 --> 01:50:10,932
Speaker 2: as it were recognized. That is, it is still an

2236
01:50:10,973 --> 01:50:13,932
Speaker 2: incredibly dumb machine that hasn't a real clue about anything.

2237
01:50:14,213 --> 01:50:16,333
Speaker 2: The more advanced it gets and the more clunky it

2238
01:50:16,412 --> 01:50:20,932
Speaker 2: gets in real terms, however, frenche thermotats, thermostats and other

2239
01:50:21,013 --> 01:50:23,692
Speaker 2: handy mechanized equipment are sometimes useful.

2240
01:50:23,772 --> 01:50:24,372
Speaker 3: That's from Allen.

2241
01:50:24,853 --> 01:50:26,652
Speaker 2: I mean, he's sort of he has a point there,

2242
01:50:26,652 --> 01:50:29,293
Speaker 2: because I mean, a land large language model is just

2243
01:50:30,412 --> 01:50:35,293
Speaker 2: running probabilities of what the most likely thing would come

2244
01:50:35,372 --> 01:50:38,772
Speaker 2: up next in a very comics complex way, and they've

2245
01:50:38,812 --> 01:50:42,333
Speaker 2: got parameters to put it into certain areas and such,

2246
01:50:42,853 --> 01:50:45,253
Speaker 2: and so that's why it gets in trouble with advanced

2247
01:50:45,893 --> 01:50:49,013
Speaker 2: you know, mathematics. It can't do advanced mathematics because it

2248
01:50:49,093 --> 01:50:56,772
Speaker 2: can't really create. It can only put together what's happened before.

2249
01:50:57,492 --> 01:51:00,213
Speaker 2: And that's that's why small language models are quite good,

2250
01:51:00,253 --> 01:51:02,412
Speaker 2: because you know, like you train it on a specific

2251
01:51:02,492 --> 01:51:05,173
Speaker 2: thing to do a specific thing, and that that's kind

2252
01:51:05,213 --> 01:51:07,053
Speaker 2: of what's taking over a little bit. Like if if

2253
01:51:07,093 --> 01:51:08,612
Speaker 2: you're in a law firm, right, you could train it

2254
01:51:08,652 --> 01:51:12,492
Speaker 2: on property law. Yeah, and then and then all that's

2255
01:51:12,532 --> 01:51:14,253
Speaker 2: information because there's a lot of repetition in it, right,

2256
01:51:14,293 --> 01:51:16,972
Speaker 2: because it's specialized. But hence the hallucinations as well, right

2257
01:51:17,213 --> 01:51:19,612
Speaker 2: that we still see that it's making a probability on

2258
01:51:19,732 --> 01:51:22,852
Speaker 2: the most likely scenario, and then sometimes it gets that

2259
01:51:23,293 --> 01:51:27,053
Speaker 2: prediction wrong and creates these hallucinations of what it thought

2260
01:51:27,093 --> 01:51:29,372
Speaker 2: it was going to be accurate, and it clearly isn't.

2261
01:51:29,532 --> 01:51:31,372
Speaker 3: Yeah, well, I mean you just look up online.

2262
01:51:31,412 --> 01:51:31,652
Speaker 18: You can.

2263
01:51:31,772 --> 01:51:33,692
Speaker 2: There's there's a great video on there with someone asking

2264
01:51:35,293 --> 01:51:38,933
Speaker 2: chet GPT how manys are in Strawberry and it's just

2265
01:51:38,973 --> 01:51:42,093
Speaker 2: a really long argument and chet GP you can't get

2266
01:51:42,093 --> 01:51:42,253
Speaker 2: to it.

2267
01:51:42,372 --> 01:51:44,492
Speaker 3: Yeah, that's so good, Philip. How are you?

2268
01:51:46,053 --> 01:51:48,293
Speaker 17: Hey, I'm on the same line of it. Strawberry scenario.

2269
01:51:48,572 --> 01:51:53,732
Speaker 17: So this is a movie called Snake Eyes and I saw. Yeah,

2270
01:51:54,452 --> 01:51:54,972
Speaker 17: I haven't.

