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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 brigitat and this is

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Speaker 1: there are No Girls on the Internet. I'm here as

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Speaker 1: my producer, Mike. Mike, how you doing today?

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Speaker 2: Doing pretty good? There's a lot of smoke in the air,

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Speaker 2: but other than that of do pretty good.

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Speaker 1: Well, let's see if you can clear out some of

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Speaker 1: that smoke with news stories that you may have missed

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Speaker 1: this week on the internet. So we have to start

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Speaker 1: with talking about the Supreme Court. By now, you've probably

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Speaker 1: seen that the Supreme Court ruled on affirmative action, ruling

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Speaker 1: that race can no longer play a part in college admissions. Notably,

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Speaker 1: the decision just applies to race. It does not applied

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Speaker 1: to things like gender or legacy status or donor status

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Speaker 1: being considered in college admissions. So I thought, what better

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Speaker 1: time than now to revisit some of the myths about

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Speaker 1: affirmative action and break down some of the online chatter

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Speaker 1: that I'm seeing and have seen about the issue. So first,

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Speaker 1: I think it's really easy to think that affirmative action

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Speaker 1: has only helped black folks like me, but that is

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Speaker 1: not the case. In fact, that is actually not even

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Speaker 1: a little bit true. According to the United States Labor Department,

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Speaker 1: the primary beneficiaries of affirmative action are white women, and

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Speaker 1: because the Supreme Court ruling only applies to race and

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Speaker 1: not gender, that means that white women will continue to benefit.

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Speaker 1: What's weird is that, despite being the biggest beneficiaries of

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Speaker 1: affirmative action, white women are also more likely to be

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Speaker 1: against affirmative action. This is according to a twenty fourteen

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Speaker 1: Cooperative Congressional Election study that found that nearly seventy percent

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Speaker 1: of the twenty thousand, six hundred and ninety four self

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Speaker 1: identified non Hispanic white women surveyed either somewhat or strongly

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Speaker 1: opposed affirmative action. So I've already seen a lot of

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Speaker 1: people online saying that the reason why this happened and

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Speaker 1: the reason why affirmative action was struck down was because

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Speaker 1: Asian American people were big mad about not getting into Harvard. Now,

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Speaker 1: I get how it's easy to think that, but it's

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Speaker 1: not exactly true. I would actually argue that Asian American

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Speaker 1: people were being exploited by one person who pretended to

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Speaker 1: actually care about them and their interests in order to

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Speaker 1: better serve his own agenda right like tail as old

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Speaker 1: as time, and it's also worth pointing out that it's

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Speaker 1: just messed up. I think to blame this on Asian

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Speaker 1: Americans as a group. In fact, Asian Americans are actually

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Speaker 1: more likely to be supportive of affirmative action. According to

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Speaker 1: NBC polling found that a higher share of Asian Americans

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Speaker 1: support affirmative action, with fifty three percent who have heard

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Speaker 1: of the policy saying that it's a good thing. Another

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Speaker 1: nineteen percent say it's a negative thing. A different twenty

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Speaker 1: twenty two survey, which polled registered Asian American voters, found

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Speaker 1: that sixty nine percent of them favored affirmative action programs

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Speaker 1: designed to help black people, women, and other minorities get

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Speaker 1: better access to higher education. So branding Asian Americans as

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Speaker 1: against affirmative action just is not correct. You know, we've

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Speaker 1: already living through a time where there is so much

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Speaker 1: anti Asian hate going on, and we need to be

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Speaker 1: Chris's so clear about this. This ruling is a symptom

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Speaker 1: of white supremacy full stop, and that means that it

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Speaker 1: is a victory for the same white supremacist who are

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Speaker 1: fueling that current wave of anti Asian hate. So When

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Speaker 1: people try to blame this ruling on Asian Americans, they

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Speaker 1: are falling into the trap of white supremacy that pits

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Speaker 1: marginalized groups against each other in service of white supremacy.

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Speaker 1: So how did we get here, Well, it all started

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Speaker 1: with conservative litigant Edward Blum. I say litigant because that's

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Speaker 1: what he is, right, Like, if you go to his

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Speaker 1: Wikipedia entry, that's how it describes him. He is a

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Speaker 1: professional lawsuit bringer. He connects potential plaintiffs with attorneys who

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Speaker 1: are willing to represent them in test cases, which he

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Speaker 1: then tries to use to set and establish legal presidents.

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Speaker 1: He's the founder and also the only member of the

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Speaker 1: Project on Fair Representation, which he founded in two thousand

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Speaker 1: and five, which focuses on voting, education, contracting, employment, racial quotas,

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Speaker 1: and racial reparations. So you can kind of think of

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Speaker 1: him as like a professional like he isn't even a lawyer.

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Speaker 1: He's just wealthy and well connected. He is actually responsible

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Speaker 1: for the gutting of the Voting Rights Act. This is

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Speaker 1: from the Guardian. Shelby County versus Holder, a case that

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Speaker 1: he sponsored in twenty thirteen led to the Supreme Court

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Speaker 1: to overturn a key provision of the nineteen sixty five

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Speaker 1: Voting Rights Act. Now, Blum told The Guardian that he

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Speaker 1: was worried from the fallout of that ruling, even though,

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Speaker 1: like it's not clear what he thought was going to happen,

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Speaker 1: But that ruling spurred conservative legislators in Texas, North Carolina

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Speaker 1: and other states to revive laws that the Justice Department

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Speaker 1: had previously blocked or were expected to block on the

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Speaker 1: grounds that there were vehicles for voter suppression. So, basically,

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Speaker 1: Blum has been trying to come for affirmative action since

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Speaker 1: twenty thirteen. Y'all might remember that back then he was

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Speaker 1: working with Abigail Fisher, who was this white woman with

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Speaker 1: red hair like. If you don't remember her name, you

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Speaker 1: probably remember this image of her standing on the Supreme

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Speaker 1: Court steps with Blum. She had this like red hair like.

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Speaker 1: You might remember that image in your mind. So who

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Speaker 1: is Abbagio Fresh? Well, Abigail Fisher did not get into

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Speaker 1: the University of Texas because her GPA was mid. Basically,

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Speaker 1: she sued the University of Texas at Austin in two

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Speaker 1: thousand and eight after it denied her admission. Interestingly, she

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Speaker 1: sued them when she was thirty so like several years

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Speaker 1: after this denial took place, her GPA was three five

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Speaker 1: nine as a senior, which put her just below the

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Speaker 1: cutoff of the state law requiring University of Texas to

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Speaker 1: accept students that graduate in the top ten percent of

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Speaker 1: her class. So she was not in the top ten

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Speaker 1: percent of her class. She was under the top ten

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Speaker 1: percent of her class, although she argued that her extra

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Speaker 1: curriculum activities, combined with her academic record, would have qualified

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Speaker 1: her if UT had not used race as a factor

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Speaker 1: in its holistic approach to selecting the remainder of its

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Speaker 1: twenty eighteen freshman class, in what she said was a

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Speaker 1: violation of the Fourteenth Amendments Equal Protection Clause. Now, it

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Speaker 1: didn't work with Abigail, And if folks remember, she actually

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Speaker 1: kind of was treated like a little bit of a joke. Mike,

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Speaker 1: do you remember this at all?

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Speaker 2: Well, I have to admit I don't, But like hearing

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Speaker 2: you describe it, it's pretty wild that she feels like

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Speaker 2: race and all of the historical baggage that comes with

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Speaker 2: that should not be considered for college admission, and yet

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Speaker 2: you know her extra curricular activities should and that they

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Speaker 2: somehow counted up to make up for her totally middling gpa,

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Speaker 2: and somehow she wants like the law to come in

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Speaker 2: on this side. I don't know, it's I actually don't

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Speaker 2: remember this story. I kind of wish I did, but

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Speaker 2: it sounds wild.

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Speaker 1: Yeah, she was, I have to say, like roundly mocked.

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Speaker 1: I think because her gpa, Like it's a fine gpa

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Speaker 1: three five nine, it's like nothing to sneeze at, but

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Speaker 1: it's not an it's fine, I have an automatic acceptance

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Speaker 1: to the school of your choice, Like you would still

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Speaker 1: have to work and be well rounded and all of

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Speaker 1: that to be accepted with a GPA like that. I

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Speaker 1: think the reason why she was sort of treated as

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Speaker 1: a joke is because she was just not a very

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Speaker 1: sympathetic character, right, Like, she had a pretty good, not

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Speaker 1: great GPA, and I think that she came off as

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Speaker 1: somebody who was entitled, Like, you know, I believe that

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Speaker 1: I'm good enough to get into this college, Like I

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Speaker 1: can't imagine that it was me that my GPA and

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Speaker 1: my extracurriculars weren't as good as I thought they were

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Speaker 1: in my mind, I have been wrong because I did

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Speaker 1: not get into.

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Speaker 2: Ut Yeah, I think entitled sounds about right based on

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Speaker 2: this story.

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Speaker 1: So Blum, when the Abigail thing didn't work, Blum stepped

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Speaker 1: back and regrouped, and he came back and decided to

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Speaker 1: make the face of the victims in scare quotes of

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Speaker 1: affirmative action Asian Americans. So this is why I say

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Speaker 1: it's not accurate to say that Asian Americans were angry

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Speaker 1: about not having gotten into their colleges of choice, and

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Speaker 1: that that is why affirmative action has been rolled back.

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Speaker 1: It is because this guy made a deliberate, specific choice

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Speaker 1: to make Asian American students the faith of the quote

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Speaker 1: victims of affirmative action. So Blum and his group brought

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Speaker 1: two cases that led to this decision, one on behalf

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Speaker 1: of Asian American students who claim that Harvard's admission policies

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Speaker 1: discriminated against them, and another for white and Asian students

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Speaker 1: denied admission by the University of North Carolina. So why

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Speaker 1: I bring this up is because it is a great

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Speaker 1: example of how effective it is to pit marginalized groups

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Speaker 1: against one another in service of white supremacy. Because I

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Speaker 1: believe that these Asian American students probably thought that Blum

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Speaker 1: had their interests, you know, at heart, and were doing

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Speaker 1: this for like genuinely doing this to serve their interests,

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Speaker 1: and they might have like genuinely had questions about the

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Speaker 1: policies at these universities or whatever. However, this ruling I

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Speaker 1: don't think is act going to help these Asian American

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Speaker 1: students because it basically, I think is going to help

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Speaker 1: white people. If legacies and donors and people who are

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Speaker 1: well connected and white women are still allowed to get

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Speaker 1: whatever leg up that university admissions policies have been giving them,

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Speaker 1: I don't see how that will actually end up helping

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Speaker 1: these Asian American students. And so I think that these

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Speaker 1: students have been misled by Blum and the in fact,

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Speaker 1: are having their interests exploited by somebody in service of

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Speaker 1: their larger agenda to roll back progress in this country,

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Speaker 1: which they have done time and time again. Like has

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Speaker 1: made no mistake about that being what they want to do.

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Speaker 2: Yeah, it seems pretty transparent that is what he's after here.

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Speaker 1: Yeah. And you know, just something else that I find

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Speaker 1: interesting about these conversations around affirmative action is just how

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Speaker 1: many people in the United States think that black people

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Speaker 1: one get automatic entrants to whatever college we want, and

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Speaker 1: two get to go to college for free. I've had

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Speaker 1: people tell me that's to my face. It is a

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Speaker 1: This might sound shocking or surprising, it is a commonly

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Speaker 1: held miss piece of misinformation that people actually genuinely believe.

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Speaker 1: And again, it really goes to show you how insidious

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Speaker 1: but also nonsensical racialized misinformation is because it doesn't even

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Speaker 1: really have to make sense if it feels true and

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Speaker 1: speaks to people's worst imaginings of marginalized people. Because think

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Speaker 1: about it, if black people can get into any college

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Speaker 1: we want automatic acceptance and also attend college for free

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Speaker 1: takes spots from white students. And then another piece of

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Speaker 1: like commonly held misinformation that it is mostly black people

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Speaker 1: who are like living off welfare or living off of

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Speaker 1: the system more than other groups. How do those two

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Speaker 1: things make sense together? Right? Like, how is it that

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Speaker 1: like black people can go to any college they want

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Speaker 1: for free, but also are the largest recipes and some

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Speaker 1: things like welfare and social programs. It doesn't make any sense.

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Speaker 2: No, it totally doesn't make sense.

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Speaker 1: And I think, like, I don't know, it's just a

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Speaker 1: it's just a tough time. I think when we think

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Speaker 1: about like where our country is. I can't help but

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Speaker 1: mention that we're talking about this almost a year to

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Speaker 1: the day after the role after Supreme the Supreme Court

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Speaker 1: rolled back row you know, book bands left and right. Like,

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Speaker 1: it does feel like we're in this place where the

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Speaker 1: very fabric of our country is being eroded and it

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Speaker 1: is difficult to know what to do. Like and people

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Speaker 1: who say, like, oh, you got to go out and vote.

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Speaker 1: The thing that gets me about that that narrative of

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Speaker 1: the like voting narrative, And obviously voting is pretty important,

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Speaker 1: but I think it was, like what the majority of

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Speaker 1: the justices who helped usher this decision in, we're a point,

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Speaker 1: we're put there by presidents who lost the popular vote.

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Speaker 1: So I don't think we can vote our way out

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Speaker 1: of That's like, that's not really how it works. And

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Speaker 1: so people being like, oh, the importance of voting, it

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Speaker 1: doesn't really seem like a good answer to the kind

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Speaker 1: of cosmic problem it feels like we're facing right now.

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Speaker 2: Yeah, I get that, Like voting is totally necessary and

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Speaker 2: it's completely insufficient. Yeah, I mean I could go on

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Speaker 2: about Supreme Court reform, which is also necessary and also insufficient, right,

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Speaker 2: like this is a cultural thing. You were just talking

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Speaker 2: about those irreconcilable, nonsensical beliefs that people have about affirmative

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Speaker 2: action and the like, Yeah, the ways in which black

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Speaker 2: people are like super privileged in our American society and

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Speaker 2: like get all this free stuff given to them, and

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Speaker 2: how like white people are excluded from this and that,

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Speaker 2: and it's obviously so bananas, right, Like that's just like

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Speaker 2: it's obviously not true.

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Speaker 1: I mean, I got my free car from the government.

