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Speaker 1: Hey, everybody out there in the Pacific Northwest or with

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Speaker 1: access to an airport or a car rental place that

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Speaker 1: can get you to the Pacific Northwest specifically at the

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Speaker 1: end of January. We'll see you in Seattle, Portland, and

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Speaker 1: San Francisco.

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Speaker 2: That's right to. Our new live show for twenty twenty

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Speaker 2: four is Seattle, Washington January twenty fourth at the Paramount Theater,

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Speaker 2: then Portland at our Homeway from Home at Revolution Hall

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Speaker 2: in the twenty fifth, and then winding it all up

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Speaker 2: at Sketchfest on the twenty six at the Sydney Goldstein Theater.

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Speaker 1: Very nice. If you want tickets, if you want information,

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Speaker 1: if you want tickets, you can go to a couple

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Speaker 1: of places. You can go to our link tree at

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Speaker 1: Linktree slash sysk, and you can go to our home

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Speaker 1: on the web, Stuff youshould Know dot com. Click on

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Speaker 1: the tour button and it'll take you to all of

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Speaker 1: the beautiful places you can go to buy your tickets

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Speaker 1: and we'll see you guys in January.

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Speaker 2: Welcome to Stuff you Should Know, a production of iHeartRadio.

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Speaker 1: Hey, and welcome to the podcast. I'm Josh, and there's

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Speaker 1: Chuck and Jerry's here too, and this is stuff you

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Speaker 1: should know, the last edition of the two to the

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Speaker 1: to the Deuce Tray.

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Speaker 2: Oh, is this our final of the year?

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Speaker 1: It is. It's the last one of twenty twenty three, Chuck.

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Speaker 1: We recorded all of the episodes that we're ever going

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Speaker 1: to record in the year twenty twenty three. Isn't that amazing?

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Speaker 2: Yeah? And hey, you know, since you brought that up,

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Speaker 2: can I say something?

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Speaker 1: Sure?

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Speaker 2: Spotify, who carries our show, as do all platforms, they

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Speaker 2: have this really cool thing they send out called the

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Speaker 2: wrap Wrap I guess is in year end wrap up

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Speaker 2: kind of thing, and they they sent us as a

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Speaker 2: show our own statistical analysis, but then they send individual

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Speaker 2: users their own and we just had a lot of

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Speaker 2: great listeners sending us in their rap statistics like hey,

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Speaker 2: I'm in the top one percent of stuff you should

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Speaker 2: know listenership, And it was just really neat to see

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Speaker 2: all that stuff coming in, So thank you.

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Speaker 1: It really is. It's amazing and everybody's so proud of it.

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Speaker 1: It's so great to see. So no matter what percentage

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Speaker 1: you're in, if you are proud enough to send an

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Speaker 1: email or post it, kudos to you, because we're proud

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Speaker 1: of you right back. I do think, though, Chuck, that

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Speaker 1: we probably should shout out the person who wrote in

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Speaker 1: with the far and away the largest number of listening

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Speaker 1: minutes according to Spotify.

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Speaker 2: Yeah, who's that?

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Speaker 1: That is Aravin Cancerla, who is in the top point

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Speaker 1: zero five percent of listeners and based on the eighty

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Speaker 1: thousand plus eighty six, seven hundred and seventy two minutes,

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Speaker 1: I don't see how there could be anybody else in that,

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Speaker 1: you know, in the remaining what point zero's five percent left?

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Speaker 2: Yeah. I did a little back of the envelope math

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Speaker 2: and that that's something somewhere between twenty five and thirty

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Speaker 2: hours of stuff you should know a week.

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Speaker 1: Yeah, that just it's a lot.

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Speaker 2: Doesn't seem possible. So I have suspicions that this person

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Speaker 2: might have just played it on a loop so they could,

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Speaker 2: you know, and then just went out shopping or whatever.

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Speaker 1: I don't know. I think Aravin strikes me as a

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Speaker 1: pretty straightforward person. So grats to Airvind and also seriously,

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Speaker 1: thank you to everybody who listens to us so much

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Speaker 1: that you get statistics at the end of the year

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Speaker 1: that make you proud. I mean, that's amazing, guys. Thank

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Speaker 1: you very much.

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Speaker 2: Yeah, thanks to Spotify. That's a cool service that they

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Speaker 2: or I don't know if is that a service, whatevers

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Speaker 2: cool thing they do.

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Speaker 1: It's a service, it's a public service.

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Speaker 2: We were downloaded in one hundred and sixty three countries.

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Speaker 1: Oh, I didn't know there were that many countries.

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Speaker 2: Which is we looked it up. It was actually something

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Speaker 2: like one hundred and ninety. So like that's most of

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Speaker 2: the countries.

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Speaker 1: Yeah, I would say that's the vast majority of them.

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Speaker 1: And by the way, everybody I knew that there were

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Speaker 1: more countries than that.

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Speaker 2: I was joking and quickly I saw that. I don't

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Speaker 2: know if you went through that yet, Josh, but we

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Speaker 2: our third biggest country of growth was Mexico. Oh, no way,

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Speaker 2: And I'm gaming. I'm gaming. I'm aiming for a show

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Speaker 2: in Mexico City. I'd like to do that. We just

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Speaker 2: don't know if like people would come. So, you know,

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Speaker 2: if we can get like a thousand people in a

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Speaker 2: room in Mexico City, I think that might be a

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Speaker 2: fun thing to do.

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Speaker 1: Yeah, especially if it's a room with seats.

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Speaker 2: Yeah, so we should get at least five hundred emails

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Speaker 2: saying at least two people will come and then that

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Speaker 2: means we might go all right, So anyway, should we

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Speaker 2: get on with barbaric practices?

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Speaker 1: Yeah, let's because I find this endlessly fascinating. Olivia helped

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Speaker 1: us with this, and basically what we're doing here is

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Speaker 1: reversing what we already kind of like to do smugly,

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Speaker 1: which is look back fifty one hundred, two hundred years

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Speaker 1: and be like, look at how backwards and antiquated those

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Speaker 1: people were back then, Like even in as recent is

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Speaker 1: the nineties. I remember in the mid nineties, I was

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Speaker 1: I smoked on an airplane on the way to Amsterdam. Yeah,

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Speaker 1: and like there was it was just like the last

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Speaker 1: three rows were smoking. But it's not like it was

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Speaker 1: sectioned off. There wasn't even a curtain. It's just like

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Speaker 1: this is the smoking section, even though the entire plane's

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Speaker 1: being covered in your cigarette smoke. This was the nineties, man.

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Speaker 2: Yeah, the first time I flew to Europe there was

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Speaker 2: smoking in like that was it would have been ninety six.

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Speaker 1: Yeah, I mean imagine that today. I mean you would

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Speaker 1: literally go to federal prison if you tried to light

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Speaker 1: a cigarette on an airplane today.

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Speaker 3: You know, Yeah, I mean it's about the fire, but sure, yeah,

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Speaker 3: a few decades before that there was jello salad where

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Speaker 3: it was all the rage and like the weirdest jello salad.

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Speaker 1: If you've never just kind of taken a stroll down

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Speaker 1: memory lane and looked up like pictures of jello molds

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Speaker 1: from the fifties to the seventies, yeah, treat yourself and

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Speaker 1: go do that. But make sure you have not had

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Speaker 1: lunch yet, because you're gonna want to gag when you

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Speaker 1: see a lot of them. That's another fun thing to

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Speaker 1: judge people for being stupid with cello because I don't

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Speaker 1: know if we said it yet. We're going to do

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Speaker 1: the opposite. We're going to look forward and try to

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Speaker 1: figure out what our descendants are going to ridicule us

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Speaker 1: or look down at us about, right, what will we

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Speaker 1: seem primitive or barbaric or ridiculous about?

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Speaker 2: That's right? But what we have before us are seven.

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Speaker 2: I think a little more serious things than jello molds

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Speaker 2: and spanking kids is on up there. However, it really

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Speaker 2: depends on who you ask, because about half of Americans

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Speaker 2: still think and this is a quote, and this from

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Speaker 2: a survey a couple of years ago from the American

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Speaker 2: Family Survey quote, it is sometimes necessary to discipline a

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Speaker 2: child with a good, hard spanking, and half.

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Speaker 1: Of those respondents said almost under their breath feels so right.

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Speaker 2: Yeah, I mean, big change from you know, back in

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Speaker 2: the day. They have another stat from sixty eight when

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Speaker 2: ninety four percent of parents said, yeah, hit your kids,

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Speaker 2: it's awesome.

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Speaker 1: You drop. That's a big drop.

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Speaker 2: But things are really changing because a third of the

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Speaker 2: respondents between eighteen and twenty nine agree with spanking, compared

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Speaker 2: to fifty percent of the overall survey. So it's something

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Speaker 2: that's going out of fashion for sure.

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Speaker 1: Yeah, it seems to be following a larger trend of

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Speaker 1: moving away from social acceptance of violence in any form,

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Speaker 1: and it's being supported by studies that find like, yeah,

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Speaker 1: it's actually good if you don't spank your kids, because

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Speaker 1: not only has there never been a study that shows

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Speaker 1: it improves children's behavior, study after study keeps suggesting it

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Speaker 1: does the opposite. It actually maladjusts children. I mean, I

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Speaker 1: can't imagine what a well adjusted person I would be

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Speaker 1: if I hadn't been spanked that handful of times when

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Speaker 1: I was a kid.

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Speaker 2: Yeah, you know, I was. We're always trying to poke

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Speaker 2: around to find, you know, the other opinion on something,

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Speaker 2: just to take a look at it, and you know

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Speaker 2: there are people who don't agree. I saw this one

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Speaker 2: professor from the Oklahoma State Robert Larzelli. I can't even

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Speaker 2: read my handwriting now, but he said that the studies

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Speaker 2: that are out there are flawed for a couple of reasons. One,

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Speaker 2: he says, these studies that say that if you spank

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Speaker 2: kids more, there that leads to them actually acting out more.