2271
01:51:54,772 --> 01:51:56,572
Speaker 24: Actually seen the movie, but I've seen but some pieces,

2272
01:51:56,652 --> 01:51:59,572
Speaker 24: you know, in this particular, you start asking Ai like

2273
01:51:59,652 --> 01:52:01,652
Speaker 24: I did last nime and my mate and we just

2274
01:52:01,692 --> 01:52:05,133
Speaker 24: started asking Ai, okay, is there any relevance to Snake

2275
01:52:05,133 --> 01:52:07,772
Speaker 24: Eyes and Charlie Kirk's murder? And he goes, no, no,

2276
01:52:07,812 --> 01:52:08,173
Speaker 24: there's nothing.

2277
01:52:08,572 --> 01:52:09,093
Speaker 17: Okay, cool.

2278
01:52:09,293 --> 01:52:13,372
Speaker 24: What was the executioner's name? And it was like Charlie Kirkland,

2279
01:52:13,612 --> 01:52:16,333
Speaker 24: It's pretty close. And then you go, okay, there's a

2280
01:52:16,372 --> 01:52:20,012
Speaker 24: boxing match in that movie. And then you start asking them, okay,

2281
01:52:20,093 --> 01:52:22,812
Speaker 24: what was the box's name? I wanted the name was Tyler?

2282
01:52:22,973 --> 01:52:23,452
Speaker 3: Okay, cool?

2283
01:52:23,532 --> 01:52:25,452
Speaker 17: Does he have a nickname? And then Ai comes back

2284
01:52:25,452 --> 01:52:27,692
Speaker 17: and says, no, he doesn't have a nickname, okay, because

2285
01:52:27,732 --> 01:52:30,812
Speaker 17: I thought the nickname was the executioner or the terminator

2286
01:52:30,933 --> 01:52:33,572
Speaker 17: or something. And it comes back and clarifies and says that, no, No,

2287
01:52:33,772 --> 01:52:34,652
Speaker 17: the actual thing was.

2288
01:52:34,812 --> 01:52:39,293
Speaker 24: They there's something to the effect of the exterminator or

2289
01:52:39,812 --> 01:52:41,852
Speaker 24: the something along the lines.

2290
01:52:41,652 --> 01:52:45,213
Speaker 3: Of a what do they call when they're shooter assassin?

2291
01:52:46,093 --> 01:52:49,293
Speaker 17: Yeah, something like that, you know, Tyler, the assassin, whatever

2292
01:52:49,412 --> 01:52:51,213
Speaker 17: his name was. But it was quite funny because it

2293
01:52:51,213 --> 01:52:53,333
Speaker 17: didn't want to answer. I knew the answer already, and

2294
01:52:53,412 --> 01:52:55,933
Speaker 17: you ask these questions and it doesn't answer. It just

2295
01:52:56,013 --> 01:52:57,772
Speaker 17: goes around and he goes, oh, yeah, actually, you got

2296
01:52:57,812 --> 01:52:59,572
Speaker 17: me right, I'm wrong you're.

2297
01:52:59,492 --> 01:53:02,372
Speaker 3: Right boozed it. It's one's certain.

2298
01:53:02,652 --> 01:53:05,293
Speaker 2: And then you pointed out that it's wrong, and then

2299
01:53:05,333 --> 01:53:07,213
Speaker 2: it says, oh, I should have spotted that, and then

2300
01:53:07,253 --> 01:53:10,932
Speaker 2: it throws something else that's wrong out, and it's about.

2301
01:53:10,732 --> 01:53:14,372
Speaker 17: That almost then and it goes, I've got nothing.

2302
01:53:14,612 --> 01:53:16,813
Speaker 16: I mean, come on, this is a good conversation.

2303
01:53:17,093 --> 01:53:19,412
Speaker 3: Yeah, and it's so polite when it does it as well.

2304
01:53:19,692 --> 01:53:21,732
Speaker 3: You bang on and say, oh, you're so right. I

2305
01:53:22,013 --> 01:53:24,253
Speaker 3: forgot about that element, and then here's the here's some more.

2306
01:53:25,173 --> 01:53:27,412
Speaker 17: I'm polite talking to it. I always say please and

2307
01:53:27,532 --> 01:53:30,692
Speaker 17: take yeah, why I always do. It's quite funny.

