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Speaker 1: From UFOs to psychic powers and government conspiracies. History is

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Speaker 1: riddled with unexplained events. You can turn back now or

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Speaker 1: learn this stuff they don't want you to know. A

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Speaker 1: production of iHeartRadio.

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Speaker 2: Hello, welcome back to the show. My name is Matt,

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Speaker 2: my name is Noel.

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Speaker 3: They call me Ben. We're joined as always with our

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Speaker 3: super producer Dylan the Tennessee pal Fagan. Most importantly, you

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Speaker 3: are here. That makes this the stuff they don't want

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Speaker 3: you to know. Thank you, as always, fellow conspiracy realists

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Speaker 3: for joining us. If you are hearing our strange news.

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Speaker 3: We'd also like to welcome you to November tenth, twenty

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Speaker 3: twenty five Procedural new from Tennessee. We are recording reading

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Speaker 3: this on Wednesday, November fifth, so a lot of stuff

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Speaker 3: might change.

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Speaker 2: Sure, And November fifth the day we're in right this moment,

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Speaker 2: it's the morning after a big election here in the US,

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Speaker 2: and uh yeah.

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Speaker 4: You're you're myleogs may vary how you're feeling about it,

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Speaker 4: but some people are quite thrilled and some people are

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Speaker 4: doom saying.

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Speaker 3: Yeah, way yeah. And congratulations to Virginia for the collective action.

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Speaker 3: Congratulations to everybody who took the time to vote. Now,

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Speaker 3: did you guys, are you guys people who vote early

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Speaker 3: or do you like to go day up?

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Speaker 2: I'm like, I'm like morning after. I like to cast one.

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Speaker 3: Yeah, you like to show up to the library and say,

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Speaker 3: why isn't there a line?

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Speaker 2: Yeah, what's going on?

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Speaker 4: Oh no, it's the old spirit of the staircase conundrum.

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Speaker 4: Now I love it. Yeah, I went to my local

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Speaker 4: elementary school right my Neighborhoo. I didn't even have to

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Speaker 4: get on the main road and went around four o'clock

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Speaker 4: and not a line to speak of. Very very smooth.

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Speaker 2: It's something I've been noticing every election. On election day,

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Speaker 2: there doesn't seem to be many people out because so

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Speaker 2: many people are making use of those early voting things,

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Speaker 2: which is.

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Speaker 4: Not a popular measure for the Republicans, if I'm not mistaken.

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Speaker 4: They are very against mail in ballots and early voting,

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Speaker 4: arguing that it is a system that is rife with fraud,

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Speaker 4: though I don't know that I've seen any compelling evidence

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Speaker 4: to that.

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Speaker 3: Yeah, it's it's cool to vote early. It's cool to

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Speaker 3: vote the date of I like it. I like walking

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Speaker 3: up in the line. Not to be too Larry David

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Speaker 3: about it. I know it's technically here in our fair

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Speaker 3: state of Georgia is technically illegal to bring snacks and water,

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Speaker 3: but I dig it. Everybody in Atlanta at least is

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Speaker 3: vaguely related. We talked about that with our pal Michael

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Speaker 3: render Killer Mike. Uh some of us might know him

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Speaker 3: as Uh. So we hope you had the opportunity to vote,

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Speaker 3: and we are using the word opportunity correctly. So, uh folks, Uh, look,

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Speaker 3: stuff is wild. We're hurtling headlong forward twenty twenty six.

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Speaker 3: We got a lot of strange news to get to.

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Speaker 3: Where should we start?

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Speaker 2: We got robots.

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Speaker 4: I love a robot bee Boop meet meat more Yep,

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Speaker 4: very much.

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Speaker 2: So we have robots. Let's talk robots. After a break

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Speaker 2: and we've returned. Guess what, guys what Elon Musk? That's

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Speaker 2: it from.

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Speaker 3: Earlier, from earlier.

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Speaker 4: Yeah, yeah, yeah, yeah, I love he's got good vibes.

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Speaker 2: Yeah. Well, okay, so what's that's for shock value? We're

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Speaker 2: gonna talk about Ela Musk and Tesla and all the

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Speaker 2: various companies that are run by Elon Musk that are

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Speaker 2: all kind of interconnected, as we've seen in a bunch

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Speaker 2: of ways. I think very soon Tesla and the Optimist

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Speaker 2: robot are going to part ways and it'll be like

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Speaker 2: the Optimist company or you know, it'll be something like that.

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Speaker 2: That's that'll happen. Just to get specific investment.

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Speaker 3: Point of order, mister Frederick, We the fellow conspiracy realist,

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Speaker 3: aren't going to let you go on this one. What's

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Speaker 3: an Optimist?

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Speaker 4: Is it Prime?

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Speaker 2: Oh? There will be I guarantee there will be an

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Speaker 2: attempt to make an Optimist Prime branded robot. But yes,

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Speaker 2: Optimist is the bipedal robot that the company Tesla is

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Speaker 2: currently attempting to create something that is commercially viable that

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Speaker 2: would exist in homes and in you know, businesses, in

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Speaker 2: places to be a helper, a robot helper just like

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Speaker 2: a human, but a robot. And there are a lot

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Speaker 2: of companies out there that are attempting to make these.

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Speaker 2: We've talked about it a few times, I think specifically

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Speaker 2: on Strange News. Another big company is Figure. Another big

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Speaker 2: company is Agibot, whose website we cannot visit by the way,

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Speaker 2: if we're connected to our iHeartRadio system, because it's a

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Speaker 2: site in China that they specifically say has a threat.

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Speaker 2: Oh yeah, so we can't go. Sorry, Agabot, I can't

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Speaker 2: even learn about your tech because I'm here in the US.

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Speaker 2: Figure though, has an incredible bot that you should all

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Speaker 2: check out called a Figure three that we have mentioned before.

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Speaker 2: Very stylish looking robot with some cool gray mesh stuff

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Speaker 2: going on. Very almost human.

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Speaker 3: Anthropomorphized for sure.

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Speaker 2: Yes, and all of these companies are attempting to make

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Speaker 2: something that would actually be a viable product for humans

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Speaker 2: to buy. That then you could have you know, thousands,

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Speaker 2: if not millions of these things shipped out to homes

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Speaker 2: and to buyers. The big question is who in the

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Speaker 2: heck is going to buy one of these things that

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Speaker 2: is going to cost as much as a car, if

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Speaker 2: not more than a car.

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Speaker 4: If you'll see any of these tiktoks or you know,

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Speaker 4: reels or whatever making the rounds of people just basically

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Speaker 4: trying to get robots to malfunction absurdly, like run into

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Speaker 4: walls and like trying to get them to cook and

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Speaker 4: have them flinging food all over the place and throwing

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Speaker 4: pots and pans everywhere. And I don't know exactly the context.

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Speaker 4: On first glance, it makes it seem like the robots

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Speaker 4: are just not good at what they're supposed to be

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Speaker 4: good at but upon further examination, it does feel like

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Speaker 4: some of these folks are like trying their best to

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Speaker 4: make them do awful stuff. Yeah, but yeah, anyway.

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Speaker 2: Well, and how could you ever tell that it's not

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Speaker 2: just a generated video. I mean, just because it doesn't

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Speaker 2: have that Soura mark on it doesn't mean it's not

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Speaker 2: a Saura video or something similar, you know. But at

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Speaker 2: the same time, we have seen these machines just malfunction

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Speaker 2: because they're not quite ready yet for these advanced levels

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Speaker 2: of vactuation and things like that that you see in

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Speaker 2: production lines for vehicles, production lines all over the place

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Speaker 2: that actually have that built in singular movement right picks

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Speaker 2: up a piece of whatever it's working on, does whatever

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Speaker 2: supposed work on, then puts it back down. That kind

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Speaker 2: of movement that is a trained specific movement to be

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Speaker 2: done by a robot. When there's an assembly line where

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Speaker 2: there's going to be a known object in a known place,

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Speaker 2: in a known angle or something. Sure, that movement occurs

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Speaker 2: every time an.

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Speaker 3: Ada Z routine.

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Speaker 2: Yes, So how do you get one of these bipedal

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Speaker 2: robots to do that thing when you've got so many variables?

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Speaker 2: When that can of Coca Cola that you want the

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Speaker 2: robot to go pick up for you out of the

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Speaker 2: fridge and bring it to you and open it or whatever.

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Speaker 3: Without squeezing too hard.

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Speaker 2: Oh yeah, there's so much stuff you got to do

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Speaker 2: just to make that happen. Right, you have to have

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Speaker 2: an awareness of everything and those small movements. How do

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Speaker 2: you get that exactly right? Well, Tesla and the Optimist

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Speaker 2: team is attempting to make this happen with humans, with

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Speaker 2: human test subjects.

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Speaker 4: Well, it was funny when you say that, Matt. It

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Speaker 4: made me immediately think of that event where the Optimist

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Speaker 4: robots were sort of trotted out, but it turns out

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Speaker 4: they were being remotely controlled by humans from off site

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Speaker 4: or in another room, even like you know, elaborate expensive puppets.

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Speaker 3: So the old question would be how do you do that?

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Speaker 3: Which is the question people posed with the mechanical turt

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Speaker 3: of old If we recall.

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Speaker 2: That, yes, absolutely, and you're right. All the telemetric I

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Speaker 2: think they call it telemetric controls or something where someone

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Speaker 2: has actually got a control system and then they're the

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Speaker 2: ones doing that. There are a lot of allegations that

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Speaker 2: many of these the higher ups in these robotics companies

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Speaker 2: will get one of their machines telemetrically controlled. When you've

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Speaker 2: got a huge investor, maybe rolling through, which.

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Speaker 4: Seems like fraud. I mean, that just seems like absolute fraud.

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Speaker 2: Well, and I think there's probably on the level discussion like, hey,

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Speaker 2: this is what they will be able to do, and

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Speaker 2: you can see how they move and all this. We're

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Speaker 2: on our way there. We just need more funding kind.

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Speaker 3: Of Yeah, this is our this is our demo of

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Speaker 3: the bomb, right and if you give us enough money,

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Speaker 3: we can make a bomb.

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Speaker 2: Oh for sure, and you're gonna low our bombs, you're

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Speaker 2: gonna buy them.

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Speaker 3: And yeah, and we have a subscription model, right follow

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Speaker 3: us on x dot com for sure.

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Speaker 2: Absolutely. Okay, So how do you get these or us

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Speaker 2: to do it? You have human test subjects. This is

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Speaker 2: where we get to the Optimist Lab. Business Insider and

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Speaker 2: Futurism and other places are calling it a secret Tesla

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Speaker 2: lab at the engineering headquarters for Tesla and Palo Alto

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Speaker 2: in California, and they're saying it's all glass and it

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Speaker 2: looks super futuristic. But inside this place you will find

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Speaker 2: something very similar to maybe what you remember seeing. If

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Speaker 2: you watched the HBO series Westworld. These glass kind of

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Speaker 2: enclosures where stuff happens. You remember this on the.

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Speaker 3: Testing Yeah you remember this?

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Speaker 2: Yeah, okay, so but inside here for real, in real life,

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Speaker 2: there are dozens and dozens of human beings in a

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Speaker 2: motion capture suit, wearing a backpack that goes up to

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Speaker 2: about forty pounds depending on what the battery pack simulated

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Speaker 2: for the robot would be. And they do things like

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Speaker 2: stand in a rest position that the robot would be in,

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Speaker 2: which is specific and detailed, right, what an option just

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Speaker 2: looks like at rest? Then they move one arm up,

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Speaker 2: they lean down a little bit, pick up a cup

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Speaker 2: on the table, pick it up like a robot, hold

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Speaker 2: it like a robot, and then put it back down.