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Speaker 1: I got I got my college was paid for. I

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Speaker 1: could go to Edny College that I wanted to for free,

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Speaker 1: and yet I still decided to attend a shitty state

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Speaker 1: school in North Carolina. Weird. I know when I could,

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Speaker 1: when I could, when I could have gone to Harvard,

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Speaker 1: I could just I don't have to apply. I can

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Speaker 1: just show up and be like, I'm here Harvard. I

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Speaker 1: just get to go for free. You know, we got

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Speaker 1: all this free stuff. You know how it goes. I

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Speaker 1: guess you don't cause you're white. You don't get it.

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Speaker 2: No, I don't get it. I'm shut out. I'm you know,

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Speaker 2: I'm completely shut out from all these things.

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Speaker 1: I Mean, everybody knows that. For too long it has

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Speaker 1: been the black woman who has had her boot on

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Speaker 1: the throat of white men like you, Mike.

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Speaker 2: This shit is so sad though, because like affirmative action

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Speaker 2: was so positive, right, Like it made colleges more diverse.

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Speaker 2: And it's interesting because it is a population level intervention

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Speaker 2: that is designed to address population harms that are have

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Speaker 2: been done to black people over generations due to a

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Speaker 2: legacy of slavery and then Jim Crow and whatever term

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Speaker 2: historians want to give to this era we're currently living through.

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Speaker 2: There are like deep systemic forces keeping black people down,

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Speaker 2: and affirmative action in college admissions is one way to

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Speaker 2: address those population level harms. And also it is like

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Speaker 2: a population level remedy to not just like provide justice

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Speaker 2: for those past harms, but also like fix the future

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Speaker 2: by making our society more equal and providing more paths

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Speaker 2: to opportunities so that the people who graduate from college

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Speaker 2: and go on to have more influence in society and

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Speaker 2: government and business and our communities are a more diverse

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Speaker 2: set of people, which just makes us all better, healthier,

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Speaker 2: more resilient.

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Speaker 1: Well, I think that's the point. I think that the

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Speaker 1: reason why people like Lum and the people who support

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Speaker 1: this rolling back. They don't want a world where people

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Speaker 1: who look like me have a chance to beat, to

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Speaker 1: even have a I wouldn't even say equal footing to

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Speaker 1: be in those positions. And I also think like they

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Speaker 1: for so long have been drumming up this fear about

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Speaker 1: sending your kids to college. Your colleges are going to

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Speaker 1: be hotbeds of like woke indoctrination or whatever. I think

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Speaker 1: that for them, rolling back affirmative action is another way

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Speaker 1: to be like, Oh, I can safely send my kids

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Speaker 1: to school. There's going to be less marginalized people there.

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Speaker 1: They will be less likely to be influenced by those people.

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Speaker 1: What they're actually doing is preventing their children from being

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Speaker 1: around different people, people who have different backgrounds, different perspectives.

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Speaker 1: That is what college is all about. And so it's

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Speaker 1: so funny how these are the same people who have

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Speaker 1: been able to call, you know, other people like, oh,

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Speaker 1: you're snowflakes. You're so afraid of what happens at college campuses.

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Speaker 1: You're not letting speakers come, blah blah blah, all of that,

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Speaker 1: When it's really them, they're the ones who are afraid,

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Speaker 1: are afraid. They're the ones who don't want their you know,

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Speaker 1: daughters to have to go to school with people who

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Speaker 1: are different. And I think that's really what it comes

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Speaker 1: down to. That's why these people are championing this. I

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Speaker 1: think it's exactly what you said, that they that they

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Speaker 1: that they want. It feels like payback for what little

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Speaker 1: progress black and brown people have made in this country,

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Speaker 1: and it is a way to kind of, hopefully from

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Speaker 1: their perspective, make it so that when their kids go

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Speaker 1: to college, they are not having to come home on

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Speaker 1: weekends and be like, Dad, are you a racist? Dad?

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Speaker 1: Are you like, do you have like shitty attitudes about

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Speaker 1: people of color?

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Speaker 2: Yeah? And I think it's important to also, you know,

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Speaker 2: to not think about this in a vacuum, but to

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Speaker 2: consider it in the context of what other changes and

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Speaker 2: policy debates are happening around higher education. I suspect a

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Speaker 2: lot of the people who are cheering this ruling are

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Speaker 2: the same people who are demanding, you know, budget cuts

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Speaker 2: to universities, demanding that curriculum be stripped of any sort

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Speaker 2: of discussion of you know, race or gender identity, and

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Speaker 2: also probably the same people who are you know, insisting

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Speaker 2: that what little resources are left over for higher education

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Speaker 2: all get funneled into you know stem or you know

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Speaker 2: engineering or technical skills, which are like great fields. I

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Speaker 2: love science, I love technology, I love engineers, I love math.

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Speaker 2: These are great things and people should definitely pursue them,

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Speaker 2: but they are not sufficient for a well rounded education.

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Speaker 2: They don't They alone do not train people how to

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Speaker 2: be good citizens who contribute to a good society on

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Speaker 2: their own. And I think the people who are advocating

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Speaker 2: for that really view college as just like a certificate

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Speaker 2: program that makes people eligible for such and such a job.

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Speaker 2: And in some ways, you know, I think those criticisms

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Speaker 2: are maybe kind of fair, but in the bigger ways,

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Speaker 2: I think they completely miss the mark of what a

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Speaker 2: college education can be to opening up people's minds to

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Speaker 2: this world that we all live in together and this

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Speaker 2: life that each individual student lives on their own, and

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Speaker 2: just you know, shutting people out, making it a more

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Speaker 2: narrow experience in terms of the people that aud and

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Speaker 2: gets to interact with, a more narrow experience in terms

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Speaker 2: of the ideas and subject matter that they get exposed to.

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Speaker 2: Just yeah, just really is limiting and narrow and sad. Yeah,

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Speaker 2: it's sad and also scary and reinforces a white supremacist

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Speaker 2: worldview of oppression.

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Speaker 1: Let's say a quick break at our back. So this

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Speaker 1: is not the only thing happening in the courts right now.

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Speaker 1: Let's talk about open ai versus the people. A new

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Speaker 1: class action lawsuit that's been filed against open ai, the

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Speaker 1: company run by Sam Altman that develops chat GPT, which

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Speaker 1: has kind of become like the face of AI. The

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Speaker 1: lawsuit alleges that open ai stole quote massive amounts of

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Speaker 1: personal data to train chat GPT. The lawsuit claims that

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Speaker 1: open ai has basically been mining the digital footprints of

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Speaker 1: you and me, regular people, whether you use AI tools

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Speaker 1: or not. Open ai crawls the Internet to collect huge

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Speaker 1: amounts of datas to train its programs to learn language,

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Speaker 1: including a lot from social media sites. The lawsuit claims

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Speaker 1: that open ai went far beyond simply scraping the public Internet,

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Speaker 1: and that the data access included quote private information and

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Speaker 1: private conversation, medical data, including information about children. Essentially every

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Speaker 1: piece of data exchanged on the Internet it could take

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Speaker 1: without notice to the owners or users of such data,

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Speaker 1: much less without anyone's permission end quote. So this amounted

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Speaker 1: to quote the negligent and otherwise illegal theft of personal

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Speaker 1: data of millions of Americans who do not even use

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Speaker 1: Ai tools, The lawsuit claims, so it's about everybody, whether

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Speaker 1: you use chat GBT or not. The suit also claims

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Speaker 1: that open ai stores and discloses users private information, including

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Speaker 1: the details they enter to create open Ai accounts, their

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Speaker 1: chatlog data, and social media information. And in case you're thinking, well,

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Speaker 1: I've never used chat, GPT or another open ai program,

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Speaker 1: the suit says that it also disclosed the data of

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Speaker 1: people who use sites that were integrated with chat SHEPT.

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Speaker 1: That includes sites like Snapchat, Stripe, Spotify, Microsoft Teams, and slack.

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Speaker 2: Now.

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Speaker 1: The lawsuit is calling for open ai to basically pause

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Speaker 1: until this is all dealt with, by issuing a temporary

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Speaker 1: freeze on commercial access to and commercial development of open

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Speaker 1: AI's products until the company has implemented more regulations and safeguards,

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Speaker 1: including allowing people to opt out of data collection and

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Speaker 1: preventing its products from surpassing human intelligence and harming others.

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Speaker 1: The lawsuit also seeks financial compensation for people whose data

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Speaker 1: was access to train bots.

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Speaker 2: Yeah there's a lot there, and I will admit that

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Speaker 2: I don't know the details behind that, claims in this lawsuit.

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Speaker 2: But the claims of the lawsuit are pretty intense, right,

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Speaker 2: like the idea that they didn't just scrape the open Internet,

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Speaker 2: but they essentially, and I'm paraphrasing here, if I understand correctly,

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Speaker 2: like looked into every nook and cranny of to find

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Speaker 2: any piece of private information that was not locked down,

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Speaker 2: whether it should have been or not, and then use that.

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Speaker 2: And that's that's not cool.

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Speaker 1: Yeah, the lawsuit makes it sound like it wasn't just

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Speaker 1: a once over. It was like a deep cavity search

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Speaker 1: of these Internet nook and crannies any information that was

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Speaker 1: out there. According to this lawsuit, it took to train

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Speaker 1: their models. And I think this lawsuit makes it pretty

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Speaker 1: clear that AI is developing way more quickly than we

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Speaker 1: have guardrails for, and clearly we need those guardrails. The

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Speaker 1: people who make AI are in such a rush to

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Speaker 1: skip over that part that now we're already in the

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Speaker 1: like convincing everyone that AI is the future and that

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Speaker 1: we all need to get on board part, But we

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Speaker 1: don't even necessarily know what it is we're getting on

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Speaker 1: board with. How will we be used, how will we

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Speaker 1: be exploited, will we be harmed? Who will make money

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Speaker 1: off of us. These are all questions that have not

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Speaker 1: really been answered, even by Sam Altman, the head of Chat,

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Speaker 1: GPT and open Ai. He has not answered these questions,

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Speaker 1: and so I do think it just shows that we

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Speaker 1: can't just move so quickly for technology that is so powerful.

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Speaker 1: I don't want to get to the part where I

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Speaker 1: just accept all of this as like the future without

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Speaker 1: answering some of these critical questions about my role in this,

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Speaker 1: even if I'm somebody who's never used an open ai

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Speaker 1: program a day in my life.

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Speaker 2: Yeah, this is a super interesting story and I'm really

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Speaker 2: interested to see where it goes and how it evolves.

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Speaker 2: I think a pretty interesting thing about it is that

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Speaker 2: it's a class action suit brought on the behalf of

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Speaker 2: a bunch of individuals, but really those individuals represent all

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Speaker 2: of us. And it's completely consistent with a lot of

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Speaker 2: stuff that Sam Altman has said. That they would just

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Speaker 2: take what data they could get right whether they had

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Speaker 2: permission to use it or not, that wasn't their concern.

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Speaker 2: They just wanted to get the data train these models

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Speaker 2: get as much data as they could, and it's an

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Speaker 2: interesting contrast with some other domains that have been using

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Speaker 2: AI video games. For example, I read a story earlier

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Speaker 2: this week about Valve, the company that owns Steam, and

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Speaker 2: how they had rejected a developer's game because it relied

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Speaker 2: on some AI and he couldn't prove that he owned

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Speaker 2: the rights to the underlying intellectual property that was used

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Speaker 2: to train the AI. And I think that's related here,

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Speaker 2: because in that story it's business entities who are very

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Speaker 2: prone to suing each other, especially over things like intellectual property.

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Speaker 2: And I think there is this sense the Internet and

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Speaker 2: the data that belongs to you and me, ordinary Internet

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Speaker 2: users who are not engaged in some sort of commercial activity,

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Speaker 2: but just like using the Internet, there's a sense that

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Speaker 2: that data is just like up for grabs. And this lawsuit,

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Speaker 2: if I understand it, correctly, says that that's not the case.

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Speaker 2: That actually we should have some expectation of privacy, regardless

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Speaker 2: of whether you're trying to build the world's greatest AI

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Speaker 2: tool or anything. And so I think that's an interesting

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Speaker 2: aspect of it too, that I hope gets affirmed.

435
00:25:35,640 --> 00:25:37,320
Speaker 1: Yeah, this is I mean, I could talk all day.

436
00:25:37,359 --> 00:25:40,480
Speaker 1: I find this so fascinating, But there is a precedent

437
00:25:40,600 --> 00:25:45,240
Speaker 1: online where when we're talking about regular people are information

438
00:25:45,400 --> 00:25:49,320
Speaker 1: the things that we put online when it can financially

439
00:25:49,480 --> 00:25:54,000
Speaker 1: benefit somebody else, then there tends to be this idea

440
00:25:54,040 --> 00:25:57,000
Speaker 1: of like, well, the Internet is just like ideas and

441
00:25:57,119 --> 00:26:00,360
Speaker 1: data and information out there and who knows who owns one,

442
00:26:00,480 --> 00:26:03,760
Speaker 1: and like whenever somebody else can benefit. But it's interesting

443
00:26:03,760 --> 00:26:07,040
Speaker 1: to me when it's like business entities. Of course they

444
00:26:07,080 --> 00:26:10,439
Speaker 1: need to protect their intellectual property and their data, and

445
00:26:10,440 --> 00:26:12,760
Speaker 1: so it's like, you know, I think, taking a step

446
00:26:12,800 --> 00:26:19,000
Speaker 1: back here, it's easy to create technological innovation when you steal,

447
00:26:19,280 --> 00:26:22,080
Speaker 1: when you just take things that don't belong to you. Like,

448
00:26:22,119 --> 00:26:24,760
Speaker 1: that's just what's going on here. When we talked about

449
00:26:24,760 --> 00:26:28,640
Speaker 1: it in our episode about aiart, when the Lensa selfies

450
00:26:28,640 --> 00:26:31,680
Speaker 1: were all over social media, half of those quote AI

451
00:26:32,119 --> 00:26:36,760
Speaker 1: generated selfies had watermarks on them. They weren't AI generated,

452
00:26:36,800 --> 00:26:40,199
Speaker 1: they were stolen. They were art that a human artist

453
00:26:40,280 --> 00:26:44,800
Speaker 1: made and put online, and that Lensas their AI art

454
00:26:45,160 --> 00:26:48,879
Speaker 1: selfie app just stole. So yeah, it's easy to rush

455
00:26:49,320 --> 00:26:52,679
Speaker 1: technological innovation when you just take what doesn't belong to you.

