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Speaker 1: There Are No Girls on the Internet. As a production

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

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Speaker 1: is there Are No Girls on the Internet. Happy New Year.

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Speaker 1: Now that the holidays are officially over, I'm looking at

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Speaker 1: what's in and what's out in tech for twenty twenty four.

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Speaker 1: And to do that I joined my friends Samantha and

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Speaker 1: Annie over at the podcast stuff. Mom never told you

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Speaker 1: to run through what we should be leaving in twenty

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Speaker 1: twenty three, I gotta ask what tech things are you

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Speaker 1: leaving in twenty twenty three? After you listen to the episode,

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Speaker 1: be sure to let me know.

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Speaker 2: For the topic today that you brought us. You're talking

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Speaker 2: about some of the things tech in the tech world

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Speaker 2: that we should leave behind.

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Speaker 3: Are wont exactly?

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Speaker 4: Yeah?

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Speaker 1: If you all know that cartoon that you might see

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Speaker 1: on Twitter a lot where it's like a woman stepping

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Speaker 1: from one year to the next, and behind her she's

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Speaker 1: leaving behind all of this baggage from the previous year,

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Speaker 1: like fake friends or heartbreak, and then across over her shoulder,

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Speaker 1: she's carrying what she's carrying into the next year. So

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Speaker 1: like love or focus. Do you know the image that

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Speaker 1: I'm talking about.

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Speaker 4: I don't.

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Speaker 2: I had never seen it, but I'm looking at it now.

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Speaker 4: Yes, So buffet about that image.

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Speaker 1: It was created by a British Ghanaian graphic artist named

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Speaker 1: Penniel and Chill and it first appeared on the internet

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Speaker 1: in twenty fourteen, and Buzzby has a great interview with her.

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Speaker 1: But basically, this image has been totally memified and it

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Speaker 1: kind of pops up around this time of year every

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Speaker 1: year to sort of fit the theme of like new Year,

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Speaker 1: new me. Sometimes it's a joke, which this artist says

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Speaker 1: that she doesn't always agree with or like the way

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Speaker 1: that people like do their spins on it. But it's

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Speaker 1: really become a part of the shorthand of this time

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Speaker 1: of year of like what we're leaving behind and what

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Speaker 1: we're taking with us into the new year. So when

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Speaker 1: that picture was making its rounds recently around this time

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Speaker 1: of year, you know, I had just wrapped up podcasting

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Speaker 1: for my own podcast. There are no girls on the

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Speaker 1: internet talking about technology and identity and really asking questions

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Speaker 1: about like what we get right and what we get

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Speaker 1: wrong you know, in those in those arenas. So this

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Speaker 1: image got me thinking what tech things should just stay

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Speaker 1: in twenty twenty three, and that we should not be

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Speaker 1: bringing with us into twenty twenty four. So I've got

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Speaker 1: my list of things that should stay. We're like, we're

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Speaker 1: done with them, We're not, we should not be bringing

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Speaker 1: them with us into the new year.

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Speaker 2: Yeah, And I was thinking about this too. This has

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Speaker 2: been a big year for tech. This has felt like

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Speaker 2: a very monumental like a lot of things are changing,

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Speaker 2: and still looking at this list, I was like, oh, yeah, yah, Yeah,

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Speaker 2: it can feel strange because time feels so strange now,

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Speaker 2: but that did all happen this year. Yeah, So what

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Speaker 2: is on your list? Let's get into it.

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Speaker 1: So, as you said, like, it's been a big year

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Speaker 1: for conversation around technology, that they're big right now, and

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Speaker 1: I would say there probably is not a bigger conversation

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Speaker 1: than the one happening around AI. Right So we're all

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Speaker 1: like rightly talking about how AI could change the way

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Speaker 1: that we do our jobs or the way that artistic

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Speaker 1: projects get created, and those are all important conversations to

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Speaker 1: be having, But there is one very serious conversation about

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Speaker 1: the reality of AI, and that is how AI is

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Speaker 1: being used to do things like violate consent and further

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Speaker 1: use technology to make it seem like our bodies are

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Speaker 1: just like up for grabs once we show up online.

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Speaker 4: So case in point newdify apps.

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Speaker 1: So newdify apps are kind of the catch all term

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Speaker 1: for apps or platforms that promise to use AI to

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Speaker 1: generate non consensual nude or semi nude images of anyone.

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Speaker 1: These kinds of apps have been exploding in popularity. In

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Speaker 1: September alone, twenty four million people visited newdify apps or

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Speaker 1: undressing websites, according to the social network analysis company Graphica.

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Speaker 1: So in their analysis they really talk about how we've

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Speaker 1: how this year specifically, we've seen this shift where new

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Speaker 1: toify apps kind of went from this niche, underground custom

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Speaker 1: thing where like if you were like a singular creep

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Speaker 1: who had a singular fixation on one person, you could

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Speaker 1: find a marketplace for non consensual images to be made

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Speaker 1: of somebody, but it was like a niche custom thing.

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Speaker 1: Now in twenty twenty three, we have really seen that

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Speaker 1: shift to where these are fleshed out monetized online businesses

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Speaker 1: complete with advertising apparatuses. Graphica found that the volume of

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Speaker 1: referral link spam for these kinds of services has increased

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Speaker 1: by more than two thousand percent on social media platforms

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Speaker 1: like Reddit and Twitter since the beginning of twenty twenty three,

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Speaker 1: and this set of fifty two Telegram groups used to

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Speaker 1: access non consensual image. Sites like these contain at least

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Speaker 1: one million users as of September of twenty twenty three,

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Speaker 1: so they have really exploded in popular Yet I think

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Speaker 1: that we're only now, like only recently, come to have

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Speaker 1: any kind of conversation about what that means, not just

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Speaker 1: for you know, the women who are overwhelmingly targeted by

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Speaker 1: these kinds of apps, but also what it means for

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Speaker 1: our digital landscape more broadly.

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Speaker 3: Okay, I'm not gonna lie.

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Speaker 5: None of this is familiar to me as many social

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Speaker 5: media things as I'm involved in.

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Speaker 3: Didn't know this was a thing. Didn't know.

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Speaker 1: It's pretty despicable. It first got on my radar. It

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Speaker 1: first seemed to be rolled out to like more mainstream

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Speaker 1: platforms with celebrities, so it'll be like, oh, you can

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Speaker 1: get a nude of any celebrity. And now it's like,

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Speaker 1: not just celebrities, it's like anybody who can get it.

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Speaker 1: You can generate a non consensual nude of anybody. And

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Speaker 1: so one of the ways that this is becoming more

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Speaker 1: and more ubiquitous is just seeing the advertisements for it

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Speaker 1: on social media. After some journalists at Bloomberg were looking

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Speaker 1: into the popularity kinds of apps, they contacted both TikTok

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Speaker 1: and Facebook to ask them about you know, it's okay.

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Speaker 4: It seems like these really.

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Speaker 1: Gross apps are being allowed to advertise on your platforms.

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Speaker 1: And both Facebook and TikTok I will say, you know,

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Speaker 1: to their credit when Bloomberg reached out to them, they

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Speaker 1: both did some work to block search terms related to

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Speaker 1: newify apps, but one social media platform notably did not

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Speaker 1: do that. Can you guess what platform? That was?

