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Speaker 1: Fellow conspiracy realist. First off, we want to thank you

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Speaker 1: for joining us this evening. Statistics prove that you are

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Speaker 1: probably listening to this show on a mobile device.

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Speaker 2: It's true, or recording this show well, occasionally glancing down

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Speaker 2: at your mobile device. No, not typically, but we're doing classics,

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Speaker 2: so I was just checking the feed. But see, yeah,

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Speaker 2: they were kind of tethered to these things, aren't.

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Speaker 3: We looking at mine? Here it says something about who's

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Speaker 3: this Lindsey Graham character? And then a bunch of.

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Speaker 1: Old ladybugs lady, Yes, ladybug lady, how is ladybugs?

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Speaker 2: Uh?

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Speaker 1: Back in back in twenty twenty, right around Halloween, the

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Speaker 1: most wonderful time of the year, folks, we asked ourselves

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Speaker 1: about the sudden proliferation of the technology called the smartphone,

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Speaker 1: which very much looked like a thing out of SIDEI

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Speaker 1: Fi franchises, similar to Star Trek. It had been predicted

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Speaker 1: for a long time. DARPA and parts of the Deep State,

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Speaker 1: just to be honest, had cell phone technology way before

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Speaker 1: the public. But we were worried about what we later

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Speaker 1: decided to call the dopamine casino of it all.

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Speaker 2: How quaint our take must be company compared to the

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Speaker 2: episode on chat GPT Psychosis that we just recorded.

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Speaker 3: Oh geez, just think about all the things you do

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Speaker 3: with that phone that's in your pocket right now, or

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Speaker 3: that you're staring at because now you're contemplating.

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Speaker 2: It because we made it.

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Speaker 1: We've naughty out of mind for you, and so now

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Speaker 1: now the boys called you out.

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Speaker 4: And you're looking at your phone.

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Speaker 2: Well, I think a concept too that that's come up

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Speaker 2: a lot in terms of like the way our attention

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Speaker 2: is divided. Matt Damon was recently doing the rounds for

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Speaker 2: the Netflix movie that he's in. It's like about some

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Speaker 2: kind of heist. I think, I forget what it's called,

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Speaker 2: something like the heist, but about second screen viewing, about

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Speaker 2: how so many screenplays are being required to be written

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Speaker 2: or notated to death by executives in order to accommodate

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Speaker 2: what's called second screen viewing, the idea that our attention

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Speaker 2: is always on our phone, so you better make sure

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Speaker 2: that people that are half paying attention to your movie

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Speaker 2: can follow along.

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Speaker 3: What were you guys saying?

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Speaker 2: Sorry?

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Speaker 1: Yeah, I can always have exposition similar to how MPR

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Speaker 1: whenever they come back from an ad break, they'll tell

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Speaker 1: you what was happening. So Dylan, Matt Nol, we are

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Speaker 1: a conspiracy show and we're talking about what happens when

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Speaker 1: we become our phones.

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Speaker 2: Yeah, let's ro all this quaint take right now from

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Speaker 2: twenty to.

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Speaker 5: Twenty from UFOs to psychic powers and government conspiracies. History

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Speaker 5: is real with unexplained events. You can turn back now

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Speaker 5: or learn this stuff they don't want you to know.

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Speaker 5: A production of iHeartRadio.

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Speaker 3: Hello, welcome back to the show. My name is Matt,

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Speaker 3: my name is Nolan.

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Speaker 2: They call me Ben.

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Speaker 1: We are joined as always with our super producer Paul.

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Speaker 1: Mission Control decands. Most importantly, you are you, You are here,

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Speaker 1: and that makes this stuff they don't want you to know.

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Speaker 1: Shout out to everybody listening to today's episode on a

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Speaker 1: smartphone or mobile device. In a way, this is very

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Speaker 1: much for you. It's for everybody all the time, but

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Speaker 1: this one's especially for you. One question I thought we

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Speaker 1: could start with today, Guys, what did you do on

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Speaker 1: your phone today? Just however much you feel comfortable disclosing

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Speaker 1: to millions of people.

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Speaker 2: And by too much, too much, I mean it's like

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Speaker 2: the mission control center of my life. I do a

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Speaker 2: lot of my work on my phone I like enter

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Speaker 2: data into Google Docs on my phone. I do your

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Speaker 2: research on my phone. I do social media on my phone.

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Speaker 2: It's kind of embarrassing, honestly.

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Speaker 3: Well it's yeah, it's interesting. So I think, well, I'm

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Speaker 3: not sure for all of us, but I'm on a

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Speaker 3: laptop and almost all of my work happens there. But

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Speaker 3: if I ever have to leave my home office for

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Speaker 3: any reason, that phone is just in my hand and

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Speaker 3: it's doing all that same stuff. Or of course playing

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Speaker 3: Call of Duty or Best Fiends.

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Speaker 1: Right there, we go, check that one off the box.

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Speaker 1: We got in front of it this time. Awesome. So

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Speaker 1: you know, that's a really great point because a peep

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Speaker 1: behind the curtain. Your individual mileage may vary, folks, but

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Speaker 1: Matt Nol and I do a ton of stuff all

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Speaker 1: the time. As a matter of fact. Before or I

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Speaker 1: guess it's okay to say this publicly, before I've complained

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Speaker 1: to you guys about it, and I've said, you know,

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Speaker 1: the fact that we're kind of always available makes us

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Speaker 1: like very underpaid anesthesiologists. We're just sort of always on

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Speaker 1: the clock, you know.

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Speaker 3: What I mean, ready to get someone prepared to fix

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Speaker 3: a problem. Surgeons, firefighters, there you go surgeons. I think

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Speaker 3: we're surgeons.

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Speaker 2: Well. We also refer to like problems in the podcast

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Speaker 2: world as fire drills quite often, so I think we

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Speaker 2: are sort of firefighters in our own ways.

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Speaker 3: Nice fire surgeons.

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Speaker 1: There we go, fire surgeons, rocket fighters there we go.

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Speaker 1: Put it on the T shirts and so personally, I

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Speaker 1: did a lot of stuff on my phone today and

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Speaker 1: I did something that I don't usually do well.

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Speaker 4: I did this yesterday.

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Speaker 1: I kept track of what I was doing. I didn't

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Speaker 1: check every time I touched my phone, but I checked

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Speaker 1: all the weird things. I bought stuff, I looked through

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Speaker 1: social media and blah blah blah, all the YadA YadA yadas.

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Speaker 1: But one of the things I did today was post

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Speaker 1: an informal question on Twitter. They asked, has your phone

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Speaker 1: or an app on your phone ever seemed to know

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Speaker 1: too much about you? And if so, what freaked you

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Speaker 1: out the most? You can find the responses there on apt, bembull, ANDHSW.

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Speaker 1: But for our purposes today, one thing that most people

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Speaker 1: talked about was, of course, targeted advertising. We see stories

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Speaker 1: about someone saying, you know, I coughed around my phone

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Speaker 1: and then instantly when I pulled up Instagram or something.

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Speaker 1: I was receiving sponsored ads.

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Speaker 2: For Hals the Dea.

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Speaker 1: Yeah, and a lot of people reporting like their microphones

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Speaker 1: somehow seemed to know stuff, or their conversations with someone,

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Speaker 1: even a wave from their devices somehow led to what

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Speaker 1: they see as a targeted ad. There's a little bit

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Speaker 1: of better Minhoff in there, for sure, but there are

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Speaker 1: also a lot of shenanigans. But the thing is, there's

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Speaker 1: a problem with this. We've talked about targeted ads before

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Speaker 1: everyone knows they exist. These are the things your smartphones

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Speaker 1: and your telecom your ad industries want you to know about.

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Speaker 1: They want you to see an ad for Holes or

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Speaker 1: Best Fiends or whatever. But that's because it's the stuff

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Speaker 1: they want you to know that they know about you.

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Speaker 1: But today's episode is different. Today's episode goes in a

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Speaker 1: different direction. It is about the stuff they don't want

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Speaker 1: you to know about you and your phone.

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Speaker 4: Here are the facts.

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Speaker 2: So what is a smartphone? It's sort of like just

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Speaker 2: a phone these days. But you know, I know people

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Speaker 2: that have decided to not go that route and still

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Speaker 2: use a dumb phone. I guess nobody really calls them that,

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Speaker 2: but they're basically phones that if they have Internet connective

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Speaker 2: are rudimentary at best. Maybe they can send text messages,

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Speaker 2: probably can't send pictures very well. Oftentimes, when you're communicating

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Speaker 2: on a smartphone with somebody with a dumb phone, things

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Speaker 2: appear in blocks or weird ways. You have little message

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Speaker 2: sent kind of things. Because these are much more old

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Speaker 2: school communication tools. Smart their phones their phones. Yeah, first

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Speaker 2: and foremost, I remember I used to have a razor.

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Speaker 2: You guys remember the razor? Yeah, yeah, and that was

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Speaker 2: that was like, oh god, it's a color screen and

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Speaker 2: it was like, you know, a tiny little color screen.

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Speaker 2: Was looking back at now seems laughable. But a smartphone

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Speaker 2: is essentially a cell phone with a bevy of highly

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Speaker 2: advanced features like what we're talking about. Yeah, oh man, Matt,

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Speaker 2: is that your burner? This side hustle?

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Speaker 3: This was my first cell phone, you guys.

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Speaker 4: Oh my god, is that a Motorola.

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Speaker 3: It's a Kyo Sera.

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Speaker 2: I don't even know that brand.

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Speaker 3: QCP five Oh I have those amazing Still.

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Speaker 1: I have a Nokia, but I just attached to chain

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Speaker 1: to it and use it for personal defense because these

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Speaker 1: things are indestructible. But you're I love the point you're

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Speaker 1: making because it's like if you own a smartphone, or

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Speaker 1: if you buy an iPhone or an Android or something,

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Speaker 1: saying that you're gonna call someone or knowing that you

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Speaker 1: can use it to make phone calls is very much

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Speaker 1: like a lower priority feature. Now you want it to

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Speaker 1: be online, you want to have certain slick apps and

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Speaker 1: so on.

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Speaker 2: Well, yeah, and we recently talked about on a listener

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Speaker 2: Mail episode about you know, robocall scams and things like that,

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Speaker 2: and how they're often targeted to older generations because they're

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Speaker 2: the ones that are still using phones to make calls

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Speaker 2: or have land lines. You know. I haven't had a

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Speaker 2: landline in probably fifteen years, you know. And I very rare.

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Speaker 2: I mean I use my phone to make calls for

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Speaker 2: work often because we do conference calls, but now that's

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Speaker 2: pivoted to you know, Google hangouts or Microsoft teams or

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Speaker 2: Zoom or what have you. So very rarely use my

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Speaker 2: phone to make calls. And oftentimes if I get a

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Speaker 2: call that I from a number I don't recognize, I

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Speaker 2: don't answer it. And if someone calls me out of

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Speaker 2: the blue, I low key resent them for it. Sorry

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Speaker 2: about that, No, not, I'm not you mad. I'm always

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Speaker 2: happy to hear from either of you, guys. But I'm

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Speaker 2: just saying in general and out of the blue call

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Speaker 2: feels like, why didn't we schedule this? You know, this

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Speaker 2: should have been like on the books. You know, you

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Speaker 2: can't just call me out of nowhere because it implies

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Speaker 2: it requires an immediate reaction. Although there's also that kind

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Speaker 2: of expectation with texting to people text you aggressively because

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Speaker 2: they want you to text back right away, and if

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Speaker 2: you don't, it can kind of weird people out sometimes.

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Speaker 2: But basically we are in the world of smartphones. There's

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Speaker 2: no putting that Badger back in the bag or Genie

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Speaker 2: back in the jar or what have you. Because people

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Speaker 2: are using their phones for so many other things, calling

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Speaker 2: being absolutely secondary, and at this point it really might

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Speaker 2: as well be an extra feature. But I guess it's

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Speaker 2: bundled in because the telecom companies still want to have

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Speaker 2: that infrastructure for making calls, right, I mean yeah.

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Speaker 1: And they want that voice data right so they can

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Speaker 1: frank abite you later, all.

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Speaker 4: Right, allegedly sure.

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Speaker 2: But the.

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Speaker 1: One thing that's fascinating to me about this is how

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Speaker 1: quickly this became normalized, especially in the West. I mean,

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Speaker 1: if you ever want to have a fun rabbit hole,

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Speaker 1: try looking into all the in if you're a hip

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Speaker 1: hop head like I am, try looking into all the

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Speaker 1: old technological brags of hip hop songs of yesteryear, you know,

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Speaker 1: when like jay z Is is like I'm too cold,

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Speaker 1: Motorola two way pager, like that was fancy. That was

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Speaker 1: a razor at the time. And now that stuff seems

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Speaker 1: so antiquated because it evolves so quickly. But that's the

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Speaker 1: front of the curtain evolution eye the curtain stuff is

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Speaker 1: much different and also evolving at a much faster pace.

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Speaker 3: Yeah, but it's it's harder to trace the stuff with

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Speaker 3: a two a pager.

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Speaker 2: I'm just you know, that's true.

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Speaker 1: That's true. That's why it's so cold. In a very

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Speaker 1: short amount of time, as Drake would say this stuff,

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Speaker 1: the smartphone usage went from zero to one hundred and

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Speaker 1: as we record this episode right now, this is a

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Speaker 1: statistic that baffled me. There are approximately ten billion mobile

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Speaker 1: devices in use, and yes, to confirm, that's more than

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Speaker 1: the current human population of the planet. I checked just

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Speaker 1: in case.

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Speaker 2: So wait, is that just for like people that have

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Speaker 2: like two phones. That's part of the world, And why

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Speaker 2: would you do that. That's very suspicious. Oh that's true.

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Speaker 2: You might get issued one for work. That's the good point.

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Speaker 3: SI side piece burners also everywhere. Also true.