456
00:26:53,080 --> 00:26:56,119
Speaker 1: And I think that it's time for us as regular

457
00:26:56,240 --> 00:26:59,280
Speaker 1: people to say, yeah, some stuff belongs to me, My

458
00:26:59,440 --> 00:27:02,240
Speaker 1: data belongs to me, if you don't have my permission

459
00:27:02,280 --> 00:27:05,840
Speaker 1: to my you know, private information and my private data

460
00:27:05,840 --> 00:27:08,560
Speaker 1: that I put online, you can't just scrape bit for profit.

461
00:27:08,960 --> 00:27:12,640
Speaker 1: Unacceptable And I'm curious to see how this lawsuit shakes out.

462
00:27:12,960 --> 00:27:16,360
Speaker 2: Yeah, it's an interesting take you just articulated there. It's

463
00:27:16,359 --> 00:27:23,040
Speaker 2: like an anti colonialist approach to privacy and protection of

464
00:27:23,119 --> 00:27:28,399
Speaker 2: intellectual property because you know, it kind of feels like

465
00:27:28,440 --> 00:27:31,480
Speaker 2: there's parallels, right, like if you can just go and

466
00:27:32,000 --> 00:27:38,040
Speaker 2: take resources from another people and make them your own, Yeah,

467
00:27:38,040 --> 00:27:41,880
Speaker 2: that's going to fuel a lot of growth and wealth.

468
00:27:42,480 --> 00:27:47,679
Speaker 2: And I think that's something that we've seen happen a

469
00:27:47,680 --> 00:27:50,359
Speaker 2: lot of times throughout history. People love to take stuff

470
00:27:50,359 --> 00:27:54,159
Speaker 2: from other people, and you know that's also something that

471
00:27:54,320 --> 00:27:58,919
Speaker 2: is really facilitated by othering specific groups.

472
00:27:59,480 --> 00:28:02,679
Speaker 1: Oh that is a great setup to this whole story

473
00:28:02,680 --> 00:28:06,480
Speaker 1: with Canva. Okay, so last year the graphic design company Canva,

474
00:28:06,520 --> 00:28:09,119
Speaker 1: who I actually I actually love Canava. Use it almost

475
00:28:09,119 --> 00:28:11,199
Speaker 1: every day if you don't use Canva. It is a

476
00:28:11,240 --> 00:28:13,960
Speaker 1: tool that makes it easy to create kind of plug

477
00:28:13,960 --> 00:28:16,840
Speaker 1: and play drag and drop graphics. Many folks use it

478
00:28:16,880 --> 00:28:18,639
Speaker 1: for work because they don't have access to a graphic

479
00:28:18,640 --> 00:28:23,000
Speaker 1: designer so Canva last year announced its text to image app,

480
00:28:23,200 --> 00:28:26,240
Speaker 1: which uses AI to curate and design images. According to

481
00:28:26,280 --> 00:28:29,200
Speaker 1: their website, they say, with this magic new feature, you

482
00:28:29,240 --> 00:28:32,560
Speaker 1: can turn your imagination into reality by watching your words

483
00:28:32,560 --> 00:28:35,960
Speaker 1: transform into stunning one of the kind images. In the announcement,

484
00:28:36,000 --> 00:28:38,800
Speaker 1: Canvas says that they invested heavily in safety measures that

485
00:28:38,920 --> 00:28:41,520
Speaker 1: help the millions of people using our platform be a

486
00:28:41,520 --> 00:28:44,040
Speaker 1: good human and minimize the risk to our platform being

487
00:28:44,120 --> 00:28:47,440
Speaker 1: used to produce unsafe content for text to image. This

488
00:28:47,520 --> 00:28:50,640
Speaker 1: includes automated reviews of input prompts for terms that might

489
00:28:50,720 --> 00:28:54,120
Speaker 1: generate unsafe imagery and of output images for a range

490
00:28:54,120 --> 00:28:58,440
Speaker 1: of categories including adult content, hate and abuse. And apparently

491
00:28:59,280 --> 00:29:04,360
Speaker 1: these unsafe images include hairstyles of black women like me.

492
00:29:04,920 --> 00:29:08,760
Speaker 1: That's according to Adriel Parker, a diversity and inclusion professional

493
00:29:08,920 --> 00:29:11,680
Speaker 1: and the author of the Inclusive Leadership Journal, who posted

494
00:29:11,720 --> 00:29:14,240
Speaker 1: on LinkedIn that she was playing around with Canvas text

495
00:29:14,320 --> 00:29:16,880
Speaker 1: to Image app and prompted it to generate a picture

496
00:29:16,920 --> 00:29:18,800
Speaker 1: of a black woman with bant two knots, which, if

497
00:29:18,800 --> 00:29:21,440
Speaker 1: you don't know what that is, it's a black natural

498
00:29:21,480 --> 00:29:24,600
Speaker 1: hairstyle where you kind of like twist your hair up

499
00:29:24,600 --> 00:29:27,360
Speaker 1: into little, tiny, little buns all over your head. It's

500
00:29:27,400 --> 00:29:29,000
Speaker 1: a cute style. I can't really pull it off, but

501
00:29:29,040 --> 00:29:31,440
Speaker 1: it's a cute style. And when she did this, an

502
00:29:31,560 --> 00:29:35,440
Speaker 1: error appeared telling me that bantwo may result in unsafe

503
00:29:35,520 --> 00:29:39,680
Speaker 1: or offensive content. In response to her LinkedIn posts, Canva apologize,

504
00:29:39,720 --> 00:29:42,400
Speaker 1: saying yes, we fix this in text to image and

505
00:29:42,440 --> 00:29:45,480
Speaker 1: have raised the elements concerned with that team too. This

506
00:29:45,520 --> 00:29:47,800
Speaker 1: is from their trust and safety product lead at Canva,

507
00:29:47,840 --> 00:29:50,480
Speaker 1: who also said thank you for flagging it, Adrielle Parker.

508
00:29:50,920 --> 00:29:53,080
Speaker 1: These are actually a great pair of examples of the

509
00:29:53,120 --> 00:29:55,560
Speaker 1: balancing act that we have. On the one hand, if

510
00:29:55,600 --> 00:29:58,480
Speaker 1: the safety's over trigger, like in text to image, it

511
00:29:58,520 --> 00:30:01,360
Speaker 1: can result in perceptions like raise here. On the other

512
00:30:01,880 --> 00:30:04,840
Speaker 1: if the safeties don't trigger, we can end up showing

513
00:30:04,880 --> 00:30:08,400
Speaker 1: offensive results like in the elemental search that you've also highlighted.

514
00:30:08,800 --> 00:30:12,080
Speaker 1: He continued, of course, we do strive for simply not

515
00:30:12,120 --> 00:30:15,440
Speaker 1: having offensive representations. That is not easy to do at scale,

516
00:30:15,440 --> 00:30:17,720
Speaker 1: and feedback like yours is crucial to helping us find

517
00:30:17,760 --> 00:30:21,200
Speaker 1: things that slip through the gaps. But Parker wasn't having

518
00:30:21,280 --> 00:30:24,640
Speaker 1: any of it right. Parker said, honestly, doubling down on

519
00:30:24,680 --> 00:30:26,440
Speaker 1: a message and implying that on an image of black

520
00:30:26,440 --> 00:30:29,320
Speaker 1: women with natural hairstyles like banto knots is potentially unsafe

521
00:30:29,320 --> 00:30:32,840
Speaker 1: for inappropriate content for your community, is not it? At

522
00:30:32,840 --> 00:30:35,520
Speaker 1: the very least craft and more thoughtful and intentional can

523
00:30:35,600 --> 00:30:38,560
Speaker 1: Responts again, please do better. There are plenty of folks

524
00:30:38,640 --> 00:30:41,680
Speaker 1: out there like myself who are already and willing to support.

525
00:30:42,440 --> 00:30:46,000
Speaker 1: So yeah, I mean I again. I love Canva and

526
00:30:46,040 --> 00:30:48,560
Speaker 1: I have been a big champion of Canvas Story, which

527
00:30:48,600 --> 00:30:51,280
Speaker 1: is like very inspiring. Was founded by a woman who

528
00:30:51,320 --> 00:30:54,440
Speaker 1: got lots of nose for funding until finally getting as

529
00:30:54,440 --> 00:30:57,360
Speaker 1: to the rest is history. But I do think this

530
00:30:57,480 --> 00:31:01,240
Speaker 1: kind of goes to show that technology like AI really

531
00:31:01,360 --> 00:31:03,800
Speaker 1: is not neutral. It's easy to think that AI is

532
00:31:03,880 --> 00:31:06,400
Speaker 1: like a computer brain. Look a a sci fi movie

533
00:31:06,400 --> 00:31:09,160
Speaker 1: that is just like spitting out computer stuff that we

534
00:31:09,160 --> 00:31:11,440
Speaker 1: don't understand. That is not coming from us. That is

535
00:31:11,480 --> 00:31:15,440
Speaker 1: not true. It is being trained and programmed by humans.

536
00:31:15,720 --> 00:31:19,080
Speaker 1: And as humans, we all have biases and baggage, and

537
00:31:19,120 --> 00:31:23,600
Speaker 1: if you're not careful, those things can just be recreated

538
00:31:23,840 --> 00:31:27,840
Speaker 1: by that technology. And I would say having those biases

539
00:31:28,080 --> 00:31:31,800
Speaker 1: be even more entrenched in our society because the technology

540
00:31:31,840 --> 00:31:34,680
Speaker 1: is so powerful We've talked a bit about this off Mike.

541
00:31:35,520 --> 00:31:39,360
Speaker 1: You have some like very interesting questions about what's going

542
00:31:39,360 --> 00:31:41,280
Speaker 1: on here, right, Mike, I do.

543
00:31:41,520 --> 00:31:45,040
Speaker 2: I am so curious. My primary question is this, and

544
00:31:45,080 --> 00:31:47,320
Speaker 2: I would love for somebody from Canva to come on

545
00:31:47,360 --> 00:31:49,400
Speaker 2: the show and answer it for us.

546
00:31:49,440 --> 00:31:52,480
Speaker 1: Oh my god, as a Canva devote hey, I would

547
00:31:52,680 --> 00:31:53,240
Speaker 1: welcome that.

548
00:31:53,960 --> 00:31:57,160
Speaker 2: Yeah. So, like my big question is how did the

549
00:31:57,240 --> 00:32:01,320
Speaker 2: word band two end up on the disoey right? And Like,

550
00:32:01,400 --> 00:32:05,560
Speaker 2: I've been thinking about this a lot, and you know,

551
00:32:05,600 --> 00:32:08,040
Speaker 2: maybe it's naive of me, but I really want to

552
00:32:08,080 --> 00:32:10,400
Speaker 2: give them the benefit of the doubt here that you know,

553
00:32:11,040 --> 00:32:14,600
Speaker 2: there was no malice, that Canva was really just trying

554
00:32:14,640 --> 00:32:22,080
Speaker 2: to like prevent their platform from producing offensive images, and

555
00:32:22,120 --> 00:32:23,920
Speaker 2: they messed it up, Like they said, they got this

556
00:32:24,000 --> 00:32:27,080
Speaker 2: balancing act wrong. It's pretty rare that I'm willing to

557
00:32:27,080 --> 00:32:29,600
Speaker 2: give tech companies the benefit of the doubt, but I'm

558
00:32:29,640 --> 00:32:32,360
Speaker 2: willing to do that here, maybe just because for the

559
00:32:32,360 --> 00:32:35,200
Speaker 2: sake of argument. It makes things more interesting. It's like,

560
00:32:35,440 --> 00:32:38,240
Speaker 2: how did the word band tu end up on that list?

561
00:32:38,280 --> 00:32:40,880
Speaker 2: It's it's a name for hashtyle, it's a name for

562
00:32:40,960 --> 00:32:44,920
Speaker 2: a people. It is not a slur and it. You know,

563
00:32:44,960 --> 00:32:48,240
Speaker 2: it really made me think that at the end of

564
00:32:48,280 --> 00:32:53,080
Speaker 2: the day, you can have like the fanciest, most sophisticated

565
00:32:53,240 --> 00:32:58,600
Speaker 2: AI platform available, but between the outputs of that platform

566
00:32:58,720 --> 00:33:02,200
Speaker 2: and the end users, there is a list of words

567
00:33:02,600 --> 00:33:05,440
Speaker 2: that what are more humans have put together and been

568
00:33:05,480 --> 00:33:07,840
Speaker 2: like yep, that one's not allowed, that one's not allowed.

569
00:33:08,280 --> 00:33:10,240
Speaker 2: Like is that how it happened? How did this happen?

570
00:33:10,480 --> 00:33:12,239
Speaker 2: That's one of my questions, how did this happen? How

571
00:33:12,240 --> 00:33:15,000
Speaker 2: did this word get on that list? And then the

572
00:33:15,040 --> 00:33:19,520
Speaker 2: other question is less direct, but and you know, you

573
00:33:19,560 --> 00:33:22,680
Speaker 2: and I talked about this a little bit of like

574
00:33:24,880 --> 00:33:32,960
Speaker 2: where where is the line where trying to police an

575
00:33:33,000 --> 00:33:38,440
Speaker 2: algorithm so that it never produces anything offensive crosses over

576
00:33:38,560 --> 00:33:42,640
Speaker 2: into territory where it starts to other people who are

577
00:33:42,680 --> 00:33:45,680
Speaker 2: just trying to use it and like you said, reinforce

578
00:33:45,880 --> 00:33:54,880
Speaker 2: those entrenched you know, racism, sexisms, all the isms that

579
00:33:56,440 --> 00:34:01,440
Speaker 2: we try so hard on this show to fight against.

580
00:34:01,840 --> 00:34:06,280
Speaker 2: Where is that line? It's a hard question, it's a.

581
00:34:06,240 --> 00:34:08,960
Speaker 1: Good question, and I would say, like, I don't know

582
00:34:09,000 --> 00:34:11,200
Speaker 1: if this is the answer, and it might sound simplistic,

583
00:34:11,239 --> 00:34:13,960
Speaker 1: but this is why you need to make sure from

584
00:34:14,000 --> 00:34:17,279
Speaker 1: the start you have people in the room who are

585
00:34:17,480 --> 00:34:22,640
Speaker 1: training and designing and building this technology, who look like

586
00:34:23,080 --> 00:34:25,680
Speaker 1: and can share the background of the people who are

587
00:34:25,680 --> 00:34:28,960
Speaker 1: going to be using it, because I mean, again, I

588
00:34:29,000 --> 00:34:32,040
Speaker 1: don't know, but I can imagine someone being like, ooh, bantu,

589
00:34:32,560 --> 00:34:34,799
Speaker 1: I don't really know if that's an offensive term or not.