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Speaker 3: Probably Twitter? Obviously I'm not calling name, No, let's.

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Speaker 4: Call on Twitter still, so it was Twitter.

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Speaker 1: They were like, we're actually fine with these neuify apps, Like,

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Speaker 1: we don't see a problem.

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Speaker 4: So this is from vice.

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Speaker 1: Searching the word undress on TikTok brings up no results

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Speaker 1: in either the top or video tabs. Instead, the platform

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Speaker 1: warns users that the phrase may be associated with activity

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Speaker 1: that violates the platform's guidelines. Searching the same term on

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Speaker 1: Instagram similarly brings up no results. Searching undress on Twitter, however,

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Speaker 1: readily surfaces a verified account with nearly twenty thousand followers

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Speaker 1: promoting new toify app services. So let's say that you

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Speaker 1: were to search Twitter for the word in dress instead

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Speaker 1: of undress. Twitter is actually like, wait, were you actually

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Speaker 1: meeting to search for undressed? And it prompts you to

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Speaker 1: search for undress instead. So where other platforms are like, no,

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Speaker 1: we can't have that on our on our platforms, Twitter

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Speaker 1: is like not just allowing it on platforms, but like

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Speaker 1: helping people to search it when they when they get

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Speaker 1: it wrong. And so you might see people if you

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Speaker 1: ever see newify apps advertise on these platforms, sometimes they

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Speaker 1: like the a word is misspelled, or like there's a

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Speaker 1: space between a couple some of the letters to try

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Speaker 1: to evade being picked up and like knocked off the platform.

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Speaker 4: But it generally.

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Speaker 1: This does not seem like Twitter has a problem with

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Speaker 1: these kinds of services being advertised on their platform.

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Speaker 5: And I feel like this is a big question with

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Speaker 5: like legalities, especially on public platforms like that in general.

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Speaker 5: But like I'm talking about doing news and such like it.

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Speaker 3: Seems like it should be illegal.

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Speaker 5: It seems like this could be one of those things,

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Speaker 5: especially like if you think about revenge born and all

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Speaker 5: of that being illegal in so many places, Like, is

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Speaker 5: that not a thing?

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Speaker 2: Is that?

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Speaker 5: Can you not take it to court or at least

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Speaker 5: try to stop these images from happening?

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Speaker 4: So that is a great question.

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Speaker 1: When I first saw these, I was like, this has

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Speaker 1: got to be illegal, And it turns out that right now,

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Speaker 1: depending on where you live, it is probably not illegal

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Speaker 1: to do this.

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Speaker 4: It depends on your jurisdiction. But there is.

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Speaker 1: Currently no federal law criminalizing using AI to generate non

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Speaker 1: consensual deep fake images. Representative Evet Clark out of New

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Speaker 1: York has actually proposed legislation that would criminalize making, you know,

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Speaker 1: non consensual deep fakes, but as of right now, kind

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Speaker 1: of unbelievably, there is not any legislation federally that prevents

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Speaker 1: somebody from doing that. And Yeah, I just think that, like,

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Speaker 1: as this kind of technology becomes more ubiquitous in our culture,

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Speaker 1: I think it adds to this idea that just by

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Speaker 1: showing up on the Internet, women are fair game for

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Speaker 1: anybody who wants to sexualize us, and that I think

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Speaker 1: it's getting to be getting to a point where the

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Speaker 1: idea is like, well, if you didn't want someone to

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Speaker 1: use your images in that way?

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Speaker 4: Why did you post them to Instagram? Right?

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Speaker 5: Like?

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Speaker 1: And I think that we really got to have a

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Speaker 1: real serious think and a serious conversation about if that

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Speaker 1: is going to be a social media climate that we want.

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Speaker 4: And I think that that kind of thinking is the kind.

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Speaker 1: Of thinking that we need to leave behind in twenty

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Speaker 1: twenty three, right, Like, women should be able to show

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Speaker 1: up online without non consensual sexualization just being the cost

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Speaker 1: of showing up, and so let's leave that behind in

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Speaker 1: twenty twenty four.

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Speaker 4: We are not bringing it with.

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Speaker 2: Us, please please, Yeah. And that's one of the very

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Speaker 2: frustrating thing since I did see a lot about this,

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Speaker 2: and we even had an episode bridget about it about

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Speaker 2: journalists getting targeted by stuff, yeah, and revenge porn and

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Speaker 2: things like that, and this is just making it so

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Speaker 2: much easier and it's harder to for some people, for

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Speaker 2: all of us to ascertain like what is real and

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Speaker 2: what is not real. But it does kind of disproportionately

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Speaker 2: impact women and marginalized folks. And that's just one example

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Speaker 2: of technology doing that that you have on your list.

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Speaker 1: Right, yes, And I think, like, just like what you said,

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Speaker 1: I think that there are specific groups that we see

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Speaker 1: being targeted for this kind of harm first, and then it's.

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Speaker 4: Like, oh nah.

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Speaker 1: And then like when those groups, when it's allowed to

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Speaker 1: happen to those groups, it's like, oh, well, you know,

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Speaker 1: nobody really does anything. And then it's like, oh, surprise,

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Speaker 1: Now it's everybody's problem. Now we just live in a

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Speaker 1: society where like this is commonplace, and like maybe when

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Speaker 1: this was happening to specific groups of people, if you

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Speaker 1: had done something and take it seriously, then we wouldn't

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Speaker 1: have allowed it to just become ubiquitous. Right, And I

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Speaker 1: don't think anybody wants to live in a culture where

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Speaker 1: anybody is fair game just because they were all they

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Speaker 1: put a picture of themselves on the Internet, to have

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Speaker 1: that picture be distorted and sexualized.

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Speaker 4: So absolutely right.

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Speaker 1: So that brings me to another thing that we should

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Speaker 1: not be taking with us into twenty twenty four, and

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Speaker 1: that as content moderation policies that really hurt women and

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Speaker 1: other marginalized people.

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Speaker 4: So, as you were talking.

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Speaker 1: About with AI right now, AI is used in content

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Speaker 1: moderation that really does a lot of the work of

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Speaker 1: deciding what gets amplified and what gets suppressed on social media.

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Speaker 1: This technology, however, also objectifies women's bodies, and it's much

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Speaker 1: more likely to flag images that involve women or include

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Speaker 1: women as racy or inappropriate, and thus those images are

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Speaker 1: more likely to be suppressed on social media sites. So

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Speaker 1: The Guardian actually put together like a really interesting investigation

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Speaker 1: into this, where they had journalists use AI tools to

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Speaker 1: analyz hundreds of photos of men and women in their underwear,

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Speaker 1: working out using medical tests and with partial nudity, and

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Speaker 1: found that the AI used will tag photos of women

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Speaker 1: in everyday situations as sexually suggestive. So I'm talking about

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Speaker 1: images of like women in their underwear fully covered, or

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Speaker 1: women working out at the gym fully covered, right, like

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Speaker 1: images that we would recognize as not racy, but because

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Speaker 1: they include women, the tools that are being used to

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Speaker 1: make decisions about how content is moderated will be like, no,

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Speaker 1: that's racy, can't have that.

