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

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Speaker 1: Follow this and our wide range of podcasts now on iHeartRadio.

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Speaker 2: All Right, you Great New Zealand's Welcome to Matt and

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Speaker 2: Tyler Full Show Podcast number.

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Speaker 3: Six.

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Speaker 2: He's for the twenty first of October twenty twenty five.

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Speaker 2: Really good, deep intense show went really deep on artificial

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Speaker 2: intelligence and the chances of terrible things happening or good

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Speaker 2: things happening. But we didn't get round to whether you

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Speaker 2: should let your kids win or not. Yeah, in terms

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Speaker 2: of resilience and as they're growing up. So we'll get

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Speaker 2: to that one tomorrow.

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Speaker 4: Looking forward to that.

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Speaker 2: But we wallow out into two hours on artificial intelligence.

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Speaker 4: Absolutely rinse. It was a great chat though, so download,

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Speaker 4: subscribe and give us a great review.

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Speaker 2: And you've seen bazill let you go, so give them

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Speaker 2: a taste.

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Speaker 4: K all right, then we love you little.

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Speaker 1: Big stories, league issues, the big trends and everything in between.

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Speaker 1: Matt Heath and Taylor Adams Afternoons News Talk sa'd.

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Speaker 4: Be very good. Afternoons youre welcome into Tuesday. Really good

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Speaker 4: of your company as always, Get a met.

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Speaker 2: Get a Tyler, Get a Great New Zealander with you're

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Speaker 2: listening across our beautiful nation. Got a great three hours

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Speaker 2: of talk back coming your way, So looking forward to

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Speaker 2: having some good conversations on one hundred and eighty ten

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Speaker 2: eighty and ninety two ninety two as.

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Speaker 4: Usual, huge show for you. Just before we get to that,

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Speaker 4: I've got a question for you, met and for our listeners.

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Speaker 4: Now it's a funny day to ask this, but it

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Speaker 4: has been starting to get quite beautiful and sunny and warm,

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Speaker 4: and we're gonna hopefully get more of that as we

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Speaker 4: get closer into the summer months, and of course that

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Speaker 4: means barbecue season. I don't have a barbecue at the moment,

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Speaker 4: so I'm knee deep in. I didn't realize how many

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Speaker 4: options that we're out there. I'm knee deep in trying

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Speaker 4: to find a barbecue, and everybody's been telling me to

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Speaker 4: buy a webber. But I've got to tell you, I'm

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Speaker 4: liking the look of the old jumbuck.

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Speaker 2: How sort of bigger barbecue you going for? You going

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Speaker 2: for those? One sort of a monster one about as

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Speaker 2: big as an indoor kitchen kind of situation?

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Speaker 4: Now, nice, wouldn't that? I mean, that's that's why I'm

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Speaker 4: throwing this question out because I'd love to go full hog.

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Speaker 2: But look, my neighbors put a full hog barbecue on

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Speaker 2: has Berm and it's been sitting there for about two weeks.

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Speaker 4: Oh, where's address?

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Speaker 2: Well, I wouldn't want to give out my address on

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Speaker 2: the air.

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Speaker 4: Oh not your address.

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Speaker 2: Yeah, but if I give out my neighor's address, it's

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Speaker 2: my address.

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Speaker 4: Okay, yeah, good point on.

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Speaker 2: The house beside my neighbor.

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Speaker 4: If it's still there, just put my name on it

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Speaker 4: and I'll be around.

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Speaker 2: I do know the fact that's been out there for

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Speaker 2: so long. I mentioned there's been a few tire kickers

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Speaker 2: that have decided to reject it.

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Speaker 4: But the old jump back. I mean, look, everybody knows

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Speaker 4: I don't like to spend too much money and I

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Speaker 4: want the biggest barbie barbecue I can get for the

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Speaker 4: lowest price. But if anyone out there is rocket out

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Speaker 4: jump buck, how big do you need it? I want

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Speaker 4: at least a six burner. I really got to have

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Speaker 4: a six burner. We are we thing on the side

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Speaker 4: for the pan.

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Speaker 2: Yeah right, I'm a big fan. I'm running the Ziggler

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Speaker 2: and Brown, The Ziggler and Brown zigg by Ziggler and

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Speaker 2: Brown see this is good stuff. That is a great

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Speaker 2: barbe It's not a big barbecue, but I've been very

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Speaker 2: impressed with its performance. I've got to say over that,

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Speaker 2: you know, it's kind of like a webber. Yeah, but

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Speaker 2: I've done some good stuff, mate, I'm.

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Speaker 4: Just oh Jesus, the sixty looking six.

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Speaker 2: I've done some good stuff with the fillets on that.

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Speaker 4: Okay, Ziggler and Brown going right to the top of

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Speaker 4: the list, I think. But again, I know I've been

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Speaker 4: talking about jump Buck, and this is an ad for

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Speaker 4: jump Buck.

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Speaker 2: I don't know what jump Buck is. I just want

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Speaker 2: to jump. I just jump back jump you.

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Speaker 4: Yeah, it's it's let's call it a more budget option,

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Speaker 4: but whether it's worth.

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Speaker 2: It budget if you're looking at it exactly.

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Speaker 4: So, I just need some reassurance that if I go

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Speaker 4: jump Buck, it's not going to fall a part on

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Speaker 4: me within two weeks.

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Speaker 2: So you're going for like the full Yeah, you're going

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Speaker 2: for some kind of massive, big deal. What I mean,

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Speaker 2: how many it's only you and may how many people

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Speaker 2: you need to feed with that?

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Speaker 4: But you know it's a status symbol, wasn't it. You know,

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Speaker 4: when if nobody nobody comes around my house because I

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Speaker 4: don't have any friends.

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Speaker 2: But if they did, it then be able to say

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Speaker 2: I'm your friend.

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Speaker 4: Yeah, you are my friend mate. No that's not quite true,

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Speaker 4: but yeah, it's a status symbol. You want to be

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Speaker 4: proud of your barbecue.

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Speaker 2: Well, I'm proud of my little my little Ziggi.

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Speaker 4: As you should be. Right on to today's show after

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Speaker 4: three o'clock, we want to even chat about whether you

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Speaker 4: should ever let your children win and a competition. Now,

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Speaker 4: this was after a Jorgan Clop, very successful longtime manager

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Speaker 4: of LIVERPOOLFC. He is phenomenal what he has done for

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Speaker 4: that particular football team. But he sat down for this

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Speaker 4: podcast it's called Diary of a CEO and he talked

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Speaker 4: about his upbringing and he said his father was a

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Speaker 4: traveling salesman and a former amateur goalkeeper himself, and he

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Speaker 4: had big expectations for a young Yorgan Klop, and he

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Speaker 4: said he was a bit offraid that Jorgan might not

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Speaker 4: be ambitious enough and wanted his son to be a sportsman,

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Speaker 4: excelling in everything from soccer to tennis to skian. Then

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Speaker 4: he was asked about whether his father ever less him

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Speaker 4: winned anything, and here's what he said, who.

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Speaker 2: Knows if it was right. Probably was right.

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Speaker 5: I don't know if it was not nice in a

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Speaker 5: way when you tell the story, it's like, my god, Gamma,

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Speaker 5: let the poor boy win or whatever.

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Speaker 2: It had no chance.

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Speaker 5: It's just you stand on the touch line and you're

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Speaker 5: onto the halfway line.

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Speaker 6: And when you when you look back over the course

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Speaker 6: of your career, are there moments where you have flashback

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Speaker 6: to lessons that he taught you, or principles or values

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Speaker 6: that he taught you that you think, gosh, I got

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Speaker 6: that from my dad, the ones kill.

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Speaker 5: I realized that my dad had without knowing that time.

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Speaker 5: It was a skill he could speak publicly. You don't

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Speaker 5: know that you have that, but I have it so victively.

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Speaker 4: He said that his father never let him win at

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Speaker 4: the skiing racers, at the running races, no matter what,

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Speaker 4: his father always beat him if he could, to try

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Speaker 4: and build resilience, I think was his points.

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Speaker 2: Yeah, Base, you can't let your kids win. It's dishonest

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Speaker 2: and it doesn't it's dishonest, and it doesn't encourage growth,

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Speaker 2: and it definitely does not prepare them for reality because

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Speaker 2: no one else is going to let you win in life.

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Speaker 2: So what are you doing? What are you doing as

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Speaker 2: a parent. You're trying to build competence in your kids.

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Speaker 2: That's what you're trying to do. And look, you can't

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Speaker 2: absolutely smash them. And you know, chin music with a

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Speaker 2: hard cricket ball at the age of three is probably

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Speaker 2: not the best way to go because there is a balance.

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Speaker 2: You want them to be confident, but you just can't

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Speaker 2: give them a pass absolutely, and you can't tell them

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Speaker 2: if they didn't play well. And that's why everyone knows

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Speaker 2: that the player of the day thing actually needs to

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Speaker 2: be given to the player of the day. But if

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Speaker 2: they didn't play well, you've got to say on the

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Speaker 2: way home that they didn't play well. I mean I

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Speaker 2: used to get a good serve in the car on

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Speaker 2: the way home if I hadn't helped out. Gosh. I

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Speaker 2: remember once my football team lost and I tried to

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Speaker 2: blame the goalie, and my father has never been so angry.

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Speaker 2: He said, that goal was scored because you were useless,

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Speaker 2: not because the goalie. The ball should never got anywhere

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Speaker 2: near the goalie.

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Speaker 4: It's a big ghost of reality, but that is real life.

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Speaker 2: It's all about resilience. Yeah, I mean, that's what you're doing,

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Speaker 2: and try and encourage your kids to grow and just

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Speaker 2: giving them a free parcel the time. No, no, anyway

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Speaker 2: that it's about out after three.

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Speaker 4: Yeah, looking forward to that after two o'clock. Is artificial

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Speaker 4: intelligence going to destroy jobs? Or maybe create some new

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Speaker 4: ones that we don't expect? New research shows the labor

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Speaker 4: market disruption is clearly coming when it comes to AI.

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Speaker 4: But the true challenge, they say, for governments and businesses

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Speaker 4: is ensuring that workers have the skills and apt adaptability

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Speaker 4: needed to use the technology and to face the future

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Speaker 4: with AI.

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Speaker 2: Yeah, so this is an interesting one. We've had AI

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Speaker 2: for a while now, readily accessible since probably the end

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Speaker 2: of twenty twenty two. Everyone's been jumping on it. So

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Speaker 2: we've got a bit of an angle on it now,

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Speaker 2: I've got a head around it. We've seen it settling

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Speaker 2: a little bit into systems. So do you agree that

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Speaker 2: it won't wipe out your job? It will just transform it?

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Speaker 2: Are you retraining or is your work retraining you? Or

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Speaker 2: is it just eliminating people? And you're seeing more and

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Speaker 2: more empty desks around your workplace ndred and eighteen eighty.

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Speaker 4: Looking forward to that after too. But right now, let's

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Speaker 4: have a chat about your emergency preparedness, so as we know,

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Speaker 4: more than seven thousand, five hundred homes and businesses without

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Speaker 4: power as the storm hits the Low North Island and

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Speaker 4: the top of the South Sadly, one person has died

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Speaker 4: after being hit by a falling branch on Wellington's Mount Victoria.

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Speaker 4: Several main roads are closed in the South Island this morning.

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Speaker 4: Extensive orange weather warnings remain in Damaging gales of up

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Speaker 4: to one hundred and thirty k's are expected during a

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Speaker 4: week of wild weather. Pretty significant.

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Speaker 2: Yeah, and this kind of circles background to the barbecue question, really,

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Speaker 2: how long can you last without power? Do you have

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Speaker 2: a power back up? Do you have ways to cook

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Speaker 2: and prepare food? Have you got an emergency set up?

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Speaker 2: As I said before, I have absolutely no plan or

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Speaker 2: set up, and I should probably because there seems to

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Speaker 2: be quite a few power outages in this country, not

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Speaker 2: just emergency and weather events, but with the grid under

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Speaker 2: so much pressure, there could be power. That's how long

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Speaker 2: could you survive without without the without electricity and a

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Speaker 2: good barbecue with a couple of LPG canisters floating around.

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Speaker 4: That'll keep you going for at least a couple of days.

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Speaker 2: Or this Busines says, man up and get a charcoal barbecue.

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Speaker 2: If you've got a lot of charcoal on the side. Boy,

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Speaker 2: there's a lot of people that want to talk about

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Speaker 2: barbecues as well, so I think. I think, but the

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Speaker 2: two topics kind of work together because a barbecue and

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Speaker 2: if you've got an electric stove and the power goes down,

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Speaker 2: then having a good barbecue to set up on the deck.

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Speaker 4: Yep with an LPG cylinder ready to go. Yeah, yeah,

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Speaker 4: Well charcoal or charcoal is some of those texts texting

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Speaker 4: and oh eighte hundred eighty ten eighty is the number

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Speaker 4: to call. How prepared are you? How long could you

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Speaker 4: last without power? And what have you got in your

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Speaker 4: emergency kit? Or what have you got to keep power

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Speaker 4: on your home? Really can never chat with you.

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Speaker 2: On the topic that we're talking about. After two the

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Speaker 2: sticks Is says, unfortunately AI is way too smart to

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Speaker 2: be able to replace Matt.

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Speaker 4: It's good.

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Speaker 2: You can do it down, you can choke it.

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Speaker 4: Don't fight it, mate. It is quarter past one, be

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Speaker 4: very shortly. Oh eight hundred and eighty ten eighties the

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Speaker 4: number to call.

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Speaker 1: The big stories, the big issues, the big trends, and

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Speaker 1: everything in between. Matt Heath and Taylor Adams afternoons used

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Speaker 1: talks B news talks.

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Speaker 4: It be eighteen pass one. Can I mention what you

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Speaker 4: just said?

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Speaker 2: Yeah?

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Speaker 4: So Matte just said, I you have you got to

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Speaker 4: get canvas tins and I said no, and then he said,

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Speaker 4: you want one of my eyes? Just let up. We

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Speaker 4: had to come back on here. But I'd love that

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Speaker 4: canvas tin mate.

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Speaker 7: Yeah.

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Speaker 2: Well, I'm just moving houses and I got myself one.

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Speaker 2: I got myself an inflatable one.

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Speaker 4: Yes, that is a nice tint.

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Speaker 2: So I've got an old it's not actually that old.

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Speaker 2: It's about five years old. That that, you know, the

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Speaker 2: old school pigs sort of one.

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Speaker 4: Here here, Yeah, this is a great day.

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Speaker 2: It's the Kakapo five, I believe.

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Speaker 4: Oh that's that's a good tint.

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Speaker 2: Yeah here here yeah.

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Speaker 4: Is that a full mana.

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Speaker 2: Yep, yeah, yep, No, it's a five five? Oh this

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Speaker 2: is good. Yeah.

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Speaker 4: I mean talking about emergency preparedness. I could live in

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Speaker 4: that sleeps five Yeah, good times. All right, I'll take it.

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Speaker 4: We'll sort out the details later on.

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Speaker 2: In the case of a storm, it's not a good

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Speaker 2: idea to just abandon the house and go out and

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Speaker 2: sleep in a tent on the front. That is a

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Speaker 2: good point safety.

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Speaker 4: There's not official advice there jumping into the tent. Kates,

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Speaker 4: how are you this afternoon?

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Speaker 7: God?

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Speaker 4: How are you very good? So you're facing a bit

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Speaker 4: of winding Carterton at the moment, are you?

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Speaker 8: Oh?

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Speaker 9: Yeah, Well we ended up with a day off. Well,

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Speaker 9: I ended up with the day off. The POW's been

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Speaker 9: off since about half the six this morning. Oh and

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Speaker 9: of course so much wind as a roof in the

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Speaker 9: main street and cartertain that look very dodgy. So we

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Speaker 9: weren't allowed into our building. I worked for a business

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Speaker 9: down on the high street and so we got an

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Speaker 9: unexpected day off. But I've got my free standing log

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Speaker 9: burner going. I've got my little but tane gas bottle

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Speaker 9: with its little stands. I've been able to water and

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Speaker 9: have cups of tea. And yeah, I'm busy planning tea

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Speaker 9: and how to cook it on top of the fire.

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Speaker 2: And so how long do you think you how do

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Speaker 2: you think? How long do you think you could comfortably last?

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Speaker 2: For Kate? What kind of supplies have you got?

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Speaker 9: Well, we've got all sorts of ways of looking after

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Speaker 9: things and keeping going. But my biggest worries all the

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Speaker 9: food that starts to spoil you. Yeah, that's the biggest concern,

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Speaker 9: and also the fact we can't really function at work

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Speaker 9: without being able to plug your laptops in and get

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Speaker 9: your printers going and like that.

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Speaker 2: But are you're missing that's online a lot, Kate.

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Speaker 9: Oh yeah, yeah. I went from all firm over here

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Speaker 9: and we're constantly so you're missing that.

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Speaker 2: I mean, are you missing that? I mean, I assume

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Speaker 2: your WiFi is down.

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Speaker 9: Oh yeah, yeah, but I'm just enjoying it.

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Speaker 10: I think it would be.

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Speaker 4: Good for you.

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Speaker 2: Yeah, yeah, Well, are there any hacks about that? I mean,

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Speaker 2: that's an interesting one about keeping food from because you know,

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Speaker 2: once your fridge goes off, that's I mean, when you

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Speaker 2: freezer defrost, that's a massive disaster. I don't know. I

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Speaker 2: have this house and it had this kind of box

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Speaker 2: that hung out. It had been it had been covered

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Speaker 2: over in the kitchen, but sort of I think it's

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Speaker 2: got a meat box that hung out so you can

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Speaker 2: do something. It's a bit of a I put it

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Speaker 2: in something and put it outside it.

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Speaker 4: We know.

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Speaker 9: I just I just think don't know a fridge and

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Speaker 9: freezer any more than you have to that you have

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Speaker 9: them keep it in there. So yeah, just fingers and

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Speaker 9: toes crossed. But yeah, they said that we were supposed

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Speaker 9: to have it back on by about courts to eleven,

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Speaker 9: but here when still ourself.

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Speaker 2: Yeah, that's that's that's rough. Do you motivate you to

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Speaker 2: maybe think about getting some kind of generator system? You

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Speaker 2: know you can you can have you can have your

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Speaker 2: you know, your your gas generators or your petrol generators,

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Speaker 2: but you can also get your just those those new

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Speaker 2: or just plug in battery ones.

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Speaker 9: I suppose there's a certain sense in that, just for

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Speaker 9: no other reason, just keep your fridge freezer going at

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Speaker 9: the end of the day, and we've got ways of

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Speaker 9: feeding ourselves and stuff. We'll just crank the barbecue up.

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Speaker 4: But you know, you can get the and this is

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Speaker 4: an ad for them. It's just the only brand I

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Speaker 4: can think of. I think they're called brass monkeys that

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Speaker 4: are effectively electric callers that you can plug into your

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Speaker 4: car and they run off your car battery. But those things,

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Speaker 4: you know, that primarily for camping, but you just plug

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Speaker 4: them into the old nine vault and that will keep

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Speaker 4: you going for as long as your car battery loss

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Speaker 4: isn't that's one I've.

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Speaker 9: Never heard of it. The only brass monkey I know

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Speaker 9: is remember there was that saying, freeze the balls off

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Speaker 9: a breath monkey. Yeah yeah, and that's not being rude.

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Speaker 9: What it is the breath monkey is what these have

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Speaker 9: on sailing ships. And it was like a contraption that

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Speaker 9: used to hold the cannon balls gold. It throws the

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Speaker 9: balls off a breath monkey.

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Speaker 4: Interesting, good learns from that new today. Absolutely, so hang

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Speaker 4: on a minute.

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Speaker 2: So the brass monkey contracted and as a result, the

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Speaker 2: balls popped out.

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Speaker 9: Well, no, just whatever happened. It was this this container

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Speaker 9: on board the sailing ship that held stacks of cannon balls,

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Speaker 9: and when it got so cold, the balls would freeze. Yeah,

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Speaker 9: on these brass monkeys, and then they couldn't use them, right, all.

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Speaker 4: Right, the things you learn on this show.

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Speaker 2: Okay, yeah, well I tell you good good luck here

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Speaker 2: in good work.

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Speaker 9: It's so good to have you guys back on. We've

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Speaker 9: been without there'd be all morning. So I've been having

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Speaker 9: severe withdraw symptoms.

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Speaker 2: How are you listening?

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Speaker 4: If you if you oh on the wireless.

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Speaker 9: But when the power went out this morning, I looked

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Speaker 9: the radio as well for whether the tower in the

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Speaker 9: Wirerappa bellover. I don't know, but I need to be

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Speaker 9: in the radio on about half an hour going yeah,

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Speaker 9: we're back.

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Speaker 2: Well, good to have you along. Thank you for your

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Speaker 2: col Kate.

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Speaker 4: Yeah great call I wait one hundred and eighty ten

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00:15:44,573 --> 00:15:46,013
Speaker 4: eighties and I'm going to call how long do you

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00:15:46,013 --> 00:15:48,013
Speaker 4: think you could survive in a power cut? And what

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00:15:48,093 --> 00:15:50,013
Speaker 4: have you got is a bit of a backup? What's

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00:15:50,053 --> 00:15:51,773
Speaker 4: in your emergency kits?

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Speaker 2: Ben? How are you welcome to the show?

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Speaker 11: Are you get to go? Yeah, I've got got a

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Speaker 11: little generator. Especially it was on the marketplace. It was

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Speaker 11: the surf off so if in club and New Brighton

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Speaker 11: there they got given a whole lot after the earthquake

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Speaker 11: and there'd be something around for about ten years. And yeah,

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Speaker 11: they just flipped them off and I'll put it up

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Speaker 11: for three hundred bucks and that's a three killer what one?

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Speaker 11: And it was great the other year when we had

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Speaker 11: a lot of power cuts because there's three killer I

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Speaker 11: can run a heater, the TV, the fridge and the internet.

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Speaker 11: It made them off it and yeah, no, we're good.

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Speaker 11: Airs like to a twenty liter? Can a guess the

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00:16:32,533 --> 00:16:33,933
Speaker 11: last twenty four hours?

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Speaker 2: Right? And have you got do you keep a good

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Speaker 2: supply of gas on on on premises.

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Speaker 11: I've got a barbecue, so yeah, there's always got a

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Speaker 11: full teenth. But apart from that, you know, as long

384
00:16:45,293 --> 00:16:47,813
Speaker 11: as you've got the generator, you can run pretty much.

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Speaker 11: You can't run even and get it once because they'll

386
00:16:49,733 --> 00:16:52,413
Speaker 11: overload it, but you can run, you know, you can.

387
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Speaker 11: You can keep yourself going as long as you've got

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Speaker 11: a guess support.

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Speaker 4: Yeah, and you've got a bit of solar as well,

390
00:16:57,733 --> 00:17:00,933
Speaker 4: being which obviously adds to the keeping yourself going.

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Speaker 11: Yeah, we got solid during the day and that just

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Speaker 11: feeds the grid to keep the power buill down. But

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Speaker 11: we don't have the battery right, Your batteries are too expensive,

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00:17:09,213 --> 00:17:11,173
Speaker 11: but the solo does keep the power bull right.

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Speaker 2: Now, have you got this with with this in mind,

396
00:17:14,973 --> 00:17:17,053
Speaker 2: with emergency in mind?

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Speaker 10: Or is it?

398
00:17:18,013 --> 00:17:19,973
Speaker 2: Is it just something that you've got going.

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Speaker 11: Just a lot of the guys up work. We're checking

400
00:17:23,293 --> 00:17:25,413
Speaker 11: solar panels on the roof and getting fifty all the

401
00:17:25,413 --> 00:17:28,093
Speaker 11: power bills and stuff, and I'm like, well, I'm into that.

402
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Speaker 11: So it's up to mine. About five years four or

403
00:17:30,613 --> 00:17:34,013
Speaker 11: five years ago, I haven't had a power over one

404
00:17:34,053 --> 00:17:35,013
Speaker 11: hundred and twenty Bucks.

405
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Speaker 8: Since nice.

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00:17:35,973 --> 00:17:38,693
Speaker 2: How long do you reckon you could go comfortably and

407
00:17:38,733 --> 00:17:40,973
Speaker 2: a complete power outage in your area? Ben?

408
00:17:43,133 --> 00:17:45,253
Speaker 11: Probably probably a week, maybe a week and a half.

409
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Speaker 11: I don't keep it. I don't keep much food in

410
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Speaker 11: the cupboards, you know. I like to go to the

411
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Speaker 11: grocery story for a couple of days. So, yeah, a.

412
00:17:53,613 --> 00:17:55,733
Speaker 4: Week's pretty good. I think that would be better than

413
00:17:55,813 --> 00:17:58,173
Speaker 4: ninety nine percent of New Zealanders.

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Speaker 2: Do you have any other survival supplies? Ben?

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Speaker 8: Have you?

416
00:18:02,533 --> 00:18:04,413
Speaker 2: Are you running any other kind of set up, any

417
00:18:04,413 --> 00:18:05,013
Speaker 2: other kind of kit?

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00:18:05,853 --> 00:18:08,333
Speaker 11: Yeah, we'll be actually good caravan that we use it,

419
00:18:09,173 --> 00:18:10,973
Speaker 11: you know, used to go away and it's got a

420
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Speaker 11: solar panel on the roof, and it's got a caller

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00:18:12,773 --> 00:18:15,653
Speaker 11: and share and stuff enough, So yeah, whisk the risk.

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Speaker 11: You're just going to gout to the care of the Yeah.

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Speaker 2: You gay got to sort it. You're good for the apocalypse,

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Speaker 2: go yeah.

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00:18:20,133 --> 00:18:21,893
Speaker 4: Oh one hundred and eighty ten eighty is the number

426
00:18:21,893 --> 00:18:24,533
Speaker 4: to call. How long do you think you could survive

427
00:18:24,573 --> 00:18:27,173
Speaker 4: in a power cut? Or live comfortably in a power cut?

428
00:18:27,493 --> 00:18:30,213
Speaker 4: It is twenty six past one? Nine nine two is

429
00:18:30,293 --> 00:18:31,173
Speaker 4: the text number.

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Speaker 8: Oh.

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00:18:31,413 --> 00:18:33,213
Speaker 2: This explains that because I was trying to work out

432
00:18:33,213 --> 00:18:35,013
Speaker 2: how the brass and monkey worked with the contraction. A

433
00:18:35,053 --> 00:18:37,693
Speaker 2: brass monkey was a triangle that stacked cannonballs. When it

434
00:18:37,733 --> 00:18:40,373
Speaker 2: got really cold, the triangle would shrink in the canniboards

435
00:18:40,533 --> 00:18:44,093
Speaker 2: would fall off. Ah nice, cheers, Andy, All right, freeze times.

436
00:18:44,693 --> 00:18:46,093
Speaker 2: How is the one with the witch work.

437
00:18:48,213 --> 00:18:52,453
Speaker 1: The headlines and the hard questions? It's the mic asking breakfast.

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00:18:52,613 --> 00:18:55,093
Speaker 2: Those budgets? How lockdown are they? I mean, how open

439
00:18:55,173 --> 00:18:56,453
Speaker 2: to creep are they?

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Speaker 12: So the Transport Agency has taken a very conservative approach

441
00:18:59,493 --> 00:19:01,973
Speaker 12: to the budget. They're what's called the P ninety five

442
00:19:02,093 --> 00:19:04,333
Speaker 12: rather than P fifty, which technically means there are a

443
00:19:04,373 --> 00:19:06,653
Speaker 12: lot more confidence around the numbers than they are with

444
00:19:06,933 --> 00:19:07,533
Speaker 12: a P fifty.

445
00:19:07,573 --> 00:19:09,013
Speaker 13: So they've done quite a work.

446
00:19:08,813 --> 00:19:11,413
Speaker 12: And it's they're conservatives, but there's no doubt that their

447
00:19:11,453 --> 00:19:12,613
Speaker 12: expensive projects mike for them.

448
00:19:12,613 --> 00:19:13,213
Speaker 3: Okay about that?

449
00:19:13,413 --> 00:19:14,573
Speaker 2: How long is this stage?

450
00:19:14,853 --> 00:19:17,373
Speaker 12: So what's happening now as we're going through a prioritization

451
00:19:17,453 --> 00:19:19,693
Speaker 12: exercise as a government. We've now got all of the

452
00:19:19,733 --> 00:19:21,493
Speaker 12: investment cases and we're going to go through a bit

453
00:19:21,493 --> 00:19:23,413
Speaker 12: of a process of the government. You can't build everything

454
00:19:23,453 --> 00:19:25,013
Speaker 12: all at once, and we've always said that.

455
00:19:26,093 --> 00:19:28,853
Speaker 2: Back tomorrow at six Am the Mike Husking Breakfast with

456
00:19:28,933 --> 00:19:30,853
Speaker 2: Baby's Real Estate News Talk z B.

457
00:19:31,133 --> 00:19:33,693
Speaker 4: Very good afternoon, Cheu. It is twenty nine past one,

458
00:19:33,733 --> 00:19:36,213
Speaker 4: so many tips coming through about emergency preparedness.

459
00:19:36,333 --> 00:19:39,133
Speaker 2: We spove seven days without power or buying food after

460
00:19:39,333 --> 00:19:42,573
Speaker 2: cyconon Gabrielle. We live in Napier. Took six days for

461
00:19:42,613 --> 00:19:44,613
Speaker 2: the meat to go off and the deep freeze survive

462
00:19:44,733 --> 00:19:46,013
Speaker 2: with cooking on the barbecue.