2308
01:53:30,893 --> 01:53:33,732
Speaker 3: Yeah, thank you, thank you so on your fellow Philip.

2309
01:53:33,853 --> 01:53:36,293
Speaker 2: Yeah, there has been talk that all the pleases and

2310
01:53:36,372 --> 01:53:38,572
Speaker 2: thank you are using up a huge amount of energy.

2311
01:53:39,572 --> 01:53:41,293
Speaker 2: It's about the worst thing you can do for the environment.

2312
01:53:41,333 --> 01:53:43,492
Speaker 2: And be polite to a freedom like a dog.

2313
01:53:43,612 --> 01:53:47,333
Speaker 3: It's good for it's good for the planet. All right,

2314
01:53:47,412 --> 01:53:48,852
Speaker 3: take you more of your calls. I know one hundred

2315
01:53:48,853 --> 01:53:50,612
Speaker 3: and eighty ten eighty if you want to take through

2316
01:53:50,652 --> 01:53:53,333
Speaker 3: nine two ninety two is that number? How is AI

2317
01:53:53,492 --> 01:53:56,972
Speaker 3: transforming your workplace? Was the fear about it taking jobs left,

2318
01:53:57,133 --> 01:54:00,612
Speaker 3: right and center unfounded or has it not come to pass?

2319
01:54:00,692 --> 01:54:01,253
Speaker 3: Love to hear from you?

2320
01:54:01,333 --> 01:54:05,333
Speaker 1: It is nine to four the big stories, the big issues,

2321
01:54:05,572 --> 01:54:09,492
Speaker 1: the big trends, and everything in between. Heathan Tyler Adams

2322
01:54:09,612 --> 01:54:11,852
Speaker 1: Afternoons used TALKSB.

2323
01:54:12,333 --> 01:54:16,452
Speaker 3: News Talks AB. We are talking about artificial intelligence increasing

2324
01:54:16,532 --> 01:54:20,333
Speaker 3: productivity and in some cases increasing the number of jobs.

2325
01:54:20,452 --> 01:54:22,652
Speaker 3: Is that what you're found in your workplace? Hey, guys

2326
01:54:22,812 --> 01:54:23,093
Speaker 3: is Peter.

2327
01:54:23,213 --> 01:54:26,453
Speaker 2: AI is just in at sympancy, with biggest current limitations

2328
01:54:26,492 --> 01:54:28,372
Speaker 2: being the number of people who actually understand how to

2329
01:54:28,452 --> 01:54:30,612
Speaker 2: use it. Think spreadsheets. It took years for people to

2330
01:54:30,692 --> 01:54:34,533
Speaker 2: understand them before becoming widely used then indispensable. In my

2331
01:54:34,653 --> 01:54:38,493
Speaker 2: humble opinion, effectively deployed AI could have provided a life

2332
01:54:38,533 --> 01:54:43,212
Speaker 2: saving early warning systems for recent landslides instead of relying

2333
01:54:43,333 --> 01:54:47,972
Speaker 2: on human decisions. By the way, the most recent comment

2334
01:54:47,973 --> 01:54:50,413
Speaker 2: from all improves, he knows nothing about AI. It's already

2335
01:54:50,453 --> 01:54:53,213
Speaker 2: well beyond llms, which are just one form of AI.

2336
01:54:53,573 --> 01:54:56,373
Speaker 2: Neural networks are another form of AI. Stick jobs like

2337
01:54:56,772 --> 01:54:58,293
Speaker 2: plumber or electricians.

2338
01:54:58,293 --> 01:55:00,613
Speaker 3: Stick to jobs if you don't want to be threatened

2339
01:55:00,653 --> 01:55:04,533
Speaker 3: by AI. Chairs Peter, Yeah, very interesting and this one, guys,

2340
01:55:04,613 --> 01:55:08,892
Speaker 3: AI improves improves most processes with human overview. So if

2341
01:55:08,933 --> 01:55:11,772
Speaker 3: it improves outcomes, then just do it. As performances everything

2342
01:55:11,812 --> 01:55:15,213
Speaker 3: New Zealand is too far behind globally already. Soon AI

2343
01:55:15,293 --> 01:55:20,173
Speaker 3: surgeons will take over, as will AI robots. Still hate AI.