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Speaker 4: On social media.

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Speaker 1: And so as a result of this, social media companies

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Speaker 1: that leverage these algorithms have suppressed honestly countless images of

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Speaker 1: featuring women's bodies. We know that this hurts women led businesses,

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Speaker 1: and this is reading about how a shapewear company essentially

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Speaker 1: can't advertise on social media because images of women's bodies

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Speaker 1: fully covered in shapewear will just be suppressed by these algorithms,

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Speaker 1: and they essentially cannot advertise their product on social media,

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Speaker 1: which is like where you advertise products in twenty twenty three.

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Speaker 1: This also like not only does it hurt female led businesses,

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Speaker 1: it also has medical impacts. They found that this disparity

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Speaker 1: is also true for medical images. AI was shown images

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Speaker 1: from the US National Cancer Institute demonstrating how to do

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Speaker 1: a clinical breast exam. Google's AI gave this photo the

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Speaker 1: highest score for raciness. Microsoft's AI was eighty two percent

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Speaker 1: confident that these images of women doing breast exams was

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Speaker 1: explicit sexually in nature, and Amazon classified it as representing

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Speaker 1: explicit nudity. This is also true for pregnant bellies. If

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Speaker 1: you are heavily pregnant and showing a pregnant belly on

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Speaker 1: social media platforms, AI is much more likely to deem

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Speaker 1: that image to be racy and then suppress that in

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Speaker 1: their content moderation tools.

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Speaker 4: And so you really get a sense.

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Speaker 1: Of the way that these platforms are creating a disproportion

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Speaker 1: it cost for being a woman who shows up online

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Speaker 1: like it prevents women from being able to express themselves.

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Speaker 1: It prevents women from being able to get medical information

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Speaker 1: about our bodies, and ultimately it's just not fair, like

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Speaker 1: we shouldn't have to deal with this just because we

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Speaker 1: showed up on our social media platform with our bodies,

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Speaker 1: Like there's nothing wrong with women's bodies, They're not racy

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Speaker 1: or explicit just by us having.

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Speaker 2: Them right, right, And I believe Smith and I we

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Speaker 2: talked about this about YouTube because YouTube had a similar

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Speaker 2: strange thing that was happening where it was flagging videos

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Speaker 2: of children the young girls. Is just like, this is

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Speaker 2: so sexual and it's just like literally young girls, and

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Speaker 2: it's it's telling to what is going on in our society,

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Speaker 2: the problems of objectifying and sexualizing women. But also, this

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Speaker 2: is a big thing that we talked about with you,

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Speaker 2: Bridget when it matters who's making these things, who's doing

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Speaker 2: this stuff. And AI is a pretty new space, but

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Speaker 2: I've already seen a lot of conversations about about the

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Speaker 2: importance of who is working on it. So that's part

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Speaker 2: of what is going on here, right totally.

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Speaker 1: So The Guardian spoke to Margaret Mitchell of the AI

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Speaker 1: company Hugging Face, who said that she believes that the

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Speaker 1: photos used to train these algorithms were probably just being

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Speaker 1: labeled by straight men who probably associate men working out

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Speaker 1: with fitness, but maybe consider an image of a woman

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Speaker 1: working out as being like racy or sexual or explicit,

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Speaker 1: even though it's like the same theme, the same content.

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Speaker 1: And so it's possible that these ratings seem gender biased

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Speaker 1: in the US and in Europe because the labelers might

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Speaker 1: be from a place that might have more conservative cultures, right,

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Speaker 1: And so yeah, it really matters who is building technology,

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Speaker 1: who's in the room and the technology gets built, who

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Speaker 1: is training it, who is rolling it out, who is

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Speaker 1: thinking about it, who's writing about it, who's talking about it.

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Speaker 1: If these people are mostly men, then like, of course

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Speaker 1: women and other marginalized people are not showing up in

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Speaker 1: an equitable way. With all the conversations that we have

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Speaker 1: around things like inclusion and diversity and equity and tech,

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Speaker 1: I don't I'm not like harping on those just because

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Speaker 1: it's like nice to do or it's like the right

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Speaker 1: thing to do, which it is it is because eventually

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Speaker 1: the technology that gets built is worse, is less inclusive,

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Speaker 1: is more dangerous, like it includes less people.

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Speaker 4: And that ends up hurting all of us. Yes, it does.

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Speaker 2: And speaking of that, you have another point on here,

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Speaker 2: going back to something we were talking about earlier about

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Speaker 2: women in journalism.

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Speaker 1: Yes, yes, so I'm so glad that was a great transition,

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Speaker 1: which is that you know, it's like one of the

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Speaker 1: reasons why I started my own podcast about this is

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Speaker 1: that we unfortunately have a tech culture that can treat

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Speaker 1: women like perpetual outsiders, right, whether by accident or with intention,

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Speaker 1: and that is something that we gotta leave in twenty

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Speaker 1: twenty three, really because I have left it like in

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Speaker 1: like many many many years past, but this is the

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Speaker 1: time that we should be leaving it in the past

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Speaker 1: because exactly what you said, it is so critical. These

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Speaker 1: tools are going to be shaping our world and how

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Speaker 1: we understand our world. So we've got to make sure

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Speaker 1: that people who are publicly talking about it and are

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Speaker 1: included in that conversation are done so in a way

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Speaker 1: that does not treat them like perpetual outsiders. And so

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Speaker 1: women who are working for that AI company that I

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Speaker 1: mentioned before, hugging face, but you can sort of think

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Speaker 1: of as like a competitor to open Ai, the company

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Speaker 1: that makes chat GPT, which is run by that.

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Speaker 4: Guy Sam Altman.

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Speaker 1: Hugging Face has a lot of women who work there,

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Speaker 1: and these women do a ton of.

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Speaker 4: Like public speaking about tech and AI.

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Speaker 1: And the media, which is great, and I also think

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Speaker 1: again it's important because it can help to change the

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Speaker 1: face of like who we think of as somebody who

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Speaker 1: gets to speak or gets to have.

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Speaker 4: An opinion when it comes to technology, and that's great.

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Speaker 1: However, the women at Hugging Face also noticed that when

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Speaker 1: they were doing public speaking about AI with the press,

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Speaker 1: times would get like sexist or otherwise kind of messed

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Speaker 1: up questions during interviews that just like really highlighted that

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Speaker 1: they were not necessarily being treated like people who belonged

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Speaker 1: in the space. Margaret Mitchell, she is Hugging Faces chief

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Speaker 1: ethics scientist, framed it as a research question. She asked

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Speaker 1: what are patterns and how journalists talk to and about

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Speaker 1: women and AI. She found that compared to male peers,

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Speaker 1: there is a disproportionate focus from the press on their ages,

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Speaker 1: their motherhood, their physical appearance or behaviors, their failures, and

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Speaker 1: what AI gossip they could provide, rather than their like

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Speaker 1: technical work. And these are people who are incredibly accomplished.

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Speaker 1: They're like doing very important work that they've gone to

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Speaker 1: school for been trained for in an article.

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Speaker 4: If you get to interview.

