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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 bridgeitat and this is

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Speaker 1: there are No Girls on the Internet. I am here

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Speaker 1: with my producer Mike, and here's what you may have

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Speaker 1: missed this week on the Internet. So, as y'all know,

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Speaker 1: in the pandemic, Zoom became such.

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Speaker 2: An ubiquitous part of all of our lives.

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Speaker 1: You would have your work meeting on Zoom, through therapy

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Speaker 1: on Zoom, your happy hour on Zoom, your book club

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Speaker 1: on Zoom. But Zoom is still a tech company, and

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Speaker 1: tech companies can be very tricky, and that they love

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Speaker 1: to quietly slip some updates into their terms of service,

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Speaker 1: you know that thing we all click without really reading

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Speaker 1: too much. Last week, people started posting screenshots of Zoom's

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Speaker 1: new policies that state that people who use Zoom, just

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Speaker 1: by using the platform, consent to the company's use, collection,

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Speaker 1: and storage of quote service generated data for any This

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Speaker 1: includes quote training and tuning of algorithms and models. And

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Speaker 1: so just by showing up to a Zoom meeting, you

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Speaker 1: are consenting to use whatever comes up in that meeting,

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Speaker 1: whatever you talk about, images, whatever, for Zoom's quote machine learning,

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Speaker 1: artificial intelligence, training and testing, among other uses. According to

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Speaker 1: zd net, just by showing up on Zoom, users would

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Speaker 1: then give Zoom quote a perpetual, worldwide, non exclusive, royalty free,

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Speaker 1: sub licensable and transferable license and all other rights required

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Speaker 1: or necessary to redistribute, publish, import, access, use, store, transmit, review, disclose, preserve, extract, modify, reproduce, share, use, display, copy, distribute, translate, transcribe,

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Speaker 1: create derivative works of and process all of our content.

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Speaker 1: That's pretty bad. That is a lot of like just

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Speaker 1: by showing up to this meeting, you know, that's well,

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Speaker 1: that's a lot of data to be giving over to

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Speaker 1: Zoom just to show up. So Zoom told Vice that

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Speaker 1: they actually made this change to their terms of service

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Speaker 1: back in March. Notably, it sounds like Zoom didn't really

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Speaker 1: go out of their way to make this super public

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Speaker 1: until this week users started posting screenshots of this new

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Speaker 1: terms of service. After all the backlash and outcry, Zoom

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Speaker 1: then put out a blog post this week where they

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Speaker 1: kind of walked back this terms of service and basically said, yeah,

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Speaker 1: you know all that stuff that I just read from

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Speaker 1: their terms of service, they promise, promise promise pinky promise

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Speaker 1: that they will not do it without users consent, like

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Speaker 1: opting into it, promising that Zoom will not use audio,

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Speaker 1: video or chat customer content to train our artificial intelligence

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Speaker 1: models without your consent. That was a big part of

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Speaker 1: the issue before that Zoom users basically had to consent

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Speaker 1: to this. There was no opt out. One interesting thing

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Speaker 1: is that folks were asking, like what about people who

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Speaker 1: use Zoom for like therapy sessions, like my therapy happened

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Speaker 1: over Zoom, or you know, medical reasons. It does sound

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Speaker 1: like those use cases are completely different Zoom contracts, and

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Speaker 1: so Zoom is required to keep those conversations like hip

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Speaker 1: hoop compliant.

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Speaker 3: Is that right, Mike, Yeah, I believe that's right. I

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Speaker 3: think that it's like a separate category of contract that

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Speaker 3: enterprise clients who have Hippo protection needs signed than general

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Speaker 3: users like most of us.

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Speaker 1: So yeah, it sounds like that for most of us

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Speaker 1: general Zoom users, there was no way to opt out

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Speaker 1: of this. If you were gonna use Zoom, you had

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Speaker 1: to consent to let Zoom use your data in this way.

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Speaker 1: But now they are saying promising that they will allow

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Speaker 1: users to opt out. However, the d Net pointed out

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Speaker 1: that Sean Hogel, business and intellectual property attorney, is not

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Speaker 1: so sure on why. Combinator Hogel wrote, quote, Zoom's lawyers

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Speaker 1: are trying to pull a fast one with these revised terms.

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Speaker 1: The new sentence on user consent being required to train

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Speaker 1: AI applies only to customer content, not service generated data.

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Speaker 1: Zoom can use this data, which is derived from your

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Speaker 1: conference and materials without your consent. Also, surprise, in their

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Speaker 1: blog posts clarifying their terms of service as it pertains

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Speaker 1: to AI, Zoom assures all of us, no, no, no, don't worry.

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Speaker 1: It sounds scary, trust us. This is gonna be good

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Speaker 1: for you. It's for your own good, pointing to the

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Speaker 1: ability to opt into Zoom settings for things like meeting

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Speaker 1: transcriptions and summaries and how it will quote improve the

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Speaker 1: performance and accuracy of these AI services. So if you

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Speaker 1: want to use these services with Zoom, then you will

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Speaker 1: have to opt in to sharing your data with the platform.

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Speaker 1: Something that I find kind of interesting about this, Like

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Speaker 1: I was talking to somebody about this and they were like, well,

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Speaker 1: what do I care if, like Zoom wants to use

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Speaker 1: my data in this way? Like, explain it to me.

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Speaker 1: Like I'm five. Why this is a problem, Well, first

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Speaker 1: of all is there are pretty big intellectual property concerns

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Speaker 1: anybody who uses a platform like Zoom to discuss things

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Speaker 1: where their ownership of those things is key. So like

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Speaker 1: for me, I'm a podcaster, if I were having podcast

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Speaker 1: interviews on Zoom, Zoom can essentially say oh, hey, actually,

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Speaker 1: because you use Zoom, we could have the rights to

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Speaker 1: what you talk about in this episode. You no longer

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Speaker 1: have the rights. We've talked about how folks like Sarah

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Speaker 1: Silverman are suing companies like Open Ai for intellectual property

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Speaker 1: threats because they have taken her intellectual property which she owns,

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Speaker 1: to train their algorithms and machine learning. So if you

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Speaker 1: are somebody who has to discuss scripts that you own

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Speaker 1: or discuss creative content that you own, using Zoom under

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Speaker 1: those original terms of service would not be a really

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Speaker 1: secure place to do that, because just by using Zoom,

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Speaker 1: you're kind of giving them consent and ownership to use

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Speaker 1: that intellectual property however they see fit to make money

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Speaker 1: for their company.

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Speaker 3: And not just intellectual property, but any sort of trade

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Speaker 3: secrets or internal information that you wanted to keep private

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Speaker 3: among you and your your team your business associates, your

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Speaker 3: partner anything, right, Like it's chat GPT can just using

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Speaker 3: that as one example. But you know a lot of

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Speaker 3: these large language models are different, and who knows how

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Speaker 3: many of them work. But chat GPT seems pretty good

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Speaker 3: at repeating back information from specific copyrighted works, you know,

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Speaker 3: specific pieces of its training corpus. And so who's to

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Speaker 3: say that what might happen to something that you say

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Speaker 3: during a Zoom meeting that you think is private, that

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Speaker 3: then gets used to train a large language model. It's

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Speaker 3: a pretty like brazen attack on privacy and confidentiality.

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Speaker 1: Well exactly that, And I think that's another big issue

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Speaker 1: is that yes to all of the intellectual property concerns

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Speaker 1: and trade secret concerns, kind of like what you were saying,

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Speaker 1: it's also just kind of the principle that people's private

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Speaker 1: meetings should not be fodder for training Zoom's AI that

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Speaker 1: like that dynamic where by by just by using this

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Speaker 1: software that has become so ubiquitous to all of our lives,

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Speaker 1: how work gets done, how people communicate and connect with

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Speaker 1: each other, that just by showing up on that platform,

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Speaker 1: you are consenting that basically you're like an employee of

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Speaker 1: Zoom that whatever you whatever comes up in that space

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Speaker 1: belongs to them, and that's like a fundamental dynamics that

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Speaker 1: I think that it's it's the principle of that that's

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Speaker 1: kind of what why people felt so outraged by this

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Speaker 1: move by Zoom. It's just like the fundamental shift that

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Speaker 1: we as users of these programs are just fodder for

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Speaker 1: whatever Zoom wants to do, and however Zoom wants to

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Speaker 1: make money, and I think that people are The fact

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Speaker 1: that people really raised hell about this to the point

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Speaker 1: where Zoom had to like walk it back, I think

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Speaker 1: shows that there's a little bit of a shift happening

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Speaker 1: in terms of how folks are thinking about the relationship

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Speaker 1: between themselves tech companies and how their data is used

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Speaker 1: or misused. And this is not the first time that

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Speaker 1: Zoom has really stepped in it with regards to things

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Speaker 1: like user privacy. Mike, you got a little bit of

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Speaker 1: a check from Zoom because of this, right.

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Speaker 3: That's right. I got to check for fifty three dollars

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Speaker 3: from a class action settlement against them.

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Speaker 1: Wow, what did you spend that whopping fifty three dollars on?

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Speaker 3: I think I got some uber eats.

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Speaker 1: Well, the reason why you were able to get that

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Speaker 1: Uber eats is because Zoom had to pay out a

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Speaker 1: historic eighty five million dollar lawsuit for zoom bombing, basically

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Speaker 1: not having secure enough system that allowed for bad actors

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Speaker 1: to disrupt the privacy of folks as private meetings. This

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Speaker 1: has happened to me before. I have been on pretty

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Speaker 1: high stakes important calls on Zoom where like presentations about

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Speaker 1: things that are like important but need to be secure

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Speaker 1: for specific security reasons, and bad actors and folks looking

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Speaker 1: to disrupt have been able to gain access to those

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Speaker 1: spaces to disrupt them. So it's pretty serious, Like it's

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Speaker 1: it's pretty high stakes what we're talking about. And like

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Speaker 1: I said, I think that people are just starting to

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Speaker 1: ask questions about whether or not these tech companies are

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Speaker 1: actually obviously they're not acting with our best interests as

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Speaker 1: users in mind, but like how much are they not

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Speaker 1: acting with our best interest as users in mind? I

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Speaker 1: think really recently there have been a lot of big

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Speaker 1: examples of people being like, wait a minute, you're using

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Speaker 1: my data or my work or my intellectual property for

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Speaker 1: what exactly, and really pushing back against that dynamic that

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Speaker 1: everything of their should just be for the taking of

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Speaker 1: whoever wants it for them to make money. For instance,

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Speaker 1: this week, this guy Benji Smith was rightly like kind

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Speaker 1: of bullied by authors of the Internet into taking his platform,

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Speaker 1: prose Craft down. Prosecraft was meant to use AI to

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Speaker 1: analyze the structure of a whole library of thousands and

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Speaker 1: thousands of books looks he notably did not have any

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Speaker 1: right to be using or reproducing. Authors like rox Sane

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Speaker 1: Gay were pissed, and eventually he took the platform down

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Speaker 1: because it turns out that authors really don't love it

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Speaker 1: when their material is being taken without their consent, so

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Speaker 1: they spoke up about it until he put out a

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Speaker 1: statement apologizing and eventually was like, oh yeah, wow, like

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Speaker 1: y'all really hated this, and announced he was closing up shop.