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Speaker 1: I also think there may be more than ten billion

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Speaker 1: in use as we record this because one thing that

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Speaker 1: one thing I think a lot of people, especially in

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Speaker 1: the US, don't know, is that there are some phones

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Speaker 1: that are designed to have two SIM cards in the

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Speaker 1: phone so they can function like two different phones. I

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Speaker 1: found out about it when I saw some news in China,

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Speaker 1: I can't remember which city about these guys who were

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Speaker 1: cheating on their spouses and then their their secret SIM

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Speaker 1: cards got found.

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Speaker 2: Tut tut. That's like prison rules, man. Yeah, like seriously.

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Speaker 2: And also, you know, because of these smart devices, we

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Speaker 2: hear a lot of other stories like that, Like I

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Speaker 2: think you know Gavin Rossdale from the band Bush he

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Speaker 2: used to be married to Gwen Stefani, you know, amazing

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Speaker 2: solo artists, but also from the band No Doubt, And

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Speaker 2: I believe that he got caught cheating because he had

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Speaker 2: the Family iPad synced with his personal device. Because with

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Speaker 2: Apple you sink all your Apple products together and so

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Speaker 2: it came up on the Family iPad. The like dudes

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Speaker 2: that his mistress was sending him and so his wife

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Speaker 2: found out, possibly even his kids. I can either confirm

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Speaker 2: nor deny to that app.

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Speaker 1: Okay, so prison rules, you're talking about multiple SIM cards,

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Speaker 1: not marriage.

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Speaker 2: I'm talking about multiple SIM cards. Okay, yeah, yeah, for sure.

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Speaker 2: The way we don't hear about SIM cards. Remember that

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Speaker 2: that heroin smuggling cat, that cat also had SIM cards

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Speaker 2: in the little pouch around its neck.

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Speaker 3: It really feels like we need to consult Professor Wilson

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Speaker 3: on this whole thing. I feel like he would know

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Speaker 3: a lot about all of this legend. Legend.

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Speaker 1: Professor Wilson, we started, if you're listening, Doc, we started

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Speaker 1: a little late today because we had to talk about

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Speaker 1: the amazing emails this guy's been sending. Just check Professor

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Speaker 1: Wilson on Twitter. I'm sure he'll show up and hope

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Speaker 1: he's well. Professor Wilson probably has a cell phone or

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Speaker 1: smartphone excuse me, not a cellular phone. We wanted to

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Speaker 1: gather some up to date statistics about just the lay

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Speaker 1: of the digital land for smartphones, and we've got info

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Speaker 1: from as recent as we could get it. We've got

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Speaker 1: a lot from twenty twenty. There's some things where we

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Speaker 1: had to go back to twenty nineteen, and there are

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Speaker 1: a couple of little slips, but some of this is

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Speaker 1: going to be news to a lot of us, just

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Speaker 1: the enormity of what's going on with smartphones.

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Speaker 3: Yes, so we talked about the ten billion devices that

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Speaker 3: are being used. Well, as of this year, there are

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Speaker 3: three point five billion humans, well mostly humans hopefully, who

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Speaker 3: are actually using these smartphones. And it's pretty crazy because

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Speaker 3: in twenty nineteen, eighty one percent of the United States

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Speaker 3: residents people living in the US had a smartphone, and

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Speaker 3: that is obviously a number that is going to continue

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Speaker 3: to rise. And here are some of the stats. Forty

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Speaker 3: seven percent say they absolutely could not live without a smartphone.

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Speaker 3: I certainly believe that, as I'm sure you do too.

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Speaker 3: In comparison, ninety nine point three percent of all internet

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Speaker 3: us in China use mobile devices to go online, so

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Speaker 3: not a laptop, not a laptop at all. When you

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Speaker 3: think about forty seven percent saying they couldn't live without one,

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Speaker 3: I'm just going to assume that a good portion of

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Speaker 3: that number has to do with people who just use

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Speaker 3: their their smartphone to access the Internet and things. Sure,

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Speaker 3: in comparison, in China. It's like so many people are

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Speaker 3: just using their mobile devices.

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Speaker 2: It makes me ask you really quickly, Matt, like, what

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Speaker 2: were we doing before smartphones? Clearly not living? And what

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Speaker 2: was that?

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Speaker 3: Like? We were blackberrying, we were, as Ben said, razoring,

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Speaker 3: we were playing snake texting each other forgetting. We were

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Speaker 3: actively forgetting all of the phone numbers that we had

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Speaker 3: in our heads.

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Speaker 1: Game Boys, we're playing game Boys Nintendo DS. I guess

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Speaker 1: the is the Homo sapien to that neanderthal, but the yeah,

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Speaker 1: I would say it's affected our.

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Speaker 4: Cognition for sure.

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Speaker 1: We're thinking less deeply about things, and now we're thinking

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Speaker 1: about more things. We've said this before. We're thinking across

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Speaker 1: the axis rather than up and down.

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Speaker 2: So we know, like we.

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Speaker 1: Have, like the first paragraph of Wikipedia, knowledge about a

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Speaker 1: ton of stuff, but we don't have as much depth

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Speaker 1: of learning.

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Speaker 4: Right.

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Speaker 1: Just this is not a ding on any individual. This

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Speaker 1: is a purposeful, brutal ding on.

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Speaker 3: Our species because part of this where we would store

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Speaker 3: all that stuff. It's now here in your hand. Oh god,

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Speaker 3: that was kind of weird. Here it's not here, it's

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Speaker 3: on this Oh look at that. Who ooh. Refresh the

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Speaker 3: tape for this episode.

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Speaker 2: I think we've been in the before times we even

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Speaker 2: had like kind of bar conversations about what happens to

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Speaker 2: that part of our brain that used to store phone numbers.

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Speaker 2: Does it evolve into something cooler or do we just

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Speaker 2: lose it? And I don't know the answer to that.

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Speaker 2: I tend towards we just lose it, or I just

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Speaker 2: get subsumed with more kind of useless, shallow dive facts

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Speaker 2: that we can then spit out at parties.

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Speaker 1: Mainly I would which we and every every one of

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Speaker 1: our colleagues are like the worst at parties, which is

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Speaker 1: we're gonna have to start a game called the actualies.

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Speaker 1: I'm kidding, I do. I have put concerted efforts into

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Speaker 1: not not being that person at parties. But but you're right,

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Speaker 1: I would say, if we wanted to be optimistic about it,

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Speaker 1: perhaps we could say that that that skill of memorization,

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Speaker 1: rather than being atrophied, has just been re channeled, because

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Speaker 1: for a time we were memorizing many more passwords, right,

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Speaker 1: which would I think occur in the same part of

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Speaker 1: the brain that retains phone numbers. But now you know

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Speaker 1: increasingly your phone or your device will recall passwords for

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Speaker 1: you or just say hey, let me go go look

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Speaker 1: at your face. All right, here's all your banking information.

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Speaker 1: That's pretty common with iPhones especially, and we use them

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Speaker 1: a lot. I'm still working this one out, and I

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Speaker 1: wanted to see how you guys felt about this number.

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Speaker 1: You Matt, you know everybody listening. So overall, it seems

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Speaker 1: that the average smartphone user across the planet checks their device,

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Speaker 1: whatever it is, at least fifty eight times per day.

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Speaker 1: We also tend to vastly underestimate how often we check

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Speaker 1: our phones, and there's fifty eight is probably still a

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Speaker 1: really low number. Because there's a study we're going to

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Speaker 1: get to later in today's episode that found in the

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Speaker 1: United Kingdom, people on average thought they checked their phone

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Speaker 1: thirty seven times a day. That was their best guest

358
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Speaker 1: when they asked all these people, but the actual number

359
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Speaker 1: was much closer to eighty five times a day. Does

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Speaker 1: that sound reasonable to you, guys or does that sound

361
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Speaker 1: like off base one way or another?

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Speaker 3: Real that does not sound off base, No, it tracks sure.

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00:20:03,280 --> 00:20:06,920
Speaker 3: If you if you use an iOS device and you

364
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Speaker 3: want to get insights into yourself and how you are

365
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Speaker 3: your phone, open up the thing that's in your settings

366
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Speaker 3: it's very close to the top. That's called screen time,

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Speaker 3: and you will see I have just so you know,

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Speaker 3: I've disabled mine on my phone. I disabled it almost

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Speaker 3: as soon as it became a feature, and I think

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Speaker 3: you should too, unless you are using it for a

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Speaker 3: specific reason like preventing children your children from using your phone,

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Speaker 3: or preventing yourself even from using your phone at certain

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Speaker 3: times or certain apps as a self regulating technique. Because

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Speaker 3: that thing alone, that little feature stores a ton of

375
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Speaker 3: information about you, as your phone does it.

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Speaker 1: So wait though, but Matt does disabling it prevent the

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Speaker 1: device from collecting that information or just prevent it from

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Speaker 1: displaying that information to you.

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Speaker 3: You can disable a lot of stuff in your iOS

380
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Speaker 3: device that has to do with what we're going to

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Speaker 3: talk about today, and that is one of the one

382
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Speaker 3: of the things that you can do.

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Speaker 2: It's also a good way to feel like an absolute

384
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Speaker 2: technology junkie when it like serves you up with that

385
00:21:20,680 --> 00:21:23,479
Speaker 2: information weekly, like there's a like automatically. I think by

386
00:21:23,480 --> 00:21:26,680
Speaker 2: default it gives you reports like your screen time is up,

387
00:21:27,000 --> 00:21:29,159
Speaker 2: you know, blah blah blah. And I'm certain that because

388
00:21:29,160 --> 00:21:32,560
Speaker 2: of all this COVID business people's screen times are way

389
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Speaker 2: the hell up, you know, and then it becomes like, Okay,

390
00:21:36,000 --> 00:21:39,119
Speaker 2: I'm an alcoholic, I need to drink less. You know

391
00:21:39,240 --> 00:21:42,439
Speaker 2: it's bad for me. How do I self regulate? Like

392
00:21:42,480 --> 00:21:44,159
Speaker 2: you say? And the fact that we even have to

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Speaker 2: think about it like that sort of proves the whole

394
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Speaker 2: idea that it is hijacking our brains and making us

395
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Speaker 2: crave that feedback that we get from checking the phone.

396
00:21:54,680 --> 00:21:56,800
Speaker 2: What if there's a new thing. What if I get

397
00:21:56,840 --> 00:22:00,240
Speaker 2: a notification? Oh, let me drag down and refresh my

398
00:22:00,440 --> 00:22:02,760
Speaker 2: Facebook feed. Maybe there's a cool new thing that I

399
00:22:02,880 --> 00:22:04,320
Speaker 2: must know about instantly.

400
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Speaker 1: Exactly, Yeah, and well put so, so we know that

401
00:22:09,680 --> 00:22:12,439
Speaker 1: that number of tracks, if anything, it's it's probably going

402
00:22:12,520 --> 00:22:15,000
Speaker 1: to be a little bit low for at least half

403
00:22:15,040 --> 00:22:17,240
Speaker 1: of the logically for about half the audience.

404
00:22:17,359 --> 00:22:17,560
Speaker 2: Right.

405
00:22:18,920 --> 00:22:23,040
Speaker 1: More than sixty percent of smartphone users have made a

406
00:22:23,160 --> 00:22:27,000
Speaker 1: purchase of some sort on their device. Not to pick

407
00:22:27,040 --> 00:22:31,040
Speaker 1: on Apple, but the way that they sandbox is purposely

408
00:22:31,119 --> 00:22:34,359
Speaker 1: designed to make it easy, or they would say, seamless,

409
00:22:34,480 --> 00:22:37,320
Speaker 1: to buy things on the device, to the chagrin if

410
00:22:37,320 --> 00:22:40,800
Speaker 1: I imagine many many parents who will later find out

411
00:22:41,119 --> 00:22:43,360
Speaker 1: that their kid has bought YadA, YadA, YadA.

412
00:22:43,320 --> 00:22:47,439
Speaker 3: And husbands and spouses. Just kidding, just kidding.

413
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Speaker 2: So while we're on.

414
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Speaker 1: The subject of money, let's also point out in app

415
00:22:53,240 --> 00:22:57,600
Speaker 1: advertising is going to rise to two hundred billion dollars

416
00:22:57,760 --> 00:23:01,639
Speaker 1: US by just next year. In four years after that,

417
00:23:01,720 --> 00:23:04,840
Speaker 1: by twenty twenty five, this number is projected to grow

418
00:23:05,200 --> 00:23:07,800
Speaker 1: as long as we don't burn everything down, projected to

419
00:23:07,840 --> 00:23:11,600
Speaker 1: grow to around two hundred and twenty six billion, and

420
00:23:12,560 --> 00:23:15,320
Speaker 1: it's gonna keep going. We spend so much time staring

421
00:23:15,359 --> 00:23:18,480
Speaker 1: at these little screens for one reason or another. In fact,

422
00:23:19,600 --> 00:23:24,520
Speaker 1: smartphones are smartphones as the doorway to the Internet for

423
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Speaker 1: a lot of people are leaving other media platforms in

424
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Speaker 1: the dust. Seventy percent of all the media time like

425
00:23:33,920 --> 00:23:38,800
Speaker 1: around ends up being spent on smartphones, which is nuts.

426
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Speaker 1: It's not saying that people are, you know, necessarily listening

427
00:23:41,960 --> 00:23:44,760
Speaker 1: to a ton less radio or something. They're just a

428
00:23:44,800 --> 00:23:47,879
Speaker 1: ton more people with very easy access to stuff.

429
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Speaker 2: It's just all integrated. I mean, I remember when the

430
00:23:50,240 --> 00:23:53,280
Speaker 2: first iPhone came out out. It seemed like magic. You know,

431
00:23:53,359 --> 00:23:56,680
Speaker 2: everything from the touch screen implementation to the way the

432
00:23:56,800 --> 00:23:59,280
Speaker 2: keyboard was virtual. And obviously it was clunky at first,

433
00:23:59,280 --> 00:24:01,320
Speaker 2: but it seemed to me at the time, and just

434
00:24:01,359 --> 00:24:03,760
Speaker 2: the idea that, oh, I can watch YouTube on my

435
00:24:03,840 --> 00:24:06,480
Speaker 2: phone or I can it's literally like a handheld computer.