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Speaker 1: Them and you ask about their age, or you ask

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Speaker 1: if they have kids, or you ask you know, about

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Speaker 1: their the way they look, it's it's so limiting because

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Speaker 1: it's like you have somebody who has dedicated their life

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Speaker 1: to this very important technology and this is what you

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Speaker 1: ask them. Like it's such a it's such a miss

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Speaker 1: on part on the part of the journalists.

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Speaker 5: It's like, I thought you by now at this point

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Speaker 5: in AI there would be more things to talk about.

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Speaker 5: There's so many questions that we should be asking again,

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Speaker 5: things like how are how are sexist issues being handled

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Speaker 5: in AI? How are you protecting women in AI? And

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Speaker 5: not about the individual's person, be like, so you got

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Speaker 5: a kid, you gotta be on the computer while you

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Speaker 5: have a kid, how.

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Speaker 3: Are you gonna do that? Like, well, this seems so

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Speaker 3: like far FoST Like why is this? Is this a

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Speaker 3: s good?

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Speaker 4: Are you?

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Speaker 3: Are we still doing this?

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Speaker 4: What's frustrating? It's like they would never ask a man.

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Speaker 1: If you were talking to like a powerful man, you

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Speaker 1: would never be like, oh, well, who watches your kids

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Speaker 1: while you're working?

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Speaker 4: Or like how do how do how do you juggle

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Speaker 4: being a dad and like being a scientist?

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Speaker 1: Like these are things that would not that like would

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Speaker 1: not come up. They're only coming up because they're women.

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Speaker 1: Doctor Sasha Lucioni, who was an AI researcher and a

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Speaker 1: climate lead at hucking Face, was in this like pretty

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Speaker 1: glowing piece for Adweek. The piece was great, but the

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Speaker 1: headline to the article read this AI ethics expert juggles

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Speaker 1: motherhood and a tech career and people had to like

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Speaker 1: raise hell to get them to change it. And yeah,

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Speaker 1: it's just like I do think that there is a

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Speaker 1: place for conversations about what it looks like to juggle

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Speaker 1: career and family and all of that, But those are

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Speaker 1: not conversations that should only be happening to women, and

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Speaker 1: there's a time and a place for them, right, Like

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Speaker 1: if you're meant to be interviewing somebody about their technical prowess,

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Speaker 1: that I don't see how that is is relevant to

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Speaker 1: their technical expertise.

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Speaker 5: Right, And that piece in the self the title is

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Speaker 5: so condescending, is that that's note like.

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Speaker 3: Aw, look at you how cute? Look at you doing that?

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Speaker 5: You go, you go girl, I'm so proud of you

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Speaker 5: instead of seeing that professional scientists who has more degrees

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Speaker 5: and more experience than the person who actually probably wrote

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Speaker 5: the article. I don't know for sure, but just that

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Speaker 5: level of like proble, Wow, really, what are you doing?

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Speaker 5: Like is it because you feel insecure that you need

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Speaker 5: to be condescending but maybe like passive aggressively like but no, no, no,

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Speaker 5: I'm really impressed.

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Speaker 1: Really, it is so condescending, and so, in an effort

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Speaker 1: to help the space be better, the women who work

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Speaker 1: at Hugging Face actually developed a guide for journalists to

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Speaker 1: help get it right, to help create a better dynamic

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Speaker 1: so that it's not just condescending question and sexist question

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Speaker 1: after sexist question. The guide reads the real achievements of

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Speaker 1: women on our team often get overshadowed by a focus

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Speaker 1: on personal and sometimes very intrusive details that are not

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Speaker 1: relevant to their work. With all the amazing press attention

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Speaker 1: we get at Hugging Face, we're bound to see some

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Speaker 1: journalists rely on outdated tropes. Lately, we've seen more reporters

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Speaker 1: ignoring our amazing achievements of our ses and theays and

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Speaker 1: instead focusing on stereotypes in tech. One of the things

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Speaker 1: that I love about the guys that they put out

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Speaker 1: is that it really highlights the importance of not treating

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Speaker 1: tech like a place where women don't belong. And so,

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Speaker 1: for instance, the guide reads, don't rely on antiquated stereotypes

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Speaker 1: about women in tech. This includes describing women as outsiders

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Speaker 1: in the field, which only serves to reinforce the idea

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Speaker 1: that women don't belong in tech. An example of a

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Speaker 1: problematic sentence they give despite being a woman in a

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Speaker 1: male dominated field, brook Brookie has made a name for

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Speaker 1: herself in the tech industry better Is through her brilliant

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Speaker 1: results on magic and large language models. Brooke Brookie made

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Speaker 1: a name for herself in the tech industry. And so

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Speaker 1: this I think is really key, and I think people

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Speaker 1: do this without even really meaning to you, is that

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Speaker 1: the tech industry, women and queer folks and trans folks

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Speaker 1: and black folks and folks with disabilities and all different

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Speaker 1: kinds of people who are traditionally marginaliznsed in our society

441
00:22:56,640 --> 00:22:59,720
Speaker 1: have been at the beginning of technology and have been

442
00:23:00,160 --> 00:23:02,520
Speaker 1: the very start. We have this idea that like, oh,

443
00:23:02,560 --> 00:23:05,280
Speaker 1: tech is this like white male CIS boys club and

444
00:23:05,320 --> 00:23:07,520
Speaker 1: anybody else is trying to break their way in, and

445
00:23:07,560 --> 00:23:10,320
Speaker 1: that's I can understand why people feel that way, But

446
00:23:10,880 --> 00:23:12,760
Speaker 1: we have been there from the very beginning. And if

447
00:23:12,880 --> 00:23:15,800
Speaker 1: if you don't always hear our stories or our voices,

448
00:23:15,880 --> 00:23:18,840
Speaker 1: it's not because we're not there, it's because specific choices

449
00:23:18,880 --> 00:23:21,239
Speaker 1: have been made to keep to like sideline us and

450
00:23:21,280 --> 00:23:24,560
Speaker 1: to turn down our voices. And so really starting from

451
00:23:24,560 --> 00:23:27,480
Speaker 1: a place that like, we do belong in these conversations,

452
00:23:27,480 --> 00:23:29,760
Speaker 1: we do belong in tech, we do belong in the sector.

453
00:23:29,960 --> 00:23:34,040
Speaker 1: We're not outsiders, I think is really really important, and

454
00:23:34,280 --> 00:23:37,960
Speaker 1: it also just really matters. You know, if the people

455
00:23:37,960 --> 00:23:41,680
Speaker 1: who are building technology and talking about technology and thinking

456
00:23:41,760 --> 00:23:45,880
Speaker 1: about technology are only one type of person, the tech

457
00:23:45,880 --> 00:23:46,400
Speaker 1: that gets.

458
00:23:46,240 --> 00:23:47,879
Speaker 4: Built is going to be a whole lot worse.

459
00:23:47,920 --> 00:23:50,760
Speaker 1: And so all of us benefit when more voices are

460
00:23:50,800 --> 00:23:54,600
Speaker 1: included and feel included and feel empowered to join the conversation.