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Speaker 2: He's saying, no, kids, it's the kids that are acting

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Speaker 2: out more that are it's a chicken in the egg thing. Yes,

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Speaker 2: I've seen the ones getting spanked more.

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Speaker 1: I've seen that, and I think I found in a

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Speaker 1: Scientific American article there was at least one group that

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Speaker 1: managed to control for that and basically have shown like, no,

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Speaker 1: it's it's it actually does have this effect on kids.

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Speaker 1: The problem that what'd you say, Lara Zelli?

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Speaker 2: I think so if my writing is any indicator.

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Speaker 1: That what Larazzelli's saying is that these studies don't they

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Speaker 1: don't start following kids and from like birth to twenty

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Speaker 1: five or thirty and then see you know where you spanked,

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Speaker 1: where you're not spanked it's all just like they might.

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Speaker 1: They might peek in on a kid who's in at

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Speaker 1: the spanking age and look at their behavior. Then you

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Speaker 1: just can't parse it apart. So there's not really good

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Speaker 1: quality studies. But I saw it put like this. Even

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Speaker 1: if there are no studies that conclusively show spanking is

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Speaker 1: bad for kids or produces malajusted behavior in kids, there

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Speaker 1: are plenty of studies that seem to suggest that. There

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Speaker 1: aren't any studies that seem to suggest otherwise that it's

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Speaker 1: it's actually good, it's actually it's effective to spank your kids.

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Speaker 1: And so the argument that I've seen is like, why

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Speaker 1: why do it? Then?

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Speaker 2: Yeah, I saw a First of all, I'm a parent.

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Speaker 2: I can't in a million years imagine hitting Ruby for

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Speaker 2: any reason that's nice. It makes me want to cry

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Speaker 2: just thinking about that. It's terrible for our family. But

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Speaker 2: I did find a study from twenty eighteen that I

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Speaker 2: found in from NPR. They didn't do the study, but

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Speaker 2: they were, you know, did a thing on it.

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Speaker 1: I'm sure they were hot and heavy.

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Speaker 2: Oh of course it was what they claim. And it

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Speaker 2: seems like probably one of the most robust studies at

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Speaker 2: least that looks at countries that have banned spanking, because

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Speaker 2: I think something like sixty two countries have banned spanking

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Speaker 2: starting with Sweden in nineteen seventy nine.

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Speaker 1: Did you even know there were that many countries?

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Speaker 2: But they followed four or they used four hundred thousand

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Speaker 2: children from kids from eighty eight countries, so that's pretty good.

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Speaker 2: Fifty eight countries have the bands in thirty don't. I'm

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Speaker 2: not sure which one it is. But what they found

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Speaker 2: when what they were tracking was incidences of kids fighting,

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Speaker 2: like you know, getting in fights at school, right and

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Speaker 2: in the countries that have banned spanking, there was a

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Speaker 2: school fighting reduction by sixty nine percent in boys in girls,

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Speaker 2: which is I mean, that is pretty substantial. I was

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Speaker 2: curious about the United States because you know, we both

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Speaker 2: grew up, like my dad was my elementary school principal,

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Speaker 2: and they were he spanked me and other kids. It's

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Speaker 2: ridiculous to think about, but apparently in seventy seven the

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Speaker 2: Supreme Court of the United States gave the power to

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Speaker 2: the states. These days, ninety percent of schools don't use

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Speaker 2: corporal punishment, but it is still legal in seventeen states.

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Speaker 2: There are restrictions in place in a lot of those

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Speaker 2: like maybe your parents have to sign a thing. I said, sure,

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Speaker 2: hit my kid. But seventeen percent of states you can

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Speaker 2: still do this, with Mississippi leading the way in the

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Speaker 2: most spankings. And the other thing I found out that

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Speaker 2: we should point out is that black males are twice

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Speaker 2: as likely to be spanked than anyone. And get this,

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Speaker 2: sixteen point five percent of kids that are corporately punished

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Speaker 2: in schools in America today are disabled. My god, usually

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Speaker 2: it's an intellectual disability. Wow. Is that so not disturbing?

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Speaker 1: Yeah, of course it's disturbing. That's horrible. That's one of

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Speaker 1: the most horrible statistics you've ever spouted out. And I

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Speaker 1: should say, also just want to verify for the listeners

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Speaker 1: in any of those sixty two countries where spanking is banned,

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Speaker 1: you're talking about like public spanking, like in school in

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Speaker 1: seventeen states. In schools, you're allowed to do that.

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Speaker 2: Right, Yes, a teacher or a principal and they say

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Speaker 2: it's you know. And this is one of the other

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Speaker 2: problems that that professor had is that those studies, he says,

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Speaker 2: lump everyone in together as in, like the parents who

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Speaker 2: do it as the very last resort after several other

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Speaker 2: attempts at discipline or parents are just like, oh, you

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Speaker 2: screwed up, you know, let's hit you or whatever. And

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Speaker 2: apparently most, you know, almost all the schools, it is

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Speaker 2: a last resort, as in they've tried other things, but

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Speaker 2: you know, it's just I don't know, I got to

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Speaker 2: try not to judge people, but don't hit your kids.

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Speaker 1: Well, I was gonna say, it's it's legal in seventeen

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Speaker 1: states for schools to spank kids. It's legal in all

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Speaker 1: fifty states for a parent to spank kids. That's there's

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Speaker 1: not really anything coming down the horizon that makes it

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Speaker 1: seem like that's ever going to be banned. But it

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Speaker 1: does seem generationally like we're moving away from spanking pretty rapidly.

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Speaker 2: Yeah, my spankings as a kid, we're very infrequent and

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Speaker 2: very organized, as in it was never done in the

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Speaker 2: heat of anger, like just getting slapped or something. It

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Speaker 2: was like, all right, go to the bathroom and spend

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Speaker 2: ten minutes, you know, upset and card and then I

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Speaker 2: got spanked with a bolo paddle. You know, the little

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Speaker 2: bolo paddle games.

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Speaker 1: I know, the bolo tie.

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Speaker 2: No, the bolo paddle where you a little light plywood

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Speaker 2: paddle with a ball hard. Yeah, sure, that's that was

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Speaker 2: the spanking device.

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Speaker 1: In mind, those things are made of like balsa wood.

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Speaker 2: Yeah, it wasn't you bad. It's stung.

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Speaker 1: But you know, how about all of your spankings that

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Speaker 1: had grown.

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Speaker 2: Up that's usually involves leather.

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Speaker 1: Okay, you you want to move on to the next one.

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Speaker 2: Yeah, let's let's move on to chemotherapy.

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Speaker 1: Okay. So chemotherapy is one of these things where, if

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Speaker 1: you start kind of putting it down today, what you're

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Speaker 1: talking about is our current modern medical miracle that since

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Speaker 1: the nineties has reduced the cancer death rate by twenty

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Speaker 1: five percent. Yeah, it's it's a really big deal that

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Speaker 1: we have chemotherapy now, it's saved a lot of lives.

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Speaker 2: Yeah, this is not poopoo in chemotherapy.

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Speaker 1: No, it's not the reason why we can probably guess

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Speaker 1: that our descendants down the line are going to look

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Speaker 1: at chemotherapy is fairly primitive and barbaric is because it's

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Speaker 1: so indiscriminate in how it harms the body. It harms

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Speaker 1: the whole body in order to kill the cancer cells. Right,

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Speaker 1: and we're moving it seems like much more toward far,

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Speaker 1: far more specific and tailored medicine, and so all of

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Speaker 1: the side effects, in the horribleness that come with chemotherapy,

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Speaker 1: even though it does save lives, will be going away

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Speaker 1: in future decades.

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Speaker 2: It looks like, yeah, and it seems like and we're

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Speaker 2: going to talk about a few ways that things are

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Speaker 2: becoming more specific, but that seems to be the way

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Speaker 2: it's all trying to go is instead of just like

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Speaker 2: killing all the cells, let's see if they can just

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Speaker 2: specifically target cancer cells and then eventually, you know, get

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Speaker 2: down to you know, the human specific targeting of things,

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Speaker 2: which would be amazing obviously, yeah, you know, patient specific, right.

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Speaker 2: But one of the first ones is antibody drug conjugates,

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Speaker 2: and this is a type of chemotherapy, but it combines chemotherapy,

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Speaker 2: like the drugs used in chemotherapy, with monoclonal antibodies, which

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Speaker 2: are you know, lab just like antibodies that we have

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Speaker 2: in our body, except they're created in a lab.

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Speaker 1: Right, And so what happens is we inject these drugs,

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Speaker 1: these antibody drug conjugates into a patient with cancer, and

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Speaker 1: those antibodies are designed to go seek out that tumor

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Speaker 1: the specific kind of tumor that that patient has, Yeah,

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Speaker 1: and attached to that tumor, that cancer sell and it

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Speaker 1: delivers that payload of chemo drugs to it, says here

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Speaker 1: you go, here's a nice little present, and then turns

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Speaker 1: around and runs, and then in the background, the cell

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Speaker 1: explodes and the antibody like ends up on its chest

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Speaker 1: but lives to fight another day.

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Speaker 2: Yeah, exactly. That's if we're headed in that direction. That's fantastic. Yeah.

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Speaker 2: Vaccines is another one, the mr NA vaccines that we

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Speaker 2: detailed back when those came out for COVID. Two of

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Speaker 2: the most successful versions of those vaccines were originally brought

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Speaker 2: about to begin with as tumor vaccines, and the idea

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Speaker 2: is to use sort of that same technology to just

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Speaker 2: specifically target tumors themselves. So it's not like a vaccine

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Speaker 2: to prevent a disease. It's a shot that will essentially

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Speaker 2: specifically shrink a tumor.

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Speaker 1: Yeah, just like the mRNA vaccines for COVID train the

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Speaker 1: body to look for and respond to COVID viruses, saying like, hey,

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Speaker 1: if you see anything with this little horn on it,

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Speaker 1: the spike, go after it. They're doing the same thing

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Speaker 1: with tumors, right, So that's boosting the immune response. It's

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Speaker 1: also training immune response. So technically it does qualify as

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Speaker 1: a vaccine. And because like we talked about in the

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Speaker 1: COVID vaccine episode, this mRNA technology is just so you

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Speaker 1: can just it's just like ready to wear vaccines basically.