436
00:24:06,800 --> 00:24:10,639
Speaker 2: And it is more so every single new generation because

437
00:24:10,640 --> 00:24:12,680
Speaker 2: of the way that you know, the way it rose

438
00:24:12,720 --> 00:24:15,639
Speaker 2: to prominence so quickly. That's just how technology works now,

439
00:24:15,640 --> 00:24:19,119
Speaker 2: because of the speed of processors and the exponential improvement

440
00:24:19,240 --> 00:24:22,600
Speaker 2: of technology and streamlining of technology. Not to mention that,

441
00:24:22,720 --> 00:24:24,760
Speaker 2: like you know, all of this is built on the

442
00:24:24,800 --> 00:24:27,560
Speaker 2: idea of stuff is free. You know, oh, the apps

443
00:24:27,560 --> 00:24:30,320
Speaker 2: are free. You can buy an app stuff if you

444
00:24:30,320 --> 00:24:32,840
Speaker 2: want to, but you don't have to. But it sure

445
00:24:32,840 --> 00:24:35,040
Speaker 2: does make it a better experience if you buy those

446
00:24:35,080 --> 00:24:38,280
Speaker 2: extra widgets or gems or power ups or what have you,

447
00:24:38,840 --> 00:24:41,360
Speaker 2: or you pay for the premium version so you don't

448
00:24:41,359 --> 00:24:43,080
Speaker 2: have to get serve those ads all the time.

449
00:24:43,600 --> 00:24:44,199
Speaker 4: Freemium.

450
00:24:44,480 --> 00:24:48,160
Speaker 1: Yeah, that brings me to another question, only tangentially related.

451
00:24:48,600 --> 00:24:51,399
Speaker 1: We all know a lot of early adopters, right, just

452
00:24:51,760 --> 00:24:55,080
Speaker 1: the nature of our social networks and friendships. Are you

453
00:24:55,160 --> 00:24:58,480
Speaker 1: guys people who buy the latest version of an iPhone

454
00:24:58,520 --> 00:25:01,200
Speaker 1: like you were you Twitter paid about the iPhone twelve?

455
00:25:01,440 --> 00:25:02,119
Speaker 2: Absolutely not.

456
00:25:03,000 --> 00:25:05,320
Speaker 3: My first iPhone was a four.

457
00:25:05,800 --> 00:25:07,359
Speaker 2: I had an early one. I had I think I

458
00:25:07,400 --> 00:25:09,960
Speaker 2: had one of the real small ones, the original that

459
00:25:10,000 --> 00:25:12,600
Speaker 2: were kind of beveled edges, you know, that was kind

460
00:25:12,600 --> 00:25:14,400
Speaker 2: of shiny on the back. I had one of those.

461
00:25:15,960 --> 00:25:18,320
Speaker 2: And the thing about those, they just become bricks man

462
00:25:18,440 --> 00:25:20,439
Speaker 2: like it was probably it's probably in a drawer somewhere,

463
00:25:20,800 --> 00:25:23,600
Speaker 2: you know, no idea, because they become useless, you know,

464
00:25:23,640 --> 00:25:27,760
Speaker 2: because they basically designed the software so that they no

465
00:25:27,840 --> 00:25:30,320
Speaker 2: longer work on the old phones. And obviously we understand

466
00:25:30,359 --> 00:25:32,159
Speaker 2: all of that about planned OPSO lessons, and that's a

467
00:25:32,200 --> 00:25:35,359
Speaker 2: whole nother discussion. But no, I wait till it's affordable.

468
00:25:35,480 --> 00:25:37,639
Speaker 2: I wait till it's like part of a deal to

469
00:25:37,760 --> 00:25:39,960
Speaker 2: re up my plan or whatever. I'm never going to

470
00:25:40,080 --> 00:25:44,639
Speaker 2: drop the full price on a new device. I'm not

471
00:25:44,680 --> 00:25:45,080
Speaker 2: that guy.

472
00:25:45,800 --> 00:25:49,800
Speaker 1: I gotta half heartily apologize and misled you guys just

473
00:25:49,880 --> 00:25:53,080
Speaker 1: a bit. Part of the reason I'm specifically asking about

474
00:25:53,160 --> 00:25:58,919
Speaker 1: the Apple iPhone twelve pro is that I got a

475
00:25:58,960 --> 00:26:03,520
Speaker 1: ton of questions over the past few days, people asking

476
00:26:03,720 --> 00:26:07,119
Speaker 1: whether I had done the vo for an Apple announcement.

477
00:26:07,760 --> 00:26:10,480
Speaker 1: And we record so much stuff that, you know, it's

478
00:26:10,480 --> 00:26:13,360
Speaker 1: easy for the three of us to not remember all

479
00:26:13,440 --> 00:26:17,320
Speaker 1: the stuff we may have recorded, and I went back

480
00:26:17,359 --> 00:26:21,000
Speaker 1: and checked after like the fifth or tenth person reached

481
00:26:21,000 --> 00:26:24,000
Speaker 1: out to me, and I've got what I've decided to

482
00:26:24,080 --> 00:26:27,600
Speaker 1: call a voppel ganger, like vo plus doppelganger. That dude

483
00:26:27,720 --> 00:26:31,560
Speaker 1: sounds like me. I don't Apple may have like done

484
00:26:31,640 --> 00:26:33,280
Speaker 1: something like it's weird.

485
00:26:33,320 --> 00:26:35,679
Speaker 3: Okay, tell us how to find it? How do we

486
00:26:35,720 --> 00:26:36,080
Speaker 3: find it?

487
00:26:36,440 --> 00:26:41,480
Speaker 1: Yeah, you can check it out by going to YouTube

488
00:26:41,680 --> 00:26:47,440
Speaker 1: and search for Apple event October thirteenth. It's it's weird.

489
00:26:47,920 --> 00:26:51,200
Speaker 1: I don't think there's anything that fair. I think it's

490
00:26:51,359 --> 00:26:54,000
Speaker 1: just that somewhere out there, I have a vocal twin.

491
00:26:54,320 --> 00:26:57,080
Speaker 1: Who are you, mysterious voppelganger?

492
00:26:57,280 --> 00:26:59,159
Speaker 2: I got one of those two. Somebody sent me with that.

493
00:26:59,160 --> 00:27:00,399
Speaker 2: I don't even remember what it was, but it was

494
00:27:00,440 --> 00:27:02,959
Speaker 2: some like ad or something, and maybe it was on

495
00:27:02,960 --> 00:27:05,439
Speaker 2: one of the message boards or the show pages for

496
00:27:05,560 --> 00:27:07,679
Speaker 2: either this or Ridiculous History. But I went back and

497
00:27:07,720 --> 00:27:10,280
Speaker 2: listened to you and it was real. It was spot on.

498
00:27:10,440 --> 00:27:13,040
Speaker 2: Like it freaked me out a little bit, you know, So,

499
00:27:13,240 --> 00:27:15,240
Speaker 2: I don't know what's the play here?

500
00:27:15,760 --> 00:27:20,520
Speaker 1: Your next bet, you're up next? But anyway, we digress.

501
00:27:21,040 --> 00:27:25,600
Speaker 1: We're just confirming that we're not secretly recording stuff and

502
00:27:25,640 --> 00:27:27,919
Speaker 1: claiming we don't know what it is. With all this

503
00:27:27,960 --> 00:27:31,639
Speaker 1: stuff in mind about smartphones and about how they have

504
00:27:32,600 --> 00:27:36,800
Speaker 1: entrenched themselves into society and culture, it shouldn't come as

505
00:27:36,800 --> 00:27:40,000
Speaker 1: a surprise that millions upon millions of people somehow work

506
00:27:40,000 --> 00:27:43,320
Speaker 1: in this industry. The current guesses I found said that

507
00:27:43,359 --> 00:27:47,200
Speaker 1: there are about fourteen million jobs that are directly related

508
00:27:47,240 --> 00:27:51,240
Speaker 1: to the mobile industry, but the number of associated jobs.

509
00:27:51,440 --> 00:27:52,840
Speaker 2: Has to be much much higher.

510
00:27:52,920 --> 00:27:56,400
Speaker 1: Right, Think of marketing firms where there's one person who

511
00:27:56,480 --> 00:28:01,119
Speaker 1: specializes in mobile experience, right, or or someone at a

512
00:28:01,160 --> 00:28:05,840
Speaker 1: tech company who specializes in mobile ux they're still associated.

513
00:28:06,040 --> 00:28:07,840
Speaker 3: There is a great deal of money. We already talked

514
00:28:07,840 --> 00:28:10,680
Speaker 3: about it. There's more on the horizon. And make no mistake,

515
00:28:10,920 --> 00:28:13,879
Speaker 3: if you own a smartphone, or somebody who lives in

516
00:28:14,000 --> 00:28:18,080
Speaker 3: close proximity to you, especially in your house, if they've

517
00:28:18,119 --> 00:28:21,560
Speaker 3: got a smartphone, you are a piece of this ecosystem

518
00:28:21,680 --> 00:28:26,680
Speaker 3: in equation. And just get ready because as we keep

519
00:28:26,720 --> 00:28:30,320
Speaker 3: going in this episode, it's gonna get weirder and scarier.

520
00:28:31,920 --> 00:28:36,119
Speaker 1: Yeah, right, And if you're listening to this show, you

521
00:28:36,160 --> 00:28:39,520
Speaker 1: know you're probably already very well aware of how this

522
00:28:39,640 --> 00:28:43,160
Speaker 1: process works. You, like us, have seen the social dilemma.

523
00:28:43,200 --> 00:28:46,600
Speaker 1: But more importantly, you've experienced it yourself. Your data on

524
00:28:46,680 --> 00:28:49,760
Speaker 1: social apps, that's a revenue stream, and you don't get

525
00:28:49,760 --> 00:28:53,240
Speaker 1: a piece of that pie. Your data from your telecom carrier,

526
00:28:53,720 --> 00:28:57,680
Speaker 1: same thing, your internet browser, every single app, or virtually

527
00:28:57,720 --> 00:29:00,520
Speaker 1: every single app on your phone. Each of those agrigations

528
00:29:00,560 --> 00:29:03,920
Speaker 1: of information are packaged up and they're sold to different

529
00:29:03,920 --> 00:29:07,760
Speaker 1: places with varying levels of anonymity.

530
00:29:07,240 --> 00:29:10,560
Speaker 4: And you do not get a piece of that either, which.

531
00:29:10,400 --> 00:29:12,480
Speaker 1: I think is one of the main things, one of

532
00:29:12,520 --> 00:29:15,600
Speaker 1: the pettiest things we winged about in the Big Data episode.

533
00:29:16,040 --> 00:29:19,880
Speaker 1: So instead we all stare into this digital abyss and

534
00:29:19,920 --> 00:29:27,600
Speaker 1: there's this dizzy, neuro chemically manipulative ping of likes and follows, subscribes, notifications.

535
00:29:27,600 --> 00:29:30,520
Speaker 1: They blow up our synapsies like these gaudy lights of

536
00:29:30,560 --> 00:29:32,800
Speaker 1: a casino slot machine.

537
00:29:32,960 --> 00:29:34,520
Speaker 4: And that's where we're at.

538
00:29:34,600 --> 00:29:37,440
Speaker 1: Like you said, this CAUs this has caused some people

539
00:29:37,480 --> 00:29:40,560
Speaker 1: to consciously limit their time or to maybe be more

540
00:29:40,600 --> 00:29:43,760
Speaker 1: mindful of it, like get those focus apps than say,

541
00:29:43,840 --> 00:29:48,720
Speaker 1: you know, hey, JABRONI get out of Instagram. You're here

542
00:29:48,760 --> 00:29:51,480
Speaker 1: for your Outlook email or something like that.

543
00:29:51,760 --> 00:29:54,240
Speaker 2: Yeah, there's some apps that will even block you from

544
00:29:54,280 --> 00:29:57,959
Speaker 2: accessing certain other apps. It's like you literally have to

545
00:29:58,040 --> 00:30:01,160
Speaker 2: like it's like a digital chastity belt or something like that,

546
00:30:01,280 --> 00:30:03,760
Speaker 2: you know, or you have to make sure that you

547
00:30:04,240 --> 00:30:08,560
Speaker 2: won't be tempted by you know, the sweet, sweet evil

548
00:30:08,640 --> 00:30:11,840
Speaker 2: fruit of digital connectivity. You know what I mean.

549
00:30:12,360 --> 00:30:15,480
Speaker 1: Yeah, how is that different from medicine that has prescribed

550
00:30:15,640 --> 00:30:18,160
Speaker 1: to address the side effects of another medication?

551
00:30:18,560 --> 00:30:21,680
Speaker 2: That's exactly No, it's I think it's it's not different

552
00:30:21,720 --> 00:30:23,720
Speaker 2: at all, terrifying.

553
00:30:23,760 --> 00:30:27,280
Speaker 1: But today's question, right, is this, how far does this

554
00:30:27,480 --> 00:30:31,200
Speaker 1: data gathering go? How will it affect you, your loved ones,

555
00:30:31,320 --> 00:30:34,520
Speaker 1: your pocketbook, your region, and this species in the future.

556
00:30:34,920 --> 00:30:39,200
Speaker 1: Why are more and more tech savvy people becoming just

557
00:30:39,240 --> 00:30:43,640
Speaker 1: like in social dilemma, doomsday profits telling us you are

558
00:30:43,920 --> 00:30:46,880
Speaker 1: your phone, we'll tell you afterword.

559
00:30:47,480 --> 00:30:58,920
Speaker 4: Oddly enough from our sponsor. Here's where it gets crazy.

560
00:31:00,040 --> 00:31:04,200
Speaker 1: Perbly aside, they are saying it because they are absolutely correct.