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Speaker 1: On the show, We're actually gonna be talking to another author,

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Speaker 1: Jane Friedman, who recently found out that somebody or somebody's

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Speaker 1: is impersonating her name as an author and using AI

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Speaker 1: to create like poorly written AI generated knockoffs of her

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Speaker 1: books under her name, and that Amazon then added those

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Speaker 1: books that were not written by her to her Goodreads

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Speaker 1: author profile, and Google, for instance, just recently went on

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Speaker 1: record as saying that their AI systems should have the

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Speaker 1: right to mind publishers work unless those publishers specifically opt

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Speaker 1: out of doing so. When asked how a system like

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Speaker 1: this might work, Google didn't say they didn't really have

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Speaker 1: an answer. And I think that we've really hit a

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Speaker 1: wall with how regular people are thinking about the future

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Speaker 1: and the way that tech companies are using all of us.

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Speaker 1: I think folks are starting to push back against this

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Speaker 1: idea that everything we do, everything we do online, everything

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Speaker 1: we say, whatever we bring to these platforms can just

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Speaker 1: go to enriching these tech companies that already have so much,

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Speaker 1: have so much money, and have so much impact on

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Speaker 1: how all of us live our lives.

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Speaker 3: I hope you're right.

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Speaker 2: Let's take a quick break at our back.

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Speaker 1: So speaking of that, we have to talk about this

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Speaker 1: black woman in Detroit who was falsely arrested. This story

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Speaker 1: is infuriating. So back in February, a thirty two year old,

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Speaker 1: eight month pregnant black woman in Detroit named Portia Woodruff

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Speaker 1: was arrested for our carjacking that she did not commit.

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Speaker 1: Why well, facial recognition technology misidentified her, as it often

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Speaker 1: does with black people. The New York Times reports that

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Speaker 1: when the police came to her house, she actually gestured

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Speaker 1: to her stomach to indicate how pregnant she was and

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Speaker 1: that like how unlikely it would be to have an

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Speaker 1: eight month pregnant woman commit at armed carjacking and robbery. However,

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Speaker 1: that did not stop them from putting her in handcuffs

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Speaker 1: in front of her neighbors and her four babies, leaving

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Speaker 1: her four babies at home with her fiance as they cried,

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Speaker 1: and taking her to the Detroit Detention Center for booking.

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Speaker 1: She says that she was held for eleven hours, questioned

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Speaker 1: about a crime that she had no knowledge of, and

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Speaker 1: had her phone ceased to be searched for evidence. She

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Speaker 1: told The New York Times, I was having contractions in

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Speaker 1: the holding cell. My back was sending me sharp pains.

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Speaker 1: I was having spasms. I think I was probably having

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Speaker 1: a panic attacked. And the reason that she knows all

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Speaker 1: of this is because she's a nursing school student. I

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Speaker 1: was hurting sitting on those concrete benches. So she was

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Speaker 1: charged with robbery and carjacking, released on one hundred thousand

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Speaker 1: dollars bond, and when she was released on bond, went

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Speaker 1: straight to the hospital where she was diagnosed with dehydration

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Speaker 1: and had to be given two bags of ivy fluids.

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Speaker 1: A month later, in March, the Wayne County Prosecutor dismissed

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Speaker 1: the case against her, and this week she filed a

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Speaker 1: lawsuit for wrongful arrest against the City of Detroit in

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Speaker 1: the US District Court of the Eastern District of Michigan.

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Speaker 1: Portia is the sixth person to report being falsely accused

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Speaker 1: of a crime because of facial recognition technology. All six

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Speaker 1: of these people have been black. Because we already know

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Speaker 1: that facial recognition technology disproportionately misidentifies blackfaces, Portia Woodriff is

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Speaker 1: the first woman reporting it. So basically what happened in

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Speaker 1: this situation is that a man called the police to

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Speaker 1: report that he had picked up a woman who he

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Speaker 1: had been drinking with outside of a liquor store, and

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Speaker 1: that this woman pulled out a gun, robbed him, and

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Speaker 1: stole his car. The police department uses a facial recognition

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Speaker 1: vendor called data Works Plus to run unknown faces against

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Speaker 1: a database of criminal mug shots. The system returns matches

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Speaker 1: ranked by their likeliness of being the same person. A

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Speaker 1: human analyst is ultimately responsible for deciding if any of

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Speaker 1: the matches are a potential suspect. The police report said

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Speaker 1: that the crime analyst gave the investigator Portia Woodroff's name

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Speaker 1: based on a match to a twenty fifteen mugshot that

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Speaker 1: she had because she had been arrested for being pulled

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Speaker 1: over while driving with an expired license. They then showed

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Speaker 1: the victim six photos of black women, and the police

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Speaker 1: say that he the victim picked out Portia Woodroff's picture.

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Speaker 1: If Portia gets her settlement, which she should, this would

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Speaker 1: be the third case where the city would have to

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Speaker 1: settle for folks who have been falsely arrested of a

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Speaker 1: crime because of faulty facial recognition technology. The New York

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Speaker 1: Times reports that on average, Detroit runs over one hundred

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Speaker 1: and twenty five facial recognition searches a year, almost entirely

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Speaker 1: on black men. An attorney for the ACLU, who represented

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Speaker 1: a man who was falsely arrested for shoplifting because of

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Speaker 1: facial recognition technology, is trying to get Detroit to gree

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Speaker 1: to collect more evidence in cases involving automated face searches

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Speaker 1: to end what he called the facial recognition to line

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Speaker 1: up pipeline so I need to be clear. It is

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Speaker 1: not like we do not already know the dangers of

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Speaker 1: the use of this kind of technology. Doctor joy Bolomwini,

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Speaker 1: researcher at MIT back in twenty eighteen, did a whole

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Speaker 1: bunch of research showing that facial recognition technology routinely fails

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Speaker 1: to accurately read the faces of marginalized people. There's an

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Speaker 1: entire fascinating Netflix documentary about it called Hidden Bias. However,

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Speaker 1: apparently police officers in Detroit have not seen this film

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Speaker 1: and have not heard of this research. This is a

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Speaker 1: good example of how technology can really harm marginalized people

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Speaker 1: and how we know when that technology is allowed to

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Speaker 1: become commonplace. Anyway, it is a real problem, like there

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Speaker 1: are real world consequences, and unfortunately this technology, despite being

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Speaker 1: knowingly faulty, is also becoming really commonplace. You have probably

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Speaker 1: gone into venues that use facial recognition technology and might

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Speaker 1: not have even known it, and that then used like

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Speaker 1: Madison Square, Garden City Field, Cleveland's First Energy Stadium, Miami's

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Speaker 1: Hard Rock Stadium, the Pachanga Arena in San Diego, and

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Speaker 1: others used this technology on fans entering venues for concerts

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Speaker 1: and sporting events. I've heard horror stories like there was

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Speaker 1: a story of a young girl who was being who

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Speaker 1: her parents had dropped her off at a skating rink

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Speaker 1: and I think it was in Chicago, and the skating

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Speaker 1: rink happened to be using facial recognition technology it misidentified

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Speaker 1: her as someone else, and the skating rink owners basically

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Speaker 1: threw this girl into the night without her parents because

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Speaker 1: they said that she was someone that she was not

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Speaker 1: based on this technology. There was another case with Madison

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Speaker 1: Square Garden where an attorney who was representing the parent

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Speaker 1: group of Madison Square Garden. She was representing them and

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Speaker 1: in a legal suit, but not directly involved with Madison

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Speaker 1: Square Garden, just like their parent group, and that apparently

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Speaker 1: that parent group has a rule where if you are

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Speaker 1: not just one of the lawyers involved in a litigation

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Speaker 1: against Madison Square Garden or their parent company, even if

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Speaker 1: you are a lawyer who just works at that firm,

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Speaker 1: you are not allowed to come into Madison Square Garden.

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Speaker 1: And so she was taken her kid to like see

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Speaker 1: the rockets for Christmas. They stop, we're at the door

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Speaker 1: and they say, we know you're an attorney and you

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Speaker 1: can't come in. And so this technology is becoming more

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Speaker 1: and more and more used and commonplace, even though we

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Speaker 1: already know it is disproportionately misidentifying black folks, women and

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Speaker 1: folks of color. In fact, the organization Fight for Our

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Speaker 1: Future organized a boycott led by musicians like Tom Morello

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Speaker 1: of Rage against the machine of venues that utilize this technology,

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Speaker 1: and they actually got smaller venues like House of Yes

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Speaker 1: and Brooklyn one of my favorite spots, the Lyric Hyperion

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Speaker 1: in Los Angeles, and the Black Cat Here in DC.

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Speaker 1: These smaller venues actually pledged not to use facial recognition

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Speaker 1: at their shows. But as this technology grows, I think

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Speaker 1: these problems are going to become more and more pronounced

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Speaker 1: and happened more and more often, with bigger and bigger consequences.

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Speaker 1: It's one thing to not be let in for a concert.

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Speaker 1: It's quite another to spend eleven days in jail when

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Speaker 1: you're eight months pregnant.

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Speaker 3: The fact that she was so pregnant and had to

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Speaker 3: spend like her eighth months of pregnancy in jail feels

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Speaker 3: pretty It feels inhumane.

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Speaker 1: It does feel inhumane, And something that she said that

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Speaker 1: kind of like broke my heart was that she said

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Speaker 1: that she was as horrible as it was to be

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Speaker 1: very pregnant in this holding cell for eleven hours, that

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Speaker 1: she was kind of happy that she was so pregnant

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Speaker 1: because at least it provided some kind of a seed

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Speaker 1: of doubt to the police officers and investigators that this

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Speaker 1: could not have been her, because the victim in the

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Speaker 1: carjacting did not say that this person, the person who

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Speaker 1: did this to him, was pregnant, and so oh, it's

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Speaker 1: pretty obvious when someone is eight months pregnant. So she

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Speaker 1: said that as much as it was horrifying to her,

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Speaker 1: which I'm sure it was, being pregnant, she believes it

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Speaker 1: was almost kind of a saving grace because at least

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Speaker 1: it's the police officers had put some doubt in their

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Speaker 1: mind that they had the correct person.

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Speaker 3: Maybe we just need to figure out a way to

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Speaker 3: move that doubt a little bit further up the causal

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Speaker 3: chain of events and like not arrest the pregnant, innocent

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Speaker 3: women from the beginning.