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Speaker 2: Yeah.

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Speaker 1: Apparently, yeah, apparently they they have reached a turning point

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Speaker 1: and in the next five years a lot of cancer

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Speaker 1: researchers are saying We're going to see a lot more

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Speaker 1: cancer vaccines coming down the pike.

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Speaker 2: Amazing. And then what I talked about earlier, like making

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Speaker 2: like targeting cancer cell specifically is like a great direction

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Speaker 2: to go, but really getting into personalized cancer care will

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Speaker 2: be the next step beyond that, Like, hey, I'm going

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Speaker 2: to identify exactly what kind of tumor that you have

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Speaker 2: in your body and not just maybe this kind of

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Speaker 2: tumor and you know, treat like get to patient specific

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Speaker 2: levels of treatment. And you know, I know we poo

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Speaker 2: pooed AI in certain respects, but this is a place

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Speaker 2: where AI can really probably do a lot of good.

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Speaker 1: Yeah, I think we should just clarify or position on

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Speaker 1: it if I can speak for both of us. Sure,

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Speaker 1: as long as AI is not taking over the world

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Speaker 1: or damaging humanity in some terrible way. I'm all for

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Speaker 1: all the great ways that can help things, and this

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Speaker 1: is a really sterling example of that.

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Speaker 2: Yeah, it gave us a new Beatles song.

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Speaker 1: Yeah, I would say that's right in the middle for me.

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Speaker 1: But the I think what you were talking about was

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Speaker 1: taking a sample of the specific tumor that a specific

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Speaker 1: person has, analyzing its genetic makeup, and then looking at

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Speaker 1: that genetic makeup thanks to AI spitting out all of

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Speaker 1: the information that we need from analyzing that huge genome,

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Speaker 1: saying oh, this is an achilles heel, this is another weakness,

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Speaker 1: this is another way we can attack it, and then

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Speaker 1: tailoring the treatment for that specific tumor like that tumor,

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Speaker 1: Like you said, now that kind of tumor that tumor

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Speaker 1: is getting at tech. It's so specific you could name

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Speaker 1: the tumor, name the tumor Melvin. Melvin is toast when

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Speaker 1: you're using precision or personal cancer treatments.

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Speaker 2: Yeah, you know, this kind of stuff could even be possible. Now,

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Speaker 2: it's just really really expensive to target a specific tumor

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Speaker 2: for a specific human And so the idea is hopefully

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Speaker 2: with the help of AI, they can just reduce a

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Speaker 2: lot of the you know, the cost basically for doing that,

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Speaker 2: So it becomes instead of something that's not even you know,

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Speaker 2: not even something worth pursuing or able to be pursued

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Speaker 2: because of finances, something' is like, oh yeah, just step

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Speaker 2: right up and we'll spit out your treatment.

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Speaker 1: Yeah. And as more more does the cost come down,

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Speaker 1: more people use it, which means more people using it

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Speaker 1: allows for greater chance of new breakthrough. So yeah, hopefully

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Speaker 1: we're going to have cancer licked in the future. I

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Speaker 1: saw somebody suggest that it'll end up being kind of

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Speaker 1: like a chronic disease akin to diabetes in the.

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Speaker 2: Future, just something you can live fairly healthily with.

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Speaker 1: Yeah, you can manage and there'll be plenty of drugs

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Speaker 1: to keep you going amazing. You want to take a break, Yeah, okay,

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Speaker 1: well then let's do that.

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Speaker 2: All right. Next up on the list of things that

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Speaker 2: people may one day look back and say, why did

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Speaker 2: you do it that way? Dummies of the twenty first

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Speaker 2: century is organ transplants. We have a pretty great episode

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Speaker 2: on organ transplants somewhere in our back catalog. It feels

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Speaker 2: like a long time ago. It was a little while,

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Speaker 2: but what we're basically talking about, and again, organ transplants awesome.

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Speaker 2: It's amazing how far they've come in the past, you know,

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Speaker 2: since they've been doing them. But rejection rates are still

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Speaker 2: an issue, up to ten to fifteen percent for kidneys

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Speaker 2: for instance. And then also the fact that you know,

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Speaker 2: transporting organs, getting them to the people in time can

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Speaker 2: still be an issue. Seventeen Americans die every day waiting

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Speaker 2: on organs. And it's also inequitable in that, you know,

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Speaker 2: generally people that have are the most funded, get the

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Speaker 2: most organs for transplant. But there's a better way forward, right.

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Speaker 1: Yeah, I just want to say one I found a

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Speaker 1: stat that I found rather shocking. One in five donated

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Speaker 1: kidneys goes unused. It goes to waste, even though people

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Speaker 1: die waiting for kidneys. That's just how kluge the whole

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Speaker 1: setup is right now. Yeah, So, yeah, they're trying to

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Speaker 1: fix the process and the system and the organization in

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Speaker 1: charge of that in the United States, But like further

404
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Speaker 1: down the pike on a longer timeline, the goal is organagenesis,

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Speaker 1: which is what it sounds like. It's creating entirely new

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Speaker 1: organs from cells from scratch. It's like watch this grow.

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Speaker 1: You remember those little dinosaur sponges that you added water

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Speaker 1: to and they just grew, grew, grew. It's kind of

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Speaker 1: like that, but with fully functioning organs.

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Speaker 2: Yeah. How far are we away from someone trying to

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Speaker 2: grow a human out in a lab?

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Speaker 1: I'm sure somebody's probably trying it already, but I don't

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Speaker 1: know how long it'll be till they're successful.

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Speaker 2: I mean, we had Dolly the sheep, that Dolly was

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Speaker 2: a clone, right.

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Speaker 1: Yes, And I don't know if everybody's read our book,

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Speaker 1: and if you haven't, I'll just go ahead and share

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Speaker 1: with you a little fact from it. Yeah, passage of

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Speaker 1: dramatic reading. Apparently Dolly was named Dolly because she was

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Speaker 1: grown from a mammary cell. So it was a nineties

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Speaker 1: haha joke about Dolly Park.

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Speaker 2: Yeah. I think we might have said that in the

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Speaker 2: Delli parton episode.

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Speaker 1: Didn't make or did If we did, I'll have to

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Speaker 1: go back and listen. If so, we'll edit this part out.

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Speaker 2: No, no, no, the beers repeating, I think.

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Speaker 1: Okay, So, yes, we're a little ways off, because not

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Speaker 1: only Chuck, are we not capable of growing human from cells.

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Speaker 1: We're not capable of growing kidneys or hearts, but we

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Speaker 1: are somewhere. We've grown and successfully transplanted windpipes, bladders fairly

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Speaker 1: like simple organs and structures. But I mean simple as

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Speaker 1: like a relative term, because we're talking about something that

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Speaker 1: was grown from that person's own cells into the very

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Speaker 1: piece of equipment that they needed and then put in

435
00:24:43,800 --> 00:24:44,879
Speaker 1: them and it worked.

436
00:24:45,600 --> 00:24:48,720
Speaker 2: Yeah, which is remarkable. I know that they can do

437
00:24:48,800 --> 00:24:52,840
Speaker 2: this with, at least right now, the epidermis. So if

438
00:24:52,840 --> 00:24:56,639
Speaker 2: you're a burn victim, you can get your own stem

439
00:24:56,680 --> 00:25:00,359
Speaker 2: cells and you can get some new epidermis of about

440
00:25:00,400 --> 00:25:02,960
Speaker 2: to say, just skin, but they're working on growing like

441
00:25:03,000 --> 00:25:05,080
Speaker 2: the entire thickness of the skin. They're not there yet,

442
00:25:05,480 --> 00:25:09,119
Speaker 2: but they can now grow epidermis from your own stem

443
00:25:09,119 --> 00:25:14,040
Speaker 2: cells in a lab and they transfer it to something

444
00:25:14,080 --> 00:25:17,439
Speaker 2: called fibrine, which is a protein that really kind of

445
00:25:17,440 --> 00:25:19,960
Speaker 2: helps your blood clot when you get a cut or something,

446
00:25:20,760 --> 00:25:23,439
Speaker 2: and then they put it on your body and it

447
00:25:23,720 --> 00:25:27,480
Speaker 2: just it goes and then you're done.

448
00:25:27,720 --> 00:25:29,320
Speaker 1: Yeah, that's it makes that sound.

449
00:25:29,560 --> 00:25:31,600
Speaker 2: It takes you know, obviously it's a process. I was

450
00:25:31,640 --> 00:25:34,840
Speaker 2: just kidding around, But right now they can't like grow

451
00:25:35,280 --> 00:25:38,720
Speaker 2: skin that grows hair or sebaceous glands and stuff like that.

452
00:25:38,760 --> 00:25:41,120
Speaker 2: But if you're a burn victim and you can get

453
00:25:41,160 --> 00:25:46,160
Speaker 2: you know, your own epidermis to replace you know, your

454
00:25:46,200 --> 00:25:49,440
Speaker 2: scarred skin on your body, then that's pretty amazing.