561
00:31:04,480 --> 00:31:06,760
Speaker 1: They've been trying to warn us about this for a while.

562
00:31:07,080 --> 00:31:09,200
Speaker 1: But this goes this goes back to one of my

563
00:31:09,280 --> 00:31:11,400
Speaker 1: problems with social dilemma. I think we've talked about it,

564
00:31:11,400 --> 00:31:16,560
Speaker 1: but maybe we haven't talked about it. While those folks

565
00:31:16,720 --> 00:31:21,120
Speaker 1: are providing valid, valuable insight. They're doing it after they

566
00:31:21,200 --> 00:31:26,720
Speaker 1: made a fortune, and morally, I am conflicted with by

567
00:31:27,160 --> 00:31:29,840
Speaker 1: you know, I don't know how to address the immense

568
00:31:29,920 --> 00:31:32,840
Speaker 1: privilege of that, right, Like you feel bad after you've

569
00:31:32,840 --> 00:31:35,080
Speaker 1: got your dragon's horde of gold and you sit on

570
00:31:35,120 --> 00:31:39,200
Speaker 1: that hill and tell people about the dangers of the

571
00:31:39,280 --> 00:31:41,280
Speaker 1: thing you created. But I don't see him giving them

572
00:31:41,360 --> 00:31:41,880
Speaker 1: money away.

573
00:31:42,400 --> 00:31:44,800
Speaker 3: Yeah, but what would what would the money do? This

574
00:31:44,840 --> 00:31:48,520
Speaker 3: is my only counterpoint. What would I mean? I guess

575
00:31:48,520 --> 00:31:50,040
Speaker 3: if they were just trying to do good in the

576
00:31:50,040 --> 00:31:53,120
Speaker 3: world and just be you know, proactive about giving their

577
00:31:53,160 --> 00:31:56,440
Speaker 3: money to some charity, right, but we know that doesn't

578
00:31:56,480 --> 00:32:01,080
Speaker 3: always work, just throwing money at a problem. I'm I

579
00:32:01,080 --> 00:32:03,240
Speaker 3: I'm not saying I'm completely on their side. I just

580
00:32:03,400 --> 00:32:07,160
Speaker 3: know that I know for a fact that the human beings,

581
00:32:07,200 --> 00:32:09,600
Speaker 3: the very low number of human beings that were in

582
00:32:09,640 --> 00:32:14,640
Speaker 3: the rooms that created the the the pioneering apps in

583
00:32:14,840 --> 00:32:19,520
Speaker 3: things that we use now ubiquitously, those people didn't fully

584
00:32:19,600 --> 00:32:22,360
Speaker 3: understand what they were doing. They knew they were trying

585
00:32:22,400 --> 00:32:27,520
Speaker 3: to build something like that was addictive to some extent. Right, No,

586
00:32:27,760 --> 00:32:30,920
Speaker 3: wasn't there, guys, isn't there wasn't there a class or

587
00:32:30,960 --> 00:32:34,000
Speaker 3: something that everybody took, or there's like connective tissue between

588
00:32:34,000 --> 00:32:34,800
Speaker 3: a lot of these things.

589
00:32:35,080 --> 00:32:38,160
Speaker 2: There is, and I would I would need to double

590
00:32:38,240 --> 00:32:40,200
Speaker 2: check what the name of the class was, but essentially

591
00:32:40,600 --> 00:32:42,320
Speaker 2: it was a class that was offered at Stanford. And

592
00:32:42,360 --> 00:32:45,600
Speaker 2: so we know Stanford is kind of like a feeder colony,

593
00:32:45,680 --> 00:32:49,880
Speaker 2: I guess for you know, Silicon Valley startup culture essentially,

594
00:32:50,760 --> 00:32:53,640
Speaker 2: and there was a particular class that everyone that ended

595
00:32:53,720 --> 00:32:56,640
Speaker 2: up in high levels of this ux design for these

596
00:32:56,680 --> 00:32:59,680
Speaker 2: types of apps all took. And it essentially had to

597
00:32:59,680 --> 00:33:04,959
Speaker 2: do with harnessing psychology and weaponizing it essentially. I mean

598
00:33:05,000 --> 00:33:08,120
Speaker 2: this really wouldn't have said it like that at the time,

599
00:33:08,520 --> 00:33:10,320
Speaker 2: but everyone kind of knew what they were doing and

600
00:33:10,360 --> 00:33:15,040
Speaker 2: they thought it very clever, and obviously it was. But hindsight,

601
00:33:15,200 --> 00:33:17,280
Speaker 2: a lot of these folks that spoke to this in

602
00:33:17,320 --> 00:33:21,360
Speaker 2: the Social Dilemma kind of make the argument of, oh,

603
00:33:21,360 --> 00:33:23,440
Speaker 2: we didn't realize how far it would go, but we

604
00:33:23,520 --> 00:33:26,720
Speaker 2: knew exactly what it was. So I see your dilemma, Ben,

605
00:33:26,960 --> 00:33:28,840
Speaker 2: that there is sort of like you can't have it

606
00:33:28,880 --> 00:33:30,960
Speaker 2: both ways thing. One of the guys who's featured in

607
00:33:31,000 --> 00:33:34,640
Speaker 2: the Social Dilemma, Gus sorry, guys, I'm being bad with names.

608
00:33:34,640 --> 00:33:37,400
Speaker 2: But he's one of the main speakers and he does

609
00:33:37,440 --> 00:33:40,200
Speaker 2: a lot of ted talk type things about the evils

610
00:33:40,280 --> 00:33:47,560
Speaker 2: of this technology and has essentially a program that's about AI,

611
00:33:48,920 --> 00:33:52,440
Speaker 2: you know, basically kindness in AI or sort of like

612
00:33:52,560 --> 00:33:58,480
Speaker 2: rethinking AI and rethinking these kinds of technologies. But you're right, like,

613
00:33:58,720 --> 00:34:01,360
Speaker 2: is he giving the money away like he made a mint,

614
00:34:01,520 --> 00:34:03,640
Speaker 2: you know, being a part of this thing. But he

615
00:34:03,760 --> 00:34:06,720
Speaker 2: was sort of the guy who flagged with Facebook early on.

616
00:34:06,800 --> 00:34:09,080
Speaker 2: He probably is the one who has the best argument.

617
00:34:09,520 --> 00:34:13,520
Speaker 3: That's the Tristan guy, but it's spelled pronounced differently that Tristan.

618
00:34:15,440 --> 00:34:18,239
Speaker 2: He has the best argument of like being somewhat benevolent

619
00:34:18,320 --> 00:34:21,080
Speaker 2: in this whole thing, because he flagged it early at

620
00:34:21,120 --> 00:34:25,120
Speaker 2: Facebook and essentially got shut down even after it made

621
00:34:25,120 --> 00:34:28,040
Speaker 2: a bunch of noise, this idea that this was going

622
00:34:28,120 --> 00:34:30,919
Speaker 2: to lead to something really bad, and then he got

623
00:34:30,960 --> 00:34:34,120
Speaker 2: sort of shut down, and then he left because he

624
00:34:34,120 --> 00:34:36,399
Speaker 2: couldn't be a part of it anymore. So I kind

625
00:34:36,400 --> 00:34:39,759
Speaker 2: of see him as being the kind of winner, the

626
00:34:39,760 --> 00:34:42,200
Speaker 2: guy that comes out looking a little bit more like

627
00:34:42,480 --> 00:34:44,759
Speaker 2: he's practicing what he preaches in this documentary.

628
00:34:45,200 --> 00:34:47,719
Speaker 1: Yeah, understood, and thank you guys again. I don't know

629
00:34:48,200 --> 00:34:55,080
Speaker 1: how to intellectually navigate it, but the social dilemma is

630
00:34:55,760 --> 00:34:59,880
Speaker 1: worth watching. My personal principle is I don't like to

631
00:35:00,120 --> 00:35:03,680
Speaker 1: judge people too harshly when I'm seeing a curated version

632
00:35:03,760 --> 00:35:07,560
Speaker 1: of them, like on a documentary or something. So social

633
00:35:07,560 --> 00:35:11,000
Speaker 1: dilemma exists, though tangentially related.

634
00:35:10,560 --> 00:35:11,680
Speaker 4: To this episode.

635
00:35:11,719 --> 00:35:15,040
Speaker 1: To go back to the concept of what we're talking

636
00:35:15,040 --> 00:35:20,479
Speaker 1: about today, you are your phone as a concept goes

637
00:35:20,600 --> 00:35:27,240
Speaker 1: far far beyond targeted advertising and psychologically manipulative ux design,

638
00:35:28,840 --> 00:35:31,600
Speaker 1: way back as far back as twenty fifteen, and to

639
00:35:31,640 --> 00:35:35,040
Speaker 1: be honest, before twenty fifteen, people were sounding the alarm

640
00:35:35,840 --> 00:35:37,920
Speaker 1: a few years back, and there were a couple of

641
00:35:37,960 --> 00:35:41,160
Speaker 1: startups in Silicon Valley that had a bright idea. They

642
00:35:41,200 --> 00:35:43,759
Speaker 1: were lenders who want They wanted to be disruptors in

643
00:35:43,800 --> 00:35:46,320
Speaker 1: the financial market, and you know, disruptors is one of

644
00:35:46,320 --> 00:35:49,320
Speaker 1: those buzzwords it gets thrown around. Here's what they wanted

645
00:35:49,320 --> 00:35:52,960
Speaker 1: to do. They said, why don't we base personal loan

646
00:35:53,080 --> 00:35:58,920
Speaker 1: decisions on analyzes of data that we get from people's phones.

647
00:35:59,320 --> 00:36:02,040
Speaker 1: That means exactly what you think it means, folks, knowing

648
00:36:02,160 --> 00:36:05,440
Speaker 1: how you use your phone on a granular level, it

649
00:36:05,520 --> 00:36:07,960
Speaker 1: reveals a lot about you. They want it, you know,

650
00:36:08,000 --> 00:36:11,080
Speaker 1: they were what's another buzzword, They were going blue sky right,

651
00:36:11,320 --> 00:36:14,320
Speaker 1: and they were saying, Look, we want all the texts,

652
00:36:14,360 --> 00:36:17,560
Speaker 1: we want all the emails, the GPS coordinates, we want

653
00:36:17,600 --> 00:36:21,279
Speaker 1: social media posts, we want all the receipts that are sent,

654
00:36:21,360 --> 00:36:25,360
Speaker 1: you know, for like your Uber ride or you're purchased

655
00:36:25,400 --> 00:36:28,480
Speaker 1: through an app, And we want to take all of

656
00:36:28,520 --> 00:36:33,200
Speaker 1: that stuff and then build a collage about this person,

657
00:36:33,360 --> 00:36:39,000
Speaker 1: build like a very accurate persona and like a profile eser.

658
00:36:40,080 --> 00:36:44,600
Speaker 1: And these patterns of behavior that by themselves or grains

659
00:36:44,600 --> 00:36:49,080
Speaker 1: of sand and innocuous together they can be fused to

660
00:36:49,160 --> 00:36:53,320
Speaker 1: make this gigantic glass sculpture of you, which is weirdly

661
00:36:54,360 --> 00:36:56,239
Speaker 1: bad announce. Okay, these aren't all going to work, but

662
00:36:56,280 --> 00:36:59,759
Speaker 1: you see what I'm saying. And this behavior can correlate

663
00:37:00,239 --> 00:37:05,399
Speaker 1: in their minds with a person's likelihood to repay alone or.

664
00:37:05,400 --> 00:37:06,600
Speaker 2: To default on a loan.

665
00:37:07,200 --> 00:37:10,520
Speaker 1: And we've got like specific stuff they found, like it's

666
00:37:10,560 --> 00:37:15,799
Speaker 1: even more obscure than this person you know, goes to

667
00:37:16,200 --> 00:37:18,680
Speaker 1: Applebee's a five point thirty every Thursday.

668
00:37:19,200 --> 00:37:22,560
Speaker 3: Which would be a great indicator that you are just

669
00:37:22,600 --> 00:37:25,280
Speaker 3: going to get the best loans that you are a paragon.

670
00:37:27,360 --> 00:37:30,719
Speaker 3: But yeah, it's things like how often do you recharge

671
00:37:30,760 --> 00:37:34,840
Speaker 3: your phone's battery? How many miles do you travel in

672
00:37:34,920 --> 00:37:39,279
Speaker 3: a given day? Oh here here's my personal favorite one,

673
00:37:40,120 --> 00:37:41,200
Speaker 3: and thank you for finding this.

674
00:37:41,280 --> 00:37:41,480
Speaker 2: Ben.

675
00:37:42,680 --> 00:37:45,799
Speaker 3: When you enter a new contact into your phone, do

676
00:37:45,880 --> 00:37:49,280
Speaker 3: you take the time to put a last name in there?

677
00:37:49,800 --> 00:37:52,520
Speaker 3: And are you capitalizing the first letter? Are you making

678
00:37:52,560 --> 00:37:56,920
Speaker 3: it look like a true good Rolodex within your phone?

679
00:37:57,000 --> 00:38:00,279
Speaker 3: Because if you are you my friend, are worthy of

680
00:38:00,320 --> 00:38:01,520
Speaker 3: the highest of credits?

681
00:38:01,680 --> 00:38:03,640
Speaker 2: Or do you just put Steve Weed guy?

682
00:38:04,080 --> 00:38:04,279
Speaker 4: You know?

683
00:38:04,560 --> 00:38:08,640
Speaker 2: Like, yeah, these things matter. I mean it's interesting because

684
00:38:08,680 --> 00:38:11,080
Speaker 2: it's like it's not that far removed from the current

685
00:38:11,160 --> 00:38:14,120
Speaker 2: credit rating system. I mean, it's all about mapping our

686
00:38:14,160 --> 00:38:18,880
Speaker 2: decisions and penalizing us or we're rewarding us accordingly. I

687
00:38:18,880 --> 00:38:19,759
Speaker 2: mean it's like do we.