463
00:19:46,053 --> 00:19:46,453
Speaker 4: Interesting.

464
00:19:46,533 --> 00:19:49,893
Speaker 2: Good on you days, Michelle says, feeling smug. I've been

465
00:19:49,933 --> 00:19:52,373
Speaker 2: off grid for twelve years. Chuckle every time the power

466
00:19:52,413 --> 00:19:54,693
Speaker 2: goes out and people are whining on the community page.

467
00:19:55,053 --> 00:19:57,333
Speaker 2: But I do offer water and a shower, et cetera.

468
00:19:57,413 --> 00:20:01,293
Speaker 2: Good on you, so they're off the grid, never needed power.

469
00:20:01,453 --> 00:20:04,813
Speaker 2: Also on the community page, so off the grid, but

470
00:20:05,213 --> 00:20:08,293
Speaker 2: enough to power Facebook and listen to news talk. Said

471
00:20:09,093 --> 00:20:11,813
Speaker 2: I own a sixty liter brass Monkey FREDG freezer with

472
00:20:11,893 --> 00:20:15,773
Speaker 2: battery and solar blanket which is endless supplier's power. I

473
00:20:15,893 --> 00:20:18,973
Speaker 2: use camping battery never goes flat. Good old solo blanket,

474
00:20:19,053 --> 00:20:19,773
Speaker 2: sol a blanket.

475
00:20:19,813 --> 00:20:21,853
Speaker 4: Never heard of. That sounds good though. Oh eight one

476
00:20:21,893 --> 00:20:24,292
Speaker 4: hundred eighty ten eighty is the number to call if

477
00:20:24,293 --> 00:20:26,493
Speaker 4: you can't get through keep trying. We've got full boards

478
00:20:26,493 --> 00:20:28,373
Speaker 4: at the moment, but love to hear what you've got

479
00:20:28,373 --> 00:20:30,693
Speaker 4: in your emergency kit and how long can you keep

480
00:20:30,733 --> 00:20:32,453
Speaker 4: the lights on if there is a power cut or

481
00:20:32,453 --> 00:20:33,773
Speaker 4: some sort of emergency.

482
00:20:33,773 --> 00:20:36,572
Speaker 2: Hey, guys, we can live indefinitely without power as we

483
00:20:36,613 --> 00:20:39,093
Speaker 2: live in a bus mostly off grid. We don't have

484
00:20:39,093 --> 00:20:41,373
Speaker 2: a kit as it's all on hand on the bus,

485
00:20:41,533 --> 00:20:44,053
Speaker 2: so hopefully will be okay.

486
00:20:44,253 --> 00:20:45,573
Speaker 4: It's like cozy you.

487
00:20:45,573 --> 00:20:47,613
Speaker 2: An agrophobe, if you don't aergophobic, if you don't like

488
00:20:47,653 --> 00:20:50,933
Speaker 2: going outside. Yeah, it's like when when the power goes down,

489
00:20:50,973 --> 00:20:52,453
Speaker 2: the people that are off the grid, that's when they

490
00:20:52,493 --> 00:20:54,213
Speaker 2: come into their own you know this, Look at me.

491
00:20:54,373 --> 00:20:56,493
Speaker 2: Everyone's like me now, and I'm the champion. It was

492
00:20:56,493 --> 00:20:59,093
Speaker 2: like in COVID with the agrophobics, they thought they'd inherited

493
00:20:59,093 --> 00:20:59,613
Speaker 2: the world.

494
00:20:59,933 --> 00:21:01,973
Speaker 4: Yeah, that's right, now's our time.

495
00:21:02,253 --> 00:21:07,013
Speaker 2: Look at us now, I'm you know, legislated to be inside.

496
00:21:06,773 --> 00:21:09,453
Speaker 4: Twenty nine to two and a mob at taking your

497
00:21:09,453 --> 00:21:11,813
Speaker 4: CALLZ one hundred eighty ten eighty.

498
00:21:14,213 --> 00:21:17,773
Speaker 14: US talks at the headlines with blue Bubble taxis. It's

499
00:21:17,853 --> 00:21:21,253
Speaker 14: no trouble with a blue bubble. Flights have resumed in

500
00:21:21,293 --> 00:21:24,533
Speaker 14: and out of Wellington Airport after a four hour suspension

501
00:21:24,893 --> 00:21:28,853
Speaker 14: during severe winds tipping one hundred and forty kilometers an hour.

502
00:21:29,493 --> 00:21:32,693
Speaker 14: Amn's died after being hit by a falling branch on

503
00:21:32,773 --> 00:21:36,853
Speaker 14: a track on Wellington's Mount Victoria. This morning, Wellington wided

504
00:21:36,933 --> 00:21:38,973
Speaker 14: up a Hawk's Bay and the South Island are being

505
00:21:38,973 --> 00:21:42,093
Speaker 14: battered by strong winds, with power out to thousands of

506
00:21:42,133 --> 00:21:45,853
Speaker 14: homes in the Lower and central North Island. Floodings closed

507
00:21:45,893 --> 00:21:49,292
Speaker 14: Lewis Pass and a number of West Coast and Canterbury roads.

508
00:21:50,373 --> 00:21:53,893
Speaker 14: A one used correspondence permission to cover Defense Minister Judith

509
00:21:53,893 --> 00:21:58,213
Speaker 14: Collins Pentagon meeting in the US has been revoked an

510
00:21:58,333 --> 00:22:01,773
Speaker 14: order coming from the Secretary of War Office.

511
00:22:01,933 --> 00:22:03,533
Speaker 2: Torpedo seven will return.

512
00:22:03,293 --> 00:22:05,653
Speaker 14: To being an online only retailer by the end of

513
00:22:05,693 --> 00:22:08,253
Speaker 14: summer after being sold by the Warehouse Group for one

514
00:22:08,493 --> 00:22:12,733
Speaker 14: dollar in February last year. Christ Church Central's labor MP

515
00:22:12,933 --> 00:22:15,733
Speaker 14: Duncan Web is calling it quits next year. He's been

516
00:22:15,773 --> 00:22:19,853
Speaker 14: in the job since twenty seventeen. A decisive move. More

517
00:22:19,893 --> 00:22:23,013
Speaker 14: about to pe Torpedo seven to return to online only

518
00:22:23,173 --> 00:22:27,013
Speaker 14: sales you can see at z Herald Premium. Now back

519
00:22:27,013 --> 00:22:28,733
Speaker 14: to matt Ethan Tyler Adams.

520
00:22:28,413 --> 00:22:30,453
Speaker 4: Thank you very much, Raylan. So we are talking about

521
00:22:30,453 --> 00:22:33,533
Speaker 4: emergency preparedness. It's obviously pretty significant in the lower part

522
00:22:33,533 --> 00:22:35,613
Speaker 4: of the North Island and top of the South as

523
00:22:35,693 --> 00:22:37,853
Speaker 4: we speak. So what have you got in your emergency

524
00:22:37,933 --> 00:22:40,813
Speaker 4: kits and how long can you live comfortably without power?

525
00:22:40,973 --> 00:22:44,773
Speaker 2: Peter, welcome to show your thoughts on emergency preparedness if

526
00:22:44,773 --> 00:22:45,293
Speaker 2: that's a word.

527
00:22:46,853 --> 00:22:54,053
Speaker 3: Well, I've got five barbecues. Five yeah, and just for you, Tyler,

528
00:22:54,333 --> 00:22:57,893
Speaker 3: I've got two webbers, one to charcoal, one to get yep,

529
00:22:58,133 --> 00:23:00,493
Speaker 3: and agains I've had for over.

530
00:23:00,293 --> 00:23:03,173
Speaker 4: Twenty years, solid generational.

531
00:23:04,133 --> 00:23:07,053
Speaker 3: It's the que barbecue and I'm sure it will be

532
00:23:07,213 --> 00:23:10,413
Speaker 3: more as an ample spar for what you need. Brilliant,

533
00:23:10,493 --> 00:23:16,893
Speaker 3: brilliant barbecues. You can't fault them. Oh, probably a lot

534
00:23:16,893 --> 00:23:17,933
Speaker 3: more than what you're going to afford.

535
00:23:18,613 --> 00:23:21,613
Speaker 4: That's no jumper because I wouldn't.

536
00:23:21,253 --> 00:23:22,813
Speaker 3: Have pad for over twenty years if I was going

537
00:23:22,853 --> 00:23:23,413
Speaker 3: to sell it to you.

538
00:23:25,413 --> 00:23:26,053
Speaker 10: So there you go.

539
00:23:26,373 --> 00:23:30,373
Speaker 3: But yeah, look, I swear by the weather. They're a

540
00:23:30,373 --> 00:23:33,773
Speaker 3: little bit more expensive, but they're bloody good. They work

541
00:23:33,853 --> 00:23:37,253
Speaker 3: very well and they cook your food beautifully. So they

542
00:23:37,333 --> 00:23:39,253
Speaker 3: use them as an oven, as a as a grill,

543
00:23:39,533 --> 00:23:42,093
Speaker 3: as whatever. You want to do, and they'll cook the

544
00:23:42,133 --> 00:23:42,693
Speaker 3: food for you.

545
00:23:43,253 --> 00:23:43,493
Speaker 4: I guess.

546
00:23:43,533 --> 00:23:46,053
Speaker 2: The other problem with that and in an emergency weather event,

547
00:23:46,093 --> 00:23:48,573
Speaker 2: you're having to hit outside to do the into the

548
00:23:48,613 --> 00:23:49,733
Speaker 2: elements to cook dinner.

549
00:23:50,173 --> 00:23:53,093
Speaker 3: Oh look, I've got my set up here, beautifully set

550
00:23:53,173 --> 00:23:57,373
Speaker 3: up under a purglar, so it's all very well to proof.

551
00:23:57,653 --> 00:23:58,373
Speaker 13: Yeah.

552
00:23:58,533 --> 00:23:58,733
Speaker 11: Yeah.

553
00:23:58,973 --> 00:24:01,253
Speaker 3: The only barbecue I wouldn't be able to use probably

554
00:24:01,373 --> 00:24:05,613
Speaker 3: is the smoker because that's electric. But but the other

555
00:24:05,813 --> 00:24:08,253
Speaker 3: the others are either charcoal will get So.

556
00:24:08,373 --> 00:24:10,693
Speaker 2: How long does it take you to your charcoal barbq

557
00:24:10,933 --> 00:24:12,533
Speaker 2: ready to cook, Peter?

558
00:24:13,213 --> 00:24:14,773
Speaker 3: Oh, probably forty five minutes.

559
00:24:14,973 --> 00:24:15,853
Speaker 2: Oh yeah, yeah.

560
00:24:16,293 --> 00:24:19,853
Speaker 3: Preparedness, Yeah yeah, you know you want to let those

561
00:24:19,893 --> 00:24:22,413
Speaker 3: cold get nice and gray. But but the thing with

562
00:24:22,453 --> 00:24:26,013
Speaker 3: the charcoal barbecue, five or six hours later, you can

563
00:24:26,053 --> 00:24:30,053
Speaker 3: put a pedal over on it and cook that as well.

564
00:24:32,693 --> 00:24:33,173
Speaker 15: Exactly.

565
00:24:33,213 --> 00:24:37,013
Speaker 3: Nothing wrong with a pair. Egency keeps you going. And

566
00:24:37,093 --> 00:24:39,813
Speaker 3: the other thing is your freezers. Make sure your freezing

567
00:24:39,813 --> 00:24:42,373
Speaker 3: is there full. So your freezer will give you six

568
00:24:42,413 --> 00:24:46,893
Speaker 3: to eight days of food. It's still, you know, not

569
00:24:46,893 --> 00:24:49,813
Speaker 3: not you've started to four out after that time around

570
00:24:49,813 --> 00:24:52,693
Speaker 3: the six or or eighth day. As long as that's

571
00:24:52,733 --> 00:24:55,813
Speaker 3: full they'll maintain that that that chill for a.

572
00:24:55,773 --> 00:24:58,693
Speaker 2: Long long time, right, because each each better frozen food

573
00:24:58,813 --> 00:25:01,933
Speaker 2: is helping keeping the other bit of frozen food frozen.

574
00:25:01,973 --> 00:25:05,373
Speaker 3: I guess you did, right, and and and an old

575
00:25:05,413 --> 00:25:07,733
Speaker 3: an old well, it's not an old, wide stout old trick.

576
00:25:07,813 --> 00:25:10,853
Speaker 3: If you freeze is not full, have something in there

577
00:25:10,933 --> 00:25:15,133
Speaker 3: like screwed up paper, because you condemn that face up.

578
00:25:15,613 --> 00:25:19,093
Speaker 3: And so everything that's in there will help to as

579
00:25:19,133 --> 00:25:22,773
Speaker 3: you say, keep freezing everything else that's in there. So

580
00:25:23,453 --> 00:25:25,533
Speaker 3: the freezer that's got up and it will will will

581
00:25:25,613 --> 00:25:26,453
Speaker 3: last a long time.

582
00:25:26,733 --> 00:25:29,013
Speaker 2: What about what a supply? Do you ever get involved

583
00:25:29,013 --> 00:25:31,013
Speaker 2: in any sort of re Welsh type situations?

584
00:25:32,733 --> 00:25:33,413
Speaker 3: Walsh?

585
00:25:33,493 --> 00:25:36,253
Speaker 2: Well, you know people say that that that the water

586
00:25:36,333 --> 00:25:39,373
Speaker 2: and the system in your toilet is as long as

587
00:25:39,373 --> 00:25:42,653
Speaker 2: it hasn't gone down into the actual bowl area, that

588
00:25:42,693 --> 00:25:43,613
Speaker 2: water is good to go.

589
00:25:43,773 --> 00:25:47,373
Speaker 3: Right Yeah, well yeah, exactly any system. Yeah, you can

590
00:25:47,453 --> 00:25:50,133
Speaker 3: use that water in your system. Yep, definitely, definitely. It's

591
00:25:51,293 --> 00:25:54,413
Speaker 3: it's still all drinkable. I mean, obviously you're gonna have

592
00:25:54,533 --> 00:26:00,933
Speaker 3: a little bit of perhaps it's not going to be

593
00:26:00,933 --> 00:26:03,013
Speaker 3: obviously free. Well, it is free. It's when it comes

594
00:26:03,013 --> 00:26:04,813
Speaker 3: down to text. So as soon as you're plucked. Its

595
00:26:04,853 --> 00:26:06,013
Speaker 3: free water that goes in there.

596
00:26:06,133 --> 00:26:06,293
Speaker 4: Yeah.

597
00:26:06,333 --> 00:26:10,813
Speaker 2: Yeah, you've got a little bowl of free water exactly, exactly.

598
00:26:11,573 --> 00:26:14,053
Speaker 3: All those things are usable and and also keeping your

599
00:26:14,053 --> 00:26:17,733
Speaker 3: stare of fresh water. Yeah, you know, a few go

600
00:26:17,853 --> 00:26:20,653
Speaker 3: into the water to replace every now and then.

601
00:26:21,333 --> 00:26:22,453
Speaker 10: Yep, yep, definitely.

602
00:26:22,773 --> 00:26:25,813
Speaker 3: So a couple of how long, five big?

603
00:26:26,933 --> 00:26:28,573
Speaker 2: How long are you good to go? If the power

604
00:26:28,573 --> 00:26:33,573
Speaker 2: goes down? Pet Peter, Pete. I'll call you Pete is fine, fine.

605
00:26:35,133 --> 00:26:35,733
Speaker 8: Friend.

606
00:26:35,373 --> 00:26:37,213
Speaker 3: A friend of mine used to call me peta.

607
00:26:37,213 --> 00:26:38,213
Speaker 11: You used to worrying me a little bit.

608
00:26:38,253 --> 00:26:39,973
Speaker 3: Sometimes speak them around of dinner together.

609
00:26:40,213 --> 00:26:42,093
Speaker 4: A vibe about you, which is a nice thing.

610
00:26:42,933 --> 00:26:44,572
Speaker 8: Oh, thank you very much, thank you very much.

611
00:26:44,613 --> 00:26:45,253
Speaker 16: Appreciate that.

612
00:26:46,133 --> 00:26:48,693
Speaker 3: I would say we would, we would, we would survive

613
00:26:48,853 --> 00:26:50,093
Speaker 3: a good week or more.

614
00:26:50,373 --> 00:26:50,893
Speaker 10: I mean.

615
00:26:52,213 --> 00:26:54,533
Speaker 3: We've got I've also got which I which I care

616
00:26:54,573 --> 00:26:56,053
Speaker 3: in the back of my youth as one of those

617
00:26:56,573 --> 00:27:01,293
Speaker 3: little single burners that takes the aerosol gas cans. Now,

618
00:27:01,413 --> 00:27:03,933
Speaker 3: those for the aerosol gas cans, they will last over

619
00:27:04,013 --> 00:27:09,133
Speaker 3: eight hours continuous burning on on five So I've got

620
00:27:09,653 --> 00:27:12,253
Speaker 3: at least eight or nine of those in my shed

621
00:27:12,373 --> 00:27:16,413
Speaker 3: or the trimmer shed in my youth, and so there's

622
00:27:16,453 --> 00:27:19,573
Speaker 3: plenty of place that's just a trainy of access to that,

623
00:27:19,813 --> 00:27:22,173
Speaker 3: and the ability just to bail up a jukes for

624
00:27:22,213 --> 00:27:25,292
Speaker 3: hot water, just the one little single burner and they

625
00:27:25,293 --> 00:27:30,573
Speaker 3: get pretty hot. So yeah, I'd say we could provide

626
00:27:30,573 --> 00:27:34,293
Speaker 3: and we had water for flying. After a period of time,

627
00:27:35,253 --> 00:27:37,292
Speaker 3: we could survive a couple of weeks. I'll say at

628
00:27:37,373 --> 00:27:39,293
Speaker 3: least a good week and a half to two weeks.

629
00:27:39,653 --> 00:27:43,413
Speaker 2: You barbecue at Peter's house. Next time the power goes out.

630
00:27:43,293 --> 00:27:45,533
Speaker 4: Got five of them? Well, well get it out.

631
00:27:45,533 --> 00:27:50,573
Speaker 2: Hey, this is a really good tap from Linda. We

632
00:27:50,773 --> 00:27:53,053
Speaker 2: are rural and always ready. Best top, have lots of

633
00:27:53,133 --> 00:27:56,493
Speaker 2: outdoor solar lights. Then bring them in at night. Get

634
00:27:56,493 --> 00:28:00,133
Speaker 2: your solar lights that you know that you're what do

635
00:28:00,213 --> 00:28:03,573
Speaker 2: you call it staking into the garden. Have heaps of them.

636
00:28:03,813 --> 00:28:05,733
Speaker 2: They get all charged up in the day and then

637
00:28:05,773 --> 00:28:06,493
Speaker 2: you bring them at night.

638
00:28:06,573 --> 00:28:08,013
Speaker 4: No more candles. What a great tip.

639
00:28:08,453 --> 00:28:09,093
Speaker 2: That's brilliant.

640
00:28:09,293 --> 00:28:11,653
Speaker 4: Oh eight, one hundred eighty ten eighty is the number

641
00:28:11,693 --> 00:28:13,373
Speaker 4: to call love to hear what you've got in your

642
00:28:13,413 --> 00:28:15,573
Speaker 4: emergency kat. How long do you think you could survive

643
00:28:15,653 --> 00:28:18,013
Speaker 4: comfortably without power? If you want to seen to tax

644
00:28:18,093 --> 00:28:20,413
Speaker 4: ninety two niney two is that number back of the

645
00:28:20,693 --> 00:28:21,293
Speaker 4: twenty to two.

646
00:28:21,853 --> 00:28:24,853
Speaker 13: It's a fresh take on took back. It's Matt Heathen

647
00:28:24,973 --> 00:28:25,773
Speaker 13: Taylor Adams.

648
00:28:25,773 --> 00:28:29,293
Speaker 1: Afternoons have your say on eight hundred eighty ten eighty

649
00:28:29,413 --> 00:28:30,293
Speaker 1: youth talks'd b.

650
00:28:30,453 --> 00:28:33,693
Speaker 4: For a good afternoons You we're talking about emergency preparedness.

651
00:28:33,693 --> 00:28:37,093
Speaker 4: How long do you think you could live comfortably without power? Oh,

652
00:28:37,213 --> 00:28:38,773
Speaker 4: one hundred eighty ten eighty is the number to call.

653
00:28:38,853 --> 00:28:40,973
Speaker 2: Wrap a cold pie and tinfoil, Whack it on top

654
00:28:41,013 --> 00:28:43,253
Speaker 2: of your UT's engine, a couple of laps around the block,

655
00:28:43,293 --> 00:28:45,653
Speaker 2: hot and good to go. Make sure it's secure, though

656
00:28:46,253 --> 00:28:49,133
Speaker 2: you won't like mince on your exhaust. Manifolds his bill

657
00:28:49,293 --> 00:28:51,093
Speaker 2: tried that before. I had a mate that used to

658
00:28:51,773 --> 00:28:53,533
Speaker 2: He used to cook a chicken and roasted chicken on

659
00:28:53,573 --> 00:28:56,493
Speaker 2: the way up from invery cargo see man in Donedan.

660
00:28:56,613 --> 00:28:58,093
Speaker 4: Did he have a we pan like a roasting pan?

661
00:28:58,173 --> 00:28:58,973
Speaker 4: Did he just put the chricken?

662
00:28:59,013 --> 00:29:01,533
Speaker 2: He just put on the manifold, Wrap it in tinfoil

663
00:29:01,653 --> 00:29:04,293
Speaker 2: right as car ran pretty hot? Actually to be fair

664
00:29:04,453 --> 00:29:09,773
Speaker 2: yep is V eight. But yeah, so one time anyway,

665
00:29:09,813 --> 00:29:11,453
Speaker 2: this is a different story, but I think one time

666
00:29:11,493 --> 00:29:13,333
Speaker 2: he's trying to cook chicken on the way out, Old

667
00:29:13,413 --> 00:29:15,773
Speaker 2: Doug and then he accidentally ran through a flock of

668
00:29:15,813 --> 00:29:17,933
Speaker 2: sheep on the road and it was absolute carnish.

669
00:29:17,573 --> 00:29:18,213
Speaker 4: But it's unfortunate.

670
00:29:18,253 --> 00:29:19,773
Speaker 2: It's a different story for a different day.

671
00:29:19,813 --> 00:29:20,973
Speaker 4: That check was ready to go.

672
00:29:21,493 --> 00:29:24,413
Speaker 2: When you make up your emergency kit, guys, don't forget

673
00:29:24,413 --> 00:29:25,533
Speaker 2: food for your poochers.

674
00:29:25,653 --> 00:29:27,013
Speaker 4: Yeah, that's a biggie.

675
00:29:26,653 --> 00:29:28,173
Speaker 2: And if worse comes to worse, you can get on

676
00:29:28,173 --> 00:29:29,093
Speaker 2: the jelly meat yourself.

677
00:29:30,013 --> 00:29:37,413
Speaker 4: Good point, Leete.

678
00:29:36,253 --> 00:29:39,853
Speaker 8: This afternoon, guys, great, great, great radio.

679
00:29:40,053 --> 00:29:41,693
Speaker 2: Well thanks for listening and thanks for calling.

680
00:29:42,813 --> 00:29:45,413
Speaker 8: Yeah, no worries. Hey, I'm from a Hawks bay and.

681
00:29:45,533 --> 00:29:47,613
Speaker 15: We know all about power cuts.

682
00:29:47,893 --> 00:29:52,373
Speaker 8: Yeah, a powerful long period of times. But I just

683
00:29:52,373 --> 00:29:54,613
Speaker 8: wanted to ring up just because when you guys were

684
00:29:54,653 --> 00:29:57,973
Speaker 8: talking about this, it reminded me that there's actually a

685
00:29:58,093 --> 00:30:00,253
Speaker 8: backup power point that you can put on an invert

686
00:30:01,773 --> 00:30:05,173
Speaker 8: the Thronius inverter, and what that does is during the daytime,

687
00:30:05,413 --> 00:30:08,413
Speaker 8: it can give you up to three killer wats of

688
00:30:08,653 --> 00:30:14,373
Speaker 8: power that can run like your treezes or just anything

689
00:30:14,413 --> 00:30:17,213
Speaker 8: you just need to run. Basically, an extension lead out

690
00:30:17,213 --> 00:30:19,853
Speaker 8: from your inverder and you can plug yourself in. But

691
00:30:19,893 --> 00:30:22,373
Speaker 8: it's the key is that it's only during the daytime.

692
00:30:22,533 --> 00:30:24,493
Speaker 8: You can't actually use it during the night.

693
00:30:24,733 --> 00:30:27,373
Speaker 4: So when do you run the inverter is running off

694
00:30:27,413 --> 00:30:28,853
Speaker 4: your solar kit, is that right?

695
00:30:30,133 --> 00:30:30,333
Speaker 17: Yeah?

696
00:30:30,413 --> 00:30:32,813
Speaker 8: Yeah, So your solar panels are on your roof, right,

697
00:30:32,933 --> 00:30:35,973
Speaker 8: that comes down into you inverter. And when you've got

698
00:30:36,013 --> 00:30:39,813
Speaker 8: no power from your eighty side, so from your switchboard

699
00:30:39,813 --> 00:30:43,653
Speaker 8: into your invert base is an internal bypass in the

700
00:30:43,733 --> 00:30:46,293
Speaker 8: inverter and it sends the power down to a little

701
00:30:46,933 --> 00:30:49,173
Speaker 8: power point next to your inverter and.

702
00:30:49,133 --> 00:30:54,453
Speaker 4: That will give you power smart to that point only right,

703
00:30:54,573 --> 00:30:56,293
Speaker 4: So like you don't have to have a battery, but

704
00:30:56,373 --> 00:30:57,413
Speaker 4: you can't stick this thing on.

705
00:30:57,493 --> 00:31:00,413
Speaker 8: So it was just a yeah, a good way of

706
00:31:00,613 --> 00:31:03,693
Speaker 8: a deeper way of having power without having batteries to

707
00:31:03,773 --> 00:31:06,053
Speaker 8: run like you know, your whole house.

708
00:31:06,293 --> 00:31:09,693
Speaker 2: So yeah, good on yourself for calling Lee, Thanks for listening.

709
00:31:09,693 --> 00:31:11,013
Speaker 2: There's a top because a lot of people are coming

710
00:31:11,013 --> 00:31:13,973
Speaker 2: through with the sole of thing. Hey, guys, we should

711
00:31:14,013 --> 00:31:16,133
Speaker 2: all have power backup of some sort in case the

712
00:31:16,133 --> 00:31:18,493
Speaker 2: power goes out. However, at the cost we are currently

713
00:31:18,493 --> 00:31:21,253
Speaker 2: paying for power, my power supply company should be bringing

714
00:31:21,253 --> 00:31:22,973
Speaker 2: a generator and leaving it for me to use at

715
00:31:22,973 --> 00:31:25,453
Speaker 2: any time. That they cannot supply the power pricing as

716
00:31:25,453 --> 00:31:30,093
Speaker 2: a body jokes is simon, But am I where am I?

717
00:31:30,293 --> 00:31:32,333
Speaker 2: This is the one? Hi, guys, I don't think everyone

718
00:31:32,373 --> 00:31:36,613
Speaker 2: knows that if you have solo without batteries and backfeed

719
00:31:36,653 --> 00:31:39,013
Speaker 2: the grid, they will not have power in a power

720
00:31:39,053 --> 00:31:42,213
Speaker 2: cut because the back feed could eletrocute a linesman working

721
00:31:42,213 --> 00:31:43,253
Speaker 2: on the line. Cheers Bob.

722
00:31:43,573 --> 00:31:46,653
Speaker 4: Right, So well, they must cut off the feed in

723
00:31:46,653 --> 00:31:48,853
Speaker 4: a power cut, so it's not feeding back to the line.

724
00:31:48,853 --> 00:31:51,693
Speaker 2: I suppose, muss is I've got a paddock with cheap cows,

725
00:31:51,773 --> 00:31:53,853
Speaker 2: chickens and pegs, so I'm never going to starve with

726
00:31:53,973 --> 00:31:57,093
Speaker 2: a mess of vegetable garden. Well, eventually you're gonna eat

727
00:31:57,093 --> 00:31:59,413
Speaker 2: all the chaos and the chickens and the pegs. Yeah, could,

728
00:31:59,413 --> 00:32:02,133
Speaker 2: depending depending how much you're eating. But so far, Mars

729
00:32:02,173 --> 00:32:03,413
Speaker 2: is going to survive a lot longer than the rest

730
00:32:03,413 --> 00:32:04,413
Speaker 2: of us, that's for sure.

731
00:32:04,253 --> 00:32:07,533
Speaker 4: Very true. Well, let's go to Joe Enne get a Joeanne.

732
00:32:08,453 --> 00:32:13,053
Speaker 17: Hi, Oh not too bad, A little bit windy.

733
00:32:12,893 --> 00:32:13,893
Speaker 4: Here, Yeah you're in.