455
00:25:49,800 --> 00:25:53,359
Speaker 1: It's kind of akin to laying sod, but with skin,

456
00:25:54,040 --> 00:25:59,479
Speaker 1: you know. Sure, So right now, I think the state

457
00:25:59,560 --> 00:26:02,800
Speaker 1: of the art with organ and genesis that extra oh

458
00:26:02,840 --> 00:26:05,800
Speaker 1: trips me up and I like to add syllables. So

459
00:26:05,840 --> 00:26:12,240
Speaker 1: that's a real tricky one is growing organs in other animals,

460
00:26:13,280 --> 00:26:16,639
Speaker 1: And as we'll see, hopefully we're going to be moving

461
00:26:16,680 --> 00:26:19,320
Speaker 1: away from that because to take that organ from the

462
00:26:19,359 --> 00:26:21,679
Speaker 1: animal and transplant it into a human, you kill that

463
00:26:21,760 --> 00:26:25,440
Speaker 1: animal in the process, right Like you don't. You can't

464
00:26:25,440 --> 00:26:27,359
Speaker 1: take a pig's heart and be like, good luck with

465
00:26:27,400 --> 00:26:29,080
Speaker 1: the rest of your life, because it doesn't have a

466
00:26:29,160 --> 00:26:32,800
Speaker 1: rest of its life. It's missing its heart. And from

467
00:26:32,840 --> 00:26:35,560
Speaker 1: a lot of the trends that I've seen, it seems

468
00:26:35,840 --> 00:26:38,840
Speaker 1: like a fairly safe bet that we're moving in a

469
00:26:38,880 --> 00:26:43,159
Speaker 1: direction where animal welfare is going to become more and

470
00:26:43,240 --> 00:26:47,600
Speaker 1: more and more important to where how we treat animals

471
00:26:47,640 --> 00:26:50,919
Speaker 1: will be maybe the most critical thing that people will

472
00:26:51,040 --> 00:26:53,640
Speaker 1: of the future will look back at us on you.

473
00:26:53,600 --> 00:26:57,040
Speaker 2: Know, yeah, which that's coming up in a more robust

474
00:26:57,080 --> 00:26:59,920
Speaker 2: way in a second. But to finish this up, there's

475
00:27:00,119 --> 00:27:05,040
Speaker 2: also three D bioprinting, which is pretty amazing. I remember

476
00:27:05,160 --> 00:27:08,040
Speaker 2: telling the story one time. I've known two people in

477
00:27:08,080 --> 00:27:14,560
Speaker 2: my life who were born without an external ear, and

478
00:27:15,520 --> 00:27:19,080
Speaker 2: the process back then was they formed a sort of

479
00:27:19,080 --> 00:27:24,160
Speaker 2: a skeleton of the shape of an ear, and if

480
00:27:24,160 --> 00:27:25,840
Speaker 2: I'm not I'm not sure what it was made of,

481
00:27:26,160 --> 00:27:29,680
Speaker 2: I think cartilage. But if I'm not mistaken, that then

482
00:27:30,160 --> 00:27:34,560
Speaker 2: was there was like a skin bubble around it, and

483
00:27:34,560 --> 00:27:37,480
Speaker 2: then they would suck the air out of that skin

484
00:27:37,520 --> 00:27:42,159
Speaker 2: bubble very quickly to onto that cartilage to form you

485
00:27:42,200 --> 00:27:46,560
Speaker 2: know what looked close enough to an external ear. And

486
00:27:46,600 --> 00:27:49,199
Speaker 2: I say external like you know, the ear parts or.

487
00:27:49,280 --> 00:27:50,400
Speaker 1: Yeah, I know it's talking about.

488
00:27:51,520 --> 00:27:53,240
Speaker 2: And I've known two people in my life that had

489
00:27:53,240 --> 00:27:55,320
Speaker 2: that done and they, you know, back in the day,

490
00:27:55,400 --> 00:27:56,960
Speaker 2: it was not like it is now. I think the

491
00:27:57,000 --> 00:28:01,400
Speaker 2: three D bioprinting of ears is much much further along

492
00:28:01,400 --> 00:28:03,040
Speaker 2: and they look much better than they used to. And

493
00:28:03,040 --> 00:28:05,359
Speaker 2: that's kind of the point, right, But they're thinking that,

494
00:28:05,840 --> 00:28:08,880
Speaker 2: you know, maybe one day we can three D bioprint

495
00:28:08,920 --> 00:28:09,359
Speaker 2: a liver.

496
00:28:10,119 --> 00:28:13,600
Speaker 1: Yeah, pretty amazing, and that'll kind of come up well

497
00:28:13,640 --> 00:28:16,239
Speaker 1: in the next section two. So I say we move

498
00:28:16,280 --> 00:28:18,080
Speaker 1: on to the next section because it does kind of

499
00:28:18,119 --> 00:28:19,639
Speaker 1: tie into what we were talking about.

500
00:28:19,680 --> 00:28:21,400
Speaker 2: Just now, that's right, let's do it.

501
00:28:21,960 --> 00:28:26,160
Speaker 1: So getting meat from animals is probably something that will

502
00:28:26,200 --> 00:28:29,639
Speaker 1: really be looked down upon in the future because we

503
00:28:29,720 --> 00:28:35,119
Speaker 1: already have techniques that will that make it so we

504
00:28:35,119 --> 00:28:39,400
Speaker 1: don't really need live animals to create meat to eat meat,

505
00:28:40,880 --> 00:28:43,840
Speaker 1: and yet we're still eating meat. And that's despite and

506
00:28:43,880 --> 00:28:46,520
Speaker 1: I'm very much guilty of this too, that's despite knowing

507
00:28:46,560 --> 00:28:51,200
Speaker 1: how horrific and terrible factory farming is for the animals themselves,

508
00:28:51,200 --> 00:28:54,720
Speaker 1: for the environment. People just really love meat and it's

509
00:28:54,760 --> 00:28:57,040
Speaker 1: tough to give up. So rather than forcing people to

510
00:28:57,040 --> 00:28:59,560
Speaker 1: give up, there's other alternatives that people are working on

511
00:28:59,560 --> 00:29:03,400
Speaker 1: to replace. We're going to need to do that too, because, apparently, Chuck,

512
00:29:04,400 --> 00:29:07,520
Speaker 1: the growing demand for meat is going to be totally

513
00:29:07,640 --> 00:29:09,520
Speaker 1: unsustainable in the next couple decades.

514
00:29:10,040 --> 00:29:12,840
Speaker 2: Yeah, I mean, there are statistics like the UN will

515
00:29:12,880 --> 00:29:15,719
Speaker 2: throw out that say we're going to by the year

516
00:29:15,760 --> 00:29:17,960
Speaker 2: twenty fifty, the meat demand means we're going to have

517
00:29:18,040 --> 00:29:20,440
Speaker 2: to produce fifty to one hundred percent more meat than

518
00:29:20,480 --> 00:29:24,520
Speaker 2: we do now. But there's also other people saying, like, Hey,

519
00:29:24,600 --> 00:29:29,120
Speaker 2: this whole notion of you know, wealthier countries eat meat

520
00:29:29,120 --> 00:29:32,160
Speaker 2: because they can afford it and countries that are more

521
00:29:32,160 --> 00:29:36,640
Speaker 2: developing eat agriculturally largely or vegetarian because they're forced to,

522
00:29:37,520 --> 00:29:40,600
Speaker 2: isn't really the case any or at least moving forward,

523
00:29:40,640 --> 00:29:43,480
Speaker 2: it looks like because what they found is the emerging

524
00:29:43,520 --> 00:29:47,280
Speaker 2: trend is that people are eating less meat once they

525
00:29:47,520 --> 00:29:50,120
Speaker 2: get enough of wealth to afford it for a bunch

526
00:29:50,160 --> 00:29:52,080
Speaker 2: of reasons, and one of which is what you're talking about,

527
00:29:52,120 --> 00:29:56,200
Speaker 2: is there's just a forever changing way that humans look

528
00:29:56,280 --> 00:29:59,840
Speaker 2: at animals and animal welfare for one. And also you

529
00:30:00,320 --> 00:30:02,440
Speaker 2: red meat in the fact that it's terrible for your

530
00:30:02,480 --> 00:30:04,160
Speaker 2: body is another one.

531
00:30:04,040 --> 00:30:08,600
Speaker 1: And terrible for the environment livestock raising that includes transportation,

532
00:30:08,840 --> 00:30:12,080
Speaker 1: tractor emissions, but also methane from the cow shooting ducks

533
00:30:12,120 --> 00:30:14,640
Speaker 1: all the time that it counts for fourteen and a

534
00:30:14,680 --> 00:30:18,600
Speaker 1: half percent of global greenhouse gas emissions. So it has

535
00:30:18,680 --> 00:30:23,360
Speaker 1: like this triple impact, triple negative impact on the animal's welfare,

536
00:30:23,760 --> 00:30:28,160
Speaker 1: the human body, and the health of the earth. And

537
00:30:28,440 --> 00:30:31,320
Speaker 1: for those reasons, it does seem like people in wealthier

538
00:30:31,360 --> 00:30:34,520
Speaker 1: countries are starting to move away from meat, and so

539
00:30:34,640 --> 00:30:39,480
Speaker 1: I think the Organization for Economic Cooperation and Development they

540
00:30:39,520 --> 00:30:42,480
Speaker 1: released an Agricultural Outlook within the last couple of years

541
00:30:42,880 --> 00:30:45,760
Speaker 1: and they predicted that around twenty seventy five, the whole

542
00:30:45,800 --> 00:30:50,400
Speaker 1: world will start moving away from meat and that eventually

543
00:30:50,400 --> 00:30:53,640
Speaker 1: we're just going to stop eating what's called carcass meat

544
00:30:54,320 --> 00:30:56,000
Speaker 1: very appropriately altogether.

545
00:30:56,640 --> 00:30:59,240
Speaker 2: Yeah, can I tell you something really quick about a

546
00:30:59,480 --> 00:31:03,640
Speaker 2: Instagram all today. Yeah, it has to do with the

547
00:31:03,680 --> 00:31:07,560
Speaker 2: cows and the methane. Actually, our colleague, our old friend,

548
00:31:07,560 --> 00:31:10,120
Speaker 2: Tamika at work posted this, yeah, and it was a

549
00:31:10,200 --> 00:31:13,200
Speaker 2: video of a guy that was showed how they treat

550
00:31:13,240 --> 00:31:16,560
Speaker 2: bloat in cows. Have you seen this? No, when a

551
00:31:16,600 --> 00:31:21,880
Speaker 2: cow gets bloated with gas, they stick a needle into

552
00:31:22,080 --> 00:31:27,320
Speaker 2: the cow's stomach, releasing methane and they light that thing

553
00:31:28,680 --> 00:31:31,320
Speaker 2: so they can see a flame to judge like how

554
00:31:31,400 --> 00:31:34,320
Speaker 2: much gas is still in there. Oh wow, So there

555
00:31:34,360 --> 00:31:36,920
Speaker 2: was a video of a cow with a with a

556
00:31:37,080 --> 00:31:38,520
Speaker 2: blowtorch coming out of its side.