461
00:23:54,960 --> 00:23:57,080
Speaker 5: You would think that would be an obvious and also

462
00:23:57,119 --> 00:23:59,280
Speaker 5: that it would be it would save money, It would

463
00:23:59,280 --> 00:24:03,160
Speaker 5: save money, and all the revamping and redoing of everything

464
00:24:03,200 --> 00:24:06,800
Speaker 5: that you knew, well I would expect you knew. But

465
00:24:07,160 --> 00:24:09,720
Speaker 5: that could go wrong when you don't have the right people,

466
00:24:09,880 --> 00:24:13,080
Speaker 5: or all the people at least represented in this conversation,

467
00:24:13,200 --> 00:24:15,920
Speaker 5: especially if you're wanting these people to use your technology.

468
00:24:20,560 --> 00:24:23,200
Speaker 1: You're so right, And like, what's interesting is I once

469
00:24:23,280 --> 00:24:26,199
Speaker 1: read this book called Mothers of Invention, all about how

470
00:24:26,720 --> 00:24:31,360
Speaker 1: things like misogyny and bias around women stifle innovation. And

471
00:24:32,000 --> 00:24:35,000
Speaker 1: something that's really struck me from that book is how

472
00:24:35,080 --> 00:24:37,960
Speaker 1: much money gets left on the table because of things

473
00:24:38,000 --> 00:24:40,680
Speaker 1: like gender bias and misogyny. It's like, you would think

474
00:24:40,680 --> 00:24:43,920
Speaker 1: in a capitalistic society that for profit companies would want

475
00:24:44,119 --> 00:24:47,359
Speaker 1: money above anything. You might be surprised because they are

476
00:24:47,400 --> 00:24:52,600
Speaker 1: perfectly willing to lose money if it means further entrenching

477
00:24:52,840 --> 00:24:54,919
Speaker 1: gender bias and massogey here.

478
00:24:54,960 --> 00:24:57,399
Speaker 4: Yeah, which is so nonsensical.

479
00:24:57,359 --> 00:24:59,880
Speaker 5: Right, I guess it's power over money, even though money

480
00:25:00,080 --> 00:25:01,280
Speaker 5: can be considered power.

481
00:25:01,720 --> 00:25:03,440
Speaker 1: Yeah.

482
00:25:03,760 --> 00:25:05,680
Speaker 2: Yeah, we were doing I was trying to do a

483
00:25:05,720 --> 00:25:09,040
Speaker 2: wrap up episode like this about video games and board games,

484
00:25:09,520 --> 00:25:14,200
Speaker 2: and I ran across kind of a stunning, upsetting amount

485
00:25:14,440 --> 00:25:19,920
Speaker 2: of how often people in power who are dudes, were like,

486
00:25:20,720 --> 00:25:25,040
Speaker 2: it's just women don't make good stuff, and they would

487
00:25:25,160 --> 00:25:28,040
Speaker 2: literally be like, uh, people don't buy those things, those

488
00:25:28,040 --> 00:25:31,200
Speaker 2: party games, Why yes they do.

489
00:25:32,720 --> 00:25:35,440
Speaker 1: Yeah, look at the demographics of who is actually playing

490
00:25:35,520 --> 00:25:37,960
Speaker 1: video games it is a lot of women, and so

491
00:25:38,119 --> 00:25:43,520
Speaker 1: set like I think not making video games and stuff

492
00:25:43,560 --> 00:25:46,840
Speaker 1: that like women want. When a big chunk of your

493
00:25:46,840 --> 00:25:49,520
Speaker 1: customer base just is women, it's a fact that is

494
00:25:49,560 --> 00:25:51,159
Speaker 1: on you, that is like you don't know how to

495
00:25:51,200 --> 00:25:52,399
Speaker 1: do it or unwilling to do it.

496
00:25:52,400 --> 00:25:55,520
Speaker 4: It's like any anything that you could say is just

497
00:25:55,520 --> 00:25:57,280
Speaker 4: an excuse to not do it right.

498
00:25:57,520 --> 00:26:00,440
Speaker 5: Like you look at Animal Crossing and the fact they

499
00:26:00,960 --> 00:26:05,040
Speaker 5: made bank, especially during COVID and quarantine, and you're like

500
00:26:05,200 --> 00:26:07,159
Speaker 5: you sure, are you sure?

501
00:26:08,160 --> 00:26:08,760
Speaker 4: Yeah?

502
00:26:08,880 --> 00:26:11,120
Speaker 2: This guy in question was he was saying, like all

503
00:26:11,119 --> 00:26:13,679
Speaker 2: the games women like to play, which are like in

504
00:26:13,760 --> 00:26:18,040
Speaker 2: his mind, are much more too political or two like parties,

505
00:26:18,400 --> 00:26:24,000
Speaker 2: like Animal Mobile games like aren't good and don't make money.

506
00:26:24,040 --> 00:26:26,720
Speaker 4: And it's like, uh, are you sure?

507
00:26:28,560 --> 00:26:29,240
Speaker 3: I can't?

508
00:26:29,880 --> 00:26:30,640
Speaker 4: Why?

509
00:26:30,920 --> 00:26:31,280
Speaker 1: Oh?

510
00:26:31,840 --> 00:26:34,879
Speaker 2: This also reminds me of it's kind of upsetting, but

511
00:26:34,920 --> 00:26:38,800
Speaker 2: it reminds me of when Sally Ride was, you know,

512
00:26:38,840 --> 00:26:41,679
Speaker 2: getting ready to go to space and they would have

513
00:26:41,720 --> 00:26:44,520
Speaker 2: these press conferences with her and all these men and

514
00:26:44,520 --> 00:26:47,640
Speaker 2: the men they're asking like all these like questions related

515
00:26:47,640 --> 00:26:49,679
Speaker 2: to the jab and her. They'd be like, how are

516
00:26:49,720 --> 00:26:51,560
Speaker 2: you gonna put your makeup on space?

517
00:26:54,960 --> 00:26:58,760
Speaker 4: What it has?

518
00:26:58,800 --> 00:27:02,119
Speaker 2: It? Like it's still there that kind of do we

519
00:27:02,200 --> 00:27:05,240
Speaker 2: still gender these things and we still other people And

520
00:27:05,280 --> 00:27:08,200
Speaker 2: it does really matter who's writing about it and who

521
00:27:09,040 --> 00:27:12,720
Speaker 2: whose stories are getting published or getting that traction, because

522
00:27:13,840 --> 00:27:18,000
Speaker 2: you know, if it's mostly white men's stories who's getting

523
00:27:18,040 --> 00:27:22,040
Speaker 2: like all of the attention, then that does help reinforce

524
00:27:22,080 --> 00:27:24,440
Speaker 2: this idea that yeah, it's this white male space when

525
00:27:24,480 --> 00:27:27,920
Speaker 2: it isn't and it hasn't been, as you said, yeah.

526
00:27:27,840 --> 00:27:28,560
Speaker 4: Did you ever hear?