734
00:32:15,653 --> 00:32:20,933
Speaker 17: Yeah, yeah, still got power. Two. I've just got two

735
00:32:21,573 --> 00:32:24,013
Speaker 17: tips that people don't normally, well a lot of people

736
00:32:24,013 --> 00:32:27,893
Speaker 17: don't really and after crop we were in christ Church

737
00:32:27,933 --> 00:32:31,013
Speaker 17: with the earthquake. If you don't have some cash in

738
00:32:31,053 --> 00:32:34,213
Speaker 17: your kit, of course, well probably you can't. There's no

739
00:32:34,813 --> 00:32:39,693
Speaker 17: there's no all that stuff. So yeah, that even if

740
00:32:39,733 --> 00:32:41,573
Speaker 17: it's only a hundred bucks or something to give you

741
00:32:42,253 --> 00:32:43,693
Speaker 17: a chance that less you can go to a dairy

742
00:32:43,733 --> 00:32:47,653
Speaker 17: and get something. And the and the other one is

743
00:32:48,253 --> 00:32:51,133
Speaker 17: and it's a power thing too. If you've got a

744
00:32:51,173 --> 00:32:54,933
Speaker 17: freezer that's half full, say of your meat or whatever.

745
00:32:55,573 --> 00:32:58,333
Speaker 17: If you keep old milk bottles with water in or

746
00:32:58,373 --> 00:33:02,253
Speaker 17: fizzy bottles, whatever it all, it's actually cheaper to run

747
00:33:02,293 --> 00:33:05,813
Speaker 17: your freezers and you can use it if there's if

748
00:33:05,853 --> 00:33:08,133
Speaker 17: you're in a disaster, because you've got your waters to

749
00:33:08,373 --> 00:33:09,133
Speaker 17: the polight as well.

750
00:33:09,453 --> 00:33:12,173
Speaker 4: Great tip, Oh right, genius.

751
00:33:12,173 --> 00:33:14,333
Speaker 2: So if you've got space, you just freeze a bunch

752
00:33:14,333 --> 00:33:15,533
Speaker 2: of a bunch of water in there.

753
00:33:16,173 --> 00:33:18,893
Speaker 17: Yeah, just put it at the bottom. The more the

754
00:33:18,973 --> 00:33:22,093
Speaker 17: fullier freezer is, the less it's having to work. So

755
00:33:22,133 --> 00:33:24,693
Speaker 17: therefore you're killing two birds with one stone.

756
00:33:25,413 --> 00:33:25,853
Speaker 2: Brilliant.

757
00:33:26,333 --> 00:33:29,893
Speaker 4: That is a great tip. Yeah, thank you very much, Joanne.

758
00:33:29,893 --> 00:33:32,173
Speaker 4: It doesn't sound doesn't sound too bad in the background now,

759
00:33:32,253 --> 00:33:33,853
Speaker 4: but hopefully it's eased up a bit.

760
00:33:35,333 --> 00:33:39,333
Speaker 17: So that's the second there's no rain, but it's how

761
00:33:40,213 --> 00:33:41,573
Speaker 17: publishments going everywhere?

762
00:33:42,933 --> 00:33:45,733
Speaker 2: Will just last question, when you do you get you

763
00:33:45,813 --> 00:33:47,453
Speaker 2: that one hundred bucks you're putting away? What you'd have

764
00:33:47,493 --> 00:33:49,853
Speaker 2: it in fives, wouldn't you? There would be the best

765
00:33:49,853 --> 00:33:50,253
Speaker 2: way to go.

766
00:33:51,293 --> 00:33:53,093
Speaker 17: Well, definitely not fifties. If you have to send the

767
00:33:53,133 --> 00:33:58,813
Speaker 17: kids down to pick anything up, yeah, yeah, yeah, whatever,

768
00:33:58,973 --> 00:34:01,533
Speaker 17: you know, just as long as you've got something tucked

769
00:34:01,533 --> 00:34:04,693
Speaker 17: away if you can, because I mean even if you

770
00:34:04,773 --> 00:34:07,413
Speaker 17: had to leave town to drive somewhere, you're going to

771
00:34:07,453 --> 00:34:09,893
Speaker 17: have to put pet in yeah, and you can't get

772
00:34:09,933 --> 00:34:10,813
Speaker 17: it out of the machine.

773
00:34:10,893 --> 00:34:13,693
Speaker 4: So good on you, Thank you for you great good thinking.

774
00:34:14,133 --> 00:34:17,172
Speaker 2: I keep twenty thousand dollars and two dollar coins at

775
00:34:17,213 --> 00:34:18,373
Speaker 2: all times for emergencies.

776
00:34:18,693 --> 00:34:21,013
Speaker 4: Good on you. You bury that in the bag, garden

777
00:34:21,053 --> 00:34:23,533
Speaker 4: or something. Oh, one hundred eighty ten eighty is the

778
00:34:23,613 --> 00:34:25,853
Speaker 4: number to call love to hear what you've got in

779
00:34:25,893 --> 00:34:28,573
Speaker 4: your emergency kettle. How prepared would you be if you

780
00:34:28,613 --> 00:34:29,933
Speaker 4: had an emergency and the power went out?

781
00:34:29,973 --> 00:34:31,573
Speaker 2: This is a good point from Blair. Hey, guys, remember

782
00:34:31,573 --> 00:34:33,692
Speaker 2: most cars have radio, so if you have a power cut,

783
00:34:33,933 --> 00:34:36,173
Speaker 2: but do not have a battery power transistor. There's always

784
00:34:36,173 --> 00:34:37,693
Speaker 2: the car radio for updates.

785
00:34:37,773 --> 00:34:40,453
Speaker 4: Yeah, good point. Eleven to three two.

786
00:34:42,093 --> 00:34:45,652
Speaker 1: Matt Heath Taylor Adams taking your calls on eight hundred

787
00:34:45,693 --> 00:34:46,533
Speaker 1: and eighty ten eighty.

788
00:34:46,613 --> 00:34:49,733
Speaker 13: It's Mad Heathen Taylor Adams Afternoons.

789
00:34:49,213 --> 00:34:53,413
Speaker 4: News Dogs be afternoon. It is eight to two, and

790
00:34:53,493 --> 00:34:56,133
Speaker 4: we are talking about whether you could survive or how

791
00:34:56,173 --> 00:34:59,573
Speaker 4: long could you survive comfortably without power if emergency hit

792
00:34:59,613 --> 00:35:00,333
Speaker 4: your property.

793
00:35:00,653 --> 00:35:03,212
Speaker 2: The six says we survived twenty days without power, water,

794
00:35:03,253 --> 00:35:06,413
Speaker 2: and sewage as a result of the christ Church earthquakes. Yeah,

795
00:35:07,013 --> 00:35:08,253
Speaker 2: of course a few people went through that.

796
00:35:08,613 --> 00:35:10,533
Speaker 4: Yeah, that was a long time that one, Bob.

797
00:35:11,493 --> 00:35:14,493
Speaker 18: Yes, the first thing that brought me to your program

798
00:35:14,653 --> 00:35:16,493
Speaker 18: was brass brass monkeys about.

799
00:35:16,253 --> 00:35:17,093
Speaker 3: An hour ago.

800
00:35:19,773 --> 00:35:23,173
Speaker 4: From cap And just as a point.

801
00:35:22,973 --> 00:35:26,692
Speaker 18: On that, brass monkeys. In fact, today is actually Trafalgerday,

802
00:35:26,733 --> 00:35:28,172
Speaker 18: you know, Lord Nelson.

803
00:35:27,853 --> 00:35:28,293
Speaker 7: And all that.

804
00:35:28,733 --> 00:35:33,173
Speaker 18: Yeah, brass monkeys are. In fact, we're critical to that

805
00:35:33,253 --> 00:35:36,893
Speaker 18: ship that held the that wasn't cold. So it's a

806
00:35:37,053 --> 00:35:40,093
Speaker 18: practable Tomnure met today on this particular day.

807
00:35:40,213 --> 00:35:41,772
Speaker 2: Yeah, what are you thanks for that? Bob?

808
00:35:42,693 --> 00:35:47,493
Speaker 18: Anyway, support emergency support. I've been well, my wife has

809
00:35:47,533 --> 00:35:51,693
Speaker 18: gone now, but we've been totally independent of whatever needs.

810
00:35:51,933 --> 00:35:54,773
Speaker 18: For the last thirty of thirty five years. We've got

811
00:35:54,773 --> 00:35:59,213
Speaker 18: a motor home and we it's parked in our drive

812
00:35:59,533 --> 00:36:04,333
Speaker 18: two meters from our back door. So anytime there's an emergency,

813
00:36:04,533 --> 00:36:05,413
Speaker 18: we just move in.

814
00:36:05,973 --> 00:36:08,853
Speaker 4: Everything is there smart? Is it all all? Can you

815
00:36:08,893 --> 00:36:10,613
Speaker 4: move it? Is it all guessed up? Ready to fly

816
00:36:10,693 --> 00:36:12,213
Speaker 4: if you need to? Or is it stationary?

817
00:36:12,813 --> 00:36:17,373
Speaker 18: I'm off down country day after tomorrow, but it parks

818
00:36:17,453 --> 00:36:18,933
Speaker 18: up the drive. We build a house to fit the

819
00:36:18,973 --> 00:36:24,253
Speaker 18: motor home, so we use it as emergency well not accommodation.

820
00:36:24,613 --> 00:36:26,453
Speaker 18: I go and live at when visitors come to start,

821
00:36:26,493 --> 00:36:29,093
Speaker 18: give them my bed and making mugg out and motihome nice.

822
00:36:29,333 --> 00:36:34,453
Speaker 2: Yeah, just go and sleep in their motor home just

823
00:36:34,493 --> 00:36:35,853
Speaker 2: because it's cozy. I love it.

824
00:36:35,933 --> 00:36:38,613
Speaker 4: Some of them are super comfy, lovely, get your own space.

825
00:36:38,853 --> 00:36:42,533
Speaker 18: It's beautiful, it is it's I mean, there are thousands

826
00:36:42,573 --> 00:36:45,133
Speaker 18: now the own mode homes in this country. So they're

827
00:36:45,173 --> 00:36:47,252
Speaker 18: all in the same position. Because all these modi homes

828
00:36:47,333 --> 00:36:50,413
Speaker 18: now are totally self sufficient. They most of them, I

829
00:36:50,413 --> 00:36:52,133
Speaker 18: mean some of them even work from home. And them

830
00:36:52,173 --> 00:36:54,453
Speaker 18: you know, they were all the solar power, run the

831
00:36:54,453 --> 00:36:58,973
Speaker 18: batteries and with the batteries, et cetera, and whatever happens

832
00:36:58,973 --> 00:37:01,253
Speaker 18: in the rest of the world that totally self sufficient.

833
00:37:01,333 --> 00:37:03,333
Speaker 2: How much food you got How much food have you

834
00:37:03,373 --> 00:37:05,333
Speaker 2: got stored up, Bob for emergencies.

835
00:37:06,333 --> 00:37:12,173
Speaker 18: Well, there's always bag beans, yep. And there's oil, and

836
00:37:12,173 --> 00:37:15,772
Speaker 18: there's water. Basically, I mean there's not fresh bridge, but

837
00:37:15,893 --> 00:37:19,533
Speaker 18: basically there's crew. There's baked beans and sardis and that

838
00:37:19,573 --> 00:37:20,133
Speaker 18: sort of stuff.

839
00:37:20,293 --> 00:37:22,133
Speaker 2: Get a long way on mag beans and you're running

840
00:37:22,133 --> 00:37:23,973
Speaker 2: a manual can opener, because that can be a trick

841
00:37:24,013 --> 00:37:27,493
Speaker 2: for for young survivalist players just trying to have.

842
00:37:27,573 --> 00:37:31,853
Speaker 4: Never owned an automatic old school love a Bob, Thank

843
00:37:31,893 --> 00:37:32,573
Speaker 4: you very much.

844
00:37:32,893 --> 00:37:33,613
Speaker 2: Think you there you go?

845
00:37:33,773 --> 00:37:33,973
Speaker 16: Yeah?

846
00:37:33,973 --> 00:37:37,773
Speaker 2: I mean those of us with motor homes good to go, Yeah, exactly.

847
00:37:37,813 --> 00:37:38,013
Speaker 6: Yeah.

848
00:37:38,013 --> 00:37:40,813
Speaker 2: And then if you've watched a disaster movie, people trying

849
00:37:40,813 --> 00:37:42,692
Speaker 2: to relieve down the motorhomes do do a lot better.

850
00:37:42,733 --> 00:37:44,773
Speaker 4: Yeah, yeah, they go far at a motor home. I

851
00:37:44,813 --> 00:37:46,613
Speaker 4: think we've got time saying he Nadi.

852
00:37:46,813 --> 00:37:49,013
Speaker 7: Henadi killed a cordwall.

853
00:37:49,013 --> 00:37:51,133
Speaker 4: Hell, are you guys very well? What do you reckon?

854
00:37:51,493 --> 00:37:51,692
Speaker 11: Hey?

855
00:37:52,333 --> 00:37:54,013
Speaker 7: Oh, I'm just going to make it real quick. You know,

856
00:37:54,733 --> 00:37:57,453
Speaker 7: for thousands of years, she meant humans have survived on

857
00:37:57,493 --> 00:38:00,733
Speaker 7: the earth, and it took two generations to forget all that.

858
00:38:01,653 --> 00:38:05,853
Speaker 7: We're screwed. We're pretty much screwed. Apocalyptic evince everyone with

859
00:38:05,973 --> 00:38:09,173
Speaker 7: motor homes and baked beans inking a laugh forever. Yeah,

860
00:38:09,253 --> 00:38:11,053
Speaker 7: you need to return to the fen word and learn

861
00:38:11,053 --> 00:38:13,573
Speaker 7: how to grow. So you know, that's a bit of

862
00:38:13,613 --> 00:38:15,453
Speaker 7: wise words for everyone out there, and you guys have

863
00:38:15,493 --> 00:38:16,093
Speaker 7: a good day.

864
00:38:16,333 --> 00:38:18,013
Speaker 2: Thank you so much. It's such a such a good

865
00:38:18,013 --> 00:38:20,573
Speaker 2: point because you know, for all of our history we've

866
00:38:20,573 --> 00:38:23,653
Speaker 2: lived outside, and now most of us are just freaking

867
00:38:23,693 --> 00:38:26,573
Speaker 2: out that we can't live in a house with all

868
00:38:26,613 --> 00:38:31,333
Speaker 2: the food, walls, carpet, things to sit on, without lights

869
00:38:31,893 --> 00:38:36,413
Speaker 2: and cooked food, and and you know people freak out

870
00:38:36,453 --> 00:38:39,652
Speaker 2: about the internet. I mean, as that should be on

871
00:38:39,773 --> 00:38:40,212
Speaker 2: us too.

872
00:38:40,773 --> 00:38:43,293
Speaker 4: You know, we're getting weaker, You're getting weaker reliant on

873
00:38:43,333 --> 00:38:45,453
Speaker 4: these everyone takes the time.

874
00:38:45,173 --> 00:38:47,773
Speaker 2: To learn to be able to just live in the

875
00:38:47,773 --> 00:38:49,973
Speaker 2: bush for a week and be self sufficient.

876
00:38:50,093 --> 00:38:52,613
Speaker 4: Definitely. Yeah, I wonder if you teaches that we might

877
00:38:52,613 --> 00:38:54,172
Speaker 4: get him back. He sounds like a man who knows

878
00:38:54,173 --> 00:38:56,373
Speaker 4: that sort of stuff. I mean, that's my fear. If

879
00:38:56,413 --> 00:38:58,213
Speaker 4: you get me out in a bush trying to grow things,

880
00:38:58,213 --> 00:38:59,893
Speaker 4: then I'm not making it more than two days.

881
00:38:59,933 --> 00:39:01,413
Speaker 2: I was hanging out with this great u zeallytic called

882
00:39:01,413 --> 00:39:04,133
Speaker 2: Sam the Trapman the other day, and you know he

883
00:39:04,213 --> 00:39:10,573
Speaker 2: makes me feel what's the word useless because he can

884
00:39:10,613 --> 00:39:11,973
Speaker 2: just eager literally live off the land.

885
00:39:12,053 --> 00:39:15,293
Speaker 4: Yeah, an impressive through that man. Absolutely right, great discussion.

886
00:39:15,333 --> 00:39:17,813
Speaker 4: Thank you very much to everyone who text and called

887
00:39:17,813 --> 00:39:20,252
Speaker 4: on that one. And if you're in the Lower North

888
00:39:20,293 --> 00:39:21,973
Speaker 4: Island and the top of the South hope you were

889
00:39:22,013 --> 00:39:25,613
Speaker 4: staying safe and hopefully that weather starts to ease soon.

890
00:39:25,693 --> 00:39:27,973
Speaker 4: Coming up after two o'clock, let's have a chat about

891
00:39:28,013 --> 00:39:32,172
Speaker 4: artificial intelligence. Is it going to destroy jobs or create them?

892
00:39:32,653 --> 00:39:35,093
Speaker 2: Yeah, well there's no artificial intelligence in the bush just yet,

893
00:39:35,173 --> 00:39:35,453
Speaker 2: is there?

894
00:39:35,573 --> 00:39:37,453
Speaker 4: Thank God for that that has coming up. Oh wait,

895
00:39:37,533 --> 00:39:40,172
Speaker 4: hundred eighty ten eighty is the number called nine two

896
00:39:40,293 --> 00:39:41,933
Speaker 4: nine two is the text number.

897
00:39:41,693 --> 00:39:43,172
Speaker 2: We might have to go bush to get away from

898
00:39:43,173 --> 00:39:44,133
Speaker 2: the robots at some point.

899
00:39:44,173 --> 00:39:45,933
Speaker 4: Absolutely, News, it's coming.

900
00:39:45,773 --> 00:39:51,573
Speaker 13: Up talking with you all afternoon.

901
00:39:51,893 --> 00:39:55,453
Speaker 1: It's Matt Heathen Taylor Adams Afternoons News Talks.

902
00:39:55,493 --> 00:39:58,933
Speaker 4: It'd be afternoon to you. Welcome back into the show.

903
00:39:58,973 --> 00:40:01,853
Speaker 4: It is six past two, so let's get into this

904
00:40:01,893 --> 00:40:05,053
Speaker 4: one and it's going to be interesting. Artificial intelligence? Is

905
00:40:05,053 --> 00:40:08,893
Speaker 4: it going to destroy jobs as some recent researchers warned,

906
00:40:09,093 --> 00:40:13,013
Speaker 4: or create them? So it's a paper by Google's chief economist.

907
00:40:13,093 --> 00:40:17,173
Speaker 4: His name is Fabian Quarto Mala, and also Diane Coyle,

908
00:40:17,213 --> 00:40:20,813
Speaker 4: who is a renowned British economist. They show that labor

909
00:40:20,853 --> 00:40:23,653
Speaker 4: market disruption is certainly coming when it comes to AI,

910
00:40:23,893 --> 00:40:28,293
Speaker 4: but they say the true challenges for governments and businesses

911
00:40:28,373 --> 00:40:31,893
Speaker 4: is ensuring that workers have the skills and adaptability needed

912
00:40:31,933 --> 00:40:35,053
Speaker 4: to use the technology. Only then, they say, will the

913
00:40:35,213 --> 00:40:38,973
Speaker 4: AI drive productivity and raise living standards for us all.

914
00:40:39,053 --> 00:40:41,653
Speaker 2: Well, their main point here is that they think predictions

915
00:40:41,653 --> 00:40:45,773
Speaker 2: that I will wipe out jobs have been exaggerated, and

916
00:40:45,853 --> 00:40:49,693
Speaker 2: they write that most past technology technological revolutions led to

917
00:40:50,333 --> 00:40:54,333
Speaker 2: net employment growth. And look, as I was saying before,

918
00:40:56,173 --> 00:41:01,053
Speaker 2: general access to large language models like chat GPT has

919
00:41:01,053 --> 00:41:04,413
Speaker 2: been around for since probably the end of twenty twenty

920
00:41:04,413 --> 00:41:06,893
Speaker 2: two is when people first started tinkering with it. So

921
00:41:06,893 --> 00:41:08,213
Speaker 2: it's been around a lot, and it's the up to

922
00:41:08,373 --> 00:41:14,772
Speaker 2: take has been exponential, So you know, we've got used

923
00:41:14,773 --> 00:41:17,413
Speaker 2: to it. We've had it around for it's been really available.

924
00:41:17,493 --> 00:41:20,172
Speaker 2: So what are you seeing? Read A bit? I can't

925
00:41:20,173 --> 00:41:22,973
Speaker 2: say that we need to replace me with AI readily available.

926
00:41:23,373 --> 00:41:24,773
Speaker 2: So what are you seeing? Do you agree that it

927
00:41:24,813 --> 00:41:28,373
Speaker 2: won't wipe out your jobs? Are you retraining with it?

928
00:41:28,093 --> 00:41:33,053
Speaker 2: Is it transforming rather than destroying your job? And as

929
00:41:33,093 --> 00:41:35,973
Speaker 2: your work, are retraining for it? So according to these

930
00:41:36,013 --> 00:41:40,453
Speaker 2: guys history, you know, they talk about agriculture, once employed

931
00:41:40,453 --> 00:41:43,773
Speaker 2: sixty percent of the US workers, now under five percent.

932
00:41:43,853 --> 00:41:46,893
Speaker 2: So they're just they're talking about how jobs jobs evolve

933
00:41:46,933 --> 00:41:50,013
Speaker 2: over time. Computerization since the nineteen seventies cost three point

934
00:41:50,053 --> 00:41:52,853
Speaker 2: five million jobs, according to these two, but created over

935
00:41:52,933 --> 00:41:54,173
Speaker 2: nineteen million jobs.

936
00:41:55,133 --> 00:41:57,652
Speaker 4: It is interesting. I mean they mentioned there that at

937
00:41:57,693 --> 00:42:00,173
Speaker 4: the nineteen to fifty UIs census listed two hundred and

938
00:42:00,213 --> 00:42:05,173
Speaker 4: seventy one occupations. Today only one remains elevator operator. And

939
00:42:05,253 --> 00:42:08,093
Speaker 4: I imagine an elevator operator. I don't know how much

940
00:42:08,893 --> 00:42:11,933
Speaker 4: longevity that's got left in it, certainly for another few

941
00:42:12,013 --> 00:42:14,213
Speaker 4: years at least, but whether AI comes in and starts

942
00:42:14,253 --> 00:42:14,853
Speaker 4: to operate that so.

943
00:42:15,053 --> 00:42:17,213
Speaker 2: I can think of another operator that doesn't exist anymore.

944
00:42:17,453 --> 00:42:18,733
Speaker 2: Was that the phone operator?

945
00:42:18,813 --> 00:42:18,933
Speaker 15: Oh?

946
00:42:19,013 --> 00:42:19,813
Speaker 4: Yes, the person that.

947
00:42:19,853 --> 00:42:22,613
Speaker 2: Used to plug things in? Yeah, so they went anyway,

948
00:42:22,613 --> 00:42:26,533
Speaker 2: what about typing polls? They've completely gone yep. According to

949
00:42:26,613 --> 00:42:31,373
Speaker 2: these guys, technology typically effects tasks, not entire jobs, so

950
00:42:32,573 --> 00:42:35,133
Speaker 2: they talk about jobs being essentially a bundle of tasks

951
00:42:35,133 --> 00:42:37,813
Speaker 2: and eliminating an entire job requires the automation of a

952
00:42:37,853 --> 00:42:41,773
Speaker 2: significant proportion of its underlying duties, and they think that

953
00:42:41,853 --> 00:42:47,253
Speaker 2: this regularly happens. So, for example, they talk about radiologists.

954
00:42:47,253 --> 00:42:49,813
Speaker 2: In twenty sixteen, it was predicted that AI would replace

955
00:42:49,933 --> 00:42:53,853
Speaker 2: radiologists within five years. You demand for radiologists has surged

956
00:42:53,893 --> 00:42:56,733
Speaker 2: because their job is not just to study images, but

957
00:42:56,773 --> 00:42:59,773
Speaker 2: also to analyze medical records, advise doctors, talk to patients

958
00:42:59,813 --> 00:43:03,693
Speaker 2: and interpret findings instead of going extinct. Radiologists are thriving

959
00:43:03,773 --> 00:43:07,413
Speaker 2: by incorporating AI into their workflow, which is very positive.

960
00:43:07,533 --> 00:43:10,613
Speaker 2: So is that what's happing. You're thriving because you're incorporating

961
00:43:10,933 --> 00:43:13,293
Speaker 2: AI on your workflow or are you being erased?

962
00:43:13,413 --> 00:43:15,573
Speaker 4: Yeah, love to hear from you, oh eight hundred eighty

963
00:43:15,653 --> 00:43:18,573
Speaker 4: ten eighty Are you doing anything different with the emergence

964
00:43:18,613 --> 00:43:21,453
Speaker 4: of AI or are you getting training within your job

965
00:43:21,533 --> 00:43:24,333
Speaker 4: as we speak, to better utilize it. Love to hear

966
00:43:24,333 --> 00:43:26,172
Speaker 4: from you on Oh eight hundred eighty ten eighty nine

967
00:43:26,253 --> 00:43:28,133
Speaker 4: two nine two is the text number.

968
00:43:28,493 --> 00:43:33,693
Speaker 2: Has the threat of AI been greatly exaggerated, as Fabian

969
00:43:34,093 --> 00:43:36,853
Speaker 2: Couteau Malay and Diane Coyle claim, this will.

970
00:43:36,773 --> 00:43:38,692
Speaker 4: Be a good one. It is ten pass two. Come

971
00:43:38,733 --> 00:43:41,413
Speaker 4: on through. Oh eight hundred eighty ten eighty is that number?

972
00:43:41,773 --> 00:43:42,013
Speaker 19: Wow?

973
00:43:42,933 --> 00:43:47,293
Speaker 1: Your home of afternoon talk Mad Heathen Taylor Adams afternoons

974
00:43:47,333 --> 00:43:50,133
Speaker 1: call oh eight hundred eighty ten eighty us talk.

975
00:43:50,013 --> 00:43:50,333
Speaker 12: Z B.

976
00:43:52,133 --> 00:43:56,333
Speaker 4: Very good afternoon, thirteen past two. We're talking about artificial intelligence?

977
00:43:56,333 --> 00:43:59,693
Speaker 4: Will it destroy jobs or create jobs? A research paper

978
00:44:00,453 --> 00:44:04,213
Speaker 4: by the heads of old Google's chief economists. I should

979
00:44:04,213 --> 00:44:08,213
Speaker 4: say a Fabian Cola Mala and also a very renowned

980
00:44:08,333 --> 00:44:11,933
Speaker 4: British economist Diane Coyle say, well, that are quite positive

981
00:44:11,973 --> 00:44:14,533
Speaker 4: about the future of AI and the jobs that could create.

982
00:44:14,693 --> 00:44:16,533
Speaker 4: So do you think your job is safe?

983
00:44:16,533 --> 00:44:16,652
Speaker 6: Oh?

984
00:44:16,653 --> 00:44:18,212
Speaker 4: Wait, one hundred and eighty ten eighty is an number

985
00:44:18,213 --> 00:44:18,413
Speaker 4: to call.

986
00:44:18,413 --> 00:44:20,533
Speaker 2: I mean they make a bold claim that AI will

987
00:44:20,573 --> 00:44:23,333
Speaker 2: not take your job, it will make your job more meaningful.

988
00:44:23,813 --> 00:44:24,733
Speaker 2: That's a bit claim.

989
00:44:24,813 --> 00:44:26,853
Speaker 4: I mean it sounds positive. I hope that's the case,

990
00:44:26,933 --> 00:44:28,933
Speaker 4: but whether that is going to be the case, love

991
00:44:28,973 --> 00:44:31,013
Speaker 4: to hear your thoughts. Nine two nine two is the

992
00:44:31,053 --> 00:44:33,653
Speaker 4: text number you are? How are you.

993
00:44:35,613 --> 00:44:36,053
Speaker 10: Very well?

994
00:44:36,093 --> 00:44:39,333
Speaker 4: And yourselves very good? What's your thoughts about this positivity

995
00:44:39,333 --> 00:44:41,133
Speaker 4: when it comes to AI and jobs.

996
00:44:41,573 --> 00:44:45,733
Speaker 20: I actually I do believe it. I've got an automation

997
00:44:45,853 --> 00:44:50,212
Speaker 20: and AI implementation consulting them. And what I'd say is

998
00:44:50,373 --> 00:44:53,333
Speaker 20: that probably about a year and a half go, maybe

999
00:44:53,413 --> 00:44:56,893
Speaker 20: year ago, there's a lot of focus just on AI

1000
00:44:57,013 --> 00:45:02,212
Speaker 20: for AI's sake. Yeah, businesses just get on that, get

1001
00:45:02,253 --> 00:45:06,253
Speaker 20: on that train. But now people have started sort of

1002
00:45:06,293 --> 00:45:09,493
Speaker 20: starting a look at a bit differently. They're starting to say, Okay,

1003
00:45:09,533 --> 00:45:13,133
Speaker 20: what's the actual strategic goal that I'm trying to hit

1004
00:45:13,693 --> 00:45:16,252
Speaker 20: with AI rather than just don't forget about a what's

1005
00:45:16,253 --> 00:45:19,053
Speaker 20: the strategic goal? And then how how can AI fit

1006
00:45:19,133 --> 00:45:22,653
Speaker 20: into that? And then's and now they're starting to say, okay,

1007
00:45:22,653 --> 00:45:24,973
Speaker 20: now how do we train our people to be working

1008
00:45:25,013 --> 00:45:29,252
Speaker 20: with that process? And then now what's happening which is real,

1009
00:45:29,333 --> 00:45:32,133
Speaker 20: which is new probably this year, is they're saying, now,

1010
00:45:32,133 --> 00:45:34,533
Speaker 20: how do I redeploy that person so that they can

1011
00:45:34,533 --> 00:45:37,093
Speaker 20: add more value in the above the line. So they're

1012
00:45:37,453 --> 00:45:39,613
Speaker 20: they're looking at it, they're looking at it from a

1013
00:45:39,653 --> 00:45:43,133
Speaker 20: productivity point of view to date, and now they're starting

1014
00:45:43,213 --> 00:45:45,493
Speaker 20: to say, okay, now I released that twenty percent of

1015
00:45:45,573 --> 00:45:48,973
Speaker 20: the person's sort of job that could be automated, how

1016
00:45:49,333 --> 00:45:53,693
Speaker 20: do we then get amplify them to make more revenue

1017
00:45:53,693 --> 00:45:54,253
Speaker 20: for example?