557
00:31:38,560 --> 00:31:41,360
Speaker 1: Essentially wow, what was the cow's expression?

558
00:31:41,520 --> 00:31:43,520
Speaker 2: Like, Well, all I saw was the cow, but the

559
00:31:44,240 --> 00:31:47,840
Speaker 2: guy who was hosting the video his expression was horrified

560
00:31:47,880 --> 00:31:50,600
Speaker 2: because he was like, you know, can you believe that

561
00:31:50,640 --> 00:31:51,880
Speaker 2: this is where we are in the world.

562
00:31:52,320 --> 00:31:55,240
Speaker 1: Yeah, I totally can believe that. That doesn't surprise me at all,

563
00:31:55,280 --> 00:31:56,160
Speaker 1: to tell you the truth.

564
00:31:56,320 --> 00:31:57,920
Speaker 2: And they're trying to help the cow, but it's you know,

565
00:31:57,960 --> 00:32:01,800
Speaker 2: the cows. The reason cow's there is because of factory partment.

566
00:32:02,160 --> 00:32:04,760
Speaker 1: Right. Tamiko, by the way, has one of the better

567
00:32:05,320 --> 00:32:08,360
Speaker 1: non celebrity Instagram feeds you can find.

568
00:32:08,760 --> 00:32:10,720
Speaker 2: Yeah to me, because this is great. It's good stuff.

569
00:32:11,160 --> 00:32:14,680
Speaker 2: But what you were saying about moving away from what'd

570
00:32:14,680 --> 00:32:17,680
Speaker 2: you call it, carcass meat? Yeah, there are two main

571
00:32:17,720 --> 00:32:20,600
Speaker 2: ways that that's happening right now, and that is obviously

572
00:32:21,160 --> 00:32:25,440
Speaker 2: what they call novel vegan meat replacements, you know, fake meat,

573
00:32:25,480 --> 00:32:30,480
Speaker 2: impossible stuff, beyond stuff, and then lab grown meat, which

574
00:32:31,000 --> 00:32:32,720
Speaker 2: did we do a whole episode on lab grown meat?

575
00:32:32,800 --> 00:32:33,960
Speaker 1: Yeah? We did a while back.

576
00:32:34,240 --> 00:32:35,960
Speaker 2: I thought so we should.

577
00:32:35,760 --> 00:32:38,200
Speaker 1: Update it like we did the recycling episode, you know,

578
00:32:38,240 --> 00:32:41,360
Speaker 1: like so much stuff has changed since total I'm sure, well,

579
00:32:41,360 --> 00:32:44,240
Speaker 1: we'll update it eventually. But lab grow meat or cultured

580
00:32:44,280 --> 00:32:47,360
Speaker 1: meat is exactly what it sounds like. You use a bioreactor,

581
00:32:47,480 --> 00:32:51,800
Speaker 1: sometimes a three D like bioprinter using animal cells to

582
00:32:51,920 --> 00:32:57,720
Speaker 1: recreate meat, and they I think the there's a consulting

583
00:32:57,760 --> 00:33:00,920
Speaker 1: group called at Kearney, they predict that by twenty forty,

584
00:33:00,960 --> 00:33:04,360
Speaker 1: which is not that long off everybody, the sixty percent

585
00:33:04,400 --> 00:33:09,120
Speaker 1: of global meat consumption will be from cultured or non

586
00:33:09,240 --> 00:33:13,960
Speaker 1: vegan meat replacements. Yeah, like that's significant. That's a huge change.

587
00:33:14,320 --> 00:33:17,120
Speaker 1: Like there may be countries that are developing now that

588
00:33:17,200 --> 00:33:20,960
Speaker 1: won't even eat carcass meat when they become wealthy because

589
00:33:21,280 --> 00:33:23,880
Speaker 1: the replacements will have become so great there'll be no

590
00:33:24,040 --> 00:33:25,160
Speaker 1: reason to eat meat.

591
00:33:26,400 --> 00:33:29,719
Speaker 2: Yeah. You know, if you ask the CEOs of beyond

592
00:33:29,760 --> 00:33:33,160
Speaker 2: it impossible, they're going to say, then fifteen years there

593
00:33:33,200 --> 00:33:36,400
Speaker 2: will be no more eating of meat. Yeah, that's a

594
00:33:36,400 --> 00:33:39,280
Speaker 2: little ambitious, and I think that, you know, maybe they're

595
00:33:39,280 --> 00:33:43,480
Speaker 2: trying to drive up the stock price, so that's probably

596
00:33:43,480 --> 00:33:45,480
Speaker 2: not going to be the case, but that that at

597
00:33:45,640 --> 00:33:49,880
Speaker 2: Kearney group prediction, like, that's that seems quite possible.

598
00:33:50,000 --> 00:33:52,800
Speaker 1: I buy that, especially if there's a couple of challenges

599
00:33:52,840 --> 00:33:56,200
Speaker 1: that are overcome by then, which is you know, that's

600
00:33:56,200 --> 00:34:01,120
Speaker 1: seventeenth sorry, sixteen years now away, and that's plenty of

601
00:34:01,200 --> 00:34:06,280
Speaker 1: time to overcome some relative speed bumps. One is replicating no,

602
00:34:06,440 --> 00:34:09,239
Speaker 1: I think they have flavor kind of locked, at least

603
00:34:09,239 --> 00:34:10,759
Speaker 1: as far as cultured meat goes.

604
00:34:10,920 --> 00:34:13,560
Speaker 2: Yeah, texture textures.

605
00:34:13,040 --> 00:34:15,719
Speaker 1: The problem, because you don't want to eat like a

606
00:34:15,760 --> 00:34:22,440
Speaker 1: little a little scobie of beef that tastes just like

607
00:34:22,520 --> 00:34:25,600
Speaker 1: beef but looks like a scobi from a kombucha batch.

608
00:34:26,160 --> 00:34:29,800
Speaker 1: No one wants to eat that. And yet Japanese researchers

609
00:34:29,840 --> 00:34:34,160
Speaker 1: recently showed I think according to freethink, this great website

610
00:34:34,160 --> 00:34:37,840
Speaker 1: I found in twenty twenty one, they recreated a Wagoo

611
00:34:37,960 --> 00:34:41,480
Speaker 1: steak which has got some of the most complex marbling

612
00:34:41,520 --> 00:34:44,640
Speaker 1: of fat mixed in with the meat that you could

613
00:34:44,680 --> 00:34:49,319
Speaker 1: possibly ever come across, and they faithfully recreated one. I'm

614
00:34:49,360 --> 00:34:52,120
Speaker 1: sure it cost them a million and a half dollars

615
00:34:52,120 --> 00:34:54,400
Speaker 1: to make that one steak, but there was a proof

616
00:34:54,440 --> 00:34:57,560
Speaker 1: of concept that it can be done. The other big

617
00:34:57,640 --> 00:35:01,520
Speaker 1: challenge is right now, when you're making that Wagou steak

618
00:35:01,800 --> 00:35:05,680
Speaker 1: from cellular culture, you actually need to take it from

619
00:35:05,680 --> 00:35:08,919
Speaker 1: an unborn calf as you slaughter the mom. I don't

620
00:35:08,920 --> 00:35:11,640
Speaker 1: think the mom has to be slaughtered. I think they

621
00:35:11,719 --> 00:35:14,680
Speaker 1: just take it while they're slaughtering the mom. Yeah, and

622
00:35:14,719 --> 00:35:17,120
Speaker 1: that's what they used to grow meat. Right now, and

623
00:35:17,160 --> 00:35:19,600
Speaker 1: a lot of people are like, no, still, I'm not

624
00:35:19,680 --> 00:35:23,960
Speaker 1: okay with that. It's still an animal suffers somehow, some way.

625
00:35:25,040 --> 00:35:28,440
Speaker 1: And so there's a company called Meetable, a Dutch company

626
00:35:28,440 --> 00:35:30,520
Speaker 1: that said, we got this, we got our way around this.

627
00:35:31,080 --> 00:35:34,520
Speaker 2: Yeah, they made us sausage in July twenty twenty two

628
00:35:35,239 --> 00:35:40,239
Speaker 2: that was lab grown sausage, lab grown pork. But it

629
00:35:40,320 --> 00:35:43,080
Speaker 2: was it did not use I don't think we said.

630
00:35:43,120 --> 00:35:46,600
Speaker 2: What it's called fetal bovine serum. Is that blood drawn

631
00:35:46,680 --> 00:35:49,319
Speaker 2: from the cow's fetus, and that's what you said, it's

632
00:35:49,360 --> 00:35:52,359
Speaker 2: typically used, but they didn't use that at all. It

633
00:35:52,480 --> 00:35:56,640
Speaker 2: was you know, there was no animal involved.

634
00:35:56,840 --> 00:35:59,359
Speaker 1: Yeah, they used cells from like a live animal that

635
00:35:59,440 --> 00:36:00,440
Speaker 1: was unharmed by it.

636
00:36:00,480 --> 00:36:02,320
Speaker 2: Well yeah, yeah, that's what I meant. No, no animal

637
00:36:02,520 --> 00:36:04,160
Speaker 2: involved is in their death?

638
00:36:04,239 --> 00:36:07,480
Speaker 1: Was not involved, right right, exactly? Yeah, the animal couldn't

639
00:36:07,480 --> 00:36:10,839
Speaker 1: have cared less either way. From what I understand they did.

640
00:36:11,640 --> 00:36:14,239
Speaker 1: They were in the process of having they were being degassed,

641
00:36:14,280 --> 00:36:16,840
Speaker 1: so they had bigger fish to fry than somebody scraping

642
00:36:16,880 --> 00:36:18,360
Speaker 1: a few cells off their hind quarters.

