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Speaker 1: There Are No Girls on the Internet, as a production

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Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget and this is

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Speaker 1: There Are No Girls on the Internet. I'm here with

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Speaker 1: my producer Mike. Mike, thank you for being here as.

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Speaker 2: Always, Bridget, thanks for having me pleasure to be here.

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Speaker 1: And here's what you may have missed this week on

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Speaker 1: the Internet. But first we have a little I don't know,

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Speaker 1: good news to commemorate share I don't know. I was

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Speaker 1: surprised to find that earlier this week there Are No

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Speaker 1: Girls on the Internet. Had a birthday. We turned three.

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Speaker 1: Three years ago. We were just launching this show. It feels, honestly,

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Speaker 1: feels more like ten. I'm being honest, but I wanted

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Speaker 1: to say something. I literally didn't realize it. You know,

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Speaker 1: I was looking at that Facebook like on this day

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Speaker 1: and three years ago, on this day, on that day,

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Speaker 1: I was literally begging my friends to leave me good

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Speaker 1: reviews because I was terrified that no one was going

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Speaker 1: to listen to this show. I was terrified that it

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Speaker 1: was going to be such a flop. I was terrified

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Speaker 1: that it was going to be just me talking to myself,

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Speaker 1: which maybe it was for a little while, but yeah,

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Speaker 1: three years later going strong, and I wanted to thank

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Speaker 1: you Mike for all the support. You know, I still

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Speaker 1: remember when we were first trying to get the show

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Speaker 1: green lit or green lighted, whatever the past tense of

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Speaker 1: green lit is. And it was a process and I

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Speaker 1: really was the first thing I ever conceptualized and pitched

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Speaker 1: and got you know, got to see in the world

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Speaker 1: all on my own. It was my first time ever

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Speaker 1: doing that. It was a real process. And I just

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Speaker 1: want to thank everybody who listens, Like, thank you listeners.

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Speaker 1: It really truly me mean so much to me. Every

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Speaker 1: now and then someone will DM me and say something like,

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Speaker 1: oh I love the show or like you said, talked

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Speaker 1: about X y Z and I really liked it. You

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Speaker 1: have no idea what that means to me. Like I

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Speaker 1: don't make a ton of money doing this. The reason

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Speaker 1: why I do it, like I don't want to wish,

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Speaker 1: The reason why I do it is genuinely because it

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Speaker 1: really means so much to connect with listeners and to

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Speaker 1: tell these stories and to make sure that everybody gets

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Speaker 1: to have their stories of the ways that they have

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Speaker 1: impacted technology and the internet told. And so yeah, thank

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Speaker 1: you to everybody who listened. Everybody who's ever left a review,

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Speaker 1: everybody who's ever sent a DM. It means the world

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Speaker 1: to me, truly, And thank you Mike for going on

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Speaker 1: this journey with me.

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Speaker 2: Yeah, happy birthday, Tang Godie, Happy birthday, Bridget. Three years.

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Speaker 2: You know, we are solidly toddling at this point. And similarly,

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Speaker 2: it's been such an honor and a privilege for me

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Speaker 2: to be part of this. I don't think there's anything

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Speaker 2: I've ever done that has felt more important than helping

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Speaker 2: produce this show. And I'm just so happy to have

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Speaker 2: been able to play a part in helping you produce it.

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Speaker 2: And you know, I see a lot of those reviews.

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Speaker 2: We get real reviews now, not just your friends who

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Speaker 2: have been conversed into leaving them. People leave real reviews

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Speaker 2: that are nice and kind, and you know, people thank

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Speaker 2: you for producing the show and creating it and say

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Speaker 2: what it means to them, And I get it. It's

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Speaker 2: great and it means so much to see them to me,

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Speaker 2: and I know it means a lot to you, Bridget.

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Speaker 2: And so listeners, if you appreciate this show and you

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Speaker 2: wanted to leave a review saying that it means so

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Speaker 2: much to Bridget, so as like a birthday present.

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Speaker 1: I didn't make him say this, she.

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Speaker 2: Didn't, but you know it's it's a birthday. And so

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Speaker 2: if anybody wants to leave a review as a birthday

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Speaker 2: present for bridget of the show, please do. It means

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Speaker 2: so much to her. She reads every one of them,

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Speaker 2: like multiple times, like a crazy person. Not it's fine,

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Speaker 2: you know, like a.

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Speaker 1: Normal person who cares about the quality of the thing

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Speaker 1: that she produces.

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Speaker 2: That's right, like a totally normal person. So leave a

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Speaker 2: review or don't, you know, do whatever, just listen or not.

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Speaker 2: Do what feels good. But thank you for letting me

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Speaker 2: be part of the show.

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Speaker 1: Oh my god, it's watching you grow. As for the

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Speaker 1: first two seasons you were totally behind the scenes and

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Speaker 1: now like we you we do this newscast together every week.

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Speaker 1: And yeah, watching you grow as a producer and as

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Speaker 1: on Mike Talent, it's been great. And I just want

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Speaker 1: to say to anybody listening, if you are thinking, like, wow,

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Speaker 1: three years of doing this show, maybe I'll have a

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Speaker 1: show one day. Do it. If I if I could

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Speaker 1: conceptualize this show, get it off the ground, do it

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Speaker 1: for three years. I'm such a fuck up. If I

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Speaker 1: can do it. You can do it and probably do

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Speaker 1: it better than me. So I want to. I mean,

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Speaker 1: like I don't want to go on too long, but

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Speaker 1: it means a lot to me, and I I want

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Speaker 1: other people to do it too, Like I want to

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Speaker 1: be a model for other people that it is possible.

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Speaker 1: So if you ever wanted to have a podcast or

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Speaker 1: have a platform, you could do that. You should do that.

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Speaker 1: If I can do it, you can do it. Go forth,

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Speaker 1: and I'll leave it there. Happy birthday. There are no

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Speaker 1: girls on the internet.

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Speaker 2: Happy birthday.

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Speaker 1: Okay. So let's get into some of these news stories.

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Speaker 1: So there was a whole lot of legal movement to

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Speaker 1: put some guardrails around the public and AI this week.

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Speaker 1: Let's take a look at some of what's going on.

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Speaker 1: First up, the FTC, the Federal Trade Commission has opened

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Speaker 1: an expansive investigation into Open AI's the company hind Chat

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Speaker 1: geept that it's kind of become like the AI company,

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Speaker 1: right like Sam Altman is like kind of becoming the

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Speaker 1: face of AI in the United States. The FTC is

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Speaker 1: investigating whether or not Chat GPT is flouting consumer protection

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Speaker 1: laws by putting personal reputations and data at risk. Before

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Speaker 1: we got to this point, the FTC had been warning

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Speaker 1: Open AI. They basically were like, Hey, have y'all ever

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Speaker 1: heard about laws? Well, just because you run a tech

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Speaker 1: company doesn't mean that laws don't exist. They actually apply

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Speaker 1: to you. The FTC issued multiple warnings that existing consumer

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Speaker 1: protection laws do apply to AI, but it is a

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Speaker 1: little bit wonky right now because as of right now,

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Speaker 1: it is not totally clear what the regulations are as

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Speaker 1: they pertain to AI and who was supposed to be

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Speaker 1: enforcing them. The Washington Post reports that the United States

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Speaker 1: has trailed other governments in drafting AI legislation and regulating

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Speaker 1: the privacy risks associated with the technology. Countries within the

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Speaker 1: European Union have taken steps to limit US companies chatbots

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Speaker 1: under the block's privacy law, the General Data Protect Regulation

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Speaker 1: or GDPR, which we talked about a few times on

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Speaker 1: the show, like Italy temporarily blocked chat GPT from operating

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Speaker 1: there due to data privacy concerns, and Google had to

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Speaker 1: postpone the launch of its chat barred more on Google

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Speaker 1: and bard in a moment after receiving requests for privacy

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Speaker 1: assessments from the Irish Data Protection Commission. The European Union

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Speaker 1: is also expected to pass AI legislation by the end

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Speaker 1: of the year. So in the same way that other countries,

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Speaker 1: like the crap food in the United States would probably

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Speaker 1: be illegal in other countries, other countries are regulating the

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Speaker 1: United States as tech while US lawmakers are basically dragging

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Speaker 1: their feed. Senate Majority Leader Chuck Schumer has predicted that

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Speaker 1: new AI legislation is still months away. So we talked

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Speaker 1: a little bit about this in a previous newscast. But

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Speaker 1: that radio DJ who was suing chat gpt for making

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Speaker 1: up lies that he was fired for embezzlement from an

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Speaker 1: organization that he never worked for. Also, there was another

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Speaker 1: similar lawsuit where chat gpt made up that a lawyer

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Speaker 1: had made sexually suggestive means and attempted to inappropriately touch

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Speaker 1: a student on a class trip, which never happened. So

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Speaker 1: you can sort of get a sense of what kinds

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Speaker 1: of stories chat GBT is making up about real people,

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Speaker 1: and why these real people are not thrilled that AI

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Speaker 1: is making up these kinds of hallucinations about them. When

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Speaker 1: AI spits out a fact that it's not actually correct

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Speaker 1: or not based in truth or reality, that's called a hallucination.

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Speaker 1: So the FTC is calling on open ai to provide

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Speaker 1: detailed descriptions of all complaints that it's received of its

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Speaker 1: product making false, misleading, disparaging, or harmful statements about people,

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Speaker 1: and the agency is also looking into whether or not

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Speaker 1: open ai engaged in unfair or deceptive practices that resulted

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Speaker 1: in reputational harm to consumers. The Washington Post has a

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Speaker 1: real deep dive into this investigation. But what's really annoying

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Speaker 1: about this to me is that in that piece they

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Speaker 1: also quote a couple of folks who oppose this kind

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Speaker 1: of regulation, and they do so by really setting up

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Speaker 1: this kind of choice between protecting consumers and innovation, as

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Speaker 1: if anybody who would have questions or be interested in

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Speaker 1: investigating how this technology is going to be used is

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Speaker 1: automatically trying to stifle tech innovation. And I firmly believe

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Speaker 1: that we can and need to do both.

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Speaker 2: Yeah, false choice is such a good way to describe that.

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Speaker 2: I mean, you can have innovation in a way that

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Speaker 2: protects privacy. Like none of us live under the illusion

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Speaker 2: that we're going to have complete privacy on the Internet,

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Speaker 2: but like there can be some kind of reasonable safeguards

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Speaker 2: where people have some kind of expectation of privacy, protection

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Speaker 2: of copyrighted information, something anything, as opposed to just like

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Speaker 2: everything is completely up for grabs, right, Like you mentioned

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Speaker 2: that Google had to postpone the launch of Barred after

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Speaker 2: Ireland requested a price assessment. The fact that they had

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Speaker 2: to postpone the launch just because they were like asked

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Speaker 2: for an assessment really says a lot.

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Speaker 1: It does. So let's talk about Google. That's a great segue.

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Speaker 1: So Google is being accused of quote, stealing everything that's

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Speaker 1: ever been shared on the Internet. This is from a

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Speaker 1: class action lawsuit from a group of individuals who claimed

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Speaker 1: that Google brote copyright law and collected people's personal information

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Speaker 1: without consent. So this case comes after Google changed its

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Speaker 1: AI privacy policy to say that the company reserves the

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Speaker 1: right to scrape all the Internet's public information for its

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Speaker 1: artificial intelligence projects. Ryan Clarkson, he's the lawyer who's representing

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Speaker 1: the plaintiffs in this case, is really arguing against the

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Speaker 1: precedent that you just laid out, Mike, that Google and

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Speaker 1: tech companies just get to own anything any of us

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Speaker 1: has ever done, searched, typed online. He says Google does

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Speaker 1: not own the Internet. It does not own our creative works.

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Speaker 1: It does not own our expressions of personhood, pictures of

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Speaker 1: our families and children, or anything else simply because we

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Speaker 1: share it online. We have only recently learned that Google

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Speaker 1: has been taking everything ever created or shared online by

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Speaker 1: millions of Internet users, including all our personal information, creative works,

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Speaker 1: and professional works, and using all of that data to

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Speaker 1: train and build commercial AI products. The plaintiffs in this

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Speaker 1: case are a pretty wide range of folks. A New

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Speaker 1: York Times bestselling author, a six year old boy, a

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Speaker 1: software developer, a TikTok influencer, an actor, and several others.

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Speaker 1: And I should say, like, I am no lawyer, but

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Speaker 1: per Gizmoto, who did some great reporting on this, Google

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Speaker 1: might have actually kind of disclosed all of this in

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Speaker 1: their privacy policy, you know that thing that we all

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Speaker 1: just scroll to the bottom of and instantly click without

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Speaker 1: reading a word of it. Actually, Mike, do you read those.

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Speaker 2: I wish I've read them more often than I do.

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Speaker 2: Sometimes I look through them. Usually I don't. I should.

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Speaker 2: It's shameful that I don't, But like, who's going to

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Speaker 2: read those things rightly? Often they are written in a

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Speaker 2: way to like intentionally discourage people from reading them intentionally

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Speaker 2: be up to Some of them are written in a

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Speaker 2: way that is accessible to like facilitate people to actually

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Speaker 2: understand how their data is being used. But the irony

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Speaker 2: is that the companies that take the time to write

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Speaker 2: their privacy disclosure or their end user license in a

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Speaker 2: way that people can understand, those are the companies that

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Speaker 2: generally actually respect their users and their privacy. It's the

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Speaker 2: ones where it's like a two hundred page document of

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Speaker 2: dense legalese that you know they're just using your data

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Speaker 2: for anything they can.

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Speaker 1: Well, that's exactly what this attorney says Google is doing.

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Speaker 1: So Google's privacy policy used to read that it uses

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Speaker 1: publicly available information to train language models such as Google Translate.

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Speaker 1: But obviously, as you said, most people don't read that,

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Speaker 1: and even more importantly, does the general public truly understand

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Speaker 1: what what it means to train language models. Like if

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Speaker 1: you told me that my data was being used to

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Speaker 1: train a language model, I don't know that I would

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Speaker 1: automatically assume that that means that it's going to train

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Speaker 1: a kind of AI, or that you're talking about a

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Speaker 1: kind of AI. I don't know that that's like a

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Speaker 1: thing that everybody knows and so kind of to your point,

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Speaker 1: if it's written in such a dense and obscure way,

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Speaker 1: how can you really say that like, oh, well, we

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Speaker 1: made people aware. That's what this lawsuit is arguing.