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Speaker 1: Yeah, I mean, that's what the lawsuit is calling for,

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Speaker 1: that when facial recognition gives a match, doing actual police

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Speaker 1: work and actual investigating to see if you have the

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Speaker 1: right person before you arrest them and hold them for

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Speaker 1: eleven hours, I said, actially if they're pregnant, but even

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Speaker 1: if they're not that like, I think it might be

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Speaker 1: a case where it's very easy to just rely on

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Speaker 1: the technology because you have it and you can that

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Speaker 1: cover of saying like this is why we arrested her,

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Speaker 1: right like the City of Detroit is saying like, oh, well,

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Speaker 1: in this case, her arrest was actually warranted because of

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Speaker 1: the situation, because the facial recognition technology said it was her.

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Speaker 1: The victim picked her up and up picked her out

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Speaker 1: on a lineup of six. But you know, anybody who's

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Speaker 1: ever done one of those police lineups knows how faulty

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Speaker 1: those are too. So relying on two different faulty mechanisms

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Speaker 1: facial recognition technology, which we know is faulty, and just

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Speaker 1: a human being's ability to recognize faces at a moment

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Speaker 1: that was probably like scary or traumatic. Just relying on

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Speaker 1: those two things that are so faulty to lock somebody

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Speaker 1: up for eleven hours is not enough. And so I agree,

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Speaker 1: I think that we need to have clearly, we need

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Speaker 1: to have protocols in place where this kind of thing

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Speaker 1: doesn't happen, and that human investigators are doing work, which

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Speaker 1: I know and nobody likes to do extra work, but

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Speaker 1: that you can't just cut horners when it's people's lives

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Speaker 1: like this, And it sounds like that's happened too many

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Speaker 1: times in Detroit. But I think the fact that the

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Speaker 1: powers that be are like, let's start using it first,

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Speaker 1: make it commonplace first, and then the Kinks will reveal it.

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Speaker 1: It seems like they're just saying that, like it's okay.

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Speaker 1: The human cost, the human impact is just part of

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Speaker 1: working at the Kinks. And if an eight month pregnant

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Speaker 1: woman had to, you know, be dehydrated in a cell

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Speaker 1: for an eleven hours, that's just the cost of getting

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Speaker 1: on the road of perfectedness technology so that we can

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Speaker 1: have a pitch perfect digital surveillance state. The whole thing

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Speaker 1: is like a big castle role of awful when I

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Speaker 1: think about it that way. So speaking of a big

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Speaker 1: cast role of awful, let's talk about Linda Yakarino, new

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Speaker 1: CEO question Mark of Twitter. She had her first big

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Speaker 1: splashy Boss of Twitter televised interview this week. By the way,

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Speaker 1: you may have realized that we're not calling it X,

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Speaker 1: We're calling it Twitter. I am going to continue calling

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Speaker 1: it Twitter. I think it will be funny if X

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Speaker 1: doesn't take, and I'm doing my part to make sure

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Speaker 1: that it does not, so we will continue to call

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Speaker 1: it Twitter. I call Facebook Facebook for the most part.

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Speaker 1: Every now and then, like if I'm reading a statement

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Speaker 1: from the company, I'll slip into meta every now and then.

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Speaker 1: But x's is not going to take. I'm not doing it.

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Speaker 1: I'm not doing it.

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Speaker 2: Are you doing it?

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Speaker 3: I guess I'm trying, but it just feels so awkward

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Speaker 3: that it feels weird every time I say X.

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Speaker 1: So basically, as you reported, a couple of months ago,

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Speaker 1: Linda Yacanaro was made Twitter's new CEO. It was a

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Speaker 1: weird situation because during that time Twitter had like very

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Speaker 1: big moves, like they changed the name, they announced the payouts,

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Speaker 1: which we'll get to later, all of these big changes,

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Speaker 1: and these changes seem to be from the outside, just

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Speaker 1: Elon Musk's whims, and then Yak and Arrow would have

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Speaker 1: to sort of play catch up and like have a

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Speaker 1: very enthusiastic thread of tweets about whatever random change they

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Speaker 1: just made. It just did not seem like the behavior

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Speaker 1: of someone who is the CEO of the company, which

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Speaker 1: she supposedly is she often publicly anyway seemed to be

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Speaker 1: like an afterthought, like she I remember the day that

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Speaker 1: they announced the Twitter payouts, she posted like, oh, in

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Speaker 1: order to be eligible for payouts, you have to have

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Speaker 1: forty four million impressions a month or whatever, and then

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Speaker 1: Elon Musk that same day said like, oh, well we're

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Speaker 1: using internal metrics to determine who gets payouts. There's no

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Speaker 1: way to know who gets what publicly. And it's like, well,

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Speaker 1: then why did she just tweet? Literally is something else?

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Speaker 1: Like why didn't you at least tell her that? So

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Speaker 1: she didn't tweet that? And then have you say something

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Speaker 1: totally different and then it's like, okay, well my tweets

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Speaker 1: has a completely different thing. Get on the same page. Basically,

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Speaker 1: what I'm saying is that it did not seem like

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Speaker 1: CEO behavior to me publicly, and so I was sort

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Speaker 1: of like happy to see that she was doing some

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Speaker 1: kind of a public facing CEO ish interview. Let's talk

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Speaker 1: about how it went. So you really don't need to

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Speaker 1: watch the whole thing. There's really nothing too surprising that

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Speaker 1: you need to see if you didn't watch it. Basically,

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Speaker 1: Yack and Aro did a lot of spin around Twitter

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Speaker 1: being a healthy place. It is very clear that she

434
00:24:34,840 --> 00:24:37,520
Speaker 1: really wants brands to feel safe on the platform and

435
00:24:37,560 --> 00:24:41,600
Speaker 1: come back. Many brands have left Twitter rightly so since

436
00:24:41,640 --> 00:24:44,159
Speaker 1: Elon Musk took over, because they probably don't want to

437
00:24:44,160 --> 00:24:48,000
Speaker 1: see their advertising next to like, you know, slurs or

438
00:24:48,280 --> 00:24:52,080
Speaker 1: hate speech or child sexual abuse material. So a lot

439
00:24:52,080 --> 00:24:55,760
Speaker 1: of brands left the platform and took their advertising revenue

440
00:24:55,800 --> 00:24:58,879
Speaker 1: with them. Yack and Arrow was really excited to talk

441
00:24:58,920 --> 00:25:01,720
Speaker 1: about the fact that too big brands State Farm and

442
00:25:01,800 --> 00:25:04,840
Speaker 1: Coke have come back to the platform. Although funny enough

443
00:25:05,040 --> 00:25:09,680
Speaker 1: Media Matters after this interview showed Coke and State Farm

444
00:25:09,760 --> 00:25:12,439
Speaker 1: ads where it's like here here their ads are on

445
00:25:12,480 --> 00:25:15,560
Speaker 1: the platform next to hate speech and harassment, like good job.

446
00:25:16,640 --> 00:25:20,800
Speaker 1: And she really kept championing this idea of Twitter being

447
00:25:20,840 --> 00:25:23,680
Speaker 1: a place force free expression like that was like she'd

448
00:25:24,040 --> 00:25:27,120
Speaker 1: probably said that three different times in this interview. As

449
00:25:27,160 --> 00:25:29,439
Speaker 1: someone who has done a little bit of work around

450
00:25:29,480 --> 00:25:32,800
Speaker 1: platforms and sort of how people think about platforms, researchers

451
00:25:32,800 --> 00:25:35,160
Speaker 1: think about platforms, something that she did that I really

452
00:25:35,160 --> 00:25:38,960
Speaker 1: want to call out is that she repeated a lot

453
00:25:38,960 --> 00:25:43,159
Speaker 1: of made up metrics that truly mean nothing, like truly

454
00:25:43,200 --> 00:25:46,440
Speaker 1: are meaningless. She said, I can considantly sit in front

455
00:25:46,440 --> 00:25:48,480
Speaker 1: of you and say that ninety nine point nine percent

456
00:25:48,520 --> 00:25:52,639
Speaker 1: of all posted impressions are healthy. I don't know what

457
00:25:52,680 --> 00:25:57,800
Speaker 1: that means. It's just it's like not a metric that

458
00:25:57,880 --> 00:26:01,040
Speaker 1: any researcher would be like, Oh, sure, ninety nine point

459
00:26:01,119 --> 00:26:03,600
Speaker 1: nine percent of all posted impressions are healthy. She just

460
00:26:03,640 --> 00:26:06,280
Speaker 1: made that up, and it probably sounds good. It's probably

461
00:26:06,359 --> 00:26:08,679
Speaker 1: something they're going to be using in their marketing to

462
00:26:08,720 --> 00:26:10,840
Speaker 1: try to get brands back to the platform because it's

463
00:26:10,920 --> 00:26:13,080
Speaker 1: it's very it's a very brand safe platform. If you

464
00:26:13,080 --> 00:26:17,480
Speaker 1: didn't know, like lots of healthy discussion and free expression

465
00:26:17,640 --> 00:26:21,920
Speaker 1: is taking place there. Coke and State Farm and other advertisers,

466
00:26:21,960 --> 00:26:25,040
Speaker 1: please come back, We love you. There was this exchange

467
00:26:25,280 --> 00:26:27,560
Speaker 1: that I feel like is a pretty good example of

468
00:26:27,640 --> 00:26:31,640
Speaker 1: the kind of say nothing pr tech spen that I'm

469
00:26:31,680 --> 00:26:32,280
Speaker 1: talking about.

470
00:26:32,680 --> 00:26:35,400
Speaker 4: I can confidently sit in front of you and say

471
00:26:35,440 --> 00:26:39,960
Speaker 4: that ninety nine zero point nine percent of how all

472
00:26:40,040 --> 00:26:42,480
Speaker 4: posted impressions are healthy?

473
00:26:42,520 --> 00:26:44,800
Speaker 2: How do you define health? Say? Though? Is porn healthy?

474
00:26:45,640 --> 00:26:48,320
Speaker 2: Are conspiracy theories healthy? You know?

475
00:26:48,440 --> 00:26:53,000
Speaker 4: It goes back to my point about our success with

476
00:26:53,200 --> 00:26:58,520
Speaker 4: freedom of speech not reach, and if it's if it

477
00:26:58,760 --> 00:27:05,600
Speaker 4: is lawful, but it's awful. It's extraordinarily difficult for you

478
00:27:05,640 --> 00:27:07,720
Speaker 4: to see it. But how many millions of people follow

479
00:27:07,800 --> 00:27:08,480
Speaker 4: Kanye West?

480
00:27:09,040 --> 00:27:11,600
Speaker 2: Lawful but awful and he's allowed back on.