527
00:27:28,600 --> 00:27:30,359
Speaker 1: I mean, I'm sure you have all have heard this story,

528
00:27:30,359 --> 00:27:32,560
Speaker 1: but it really sticks with me that when Sally Ride

529
00:27:32,640 --> 00:27:34,520
Speaker 1: was going to space, she's going to be there for

530
00:27:34,600 --> 00:27:39,240
Speaker 1: one week, and the like scientists or engineers at NASA whoever,

531
00:27:39,600 --> 00:27:41,080
Speaker 1: were like, they gave her a hunt.

532
00:27:41,280 --> 00:27:44,040
Speaker 4: Was it two hundred tampons or one hundred tampons?

533
00:27:44,160 --> 00:27:46,760
Speaker 1: She'd be there for one week? And when she was like, oh,

534
00:27:47,119 --> 00:27:49,200
Speaker 1: one hundred tampons, they were like, will that be enough?

535
00:27:51,000 --> 00:27:54,440
Speaker 1: Like these are the greatest scientific mott Like, right, nobody

536
00:27:54,440 --> 00:27:58,919
Speaker 1: can tell me that like male scientists are like better

537
00:27:59,119 --> 00:28:00,880
Speaker 1: than women when that's what's going on?

538
00:28:02,600 --> 00:28:02,800
Speaker 3: Right?

539
00:28:03,440 --> 00:28:06,440
Speaker 2: Can you imagine if you honestly believe that was true

540
00:28:06,960 --> 00:28:10,600
Speaker 2: and then for to expect the women in your life

541
00:28:10,720 --> 00:28:13,320
Speaker 2: or the people who menstrate in your life to just

542
00:28:13,359 --> 00:28:14,240
Speaker 2: be going through.

543
00:28:14,080 --> 00:28:14,960
Speaker 3: Two hundred and.

544
00:28:17,320 --> 00:28:18,760
Speaker 5: We have to pay that much when you were like

545
00:28:18,800 --> 00:28:21,359
Speaker 5: looking at tampons and they're like thirty for what is it,

546
00:28:21,480 --> 00:28:22,840
Speaker 5: like eight dollars or thirty?

547
00:28:23,000 --> 00:28:27,159
Speaker 4: They're like thirty. So it's like how much that I

548
00:28:27,160 --> 00:28:28,240
Speaker 4: have so many questions?

549
00:28:28,280 --> 00:28:29,920
Speaker 3: I have so many There's so many questions.

550
00:28:29,960 --> 00:28:32,960
Speaker 5: There's so many questions, and to be fair and like

551
00:28:33,000 --> 00:28:34,800
Speaker 5: in everything, like how did you think this was going

552
00:28:34,880 --> 00:28:36,959
Speaker 5: to work? Like who invented this? This is not this

553
00:28:37,040 --> 00:28:38,200
Speaker 5: is this doesn't work like that?

554
00:28:38,280 --> 00:28:39,280
Speaker 3: What are you doing?

555
00:28:40,160 --> 00:28:44,120
Speaker 1: God? Sometimes like oh, this is such a non secutor

556
00:28:44,480 --> 00:28:49,360
Speaker 1: sometimes when I think about gender dynamics, like I remember

557
00:28:49,400 --> 00:28:52,920
Speaker 1: reading this tweet from a guy who was like, people

558
00:28:52,960 --> 00:28:55,600
Speaker 1: think Taylor Swift is so pretty, but here she is

559
00:28:55,640 --> 00:28:57,960
Speaker 1: without makeup, and it's like, did you think that she was.

560
00:28:57,920 --> 00:29:00,640
Speaker 4: Born with cat eyes and like break red lips?

561
00:29:01,320 --> 00:29:03,640
Speaker 1: And you thought all of us thought that You clearly

562
00:29:03,680 --> 00:29:05,960
Speaker 1: thought that, but you thought all of us thought that,

563
00:29:06,120 --> 00:29:08,560
Speaker 1: Like and then you're like you're supposed to be getting

564
00:29:08,560 --> 00:29:11,040
Speaker 1: one over on us women, Like the whole thing is

565
00:29:11,080 --> 00:29:11,760
Speaker 1: just laughable.

566
00:29:12,680 --> 00:29:16,880
Speaker 2: Yeah, it's like such a great example of who we

567
00:29:16,960 --> 00:29:20,920
Speaker 2: consider the norm in society whose experience we consider the

568
00:29:20,960 --> 00:29:24,880
Speaker 2: norm we base it on, because I was thinking about

569
00:29:24,880 --> 00:29:29,480
Speaker 2: that recently too, about this whole idea that I feel

570
00:29:29,480 --> 00:29:32,920
Speaker 2: like has faded away largely but not always. But like

571
00:29:33,120 --> 00:29:35,120
Speaker 2: women who would wake up early and put on makeup

572
00:29:35,320 --> 00:29:40,000
Speaker 2: so their partner would not see them without makeup. So

573
00:29:40,040 --> 00:29:44,040
Speaker 2: it's like, it's like a lot of us feel that

574
00:29:44,120 --> 00:29:48,000
Speaker 2: we have to do this thing because then we'll get

575
00:29:48,040 --> 00:29:51,320
Speaker 2: this complaint from this random jackets.

576
00:29:50,840 --> 00:29:53,000
Speaker 4: On social media.

577
00:29:53,120 --> 00:29:55,480
Speaker 2: But then it's also like, dude, what do you think?

578
00:29:55,840 --> 00:30:00,920
Speaker 2: It's strange. It's like a strange dynamic that is happening

579
00:30:01,160 --> 00:30:03,840
Speaker 2: where you feel the pressure to do it. But then

580
00:30:03,960 --> 00:30:07,920
Speaker 2: also shouldn't they shouldn't people realize how much work.

581
00:30:07,760 --> 00:30:08,520
Speaker 4: It is, right?

582
00:30:08,600 --> 00:30:11,440
Speaker 5: Yeah, And it kind of goes into that another question

583
00:30:11,600 --> 00:30:13,880
Speaker 5: that I'm kind of concerned about I've had, like I've

584
00:30:13,880 --> 00:30:16,080
Speaker 5: been noticing more and more on TikTok they just have

585
00:30:16,160 --> 00:30:18,680
Speaker 5: automatic filters, and a lot of people talk about the

586
00:30:18,680 --> 00:30:20,720
Speaker 5: fact that they use different filters, especially because they don't

587
00:30:20,720 --> 00:30:22,760
Speaker 5: want to wear makeup and all that, and this feels

588
00:30:22,880 --> 00:30:25,280
Speaker 5: makes them feel better, which all all in on whatever

589
00:30:25,440 --> 00:30:29,200
Speaker 5: you do, you but then this level of expectation that

590
00:30:29,680 --> 00:30:34,440
Speaker 5: especially men, especially I'm gonna say sis, heterosexual men think

591
00:30:34,440 --> 00:30:36,640
Speaker 5: that this is what you should look like. And if

592
00:30:36,680 --> 00:30:39,720
Speaker 5: you don't look like that in real life, holy catfish

593
00:30:39,800 --> 00:30:42,120
Speaker 5: me and you've lied to me. Like in this new

594
00:30:42,240 --> 00:30:45,840
Speaker 5: level of standard, a beauty standard, that might been like, Okay,

595
00:30:45,840 --> 00:30:47,920
Speaker 5: this is good for us because it makes us kind

596
00:30:47,920 --> 00:30:48,440
Speaker 5: of feel better.