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Speaker 2: Yeah, the idea that your data might be used to

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Speaker 2: train a language model. Until a few months ago, when

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Speaker 2: chat GPT had its big, splashy debut, nobody would have

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Speaker 2: thought that training a language model meant that an AI

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Speaker 2: chatbot would be able to repeat verbatim lengthy passages of

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Speaker 2: a book you wrote, or a chapter you wrote, or

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Speaker 2: an article that you wrote. Nobody would have thought that,

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Speaker 2: because of course they can't just repeat your copyrighted material

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Speaker 2: as if they own it. And yet that's exactly what

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Speaker 2: they're doing. Ooh, I'm glad.

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Speaker 1: That you brought that up. More on that in a moment.

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Speaker 1: So in the class action case against Google, the plaintiffs

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Speaker 1: in the case are asking for five million dollars in damages,

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Speaker 1: and they're asking the court for a temporary freeze on

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Speaker 1: commercial use of AI technology until guardrails are in place.

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Speaker 1: They also ask for quote data dividends to be paid

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Speaker 1: to every person whose information was used to develop Google's AI,

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Speaker 1: which will probably be so many people. But to your

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Speaker 1: point about not thinking that AI would be able to

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Speaker 1: just duplicate it verbatim a chapter from your book, that

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Speaker 1: is exactly why. Sarah Silverman, Yes, that's Sarah Silverman, the comedian,

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Speaker 1: is also suing open AI. Sarah Silverman is suing open

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Speaker 1: Ai and Meta alleging that the firms trained their large

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Speaker 1: language model on copyrighted materials, including works that she published

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Speaker 1: without obtaining consent. It's a little bit complicated, but basically

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Speaker 1: her legal team says that one of chat GPT's data

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Speaker 1: sets pulled from quote shadow libraries of a legally available

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Speaker 1: copyrighted mater, which are available in bulk online. Sarah Silverman's

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Speaker 1: attorneys had an exchange with chat geetbt, which then became

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Speaker 1: an exhibit or like a piece of evidence in their

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Speaker 1: lawsuit where basically her legal team asked chat geet bt

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Speaker 1: to summarize her memoir Bedwetter U fun fact, Sarah Silverman

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Speaker 1: is a bedwetter. Her memoir is all about her being

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Speaker 1: a bedwetter, and chat GPT, when asked this, outlined entire

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Speaker 1: parts of her book, including parts of her book that

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Speaker 1: were reproduced verbatim. So it's exactly what you're saying that

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Speaker 1: nobody would expect that a book that you a copyrighted book,

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Speaker 1: your intellectual property chat geetpt, would just be reproducing verbatim

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Speaker 1: large sections of it without your consent or knowledge necessarily

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Speaker 1: Like that, that is a problem, I think.

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Speaker 2: Yeah, Google's not particularly innovative here. The idea that all

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Speaker 2: information on the Internet is free and should be available

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Speaker 2: to everyone is not new. Right in the early aughts,

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Speaker 2: we were all downloading songs and movies on Naptre and

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Speaker 2: then lime wire, and then you Torrent and a million

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Speaker 2: other Torrent clients until HBO and Metallica and you know,

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Speaker 2: a bunch of other production companies that didn't like that

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Speaker 2: sued the hell out of everyone and shut it all

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Speaker 2: down because copyright law, right, like, because copyright law. And

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Speaker 2: the only thing that's different here is that instead of

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Speaker 2: end users in college dorms downloading stuff for their personal consumption,

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Speaker 2: it is giant tech companies downloading illegally obtained copyrighted material

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Speaker 2: to train their language models. That's the only difference here.

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Speaker 1: Oh my god, ask mister and missus Todd who ruined

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Speaker 1: multiple family desktop computers trying to download music and movies

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Speaker 1: from Kazah and LimeWire in the aughts, it was definitely.

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Speaker 2: Me where you just like install everything.

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Speaker 1: I don't think I had any sense that it was bad.

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Speaker 1: Like like back then, you didn't necessarily know that if

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Speaker 1: you like, if you're like I'm going to get every

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Speaker 1: Pink Floyd album ever ever release, I'm gonna just download

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Speaker 1: them all. I didn't have the sense that that was

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Speaker 1: like a bad thing to do, where that was like not,

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Speaker 1: we didn't we didn't know. Also, I was like fifteen,

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Speaker 1: I didn't care about the you know, well being of

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Speaker 1: the family desktop. Oh my god. It's My parents still

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Speaker 1: to this day are like she has ruined multiple computers,

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Speaker 1: shunk off off of the Internet.

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Speaker 2: It's a funny analogy, but it's kind of apt, right.

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Speaker 2: It's like, this new technology enabled access to all of

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Speaker 2: this data and information, and we were prepared to handle

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Speaker 2: that responsibility. And it sounds like several desktops in the

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Speaker 2: Todd family household suffered the consequences.

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Speaker 1: Today's kids will never know what it will be like

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Speaker 1: to download a song that was mislabeled. You know.

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Speaker 2: There were multiple times in the late auts when the

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Speaker 2: new death Punk album was leaked, except it was not

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Speaker 2: a new Daft Punk album. It was just like DJ

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Speaker 2: whoever you know, like labeling their track as Daft Punk.

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Speaker 2: I listened to all them, some of them. I was like, yeah,

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Speaker 2: this kind of slaps oh boy, this is kind of

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Speaker 2: a weird direction for them.

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Speaker 1: Then you get to college and you're like, have you

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Speaker 1: all heard that new Daft Punk single rat Attack? And

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Speaker 1: you were They're like, what that's not a tad punk song? Yeah.

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Speaker 1: Downloading stuff on kaza and LimeWire led to a lot

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Speaker 1: of like misunderstandings about who did what music and what

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Speaker 1: music belonged to what group today's kids will never know.

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Speaker 3: Let's take a quick break at our back.

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Speaker 1: So we're summarizing all of these different AI related investigations

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Speaker 1: and lawsuits from this week. This one hasn't reached lawsuit

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Speaker 1: levels yet, but voice actors are very angry after having

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Speaker 1: AI versions of their voices used in pornographic video games.

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Speaker 1: I gotta give major shout outs to Austin Wood at

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Speaker 1: games Radar for his reporting on this because it was

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Speaker 1: very in depth. So for those of y'all who don't know,

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Speaker 1: modding a video game is when a video game's code

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Speaker 1: is altered to create a new version of that game.

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Speaker 1: It's very popular with role playing games first person shooters.

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Speaker 1: Don't feel bad if you didn't know this. I actually

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Speaker 1: didn't know this was a popular thing either until researching

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Speaker 1: this episode, but it is. You can do this on

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Speaker 1: a platform called Nexus Mods. So modified versions of the

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Speaker 1: video game Skyrim used AI deep fakes of voice actors

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Speaker 1: from the original game, and some of those modified versions

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Speaker 1: were pornographic in nature. Some of these actors have now

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Speaker 1: publicly spoken out against the practice of scraping and cloning

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Speaker 1: their vocal performances without their permission, especially for pornographic purposes.

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Speaker 1: I am a voice actor. In addition to doing this podcast,

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Speaker 1: every now and then you'll catch me doing voiceover for things.

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Speaker 1: If you ever think you hear my voice in a

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Speaker 1: commercial or something, it probably is my voice. Because I

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Speaker 1: am a voice actor, so I can completely understand why

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Speaker 1: these voice actors are pissed. I would also be pissed.

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Speaker 1: The official stance of Nexus mods is that AI generated

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Speaker 1: mod content is not against their rules. However, these voice

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Speaker 1: actors are angry. Anime and Elder Scrolls voice actor Kyle

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Speaker 1: McCary said, please tell me if you find my voice

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Speaker 1: print somewhere like this I don't want people using AI

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Speaker 1: to put words in my mouth. Abby Veffer, who voiced

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Speaker 1: several characters in the game Genshin Impact and the Elder

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Speaker 1: Scrolls Online, wrote, if you find my voice in any

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Speaker 1: of these mods, please let me know so I can

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Speaker 1: request it be taken down. I do not, and never

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Speaker 1: will consent to my voice being used for AI synthesis, cloning,

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Speaker 1: deep things, et cetera. This is not okay. Ryan Lawton,

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Speaker 1: whose voice can be found on the likes of Diablo four,

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Speaker 1: hit Man two, and Return of ober Din, said this

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Speaker 1: is wrong on every level. It is disturbing. Wtf is

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Speaker 1: wrong with people? AI voice cloning is out of hand.

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Speaker 1: Do not support the AI replication of VA voices in

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Speaker 1: any way, shape or form. Please let me know if

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Speaker 1: you ever encounter my voice being used like this. I

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Speaker 1: do not give consent. Now. The counter argument is that

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Speaker 1: you know, if a lot of these people making these

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Speaker 1: you know, mod of games, which is, there's nothing wrong

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Speaker 1: with like making a mod of game, but a lot

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Speaker 1: of people who make them are just independent creators. And

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Speaker 1: so the counter argument is that you would have to

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Speaker 1: use a professional voice actor, you would have to pay them,

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Speaker 1: and so that makes using voiceover talent in modified games

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Speaker 1: less accessible. But and I sort of like, kind of

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Speaker 1: understand that argument. But the bottom line is just using

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Speaker 1: AI to essentially steal the work and the voice of

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Speaker 1: voice professionals just cheats that professional out of work, and

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Speaker 1: I think it devalues all of our work. It actually

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Speaker 1: really reminds me of what's going on with the SAG

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Speaker 1: strikes and the writer strikes. I saw this reporter today

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Speaker 1: that one studio's AI proposal for sag Actra included scanning

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Speaker 1: a background actor's likeness for one day's worth of pay,

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Speaker 1: and then using their likeness forever in any form without

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Speaker 1: any pay or consent. So you would be paid for

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Speaker 1: one day's works as talent, and then they can just

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Speaker 1: use your image via AI forever and not pay you

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Speaker 1: over again.

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Speaker 2: I saw a really good take about that online. In fact,

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Speaker 2: like that exact thing. Somebody made the great point that, like,

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Speaker 2: it's already pretty simple to use AI to like create

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Speaker 2: background actors. So if all you wanted was some background actors,

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Speaker 2: like in a scene, you could do that using AI.

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Speaker 2: The only reason that you would want an actor to

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Speaker 2: sign an agreement like this to be in the background

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Speaker 2: of some scene as an extra. Is in the event

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Speaker 2: that that actor then becomes famous at some point down

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Speaker 2: the road, then you the studio have their consent to

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Speaker 2: use their likeness in perpetuity. I thought that was a

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Speaker 2: really interesting and insightful take on exactly that kind of clause.

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Speaker 1: Yeah, I think we are. I just hate this kind

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Speaker 1: of like new normal that is being I think pushed

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Speaker 1: on us, forced on us, especially as creatives, that this

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Speaker 1: is what we have to accept that like, yeah, that

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Speaker 1: like studios would be able to behave in this way

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Speaker 1: that just seems so tailor made to short and exploit

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Speaker 1: us the workers and make somebody else rich.

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Speaker 2: Yeah, I mean absolutely, that's the whole name of the

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Speaker 2: game here for this kind of thing. Right Like, nobody

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Speaker 2: is cloning voice actors' voices and inserting them in porn

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Speaker 2: because they want to support those actors, right like, They're

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Speaker 2: doing it because they want to take the value that

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Speaker 2: those actors have created and just expropriate it and own

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Speaker 2: it without having to pay them.

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Speaker 1: And I think it really comes back to what you

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Speaker 1: were saying earlier about what is treated as communal and

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Speaker 1: what people in power feel entitled to. Right Like, as

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Speaker 1: a voice professional, a voiceover actor myself. My voice is

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Speaker 1: really important to me. It's mine. I have shaped it

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Speaker 1: and honed it over many many years. I've taken vocal coaching.

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Speaker 1: I've done breathing exercises. I'm getting a nasal surgery in

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Speaker 1: two weeks so that the quality of my voice and

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Speaker 1: throat and breathing comes through. It is important to me.

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Speaker 1: It is something that I have owned. It is mine,

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Speaker 1: and that the fact that using technol people will be like, well,

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Speaker 1: I can just take it. I don't have to pay

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Speaker 1: you for it or get your consent. It's mine now. Uh.

433
00:25:05,800 --> 00:25:08,240
Speaker 1: I really think that, like, it's not. It's not just

434
00:25:08,280 --> 00:25:12,000
Speaker 1: a financial or a labor issue. It definitely is those things,

435
00:25:12,119 --> 00:25:15,040
Speaker 1: but it's also an ethical and philosophical question of like,

436
00:25:15,320 --> 00:25:18,439
Speaker 1: don't some things just belong to the people they belong to?

437
00:25:18,640 --> 00:25:20,439
Speaker 1: Like I don't want to. I don't want to, like

438
00:25:20,840 --> 00:25:23,679
Speaker 1: go off on a tangent. When I was young and

439
00:25:23,720 --> 00:25:28,119
Speaker 1: growing up, I was had to really become comfortable with

440
00:25:28,160 --> 00:25:30,679
Speaker 1: the way that my speaking voice sounds. It was really

441
00:25:30,760 --> 00:25:34,440
Speaker 1: a process. I have a high voice. I have been

442
00:25:34,480 --> 00:25:38,480
Speaker 1: called shrill before. There might that might not surprise people

443
00:25:38,520 --> 00:25:43,600
Speaker 1: listening to this show. I have been told that I

444
00:25:43,600 --> 00:25:47,920
Speaker 1: don't talk black enough, that I talk too black. It

445
00:25:48,000 --> 00:25:51,720
Speaker 1: was it's a it was an incredibly personal journey to

446
00:25:51,840 --> 00:25:54,080
Speaker 1: get to a place where I speak for a living.

447
00:25:54,600 --> 00:25:57,920
Speaker 1: I'll just leave it there, and that journey is mine.