481
00:27:13,240 --> 00:27:17,159
Speaker 4: You know, Kanye, who hasn't rejoined the platform yet but

482
00:27:17,600 --> 00:27:23,040
Speaker 4: is planning to do so, will operate within the very

483
00:27:23,080 --> 00:27:28,040
Speaker 4: specific policies that we have established that we're clear on

484
00:27:28,480 --> 00:27:32,920
Speaker 4: that everyone who's watching this or listening on spaces can

485
00:27:33,040 --> 00:27:37,800
Speaker 4: access themselves, and we have an extraordinary team of people

486
00:27:38,240 --> 00:27:42,840
Speaker 4: who are overseeing, hands on keyboards, monitoring all day, every

487
00:27:42,920 --> 00:27:46,040
Speaker 4: day to make sure that that ninety nine point nine

488
00:27:46,040 --> 00:27:49,520
Speaker 4: to nine percent of impressions remain.

489
00:27:49,400 --> 00:27:50,199
Speaker 2: At that number.

490
00:27:50,480 --> 00:27:52,920
Speaker 4: But we also have to remember what's at the core

491
00:27:53,680 --> 00:27:58,680
Speaker 4: of free expression. You might not agree with what everyone

492
00:27:58,760 --> 00:28:01,439
Speaker 4: is saying. We want to make it a healthy debate

493
00:28:01,760 --> 00:28:07,080
Speaker 4: and discourse, but free expression at its core will really

494
00:28:07,200 --> 00:28:12,280
Speaker 4: really only survive when someone you don't agree with says

495
00:28:12,359 --> 00:28:14,400
Speaker 4: something you don't agree with.

496
00:28:14,960 --> 00:28:17,200
Speaker 1: I mean, so, what are you what are your takeaways

497
00:28:17,240 --> 00:28:18,240
Speaker 1: on that? Bet?

498
00:28:18,480 --> 00:28:21,719
Speaker 3: Oh, it was pretty empty. She used a lot of

499
00:28:21,920 --> 00:28:27,000
Speaker 3: rhyming phrases.

500
00:28:24,480 --> 00:28:26,920
Speaker 1: The business world to Johnny cochrane.

501
00:28:27,359 --> 00:28:30,760
Speaker 3: Yeah, She was asked, how do you define that, and

502
00:28:30,800 --> 00:28:35,760
Speaker 3: she responded, I think with another rhyme. At first she

503
00:28:35,840 --> 00:28:39,600
Speaker 3: said ninety nine point nine percent of posts are healthy,

504
00:28:39,680 --> 00:28:41,840
Speaker 3: which like, what does that mean that she says ninety

505
00:28:41,920 --> 00:28:45,440
Speaker 3: nine point ninety nine percent, which further makes it seem

506
00:28:45,480 --> 00:28:49,280
Speaker 3: like she's just making this number up. And it just

507
00:28:49,320 --> 00:28:56,120
Speaker 3: sounded like very pr speech in a way that was

508
00:28:56,160 --> 00:29:00,200
Speaker 3: like transparently, so like she wasn't even trying to make

509
00:29:00,240 --> 00:29:05,360
Speaker 3: it seem like she was actually talking about these problems.

510
00:29:05,440 --> 00:29:08,920
Speaker 3: She sees no problems. From her point of view. She

511
00:29:09,000 --> 00:29:14,840
Speaker 3: is they have solved all the problems, and it's just

512
00:29:15,120 --> 00:29:20,200
Speaker 3: all about free speech, which to her means anybody can

513
00:29:20,200 --> 00:29:20,840
Speaker 3: say anything.

514
00:29:21,560 --> 00:29:27,080
Speaker 1: So her obsession with free expression and open dialogue, healthy dialogue.

515
00:29:27,680 --> 00:29:30,800
Speaker 1: I don't think that's really what we're talking about when

516
00:29:30,800 --> 00:29:34,560
Speaker 1: we're talking about problems on platforms like Twitter. I don't

517
00:29:34,560 --> 00:29:37,360
Speaker 1: think that anybody could say that they go on the

518
00:29:37,440 --> 00:29:41,239
Speaker 1: platforms like Twitter and they only see takes they agree with.

519
00:29:41,960 --> 00:29:45,840
Speaker 1: It's such a like false reality that like, oh, what

520
00:29:45,920 --> 00:29:48,520
Speaker 1: people are upset about is that they're seeing takes that

521
00:29:48,560 --> 00:29:51,800
Speaker 1: they don't agree with. But have you ever considered, dear user,

522
00:29:52,040 --> 00:29:53,920
Speaker 1: that that it's free to have speech? It's like, no,

523
00:29:54,000 --> 00:29:57,400
Speaker 1: that's not really what people are upset about, Like me

524
00:29:57,480 --> 00:30:00,320
Speaker 1: being called a slur is not on a pian that

525
00:30:00,400 --> 00:30:03,040
Speaker 1: I don't agree with. Right, And here's an actual metric.

526
00:30:03,240 --> 00:30:06,320
Speaker 1: According to Twitter's own API, usage of the N word

527
00:30:06,480 --> 00:30:09,760
Speaker 1: on Twitter jump from about three hundred and ninety instances

528
00:30:10,080 --> 00:30:13,360
Speaker 1: on October twenty sixth, twenty twenty two, before Elon took

529
00:30:13,400 --> 00:30:16,680
Speaker 1: over to twenty seven thousand, seven hundred and six on

530
00:30:16,720 --> 00:30:19,600
Speaker 1: November fifth, twenty twenty two, after Elon took over. So

531
00:30:19,720 --> 00:30:23,920
Speaker 1: that is an actual, tangible, specific metric. And that's what

532
00:30:23,960 --> 00:30:27,200
Speaker 1: I'm talking about. If there are about thirty thousand people

533
00:30:27,320 --> 00:30:29,640
Speaker 1: calling me a slur on the platform, that is not

534
00:30:29,960 --> 00:30:33,040
Speaker 1: thirty thousand takes that I happen to not agree with.

535
00:30:33,160 --> 00:30:35,800
Speaker 1: That is not discourse, That is not free expression. And

536
00:30:35,840 --> 00:30:40,160
Speaker 1: so she's doing this very tricky rhetorical thing where she's

537
00:30:40,160 --> 00:30:44,600
Speaker 1: making it seem like people's complaints and concerns about the

538
00:30:44,640 --> 00:30:47,720
Speaker 1: way Twitter is being moderated now is grounded from a

539
00:30:47,760 --> 00:30:51,040
Speaker 1: place of not wanting to see opinions they don't agree with.

540
00:30:51,480 --> 00:30:55,760
Speaker 1: Who owns any social media platform, ever, is only engaging

541
00:30:55,760 --> 00:30:58,520
Speaker 1: with or seeing opinions they say they agree with. That's

542
00:30:58,560 --> 00:31:01,720
Speaker 1: not what people are saying, and it's like this insistence

543
00:31:01,760 --> 00:31:06,560
Speaker 1: on digging into this false, self serving reality. Really it's

544
00:31:06,600 --> 00:31:10,920
Speaker 1: just so obvious. I also think like the person interviewing

545
00:31:10,960 --> 00:31:13,120
Speaker 1: her in this clip really did a good job of

546
00:31:13,200 --> 00:31:15,800
Speaker 1: kind of trying to hold her accountable a little bit

547
00:31:15,800 --> 00:31:17,360
Speaker 1: for some of the things that she was saying, like

548
00:31:18,240 --> 00:31:22,200
Speaker 1: saying like, oh, we are going to be focusing on

549
00:31:22,320 --> 00:31:26,560
Speaker 1: healthy speech on the platform. Kanye West tweeted a literal

550
00:31:26,800 --> 00:31:30,840
Speaker 1: swastika and got his platform back, and so she's saying, oh, well,

551
00:31:31,400 --> 00:31:34,680
Speaker 1: he's going to be operating under specific rules. First of all,

552
00:31:34,760 --> 00:31:37,320
Speaker 1: Kanye Kanye West is probably not doing anything that you

553
00:31:37,480 --> 00:31:39,000
Speaker 1: expect him to be doing at this point. We all,

554
00:31:39,040 --> 00:31:43,160
Speaker 1: it's all clear. What she's actually saying is that he

555
00:31:43,240 --> 00:31:48,959
Speaker 1: will not be eligible for the revenue sharing model and

556
00:31:49,000 --> 00:31:52,440
Speaker 1: that advertising won't be next to his tweets. But he

557
00:31:52,520 --> 00:31:55,360
Speaker 1: still has millions and millions and millions of followers. He

558
00:31:55,400 --> 00:31:58,680
Speaker 1: has more followers than there are Jewish people worldwide. And

559
00:31:58,680 --> 00:32:00,000
Speaker 1: so you're gonna tell me that when he tweets a

560
00:32:00,080 --> 00:32:03,760
Speaker 1: swastika it's no big deal, because oh, don't worry, he's

561
00:32:03,800 --> 00:32:05,480
Speaker 1: not going to it's not gonna be add sex to

562
00:32:05,520 --> 00:32:09,080
Speaker 1: that like she speaks like somebody for whom the only

563
00:32:09,200 --> 00:32:11,760
Speaker 1: thing she can think about. The only measure of success

564
00:32:11,840 --> 00:32:15,760
Speaker 1: of this huge, important platform is whether or not brands

565
00:32:15,800 --> 00:32:19,640
Speaker 1: are there, and that Kanye West using his millions of

566
00:32:19,680 --> 00:32:24,440
Speaker 1: followers to tweet anti Semitic garbage is only a problem

567
00:32:24,600 --> 00:32:27,280
Speaker 1: inso much as it affects advertising. There's no other reason

568
00:32:27,440 --> 00:32:28,840
Speaker 1: why one would have a problem with that.

569
00:32:29,920 --> 00:32:33,560
Speaker 3: The only other reasons that she articulates to you know,

570
00:32:33,680 --> 00:32:38,240
Speaker 3: care about content moderation policies is to ensure that free

571
00:32:38,280 --> 00:32:43,840
Speaker 3: speech is protected, like by her telling you know, we

572
00:32:43,920 --> 00:32:49,920
Speaker 3: should welcome the opportunity to see swastikas and hate speech

573
00:32:49,960 --> 00:32:52,200
Speaker 3: and slurs, right because it's like a difference of opinion,

574
00:32:52,240 --> 00:32:56,479
Speaker 3: and that's valuable in itself, which which is just wrong, right,

575
00:32:56,560 --> 00:33:02,080
Speaker 3: Like we we don't want that need to be rules

576
00:33:02,200 --> 00:33:07,040
Speaker 3: for discourse in polite society, and hate speech has no

577
00:33:07,120 --> 00:33:07,720
Speaker 3: place there.

578
00:33:08,240 --> 00:33:12,360
Speaker 1: But then stepping back about her saying like, oh, we

579
00:33:12,400 --> 00:33:17,120
Speaker 1: want to foster healthy engagement and healthy dialogue on the platform.