688
00:38:20,560 --> 00:38:24,239
Speaker 3: But they're all they're all financially linked. Credit is now

689
00:38:24,280 --> 00:38:25,600
Speaker 3: linked to your financials.

690
00:38:25,680 --> 00:38:27,000
Speaker 2: I get it. But what I'm asking you is do

691
00:38:27,040 --> 00:38:30,560
Speaker 2: you opt in to being tracked by experience? Do you

692
00:38:30,600 --> 00:38:33,920
Speaker 2: opt in to being tracked by all these credit reporting agencies?

693
00:38:34,080 --> 00:38:36,319
Speaker 2: It just happens by virtue of you having a social

694
00:38:36,360 --> 00:38:39,160
Speaker 2: security number, And I mean you don't when you get

695
00:38:39,200 --> 00:38:41,359
Speaker 2: that social security number, you're an infant. You don't have

696
00:38:41,680 --> 00:38:44,239
Speaker 2: the ability to say I don't want that, get me

697
00:38:44,640 --> 00:38:47,120
Speaker 2: off the grid, you know. So you are basically by

698
00:38:47,200 --> 00:38:50,400
Speaker 2: default part of this system. So we're already kind of

699
00:38:50,440 --> 00:38:52,680
Speaker 2: in this. This is just an escalation of it, or

700
00:38:52,760 --> 00:38:55,960
Speaker 2: like almost more something like approaching sesame credit, which we've

701
00:38:55,960 --> 00:38:59,560
Speaker 2: talked about, and the way that in China, I believe

702
00:38:59,640 --> 00:39:03,440
Speaker 2: they use social media statuses to track you and to

703
00:39:03,520 --> 00:39:07,000
Speaker 2: prove you for entrance and exit and premium flights and

704
00:39:07,120 --> 00:39:08,839
Speaker 2: all the kind of black mirror stuff. You know.

705
00:39:09,560 --> 00:39:13,960
Speaker 1: So wait, michig control, give me a record scratch rewind.

706
00:39:15,239 --> 00:39:18,320
Speaker 1: Does this mean some of us are asking that always

707
00:39:18,360 --> 00:39:21,440
Speaker 1: having your phone around like fourteen percent charge or something

708
00:39:21,719 --> 00:39:25,760
Speaker 1: means you'll be considered a risky person loanwise? The answer

709
00:39:25,800 --> 00:39:28,320
Speaker 1: is yeah, well something like that is in the cards.

710
00:39:28,760 --> 00:39:31,120
Speaker 3: Well let me plug this in then, right.

711
00:39:31,200 --> 00:39:34,919
Speaker 1: The only real question in the conspiracy here is how

712
00:39:35,000 --> 00:39:38,640
Speaker 1: much data they can get, and this is their conspiracy,

713
00:39:38,680 --> 00:39:41,040
Speaker 1: these kinds of entities, and how much they have to

714
00:39:41,120 --> 00:39:46,640
Speaker 1: disclose about their analytical methods, their proprietary algorithms. It is

715
00:39:46,719 --> 00:39:48,960
Speaker 1: safe to say and This is an assumption, but it's

716
00:39:48,960 --> 00:39:51,400
Speaker 1: safe to say number one, they want all of the

717
00:39:51,520 --> 00:39:55,880
Speaker 1: data picture some version of that Gary Oldman meme or

718
00:39:55,960 --> 00:39:59,600
Speaker 1: GIF where he's screaming everything. And two they want none

719
00:39:59,640 --> 00:40:03,040
Speaker 1: of the old Why would you this as a business?

720
00:40:03,120 --> 00:40:06,080
Speaker 1: It loses its edge if other people can do the

721
00:40:06,120 --> 00:40:10,239
Speaker 1: same thing. So wait, you might be saying, well, guys, guys, guys,

722
00:40:10,320 --> 00:40:13,440
Speaker 1: I'm a longtime listener conspiracy realist. I don't have a

723
00:40:13,440 --> 00:40:16,200
Speaker 1: bunch of garbage apps on my phone, okay, and you know, heck,

724
00:40:16,360 --> 00:40:19,360
Speaker 1: now that I know this might affect my financial future,

725
00:40:19,719 --> 00:40:21,960
Speaker 1: I'm just going to stick to text. I'm just going

726
00:40:22,040 --> 00:40:24,960
Speaker 1: to stick to calls because if I don't generate a

727
00:40:25,000 --> 00:40:28,680
Speaker 1: bunch of information, then these people will have nothing to

728
00:40:28,960 --> 00:40:33,879
Speaker 1: use against me. And to that we can only respond what.

729
00:40:37,120 --> 00:40:39,520
Speaker 2: Don't be so sure? Yeah, I wouldn't bet on it.

730
00:40:40,000 --> 00:40:45,040
Speaker 2: Study published in Science, that's right, Science Science publisher found

731
00:40:45,080 --> 00:40:48,080
Speaker 2: the metadata alone of this information, you know, in the

732
00:40:48,080 --> 00:40:50,480
Speaker 2: same way that we know the NSA was tracking getting

733
00:40:50,520 --> 00:40:53,680
Speaker 2: really good info using only metadata. They didn't have to

734
00:40:53,760 --> 00:40:56,400
Speaker 2: monitor your call. They could just tell things by the

735
00:40:56,480 --> 00:40:59,000
Speaker 2: length of the call and analyze that web of who

736
00:40:59,000 --> 00:41:01,400
Speaker 2: you're calling and who those are calling, et cetera, and

737
00:41:01,440 --> 00:41:03,120
Speaker 2: then just tracking all of that, they can get a

738
00:41:03,160 --> 00:41:06,360
Speaker 2: pretty decent picture just based on that alone. And this

739
00:41:06,520 --> 00:41:11,719
Speaker 2: study pointed out that this information actually can reveal interesting

740
00:41:11,840 --> 00:41:16,160
Speaker 2: economic status type information, just things like when the calls

741
00:41:16,200 --> 00:41:21,800
Speaker 2: were made, the length of the calls, when they were received,

742
00:41:22,880 --> 00:41:26,080
Speaker 2: when text messages were sent, and which cell phone towers

743
00:41:26,160 --> 00:41:29,200
Speaker 2: the texts or calls were pinging off of. And the

744
00:41:29,239 --> 00:41:33,080
Speaker 2: researchers analyzed again, just the metadata that we just described

745
00:41:33,440 --> 00:41:36,960
Speaker 2: and were able to build a one of those profiles

746
00:41:37,000 --> 00:41:39,200
Speaker 2: that we're talking about, or take it a step further,

747
00:41:39,400 --> 00:41:44,839
Speaker 2: an algorithm that could predict behavior and could correlate that

748
00:41:44,880 --> 00:41:49,520
Speaker 2: with wealth or lack thereof of a given phone user,

749
00:41:50,000 --> 00:41:52,680
Speaker 2: and it gets even crazier, even more granular.

750
00:41:52,920 --> 00:41:56,280
Speaker 1: This is I don't know. Whenever we talk about this stuff,

751
00:41:56,360 --> 00:42:01,040
Speaker 1: I'm always torn between being like, that's amazing, actively fascinating

752
00:42:01,120 --> 00:42:06,400
Speaker 1: and impressive, and going that's terrifying and objectively frightening.

753
00:42:06,880 --> 00:42:08,239
Speaker 3: Because they're able.

754
00:42:07,960 --> 00:42:11,839
Speaker 1: To, uh, these scientists were able to answer even more

755
00:42:11,880 --> 00:42:15,520
Speaker 1: specific questions again just based on that very bare bones data.

756
00:42:15,840 --> 00:42:18,879
Speaker 1: They were able to say, this house obviously doesn't have electricity,

757
00:42:19,840 --> 00:42:23,560
Speaker 1: and therefore, if they you know, can't pay their electric bill.

758
00:42:23,680 --> 00:42:25,960
Speaker 1: How does that work out for their loans?

759
00:42:26,440 --> 00:42:29,279
Speaker 3: Oh, because it wasn't being charged while it was being

760
00:42:29,400 --> 00:42:30,920
Speaker 3: geolocated there. Wow.

761
00:42:31,120 --> 00:42:34,520
Speaker 2: Okay, but is this somewhere in the terms of service,

762
00:42:35,080 --> 00:42:37,279
Speaker 2: like at some point you have to sort of give

763
00:42:37,360 --> 00:42:40,280
Speaker 2: consent for this information to be used in these ways?

764
00:42:40,360 --> 00:42:40,560
Speaker 1: Right?

765
00:42:41,160 --> 00:42:41,400
Speaker 3: Or no?

766
00:42:42,280 --> 00:42:44,239
Speaker 2: Is it just by virtue of having the device? Like

767
00:42:44,320 --> 00:42:46,799
Speaker 2: at what point? At what line of text? In these

768
00:42:46,920 --> 00:42:48,240
Speaker 2: massive terms of service?

769
00:42:48,840 --> 00:42:49,040
Speaker 4: Is that?

770
00:42:49,560 --> 00:42:51,800
Speaker 2: Is it per app? Is it for the whole phone

771
00:42:51,840 --> 00:42:52,400
Speaker 2: at large?

772
00:42:52,719 --> 00:42:53,320
Speaker 4: It's tricky.

773
00:42:53,719 --> 00:42:57,439
Speaker 3: Yeah, here's here's my mission for you know, find that

774
00:42:57,680 --> 00:43:00,560
Speaker 3: piece of text. I dare you hundred percent?

775
00:43:00,680 --> 00:43:04,839
Speaker 2: I mean, basically speaking, I'm speaking not hypothetically, but it's

776
00:43:04,920 --> 00:43:08,400
Speaker 2: it's it's, of course a needle in a haystack situation.

777
00:43:08,560 --> 00:43:09,600
Speaker 2: If it even exists.

778
00:43:10,320 --> 00:43:12,920
Speaker 1: It does it does exist. One of my one of

779
00:43:12,920 --> 00:43:17,440
Speaker 1: my old colleagues, actually was mentioned this person on air before,

780
00:43:17,480 --> 00:43:19,960
Speaker 1: but I'll try to keep it kind of anonymous. They

781
00:43:20,000 --> 00:43:26,000
Speaker 1: were having some pretty good success making readable terms of

782
00:43:26,080 --> 00:43:30,480
Speaker 1: service and conditions, and then they got their funding pulled

783
00:43:31,000 --> 00:43:34,280
Speaker 1: because there is no benefit to many of these entities

784
00:43:34,360 --> 00:43:39,120
Speaker 1: for the end user to understand those TOS documents, and

785
00:43:39,200 --> 00:43:42,480
Speaker 1: usually the line will be something like I'm not citing

786
00:43:42,480 --> 00:43:46,000
Speaker 1: a specific example here, but the line will be something like,

787
00:43:47,080 --> 00:43:52,720
Speaker 1: by agreeing to this service, the customer, the user also

788
00:43:53,000 --> 00:43:59,680
Speaker 1: agrees that the provider can use data collected and provide

789
00:43:59,719 --> 00:44:03,960
Speaker 1: a third parties as necessary to improve service. And so

790
00:44:04,000 --> 00:44:08,560
Speaker 1: they're like third parties is a huge chasm of a term,

791
00:44:08,719 --> 00:44:13,040
Speaker 1: and so is improve service. Right, They're saying, we're improving

792
00:44:13,120 --> 00:44:16,040
Speaker 1: your service. If they can find any kind of rationalization

793
00:44:16,120 --> 00:44:18,320
Speaker 1: for that, then they can do whatever they want, especially

794
00:44:18,360 --> 00:44:20,719
Speaker 1: because the other thing that always goes with it, just

795
00:44:20,760 --> 00:44:23,319
Speaker 1: like as Forrest Gump would say, peas and carrots, is

796
00:44:23,440 --> 00:44:27,920
Speaker 1: the is that lovely little line. The terms of service

797
00:44:27,960 --> 00:44:28,560
Speaker 1: can change at.

798
00:44:28,520 --> 00:44:31,600
Speaker 3: Anytime, and they do. They get updated pretty frequently.

799
00:44:32,000 --> 00:44:34,400
Speaker 2: But doesn't I mean you have to like, do they

800
00:44:34,400 --> 00:44:37,160
Speaker 2: serve it to you again? I don't recall having been

801
00:44:37,640 --> 00:44:41,719
Speaker 2: reserved terms of service to agree again when things have

802
00:44:41,760 --> 00:44:43,120
Speaker 2: been significantly changed.

803
00:44:43,600 --> 00:44:47,000
Speaker 1: Sometimes I'll have to say there's a privacy announcement or something,

804
00:44:47,000 --> 00:44:49,040
Speaker 1: but you don't have to sign anything.

805
00:44:49,120 --> 00:44:50,560
Speaker 4: They're just telling you what's happening.

806
00:44:50,800 --> 00:44:53,719
Speaker 3: Yeah. Well, and sometimes you'll notice not necessarily with iOS,

807
00:44:53,760 --> 00:44:56,760
Speaker 3: but with other devices and other apps and things like that.

808
00:44:57,239 --> 00:45:00,440
Speaker 3: With an update. You'll just have to click eye agree

809
00:45:00,480 --> 00:45:03,319
Speaker 3: again and you won't even realize that you're doing it.

810
00:45:03,600 --> 00:45:06,720
Speaker 3: I'm serious. You're opening a new app that's just being updated.

811
00:45:06,760 --> 00:45:08,839
Speaker 3: You're like, oh, let me get past this. You don't

812
00:45:08,840 --> 00:45:11,120
Speaker 3: even realize. I've done it so many times in my life.

813
00:45:11,160 --> 00:45:11,720
Speaker 3: It's insane.

814
00:45:11,760 --> 00:45:12,799
Speaker 4: It's so ridiculous.