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Speaker 2: And guys, they have to do these tasks for hours

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Speaker 2: and hours and hours a day until they get a break.

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Speaker 2: They have to get on a daily basis, these human

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Speaker 2: test subjects, they're called data collectors.

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Speaker 3: They have to get.

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Speaker 2: Yeah, they have to get four hours of footage per

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Speaker 2: day of themselves as a robot doing things. And we're

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Speaker 2: talking about vacuuming. What does vacuuming look like? And holding

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Speaker 2: specific sizes of vacuums.

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Speaker 3: It reminds me a lot of mocap o cap.

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Speaker 2: I was thinking the same thing. It is amazing, the

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Speaker 2: whole thing is motion capture. But I'm just thinking about

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Speaker 2: this weird world we're in now, where we've got human

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Speaker 2: data annotators that are taking all of the stuff on

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Speaker 2: the web that these large language models are being trained

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Speaker 2: on and putting little notes about what this is, what

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Speaker 2: this is about, what is being said in this website

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Speaker 2: about this subject. We've got humans in lab scenarios doing

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Speaker 2: the things that robots will one day do to train

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Speaker 2: the robots. It feels so strange and wrong to me.

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Speaker 2: I just wanted to see if you guys felt that

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Speaker 2: same way, knowing that there are human beings training these

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Speaker 2: robots that will eventually be walking around and sold to

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Speaker 2: people that have houses like the ones on figure dot

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Speaker 2: AI's website. It's like, the most they've got this robot

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Speaker 2: walking around the most palatial, marble floored places, you know,

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Speaker 2: five million dollar homes on the exterior, walking something in

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Speaker 2: on the gravel driveway. I'm just trying to imagine what

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Speaker 2: the hell are these things going to actually be doing,

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Speaker 2: and who's going to be owning them? And why is

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Speaker 2: it such a big deal and how could such a

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Speaker 2: huge amount of the global economy being pushed into this stuff?

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Speaker 2: As this is the thing, right it? But somehow the

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Speaker 2: human toil still isn't enough to get a Tesla robot,

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Speaker 2: one of these optimist guys to do some pretty basic

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Speaker 2: things like go get a coke out of the refrigerator

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Speaker 2: for Mark Benioff.

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Speaker 3: And your series. And I love Matt the Matt Nole.

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Speaker 3: I love the comparison of a large language model sort

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Speaker 3: of plagiarizing earlier human thought. And now the idea is

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Speaker 3: that we are creating a thing that is taking an

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Speaker 3: l l M approach to physical action, right, so scraping

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Speaker 3: the idea of taking a coat from a refrigerator, or

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Speaker 3: scraping the idea of you know, scraping a floor or

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Speaker 3: brooming sweeping. Sweeping is the word I'm still learning English.

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Speaker 4: Same, you know. It's that's really interesting, because isn't the

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Speaker 4: isn't the crux of like why we don't yet have

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Speaker 4: Rosy the robot level, you know, autonomous assistance, the nature

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Speaker 4: of mapping to a space and avoiding all of the

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Speaker 4: random things that could pop up as impediments to a

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Speaker 4: simple task, and that's where the adaptation part is so important,

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Speaker 4: not just learning the moves right. It's bigger than that.

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Speaker 2: Well yeah, and yes, absolutely, mapping environment is massive. And

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Speaker 2: also and as you're saying the variables, you think about

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Speaker 2: the weimos and automatic driving cars and all that stuff

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Speaker 2: and attempting to avoid something like a cat that runs

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Speaker 2: across the street. I don't know if you guys saw

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Speaker 2: the there's a national news story about a cat that

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Speaker 2: got hit by a weimo and how big of a

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Speaker 2: deal it was.

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Speaker 3: I'm read up on cat news for sure.

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Speaker 5: We go.

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Speaker 4: Yeah, we follow that hashtag. Also, like you see those

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Speaker 4: weamos get spun up into a tizzy because of something

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Speaker 4: really simple and all of a sudden, they're like, does

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Speaker 4: not compute.

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Speaker 2: You know, it's a machine that's attempting to make the

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Speaker 2: next best decision and that is not an easy thing.

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Speaker 2: That's a reason why our brains are so awesome because

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Speaker 2: we can do that pretty quickly, and we can kind

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Speaker 2: of make multiple decisions at once and think about the

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Speaker 2: future decision, what that's going to mean, and the past

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Speaker 2: decisions that we made, and how this one's going to

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Speaker 2: be different than the one we made.

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Speaker 3: Before and not consciously think about that your background software humans,

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Speaker 3: it's doing a lot of work for you.

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Speaker 2: Yes, but the way our stuff is attenuated, right, all

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Speaker 2: these limbs we've got in our sense machinery that we've

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Speaker 2: got going, it's just been here. It's there's been millions

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Speaker 2: of years, if not hundreds and hundreds of thousands of

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Speaker 2: years of evolution that goes into making this stuff work.

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Speaker 3: With millions pro preception was a hard earned sensory trait.

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Speaker 4: It also reminds me of like the whole I know,

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Speaker 4: Kung Fu thing and the Matrix where all of a

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Speaker 4: sudden they just dump all of the wealth of human

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Speaker 4: knowledge into you know, Neo's brain and now all of

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Speaker 4: a sudden he can do all of the martial arts.

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Speaker 4: But that's like mega sci fi, and that's you know,

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Speaker 4: trying to do it in a human with some sort

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Speaker 4: of brain computer interface, also relying on the malleability of

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Speaker 4: that brain. And what they're trying to do is teach

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Speaker 4: robots how to be human really quickly, you know, rapidly

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Speaker 4: with this like information dump, and I just think it's

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Speaker 4: much more nuanced than that.

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Speaker 2: Yeah, No, I agree, I agree. It puts a pit

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Speaker 2: in my stomach when I when I read about it

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Speaker 2: and I watch this stuff, because you can just see

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Speaker 2: it coming. You've got You've got Elon Musk saying things like, oh,

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Speaker 2: we're gonna have five thousand of these optimist robots ready

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Speaker 2: by the end of the year. And this is him

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Speaker 2: talking to potential investors, right, and then he says, well,

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Speaker 2: hopefully we're going to be creating, you know, to the

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Speaker 2: scale of a million robots per year eventually. And it's

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Speaker 2: a multi trillion dollar market according to the people who

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Speaker 2: have money who invest money in things. But nobody can

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Speaker 2: fully grasp who's actually going to buy these and how

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Speaker 2: are you going to get that many in the marketplace

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Speaker 2: to make this a multi trillion dollar market.

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Speaker 3: And after a certain economic threshold, what consumer base will

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Speaker 3: exist to afford them?

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Speaker 2: Because all the jobs are going away, not all of them,

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Speaker 2: a lot of the jobs are going away, and the

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Speaker 2: people with money are deciding that that's the good thing

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Speaker 2: to do. And the people who use AI in their

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Speaker 2: corner offices, you know, in the top of Manhattan every

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Speaker 2: day say AI is the greatest. Oh my god, my

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Speaker 2: AI chatbot can do incredible things. Oh wow, this is

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Speaker 2: the future. Everyone should be doing this. Why do I

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Speaker 2: need this staff? They can't get can't collate all my

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Speaker 2: notes the way this chatbot can.

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Speaker 3: Also, I will statistically be dead by eighty four human

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Speaker 3: years no matter what the hell I do. So they're

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Speaker 3: not incentivized to think in the long term.

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Speaker 2: Oh for sure. So let's watch this quick clip that

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Speaker 2: was posted on x by mister Mark Benioff.

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Speaker 5: Uh.

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Speaker 2: Mark Bennioff, guy with money. No, I'm just sorry. You

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Speaker 2: could look him up. He's a very interesting person, Salesforce CEO.

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Speaker 2: He was hanging out, I guess with Elon Musk just

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Speaker 2: right next to him, and he was getting a little

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Speaker 2: demonstration of the optimist bot not long ago. So let's

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Speaker 2: watch this clip together. You can listen to it at home.

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Speaker 2: You can also find it on x slash. Bennioff being

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Speaker 2: Thrones guy.

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Speaker 4: He's related to David Benioff of Oh maybe, Oh, Game

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Speaker 4: of Thrones fame. They're like, I did not know second cousins.

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Speaker 2: That is very cool and congratulations to both of you guys.

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Speaker 4: I had to check.

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Speaker 2: Uh, let's watch this really quickly, just so we can

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Speaker 2: have an understanding of this is currently what an optimist

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Speaker 2: spot looks like when it's being shown off to a

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Speaker 2: potential investor.

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Speaker 5: Hey, optimists, what are you doing there?

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Speaker 4: Oh boy? Just chilling ready to help?

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Speaker 5: Hey optimist, do you know where I can get a coke?

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Speaker 4: Sorry? I don't.

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Speaker 3: And have a real time info, but I can take

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Speaker 3: Oh my god, this guy sounds like me.

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Speaker 5: Great, Yes, let's do that. Let's get a.

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Speaker 6: Lag is intolerable, awesome, Let's into the kitchen and okay, okay, go,

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Speaker 6: I think it's I think we need to give it

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Speaker 6: a bit more.

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Speaker 2: Okay, it's slowly turning and it's starting.

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Speaker 3: It's doing a little walk.

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Speaker 2: Listen your pants.

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Speaker 3: Yeah, clank clank clank.

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Speaker 2: Isn't that crazy?

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Speaker 3: It's not very impressive, right, I mean, are humans impressive?

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Speaker 3: It's I I think we don't. I think we should

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Speaker 3: not Dylan beat me here. I think we should not

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Speaker 3: immediately on prototype ideas. The precedent is there, Matt.

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Speaker 2: What do you mean?

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Speaker 4: Well, I just meant it's it's been a minute. I

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Speaker 4: just feel like they should be a little bit further

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Speaker 4: along than that. Just I don't know. It just seemed

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Speaker 4: really unimpressive, very lackluster, Like I don't know.

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Speaker 3: Sure, Yeah, the Wright brothers were unimpressive, right, I disagree.

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Speaker 4: They're playing did a thing this? This is this guy

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Speaker 4: just I don't know. I get that it's hard to

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Speaker 4: make these things. I get that the technology is tough,

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Speaker 4: but it's just like that's what you're trotting out, Like,

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Speaker 4: I mean, the lag alone is.

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Speaker 3: Just like really, I'm sorry, I just trot's an interesting

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Speaker 3: word too, because it's definitely trotting like it pooped it

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Speaker 3: span Well.

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Speaker 2: It's just strange to me, guys, because I seem to

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Speaker 2: recall this company called Boston Dynamics that I watched, well, yes,

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Speaker 2: I think we all watched in awe as Boston Dynamics

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Speaker 2: every year, every quarter would roll out more and more

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Speaker 2: and impressively dexterous robots of you know, varying forms. And

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Speaker 2: we've been watching that for well over a decade now,

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Speaker 2: like well over a decade. So it just makes me

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Speaker 2: wonder what the difference is, right, And I think one

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Speaker 2: of the primary things is early on in the Boston

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Speaker 2: Dynamics videos, you had bots that were they were kind

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Speaker 2: of chained up, right, they had a power source, they

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Speaker 2: were helped a little bit. Yeah, But then eventually you

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Speaker 2: move on to those I guess you could call it

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Speaker 2: the dog bots, the the four legged bots that were

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Speaker 2: crazy dexterous, and then not long ago at all, you've

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Speaker 2: got the ones I can't remember the name.

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Speaker 4: Of, the backpacky looking ones. Yeah, would like yeah, do

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Speaker 4: like flips and stuff.