580
00:33:17,600 --> 00:33:19,840
Speaker 1: Ever since Twitter announced these payouts, which we have a

581
00:33:19,840 --> 00:33:21,640
Speaker 1: big update about that for you at the end of

582
00:33:21,680 --> 00:33:25,640
Speaker 1: the show. But the platform has just gotten it's gone

583
00:33:25,680 --> 00:33:27,760
Speaker 1: from you know that you don't know. I don't know

584
00:33:27,760 --> 00:33:30,240
Speaker 1: if you're familiar with the expression it's gone from worse

585
00:33:30,280 --> 00:33:32,960
Speaker 1: to worser, if anything, like it's gone from from worse

586
00:33:33,000 --> 00:33:39,200
Speaker 1: to worser, if anything, it is. I have definitely seen

587
00:33:39,560 --> 00:33:46,320
Speaker 1: an uptick in more accounts rage baiting and clickbaiting and

588
00:33:46,400 --> 00:33:50,680
Speaker 1: outrage baiting because there is now a financial incentive to

589
00:33:50,720 --> 00:33:53,680
Speaker 1: do so. You might not even ever get that check, hint, hint.

590
00:33:53,960 --> 00:33:58,280
Speaker 1: But now that that that has been financially incentivized, I

591
00:33:58,320 --> 00:34:01,920
Speaker 1: have seen more and more people give more and more

592
00:34:02,320 --> 00:34:06,560
Speaker 1: asinine tweets that are just clearly meant to anger you

593
00:34:06,760 --> 00:34:11,239
Speaker 1: into engaging because they think that it is financially incentivized. Like,

594
00:34:11,239 --> 00:34:15,000
Speaker 1: there was this account earlier this week that like posted

595
00:34:15,000 --> 00:34:18,360
Speaker 1: a picture of a woman who runs a coffee truck

596
00:34:18,920 --> 00:34:23,279
Speaker 1: in Vancouver, and she made a cute TikTok where she's like, Oh,

597
00:34:23,280 --> 00:34:25,800
Speaker 1: I make my coffee and I like, I'm really polite

598
00:34:25,840 --> 00:34:29,560
Speaker 1: to everybody. And this guy, I guess, just because he

599
00:34:29,600 --> 00:34:33,879
Speaker 1: didn't happen to find this woman like attractive, personally took

600
00:34:33,920 --> 00:34:37,400
Speaker 1: her TikTok from TikTok, posted it on Twitter with like

601
00:34:37,440 --> 00:34:39,279
Speaker 1: a mean comment, and the only thing this woman had

602
00:34:39,320 --> 00:34:42,120
Speaker 1: done wrong was just like exists and like happened to

603
00:34:42,120 --> 00:34:44,440
Speaker 1: put her existence on Twitter. And so it's like these

604
00:34:44,520 --> 00:34:47,839
Speaker 1: kinds of things that are that are kind of relying

605
00:34:48,040 --> 00:34:52,120
Speaker 1: on people like me replying and being like that's awful,

606
00:34:52,320 --> 00:34:54,919
Speaker 1: how dare you like blah blah blah because they think

607
00:34:54,960 --> 00:34:57,440
Speaker 1: it will put money in their pocket. I saw this,

608
00:34:57,560 --> 00:35:02,520
Speaker 1: like pretty well known right wing grifter doing that thing

609
00:35:02,760 --> 00:35:07,280
Speaker 1: where he posted an image of a really sexy looking

610
00:35:07,360 --> 00:35:10,600
Speaker 1: woman dressed in a UPS uniform and he tweeted it

611
00:35:10,600 --> 00:35:13,279
Speaker 1: with the caption, you get a package and she's your

612
00:35:13,320 --> 00:35:16,120
Speaker 1: delivery driver? What are you saying to her? And it's

613
00:35:16,160 --> 00:35:22,040
Speaker 1: like just the lowest of the lowest quality engagement bait

614
00:35:22,120 --> 00:35:27,080
Speaker 1: of like that you see on like your MEM's Facebook page.

615
00:35:27,520 --> 00:35:30,840
Speaker 1: That I mean, that is not healthier discourse. That's not

616
00:35:30,920 --> 00:35:34,960
Speaker 1: better discourse. People being incentivized to have the worst or

617
00:35:34,960 --> 00:35:37,920
Speaker 1: most obnoxious takes that they can to get people to

618
00:35:38,000 --> 00:35:39,799
Speaker 1: engage with them so they can put money in their

619
00:35:39,840 --> 00:35:43,240
Speaker 1: pocket is not making anybody's discourse healthier. It's just making

620
00:35:43,280 --> 00:35:48,640
Speaker 1: us all more annoyed, myself specifically.

621
00:35:53,080 --> 00:35:53,600
Speaker 2: More After a.

622
00:35:53,640 --> 00:36:05,960
Speaker 5: Quick break, let's get right back into it.

623
00:36:08,200 --> 00:36:12,440
Speaker 1: Linda Yakarvino's Big first public interview as Twitter CEO was

624
00:36:12,520 --> 00:36:15,360
Speaker 1: happening against kind of a weird backdrop. I have to

625
00:36:15,360 --> 00:36:17,040
Speaker 1: give a little bit of a heads up for child

626
00:36:17,120 --> 00:36:20,200
Speaker 1: abuse on this one, because the day before her big interview,

627
00:36:20,520 --> 00:36:23,040
Speaker 1: Nick Pickles, who is the head of Global Government Affairs

628
00:36:23,080 --> 00:36:26,239
Speaker 1: at Twitter, was grilled by the Australian Parliament about why

629
00:36:26,360 --> 00:36:30,240
Speaker 1: Twitter restored the account of someone who shared a still

630
00:36:30,520 --> 00:36:35,400
Speaker 1: from childhood sexual abuse material. Remember that Elon Musk touted

631
00:36:35,400 --> 00:36:37,839
Speaker 1: how he was going to crack down on childhood sexual

632
00:36:37,880 --> 00:36:41,200
Speaker 1: abuse material on the platform and was like bolstering this

633
00:36:41,480 --> 00:36:45,880
Speaker 1: zero tolerance policy, and then he actually criticized Twitter's Twitter's

634
00:36:45,880 --> 00:36:50,560
Speaker 1: former leadership as being too busy censoring conservatives and speech

635
00:36:50,719 --> 00:36:54,200
Speaker 1: to care about protecting children online. Well, now it sounds

636
00:36:54,239 --> 00:36:56,640
Speaker 1: like the new policy is kind of like hmm, depends

637
00:36:56,640 --> 00:36:59,960
Speaker 1: on the vibe. So a right wing social media influence.

638
00:37:00,280 --> 00:37:03,399
Speaker 1: Dominic McGee who goes by dom Luker, who has over

639
00:37:03,440 --> 00:37:05,480
Speaker 1: a half a million followers on the platform and it's

640
00:37:05,480 --> 00:37:09,400
Speaker 1: big into Cubanon posted an image from a well known

641
00:37:09,640 --> 00:37:13,600
Speaker 1: child sexual abuse material video. He said that he posted

642
00:37:13,600 --> 00:37:17,400
Speaker 1: these images to spread awareness about child trafficking. The still

643
00:37:17,680 --> 00:37:20,480
Speaker 1: is from a video that is known to investigators. It's

644
00:37:20,480 --> 00:37:22,920
Speaker 1: actually the specific video that led to the arrest of

645
00:37:23,000 --> 00:37:25,719
Speaker 1: Josh Dugger from the show nineteen Kids in Counting. The

646
00:37:25,840 --> 00:37:28,400
Speaker 1: Washington Post reports that when this image was posted to Twitter,

647
00:37:28,680 --> 00:37:32,520
Speaker 1: it got more than three million views and eight thousand retweets,

648
00:37:32,960 --> 00:37:37,560
Speaker 1: and when he posted it to Twitter, he watermarked the image,

649
00:37:37,640 --> 00:37:41,239
Speaker 1: which I feel like is a special kind of like

650
00:37:41,280 --> 00:37:44,600
Speaker 1: you gotta be a real creep to do something like that.

651
00:37:45,440 --> 00:37:46,280
Speaker 3: Yeah.

652
00:37:46,480 --> 00:37:49,040
Speaker 1: So Nick Pickles, the head of Global Government Affairs at Twitter,

653
00:37:49,719 --> 00:37:53,560
Speaker 1: basically said that he felt the person that shared that

654
00:37:53,680 --> 00:37:58,440
Speaker 1: image did so because he was outraged by the image, which, again,

655
00:37:59,000 --> 00:38:01,520
Speaker 1: that is not a zero to Laban's policy, right, That's

656
00:38:01,520 --> 00:38:05,000
Speaker 1: not what a zero tolerance policy is. Pickles testified, quote,

657
00:38:05,600 --> 00:38:08,360
Speaker 1: one of the challenges we see is, for example, people

658
00:38:08,440 --> 00:38:10,759
Speaker 1: sharing this content out of outrage because they want to

659
00:38:10,840 --> 00:38:13,960
Speaker 1: raise awareness of an issue and see something in the media.

660
00:38:14,040 --> 00:38:16,759
Speaker 1: So I guess I'll if you put like a sad

661
00:38:16,760 --> 00:38:19,960
Speaker 1: face emog next to it, or like you're like this,

662
00:38:20,239 --> 00:38:24,640
Speaker 1: like hate this, then like it's okay. You know, there

663
00:38:24,719 --> 00:38:27,160
Speaker 1: is nothing in Twitter's terms of services says it's okay

664
00:38:27,200 --> 00:38:30,120
Speaker 1: to share child sexual abuse material if the user is

665
00:38:30,120 --> 00:38:34,279
Speaker 1: doing it because they're outraged or looking to quote raise awareness.

666
00:38:34,760 --> 00:38:34,880
Speaker 4: Uh.

667
00:38:35,160 --> 00:38:38,600
Speaker 1: The policy says viewing, sharing, or linking to child sexual

668
00:38:38,600 --> 00:38:42,680
Speaker 1: exploitation material contributes to the revictimization of the depicted child,

669
00:38:42,960 --> 00:38:46,319
Speaker 1: and it's one of the platform's most serious violations. And

670
00:38:46,840 --> 00:38:51,440
Speaker 1: it's just beyond obvious that, regardless of anybody's intent, nobody

671
00:38:51,480 --> 00:38:54,560
Speaker 1: should be sharing that kind of material ever, point blank,

672
00:38:54,640 --> 00:38:57,840
Speaker 1: full stop, end of sentence. And it really kind of

673
00:38:57,840 --> 00:38:59,680
Speaker 1: goes back to something that came up in our interview

674
00:38:59,719 --> 00:39:02,680
Speaker 1: with mind Miles Klee two weeks ago, the author of

675
00:39:02,680 --> 00:39:07,840
Speaker 1: that Rolling Stone article about the trafficking movie Sound of Freedom,

676
00:39:08,280 --> 00:39:12,759
Speaker 1: where so much is done to quote raise awareness, like,

677
00:39:12,760 --> 00:39:15,040
Speaker 1: oh well, even if it's even if it's upsets you,

678
00:39:15,480 --> 00:39:18,200
Speaker 1: it raises awareness and that's okay, And it's like, no, actually,

679
00:39:18,239 --> 00:39:22,680
Speaker 1: it revictimizes the person depicted in this harmful image, and

680
00:39:22,760 --> 00:39:25,400
Speaker 1: it has no place. It's not discourse, it has no

681
00:39:25,480 --> 00:39:26,600
Speaker 1: place in place society.