815
00:45:12,840 --> 00:45:18,160
Speaker 1: And I love the the slick psychological UX design where

816
00:45:18,200 --> 00:45:22,600
Speaker 1: it's clicking a button that said like the the yes

817
00:45:22,719 --> 00:45:30,240
Speaker 1: button is disguised as something like uh, okay or thank you, continue, continue, yeah, yeah,

818
00:45:30,280 --> 00:45:35,160
Speaker 1: and the no button is nested several different interactions down.

819
00:45:35,480 --> 00:45:38,319
Speaker 1: It's like under a learn more, under a learn more

820
00:45:38,560 --> 00:45:39,680
Speaker 1: under a learn more.

821
00:45:39,880 --> 00:45:41,840
Speaker 3: Or you just can't you can't move forward. If you

822
00:45:41,840 --> 00:45:43,799
Speaker 3: say no, then okay, you can use it.

823
00:45:44,040 --> 00:45:47,200
Speaker 2: You will occasionally see this is rarer and rarer. I

824
00:45:47,239 --> 00:45:49,640
Speaker 2: think ones where they will actually force you to, at

825
00:45:49,640 --> 00:45:52,440
Speaker 2: the very least scroll all the way to the bottom,

826
00:45:53,080 --> 00:45:56,759
Speaker 2: the implication being okay, I I blasted through this. All

827
00:45:56,800 --> 00:45:58,799
Speaker 2: of the text was in front of my eyeballs at

828
00:45:58,800 --> 00:46:01,440
Speaker 2: some point, and that's say, maybe to provide more protection

829
00:46:01,960 --> 00:46:04,720
Speaker 2: for the for the developer, you know, for the company,

830
00:46:05,400 --> 00:46:07,320
Speaker 2: But it doesn't seem like a necessity, it seems like

831
00:46:07,360 --> 00:46:09,719
Speaker 2: something they're legal, was like, we should probably do this

832
00:46:10,520 --> 00:46:13,680
Speaker 2: just to like cover our assets a little more.

833
00:46:13,920 --> 00:46:17,960
Speaker 1: In the most token effort possible. And so here we

834
00:46:18,000 --> 00:46:20,360
Speaker 1: have proven that it doesn't take a bunch of data.

835
00:46:20,719 --> 00:46:20,919
Speaker 2: Right.

836
00:46:21,600 --> 00:46:24,680
Speaker 1: So as for the strategy of using a phone less

837
00:46:24,800 --> 00:46:28,880
Speaker 1: and less right, or disclosing less and less info to

838
00:46:28,960 --> 00:46:32,600
Speaker 1: later be used against you or to just be used

839
00:46:32,640 --> 00:46:35,160
Speaker 1: in some way with your informed consent, which we just

840
00:46:35,200 --> 00:46:38,879
Speaker 1: talked about how tricky that is. As for that strategy,

841
00:46:39,239 --> 00:46:41,960
Speaker 1: we're going to have to return to the study we

842
00:46:42,080 --> 00:46:44,279
Speaker 1: mentioned at the top of the show, and we'll do

843
00:46:44,360 --> 00:46:46,080
Speaker 1: this afterward from our sponsor.

844
00:46:53,760 --> 00:46:54,359
Speaker 4: So we're back.

845
00:46:54,520 --> 00:46:57,560
Speaker 1: Here's the study we mentioned about how often you touch

846
00:46:57,600 --> 00:47:01,280
Speaker 1: your phone or interact with it. There were four researchers

847
00:47:01,400 --> 00:47:05,320
Speaker 1: based in the United Kingdom. They installed a usage tracking

848
00:47:05,440 --> 00:47:09,600
Speaker 1: app consensually on the smartphones of twenty three students and

849
00:47:09,719 --> 00:47:12,879
Speaker 1: faculty at the University of Lincoln. They let it run

850
00:47:12,960 --> 00:47:16,120
Speaker 1: for two weeks and then they examined the results, and

851
00:47:16,160 --> 00:47:19,960
Speaker 1: there's an excellent summarization of this by an author named

852
00:47:20,040 --> 00:47:20,920
Speaker 1: Nicholas Carr.

853
00:47:21,480 --> 00:47:24,799
Speaker 3: They realized that just by measuring one simple thing, they

854
00:47:24,800 --> 00:47:27,839
Speaker 3: could tell a whole host of information, a whole bunch

855
00:47:27,880 --> 00:47:30,920
Speaker 3: of things about a person's daily routine. And all that

856
00:47:31,080 --> 00:47:35,279
Speaker 3: is is when an individual is using their phone. That's

857
00:47:35,320 --> 00:47:37,600
Speaker 3: really all you have to measure, and you can see

858
00:47:37,800 --> 00:47:38,840
Speaker 3: almost the whole picture.

859
00:47:39,160 --> 00:47:42,480
Speaker 1: When are you asleep? Phone usage will tell you when

860
00:47:42,520 --> 00:47:44,279
Speaker 1: do you wake up. It turns out that all of

861
00:47:44,320 --> 00:47:46,960
Speaker 1: these test subjects except one use their phone as an

862
00:47:47,000 --> 00:47:50,239
Speaker 1: alarm clock, and one hundred percent of them said the

863
00:47:50,320 --> 00:47:52,840
Speaker 1: last thing they do before they go to sleep is

864
00:47:52,920 --> 00:47:56,040
Speaker 1: check their phone. As for gaps and phone usage during

865
00:47:56,080 --> 00:47:58,480
Speaker 1: the day, pretty good indicator of a nap.

866
00:47:58,719 --> 00:47:59,600
Speaker 4: It turns out.

867
00:48:00,360 --> 00:48:01,720
Speaker 3: How crazy a nap.

868
00:48:02,120 --> 00:48:03,680
Speaker 2: I can't. I don't know how to nap. I'm really

869
00:48:03,680 --> 00:48:05,520
Speaker 2: bad at napping. Do you guys know how to nap?

870
00:48:06,120 --> 00:48:06,279
Speaker 4: Oh?

871
00:48:06,320 --> 00:48:08,239
Speaker 3: We need to ask everybody out in the UK, like,

872
00:48:08,520 --> 00:48:11,800
Speaker 3: is it easier to nap there? Or what's the deal?

873
00:48:12,120 --> 00:48:13,800
Speaker 1: Is it the gloomy weather that helps?

874
00:48:13,880 --> 00:48:15,480
Speaker 2: Oh? I could do it. I could do it.

875
00:48:15,600 --> 00:48:20,080
Speaker 1: I actually have the opposite problem though. I can't sleep

876
00:48:20,160 --> 00:48:23,080
Speaker 1: for like six hours in a stretch, so I just

877
00:48:23,200 --> 00:48:27,200
Speaker 1: kind of the pandemic has made it real weird too, you.

878
00:48:27,160 --> 00:48:30,080
Speaker 2: Know, grab grab it where you can, a few winks

879
00:48:30,080 --> 00:48:33,320
Speaker 2: here and there, as they say, exactly.

880
00:48:33,160 --> 00:48:36,719
Speaker 1: And why does this not sleep patterns? But why does

881
00:48:36,760 --> 00:48:39,360
Speaker 1: this matter that so much can be gleaned from this

882
00:48:39,640 --> 00:48:43,480
Speaker 1: innocuous information. Well, it's because both of those studies depend

883
00:48:43,760 --> 00:48:48,160
Speaker 1: entirely on this very basic info, and that means that

884
00:48:49,680 --> 00:48:53,560
Speaker 1: there's an entire new universe out there. When we look

885
00:48:53,600 --> 00:48:56,040
Speaker 1: at the other stuff, the other data that can be pulled,

886
00:48:56,080 --> 00:48:58,640
Speaker 1: we get to the world of apps, how we use them,

887
00:48:58,840 --> 00:49:02,520
Speaker 1: when we use them, When, for instance, do you tend

888
00:49:02,560 --> 00:49:06,120
Speaker 1: to order delivery food? Or when do you search? What

889
00:49:06,160 --> 00:49:07,920
Speaker 1: do you search for? What do we watch, what do

890
00:49:08,000 --> 00:49:10,960
Speaker 1: we listen to when do we do that? What kind

891
00:49:10,960 --> 00:49:13,400
Speaker 1: of pictures do we post on social media? And what

892
00:49:13,480 --> 00:49:16,120
Speaker 1: kind of pictures do we like? Even if those are

893
00:49:16,160 --> 00:49:20,200
Speaker 1: private to you, they are not private to the companies

894
00:49:20,239 --> 00:49:25,080
Speaker 1: that have the ability to wield this information. We're building

895
00:49:25,160 --> 00:49:27,360
Speaker 1: these sculptures of ourselves, and most of the time we

896
00:49:27,400 --> 00:49:29,680
Speaker 1: don't know how sophisticated they are.

897
00:49:29,800 --> 00:49:32,240
Speaker 2: Well, and I don't know if we've ever gotten fully

898
00:49:32,239 --> 00:49:33,759
Speaker 2: to the bottom of this, but I think we've all

899
00:49:33,800 --> 00:49:37,399
Speaker 2: had that experience, or we had some little throwaway conversation

900
00:49:37,600 --> 00:49:41,480
Speaker 2: about a thing, a topic, a movie, a record, what

901
00:49:41,640 --> 00:49:43,400
Speaker 2: have you, and then all of a sudden you're getting

902
00:49:43,440 --> 00:49:46,040
Speaker 2: served up ads for that thing, and in your mind

903
00:49:46,080 --> 00:49:48,640
Speaker 2: it could only be based on this notion that your

904
00:49:48,680 --> 00:49:51,839
Speaker 2: phone is listening to you. And I think we did

905
00:49:52,000 --> 00:49:54,759
Speaker 2: cover something along these lines where you know the microphone

906
00:49:54,840 --> 00:49:58,359
Speaker 2: is on and transmitting or what.

907
00:49:58,440 --> 00:50:01,279
Speaker 3: I don't know, well, especially if you're using on an

908
00:50:01,280 --> 00:50:05,400
Speaker 3: iOS device, theory, or on any other device, you're what

909
00:50:05,520 --> 00:50:07,879
Speaker 3: is the what is the term for that, the concierge

910
00:50:07,960 --> 00:50:12,600
Speaker 3: or your assistant whatever, whatever the personal assistant is, it

911
00:50:12,680 --> 00:50:17,000
Speaker 3: is always listening. There is a Google Home like on

912
00:50:17,040 --> 00:50:20,040
Speaker 3: the other side of this wall. I'm pointing directly ahead

913
00:50:20,040 --> 00:50:24,480
Speaker 3: of me for everyone listening. And I realized that while

914
00:50:24,520 --> 00:50:26,719
Speaker 3: I'm in my office with the door closed, on the

915
00:50:26,800 --> 00:50:29,920
Speaker 3: other side of that wall, it can hear me. So

916
00:50:30,440 --> 00:50:33,400
Speaker 3: I I am just now I'm having this realization in

917
00:50:33,440 --> 00:50:37,960
Speaker 3: real time. Every time we podcast, my Google Home is

918
00:50:38,040 --> 00:50:41,840
Speaker 3: listening to everything. I'm saying, Hey Google, no, no.

919
00:50:41,680 --> 00:50:48,440
Speaker 2: No no, But did try everyone at homes? Google Homes

920
00:50:48,520 --> 00:50:49,560
Speaker 2: just went bonkers.

921
00:50:49,640 --> 00:50:54,600
Speaker 3: Just okay, sorry, I don't even know where were that.

922
00:50:54,600 --> 00:50:57,200
Speaker 3: That really just weirded me out. I'm gonna have to

923
00:50:57,840 --> 00:50:59,000
Speaker 3: disable that thing somehow.

924
00:50:59,160 --> 00:51:02,640
Speaker 1: Here's a here's a at I'm not being sarcastic because

925
00:51:02,680 --> 00:51:05,040
Speaker 1: a genuinely fun thing I've been doing in an experiment

926
00:51:05,360 --> 00:51:08,719
Speaker 1: that you can try at home, folks. When I was

927
00:51:08,840 --> 00:51:12,400
Speaker 1: convinced about this, and that was several years ago, based

928
00:51:12,440 --> 00:51:14,640
Speaker 1: on you know, our collective work here on stuff they

929
00:51:14,680 --> 00:51:18,360
Speaker 1: want you to know, I decided a while back that

930
00:51:18,400 --> 00:51:21,120
Speaker 1: I was going to try to hack my targeted ads

931
00:51:21,280 --> 00:51:23,760
Speaker 1: if I couldn't get away from them, because you can't.

932
00:51:23,880 --> 00:51:28,520
Speaker 1: It's very difficult to one hundred percent eliminate this phenomenon.

933
00:51:29,000 --> 00:51:31,480
Speaker 1: So I said, Okay, well, maybe I'll just try to

934
00:51:31,760 --> 00:51:37,600
Speaker 1: make the digital autobiography of me into a very like elitist,

935
00:51:37,760 --> 00:51:40,440
Speaker 1: wealthy person. I think we mentioned this earlier, right when

936
00:51:40,440 --> 00:51:43,280
Speaker 1: I started like just searching for fancy watches.

937
00:51:43,640 --> 00:51:47,359
Speaker 2: Yeah right, yachts, Yes, yachts, And I've had it.

938
00:51:47,600 --> 00:51:49,080
Speaker 4: I've actually had some good.

939
00:51:48,920 --> 00:51:54,480
Speaker 2: Results, well, exclusively listening to Steely Dan, you know, really

940
00:51:54,600 --> 00:51:56,960
Speaker 2: putting out that vibe into the universe. You know that

941
00:51:57,040 --> 00:51:58,640
Speaker 2: you are a yachtsman.

942
00:51:58,600 --> 00:52:03,239
Speaker 1: Yeah, searching searching for stuff like dangers of peasant uprising

943
00:52:03,560 --> 00:52:07,600
Speaker 1: and and order and endangered meat.

944
00:52:07,840 --> 00:52:11,160
Speaker 3: You know, all all the hits, best fox, hunting dogs.

