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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 brigittat and this is

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Speaker 1: there are no girls.

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Speaker 2: On the Internet.

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Speaker 1: So Mike, as you can probably tell, we are not

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Speaker 1: recording this when we usually record for the Round US,

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Speaker 1: which is Thursday late night. It is Friday early morning.

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Speaker 1: I'm not a morning person. Do you consider yourself a

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Speaker 1: morning person?

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Speaker 3: I don't. I've always aspired to be a morning person,

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Speaker 3: and on those occasions when I manage to get out

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Speaker 3: of bed early, I feel really proud of myself and

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Speaker 3: like a little bit smug about it. But truth be told,

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Speaker 3: that left my own devices, I would sleep in late

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Speaker 3: every single day.

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Speaker 1: I pretty much do sleep in late every single day.

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Speaker 1: So if I'm not sounding my most bride eyed and

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Speaker 1: bushy tailed, it is because it is early in the morning,

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Speaker 1: and I don't I'm not usually on a microphone right

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Speaker 1: when I wake up. I was traveling to New York

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Speaker 1: for a conference about disinformation and my train was late

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Speaker 1: and I got in late, and I was like, you

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Speaker 1: know what, I don't have it in me. But this morning,

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Speaker 1: this is this is the truth. We got this right?

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Speaker 3: Yeah? Well, and listeners, this is really a sign of

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Speaker 3: how much Bridget loves and respects you that she is

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Speaker 3: so committed to putting this podcast out that she is

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Speaker 3: getting up early in the morning. There are not a

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Speaker 3: lot of things that can get Bridget up on the

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Speaker 3: microphone early in the morning.

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Speaker 1: Surely nothing that's so true. So I'm phasing in a

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Speaker 1: new banter idea. Mike, you you kind of don't have TikTok.

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Speaker 1: You've you've had it, You've you've had flirtations with it.

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Speaker 1: I know you have a like a philosophical opposition to

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Speaker 1: TikTok or something. You you're basically you're not on TikTok.

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Speaker 3: I'm not on TikTok. I wouldn't call it a philosophical opposition.

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Speaker 3: I am kind of where of it, but like, I

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Speaker 3: don't know, I don't have it. I've been thinking lately

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Speaker 3: like maybe I'm being ridiculous and I should just get it,

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Speaker 3: but I don't currently have it.

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Speaker 1: So that means that there are entire trends and discourses

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Speaker 1: and all of that happening that you're just like you

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Speaker 1: just miss you. I'll be like, oh, did you see

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Speaker 1: such and such, and You're like, I don't know what

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Speaker 1: you're saying, I don't know what those words mean. So

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Speaker 1: new kind of segment we're trying out. Bridgett explains TikTok's

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Speaker 1: to Mike. I love TikTok. I watch it constantly. I

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Speaker 1: have a timer on my phone to when I've been

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Speaker 1: on for an hour, we'll be like, oh, you've had

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Speaker 1: your hour of TikTok a day. It used to be

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Speaker 1: an hour of Instagram. I can tell you the last

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Speaker 1: time I spent ten minutes on Instagram TikTok. That's where

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Speaker 1: I need my timer. So I somebody who is very

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Speaker 1: tuned into what's happening on TikTok, I'm going to take

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Speaker 1: a stab at explaining popping TikTok discourse to Mike somebody

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Speaker 1: who is barely on TikTok with me.

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Speaker 3: Yeah, this probably won't be embarrassing for me at all.

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Speaker 1: Okay, So there's this thing, this trend on TikTok. I

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Speaker 1: should say something about TikTok and also social media more generally,

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Speaker 1: is that you really never know if what you're watching

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Speaker 1: is a skit, like somebody is just making up a

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Speaker 1: scenario that's never happened just to get engagement. And so

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Speaker 1: I suspect that most of what I'm about to say

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Speaker 1: is being done by people who are doing it on

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Speaker 1: purpose to get people like me to talk about it.

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Speaker 1: And here I am so. I saw this trend challenge

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Speaker 1: whatever on TikTok where women who were married to men

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Speaker 1: were making a small mess of ketchup on their kitchen tables,

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Speaker 1: calling their husband or their boyfriend in and being like, hey,

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Speaker 1: can you clean this ketchup? And first of all, all

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Speaker 1: the men are like, why are you asking me to

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Speaker 1: clean up a random pile of ketchup that you made

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Speaker 1: seemingly with intention because it's a perfect circle, while also

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Speaker 1: filming me like what's going on? But the challenge is

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Speaker 1: does your husband or boyfriend know how to clean up

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Speaker 1: up a pile of ketchup effectively? And what would he do?