590
00:34:35,160 --> 00:34:37,759
Speaker 1: We'll just add it to the to the problematic terms list.

591
00:34:37,840 --> 00:34:42,920
Speaker 1: And that is so deeply marginalizing to just sort of say, like,

592
00:34:43,239 --> 00:34:45,440
Speaker 1: even if it's done with good intentions, to just sort

593
00:34:45,440 --> 00:34:47,120
Speaker 1: of say, like, well, just sides up the issue by

594
00:34:47,120 --> 00:34:51,839
Speaker 1: saying any word that is indicative of blackness or rape,

595
00:34:51,920 --> 00:34:55,200
Speaker 1: like race, we're just going to add that to the list.

596
00:34:55,320 --> 00:34:57,799
Speaker 1: Interestingly enough, I have heard in our episode that we

597
00:34:57,880 --> 00:35:00,839
Speaker 1: did with Nandaine Jammy from check my Adds, the ad

598
00:35:00,840 --> 00:35:04,640
Speaker 1: tech watchdog organization. She's talked about how brands who don't

599
00:35:04,640 --> 00:35:08,000
Speaker 1: want their advertising or their images next to things that

600
00:35:08,040 --> 00:35:10,920
Speaker 1: are offensive will just be like, Okay, anything to do

601
00:35:11,000 --> 00:35:15,560
Speaker 1: with blackness, gender, sexuality, and ething like that, we'll just

602
00:35:15,840 --> 00:35:18,799
Speaker 1: not include that. And so they're doing it because they

603
00:35:18,840 --> 00:35:21,319
Speaker 1: don't want to have to figure out like is this

604
00:35:21,360 --> 00:35:23,440
Speaker 1: going to be brand safe? Is this not? But in

605
00:35:23,560 --> 00:35:27,840
Speaker 1: doing so, they are just further marginalizing communities and identities

606
00:35:27,880 --> 00:35:30,480
Speaker 1: who are already marginalized. And I guess yeah, like this

607
00:35:30,560 --> 00:35:33,799
Speaker 1: is why it's important to have inclusive teams. Like it's

608
00:35:33,840 --> 00:35:36,360
Speaker 1: not just because it's the right thing to do or

609
00:35:36,360 --> 00:35:38,960
Speaker 1: the nice thing to do, which it is, it's because

610
00:35:39,120 --> 00:35:43,680
Speaker 1: that is how we can make decisions that are not marginalizing,

611
00:35:44,000 --> 00:35:46,440
Speaker 1: even decisions that are made with like good intentions of

612
00:35:46,480 --> 00:35:48,800
Speaker 1: not wanting to offend or not wanting to have offensive

613
00:35:48,800 --> 00:35:50,120
Speaker 1: things on your on your platform.

614
00:35:50,800 --> 00:35:53,799
Speaker 2: Hell yeah, you know, can't be neutral on a moving train.

615
00:35:54,200 --> 00:36:12,000
Speaker 1: Nope, you cannot. More. After a quick break, let's get

616
00:36:12,080 --> 00:36:16,319
Speaker 1: right back into it. So let's quickly talk about this

617
00:36:16,400 --> 00:36:20,040
Speaker 1: kind of interesting new study. So you might associate falling

618
00:36:20,080 --> 00:36:23,560
Speaker 1: for fake news with your boomer parents, but younger folks

619
00:36:23,640 --> 00:36:27,120
Speaker 1: might actually be the most susceptible to misinformation online. This

620
00:36:27,160 --> 00:36:29,759
Speaker 1: is according to a new test developed by some psychologists

621
00:36:29,760 --> 00:36:32,600
Speaker 1: at the University of Cambridge, which is the first validated

622
00:36:32,880 --> 00:36:36,560
Speaker 1: misinformation susceptibility test that is meant to give an indication

623
00:36:36,640 --> 00:36:39,560
Speaker 1: of how vulnerable a person is to being duped by

624
00:36:39,640 --> 00:36:43,200
Speaker 1: fake news online. So they paired examples of real news

625
00:36:43,239 --> 00:36:46,359
Speaker 1: from real news outlets like Pew and Reuters, and then

626
00:36:46,400 --> 00:36:51,080
Speaker 1: they made confusingly credible headlines similar to the misinformation encountered

627
00:36:51,120 --> 00:36:55,000
Speaker 1: in the wild, in an unbiased way using chatchbt hope

628
00:36:55,040 --> 00:36:59,160
Speaker 1: their data was safe when they were using it. It

629
00:36:59,160 --> 00:37:02,320
Speaker 1: went out there. Okay, so here's what the test found.

630
00:37:02,560 --> 00:37:05,600
Speaker 1: The good news is that we're actually not doing two

631
00:37:05,680 --> 00:37:11,280
Speaker 1: too badly. On average, adult US citizens correctly classified two thirds,

632
00:37:11,360 --> 00:37:13,520
Speaker 1: or sixty five percent of headlines that they were shown

633
00:37:13,760 --> 00:37:16,359
Speaker 1: as either real or faked. That's actually pretty good news.

634
00:37:16,400 --> 00:37:18,000
Speaker 1: I'm surprised that it was that good.

635
00:37:18,400 --> 00:37:24,080
Speaker 2: Yeah, it is nice to highlight the good news. Yeah,

636
00:37:24,760 --> 00:37:27,160
Speaker 2: maybe people are the dumb dums we think they are.

637
00:37:27,760 --> 00:37:30,920
Speaker 1: Maybe they're not. They did find that younger adults are

638
00:37:30,960 --> 00:37:34,239
Speaker 1: worse than older adults and identifying false headlines, and that

639
00:37:34,320 --> 00:37:38,040
Speaker 1: the more time someone spent online recreationally, the less likely

640
00:37:38,080 --> 00:37:41,160
Speaker 1: they were to be able to fell real news from misinformation.

641
00:37:41,880 --> 00:37:45,320
Speaker 1: Some thirty percent of those spending zero to two recreation

642
00:37:45,440 --> 00:37:48,239
Speaker 1: hours online each day got a high score, compared to

643
00:37:48,280 --> 00:37:52,160
Speaker 1: just fifteen percent of those spending nine or more hours online.

644
00:37:52,360 --> 00:37:54,520
Speaker 2: So that's super interesting. We do need to keep in

645
00:37:54,600 --> 00:37:56,960
Speaker 2: mind that this was a cross sectional study, so we

646
00:37:57,000 --> 00:37:59,560
Speaker 2: can't say what does that mean, So it means that's

647
00:37:59,560 --> 00:38:02,360
Speaker 2: a great question. Bridget across sectional study is a study.

648
00:38:02,480 --> 00:38:07,640
Speaker 2: It was conducted at a single moment in time across

649
00:38:07,760 --> 00:38:11,240
Speaker 2: a group of people. Right, so I just found associations

650
00:38:11,280 --> 00:38:14,520
Speaker 2: the result you just described there. You know, they split

651
00:38:14,560 --> 00:38:17,360
Speaker 2: people into two groups of people who spent zero to

652
00:38:17,440 --> 00:38:21,120
Speaker 2: two hours online each day versus a group who spent

653
00:38:21,239 --> 00:38:23,560
Speaker 2: nine or more hours online eachd Right, we've got these

654
00:38:23,600 --> 00:38:26,520
Speaker 2: two extreme groups. They're extreme in the sense that they're

655
00:38:26,640 --> 00:38:30,920
Speaker 2: very different. And they found that those in the you know,

656
00:38:31,239 --> 00:38:34,160
Speaker 2: the people who only spent two hours online each day,

657
00:38:34,600 --> 00:38:37,239
Speaker 2: thirty percent got a high score, meaning that they were

658
00:38:37,280 --> 00:38:41,600
Speaker 2: pretty good at telling apart misinformation from real information, and

659
00:38:41,640 --> 00:38:44,560
Speaker 2: the people who spent nine or more hours online got

660
00:38:44,560 --> 00:38:50,280
Speaker 2: a low score. You don't only And so that's interesting.

661
00:38:51,360 --> 00:38:54,360
Speaker 2: And so one might want to jump to the conclusion that, like, okay,

662
00:38:54,400 --> 00:38:59,879
Speaker 2: spending time online on social media makes one worse at

663
00:39:00,719 --> 00:39:05,000
Speaker 2: telling apart the difference between truth and falsehood. But that's

664
00:39:05,080 --> 00:39:09,400
Speaker 2: not a conclusion that this data supports. Right, It's just

665
00:39:09,440 --> 00:39:12,400
Speaker 2: as likely, based on these data from this cross sectional

666
00:39:12,400 --> 00:39:15,040
Speaker 2: study that was conducted a single moment in time, that

667
00:39:15,239 --> 00:39:20,279
Speaker 2: maybe those people spend more time online because they're more

668
00:39:20,320 --> 00:39:25,480
Speaker 2: susceptible to misinformation, and all the misinformation they're encountering out

669
00:39:25,520 --> 00:39:29,239
Speaker 2: there is more appealing to them, or more enraging or

670
00:39:29,280 --> 00:39:31,960
Speaker 2: emotionally a baka to them or something like that. So

671
00:39:32,000 --> 00:39:37,319
Speaker 2: this study can't say that spending time online causally makes

672
00:39:37,360 --> 00:39:41,920
Speaker 2: anybody better or worse at telling apart misinformation from truth.

673
00:39:42,440 --> 00:39:45,319
Speaker 2: What this study does tell us is that people who

674
00:39:45,640 --> 00:39:48,240
Speaker 2: spend a lot of time online, those are the people

675
00:39:48,280 --> 00:39:52,160
Speaker 2: that have the biggest problem telling apart truth.

676
00:39:52,239 --> 00:39:55,160
Speaker 1: From Mike, who died and made you the expert on

677
00:39:55,239 --> 00:39:57,680
Speaker 1: studies like this and how they're done.

678
00:39:57,600 --> 00:40:01,200
Speaker 2: I mean, nobody died. I'm not the expert. I do

679
00:40:01,320 --> 00:40:04,520
Speaker 2: have a PhD in psychology. This is the kind of

680
00:40:04,520 --> 00:40:09,279
Speaker 2: thing that I do, you know. I Experimental design is

681
00:40:09,680 --> 00:40:13,680
Speaker 2: something that I get paid to do. So I'm not

682
00:40:13,880 --> 00:40:18,640
Speaker 2: the expert. But I think most experts would agree that

683
00:40:19,160 --> 00:40:22,759
Speaker 2: one cannot infer causality from a cross sectional design like this,

684
00:40:22,840 --> 00:40:24,480
Speaker 2: which is fine. We can still learn a lot of

685
00:40:24,560 --> 00:40:26,600
Speaker 2: valuable things about these associations.

686
00:40:27,000 --> 00:40:30,160
Speaker 1: Okay, so maybe Unsurprisingly, people who get their news mostly

687
00:40:30,160 --> 00:40:32,799
Speaker 1: from social media scored worse than people who get their

688
00:40:32,840 --> 00:40:37,200
Speaker 1: news from legacy newers. Organizations like MPR or the Associated Press.

689
00:40:37,200 --> 00:40:40,200
Speaker 2: And again we can't make any inferences about the direction

690
00:40:40,239 --> 00:40:43,440
Speaker 2: of causality there, but it is a very interesting association.

691
00:40:43,880 --> 00:40:47,959
Speaker 1: And lastly, there was a party split. Democrats performed better

692
00:40:48,000 --> 00:40:50,480
Speaker 1: than Republicans on this test, with thirty three percent of

693
00:40:50,520 --> 00:40:54,400
Speaker 1: Democrats achieving high scores compared to as fourteen percent of Republicans. However,

694
00:40:54,880 --> 00:40:57,719
Speaker 1: almost a quarter of both parties as followers were in

695
00:40:57,760 --> 00:40:59,440
Speaker 1: the low scoring bracket.

696
00:41:00,360 --> 00:41:02,520
Speaker 2: One thing I really liked about the method that they

697
00:41:02,640 --> 00:41:05,840
Speaker 2: used for this test is that the fake story stories

698
00:41:06,040 --> 00:41:09,520
Speaker 2: were truly fake. They were literally made up by chat GPT,

699
00:41:10,000 --> 00:41:13,440
Speaker 2: so there's zero room for anybody to whine about research

700
00:41:13,560 --> 00:41:15,479
Speaker 2: or bias or anything like that.

701
00:41:15,760 --> 00:41:18,439
Speaker 1: So I think it's kind of interesting to see these

702
00:41:18,560 --> 00:41:22,200
Speaker 1: commonly held believes about misinformation and who was following for

703
00:41:22,239 --> 00:41:24,799
Speaker 1: it be challenged, right, Like, it's easy to think that

704
00:41:24,840 --> 00:41:27,520
Speaker 1: young people who grew up online as digital natives are

705
00:41:27,560 --> 00:41:30,160
Speaker 1: like too savvy to fall for fake news. This is

706
00:41:30,160 --> 00:41:32,319
Speaker 1: a good reminder that can happen to the best of us,

707
00:41:32,680 --> 00:41:35,760
Speaker 1: even me. I fall for it, Mike, Have you fallen

708
00:41:35,800 --> 00:41:36,280
Speaker 1: for it.

709
00:41:36,760 --> 00:41:40,719
Speaker 2: For fake news? Yeah? I do? You know you're you're

710
00:41:40,719 --> 00:41:43,560
Speaker 2: in It's nice to see her are commonly held attitudes challenged,

711
00:41:43,719 --> 00:41:49,680
Speaker 2: and it's nice to see some data affirming the fact

712
00:41:49,719 --> 00:41:51,680
Speaker 2: that there is wisdom in age. That's kind of a

713
00:41:51,760 --> 00:41:55,160
Speaker 2: nice thing to take away. But I'm just kind of

714
00:41:55,160 --> 00:41:57,359
Speaker 2: like stalling for time. Have I fallen for fake news? Yeah?