1018
00:45:54,813 --> 00:45:57,573
Speaker 2: So what kind of so what kind of just to describe,

1019
00:45:57,613 --> 00:46:01,013
Speaker 2: what's a common you know, strategic value that people see

1020
00:46:01,053 --> 00:46:04,373
Speaker 2: in AI. What are the common things they want AI

1021
00:46:04,413 --> 00:46:04,732
Speaker 2: to do?

1022
00:46:05,653 --> 00:46:08,253
Speaker 20: Okay, So the biggun of that it just does time.

1023
00:46:08,493 --> 00:46:11,693
Speaker 20: But one of the other major things that is around

1024
00:46:12,173 --> 00:46:16,253
Speaker 20: around documentation. Okay, So if you've got to regulated an industry,

1025
00:46:16,333 --> 00:46:20,133
Speaker 20: let's talk about mortgage brokers. They talk about, you know, insurance,

1026
00:46:20,253 --> 00:46:22,893
Speaker 20: thet's talk about pension funds, things like that. They're very

1027
00:46:22,933 --> 00:46:26,893
Speaker 20: heavy on documentation. And what ends up happening is is

1028
00:46:27,373 --> 00:46:31,733
Speaker 20: relatively highly paid brokers or pench and fund managers or

1029
00:46:32,733 --> 00:46:35,253
Speaker 20: they end up spending quite a lot of their time

1030
00:46:35,493 --> 00:46:40,212
Speaker 20: and making sure that all that documentation is correct. Yes, right,

1031
00:46:40,253 --> 00:46:42,172
Speaker 20: and it's there right now.

1032
00:46:42,333 --> 00:46:42,453
Speaker 8: Now.

1033
00:46:42,493 --> 00:46:44,493
Speaker 20: I'm not saying AI is the only thing involving in that,

1034
00:46:44,533 --> 00:46:48,773
Speaker 20: because it's really a mixture of automation and AI, right,

1035
00:46:48,893 --> 00:46:53,253
Speaker 20: those two things working together and people. Right, But you

1036
00:46:53,293 --> 00:46:58,853
Speaker 20: can knock out about you know, I'm being optimistic fifty percent,

1037
00:46:58,893 --> 00:47:02,173
Speaker 20: but probably more likely twenty to twenty five thirty percent

1038
00:47:02,693 --> 00:47:06,333
Speaker 20: of those those things you can knock out by using

1039
00:47:06,373 --> 00:47:09,973
Speaker 20: AI and workflow. Right now, now, once you've done that,

1040
00:47:10,173 --> 00:47:12,973
Speaker 20: you're either saying, okay, now that you've saved them twenty

1041
00:47:12,973 --> 00:47:16,093
Speaker 20: five of their time, Well, how are we going to

1042
00:47:16,133 --> 00:47:18,893
Speaker 20: now utilize those highly skilled people again, Well, we're going

1043
00:47:18,933 --> 00:47:21,053
Speaker 20: to utilize them by going out and finding more customers,

1044
00:47:21,053 --> 00:47:24,733
Speaker 20: by servicing the existing customers that you've you've got more effectively.

1045
00:47:25,493 --> 00:47:25,732
Speaker 11: Right.

1046
00:47:26,573 --> 00:47:30,373
Speaker 20: So, So, but that that piece there where I think

1047
00:47:30,453 --> 00:47:33,373
Speaker 20: people have been really worried about it taking jobs. But

1048
00:47:33,653 --> 00:47:36,973
Speaker 20: I think what's happened this year as people are now

1049
00:47:37,013 --> 00:47:39,333
Speaker 20: sort of going Okay, now I'm starting to see how

1050
00:47:39,373 --> 00:47:42,773
Speaker 20: I can free more time up for my people. What

1051
00:47:42,773 --> 00:47:47,213
Speaker 20: what strategic goal am I going to apply them to?

1052
00:47:47,933 --> 00:47:48,133
Speaker 11: Right?

1053
00:47:48,493 --> 00:47:51,253
Speaker 20: So that's that's almost going okay, Well, we're not going

1054
00:47:51,253 --> 00:47:53,573
Speaker 20: to knock out people. We're going to keep the people

1055
00:47:54,173 --> 00:47:55,453
Speaker 20: and we're just going to make them do more of

1056
00:47:55,453 --> 00:47:57,933
Speaker 20: the things that they're good at, yeah, right, rather or

1057
00:47:58,013 --> 00:47:59,093
Speaker 20: other high value things.

1058
00:47:59,253 --> 00:48:01,812
Speaker 2: But what about what does that mean for because say,

1059
00:48:02,013 --> 00:48:05,173
Speaker 2: just think of a legal firm. There is there's a

1060
00:48:05,253 --> 00:48:08,172
Speaker 2: pathway from the bottom up where you do a lot

1061
00:48:08,213 --> 00:48:11,053
Speaker 2: of filing. You know, what would you call a legal assistant.

1062
00:48:11,173 --> 00:48:12,252
Speaker 2: I can't remember the league work.

1063
00:48:12,333 --> 00:48:12,533
Speaker 4: Yeah.

1064
00:48:12,613 --> 00:48:14,453
Speaker 2: Yeah, And so so is that going to be a

1065
00:48:14,493 --> 00:48:17,413
Speaker 2: situation where where there isn't straight out of law school

1066
00:48:17,693 --> 00:48:19,853
Speaker 2: a entry level job that you can get to move

1067
00:48:19,933 --> 00:48:22,613
Speaker 2: up because someone further up the chain is using AI

1068
00:48:22,813 --> 00:48:26,933
Speaker 2: to do that that league work, that monkey work, one

1069
00:48:26,973 --> 00:48:27,733
Speaker 2: of a better words.

1070
00:48:28,373 --> 00:48:31,293
Speaker 20: It's possible. But what I'd say is the entire legal

1071
00:48:31,333 --> 00:48:35,053
Speaker 20: industry is right for disrupt they know it, right, that's

1072
00:48:34,853 --> 00:48:36,733
Speaker 20: a that's a that's a little bit of it. And

1073
00:48:36,533 --> 00:48:39,173
Speaker 20: it's also the same and consulting, and you know there

1074
00:48:39,293 --> 00:48:43,573
Speaker 20: are there are levels of work in in there that

1075
00:48:43,613 --> 00:48:47,613
Speaker 20: you won't be able to that won't won't need people for.

1076
00:48:48,053 --> 00:48:50,533
Speaker 20: But what you will need is you'll need almost like

1077
00:48:50,573 --> 00:48:54,573
Speaker 20: a shepherd for the AIS. So you're going to have

1078
00:48:54,613 --> 00:48:58,853
Speaker 20: to have people who know know the not thinking to

1079
00:48:58,853 --> 00:49:02,413
Speaker 20: get subject matter expertise, but also know how to manage

1080
00:49:02,813 --> 00:49:06,692
Speaker 20: agents for example, Yeah, right, a number of a number

1081
00:49:06,733 --> 00:49:09,493
Speaker 20: of agents that that's that probably the most.

1082
00:49:10,853 --> 00:49:11,853
Speaker 11: Yes al agent.

1083
00:49:11,973 --> 00:49:13,973
Speaker 2: So just just for people that are listening, just can

1084
00:49:14,013 --> 00:49:15,533
Speaker 2: you explain what an AI agent is?

1085
00:49:16,373 --> 00:49:16,613
Speaker 18: Well?

1086
00:49:16,893 --> 00:49:21,933
Speaker 20: Yeah, okay, so so it's a it's a million dollar question.

1087
00:49:22,053 --> 00:49:24,252
Speaker 20: Lots of people argue about what exactly they are.

1088
00:49:25,013 --> 00:49:25,692
Speaker 11: So, so.

1089
00:49:27,333 --> 00:49:29,213
Speaker 20: You know how you'll be in chat GPT, and you

1090
00:49:29,253 --> 00:49:33,773
Speaker 20: would you would put a prompt and into chat GPT

1091
00:49:33,933 --> 00:49:38,652
Speaker 20: to say, you know, ask uh, let's let's say you

1092
00:49:38,693 --> 00:49:42,373
Speaker 20: want to research a company, right, you're you're, you're. The

1093
00:49:42,413 --> 00:49:44,933
Speaker 20: way that you work in chat GPT is you'd write

1094
00:49:44,933 --> 00:49:47,293
Speaker 20: out sort of prompt for giving it a context and

1095
00:49:47,333 --> 00:49:50,133
Speaker 20: giving it instructions about what you want to want to

1096
00:49:50,413 --> 00:49:54,133
Speaker 20: get out of that out of that exercise. Now, what

1097
00:49:54,213 --> 00:50:00,373
Speaker 20: an agent does is basically codifies. So it's basically as

1098
00:50:00,493 --> 00:50:03,652
Speaker 20: it's like a repetitive prompt. You've you've been a lot

1099
00:50:03,693 --> 00:50:06,453
Speaker 20: of instructions you've given it. You've told it to get

1100
00:50:06,493 --> 00:50:12,493
Speaker 20: a look at specific professional texts for example, you've given

1101
00:50:12,493 --> 00:50:16,653
Speaker 20: it a role. You've said you are an expert pitching

1102
00:50:16,693 --> 00:50:20,373
Speaker 20: fund manager. Yeah, right right, And you're giving it a

1103
00:50:20,413 --> 00:50:23,413
Speaker 20: set of instructions because you want a repeatable outcome or

1104
00:50:23,893 --> 00:50:27,933
Speaker 20: an outcome that sits within a band of nondeterministic bands.

1105
00:50:28,053 --> 00:50:28,212
Speaker 21: Right.

1106
00:50:28,973 --> 00:50:32,053
Speaker 20: And so what it means then is that you don't

1107
00:50:32,053 --> 00:50:34,212
Speaker 20: have to write out your prompt every time. You can

1108
00:50:34,253 --> 00:50:36,933
Speaker 20: go to your agent and it can perform that those duties. Now,

1109
00:50:36,933 --> 00:50:38,453
Speaker 20: I'll just give you one example that it comes with

1110
00:50:38,693 --> 00:50:41,413
Speaker 20: different examples of what it can do, but it's basically

1111
00:50:41,813 --> 00:50:44,853
Speaker 20: codifying a repeatable process.

1112
00:50:46,013 --> 00:50:47,733
Speaker 2: Sending it off, and it's on its mission for you.

1113
00:50:48,733 --> 00:50:51,573
Speaker 4: Yeah, So wouldn't that be a way for newcomers into

1114
00:50:51,613 --> 00:50:54,373
Speaker 4: an industry like the legal profession, because we've talked about that.

1115
00:50:54,373 --> 00:50:56,973
Speaker 4: That's the way they can future proof themselves, right, is

1116
00:50:57,013 --> 00:51:01,933
Speaker 4: knowing more about how to shepherd AI than the partners

1117
00:51:01,973 --> 00:51:04,093
Speaker 4: of a particular firm. So they're bringing that school to

1118
00:51:04,133 --> 00:51:06,493
Speaker 4: the table and still have that ability maybe to move

1119
00:51:06,533 --> 00:51:08,253
Speaker 4: through Absolutely.

1120
00:51:08,493 --> 00:51:10,933
Speaker 20: That's you know, I like, there's so you've just got

1121
00:51:10,973 --> 00:51:13,493
Speaker 20: to You've got to. AI is not perfect and it's

1122
00:51:13,533 --> 00:51:15,973
Speaker 20: not magic. It has got lots of flaws at the moment,

1123
00:51:16,013 --> 00:51:18,533
Speaker 20: it really really has, and people have to be you

1124
00:51:18,653 --> 00:51:20,693
Speaker 20: have to have what we call human under loop. The

1125
00:51:20,853 --> 00:51:23,773
Speaker 20: human has to be in the loop. Right, there's no

1126
00:51:24,093 --> 00:51:26,813
Speaker 20: I would not let an AI make a decision for me.

1127
00:51:27,053 --> 00:51:30,613
Speaker 20: I would get it to propose something and then I've

1128
00:51:30,613 --> 00:51:34,533
Speaker 20: got and I do adject myself or other agents that

1129
00:51:34,573 --> 00:51:37,053
Speaker 20: I go and apply to that to stress test that

1130
00:51:37,213 --> 00:51:39,172
Speaker 20: and throw out red flags or whatever.

1131
00:51:39,373 --> 00:51:41,772
Speaker 4: Right, you probably just answered the question I had one,

1132
00:51:41,853 --> 00:51:44,893
Speaker 4: which was why not let an AI be an agent

1133
00:51:44,933 --> 00:51:47,693
Speaker 4: of another AI, but clearly there's a long way to

1134
00:51:47,693 --> 00:51:49,893
Speaker 4: go before that would be a safe thing to do.

1135
00:51:50,773 --> 00:51:52,772
Speaker 20: Yeah, but I mean, I think I think the other

1136
00:51:52,813 --> 00:51:55,613
Speaker 20: thing is that that, like I say, AI is not magic.

1137
00:51:56,093 --> 00:51:59,212
Speaker 20: It is a machine still, right, and you've got and

1138
00:51:59,493 --> 00:52:01,373
Speaker 20: you've really got to make sure that you've got people

1139
00:52:01,453 --> 00:52:05,893
Speaker 20: in there looking out the outputs and making the final decisions.

1140
00:52:05,893 --> 00:52:08,213
Speaker 20: That's that's absolutely absolutely critical.

1141
00:52:08,813 --> 00:52:14,172
Speaker 2: It's not magic now, but it exponentially has grown as

1142
00:52:14,213 --> 00:52:17,333
Speaker 2: a large language lange and it felt like the increases

1143
00:52:17,333 --> 00:52:19,613
Speaker 2: were really rapid, and then it feels like it's tailed

1144
00:52:19,653 --> 00:52:21,973
Speaker 2: off a little bit, but there is, there's lots of

1145
00:52:21,973 --> 00:52:25,733
Speaker 2: different applications coming through, but it is advancing an incredible

1146
00:52:25,853 --> 00:52:28,173
Speaker 2: rate and you know, the sam Ackmans of this world

1147
00:52:28,213 --> 00:52:31,253
Speaker 2: have got big plans for it. So yeah, is it

1148
00:52:31,493 --> 00:52:35,373
Speaker 2: just an ever ever shifting goalpost and what you set

1149
00:52:35,453 --> 00:52:38,733
Speaker 2: up now might just change with chat GBT seven.

1150
00:52:40,093 --> 00:52:43,293
Speaker 20: Yeah, but I think I think that what's what's also

1151
00:52:43,333 --> 00:52:46,172
Speaker 20: happening along along this and Sam Olton also the same

1152
00:52:46,413 --> 00:52:49,333
Speaker 20: It says that soften which is which is you know

1153
00:52:49,373 --> 00:52:53,693
Speaker 20: there are some fundamental sort of principles to it, Like

1154
00:52:54,093 --> 00:52:57,013
Speaker 20: you know, you've got to you've got to train for

1155
00:52:57,173 --> 00:52:59,933
Speaker 20: people to use it properly. Like, what's happening at the

1156
00:52:59,933 --> 00:53:01,933
Speaker 20: moment is shot a lot of shadow. Are you looking

1157
00:53:01,933 --> 00:53:04,213
Speaker 20: at all about that? Where where people are people are

1158
00:53:04,213 --> 00:53:07,172
Speaker 20: bringing the chat their GPT into work and using that

1159
00:53:07,333 --> 00:53:10,373
Speaker 20: and a really unstructured way. Now, what we used to

1160
00:53:10,533 --> 00:53:13,732
Speaker 20: what we used to do in all businesses and all

1161
00:53:13,773 --> 00:53:17,213
Speaker 20: my experience was we would be really really pedantic about

1162
00:53:17,213 --> 00:53:20,093
Speaker 20: what process people were using to get to a particular outcome,

1163
00:53:20,373 --> 00:53:22,613
Speaker 20: and you would have that as part of your operating model.

1164
00:53:22,853 --> 00:53:26,413
Speaker 20: Right now, what's happening is they're basically going that's basically

1165
00:53:26,413 --> 00:53:28,573
Speaker 20: been thrown out the window to some degree and said, okay,

1166
00:53:28,573 --> 00:53:30,053
Speaker 20: you just used GBT to do it.

1167
00:53:30,333 --> 00:53:30,893
Speaker 11: No way man.

1168
00:53:31,293 --> 00:53:35,533
Speaker 20: You know that's basically and that's basically an unqualified outcome

1169
00:53:35,533 --> 00:53:37,853
Speaker 20: that you're leaving to come into your organization. That we

1170
00:53:37,973 --> 00:53:40,813
Speaker 20: can't allow those that to happen. You've got to have

1171
00:53:40,893 --> 00:53:43,773
Speaker 20: principles around that to be able to be able to

1172
00:53:43,813 --> 00:53:46,333
Speaker 20: go and he sum Olton goes goes on about that

1173
00:53:46,413 --> 00:53:49,293
Speaker 20: quite a lot. As you've got to train people very well.

1174
00:53:49,333 --> 00:53:51,253
Speaker 20: You've got to make sure you're doing your change management

1175
00:53:51,293 --> 00:53:54,173
Speaker 20: and adoption and dealing with people and making sure that

1176
00:53:54,213 --> 00:53:56,613
Speaker 20: they're not fearful and all those sorts of all those

1177
00:53:56,613 --> 00:53:59,333
Speaker 20: sorts of things as well as you know how you

1178
00:53:59,333 --> 00:54:02,453
Speaker 20: know the technicalities of getting the best out of your AI.

1179
00:54:02,613 --> 00:54:04,533
Speaker 2: You know, Well, thank you so much, chef for calling

1180
00:54:04,613 --> 00:54:06,013
Speaker 2: in with that. It's great to ge niques. But what's

1181
00:54:06,013 --> 00:54:07,773
Speaker 2: the name of your operation?

1182
00:54:08,453 --> 00:54:11,853
Speaker 20: Well, jeez, plug plug we love a plug plug away.

1183
00:54:12,653 --> 00:54:17,413
Speaker 20: Mine is called pro pro Gentic, pro ge Ntic progen

1184
00:54:17,613 --> 00:54:19,813
Speaker 20: dot cog. Indeed we're based in.

1185
00:54:19,813 --> 00:54:22,893
Speaker 2: Cussia, Tugentic. All right, next one, appreciate it.

1186
00:54:22,933 --> 00:54:25,093
Speaker 4: What a great call. Thank you very much. Can you

1187
00:54:25,133 --> 00:54:28,093
Speaker 4: hear from you? Oh eight hundred at eighty ten eighty

1188
00:54:28,133 --> 00:54:30,453
Speaker 4: spit it out, tyler? What are you doing to future

1189
00:54:30,453 --> 00:54:32,493
Speaker 4: proof yourself with the advancement of AI?

1190
00:54:32,813 --> 00:54:36,613
Speaker 2: Will AI destroy your job or make work more meaningful?

1191
00:54:36,853 --> 00:54:39,013
Speaker 4: Come on through, It's twenty four past two.

1192
00:54:40,133 --> 00:54:42,533
Speaker 2: Here's a question, what if your home could buy your

1193
00:54:42,613 --> 00:54:45,973
Speaker 2: next one? Sounds impossible, right, That's exactly what we thought

1194
00:54:46,053 --> 00:54:49,213
Speaker 2: until we talked with the Staircase crew on their podcast.

1195
00:54:49,573 --> 00:54:52,253
Speaker 2: They'ved how thousands of Kiwis use the equity in their

1196
00:54:52,253 --> 00:54:55,853
Speaker 2: homes to build wealth through smart property investing. No tracks,

1197
00:54:55,933 --> 00:54:58,693
Speaker 2: just real numbers. Real strategy and a team that actually

1198
00:54:58,733 --> 00:55:00,973
Speaker 2: knows what they're doing. And the best part is they

1199
00:55:01,013 --> 00:55:03,613
Speaker 2: explain it in plain English. You don't have to be

1200
00:55:03,653 --> 00:55:06,253
Speaker 2: a finance nerd to get it. We've got into it

1201
00:55:06,333 --> 00:55:09,813
Speaker 2: on the Alternative Guide to Property Investment on the Staircase podcast.

1202
00:55:09,893 --> 00:55:13,013
Speaker 2: The myths, the mistakes, the mindset shifts that turn your

1203
00:55:13,053 --> 00:55:17,093
Speaker 2: home into your first investment tool. We cover all of it.

1204
00:55:17,373 --> 00:55:19,613
Speaker 2: We're to start what to watch out for and how

1205
00:55:19,653 --> 00:55:22,133
Speaker 2: to build a plan that works for you. Go check

1206
00:55:22,173 --> 00:55:25,773
Speaker 2: it out on iHeartRadio, find it at Staircase, dot co,

1207
00:55:26,213 --> 00:55:28,533
Speaker 2: dot and z. It's eye opening stuff.

1208
00:55:32,493 --> 00:55:33,893
Speaker 13: Matt Heath and Tyler Adams.

1209
00:55:33,933 --> 00:55:37,133
Speaker 1: Afternoons, call oh eight hundred eighty ten eighty on News

1210
00:55:37,213 --> 00:55:38,053
Speaker 1: Talk ZB.

1211
00:55:38,093 --> 00:55:41,293
Speaker 4: Very good afternoon to you. So is AI going to

1212
00:55:41,293 --> 00:55:43,772
Speaker 4: create more jobs than it destroys? What do you reckon?

1213
00:55:43,813 --> 00:55:45,773
Speaker 4: I eighte hundred eighty ten eighty is the number to

1214
00:55:45,813 --> 00:55:46,813
Speaker 4: call these people?

1215
00:55:46,813 --> 00:55:50,733
Speaker 2: What are they again? That's the chief There's Diane Coyle

1216
00:55:50,813 --> 00:55:56,653
Speaker 2: who's a at Cambridge Public Economics Advisor.

1217
00:55:56,293 --> 00:56:00,813
Speaker 4: YEP, and then there's Google's chief economist Fabian Quarto Mila,

1218
00:56:01,253 --> 00:56:04,573
Speaker 4: who both are somewhat positive about the future of jobs

1219
00:56:04,613 --> 00:56:04,973
Speaker 4: and AI.

1220
00:56:05,133 --> 00:56:09,413
Speaker 2: Well massively. They predicted AO well the egg that the

1221
00:56:09,653 --> 00:56:13,172
Speaker 2: claims that AI will wipe out jobs are massively exaggerated.

1222
00:56:13,453 --> 00:56:18,693
Speaker 2: They point to electricians and they say AI has created

1223
00:56:18,693 --> 00:56:21,333
Speaker 2: the need for one hundred and thirty thousand additional electricians

1224
00:56:21,373 --> 00:56:24,493
Speaker 2: in the coming years in the US, largely because they

1225
00:56:24,653 --> 00:56:27,493
Speaker 2: need to build all the data centers and manufacturing facilities.

1226
00:56:27,733 --> 00:56:30,093
Speaker 2: But what I say to that is what about I mean,

1227
00:56:30,093 --> 00:56:32,732
Speaker 2: I've been looking at those Tesla robots that Elon musk

1228
00:56:32,813 --> 00:56:33,613
Speaker 2: pumping out.

1229
00:56:33,733 --> 00:56:37,013
Speaker 4: And it's good for the Sparkys for a couple of years,

1230
00:56:37,013 --> 00:56:38,613
Speaker 4: and that once those robots.

1231
00:56:38,733 --> 00:56:42,053
Speaker 2: Until the computers get hands. Yeah, I mean, I don't know,

1232
00:56:42,093 --> 00:56:44,772
Speaker 2: maybe that maybe that'll never come. Maybe it'll maybe it'll

1233
00:56:44,813 --> 00:56:48,973
Speaker 2: be as Juan said, it's got to be overseen. So

1234
00:56:49,013 --> 00:56:52,533
Speaker 2: you just increase productivity. So you've got one knowledgeable electrician

1235
00:56:52,693 --> 00:56:56,853
Speaker 2: and he's overseeing you know, ten terrifying robots.

1236
00:56:56,653 --> 00:56:59,252
Speaker 4: Have a human shepherd. Yeah, Oh, one hundred and eighty.

1237
00:56:59,253 --> 00:57:00,453
Speaker 4: Ten eighty is the number to.

1238
00:57:00,413 --> 00:57:02,693
Speaker 2: Call, Jason, Welcome to the show.

1239
00:57:04,013 --> 00:57:06,693
Speaker 4: Hello, boys, how are you very well? You want to

1240
00:57:06,773 --> 00:57:08,812
Speaker 4: kick back a little bit or on what one was saying?

1241
00:57:09,653 --> 00:57:12,373
Speaker 19: Yeah, I think he's just another one of these guys,

1242
00:57:12,013 --> 00:57:16,133
Speaker 19: that's gone a man of money at AI. So I

1243
00:57:16,173 --> 00:57:18,813
Speaker 19: mean everything he said was just backwards. It sounds like

1244
00:57:18,813 --> 00:57:20,373
Speaker 19: a five year old talking. I mean, I don't mean

1245
00:57:20,373 --> 00:57:23,893
Speaker 19: to be disrespectful because he can't not need to defend himself,

1246
00:57:23,893 --> 00:57:26,373
Speaker 19: but you know he's basically saying that people are going

1247
00:57:26,453 --> 00:57:27,493
Speaker 19: to be able to do more work.

1248
00:57:27,533 --> 00:57:29,013
Speaker 20: Well that's exactly right, Like.

1249
00:57:29,693 --> 00:57:31,893
Speaker 19: An accountant will be able to do twice as much

1250
00:57:31,933 --> 00:57:34,573
Speaker 19: work because the information will get fed into AI and

1251
00:57:34,613 --> 00:57:36,853
Speaker 19: it'll get spat out, which means half the accountants will

1252
00:57:36,853 --> 00:57:39,373
Speaker 19: get laid off. I mean, I got it to write

1253
00:57:39,373 --> 00:57:42,013
Speaker 19: me a letter of offer yesterday, which was well above

1254
00:57:42,213 --> 00:57:45,653
Speaker 19: my pay grade. To write it was fantastic, and once

1255
00:57:45,693 --> 00:57:48,813
Speaker 19: I got it, I just you know, the bells rang,

1256
00:57:48,893 --> 00:57:50,613
Speaker 19: and I went through and peaked a few little bits

1257
00:57:50,613 --> 00:57:53,413
Speaker 19: and pieces looked like it was written by bell gullies

1258
00:57:53,413 --> 00:57:53,773
Speaker 19: in town.

1259
00:57:56,053 --> 00:57:59,333
Speaker 2: Do you still would you be confident enough to use

1260
00:57:59,453 --> 00:58:02,613
Speaker 2: that without having someone qualified to go through?

1261
00:58:04,653 --> 00:58:08,933
Speaker 19: Absolutely yes. I mean, like I say, it run a

1262
00:58:08,933 --> 00:58:10,653
Speaker 19: whole lot of bells, so there's a whole lot of

1263
00:58:10,653 --> 00:58:12,613
Speaker 19: things that it came up with it I didn't think about.

1264
00:58:13,533 --> 00:58:16,693
Speaker 19: So yeah, one hundred percent what I did of sorry

1265
00:58:16,733 --> 00:58:21,733
Speaker 19: sent son.

1266
00:58:20,093 --> 00:58:24,253
Speaker 2: A situation where just everything gets done because generally speaking,

1267
00:58:24,293 --> 00:58:27,373
Speaker 2: things are back backlogged and we're not getting through everything

1268
00:58:27,373 --> 00:58:30,693
Speaker 2: we need to get done. So maybe a person teaches

1269
00:58:30,733 --> 00:58:33,093
Speaker 2: their job but does ten times the work and then

1270
00:58:33,133 --> 00:58:36,093
Speaker 2: everything and gets more efficient and productivity. Because one thing's

1271
00:58:36,093 --> 00:58:37,933
Speaker 2: for sure in New Zealand's got terrible productivity.

1272
00:58:39,093 --> 00:58:44,093
Speaker 19: Well right now it can. It can make lawyers and

1273
00:58:44,133 --> 00:58:47,253
Speaker 19: accountants a whole lot faster, and lots of other people

1274
00:58:47,373 --> 00:58:49,453
Speaker 19: doing lots of other lots of other things can make

1275
00:58:49,453 --> 00:58:51,293
Speaker 19: them a whole lot faster. In a couple of years,

1276
00:58:51,893 --> 00:58:55,773
Speaker 19: that'll double. So instead of instead of it harving the

1277
00:58:55,773 --> 00:58:59,813
Speaker 19: amount of lawyers'll it'll you know, there'll be only a

1278
00:58:59,853 --> 00:59:03,893
Speaker 19: quarter as many, and it'll just get progressively more and more.

1279
00:59:03,973 --> 00:59:06,813
Speaker 19: Like so all the white collar guys, it all have

1280
00:59:06,893 --> 00:59:10,853
Speaker 19: mortgages and families and cars, and they're all getting laid

1281
00:59:10,853 --> 00:59:15,093
Speaker 19: off any matter of time or or massively reduced because

1282
00:59:15,133 --> 00:59:17,733
Speaker 19: each one of them will be able to do twice

1283
00:59:17,773 --> 00:59:18,413
Speaker 19: as much work.