682
00:39:27,239 --> 00:39:31,480
Speaker 3: Yeah, and raises awareness towards what end. Like the field

683
00:39:31,480 --> 00:39:36,920
Speaker 3: of public health is littered with failed awareness campaigns that

684
00:39:37,760 --> 00:39:42,680
Speaker 3: did successfully raise awareness of something but achieved zero impact

685
00:39:42,800 --> 00:39:47,319
Speaker 3: in like reducing the harm of it or reducing the

686
00:39:47,360 --> 00:39:52,719
Speaker 3: scope of how many people it affected. So like, how

687
00:39:52,719 --> 00:39:55,840
Speaker 3: does it help anyone to raise awareness of the specifics

688
00:39:55,880 --> 00:40:03,360
Speaker 3: of an image of childhood sexual abuse material. It just

689
00:40:03,400 --> 00:40:06,760
Speaker 3: reinforces the idea that the people who are like deep

690
00:40:06,880 --> 00:40:13,600
Speaker 3: down this rabbit hole of like fixation on trafficking, Like

691
00:40:15,360 --> 00:40:17,319
Speaker 3: it just feels like a lot of them just kind

692
00:40:17,320 --> 00:40:19,680
Speaker 3: of like it, right, Like it's like a smoke screen

693
00:40:19,840 --> 00:40:21,040
Speaker 3: to share stuff.

694
00:40:21,440 --> 00:40:23,879
Speaker 1: Yeah, that came up in that mild Cley interview as well,

695
00:40:23,960 --> 00:40:29,120
Speaker 1: that like that film is like very gratuitous, and so

696
00:40:29,200 --> 00:40:31,840
Speaker 1: all these people who are like, we are trying to

697
00:40:31,880 --> 00:40:35,880
Speaker 1: raise awareness about trafficking are also like watching a movie

698
00:40:35,880 --> 00:40:38,839
Speaker 1: that kind of like has a gratuitous depiction of it.

699
00:40:39,120 --> 00:40:42,799
Speaker 1: And so it's like, well, you're kind of consuming the

700
00:40:42,880 --> 00:40:45,120
Speaker 1: thing with which you are saying repulses you in a

701
00:40:45,120 --> 00:40:48,279
Speaker 1: way that is like very weird. And you know, this

702
00:40:48,360 --> 00:40:51,399
Speaker 1: idea of raising awareness. I don't think that people need

703
00:40:51,440 --> 00:40:55,360
Speaker 1: to see an image from childhood sexual abuse material to

704
00:40:55,520 --> 00:40:58,799
Speaker 1: know that that is wrong and that is bad. I

705
00:40:58,840 --> 00:41:01,680
Speaker 1: truly don't think people to see that to have their aware.

706
00:41:01,800 --> 00:41:05,160
Speaker 1: I think that most people already know it's wrong and horrible.

707
00:41:05,400 --> 00:41:08,160
Speaker 1: I don't think that that image really helps change anybody's mind,

708
00:41:08,600 --> 00:41:12,680
Speaker 1: and in that interview with Linda Yakerino. I do think that,

709
00:41:13,000 --> 00:41:15,640
Speaker 1: you know, the way that the tenor of that interview

710
00:41:16,360 --> 00:41:20,360
Speaker 1: seem to me that folks are interested in asking pointed

711
00:41:20,440 --> 00:41:22,719
Speaker 1: questions to get at what the truth is because they

712
00:41:22,840 --> 00:41:26,640
Speaker 1: understand that this platform is so important to our discourse.

713
00:41:27,520 --> 00:41:29,440
Speaker 1: And I think kind of in the same way that

714
00:41:29,480 --> 00:41:31,800
Speaker 1: the tide is sort of turning around how we respond

715
00:41:31,880 --> 00:41:35,040
Speaker 1: to big tech companies who want to be using us

716
00:41:35,080 --> 00:41:38,600
Speaker 1: and everything we do online to train their AI. I

717
00:41:38,600 --> 00:41:41,560
Speaker 1: think that we're kind of seeing folks turn on Musk

718
00:41:41,680 --> 00:41:44,320
Speaker 1: and the people in his orbit as well. Casey Newton

719
00:41:44,440 --> 00:41:47,440
Speaker 1: published a piece over at platform Or called It's time

720
00:41:47,480 --> 00:41:49,920
Speaker 1: to change how we cover Elon Musk. In it, he

721
00:41:50,000 --> 00:41:55,360
Speaker 1: breaks down Musks many many, many many lies and calls

722
00:41:55,520 --> 00:41:59,239
Speaker 1: for a shift and how journalists cover him kind of

723
00:41:59,440 --> 00:42:02,879
Speaker 1: not totally dissimilar to Trump, Like how do you cover

724
00:42:03,000 --> 00:42:06,640
Speaker 1: someone who lies so much? I mostly agree with the

725
00:42:06,640 --> 00:42:08,920
Speaker 1: substance of the piece. I have a couple of big quibbles.

726
00:42:09,280 --> 00:42:13,040
Speaker 1: One is that Newton says that in business reporting, it

727
00:42:13,120 --> 00:42:17,160
Speaker 1: is generally commonplace to take CEOs at their word and

728
00:42:17,239 --> 00:42:20,160
Speaker 1: like not assume that they're just lying, bold faced lying

729
00:42:20,200 --> 00:42:22,600
Speaker 1: to you. I would really push back on that. I

730
00:42:22,600 --> 00:42:25,279
Speaker 1: would say, like, yeah, if you're lazy, Yeah, if you

731
00:42:25,440 --> 00:42:30,279
Speaker 1: like can't imagine a CEO ever lying or distorting the truth. Yeah,

732
00:42:30,280 --> 00:42:32,600
Speaker 1: if you are caught up in the fact that this

733
00:42:32,680 --> 00:42:35,560
Speaker 1: person has power and a company and money and access

734
00:42:35,560 --> 00:42:38,760
Speaker 1: and privilege, and therefore us are bought into this idea

735
00:42:38,800 --> 00:42:43,240
Speaker 1: that they are smart and worthy and valid and upstanding. Sure, maybe,

736
00:42:43,320 --> 00:42:45,480
Speaker 1: but I think that for the most part, I don't

737
00:42:45,840 --> 00:42:47,520
Speaker 1: when I'm if I'm talking to a CEO, I'm not

738
00:42:47,520 --> 00:42:50,000
Speaker 1: gonna assume that CEO is automatically telling me the truth.

739
00:42:50,160 --> 00:42:55,600
Speaker 1: And it's also worth noting who, like which specific journalists

740
00:42:56,120 --> 00:42:59,719
Speaker 1: take hucksters and liars like Elon Musk at their word,

741
00:42:59,719 --> 00:43:03,320
Speaker 1: because I sure didn't right to cover Musk without mentioning

742
00:43:03,760 --> 00:43:07,920
Speaker 1: his deep history of lies, his embrace, his open embrace

743
00:43:07,960 --> 00:43:11,320
Speaker 1: of things like white supremacist talking points and eugenesis talking points,

744
00:43:11,800 --> 00:43:16,239
Speaker 1: failing to pay his bills, fostering workplaces that foster sexism

745
00:43:16,360 --> 00:43:20,920
Speaker 1: and racial mistreatment, cracking down on journalists and freedom of speech, etc. Etc. Etc.

746
00:43:21,160 --> 00:43:24,840
Speaker 1: Like union busting, like blaytant transphobia, all of that, choosing

747
00:43:24,920 --> 00:43:27,960
Speaker 1: that those things are not part of the story and

748
00:43:28,080 --> 00:43:31,200
Speaker 1: choosing to not to not include the men stories is

749
00:43:31,280 --> 00:43:33,839
Speaker 1: a choice. It is not being objective, it is not

750
00:43:33,880 --> 00:43:36,480
Speaker 1: being unbiased. It is a choice. And I do think

751
00:43:36,480 --> 00:43:39,480
Speaker 1: that the tech press has spent a long time really

752
00:43:39,480 --> 00:43:42,560
Speaker 1: not including these things that I believe are so at

753
00:43:42,600 --> 00:43:45,640
Speaker 1: the core of who Elon Musk is, not just as

754
00:43:45,640 --> 00:43:48,359
Speaker 1: a person, but as a leader as well, and they

755
00:43:48,600 --> 00:43:52,719
Speaker 1: basically helped him rise to power. They helped give him

756
00:43:52,760 --> 00:43:57,160
Speaker 1: this reputation as a brilliant genius who you know, is

757
00:43:57,160 --> 00:44:00,759
Speaker 1: playing three dimensional chests, who is so brilliant we could

758
00:44:00,800 --> 00:44:04,200
Speaker 1: never fully understand the moves that he's making because he's

759
00:44:04,200 --> 00:44:07,319
Speaker 1: just too smart, when in reality he's actually just like

760
00:44:07,719 --> 00:44:11,239
Speaker 1: a wealthy manchild born on third base who thinks he

761
00:44:11,320 --> 00:44:14,680
Speaker 1: hit a home run, right Like. I think that a

762
00:44:14,719 --> 00:44:19,200
Speaker 1: lot of tech folks got really invested in this idea

763
00:44:19,360 --> 00:44:22,719
Speaker 1: of who Musk was telling them. By the time they

764
00:44:23,520 --> 00:44:27,040
Speaker 1: opened their eyes to who Musk actually is and who

765
00:44:27,040 --> 00:44:29,000
Speaker 1: he has been for a really long time, and who

766
00:44:29,000 --> 00:44:31,960
Speaker 1: he has made clear time and time again who he is,

767
00:44:32,440 --> 00:44:34,479
Speaker 1: I think they were like, oh wait, I have spent

768
00:44:34,480 --> 00:44:38,080
Speaker 1: a long time not asking these questions, and so you know,

769
00:44:38,360 --> 00:44:42,200
Speaker 1: I'm glad folks are getting there I do think the

770
00:44:42,239 --> 00:44:43,960
Speaker 1: tide is turning a little bit. I think that like

771
00:44:44,880 --> 00:44:48,279
Speaker 1: the fact that this this big first interview of the

772
00:44:48,320 --> 00:44:51,840
Speaker 1: new CEO wasn't just a cushy you know, Q and

773
00:44:51,880 --> 00:44:54,880
Speaker 1: a about her rise to power whatever, but it was

774
00:44:54,920 --> 00:44:57,920
Speaker 1: like real questions of substance about this platform that is

775
00:44:57,960 --> 00:45:00,160
Speaker 1: so important to all of our lives and our democracy.