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Speaker 1: So if asked? And I gotta say, y'all the bar

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Speaker 1: is in hell. I was like, so if you're if

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Speaker 1: your man doesn't know how to do it embarrassing all

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Speaker 1: if you're a man doesn't and you're like, Wow, my

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Speaker 1: husband really knows how to clean up a pile of ketchup?

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Speaker 1: If almost the man, I would be so insulted that

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Speaker 1: you would think that, like that like that like like, hey, ladies,

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Speaker 1: does your man clean up piles of ketchup on the table?

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Speaker 1: That like that's the marker of like a good confident man.

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Speaker 3: So just to repeat this back to make sure that

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Speaker 3: I've understood it. Uh, the woman will call her husband

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Speaker 3: or boyfriend over look him in the eye while she

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Speaker 3: squeezes out some ketchup onto the there. Oh, it's already there. Okay.

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Speaker 3: So he comes in and she's like, somehow, through mysterious circumstances,

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Speaker 3: there's ketchup here. I don't know what to do. I

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Speaker 3: need you to help me by cleaning it up.

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Speaker 1: And for some reason, her phone is out she's recording

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Speaker 1: hold the whole interaction, which to me, it's like somebody

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Speaker 1: in my household. Actually, that would never happen unless somebody

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Speaker 1: was about to like give me happy news. I think.

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Speaker 1: I don't think anybody in my life would ever be

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Speaker 1: purporting to have a normal interaction with me while also

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Speaker 1: filming me.

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Speaker 3: Now that's a little strange. But like, how does Wooden

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Speaker 3: screw up cleaning up a little bit of ketchup from

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Speaker 3: the counter?

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Speaker 1: Great question. So it is a fair amount of ketchup,

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Speaker 1: So it's not just like a doll up. It's like

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Speaker 1: a little pile of ketchup if you I mean, it's

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Speaker 1: hard to explain, but if you ever had to clean

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Speaker 1: something that's like pretty sticky off of a surface, you

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Speaker 1: know what I'm talking about. You have to sort of

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Speaker 1: come at it at a specific angle. A lot of

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Speaker 1: these guys are going at it from the top, like

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Speaker 1: they put a paper They put a single paper towel

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Speaker 1: down on it, which is already like the wrong tool

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Speaker 1: for the job, and then they sort of full hand

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Speaker 1: palmet from the top and then move their hand around,

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Speaker 1: so essentially they're smearing it around, is what they're doing.

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Speaker 1: They're like or like finger painting with it at that point.

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Speaker 3: Yeah, like like wax on, wax off from like the

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Speaker 3: karate kids exactly.

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Speaker 1: Yeah, so some of these men don't know how to

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Speaker 1: clean ketchup off a table, or they're pretending not to. Again,

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Speaker 1: whenever I am watching tiktoks and I'm watching a seemingly

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Speaker 1: mundane moment, I always been thinking, like, well, why did

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Speaker 1: you have your phone? Why are you recording this? Like

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Speaker 1: like it has to be some sort of setup. But

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Speaker 1: in any event, that is what's going on in certain

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Speaker 1: corners of TikTok. Men either cannot clean ketchup off of

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Speaker 1: a table or they're getting lots of praise and like, oh,

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Speaker 1: what a good husband for knowing how to and being

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Speaker 1: willing to clean up ketchup from the table. We deserve better.

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Speaker 1: Women deserve better. Frankly, men deserve better. Everybody deserves better.

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Speaker 1: Nobody wins in this scenario. The bar is in hell.

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Speaker 3: Maybe you need to or like, we need to do

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Speaker 3: a PSA or something like create a little instructional video

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Speaker 3: on how to clean ketchup off a table.

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Speaker 1: Men, is your wife putting a phone in your face

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Speaker 1: and asking you if you know how to clean up

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Speaker 1: ketchup off a countertop. Don't worry, it happens to a

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Speaker 1: lot of us. Here's what you need to do. Oh,

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Speaker 1: that's helpful. We'll flesh it out. We'll flesh it out

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Speaker 1: in further episode. So that is what's going on on, TikTok.

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Speaker 1: Thank you for letting me to explain this to you.

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Speaker 1: Do you feel like you're missing out by not having

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Speaker 1: heard this discourse?