448
00:25:58,359 --> 00:26:00,800
Speaker 1: And the fact that a company would feel like they

449
00:26:00,840 --> 00:26:04,760
Speaker 1: can just take that from me, I think, really, it's

450
00:26:04,840 --> 00:26:07,280
Speaker 1: not just a financial or labor concern. It is also

451
00:26:07,320 --> 00:26:10,040
Speaker 1: an ethical and philosophical concern of of what we are

452
00:26:10,080 --> 00:26:13,840
Speaker 1: comfortable with companies mining and other people mining for their

453
00:26:13,880 --> 00:26:17,160
Speaker 1: own financial benefit. And I really agree with what this

454
00:26:17,320 --> 00:26:20,119
Speaker 1: voice actor, Zayan Shackt, had to say. He says, if

455
00:26:20,200 --> 00:26:22,880
Speaker 1: you want voice acting, pay an actor. If you can't

456
00:26:22,880 --> 00:26:25,280
Speaker 1: afford an actor, ask around and see if someone will

457
00:26:25,280 --> 00:26:27,280
Speaker 1: do it for free. You can't find someone to do

458
00:26:27,320 --> 00:26:30,480
Speaker 1: it for free, you don't get voice acting. It's one

459
00:26:30,480 --> 00:26:32,440
Speaker 1: thing to grab an actor's voice and make it say

460
00:26:32,440 --> 00:26:35,040
Speaker 1: a silly meme, he said, it's another to make them

461
00:26:35,080 --> 00:26:38,080
Speaker 1: engage in sexual acts. I have nothing against not Safe

462
00:26:38,080 --> 00:26:40,639
Speaker 1: for work mods. I have nothing against not safe for

463
00:26:40,720 --> 00:26:42,720
Speaker 1: work art. But at the core of this is a

464
00:26:42,760 --> 00:26:46,560
Speaker 1: fundamental disrespect for the original voice actor, seeing them as

465
00:26:46,680 --> 00:26:50,080
Speaker 1: pure data to be molded and not an individual consent.

466
00:26:50,280 --> 00:26:53,399
Speaker 1: Is everything, and if you're creating sexual content without consent

467
00:26:53,560 --> 00:26:57,679
Speaker 1: of the involved parties, that's just vile. And I actually

468
00:26:57,680 --> 00:27:00,480
Speaker 1: do think it's kind of almost worse that they were

469
00:27:00,520 --> 00:27:03,760
Speaker 1: taking vo actors and then using AI to put them

470
00:27:03,760 --> 00:27:07,479
Speaker 1: into sexually explicit and pornographic games, because there's plenty of

471
00:27:07,960 --> 00:27:13,200
Speaker 1: voice actors who their specialty, their thing is pornographic content, right,

472
00:27:13,359 --> 00:27:16,840
Speaker 1: that's a lane that voice actors can go into, and

473
00:27:17,440 --> 00:27:20,760
Speaker 1: I think it's it's bad all around, but I think

474
00:27:20,800 --> 00:27:24,959
Speaker 1: it's worse to undermine the their rates. I think if

475
00:27:24,960 --> 00:27:28,399
Speaker 1: you're a voice actor that specifically works in pornographic or

476
00:27:28,440 --> 00:27:33,159
Speaker 1: sexual explicit content, you're probably being undermined in all types

477
00:27:33,160 --> 00:27:35,760
Speaker 1: of ways. You probably really have to keep a hard

478
00:27:35,840 --> 00:27:38,520
Speaker 1: line to keep people from exploiting your labor. And the

479
00:27:38,560 --> 00:27:42,679
Speaker 1: fact that these people would just turn around and people

480
00:27:42,680 --> 00:27:47,040
Speaker 1: who don't who specifically are not doing pornographic voice actor

481
00:27:47,080 --> 00:27:51,040
Speaker 1: work AI clone them. It then undermines the people who

482
00:27:51,280 --> 00:27:53,040
Speaker 1: do that work, which there are plenty of, and so

483
00:27:53,040 --> 00:27:55,800
Speaker 1: it's just like it's another it's just very insulting and

484
00:27:55,920 --> 00:27:58,000
Speaker 1: very degrading, and at the end of the day, it's

485
00:27:58,040 --> 00:28:01,879
Speaker 1: really about not paying people for their labor. Not paying

486
00:28:01,920 --> 00:28:05,840
Speaker 1: people what they deserve. It worries me that we're talking

487
00:28:05,880 --> 00:28:09,760
Speaker 1: about the financial implications of this, but not the deeper

488
00:28:09,800 --> 00:28:12,520
Speaker 1: implications of what all of this means. That people feel

489
00:28:12,560 --> 00:28:15,040
Speaker 1: so entitled to just take whatever they want from people

490
00:28:15,080 --> 00:28:17,520
Speaker 1: and not pay them or get their consent totally.

491
00:28:18,880 --> 00:28:20,400
Speaker 2: You know, it goes back to what we were talking

492
00:28:20,400 --> 00:28:23,760
Speaker 2: about about that Google lawsuit, where they just took it

493
00:28:23,800 --> 00:28:26,600
Speaker 2: because it was there and they could and felt entitled

494
00:28:27,040 --> 00:28:31,119
Speaker 2: to it. So it's similar in that regard. But you

495
00:28:31,240 --> 00:28:34,280
Speaker 2: also make a great point that it's not just about

496
00:28:34,600 --> 00:28:37,840
Speaker 2: unpaid labor of it and how wrong that is. It

497
00:28:37,920 --> 00:28:40,000
Speaker 2: is about that, but it's not just about that. The

498
00:28:40,040 --> 00:28:43,280
Speaker 2: idea that you would take someone's voice and put it

499
00:28:43,440 --> 00:28:50,600
Speaker 2: into sexually explicit scenarios without their consent, that's really vile,

500
00:28:50,960 --> 00:28:54,200
Speaker 2: Like the quote that you read, Vile is the word

501
00:28:54,200 --> 00:29:01,360
Speaker 2: for it. It's messed up. It's disturbing. That shouldn't be allowed, right,

502
00:29:01,480 --> 00:29:05,600
Speaker 2: Like people should have a say over whether or not

503
00:29:05,680 --> 00:29:11,080
Speaker 2: their voice gets used to get somebody else off, like ugh,

504
00:29:12,320 --> 00:29:16,400
Speaker 2: and also whose voices, like I don't know whose voices

505
00:29:16,440 --> 00:29:21,320
Speaker 2: are getting used without their consent in these sexually explicit

506
00:29:21,760 --> 00:29:27,080
Speaker 2: situations and games. I'm guessing it's not men, right, like,

507
00:29:27,240 --> 00:29:33,840
Speaker 2: it's probably women. So there's that as well, the misogynistic

508
00:29:33,960 --> 00:29:38,320
Speaker 2: angle to it that I don't know. Maybe people are

509
00:29:38,520 --> 00:29:41,880
Speaker 2: doing it with a bunch of men's voices, but I

510
00:29:42,040 --> 00:29:46,120
Speaker 2: have to suspect that it's mainly women that are having

511
00:29:46,200 --> 00:29:51,719
Speaker 2: their voices stolen and used in sexual ways without their consent. Uh,

512
00:29:51,960 --> 00:29:54,400
Speaker 2: and that feels extra gross.

513
00:29:54,800 --> 00:29:58,600
Speaker 1: I can tell you that it's disproportionately women when it

514
00:29:58,600 --> 00:30:01,200
Speaker 1: comes to AI fakes. This was a study that I

515
00:30:01,240 --> 00:30:04,440
Speaker 1: saw that was specifically about visual fakes like graphics and videos,

516
00:30:04,440 --> 00:30:06,800
Speaker 1: but I can only imagine that would also extend to

517
00:30:07,680 --> 00:30:09,640
Speaker 1: voiceover fakes and audio fakes as well.

518
00:30:10,080 --> 00:30:10,200
Speaker 3: Uh.

519
00:30:10,320 --> 00:30:14,400
Speaker 1: It is overwhelmingly women who are put in sexual positions

520
00:30:15,080 --> 00:30:18,680
Speaker 1: via AI. And I think we've already seen this. We've

521
00:30:18,720 --> 00:30:21,600
Speaker 1: we've already seen a marketplace for AI deep fakes. I

522
00:30:21,600 --> 00:30:24,080
Speaker 1: think it's going to be incredibly disruptive to our democracy

523
00:30:24,800 --> 00:30:27,760
Speaker 1: when people are able to make convincing deep fakes of

524
00:30:27,840 --> 00:30:30,280
Speaker 1: women and women of color and other marginalized candidates trying

525
00:30:30,320 --> 00:30:33,200
Speaker 1: to run for office or hold public office. I don't

526
00:30:33,200 --> 00:30:36,000
Speaker 1: think that we're talking enough about the deep, deep threat

527
00:30:36,080 --> 00:30:38,760
Speaker 1: at that place to our to our democracy. Into all

528
00:30:38,760 --> 00:30:41,320
Speaker 1: of us. But I think that I think that you're right.

529
00:30:41,840 --> 00:30:44,640
Speaker 1: I think that the consent part of it is really

530
00:30:44,680 --> 00:30:47,800
Speaker 1: troubling to me. Zay In Shack has another good quote.

531
00:30:47,800 --> 00:30:51,200
Speaker 1: He says, see how people react when you tell them no, no,

532
00:30:51,360 --> 00:30:54,120
Speaker 1: you cannot use my voice and likeness against my will.

533
00:30:54,480 --> 00:30:56,920
Speaker 1: No you cannot make me say the meme. People go

534
00:30:57,120 --> 00:31:00,240
Speaker 1: feral and the mask comes off. I think that we're

535
00:31:00,240 --> 00:31:02,640
Speaker 1: at that point where the mask is off. I think

536
00:31:02,680 --> 00:31:05,640
Speaker 1: that people with power and institutions with power feel that

537
00:31:05,680 --> 00:31:09,560
Speaker 1: they are entitled to mine and use anyone however they

538
00:31:09,600 --> 00:31:11,480
Speaker 1: want if it means that they make money. So if

539
00:31:11,480 --> 00:31:15,160
Speaker 1: that's me Frigid as a voice actor, if that's some

540
00:31:16,120 --> 00:31:18,760
Speaker 1: parent who put their kids picture on Google and now

541
00:31:18,760 --> 00:31:20,360
Speaker 1: that picture is going to be used to train an

542
00:31:20,400 --> 00:31:25,920
Speaker 1: AI model, If that's Sarah Silverman who wrote a touching

543
00:31:26,040 --> 00:31:30,160
Speaker 1: memoir about her life that chat GBT just steals. I

544
00:31:30,200 --> 00:31:33,080
Speaker 1: think that the mask is off, and it is clear

545
00:31:33,240 --> 00:31:36,880
Speaker 1: that if institutions and people with power can make money

546
00:31:36,920 --> 00:31:39,680
Speaker 1: off of it, all bets are off. They will take

547
00:31:39,720 --> 00:31:42,239
Speaker 1: it and they will see it as theirs, and they

548
00:31:42,240 --> 00:31:45,479
Speaker 1: will expect us to praise them for it. To be

549
00:31:45,520 --> 00:31:47,360
Speaker 1: like oh, it's innovation. It's gonna make all of our

550
00:31:47,400 --> 00:31:50,720
Speaker 1: lives better. They will expect us to praise them for

551
00:31:50,880 --> 00:31:54,720
Speaker 1: taking from us, And that's the thing I'm like, really

552
00:31:54,760 --> 00:31:57,120
Speaker 1: think we need to really pump the brakes on.

553
00:31:58,280 --> 00:32:01,360
Speaker 2: Yeah, I think the mask is off is a good

554
00:32:01,360 --> 00:32:04,920
Speaker 2: phrase for it. Like, they have the technology, they can

555
00:32:05,000 --> 00:32:10,240
Speaker 2: do it. So the only thing standing between doing it

556
00:32:10,280 --> 00:32:13,000
Speaker 2: and not doing it is the ethics of it and

557
00:32:13,480 --> 00:32:17,320
Speaker 2: are there standards? Are we going to live in a

558
00:32:17,360 --> 00:32:24,840
Speaker 2: society where people's voices are not ripped off and used

559
00:32:24,880 --> 00:32:27,560
Speaker 2: in ways that they didn't consent to or not? And

560
00:32:27,720 --> 00:32:29,760
Speaker 2: who is going to line up on which side of that?

561
00:32:30,840 --> 00:32:32,360
Speaker 2: I think that's what we're looking at right now.

562
00:32:32,760 --> 00:32:35,280
Speaker 1: So let's talk about that. Because Nexus mods I said

563
00:32:35,320 --> 00:32:38,360
Speaker 1: earlier that they did not have a rule against AI

564
00:32:38,480 --> 00:32:42,080
Speaker 1: generated content, they now have come out and said that

565
00:32:42,120 --> 00:32:45,760
Speaker 1: they may remove AI generated content in response to quote

566
00:32:46,040 --> 00:32:48,800
Speaker 1: a credible complaint from a party who feels that a

567
00:32:48,840 --> 00:32:51,680
Speaker 1: mod is damaging to them, including a voice actor or

568
00:32:51,680 --> 00:32:54,960
Speaker 1: an asset owner. It also advised users to avoid using

569
00:32:54,960 --> 00:32:58,240
Speaker 1: these tools AI tools unless you have explicit permission to

570
00:32:58,360 --> 00:33:01,959
Speaker 1: use all the assets. Actor consent has clearly been ignored

571
00:33:02,000 --> 00:33:05,440
Speaker 1: by many Nexus mods even added that this is particularly

572
00:33:05,480 --> 00:33:09,080
Speaker 1: true of AI generated voice acting, but has not stemmed

573
00:33:09,120 --> 00:33:11,360
Speaker 1: the tide. And I think that really goes back to

574
00:33:11,400 --> 00:33:13,480
Speaker 1: our conversation from a few weeks ago, that it does

575
00:33:13,520 --> 00:33:16,560
Speaker 1: seem to be that right now, the only guard rail

576
00:33:17,120 --> 00:33:20,720
Speaker 1: seems to be protecting intellectual property or some kind of

577
00:33:20,760 --> 00:33:25,480
Speaker 1: an asset, right the question of like is this We're

578
00:33:25,520 --> 00:33:27,800
Speaker 1: not wrestling with it as an ethical dilemma, only a

579
00:33:27,800 --> 00:33:29,880
Speaker 1: financial one, And so the only person who could be

580
00:33:29,960 --> 00:33:32,440
Speaker 1: like I have been harmed would have to be like

581
00:33:33,080 --> 00:33:35,320
Speaker 1: I have been harmed in some material way because my

582
00:33:35,400 --> 00:33:40,640
Speaker 1: intellectual property, a business thing that needs consideration, is at risk.

583
00:33:41,640 --> 00:33:45,200
Speaker 1: I just feel like we only care when when it's

584
00:33:45,200 --> 00:33:48,600
Speaker 1: like a business interest. We're not interested in having a

585
00:33:48,600 --> 00:33:50,800
Speaker 1: conversation about the ethical dilemmas.