1284
00:59:18,933 --> 00:59:22,533
Speaker 4: Yeah, but that that human shepherd element, do you not

1285
00:59:22,653 --> 00:59:25,053
Speaker 4: still think that will have to remain at some some

1286
00:59:25,333 --> 00:59:27,653
Speaker 4: element there jasent When we talk about the legal reficion,

1287
00:59:27,733 --> 00:59:29,813
Speaker 4: you say it's going to replace lawyers. So then what

1288
00:59:29,933 --> 00:59:32,733
Speaker 4: happens to the justice system. Do the judges get replaced

1289
00:59:32,733 --> 00:59:35,933
Speaker 4: by AIS? Is that AI trying to defend against other AIS.

1290
00:59:35,933 --> 00:59:37,373
Speaker 4: You see what I'm saying is there's got to be

1291
00:59:37,413 --> 00:59:38,493
Speaker 4: some controlsant place.

1292
00:59:38,573 --> 00:59:41,733
Speaker 19: Probably not, but judges will Judges will get AI to

1293
00:59:41,773 --> 00:59:43,533
Speaker 19: write a lot of their reports so they'll be able.

1294
00:59:43,533 --> 00:59:45,253
Speaker 19: There won't be anywhere there as busy, So you probably

1295
00:59:45,253 --> 00:59:48,933
Speaker 19: won't need as many judges because you know the judge, right,

1296
00:59:49,053 --> 00:59:51,773
Speaker 19: the judge will packed the notes, you'll feed in another

1297
00:59:51,813 --> 00:59:54,773
Speaker 19: one that was similar, and the AI will spit it out.

1298
00:59:54,813 --> 00:59:56,173
Speaker 19: Then he all goes through and do it in half

1299
00:59:56,213 --> 00:59:57,813
Speaker 19: the time. So instead of taking a day to do it,

1300
00:59:57,893 --> 00:59:58,773
Speaker 19: take a half day to do it.

1301
00:59:59,453 --> 01:00:01,813
Speaker 2: Yeah, that's an interesting point. Just for example.

1302
01:00:01,933 --> 01:00:05,613
Speaker 19: I mean, you don't need as many judges. And you

1303
01:00:05,653 --> 01:00:09,133
Speaker 19: know that the roofer it's not going to fit roofing

1304
01:00:09,133 --> 01:00:11,493
Speaker 19: at all, except all the lawyers in the accountants are

1305
01:00:11,533 --> 01:00:12,813
Speaker 19: going to get laid off and they're the ones that

1306
01:00:12,813 --> 01:00:15,333
Speaker 19: employ roofs to do their house up. So they're all

1307
01:00:15,333 --> 01:00:16,253
Speaker 19: getting you know.

1308
01:00:16,253 --> 01:00:19,013
Speaker 2: They get yeah, yeah, I see what you're saying.

1309
01:00:19,213 --> 01:00:21,413
Speaker 19: It's going to fit everything. So all these guys that

1310
01:00:21,453 --> 01:00:23,253
Speaker 19: come along, I think it's one of the most dangerous

1311
01:00:23,293 --> 01:00:26,933
Speaker 19: things that's ever come. I think this is one of

1312
01:00:26,973 --> 01:00:30,413
Speaker 19: the most dangerous things that's ever been invented or introduced

1313
01:00:30,413 --> 01:00:31,853
Speaker 19: to humanity.

1314
01:00:31,693 --> 01:00:35,173
Speaker 2: So you don't buy the argument, Jason, that we've gone

1315
01:00:35,213 --> 01:00:39,213
Speaker 2: through all these revolutions, the agricultural revolution, the industrial revolution,

1316
01:00:39,453 --> 01:00:43,933
Speaker 2: the computerization since the nineteen seventies, and each one of

1317
01:00:43,973 --> 01:00:48,853
Speaker 2: those revolutions has increased the amount of jobs. Because you know,

1318
01:00:49,133 --> 01:00:51,293
Speaker 2: throughout a lot of these things, we've also had a

1319
01:00:51,333 --> 01:00:55,133
Speaker 2: woman entering the workforce and greater numbers, and the number's

1320
01:00:55,133 --> 01:00:58,413
Speaker 2: gone down. So do you think that, just to let

1321
01:00:58,493 --> 01:01:01,373
Speaker 2: me finish, Jason, do you think that this is just

1322
01:01:01,653 --> 01:01:04,013
Speaker 2: completely different than those other revolutions.

1323
01:01:04,813 --> 01:01:06,893
Speaker 19: Well, I'll give you an example. I think it's very,

1324
01:01:07,013 --> 01:01:11,173
Speaker 19: very different, And I'll give you an example. You know,

1325
01:01:11,253 --> 01:01:15,733
Speaker 19: like immigration has been great, you know, for a lot

1326
01:01:15,773 --> 01:01:18,653
Speaker 19: of countries for a lot of years. But how do

1327
01:01:18,693 --> 01:01:20,853
Speaker 19: you think, how did the Indians in America and what

1328
01:01:21,133 --> 01:01:23,813
Speaker 19: do they think of immigration? Or they have original is

1329
01:01:23,853 --> 01:01:25,853
Speaker 19: in Australia, what do they think of immigration?

1330
01:01:25,893 --> 01:01:26,093
Speaker 4: You know?

1331
01:01:26,173 --> 01:01:29,693
Speaker 19: So like you think, yes, these things can be great,

1332
01:01:29,853 --> 01:01:32,053
Speaker 19: but also they can be disastrous. And I think this

1333
01:01:32,053 --> 01:01:34,573
Speaker 19: particular one is going to be disastrous because it's literally

1334
01:01:34,613 --> 01:01:39,413
Speaker 19: going to send huge portions of the of the workforce

1335
01:01:39,493 --> 01:01:42,693
Speaker 19: to do the goal cues. And what's the lawyer're going

1336
01:01:42,693 --> 01:01:45,093
Speaker 19: to do? I mean, he's a useless individual. You can't

1337
01:01:45,133 --> 01:01:46,973
Speaker 19: dig a hole or put in a post or fix

1338
01:01:47,013 --> 01:01:48,853
Speaker 19: a car. All he knows how to do is be

1339
01:01:48,893 --> 01:01:51,493
Speaker 19: a lawyer for everything else.

1340
01:01:51,573 --> 01:01:53,853
Speaker 2: But isn't there a certain part of all these things,

1341
01:01:53,973 --> 01:01:58,253
Speaker 2: which is the you know, we crave human interaction, and

1342
01:01:58,773 --> 01:02:00,773
Speaker 2: a lot of what we do is how we socialize

1343
01:02:00,773 --> 01:02:03,653
Speaker 2: with very social creatures. So a lot of what, a

1344
01:02:03,653 --> 01:02:04,773
Speaker 2: lot of what what? What?

1345
01:02:04,773 --> 01:02:04,893
Speaker 10: What?

1346
01:02:04,933 --> 01:02:05,093
Speaker 15: You do?

1347
01:02:05,533 --> 01:02:08,613
Speaker 2: You think that's rubbish, You think that we're not social creatures.

1348
01:02:09,053 --> 01:02:09,893
Speaker 19: Not what we are.

1349
01:02:09,933 --> 01:02:12,333
Speaker 2: But you try living by yourself and how long would

1350
01:02:12,373 --> 01:02:16,013
Speaker 2: you go living byself in the bush with GBT and.

1351
01:02:16,013 --> 01:02:18,733
Speaker 19: Listen, that's all lovely and romantic, but the reality is,

1352
01:02:20,093 --> 01:02:22,893
Speaker 19: if some if there's a lawyer out there that can

1353
01:02:23,253 --> 01:02:26,013
Speaker 19: reduce us operating cost of his business by fifty percent

1354
01:02:26,693 --> 01:02:29,133
Speaker 19: by using AI and second half as lawyers and turning

1355
01:02:29,173 --> 01:02:30,813
Speaker 19: over just the same amount of money, he's going to

1356
01:02:30,813 --> 01:02:31,933
Speaker 19: do it every single time.

1357
01:02:32,573 --> 01:02:37,253
Speaker 4: M Well, Jason, I mean I'm not saying that you're wrong.

1358
01:02:38,533 --> 01:02:41,973
Speaker 19: It's not. It's just it's clearly the reality. It's already

1359
01:02:41,973 --> 01:02:42,733
Speaker 19: happening right now.

1360
01:02:44,173 --> 01:02:45,853
Speaker 2: I mean, I mean, what what I mean? I pushed

1361
01:02:45,853 --> 01:02:47,613
Speaker 2: back a little bit on on some of it. I

1362
01:02:47,613 --> 01:02:50,173
Speaker 2: think I think in any revolution there are definitely winners.

1363
01:02:50,173 --> 01:02:54,013
Speaker 2: There's definitely winners and losers. Jason, absolutely, But I think

1364
01:02:54,053 --> 01:02:56,973
Speaker 2: that human side of things is incredibly important. Otherwise, why

1365
01:02:56,973 --> 01:02:58,933
Speaker 2: do we go to why do we go to rock

1366
01:02:58,973 --> 01:03:02,693
Speaker 2: concerts when you can hear that exact music on a

1367
01:03:02,853 --> 01:03:06,213
Speaker 2: on a recording and played better because they've spent more

1368
01:03:06,213 --> 01:03:09,133
Speaker 2: time playing it. So there is a huge part of

1369
01:03:09,493 --> 01:03:13,133
Speaker 2: any bit of business and different ways of earning money

1370
01:03:13,533 --> 01:03:16,573
Speaker 2: that are down to the human contact that we crave.

1371
01:03:16,773 --> 01:03:17,693
Speaker 10: I'll give you, I'll give you.

1372
01:03:17,853 --> 01:03:20,733
Speaker 19: I'll give you a really good example right of something

1373
01:03:20,773 --> 01:03:24,333
Speaker 19: that's similar to this. Amazon. Amazon has started up and

1374
01:03:24,373 --> 01:03:27,173
Speaker 19: it's become massive. And how many businesses in America has

1375
01:03:27,293 --> 01:03:29,813
Speaker 19: put out a business shops and things. I mean, could

1376
01:03:29,853 --> 01:03:32,333
Speaker 19: we say it's to put one hundred thousand businesses out

1377
01:03:32,333 --> 01:03:34,333
Speaker 19: of business? Do you think that will be a fair

1378
01:03:34,333 --> 01:03:35,853
Speaker 19: and a fair and reasonable number?

1379
01:03:37,333 --> 01:03:39,653
Speaker 2: But I believe it. I believe it. Jason, Well, it's.

1380
01:03:39,493 --> 01:03:41,813
Speaker 19: Me's say a hundred thousand, right, So one hundred thousand

1381
01:03:41,813 --> 01:03:44,973
Speaker 19: mom and dads that don't have a job now, right,

1382
01:03:45,013 --> 01:03:47,973
Speaker 19: And there's a small amount of senior management in Amazon.

1383
01:03:48,693 --> 01:03:53,453
Speaker 19: Let's say there's five thousand small managers of senior management

1384
01:03:53,573 --> 01:03:55,013
Speaker 19: and Amazon running the company.

1385
01:03:55,093 --> 01:03:55,253
Speaker 8: Right.

1386
01:03:55,493 --> 01:03:58,413
Speaker 2: Yeah. I mean you make a really good argument, Jason,

1387
01:03:58,773 --> 01:04:02,293
Speaker 2: And that is definitely the other side to it that

1388
01:04:01,853 --> 01:04:03,973
Speaker 2: these two people are trying to put up the other

1389
01:04:04,013 --> 01:04:06,413
Speaker 2: one but have to go to the eat news headlines.

1390
01:04:06,413 --> 01:04:07,573
Speaker 2: But thanks so much for calling, Jason.

1391
01:04:07,653 --> 01:04:09,613
Speaker 4: Yeah, good call. So one hundred and eighteen eighty is

1392
01:04:09,613 --> 01:04:11,653
Speaker 4: the number to call love to hear your thoughts about

1393
01:04:11,653 --> 01:04:14,093
Speaker 4: whether AI is going to take your jobs or create

1394
01:04:14,173 --> 01:04:15,853
Speaker 4: new ones. It is twenty five to.

1395
01:04:15,853 --> 01:04:19,053
Speaker 13: Three deuce talks.

1396
01:04:19,053 --> 01:04:22,493
Speaker 14: There be headlines with blue bubble taxis, it's no trouble

1397
01:04:22,533 --> 01:04:25,373
Speaker 14: with a blue bubble. Two Capra vans have rolled in

1398
01:04:25,493 --> 01:04:29,133
Speaker 14: strong winds near Kaikoda, with one blocking State Highway one

1399
01:04:29,173 --> 01:04:32,853
Speaker 14: near Hapuku River Bridge just north of Caikota. There are

1400
01:04:32,893 --> 01:04:37,173
Speaker 14: no life threatening injuries. The persons died in Wellington this morning,

1401
01:04:37,453 --> 01:04:40,053
Speaker 14: hit by a branch on a track on Mont Victoria.

1402
01:04:40,693 --> 01:04:44,253
Speaker 14: Flights have resumed at Wellington Airport, but thousands of homes

1403
01:04:44,253 --> 01:04:48,333
Speaker 14: in Wellington and Manawatu Huanganui have lost power. Further south,

1404
01:04:48,373 --> 01:04:51,773
Speaker 14: flooding and slips have closed several main South Island roads,

1405
01:04:51,813 --> 01:04:56,453
Speaker 14: including a State Highway seven and Lewis Pass. The Government's

1406
01:04:56,493 --> 01:05:00,213
Speaker 14: appointing a Crown Facilitator to help White Taki District Council

1407
01:05:00,533 --> 01:05:04,293
Speaker 14: add assessment of networks to its water service delivery plan

1408
01:05:04,733 --> 01:05:09,573
Speaker 14: is required under new local Water Doune well laws. Torpedo

1409
01:05:09,693 --> 01:05:12,533
Speaker 14: seven will return to being an online only retailer by

1410
01:05:12,573 --> 01:05:14,933
Speaker 14: the end of summer. It was sold by the Warehouse

1411
01:05:14,973 --> 01:05:18,573
Speaker 14: Group for one dollar in February last year, and christ

1412
01:05:18,653 --> 01:05:21,653
Speaker 14: Church Central's labor MP Duncan Weber is calling it quits

1413
01:05:21,733 --> 01:05:24,493
Speaker 14: next year. He's been in the job since twenty seventeen.

1414
01:05:25,533 --> 01:05:28,453
Speaker 14: Alliance Farmers back two hundred and seventy dollar milliar is

1415
01:05:28,493 --> 01:05:32,053
Speaker 14: two hundred and seventy million dollar deal for Irish company

1416
01:05:32,053 --> 01:05:35,333
Speaker 14: to take control of meat processor. Find out more at

1417
01:05:35,413 --> 01:05:38,733
Speaker 14: NZ Herald Premium. Back to Matt Eath and Tyler Adams.

1418
01:05:38,413 --> 01:05:40,773
Speaker 4: Thank you very much, Raylane having a good discussion about

1419
01:05:40,773 --> 01:05:43,493
Speaker 4: AI and whether it will create or destroy jobs.

1420
01:05:43,573 --> 01:05:48,533
Speaker 2: So just to summarize what these two are trying to

1421
01:05:48,573 --> 01:05:50,693
Speaker 2: say in this article, and then you know the high

1422
01:05:50,733 --> 01:05:53,133
Speaker 2: level of economists you know, and one of them, of course,

1423
01:05:53,213 --> 01:05:56,053
Speaker 2: is massively compromised because they work for Google yep, which

1424
01:05:56,093 --> 01:05:58,493
Speaker 2: is a big proponent of AI. Obviously very true and

1425
01:05:58,813 --> 01:06:00,413
Speaker 2: trying to make as much money as they can from it,

1426
01:06:00,493 --> 01:06:04,693
Speaker 2: but this is their take. AI, like most technologies, will

1427
01:06:04,733 --> 01:06:08,293
Speaker 2: transform but not destroy the labor market. It automates repetitive tasks,

1428
01:06:08,373 --> 01:06:12,853
Speaker 2: freeing humans for creative and interpersonal work. New technologies historically

1429
01:06:12,933 --> 01:06:15,853
Speaker 2: generate more employment than they displace, and AI will be

1430
01:06:15,933 --> 01:06:19,573
Speaker 2: no different from engineers building data centers, to teachers using

1431
01:06:19,613 --> 01:06:23,493
Speaker 2: AI tools, et cetera. With reskilling, societies can harness AI

1432
01:06:23,613 --> 01:06:27,013
Speaker 2: to increase productivity, create higher value jobs, and improve quality

1433
01:06:27,053 --> 01:06:28,213
Speaker 2: of life across the board.

1434
01:06:28,813 --> 01:06:29,133
Speaker 13: I get.

1435
01:06:29,173 --> 01:06:33,573
Speaker 2: The other side of it, which Jason was making was

1436
01:06:33,613 --> 01:06:38,453
Speaker 2: that AIS scale and speed will outpace society's ability to adapt.

1437
01:06:38,693 --> 01:06:42,213
Speaker 2: Unlike previous waves. It affects cognitive as well as manual work,

1438
01:06:42,293 --> 01:06:47,373
Speaker 2: threatening white colstability. Retraining programs often sound promising, but they

1439
01:06:47,373 --> 01:06:51,533
Speaker 2: fail to reach those most in need. Automation could concentrate

1440
01:06:51,613 --> 01:06:54,693
Speaker 2: wealth and power amongst just the tech elites, hollowing out

1441
01:06:54,693 --> 01:06:58,853
Speaker 2: middle class incomes without a proactive redistribution in public support,

1442
01:06:58,933 --> 01:07:02,253
Speaker 2: AI could deepen the massive inequality and leave millions and

1443
01:07:02,333 --> 01:07:05,613
Speaker 2: millions behind. So that's summary he's saying. So if you

1444
01:07:05,653 --> 01:07:10,093
Speaker 2: have the Amazon thing, say for an example, it's an innovation,

1445
01:07:11,493 --> 01:07:16,493
Speaker 2: hundreds of thousands of businesses potentially lost, and then as

1446
01:07:16,533 --> 01:07:18,533
Speaker 2: a result, all those people that had those sort of

1447
01:07:18,533 --> 01:07:21,893
Speaker 2: middle class jobs no longer hire the people to do that,

1448
01:07:21,933 --> 01:07:23,533
Speaker 2: build the roofs on their houses, et cetera.

1449
01:07:23,733 --> 01:07:26,013
Speaker 4: Yeah, but it fails to take into account what I

1450
01:07:26,053 --> 01:07:28,213
Speaker 4: think it is. It's a big pushback on our ever

1451
01:07:28,253 --> 01:07:31,493
Speaker 4: increasing digital world. And you mentioned your kids in particular,

1452
01:07:31,573 --> 01:07:33,973
Speaker 4: they talk about brain rot. They're very aware of that.

1453
01:07:34,333 --> 01:07:36,733
Speaker 4: And study after study, this one here, so it showed

1454
01:07:36,773 --> 01:07:39,733
Speaker 4: this is a UK study sixteen to twenty one year

1455
01:07:39,733 --> 01:07:42,213
Speaker 4: olds found that forty six percent they would prefer to

1456
01:07:42,253 --> 01:07:44,733
Speaker 4: be young in a world without the internet. So more

1457
01:07:44,773 --> 01:07:47,493
Speaker 4: and more there's this pushback on that digital world and

1458
01:07:47,533 --> 01:07:50,293
Speaker 4: the technology that is overwhelming us. And I know that's

1459
01:07:50,293 --> 01:07:52,853
Speaker 4: clinging to a hope that may never eventuate, but I

1460
01:07:52,893 --> 01:07:56,013
Speaker 4: still think that human connection is a big part of it.

1461
01:07:56,293 --> 01:07:59,013
Speaker 2: Yeah. Well, we'll be back in a couple of moments

1462
01:07:59,053 --> 01:08:01,293
Speaker 2: with Jerry and Dominic, who are both pushing back on

1463
01:08:01,373 --> 01:08:02,533
Speaker 2: a positive take on Ali.

1464
01:08:02,653 --> 01:08:05,853
Speaker 4: Okay, there's a big good nineteen two three, the.

1465
01:08:05,693 --> 01:08:10,133
Speaker 1: Big stories, the big issues and everything in between.

1466
01:08:10,373 --> 01:08:13,133
Speaker 13: Matt Heathan Tyler Adams Afternoons.

1467
01:08:12,773 --> 01:08:14,613
Speaker 4: US Talks, it'd be sixteen to three.

1468
01:08:14,693 --> 01:08:17,653
Speaker 2: The six says why will we need teachers when we

1469
01:08:17,813 --> 01:08:20,693
Speaker 2: just ask Ai the answer? But that's just assuming that

1470
01:08:20,733 --> 01:08:24,852
Speaker 2: all teachers do is in part knowledge from one to another.

1471
01:08:25,173 --> 01:08:29,412
Speaker 2: I mean, teachers have a lot of roles mentors, emotional support,

1472
01:08:29,652 --> 01:08:33,213
Speaker 2: human connection exactly. There's a lot that teachers do that

1473
01:08:33,333 --> 01:08:39,612
Speaker 2: isn't that isn't just pure explaining of stuff in the world. Jerry,

1474
01:08:39,692 --> 01:08:41,532
Speaker 2: your thoughts on this and welcome to the show.

1475
01:08:43,093 --> 01:08:46,412
Speaker 15: Oh yeah, it's really interesting. I'm enjoying it. I read

1476
01:08:46,412 --> 01:08:48,892
Speaker 15: the article in the Herald. I thought it was a

1477
01:08:48,933 --> 01:08:55,173
Speaker 15: good article, but with a very limited time frame. And yes,

1478
01:08:55,732 --> 01:09:03,293
Speaker 15: I also thought Jason's comments were good. But also there's

1479
01:09:03,612 --> 01:09:06,412
Speaker 15: a couple of things missing that are huge to me.

1480
01:09:06,572 --> 01:09:11,732
Speaker 15: At least one is robots, how to robots that is

1481
01:09:11,812 --> 01:09:16,612
Speaker 15: for this, Yeah, and that's sort of was popular, but

1482
01:09:17,093 --> 01:09:21,612
Speaker 15: you know, it's sort of been overtaken by AI. But

1483
01:09:21,732 --> 01:09:23,732
Speaker 15: I think if you really want to think it through

1484
01:09:23,812 --> 01:09:26,372
Speaker 15: and think about things like, well, the only work left

1485
01:09:26,492 --> 01:09:29,732
Speaker 15: is going to be manual laborers. Well, you know you've

1486
01:09:29,772 --> 01:09:31,652
Speaker 15: got robots, you've got to factor them in.

1487
01:09:32,293 --> 01:09:34,293
Speaker 2: Yeah, I mean I was thinking the same thing. I

1488
01:09:34,333 --> 01:09:35,892
Speaker 2: was thinking the same thing Jerry in the article when

1489
01:09:35,893 --> 01:09:38,613
Speaker 2: it talks about one hundred and thirty thousand additional electricians

1490
01:09:38,893 --> 01:09:41,892
Speaker 2: being needed in the coming years. And that's assuming, as

1491
01:09:41,933 --> 01:09:43,772
Speaker 2: you say, Jerry, that they don't get to the point

1492
01:09:43,933 --> 01:09:47,173
Speaker 2: where they can have robots that have hands that can

1493
01:09:47,213 --> 01:09:48,333
Speaker 2: do that work.

1494
01:09:49,732 --> 01:09:52,492
Speaker 15: Yeah, and they'll be far beyond what we see now.

1495
01:09:53,612 --> 01:09:57,452
Speaker 15: Another but I think probably in a way, the most

1496
01:09:57,652 --> 01:10:00,492
Speaker 15: the bigger issue issue. And frankly, I mean I'm an

1497
01:10:00,492 --> 01:10:03,692
Speaker 15: old guy. Ain't going to affect me little or none,

1498
01:10:03,772 --> 01:10:09,093
Speaker 15: but I concerned myself with my kids and my grandkids.

1499
01:10:08,732 --> 01:10:10,212
Speaker 10: And that there's.

1500
01:10:10,013 --> 01:10:17,013
Speaker 15: Something called AGI. I think it stands for artificial general intelligence.

1501
01:10:17,452 --> 01:10:27,213
Speaker 15: But it's where the intellectual cognitive ability of AI exceeds

1502
01:10:27,732 --> 01:10:33,013
Speaker 15: that of humans. So we're already seeing things like computers

1503
01:10:33,093 --> 01:10:38,053
Speaker 15: that can beat chess masters and things like that, and

1504
01:10:38,093 --> 01:10:41,173
Speaker 15: they're very useful and it's expanding all the time. But

1505
01:10:41,333 --> 01:10:46,652
Speaker 15: when you get to where AGI comes in, and you read,

1506
01:10:46,893 --> 01:10:49,852
Speaker 15: depending on what you read, forecast go is that it

1507
01:10:49,933 --> 01:10:53,412
Speaker 15: will be as little as five years or maybe the

1508
01:10:53,532 --> 01:10:57,092
Speaker 15: longer ones that are you know, they're sensible or probably

1509
01:10:57,133 --> 01:11:01,213
Speaker 15: out to maybe fifteen years, maybe twenty even. But that's

1510
01:11:01,333 --> 01:11:04,412
Speaker 15: kind of at the end. So what does that employ

1511
01:11:05,173 --> 01:11:09,772
Speaker 15: I would imply. I mean, well, it implies that that

1512
01:11:11,253 --> 01:11:15,173
Speaker 15: the machines, you know, what is controlled by AI is

1513
01:11:15,213 --> 01:11:19,293
Speaker 15: smarter than us. So the idea that we're going to

1514
01:11:19,333 --> 01:11:23,692
Speaker 15: be using AI to do something for us, which we're

1515
01:11:24,213 --> 01:11:27,372
Speaker 15: then going to modify. Like my daughter is a general

1516
01:11:27,412 --> 01:11:30,452
Speaker 15: manager of a company and she uses AI a lot

1517
01:11:30,532 --> 01:11:34,172
Speaker 15: and loves it, like for writing things like somebody earlier mentioned.

1518
01:11:35,173 --> 01:11:39,173
Speaker 15: But she writes it, she gives it some directions on

1519
01:11:39,253 --> 01:11:43,412
Speaker 15: what she wants to accomplish, and then it writes something

1520
01:11:43,492 --> 01:11:47,013
Speaker 15: for her, and then she edits it and makes it

1521
01:11:47,412 --> 01:11:51,053
Speaker 15: sure that it's consistent and sensible and is saying what

1522
01:11:51,093 --> 01:11:55,053
Speaker 15: she wants it to say. Well, that's that's gone with AGI.

1523
01:11:55,893 --> 01:11:59,372
Speaker 15: It's not going to work that way. It's not us

1524
01:11:59,412 --> 01:12:04,973
Speaker 15: exerting using the AI as sort of a bit of

1525
01:12:05,013 --> 01:12:06,293
Speaker 15: a cheap labor.

1526
01:12:06,732 --> 01:12:09,532
Speaker 2: Yeah. I mean, but I've sort of been reading quite

1527
01:12:09,532 --> 01:12:12,253
Speaker 2: a lot on this, and you know, I'm quite obsessed

1528
01:12:12,293 --> 01:12:14,532
Speaker 2: with the whole thing, but I'm not convinced, and more

1529
01:12:14,572 --> 01:12:19,213
Speaker 2: people are becoming less convinced that a land large language model,

1530
01:12:19,293 --> 01:12:23,892
Speaker 2: which is what chat, GPT is and Grockers and all

1531
01:12:23,933 --> 01:12:27,893
Speaker 2: the AIS that we're using now, actually leads to artificial

1532
01:12:27,973 --> 01:12:33,652
Speaker 2: general intelligence, that that is the path to it, because really,

1533
01:12:33,692 --> 01:12:36,572
Speaker 2: what a large language model is is just you know,

1534
01:12:37,053 --> 01:12:41,652
Speaker 2: breaking readable units of language into tokens and then the

1535
01:12:41,692 --> 01:12:44,892
Speaker 2: probabilities of what's the next thing to do. So it's

1536
01:12:44,933 --> 01:12:51,973
Speaker 2: an algorithmic reprocessing of information, right as opposed to artificial

1537
01:12:52,013 --> 01:12:55,173
Speaker 2: general intelligence, which is something that's even quite hard for

1538
01:12:55,293 --> 01:12:59,372
Speaker 2: us to define what it is. So as I read

1539
01:12:59,532 --> 01:13:01,933
Speaker 2: about it, and you've got Sam Ackman, who's obviously the

1540
01:13:01,973 --> 01:13:06,612
Speaker 2: CEO of Chat, GPT, open AI, and he's talking up

1541
01:13:06,652 --> 01:13:10,572
Speaker 2: the large language model like it's heading towards AGI. But

1542
01:13:10,772 --> 01:13:15,092
Speaker 2: I'm just not convinced. And I sort of see and look,

1543
01:13:15,173 --> 01:13:18,333
Speaker 2: I'm no genius or expert on this or anything, but

1544
01:13:18,412 --> 01:13:21,852
Speaker 2: I see a sort of a plateauing of the sort

1545
01:13:21,853 --> 01:13:26,412
Speaker 2: of an exponential growth in the capability of large language models.

1546
01:13:26,412 --> 01:13:28,732
Speaker 2: But I feel like it's tapering off a bit. So

1547
01:13:29,053 --> 01:13:30,532
Speaker 2: what's your thought on that, Jerry? Do you think a

1548
01:13:30,612 --> 01:13:32,813
Speaker 2: large language model can lead to AGI?