2344
01:55:20,333 --> 01:55:23,773
Speaker 2: Whatever you guys say, says this Texan okay, so whatever

2345
01:55:23,812 --> 01:55:25,373
Speaker 2: we say that person says hate AI.

2346
01:55:25,573 --> 01:55:29,413
Speaker 3: Yep to let AI hate out there. Hi, Matt and Tyler.

2347
01:55:29,453 --> 01:55:32,453
Speaker 2: Your topic re AI jobs as per the predictions of economists?

2348
01:55:32,573 --> 01:55:35,493
Speaker 2: Why did God invent economists because he wanted to make

2349
01:55:35,573 --> 01:55:36,852
Speaker 2: weather forecasters look good?

2350
01:55:37,893 --> 01:55:42,093
Speaker 3: It's not bad. Well done right, Thank you very much.

2351
01:55:42,213 --> 01:55:43,413
Speaker 3: That was a great discussion.

2352
01:55:43,573 --> 01:55:46,493
Speaker 2: And also the economists putting a headline AI is not

2353
01:55:46,573 --> 01:55:49,653
Speaker 2: going to steal your job after having four thousand articles

2354
01:55:49,653 --> 01:55:52,333
Speaker 2: saying it will steal your job, got us to click

2355
01:55:52,373 --> 01:55:54,892
Speaker 2: on it and talk about it. So economists is probably

2356
01:55:54,973 --> 01:55:57,893
Speaker 2: using AI to decide what AI stories we want to

2357
01:55:57,933 --> 01:55:58,333
Speaker 2: talk about.

2358
01:55:58,413 --> 01:56:01,932
Speaker 3: Yeah, well create economist. Yeah all right, ladies and gentlemen.

2359
01:56:02,013 --> 01:56:05,413
Speaker 2: That brings us to the end of a Matt Heath

2360
01:56:05,493 --> 01:56:08,533
Speaker 2: and Tyler Adams afternoons on news Talk z B the

2361
01:56:08,573 --> 01:56:12,333
Speaker 2: Wonderful Heather Duple. See Ellen is up next. We'll be

2362
01:56:12,413 --> 01:56:19,573
Speaker 2: back tomorrow live twelve to four my News Talks, he'd

2363
01:56:19,613 --> 01:56:22,573
Speaker 2: be so please tune in. We love to hang We

2364
01:56:22,613 --> 01:56:24,613
Speaker 2: would love to hang out with you again tomorrow. But

2365
01:56:24,733 --> 01:56:27,852
Speaker 2: right now, Tyler, why am I playing this Bruce Springsteen song?

2366
01:56:28,173 --> 01:56:31,453
Speaker 3: What a lovely song my hometown? Because of course we

2367
01:56:31,653 --> 01:56:35,733
Speaker 3: had a full noise, wide ranging discussion that started off

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01:56:35,812 --> 01:56:38,653
Speaker 3: about the influence of celebrity opinion when it comes to

2369
01:56:38,733 --> 01:56:42,932
Speaker 3: politics because he's written this other song. But when everywhere

2370
01:56:42,973 --> 01:56:45,693
Speaker 3: that discussion, we loved it. I loved it. Well, tell

2371
01:56:45,733 --> 01:56:47,852
Speaker 3: you what I love the song. So let's shut up

2372
01:56:47,893 --> 01:56:49,853
Speaker 3: and play it. See tomorrow, see you tomorrow.

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01:56:57,733 --> 01:57:00,853
Speaker 14: Sixty running.

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01:57:08,413 --> 01:57:10,413
Speaker 13: And there was not a.

2375
01:57:14,453 --> 01:57:15,613
Speaker 3: Madam Tyler Tyler.

2376
01:57:17,413 --> 01:57:19,973
Speaker 1: For more from News Talks at b listen live on

2377
01:57:20,133 --> 01:57:23,052
Speaker 1: air or online, and keep our shows with you wherever

2378
01:57:23,133 --> 01:57:25,693
Speaker 1: you go with our podcasts on iHeartRadio.