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Speaker 2: I can do flips, it can analyze, you know, it's

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Speaker 2: terrain and make kind of jumps and make those decisions.

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Speaker 2: It just makes me wonder. I wonder if it's a

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Speaker 2: price point thing and they're just going into a trying

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Speaker 2: to figure out how do you make something that is

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Speaker 2: going to be affordable by somebody in a mansion.

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Speaker 4: But like, also, I know where the kitchen is, you

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Speaker 4: know what I mean, Like, if you can't get me

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Speaker 4: the coke, then what use are you. It's just an

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Speaker 4: odd demonstration for that to Who's that going to impress?

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Speaker 4: Is my question? And you're right, Matt, it is all

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Speaker 4: of that history of remembering those videos of those other

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Speaker 4: prototypes and just being really that yeah, I'm not trying.

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Speaker 3: It's not a bad question, though, I think it's an

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Speaker 3: astute question. We can say this way. Look, the primary

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Speaker 3: issue here is a technology arms race, right, so now

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Speaker 3: the pitch for optimists appears to be we can make

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Speaker 3: some kind of whole home grown robot that is doing

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Speaker 3: things that other countries can already do. So that's our lag, right.

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Speaker 3: I think Nola used the word lag earlier, that was

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Speaker 3: not in direct If you look at the parable of

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Speaker 3: what's happening here, you can also see earlier historical precedence,

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Speaker 3: like the Taming of the Horse. Right, There's a reason

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Speaker 3: Jengis caught rolled through with a cavalry and it took

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Speaker 3: Western Europe a ton of time to figure it out.

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Speaker 3: So maybe this is a marketing grift. And I hate

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Speaker 3: to say it, you guys, but our buddy Elon doesn't

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Speaker 3: have the best track record for discovering stuff. He has

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Speaker 3: a track record for appropriating stuff.

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Speaker 4: Well, he also has a track record of way over

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Speaker 4: promising and way under delivering.

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Speaker 2: Yeah, and then just throwing a bunch of humans at

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Speaker 2: the hard problem. Right, And that's currently what's happening in

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Speaker 2: the space program in Tesla Optimus. But he's in a

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Speaker 2: board room very recently, what is this? This was on

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Speaker 2: the Verge from October twenty third. There was an earnings

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Speaker 2: call where Tesla CEO Ela Musk basically said I need

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Speaker 2: a trillion dollar package pay package to make this robot

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Speaker 2: army if you can go through and get the full quote.

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Speaker 2: Here's one My fundamental concern with regard to how much

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Speaker 2: voting control I have in Tesla, is if I go

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Speaker 2: ahead and build this enormous robot army, can I just

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Speaker 2: be ousted at some point in the future. That's my

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Speaker 2: biggest concern. The idea that the creator of the robot

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Speaker 2: army would no longer have full control of the robot

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Speaker 2: army he has built. It's kind of freaky.

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Speaker 4: Yeah, well, as if that's supposed to be how it

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Speaker 4: works anyway, right, Like you know what I mean? Like,

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Speaker 4: is he meant to be like their overlord? Like? Is

436
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Speaker 4: that what he's saying? I need control? Control? In what respect?

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Speaker 4: Are you not designing and building a product that can

438
00:24:01,560 --> 00:24:05,439
Speaker 4: then be used by other organizations and entities This idea

439
00:24:05,440 --> 00:24:08,119
Speaker 4: of control, I'm a little confused about what he means.

440
00:24:08,119 --> 00:24:09,800
Speaker 4: It almost makes it sound like he wants to be

441
00:24:09,840 --> 00:24:11,400
Speaker 4: the one wielding the robot army.

442
00:24:12,480 --> 00:24:16,160
Speaker 2: One could gather that. I would say, without a lot

443
00:24:16,200 --> 00:24:20,520
Speaker 2: of elucidation on what he meant, I ran waiver on this. Guys.

444
00:24:20,920 --> 00:24:23,760
Speaker 2: Elon Musk says he needs one trillion to control Tesla's

445
00:24:23,840 --> 00:24:26,040
Speaker 2: robot Army. That's the Verge. That's one of the ones

446
00:24:26,080 --> 00:24:29,240
Speaker 2: you can look up. Also secret elon Musk Lab collecting

447
00:24:29,320 --> 00:24:32,000
Speaker 2: data on every human activity to train robots. That's the

448
00:24:32,000 --> 00:24:34,720
Speaker 2: futurism article. And then the Business Insider one that that

449
00:24:34,800 --> 00:24:37,880
Speaker 2: one's based on is inside the glass walled Tesla lab,

450
00:24:37,880 --> 00:24:40,720
Speaker 2: where workers train the Optimist robot to act like a human.

451
00:24:40,960 --> 00:24:44,600
Speaker 2: That's from Business Insider. Those are the main stories. We'll

452
00:24:44,600 --> 00:24:47,440
Speaker 2: be right back afterward from our sponsor.

453
00:24:51,920 --> 00:24:55,280
Speaker 4: And we've returned. I got two today. I think, both

454
00:24:55,320 --> 00:24:57,800
Speaker 4: of which might be worth the larger discussion, so I'm

455
00:24:57,800 --> 00:25:00,639
Speaker 4: going to keep each of them a little bit bite size.

456
00:25:00,640 --> 00:25:02,359
Speaker 4: But we're going to start with a topic that I

457
00:25:02,400 --> 00:25:04,959
Speaker 4: don't think we discussed in the first place, the recent

458
00:25:05,160 --> 00:25:10,880
Speaker 4: daylight heist at the famous Louver Museum in Paris, France

459
00:25:11,240 --> 00:25:15,640
Speaker 4: on October nineteenth, where thieves managed to steal royal jewels

460
00:25:16,320 --> 00:25:19,320
Speaker 4: in the middle of the day, like during visiting hours.

461
00:25:19,640 --> 00:25:22,159
Speaker 4: I believe there was an evacuation that was called. I

462
00:25:22,280 --> 00:25:26,240
Speaker 4: don't have too much information on exactly how that went down.

463
00:25:26,280 --> 00:25:29,040
Speaker 4: That's sort of outside the scope of this story, but

464
00:25:29,119 --> 00:25:33,400
Speaker 4: it did happen, and what also happened was it revealed

465
00:25:33,520 --> 00:25:37,000
Speaker 4: or brought to light some past investigations from twenty fourteen

466
00:25:37,880 --> 00:25:47,280
Speaker 4: about the organization or the museum's incredibly outdated and dangerously

467
00:25:48,680 --> 00:25:54,440
Speaker 4: vulnerable it security systems. Reading here from Let's see Business

468
00:25:54,600 --> 00:26:00,480
Speaker 4: Standard in a piece by Rim Jim Singh, this is

469
00:26:00,520 --> 00:26:02,720
Speaker 4: out of New Delhi, and he says here The Louver,

470
00:26:02,760 --> 00:26:04,880
Speaker 4: one of the world's most visited and famous museums, faced

471
00:26:04,880 --> 00:26:07,280
Speaker 4: a shocking theft on October nineteen. Thieves managed to steal

472
00:26:07,280 --> 00:26:11,200
Speaker 4: priceless royal jewels in broad daylight, raising serious security questions

473
00:26:11,200 --> 00:26:14,520
Speaker 4: about the museum. Following the heist, investigations revealed that the

474
00:26:14,560 --> 00:26:17,960
Speaker 4: Louver had been struggling with serious cybersecurity and maintenance issues

475
00:26:17,960 --> 00:26:20,679
Speaker 4: for over a decade, many of which had been repeatedly

476
00:26:20,720 --> 00:26:25,320
Speaker 4: flagged but never fixed. According to French daily paper Liberation.

477
00:26:26,520 --> 00:26:29,919
Speaker 4: Those aforementioned vulnerabilities that came to light in a previous

478
00:26:29,960 --> 00:26:34,040
Speaker 4: investigation were from December of twenty fourteen, when the National

479
00:26:34,080 --> 00:26:38,480
Speaker 4: Agency for the Security of Information Systems, a French agency,

480
00:26:39,040 --> 00:26:44,360
Speaker 4: audited the museum's security systems, tested the stability and vulnerability

481
00:26:44,400 --> 00:26:47,800
Speaker 4: of their network and other critical systems that included video

482
00:26:47,840 --> 00:26:52,000
Speaker 4: surveillance systems, alarms, and access controls. And a twenty six

483
00:26:52,080 --> 00:26:56,120
Speaker 4: page confidential report that was released. They found that typing

484
00:26:56,160 --> 00:27:02,000
Speaker 4: the word louver could get someone into their entire video surveillances,

485
00:27:02,960 --> 00:27:06,600
Speaker 4: while typing in the word fails granted access to a

486
00:27:07,040 --> 00:27:12,719
Speaker 4: proprietary software system developed by the Fales Group. Yeah, so

487
00:27:13,359 --> 00:27:16,760
Speaker 4: they were warned the LOUP that the applications and systems

488
00:27:16,760 --> 00:27:21,360
Speaker 4: deployed on the security network present numerous vulnerabilities, also referring

489
00:27:21,400 --> 00:27:26,040
Speaker 4: to outdated operating systems that needed upgrading. Literally using Windows

490
00:27:26,119 --> 00:27:29,760
Speaker 4: two thousand, You guys might recall that relatively short lived

491
00:27:30,119 --> 00:27:33,639
Speaker 4: OS out of Microsoft, a system, by the way, that

492
00:27:33,680 --> 00:27:38,280
Speaker 4: has not been supported in terms of updates, etc. For

493
00:27:39,400 --> 00:27:42,879
Speaker 4: NIH on over a decade. By twenty seventeen, the article

494
00:27:42,920 --> 00:27:46,080
Speaker 4: goes on another audit by the National Institute for Advanced

495
00:27:46,080 --> 00:27:49,320
Speaker 4: Studies and Security and Justice found many of the same

496
00:27:49,359 --> 00:27:52,520
Speaker 4: issues persisted. So this is two years after that initial

497
00:27:52,560 --> 00:27:56,840
Speaker 4: twenty six page review. The report warned that quote serious

498
00:27:56,840 --> 00:27:59,679
Speaker 4: deficiencies were observed in the overall system that the museum

499
00:27:59,720 --> 00:28:02,919
Speaker 4: could no longer ignore the potential risk of a breach.

500
00:28:03,640 --> 00:28:08,240
Speaker 4: This forty page report also highlighted issues including accessible rooftop

501
00:28:08,359 --> 00:28:10,400
Speaker 4: panels you know the kind you might see the pink

502
00:28:10,440 --> 00:28:14,560
Speaker 4: panther lowering self down from or like a mission impossible situation,

503
00:28:14,760 --> 00:28:20,879
Speaker 4: outdated video surveillance rooftops that were accessible during renovation work. Also,

504
00:28:20,920 --> 00:28:24,840
Speaker 4: it mentioned computers that run obsolete operating systems like Windows

505
00:28:24,880 --> 00:28:30,480
Speaker 4: two thousand and XP running without antivirus software or any

506
00:28:30,600 --> 00:28:36,080
Speaker 4: kind of you know, standardized password protection policy. As part

507
00:28:36,119 --> 00:28:39,080
Speaker 4: of a corporation, we are all. It's annoying when it happens,

508
00:28:39,080 --> 00:28:40,400
Speaker 4: but it makes a lot of sense that we get

509
00:28:40,400 --> 00:28:43,760
Speaker 4: notifications for changing our passwords periodically, and it has to

510
00:28:43,800 --> 00:28:49,040
Speaker 4: be relative different enough from the previous one. Some organizations

511
00:28:49,080 --> 00:28:52,040
Speaker 4: also require strong passwords that are autogenerated that like have

512
00:28:52,160 --> 00:28:54,320
Speaker 4: like tons of crazy keys. But here it's looking like

513
00:28:54,440 --> 00:28:56,480
Speaker 4: the louver or the word louve would have gotten you

514
00:28:56,520 --> 00:28:57,560
Speaker 4: int some key systems.