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Speaker 3: No, I feel actually dumber having it. Okay, let's but

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Speaker 3: thank you for telling me anyway, of course, I do

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Speaker 3: feel a little bit more.

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Speaker 1: Informed Okay, so let's move on to an update on

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Speaker 1: a story that we talked about a little while ago. So,

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Speaker 1: after a Row was overturned by the Supreme Court, everybody

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Speaker 1: was looking at tech companies. After all, in the days

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Speaker 1: before ROW, abortion was criminalized, but we didn't all carry

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Speaker 1: like surveillance devices with us in our pocket tracking our

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Speaker 1: every move. So it really was a moment in time

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Speaker 1: where tech companies really needed to stay up or at

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Speaker 1: least clarify and be transparent about how they would be

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Speaker 1: saving data related to abortions. Google announced pretty soon after

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Speaker 1: the Supreme Court ruled on ROW that they were going

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Speaker 1: to be proactively deleting sensitive location data like when somebody

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Speaker 1: visits an abortion clinic from their search history. I believe,

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Speaker 1: like if I'm mistaken and someone is like, no, that's

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Speaker 1: not right, please correct me. My remembering of this event

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Speaker 1: is that Google did this proactively, that they were not

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Speaker 1: like pushed by a campaign. At the time, I was

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Speaker 1: pretty plugged into like tech platform accountability campaigns at the

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Speaker 1: intersection of you know, platform accountability and gender justice, and

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Speaker 1: so as far as I know, Google came out of

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Speaker 1: the gate and proactively announced this. That's that was my understanding,

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Speaker 1: But thanks to the advocacy organization Accountable Tech, we know

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Speaker 1: that Google said they would proactively be deleting people's search

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Speaker 1: history and location data when they visited abortion clinics. However,

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Speaker 1: they just kind of didn't do that. So Accountable Tech

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Speaker 1: did this really interesting test where they bought brand new

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Speaker 1: Android phones, drove to abortion clinics, and found that Google

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Speaker 1: did retain that location information. So Google gave the usual

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Speaker 1: song and dance, we care about user privacy, We're doing

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Speaker 1: all we can, blah blah blah, and it said that

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Speaker 1: the company planned to go along with changes to implement

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Speaker 1: this change in location retention policies in early twenty twenty two,

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Speaker 1: as promised. So what's the update here? While they really

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Speaker 1: haven't done that here, it is twenty twenty four and

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Speaker 1: they're still, like, I guess, still working on it, still

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Speaker 1: working at the kinks whatever. Accountable Tax just released another

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Speaker 1: study this week that found that the company still was

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Speaker 1: not deleting all location history in all cases as promised,

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Speaker 1: though I will say Google's rate of retention has improved

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Speaker 1: a little bit. So the rate of retention of location

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Speaker 1: information decreased from sixty percent of tested cases a measurement

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Speaker 1: taken five months after Google's pledge to fifty percent of

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Speaker 1: tested cases in the most recent experiment, fifty percent, Like

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Speaker 1: it's a coin flip of whether or not it's they're

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Speaker 1: actually gonna do it.

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Speaker 3: Yeah, that's not a very good metric. When you started,

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Speaker 3: I was like, Okay, well, maybe there's like a couple

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Speaker 3: edge cases they creep through. Fifty percent is like, that's

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Speaker 3: pretty embarrassing.

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Speaker 1: So, according to the Guardian, here's how they did this

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Speaker 1: newest test. Researchers from Accountable Tech ran eight tests in

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Speaker 1: seven states, Pennsylvania, Texas, Nevada, Florida, New York, Georgia, and

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Speaker 1: North Carolina. In four out of the eight tests, the

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Speaker 1: route to planned parenthood was retained in the device's location history,

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Speaker 1: though the name of the clinic was scrubbed. But it

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Speaker 1: wouldn't be like that hard if somebody was really trying

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Speaker 1: to paint a portrait about where you were, it really

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Speaker 1: wouldn't be that hard to be like, oh, it was

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Speaker 1: planned parenthood. Data on searches for abortion clinics was still

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Speaker 1: retained in the web and activity history as in the

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Speaker 1: researchers first test, so that has not changed like when

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Speaker 1: they first did this test, they were still when you

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Speaker 1: searched for abortion clinics, that information was still retained in

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Speaker 1: your web activity. It's still it is still retained in

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Speaker 1: your web activity in twenty twenty four. So the director