945
00:52:11,239 --> 00:52:13,279
Speaker 1: Best fox. Oh that's a good one. Hang on, I'm

946
00:52:13,280 --> 00:52:16,640
Speaker 1: going to make a note, but but you're right. I mean,

947
00:52:16,680 --> 00:52:21,200
Speaker 1: we're we're right here. The companies that are seeking to

948
00:52:21,280 --> 00:52:24,040
Speaker 1: use this data are going to make big, life changing

949
00:52:24,120 --> 00:52:28,200
Speaker 1: decisions based on their perceived their self perceived ability to

950
00:52:28,320 --> 00:52:32,560
Speaker 1: predict what they see as your future actions. This could

951
00:52:32,600 --> 00:52:37,200
Speaker 1: be one of the most amazing, noble, beautiful things in

952
00:52:37,280 --> 00:52:40,799
Speaker 1: the story of human technology, because think about it. This

953
00:52:40,880 --> 00:52:44,279
Speaker 1: would mean that it is possible for the groups that

954
00:52:44,360 --> 00:52:47,759
Speaker 1: are aggregating the information like this to step in and say,

955
00:52:48,120 --> 00:52:53,000
Speaker 1: provide preventative financial or mental health counseling. If their algorithm

956
00:52:53,040 --> 00:52:55,560
Speaker 1: indicates that someone's going to be in trouble down the road,

957
00:52:55,920 --> 00:52:58,080
Speaker 1: they could save people in a very real way.

958
00:52:58,320 --> 00:53:00,520
Speaker 3: Oh yeah, and that's exactly what's good to happen.

959
00:53:01,160 --> 00:53:08,080
Speaker 2: Yeah, well you think, right, are we relying on like

960
00:53:08,880 --> 00:53:12,200
Speaker 2: benevolence on the part of these technology companies.

961
00:53:12,480 --> 00:53:15,840
Speaker 3: Well, I mean, maybe we're seeing a turn, guys. Maybe

962
00:53:15,840 --> 00:53:18,719
Speaker 3: this is this is like our bright future. Maybe we're

963
00:53:18,760 --> 00:53:19,680
Speaker 3: gonna be okay.

964
00:53:19,800 --> 00:53:22,880
Speaker 1: Yeah, maybe they'll all be like Bill Murray's character at

965
00:53:22,880 --> 00:53:26,880
Speaker 1: the end is scrooged. I don't know, but that's you

966
00:53:26,920 --> 00:53:28,920
Speaker 1: can tell where a lot of fun at parties, folks,

967
00:53:28,960 --> 00:53:32,520
Speaker 1: because that's not going to happen. That's not what's happening now.

968
00:53:32,719 --> 00:53:35,799
Speaker 1: There's just more money to be made in creating these

969
00:53:35,840 --> 00:53:42,040
Speaker 1: extremely educated guesses about your future and then punishing you

970
00:53:42,120 --> 00:53:46,640
Speaker 1: for those guesses. Again, they are guesses in advance. To

971
00:53:46,760 --> 00:53:50,720
Speaker 1: paraphrase Minority Report, this is sort of like financial pre crime.

972
00:53:51,880 --> 00:53:55,520
Speaker 1: And I like the point you made earlier about how

973
00:53:55,560 --> 00:54:01,040
Speaker 1: nobody really opts into credit bureaus in the at least,

974
00:54:02,280 --> 00:54:06,480
Speaker 1: and it's the same thing. But to be fair, it

975
00:54:06,520 --> 00:54:10,520
Speaker 1: would be misleading for us to talk about these inarguably

976
00:54:10,680 --> 00:54:15,120
Speaker 1: terrible consequences inherent in this strategy if we didn't also

977
00:54:15,200 --> 00:54:18,720
Speaker 1: point out the arguments made by the proponents. And these

978
00:54:19,080 --> 00:54:23,239
Speaker 1: arguments are pretty valid if theoretical. This is the sort

979
00:54:23,280 --> 00:54:25,160
Speaker 1: of the other side of the money grab.

980
00:54:25,480 --> 00:54:29,399
Speaker 2: Yeah, I mean, there is, of course this notion that

981
00:54:29,440 --> 00:54:31,880
Speaker 2: this is exactly what we want, This is exactly what

982
00:54:31,920 --> 00:54:36,560
Speaker 2: we need, we deserve this, this is giving us better results.

983
00:54:36,600 --> 00:54:40,919
Speaker 2: It's allowing us to be served better information, less redundant,

984
00:54:41,120 --> 00:54:45,600
Speaker 2: you know, first off, as is discussed in behavior Revealed

985
00:54:45,640 --> 00:54:50,640
Speaker 2: in Mobile Phone Usage Predicts Credit Repayment by Daniel Buerkigrin

986
00:54:51,239 --> 00:54:54,880
Speaker 2: and Darryl Grisson. Many households in the developing world do

987
00:54:55,040 --> 00:55:00,440
Speaker 2: not have formal financial history period, no established credit, meaning

988
00:55:00,520 --> 00:55:02,799
Speaker 2: the odds are massively stacked against them when it comes

989
00:55:02,840 --> 00:55:05,080
Speaker 2: time to ask for a loan. So, outside of what

990
00:55:05,120 --> 00:55:08,640
Speaker 2: I just said initially the idea of serving us better results,

991
00:55:08,680 --> 00:55:11,759
Speaker 2: this is a very very important and potentially positive thing.

992
00:55:11,800 --> 00:55:14,239
Speaker 2: I know plenty of people that just decided they were

993
00:55:14,280 --> 00:55:16,360
Speaker 2: too freaked out to ever get a credit card, and

994
00:55:16,400 --> 00:55:19,239
Speaker 2: now they're thirty six, thirty seven years old and have

995
00:55:19,360 --> 00:55:21,440
Speaker 2: zero credit. Where do you go from there?

996
00:55:21,640 --> 00:55:26,160
Speaker 3: Well, thankfully you've you've got your mobile phone and you're

997
00:55:26,200 --> 00:55:28,719
Speaker 3: doing all your stuff on it. And these companies now

998
00:55:28,880 --> 00:55:32,200
Speaker 3: have an opportunity to look at that data and maybe,

999
00:55:32,800 --> 00:55:35,600
Speaker 3: just maybe by using it, they're going to be able

1000
00:55:35,600 --> 00:55:39,440
Speaker 3: to extend credit to you or to any you know,

1001
00:55:39,520 --> 00:55:42,160
Speaker 3: to anyone on the what would be considered the fringes

1002
00:55:42,320 --> 00:55:45,920
Speaker 3: of the financial world, people that don't have that established credit,

1003
00:55:46,400 --> 00:55:49,400
Speaker 3: and with that data, it's giving them an insight in

1004
00:55:50,080 --> 00:55:54,319
Speaker 3: to essentially predict what your credit will look like, what

1005
00:55:54,360 --> 00:55:58,400
Speaker 3: your financial history will be like. Oh that is so weird.

1006
00:55:59,080 --> 00:55:59,680
Speaker 2: Yeah, I mean.

1007
00:56:00,040 --> 00:56:03,520
Speaker 3: That's a positive, right, somebody who needs a loan or

1008
00:56:03,560 --> 00:56:07,239
Speaker 3: needs to establish credit, but hasn't yet. Would this could

1009
00:56:07,239 --> 00:56:08,520
Speaker 3: be a way in right?

1010
00:56:09,000 --> 00:56:12,920
Speaker 1: And their method actually is better than the traditional methods

1011
00:56:13,000 --> 00:56:14,320
Speaker 1: using credit prow information.

1012
00:56:14,560 --> 00:56:16,680
Speaker 2: But wouldn't you think you would have to be like, Okay,

1013
00:56:16,760 --> 00:56:20,080
Speaker 2: I'm on board, here's my stuff, take a look, make

1014
00:56:20,120 --> 00:56:24,040
Speaker 2: an assessment in the same way you'd submit documents for

1015
00:56:24,120 --> 00:56:26,840
Speaker 2: a credit check or whatever for like getting a mortgage,

1016
00:56:27,600 --> 00:56:30,440
Speaker 2: like they already have it. Like that's my question. I mean,

1017
00:56:30,480 --> 00:56:33,080
Speaker 2: I guess the answer is yes. I would just want

1018
00:56:33,120 --> 00:56:35,160
Speaker 2: to know that I could sort of turn the switch

1019
00:56:35,239 --> 00:56:37,920
Speaker 2: on and off and be like, Okay, I need you

1020
00:56:38,000 --> 00:56:40,520
Speaker 2: to use this information here. I consent for you to

1021
00:56:40,640 --> 00:56:43,800
Speaker 2: use my data in this way. But that's blue sky,

1022
00:56:44,160 --> 00:56:45,520
Speaker 2: I mean, that's it. I don't know.

1023
00:56:45,840 --> 00:56:50,160
Speaker 1: Yeah, it's tough, and it's it's it's hidden in byzantine

1024
00:56:50,320 --> 00:56:54,719
Speaker 1: fine prints pretty often. And when we're talking about this approach,

1025
00:56:54,960 --> 00:56:57,759
Speaker 1: or at least this specific study, they're talking about the

1026
00:56:57,840 --> 00:57:00,960
Speaker 1: developing world, and it is valid. It could be huge

1027
00:57:01,400 --> 00:57:06,200
Speaker 1: because this would open possibly open the doors of financial

1028
00:57:06,200 --> 00:57:11,839
Speaker 1: opportunity for people that the industry calls unbanked, which I

1029
00:57:11,880 --> 00:57:15,160
Speaker 1: think is a dehumanizing term. It basically means people who

1030
00:57:15,160 --> 00:57:19,040
Speaker 1: don't have that financial history they don't have mortgages, credit cards,

1031
00:57:19,640 --> 00:57:23,640
Speaker 1: checking accounts, et cetera, et cetera. They probably use their

1032
00:57:23,720 --> 00:57:27,960
Speaker 1: mobile phone or use an entirely online banking system or

1033
00:57:28,000 --> 00:57:31,960
Speaker 1: transactional app to trade stuff. So this could help those people,

1034
00:57:32,000 --> 00:57:35,000
Speaker 1: of course, if the models are accurate, which they seem

1035
00:57:35,040 --> 00:57:37,600
Speaker 1: to be, and if they are used in an ethical way.

1036
00:57:38,360 --> 00:57:41,480
Speaker 3: Hah, are these anywhere?

1037
00:57:42,480 --> 00:57:46,400
Speaker 2: Yes? Yes, it's not too too I mean it's sort

1038
00:57:46,400 --> 00:57:50,160
Speaker 2: of an escalation and a different, you know, step in

1039
00:57:50,200 --> 00:57:53,400
Speaker 2: this process. But it's like Venmo gave people that didn't

1040
00:57:53,400 --> 00:57:56,160
Speaker 2: have a bank account the ability to digitally send and

1041
00:57:56,200 --> 00:57:58,840
Speaker 2: receive funds, and that's a big deal for a lot

1042
00:57:58,840 --> 00:58:00,760
Speaker 2: of people too. So I mean, I can I can

1043
00:58:00,800 --> 00:58:03,480
Speaker 2: see the potential positive side of this.

1044
00:58:04,360 --> 00:58:06,640
Speaker 3: Yeah, but they had to have bank accounts or something

1045
00:58:06,720 --> 00:58:09,080
Speaker 3: for the money, youl, do you have some kind of money?

1046
00:58:09,360 --> 00:58:09,600
Speaker 2: Oh?

1047
00:58:09,640 --> 00:58:11,600
Speaker 3: Well, okay, so you have to have a PayPal account.

1048
00:58:11,640 --> 00:58:13,200
Speaker 2: That's I don't think they even have to anymore. You

1049
00:58:13,240 --> 00:58:15,240
Speaker 2: can have it. You can get a Venmo debit card

1050
00:58:15,520 --> 00:58:18,760
Speaker 2: where independent of any bank, you can link a bank

1051
00:58:18,800 --> 00:58:21,600
Speaker 2: account to Venmo, but Venmo money can go into a

1052
00:58:21,720 --> 00:58:25,560
Speaker 2: Venmo debit card. And then there's even stuff for like kids,

1053
00:58:25,600 --> 00:58:27,919
Speaker 2: like my daughter has this thing called green Light that's

1054
00:58:27,960 --> 00:58:29,960
Speaker 2: a debit card for kids, and she doesn't she's not

1055
00:58:30,000 --> 00:58:32,400
Speaker 2: old enough to have a bank account. So it's you know,

1056
00:58:32,440 --> 00:58:35,560
Speaker 2: there are things that, again in this financial side of things,

1057
00:58:35,640 --> 00:58:38,280
Speaker 2: could be seen as like, you know, forward thinking and

1058
00:58:38,560 --> 00:58:42,400
Speaker 2: positive because if you're not, if you don't have access

1059
00:58:42,440 --> 00:58:45,280
Speaker 2: to funds online, you are cut out of a whole

1060
00:58:45,320 --> 00:58:49,240
Speaker 2: lot of the economy, you know, because especially being stuck

1061
00:58:49,240 --> 00:58:52,040
Speaker 2: at home like we have been for so long. I mean, well,

1062
00:58:52,040 --> 00:58:53,240
Speaker 2: if you don't want to go to the story because

1063
00:58:53,240 --> 00:58:55,200
Speaker 2: you're immuno compromise, you need to be able to buy

1064
00:58:55,240 --> 00:58:58,320
Speaker 2: your groceries using you know, one of these apps, and

1065
00:58:58,600 --> 00:59:00,000
Speaker 2: you need to be able to pay for it somehow.