597
00:30:48,440 --> 00:30:50,280
Speaker 3: But at the same time, what is it leading to?

598
00:30:51,120 --> 00:30:53,959
Speaker 1: That's such an interesting question, And I guess I just

599
00:30:53,960 --> 00:30:59,959
Speaker 1: think that like a lot of men, like sis, heterosexual men,

600
00:31:00,960 --> 00:31:04,600
Speaker 1: I think, are really willing to live in a fantasy world.

601
00:31:04,880 --> 00:31:06,840
Speaker 4: Like they're really like it's like if.

602
00:31:06,760 --> 00:31:08,520
Speaker 1: You if you think that you know, some of those

603
00:31:08,560 --> 00:31:11,920
Speaker 1: TikTok filters they give you like glitter on your eyes,

604
00:31:12,080 --> 00:31:13,960
Speaker 1: if you thought that, like somebody came out of the

605
00:31:14,000 --> 00:31:17,160
Speaker 1: womb with like glitter on their eyes, Like I sometimes

606
00:31:17,240 --> 00:31:18,880
Speaker 1: like it's it's like, honestly, it's one of the reasons

607
00:31:18,920 --> 00:31:21,160
Speaker 1: why I don't really remove my body hair, because when

608
00:31:21,160 --> 00:31:23,320
Speaker 1: you add up how much time it takes to do it,

609
00:31:23,320 --> 00:31:25,400
Speaker 1: it's like it's actually not it's a lot of work,

610
00:31:25,800 --> 00:31:28,840
Speaker 1: And it's like do I want to do extra work

611
00:31:29,080 --> 00:31:30,960
Speaker 1: and pay whatever extra money it takes to buy the

612
00:31:31,000 --> 00:31:34,360
Speaker 1: razor and this and that to feed into a fantasy that.

613
00:31:34,560 --> 00:31:36,240
Speaker 4: Adult women don't have body hair.

614
00:31:36,360 --> 00:31:39,080
Speaker 1: No, I don't like if you're if you're like you

615
00:31:39,120 --> 00:31:41,960
Speaker 1: should by now know that adult women do you generally

616
00:31:41,960 --> 00:31:43,920
Speaker 1: have body hair. And I'm not I'm not interested in

617
00:31:45,400 --> 00:31:50,440
Speaker 1: perpetuating this like very weird fantasy world where this becomes

618
00:31:50,480 --> 00:31:52,080
Speaker 1: how women are in the world.

619
00:31:52,360 --> 00:31:53,720
Speaker 4: And then if you see a woman.

620
00:31:53,520 --> 00:31:56,160
Speaker 1: Who is not doing that, it's it's not it's like

621
00:31:56,880 --> 00:31:58,120
Speaker 1: out of sync with how you think.

622
00:31:57,960 --> 00:31:59,440
Speaker 4: Women should exist. I guess I'll say I don't.

623
00:31:59,440 --> 00:32:02,680
Speaker 5: I don't know if that fence no, Yeah, it absolutely does.

624
00:32:02,760 --> 00:32:05,840
Speaker 5: It kind of again, it faes into this narrative that

625
00:32:05,920 --> 00:32:10,040
Speaker 5: women are supposed to be this higher level of beauty

626
00:32:10,080 --> 00:32:13,400
Speaker 5: standard in order to fit with a norm which is

627
00:32:13,440 --> 00:32:16,760
Speaker 5: so much less work from men in general.

628
00:32:17,040 --> 00:32:21,360
Speaker 2: Yeah, and when we're looking at like tech, I mean,

629
00:32:21,400 --> 00:32:23,560
Speaker 2: so many of the things that we've talked about on

630
00:32:23,640 --> 00:32:26,280
Speaker 2: here do you play into that, whether it's filters or

631
00:32:26,280 --> 00:32:28,800
Speaker 2: social media, of these kind of beauty standards that are

632
00:32:28,800 --> 00:32:36,280
Speaker 2: getting perpetuated. Just the vitriol women can receive just by

633
00:32:37,840 --> 00:32:43,840
Speaker 2: here's my face on social media. So it is really

634
00:32:43,960 --> 00:32:50,640
Speaker 2: unfortunate because it's we can't ignore that that does happen.

635
00:32:50,680 --> 00:32:53,000
Speaker 2: And I know I've told the story before, but I

636
00:32:53,040 --> 00:32:56,280
Speaker 2: have a lovely collage of all of the horrible things

637
00:32:56,320 --> 00:32:59,200
Speaker 2: people have said to me online. But it's also like,

638
00:33:01,320 --> 00:33:04,280
Speaker 2: why can't we just these are women who are doing

639
00:33:04,320 --> 00:33:08,160
Speaker 2: amazing things in AI. We don't have to talk about

640
00:33:08,160 --> 00:33:11,520
Speaker 2: how they look. We really don't, right, we really don't.

641
00:33:11,920 --> 00:33:13,680
Speaker 5: We don't need to know how many children she has.

642
00:33:13,720 --> 00:33:15,400
Speaker 5: We don't need to know she has a husband or

643
00:33:15,560 --> 00:33:18,120
Speaker 5: a partner. Like, that's not we want to know. Are

644
00:33:18,120 --> 00:33:21,520
Speaker 5: you doing better AI that protects people? Yeah, wonderful, let's

645
00:33:21,560 --> 00:33:22,200
Speaker 5: support that.

646
00:33:22,400 --> 00:33:24,840
Speaker 1: Yes, And it's such a yeah, it's just such a

647
00:33:24,880 --> 00:33:29,000
Speaker 1: missed opportunity because like the majority of people who are

648
00:33:30,360 --> 00:33:33,960
Speaker 1: making sure that AI is safe or more inclusive, or

649
00:33:33,960 --> 00:33:38,040
Speaker 1: doesn't harm people, or is ethical or doing really interesting work,

650
00:33:38,120 --> 00:33:41,280
Speaker 1: those people tend to also be women, people of color,

651
00:33:41,480 --> 00:33:45,600
Speaker 1: people who are not necessarily treated as the norm in tech.

652
00:33:45,640 --> 00:33:48,000
Speaker 1: And so when you have an opportunity to talk to

653
00:33:48,080 --> 00:33:51,240
Speaker 1: these people, actually use it because what they are working

654
00:33:51,280 --> 00:33:54,960
Speaker 1: on probably really matters, and like it matters to all

655
00:33:54,960 --> 00:33:57,080
Speaker 1: of us. Even if you're not somebody who is a Katechi,

656
00:33:57,520 --> 00:34:00,200
Speaker 1: you're not somebody who thinks of yourself as like somebody

657
00:34:00,240 --> 00:34:02,160
Speaker 1: who thinks about AI a lot.

658
00:34:02,320 --> 00:34:04,360
Speaker 4: This stuff is going to matter to everybody.