1549
01:13:35,053 --> 01:13:40,092
Speaker 15: That's probably above my pay grade. But in mind having

1550
01:13:40,173 --> 01:13:44,492
Speaker 15: said that there, I read a recent article on them.

1551
01:13:44,652 --> 01:13:47,412
Speaker 15: I'm probably this is the gist of it. I might

1552
01:13:47,492 --> 01:13:51,412
Speaker 15: not have it exactly right, but it was talking about

1553
01:13:51,492 --> 01:13:58,892
Speaker 15: people working on AI and they decided that they wanted

1554
01:13:58,973 --> 01:14:00,732
Speaker 15: to go in a different direction.

1555
01:14:00,933 --> 01:14:02,253
Speaker 10: They wanted to.

1556
01:14:01,853 --> 01:14:05,652
Speaker 15: Explore another avenue for advancing AI.

1557
01:14:07,253 --> 01:14:08,253
Speaker 10: That would.

1558
01:14:09,692 --> 01:14:12,732
Speaker 15: I suppose you could say, obsolete the work that they

1559
01:14:12,772 --> 01:14:16,013
Speaker 15: had up to that point. But they were doing that

1560
01:14:16,732 --> 01:14:21,492
Speaker 15: in something that AI itself was involved in. Yeah, AI

1561
01:14:22,013 --> 01:14:26,812
Speaker 15: AI started creating roadblocks what they wanted to do.

1562
01:14:27,173 --> 01:14:28,732
Speaker 2: I hear these stories.

1563
01:14:29,053 --> 01:14:32,892
Speaker 15: Yeah, now you think about that bit uh and and

1564
01:14:33,133 --> 01:14:35,652
Speaker 15: what you're suggesting that this is not going to lead

1565
01:14:35,652 --> 01:14:39,732
Speaker 15: to a big deal. I think I think it's Well,

1566
01:14:40,333 --> 01:14:42,812
Speaker 15: if I was a doom sayer, which I'm not, but

1567
01:14:42,893 --> 01:14:45,812
Speaker 15: if I was, I was at the end of the world, well.

1568
01:14:45,973 --> 01:14:49,772
Speaker 4: Yeah, okay, well yeah, that positive, Jerry. I mean just seriously, though,

1569
01:14:49,853 --> 01:14:53,092
Speaker 4: if a g I, hypothetically we get to a G I,

1570
01:14:53,093 --> 01:14:55,053
Speaker 4: I think it takes it takes it out of our

1571
01:14:55,133 --> 01:14:57,572
Speaker 4: hands that at that point we've got very little control.

1572
01:14:57,652 --> 01:15:00,812
Speaker 2: When we have an intelligent intelligence that's exponentially going to

1573
01:15:00,853 --> 01:15:04,133
Speaker 2: the point where we are like what answer to us, Yeah,

1574
01:15:04,213 --> 01:15:08,253
Speaker 2: to the AHI in terms of our functionality is like

1575
01:15:09,412 --> 01:15:12,532
Speaker 2: functionally to us. Then then that that definitely becomes a problem.

1576
01:15:12,973 --> 01:15:15,772
Speaker 4: Yeah, And we don't know what it will do when

1577
01:15:15,772 --> 01:15:16,412
Speaker 4: we become.

1578
01:15:16,213 --> 01:15:19,772
Speaker 2: A rounding era for the success of uh, you know,

1579
01:15:20,133 --> 01:15:22,532
Speaker 2: the planet, then that becomes a problem.

1580
01:15:22,973 --> 01:15:26,372
Speaker 15: Well, we don't, we don't know, and uh and it

1581
01:15:26,452 --> 01:15:30,652
Speaker 15: can get very scary, and don't you know, you can

1582
01:15:31,173 --> 01:15:35,173
Speaker 15: think of ways where where it doesn't become really.

1583
01:15:35,013 --> 01:15:38,652
Speaker 4: Scary, you know, as much.

1584
01:15:38,492 --> 01:15:40,732
Speaker 15: As I love you, like if you think of if

1585
01:15:40,732 --> 01:15:44,213
Speaker 15: you think of Google and those people, I think they

1586
01:15:44,652 --> 01:15:49,452
Speaker 15: and you you think about their not so much what

1587
01:15:49,492 --> 01:15:52,333
Speaker 15: they're doing or what they believe, but what they want

1588
01:15:52,652 --> 01:15:55,692
Speaker 15: people to to how they want them to view AI.

1589
01:15:55,652 --> 01:16:00,173
Speaker 15: I don't think it's in their interest to start promoting

1590
01:16:00,652 --> 01:16:04,772
Speaker 15: a g I if then I wouldn't, I wouldn't talk

1591
01:16:04,812 --> 01:16:07,293
Speaker 15: about it. I'd be working furiously to try to be

1592
01:16:07,333 --> 01:16:09,933
Speaker 15: the first off the block, but I wouldn't want to

1593
01:16:09,933 --> 01:16:13,492
Speaker 15: talk about it. Yeah, some people will say I've heard

1594
01:16:13,492 --> 01:16:15,892
Speaker 15: some people say, well, this is we just we just

1595
01:16:15,933 --> 01:16:19,572
Speaker 15: need to regulate this. Well good luck, Yeah, I mean yeah,

1596
01:16:19,853 --> 01:16:22,093
Speaker 15: and they've got to keep it in hands, keep it

1597
01:16:22,133 --> 01:16:22,732
Speaker 15: out of the hand.

1598
01:16:23,253 --> 01:16:24,812
Speaker 2: Yeah, I'm sorry, Jerry, We've just got to go on

1599
01:16:24,853 --> 01:16:26,293
Speaker 2: air break. But yeah, I get what you're saying. And

1600
01:16:26,412 --> 01:16:31,053
Speaker 2: in this article is suspiciously written by a Google economist.

1601
01:16:31,173 --> 01:16:33,293
Speaker 4: Yeah, funny that it is eight minutes to three. Beg

1602
01:16:33,372 --> 01:16:35,293
Speaker 4: very shortly, the.

1603
01:16:35,333 --> 01:16:37,892
Speaker 1: Issues that affect you and a bit of fun along

1604
01:16:37,893 --> 01:16:42,372
Speaker 1: the way. Matt Heath and Tyler Adams Afternoons News TALKSB.

1605
01:16:42,772 --> 01:16:45,093
Speaker 4: News Talks the B five to three. A whole bunch

1606
01:16:45,093 --> 01:16:46,772
Speaker 4: of tecks have been coming through A nine two ninety.

1607
01:16:47,093 --> 01:16:49,133
Speaker 2: Yeah, pretty bit to cut off Jerry for the ads

1608
01:16:49,133 --> 01:16:51,013
Speaker 2: though he was a great chat. What a great call.

1609
01:16:51,093 --> 01:16:53,213
Speaker 2: Thanks so much, Jerry. If you're still listening, guys, A

1610
01:16:53,293 --> 01:16:55,452
Speaker 2: I was already taking jobs. This is text to Grant.

1611
01:16:55,492 --> 01:16:57,892
Speaker 2: I was at a conference last week and they had

1612
01:16:57,933 --> 01:17:01,452
Speaker 2: their UK businesses dial and their challenges ahead. Were clients

1613
01:17:01,452 --> 01:17:04,133
Speaker 2: expecting lower cost due to the time to provide what

1614
01:17:04,213 --> 01:17:07,693
Speaker 2: this global firm does along with managing the reduction and graduates.

1615
01:17:07,933 --> 01:17:11,652
Speaker 2: It's real and it's happening now. Thank you for your text, Grant. Hi, guys,

1616
01:17:11,732 --> 01:17:15,612
Speaker 2: AI will reduce the need for skilled people to do

1617
01:17:15,732 --> 01:17:18,452
Speaker 2: mundane task, but those skilled people will still need to

1618
01:17:18,452 --> 01:17:21,933
Speaker 2: provide assurance for those tasks. The worry is, this is

1619
01:17:21,933 --> 01:17:23,892
Speaker 2: what Jason was at. The worry is that the companies

1620
01:17:23,933 --> 01:17:28,293
Speaker 2: won't retain staff for assurance or have the subject matter

1621
01:17:28,333 --> 01:17:33,092
Speaker 2: expertise at all. I mean, yeah, very easy. Just unplug

1622
01:17:33,133 --> 01:17:35,213
Speaker 2: it and go outside, plant something and go fishing. People

1623
01:17:35,293 --> 01:17:36,772
Speaker 2: have done it for thousands of years. Not a problem.

1624
01:17:36,893 --> 01:17:38,492
Speaker 2: Go Bush, Yeah, go Bush.

1625
01:17:38,532 --> 01:17:40,772
Speaker 4: If only it was that easy. But it sounds pretty good.

1626
01:17:41,053 --> 01:17:43,053
Speaker 4: We're going to carry this on because we've had so

1627
01:17:43,213 --> 01:17:45,652
Speaker 4: many phone calls and thousands of decks. So I'd love

1628
01:17:45,692 --> 01:17:49,093
Speaker 4: to hear your thoughts about AI and the rapid advancement.

1629
01:17:49,133 --> 01:17:51,013
Speaker 4: Are you worried about your job? Do you think it's

1630
01:17:51,053 --> 01:17:53,572
Speaker 4: going to create more jobs than it takes away?

1631
01:17:53,973 --> 01:17:56,333
Speaker 2: Kate thinks it's the end of all good. He's right,

1632
01:17:56,452 --> 01:17:59,172
Speaker 2: it's the end of the world. We've created the next species.

1633
01:17:59,293 --> 01:18:02,213
Speaker 4: Quite possible, Quite possible. It's going to be an interesting

1634
01:18:02,293 --> 01:18:02,852
Speaker 4: ride anyway.

1635
01:18:03,173 --> 01:18:05,812
Speaker 2: I don't think so, yeah, I don't think. I don't

1636
01:18:05,812 --> 01:18:08,932
Speaker 2: think the large lam large language model get us here,

1637
01:18:08,973 --> 01:18:11,213
Speaker 2: but yeah, to see more.

1638
01:18:11,452 --> 01:18:14,333
Speaker 4: Yep, new sport and weather is fast approaching. Oh, eight

1639
01:18:14,412 --> 01:18:17,133
Speaker 4: hundred and eighty teen eighty is that number? Andrew was

1640
01:18:17,213 --> 01:18:20,572
Speaker 4: standing by? But await right, here. We'll be back very shortly.

1641
01:18:20,612 --> 01:18:22,692
Speaker 4: Great deby company as always, hope you having a pretty

1642
01:18:22,692 --> 01:18:23,772
Speaker 4: good Tuesday afternoon.

1643
01:18:28,612 --> 01:18:31,573
Speaker 13: Your new home are insightful and entertaining.

1644
01:18:31,652 --> 01:18:36,492
Speaker 1: Talk It's Matty and Taylor Adams Afternoons on News Talk.

1645
01:18:36,372 --> 01:18:39,732
Speaker 4: Sebby Afternoon to you. Welcome back into the program and

1646
01:18:39,772 --> 01:18:43,693
Speaker 4: the discussion we've been having on artificial intelligence. So fascinating

1647
01:18:43,853 --> 01:18:49,333
Speaker 4: article penned by Google's chief economist Fabian Quarto Malay and

1648
01:18:49,492 --> 01:18:53,253
Speaker 4: renowned British economist at Cambridge Diye and Coil about the

1649
01:18:53,293 --> 01:18:55,372
Speaker 4: future of AI when it comes to the job market.

1650
01:18:55,452 --> 01:18:59,213
Speaker 4: It's fair to say they are relatively positive about that future.

1651
01:18:59,372 --> 01:19:02,133
Speaker 2: Yeah. Absolutely, and look, as we said at the end

1652
01:19:02,133 --> 01:19:04,253
Speaker 2: of last hour, one of them does work for Google,

1653
01:19:04,412 --> 01:19:07,973
Speaker 2: which is heavily in the AI game. So it's masspromised,

1654
01:19:08,053 --> 01:19:11,293
Speaker 2: you know, might be slightly comboguized. But their argument is

1655
01:19:11,333 --> 01:19:14,492
Speaker 2: that predictions that AI will wipe out jobs exaggerate the threat,

1656
01:19:15,093 --> 01:19:20,132
Speaker 2: and they talk about most past technological revolutions have actually

1657
01:19:20,253 --> 01:19:25,372
Speaker 2: led to employment growth. They're talking about the industrial revolution,

1658
01:19:25,492 --> 01:19:33,532
Speaker 2: agricultural revolution, and more recently the computerization revolution that started

1659
01:19:33,532 --> 01:19:36,372
Speaker 2: in the nineteen seventies. They say three point five million

1660
01:19:36,492 --> 01:19:41,492
Speaker 2: jobs were lost, but nineteen million jobs were created and

1661
01:19:41,532 --> 01:19:45,532
Speaker 2: in their minds are just for an example, one hundred

1662
01:19:45,532 --> 01:19:49,732
Speaker 2: and thirty thousand more electricians are needed to build the

1663
01:19:49,812 --> 01:19:54,812
Speaker 2: data centers to run AI, which is interesting because if

1664
01:19:54,853 --> 01:19:56,852
Speaker 2: what a lot of people are texting in, it's being

1665
01:19:56,933 --> 01:20:01,572
Speaker 2: hired to build our own demise. But yeah, I mean,

1666
01:20:01,652 --> 01:20:05,492
Speaker 2: and then but then you think about it, they say

1667
01:20:05,532 --> 01:20:10,013
Speaker 2: that that what it means as there's a lot of

1668
01:20:10,173 --> 01:20:13,293
Speaker 2: parts of most jobs, and so AI will take away

1669
01:20:13,492 --> 01:20:17,133
Speaker 2: some parts of jobs, not all all of it. So

1670
01:20:17,173 --> 01:20:23,572
Speaker 2: they say it's crucial that people train mid career for

1671
01:20:23,652 --> 01:20:27,333
Speaker 2: AI and bring AI in so that they believe jobs

1672
01:20:27,372 --> 01:20:30,093
Speaker 2: won't completely disappear, they'll just get more efficient. But as

1673
01:20:30,333 --> 01:20:32,492
Speaker 2: a bunch of callers have been putting out the last hour,

1674
01:20:32,933 --> 01:20:36,892
Speaker 2: what do efficiencies do? They often get rid of the

1675
01:20:37,893 --> 01:20:40,333
Speaker 2: you know, for example, I was talking before about a

1676
01:20:40,412 --> 01:20:43,812
Speaker 2: legal firm. I mean, you know there's a lot of

1677
01:20:44,053 --> 01:20:49,213
Speaker 2: filing and a lot of research and sort of base

1678
01:20:49,333 --> 01:20:51,612
Speaker 2: level entry level jobs and the legal firms that if

1679
01:20:51,652 --> 01:20:54,053
Speaker 2: AI is doing that, then you know that is a

1680
01:20:54,093 --> 01:20:54,692
Speaker 2: loss of job.

1681
01:20:54,812 --> 01:20:56,652
Speaker 4: Yeah, and how do you get people to advance? But

1682
01:20:56,692 --> 01:20:58,333
Speaker 4: what do you say? O eight one hundred and eighty

1683
01:20:58,412 --> 01:21:01,372
Speaker 4: ten eighty. Is that too much of a rosy outlook

1684
01:21:01,412 --> 01:21:04,572
Speaker 4: or do you agree that AI could benefit the job

1685
01:21:04,612 --> 01:21:05,452
Speaker 4: market in the long run.

1686
01:21:05,532 --> 01:21:08,892
Speaker 2: And I was arguing that social interaction and face to

1687
01:21:08,933 --> 01:21:13,532
Speaker 2: face human experience was incredibly important. That's why we find

1688
01:21:13,572 --> 01:21:15,772
Speaker 2: it valuable to go to an art gallery and look

1689
01:21:15,812 --> 01:21:18,053
Speaker 2: exact while some of us do. It's more exciting to

1690
01:21:18,053 --> 01:21:20,372
Speaker 2: see a painting that has actually been painted by a

1691
01:21:20,452 --> 01:21:23,293
Speaker 2: human that it is to see the exact replication of it,

1692
01:21:23,452 --> 01:21:26,812
Speaker 2: even if it looks identical. Undoubtedly, undoubted human connection is

1693
01:21:26,812 --> 01:21:29,772
Speaker 2: important to us. But the stix says re AI. We

1694
01:21:29,812 --> 01:21:32,133
Speaker 2: all want that social interaction of working with people, but

1695
01:21:32,173 --> 01:21:34,293
Speaker 2: when it comes to parting with money, I bet most

1696
01:21:34,293 --> 01:21:36,812
Speaker 2: of us will choose to use AI over the cost

1697
01:21:36,853 --> 01:21:39,253
Speaker 2: of a human I'm a graphic designer and the future

1698
01:21:39,333 --> 01:21:42,652
Speaker 2: does not look good for me. And this Texas says

1699
01:21:42,692 --> 01:21:44,572
Speaker 2: work in the film industry and allland lots of chat

1700
01:21:44,612 --> 01:21:47,093
Speaker 2: on set about the possibility of AI taking a lot

1701
01:21:47,133 --> 01:21:50,253
Speaker 2: of our jobs. No need for construction or a lot

1702
01:21:50,333 --> 01:21:53,692
Speaker 2: of the art department if it's good enough or they're

1703
01:21:53,732 --> 01:21:58,972
Speaker 2: not ideal for us, Eils, welcome to the show.

1704
01:21:59,973 --> 01:22:04,013
Speaker 21: Good day, guys, Hey I just thought I had a

1705
01:22:04,013 --> 01:22:07,253
Speaker 21: bit more context to your tunes in about fifteen minutes

1706
01:22:07,293 --> 01:22:12,692
Speaker 21: ago and the discuss about a g I. And what

1707
01:22:12,772 --> 01:22:16,452
Speaker 21: most people see is the public, public facing side of AGI,

1708
01:22:16,692 --> 01:22:19,492
Speaker 21: and that's sorry of l lambs, and that's Chap, GPT,

1709
01:22:19,692 --> 01:22:24,092
Speaker 21: grock A, claud et cetera, which are all very general

1710
01:22:25,293 --> 01:22:31,492
Speaker 21: based AIS. But a lot of people don't understand the

1711
01:22:31,492 --> 01:22:34,293
Speaker 21: the commercial activity is around AI at the moment, and

1712
01:22:34,293 --> 01:22:37,652
Speaker 21: that's not large language models, or it is large language models,

1713
01:22:37,692 --> 01:22:42,253
Speaker 21: but it's trained AI and what's called AI agents. So

1714
01:22:43,732 --> 01:22:48,652
Speaker 21: our agents are specifically trained on one particular task like

1715
01:22:48,853 --> 01:22:52,692
Speaker 21: not that not general. They're just focused on being experts

1716
01:22:52,772 --> 01:22:56,973
Speaker 21: on a particular subject matter. Much so the train just

1717
01:22:57,013 --> 01:23:00,852
Speaker 21: like you train any employee or any professional into that

1718
01:23:01,173 --> 01:23:04,932
Speaker 21: into that specific knowledge base or that specific specific subject.

1719
01:23:05,652 --> 01:23:10,812
Speaker 21: And then you have around AI space these protocols that

1720
01:23:10,973 --> 01:23:13,492
Speaker 21: title these things together. People might not have heard of

1721
01:23:13,572 --> 01:23:17,652
Speaker 21: things like a to A an MCP. So what A

1722
01:23:17,812 --> 01:23:20,692
Speaker 21: to A is as agent agents. So you can have

1723
01:23:21,452 --> 01:23:26,652
Speaker 21: an organization that's trained some AI agents on four particular tasks.

1724
01:23:26,692 --> 01:23:29,733
Speaker 21: One might both be logistics, one might be human resource,

1725
01:23:30,412 --> 01:23:32,772
Speaker 21: one might be an operations. They're not general, they're just

1726
01:23:32,812 --> 01:23:37,172
Speaker 21: trained on those specific tasks and they tie them all together.

1727
01:23:37,732 --> 01:23:39,933
Speaker 21: So you're not talking about going to chat GPT and

1728
01:23:40,013 --> 01:23:44,012
Speaker 21: asking about some freighting issues for your particular company, because

1729
01:23:44,013 --> 01:23:47,853
Speaker 21: it has no context. But when they're trained into specific

1730
01:23:47,933 --> 01:23:52,932
Speaker 21: tasks with knowledge bases from the company organization, and they're

1731
01:23:52,933 --> 01:23:56,652
Speaker 21: strung together, that's when you get some real power with AI,

1732
01:23:57,333 --> 01:24:01,093
Speaker 21: not just the general LLM as you mentioned before, it

1733
01:24:01,133 --> 01:24:04,532
Speaker 21: won't be taking anything over now. I think where it

1734
01:24:04,612 --> 01:24:10,572
Speaker 21: impacts employment is a certainly low level job, low level admin,

1735
01:24:10,812 --> 01:24:13,812
Speaker 21: data entry, et cetera. There's definitely going and is an

1736
01:24:13,812 --> 01:24:17,452
Speaker 21: impact already in that space. And there's other elements as well,

1737
01:24:17,532 --> 01:24:23,293
Speaker 21: So there's multimodal lms, which you know, image recognition, voices, recognition.

1738
01:24:23,973 --> 01:24:27,692
Speaker 21: They will get mixed into the whole AI arena and

1739
01:24:27,732 --> 01:24:31,093
Speaker 21: often lost in the scope of what AI can actually do.

1740
01:24:31,812 --> 01:24:36,772
Speaker 21: So I don't think AGI is coming anytime fast. You know, generally,

1741
01:24:37,213 --> 01:24:42,133
Speaker 21: I think we'll find AI built specifically around niches and

1742
01:24:43,333 --> 01:24:46,852
Speaker 21: issues within organizations that AI can actually tackle.

1743
01:24:48,612 --> 01:24:52,293
Speaker 2: It's an interesting I see what you're saying, really interesting point.

1744
01:24:52,372 --> 01:24:55,052
Speaker 2: I was reading this article fell and it was about

1745
01:24:55,093 --> 01:24:58,452
Speaker 2: Apple and how Apple had invested more heavily in SLM

1746
01:24:58,652 --> 01:25:02,012
Speaker 2: small language models sort of what you're talking about really

1747
01:25:02,053 --> 01:25:05,612
Speaker 2: isolated on one task because the large language model, as

1748
01:25:05,652 --> 01:25:07,452
Speaker 2: you say, has got a lot of functionality that doesn't

1749
01:25:07,452 --> 01:25:11,932
Speaker 2: need uses a lot of our to to you know,

1750
01:25:12,133 --> 01:25:15,293
Speaker 2: process a lot of information that's not directly associated with

1751
01:25:15,333 --> 01:25:17,732
Speaker 2: the task you're doing and brings in a lot of

1752
01:25:17,973 --> 01:25:21,333
Speaker 2: illusions and rubbish. But with what Apple's been doing is

1753
01:25:22,053 --> 01:25:26,612
Speaker 2: training on just the information that this particular AI needs

1754
01:25:26,652 --> 01:25:29,852
Speaker 2: to complete this small task and a lot of For

1755
01:25:29,853 --> 01:25:31,812
Speaker 2: a lot of time, everyone was going on, Apple's missed

1756
01:25:31,853 --> 01:25:34,732
Speaker 2: the boat on this AI thing, but an actual fact,

1757
01:25:34,732 --> 01:25:37,173
Speaker 2: it's looking now that they just were like, well, there's

1758
01:25:37,213 --> 01:25:39,652
Speaker 2: not a huge advantage of us to spend our resources

1759
01:25:39,692 --> 01:25:42,012
Speaker 2: on the large language models. But exactly what you're saying

1760
01:25:42,013 --> 01:25:44,093
Speaker 2: for really specific.

1761
01:25:44,973 --> 01:25:47,972
Speaker 21: The l lamb's well cooked, so to speak. They're getting

1762
01:25:47,973 --> 01:25:51,293
Speaker 21: better and better. Everybody was excited. Ratio. Everybody's disappointed with

1763
01:25:51,452 --> 01:25:55,012
Speaker 21: chat GBT five came out. Yeah, everybody's expecting a lot more.

1764
01:25:55,412 --> 01:25:57,452
Speaker 21: But they were very clever and what they did is

1765
01:25:57,492 --> 01:26:00,812
Speaker 21: they decided we don't need a big white l lamb

1766
01:26:00,933 --> 01:26:04,093
Speaker 21: for general questions. We will critique the question or the

1767
01:26:04,133 --> 01:26:06,532
Speaker 21: prompt that somebody's putting in and then we'll give it

1768
01:26:06,572 --> 01:26:08,892
Speaker 21: the correct l LAMB for them. Now I did that

1769
01:26:08,973 --> 01:26:11,532
Speaker 21: was a commercial decision for them because as you say,

1770
01:26:11,532 --> 01:26:14,333
Speaker 21: it runs it there's a lot of give years involved

1771
01:26:14,372 --> 01:26:18,173
Speaker 21: in and costs running just a general question about you know,

1772
01:26:18,213 --> 01:26:21,732
Speaker 21: what should I give my dog for flea flea treatment? Right,

1773
01:26:23,213 --> 01:26:26,253
Speaker 21: you know, there's there's a lot and so the public

1774
01:26:26,333 --> 01:26:30,572
Speaker 21: generally see you know, AI as their own experience with it,

1775
01:26:30,772 --> 01:26:33,173
Speaker 21: which is fair enough, that's all they can do. But

1776
01:26:33,213 --> 01:26:35,453
Speaker 21: there's a lot of work going on in the background

1777
01:26:35,652 --> 01:26:41,692
Speaker 21: between inference calling and also the knowledge base. And but

1778
01:26:41,812 --> 01:26:43,932
Speaker 21: there's there's a lot of acronyms I could throw out

1779
01:26:43,973 --> 01:26:46,492
Speaker 21: here which which went brought adding your value. What I

1780
01:26:46,532 --> 01:26:50,013
Speaker 21: can say is that yes, small learning languages, small l

1781
01:26:50,093 --> 01:26:53,372
Speaker 21: l ms and visual l lamps as well are all

1782
01:26:53,412 --> 01:26:57,453
Speaker 21: starting to become very powerful. It's trained on specific tasks

1783
01:26:58,133 --> 01:27:00,133
Speaker 21: and then if it's saved you a two A, which

1784
01:27:00,173 --> 01:27:04,612
Speaker 21: is Google's protocol, which means agents can understand what an

1785
01:27:04,612 --> 01:27:07,452
Speaker 21: agent is trained to do automatically. So you know, it's

1786
01:27:07,452 --> 01:27:09,133
Speaker 21: a bit like somebody from Thing up with a CV.

1787
01:27:09,293 --> 01:27:12,452
Speaker 21: How I'm on accountant, I've got an MBA and another

1788
01:27:12,692 --> 01:27:16,093
Speaker 21: person turning up because I've got this qualification. So the

1789
01:27:16,173 --> 01:27:20,093
Speaker 21: CV that takes what the LLM or the this train

1790
01:27:20,213 --> 01:27:23,253
Speaker 21: the agent is trained to do, so that happens transparently.

1791
01:27:23,372 --> 01:27:27,092
Speaker 21: So you're not dealing with a whole wide bucket of knowledge.

1792
01:27:27,093 --> 01:27:32,133
Speaker 21: It's just individually trained specialists. And that's where when you

1793
01:27:32,253 --> 01:27:34,652
Speaker 21: when you channel those together, that's when you get some

1794
01:27:34,853 --> 01:27:38,852
Speaker 21: real power and automation. When I say automation, i'm talking

1795
01:27:38,893 --> 01:27:41,333
Speaker 21: about outside of an LLM, where you can go through

1796
01:27:41,372 --> 01:27:43,612
Speaker 21: a database, you can write an email, it can send

1797
01:27:43,612 --> 01:27:48,532
Speaker 21: a text message, you can recognize a tree. Look Elon Musk.

1798
01:27:48,652 --> 01:27:51,492
Speaker 21: You know what he's done with Tom was driving. If

1799
01:27:51,492 --> 01:27:54,133
Speaker 21: somebody had said five years ago cars are driving themselves around,

1800
01:27:54,173 --> 01:27:57,412
Speaker 21: they would have laughed at you. And now, well, I

1801
01:27:57,492 --> 01:27:59,652
Speaker 21: dare say, I'm an advocate for them. But it's it's

1802
01:27:59,692 --> 01:28:03,253
Speaker 21: safer than humans driving, you know, because we're and I

1803
01:28:03,372 --> 01:28:05,133
Speaker 21: see it where it's where it's gone.

1804
01:28:05,253 --> 01:28:10,132
Speaker 4: So there's been some criticism and a lot of circles

1805
01:28:10,173 --> 01:28:12,852
Speaker 4: around chet GPT and making it open source. Do you

1806
01:28:12,893 --> 01:28:17,092
Speaker 4: see any danger with the journey that that chet GPT

1807
01:28:17,333 --> 01:28:19,572
Speaker 4: has gone down? And do you think that is dangerous

1808
01:28:19,612 --> 01:28:23,492
Speaker 4: opening it up and revealing that software to anybody who

1809
01:28:23,812 --> 01:28:25,013
Speaker 4: has the skills to modify it.

1810
01:28:26,093 --> 01:28:29,333
Speaker 21: There's lots of open source l l M. Though the

1811
01:28:29,412 --> 01:28:32,133
Speaker 21: reason there's controversy around that is because it was always

1812
01:28:32,173 --> 01:28:36,093
Speaker 21: supposed to be open source. I mean, that's when that's

1813
01:28:36,133 --> 01:28:36,932
Speaker 21: what upsets Elon.