715
00:41:57,400 --> 00:42:01,280
Speaker 2: Of course. Like earlier this week, somebody who I really

716
00:42:01,440 --> 00:42:03,400
Speaker 2: like and respect and think of as a very like

717
00:42:03,480 --> 00:42:10,759
Speaker 2: smart person sent me a post saying that Twitter was

718
00:42:10,800 --> 00:42:16,200
Speaker 2: suspending the accounts of people who blocked too many other

719
00:42:16,280 --> 00:42:19,520
Speaker 2: accounts and particularly other advertisers, And I was like, oh

720
00:42:19,560 --> 00:42:22,960
Speaker 2: my god, this is what Elon's done now. And I

721
00:42:23,040 --> 00:42:25,440
Speaker 2: was like ready to take it to the bank and

722
00:42:25,560 --> 00:42:28,279
Speaker 2: file it away and talk about it on this news.

723
00:42:28,520 --> 00:42:31,640
Speaker 1: Take it to the bank, well, you know the pod

724
00:42:32,719 --> 00:42:35,439
Speaker 1: the bank where we put are like the document where

725
00:42:35,440 --> 00:42:38,160
Speaker 1: we like our like our news idea round up bank,

726
00:42:38,480 --> 00:42:40,440
Speaker 1: not like the bank where the money is stored.

727
00:42:41,160 --> 00:42:44,719
Speaker 2: Yeah, right, the news idea round up bank. Yeah, not

728
00:42:44,840 --> 00:42:48,080
Speaker 2: the money bank. No, I don't. I don't take anything there.

729
00:42:48,440 --> 00:42:54,960
Speaker 2: I make contact. But I was like, all good to

730
00:42:55,000 --> 00:42:56,799
Speaker 2: go on it and I'll set And then I was like, well,

731
00:42:56,880 --> 00:42:59,160
Speaker 2: let me just look into this and like find some

732
00:42:59,360 --> 00:43:03,360
Speaker 2: evidence that supports this thing that this guy said, and

733
00:43:04,480 --> 00:43:07,520
Speaker 2: it just wasn't there. There was just zero evidence to

734
00:43:07,560 --> 00:43:10,640
Speaker 2: support it. There were like a couple people who said

735
00:43:10,800 --> 00:43:13,200
Speaker 2: that they thought maybe there was a rumor that it

736
00:43:13,280 --> 00:43:17,440
Speaker 2: was happening, and maybe it is happening, right Like, I

737
00:43:17,480 --> 00:43:23,040
Speaker 2: would not put it past Twitter, but I was not

738
00:43:23,080 --> 00:43:25,840
Speaker 2: able to find any evidence to suggest that that was happening.

739
00:43:26,000 --> 00:43:29,520
Speaker 2: And if listeners are aware of evidence, I'd love to

740
00:43:29,560 --> 00:43:31,359
Speaker 2: see it, you know, we can talk about it next week.

741
00:43:31,800 --> 00:43:35,319
Speaker 2: But I was totally taken in with it because it

742
00:43:35,520 --> 00:43:41,080
Speaker 2: really fit with my preconceived ideas of what kind of

743
00:43:41,640 --> 00:43:45,759
Speaker 2: nonsense shenanigans Elon Musk's Twitter would get up to. It

744
00:43:45,760 --> 00:43:49,520
Speaker 2: seemed like a totally believable thing. I wanted to believe

745
00:43:49,560 --> 00:43:52,280
Speaker 2: it because it was just one more piece of evidence

746
00:43:52,320 --> 00:43:56,799
Speaker 2: about what in nefarious people they were or he is.

747
00:43:59,080 --> 00:44:02,560
Speaker 2: But you know, fortunately there was a couple of days

748
00:44:02,560 --> 00:44:08,720
Speaker 2: between then and now, and uh, cooler heads prevailed and

749
00:44:08,920 --> 00:44:11,160
Speaker 2: now we're just talking about it as a faith thing.

750
00:44:11,880 --> 00:44:15,080
Speaker 1: Yeah. I saw that story, and I also was willing

751
00:44:15,200 --> 00:44:17,759
Speaker 1: to believe that that's something that Elon Musk would do.

752
00:44:18,040 --> 00:44:20,279
Speaker 2: Yeah, And to be clear, I still believe it is

753
00:44:20,320 --> 00:44:23,440
Speaker 2: something he would do. I just am not confident that

754
00:44:23,480 --> 00:44:24,440
Speaker 2: it is something he has.

755
00:44:24,440 --> 00:44:27,799
Speaker 1: Oh, anything that anyone would say is something I would

756
00:44:27,800 --> 00:44:29,520
Speaker 1: be like, yep, that's h would do that. I believe

757
00:44:29,520 --> 00:44:31,480
Speaker 1: he would do that. There's not there's not much out

758
00:44:31,520 --> 00:44:34,600
Speaker 1: there that I would not believe that he would do.

759
00:44:34,800 --> 00:44:37,760
Speaker 1: Just to be really clear, but something about the story

760
00:44:37,840 --> 00:44:43,840
Speaker 1: also seemed not legit to me, so I didn't share it.

761
00:44:43,920 --> 00:44:45,399
Speaker 1: I didn't like, I don't I don't know why something

762
00:44:45,440 --> 00:44:47,719
Speaker 1: about it. I was like, I don't know, like I

763
00:44:47,760 --> 00:44:49,839
Speaker 1: don't know. Uh, to be clear, I don't know if

764
00:44:49,840 --> 00:44:52,239
Speaker 1: it's if it, I cannot I can either confirm or

765
00:44:52,239 --> 00:44:53,759
Speaker 1: deny that it's happening. I have not looked into it.

766
00:44:53,800 --> 00:44:56,520
Speaker 1: But something about the story didn't seem correct to me,

767
00:44:56,600 --> 00:44:58,680
Speaker 1: and I saw it and did not engage with it.

768
00:44:58,920 --> 00:45:01,520
Speaker 1: I had a I myself, i've shared some bake news recently.

769
00:45:01,760 --> 00:45:04,160
Speaker 1: It was a and I unshared it and was like, oh,

770
00:45:04,160 --> 00:45:06,920
Speaker 1: this was not true. So it was a video that

771
00:45:07,040 --> 00:45:11,880
Speaker 1: purported to show South African firefighters who were all dancing

772
00:45:11,920 --> 00:45:15,800
Speaker 1: in an airport en route to being sent to Canada

773
00:45:15,840 --> 00:45:18,320
Speaker 1: to help fight the wildfires. It was like a cute

774
00:45:18,400 --> 00:45:21,759
Speaker 1: video of these men dancing. I shared it immediately, and

775
00:45:21,800 --> 00:45:27,600
Speaker 1: I know, you know, usually when someone shares something incorrect online,

776
00:45:27,719 --> 00:45:31,640
Speaker 1: it's because it is like speaking to some emotional need

777
00:45:31,680 --> 00:45:33,600
Speaker 1: that they have or some sort of like emotional like

778
00:45:33,719 --> 00:45:36,080
Speaker 1: place that they're in. And I can tell you exactly

779
00:45:36,320 --> 00:45:38,520
Speaker 1: what it got in me. You know, this is a

780
00:45:38,560 --> 00:45:41,320
Speaker 1: second or third day this summer that I've been unable

781
00:45:41,320 --> 00:45:44,400
Speaker 1: to leave the apartment because of the wildfire smoke. You know,

782
00:45:44,400 --> 00:45:47,400
Speaker 1: I have pretty bad asthma and a whole host of

783
00:45:47,480 --> 00:45:50,600
Speaker 1: respiratory issues, and so I really when stuff like this happens,

784
00:45:50,640 --> 00:45:54,160
Speaker 1: I really can't go outside. And seeing this video of

785
00:45:54,239 --> 00:45:58,920
Speaker 1: like smiling South African men, black men, like dancing, and

786
00:45:59,160 --> 00:46:00,719
Speaker 1: you know, the idea that like these are these are

787
00:46:00,760 --> 00:46:02,920
Speaker 1: the men who are going to like go to Canada

788
00:46:02,960 --> 00:46:07,000
Speaker 1: and like fix the smoke, it really gave me warm fuzzies.

789
00:46:07,320 --> 00:46:09,239
Speaker 1: Come to find out that video was taken like five

790
00:46:09,360 --> 00:46:12,640
Speaker 1: years ago and had nothing to do with the current wildfires.

791
00:46:12,800 --> 00:46:17,120
Speaker 1: So like, technically it's not it's not fake news, but

792
00:46:17,239 --> 00:46:21,239
Speaker 1: it is like like I'm taken out of context, but

793
00:46:21,400 --> 00:46:25,160
Speaker 1: I shared it because it spoke to my emotional needs,

794
00:46:25,200 --> 00:46:27,640
Speaker 1: like I've been stuck in the house. The idea that

795
00:46:27,680 --> 00:46:30,080
Speaker 1: these men were coming to fix the fire so that

796
00:46:30,120 --> 00:46:32,480
Speaker 1: I could go outside. Finally was really spoke to me

797
00:46:32,520 --> 00:46:34,520
Speaker 1: and I shared it, and I had done share it.

798
00:46:34,560 --> 00:46:36,880
Speaker 1: So it does definitely happen to the best of us.

799
00:46:36,920 --> 00:46:40,640
Speaker 1: It does not matter if you're young, digital native, tech savvy,

800
00:46:41,120 --> 00:46:44,920
Speaker 1: grew up online. It can happen to the best of us.

801
00:46:45,360 --> 00:46:49,000
Speaker 2: Yeah, it can. We all got to be vigilant, especially

802
00:46:49,040 --> 00:46:53,160
Speaker 2: everything else that you know, fit with our preconceived notions

803
00:46:53,200 --> 00:46:55,960
Speaker 2: of how things are going to be, or like you said,

804
00:46:56,640 --> 00:46:59,799
Speaker 2: fulfill some sort of emotional need that we have.

805
00:47:00,440 --> 00:47:03,360
Speaker 1: Facebook and Google have both been battling with the Canadian

806
00:47:03,400 --> 00:47:05,759
Speaker 1: government over a new law that was just passed in

807
00:47:05,760 --> 00:47:07,719
Speaker 1: Canada called the Online News Act.

808
00:47:07,960 --> 00:47:08,120
Speaker 2: Now.

809
00:47:08,160 --> 00:47:10,840
Speaker 1: This law would require that Google and Facebook pay news

810
00:47:10,920 --> 00:47:14,440
Speaker 1: organizations and publishers for distributing links to news stories. The

811
00:47:14,520 --> 00:47:16,640
Speaker 1: law has not gone into effect just yet, but once

812
00:47:16,640 --> 00:47:19,080
Speaker 1: it does, Google and Facebook say that they're going to

813
00:47:19,080 --> 00:47:23,200
Speaker 1: start removing news articles by Canadian publishers from their services

814
00:47:23,200 --> 00:47:26,360
Speaker 1: in the country. According to NPR, the Canadian government estimates

815
00:47:26,360 --> 00:47:29,040
Speaker 1: that the law would result in a cash injection of

816
00:47:29,080 --> 00:47:31,480
Speaker 1: some three hundred and twenty nine million dollars into the

817
00:47:31,480 --> 00:47:34,120
Speaker 1: Canadian news industry, which, just like you're in the United

818
00:47:34,160 --> 00:47:38,080
Speaker 1: States has been plagued by news staff layoffs and other

819
00:47:38,239 --> 00:47:41,560
Speaker 1: downsizing in recent years. Supporters of this legislation have argued

820
00:47:41,600 --> 00:47:43,640
Speaker 1: that it could provide a much needed lifeline to the

821
00:47:43,680 --> 00:47:46,600
Speaker 1: ailing news industry, which has been gutted by Silicon Valley's

822
00:47:46,600 --> 00:47:50,279
Speaker 1: ironclad control of digital advertising. Now, this Canadian law is

823
00:47:50,320 --> 00:47:52,839
Speaker 1: modeled after the law in Australia. We did a whole

824
00:47:52,840 --> 00:47:55,640
Speaker 1: episode on that like a few years ago, which will

825
00:47:55,640 --> 00:47:57,840
Speaker 1: link to in the show notes, and folks might remember,

826
00:47:57,920 --> 00:48:02,080
Speaker 1: I'm sure Australians do that that tense negotiation between Facebook

827
00:48:02,120 --> 00:48:05,799
Speaker 1: and news publishers led to Facebook being temporarily shut down

828
00:48:05,800 --> 00:48:08,799
Speaker 1: in Australia. There's a similar bill here in the US

829
00:48:08,840 --> 00:48:12,080
Speaker 1: in California that would force tech companies to pay publishers,

830
00:48:12,320 --> 00:48:15,880
Speaker 1: and yep, Facebook and Google have threatened blackouts there too. Now,

831
00:48:15,920 --> 00:48:19,760
Speaker 1: both Facebook and Google like don't seem to see news

832
00:48:19,840 --> 00:48:22,560
Speaker 1: as a key part of their strategies, and it seems

833
00:48:22,600 --> 00:48:24,440
Speaker 1: like they're thinking, like, oh, it'll be easier just to

834
00:48:24,440 --> 00:48:27,920
Speaker 1: block news altogether rather than pay for it. Now, I

835
00:48:27,960 --> 00:48:30,840
Speaker 1: want to be clear that I absolutely think that something

836
00:48:30,880 --> 00:48:33,759
Speaker 1: needs to be done, but how this should play out

837
00:48:33,880 --> 00:48:36,400
Speaker 1: is like a little bit above my pay grade. MPR

838
00:48:36,520 --> 00:48:38,840
Speaker 1: has a great summary of some of the different positions

839
00:48:38,920 --> 00:48:42,480
Speaker 1: they write. While most major publishers in Canada back the

840
00:48:42,520 --> 00:48:45,240
Speaker 1: new law, outside media observers have not been so sure.