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Speaker 1: of Products of Google Maps, Marlow McGriff, disputed the findings

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Speaker 1: of this study, saying, we are upholding our promise to

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Speaker 1: delete particularly personal places from location history if these places

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Speaker 1: are identified by our systems. Any claims that we're not

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Speaker 1: doing so are patently false or misguided. So it's when

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Speaker 1: I looked into this, it's like very clear to me

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Speaker 1: that Google is sort of nippicking here. Like in one

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Speaker 1: of the cases they say that, oh, well, Google Location

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Speaker 1: services did not detect that visit as a planned parenthood

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Speaker 1: specifically so that it did not delete that location data,

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Speaker 1: but like it was a planned parenthood. So whether or

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Speaker 1: not your location services or map or whatever clocked it

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Speaker 1: that way, I don't know. It seems a little weird

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Speaker 1: to include that in a case where you wouldn't have

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Speaker 1: to delete the location services.

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Speaker 3: Yeah, it seems like they're saying, like what that spokesman

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Speaker 3: was saying is that they didn't detect that the location

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Speaker 3: was abortion clinic and so they didn't delete the trail.

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Speaker 3: But like detecting that the person has visited in abortion

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Speaker 3: clinic is a big part of what they're saying that

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Speaker 3: they're they're doing right. Like it's like saying, oh, we

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Speaker 3: you know, like they just totally didn't they missed on

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Speaker 3: half of the objective.

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Speaker 1: Yeah, and they're essentially using that as their excuse. Like,

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Speaker 1: I don't know why something about this this situation infuriates

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Speaker 1: me because it's so ridiculous. Basically, they out of the gate,

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Speaker 1: We're like, we're gonna do X, and now they're like,

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Speaker 1: well we can't do X. It's like, well, you couldn't

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Speaker 1: do it. It's like exactly what accountable tech and abortion

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Speaker 1: advocates are saying is that you're not you're doing a

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Speaker 1: sloppy job of it. And they're like, well, we agree,

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Speaker 1: we're doing a sloppy job. One could say, we're not

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Speaker 1: even really doing it at all. It's like, well, yeah,

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Speaker 1: that's what we're saying. It's like it seems to be

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Speaker 1: everybody is saying the same thing. It's not getting done.

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Speaker 3: You know. I've heard that Google is very siloed and

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Speaker 3: it's a bunch of little fiefdoms. Uh, and I I

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Speaker 3: wonder if, like, basically what he's saying is that, like, well,

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Speaker 3: the department that was in charge of this, they did

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Speaker 3: their part, and they were just relying on the data

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Speaker 3: from some other department that wasn't involved in this, So

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Speaker 3: it's not our fault. But like it's all Google, right,

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Speaker 3: Like the whole thing that they said they were gonna

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Speaker 3: do of scrubbing data showing people visiting abortion clinics like

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Speaker 3: Soup to nuts, the whole process of identifying that data

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Speaker 3: and then scrubbing it, it's all Google right. Like this

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Speaker 3: is not a good excuse.

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Speaker 1: No, it's not a good excuse at all, And especially

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Speaker 1: when we're talking about like playing so fast and loose

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Speaker 1: with something that can, let's be really clear, could get

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Speaker 1: abortion seekers and the people who support abortion seekers arrested.

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Speaker 1: Like we're not talking about something that is frivolous. We're

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Speaker 1: talking about something that really matters. And so I think,

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Speaker 1: I think, like playing this weird blame game and you know,

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Speaker 1: using all these excuses really doesn't cut it for me

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Speaker 1: when we're talking about something so serious that they proactively

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Speaker 1: said that they could and would do so. According to

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Speaker 1: Accountable Tech, it's about fifty to fifty whether or not

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Speaker 1: this data is deleted or not by Google, they say,

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Speaker 1: with the same odds as a coin flip an abortion

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Speaker 1: seekers location data might still be retained and used to

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Speaker 1: prosecute them. On top of that, as we've seen through

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Speaker 1: the experiments, Google still retains location, search query data and

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Speaker 1: likely other incriminating data as well from email to Google

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Speaker 1: search data. Yeah, it's not great, Like this is a

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Speaker 1: pretty big deal that I think already has had pretty

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Speaker 1: big implications for folks who need abortions. The company receives

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Speaker 1: and responds to tens of thousands of law enforcement requests

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Speaker 1: for data from its vast tropes of user data, and

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Speaker 1: complies with eighty percent of those requests with some level

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Speaker 1: of information. This is according to Google's transparency report. And

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Speaker 1: on top of that, law enforcement agencies and police have

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Speaker 1: so or to make increasing use of this new category

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Speaker 1: of search warrant called reverse search warrants. The Guardian says

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Speaker 1: in that category are geofence location warrants, which police use

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Speaker 1: to come up with a list of suspects by seeking

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Speaker 1: out information on all users whose devices have been detected

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Speaker 1: in a certain place at a certain time. Many activists

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Speaker 1: worry law enforcement would use these search warrants to collect

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Speaker 1: data to find and prosecute or investigate those seeking abortions.