515
00:28:58,560 --> 00:28:59,600
Speaker 2: So that's really that.

516
00:28:59,680 --> 00:29:03,080
Speaker 4: I just I think, yeah, just really quickly a little

517
00:29:03,080 --> 00:29:07,120
Speaker 4: more details about the heist. Tappened on October nineteenth. Burglars

518
00:29:07,520 --> 00:29:10,240
Speaker 4: robbed the place in broad daylight at nine thirty am

519
00:29:10,360 --> 00:29:13,200
Speaker 4: after opening. They used a truck mounted ladder to get

520
00:29:13,200 --> 00:29:17,080
Speaker 4: to a second floor balcony, broke a window using grinders

521
00:29:17,520 --> 00:29:20,280
Speaker 4: like you would, you know, like the pink panther triggered

522
00:29:20,320 --> 00:29:22,880
Speaker 4: alarms and entered the Apollo Gallery, which is where the

523
00:29:22,960 --> 00:29:29,360
Speaker 4: Crown Jewels are housed French historical artifacts. Visitors were quickly

524
00:29:29,400 --> 00:29:32,920
Speaker 4: evacuated as police arrived, but the robbers had already escaped.

525
00:29:33,480 --> 00:29:37,840
Speaker 4: This included nine pieces of royal jewelry from the Napoleonic era,

526
00:29:38,040 --> 00:29:42,160
Speaker 4: as well as items from Queen Marie Amalie, Queen Hortense,

527
00:29:42,240 --> 00:29:46,680
Speaker 4: and Empress Marie Louise. The tierra in fact of Empress

528
00:29:46,720 --> 00:29:50,120
Speaker 4: Eugene encrusted with two thousand diamonds and two hundred pearls,

529
00:29:50,240 --> 00:29:54,280
Speaker 4: was stolen as well priceless priceless stuff. Manuel Macrone, the

530
00:29:54,360 --> 00:29:57,600
Speaker 4: President of France, referred to the heist as a attack

531
00:29:57,720 --> 00:30:01,400
Speaker 4: on our heritage that we cherish because it is our history.

532
00:30:02,080 --> 00:30:03,920
Speaker 4: So this is really a big problem for the loof

533
00:30:03,920 --> 00:30:06,080
Speaker 4: and it seems like something's gonna have to get someone's

534
00:30:06,080 --> 00:30:06,880
Speaker 4: gonna have to answer for this.

535
00:30:07,480 --> 00:30:11,240
Speaker 2: Well, there are four suspects I think as of today

536
00:30:11,240 --> 00:30:14,440
Speaker 2: November fifth, that are any let's hear it. Well, there

537
00:30:14,440 --> 00:30:18,160
Speaker 2: are four suspects in custody, but the whereabouts currently of

538
00:30:18,200 --> 00:30:20,080
Speaker 2: the royal jewels and a lot of the other stuff

539
00:30:20,120 --> 00:30:23,280
Speaker 2: that was taken is unknown. It seems like those folks

540
00:30:23,280 --> 00:30:27,000
Speaker 2: aren't talking. They appear to be well, at least according

541
00:30:27,000 --> 00:30:30,240
Speaker 2: to several sources. I'm looking at ABC News right now,

542
00:30:30,320 --> 00:30:33,280
Speaker 2: and before I was looking at The New York Times

543
00:30:33,320 --> 00:30:36,720
Speaker 2: has something on the power of DNA databases in solving crimes.

544
00:30:37,480 --> 00:30:43,719
Speaker 2: CNN calls them local petty criminals in Paris. It seems

545
00:30:43,760 --> 00:30:46,240
Speaker 2: like they've at least got a few people, and if

546
00:30:46,280 --> 00:30:49,560
Speaker 2: they actually talk, then you might get answers. Otherwise, it

547
00:30:49,600 --> 00:30:52,440
Speaker 2: seems like maybe some of that stuff already got fenced.

548
00:30:52,760 --> 00:30:54,600
Speaker 4: Oh, I wouldn't be surprised if they're not. They're not

549
00:30:54,640 --> 00:30:56,960
Speaker 4: able to recover it, certainly. I think they're going to

550
00:30:57,000 --> 00:30:59,240
Speaker 4: get some convictions or at very least find someone to

551
00:30:59,280 --> 00:31:01,880
Speaker 4: take the fall for pr purposes. But I would be

552
00:31:01,880 --> 00:31:03,760
Speaker 4: surprised if they got all of the pieces back.

553
00:31:03,840 --> 00:31:05,680
Speaker 2: But we'll see very weird stuff.

554
00:31:06,000 --> 00:31:09,160
Speaker 4: M moving on to one then, I think that you'll

555
00:31:09,160 --> 00:31:11,760
Speaker 4: be interested in it may well be something that's very

556
00:31:11,840 --> 00:31:16,560
Speaker 4: much on your radar uput entirely intended. Ukraine's game style

557
00:31:16,720 --> 00:31:21,520
Speaker 4: drone system changes how soldiers track and plan strikes coming from.

558
00:31:21,800 --> 00:31:24,360
Speaker 3: Could you tell us about more about this, like are

559
00:31:24,400 --> 00:31:25,320
Speaker 3: they getting points?

560
00:31:25,600 --> 00:31:28,600
Speaker 4: Yes, get it's crazy man Amazon for war is what

561
00:31:28,640 --> 00:31:31,960
Speaker 4: it's being referred to. It's a platform called Brave one

562
00:31:32,400 --> 00:31:39,160
Speaker 4: that allows soldiers to earn points for carrying out successful

563
00:31:39,240 --> 00:31:41,520
Speaker 4: drone attacks. And I swear I had to do a

564
00:31:41,640 --> 00:31:47,320
Speaker 4: double triple quadruple take on this and exchange them for weaponry.

565
00:31:48,480 --> 00:31:53,640
Speaker 4: It's like a marketplace of like drones and artillery, and

566
00:31:53,760 --> 00:31:57,880
Speaker 4: it's a point system that's basically like almost like you know,

567
00:31:57,960 --> 00:31:59,720
Speaker 4: Dave and Buster points where you can go into the

568
00:31:59,760 --> 00:32:02,560
Speaker 4: little thing and exchange it for like, you know, a

569
00:32:02,600 --> 00:32:05,160
Speaker 4: million points might get you a PlayStation or something like that.

570
00:32:05,200 --> 00:32:07,080
Speaker 3: Oh, thank you for the DMB reference.

571
00:32:07,240 --> 00:32:10,320
Speaker 4: Well, we love DMB and the points system I think

572
00:32:10,400 --> 00:32:12,760
Speaker 4: is appropriate there, although it seems like this one might

573
00:32:12,800 --> 00:32:14,720
Speaker 4: be a little more weighted in the favor of the

574
00:32:14,880 --> 00:32:18,719
Speaker 4: point earner, because it's an incentive that seems to be working.

575
00:32:19,520 --> 00:32:24,800
Speaker 4: Writing for Interesting Engineering, Sujita Sinha says a video game

576
00:32:24,800 --> 00:32:28,720
Speaker 4: style drone attack system has gone viral among Ukrainian military units,

577
00:32:28,760 --> 00:32:33,280
Speaker 4: turning real life warfare into a digital competition. The first

578
00:32:33,400 --> 00:32:38,200
Speaker 4: Deputy Prime Minister of Ukraine, Mikhailo Federov, spoke to the

579
00:32:38,200 --> 00:32:42,520
Speaker 4: Guardian newspaper saying that drone teams killed or wounded eighteen

580
00:32:42,600 --> 00:32:46,760
Speaker 4: thousand Russian soldiers. In September, around four hundred drone units

581
00:32:46,880 --> 00:32:49,160
Speaker 4: now take part in the system, up from only ninety

582
00:32:49,160 --> 00:32:53,200
Speaker 4: five in August. He says it's truly become incredibly popular.

583
00:32:53,400 --> 00:32:56,960
Speaker 4: The system works through this online platform called Brave one,

584
00:32:57,000 --> 00:33:00,120
Speaker 4: which is often described as Amazon for war, which for

585
00:33:00,280 --> 00:33:03,760
Speaker 4: one hundred different kinds of drones and other autonomous systems.

586
00:33:04,320 --> 00:33:07,200
Speaker 4: There is a leader board for this, y'all. Truly like

587
00:33:07,400 --> 00:33:10,440
Speaker 4: you know, like in some kind of you know, call

588
00:33:10,480 --> 00:33:15,400
Speaker 4: of duty situation. The Bonus system was launched over a

589
00:33:15,440 --> 00:33:18,360
Speaker 4: year ago. The article goes on to motivate drone teams

590
00:33:18,360 --> 00:33:23,760
Speaker 4: and enhance efficiency on the battlefield. It also apparently generates

591
00:33:23,800 --> 00:33:28,720
Speaker 4: some very interesting and allegedly useful information in terms of

592
00:33:28,840 --> 00:33:32,080
Speaker 4: mapping out the way these attacks are going down. It

593
00:33:32,080 --> 00:33:35,040
Speaker 4: adds this level of transparency, and we'll get to that

594
00:33:35,080 --> 00:33:38,280
Speaker 4: in a second. The program now extends beyond combat drones.

595
00:33:38,480 --> 00:33:42,560
Speaker 4: Artillery and reconnaissance teams also earn points for confirmed strikes

596
00:33:42,680 --> 00:33:46,920
Speaker 4: or for spotting enemy targets. Even logistics units can now

597
00:33:47,160 --> 00:33:51,400
Speaker 4: score points using autonomous vehicles. It's described in the piece

598
00:33:51,440 --> 00:33:56,400
Speaker 4: here as uber targeting, not like awesome targeting, but uber

599
00:33:56,600 --> 00:33:59,280
Speaker 4: like the ride hailing app. The idea that you drop

600
00:33:59,320 --> 00:34:02,400
Speaker 4: a pin on a map, like you for calling yourself

601
00:34:02,400 --> 00:34:06,240
Speaker 4: an uber, and then the next available unit picks up

602
00:34:06,240 --> 00:34:11,759
Speaker 4: on that pen and carries out the attack. It says

603
00:34:11,760 --> 00:34:15,200
Speaker 4: here points are awarded for specific types of missions. Killing

604
00:34:15,239 --> 00:34:19,120
Speaker 4: an enemy drone operator, as an example, earns twenty five points,

605
00:34:19,640 --> 00:34:23,200
Speaker 4: while capturing a Russian soldier earns one hundred and twenty points.

606
00:34:23,920 --> 00:34:27,719
Speaker 4: And this has been approved directly by the Ukrainian cabinet.

607
00:34:28,560 --> 00:34:32,160
Speaker 4: And apparently this is a I think I knew to

608
00:34:32,160 --> 00:34:35,319
Speaker 4: some degree the level of technology being employed in this war,

609
00:34:36,120 --> 00:34:39,759
Speaker 4: but their systems on the Ukrainian side have gotten increasingly

610
00:34:39,800 --> 00:34:42,600
Speaker 4: more technical. They've been at war for four years now

611
00:34:42,840 --> 00:34:48,040
Speaker 4: and they are finding ways to They're looking for efficiencies.