643
00:36:18,360 --> 00:36:19,879
Speaker 2: You know, it's like I got a blowtorch coming out

644
00:36:19,880 --> 00:36:21,200
Speaker 2: of my exactly.

645
00:36:22,080 --> 00:36:24,879
Speaker 1: Uh, okay, I say we move on. Oh but first, Chuck,

646
00:36:24,960 --> 00:36:29,080
Speaker 1: let's take a break, because it's it's that kind of time.

647
00:36:29,719 --> 00:36:30,239
Speaker 2: Let's do it.

648
00:36:51,239 --> 00:36:56,080
Speaker 1: Okay, we're back, Charles. We're talking about what people in

649
00:36:56,120 --> 00:36:57,920
Speaker 1: the future are going to think of us based on

650
00:36:57,960 --> 00:37:00,880
Speaker 1: the stuff we do today that may seem primitive, and

651
00:37:00,960 --> 00:37:03,720
Speaker 1: one of them might not seem well, it could seem primitive,

652
00:37:03,719 --> 00:37:07,920
Speaker 1: it'll seem quaint. Probably is driving a car yourself, Yeah,

653
00:37:08,040 --> 00:37:11,600
Speaker 1: or maybe even owning your own car, because the predictions

654
00:37:11,600 --> 00:37:16,200
Speaker 1: for the future are that car hailing apps will become

655
00:37:16,239 --> 00:37:18,799
Speaker 1: so ubiquitous that you're gonna need your own car less

656
00:37:18,800 --> 00:37:21,879
Speaker 1: and less and less. This is a prediction from Karas

657
00:37:21,920 --> 00:37:25,759
Speaker 1: Swisser Swisher the New York Times tech colonists. Sorry Kara,

658
00:37:27,520 --> 00:37:30,920
Speaker 1: that in not too many years, owning your own car

659
00:37:31,000 --> 00:37:34,280
Speaker 1: is going to become obsolete. And then eventually the next step.

660
00:37:34,440 --> 00:37:37,719
Speaker 1: This is me adding to that prediction, those cars that

661
00:37:37,840 --> 00:37:39,920
Speaker 1: pick you up when you use a ride hailing app

662
00:37:40,120 --> 00:37:42,600
Speaker 1: will not have a driver in them. You will just

663
00:37:42,640 --> 00:37:43,640
Speaker 1: get in the back and go.

664
00:37:44,320 --> 00:37:47,000
Speaker 2: Yeah. I mean, self driving cars has been in the

665
00:37:47,040 --> 00:37:50,640
Speaker 2: news a lot over the past you know, decade or so.

666
00:37:51,360 --> 00:37:54,520
Speaker 2: I remember being in San Francisco, a couple of years

667
00:37:54,560 --> 00:37:58,759
Speaker 2: ago and seeing a car with a crazy contraption on top,

668
00:37:59,480 --> 00:38:00,880
Speaker 2: and I was like, what in the world is that?

669
00:38:01,120 --> 00:38:03,959
Speaker 2: And is that a Google Maps or a Google Earth

670
00:38:04,120 --> 00:38:05,760
Speaker 2: like thing taking pictures.

671
00:38:05,400 --> 00:38:06,960
Speaker 1: That car's wearing braces.

672
00:38:07,400 --> 00:38:10,120
Speaker 2: Then I looked inside. It's like no, no, no, there's

673
00:38:10,160 --> 00:38:13,000
Speaker 2: no human in that car. And it kind of startled me.

674
00:38:13,160 --> 00:38:15,440
Speaker 1: But just showing witchcraft, right.

675
00:38:16,960 --> 00:38:19,719
Speaker 2: I did. I threw a Molotov cocktail out of it

676
00:38:19,719 --> 00:38:23,040
Speaker 2: all to care of that problem. But there was a company,

677
00:38:23,600 --> 00:38:24,880
Speaker 2: I think there were you know, there's more than one

678
00:38:24,880 --> 00:38:27,320
Speaker 2: company that's trying this stuff out, but there's a company

679
00:38:27,360 --> 00:38:32,160
Speaker 2: called Cruise, which just recently in October of this year. Well,

680
00:38:32,200 --> 00:38:35,040
Speaker 2: I guess last year now of twenty twenty three, the

681
00:38:35,080 --> 00:38:40,480
Speaker 2: California state government said you can't do this, you can't

682
00:38:40,480 --> 00:38:44,239
Speaker 2: practice this anymore, no more driverless practicing out of you

683
00:38:45,320 --> 00:38:48,200
Speaker 2: because well, for a lot of reasons building up to

684
00:38:48,760 --> 00:38:52,560
Speaker 2: what was called the incident, but minor incidents involved things

685
00:38:52,600 --> 00:38:56,560
Speaker 2: like blocking ambulances, stopping in the middle of an intersection,

686
00:38:57,920 --> 00:39:01,960
Speaker 2: rearinding a bus, running red light, stuff like that. But

687
00:39:02,080 --> 00:39:05,760
Speaker 2: the big incident was when a pedestrian finally was bound

688
00:39:05,760 --> 00:39:10,160
Speaker 2: to happen, was hit in downtown San Francisco when she

689
00:39:10,320 --> 00:39:12,560
Speaker 2: was hit by another car driven by a real human,

690
00:39:13,080 --> 00:39:16,279
Speaker 2: knocked into the other lane, and then the Cruise car

691
00:39:16,480 --> 00:39:22,320
Speaker 2: apparently braked but then rolled over her anyway, pulled her forward,

692
00:39:22,440 --> 00:39:24,520
Speaker 2: and then stopped on top of her.

693
00:39:24,880 --> 00:39:26,719
Speaker 1: Just stopped. It was like, Okay, I'm fine, I don't

694
00:39:26,760 --> 00:39:28,719
Speaker 1: know what to do. I'm just gonna freeze right here

695
00:39:28,760 --> 00:39:32,000
Speaker 1: on top of this pedestrian. So yeah, Cruise is far

696
00:39:32,080 --> 00:39:36,919
Speaker 1: and away the only company from having problems with their

697
00:39:37,160 --> 00:39:40,080
Speaker 1: tech that they're working on. They're just the most recent

698
00:39:40,160 --> 00:39:43,280
Speaker 1: poster child of the problems with self driving cars.

699
00:39:43,680 --> 00:39:45,720
Speaker 2: Yeah she didn't die, by the way, No, thank.

700
00:39:45,560 --> 00:39:46,279
Speaker 1: You for saying that.

701
00:39:46,520 --> 00:39:46,960
Speaker 2: Yeah.

702
00:39:47,080 --> 00:39:50,640
Speaker 1: The point of this is, though, is that despite these setbacks,

703
00:39:51,680 --> 00:39:54,320
Speaker 1: we exist in the time of setbacks. In a couple

704
00:39:54,360 --> 00:39:56,840
Speaker 1: of decades, will exist in the time where were beyond

705
00:39:56,840 --> 00:39:59,759
Speaker 1: those setbacks and we have driverless cars. These setbacks don't

706
00:39:59,760 --> 00:40:02,560
Speaker 1: mean we're never going to have driverless cars. In fact,

707
00:40:02,560 --> 00:40:05,919
Speaker 1: even people who are super skeptical of them right now

708
00:40:06,080 --> 00:40:09,160
Speaker 1: still admit we're probably going to have them at some

709
00:40:09,360 --> 00:40:11,919
Speaker 1: point in the future. It's just a question of when.

710
00:40:12,320 --> 00:40:16,040
Speaker 1: And it seems like we're a little further behind than

711
00:40:16,080 --> 00:40:18,160
Speaker 1: we may have thought a few years ago.

712
00:40:18,960 --> 00:40:21,040
Speaker 2: Yeah, and you know one thing that if it's not,

713
00:40:21,400 --> 00:40:24,400
Speaker 2: I think the road there may not be as abrupt

714
00:40:24,520 --> 00:40:28,000
Speaker 2: because we already see in newer cars a lot of

715
00:40:28,080 --> 00:40:33,080
Speaker 2: like things like lane assistance, Like your car will correct

716
00:40:33,080 --> 00:40:36,280
Speaker 2: itself and steer itself back if it sees that it's

717
00:40:36,360 --> 00:40:39,280
Speaker 2: driving off the road, like if you're drowsy or you're

718
00:40:39,320 --> 00:40:42,120
Speaker 2: on your phone, which you should never be, so you

719
00:40:42,160 --> 00:40:45,640
Speaker 2: see like lane assistants and stuff like that. You know,

720
00:40:45,680 --> 00:40:49,000
Speaker 2: if your speed like really really changes a lot. A

721
00:40:49,000 --> 00:40:50,920
Speaker 2: lot of times cars these days will send you an

722
00:40:50,920 --> 00:40:53,879
Speaker 2: alert that says, like, you know, are you okay? Maybe

723
00:40:53,880 --> 00:40:56,799
Speaker 2: you should pull over, stuff like that. So that's sort

724
00:40:56,840 --> 00:41:00,239
Speaker 2: of like these are the intermediary steps that will lead

725
00:41:00,280 --> 00:41:03,040
Speaker 2: to full automation, and they've already come a long way,

726
00:41:03,040 --> 00:41:06,000
Speaker 2: but apparently, again with the help of AI, they could

727
00:41:06,000 --> 00:41:06,720
Speaker 2: go a lot further.

728
00:41:07,120 --> 00:41:09,120
Speaker 1: Yeah, eventually the cars is going to start talking to

729
00:41:09,200 --> 00:41:11,359
Speaker 1: itself and you'll feel so left out you just don't

730
00:41:11,360 --> 00:41:14,879
Speaker 1: even get in the driver's seat anymore. But the whole

731
00:41:14,920 --> 00:41:19,080
Speaker 1: point of removing humans from cars is to remove humans

732
00:41:19,160 --> 00:41:23,759
Speaker 1: from the equation of driving, not for our convenience necessarily,

733
00:41:23,760 --> 00:41:26,120
Speaker 1: but for our safety because we're our own worst enemies

734
00:41:26,120 --> 00:41:29,960
Speaker 1: when it comes to driving. You found a stat that recently,

735
00:41:30,080 --> 00:41:32,120
Speaker 1: it was it like twenty twenty twenty twenty one.