586
00:33:51,200 --> 00:33:56,120
Speaker 2: Yeah, I guess maybe to be generous to I don't know, society,

587
00:33:57,360 --> 00:34:00,360
Speaker 2: we haven't really had to grapple with this before. We're

588
00:34:00,440 --> 00:34:03,680
Speaker 2: kind of in like new territory here where like this,

589
00:34:03,840 --> 00:34:07,960
Speaker 2: these new technologies have enabled these new types of transgressions

590
00:34:07,960 --> 00:34:10,799
Speaker 2: where we need some new protections. But you know, I

591
00:34:10,800 --> 00:34:15,960
Speaker 2: don't have a copyright to my voice. That's not a

592
00:34:16,080 --> 00:34:19,480
Speaker 2: thing that has ever even like come up before, not

593
00:34:19,560 --> 00:34:22,400
Speaker 2: that people are trying to take it, but if somebody

594
00:34:22,440 --> 00:34:25,520
Speaker 2: wanted to, there's not a legal framework to protect it,

595
00:34:25,560 --> 00:34:29,000
Speaker 2: and like, same with you, same with everyone, right, Like

596
00:34:29,680 --> 00:34:35,759
Speaker 2: it's it really does feel like uncharted territory. And I

597
00:34:35,800 --> 00:34:40,520
Speaker 2: guess a bazillion lawyers are going to make a bazillion

598
00:34:40,640 --> 00:34:46,239
Speaker 2: dollars sorting it all out. Hopefully we land in a

599
00:34:46,239 --> 00:34:52,440
Speaker 2: place where there are some protections for people that are

600
00:34:52,440 --> 00:34:57,560
Speaker 2: based on like privacy and the right to own one's

601
00:34:57,680 --> 00:35:02,560
Speaker 2: own likeness and voice and not just completely tied to

602
00:35:04,080 --> 00:35:05,399
Speaker 2: business value.

603
00:35:05,560 --> 00:35:09,359
Speaker 1: So speaking of trying to figure out kind of a

604
00:35:09,440 --> 00:35:13,760
Speaker 1: new paradigm for how this technology fits into our lives

605
00:35:13,760 --> 00:35:16,759
Speaker 1: and accountability around it, that actually brings me to this

606
00:35:16,920 --> 00:35:20,560
Speaker 1: story with the victims of the Buffalo shooting. So, families

607
00:35:20,560 --> 00:35:23,160
Speaker 1: of the victims of the Buffalo shooting that happened last

608
00:35:23,239 --> 00:35:26,279
Speaker 1: year are assuming social media platforms. Y'll probably remember that

609
00:35:26,560 --> 00:35:29,000
Speaker 1: horrible mass shooting at the Top supermarket and a black

610
00:35:29,000 --> 00:35:32,200
Speaker 1: neighborhood in Buffalo, New York. It was absolutely horrifying. I'll

611
00:35:32,239 --> 00:35:34,879
Speaker 1: never forget it. The gunman was specifically looking for black

612
00:35:34,920 --> 00:35:38,520
Speaker 1: neighborhoods to terrorize. He researched the area online, drove two

613
00:35:38,640 --> 00:35:41,399
Speaker 1: hundred miles from his home and opened fire in the supermarket,

614
00:35:41,719 --> 00:35:44,839
Speaker 1: killing ten black people and injuring others. He's currently serving

615
00:35:44,880 --> 00:35:47,680
Speaker 1: a life sentence after pleading guilty to murder and domestic

616
00:35:47,760 --> 00:35:51,000
Speaker 1: terrorism motivated by hate, and now the relatives of those

617
00:35:51,120 --> 00:35:54,239
Speaker 1: killed and wounded said that social media platforms should share

618
00:35:54,280 --> 00:35:57,040
Speaker 1: the blame for the attack because the gunman was fueled

619
00:35:57,040 --> 00:36:00,520
Speaker 1: by racist conspiracy theories that he encountered online on social

620
00:36:00,560 --> 00:36:06,360
Speaker 1: media platforms. The suit names nearly a dozen companies Meta, Reddit, Amazon,

621
00:36:06,400 --> 00:36:10,399
Speaker 1: which owns Twitch, Google, YouTube, Discord, four Chan. They're also

622
00:36:10,680 --> 00:36:16,439
Speaker 1: including RMA Armament, a body armor manufacturer, and Vintage Firearms LLC,

623
00:36:16,640 --> 00:36:19,440
Speaker 1: a gun retailer. They basically said that, by the killer's

624
00:36:19,520 --> 00:36:23,440
Speaker 1: own admissions, these social media sites radicalized him. This is

625
00:36:23,440 --> 00:36:26,799
Speaker 1: from the lawsuit. The killer, a vulnerable teenager, was not

626
00:36:26,960 --> 00:36:29,719
Speaker 1: racist until he became addicted to social media apps and

627
00:36:29,800 --> 00:36:34,200
Speaker 1: was lured unsuspectingly into a psychological vortex by defective social

628
00:36:34,200 --> 00:36:38,560
Speaker 1: media applications designed, marketed, and pushed out by social media defendants,

629
00:36:38,760 --> 00:36:41,520
Speaker 1: and fed a steady stream of racist and white supremacist

630
00:36:41,600 --> 00:36:45,120
Speaker 1: propaganda and falsehoods by some of those same defendant's products.

631
00:36:45,480 --> 00:36:48,719
Speaker 1: Addiction to these defective social media products leads users like

632
00:36:48,800 --> 00:36:53,200
Speaker 1: him into social isolation. Once isolated, he became radicalized by

633
00:36:53,200 --> 00:36:57,279
Speaker 1: overexposure to fringe racist ideologies and was primed for the

634
00:36:57,320 --> 00:37:00,960
Speaker 1: reckless and wanton conduct of the weapons and body armored defendants.

635
00:37:01,120 --> 00:37:03,560
Speaker 1: He pulled the trigger, but he did so only after

636
00:37:03,680 --> 00:37:06,799
Speaker 1: years of exposure to addictive social media platforms, which led

637
00:37:06,800 --> 00:37:10,120
Speaker 1: to his radicalization and encouragement via the Internet to purchase

638
00:37:10,120 --> 00:37:13,239
Speaker 1: weapons and body armour to commit this heinous act. So

639
00:37:13,280 --> 00:37:16,680
Speaker 1: the lawsuit is seeking unspecified financial damages, and the victims'

640
00:37:16,680 --> 00:37:20,000
Speaker 1: families also want changes to how social media companies are operated.

641
00:37:20,160 --> 00:37:22,200
Speaker 1: Something that really broke my heart was that the mother

642
00:37:22,320 --> 00:37:24,880
Speaker 1: of one of the victims who was killed talks about

643
00:37:25,040 --> 00:37:28,840
Speaker 1: being tagged in videos depicting the murder of her child

644
00:37:28,920 --> 00:37:32,680
Speaker 1: because the killer live streamed his rampage. It also really

645
00:37:32,680 --> 00:37:36,239
Speaker 1: reminds me of the situation with Andy Parker, whose daughter

646
00:37:36,280 --> 00:37:39,239
Speaker 1: Alison Parker, and her colleague Adam Ward were shot and

647
00:37:39,320 --> 00:37:42,080
Speaker 1: killed on camera by one of her coworkers while she

648
00:37:42,120 --> 00:37:44,319
Speaker 1: was doing her job as a TV news reporter. The

649
00:37:44,400 --> 00:37:47,080
Speaker 1: video of her death is still circulating on social media

650
00:37:47,080 --> 00:37:50,359
Speaker 1: platforms to this day, and her father, Andy Parker, has

651
00:37:50,400 --> 00:37:53,279
Speaker 1: been fighting tooth and nail to have it removed. He

652
00:37:53,360 --> 00:37:56,920
Speaker 1: filed complaints to the FTC alleging that YouTube and Facebook

653
00:37:56,960 --> 00:37:59,560
Speaker 1: failed to enforce their own terms of service by keeping

654
00:38:00,200 --> 00:38:03,480
Speaker 1: certain video content off of their platforms, And the last

655
00:38:03,520 --> 00:38:05,799
Speaker 1: I heard about this was that he was trying to

656
00:38:05,880 --> 00:38:09,160
Speaker 1: have the video minted as an NFT so that he

657
00:38:09,239 --> 00:38:11,840
Speaker 1: might be able to use a copyright claim to force

658
00:38:11,920 --> 00:38:14,560
Speaker 1: platforms to remove it, which again kind of goes back

659
00:38:14,560 --> 00:38:16,160
Speaker 1: to what we were talking about before, which is that

660
00:38:16,520 --> 00:38:21,240
Speaker 1: the on he's been fighting so hard to remove essentially

661
00:38:21,280 --> 00:38:23,520
Speaker 1: a snuff video of his daughter being murdered, which he

662
00:38:23,600 --> 00:38:26,879
Speaker 1: has to see over and over and over again. He's

663
00:38:26,880 --> 00:38:29,080
Speaker 1: described how sometimes it'll have an ad in front of it,

664
00:38:29,160 --> 00:38:32,560
Speaker 1: and like that's just a terrible experience, and how he

665
00:38:32,600 --> 00:38:39,320
Speaker 1: has just exhausted the avenue of appealing to the government

666
00:38:39,440 --> 00:38:42,880
Speaker 1: and the platforms, and his only recourse now is trying

667
00:38:42,920 --> 00:38:45,759
Speaker 1: to have a copyright claim like that. It's just so

668
00:38:45,840 --> 00:38:48,279
Speaker 1: heartbreaking that that is the only way that he could

669
00:38:48,280 --> 00:38:51,200
Speaker 1: get these platforms to take a video of his daughter

670
00:38:51,239 --> 00:38:52,200
Speaker 1: being murdered down.

671
00:38:52,719 --> 00:38:58,160
Speaker 2: That is such a heartbreaking story and it just really

672
00:38:58,280 --> 00:39:01,600
Speaker 2: underscores how come plex and what a weird space we're

673
00:39:01,600 --> 00:39:04,839
Speaker 2: in where this man has dedicated his life to trying

674
00:39:04,880 --> 00:39:07,440
Speaker 2: to take down this video of his daughter being murdered,

675
00:39:08,040 --> 00:39:11,120
Speaker 2: and he's been working on it for years and he's

676
00:39:11,200 --> 00:39:15,160
Speaker 2: not being successful. Like I think most people would agree

677
00:39:15,680 --> 00:39:19,640
Speaker 2: that video should not be publicly available like it should

678
00:39:19,640 --> 00:39:22,960
Speaker 2: be taken down. It's not even like it's on fringe

679
00:39:23,120 --> 00:39:27,360
Speaker 2: sites in like dark you know, CD corners of the Internet.

680
00:39:27,840 --> 00:39:30,719
Speaker 2: He's trying to get it taken in from like YouTube

681
00:39:30,840 --> 00:39:31,840
Speaker 2: and he's failing.

682
00:39:32,400 --> 00:39:36,560
Speaker 1: Yeah, and if if he's unfortunately, he's right that if

683
00:39:36,560 --> 00:39:38,919
Speaker 1: it was a copyright claim, they would take it down

684
00:39:39,000 --> 00:39:41,040
Speaker 1: like that, they would take it down. They take those

685
00:39:41,080 --> 00:39:44,239
Speaker 1: down very quickly. But because he doesn't have some kind

686
00:39:44,239 --> 00:39:48,880
Speaker 1: of a business interest to protect, it's just I don't know,

687
00:39:49,440 --> 00:39:54,640
Speaker 1: the murder of his baby. Yeah, there's it just is online.

688
00:39:54,680 --> 00:39:56,920
Speaker 1: He just has to see it sometime. So in response

689
00:39:56,960 --> 00:39:59,759
Speaker 1: to the suit brought by the Buffalo Families, YouTube put

690
00:39:59,760 --> 00:40:02,719
Speaker 1: out a statement that honestly just really says nothing. They said,

691
00:40:02,800 --> 00:40:06,160
Speaker 1: we regularly work with law enforcement other platforms in civil

692
00:40:06,160 --> 00:40:09,319
Speaker 1: society to share intelligence and best practices. That was from

693
00:40:09,880 --> 00:40:12,440
Speaker 1: Jose Costanata said in a statement he emailed to the

694
00:40:12,440 --> 00:40:16,400
Speaker 1: Associated Press. Okay, great, good that I mean, like, that

695
00:40:16,440 --> 00:40:19,239
Speaker 1: doesn't that doesn't. That just says nothing. It doesn't, it

696
00:40:19,280 --> 00:40:20,200
Speaker 1: doesn't say anything.

697
00:40:20,680 --> 00:40:22,640
Speaker 2: That's such a like non statement.

698
00:40:22,880 --> 00:40:25,520
Speaker 1: And so I will say this that, like, there is

699
00:40:25,960 --> 00:40:30,480
Speaker 1: so much research, overwhelming research showing how platforms push users

700
00:40:30,520 --> 00:40:33,759
Speaker 1: down extremist pipelines, and they are very aware that they

701
00:40:33,760 --> 00:40:36,640
Speaker 1: are doing it. Like, according to Facebook's own internal data

702
00:40:36,800 --> 00:40:40,319
Speaker 1: from twenty sixteen, Facebook found that sixty four percent of

703
00:40:40,360 --> 00:40:44,120
Speaker 1: people who joined an extremist group on Facebook only did

704
00:40:44,160 --> 00:40:48,600
Speaker 1: so because Facebook's algorithm recommended it to them. So you know,

705
00:40:48,800 --> 00:40:53,120
Speaker 1: they're aware of it, and it's radicalization is a business

706
00:40:53,120 --> 00:40:55,840
Speaker 1: model for these platforms, right, it's not. It's not a bug,

707
00:40:55,960 --> 00:40:58,799
Speaker 1: it's not a mistake. It is a business model that

708
00:40:58,800 --> 00:41:02,080
Speaker 1: they are profiting from. And while we're all being harmed,

709
00:41:02,160 --> 00:41:07,680
Speaker 1: they're getting a rich off of it.

710
00:41:07,760 --> 00:41:20,600
Speaker 4: More after a quick break, let's get right back into it.