612
00:34:48,160 --> 00:34:50,799
Speaker 4: Feder Offset, We're thinking of this as just part of

613
00:34:50,840 --> 00:34:52,000
Speaker 4: our everyday job.

614
00:34:52,560 --> 00:34:57,560
Speaker 2: So you get points by killing enemy infantry, you get

615
00:34:58,400 --> 00:34:59,760
Speaker 2: use those points to get more drones.

616
00:35:00,719 --> 00:35:03,160
Speaker 4: It's like a video game system where you can upgrade

617
00:35:03,160 --> 00:35:04,960
Speaker 4: your car and a racer or something like that.

618
00:35:05,000 --> 00:35:07,960
Speaker 3: You know, you also get points by doing a Scottie

619
00:35:07,960 --> 00:35:13,560
Speaker 3: Pippen move and Ali you being other folks by identifying target.

620
00:35:13,320 --> 00:35:15,359
Speaker 4: Corrects the recon aspect of it, for sure.

621
00:35:15,440 --> 00:35:17,120
Speaker 3: Yeah, that's correct point of order.

622
00:35:17,280 --> 00:35:22,120
Speaker 2: How does one capture an enemy infantry person with a drone?

623
00:35:22,239 --> 00:35:25,799
Speaker 2: You like, have a drone that posentually has explosives and

624
00:35:25,960 --> 00:35:28,280
Speaker 2: you sit it in front of them and a camera

625
00:35:28,360 --> 00:35:29,120
Speaker 2: and location.

626
00:35:29,920 --> 00:35:31,960
Speaker 3: We ultimately have to send the people out.

627
00:35:32,440 --> 00:35:34,600
Speaker 4: Yeah, there's got to be some on the ground aspect

628
00:35:34,640 --> 00:35:38,080
Speaker 4: of that. I don't fully understand if the drone part

629
00:35:38,120 --> 00:35:40,759
Speaker 4: of it is. I think it's just I don't know.

630
00:35:40,800 --> 00:35:43,480
Speaker 4: It seems to me like the drones are what's capturing

631
00:35:43,560 --> 00:35:46,520
Speaker 4: it as well, Like in terms of verified you know,

632
00:35:46,680 --> 00:35:49,480
Speaker 4: there's like video evidence that this has happened, and then

633
00:35:49,520 --> 00:35:51,319
Speaker 4: that triggers earning the points or.

634
00:35:51,239 --> 00:35:51,880
Speaker 2: What have you.

635
00:35:52,000 --> 00:35:56,360
Speaker 3: Right, No, I'd say it's similar to the historical precedent

636
00:35:56,400 --> 00:36:01,240
Speaker 3: here would be the relationship between the corvids and the canids,

637
00:36:01,280 --> 00:36:04,880
Speaker 3: specifically ravens and wolves. So the ravens are kind of

638
00:36:04,880 --> 00:36:09,239
Speaker 3: the first drones, right for the wolves. So in this comparison,

639
00:36:10,320 --> 00:36:14,480
Speaker 3: you send out the drone which identifies the target, what

640
00:36:14,880 --> 00:36:19,360
Speaker 3: makes the helps the wolves know where to go. I

641
00:36:19,440 --> 00:36:24,640
Speaker 3: don't think that's new. That is a relatively workable and

642
00:36:25,000 --> 00:36:29,279
Speaker 3: trivial sort of symbiosis. But the big issue that I

643
00:36:29,320 --> 00:36:33,400
Speaker 3: think you're raising here is the idea of gamifying war

644
00:36:33,680 --> 00:36:38,880
Speaker 3: and gamifying human lots. Right, we're paying for that leader

645
00:36:38,920 --> 00:36:40,600
Speaker 3: board and blood, you know.

646
00:36:40,560 --> 00:36:44,040
Speaker 4: It's interesting. Then to that point, my girlfriend works with

647
00:36:44,120 --> 00:36:49,360
Speaker 4: kids teaching math and was really surprised when speaking to

648
00:36:49,440 --> 00:36:52,359
Speaker 4: a young person and asking what they want to do,

649
00:36:52,640 --> 00:36:54,759
Speaker 4: they said they want to go into engineering. What kind

650
00:36:54,760 --> 00:36:59,320
Speaker 4: of engineering you might ask, weaponry? Like making that jump

651
00:36:59,440 --> 00:37:02,360
Speaker 4: immediately because we were talking and it's sort of like typically

652
00:37:02,440 --> 00:37:05,239
Speaker 4: you think of maybe you get an engineering degree and

653
00:37:05,280 --> 00:37:07,760
Speaker 4: then you specialize or you get placed in a company

654
00:37:07,840 --> 00:37:09,640
Speaker 4: that does that kind of stuff. But to have these

655
00:37:09,760 --> 00:37:13,279
Speaker 4: kids already thinking weaponry, I want to do that. I

656
00:37:13,320 --> 00:37:14,799
Speaker 4: think a lot of it has to do with the

657
00:37:15,160 --> 00:37:18,680
Speaker 4: appeal of the gamification of it all. And maybe that's

658
00:37:19,120 --> 00:37:20,960
Speaker 4: a little boomer of me, you know, it's all about

659
00:37:21,000 --> 00:37:23,560
Speaker 4: violent video games or whatever, but I think there's something

660
00:37:23,560 --> 00:37:26,239
Speaker 4: to it perhaps, And this is a great line here

661
00:37:26,280 --> 00:37:28,759
Speaker 4: from the piece, and then we'll move on. That explains

662
00:37:28,800 --> 00:37:31,879
Speaker 4: what we were talking about. Feder Off goes on, thanks

663
00:37:31,880 --> 00:37:34,960
Speaker 4: to the points, we're actually starting to understand more about

664
00:37:34,960 --> 00:37:38,320
Speaker 4: what's happening in the battlefield. All the strikes have to

665
00:37:38,360 --> 00:37:41,640
Speaker 4: be verified by video, and the government then takes that data,

666
00:37:41,840 --> 00:37:46,480
Speaker 4: analyzes which scenarios played out using which combinations of weapons

667
00:37:46,520 --> 00:37:50,080
Speaker 4: and tactics and recon and all of that, and then

668
00:37:50,120 --> 00:37:55,480
Speaker 4: they're able to use that data to further fine tune

669
00:37:55,520 --> 00:38:00,720
Speaker 4: things for future you know, conflicts or future ever these

670
00:38:00,840 --> 00:38:04,839
Speaker 4: kind of skirmishes, and apparently it's doing gangbusters for the

671
00:38:04,840 --> 00:38:09,560
Speaker 4: morale of troops. Experts from the Royal United Service Institute

672
00:38:10,000 --> 00:38:15,719
Speaker 4: are urging NATO countries to not do this. It's definitely

673
00:38:16,040 --> 00:38:17,360
Speaker 4: has its detractors.

674
00:38:17,680 --> 00:38:22,879
Speaker 2: It's pretty weird because I know the POLE headmaster called

675
00:38:22,920 --> 00:38:25,560
Speaker 2: in and specifically guys gave me a bunch of crap

676
00:38:25,640 --> 00:38:27,920
Speaker 2: for playing Call of Duty Mobile and about how it's

677
00:38:27,920 --> 00:38:30,439
Speaker 2: a trash Call of Duty game and only kids play

678
00:38:30,440 --> 00:38:32,759
Speaker 2: it and that kind of thing. But I know firsthand

679
00:38:32,800 --> 00:38:36,759
Speaker 2: the poll of earning those points, right, And in that game,

680
00:38:36,800 --> 00:38:40,200
Speaker 2: you earn points by killing other players, right, That's how

681
00:38:40,239 --> 00:38:42,080
Speaker 2: you get those points, And when you get enough points,

682
00:38:42,120 --> 00:38:44,520
Speaker 2: you get new stuff, And I know exactly what that

683
00:38:44,520 --> 00:38:47,120
Speaker 2: feels like. I cannot imagine what that would be like

684
00:38:47,280 --> 00:38:50,160
Speaker 2: in this real world scenario, but I do know that

685
00:38:50,200 --> 00:38:52,120
Speaker 2: POLE is pretty universal.

686
00:38:52,200 --> 00:38:55,680
Speaker 4: Well, it's that dopamine hit and that, like, yeah, gamification

687
00:38:56,040 --> 00:39:00,000
Speaker 4: is really useful. I love gamifying my fitness stuff for example,

688
00:39:00,320 --> 00:39:03,440
Speaker 4: or just like routine things or you know, trying to optimize,

689
00:39:03,440 --> 00:39:06,680
Speaker 4: find efficiencies, you know, get your patterns locked in so

690
00:39:06,680 --> 00:39:08,680
Speaker 4: that you're getting the most bang for your buck, like

691
00:39:08,800 --> 00:39:11,040
Speaker 4: life wise. I get the I get the pull of

692
00:39:11,040 --> 00:39:13,040
Speaker 4: that too. And also been playing a hell of a

693
00:39:13,040 --> 00:39:15,440
Speaker 4: lot of Sonic Racing Cross Worlds where you earn these

694
00:39:15,480 --> 00:39:17,759
Speaker 4: tickets every time you complete a raise that you can

695
00:39:17,800 --> 00:39:20,880
Speaker 4: then save up and buy cool stuff for your racing

696
00:39:20,960 --> 00:39:21,840
Speaker 4: pod or whatever.

697
00:39:22,000 --> 00:39:22,920
Speaker 5: Mm hm.

698
00:39:23,440 --> 00:39:25,120
Speaker 4: So yeah, there you go. I think those are two.

699
00:39:25,120 --> 00:39:27,120
Speaker 4: Maybe we talk a little bit about more in the future.

700
00:39:27,160 --> 00:39:29,080
Speaker 4: Keep an eye on. Let's take a quick break here

701
00:39:29,080 --> 00:39:30,640
Speaker 4: with from our sponsor, and then we'll come back with

702
00:39:30,719 --> 00:39:37,160
Speaker 4: the last segment in today's a Strange News episode.

703
00:39:38,120 --> 00:39:40,520
Speaker 3: And we have returned with the last act of our

704
00:39:40,560 --> 00:39:44,120
Speaker 3: weekly Strange News segment. Who went to the grocery store?

705
00:39:44,600 --> 00:39:47,919
Speaker 3: Who went to the grocery store? That's out anytime? Yeah,

706
00:39:48,040 --> 00:39:51,400
Speaker 3: I think I went always. Yeah, historically I like a

707
00:39:51,440 --> 00:39:55,680
Speaker 3: grocery shop. I do so you guys order your stuff

708
00:39:55,680 --> 00:39:56,240
Speaker 3: in person.

709
00:39:57,080 --> 00:39:59,359
Speaker 4: I get delivery occasionally, but I actually like a trip

710
00:39:59,400 --> 00:40:00,120
Speaker 4: to the grocery store.

711
00:40:00,440 --> 00:40:01,040
Speaker 2: I like you too.

712
00:40:01,120 --> 00:40:03,080
Speaker 4: I like doing small shops a couple times a week

713
00:40:03,160 --> 00:40:04,399
Speaker 4: rather than like massive ones.

714
00:40:04,840 --> 00:40:08,480
Speaker 3: It's one of those rare third spaces that we talk

715
00:40:08,560 --> 00:40:12,279
Speaker 3: about in sociology. So for our last act of this

716
00:40:12,360 --> 00:40:16,080
Speaker 3: weekly Strange News segment, we wanted to introduce you to

717
00:40:16,200 --> 00:40:19,480
Speaker 3: something that might get us in trouble and indeed is

718
00:40:19,560 --> 00:40:24,000
Speaker 3: a setup for a episode in the future. Gosh, let

719
00:40:24,000 --> 00:40:28,360
Speaker 3: me step back, guys. Remember when we talked about dynamic pricing? Yeah,

720
00:40:28,600 --> 00:40:32,479
Speaker 3: not too long ago. Can we define dynamic pricing just like.