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Speaker 1: So it's not all bad though. I do have some

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Speaker 1: like good ish news from this update. Google has announced

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Speaker 1: that it planned to change the way that it stores

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Speaker 1: location history data for all users in a way that

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Speaker 1: could render responding to geofence warrants effectively impossible. The changes

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Speaker 1: include storing location data on user's devices by default, encrypting

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Speaker 1: any location data that's backed up to Google's cloud storage,

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Speaker 1: and deleting location data after three months. So this is

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Speaker 1: sort of good, Like, it's a sort of a good

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Speaker 1: way to get around that particularly invasive search warrant process. However,

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Speaker 1: as Accountable Tech points out, we are talking about a company,

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Speaker 1: Like the reason we're talking about Google today is because

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Speaker 1: they have shown themselves to be kind of slippery about

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Speaker 1: what they say they're going to do and what they

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Speaker 1: actually do when it comes to our data. Right, so

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Speaker 1: can we trust them to do this thing? That sounds

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Speaker 1: kind of like a step in the right direction? Who knows?

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Speaker 1: Accountable Text says they are not holding their breath. They

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Speaker 1: told the Guardian that yeah, this is a step in

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Speaker 1: the right direction. However, the company's inability to follow through

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Speaker 1: on previous commitments to protect location data shows that Google

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Speaker 1: cannot be trusted to meet its public commitments on its

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Speaker 1: timeline promises. We cannot take the company on its word.

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Speaker 3: Yeah, I mean, like you said, is that does sound

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Speaker 3: like a really positive step and the things Google is

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Speaker 3: saying it's doing are really good and should be commended.

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Speaker 3: But then they need to actually do those things. And

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Speaker 3: three months is a long time for data to not

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Speaker 3: be deleted, right if they continue to store sensitive data

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Speaker 3: related to abortion locations, three months is definitely enough time

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Speaker 3: for somebody to find that with a warrant or go

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Speaker 3: fishing for it.

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Speaker 1: Speaking of Google, have you noticed how Google Search sucks?

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Speaker 2: Now?

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Speaker 3: Yeah? It's bad and getting worse.

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Speaker 1: So I was googling something recently and I realized I

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Speaker 1: had just sort of trained myself to have my eyes

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Speaker 1: kind of scan over the first handful of results at

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Speaker 1: the top because they're always ads are always really scammy.

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Speaker 1: It is maddening. So if you feel like Google Search

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Speaker 1: has gotten worse lately and like just overall less useful,

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Speaker 1: full of garbage, you are not wrong, because now we

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Speaker 1: have the data to confirm that a new study from

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Speaker 1: German researchers at Leibzig University and the Center for Scalable

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Speaker 1: Data Analytics and Artificial Intelligence. We're trying to answer the

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Speaker 1: research question is Google getting worse? To answer this question,

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Speaker 1: they examined almost eight thousand product review queries on Google

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Speaker 1: Being and Duck duc Go for a year. Researchers worked

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Speaker 1: off reports that a torrent of low quality content, especially

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Speaker 1: for product search, keeps drounding out any kind of useful

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Speaker 1: information in Google search results, and what they found pretty

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Speaker 1: much confirmed that a significant amount of the results found

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Speaker 1: in response to product related queries were outright SEO product

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Speaker 1: review spam. So just like garbage ads, like people who

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Speaker 1: are taking advantage of SEO to like get their stuff

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Speaker 1: at the very top of Google Search. It is so annoying.

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Speaker 1: So basically Google Search, especially for products, is all SEO spam.