711
00:41:21,480 --> 00:41:24,239
Speaker 1: Let's talk about Twitter. So you know how we just

712
00:41:24,280 --> 00:41:28,640
Speaker 1: said an episode, a long episode all about how metas

713
00:41:28,680 --> 00:41:32,800
Speaker 1: Threads was smoking Twitter and they got millions of downloads overnight,

714
00:41:32,840 --> 00:41:37,040
Speaker 1: became the most downloaded app really quickly after Twitter was

715
00:41:37,080 --> 00:41:41,759
Speaker 1: having all of these problems and glitches. Well, totally unrelated

716
00:41:41,800 --> 00:41:44,800
Speaker 1: to that, Twitter is now saying they're giving users surprise

717
00:41:44,920 --> 00:41:50,120
Speaker 1: money from the platform's ad revenue sharing system. Isn't that interesting, so,

718
00:41:50,200 --> 00:41:52,120
Speaker 1: I mean, to me, it kind of seems like when

719
00:41:53,280 --> 00:41:56,160
Speaker 1: morale is really low at work and everybody is like

720
00:41:56,239 --> 00:42:00,279
Speaker 1: about to quit, and then management is like, oh, we're

721
00:42:00,280 --> 00:42:03,960
Speaker 1: doing bagels in the conference room. Guys like, hey, we

722
00:42:04,040 --> 00:42:07,880
Speaker 1: got cake, Like we brought lunch for everybody. Users on

723
00:42:07,920 --> 00:42:12,560
Speaker 1: Twitter were sharing screenshots of the payouts that Twitter was

724
00:42:12,600 --> 00:42:15,080
Speaker 1: promising them. Some of them were pretty big. So this

725
00:42:15,160 --> 00:42:17,759
Speaker 1: thing kind of seems like an obvious scam to me.

726
00:42:18,200 --> 00:42:20,759
Speaker 1: It does seem like Musk and the Twitter team are

727
00:42:20,840 --> 00:42:23,560
Speaker 1: just making the rules up so that they can hand

728
00:42:23,560 --> 00:42:25,960
Speaker 1: select who gets to be paid out for this program.

729
00:42:26,200 --> 00:42:28,720
Speaker 1: Tayler Laman's reports that Twitter claimed in a blog post

730
00:42:28,920 --> 00:42:31,320
Speaker 1: that creator's share of advertising revenue will be based on

731
00:42:31,400 --> 00:42:34,279
Speaker 1: a calculation of replies to their posts and monthly impressions.

732
00:42:34,560 --> 00:42:37,399
Speaker 1: It was supposed to be five million tweet impressions over

733
00:42:37,440 --> 00:42:40,720
Speaker 1: I think they said three months, but then Musk tweeted

734
00:42:40,760 --> 00:42:43,960
Speaker 1: that the payouts were actually not tied to public impressions,

735
00:42:44,120 --> 00:42:47,480
Speaker 1: but they were calculated using a proprietary metric based on

736
00:42:47,719 --> 00:42:50,680
Speaker 1: and served to other verified users, so that if you

737
00:42:50,920 --> 00:42:53,600
Speaker 1: tweet something and your verified users who bought Twitter Blue

738
00:42:53,760 --> 00:42:56,719
Speaker 1: and other people who bought Twitter Blue see it, that's

739
00:42:56,800 --> 00:42:59,080
Speaker 1: what it's based on. But then I thought, if you

740
00:42:59,120 --> 00:43:00,840
Speaker 1: paid for Twitter Blue, the whole point of doing it

741
00:43:00,880 --> 00:43:03,040
Speaker 1: was that you would see less ads. It doesn't make sense.

742
00:43:03,080 --> 00:43:04,759
Speaker 1: It really does seem like he's just making it up

743
00:43:04,760 --> 00:43:07,400
Speaker 1: as he goes along. So we really have no idea

744
00:43:07,560 --> 00:43:10,320
Speaker 1: how these payouts are being calculated. It does not seem

745
00:43:10,320 --> 00:43:11,960
Speaker 1: to be the kind of thing that is applied across

746
00:43:12,000 --> 00:43:14,480
Speaker 1: the board. And if you do want in on this program,

747
00:43:14,480 --> 00:43:16,600
Speaker 1: if you're seeing all these fat checks and thinking I

748
00:43:16,680 --> 00:43:19,080
Speaker 1: want in, I don't know what to tell you because

749
00:43:19,120 --> 00:43:21,480
Speaker 1: you need to apply to this program and you have

750
00:43:21,520 --> 00:43:23,919
Speaker 1: to be hand approved, which people are saying can take

751
00:43:24,000 --> 00:43:26,239
Speaker 1: kind of a while. So I think that a lot

752
00:43:26,280 --> 00:43:29,760
Speaker 1: of people are probably seeing these big name influencers showing

753
00:43:29,760 --> 00:43:32,560
Speaker 1: screenshots of making tons of money, and Musk is hoping

754
00:43:32,600 --> 00:43:36,000
Speaker 1: that they'll just cave and pay for Twitter Blue without

755
00:43:36,040 --> 00:43:38,759
Speaker 1: realizing that this system on how you would be paid

756
00:43:38,800 --> 00:43:41,880
Speaker 1: out does seem like it's kind of rigged. It basically

757
00:43:41,960 --> 00:43:44,800
Speaker 1: sounds like how an MLM or a pyramid scheme works.

758
00:43:45,000 --> 00:43:48,000
Speaker 1: That the people who bought in early get actual checks,

759
00:43:48,000 --> 00:43:50,360
Speaker 1: like actually get paid out, and the people who see

760
00:43:50,360 --> 00:43:52,799
Speaker 1: that and then come in because of it, well, they

761
00:43:52,880 --> 00:43:55,319
Speaker 1: just get scammed. The notice is said that they were

762
00:43:55,320 --> 00:43:58,040
Speaker 1: going to be paid in seventy two hours now. Elon

763
00:43:58,160 --> 00:44:01,960
Speaker 1: Musk and Twitter say a lot of things. They specifically

764
00:44:02,000 --> 00:44:04,560
Speaker 1: say a lot of things to people to whom they

765
00:44:04,640 --> 00:44:09,160
Speaker 1: owe money. Sometimes those things are some variation of I'm

766
00:44:09,200 --> 00:44:12,000
Speaker 1: not going to pay you. To put this in context,

767
00:44:12,440 --> 00:44:15,239
Speaker 1: Just the day before Twitter announced these payouts, they were

768
00:44:15,280 --> 00:44:18,120
Speaker 1: hit with a seven hundred and thirty six million dollar

769
00:44:18,239 --> 00:44:22,080
Speaker 1: lawsuit for allegedly refusing severance for fired employees after an

770
00:44:22,080 --> 00:44:24,719
Speaker 1: employee alleged the company is refusing to pay seferance to

771
00:44:24,760 --> 00:44:28,319
Speaker 1: her and former colleagues that could total five hundred million dollars.

772
00:44:28,880 --> 00:44:32,400
Speaker 1: So I will believe that these people are getting this

773
00:44:32,520 --> 00:44:36,920
Speaker 1: money when I see hard evidence of it hitting accounts.

774
00:44:37,719 --> 00:44:41,760
Speaker 1: We're recording this at ten forty one pm Eastern Time, Thursday,

775
00:44:41,880 --> 00:44:45,480
Speaker 1: July thirteenth. That notice is said seventy two hours. I

776
00:44:45,520 --> 00:44:49,360
Speaker 1: will happily report back if money hits accounts, but I

777
00:44:49,400 --> 00:44:54,200
Speaker 1: will believe that when I see that. I suspect I

778
00:44:54,200 --> 00:44:59,799
Speaker 1: don't know, this is my speculation. I suspect it's not

779
00:45:00,200 --> 00:45:02,799
Speaker 1: to be like a lump payment. It's gonna be like,

780
00:45:02,880 --> 00:45:06,120
Speaker 1: oh well, actually we're metering it out and you'll get

781
00:45:06,160 --> 00:45:09,680
Speaker 1: like X amount a month or something like that. I

782
00:45:09,719 --> 00:45:14,040
Speaker 1: think that Elon musk owes so many people money from

783
00:45:14,239 --> 00:45:17,600
Speaker 1: the Twitter deal that I mean, imagine all these other

784
00:45:17,920 --> 00:45:21,200
Speaker 1: creditors that are owed watching checks hit accounts. That's like

785
00:45:21,200 --> 00:45:23,440
Speaker 1: when you when you lend somebody money and then you

786
00:45:23,440 --> 00:45:26,080
Speaker 1: look on Instagram and they're in fucking grease, right, Like

787
00:45:27,560 --> 00:45:30,040
Speaker 1: it's like, what do you think?

788
00:45:30,239 --> 00:45:32,840
Speaker 2: Yeah, maybe he's gonna pay them in Twitter bucks. I

789
00:45:32,840 --> 00:45:36,960
Speaker 2: don't know. I think you're right. I'll believe it when

790
00:45:36,960 --> 00:45:40,640
Speaker 2: I see it. I don't know. It's the way that

791
00:45:40,719 --> 00:45:43,719
Speaker 2: it was phrased that you know, they have to have

792
00:45:44,040 --> 00:45:47,759
Speaker 2: what was it, five million tweet impressions each month for

793
00:45:47,760 --> 00:45:52,360
Speaker 2: the last three months. A that's going to be maybe

794
00:45:52,360 --> 00:45:55,680
Speaker 2: made a little bit harder with all the rate limits

795
00:45:55,880 --> 00:45:58,399
Speaker 2: that they had going on. Are they still going on

796
00:45:58,560 --> 00:46:02,480
Speaker 2: question mark? I don't know. Also, it's only available to

797
00:46:02,520 --> 00:46:07,640
Speaker 2: Twitter Blue users, so that's what like one percent of

798
00:46:07,680 --> 00:46:12,600
Speaker 2: Twitter users. I haven't seen estimates in a little bit,

799
00:46:12,640 --> 00:46:15,680
Speaker 2: but like not a lot of Twitter users are subscribed

800
00:46:15,680 --> 00:46:17,720
Speaker 2: to Blues, so there's just like a lot of outs

801
00:46:17,719 --> 00:46:20,880
Speaker 2: for them to not pay people. Like I don't know

802
00:46:20,880 --> 00:46:24,759
Speaker 2: why they would say this. It seems almost certain that

803
00:46:24,840 --> 00:46:27,319
Speaker 2: they're not going to pay people, and so it's just

804
00:46:27,320 --> 00:46:30,920
Speaker 2: going to create more bad will. But who knows.

805
00:46:31,040 --> 00:46:31,279
Speaker 1: I don't.

806
00:46:31,320 --> 00:46:34,200
Speaker 2: I wouldn't pretend to know what they what Elon and

807
00:46:34,280 --> 00:46:35,839
Speaker 2: Twitter are thinking, Oh.

808
00:46:35,800 --> 00:46:39,320
Speaker 1: My god, Elon Musk, I know, I know he's not listening,

809
00:46:40,200 --> 00:46:45,439
Speaker 1: but if he if they promised payouts, specific high number

810
00:46:45,520 --> 00:46:47,719
Speaker 1: payouts and then they said they were gonna pay in

811
00:46:47,760 --> 00:46:51,279
Speaker 1: a specific number of hours and then they didn't, it's

812
00:46:51,320 --> 00:46:58,279
Speaker 1: over pack it up, Like can you imagine that would be? Oh, Like,

813
00:46:58,360 --> 00:47:00,920
Speaker 1: I'm embarrassed thinking about it. I'm not even involved in this.

814
00:47:01,320 --> 00:47:03,759
Speaker 2: I mean, it's interesting to think about because the only

815
00:47:03,800 --> 00:47:07,040
Speaker 2: people who stand to receive this money are the reply

816
00:47:07,200 --> 00:47:10,080
Speaker 2: guys that sign up for Twitter Blue. So if he

817
00:47:10,160 --> 00:47:13,400
Speaker 2: screws them over, what does that look like? Where did

818
00:47:13,400 --> 00:47:14,200
Speaker 2: they go from there?

819
00:47:14,560 --> 00:47:16,719
Speaker 1: So let's talk about some of the people who were

820
00:47:16,760 --> 00:47:19,759
Speaker 1: talking about their big payouts and why I hate this.

821
00:47:20,040 --> 00:47:24,200
Speaker 1: So the people who were sharing screenshots of their big payouts.

822
00:47:24,239 --> 00:47:27,440
Speaker 1: Were some of the worst people on Twitter, people like

823
00:47:27,680 --> 00:47:29,719
Speaker 1: Fanny Johnson, who, if you don't know who that is,

824
00:47:30,040 --> 00:47:33,160
Speaker 1: right wing grifter from Turning Points USA, people like the

825
00:47:33,200 --> 00:47:36,640
Speaker 1: account and Wokeness that has one point four million followers.

826
00:47:37,080 --> 00:47:38,800
Speaker 1: They tweeted a screenshot showing that they were going to

827
00:47:38,840 --> 00:47:42,439
Speaker 1: be paid ten four hundred dollars. And don't forget Andrew Tate,

828
00:47:42,600 --> 00:47:45,560
Speaker 1: who was banned from most social media platforms, but then

829
00:47:45,600 --> 00:47:48,799
Speaker 1: when Musk bought Twitter, he brought him back. Tate was

830
00:47:48,960 --> 00:47:52,280
Speaker 1: just released from prison for rape and human trafficking charges,

831
00:47:52,560 --> 00:47:55,200
Speaker 1: and he said that Twitter owes him twenty thousand dollars

832
00:47:55,360 --> 00:47:58,920
Speaker 1: good money. It honestly does seem like it's mostly big

833
00:47:59,120 --> 00:48:01,680
Speaker 1: right wing and extreme missed influencers, the kind of people

834
00:48:01,680 --> 00:48:05,400
Speaker 1: who gamified grievance and outrage to get clicks and engagement,

835
00:48:05,640 --> 00:48:07,560
Speaker 1: which I think are exactly the kind of people that

836
00:48:07,600 --> 00:48:09,719
Speaker 1: it sounds like Musk is trying to woo to the

837
00:48:09,760 --> 00:48:13,719
Speaker 1: platform with this payout scam. But importantly, it is not

838
00:48:14,400 --> 00:48:18,000
Speaker 1: just right wing grifters. It is also lefty grifters like

839
00:48:18,120 --> 00:48:23,040
Speaker 1: Brian Cransenstein, who tweeted about earning just over twenty four

840
00:48:23,320 --> 00:48:25,799
Speaker 1: thousand dollars, which is a lot of money. So if

841
00:48:25,800 --> 00:48:27,399
Speaker 1: you don't know who that is, him and his brother

842
00:48:27,600 --> 00:48:31,200
Speaker 1: Ed were really really big on Twitter in the days

843
00:48:31,280 --> 00:48:34,279
Speaker 1: right after Trump was elected. Like if you remember, like

844
00:48:34,320 --> 00:48:36,840
Speaker 1: when hashtag resist was a thing, do you remember that

845
00:48:36,880 --> 00:48:39,759
Speaker 1: era of the Trump administration when everybody was like, yeah,

846
00:48:39,920 --> 00:48:42,040
Speaker 1: hashtag resists. They would like put it in their Twitter

847
00:48:42,080 --> 00:48:45,080
Speaker 1: bios and we were all gonna like tweet Donald Trump

848
00:48:45,120 --> 00:48:46,680
Speaker 1: out of office or something, and then it kind of

849
00:48:46,719 --> 00:48:48,240
Speaker 1: lost seem.