841
00:48:45,480 --> 00:48:48,560
Speaker 1: Tech writer Casey Newton has argued that attacks on displaying

842
00:48:48,600 --> 00:48:51,120
Speaker 1: links would effectively break the Internet if it was applied

843
00:48:51,120 --> 00:48:53,160
Speaker 1: to the rest of the web. Other critics have pointed

844
00:48:53,200 --> 00:48:55,680
Speaker 1: to the lack of transparency over who would actually receive

845
00:48:55,719 --> 00:48:58,640
Speaker 1: the cash and fusion from the tech companies. Some fear

846
00:48:58,719 --> 00:49:01,799
Speaker 1: that programs could be hid by disinformation sites that learn

847
00:49:01,840 --> 00:49:04,960
Speaker 1: how to game the system. Yet press advocates insisted that

848
00:49:05,000 --> 00:49:08,840
Speaker 1: tech companies retaliating by threatening to systemically remove news articles

849
00:49:09,120 --> 00:49:11,600
Speaker 1: will be a blow to civil society and the public's

850
00:49:11,680 --> 00:49:15,080
Speaker 1: understanding of the world. At a moment when disinformation swirls

851
00:49:15,080 --> 00:49:18,560
Speaker 1: in our public discourse, ensuring public access to credible journalism

852
00:49:18,680 --> 00:49:21,680
Speaker 1: is essential. So it's deeply disappointing to see this decision

853
00:49:21,680 --> 00:49:24,080
Speaker 1: from Google and Meta. This is from Liz Woolery, who

854
00:49:24,160 --> 00:49:27,560
Speaker 1: leads digital policy at PEN America, an organization that supports

855
00:49:27,600 --> 00:49:31,920
Speaker 1: freedom of expression. Wellery continued, as policymakers explore potential solutions

856
00:49:31,960 --> 00:49:35,080
Speaker 1: to the challenges facing the journalism industry, platforms are free

857
00:49:35,080 --> 00:49:38,760
Speaker 1: to critique, debate and offer alternatives, but reducing the public's

858
00:49:38,800 --> 00:49:41,200
Speaker 1: access to news is never the right answer.

859
00:49:41,719 --> 00:49:46,040
Speaker 2: Yeah, it's a tough one. Like, I hear what she's saying,

860
00:49:46,080 --> 00:49:51,200
Speaker 2: but like you pointed out, newsrooms are facing cuts after

861
00:49:51,920 --> 00:49:54,560
Speaker 2: decades of facing cuts. Ror Like it's it's just been

862
00:49:55,160 --> 00:49:59,680
Speaker 2: decades of cuts, cuts, cuts, cuts, And you know, there's

863
00:50:00,719 --> 00:50:04,680
Speaker 2: at least two sides to giving the public access to news.

864
00:50:04,760 --> 00:50:09,360
Speaker 2: There is making the news available to them to read

865
00:50:09,560 --> 00:50:14,839
Speaker 2: when it's been written, and there is funding the news

866
00:50:15,040 --> 00:50:20,000
Speaker 2: outlets that pay the people that write the news. And

867
00:50:20,120 --> 00:50:20,880
Speaker 2: we need both.

868
00:50:21,680 --> 00:50:26,880
Speaker 1: Yeah, I mean, I listen, I worked in media for

869
00:50:26,920 --> 00:50:29,880
Speaker 1: a long time. It's not a coincidence that that is

870
00:50:29,920 --> 00:50:31,880
Speaker 1: no longer the case for me. Broadly, I think that

871
00:50:31,920 --> 00:50:36,640
Speaker 1: Facebook has had way too much control over our media landscape.

872
00:50:36,719 --> 00:50:40,680
Speaker 1: You know, for a while, Facebook would actually direct traffic

873
00:50:40,760 --> 00:50:44,080
Speaker 1: to media sites, and now it just keeps you on Facebook,

874
00:50:44,120 --> 00:50:45,239
Speaker 1: right Like it used to be, when you click the

875
00:50:45,280 --> 00:50:47,480
Speaker 1: link on Facebook, it would take you out of Facebook.

876
00:50:47,560 --> 00:50:50,520
Speaker 1: Now Facebook is just interested in keeping you on Facebook.

877
00:50:50,800 --> 00:50:53,799
Speaker 1: When I was working in media, the great pivot to

878
00:50:53,920 --> 00:50:57,920
Speaker 1: video fraud happened, which, like I'm still angry about nobody

879
00:50:57,960 --> 00:51:01,520
Speaker 1: except for Facebook executives, are certain when Facebook is allowed

880
00:51:01,520 --> 00:51:06,000
Speaker 1: to just gut entire industries that people rely on to

881
00:51:06,000 --> 00:51:08,880
Speaker 1: stay informed like they have. And I think that Facebook

882
00:51:09,040 --> 00:51:12,560
Speaker 1: and honestly like media executives who are chasing the shiny

883
00:51:12,600 --> 00:51:16,760
Speaker 1: new thing, have vastly contributed to this precarity of media

884
00:51:16,800 --> 00:51:19,560
Speaker 1: in general. Right, Like all of my friends who still

885
00:51:19,560 --> 00:51:23,280
Speaker 1: work in media, they are all either laid off about

886
00:51:23,320 --> 00:51:27,240
Speaker 1: to be laid off, or they're like delusional, or they're

887
00:51:27,320 --> 00:51:30,840
Speaker 1: just starting a freelancing business because they know they're probably

888
00:51:30,840 --> 00:51:32,799
Speaker 1: going to be laid off and rehired to do their

889
00:51:32,840 --> 00:51:35,319
Speaker 1: same job at a fraction of the cost, so they

890
00:51:35,360 --> 00:51:37,799
Speaker 1: may as well get you a leg up on that now.

891
00:51:37,840 --> 00:51:40,720
Speaker 1: And so it's not a good time to be in media.

892
00:51:40,760 --> 00:51:44,759
Speaker 1: It is an incredibly precarious situation, and I think that

893
00:51:44,840 --> 00:51:47,080
Speaker 1: Facebook really should take a lot of the blame there.

894
00:51:47,400 --> 00:51:50,839
Speaker 1: You know, entire strategies and budgets were built around things

895
00:51:50,840 --> 00:51:53,480
Speaker 1: that Facebook said, in the case of the pivot to video,

896
00:51:53,840 --> 00:51:57,239
Speaker 1: things that Facebook said that weren't even true. They were lies, right,

897
00:51:58,160 --> 00:52:01,799
Speaker 1: and then Facebook will just change with no warning, and

898
00:52:01,920 --> 00:52:05,080
Speaker 1: all that money, all that energy, all that capacity is

899
00:52:05,160 --> 00:52:07,680
Speaker 1: just gone. So those people just get let go. And

900
00:52:07,800 --> 00:52:10,200
Speaker 1: for a while Facebook was saying like, oh, we want

901
00:52:10,200 --> 00:52:13,440
Speaker 1: to be involved in journalists, we want to save local

902
00:52:13,520 --> 00:52:18,279
Speaker 1: journalism by partnerships with local newsrooms. And now Facebook has

903
00:52:18,280 --> 00:52:21,280
Speaker 1: basically been like, hmm, no, actually we don't even need news.

904
00:52:21,320 --> 00:52:23,799
Speaker 1: And the people who were leading those partnerships have been

905
00:52:23,840 --> 00:52:26,239
Speaker 1: laid off from Facebook. And so I don't know what

906
00:52:26,280 --> 00:52:29,960
Speaker 1: the answer is, but I do think that people need

907
00:52:30,480 --> 00:52:35,440
Speaker 1: access to accurate news. I think that Facebook, like, the

908
00:52:35,760 --> 00:52:38,640
Speaker 1: answer can't just be that Facebook gets to take whatever

909
00:52:38,680 --> 00:52:41,600
Speaker 1: it wants without paying for it, and the only the

910
00:52:41,600 --> 00:52:44,360
Speaker 1: only people who benefit here are Facebook. You know, I

911
00:52:44,719 --> 00:52:46,160
Speaker 1: don't know what to do, but it's it's it is

912
00:52:46,200 --> 00:52:50,839
Speaker 1: a it doesn't look good for media, and I think

913
00:52:50,880 --> 00:52:53,680
Speaker 1: that is in part because of Facebook.

914
00:52:54,840 --> 00:52:58,600
Speaker 2: Yeah, I mean, between Facebook and Google, I think that

915
00:52:58,960 --> 00:53:03,319
Speaker 2: is most of the advertising online. I guess TikTok has

916
00:53:03,360 --> 00:53:06,879
Speaker 2: taken a big chunk out of it. I couldn't say

917
00:53:06,880 --> 00:53:09,560
Speaker 2: how much. That's not an area that I'm an expert in,

918
00:53:09,640 --> 00:53:14,160
Speaker 2: but yeah, Facebook and Google they're like it. They sell

919
00:53:14,200 --> 00:53:18,880
Speaker 2: all the advertising online. And there sure are a lot

920
00:53:18,960 --> 00:53:23,520
Speaker 2: of news outlets posting stories that we all read all

921
00:53:23,560 --> 00:53:27,920
Speaker 2: the time. It doesn't seem radical to think that they

922
00:53:27,960 --> 00:53:30,880
Speaker 2: should get some of that money. How to make that

923
00:53:30,920 --> 00:53:37,040
Speaker 2: work in practice? Like you said, it's above my pay grade.

924
00:53:38,160 --> 00:53:51,799
Speaker 1: More after a quick break, let's get right back into it.

925
00:53:53,520 --> 00:53:57,080
Speaker 1: So folks might remember that trans influencer Dylan mulvaney who

926
00:53:57,080 --> 00:53:59,600
Speaker 1: became the target of an extremist hate campaign after she

927
00:53:59,680 --> 00:54:03,439
Speaker 1: did a partnership with bud Light. Well, Dylan mulvaney broke

928
00:54:03,480 --> 00:54:06,640
Speaker 1: her silence this week. Dylan rose to prominence for her

929
00:54:06,680 --> 00:54:09,200
Speaker 1: series on TikTok, where she chronicled her life as a

930
00:54:09,200 --> 00:54:11,759
Speaker 1: trans woman. It was a cute series. It would be

931
00:54:11,800 --> 00:54:15,680
Speaker 1: like day whatever of being a girl. I thought it

932
00:54:15,719 --> 00:54:18,040
Speaker 1: was cute. People liked it. It's like why she became

933
00:54:18,360 --> 00:54:21,480
Speaker 1: kind of an influencer. Bud Light was trying to kind

934
00:54:21,520 --> 00:54:24,759
Speaker 1: of shed their frat boy image and did a brand

935
00:54:24,800 --> 00:54:28,200
Speaker 1: partnership with Dylan. Now, the logistics of this brand partnership

936
00:54:28,239 --> 00:54:35,000
Speaker 1: were way overblown. Basically, bud Light sent Dylan mulvaney one

937
00:54:35,320 --> 00:54:38,120
Speaker 1: beer can, like a promotional beer can with her face

938
00:54:38,160 --> 00:54:41,360
Speaker 1: on it. These cans were not for sale in stores,

939
00:54:41,600 --> 00:54:44,240
Speaker 1: these cans. It wasn't like they were stamping her face

940
00:54:44,280 --> 00:54:46,600
Speaker 1: on every bud Light can. Ever, that everybody had to

941
00:54:46,680 --> 00:54:49,720
Speaker 1: use it. They sent her a can with her face

942
00:54:49,719 --> 00:54:52,680
Speaker 1: on it. One she made a video on her social

943
00:54:52,760 --> 00:54:56,360
Speaker 1: media profile about bud Light talking about this can they

944
00:54:56,440 --> 00:54:58,840
Speaker 1: sent her, and that was it. You would have thought

945
00:54:58,960 --> 00:55:02,440
Speaker 1: that bud Light had named Dylan mulvany their new CEO,

946
00:55:02,960 --> 00:55:04,799
Speaker 1: and that every time that you bought a bud Light

947
00:55:05,160 --> 00:55:08,160
Speaker 1: a dollar personally went into her pocket. No, it was

948
00:55:08,239 --> 00:55:12,280
Speaker 1: just one social media post and one can. But extremist

949
00:55:12,360 --> 00:55:15,800
Speaker 1: groups melted down over this right, and to be clear,

950
00:55:16,000 --> 00:55:19,480
Speaker 1: they were explicit that this was a specific tactic right

951
00:55:19,760 --> 00:55:22,759
Speaker 1: to target and go after one brand at a time

952
00:55:22,840 --> 00:55:25,640
Speaker 1: and attack them from what they perceived as like woke

953
00:55:25,760 --> 00:55:29,240
Speaker 1: positions like partnering with a trans woman or supporting Pride

954
00:55:29,239 --> 00:55:31,640
Speaker 1: in the case of target the store. So these people

955
00:55:31,680 --> 00:55:35,120
Speaker 1: are a very vocal minority, but they know they can

956
00:55:35,200 --> 00:55:37,520
Speaker 1: drum up attention. Like we talked about many times, an

957
00:55:37,520 --> 00:55:41,680
Speaker 1: Internet hate machine, brands do not always respond well and

958
00:55:41,760 --> 00:55:45,440
Speaker 1: sometimes they just caved this bullying. Essentially, you know, extremists

959
00:55:45,440 --> 00:55:49,280
Speaker 1: would be filming themselves. I think it was like kid Rock,

960
00:55:50,440 --> 00:55:52,680
Speaker 1: which like, who the fuck is listening to kid Rock?

961
00:55:52,719 --> 00:55:55,160
Speaker 1: In twenty twenty three, but he posted a video of

962
00:55:55,239 --> 00:55:59,319
Speaker 1: himself shooting bud Light cans with a gun. People were

963
00:55:59,400 --> 00:56:02,440
Speaker 1: driving sam rollers over them, like all because of this

964
00:56:02,560 --> 00:56:06,319
Speaker 1: one can like I I can't, it's it's so it's

965
00:56:06,320 --> 00:56:09,719
Speaker 1: so ridiculous, I can't. Bud Light actually did have a

966
00:56:09,760 --> 00:56:13,239
Speaker 1: dip in sales because of these protests against bud Light.

967
00:56:13,680 --> 00:56:17,480
Speaker 1: Marketing vice president Alyssa Heinersd actually stepped away from the

968
00:56:17,520 --> 00:56:19,919
Speaker 1: company when she became a target too. It's not clear

969
00:56:19,960 --> 00:56:22,960
Speaker 1: if she was fired or she just like stepped away,

970
00:56:23,120 --> 00:56:25,360
Speaker 1: Like I read reports that she was fired, then I

971
00:56:25,400 --> 00:56:28,040
Speaker 1: read reports that bud Light was like, oh, she wasn't fired,

972
00:56:28,120 --> 00:56:31,120
Speaker 1: So I'm not totally sure she was became a target

973
00:56:31,120 --> 00:56:34,880
Speaker 1: of this too. Extremists found a picture of her drinking

974
00:56:35,280 --> 00:56:37,960
Speaker 1: a beer at all at college, like back when she

975
00:56:38,000 --> 00:56:40,000
Speaker 1: was in college, and they tried to make her look

976
00:56:40,000 --> 00:56:42,440
Speaker 1: like a hypocrite because oh, you liked beer then, but

977
00:56:42,600 --> 00:56:46,480
Speaker 1: like now you think it's freddy or whatever. It's just

978
00:56:46,480 --> 00:56:47,000
Speaker 1: so stupid.