736
00:41:32,120 --> 00:41:35,640
Speaker 2: Do you know it's twenty twenty one, But just over

737
00:41:35,680 --> 00:41:37,960
Speaker 2: the last few years, in general, it's been about thirty

738
00:41:38,000 --> 00:41:39,319
Speaker 2: to thirty three percent.

739
00:41:39,360 --> 00:41:43,160
Speaker 1: Of fatalities involved at least one of the drivers being drunk.

740
00:41:43,680 --> 00:41:46,840
Speaker 1: I couldn't find any statistics that also include drugs, but

741
00:41:47,239 --> 00:41:51,680
Speaker 1: just being drunk alone, thirty percent of people who die

742
00:41:51,719 --> 00:41:54,880
Speaker 1: in the United States die because one of the people

743
00:41:55,280 --> 00:42:00,160
Speaker 1: involved in that crash was drunk. That is unacceptable, but

744
00:42:00,239 --> 00:42:03,160
Speaker 1: it's humans. People do that. It's a terrible decision. People

745
00:42:03,160 --> 00:42:04,880
Speaker 1: think that that's not going to happen to them, and

746
00:42:05,160 --> 00:42:07,920
Speaker 1: it does, and it accounts for thousands and thousands and

747
00:42:07,960 --> 00:42:12,400
Speaker 1: thousands of deaths every year. Yeah, driverless cars don't drink.

748
00:42:12,520 --> 00:42:15,600
Speaker 1: They have other problems right now, But as we work

749
00:42:15,640 --> 00:42:18,600
Speaker 1: them out, those problems will become a part of the past,

750
00:42:18,680 --> 00:42:22,080
Speaker 1: and drunk driving accidents will become a part of the

751
00:42:22,120 --> 00:42:24,200
Speaker 1: past as well, which will be great for everybody.

752
00:42:24,719 --> 00:42:28,200
Speaker 2: Yeah, I mean, ninety four percent of any accident in

753
00:42:28,239 --> 00:42:31,680
Speaker 2: the United States involves some kind of human error. So

754
00:42:32,120 --> 00:42:35,279
Speaker 2: you know what I'm curious about is what the acceptable

755
00:42:35,320 --> 00:42:39,480
Speaker 2: percentage of driverless car error, because it seems like it

756
00:42:39,480 --> 00:42:44,359
Speaker 2: seems like human car error is just endlessly forgivable, to

757
00:42:44,440 --> 00:42:48,000
Speaker 2: the point where, you know, like every car these days

758
00:42:48,000 --> 00:42:50,560
Speaker 2: you shouldn't be able to start unless you can blow

759
00:42:50,600 --> 00:42:53,839
Speaker 2: into a breathalyzer like that technology is there.

760
00:42:54,040 --> 00:42:56,320
Speaker 1: Yeah, we're harder on computers than we are in ourselves,

761
00:42:56,400 --> 00:42:56,959
Speaker 1: is what you're saying.

762
00:42:57,000 --> 00:42:59,759
Speaker 2: Huh, well exactly, So, like what if all of a sudden,

763
00:42:59,840 --> 00:43:02,720
Speaker 2: driverless cars they prove, like you know, they can reduce

764
00:43:03,000 --> 00:43:06,960
Speaker 2: total accidents by ninety percent, there would still be people saying, like,

765
00:43:07,239 --> 00:43:10,520
Speaker 2: in those ten percent of cases where someone died, it

766
00:43:10,600 --> 00:43:12,560
Speaker 2: was some AI computer or whatever.

767
00:43:12,719 --> 00:43:13,239
Speaker 1: Yeah.

768
00:43:13,280 --> 00:43:15,080
Speaker 2: So it's just I don't know, just find it really

769
00:43:15,120 --> 00:43:18,160
Speaker 2: interesting that we still allow people to get into a

770
00:43:18,200 --> 00:43:21,440
Speaker 2: car after they've been drinking and drive, even though the

771
00:43:21,480 --> 00:43:24,760
Speaker 2: technology exists to stop that from happening.

772
00:43:24,880 --> 00:43:27,440
Speaker 1: Well, yeah, I think that it's a cognitive bias of ours.

773
00:43:27,480 --> 00:43:29,840
Speaker 1: We tend to focus on the more sensational, and the

774
00:43:29,880 --> 00:43:32,720
Speaker 1: more sensational. Is a car being driven by a computer

775
00:43:32,920 --> 00:43:39,560
Speaker 1: killing somebody than a drunk dude killing somebody in his car. Yeah,

776
00:43:39,840 --> 00:43:43,160
Speaker 1: So yeah, there's just removing people from the equation should

777
00:43:43,200 --> 00:43:48,440
Speaker 1: increase safety. It should also probably increase or decrease pollution

778
00:43:48,760 --> 00:43:50,719
Speaker 1: as a result. There's somebody who came up with the

779
00:43:50,719 --> 00:43:54,000
Speaker 1: eye popping statistic that thirty percent of the traffic in

780
00:43:54,080 --> 00:43:58,560
Speaker 1: metropolitan areas is people circling the block looking for a

781
00:43:58,600 --> 00:44:01,560
Speaker 1: place to park. If you don't own a car and

782
00:44:01,600 --> 00:44:04,160
Speaker 1: you're not driving your own car, that goes away. So

783
00:44:04,239 --> 00:44:08,760
Speaker 1: thirty percent of traffic goes away instantaneously with that, Yeah,

784
00:44:09,000 --> 00:44:13,080
Speaker 1: I mean that you have me right there. Yeah, yeah,

785
00:44:13,360 --> 00:44:16,839
Speaker 1: So driverless cars almost certainly coming down the pike, as

786
00:44:16,880 --> 00:44:19,120
Speaker 1: long as AI doesn't take over the world, of course,

787
00:44:19,160 --> 00:44:21,480
Speaker 1: I think we should caveat this entire episode with that

788
00:44:21,600 --> 00:44:24,560
Speaker 1: all of this is going to happen if AI doesn't

789
00:44:24,560 --> 00:44:25,440
Speaker 1: take over the world.

790
00:44:25,440 --> 00:44:28,319
Speaker 2: Okay, that's right, And we're going to finish up with

791
00:44:28,360 --> 00:44:32,080
Speaker 2: a couple of shorter ones that I think you're just

792
00:44:32,600 --> 00:44:37,000
Speaker 2: pretty awesome and interesting. One is the fact that sort

793
00:44:37,000 --> 00:44:39,840
Speaker 2: of the current thinking is that we we tend to

794
00:44:39,920 --> 00:44:45,480
Speaker 2: tie like progress as a nation, definitely in the United States,

795
00:44:45,480 --> 00:44:49,000
Speaker 2: but in most places around the world, to how like

796
00:44:49,160 --> 00:44:53,279
Speaker 2: robust and economy is. It's like always tied to finances

797
00:44:53,680 --> 00:44:56,200
Speaker 2: on what kind of progress we're making, and there are

798
00:44:56,239 --> 00:44:59,399
Speaker 2: people that think like sort of like with the way

799
00:44:59,440 --> 00:45:02,399
Speaker 2: we're starting to look at animals, like, you know, one

800
00:45:02,480 --> 00:45:04,920
Speaker 2: day that's not going to be the most important factor

801
00:45:04,960 --> 00:45:08,880
Speaker 2: for humans, and things like the health of the earth

802
00:45:09,120 --> 00:45:12,799
Speaker 2: and human beings health and well being both physically and

803
00:45:12,840 --> 00:45:16,840
Speaker 2: mentally is the you should equate that with the success

804
00:45:16,840 --> 00:45:19,279
Speaker 2: of a nation, And one day they're going to look

805
00:45:19,280 --> 00:45:21,600
Speaker 2: back and say, you remember when we all that we

806
00:45:21,680 --> 00:45:25,680
Speaker 2: cared about was the fact that the stock market was flesh.

807
00:45:25,800 --> 00:45:30,040
Speaker 1: Yeah, because we GDP just tells you whether an economy

808
00:45:30,120 --> 00:45:34,319
Speaker 1: is growing or shrinking, right, That's basically all it tells you,

809
00:45:35,280 --> 00:45:37,799
Speaker 1: And it leaves out a lot of stuff, like you said,

810
00:45:37,960 --> 00:45:42,239
Speaker 1: human well being, things like whether people are dying of

811
00:45:42,320 --> 00:45:46,200
Speaker 1: deaths of despair or whether they're generally happy, how many

812
00:45:46,239 --> 00:45:49,360
Speaker 1: resources are being depleted. Is anybody working on an alternative

813
00:45:49,440 --> 00:45:53,440
Speaker 1: that all of the stuff that creates that growing economy

814
00:45:53,560 --> 00:45:56,680
Speaker 1: just is totally ignored? Yeah, And I think that's what

815
00:45:56,719 --> 00:46:01,240
Speaker 1: that economist Kate Rayworth was saying, is like it's it's madness,

816
00:46:01,280 --> 00:46:04,759
Speaker 1: Like it's so ridiculous to just completely not count all

817
00:46:04,880 --> 00:46:09,319
Speaker 1: this stuff that really really counts in favor of just

818
00:46:09,400 --> 00:46:13,560
Speaker 1: this one metric, which is growth or shrinkage. And not

819
00:46:13,600 --> 00:46:17,640
Speaker 1: only is that probably going to be thought of as

820
00:46:17,719 --> 00:46:21,200
Speaker 1: ridiculous in the future. People younger people today, who are

821
00:46:21,200 --> 00:46:24,480
Speaker 1: becoming adults or who have recently become adults, they already

822
00:46:24,960 --> 00:46:28,279
Speaker 1: tend to think this way as a group. So it's

823
00:46:28,280 --> 00:46:31,720
Speaker 1: a pretty sure indicator that we're going to leave GDP

824
00:46:31,960 --> 00:46:35,360
Speaker 1: or growth behind as an indicator of the health of

825
00:46:35,400 --> 00:46:38,520
Speaker 1: an economy and start thinking more about the other stuff,

826
00:46:38,560 --> 00:46:42,080
Speaker 1: the more important stuff. And who knows what can result

827
00:46:42,080 --> 00:46:48,359
Speaker 1: from that, like what great cascading knock on effects that

828
00:46:48,360 --> 00:46:48,919
Speaker 1: that will have.