850
00:48:47,800 --> 00:48:52,120
Speaker 2: Yeah, I've kind of blocked it out like a nightmare,

851
00:48:52,320 --> 00:48:53,840
Speaker 2: but I guess it did happen.

852
00:48:54,000 --> 00:48:58,440
Speaker 1: So Brian and his brother Ed were really big in

853
00:48:58,640 --> 00:49:01,120
Speaker 1: that era. They had millions and millions of followers, and

854
00:49:01,160 --> 00:49:03,719
Speaker 1: they just had this real neck for getting a ton

855
00:49:03,760 --> 00:49:06,120
Speaker 1: of engagement on Twitter. I think it was a mix

856
00:49:06,200 --> 00:49:10,720
Speaker 1: of them playing on people's fears and anxieties and doom

857
00:49:10,760 --> 00:49:15,880
Speaker 1: scrolling and also tweeting in a really kind of bombastic, loud,

858
00:49:16,120 --> 00:49:21,080
Speaker 1: over the top, exaggerated way. They were super alarmist. They

859
00:49:21,080 --> 00:49:25,680
Speaker 1: would always tweet like all cabs breaking colon and then

860
00:49:26,400 --> 00:49:28,320
Speaker 1: it would be a like a story that was reported

861
00:49:28,400 --> 00:49:30,640
Speaker 1: yesterday or something. So it was a lot of stuff

862
00:49:30,680 --> 00:49:33,879
Speaker 1: like that they would beg for retweets, and so they

863
00:49:33,880 --> 00:49:36,120
Speaker 1: got a lot of traction on Twitter. And I think

864
00:49:36,120 --> 00:49:38,680
Speaker 1: they really did a lot of kind of capitalizing on

865
00:49:38,719 --> 00:49:43,479
Speaker 1: our outrage in those specific Trump days when people were

866
00:49:43,480 --> 00:49:46,919
Speaker 1: like really feeling lot if you were like me, really

867
00:49:47,000 --> 00:49:51,240
Speaker 1: feeling locked to your phone, really feeling like you wanted

868
00:49:51,239 --> 00:49:53,359
Speaker 1: something to do that would make you feel like you were,

869
00:49:53,560 --> 00:49:55,879
Speaker 1: you know, resisting because we were in this like very

870
00:49:55,920 --> 00:49:59,920
Speaker 1: scary time and people were really rightly angry and scared

871
00:50:00,120 --> 00:50:02,800
Speaker 1: and frightened and looking for something to fill that void.

872
00:50:02,840 --> 00:50:04,600
Speaker 1: And these voided these two fill it.

873
00:50:05,080 --> 00:50:07,319
Speaker 2: Yeah, looking for something to fill the void. And it

874
00:50:07,400 --> 00:50:12,040
Speaker 2: felt like every day there was some new thing, some

875
00:50:12,239 --> 00:50:14,480
Speaker 2: new outrage, and it was like, is this the thing

876
00:50:14,520 --> 00:50:18,560
Speaker 2: that's gonna bring down the Trump administration? And like somehow

877
00:50:18,760 --> 00:50:21,960
Speaker 2: break us out of this waking nightmare. Like I do

878
00:50:22,120 --> 00:50:25,440
Speaker 2: remember waking up every day and being like and like

879
00:50:25,520 --> 00:50:28,160
Speaker 2: opening my phone and be like is this what am

880
00:50:28,200 --> 00:50:28,920
Speaker 2: I gonna find?

881
00:50:29,760 --> 00:50:33,160
Speaker 1: It could be anything, Yeah, as the av Club put it,

882
00:50:33,440 --> 00:50:36,279
Speaker 1: they used that time to build a cottage industry out

883
00:50:36,280 --> 00:50:42,560
Speaker 1: of relentless, self serving anti Trump sentiments. Listen, I don't

884
00:50:42,600 --> 00:50:46,040
Speaker 1: want to say that they're scammers. I personally don't want

885
00:50:46,040 --> 00:50:48,520
Speaker 1: to say that, but this is how the AV Club

886
00:50:48,600 --> 00:50:51,800
Speaker 1: reported it. Quote, in twenty sixteen, their homes were raided

887
00:50:51,800 --> 00:50:53,799
Speaker 1: by the FBI because they were running a number of

888
00:50:53,840 --> 00:50:57,960
Speaker 1: websites that promoted investment scams and blatant Ponzi schemes. The

889
00:50:58,080 --> 00:51:00,920
Speaker 1: brothers were not formally charged, and with a year they

890
00:51:00,920 --> 00:51:04,560
Speaker 1: had pivoted their focus to capitalizing on anti Trump fever.

891
00:51:04,960 --> 00:51:08,600
Speaker 1: So these are people who like maybe low key scam

892
00:51:08,640 --> 00:51:12,920
Speaker 1: adjacent question mark if you are to believe this reporting.

893
00:51:13,320 --> 00:51:15,959
Speaker 2: What kind of Ponzi schemes did they say in that quote?

894
00:51:16,040 --> 00:51:16,480
Speaker 3: Blatant?

895
00:51:16,920 --> 00:51:19,640
Speaker 2: Oh, blatant, blatant schemes.

896
00:51:21,239 --> 00:51:25,160
Speaker 1: Yeah, I mean, I'm not saying it. I'm reading what

897
00:51:25,200 --> 00:51:28,960
Speaker 1: the AV Club reported. So they were both permanently banned

898
00:51:29,000 --> 00:51:32,160
Speaker 1: from Twitter in the pre Musk days for operating multiple

899
00:51:32,160 --> 00:51:36,359
Speaker 1: fake accounts and purchasing account interactions, which is big time

900
00:51:36,400 --> 00:51:40,640
Speaker 1: against Twitter's rules. Like back then Twitter actually had rules

901
00:51:40,760 --> 00:51:42,840
Speaker 1: that was like the number one no no of Twitter.

902
00:51:44,160 --> 00:51:46,960
Speaker 1: But Elon Musk when they when he kind of had

903
00:51:47,000 --> 00:51:49,239
Speaker 1: amnesty for people who had been kicked off Twitter let

904
00:51:49,320 --> 00:51:51,640
Speaker 1: them back on and now is it paying one of them?

905
00:51:51,640 --> 00:51:54,160
Speaker 1: Almost twenty five thousand dollars, and so this is one

906
00:51:54,160 --> 00:51:56,680
Speaker 1: of the reasons why I don't like this, because I

907
00:51:56,680 --> 00:52:00,400
Speaker 1: think it will obviously encourage the people on twit who

908
00:52:00,480 --> 00:52:03,320
Speaker 1: have the worst kind of discourse to keep doing what

909
00:52:03,360 --> 00:52:06,840
Speaker 1: they're doing. You know, whenever you see somebody it's like,

910
00:52:06,840 --> 00:52:09,400
Speaker 1: when I'm scrolling Twitter, you ever see somebody who you

911
00:52:09,520 --> 00:52:14,960
Speaker 1: know is intentionally making an inflammatory point because they want engagement,

912
00:52:15,040 --> 00:52:17,239
Speaker 1: because they want eyeballs. I see it all the time.

913
00:52:17,360 --> 00:52:21,720
Speaker 1: It's so difficult to resist the temptation to engage because

914
00:52:21,719 --> 00:52:23,640
Speaker 1: that's exactly but that's exactly what they want. And so

915
00:52:24,040 --> 00:52:26,920
Speaker 1: for a long time I have been saying it is

916
00:52:26,960 --> 00:52:30,120
Speaker 1: really important to not engage with people who are purposely

917
00:52:30,160 --> 00:52:33,680
Speaker 1: being inflammatory or purposely just trying to get engagement purposely

918
00:52:33,760 --> 00:52:36,080
Speaker 1: just like trying to rile you up, because you're only

919
00:52:36,120 --> 00:52:39,120
Speaker 1: helping them grow. Because you're boosting their engagement, the algorithm

920
00:52:39,160 --> 00:52:41,040
Speaker 1: is going to be like, oh, this piece of content

921
00:52:41,080 --> 00:52:43,920
Speaker 1: is getting lots and lots of engagement, so let's surface

922
00:52:43,960 --> 00:52:45,520
Speaker 1: it to more and more people. So if you dunk

923
00:52:45,560 --> 00:52:48,600
Speaker 1: on them or rage reply, you're just helping them grow.

924
00:52:48,880 --> 00:52:51,120
Speaker 1: And now it's going to be even worse because that

925
00:52:51,239 --> 00:52:54,840
Speaker 1: engagement is going to be putting actual money in their pockets.

926
00:52:55,040 --> 00:52:57,399
Speaker 1: That is, assuming that Elon Musk pays up, which we'll see.

927
00:52:57,920 --> 00:53:01,080
Speaker 1: I don't know. I think it turns social media discourse

928
00:53:01,200 --> 00:53:05,719
Speaker 1: into a Ponzi scheme where you are encouraged to have

929
00:53:05,840 --> 00:53:08,480
Speaker 1: bad takes, do whatever it takes to get eyeballs on

930
00:53:08,520 --> 00:53:10,840
Speaker 1: your page because it will put money in your pocket.

931
00:53:11,200 --> 00:53:13,760
Speaker 1: And I can't imagine this is going to make Twitter

932
00:53:13,800 --> 00:53:17,640
Speaker 1: a more pleasant place to be around, you know. I

933
00:53:17,680 --> 00:53:23,680
Speaker 1: don't think that people who are tweeting just because they

934
00:53:23,719 --> 00:53:29,640
Speaker 1: want attention and engagement is going to make us more thoughtful,

935
00:53:29,880 --> 00:53:34,000
Speaker 1: less polarized, more informed. And it just is another reason

936
00:53:34,040 --> 00:53:36,440
Speaker 1: why I think that like we're in a really strange

937
00:53:36,440 --> 00:53:37,840
Speaker 1: era when it comes to social.

938
00:53:37,640 --> 00:53:42,520
Speaker 2: Media, certainly when it comes to Twitter. Like, as you

939
00:53:42,600 --> 00:53:45,600
Speaker 2: mentioned at the top of this piece, it seems more

940
00:53:45,640 --> 00:53:50,719
Speaker 2: than coincidental that this new payout system is announced like

941
00:53:50,840 --> 00:53:56,439
Speaker 2: a couple days after Threads stole so many of their

942
00:53:56,560 --> 00:53:59,280
Speaker 2: users so much of their engagement and is like really

943
00:53:59,400 --> 00:54:04,640
Speaker 2: threatening Twitter in an existential way. But it just feels

944
00:54:04,680 --> 00:54:10,400
Speaker 2: like not even too little, too late, but like so

945
00:54:10,760 --> 00:54:16,040
Speaker 2: misguided in even trying to address the problem. You know,

946
00:54:17,360 --> 00:54:19,520
Speaker 2: people are just so sick of Twitter. They're sick of

947
00:54:19,600 --> 00:54:27,480
Speaker 2: Elon Musk and they really want something else. A lot

948
00:54:27,480 --> 00:54:30,520
Speaker 2: of people don't like Meta. It's not like it's a

949
00:54:30,520 --> 00:54:33,160
Speaker 2: big secret that Meta is a bad company that does

950
00:54:33,280 --> 00:54:37,080
Speaker 2: bad things and like hurts people to make money. And

951
00:54:37,200 --> 00:54:41,520
Speaker 2: yet people were still like eager to jump ship and

952
00:54:41,600 --> 00:54:46,400
Speaker 2: check out threads, partly because they're sick of Elon's antics.

953
00:54:47,120 --> 00:54:49,200
Speaker 2: And I think part of it is just like wanting

954
00:54:49,280 --> 00:54:53,160
Speaker 2: to stick it to him for being such a jerk

955
00:54:53,440 --> 00:54:59,799
Speaker 2: for like months and months, and his response here is

956
00:55:00,000 --> 00:55:05,120
Speaker 2: so comically flat, you know, to just like pay people.

957
00:55:06,160 --> 00:55:16,600
Speaker 2: It's like an idea born from his weird libertarian worldview

958
00:55:16,840 --> 00:55:21,760
Speaker 2: of like, oh, the only thing that matters is money.

959
00:55:21,800 --> 00:55:24,040
Speaker 2: I'm surprised he's not trying to pay him in crypto,

960
00:55:24,920 --> 00:55:25,160
Speaker 2: you know.

961
00:55:25,640 --> 00:55:27,920
Speaker 1: Oh my god, you know what it is. It's it

962
00:55:28,000 --> 00:55:30,480
Speaker 1: just hit me when you were growing up. It's the

963
00:55:30,640 --> 00:55:33,680
Speaker 1: rich kid who is a jerk and nobody likes but

964
00:55:33,840 --> 00:55:36,279
Speaker 1: he tries to buy friends. And so he's like, well,

965
00:55:36,280 --> 00:55:39,600
Speaker 1: I'm having a big party and my mom's getting We're

966
00:55:39,640 --> 00:55:42,040
Speaker 1: going to the water park and there's gonna be trampolines

967
00:55:42,120 --> 00:55:45,040
Speaker 1: and a clown and super soakers and gift bags. Be

968
00:55:45,200 --> 00:55:47,440
Speaker 1: my friend. But then he's a jerk, and so you

969
00:55:47,480 --> 00:55:49,160
Speaker 1: get there and you're like, actually, I want to leave

970
00:55:49,160 --> 00:55:51,239
Speaker 1: because you're a jerk. That's exactly what it is. Tell

971
00:55:51,239 --> 00:55:51,759
Speaker 1: me I'm wrong.

972
00:55:51,880 --> 00:55:54,600
Speaker 2: Yeah, that's exactly what it is. And also he's not

973
00:55:54,640 --> 00:55:56,120
Speaker 2: gonna pay them, Yeah you do?

974
00:55:56,160 --> 00:55:58,800
Speaker 1: You do you think he's gonna pay I mean he's.