979
00:56:47,040 --> 00:56:48,839
Speaker 2: I just gotta stop you there for just a second though,

980
00:56:48,840 --> 00:56:51,400
Speaker 2: because like, yeah, she was drinking beer. She was the

981
00:56:51,840 --> 00:56:55,440
Speaker 2: vice president of Anheuser Bush right, Like she sells beer.

982
00:56:55,800 --> 00:57:00,439
Speaker 2: The idea that that is somehow disqualifying or in baring

983
00:57:01,480 --> 00:57:06,040
Speaker 2: just emphasizes how separated from reality any of this is.

984
00:57:06,400 --> 00:57:09,960
Speaker 1: It wasn't even like a good like gotcha photo of

985
00:57:10,000 --> 00:57:11,839
Speaker 1: like look at this, like like I could see if

986
00:57:11,840 --> 00:57:13,400
Speaker 1: she was doing like a keg stand. It was just

987
00:57:13,400 --> 00:57:16,840
Speaker 1: a normal it's just like a normal person drinking at college.

988
00:57:16,880 --> 00:57:18,560
Speaker 1: Like it wasn't even like anything exciting.

989
00:57:19,040 --> 00:57:23,120
Speaker 2: Yeah, like person who sells beer drinks beer. What a hypocrite?

990
00:57:23,400 --> 00:57:26,920
Speaker 1: Right, Okay, So back to Dylan. Dylan after all of

991
00:57:26,960 --> 00:57:30,520
Speaker 1: this basically had to go offline because of the hate

992
00:57:30,560 --> 00:57:33,880
Speaker 1: that she got. That got so bad. She was stocked,

993
00:57:33,960 --> 00:57:37,280
Speaker 1: she was her rash, She feared for her life. These people,

994
00:57:37,840 --> 00:57:43,120
Speaker 1: their anger toward her was so intense, again over one

995
00:57:43,280 --> 00:57:46,919
Speaker 1: beer can and one video. So Dylan finally came back

996
00:57:46,920 --> 00:57:48,800
Speaker 1: online for the first time, and here's what she had

997
00:57:48,840 --> 00:57:49,160
Speaker 1: to say.

998
00:57:50,120 --> 00:57:52,960
Speaker 3: For a company to hire a trans person and then

999
00:57:53,040 --> 00:57:56,440
Speaker 3: not publicly stand by them is worse, in my opinion,

1000
00:57:56,520 --> 00:57:59,600
Speaker 3: than not hiring a trans person at all, because it

1001
00:57:59,640 --> 00:58:02,680
Speaker 3: gives customer's permission to be as transphobic and hateful as

1002
00:58:02,720 --> 00:58:05,800
Speaker 3: they want. And the hate doesn't end with me. It

1003
00:58:05,840 --> 00:58:09,680
Speaker 3: has serious, engraved consequences for the rest of our community.

1004
00:58:09,800 --> 00:58:12,800
Speaker 3: And you know, we're customers too. I know a lot

1005
00:58:12,800 --> 00:58:15,200
Speaker 3: of trans and queer people who love beer, and I

1006
00:58:15,240 --> 00:58:17,200
Speaker 3: have some lesbian friends who could drink some of those

1007
00:58:17,240 --> 00:58:22,080
Speaker 3: haters under the table. But to turn a blind eye

1008
00:58:22,280 --> 00:58:25,600
Speaker 3: and pretend everything is okay, it just isn't an option

1009
00:58:25,760 --> 00:58:26,200
Speaker 3: right now.

1010
00:58:26,920 --> 00:58:29,840
Speaker 1: So this is heartbreaking. And I feel like if you

1011
00:58:29,960 --> 00:58:35,920
Speaker 1: listen to Dylan's experiences and you don't feel bad for

1012
00:58:36,000 --> 00:58:39,600
Speaker 1: what she went through, that really says a lot about you.

1013
00:58:39,760 --> 00:58:43,640
Speaker 1: Like if somebody could listen to what she says that

1014
00:58:43,720 --> 00:58:47,240
Speaker 1: she experienced and still feel like, oh, well, good for her,

1015
00:58:47,480 --> 00:58:50,680
Speaker 1: she deserved it, Like you're not a good person. And

1016
00:58:50,840 --> 00:58:55,200
Speaker 1: what really is wild to me and like bums me out,

1017
00:58:55,280 --> 00:58:58,040
Speaker 1: is that nobody from bud Light reached out to her.

1018
00:58:58,280 --> 00:59:00,880
Speaker 1: And I think that that's my thing, Like just an email,

1019
00:59:01,080 --> 00:59:03,680
Speaker 1: a phone call, how you holding up? It doesn't have

1020
00:59:03,720 --> 00:59:06,160
Speaker 1: to be a public a public I mean, it should

1021
00:59:06,200 --> 00:59:08,840
Speaker 1: be a public thing, but like it's it's like a

1022
00:59:08,920 --> 00:59:11,800
Speaker 1: lack of at that point, that's like a lack of class.

1023
00:59:11,880 --> 00:59:14,959
Speaker 1: It's not even I'll get to what it says about

1024
00:59:14,960 --> 00:59:17,840
Speaker 1: how they treat people that they seek out for brand partnerships,

1025
00:59:18,200 --> 00:59:20,520
Speaker 1: But at the end of the day, it's like there's

1026
00:59:20,560 --> 00:59:23,240
Speaker 1: a standard for how people treat people, and I that

1027
00:59:23,960 --> 00:59:26,440
Speaker 1: shocks me. And I just think that, like a company

1028
00:59:26,480 --> 00:59:29,960
Speaker 1: like bud Light, if you're gonna work with marginalized people,

1029
00:59:30,440 --> 00:59:36,680
Speaker 1: particularly trans people, right now, in this climate, this drummed

1030
00:59:36,760 --> 00:59:41,880
Speaker 1: up climate of hate and targeting of those people, you

1031
00:59:42,000 --> 00:59:45,400
Speaker 1: gotta have their backs, you gotta support them. If you're

1032
00:59:45,440 --> 00:59:50,080
Speaker 1: just gonna back away and like abandon them without so

1033
00:59:50,200 --> 00:59:52,760
Speaker 1: much as an email to check up on them, If

1034
00:59:52,840 --> 00:59:57,080
Speaker 1: saying yes to your partnership means death threats for them,

1035
00:59:57,240 --> 00:59:59,920
Speaker 1: means they will have to flee their homes for their

1036
01:00:00,080 --> 01:00:02,800
Speaker 1: own safety and fear for their lives, and you're not

1037
01:00:02,840 --> 01:00:05,440
Speaker 1: even gonna check in to see how they're doing, you

1038
01:00:05,480 --> 01:00:08,080
Speaker 1: should not be working with marginalized people at all. If

1039
01:00:08,120 --> 01:00:10,720
Speaker 1: that is the case, Dylan is the one who is

1040
01:00:10,720 --> 01:00:14,480
Speaker 1: being courageous. Dylan is the one who is taking all

1041
01:00:14,520 --> 01:00:17,720
Speaker 1: of this heat simply because she said yes to bud

1042
01:00:17,760 --> 01:00:21,520
Speaker 1: Light's offer of being a brand ambassador for their brand,

1043
01:00:22,200 --> 01:00:26,520
Speaker 1: bud Light. If Dylan is being made to shoulder all

1044
01:00:26,560 --> 01:00:29,560
Speaker 1: of this for doing nothing wrong, for just saying yes

1045
01:00:29,600 --> 01:00:32,640
Speaker 1: to you, you got to stick up for her, or

1046
01:00:33,520 --> 01:00:35,520
Speaker 1: don't work with talent like that, like that's just not

1047
01:00:35,720 --> 01:00:39,160
Speaker 1: how you treat people. Like, I'm really appalled.

1048
01:00:39,920 --> 01:00:45,560
Speaker 2: Yeah, and I think brands that want to make it

1049
01:00:46,640 --> 01:00:49,920
Speaker 2: in twenty twenty three and in the future should be

1050
01:00:49,960 --> 01:00:53,360
Speaker 2: standing up for people like Dylan, right, Like that's the

1051
01:00:53,440 --> 01:00:58,360
Speaker 2: young people are tolerant. The people who are running over

1052
01:00:58,400 --> 01:01:02,600
Speaker 2: these beers with steam rollers and shit shooting them, they

1053
01:01:02,640 --> 01:01:06,840
Speaker 2: do not represent the future of America. And they're just

1054
01:01:06,880 --> 01:01:09,360
Speaker 2: so hateful, Like who would want to be associated with them?

1055
01:01:09,480 --> 01:01:13,440
Speaker 2: All Dylan did was like b trans. You know, she's

1056
01:01:13,480 --> 01:01:16,600
Speaker 2: not even accused of doing anything other than just being trans,

1057
01:01:17,600 --> 01:01:24,280
Speaker 2: and like that's enough to spur all of this hate.

1058
01:01:24,680 --> 01:01:27,160
Speaker 2: It's despicable.

1059
01:01:27,440 --> 01:01:30,320
Speaker 1: Well, that's really what these people are saying. They're you know,

1060
01:01:30,360 --> 01:01:33,080
Speaker 1: they have so many ways of kind of like masking

1061
01:01:33,080 --> 01:01:36,760
Speaker 1: it when it's like, oh, we're just concerned about girls

1062
01:01:36,800 --> 01:01:39,439
Speaker 1: in sports, we're just concerned about the safety of women

1063
01:01:39,480 --> 01:01:41,680
Speaker 1: in bathrooms. When it comes down to issues like this,

1064
01:01:41,760 --> 01:01:45,040
Speaker 1: you really just see like we don't like trans people

1065
01:01:45,040 --> 01:01:46,960
Speaker 1: and we don't want to have to see them. We

1066
01:01:47,040 --> 01:01:49,880
Speaker 1: don't think they belong in public life, we don't think

1067
01:01:49,920 --> 01:01:52,280
Speaker 1: they should be here, right, Like, the issues like this

1068
01:01:52,320 --> 01:01:54,920
Speaker 1: make it because because Dylan is not a controversial person

1069
01:01:55,160 --> 01:02:00,360
Speaker 1: like she likes cute outfits and you know, some glasses

1070
01:02:00,400 --> 01:02:02,360
Speaker 1: and stuff like she doesn't. She's not someone who like

1071
01:02:02,840 --> 01:02:05,760
Speaker 1: has done something wrong. And it just makes it so

1072
01:02:06,000 --> 01:02:09,720
Speaker 1: clear what these people are angry at. It's her existence.

1073
01:02:09,760 --> 01:02:13,320
Speaker 1: It's the fact that she exists and is affirmed. That's

1074
01:02:13,320 --> 01:02:14,080
Speaker 1: what they don't like.

1075
01:02:14,720 --> 01:02:18,240
Speaker 2: Yeah, that's a great point. It really just lays it out.

1076
01:02:18,400 --> 01:02:24,480
Speaker 2: You know, there's there's no school competition issue. There's no

1077
01:02:24,720 --> 01:02:30,080
Speaker 2: like bathroom scare, you know, none of that like smoke

1078
01:02:30,120 --> 01:02:33,280
Speaker 2: and mirrors nonsense. It's like she's a transperson and they

1079
01:02:33,280 --> 01:02:38,160
Speaker 2: don't like it. And I really feel for her, like,

1080
01:02:38,880 --> 01:02:40,760
Speaker 2: you know, she did. She didn't ask for any of this.

1081
01:02:40,960 --> 01:02:45,720
Speaker 2: A brand sent her a product and she held up

1082
01:02:45,720 --> 01:02:48,439
Speaker 2: that product on her social like that's it.

1083
01:02:48,960 --> 01:02:53,320
Speaker 1: Yeah, because they asked her to like saying yes to

1084
01:02:53,480 --> 01:02:56,800
Speaker 1: bud Light, asking her to do this gets her death

1085
01:02:56,800 --> 01:03:02,000
Speaker 1: threats and she has to go dark. You like, yeah,

1086
01:03:02,040 --> 01:03:05,040
Speaker 1: and you can't you can't even be bothered just to

1087
01:03:05,080 --> 01:03:08,680
Speaker 1: be like hey girl, you are right, Like yeah, it

1088
01:03:08,800 --> 01:03:14,760
Speaker 1: just it's despicable. It's despicable. Speaking of despicable, We're gonna

1089
01:03:14,760 --> 01:03:17,760
Speaker 1: do a new segment on the Roundup called.

1090
01:03:18,440 --> 01:03:20,000
Speaker 2: What's Elon done now?

1091
01:03:20,920 --> 01:03:22,680
Speaker 1: And I have to give major shout outs to my

1092
01:03:22,720 --> 01:03:25,479
Speaker 1: favorite podcast, WHO Weekly. If you listen to WHO Weekly,

1093
01:03:25,520 --> 01:03:29,160
Speaker 1: it's kind of inspired by their segment what's Rita up To?

1094
01:03:29,760 --> 01:03:32,960
Speaker 1: They have this amazing theme song before they do it

1095
01:03:33,000 --> 01:03:35,760
Speaker 1: where they introduce the segment. I won't sing it, but

1096
01:03:35,800 --> 01:03:37,200
Speaker 1: if you if you know the show, you know it's

1097
01:03:37,280 --> 01:03:39,640
Speaker 1: very catchy. So hopefully we can have some sort of

1098
01:03:39,720 --> 01:03:44,160
Speaker 1: like what's Elon done now? Theme song in the style

1099
01:03:44,360 --> 01:03:44,960
Speaker 1: of that one.

1100
01:03:45,560 --> 01:03:48,600
Speaker 2: Yeah, we're gonna work on it. If anybody has anything

1101
01:03:48,600 --> 01:03:50,920
Speaker 2: they'd like to share, we'd love to hear it. We

1102
01:03:51,000 --> 01:03:54,600
Speaker 2: aspire it to be anything like uh, WHO Weekly.

1103
01:03:54,800 --> 01:03:56,439
Speaker 1: I think of us as the WHO Weekly of tech.

1104
01:03:56,920 --> 01:04:01,040
Speaker 2: Okay, yeah, all right, Uh maybe someday we'll be them.