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Speaker 3: Now, yeah, it's so true, Like Google Search is just

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Speaker 3: really difficult to use for some types of queries. And

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Speaker 3: you know, it's so interesting that SEO has basically ruined

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Speaker 3: Google Search. You know, people one of the big things

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Speaker 3: people were talking about a couple months ago when you know,

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Speaker 3: chat GPT came out and everybody was all a buzz

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Speaker 3: about generative AI was the idea or concern about generative

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Speaker 3: AI producing content that gets released in the world and

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Speaker 3: then training on that content like a snake eating its

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Speaker 3: own tail thing. And I feel like we already kind

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Speaker 3: of have that with Google Search just returning in a

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Speaker 3: lot of cases garbage results that are just optimized for SEO.

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Speaker 3: Where uh like, we didn't even need generative AI to

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Speaker 3: turn the Internet into a snake eating its own tail

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Speaker 3: of low quality information, right like we we humans did

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Speaker 3: it ourselves.

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Speaker 1: Well, you know who does not agree that their search

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Speaker 1: platforms are becoming is it? Orboris? Is that the name

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Speaker 1: of that snake eating its own roboros? Or don't quote

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Speaker 1: me on that pronunciation of it. But you know who

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Speaker 1: doesn't agree with you know what I'm trying to say,

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Speaker 1: it's the morning, Give me a break. You know who

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Speaker 1: doesn't agree that that's happening?

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Speaker 3: Is it Google?

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Speaker 1: It's Google? So they're really taking a lot of heat

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Speaker 1: in this episode, I got to say. A Google spokesperson

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Speaker 1: told Mashable, but the study does not reflect the overall

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Speaker 1: quality and helpfulness of search, And they also emphasize that

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Speaker 1: the study only focuses on a narrow set of search queries,

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Speaker 1: namely product search. Again, it is wild to me that

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Speaker 1: they're like, well, yeah, of course product search sucks. Like

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Speaker 1: is that that big of a deal. Like they're almost

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Speaker 1: like using like they're almost like confirming what people are saying.

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Speaker 1: What people are saying is like, yeah, you can't use

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Speaker 1: Google the search products anymore without getting a bunch of ads.

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Speaker 1: And they're like, well that sure if you only count

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Speaker 1: product search. It's like, well that's what they're saying. It

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Speaker 1: seems like we agree.

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Speaker 3: Yeah, I mean in fairness to Google, right, Like, there

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Speaker 3: are a lot of categories where it works really well. Right, Like,

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Speaker 3: I use Google Search all the time. In a lot

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Speaker 3: of cases it works well. Product search it's terrible. And

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Speaker 3: you know, it's kind of interesting that the category of

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Speaker 3: search where it fails is the one category where people

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Speaker 3: are trying to sell stuff, right, Like, Right, isn't that interesting?

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Speaker 1: So we've been picking on Google a lot in this episode.

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Speaker 1: I will throw them a bone. So the study did

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Speaker 1: show that Google results did improve a little bit to

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Speaker 1: some extent between the start of the researchers experiment and

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Speaker 1: the end, but still they found an overall downward trend

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Speaker 1: in text quality and all three search engines, and then

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Speaker 1: when you add in things like AI generated spam, this

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Speaker 1: is only going to get worse, Like this is going

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Speaker 1: to be a problem that if they don't really take

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Speaker 1: some action on, it's just going to result in Google

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Speaker 1: being garbage, Like whenever I'm searching for a product out

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Speaker 1: the search like Ninja coffeemaker, Reddit to at least I

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Speaker 1: used to do that, but I don't know. I don't

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Speaker 1: know the state of Reddit these days, whether or not

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Speaker 1: that's going to remain a viable strategy to actually get

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Speaker 1: real information. But it should not be this hard to

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Speaker 1: get real good information curated by a human on the Internet.

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Speaker 1: Like it's like wild to me that this is where

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Speaker 1: we're at that you have to do so much just

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Speaker 1: to get real honest information online.

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Speaker 3: Yeah, adding Reddit in the search terms and you search

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Speaker 3: Google is definitely a useful thing to do. Relatedly, I mean,

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Speaker 3: this isn't part of this Google story, but it's just

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Speaker 3: an interesting thing. I was talking with somebody the other

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Speaker 3: day who's like a digital strategy person, and they were

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Speaker 3: talking about SEO and how much it's changed in recent

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Speaker 3: years where SEO used to be all about Google, right,

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Speaker 3: like optimizing your website or they would show up in

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Speaker 3: Google results. Now that's still a big piece of it,

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Speaker 3: but people are also optimizing their sites to show up

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Speaker 3: in social media platform results, so like searching Facebook, searching Twitter,

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Speaker 3: searching uh TikTok. When I thought that was like a

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Speaker 3: really interesting change from Google being basically the only game

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Speaker 3: in town for search to that no longer being the case.