975
00:55:58,760 --> 00:56:01,799
Speaker 2: Probably gonna pay like a people or something. But there

976
00:56:01,800 --> 00:56:06,160
Speaker 2: are going to be way more people who right now

977
00:56:06,440 --> 00:56:11,960
Speaker 2: at ten fifty four pm Eastern Time on July thirteenth,

978
00:56:11,960 --> 00:56:14,600
Speaker 2: Thursday night, think that they're going to get paid, and

979
00:56:14,719 --> 00:56:18,879
Speaker 2: come Sunday or Monday or Tuesday or next Friday when

980
00:56:18,880 --> 00:56:22,040
Speaker 2: we're doing the next news roundup, they're not going to

981
00:56:22,120 --> 00:56:24,879
Speaker 2: be paid. They're definitely going to outnumber the people who

982
00:56:25,160 --> 00:56:29,120
Speaker 2: got paid. And yeah, I bet they are going to

983
00:56:29,160 --> 00:56:30,080
Speaker 2: get paid in crypto.

984
00:56:30,360 --> 00:56:33,640
Speaker 1: Okay, So if you are someone listening and you are

985
00:56:33,680 --> 00:56:37,640
Speaker 1: expecting a payout, you don't have to give us specifics

986
00:56:37,840 --> 00:56:39,799
Speaker 1: and we'll keep you anonymous unless you want to be

987
00:56:39,960 --> 00:56:42,320
Speaker 1: on the record let us know if you get paid.

988
00:56:42,400 --> 00:56:44,839
Speaker 1: I am so invested, as I said, I will not

989
00:56:44,920 --> 00:56:49,600
Speaker 1: believe it until I confirm that money. But like money

990
00:56:49,640 --> 00:56:52,080
Speaker 1: isn't an account. That is dollars that you can spend.

991
00:56:53,000 --> 00:56:55,879
Speaker 1: I don't want any kind of like it's crypto. It's

992
00:56:55,880 --> 00:56:59,040
Speaker 1: a percentage. You get half now, half later. He said

993
00:56:59,040 --> 00:57:01,000
Speaker 1: he was paying the full of out at seventy two hours.

994
00:57:01,080 --> 00:57:03,399
Speaker 1: If you can help us confirm this, I'm gonna we're

995
00:57:03,440 --> 00:57:05,680
Speaker 1: gonna be doing a follow up on this. I'm very invested.

996
00:57:05,920 --> 00:57:06,759
Speaker 1: So let's find out.

997
00:57:07,200 --> 00:57:10,920
Speaker 2: You think we have Twitter blue subscribers who listen to

998
00:57:10,920 --> 00:57:11,600
Speaker 2: this podcast.

999
00:57:11,719 --> 00:57:14,120
Speaker 1: I don't know. Maybe I don't want I don't want

1000
00:57:14,160 --> 00:57:16,640
Speaker 1: to shame people for We did a whole episode about

1001
00:57:16,640 --> 00:57:20,720
Speaker 1: how like sex workers like Twitter blue, like plenty people

1002
00:57:20,800 --> 00:57:22,680
Speaker 1: have their reasons for getting Twitter blue. I don't want

1003
00:57:22,680 --> 00:57:25,200
Speaker 1: to like shame if you are a Twitter blue subscriber,

1004
00:57:25,200 --> 00:57:26,960
Speaker 1: I don't want to shame you for that choice. You

1005
00:57:27,000 --> 00:57:27,840
Speaker 1: might have your reasons.

1006
00:57:28,160 --> 00:57:30,960
Speaker 2: That's fair. Yet, if there is a Twitter blue subscriber

1007
00:57:31,120 --> 00:57:34,240
Speaker 2: listening to this podcast, please let us know. Hello at

1008
00:57:34,360 --> 00:57:39,200
Speaker 2: angoity dot com. Uh, no judgment. You know obviously you

1009
00:57:39,240 --> 00:57:44,000
Speaker 2: listen to the podcast, so you've got good taste. Tell

1010
00:57:44,080 --> 00:57:48,840
Speaker 2: us tell us why, tell us I would love to hear. Yeah, please, Uh,

1011
00:57:48,880 --> 00:57:53,760
Speaker 2: there will be absolutely no shame. We will Yeah, we

1012
00:57:53,800 --> 00:57:54,480
Speaker 2: would love to hear.

1013
00:57:54,640 --> 00:57:57,560
Speaker 1: Okay, we got to talk about the weirdest story that

1014
00:57:57,680 --> 00:58:00,400
Speaker 1: I've heard in a long time, and that is doctor

1015
00:58:00,480 --> 00:58:04,120
Speaker 1: Roxy from TikTok. So, if you've ever seen doctor Roxy,

1016
00:58:04,240 --> 00:58:07,800
Speaker 1: she's a plastic surgeon who has had lots and lots

1017
00:58:07,840 --> 00:58:10,680
Speaker 1: of TikTok followers. She would do these live streams of

1018
00:58:10,680 --> 00:58:14,560
Speaker 1: herself doing plastic surgery, and I always wondered, how is

1019
00:58:14,640 --> 00:58:17,600
Speaker 1: this allowed? Well, it turns out it's not allowed. She

1020
00:58:18,040 --> 00:58:21,640
Speaker 1: permanently lost her license to practice medicine. So her license

1021
00:58:21,720 --> 00:58:24,800
Speaker 1: was suspended back in November, and yesterday the State Medical

1022
00:58:24,800 --> 00:58:27,800
Speaker 1: Board of Ohio voted in a hearing to permanently revoke

1023
00:58:27,880 --> 00:58:31,240
Speaker 1: her license and find her four thousand, five hundred dollars

1024
00:58:31,400 --> 00:58:33,800
Speaker 1: based on her failure to meet the standard of medical care.

1025
00:58:34,080 --> 00:58:36,960
Speaker 1: The board also found that the people that she operated

1026
00:58:37,000 --> 00:58:41,760
Speaker 1: on had complications. Quote. These outcomes were not normal complications

1027
00:58:41,800 --> 00:58:44,840
Speaker 1: like those that exist in the routine practice of medicine,

1028
00:58:44,920 --> 00:58:47,880
Speaker 1: but were rather caused by recklessness in disregard for the

1029
00:58:47,960 --> 00:58:51,200
Speaker 1: rules of governing medicine in Ohio. So he said that

1030
00:58:51,280 --> 00:58:55,200
Speaker 1: her social media presence amplified her reckless behavior and accused

1031
00:58:55,240 --> 00:58:57,600
Speaker 1: her of using it to grow her brand, not educate.

1032
00:58:57,960 --> 00:59:00,919
Speaker 1: So doctor Roxy said she just wanted to teach. That's

1033
00:59:00,920 --> 00:59:03,080
Speaker 1: like why she was on social media. She really wanted

1034
00:59:03,120 --> 00:59:06,720
Speaker 1: to teach people outside of the medical field about plastic surgery,

1035
00:59:06,720 --> 00:59:08,840
Speaker 1: which is why she made these social media videos. This

1036
00:59:08,920 --> 00:59:10,880
Speaker 1: is what she said during the board hearing. She said,

1037
00:59:11,000 --> 00:59:13,360
Speaker 1: but as I stand here today, I see how many

1038
00:59:13,440 --> 00:59:17,919
Speaker 1: of those videos appeared silly and unprofessional, And like, yeah, girl,

1039
00:59:17,960 --> 00:59:21,120
Speaker 1: they sure did seem silly and unprofessional. You were live

1040
00:59:21,160 --> 00:59:23,440
Speaker 1: streaming while you were doing surgery. Yeah, they did seem

1041
00:59:23,440 --> 00:59:27,080
Speaker 1: really unprofessional. That doesn't seem professional at all. So her

1042
00:59:27,200 --> 00:59:31,200
Speaker 1: videos were wild. Some of them are just a little

1043
00:59:31,200 --> 00:59:34,280
Speaker 1: bit cringey, like her dancing in her office. Okay, fine.

1044
00:59:34,720 --> 00:59:38,800
Speaker 1: Others were shocking. So she would live stream literally while

1045
00:59:38,800 --> 00:59:42,880
Speaker 1: doing surgery. She would pause to do polls or take

1046
00:59:43,040 --> 00:59:48,240
Speaker 1: questions or interact with the audience while performing surgery. The

1047
00:59:48,240 --> 00:59:50,400
Speaker 1: New York Times reports that in one of the videos,

1048
00:59:50,600 --> 00:59:53,760
Speaker 1: doctor Roxy looks at and spoke to the camera while

1049
00:59:53,800 --> 00:59:57,240
Speaker 1: engaged in liposuction on a patient's abdomen. The board said

1050
00:59:57,480 --> 00:59:59,960
Speaker 1: a few days after that surgery, the patient was hospital

1051
01:00:00,520 --> 01:00:03,240
Speaker 1: and found to have a perforated small bow and soft

1052
01:00:03,280 --> 01:00:06,320
Speaker 1: tissue infections. This kind of thing also happened to two

1053
01:00:06,320 --> 01:00:09,280
Speaker 1: other patients of hers as well. She got warnings in

1054
01:00:09,320 --> 01:00:11,280
Speaker 1: twenty twenty and twenty twenty one about the need to

1055
01:00:11,320 --> 01:00:15,000
Speaker 1: protect patient privacy, then gave the board documents to show

1056
01:00:15,000 --> 01:00:18,760
Speaker 1: that she had completed remedial classes including ethical social media

1057
01:00:18,800 --> 01:00:21,040
Speaker 1: in December twenty twenty one, but the board said that

1058
01:00:21,080 --> 01:00:24,880
Speaker 1: she still continued to record video and do live broadcasts

1059
01:00:24,880 --> 01:00:29,040
Speaker 1: of medical procedures through October fourteenth, twenty twenty two. Woo,

1060
01:00:29,760 --> 01:00:31,920
Speaker 1: what do you even make of a story like this? Like,

1061
01:00:32,560 --> 01:00:36,760
Speaker 1: I think it just goes to show like social media

1062
01:00:36,920 --> 01:00:41,760
Speaker 1: validation and feeling like you are getting lots of engagement

1063
01:00:41,800 --> 01:00:48,200
Speaker 1: on social media can really mess with people's thinking. And

1064
01:00:49,080 --> 01:00:51,720
Speaker 1: I mean, I don't know, like a road their sense

1065
01:00:51,760 --> 01:00:52,440
Speaker 1: of judgment.

1066
01:00:53,040 --> 01:00:55,520
Speaker 2: The only thing I want to know is did she

1067
01:00:55,760 --> 01:00:56,960
Speaker 2: tag the patients.

1068
01:00:57,400 --> 01:01:01,640
Speaker 1: Some of the patients appeared to be like fine to

1069
01:01:01,720 --> 01:01:04,800
Speaker 1: be in the videos, like heyes, just getting surgery. Like

1070
01:01:05,240 --> 01:01:06,720
Speaker 1: I do have kind of a hot take on this,

1071
01:01:07,080 --> 01:01:10,000
Speaker 1: which is that I'm not speaking for myself, I'm speaking generally.

1072
01:01:10,520 --> 01:01:14,640
Speaker 1: I think in general as a society, I don't think

1073
01:01:14,720 --> 01:01:19,400
Speaker 1: people want their doctors to be talking about doctor stuff

1074
01:01:19,440 --> 01:01:23,320
Speaker 1: on social media, Like I remember reading something about these

1075
01:01:23,400 --> 01:01:27,160
Speaker 1: nurses on TikTok who were complaining about their patients in

1076
01:01:27,200 --> 01:01:29,800
Speaker 1: a public TikTok. I think they were all let go

1077
01:01:30,200 --> 01:01:32,120
Speaker 1: and there was a piece about like, well, who gets

1078
01:01:32,160 --> 01:01:35,320
Speaker 1: to complain about their jobs? Like people complain about their

1079
01:01:35,400 --> 01:01:37,600
Speaker 1: jobs on social media all the time. I think there

1080
01:01:37,640 --> 01:01:43,120
Speaker 1: is something specific about certain positions teachers, care workers, nurses,

1081
01:01:44,200 --> 01:01:49,040
Speaker 1: doctors where we are not we don't want like it's

1082
01:01:49,240 --> 01:01:54,040
Speaker 1: not appropriate, and people don't want to see their surgeon

1083
01:01:55,240 --> 01:01:58,080
Speaker 1: making this kind of content on social media. I don't

1084
01:01:58,080 --> 01:02:03,080
Speaker 1: think that people are comfortable with that, and I think like, yeah,

1085
01:02:03,120 --> 01:02:05,480
Speaker 1: there's a there's a way to there's there are plenty

1086
01:02:05,480 --> 01:02:08,520
Speaker 1: of doctors on social media who are making content about

1087
01:02:08,560 --> 01:02:11,360
Speaker 1: their their work in a way that is not this

1088
01:02:11,560 --> 01:02:13,360
Speaker 1: invasive and this unprofessional.

1089
01:02:13,960 --> 01:02:18,320
Speaker 2: Yeah, I'm stumped by this one. Like I guess if

1090
01:02:18,360 --> 01:02:21,720
Speaker 2: the patients like signed a waiver and they were like, yeah,

1091
01:02:21,840 --> 01:02:26,480
Speaker 2: I really want you to put my surgery on TikTok,

1092
01:02:28,560 --> 01:02:31,440
Speaker 2: I guess in that case, like, go for it. But

1093
01:02:32,000 --> 01:02:37,120
Speaker 2: anything short of that is wildly inappropriate.

1094
01:02:38,080 --> 01:02:40,320
Speaker 1: Oh. I totally don't agree because I think that like,

1095
01:02:40,800 --> 01:02:46,240
Speaker 1: if you are live streaming, you're you're a surgeon. You've

1096
01:02:46,240 --> 01:02:49,200
Speaker 1: got somebody's life in your hands. You've got somebody opened up,

1097
01:02:49,520 --> 01:02:53,320
Speaker 1: they're vulnerable. You should be focused on that. I don't

1098
01:02:53,360 --> 01:02:56,479
Speaker 1: know how that's possible while you're live streaming. I'm I'm

1099
01:02:56,600 --> 01:03:00,400
Speaker 1: podcasting right now, and I have to have anything, any

1100
01:03:00,520 --> 01:03:03,400
Speaker 1: visual that indicates that we're recording. I have to have

1101
01:03:03,440 --> 01:03:06,560
Speaker 1: that covered because I get I'll get distracted. I can't

1102
01:03:06,640 --> 01:03:09,800
Speaker 1: like and so like, and I'm just making a podcast.

1103
01:03:10,880 --> 01:03:12,000
Speaker 1: She's cutting people open.

1104
01:03:12,120 --> 01:03:15,880
Speaker 2: And you said she like stopped and like turned to

1105
01:03:15,920 --> 01:03:18,960
Speaker 2: the camera and like did a little veo mid surgery.