829
00:46:49,680 --> 00:46:54,960
Speaker 2: Yeah, you found this Princeton University bioethicist named Peter Singer

830
00:46:55,239 --> 00:46:57,520
Speaker 2: who talked about the fact that the circle of concern

831
00:46:58,239 --> 00:47:03,960
Speaker 2: as humankind and advances is expanding, And that's just a

832
00:47:04,000 --> 00:47:06,080
Speaker 2: wonderful thought. And you know, you see it in everything

833
00:47:06,120 --> 00:47:08,400
Speaker 2: from the fact that you know, we've laughed before it

834
00:47:08,520 --> 00:47:10,440
Speaker 2: like the mad Men episode where people used to just

835
00:47:10,760 --> 00:47:14,719
Speaker 2: willingly throw litter on the ground, to you know, we

836
00:47:14,760 --> 00:47:19,200
Speaker 2: look back at that as barbaric generally. And that's just

837
00:47:19,239 --> 00:47:22,640
Speaker 2: one small example. So as as humans are evolving down

838
00:47:22,680 --> 00:47:26,200
Speaker 2: the line, that circle of concern is expanding and people

839
00:47:26,200 --> 00:47:29,399
Speaker 2: are caring about more and more things that they didn't

840
00:47:29,440 --> 00:47:31,320
Speaker 2: care about before, and that's that's great.

841
00:47:31,480 --> 00:47:33,600
Speaker 1: Yeah. And Peter Singer, by the way, is very famous

842
00:47:34,160 --> 00:47:38,000
Speaker 1: ethicists as far as animal rights are concerned in animal welfare.

843
00:47:38,840 --> 00:47:41,120
Speaker 1: So yeah, his whole thing is like, we're gonna stop

844
00:47:41,200 --> 00:47:46,480
Speaker 1: focusing on conspicuous consumption and rather you'll be more considered

845
00:47:46,800 --> 00:47:48,839
Speaker 1: like a great person, not from your wealth, but from

846
00:47:48,840 --> 00:47:51,520
Speaker 1: your charity and your charitable giving, which would be great.

847
00:47:52,280 --> 00:47:55,480
Speaker 1: And then that circle of concern kind of leads us

848
00:47:55,480 --> 00:47:58,960
Speaker 1: to our last one too, because the most recent inclusion

849
00:47:58,960 --> 00:48:01,799
Speaker 1: into the circle of conc is the environment, the earth,

850
00:48:01,880 --> 00:48:06,640
Speaker 1: the health of the earth, and that this one is

851
00:48:06,760 --> 00:48:10,359
Speaker 1: just a sure give me, there is no way that

852
00:48:10,400 --> 00:48:12,759
Speaker 1: we're not going to be looked down upon for this

853
00:48:12,960 --> 00:48:16,080
Speaker 1: by our descendants. So that is burning fossil fuels.

854
00:48:16,480 --> 00:48:20,600
Speaker 2: Yeah, I mean in five hundred years, who knows, maybe sooner.

855
00:48:21,280 --> 00:48:23,359
Speaker 2: It seems like people will definitely look back and say,

856
00:48:23,400 --> 00:48:27,319
Speaker 2: I can't believe that we used to burn fossil fuels

857
00:48:27,360 --> 00:48:29,920
Speaker 2: like we did. And for a lot of reasons, not

858
00:48:30,040 --> 00:48:33,880
Speaker 2: just the you know the process of removing fossil fuels

859
00:48:33,920 --> 00:48:38,320
Speaker 2: and all that goes into that, or even the climate

860
00:48:38,360 --> 00:48:41,440
Speaker 2: and the ozone, which are all huge concerns obviously, but

861
00:48:41,760 --> 00:48:45,439
Speaker 2: just things like pollution and air quality and the fact

862
00:48:45,480 --> 00:48:48,920
Speaker 2: that you know that kills people and that costs so

863
00:48:49,160 --> 00:48:52,320
Speaker 2: much money in healthcare. I think there was a study

864
00:48:52,719 --> 00:48:56,400
Speaker 2: from the University of Wisconsin in Madison that said if

865
00:48:56,640 --> 00:48:59,800
Speaker 2: we stopped burning fossil fuels all together, it would have

866
00:49:00,480 --> 00:49:03,799
Speaker 2: about fifty thousand premature deaths per year because of air

867
00:49:03,880 --> 00:49:08,360
Speaker 2: quality alone, and about six hundred billion dollars annually and

868
00:49:08,440 --> 00:49:10,520
Speaker 2: the US alone and healthcare costs.

869
00:49:10,640 --> 00:49:12,840
Speaker 1: Yeah, And I think even more than looking at us

870
00:49:12,880 --> 00:49:15,440
Speaker 1: as like dumb dumbs for ignoring that we're going to

871
00:49:15,440 --> 00:49:18,520
Speaker 1: be looked at, is kind of reviled because of the

872
00:49:18,520 --> 00:49:21,840
Speaker 1: future will have delivered our descendants because of the climate

873
00:49:21,920 --> 00:49:23,640
Speaker 1: change we just allowed to happen.

874
00:49:24,080 --> 00:49:24,279
Speaker 2: Yeah.

875
00:49:24,560 --> 00:49:30,240
Speaker 1: I saw who estimate that two hundred and fifty thousand

876
00:49:30,239 --> 00:49:34,799
Speaker 1: additional deaths per year are expected to come each year

877
00:49:35,160 --> 00:49:38,840
Speaker 1: between twenty thirty and twenty fifty because of climate change

878
00:49:39,080 --> 00:49:44,040
Speaker 1: from things like heat stress, malnutrition, insect born diseases. That

879
00:49:44,160 --> 00:49:46,880
Speaker 1: an additional quarter of a million people are going to

880
00:49:46,920 --> 00:49:50,759
Speaker 1: die every year because of climate change starting six years

881
00:49:50,760 --> 00:49:54,000
Speaker 1: from now. That's nuts. So I can only imagine what

882
00:49:54,080 --> 00:49:56,680
Speaker 1: the people of you know, twenty one hundred are going

883
00:49:56,719 --> 00:49:59,560
Speaker 1: to think of us. Hopefully they'll have everything under control

884
00:49:59,600 --> 00:50:01,720
Speaker 1: by then, but they're probably going to be pretty ticked

885
00:50:01,719 --> 00:50:03,200
Speaker 1: off that they had to go to the trouble.

886
00:50:03,960 --> 00:50:05,719
Speaker 2: Yeah. I mean, you can see this coming because it

887
00:50:05,760 --> 00:50:09,480
Speaker 2: already happens now once again by seeing younger generations already

888
00:50:09,480 --> 00:50:13,719
Speaker 2: looking at previous generations as barbaric and how we treat

889
00:50:13,719 --> 00:50:14,080
Speaker 2: the earth.

890
00:50:14,160 --> 00:50:18,400
Speaker 1: For sure, I saw an RHS financial estimate or prediction

891
00:50:18,880 --> 00:50:23,400
Speaker 1: that the oil market will collapse this decade that we're

892
00:50:23,440 --> 00:50:26,040
Speaker 1: just based on trends, current trends now and the way

893
00:50:26,120 --> 00:50:29,960
Speaker 1: people think now that probably we won't be using oil

894
00:50:30,600 --> 00:50:33,520
Speaker 1: nearly as much in the next ten twenty years.

895
00:50:33,760 --> 00:50:34,360
Speaker 2: Very interesting.

896
00:50:34,560 --> 00:50:38,800
Speaker 1: Yeah, the future is interesting, Chuck, And it is the future.

897
00:50:38,920 --> 00:50:40,960
Speaker 1: As a matter of fact. It's almost twenty twenty four,

898
00:50:41,400 --> 00:50:43,440
Speaker 1: and I just want to say happy new year to everybody.

899
00:50:43,520 --> 00:50:47,960
Speaker 2: Huh, that's right, Happy new year everyone. We thank you

900
00:50:48,040 --> 00:50:50,479
Speaker 2: once again for your support. We say it all the time.

901
00:50:50,719 --> 00:50:52,839
Speaker 2: If there was no U, that would be no US.

902
00:50:53,760 --> 00:50:57,000
Speaker 2: We are always grateful that we are allowed to do

903
00:50:57,040 --> 00:50:58,280
Speaker 2: this job because you listen.

904
00:50:58,480 --> 00:51:01,160
Speaker 1: Yeah, thank you, and happy new year to everyone. Happy

905
00:51:01,160 --> 00:51:05,520
Speaker 1: birthday to you me Andy Birthday, Thanks Chuck, and we'll

906
00:51:05,560 --> 00:51:07,640
Speaker 1: see you guys next year. And if you want to

907
00:51:07,640 --> 00:51:09,480
Speaker 1: get in touch with this in the interim, in this

908
00:51:09,719 --> 00:51:12,200
Speaker 1: very short time left in twenty twenty three, you can

909
00:51:12,239 --> 00:51:16,080
Speaker 1: do it via email almost instantaneously. Wrap it up, spank

910
00:51:16,120 --> 00:51:18,279
Speaker 1: it on the bottom, put a sash onto this says

911
00:51:18,320 --> 00:51:21,680
Speaker 1: twenty twenty four, and send it off to Stuff podcast

912
00:51:22,000 --> 00:51:25,840
Speaker 1: at iHeartRadio dot com.

913
00:51:26,719 --> 00:51:29,600
Speaker 2: Stuff you Should Know is a production of iHeartRadio. For

914
00:51:29,719 --> 00:51:33,880
Speaker 2: more podcasts my heart Radio, visit the iHeartRadio app, Apple Podcasts,

915
00:51:34,000 --> 00:51:40,440
Speaker 2: or wherever you listen to your favorite shows.