1066
00:59:00,160 --> 00:59:03,320
Speaker 1: Yeah, and I think this really comes into play again

1067
00:59:03,360 --> 00:59:05,960
Speaker 1: in the developing world. You live in a region with

1068
00:59:06,000 --> 00:59:09,400
Speaker 1: no infrastructure, you know what I mean, there's literally not

1069
00:59:10,320 --> 00:59:14,160
Speaker 1: an ATM in eighty miles of you or something like that,

1070
00:59:15,280 --> 00:59:21,000
Speaker 1: or the kilometers ther results may very so this stuff,

1071
00:59:21,520 --> 00:59:25,680
Speaker 1: we can agree can be tremendously helpful, But what we're

1072
00:59:25,680 --> 00:59:28,400
Speaker 1: talking about is the next step in that process using

1073
00:59:28,440 --> 00:59:32,760
Speaker 1: that information for or against someone. And there are people

1074
00:59:32,800 --> 00:59:36,360
Speaker 1: who are trying to make a more nuanced approach. People

1075
00:59:36,440 --> 00:59:41,000
Speaker 1: like Elaine Shema. He's been working on limited applications of

1076
00:59:41,040 --> 00:59:45,160
Speaker 1: this strategy, trying to build some fences around what can

1077
00:59:45,240 --> 00:59:48,360
Speaker 1: and can't be used. Just last year he wrote something

1078
00:59:48,400 --> 00:59:52,920
Speaker 1: called effective credit scoring using limited mobile phone data, and

1079
00:59:53,360 --> 00:59:58,160
Speaker 1: he says, look, there are tons of potential privacy risk

1080
00:59:58,720 --> 01:00:02,400
Speaker 1: in these companies knowing everything about you, especially if you

1081
01:00:02,440 --> 01:00:06,560
Speaker 1: don't have again, a real kind of informed consent. So

1082
01:00:06,640 --> 01:00:10,360
Speaker 1: he says, we can get the same results with a

1083
01:00:10,360 --> 01:00:14,560
Speaker 1: more ethical approach. And all that he did was measure

1084
01:00:14,680 --> 01:00:19,680
Speaker 1: what's called airtime recharge data or data whatever you want

1085
01:00:19,720 --> 01:00:23,920
Speaker 1: to refer to it as this is this is a

1086
01:00:24,040 --> 01:00:27,400
Speaker 1: term that describes when people paid or re up on

1087
01:00:27,440 --> 01:00:31,760
Speaker 1: their prepaid mobile phones. Right, so you know, I've prepaid

1088
01:00:31,880 --> 01:00:35,960
Speaker 1: for like a month or whatever or x amount of hours,

1089
01:00:36,000 --> 01:00:39,280
Speaker 1: and then I come back and I re up at

1090
01:00:39,320 --> 01:00:43,520
Speaker 1: this given schedule. Just using that, they can build a

1091
01:00:43,560 --> 01:00:45,080
Speaker 1: similar credit prediction plan.

1092
01:00:45,160 --> 01:00:47,080
Speaker 2: But isn't the whole point of a prepaid phone that

1093
01:00:47,120 --> 01:00:51,480
Speaker 2: you have anonymity and that you aren't your name or

1094
01:00:52,280 --> 01:00:54,560
Speaker 2: identity isn't tied to it. I guess that's not the

1095
01:00:54,560 --> 01:00:56,640
Speaker 2: same as a burner phone. You could have like a

1096
01:00:56,680 --> 01:00:59,440
Speaker 2: prepaid service where you use the same phone and then

1097
01:00:59,600 --> 01:01:03,080
Speaker 2: re up. But there's two completely different things.

1098
01:01:03,560 --> 01:01:06,160
Speaker 3: No, No, there are a lot of people in the world

1099
01:01:06,160 --> 01:01:10,000
Speaker 3: who have a phone that they just pay for the

1100
01:01:10,680 --> 01:01:15,080
Speaker 3: amount of data rather than you know, a monthly charge

1101
01:01:15,160 --> 01:01:16,000
Speaker 3: or anything like that.

1102
01:01:16,240 --> 01:01:18,680
Speaker 2: But there's also ones where you can just buy a card,

1103
01:01:19,280 --> 01:01:20,920
Speaker 2: pay for it at the gas station, and then just

1104
01:01:21,040 --> 01:01:23,480
Speaker 2: enter the code and then you don't have any identity

1105
01:01:24,240 --> 01:01:25,440
Speaker 2: information tied to it.

1106
01:01:25,520 --> 01:01:29,120
Speaker 1: That's all similar technology to different uses, you know what

1107
01:01:29,120 --> 01:01:33,080
Speaker 1: I mean, got it? And for a lot of people,

1108
01:01:33,320 --> 01:01:38,320
Speaker 1: the entry point into buying full phone and then subscribing

1109
01:01:38,360 --> 01:01:40,880
Speaker 1: to a yearly plan is just too high.

1110
01:01:40,960 --> 01:01:42,040
Speaker 4: It's just unrealistic.

1111
01:01:42,120 --> 01:01:45,800
Speaker 1: So mobile phones have, I would prepaid mobile phones I

1112
01:01:45,840 --> 01:01:49,680
Speaker 1: would say, have to a degree greatly democratized access to information.

1113
01:01:50,280 --> 01:01:55,720
Speaker 1: But again, none of this stuff is without potential for misuse.

1114
01:01:56,600 --> 01:02:00,680
Speaker 1: And then here's here's the third thing. So the third

1115
01:02:00,840 --> 01:02:06,120
Speaker 1: argument for it is lending institutions are capitalistic. They don't

1116
01:02:06,480 --> 01:02:09,600
Speaker 1: get to continue existing if every loan they make results

1117
01:02:09,600 --> 01:02:12,920
Speaker 1: in defaulting, then they're in deep water. So we can't

1118
01:02:12,920 --> 01:02:16,480
Speaker 1: expect them not to use every single lever available to

1119
01:02:16,680 --> 01:02:20,880
Speaker 1: maximize their profits while minimizing their risk and to be

1120
01:02:20,960 --> 01:02:25,000
Speaker 1: extremely blunt, we cannot expect these institutions to adhere to

1121
01:02:25,120 --> 01:02:29,320
Speaker 1: codes of ethics unless those things are continually enforced with

1122
01:02:29,440 --> 01:02:35,720
Speaker 1: serious consequences. Crime and fines for crime have since we

1123
01:02:35,800 --> 01:02:38,960
Speaker 1: began this show been just a cost of doing business

1124
01:02:39,000 --> 01:02:39,720
Speaker 1: for most banks.

1125
01:02:40,480 --> 01:02:45,360
Speaker 3: Yeah, I think we've proven that countless times just on

1126
01:02:45,400 --> 01:02:50,320
Speaker 3: this show. And you know, history and articles and reality

1127
01:02:50,680 --> 01:02:53,040
Speaker 3: have further proven it way more than that.

1128
01:02:55,200 --> 01:02:59,840
Speaker 1: And so you know that kind of resigned resign grown

1129
01:03:00,120 --> 01:03:02,000
Speaker 1: means that we're close to the end of the show.

1130
01:03:02,280 --> 01:03:04,360
Speaker 1: There are some other things we've yet to talk about

1131
01:03:04,400 --> 01:03:08,600
Speaker 1: that can also leverage this technology. The medical industry using

1132
01:03:08,640 --> 01:03:12,960
Speaker 1: similar methods to predict future or present medical conditions. The

1133
01:03:13,040 --> 01:03:16,920
Speaker 1: law enforcement industry expanding its existing use of these tactics

1134
01:03:16,920 --> 01:03:20,160
Speaker 1: to predict crime other rogue entities. If we want to

1135
01:03:20,160 --> 01:03:23,920
Speaker 1: get really crazy from foreign powers, whatever your country is,

1136
01:03:23,920 --> 01:03:28,040
Speaker 1: somebody from another country using those tactics to say, identify

1137
01:03:28,200 --> 01:03:32,240
Speaker 1: target and turn assets. The dark sky is really the

1138
01:03:32,320 --> 01:03:35,640
Speaker 1: limit here. We're way past the blue version. And it's

1139
01:03:35,640 --> 01:03:38,800
Speaker 1: not a conspiracy theory. It's not some scary future thing.

1140
01:03:39,080 --> 01:03:44,160
Speaker 1: It is a conspiracy. It's real, it's happening, and a

1141
01:03:44,160 --> 01:03:46,520
Speaker 1: lot of people are just not aware that their data

1142
01:03:46,640 --> 01:03:48,840
Speaker 1: is being used in this way or will be used

1143
01:03:48,880 --> 01:03:49,960
Speaker 1: in this way very soon.

1144
01:03:50,560 --> 01:03:52,520
Speaker 2: Yeah, so what do you think? I mean? Is this

1145
01:03:52,640 --> 01:03:55,280
Speaker 2: something that keeps you up nights? Are you like many

1146
01:03:55,280 --> 01:03:58,560
Speaker 2: folks or you know we've done the research, we know

1147
01:03:58,640 --> 01:04:02,320
Speaker 2: this stuff has nefarious kind of implications. But it's like

1148
01:04:02,360 --> 01:04:05,120
Speaker 2: a trade off. But I get my cool free apps

1149
01:04:05,160 --> 01:04:07,080
Speaker 2: and I get all this other stuff, and it's worth

1150
01:04:07,120 --> 01:04:09,000
Speaker 2: it to me to do that trade off. Is that

1151
01:04:09,160 --> 01:04:12,280
Speaker 2: I'm saying, speaking the Royal we hypothetically Do you feel

1152
01:04:12,320 --> 01:04:14,760
Speaker 2: like that or do you feel like you need to

1153
01:04:14,840 --> 01:04:17,000
Speaker 2: limit your use of this stuff and get as off

1154
01:04:17,000 --> 01:04:19,400
Speaker 2: the grid as humanly possible in this day and age.

1155
01:04:19,720 --> 01:04:21,240
Speaker 2: Is this a good thing? Is this a bad thing?

1156
01:04:21,480 --> 01:04:22,439
Speaker 2: Let us know what you think.

1157
01:04:22,960 --> 01:04:26,600
Speaker 3: Yeah, and especially contact us if you have any good

1158
01:04:26,600 --> 01:04:30,360
Speaker 3: ways to subvert some of the plans that are at

1159
01:04:30,400 --> 01:04:35,920
Speaker 3: work here. We're particularly interested in those. You know, anything

1160
01:04:36,320 --> 01:04:40,160
Speaker 3: we've discussed it before, anything from a VPN to you know,

1161
01:04:40,400 --> 01:04:43,040
Speaker 3: your your actual settings on your device, like what are

1162
01:04:43,040 --> 01:04:46,480
Speaker 3: some things that you do to protect yourself and what

1163
01:04:46,560 --> 01:04:49,160
Speaker 3: can help out your fellow listeners by letting them know

1164
01:04:49,200 --> 01:04:53,280
Speaker 3: about it. You can find us on Twitter Facebook. Oh god, wait,

1165
01:04:53,360 --> 01:04:59,640
Speaker 3: what are we doing again? Instagram? We're on tumbler and

1166
01:05:00,240 --> 01:05:00,680
Speaker 3: what else?

1167
01:05:00,840 --> 01:05:03,440
Speaker 2: Bumbers tender you can find this.

1168
01:05:04,040 --> 01:05:07,320
Speaker 1: Yeah, we're on key micro loans were you?

1169
01:05:07,840 --> 01:05:11,360
Speaker 3: It's all social media, it's all coming together, it's all one.

1170
01:05:11,480 --> 01:05:16,160
Speaker 3: We are one, this castile soap. Wait what I thought

1171
01:05:16,200 --> 01:05:16,880
Speaker 3: you were going to say?

1172
01:05:16,920 --> 01:05:21,800
Speaker 1: We are one eight three three stt wytk.

1173
01:05:22,040 --> 01:05:26,160
Speaker 2: We are definitely that one for sure. Uh honestly, that's

1174
01:05:26,160 --> 01:05:30,560
Speaker 2: probably the least connected way you can get in touch

1175
01:05:30,600 --> 01:05:33,120
Speaker 2: with us, where you can just send us that send

1176
01:05:33,200 --> 01:05:36,200
Speaker 2: us your voice in electronic form and let us know

1177
01:05:36,240 --> 01:05:38,200
Speaker 2: if you want to be anonymized or don't want us

1178
01:05:38,240 --> 01:05:40,080
Speaker 2: to use it, or if you want us to refer

1179
01:05:40,080 --> 01:05:41,840
Speaker 2: to you as something else or what have you. Well,

1180
01:05:41,880 --> 01:05:44,040
Speaker 2: we will abide by those things because we are not cold,

1181
01:05:44,080 --> 01:05:47,640
Speaker 2: uncaring algorithms. We are in fact human people. We see

1182
01:05:47,720 --> 01:05:49,840
Speaker 2: true as such, it's true.

1183
01:05:49,640 --> 01:05:52,160
Speaker 3: But we just found out that these guys are trying

1184
01:05:52,160 --> 01:05:55,200
Speaker 3: to take our voices. Right, what's going on?

1185
01:05:56,360 --> 01:05:57,280
Speaker 4: Voppel gangers?

1186
01:05:57,840 --> 01:06:00,360
Speaker 3: We've gone too far? Oh no, and.

1187
01:06:02,240 --> 01:06:05,920
Speaker 1: If sorry, be afraid, be very afraid. I mean, you know,

1188
01:06:06,960 --> 01:06:10,760
Speaker 1: unlike Noel said, he had mattered humans just like anybody

1189
01:06:10,760 --> 01:06:16,120
Speaker 1: else and here from you, so feel free to send

1190
01:06:16,200 --> 01:06:18,760
Speaker 1: us an email. If you don't care for social media

1191
01:06:19,240 --> 01:06:22,560
Speaker 1: or telephones, you can reach us twenty four to seven

1192
01:06:22,640 --> 01:06:24,280
Speaker 1: until this whole thing burns down.

1193
01:06:24,560 --> 01:06:47,360
Speaker 3: We are conspiracy at iHeartRadio dot com. Stuff they don't

1194
01:06:47,360 --> 01:06:50,320
Speaker 3: want you to know. Is a production of iHeartRadio. For

1195
01:06:50,440 --> 01:06:54,360
Speaker 3: more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,

1196
01:06:54,480 --> 01:06:57,560
Speaker 3: or wherever you listen to your favorite shows.