1105
01:04:01,240 --> 01:04:04,640
Speaker 1: Oh being a WHO is so much funner. Okay, so

1106
01:04:04,680 --> 01:04:07,439
Speaker 1: you gotta ask me, Okay, Bridget, So what's Elon done now?

1107
01:04:07,640 --> 01:04:10,400
Speaker 2: Okay, Bridget, what's Elon done now?

1108
01:04:10,800 --> 01:04:13,800
Speaker 1: Well, here's what Elon's up to. So back in October,

1109
01:04:13,840 --> 01:04:17,640
Speaker 1: Twitter rolled out a TikTok competitor that mimics TikTok's swipe

1110
01:04:17,720 --> 01:04:21,760
Speaker 1: up for more videos. Functionality Musk just recently highlighted it

1111
01:04:21,960 --> 01:04:25,600
Speaker 1: introducing it to droves of new users. So how's it going?

1112
01:04:26,000 --> 01:04:28,960
Speaker 1: Because it involves Elon Musk not well, and by that

1113
01:04:29,080 --> 01:04:34,120
Speaker 1: I mean animal torture, homophobia, and violent assaults. NBC reports

1114
01:04:34,160 --> 01:04:36,880
Speaker 1: that many users using the video feature were alarmed by

1115
01:04:36,880 --> 01:04:39,720
Speaker 1: a stream of graphic videos that they encountered while scrolling

1116
01:04:39,760 --> 01:04:43,280
Speaker 1: through the feed, including videos showing gun violence, police brutality,

1117
01:04:43,600 --> 01:04:48,160
Speaker 1: physical altercations, and vaccine misinformation. So where TikTok is like, Oh,

1118
01:04:48,280 --> 01:04:51,960
Speaker 1: here's some videos about how to properly use your washing

1119
01:04:52,000 --> 01:04:54,840
Speaker 1: machine or your dishwasher or whatever. Twitter is like, here's

1120
01:04:54,880 --> 01:04:57,720
Speaker 1: some videos of a cap being abused. And because it's

1121
01:04:57,920 --> 01:05:01,600
Speaker 1: swipe up functionality, these videos are just being surface to

1122
01:05:01,640 --> 01:05:05,200
Speaker 1: you algorithmically, right. It's not like you're clicking on them

1123
01:05:05,400 --> 01:05:08,960
Speaker 1: to view them. They're just being surfaced to you. Some

1124
01:05:09,000 --> 01:05:11,520
Speaker 1: of the original posts were seen on the timelines of

1125
01:05:11,560 --> 01:05:14,439
Speaker 1: millions of users within less than twenty four hours since

1126
01:05:14,480 --> 01:05:18,120
Speaker 1: they were posted. And Musk, what's he doing now? Well,

1127
01:05:18,280 --> 01:05:21,240
Speaker 1: it sounds like he is well aware of this issue

1128
01:05:21,520 --> 01:05:23,280
Speaker 1: because he tweeted that some of the videos on the

1129
01:05:23,280 --> 01:05:25,320
Speaker 1: platform could be quite edgy.

1130
01:05:25,680 --> 01:05:27,720
Speaker 2: Ooh, he's such an edgy guy.

1131
01:05:28,280 --> 01:05:31,960
Speaker 1: So I think that that just really shows that Elon

1132
01:05:32,080 --> 01:05:35,439
Speaker 1: Musk doesn't get what people are looking for from social media,

1133
01:05:35,520 --> 01:05:38,800
Speaker 1: why they come to social media. You know, I don't

1134
01:05:38,800 --> 01:05:43,080
Speaker 1: think he understands like why people come to Twitter. No,

1135
01:05:43,520 --> 01:05:46,560
Speaker 1: I think that was clear from how he has the

1136
01:05:46,640 --> 01:05:48,120
Speaker 1: changes he has made on Twitter that he's not He

1137
01:05:48,160 --> 01:05:51,120
Speaker 1: doesn't understand like what makes it good. And I think

1138
01:05:51,600 --> 01:05:53,600
Speaker 1: you know, when people come to a platform like TikTok,

1139
01:05:53,920 --> 01:05:59,560
Speaker 1: if TikTok was all garbage videos, people wouldn't stay there, right,

1140
01:05:59,600 --> 01:06:01,560
Speaker 1: Like if it was just like videos of like stuff

1141
01:06:01,600 --> 01:06:05,240
Speaker 1: that was upsetting and you know, it might be it

1142
01:06:05,320 --> 01:06:09,160
Speaker 1: might generate engagement perhaps, but it does it won't make

1143
01:06:09,200 --> 01:06:11,680
Speaker 1: people feel good about being there. And so it almost

1144
01:06:11,680 --> 01:06:14,440
Speaker 1: feels like Elon Musk's strategy is like how can I

1145
01:06:14,520 --> 01:06:18,320
Speaker 1: make people feel bad? And how can I make showing

1146
01:06:18,400 --> 01:06:22,920
Speaker 1: up to this platform feel not good? You know, I

1147
01:06:22,920 --> 01:06:25,800
Speaker 1: just don't think that he's someone who understands what people

1148
01:06:26,000 --> 01:06:28,720
Speaker 1: are coming to social media platforms for, what they want

1149
01:06:28,720 --> 01:06:30,680
Speaker 1: to feel when they come to those platforms and what

1150
01:06:30,800 --> 01:06:31,520
Speaker 1: keeps them there.

1151
01:06:31,840 --> 01:06:36,960
Speaker 2: Yeah, it's it's not surprising that Twitter's new feature is

1152
01:06:37,240 --> 01:06:41,440
Speaker 2: stupid and bad and like offensive and mean. That's just

1153
01:06:41,520 --> 01:06:45,520
Speaker 2: like what he's going for. But I also just want

1154
01:06:45,560 --> 01:06:48,200
Speaker 2: to take a minute to step back and reflect on

1155
01:06:48,560 --> 01:06:52,600
Speaker 2: how we've all just accepted as normal that these big

1156
01:06:52,960 --> 01:06:58,640
Speaker 2: legacy social media platforms like Twitter, companies like Meta are

1157
01:06:59,240 --> 01:07:03,160
Speaker 2: they're just a band in any pretense of innovating, and

1158
01:07:03,200 --> 01:07:08,120
Speaker 2: they're just like aggressively copying each other. It's like that

1159
01:07:08,280 --> 01:07:12,120
Speaker 2: trend in the mid aus when like every restaurant had

1160
01:07:12,120 --> 01:07:14,720
Speaker 2: to become every other restaurant and like they all started

1161
01:07:14,760 --> 01:07:18,680
Speaker 2: selling chicken sandwiches and burgers. It's like every social media

1162
01:07:18,720 --> 01:07:22,600
Speaker 2: platform has to be exactly the same and like have

1163
01:07:22,680 --> 01:07:27,640
Speaker 2: the same functionality of everyone, and there's there's no innovation,

1164
01:07:27,800 --> 01:07:33,600
Speaker 2: there's just copycat And like if that if those sorts

1165
01:07:33,600 --> 01:07:37,000
Speaker 2: of videos were what I wanted, why would I go

1166
01:07:37,040 --> 01:07:39,760
Speaker 2: to Twitter? Why wouldn't I just look at it on TikTok?

1167
01:07:40,000 --> 01:07:46,560
Speaker 2: Like as a user of social media platforms, I kind

1168
01:07:46,600 --> 01:07:49,240
Speaker 2: of like that they're different, right, Like if they were

1169
01:07:49,280 --> 01:07:55,080
Speaker 2: all the same, that would be boring, And somehow, over

1170
01:07:55,120 --> 01:07:59,120
Speaker 2: the past like year or two, that just became the

1171
01:08:00,240 --> 01:08:05,680
Speaker 2: standard strategy for these big legacy companies and you would

1172
01:08:05,720 --> 01:08:08,320
Speaker 2: expect them to be embarrassed about it, but they're just

1173
01:08:08,480 --> 01:08:13,840
Speaker 2: like upfront and open about the fact that they do

1174
01:08:13,920 --> 01:08:16,720
Speaker 2: not have any ideas, that all they can do is

1175
01:08:16,800 --> 01:08:20,880
Speaker 2: try to like steal, steal the idea of somebody else.

1176
01:08:21,840 --> 01:08:24,200
Speaker 1: It reminds me so much of this article that I

1177
01:08:24,240 --> 01:08:28,479
Speaker 1: read back when Instagram last year, when Instagram basically said

1178
01:08:28,479 --> 01:08:30,680
Speaker 1: that they wanted to become TikTok and they were going

1179
01:08:30,720 --> 01:08:32,960
Speaker 1: to focus on video. I forget who said it was

1180
01:08:33,000 --> 01:08:36,200
Speaker 1: a great quote. He said that Facebook doesn't know why

1181
01:08:36,280 --> 01:08:39,599
Speaker 1: it is that people come to Instagram and they're trying

1182
01:08:39,640 --> 01:08:43,640
Speaker 1: to become more like TikTok. It's as if your mistress

1183
01:08:43,760 --> 01:08:46,680
Speaker 1: got plastic surgery to look more like your wife. You

1184
01:08:46,720 --> 01:08:49,439
Speaker 1: would be like, don't you understand what this is? Don't

1185
01:08:49,439 --> 01:08:52,599
Speaker 1: you understand? Like why I'm why I'm coming to you.

1186
01:08:52,760 --> 01:08:54,000
Speaker 1: This is not what I want at all.

1187
01:08:56,640 --> 01:09:01,920
Speaker 2: That's a good analogy.

1188
01:09:02,600 --> 01:09:05,639
Speaker 1: In our last newscast, we put out a call that

1189
01:09:06,400 --> 01:09:11,000
Speaker 1: people like Elon Musk, RFK, Joe Rogan. I might throw

1190
01:09:11,040 --> 01:09:15,800
Speaker 1: Glenn Greenwald in there, that like brand of guy. We

1191
01:09:15,880 --> 01:09:18,559
Speaker 1: don't have a name for that brand of guy. We

1192
01:09:18,680 --> 01:09:21,400
Speaker 1: have names for other brands of guy, like we've got.

1193
01:09:21,760 --> 01:09:24,720
Speaker 1: If I say Bernie bro you probably conjure up in

1194
01:09:24,800 --> 01:09:27,360
Speaker 1: your head the kind of guy I'm talking about. If

1195
01:09:27,400 --> 01:09:30,120
Speaker 1: I say, oh, that guy is a member of the

1196
01:09:30,160 --> 01:09:32,640
Speaker 1: dirt Bag Left, you probably have an idea in your

1197
01:09:32,680 --> 01:09:34,400
Speaker 1: head of what I'm talking about. But we don't have

1198
01:09:34,439 --> 01:09:38,720
Speaker 1: a name for that particular sub section of guy and

1199
01:09:38,760 --> 01:09:42,439
Speaker 1: I think we need one. And actually, listener Elizabeth dmd

1200
01:09:42,439 --> 01:09:44,800
Speaker 1: me on Instagram and said that she thinks they should

1201
01:09:44,840 --> 01:09:47,880
Speaker 1: be called tinker bells because quote, they don't feel like

1202
01:09:47,920 --> 01:09:50,280
Speaker 1: they exist if they aren't filling the internet with their

1203
01:09:50,320 --> 01:09:53,720
Speaker 1: hot takes, which I love. Thank you, Elizabeth. It is

1204
01:09:53,840 --> 01:09:57,120
Speaker 1: finally nice to have a collective term for your Elon's,

1205
01:09:57,200 --> 01:09:59,639
Speaker 1: your Joe Rogan's, and the rest of the tinker bells.

1206
01:10:00,080 --> 01:10:03,360
Speaker 2: Yeah, it's a great term, Thank you, Elizabeth. Yeah, now

1207
01:10:03,400 --> 01:10:05,160
Speaker 2: we got a term for these tinker bills.

1208
01:10:05,680 --> 01:10:08,280
Speaker 1: Well, thank you so much for listening, and Mike, as always,

1209
01:10:08,400 --> 01:10:09,880
Speaker 1: thanks for going through these stories with me.

1210
01:10:10,200 --> 01:10:12,160
Speaker 2: Thanks for having me here, Bridget it was a pleasure.

1211
01:10:14,200 --> 01:10:16,320
Speaker 1: As you can probably tell, it was kind of a

1212
01:10:16,360 --> 01:10:18,559
Speaker 1: big news week this week, and there were so many

1213
01:10:18,600 --> 01:10:20,439
Speaker 1: stories we wanted to talk about that we couldn't get

1214
01:10:20,439 --> 01:10:22,879
Speaker 1: to them all here. So to hear the full rundown,

1215
01:10:23,040 --> 01:10:26,160
Speaker 1: check out our Patreon at patreon dot com. Slash ten Goody.

1216
01:10:29,439 --> 01:10:31,559
Speaker 1: If you're looking for ways to support the show, check

1217
01:10:31,560 --> 01:10:34,200
Speaker 1: out our mark store at tenggodi dot com. Slash store.

1218
01:10:35,360 --> 01:10:37,400
Speaker 1: Got a story about an interesting thing in tech, or

1219
01:10:37,479 --> 01:10:39,320
Speaker 1: just want to say hi? You can reach us at

1220
01:10:39,360 --> 01:10:42,080
Speaker 1: Hello at tenggody dot com. You can also find transcripts

1221
01:10:42,080 --> 01:10:44,559
Speaker 1: for today's episode at tengody dot com. There Are No

1222
01:10:44,600 --> 01:10:46,719
Speaker 1: Girls on the Internet was created by me Bridget Tod.

1223
01:10:47,040 --> 01:10:50,240
Speaker 1: It's a production of iHeartRadio and Unboss Creative, edited by

1224
01:10:50,280 --> 01:10:54,360
Speaker 1: Joey pat Jonathan Strickland as our executive producer. Tari Harrison

1225
01:10:54,439 --> 01:10:57,200
Speaker 1: is our producer and sound engineer. Michael Almada is our

1226
01:10:57,200 --> 01:11:00,320
Speaker 1: contributing producer. I'm your host, Bridget Todd. If you want

1227
01:11:00,320 --> 01:11:02,720
Speaker 1: to help us grow, rate and review us on Apple Podcasts.

1228
01:11:03,479 --> 01:11:06,280
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1229
01:11:06,320 --> 01:11:09,840
Speaker 1: Apple Podcasts, or wherever you get your podcasts.