1106
01:03:19,600 --> 01:03:23,120
Speaker 2: That I guess you're right, like even if the patient

1107
01:03:23,240 --> 01:03:27,480
Speaker 2: can sense, that feels unprofessional.

1108
01:03:28,360 --> 01:03:31,960
Speaker 1: And the born said that her live streaming and social

1109
01:03:32,000 --> 01:03:36,320
Speaker 1: media presence really was like like putting patients in harm's way,

1110
01:03:36,360 --> 01:03:38,520
Speaker 1: that she was too busy focused on social media, but

1111
01:03:38,640 --> 01:03:42,000
Speaker 1: she was making like making mistakes, not routine mistakes can

1112
01:03:42,120 --> 01:03:46,480
Speaker 1: like associated with surgery mistakes, mistakes like real complications.

1113
01:03:46,840 --> 01:03:47,920
Speaker 2: You think I should stitch them?

1114
01:03:47,960 --> 01:03:48,120
Speaker 1: Up?

1115
01:03:48,240 --> 01:03:49,000
Speaker 2: Vote in my poll.

1116
01:03:50,600 --> 01:03:55,400
Speaker 1: If I get a thousand likes, I'll use anesthesia. Yeah.

1117
01:03:55,440 --> 01:03:57,920
Speaker 1: I imagine going to med school for however many years,

1118
01:03:57,960 --> 01:04:00,480
Speaker 1: and this is how it ends. I just like social

1119
01:04:00,520 --> 01:04:02,800
Speaker 1: media is a hell of a drug. We've really it's

1120
01:04:03,000 --> 01:04:03,960
Speaker 1: it's fascinating to me.

1121
01:04:08,520 --> 01:04:22,440
Speaker 4: More after a quick break, let's get right back into it.

1122
01:04:24,360 --> 01:04:26,520
Speaker 1: Okay, so real quick, something that you might actually be

1123
01:04:26,560 --> 01:04:29,400
Speaker 1: excited about. This is a special just for Mike's story.

1124
01:04:30,120 --> 01:04:32,800
Speaker 1: By twenty twenty seven, most smartphones will need to have

1125
01:04:32,840 --> 01:04:36,439
Speaker 1: replaceable batteries, yes, even your iPhone. By twenty twenty seven,

1126
01:04:36,480 --> 01:04:39,040
Speaker 1: all phones released in the EU must have a battery

1127
01:04:39,080 --> 01:04:42,440
Speaker 1: that the user can easily replace with no tools or expertise. Now,

1128
01:04:42,440 --> 01:04:46,040
Speaker 1: this law technically only applies to the European Union, but

1129
01:04:46,200 --> 01:04:50,760
Speaker 1: phone manufacturers are pretty unlikely to make one smartphone for

1130
01:04:51,000 --> 01:04:54,240
Speaker 1: Europe that has a replaceable battery, and then other smartphones

1131
01:04:54,240 --> 01:04:56,240
Speaker 1: for the rest of the world, so it will probably

1132
01:04:56,280 --> 01:04:59,760
Speaker 1: just change most, if not all, smartphones. This change is

1133
01:04:59,800 --> 01:05:03,280
Speaker 1: meant to create a circular economy for phone batteries, which

1134
01:05:03,320 --> 01:05:06,440
Speaker 1: is a manufacturing model where the resources put into the

1135
01:05:06,480 --> 01:05:09,920
Speaker 1: phones are recycled or reused as much as possible. As

1136
01:05:09,920 --> 01:05:13,720
Speaker 1: somebody who has a lot of phone problems loves to

1137
01:05:13,800 --> 01:05:17,760
Speaker 1: tinker with electronics. I like this is I feel like

1138
01:05:17,760 --> 01:05:19,280
Speaker 1: that's a good a good thing for my kit.

1139
01:05:19,360 --> 01:05:21,720
Speaker 2: I don't have a lot of phone problems.

1140
01:05:22,120 --> 01:05:26,640
Speaker 1: You never get texts, but okay.

1141
01:05:25,680 --> 01:05:29,600
Speaker 2: My phone works fine. But this is very exciting. It's

1142
01:05:29,600 --> 01:05:34,880
Speaker 2: gonna cut down on e waste. It's gonna be a

1143
01:05:34,960 --> 01:05:39,600
Speaker 2: really positive thing, even more exciting than the replaceable batteries

1144
01:05:39,640 --> 01:05:41,920
Speaker 2: for me. I don't know how it measures up in

1145
01:05:41,960 --> 01:05:46,800
Speaker 2: terms of like cutting down on E waste and greenhouse

1146
01:05:46,800 --> 01:05:50,800
Speaker 2: gases and stuff, but the fact that the EU is

1147
01:05:50,840 --> 01:05:54,200
Speaker 2: going to start requiring all cell phones to use USBC

1148
01:05:54,480 --> 01:05:57,160
Speaker 2: charging cables next year, that is going to be the

1149
01:05:57,200 --> 01:06:00,000
Speaker 2: best thing, right, Like we can all use the same

1150
01:06:00,080 --> 01:06:04,920
Speaker 2: cable to change charge our phones, to charge our laptops. Uh,

1151
01:06:05,080 --> 01:06:08,000
Speaker 2: it's just it's just gonna make everybody's life so much better.

1152
01:06:08,080 --> 01:06:11,400
Speaker 1: There should just be one cable. Let's let's simplify, like,

1153
01:06:11,400 --> 01:06:13,400
Speaker 1: there should just be one cable USBC.

1154
01:06:13,680 --> 01:06:16,720
Speaker 2: We've we've figured it out. We took a couple of

1155
01:06:16,800 --> 01:06:21,320
Speaker 2: decades with USB to really perfect the technology. We've got it.

1156
01:06:21,360 --> 01:06:24,480
Speaker 2: Now it's a little cable. We can do everything. Yeah,

1157
01:06:24,520 --> 01:06:25,720
Speaker 2: one cable. It's going to be great.

1158
01:06:26,520 --> 01:06:29,080
Speaker 1: Before we head out, I just wanted to thank everybody

1159
01:06:29,120 --> 01:06:33,080
Speaker 1: who listened to our Patreon episode on Jonah Hill, Kiki Palmer,

1160
01:06:33,160 --> 01:06:37,920
Speaker 1: and social media boundaries in controlling or abusive relationships. We

1161
01:06:38,000 --> 01:06:40,760
Speaker 1: have had such an overwhelming response, and I it's not

1162
01:06:40,800 --> 01:06:43,479
Speaker 1: really the content that I make often, So I really

1163
01:06:43,480 --> 01:06:46,600
Speaker 1: appreciate everybody who listened, everybody who chimed in with their thoughts.

1164
01:06:46,600 --> 01:06:48,920
Speaker 1: Thank you so much for listening. Not to be somebody

1165
01:06:48,920 --> 01:06:52,280
Speaker 1: who can't stop having a bee in her bonnet, but

1166
01:06:52,320 --> 01:06:55,040
Speaker 1: that seems to be the theme right now. I have

1167
01:06:55,120 --> 01:06:57,960
Speaker 1: something else that I want to say. It is not

1168
01:06:58,400 --> 01:07:04,840
Speaker 1: often that I will call in another podcast or audio professional,

1169
01:07:04,960 --> 01:07:07,200
Speaker 1: but I feel I need to do that. That person

1170
01:07:07,440 --> 01:07:11,600
Speaker 1: is Jeff Lewis of Bravo Flipping Out Fame. Now he

1171
01:07:11,680 --> 01:07:14,160
Speaker 1: has his own show on Sirius. I don't know if

1172
01:07:14,160 --> 01:07:19,240
Speaker 1: anybody caught Matt Rogers of my favorite podcast, Lost Culturistas

1173
01:07:19,760 --> 01:07:23,720
Speaker 1: on Jeff Lewis's Serious show this week. Matt Rogers is

1174
01:07:23,720 --> 01:07:26,720
Speaker 1: on a podcast called Lost Culturistas with Bowen Yang, who

1175
01:07:27,000 --> 01:07:29,440
Speaker 1: they are my best friends in my mind. It is

1176
01:07:29,480 --> 01:07:35,160
Speaker 1: my favorite podcast. We are iHeart colleagues. Bowen Yang recently

1177
01:07:35,200 --> 01:07:38,480
Speaker 1: shared that he was temporarily stepping back from Lost Culturalistas

1178
01:07:38,680 --> 01:07:40,960
Speaker 1: for a little bit to deal with mental health issues,

1179
01:07:41,160 --> 01:07:46,320
Speaker 1: specifically depersonalization. Jeff Lewis had some I think pretty disrespectful,

1180
01:07:46,440 --> 01:07:50,440
Speaker 1: degrading things to say about Bowen Yang that I found

1181
01:07:50,560 --> 01:07:54,240
Speaker 1: really insulting as a fellow I Heeart podcaster. So I

1182
01:07:54,280 --> 01:07:56,720
Speaker 1: want to talk about it. So here's a little preview

1183
01:07:56,800 --> 01:08:02,000
Speaker 1: of our conversation. I think that to trivialize and make

1184
01:08:02,040 --> 01:08:05,800
Speaker 1: fun of the way that a person is openly expressing

1185
01:08:06,320 --> 01:08:10,760
Speaker 1: dealing with a mental health issue is not acceptable. I

1186
01:08:10,800 --> 01:08:12,960
Speaker 1: found I was like personally insulted. I don't know why.

1187
01:08:13,080 --> 01:08:15,160
Speaker 1: I don't even know these people, but I was personally insulted.

1188
01:08:15,200 --> 01:08:17,400
Speaker 1: I guess as like a person of a queer person

1189
01:08:17,400 --> 01:08:19,960
Speaker 1: of color who also makes a podcast on iHeart, I

1190
01:08:20,000 --> 01:08:24,320
Speaker 1: feel like it's really not okay, it's not funny, it's

1191
01:08:24,360 --> 01:08:26,840
Speaker 1: not cute. I think it's very regressive. I think it

1192
01:08:26,880 --> 01:08:29,800
Speaker 1: sets us back to a place where people feel like

1193
01:08:29,800 --> 01:08:32,559
Speaker 1: they should feel stigma when they're going through something heavy

1194
01:08:32,720 --> 01:08:35,880
Speaker 1: or something tough. I am happy for the strides that

1195
01:08:35,920 --> 01:08:38,240
Speaker 1: we have made where people feel like they can openly

1196
01:08:38,320 --> 01:08:41,080
Speaker 1: say that they're dealing with something like depersonalization and that

1197
01:08:41,320 --> 01:08:44,360
Speaker 1: they need to take some time. We should be wanting

1198
01:08:44,400 --> 01:08:47,720
Speaker 1: to live in a society like that. Yeah, I just

1199
01:08:47,760 --> 01:08:50,000
Speaker 1: like really didn't appreciate it at all. If you want

1200
01:08:50,000 --> 01:08:51,280
Speaker 1: to hear what I have to say, check out the

1201
01:08:51,280 --> 01:08:56,439
Speaker 1: Patreon at patreon dot com slash tangote. Yeah, Jeff Lewis,

1202
01:08:56,680 --> 01:09:00,280
Speaker 1: you owe Matt Rogers an apology, You owe Bo Yang

1203
01:09:00,280 --> 01:09:03,960
Speaker 1: an apology, and you owe all I Heeart podcasters and

1204
01:09:04,000 --> 01:09:08,919
Speaker 1: apology as well for coming for our networks Sweethearts. So yeah,

1205
01:09:09,120 --> 01:09:12,559
Speaker 1: take a listen if you want. I know that sounds cryptic,

1206
01:09:13,520 --> 01:09:16,800
Speaker 1: but yeah, I just I'll end it there. Mike, thanks

1207
01:09:16,840 --> 01:09:17,639
Speaker 1: so much for being here.

1208
01:09:18,080 --> 01:09:22,599
Speaker 2: Yeah, thanks for having me as usual. And happy Tangoty birthday.

1209
01:09:22,800 --> 01:09:25,600
Speaker 1: Oh, happy Tangoty birthday to you too, and thanks so

1210
01:09:25,680 --> 01:09:28,240
Speaker 1: much for listening. Honestly, doing this show is meant the

1211
01:09:28,240 --> 01:09:31,200
Speaker 1: world to me, and I can't believe that I get

1212
01:09:31,240 --> 01:09:33,439
Speaker 1: to do this every week with people that I care about,

1213
01:09:33,479 --> 01:09:35,880
Speaker 1: and I can't believe that I get to engage with

1214
01:09:35,920 --> 01:09:38,400
Speaker 1: all of you listeners. And it's really been a dream.

1215
01:09:39,000 --> 01:09:40,479
Speaker 1: I can't even begin to tell you how much it

1216
01:09:40,520 --> 01:09:43,479
Speaker 1: means that I get to tell these stories. So thank

1217
01:09:43,520 --> 01:09:51,400
Speaker 1: you for listening as always. If you're looking for ways

1218
01:09:51,400 --> 01:09:53,599
Speaker 1: to support the show, check out our work store at

1219
01:09:53,640 --> 01:09:57,400
Speaker 1: tegoty dot com slash store. Got a story about an

1220
01:09:57,400 --> 01:09:59,320
Speaker 1: interesting thing in tech or just want to say hi,

1221
01:10:00,120 --> 01:10:02,240
Speaker 1: we just said hello at tangodi dot com. You can

1222
01:10:02,280 --> 01:10:05,200
Speaker 1: also find transcripts for today's episode at TENG Goody dot com.

1223
01:10:05,360 --> 01:10:07,000
Speaker 1: There Are No Girls on the Internet was created by

1224
01:10:07,040 --> 01:10:10,040
Speaker 1: me Bridget Cood. It's a production of iHeartRadio and Unbossed

1225
01:10:10,040 --> 01:10:14,519
Speaker 1: Creative edited by Joey Pat Jonathan Strickland as our executive producer.

1226
01:10:14,800 --> 01:10:18,040
Speaker 1: Tari Harrison is our producer and sound engineer. Michael Almada

1227
01:10:18,120 --> 01:10:21,120
Speaker 1: is our contributing producer. I'm your host, Bridget Tood. If

1228
01:10:21,160 --> 01:10:23,000
Speaker 1: you want to help us grow, rate and review.

1229
01:10:22,760 --> 01:10:23,800
Speaker 3: Us on Apple Podcasts.

1230
01:10:24,560 --> 01:10:27,400
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1231
01:10:27,439 --> 01:10:30,920
Speaker 1: Apple Podcasts, or wherever you get your podcasts.