776
00:45:00,680 --> 00:45:03,440
Speaker 1: I think it's it illustrates that, like the tide is

777
00:45:03,480 --> 00:45:05,440
Speaker 1: turning a little bit. And I guess I would just

778
00:45:05,440 --> 00:45:08,640
Speaker 1: say this that if we are reaching a point where

779
00:45:08,800 --> 00:45:11,960
Speaker 1: folks are more apt to be calling out Musk for

780
00:45:12,000 --> 00:45:14,160
Speaker 1: who he is, I hope that when the next tech

781
00:45:14,239 --> 00:45:18,520
Speaker 1: leader comes along selling us a lie and a dream

782
00:45:18,800 --> 00:45:22,240
Speaker 1: or snake oil or giving us harm and a package,

783
00:45:22,600 --> 00:45:24,279
Speaker 1: is that they've wrapped up and said it's good for

784
00:45:24,360 --> 00:45:27,320
Speaker 1: us that we are not so quick to launder that

785
00:45:27,080 --> 00:45:30,520
Speaker 1: that people with power and platforms really do the work

786
00:45:30,520 --> 00:45:33,040
Speaker 1: of asking the question of like, who is this person really?

787
00:45:33,120 --> 00:45:35,359
Speaker 1: Not not who are they saying they are? Or who

788
00:45:35,360 --> 00:45:37,680
Speaker 1: would it be nice if they were? Who are they really?

789
00:45:38,280 --> 00:45:42,200
Speaker 3: It's a good plan. I hope we do that. I

790
00:45:42,239 --> 00:45:44,120
Speaker 3: don't know. I'm a little skeptical. I think we have

791
00:45:44,200 --> 00:45:48,879
Speaker 3: got a long history of just really wanting to embrace

792
00:45:49,239 --> 00:45:56,960
Speaker 3: hucksters and not just willingly but enthusiastically get behind the

793
00:45:58,120 --> 00:46:01,680
Speaker 3: con that they're selling, because it's almost like a like

794
00:46:01,680 --> 00:46:05,880
Speaker 3: a rising tide lifts all boats, like a self serving

795
00:46:06,360 --> 00:46:09,600
Speaker 3: con serves everybody who gets behind it.

796
00:46:09,920 --> 00:46:12,640
Speaker 1: Yeah, until it doesn't, until it's revealed as a con.

797
00:46:12,719 --> 00:46:15,200
Speaker 1: And like if your folks in Trump world, you start

798
00:46:15,200 --> 00:46:15,480
Speaker 1: going to.

799
00:46:15,520 --> 00:46:20,320
Speaker 3: Jail, right, yeah, right, until it does it.

800
00:46:20,880 --> 00:46:24,359
Speaker 1: Okay. So one last thing, speaking of Elon's lives, one

801
00:46:24,440 --> 00:46:27,640
Speaker 1: big update that we promised y'all is on those Twitter payouts.

802
00:46:27,840 --> 00:46:29,960
Speaker 1: You might recall that Twitter promised they were going to

803
00:46:30,000 --> 00:46:33,680
Speaker 1: start paying out on their ad revenue sharing program to

804
00:46:33,760 --> 00:46:37,600
Speaker 1: people who drove AD dollars on the platform. People were

805
00:46:37,640 --> 00:46:41,080
Speaker 1: posting screenshots of like thousands and thousands and thousands of

806
00:46:41,160 --> 00:46:42,840
Speaker 1: dollars and they were meant to be getting from Twitter.

807
00:46:43,200 --> 00:46:48,200
Speaker 1: And I was skeptical, We'll say that's the word I'll use.

808
00:46:48,280 --> 00:46:53,719
Speaker 1: I was skeptical because Elon, much like Cheat Whitfield from

809
00:46:53,760 --> 00:46:56,759
Speaker 1: Real Housewives of Atlanta, who's sometimes known as Charay, she

810
00:46:56,880 --> 00:46:59,839
Speaker 1: don't pay. Elon Musk does not pay his bills, just

811
00:46:59,840 --> 00:47:00,680
Speaker 1: like sorry what Field.

812
00:47:01,239 --> 00:47:04,600
Speaker 3: Well, comparing Elon to charat.

813
00:47:08,320 --> 00:47:09,880
Speaker 1: I have a lot of questions from both of them,

814
00:47:09,920 --> 00:47:11,800
Speaker 1: and most of them are like, did you are you

815
00:47:11,800 --> 00:47:14,120
Speaker 1: gonna pay not to go? Don't get me on a

816
00:47:14,120 --> 00:47:16,239
Speaker 1: Housewives tangent, but no, this is.

817
00:47:16,360 --> 00:47:19,239
Speaker 3: Why people tune into you because you can take the

818
00:47:19,880 --> 00:47:23,080
Speaker 3: you know, the complicated digital things going on in the

819
00:47:23,120 --> 00:47:25,920
Speaker 3: news and put them in terms that people understand.

820
00:47:27,239 --> 00:47:32,640
Speaker 1: Elon Musk is the Charae whitfield of technology. I'll stand

821
00:47:32,640 --> 00:47:33,040
Speaker 1: by that.

822
00:47:33,480 --> 00:47:36,160
Speaker 3: You know, just like before you you were going through

823
00:47:36,280 --> 00:47:40,120
Speaker 3: all of the things that he has done openly, repeatedly

824
00:47:40,160 --> 00:47:43,520
Speaker 3: that journalists ignore when they cover him. Uh, and a

825
00:47:43,520 --> 00:47:45,640
Speaker 3: lot of them, it's like, okay, you know, fostering a

826
00:47:45,719 --> 00:47:49,360
Speaker 3: sexist and racist workplace. I can see how his people

827
00:47:49,400 --> 00:47:53,239
Speaker 3: would be cool with that, but it really truly surprises

828
00:47:53,280 --> 00:47:57,000
Speaker 3: me the extent to which people on the right seem

829
00:47:57,200 --> 00:48:03,719
Speaker 3: totally fine with their like cult leaders, personality leaders just

830
00:48:03,840 --> 00:48:06,400
Speaker 3: not paying their bills. I really would have thought that

831
00:48:06,400 --> 00:48:09,799
Speaker 3: that would be more of a problem for people on

832
00:48:09,840 --> 00:48:13,440
Speaker 3: the right who were like the Party of like personal responsibility.

833
00:48:13,440 --> 00:48:16,600
Speaker 3: According to them, it's not true, or like fiscal responsibility

834
00:48:16,640 --> 00:48:18,800
Speaker 3: also not true. I guess it's all just like not true.

835
00:48:19,200 --> 00:48:23,840
Speaker 3: But like these heroes who just openly don't pay their bills.

836
00:48:25,320 --> 00:48:28,319
Speaker 3: It's surprising, it's so interesting.

837
00:48:28,440 --> 00:48:31,160
Speaker 1: I mean, I have a new philosophy on this where

838
00:48:32,160 --> 00:48:36,040
Speaker 1: it is not fruitful to point out hypocrisy with some

839
00:48:36,120 --> 00:48:39,239
Speaker 1: of these folks of like, well, if I was like

840
00:48:40,640 --> 00:48:44,239
Speaker 1: a young person who was drowning in student loan debt,

841
00:48:44,280 --> 00:48:46,560
Speaker 1: you would be like, pay your bills, You should pay

842
00:48:46,600 --> 00:48:50,920
Speaker 1: your bills, personal responsibility, YadA, YadA, YadA. If I'm a

843
00:48:50,960 --> 00:48:53,120
Speaker 1: billionaire and I have lots of money, but I don't

844
00:48:53,120 --> 00:48:55,960
Speaker 1: pay my bills, it's like, oh, what an innovative strategy

845
00:48:56,120 --> 00:48:59,600
Speaker 1: to cut down on costs squatting.

846
00:49:00,800 --> 00:49:05,520
Speaker 3: Yeah, somebody said it a while ago that their goal

847
00:49:05,800 --> 00:49:11,840
Speaker 3: is a you know, they want the freedom to do

848
00:49:11,920 --> 00:49:15,080
Speaker 3: what they want to do and also the freedom to

849
00:49:15,120 --> 00:49:16,080
Speaker 3: tell you what to do.

850
00:49:16,360 --> 00:49:17,719
Speaker 1: It's just exactly.

851
00:49:17,520 --> 00:49:21,759
Speaker 3: Completely completely hypocritical, and it does nothing to point it out.

852
00:49:22,600 --> 00:49:26,359
Speaker 3: And maybe Elon and Yagorino have already figured that out,

853
00:49:26,400 --> 00:49:29,560
Speaker 3: and maybe they are ahead of us in their vision

854
00:49:29,880 --> 00:49:35,520
Speaker 3: of an open discourse where uh, you know, there is

855
00:49:35,600 --> 00:49:41,080
Speaker 3: no point in actually talking about inconsistencies and ideas or anything.

856
00:49:41,120 --> 00:49:44,120
Speaker 3: It's just like free speech is just people yelling slurs

857
00:49:44,160 --> 00:49:44,960
Speaker 3: at each other.

858
00:49:45,040 --> 00:49:47,000
Speaker 1: Well, why do you need to talk about things like

859
00:49:47,239 --> 00:49:51,440
Speaker 1: inconsistencies or accountability when you've got a little thing that rhymes.

860
00:49:51,800 --> 00:49:54,239
Speaker 1: You've got any but you've been practicing a little rhyme

861
00:49:54,239 --> 00:49:56,279
Speaker 1: in the Mirror'll that'll get them?

862
00:49:56,360 --> 00:49:57,759
Speaker 3: She had two things at rhyme.

863
00:49:58,320 --> 00:50:01,719
Speaker 1: I'm like trying to talk to you and also rack

864
00:50:01,840 --> 00:50:03,920
Speaker 1: my brain to come up with like a little rhyme.

865
00:50:04,520 --> 00:50:06,560
Speaker 3: Like if you could have come up with a little rhyme,

866
00:50:06,600 --> 00:50:07,800
Speaker 3: it would double our listeners.

867
00:50:07,840 --> 00:50:11,680
Speaker 1: Okay, Lydia, give me a second. When Linda talks about

868
00:50:11,680 --> 00:50:13,480
Speaker 1: the tweets, there are very few deats.

869
00:50:13,800 --> 00:50:14,880
Speaker 3: Oh that's good.