721
00:40:32,560 --> 00:40:36,200
Speaker 4: You know on the fly price fluctuations in real time

722
00:40:36,440 --> 00:40:40,919
Speaker 4: of things like fast food? Specifically, in this case, things

723
00:40:40,960 --> 00:40:44,160
Speaker 4: like groceries. The idea of even some places wanting to

724
00:40:44,320 --> 00:40:49,160
Speaker 4: change the price tag spots on the shelves to a

725
00:40:49,239 --> 00:40:52,640
Speaker 4: digital readout that can fluctuate in real time based on

726
00:40:53,000 --> 00:40:55,640
Speaker 4: the market, based on supply and demand. All of that

727
00:40:55,680 --> 00:40:58,239
Speaker 4: stuff never seemed like a great thing for consumers.

728
00:40:58,600 --> 00:41:00,160
Speaker 2: And the last time we talked about I think we

729
00:41:00,239 --> 00:41:04,040
Speaker 2: even talked about personalized pricing in a store when you

730
00:41:04,080 --> 00:41:04,600
Speaker 2: walk through.

731
00:41:05,640 --> 00:41:08,880
Speaker 3: So if we want to play a game, I would

732
00:41:09,640 --> 00:41:13,279
Speaker 3: I would recommend if you're not driving, folks, pull up

733
00:41:13,400 --> 00:41:17,359
Speaker 3: your ride share app of choice, right, if you have

734
00:41:17,400 --> 00:41:20,719
Speaker 3: an Uber or Lyft, do you guys use Uber Lift?

735
00:41:21,080 --> 00:41:24,480
Speaker 3: Matt Nol typically use Lyft because it turns delta skuyme old,

736
00:41:25,239 --> 00:41:28,800
Speaker 3: Oh nice, that's the game. Knowle's putting you on game gull.

737
00:41:28,760 --> 00:41:31,880
Speaker 4: Plarty points man points.

738
00:41:32,280 --> 00:41:35,480
Speaker 3: So here's an interesting thing you can do to see

739
00:41:35,640 --> 00:41:39,960
Speaker 3: the dynamic pricing conspiracy at work. We're going to show

740
00:41:40,000 --> 00:41:42,320
Speaker 3: you how the rabbit comes out of the hat. Folks,

741
00:41:42,920 --> 00:41:46,080
Speaker 3: go ahead and pull up these apps if you have them.

742
00:41:46,160 --> 00:41:50,360
Speaker 3: If you have one, great, if you have both. More interesting.

743
00:41:50,840 --> 00:41:57,920
Speaker 3: If you pull up an Uber ride for let's say

744
00:41:57,960 --> 00:42:02,759
Speaker 3: the iHeart Studio to the Atlanta Airport and Heartsfeld. If

745
00:42:02,800 --> 00:42:04,560
Speaker 3: you pull it up on Uber and you pull it

746
00:42:04,640 --> 00:42:07,600
Speaker 3: up on Lyft, you are going to get a different price.

747
00:42:07,800 --> 00:42:12,560
Speaker 3: If you pull it up on on the way, you

748
00:42:12,600 --> 00:42:14,640
Speaker 3: are going to get a different price. If you pull

749
00:42:14,640 --> 00:42:17,840
Speaker 3: it up next to If, for instance, Noel, Matt Tennessee

750
00:42:17,880 --> 00:42:21,400
Speaker 3: and I were hanging out with you and we all

751
00:42:21,400 --> 00:42:25,160
Speaker 3: pulled it up at the same time, we might three

752
00:42:25,239 --> 00:42:27,920
Speaker 3: out of the five of us get different prices. Some

753
00:42:28,000 --> 00:42:30,120
Speaker 3: would be higher, some would be lower.

754
00:42:30,680 --> 00:42:32,920
Speaker 4: Depending where you are. I have a bit of a

755
00:42:32,960 --> 00:42:35,160
Speaker 4: life act that probably everybody knows about. But if you

756
00:42:35,200 --> 00:42:37,920
Speaker 4: click weight and Save, it almost always comes just as

757
00:42:37,960 --> 00:42:40,560
Speaker 4: fast as the regular one, and it is significantly cheaper

758
00:42:40,680 --> 00:42:43,200
Speaker 4: right up front. But I've never clicked weight and Save

759
00:42:43,400 --> 00:42:46,040
Speaker 4: short of it being mega, mega trafficking time where it

760
00:42:46,080 --> 00:42:49,920
Speaker 4: hasn't come right away. It's almost like a trick. They

761
00:42:49,920 --> 00:42:51,279
Speaker 4: don't want you to click that one. But if you

762
00:42:51,320 --> 00:42:53,200
Speaker 4: click it, you'll save some money and they'll come just

763
00:42:53,239 --> 00:42:53,760
Speaker 4: as fast.

764
00:42:54,000 --> 00:42:56,239
Speaker 3: That's some stuff they don't want you to know. So

765
00:42:56,280 --> 00:42:58,840
Speaker 3: how how does this translate to what Kroger's up to?

766
00:43:00,000 --> 00:43:03,200
Speaker 3: Every time you step into a grocery store and thank

767
00:43:03,239 --> 00:43:05,720
Speaker 3: you for the setup there, noil, I appreciate that. Ali

768
00:43:05,760 --> 00:43:13,239
Speaker 3: youu you are entering into a vast collection mechanism as

769
00:43:13,239 --> 00:43:15,800
Speaker 3: we're going to see. You know, we talked briefly about

770
00:43:15,840 --> 00:43:23,560
Speaker 3: the increasingly monopolistic drives of grocery stores. They're making a

771
00:43:23,680 --> 00:43:28,080
Speaker 3: ton of money off you, fellow shoppers, because of the

772
00:43:28,200 --> 00:43:31,440
Speaker 3: things like the loyalty cards. But it goes way deeper

773
00:43:31,480 --> 00:43:35,279
Speaker 3: than that. There is a vast array of mechanisms that

774
00:43:35,360 --> 00:43:39,800
Speaker 3: you will not clock. They are meant to track, analyze, share,

775
00:43:40,080 --> 00:43:45,200
Speaker 3: and further influence your shopping behavior. I'd like to shout

776
00:43:45,200 --> 00:43:49,719
Speaker 3: out our longtime conspiracy realist Matthias for hipping me to this,

777
00:43:50,800 --> 00:43:54,680
Speaker 3: who was in talks with a lawyer who is currently

778
00:43:54,920 --> 00:43:57,359
Speaker 3: deep in the trenches of figuring out whether or not

779
00:43:57,440 --> 00:43:59,799
Speaker 3: this is legal. If you want to learn more, good

780
00:43:59,840 --> 00:44:04,880
Speaker 3: at epic dot org. February fourteenth, twenty twenty five. Happy

781
00:44:05,040 --> 00:44:09,680
Speaker 3: former Valentine's Day to everybody belated. Uh. The headline is

782
00:44:09,760 --> 00:44:15,279
Speaker 3: Krueger's surveillance pricing harms consumers and raises prices with or

783
00:44:15,320 --> 00:44:20,680
Speaker 3: without facial recognition. So shout out to Mayu Tobin Miyaji

784
00:44:21,040 --> 00:44:26,640
Speaker 3: who originally wrote this. Grocery stores know so much about you.

785
00:44:27,000 --> 00:44:29,880
Speaker 3: Did either of you guys ever work at a grocery store?

786
00:44:30,120 --> 00:44:32,640
Speaker 4: Never did? No, No, I know you got it did

787
00:44:32,680 --> 00:44:35,279
Speaker 4: for sure, like bag bagging and doing the carts and

788
00:44:35,280 --> 00:44:35,880
Speaker 4: all that stuff.

789
00:44:36,280 --> 00:44:39,440
Speaker 3: Okay, yeah, so we I mean we all worked in

790
00:44:39,920 --> 00:44:43,440
Speaker 3: various things before our podcasting lives together. I think we

791
00:44:43,520 --> 00:44:46,239
Speaker 3: got some mellow mushroom in the crowd. I think we

792
00:44:46,320 --> 00:44:49,760
Speaker 3: got some Piedmont driving club. We got a vegan place

793
00:44:49,800 --> 00:44:54,000
Speaker 3: out in Knoxville, Dylan, what was the vegan restaurant there

794
00:44:54,040 --> 00:44:54,799
Speaker 3: in Knoxville?

795
00:44:55,200 --> 00:44:57,000
Speaker 2: Vegi Rama, great name.

796
00:44:57,160 --> 00:45:00,239
Speaker 4: Wow, they really locked that one down.

797
00:45:00,600 --> 00:45:03,719
Speaker 2: Did you ever work at a like a Kroger or something, Ben.

798
00:45:04,200 --> 00:45:08,160
Speaker 3: Yes, I did. Yeah, Matt, thank you for asking. I did,

799
00:45:08,239 --> 00:45:11,799
Speaker 3: and they would not fire me. I eventually just had

800
00:45:11,840 --> 00:45:12,200
Speaker 3: to leave.

801
00:45:12,320 --> 00:45:13,080
Speaker 4: You were just too good.

802
00:45:14,560 --> 00:45:17,200
Speaker 3: I kept trying to I kept turning in my notice.

803
00:45:17,200 --> 00:45:19,880
Speaker 3: It was very Seinfeld. I kept turning in my notice,

804
00:45:19,880 --> 00:45:22,400
Speaker 3: and they kept scheduling me. And then one day I

805
00:45:22,520 --> 00:45:26,759
Speaker 3: just said, you know, I can't do this anymore. I'm sixteen,

806
00:45:26,880 --> 00:45:29,680
Speaker 3: I have a Monte Carlo. Stuff's looking up for me.

807
00:45:30,000 --> 00:45:31,960
Speaker 3: And they were like, all right, we'll see you Thursday.

808
00:45:32,360 --> 00:45:36,200
Speaker 3: And I simply did not see them Thursday. This was

809
00:45:36,360 --> 00:45:41,360
Speaker 3: out of time before grocery stores were collecting all this

810
00:45:41,480 --> 00:45:46,319
Speaker 3: information and started with the loyalty card. Right now, your

811
00:45:46,640 --> 00:45:50,120
Speaker 3: local grocery store, especially if it's a big chain, may

812
00:45:50,280 --> 00:45:55,239
Speaker 3: know your age, your identified gender, your race, your economic

813
00:45:55,320 --> 00:45:59,440
Speaker 3: status aka how much will this guy spend on some beings.

814
00:46:00,280 --> 00:46:07,080
Speaker 3: They'll know your family makeup, right. Does this cardholder have children?

815
00:46:07,440 --> 00:46:11,160
Speaker 3: Does this cardholder have an elderly person? Does this cardholder

816
00:46:11,200 --> 00:46:15,560
Speaker 3: perhaps use snap benefits? Yes? Oh I love that point. No,

817
00:46:16,000 --> 00:46:19,480
Speaker 3: And then the other question is what are their health conditions,

818
00:46:19,840 --> 00:46:25,719
Speaker 3: whether there are other lifestyle characteristics. Vaguely put, This, combined

819
00:46:25,840 --> 00:46:30,200
Speaker 3: with the other hidden income streams of what we call

820
00:46:30,360 --> 00:46:35,600
Speaker 3: data brokers, means that grocery stores may well be as

821
00:46:35,640 --> 00:46:39,400
Speaker 3: conspiratorial as it sounds. They may well be the next

822
00:46:39,440 --> 00:46:45,080
Speaker 3: step in an Orwellian nineteen eighty four. So let's say

823
00:46:46,280 --> 00:46:50,000
Speaker 3: we're walking up. Let's make up characters, everybody. Let's make

824
00:46:50,080 --> 00:46:55,719
Speaker 3: up a character very different from ourselves. I'll be Yulanda,

825
00:46:56,280 --> 00:47:01,120
Speaker 3: recently arrived from you praying, I'm very intro in yoga.