1814
01:28:37,933 --> 01:28:39,612
Speaker 2: I mean, that's why it's called that's called open AI

1815
01:28:39,853 --> 01:28:42,133
Speaker 2: for that reason. The idea was that they thought if

1816
01:28:42,133 --> 01:28:44,133
Speaker 2: this was all open up, then there's a species, we

1817
01:28:44,213 --> 01:28:45,852
Speaker 2: might be able to control it a little bit more

1818
01:28:45,893 --> 01:28:48,133
Speaker 2: and everyone could see what was going on. But then,

1819
01:28:48,612 --> 01:28:51,812
Speaker 2: like Sam Ackman, it's at some point when I'd like

1820
01:28:51,853 --> 01:28:55,652
Speaker 2: to be a billionaire like Elon and and you know,

1821
01:28:55,973 --> 01:28:56,612
Speaker 2: change everything.

1822
01:28:57,492 --> 01:28:59,492
Speaker 21: I think they've all made enough money out of it anyway,

1823
01:28:59,532 --> 01:29:01,932
Speaker 21: And I don't think Elon's driven by making any more

1824
01:29:01,973 --> 01:29:05,973
Speaker 21: money I think, And I'm not an Elon fanboard by

1825
01:29:06,013 --> 01:29:11,173
Speaker 21: the way, but I like like what he does. Yeah,

1826
01:29:11,253 --> 01:29:12,812
Speaker 21: so I mean it's exciting times.

1827
01:29:13,053 --> 01:29:15,812
Speaker 2: Look, do you think it's seen in the world, I feel,

1828
01:29:15,853 --> 01:29:16,772
Speaker 2: do you think do you think no?

1829
01:29:18,652 --> 01:29:21,293
Speaker 21: I think there's Look when we start getting into the robotics,

1830
01:29:21,853 --> 01:29:25,333
Speaker 21: which is nobody actually sees the development that's happening now,

1831
01:29:25,333 --> 01:29:27,732
Speaker 21: and believe you me, it's getting quite scary. What robotics

1832
01:29:27,772 --> 01:29:31,732
Speaker 21: are doing now trained by AI. That's I think that

1833
01:29:31,853 --> 01:29:35,852
Speaker 21: to me, that's when the physical AI becomes has physical ability.

1834
01:29:36,133 --> 01:29:40,732
Speaker 21: I think that's a little scary, but AI as the

1835
01:29:40,812 --> 01:29:43,293
Speaker 21: learning language models and that doesn't scare me at all.

1836
01:29:43,412 --> 01:29:45,372
Speaker 21: I look at it, do my job away. In fact,

1837
01:29:46,293 --> 01:29:50,133
Speaker 21: the business I'm involved in is writing my job out

1838
01:29:50,213 --> 01:29:53,812
Speaker 21: of the equation. You know, we do software development, and

1839
01:29:54,732 --> 01:29:57,412
Speaker 21: believe you me, everything's changed in six months. In the

1840
01:29:57,452 --> 01:30:02,213
Speaker 21: last six months. It's it's alarming. So there are a

1841
01:30:02,253 --> 01:30:05,652
Speaker 21: few industries and professions that are going to be hit.

1842
01:30:06,053 --> 01:30:08,612
Speaker 21: But on top of that, there still needs governance over

1843
01:30:08,652 --> 01:30:12,452
Speaker 21: at they still need training. But I think the low

1844
01:30:12,572 --> 01:30:16,293
Speaker 21: level jobs, certainly low level admin and data entry, and

1845
01:30:16,412 --> 01:30:20,852
Speaker 21: with a little bit of automation, those low level jobs

1846
01:30:21,452 --> 01:30:26,852
Speaker 21: will be augmented with certain AI agents, but not just

1847
01:30:27,333 --> 01:30:28,372
Speaker 21: you know check GBT.

1848
01:30:28,853 --> 01:30:31,892
Speaker 2: Yeah, well, thanks so much for your call, Phil, appreciate it.

1849
01:30:32,333 --> 01:30:34,412
Speaker 2: This text has said, Hey, guys, my sister in law

1850
01:30:34,452 --> 01:30:37,812
Speaker 2: works in the adult entertainment market. She makes twice as

1851
01:30:37,853 --> 01:30:41,092
Speaker 2: much as someone making regular adult entertainment. It's a booming

1852
01:30:41,133 --> 01:30:43,173
Speaker 2: market that's doubled in size in twelve months.

1853
01:30:43,213 --> 01:30:46,053
Speaker 4: Well done her, Oh one hundred eighty ten eighty is

1854
01:30:46,093 --> 01:30:48,852
Speaker 4: the number to call artificial intelligence. Do you think it

1855
01:30:48,933 --> 01:30:50,892
Speaker 4: is coming for your job or do you think it

1856
01:30:50,933 --> 01:30:53,013
Speaker 4: has the potential to create more jobs? Love to hear

1857
01:30:53,053 --> 01:30:55,732
Speaker 4: your thoughts. It is nineteen past three, stalk Sid be

1858
01:30:56,173 --> 01:30:58,173
Speaker 4: good afternoon. It is twenty one past three.

1859
01:30:58,452 --> 01:31:00,052
Speaker 2: In a minute, I read out a text from Paul,

1860
01:31:00,053 --> 01:31:02,692
Speaker 2: who did his masters in aid Oxford, and he is

1861
01:31:02,772 --> 01:31:07,053
Speaker 2: discussing what I was saying, that large language models are

1862
01:31:07,133 --> 01:31:11,293
Speaker 2: unlikely lead to agl Agi is on another past but Dominic,

1863
01:31:11,372 --> 01:31:12,173
Speaker 2: welcome to the show.

1864
01:31:13,293 --> 01:31:15,532
Speaker 16: Hey, thanks having us, love the show, love you your

1865
01:31:15,572 --> 01:31:17,492
Speaker 16: gros positivity and energy.

1866
01:31:17,732 --> 01:31:20,012
Speaker 2: Oh thank you so much, Dominic, and thanks for ringing.

1867
01:31:20,973 --> 01:31:23,652
Speaker 16: Oh pleasure now and I run away better go. So

1868
01:31:23,692 --> 01:31:27,333
Speaker 16: I'm just coming in a little bit behind a few callers,

1869
01:31:27,372 --> 01:31:30,052
Speaker 16: I think. So it was just one of your mates

1870
01:31:31,093 --> 01:31:32,812
Speaker 16: new call Stack was saying, you know that a one

1871
01:31:32,893 --> 01:31:36,572
Speaker 16: hundred thousand people lost their jobs because of something or

1872
01:31:36,612 --> 01:31:38,852
Speaker 16: other that that happened, but most of those people would

1873
01:31:38,853 --> 01:31:41,973
Speaker 16: have picked up other jobs and what they call it

1874
01:31:41,973 --> 01:31:45,492
Speaker 16: the big key word, the old pivot moved into other

1875
01:31:45,572 --> 01:31:49,612
Speaker 16: things or used other skill sets. When you were also

1876
01:31:49,692 --> 01:31:54,372
Speaker 16: chatting about the ages like the Industrial Revolution and revolutions

1877
01:31:54,372 --> 01:31:57,892
Speaker 16: that have come in the past and how human race

1878
01:31:57,893 --> 01:31:59,933
Speaker 16: has always done better off as a result of that.

1879
01:31:59,973 --> 01:32:03,692
Speaker 16: And you're you're exactly right, but our education system keeps

1880
01:32:03,772 --> 01:32:06,452
Speaker 16: up with that. So when you know, our edge education

1881
01:32:06,652 --> 01:32:10,612
Speaker 16: was formed, it was based on the Industrial Revolution, and

1882
01:32:11,253 --> 01:32:14,412
Speaker 16: we're still somewhat in that age and we are moving

1883
01:32:14,412 --> 01:32:17,333
Speaker 16: into an information technology age, so we are moving into

1884
01:32:17,372 --> 01:32:23,892
Speaker 16: a completely different age, and our education is lagging behind

1885
01:32:25,253 --> 01:32:28,732
Speaker 16: the world that we now face face ourselves in.

1886
01:32:28,933 --> 01:32:31,973
Speaker 2: If that makes sense, Yeah, it does. I've got a

1887
01:32:32,053 --> 01:32:34,452
Speaker 2: question for you, though, Dominic. You know, we had the

1888
01:32:34,612 --> 01:32:37,812
Speaker 2: we had the Industrial Revolution, and manufacturing was a big

1889
01:32:37,853 --> 01:32:39,732
Speaker 2: part of things. And then I remember, and I'm not

1890
01:32:39,772 --> 01:32:42,692
Speaker 2: sure which which parties said it, which government said it,

1891
01:32:42,732 --> 01:32:44,412
Speaker 2: but do you remember a lot of talk about the

1892
01:32:44,412 --> 01:32:47,572
Speaker 2: knowledge economy and New Zealand becoming a knowledge economy? That

1893
01:32:47,692 --> 01:32:50,452
Speaker 2: was that was a big buzzword for a while. You

1894
01:32:50,492 --> 01:32:54,932
Speaker 2: don't fear that we got rid of the engineering because

1895
01:32:54,933 --> 01:32:56,972
Speaker 2: we were going become a knowledge economy. And then AI

1896
01:32:57,133 --> 01:33:02,613
Speaker 2: comes along and that wipes out the use of knowledge

1897
01:33:03,133 --> 01:33:05,732
Speaker 2: and the use of a knowledge economy. And if we've

1898
01:33:05,732 --> 01:33:09,612
Speaker 2: got rid of the engineering, then then and the knowledge

1899
01:33:09,612 --> 01:33:10,812
Speaker 2: of column what have we got left.

1900
01:33:12,372 --> 01:33:15,852
Speaker 16: Yeah, I mean it's suppose fair is not a word

1901
01:33:15,893 --> 01:33:19,773
Speaker 16: that I'd probably use, but it's more we've always evolved

1902
01:33:19,933 --> 01:33:24,133
Speaker 16: as a species and our education systems kept up with that.

1903
01:33:24,213 --> 01:33:26,293
Speaker 16: As you said, you know, the whole ringing bellels and

1904
01:33:26,372 --> 01:33:30,053
Speaker 16: the industrial side of things that are now and are

1905
01:33:30,093 --> 01:33:33,732
Speaker 16: completely different. Like there's so much knowledge and information you

1906
01:33:33,772 --> 01:33:37,013
Speaker 16: can that's never been We've never had more access to

1907
01:33:37,133 --> 01:33:40,213
Speaker 16: it at the touch of a finger. We've never been

1908
01:33:40,293 --> 01:33:42,293
Speaker 16: more knowledgeable. It's how we use it and what we

1909
01:33:42,772 --> 01:33:46,173
Speaker 16: do with it that's the most important thing. And a

1910
01:33:46,213 --> 01:33:48,012
Speaker 16: few of your your callers, they all hit the nail

1911
01:33:48,053 --> 01:33:51,013
Speaker 16: on the head. It's it's keeping the creativity and the

1912
01:33:51,093 --> 01:33:55,173
Speaker 16: human aspects of things, and I think that's that's crucially important.

1913
01:33:55,213 --> 01:33:57,772
Speaker 16: And I think you know, the arts and sports plays

1914
01:33:57,772 --> 01:34:00,812
Speaker 16: a huge role in that where that's for people develop

1915
01:34:01,133 --> 01:34:06,253
Speaker 16: that connectivity, that those negotiation skills, the ability to work

1916
01:34:06,293 --> 01:34:08,253
Speaker 16: as a team and be human.

1917
01:34:09,293 --> 01:34:11,412
Speaker 2: Yeah, I mean, there's not much point in any of

1918
01:34:11,412 --> 01:34:13,692
Speaker 2: it and ass with the nes we're interacting with other humans.

1919
01:34:13,732 --> 01:34:15,772
Speaker 2: And I think that any economy has to take that

1920
01:34:15,853 --> 01:34:19,253
Speaker 2: into a into account. So the thing about humans is

1921
01:34:19,253 --> 01:34:22,293
Speaker 2: when wherever there's there's certain amount of inefficient. There's a

1922
01:34:22,293 --> 01:34:24,892
Speaker 2: certain amount of inefficiency in being a human, and that

1923
01:34:25,053 --> 01:34:28,933
Speaker 2: is because we we crave, you know, those things like

1924
01:34:29,013 --> 01:34:30,692
Speaker 2: you know, the love of a parent for a child,

1925
01:34:30,772 --> 01:34:32,772
Speaker 2: and the and the and the time of their friends

1926
01:34:32,812 --> 01:34:36,253
Speaker 2: and and you know, in any given job, like say

1927
01:34:36,612 --> 01:34:38,973
Speaker 2: Matt and Tyler afternoons, a huge part of it is

1928
01:34:39,013 --> 01:34:41,852
Speaker 2: just Tyler and I hanging out before we're on here.

1929
01:34:41,893 --> 01:34:45,132
Speaker 2: I mean, it's not hugely efficient, we hang out before

1930
01:34:45,173 --> 01:34:47,333
Speaker 2: the show, but you see see what I mean. I

1931
01:34:47,333 --> 01:34:50,093
Speaker 2: think humans will push back on full efficiency because it

1932
01:34:50,133 --> 01:34:54,492
Speaker 2: doesn't leads to the emotional outcomes we want.

1933
01:34:56,572 --> 01:35:02,053
Speaker 16: Yeah, that's that's exactly right. We're society creatures, so that

1934
01:35:02,133 --> 01:35:06,173
Speaker 16: our connection and belonging and that's that's crucially important. I

1935
01:35:06,253 --> 01:35:09,452
Speaker 16: just think that our our education system, and they're currently

1936
01:35:09,452 --> 01:35:12,892
Speaker 16: reviewing it, and it seems that we're going backwards based

1937
01:35:12,933 --> 01:35:15,732
Speaker 16: on you know, certain things that they're trying to do,

1938
01:35:15,772 --> 01:35:17,852
Speaker 16: where I think we need to have more connection and

1939
01:35:17,933 --> 01:35:24,452
Speaker 16: more human aspects of it. Embrace the technology utilized that

1940
01:35:24,772 --> 01:35:28,333
Speaker 16: you know, they're canceling it in everything. It's almost like

1941
01:35:29,173 --> 01:35:30,693
Speaker 16: it's a taboo language.

1942
01:35:31,133 --> 01:35:31,293
Speaker 10: Yea.

1943
01:35:31,572 --> 01:35:35,093
Speaker 11: The water cornerans, Oh.

1944
01:35:35,093 --> 01:35:37,532
Speaker 2: Yeah, I agree with you one hundred percent on it. Yeah, yeah,

1945
01:35:37,572 --> 01:35:39,532
Speaker 2: I mean there's no point in not embracing it because

1946
01:35:39,572 --> 01:35:41,572
Speaker 2: it's coming, whether you like it or not exactly.

1947
01:35:41,772 --> 01:35:44,572
Speaker 4: But their point on you know, that's a deep biological words.

1948
01:35:44,572 --> 01:35:47,372
Speaker 4: We have to have that connection. Without that connection, our

1949
01:35:47,452 --> 01:35:50,372
Speaker 4: mental health and and our whole biology starts to fall apart,

1950
01:35:50,452 --> 01:35:52,013
Speaker 4: no doubt about that. You go and live in the

1951
01:35:52,013 --> 01:35:54,093
Speaker 4: middle of the woods without any social connection and see

1952
01:35:54,093 --> 01:35:56,133
Speaker 4: how well you fear. But that's why I think that

1953
01:35:56,293 --> 01:35:57,612
Speaker 4: is a big part of it. And I say that

1954
01:35:57,652 --> 01:36:00,093
Speaker 4: because I'm feeling that with a technology boom that is

1955
01:36:00,133 --> 01:36:02,333
Speaker 4: happening around me, I'm starting to push back. I'm not

1956
01:36:02,452 --> 01:36:04,492
Speaker 4: using my phone as much. I'm doing the raw dogging

1957
01:36:04,732 --> 01:36:07,452
Speaker 4: that you introduced me to, which is fantastic trying to

1958
01:36:08,772 --> 01:36:11,293
Speaker 4: and I hope that is gonna save us from this

1959
01:36:11,372 --> 01:36:15,852
Speaker 4: AGI who may treat us as dex It is twenty

1960
01:36:15,893 --> 01:36:16,692
Speaker 4: seven past three.

1961
01:36:16,812 --> 01:36:22,652
Speaker 22: Back with more of your calls, very shortly, Matt Heathen,

1962
01:36:22,732 --> 01:36:26,293
Speaker 22: Tyler Adams afternoons call oh eight hundred eighty ten eighty

1963
01:36:26,372 --> 01:36:29,293
Speaker 22: On News Talk ZB, Paul says.

1964
01:36:29,053 --> 01:36:31,972
Speaker 2: I did my master's in AI at Oxford and we

1965
01:36:31,973 --> 01:36:34,972
Speaker 2: were looking specifically at general AI, not much has changed

1966
01:36:35,013 --> 01:36:39,012
Speaker 2: apart from the database sizes and accessibility through fast processing speeds.

1967
01:36:39,253 --> 01:36:41,093
Speaker 2: So he was there thirty five years ago and not

1968
01:36:41,213 --> 01:36:43,532
Speaker 2: much has changed in his opinion in thirty five years.

1969
01:36:43,812 --> 01:36:46,412
Speaker 2: The next advances will come through advances in software and

1970
01:36:46,532 --> 01:36:50,333
Speaker 2: algorithmic advances, But for now it's all what's called narrow AI,

1971
01:36:50,812 --> 01:36:55,213
Speaker 2: most task specific, but very good at polling these capabilities

1972
01:36:55,253 --> 01:36:56,333
Speaker 2: to mimic general AI.

1973
01:36:56,732 --> 01:36:56,932
Speaker 10: Yeah.

1974
01:36:56,933 --> 01:36:59,612
Speaker 2: I think we get confused because it feels like it's

1975
01:36:59,732 --> 01:37:03,133
Speaker 2: general AI, and we think that it's it's they call

1976
01:37:03,173 --> 01:37:05,652
Speaker 2: it the singularity. We feel like it's a personality, and

1977
01:37:05,692 --> 01:37:10,293
Speaker 2: we feel like it's being intuitive and creative. But it

1978
01:37:10,333 --> 01:37:13,132
Speaker 2: is still a large language model that is just creating

1979
01:37:13,173 --> 01:37:18,013
Speaker 2: tokens out of out of words and predicting the probability

1980
01:37:18,013 --> 01:37:20,612
Speaker 2: of the next the next token popping up. Yeah, so

1981
01:37:21,093 --> 01:37:25,012
Speaker 2: it's still algorithmically, just a probability machine.

1982
01:37:25,093 --> 01:37:25,892
Speaker 4: Yep, exactly.

1983
01:37:26,772 --> 01:37:29,053
Speaker 2: The six says three AI. I'm a sparking and one

1984
01:37:29,053 --> 01:37:31,333
Speaker 2: of my clients used an AI robot. It got stuck

1985
01:37:31,412 --> 01:37:33,972
Speaker 2: under the house and rusted. It's still there and job

1986
01:37:34,013 --> 01:37:35,612
Speaker 2: not done. I'm ignoring their calls.

1987
01:37:37,013 --> 01:37:39,133
Speaker 4: Thank you very much for that. I'll take you.

1988
01:37:39,572 --> 01:37:42,173
Speaker 2: I'll take you on your word. This sixer says, Hey guys,

1989
01:37:42,213 --> 01:37:44,652
Speaker 2: I take a negative outlook for AI. I believe in

1990
01:37:44,692 --> 01:37:47,932
Speaker 2: forthcoming years this will be very detrimental to society, employment,

1991
01:37:47,973 --> 01:37:51,213
Speaker 2: mental health impacted as well as for new jobs will

1992
01:37:51,213 --> 01:37:51,732
Speaker 2: be created.

1993
01:37:52,013 --> 01:37:52,253
Speaker 21: Wow.

1994
01:37:52,293 --> 01:37:54,612
Speaker 2: What a load of nonsense. Cheers Troy, Thank.

1995
01:37:54,492 --> 01:37:56,973
Speaker 4: You, thank you for that rosy outlook. We're going to

1996
01:37:57,053 --> 01:37:59,732
Speaker 4: keep this gowen after the headlines with Raylene which is

1997
01:37:59,772 --> 01:38:01,852
Speaker 4: coming up, but keen to get your views AI. Oh

1998
01:38:01,853 --> 01:38:03,572
Speaker 4: wait one hundred and eighty ten eighty do you think

1999
01:38:03,572 --> 01:38:06,572
Speaker 4: it's got a potential to create jobs or destroy them?

2000
01:38:06,572 --> 01:38:08,093
Speaker 4: And are you worried about your job?

2001
01:38:10,933 --> 01:38:13,253
Speaker 13: Us talks at be headlines.

2002
01:38:12,732 --> 01:38:15,932
Speaker 14: With blue bubble taxis it's no trouble with a blue bubble.

2003
01:38:16,372 --> 01:38:20,492
Speaker 14: Strong winds are fanning fires in Canterbury at Harpuku north

2004
01:38:20,492 --> 01:38:24,652
Speaker 14: of Caikoda and on christ Church's Russley Road. The wind

2005
01:38:24,692 --> 01:38:28,092
Speaker 14: has claimed one fatality, with a man dying in Wellington

2006
01:38:28,133 --> 01:38:30,973
Speaker 14: this morning after being hit by a falling brandt. A

2007
01:38:31,013 --> 01:38:33,652
Speaker 14: person has also been seriously injured in hawks Bay's at

2008
01:38:33,652 --> 01:38:37,333
Speaker 14: Tuckapo when a truck rolled in a gust. Another trucks

2009
01:38:37,372 --> 01:38:39,772
Speaker 14: tapped in the wided upper and two camber vans have

2010
01:38:39,893 --> 01:38:43,492
Speaker 14: rolled near kaikoder one is blocking State Highway one near

2011
01:38:43,532 --> 01:38:47,333
Speaker 14: Hapuku River Bridge. Flooding in slips of close several main

2012
01:38:47,492 --> 01:38:52,612
Speaker 14: South Island roads, including State Highway seven and Lewis Pass. Meanwhile,

2013
01:38:52,612 --> 01:38:56,333
Speaker 14: almost a dozen cars have reportedly been burnt in Faraday

2014
01:38:56,372 --> 01:38:59,772
Speaker 14: Hospital's car park, with the blaze now contained. Fire and

2015
01:38:59,812 --> 01:39:02,932
Speaker 14: Emergency says an investigator will be looking for the cause.

2016
01:39:03,853 --> 01:39:08,253
Speaker 14: The Public Service Commissioner working on teachers bargaining agreements one

2017
01:39:08,452 --> 01:39:11,732
Speaker 14: be in the country during Thursday's mega strike. The commission

2018
01:39:11,772 --> 01:39:14,852
Speaker 14: says his location is irrelevant. Is he still working and

2019
01:39:14,973 --> 01:39:18,932
Speaker 14: leading bargaining our in z host set to lose role

2020
01:39:18,933 --> 01:39:22,253
Speaker 14: amid cost cuts at public broadcaster. You can see this

2021
01:39:22,412 --> 01:39:25,773
Speaker 14: and more from media insider it in said Herald Premium.

2022
01:39:26,053 --> 01:39:28,133
Speaker 14: Back to Matt Eath and Tyler Adams.

2023
01:39:27,732 --> 01:39:30,052
Speaker 4: Thank you very much, Rayleen. Having a great discussion about

2024
01:39:30,133 --> 01:39:33,892
Speaker 4: artificial intelligence and whether it will create jobs will destroy jobs.

2025
01:39:33,973 --> 01:39:36,372
Speaker 2: Brendan says, Hi, if AI will be so efficient at

2026
01:39:36,372 --> 01:39:38,772
Speaker 2: taking over the work we do now, then it should

2027
01:39:38,812 --> 01:39:41,412
Speaker 2: be even better at identifying suggesting jobs that people could do.

2028
01:39:41,772 --> 01:39:44,333
Speaker 4: That's a good point. Yeah, it may help in that arena.

2029
01:39:44,372 --> 01:39:46,532
Speaker 2: Hey, Matt and Tyler AO reminds me of when the

2030
01:39:46,572 --> 01:39:49,092
Speaker 2: blow up doll came out and was going to replace

2031
01:39:49,133 --> 01:39:51,852
Speaker 2: my wife, but that never happened. Cheers, Well, well, it's

2032
01:39:51,933 --> 01:39:52,492
Speaker 2: glad to hear that.

2033
01:39:52,572 --> 01:39:54,732
Speaker 4: Yeah, it's an interesting analogy. But I mean, I think

2034
01:39:54,732 --> 01:39:55,532
Speaker 4: I'll follow you there.

2035
01:39:56,572 --> 01:39:59,053
Speaker 2: The block doll was never going to replace your wife, no,

2036
01:39:59,933 --> 01:40:04,333
Speaker 2: but the ai AI bots yeah yeah, could well do

2037
01:40:04,973 --> 01:40:05,972
Speaker 2: George one with the show.

2038
01:40:07,452 --> 01:40:07,612
Speaker 3: Yeah.

2039
01:40:10,652 --> 01:40:14,852
Speaker 23: Yeah, very interesting subject and you've watched to develop over

2040
01:40:14,893 --> 01:40:18,333
Speaker 23: the last twenty odd years from very simple beginnings where

2041
01:40:18,333 --> 01:40:22,173
Speaker 23: you're talking about it every day now, and I guess

2042
01:40:22,213 --> 01:40:24,892
Speaker 23: I've got a couple of worries. I've been involved with

2043
01:40:24,973 --> 01:40:28,493
Speaker 23: automation basically all my working lives since the nineteen seventies,

2044
01:40:29,452 --> 01:40:34,652
Speaker 23: and much things developed. The ideas of what you can

2045
01:40:34,692 --> 01:40:38,293
Speaker 23: do with automation have always been there because you've always

2046
01:40:38,293 --> 01:40:44,452
Speaker 23: waited for the technology, either in processing power or the

2047
01:40:44,492 --> 01:40:50,973
Speaker 23: actual software to develop. But where you are now, I

2048
01:40:51,053 --> 01:40:55,892
Speaker 23: believe is you've got the software is there, processing powers there,

2049
01:40:56,452 --> 01:41:00,412
Speaker 23: and you've got the different intelligence which has only algorithms

2050
01:41:00,412 --> 01:41:04,093
Speaker 23: that the software writers have written. But they are at

2051
01:41:04,093 --> 01:41:07,572
Speaker 23: the stage where they can talk to each other and

2052
01:41:07,572 --> 01:41:11,412
Speaker 23: it's develop things so rapidly that I honestly don't think

2053
01:41:11,933 --> 01:41:14,772
Speaker 23: that anyone trying to regulate it or keep up with it,

2054
01:41:14,732 --> 01:41:18,093
Speaker 23: it is going to be able to control. It just

2055
01:41:18,133 --> 01:41:20,612
Speaker 23: has the ability, I believe, to run away with itself.

2056
01:41:21,133 --> 01:41:23,173
Speaker 2: George, what areas did you work? Do you work in

2057
01:41:23,293 --> 01:41:27,173
Speaker 2: an automation as it was it? Manufacturing or what areas

2058
01:41:27,173 --> 01:41:27,652
Speaker 2: of automation?

2059
01:41:28,893 --> 01:41:33,333
Speaker 23: Yeah, manufacturing automatic processing gear and robotics and so forth,

2060
01:41:34,213 --> 01:41:38,572
Speaker 23: packaging equipment and things. And did a little bit of

2061
01:41:38,572 --> 01:41:42,452
Speaker 23: software myself, and not much. Most that I work with

2062
01:41:43,133 --> 01:41:46,612
Speaker 23: the software boys they specialized that, but we were always

2063
01:41:46,692 --> 01:41:51,972
Speaker 23: waiting for the processing power and things to do what

2064
01:41:52,013 --> 01:41:54,173
Speaker 23: we wanted to do and the rates we could do

2065
01:41:54,213 --> 01:42:01,652
Speaker 23: it to the point where the data storage farms thinks

2066
01:42:01,692 --> 01:42:04,692
Speaker 23: that you've got now and this is about five or

2067
01:42:04,692 --> 01:42:09,133
Speaker 23: eight years ago, we're consuming more electroc from what I

2068
01:42:09,213 --> 01:42:12,732
Speaker 23: understand is the country of Japan, and here we are

2069
01:42:13,572 --> 01:42:16,773
Speaker 23: we have developed even further from that, and it's probably.

2070
01:42:16,492 --> 01:42:18,772
Speaker 10: Ten many old more than.

2071
01:42:18,652 --> 01:42:24,293
Speaker 23: That again now and so the storage of data is

2072
01:42:24,372 --> 01:42:28,173
Speaker 23: what your AI dips into. To compare a photo of

2073
01:42:28,213 --> 01:42:32,412
Speaker 23: someone's lunch over and France compared to someone's linking lunch

2074
01:42:32,452 --> 01:42:35,532
Speaker 23: over and with this, danbold And says yeah, this is

2075
01:42:35,612 --> 01:42:38,612
Speaker 23: common or that's not so cause or a recipe here

2076
01:42:38,652 --> 01:42:43,932
Speaker 23: and a recipe here, or biology or training up. So

2077
01:42:44,093 --> 01:42:47,213
Speaker 23: that depth of storage is crucial for AI to work.

2078
01:42:47,812 --> 01:42:52,052
Speaker 23: But it's just taking so much more, so much more

2079
01:42:53,732 --> 01:42:58,893
Speaker 23: data farms, work and communications. It's as I say, what

2080
01:42:58,973 --> 01:43:01,532
Speaker 23: excuse me, is it's going to run away with yourself.