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Speaker 1: In twenty twenty two, there was new reports that, particularly

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Speaker 1: with gen Z younger folks, TikTok had already overtaken Google

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Speaker 1: as the new search engine. So people are definitely using

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Speaker 1: social media sites as search engines now, so look out Google.

432
00:23:29,119 --> 00:23:32,520
Speaker 3: That's wild that gen Z would want to search something

433
00:23:32,560 --> 00:23:34,119
Speaker 3: and be like the format I want to see that

434
00:23:34,560 --> 00:23:37,040
Speaker 3: is a short video.

435
00:23:41,560 --> 00:23:54,680
Speaker 2: Let's take a quick break at our back.

436
00:23:56,920 --> 00:23:59,480
Speaker 1: Okay, So speaking of young people on the internet, quick

437
00:23:59,520 --> 00:24:01,959
Speaker 1: heads up on story because it is pretty disturbing. It's

438
00:24:01,960 --> 00:24:04,760
Speaker 1: an issue that I feel very strongly about. I talk

439
00:24:04,760 --> 00:24:06,640
Speaker 1: about it a lot and it infuriates me. And that

440
00:24:06,800 --> 00:24:10,520
Speaker 1: is AI generated deep fakes. So if you listen to

441
00:24:10,520 --> 00:24:12,200
Speaker 1: the show, you probably already know that when it comes

442
00:24:12,200 --> 00:24:16,000
Speaker 1: to technology, we are really failing our kids, in particular

443
00:24:16,200 --> 00:24:21,800
Speaker 1: our young girls. Unchecked tech harms routinely befall on young girls,

444
00:24:21,920 --> 00:24:23,960
Speaker 1: and they're just supposed to deal with it. They're just

445
00:24:23,960 --> 00:24:25,720
Speaker 1: supposed to be like, yep. Now, this is a whole

446
00:24:25,720 --> 00:24:28,159
Speaker 1: new category of harm that you have to absorb and

447
00:24:28,200 --> 00:24:31,320
Speaker 1: there will be no accountability or recourse for it. Unacceptable

448
00:24:31,800 --> 00:24:34,240
Speaker 1: and when it comes to AI, that is no different.

449
00:24:34,280 --> 00:24:38,159
Speaker 1: So this week, a New Jersey high schooler named Francesca Manny,

450
00:24:38,359 --> 00:24:41,879
Speaker 1: a teenage victim of non consensually sexually explicit deep fakes,

451
00:24:42,080 --> 00:24:45,120
Speaker 1: joined Representative Joe Morrell from New York to share her

452
00:24:45,160 --> 00:24:48,440
Speaker 1: story and advocate for a bipartisan bill that would criminalize

453
00:24:48,480 --> 00:24:51,440
Speaker 1: sharing deep fakes at a federal level. So, this bill,

454
00:24:51,680 --> 00:24:54,760
Speaker 1: called the Preventing Deep Fakes of Intimate Images Act, was

455
00:24:54,840 --> 00:24:57,320
Speaker 1: first referred to by the House Judiciary Committee back in

456
00:24:57,359 --> 00:25:00,760
Speaker 1: December twenty twenty two, but since then no further action

457
00:25:00,880 --> 00:25:04,119
Speaker 1: has been taken and this lawmaker morel is trying to

458
00:25:04,160 --> 00:25:07,720
Speaker 1: reintroduce it. So the bill would criminalize the non consensual

459
00:25:07,760 --> 00:25:10,800
Speaker 1: sharing of sexually explicit deep fakes and create a rite

460
00:25:10,800 --> 00:25:13,240
Speaker 1: of private action so that victims who are depicted in

461
00:25:13,280 --> 00:25:15,720
Speaker 1: these images would be able to sue the creators and

462
00:25:15,760 --> 00:25:20,400
Speaker 1: distributors of those deep fakes while also remaining anonymous. Under

463
00:25:20,440 --> 00:25:23,600
Speaker 1: this proposed law, damages for sharing deep faked images without

464
00:25:23,600 --> 00:25:25,760
Speaker 1: consent go as high as one hundred and fifty thousand

465
00:25:25,800 --> 00:25:29,359
Speaker 1: dollars an imprisonment of up to ten years. If sharing

466
00:25:29,400 --> 00:25:32,600
Speaker 1: the images facilitates violence or impacts the proceedings of a

467
00:25:32,640 --> 00:25:37,280
Speaker 1: government agency. That's really important, I think, because oftentimes with

468
00:25:37,359 --> 00:25:41,840
Speaker 1: deep fakes I do sometimes they're used to terrorize individual people.