826
00:47:01,560 --> 00:47:06,719
Speaker 3: I have four kids, and I'm looking after my elderly grandmother.

827
00:47:06,920 --> 00:47:11,640
Speaker 3: So Yolanda, all right, put some folks in the spotlight, Noel,

828
00:47:11,840 --> 00:47:16,319
Speaker 3: what's a character? They get a character? Bluie Blue? All right?

829
00:47:16,400 --> 00:47:17,200
Speaker 3: Tell us about Blue.

830
00:47:17,480 --> 00:47:20,360
Speaker 4: He's the you know, the cute Australian dog, the cartoon

831
00:47:20,400 --> 00:47:21,319
Speaker 4: dog the kids love.

832
00:47:21,960 --> 00:47:26,000
Speaker 3: All right, He's a cartoon Australian dog. The kids love him.

833
00:47:26,160 --> 00:47:31,279
Speaker 3: What does Blue buy Kroger kibbles? He buys kibbles, all right,

834
00:47:31,600 --> 00:47:35,200
Speaker 3: all right, So we got Yolanda, we got Blue, Matt.

835
00:47:35,280 --> 00:47:36,200
Speaker 3: Let's get a character.

836
00:47:36,640 --> 00:47:42,000
Speaker 2: Pete Raisinson. Pete works there at the at the local

837
00:47:42,120 --> 00:47:49,600
Speaker 2: Kroger in Temecula, and Pete is he produce. Pete doesn't

838
00:47:49,640 --> 00:47:51,520
Speaker 2: make a lot of money. Pete is just you know,

839
00:47:51,719 --> 00:47:54,160
Speaker 2: works at the counter, but he makes enough money to

840
00:47:54,160 --> 00:47:57,879
Speaker 2: stay there and he can support, you know, just his

841
00:47:58,360 --> 00:48:00,960
Speaker 2: lifestyle as a twenty two year old young man.

842
00:48:01,320 --> 00:48:01,640
Speaker 4: M M.

843
00:48:02,200 --> 00:48:03,880
Speaker 3: What department does Pete work in.

844
00:48:04,160 --> 00:48:06,120
Speaker 2: He's in the checkout counter. He's one of the last

845
00:48:06,120 --> 00:48:07,360
Speaker 2: remaining checkout people.

846
00:48:07,960 --> 00:48:09,680
Speaker 3: He's a human checkout counter guy.

847
00:48:09,760 --> 00:48:13,160
Speaker 2: Yeah, yeah, yeah, that's what he does primarily. Sometimes he

848
00:48:13,200 --> 00:48:15,839
Speaker 2: stands at you know, where all the robot checkout things

849
00:48:15,880 --> 00:48:18,759
Speaker 2: are sure and just make sure nobody needs to get

850
00:48:18,800 --> 00:48:19,960
Speaker 2: an ID checked or anything.

851
00:48:20,440 --> 00:48:23,600
Speaker 3: Right. Okay, So we got Yolanda, we got Bluie, we

852
00:48:23,680 --> 00:48:27,239
Speaker 3: got Pete Raisinson, and we have one more character on

853
00:48:27,360 --> 00:48:31,720
Speaker 3: stage a courtesy of our pals, super producer Tennessee Dylan,

854
00:48:31,800 --> 00:48:33,160
Speaker 3: tell us about your character.

855
00:48:34,160 --> 00:48:38,759
Speaker 2: Chip Diggin's golf commentator, father of three, divorced Dad has

856
00:48:38,800 --> 00:48:40,840
Speaker 2: had his frozen fruit for his smoothies.

857
00:48:40,960 --> 00:48:45,560
Speaker 3: Here going by classic all right, So everything we have

858
00:48:45,719 --> 00:48:50,640
Speaker 3: learned about dynamic pricing really informs us about surveillance pricing.

859
00:48:51,000 --> 00:48:55,200
Speaker 3: We just off the dome, made up four awesome characters,

860
00:48:55,280 --> 00:48:57,719
Speaker 3: and honestly, thank you guys, I think that was great work.

861
00:48:58,320 --> 00:49:03,160
Speaker 3: Surveillance pricing will not only know all the characteristics we

862
00:49:03,239 --> 00:49:09,160
Speaker 3: have described, but will leverage this such that you could

863
00:49:09,200 --> 00:49:13,200
Speaker 3: be standing with Blue and Pete raisinsin right now in

864
00:49:13,280 --> 00:49:15,560
Speaker 3: Aisle twelve, and you could be looking at.

865
00:49:15,480 --> 00:49:17,640
Speaker 4: The same can of beans.

866
00:49:17,640 --> 00:49:20,000
Speaker 3: And that I don't know why I keep going back,

867
00:49:20,400 --> 00:49:24,200
Speaker 3: but that same can of beans might be ninety eight

868
00:49:24,280 --> 00:49:29,120
Speaker 3: cents for Bluey, it might be a dollar thirteen for

869
00:49:29,160 --> 00:49:33,560
Speaker 3: Pete Raisinson, and it might be four dollars for Yolanda.

870
00:49:33,920 --> 00:49:37,440
Speaker 3: This is a very strange thing. I think it needs

871
00:49:37,600 --> 00:49:43,440
Speaker 3: more attention, and lawmakers agree because here's the next step.

872
00:49:43,640 --> 00:49:46,440
Speaker 3: Here's the thing we didn't talk about earlier. You guys.

873
00:49:46,800 --> 00:49:50,040
Speaker 3: Facial recognition is happening in grocery stores.

874
00:49:52,120 --> 00:49:56,640
Speaker 2: Okay, So so the grocery store would be able to

875
00:49:56,760 --> 00:50:01,160
Speaker 2: identify Bluie pretty clearly, I think when Blue walks in,

876
00:50:02,000 --> 00:50:07,160
Speaker 2: countis from Australia. Yeah, yeah, sure, sure, sure for sure,

877
00:50:07,320 --> 00:50:10,680
Speaker 2: and then you know all these other characters. So you're

878
00:50:10,680 --> 00:50:14,320
Speaker 2: gonna have facial recognition that is linked to whatever card

879
00:50:14,560 --> 00:50:17,160
Speaker 2: that you use, or phone number you use, or even

880
00:50:17,200 --> 00:50:20,440
Speaker 2: sometimes the credit card or bank card that you use

881
00:50:20,520 --> 00:50:23,239
Speaker 2: to buy groceries. That all gets wrapped up into one

882
00:50:23,280 --> 00:50:25,560
Speaker 2: big piece of information about you. So in this case

883
00:50:25,560 --> 00:50:27,360
Speaker 2: it's also going to be your face.

884
00:50:29,040 --> 00:50:33,719
Speaker 3: Yes, that's correct, Matt or Pete, whichever you prefer. In

885
00:50:33,760 --> 00:50:36,520
Speaker 3: this case, it is correct. So we can posit then

886
00:50:36,880 --> 00:50:41,360
Speaker 3: that the self checkout line is not just a AI

887
00:50:41,680 --> 00:50:48,360
Speaker 3: automation rush. It is a rush on personal information. Kroger

888
00:50:48,719 --> 00:50:54,359
Speaker 3: is partnering with Microsoft. The facial recognition is being installed,

889
00:50:55,000 --> 00:50:59,399
Speaker 3: creating what they don't call surveillance pricing, what they don't

890
00:50:59,440 --> 00:51:07,000
Speaker 3: call dynamic pricing. They call it a personalized shopping experience. Now,

891
00:51:07,080 --> 00:51:11,160
Speaker 3: going back to Noel Brown's earlier or blue if you prefer, Nol,

892
00:51:11,400 --> 00:51:14,320
Speaker 3: going back to the earlier statement about things seeming awry

893
00:51:14,680 --> 00:51:20,800
Speaker 3: or JANKI, I don't think this benefits consumers at all.

894
00:51:21,040 --> 00:51:25,279
Speaker 3: I'm not sure who it benefits. What the shareholders of

895
00:51:25,360 --> 00:51:29,680
Speaker 3: the grocery store. I oh uh, Noel er Bluey said earlier.

896
00:51:30,320 --> 00:51:34,040
Speaker 3: The idea of don't call me a boomer, as we're

897
00:51:34,080 --> 00:51:36,840
Speaker 3: all getting older, and not to sound like a boomer,

898
00:51:36,960 --> 00:51:40,240
Speaker 3: nor to agree with you necessarily, Nol. But this feels

899
00:51:40,280 --> 00:51:43,279
Speaker 3: a little too far right. Shouldn't a price of a

900
00:51:43,360 --> 00:51:46,400
Speaker 3: can of beans be the same price?

901
00:51:47,040 --> 00:51:48,960
Speaker 4: It should be how much can I make can of

902
00:51:49,000 --> 00:51:52,719
Speaker 4: beans cost Michael ten dollars? No, it's yeah, I mean

903
00:51:52,719 --> 00:51:55,080
Speaker 4: I don't know. I get a certain amount of fluctuation

904
00:51:55,600 --> 00:51:59,520
Speaker 4: based on availability of products, based on the tchaining all

905
00:51:59,520 --> 00:52:03,000
Speaker 4: that stuff. This pivoting to this kind of system and

906
00:52:03,080 --> 00:52:05,160
Speaker 4: trying to sell it as though it in some way

907
00:52:05,400 --> 00:52:09,000
Speaker 4: is a benefit to anybody other than the company's is

908
00:52:10,000 --> 00:52:14,799
Speaker 4: the most absurd and egregious form of gaslighting. It's really yeah,

909
00:52:14,800 --> 00:52:15,280
Speaker 4: it's wild.

910
00:52:16,320 --> 00:52:18,799
Speaker 2: It's very strange. I don't see how they could ever

911
00:52:18,840 --> 00:52:23,960
Speaker 2: get away with it. Like, if groceries cost more for

912
00:52:24,000 --> 00:52:29,040
Speaker 2: different people, wouldn't we all raise our hands and say, hey,

913
00:52:29,040 --> 00:52:32,560
Speaker 2: this is not right. Why is this happening. You could

914
00:52:32,560 --> 00:52:36,440
Speaker 2: get your friend who gets the really low prices to

915
00:52:36,520 --> 00:52:38,799
Speaker 2: go in and buy all your groceries for you.

916
00:52:38,960 --> 00:52:40,960
Speaker 4: Remember when we talked about the Snap benefits thing may

917
00:52:41,040 --> 00:52:42,720
Speaker 4: I don't remember where this came up, but this idea

918
00:52:42,880 --> 00:52:47,239
Speaker 4: that the grocery stores weren't allowed to give discounts to

919
00:52:47,480 --> 00:52:51,839
Speaker 4: Snap holders because that in some way violated some sort

920
00:52:51,880 --> 00:52:56,320
Speaker 4: of fair play law or something. I can't remember exactly

921
00:52:56,360 --> 00:53:00,040
Speaker 4: the deal, but this seems that it squarely in a

922
00:52:59,840 --> 00:53:04,600
Speaker 4: similar gray area that's actually useless. The thing that we

923
00:53:04,600 --> 00:53:07,240
Speaker 4: were talking about before offering the discounts. If snap benefits

924
00:53:07,280 --> 00:53:09,560
Speaker 4: dried up, that would have been like doing the solid

925
00:53:09,760 --> 00:53:12,400
Speaker 4: for people in need. This just feels like it's just

926
00:53:12,440 --> 00:53:13,920
Speaker 4: absolutely the opposite of that.