870
00:50:16,440 --> 00:50:18,560
Speaker 1: That's the best I can do on short notice. If

871
00:50:18,560 --> 00:50:21,000
Speaker 1: anybody has a good rhyme out there, tell it. Tell

872
00:50:21,000 --> 00:50:24,840
Speaker 1: it to me. I'm going to like memorize it and

873
00:50:24,880 --> 00:50:26,920
Speaker 1: then drop it on the show as if I just

874
00:50:27,120 --> 00:50:29,000
Speaker 1: came up with it, and then I will not give

875
00:50:29,000 --> 00:50:32,520
Speaker 1: you credit. So that's help the homework. If that sounds

876
00:50:32,560 --> 00:50:35,640
Speaker 1: appealing to you, let me know. Okay, So back to Chara,

877
00:50:35,760 --> 00:50:42,759
Speaker 1: I mean elon so. On August fourth, Twitter put out

878
00:50:42,760 --> 00:50:45,840
Speaker 1: a statement saying, quote, the volume of people signing up

879
00:50:45,840 --> 00:50:49,399
Speaker 1: for revenue sharing has exceeded our expectations. We previously said

880
00:50:49,440 --> 00:50:51,520
Speaker 1: that payments would occur the week of July thirty. First,

881
00:50:51,960 --> 00:50:54,239
Speaker 1: we need a bit more time to review everything for

882
00:50:54,280 --> 00:50:56,800
Speaker 1: the next payout and hope to get all eligible accounts

883
00:50:56,840 --> 00:51:00,200
Speaker 1: paid as soon as possible. So basically, like the last time,

884
00:51:00,200 --> 00:51:02,960
Speaker 1: they were like within seventy two hours, it was like

885
00:51:03,000 --> 00:51:05,799
Speaker 1: a very specific time frame, and now they're like, look,

886
00:51:05,840 --> 00:51:08,000
Speaker 1: I ain't got it, So do you want me to

887
00:51:08,000 --> 00:51:09,680
Speaker 1: say you're time.

888
00:51:09,920 --> 00:51:11,759
Speaker 3: We're gonna pay you as soon as possible.

889
00:51:14,360 --> 00:51:17,240
Speaker 1: Listen, I am very familiar with the language of somebody

890
00:51:17,280 --> 00:51:20,040
Speaker 1: who like owes money but they don't have it, So

891
00:51:20,080 --> 00:51:23,520
Speaker 1: like I definitely recognize this, Like, oh, what he needs

892
00:51:23,560 --> 00:51:26,200
Speaker 1: to say is like come up with some sort of

893
00:51:26,200 --> 00:51:30,279
Speaker 1: convoluted story, convoluted but detailed story of like, well, so

894
00:51:30,360 --> 00:51:32,600
Speaker 1: I was going out to put the checks in the mail,

895
00:51:32,800 --> 00:51:34,800
Speaker 1: and I remember because it was raining and so I

896
00:51:34,840 --> 00:51:36,440
Speaker 1: had to get my coat. And when I went out

897
00:51:36,480 --> 00:51:38,440
Speaker 1: to get my coat, I realized, you got to come

898
00:51:38,520 --> 00:51:41,120
Speaker 1: up with some sort of like convoluted, detailed story. Don't

899
00:51:41,160 --> 00:51:44,480
Speaker 1: just be like mmm, it's taking and I love how

900
00:51:44,680 --> 00:51:49,000
Speaker 1: it's like because so many people were interested in this

901
00:51:49,160 --> 00:51:53,200
Speaker 1: like program, so many of you loved it and could

902
00:51:53,239 --> 00:51:55,600
Speaker 1: see that it was lucrative and worth your time. Now

903
00:51:55,680 --> 00:51:58,759
Speaker 1: because of all of that interest, you know what, Holy shit,

904
00:51:59,239 --> 00:52:02,560
Speaker 1: that's exactly See what Charay said about why her She

905
00:52:02,760 --> 00:52:05,439
Speaker 1: Buy Charret website didn't work. She was like, too many

906
00:52:05,480 --> 00:52:08,120
Speaker 1: people were interested in it, and it crashed the website.

907
00:52:08,160 --> 00:52:09,760
Speaker 1: So when you went to her website, when she launched

908
00:52:09,760 --> 00:52:12,239
Speaker 1: her line, her website didn't work. And everybody on the

909
00:52:12,239 --> 00:52:14,719
Speaker 1: show was like, you know, took you all this time

910
00:52:14,760 --> 00:52:16,719
Speaker 1: to launch this brand and your website doesn't work, And

911
00:52:16,760 --> 00:52:19,200
Speaker 1: she was like, oh well, there was so much interest

912
00:52:19,719 --> 00:52:24,279
Speaker 1: it crashed. It's actually a good thing. There's this I'm

913
00:52:24,320 --> 00:52:28,600
Speaker 1: gonna this is this comparison is apt. This comparison is apt.

914
00:52:30,040 --> 00:52:32,879
Speaker 3: X bite Elon Yes, like.

915
00:52:34,800 --> 00:52:40,719
Speaker 1: I would I see a charae Elon Musk collab in

916
00:52:40,800 --> 00:52:44,840
Speaker 1: the future. So if you are owed money from Elon Musk,

917
00:52:45,080 --> 00:52:47,279
Speaker 1: the check's not in the mail. Basically, you're not getting it.

918
00:52:47,360 --> 00:52:49,120
Speaker 1: Just like I said, I knew it. I knew he

919
00:52:49,160 --> 00:52:52,200
Speaker 1: wasn't gonna pay all. It sounds like he did pay

920
00:52:52,760 --> 00:52:55,920
Speaker 1: out like a select handful of right wing influencers, but

921
00:52:56,040 --> 00:52:58,560
Speaker 1: did not pay the vast majority of them. I think

922
00:52:58,560 --> 00:53:01,680
Speaker 1: that he's thinking that people will see these big numbers

923
00:53:01,680 --> 00:53:03,879
Speaker 1: and be like I'm gonna join, but there's the pot

924
00:53:03,920 --> 00:53:07,120
Speaker 1: of money is like empty. Just today this week they

925
00:53:07,120 --> 00:53:10,800
Speaker 1: were selling off not just old Twitter memorabilia with birds

926
00:53:10,840 --> 00:53:13,359
Speaker 1: on it, but also couches and stuff, so like not

927
00:53:13,440 --> 00:53:17,400
Speaker 1: just now off brand stuff, all their furniture. That is

928
00:53:17,440 --> 00:53:19,439
Speaker 1: not the behavior of somebody who has this big pot

929
00:53:19,440 --> 00:53:21,360
Speaker 1: of money to pay people out. Yeah. So if he

930
00:53:21,400 --> 00:53:24,560
Speaker 1: owes you money, I don't know, don't bank on it.

931
00:53:24,800 --> 00:53:26,400
Speaker 1: This is not the behavior of somebody who has the

932
00:53:26,440 --> 00:53:27,080
Speaker 1: money to pay.

933
00:53:27,160 --> 00:53:28,920
Speaker 3: Yeah. Their whole thing is that they like don't have

934
00:53:28,960 --> 00:53:32,520
Speaker 3: any money. They're leveraged up to their eyeballs. They're losing

935
00:53:32,520 --> 00:53:36,440
Speaker 3: all their advertisers and revenue. Yeah, paying out a bunch

936
00:53:36,480 --> 00:53:39,840
Speaker 3: of people doesn't really help that.

937
00:53:40,200 --> 00:53:44,960
Speaker 1: Yeah, well, I will say, at least Charet Whitfield, when

938
00:53:45,080 --> 00:53:48,200
Speaker 1: like push comes to shove, you can like back her

939
00:53:48,239 --> 00:53:51,760
Speaker 1: into a corner. She will pay. It takes some work,

940
00:53:52,080 --> 00:53:54,440
Speaker 1: but she'll pay eventually. Elon Musk comes on a thing

941
00:53:54,520 --> 00:53:57,000
Speaker 1: or two about about life from Surey Whitfield.

942
00:53:57,080 --> 00:53:59,040
Speaker 3: Yeah, I'm still waiting for those ex joggers.

943
00:54:00,719 --> 00:54:05,560
Speaker 1: Joggers, we're doing joggers. People who don't watch househongs are

944
00:54:05,560 --> 00:54:07,279
Speaker 1: like what the hell are you talking about? I also

945
00:54:07,320 --> 00:54:10,279
Speaker 1: have a little bit more to say, specifically about the

946
00:54:10,800 --> 00:54:14,480
Speaker 1: tory Lands trial for shooting Megna Stallion on how disinformation

947
00:54:14,680 --> 00:54:16,360
Speaker 1: played a role on that. Do you want to hear that?

948
00:54:16,400 --> 00:54:18,919
Speaker 1: You can find it on Patreon at patreon dot com,

949
00:54:18,960 --> 00:54:21,440
Speaker 1: slash tangoty, where you can get ad free content and

950
00:54:21,560 --> 00:54:24,600
Speaker 1: support the show. Mike, thank you so much for riunning

951
00:54:24,640 --> 00:54:27,440
Speaker 1: down these stories with me. As always, thank you for listening.

952
00:54:31,400 --> 00:54:33,520
Speaker 1: If you're looking for ways to support the show, check

953
00:54:33,520 --> 00:54:36,120
Speaker 1: out our March store at tenggodi dot com slash Store.

954
00:54:37,320 --> 00:54:39,360
Speaker 1: Got a story about an interesting thing in tech, or

955
00:54:39,440 --> 00:54:41,279
Speaker 1: just want to say hi, You can reach us at

956
00:54:41,320 --> 00:54:44,040
Speaker 1: Hello at tengody dot com. You can also find transcripts

957
00:54:44,040 --> 00:54:46,520
Speaker 1: for today's episode at tengody dot com. There Are No

958
00:54:46,560 --> 00:54:48,640
Speaker 1: Girls on the Internet was created by Me Bridget Time.

959
00:54:49,000 --> 00:54:52,200
Speaker 1: It's a production of iHeartRadio and Unboss Creative, edited by

960
00:54:52,239 --> 00:54:56,320
Speaker 1: Joey pat Jonathan Strickland as our executive producer. Tari Harrison

961
00:54:56,400 --> 00:54:59,160
Speaker 1: is our producer and sound engineer. Michael Almada is our

962
00:54:59,160 --> 00:55:02,239
Speaker 1: contributing producer. I'm your host, Bridget Todd. If you want

963
00:55:02,239 --> 00:55:03,839
Speaker 1: to help us grow, rate and review us.

964
00:55:03,719 --> 00:55:04,680
Speaker 2: On Apple podcasts.

965
00:55:05,440 --> 00:55:08,239
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

966
00:55:08,280 --> 00:55:14,080
Speaker 1: Apple Podcasts, or wherever you get your podcasts.