2081
01:43:01,572 --> 01:43:04,253
Speaker 2: And you're in your area though you would be with

2082
01:43:04,333 --> 01:43:10,853
Speaker 2: the automation, wouldn't you be utilizing small language models, isolated systems.

2083
01:43:12,372 --> 01:43:12,492
Speaker 11: Uh.

2084
01:43:12,973 --> 01:43:17,572
Speaker 23: Yeah. We competed against companies like Semens, and those sorts

2085
01:43:17,572 --> 01:43:21,452
Speaker 23: of things were there. What you started off causing, say

2086
01:43:21,572 --> 01:43:25,772
Speaker 23: PLCs and stuff, so that was sort of fixed logic

2087
01:43:25,893 --> 01:43:29,293
Speaker 23: and a fixed box full of ligand intelligence and that

2088
01:43:29,492 --> 01:43:34,133
Speaker 23: was it. But that stuff now all is all online

2089
01:43:34,213 --> 01:43:36,852
Speaker 23: and it's all going back to central PLATH storage SYS

2090
01:43:37,133 --> 01:43:42,452
Speaker 23: and and so that's all developed. And you know, you've

2091
01:43:42,492 --> 01:43:45,093
Speaker 23: only got to look at your phone five years ago.

2092
01:43:45,173 --> 01:43:50,093
Speaker 23: If you took a photo, it was about one gigabyte sorry,

2093
01:43:50,133 --> 01:43:52,972
Speaker 23: one megabyte photo, and now you look.

2094
01:43:52,853 --> 01:43:58,412
Speaker 10: At it and it's five. So that's five. The storage.

2095
01:43:59,173 --> 01:44:01,933
Speaker 2: About just deleting all your emails and the difference that

2096
01:44:01,973 --> 01:44:03,612
Speaker 2: if you want to do something, if you care about

2097
01:44:04,053 --> 01:44:08,053
Speaker 2: the environment and carbon footprints and stuff. Just emptying your emails,

2098
01:44:08,372 --> 01:44:11,253
Speaker 2: your gmails, so you've only got the ones you need.

2099
01:44:11,372 --> 01:44:13,812
Speaker 2: Otherwise they're sitting there storing, and it's being stored.

2100
01:44:14,133 --> 01:44:16,532
Speaker 23: Okay, okay, let me talk to you about that. I

2101
01:44:17,253 --> 01:44:21,173
Speaker 23: did that about a year or so ago. Three months

2102
01:44:21,213 --> 01:44:25,093
Speaker 23: after I did it, I got an email asking me,

2103
01:44:25,213 --> 01:44:27,652
Speaker 23: did I really want to delete those notes and would I.

2104
01:44:27,652 --> 01:44:29,772
Speaker 10: Like to make Yeah?

2105
01:44:30,013 --> 01:44:32,413
Speaker 3: Yeah, So they just kept it in the background.

2106
01:44:32,532 --> 01:44:36,253
Speaker 23: There's a parallel storage of all those files right point.

2107
01:44:36,333 --> 01:44:37,452
Speaker 10: Yeah. Yeah.

2108
01:44:37,492 --> 01:44:40,893
Speaker 4: But the problem is, yeah, the problem is, George, is

2109
01:44:40,933 --> 01:44:43,092
Speaker 4: we've got to keep up right where with the business

2110
01:44:43,133 --> 01:44:45,412
Speaker 4: that you're in, and it's been a criticism of what

2111
01:44:45,452 --> 01:44:47,573
Speaker 4: we do here in New Zealand when it comes to productivity.

2112
01:44:47,772 --> 01:44:50,813
Speaker 4: We've got to keep up with those advancements and technology

2113
01:44:50,812 --> 01:44:53,053
Speaker 4: because that's what other countries are doing, or else we're

2114
01:44:53,053 --> 01:44:55,173
Speaker 4: going to fall behind. So we can't just stop and

2115
01:44:55,253 --> 01:44:58,053
Speaker 4: say we're weary of where this goes, we're not going

2116
01:44:58,133 --> 01:44:59,732
Speaker 4: to do it anymore. We have to keep going.

2117
01:45:00,853 --> 01:45:05,572
Speaker 23: Oh yeah, I agree that. The other that's one aspect

2118
01:45:05,612 --> 01:45:08,852
Speaker 23: that worries me. The other aspect is that where we

2119
01:45:08,973 --> 01:45:13,812
Speaker 23: haven't mentioned the fact that everyone's assuming that AI is going.

2120
01:45:13,652 --> 01:45:17,372
Speaker 10: To be used for good. Yeah, there's to be a small.

2121
01:45:17,093 --> 01:45:19,452
Speaker 23: Section of the community out there rub in their hands

2122
01:45:19,452 --> 01:45:22,012
Speaker 23: with glee. Yeah, I mean you're just logging the scams.

2123
01:45:22,013 --> 01:45:22,812
Speaker 10: It's hopeful to go.

2124
01:45:25,133 --> 01:45:28,173
Speaker 23: Fall section of the community. They just want to rehab it.

2125
01:45:28,333 --> 01:45:31,093
Speaker 4: Yeah, and oh yeah everybody else.

2126
01:45:32,173 --> 01:45:34,812
Speaker 2: I totally agree George that that's the that's the part

2127
01:45:34,812 --> 01:45:36,492
Speaker 2: of it that it's scary to me because there's some

2128
01:45:36,532 --> 01:45:39,053
Speaker 2: people that will use it for good and productivity. There

2129
01:45:39,133 --> 01:45:41,732
Speaker 2: was some people that will use it for for direct

2130
01:45:41,812 --> 01:45:44,972
Speaker 2: evil purposes. But there were some people like the Joker

2131
01:45:45,013 --> 01:45:47,012
Speaker 2: and the Dark Night that just start fires to watch

2132
01:45:47,013 --> 01:45:50,372
Speaker 2: the world burn. And they are the really terrifying people.

2133
01:45:51,492 --> 01:45:53,172
Speaker 10: They're all I needed was a box of matches.

2134
01:45:53,492 --> 01:45:55,292
Speaker 4: Yeah, yeah, yeah, yeah exactly.

2135
01:45:55,492 --> 01:45:57,772
Speaker 2: But just finding before we let you go, George and

2136
01:45:57,812 --> 01:46:01,492
Speaker 2: your automation. Obviously there's the hardware and the software. So

2137
01:46:02,253 --> 01:46:05,732
Speaker 2: once you've invested in all the the hardware with these

2138
01:46:05,933 --> 01:46:09,452
Speaker 2: massive advantage advances in in the software part of it,

2139
01:46:09,492 --> 01:46:12,732
Speaker 2: in the automation, is the hardware up to it or

2140
01:46:12,772 --> 01:46:15,572
Speaker 2: are you always having to rebuild all the hardware.

2141
01:46:16,772 --> 01:46:20,053
Speaker 23: It's sort of all going to develop alongside it. I

2142
01:46:20,093 --> 01:46:22,452
Speaker 23: mean I got involved in the apple industry back in

2143
01:46:22,492 --> 01:46:26,652
Speaker 23: the nineteen seventies and when electronic fruit sizing it and

2144
01:46:26,812 --> 01:46:31,532
Speaker 23: color sorting it, things got brought out. And ever since

2145
01:46:31,652 --> 01:46:34,372
Speaker 23: the nineteen eighties has always been talked about.

2146
01:46:34,412 --> 01:46:36,372
Speaker 10: Color sorting and DEFX sorting.

2147
01:46:36,093 --> 01:46:39,093
Speaker 23: And so forth, and it was always a possibility, but

2148
01:46:39,253 --> 01:46:44,093
Speaker 23: under laboratory additions. And you know people go on about, oh,

2149
01:46:44,133 --> 01:46:47,492
Speaker 23: we're going to robot pick the apples and the orchids

2150
01:46:47,492 --> 01:46:52,293
Speaker 23: and so forth. Well, again under robotic condition or the

2151
01:46:52,492 --> 01:46:57,373
Speaker 23: sort of lab conditions, yes you can. But to process

2152
01:46:57,452 --> 01:47:00,772
Speaker 23: the data to keep up with a human that can

2153
01:47:00,853 --> 01:47:05,293
Speaker 23: pick an apple to side, whether it's the rip rightness

2154
01:47:05,333 --> 01:47:09,412
Speaker 23: is okay, whether there's any blemishes on it, and so more, at.

2155
01:47:09,213 --> 01:47:11,053
Speaker 10: A rate that a machine can do it.

2156
01:47:11,973 --> 01:47:14,573
Speaker 23: You still haven't gotten the processing power available.

2157
01:47:15,013 --> 01:47:16,652
Speaker 2: Yeah, thank you so much for you call, George.

2158
01:47:16,692 --> 01:47:17,772
Speaker 4: Yeah, incredibly interesting.

2159
01:47:17,893 --> 01:47:20,293
Speaker 2: So we're just shoving food in our faces and that's

2160
01:47:20,293 --> 01:47:22,173
Speaker 2: how we run the processing power. So we're still pretty

2161
01:47:22,173 --> 01:47:23,452
Speaker 2: efficient in that regard, if.

2162
01:47:23,333 --> 01:47:26,572
Speaker 4: You nicely said, it is eighteen two four back with

2163
01:47:26,652 --> 01:47:28,133
Speaker 4: more of your calls, and there's a lot of them

2164
01:47:28,173 --> 01:47:29,333
Speaker 4: coming through very shortly.

2165
01:47:30,532 --> 01:47:34,092
Speaker 1: Matt Heath Tyler Adams taking your calls on eight hundred

2166
01:47:34,093 --> 01:47:36,732
Speaker 1: and eighty ten eighty. It's mad Heath and Tyler Adams

2167
01:47:36,772 --> 01:47:38,173
Speaker 1: afternoons news talks.

2168
01:47:38,173 --> 01:47:40,133
Speaker 4: They'd be for a good afternoon to you. It is

2169
01:47:40,213 --> 01:47:41,693
Speaker 4: a quarter to four.

2170
01:47:42,253 --> 01:47:45,012
Speaker 2: Mike, welcome to the show. We're talking about AI and

2171
01:47:45,333 --> 01:47:47,973
Speaker 2: it's taking of our human jobs.

2172
01:47:49,093 --> 01:47:53,853
Speaker 24: Yes, hey, yeah, good afrites. I don't believe that eventually

2173
01:47:54,692 --> 01:47:58,293
Speaker 24: beat the priests jobs. You know, just like know what's

2174
01:47:58,333 --> 01:48:01,412
Speaker 24: happening in McDonald's, you know to go there.

2175
01:48:01,492 --> 01:48:02,173
Speaker 16: You just under it.

2176
01:48:02,293 --> 01:48:07,652
Speaker 24: In the sobsquios right, the Hunter people is gone. For example,

2177
01:48:07,772 --> 01:48:11,372
Speaker 24: the there's a warehouse he really you shouldn't the adgenal

2178
01:48:11,412 --> 01:48:16,652
Speaker 24: warehouse and all of the you know, it's automitted. It's

2179
01:48:16,772 --> 01:48:21,093
Speaker 24: that they're hiring hips of humans doing the warehouse jobs.

2180
01:48:21,133 --> 01:48:24,253
Speaker 24: They don't they don't need to so this, you know,

2181
01:48:24,333 --> 01:48:27,973
Speaker 24: unlike before. That's what you're saying that in the feral revolutions,

2182
01:48:28,253 --> 01:48:31,892
Speaker 24: they invented washing machines, they invented things that help us

2183
01:48:32,213 --> 01:48:36,093
Speaker 24: make our lives easier. Now we are threatened to lose

2184
01:48:36,133 --> 01:48:36,652
Speaker 24: our job.

2185
01:48:38,973 --> 01:48:44,013
Speaker 2: Yeyeah. But you don't you don't think that there's that

2186
01:48:44,093 --> 01:48:46,652
Speaker 2: you don't think there's other jobs that will be created.

2187
01:48:46,692 --> 01:48:49,572
Speaker 2: By AI, so you may get rid of Ye.

2188
01:48:51,612 --> 01:48:54,492
Speaker 24: I'm going on that path that the problem right now

2189
01:48:54,532 --> 01:48:57,613
Speaker 24: that we have we're fishing is based on our population.

2190
01:48:58,893 --> 01:49:03,772
Speaker 24: You know, overall populations on Earth that there's so many

2191
01:49:03,853 --> 01:49:08,852
Speaker 24: humans than than the jobs that you know, the job

2192
01:49:08,933 --> 01:49:10,773
Speaker 24: that will replace.

2193
01:49:10,893 --> 01:49:11,772
Speaker 10: It's much more.

2194
01:49:12,093 --> 01:49:14,532
Speaker 24: I don't think that we have enough just like here

2195
01:49:14,572 --> 01:49:17,213
Speaker 24: you're in it. Like now we don't have the problem,

2196
01:49:17,253 --> 01:49:20,333
Speaker 24: but we don't have enough jobs to supplay for those

2197
01:49:20,372 --> 01:49:23,293
Speaker 24: people right now they don't have a job on more

2198
01:49:24,253 --> 01:49:25,532
Speaker 24: is a what's here?

2199
01:49:26,333 --> 01:49:27,532
Speaker 3: We lose more jobs?

2200
01:49:27,692 --> 01:49:30,173
Speaker 4: Yeah, but the idea, and this is quitely a feel,

2201
01:49:30,213 --> 01:49:33,173
Speaker 4: but the idea of value might be changed drastically in

2202
01:49:33,173 --> 01:49:36,053
Speaker 4: that situation. Like if AI does start to take all

2203
01:49:36,093 --> 01:49:38,492
Speaker 4: of the jobs and we value that on productivity, so

2204
01:49:38,532 --> 01:49:42,653
Speaker 4: that picks up that value proposition, then our value arguably

2205
01:49:42,652 --> 01:49:44,932
Speaker 4: becomes human connections. So there may be other jobs that

2206
01:49:45,013 --> 01:49:48,053
Speaker 4: pop up or that you can bring value to as

2207
01:49:48,093 --> 01:49:50,653
Speaker 4: a human being that the AI has taken a productivity

2208
01:49:50,652 --> 01:49:51,412
Speaker 4: side of things away.

2209
01:49:52,853 --> 01:49:55,732
Speaker 24: Yeah, I just hope that you know, the AA will

2210
01:49:55,812 --> 01:49:59,133
Speaker 24: take the jobs that the hard jobs, the easy jobs.

2211
01:50:00,973 --> 01:50:02,812
Speaker 4: That would be nice of it if it takes the

2212
01:50:02,893 --> 01:50:07,652
Speaker 4: hard job that.

2213
01:50:05,732 --> 01:50:08,612
Speaker 24: I mean, you know, it's the commers of this, ay,

2214
01:50:09,133 --> 01:50:11,452
Speaker 24: you know, come.

2215
01:50:11,293 --> 01:50:13,692
Speaker 2: On, that's yeah that I find that very annoying.

2216
01:50:15,053 --> 01:50:18,133
Speaker 24: I mean the graphic ents, not jobs, the people you

2217
01:50:18,173 --> 01:50:21,372
Speaker 24: know that, theors, you know, those things that come on

2218
01:50:21,572 --> 01:50:23,812
Speaker 24: you know.

2219
01:50:23,973 --> 01:50:27,213
Speaker 2: Yeah, I agree, Mike. And YouTube's pushing back on that,

2220
01:50:27,452 --> 01:50:29,932
Speaker 2: which is I guess ironic because Google such owns YouTube,

2221
01:50:29,933 --> 01:50:34,013
Speaker 2: the ABC and there are the Alphabet group. But they,

2222
01:50:35,572 --> 01:50:37,892
Speaker 2: you know, are demonetizing anything that's AI.

2223
01:50:38,772 --> 01:50:39,333
Speaker 4: Which is great.

2224
01:50:39,412 --> 01:50:42,452
Speaker 2: I mean it has to be real people talking real

2225
01:50:42,492 --> 01:50:44,532
Speaker 2: stuff into real cameras. Arouse, They're not going to pay

2226
01:50:44,532 --> 01:50:47,772
Speaker 2: you any money, which may be which may be maybe

2227
01:50:47,812 --> 01:50:50,732
Speaker 2: a financial proposition for them. But I mean if I

2228
01:50:50,772 --> 01:50:53,293
Speaker 2: see some if I see anything that's generated by AI

2229
01:50:54,093 --> 01:50:56,133
Speaker 2: in a movie and advertising or whatever, it just puts

2230
01:50:56,133 --> 01:50:59,732
Speaker 2: me off that, you know, because I guess the idea

2231
01:51:00,133 --> 01:51:06,812
Speaker 2: is that, you know, AI automates repetitive tasks, but in

2232
01:51:06,812 --> 01:51:09,653
Speaker 2: the dream world, it would free up you for creative

2233
01:51:09,772 --> 01:51:11,972
Speaker 2: and social and interpersonal work, right.

2234
01:51:11,933 --> 01:51:14,612
Speaker 4: Yeah, So the idea of value just might change into

2235
01:51:14,652 --> 01:51:17,692
Speaker 4: those creative outlets that only us humans can do. For

2236
01:51:17,732 --> 01:51:20,772
Speaker 4: the time being anyway, and unless AGI gets developed, and

2237
01:51:20,812 --> 01:51:22,812
Speaker 4: there's still a lot of question marks on that a

2238
01:51:22,853 --> 01:51:25,412
Speaker 4: couple of texts before we get to the ads. This

2239
01:51:25,492 --> 01:51:28,213
Speaker 4: one says, Guys, I think AI will create jobs for

2240
01:51:28,253 --> 01:51:31,492
Speaker 4: as long as AI centers are being constructed and commissioned, etc.

2241
01:51:31,893 --> 01:51:34,293
Speaker 4: Of course, AI will also result in job losses. My

2242
01:51:34,372 --> 01:51:37,452
Speaker 4: concern is if we build these power hungry monsters here,

2243
01:51:37,732 --> 01:51:40,133
Speaker 4: they should have to have their own power generation. A

2244
01:51:40,213 --> 01:51:43,053
Speaker 4: chat GBT search uses ten times more energy than a

2245
01:51:43,093 --> 01:51:46,333
Speaker 4: Google search. Can't see AI driving my truck anytime soon,

2246
01:51:46,372 --> 01:51:47,972
Speaker 4: so I feel pretty secure right now.

2247
01:51:48,253 --> 01:51:50,333
Speaker 2: Yeah, well, I mean, have you seen there's a lot

2248
01:51:50,333 --> 01:51:54,092
Speaker 2: of trucks being driven in China AI trucks, I imagine several.

2249
01:51:54,133 --> 01:51:57,372
Speaker 2: I managed several websites as the text that would occasionally

2250
01:51:57,412 --> 01:52:00,812
Speaker 2: call upon a freelance developer to update and add additional

2251
01:52:00,812 --> 01:52:03,093
Speaker 2: features to this website. Over the past year, I have

2252
01:52:03,173 --> 01:52:05,333
Speaker 2: used chat gbt to do this instead, so no longer

2253
01:52:05,412 --> 01:52:08,333
Speaker 2: hire the developer. Chat gtb is fast, it provides cleaner code,

2254
01:52:08,452 --> 01:52:11,372
Speaker 2: and is free. I feel bad for not providing work

2255
01:52:11,372 --> 01:52:13,452
Speaker 2: for the developer now, but it's the way the world

2256
01:52:13,492 --> 01:52:17,293
Speaker 2: is going. Brian says that bruce your thoughts on this.

2257
01:52:18,492 --> 01:52:20,013
Speaker 25: Okaday, how are you guys?

2258
01:52:20,053 --> 01:52:22,452
Speaker 4: Fantastic awesome?

2259
01:52:22,492 --> 01:52:24,213
Speaker 25: First time called just a couple of points I want

2260
01:52:24,253 --> 01:52:28,333
Speaker 25: to make. I read an article recently from Harvard and

2261
01:52:28,532 --> 01:52:32,492
Speaker 25: they have predicted that AI will take away three hundred

2262
01:52:32,532 --> 01:52:39,173
Speaker 25: million dollar jobs globally and create eighty nine million jobs globally.

2263
01:52:39,612 --> 01:52:40,812
Speaker 4: Right, it's a bit of a shortfall.

2264
01:52:41,893 --> 01:52:43,812
Speaker 25: Bit of a shortfall. And the other thing I was

2265
01:52:43,893 --> 01:52:46,173
Speaker 25: at a seminar about three weeks ago from a guy

2266
01:52:46,213 --> 01:52:48,893
Speaker 25: from the States talking about AI in my profession, and

2267
01:52:48,973 --> 01:52:54,052
Speaker 25: he said, the biggest consumer we've got is that older generation,

2268
01:52:54,372 --> 01:52:56,612
Speaker 25: people who you know, have got a bit of wisdom,

2269
01:52:56,652 --> 01:53:00,852
Speaker 25: will use AIA in a responsible way to actually do

2270
01:53:01,093 --> 01:53:04,253
Speaker 25: the work that computers do. But the consume was the

2271
01:53:04,372 --> 01:53:08,133
Speaker 25: younger generation coming out of school will actually use a

2272
01:53:08,412 --> 01:53:11,772
Speaker 25: I had to actually for computers to do the work

2273
01:53:11,853 --> 01:53:14,093
Speaker 25: that humans need to do. And his last word of

2274
01:53:14,133 --> 01:53:18,053
Speaker 25: warning was use computers for computer work and humans for

2275
01:53:18,293 --> 01:53:18,972
Speaker 25: human work.

2276
01:53:19,732 --> 01:53:25,652
Speaker 2: So human work is what is it? Social work? Of

2277
01:53:25,853 --> 01:53:27,812
Speaker 2: personal work? That kind of thing creative?

2278
01:53:29,492 --> 01:53:36,932
Speaker 25: Yeah, creative work and work that actually involves emotional critical

2279
01:53:37,013 --> 01:53:42,133
Speaker 25: thinking and so forth. And this was just recently. I

2280
01:53:42,253 --> 01:53:45,133
Speaker 25: had one of the young guys as simple as this

2281
01:53:46,053 --> 01:53:48,933
Speaker 25: in my athleticscope. He put his training program into and

2282
01:53:49,093 --> 01:53:51,612
Speaker 25: I and asked it to tell him what his speed

2283
01:53:51,772 --> 01:53:54,173
Speaker 25: was going to be in a sprint in two weeks time.

2284
01:53:54,732 --> 01:53:59,893
Speaker 25: I expected out a really ridiculous figure for him in

2285
01:53:59,933 --> 01:54:01,532
Speaker 25: his sprint, which he said, Wow, I'm going to be

2286
01:54:01,612 --> 01:54:05,652
Speaker 25: really fast, but the year and I said to the

2287
01:54:05,652 --> 01:54:08,053
Speaker 25: young guy, that is totally ridiculous.

2288
01:54:08,412 --> 01:54:09,093
Speaker 7: Going to achieve that?

2289
01:54:09,652 --> 01:54:12,092
Speaker 25: Yes I will because AI?

2290
01:54:14,973 --> 01:54:18,893
Speaker 2: Yeah, there, what you actually need is a buddy to

2291
01:54:18,973 --> 01:54:21,532
Speaker 2: do it with. Really yeah, because you know there's no

2292
01:54:21,612 --> 01:54:24,612
Speaker 2: doubt that AI is, you know, changing everything. And these people,

2293
01:54:24,732 --> 01:54:26,772
Speaker 2: these optimists, this article reckon it's going to free us

2294
01:54:26,772 --> 01:54:32,093
Speaker 2: from drudgedy, drudgery and fulm creativity, a booming productivity and

2295
01:54:32,173 --> 01:54:34,573
Speaker 2: new kinds of jobs. And they say that we've been

2296
01:54:34,613 --> 01:54:37,973
Speaker 2: through lots and lots of these revolutions and it's just

2297
01:54:38,413 --> 01:54:42,213
Speaker 2: created opportunities. But you know, the skeptics say that it

2298
01:54:42,253 --> 01:54:44,453
Speaker 2: could be very different, and it's moving faster and hitting

2299
01:54:44,493 --> 01:54:48,613
Speaker 2: white collar jobs and deepening inequality unless governments do something

2300
01:54:48,733 --> 01:54:50,973
Speaker 2: to step in and businesses train people up.

2301
01:54:51,053 --> 01:54:54,172
Speaker 4: So who knows, Yeah, who knows? The brave new worlds?

2302
01:54:54,213 --> 01:54:56,852
Speaker 2: So many people writing different arguments about it. It might

2303
01:54:56,973 --> 01:54:58,972
Speaker 2: just be too complicated for anever us to predict what's

2304
01:54:59,013 --> 01:55:04,093
Speaker 2: going to happen. Maybe awesome, maybe terrible, maybe kind of

2305
01:55:04,133 --> 01:55:06,052
Speaker 2: the same as it is now, who knows? Maybe beije.

2306
01:55:06,493 --> 01:55:08,533
Speaker 4: What a beautiful place to leave it. Right there is

2307
01:55:08,893 --> 01:55:12,013
Speaker 4: seven minutes to four back very surely the.

2308
01:55:12,173 --> 01:55:16,092
Speaker 1: Big stories, the big issues, the big trends and everything

2309
01:55:16,133 --> 01:55:16,613
Speaker 1: in between.

2310
01:55:16,853 --> 01:55:20,172
Speaker 13: Matt Heath and Tyler Adams Afternoons Used Talk said.

2311
01:55:20,013 --> 01:55:23,693
Speaker 4: Be very good afternoon to you. It is five minutes

2312
01:55:23,772 --> 01:55:24,453
Speaker 4: to four.

2313
01:55:24,733 --> 01:55:26,812
Speaker 2: That brings us the end of another fantastic Matt and

2314
01:55:26,853 --> 01:55:29,772
Speaker 2: Tyler Afternoons on News Talk Dead b thank you so

2315
01:55:30,013 --> 01:55:33,733
Speaker 2: much for all your calls and texts. It's been quite

2316
01:55:33,772 --> 01:55:36,932
Speaker 2: a show. Actually loved it. There's been a lot going

2317
01:55:37,013 --> 01:55:39,932
Speaker 2: on in it's The full podcast of the show will

2318
01:55:39,933 --> 01:55:42,693
Speaker 2: be out very soon if you want to relive the

2319
01:55:42,772 --> 01:55:45,852
Speaker 2: magic or you missed anything. The Mighty Sir Paul Holmes

2320
01:55:45,933 --> 01:55:48,293
Speaker 2: Broadcast of the Year. Heather duples the Ellen is up next.

2321
01:55:48,333 --> 01:55:50,093
Speaker 2: But Tyler, why am I playing the song?

2322
01:55:50,693 --> 01:55:52,173
Speaker 4: I know it's the bed Boys, but I don't know

2323
01:55:52,213 --> 01:55:52,693
Speaker 4: what it's called.

2324
01:55:52,853 --> 01:55:54,493
Speaker 2: It's brass Monkey by the Besie Boys.

2325
01:55:54,613 --> 01:55:57,213
Speaker 4: Oh cold enough to freeze the bulls off A brass Monkey,

2326
01:55:57,253 --> 01:56:01,173
Speaker 4: I think, Kate said yeah, and and we had a

2327
01:56:01,253 --> 01:56:06,213
Speaker 4: great chat about brass monkey fridges and also emergency prepared.

2328
01:56:06,253 --> 01:56:08,173
Speaker 4: This guest a lot of very prepared people.

2329
01:56:08,613 --> 01:56:10,013
Speaker 2: Yeah, if it, what is the brass monkey and what

2330
01:56:10,053 --> 01:56:11,333
Speaker 2: does freeze the balls off a brass Mike?

2331
01:56:11,413 --> 01:56:13,333
Speaker 4: You mean, I don't know. I couldn't understand what Kate

2332
01:56:13,413 --> 01:56:14,533
Speaker 4: was saying. Now I just love the line.

2333
01:56:14,613 --> 01:56:17,533
Speaker 2: It was the brass container that whole held the cannon

2334
01:56:17,613 --> 01:56:21,373
Speaker 2: balls on chests out and when you freeze the balls

2335
01:56:21,413 --> 01:56:23,333
Speaker 2: off the brass monkeys, it's when the balls popped up.

2336
01:56:23,573 --> 01:56:25,772
Speaker 4: Glad you're listening. I got distracted when you see brass

2337
01:56:25,812 --> 01:56:28,093
Speaker 4: monkeys started talking about balls, But I'm glad you were listening.

2338
01:56:28,253 --> 01:56:33,293
Speaker 2: Good effort to thank you. All right, until tomorrow afternoon,

2339
01:56:33,333 --> 01:56:35,173
Speaker 2: wherever you are, what are you doing across this great

2340
01:56:35,253 --> 01:56:35,772
Speaker 2: nation of ours?

2341
01:56:35,812 --> 01:56:36,613
Speaker 4: Give my taste a Kimi?

2342
01:56:36,653 --> 01:56:36,892
Speaker 13: All right?

2343
01:56:36,893 --> 01:56:37,453
Speaker 2: You're seeing busy?

2344
01:56:37,493 --> 01:56:37,893
Speaker 13: Will let you go?

2345
01:56:38,133 --> 01:56:38,413
Speaker 4: Love you

2346
01:56:51,772 --> 01:56:54,653
Speaker 1: For more from News Talks'd be listen live on air

2347
01:56:54,893 --> 01:56:57,533
Speaker 1: or online and keep our shows with you wherever you

2348
01:56:57,653 --> 01:57:00,053
Speaker 1: go with our podcasts on iHeartRadio