927
00:53:14,840 --> 00:53:17,200
Speaker 2: But what we're talking about here right now is dynamic pricing, right,

928
00:53:17,280 --> 00:53:20,840
Speaker 2: We're not talking about individual pricing like personal pricing.

929
00:53:20,920 --> 00:53:24,040
Speaker 4: Yes, that's the nth degree awful version of it.

930
00:53:24,160 --> 00:53:29,680
Speaker 3: We're we're we're midway through the we're in the interim

931
00:53:29,760 --> 00:53:33,719
Speaker 3: of the spectrum, right, So it's surveillance pricing. Now it

932
00:53:33,760 --> 00:53:35,799
Speaker 3: will become individual pricing.

933
00:53:35,800 --> 00:53:41,000
Speaker 2: And surveillance pricing says like what like, therefore, this is

934
00:53:41,040 --> 00:53:41,960
Speaker 2: the price you get.

935
00:53:41,840 --> 00:53:47,320
Speaker 3: Right, Surveillance pricing says Yolanda walks in. Yolanda gets clocked

936
00:53:48,200 --> 00:53:52,040
Speaker 3: with a camera at the beginning, right, and has some

937
00:53:52,080 --> 00:53:55,920
Speaker 3: sort of profile that pulls up which dictates the price

938
00:53:56,000 --> 00:54:00,520
Speaker 3: of beans or the price of chabadi yogurt, you know.

939
00:54:00,920 --> 00:54:04,640
Speaker 3: And then you'll Londa checks out right at a self

940
00:54:04,719 --> 00:54:09,920
Speaker 3: checkout counter, and that will also clock her face, maybe

941
00:54:09,960 --> 00:54:13,520
Speaker 3: even people with her, and then it will feed the

942
00:54:13,680 --> 00:54:19,319
Speaker 3: data of the purchase into that overall profile or fingerprint,

943
00:54:19,360 --> 00:54:22,680
Speaker 3: which informs the next time you'll landa walks in and

944
00:54:22,800 --> 00:54:24,640
Speaker 3: does or does not buy chobani.

945
00:54:24,760 --> 00:54:29,520
Speaker 4: And the new thing here is this facial recognition piece. Right,

946
00:54:30,000 --> 00:54:31,560
Speaker 4: if you've if you've ever checked out at one of

947
00:54:31,600 --> 00:54:34,480
Speaker 4: these things, you'll notice you're on camera. They could argue

948
00:54:34,480 --> 00:54:36,880
Speaker 4: it's deth deterrent or what have you. But that information

949
00:54:36,960 --> 00:54:38,640
Speaker 4: is going somewhere. I don't think you have to sign

950
00:54:38,680 --> 00:54:41,160
Speaker 4: anything in the terms that say they have to delete it.

951
00:54:41,560 --> 00:54:44,600
Speaker 4: At the very least, that's what the TSA promises. I

952
00:54:44,640 --> 00:54:47,720
Speaker 4: don't think you've given any such promise from the groceries,

953
00:54:47,800 --> 00:54:48,880
Speaker 4: from the grocery chains.

954
00:54:49,040 --> 00:54:54,200
Speaker 3: We have not because it's not legally requisite. Right, there

955
00:54:54,320 --> 00:54:57,000
Speaker 3: doesn't need to be a sign that says you are

956
00:54:57,120 --> 00:55:03,680
Speaker 3: surveiled and stalled. This, I would posit is episode in

957
00:55:03,680 --> 00:55:06,279
Speaker 3: the future. This is part of what we will call

958
00:55:06,360 --> 00:55:09,319
Speaker 3: a K shaped economy, which we don't have time to

959
00:55:09,440 --> 00:55:10,400
Speaker 3: talk about today.

960
00:55:10,680 --> 00:55:11,919
Speaker 2: Well, what does that? What does that mean?

961
00:55:12,719 --> 00:55:17,080
Speaker 3: A K shaped economy is an increasing violation of what

962
00:55:17,120 --> 00:55:21,480
Speaker 3: we call the Genie index. K shaped economy means that

963
00:55:21,719 --> 00:55:27,560
Speaker 3: part of a given economic structure is spiraling and another

964
00:55:27,640 --> 00:55:33,440
Speaker 3: part of it is skyrocketing. It was ultimately untenable.

965
00:55:33,520 --> 00:55:36,120
Speaker 4: It's a big divider of class.

966
00:55:36,680 --> 00:55:40,840
Speaker 3: Sure a capital K, right, and then picture the upward

967
00:55:40,880 --> 00:55:44,239
Speaker 3: part being the one to ten percent and the downward

968
00:55:44,280 --> 00:55:50,720
Speaker 3: part being everybody else. Anyway, thanks for listening to our show, folks.

969
00:55:52,040 --> 00:55:54,239
Speaker 3: I don't know, do you guys think this surveillance and

970
00:55:54,280 --> 00:55:57,080
Speaker 3: grocery stores? Like? Am I too into it? Or is

971
00:55:57,120 --> 00:55:57,960
Speaker 3: this an episode?

972
00:55:58,040 --> 00:56:02,040
Speaker 4: Oh? It's definitely an episode. I'd be interested too. What

973
00:56:02,400 --> 00:56:07,239
Speaker 4: larger implications this might speak to about the availability of

974
00:56:07,280 --> 00:56:10,160
Speaker 4: the stuff and just how it plays into larger surveillance

975
00:56:10,160 --> 00:56:15,080
Speaker 4: state aspects and well that but also just literally just

976
00:56:15,400 --> 00:56:18,760
Speaker 4: another layer of the surveillance state, you know. And who's

977
00:56:18,760 --> 00:56:20,960
Speaker 4: to say that the government can't use that information as well,

978
00:56:21,040 --> 00:56:24,320
Speaker 4: or that it isn't feeding into some database that's accessible

979
00:56:24,719 --> 00:56:25,480
Speaker 4: that in that way.

980
00:56:26,360 --> 00:56:27,759
Speaker 2: I just don't think they can get away with it.

981
00:56:28,360 --> 00:56:30,360
Speaker 2: You could have. So what you need to do is

982
00:56:30,400 --> 00:56:34,040
Speaker 2: have if you're in the top ten percent, you need

983
00:56:34,080 --> 00:56:37,640
Speaker 2: to make a friend who's in the lowest ten percent, right,

984
00:56:37,880 --> 00:56:41,680
Speaker 2: and vice versa. Then you two go to the store

985
00:56:41,719 --> 00:56:43,759
Speaker 2: together every time you buy each other's groceries.

986
00:56:44,840 --> 00:56:48,000
Speaker 4: Criss cross yess on a train rolls.

987
00:56:48,040 --> 00:56:52,719
Speaker 3: I love it. Yeah, very well done, guys, and thank you. Yeah,

988
00:56:52,800 --> 00:56:55,640
Speaker 3: I think this can be a good episode in the future.

989
00:56:55,719 --> 00:56:58,799
Speaker 3: There's so much stuff we didn't get to, including the

990
00:56:58,840 --> 00:57:03,439
Speaker 3: fact that the online retail giant Sheen Shane I haven't

991
00:57:03,440 --> 00:57:06,360
Speaker 3: heard of them s h E I N. They have

992
00:57:06,560 --> 00:57:12,000
Speaker 3: banned the sale of sex dolls. The Interstellar Visitor three

993
00:57:12,160 --> 00:57:15,960
Speaker 3: I Atlas has changed color. We'll have updates on that.

994
00:57:16,400 --> 00:57:19,520
Speaker 3: For now, we cannot thank you enough for your time.

995
00:57:19,640 --> 00:57:23,040
Speaker 3: We cannot thank our super producer, Dylan the Tennessee pal

996
00:57:23,120 --> 00:57:26,040
Speaker 3: Fagan enough for his time, but we'll try it. Dylan,

997
00:57:26,360 --> 00:57:28,240
Speaker 3: thank you. How do we do today?

998
00:57:29,040 --> 00:57:30,520
Speaker 5: Excellent job? Boys?

999
00:57:30,880 --> 00:57:33,320
Speaker 3: Oh come on, I love it when you lie to us,

1000
00:57:33,960 --> 00:57:37,200
Speaker 3: so we want to hear from you. So join up

1001
00:57:37,240 --> 00:57:39,240
Speaker 3: with the crew. Find us out here in the dark.

1002
00:57:39,400 --> 00:57:42,000
Speaker 3: You can hit us on the telephonic devices. You can

1003
00:57:42,080 --> 00:57:45,360
Speaker 3: always write to us with awesome stuff, and you can

1004
00:57:45,400 --> 00:57:49,480
Speaker 3: find us on the lines the social needs should thou sip.

1005
00:57:50,400 --> 00:57:52,560
Speaker 4: Find us indeed all over the lines at the handle

1006
00:57:52,560 --> 00:57:55,320
Speaker 4: Conspiracy Stuffhere. We exist on Facebook with our Facebook group

1007
00:57:55,320 --> 00:57:58,920
Speaker 4: Here's where it gets crazy, on xfka, Twitter, and on YouTube.

1008
00:57:59,280 --> 00:58:01,560
Speaker 4: You can also find that on Instagram and TikTok at

1009
00:58:01,560 --> 00:58:02,760
Speaker 4: Conspiracy Stuff Show.

1010
00:58:03,320 --> 00:58:04,800
Speaker 2: We have a phone number. It is one eight to

1011
00:58:04,840 --> 00:58:08,400
Speaker 2: three three std WYTK. When you call in, give yourself

1012
00:58:08,440 --> 00:58:10,080
Speaker 2: a cool nickname and let us know within the message

1013
00:58:10,080 --> 00:58:11,680
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1014
00:58:11,720 --> 00:58:13,280
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1015
00:58:13,280 --> 00:58:13,760
Speaker 2: do that too.

1016
00:58:13,840 --> 00:58:17,760
Speaker 3: We are the entities that read each piece of correspondence

1017
00:58:17,800 --> 00:58:21,400
Speaker 3: we receive. Be well aware, yet I'm afraid sometimes the

1018
00:58:21,600 --> 00:58:25,440
Speaker 3: void writes back. For everybody who listened to the end

1019
00:58:25,480 --> 00:58:29,920
Speaker 3: of our weekly Strange News segment, there is something that

1020
00:58:30,000 --> 00:58:33,240
Speaker 3: you guys said that required me to pull up an

1021
00:58:33,240 --> 00:58:36,960
Speaker 3: old book. It's called Wealth and Poverty by George Gilder.

1022
00:58:37,880 --> 00:58:41,520
Speaker 3: Here's the quote. Wealth and poverty are the prime concerns

1023
00:58:41,560 --> 00:58:45,720
Speaker 3: of economics. But they are subjects too vast and too

1024
00:58:45,840 --> 00:58:50,800
Speaker 3: vital be left to the economist alone. I think we

1025
00:58:50,840 --> 00:58:53,880
Speaker 3: can agree with that. Find us in the Dark Conspiracy

1026
00:58:53,920 --> 00:59:13,880
Speaker 3: at iHeartRadio dot com.

1027
00:59:14,040 --> 00:59:16,080
Speaker 2: Stuff they Don't Want You to Know is a production

1028
00:59:16,200 --> 00:59:20,720
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1029
00:59:20,800 --> 00:59:23,680
Speaker 2: Apple Podcasts, or wherever you listen to your favorite shows.