659
00:34:04,360 --> 00:34:06,360
Speaker 1: And I guess that brings me to one of the

660
00:34:06,400 --> 00:34:09,160
Speaker 1: things that I'm looking forward to in twenty twenty four

661
00:34:09,280 --> 00:34:12,440
Speaker 1: that I if I'm the girl in the cartoon that

662
00:34:12,600 --> 00:34:15,480
Speaker 1: I've got this in my bag slung over my shoulder,

663
00:34:15,480 --> 00:34:16,960
Speaker 1: that I'm taking it with me to the next year,

664
00:34:17,480 --> 00:34:19,920
Speaker 1: and that is all of us, each and every one

665
00:34:19,960 --> 00:34:23,400
Speaker 1: of us, being more involved in conversations around tech. Like

666
00:34:23,960 --> 00:34:26,600
Speaker 1: I do think I have seen a shift this year

667
00:34:27,480 --> 00:34:30,360
Speaker 1: around how regular people like you and me and people

668
00:34:30,400 --> 00:34:34,520
Speaker 1: listening you know, are thinking about technology and talking about technology.

669
00:34:34,840 --> 00:34:37,399
Speaker 1: I think that we are done with this idea that

670
00:34:37,520 --> 00:34:41,200
Speaker 1: tech is only decided by a bunch of like super smart,

671
00:34:41,239 --> 00:34:43,640
Speaker 1: genius white guys who don't have to be accountable to

672
00:34:43,719 --> 00:34:46,120
Speaker 1: us at all because we're like not smart enough to

673
00:34:46,200 --> 00:34:49,400
Speaker 1: understand the brilliance that they do. That is out I

674
00:34:49,440 --> 00:34:51,319
Speaker 1: think that in twenty twenty three we are coming to

675
00:34:51,360 --> 00:34:54,640
Speaker 1: the realization that these people have been using that dynamic

676
00:34:54,800 --> 00:34:57,520
Speaker 1: as a way to essentially like get rich off of us,

677
00:34:57,600 --> 00:34:59,680
Speaker 1: and I think that we're going to start asking some

678
00:34:59,760 --> 00:35:02,640
Speaker 1: question about that dynamic and pushing it back, like, should

679
00:35:02,719 --> 00:35:04,920
Speaker 1: these companies and tech leaders be able to just get

680
00:35:05,000 --> 00:35:08,160
Speaker 1: rich off of us without us asking any questions or

681
00:35:08,200 --> 00:35:11,000
Speaker 1: having any say I think, I think I'm starting to

682
00:35:11,000 --> 00:35:14,359
Speaker 1: see people be like, No, Actually, I am smart enough

683
00:35:14,400 --> 00:35:16,880
Speaker 1: to understand that I'm being taken advantage of it, exploited,

684
00:35:17,160 --> 00:35:20,120
Speaker 1: and I have questions about that. So let's keep asking

685
00:35:20,160 --> 00:35:23,640
Speaker 1: those questions in twenty twenty four, let's bring that dynamic

686
00:35:23,880 --> 00:35:25,120
Speaker 1: into the new year with us.

687
00:35:25,440 --> 00:35:31,520
Speaker 2: Yes, yes, and of course always your show is such

688
00:35:31,560 --> 00:35:34,280
Speaker 2: a good part of that and part of that conversation,

689
00:35:34,600 --> 00:35:40,040
Speaker 2: so very eagerly awaiting the next the next season. Do

690
00:35:40,080 --> 00:35:42,000
Speaker 2: you call it seasons, frigid, We call.

691
00:35:41,880 --> 00:35:44,680
Speaker 1: It seasons, but they're really just like when I get

692
00:35:45,160 --> 00:35:46,839
Speaker 1: tired of making the show and I have to take

693
00:35:46,880 --> 00:35:47,239
Speaker 1: a break.

694
00:35:47,280 --> 00:35:50,200
Speaker 4: So we are taking a break, but we'll be back real.

695
00:35:50,120 --> 00:35:54,560
Speaker 5: Soon while you're here with us answering those questions and

696
00:35:54,680 --> 00:35:58,440
Speaker 5: asking those questions and allowing us to ask you those questions.

697
00:35:58,480 --> 00:36:01,000
Speaker 5: So we are very grateful for that and excited for

698
00:36:01,000 --> 00:36:01,879
Speaker 5: that for the new year.

699
00:36:02,560 --> 00:36:06,120
Speaker 2: Yes, yes, and we are hoping maybe we'll get to

700
00:36:06,360 --> 00:36:09,680
Speaker 2: hang out irl and do some things in twenty four.

701
00:36:09,920 --> 00:36:11,520
Speaker 3: One day, one day.

702
00:36:12,400 --> 00:36:15,360
Speaker 4: It's gonna happen soon. Stay ye tune folks.

703
00:36:15,600 --> 00:36:19,880
Speaker 2: Yes well, Bridget, thank you so much as always for

704
00:36:20,000 --> 00:36:21,839
Speaker 2: being here at the end of this year.

705
00:36:23,200 --> 00:36:24,960
Speaker 3: Where can the good listeners find you?

706
00:36:24,960 --> 00:36:26,919
Speaker 1: You can check out my podcast there are no girls

707
00:36:26,960 --> 00:36:29,160
Speaker 1: on the internet on iHeartRadio. You can find me on

708
00:36:29,239 --> 00:36:32,759
Speaker 1: Instagram at Bridget Marie and DC or on Twitter at

709
00:36:32,800 --> 00:36:33,600
Speaker 1: Bridget Marie.

710
00:36:34,040 --> 00:36:38,160
Speaker 2: Yes, and definitely go do that listeners if you haven't already, Bridgeitte,

711
00:36:38,160 --> 00:36:42,520
Speaker 2: I hope you have a good relaxing holiday, weirdo Christmas.

712
00:36:42,800 --> 00:36:46,720
Speaker 1: Yes, thank you all, Happy Mary whatever to all of y'all.

713
00:36:47,040 --> 00:36:52,040
Speaker 2: Yes, thank you and listeners. If you would like to

714
00:36:52,040 --> 00:36:54,279
Speaker 2: contact us, you can or email is Stephanie and mom

715
00:36:54,360 --> 00:36:56,520
Speaker 2: Stuff at iHeartMedia dot com. You can find us on

716
00:36:56,520 --> 00:36:58,960
Speaker 2: Twitter at mom Stuff Podcast or in instagrament TikTok at Stuff.

717
00:36:58,960 --> 00:37:00,960
Speaker 2: I never told you a ta public store and we

718
00:37:01,000 --> 00:37:03,760
Speaker 2: do have a book. Thanks as always to our super

719
00:37:03,800 --> 00:37:07,120
Speaker 2: producer Christina, our executive producer Maya, and our contributor Joey.

720
00:37:07,520 --> 00:37:09,040
Speaker 3: Thank you and happy holidays.

721
00:37:09,280 --> 00:37:11,280
Speaker 2: Yes yes, and thanks.

722
00:37:11,080 --> 00:37:11,800
Speaker 3: To you for listening.

723
00:37:11,920 --> 00:37:14,080
Speaker 2: Stefan never told you the prediction of iHeartRadio. For more

724
00:37:14,120 --> 00:37:16,160
Speaker 2: podcasts from My Heart Radio, you can check out the iHeartRadio,

725
00:37:16,160 --> 00:37:18,719
Speaker 2: Apple podcasts, or where you listen to your favorite shows.