469
00:25:42,160 --> 00:25:46,040
Speaker 1: Sometimes they're used to, you know, disrupt a political campaign

470
00:25:46,080 --> 00:25:48,159
Speaker 1: or disrupt an election, and so I think it is

471
00:25:48,200 --> 00:25:50,879
Speaker 1: important to have to have that bit in there. So

472
00:25:51,080 --> 00:25:55,560
Speaker 1: Francesca's story is truly heartbreaking. Boys in her school, like

473
00:25:55,600 --> 00:25:59,720
Speaker 1: a group of boys created and traded AI generated deep

474
00:25:59,720 --> 00:26:03,040
Speaker 1: fake nude images of her at about thirty other girls

475
00:26:03,040 --> 00:26:06,000
Speaker 1: in her class back on October twentieth, And I think

476
00:26:06,040 --> 00:26:10,040
Speaker 1: it's an important reminder that AI is threatening and harming

477
00:26:10,200 --> 00:26:13,240
Speaker 1: women and girls right now. Like this is not some

478
00:26:13,520 --> 00:26:17,280
Speaker 1: faraway conceptual threat down the line. It is happening. It

479
00:26:17,359 --> 00:26:20,520
Speaker 1: is here. Women and girls are already paying a cost

480
00:26:20,680 --> 00:26:23,480
Speaker 1: for that technology and for the fact that it's been

481
00:26:23,560 --> 00:26:28,280
Speaker 1: allowed to just run rampant unchecked. So according to the

482
00:26:28,359 --> 00:26:32,399
Speaker 1: Dutch AI company sensity. Ninety six percent of deep fakes

483
00:26:32,400 --> 00:26:36,480
Speaker 1: online are non consensual deep fake porn, surprise, surprise, the

484
00:26:36,640 --> 00:26:39,439
Speaker 1: vast majority of which targets women. So this is a

485
00:26:39,480 --> 00:26:42,560
Speaker 1: gender justice issue. It is a issue about sexual violence.

486
00:26:42,600 --> 00:26:44,600
Speaker 1: It's an issue about consent. You know, I beat this

487
00:26:44,680 --> 00:26:48,640
Speaker 1: drama lot that tech issues are gender issues, their race issues,

488
00:26:48,880 --> 00:26:50,760
Speaker 1: their identity issues. And this is a story that I

489
00:26:50,760 --> 00:26:54,159
Speaker 1: think really makes that clear. It poses a very real

490
00:26:54,280 --> 00:26:56,840
Speaker 1: threat to our democracy. It does not just threaten the

491
00:26:56,880 --> 00:26:59,040
Speaker 1: women and girls that are depicted in these images. It

492
00:26:59,080 --> 00:27:01,280
Speaker 1: threatens all of us and makes us all less safe

493
00:27:01,320 --> 00:27:04,639
Speaker 1: and secure. Because if women can't run for office or

494
00:27:04,640 --> 00:27:08,879
Speaker 1: participate in public and civic life without being depicted in

495
00:27:09,000 --> 00:27:12,840
Speaker 1: sexualized AI generated content that depicts them, we will not

496
00:27:12,920 --> 00:27:15,679
Speaker 1: have a representative democracy, right. We will not have a

497
00:27:15,680 --> 00:27:18,560
Speaker 1: society where everybody can make their voice heard, where everybody

498
00:27:18,560 --> 00:27:21,720
Speaker 1: can can participate equally in public and civic life. We

499
00:27:21,800 --> 00:27:23,480
Speaker 1: just won't have that. And so it's an issue that

500
00:27:23,480 --> 00:27:26,000
Speaker 1: impacts us all, not just the women who are depicted.

501
00:27:26,600 --> 00:27:29,000
Speaker 1: One note about this is that we're talking specifically about

502
00:27:29,080 --> 00:27:32,000
Speaker 1: AI generated deep things right now, because we're talking about

503
00:27:32,000 --> 00:27:36,080
Speaker 1: this legislation However, this kind of thing is not new, right,

504
00:27:36,520 --> 00:27:38,880
Speaker 1: bad actors have already been doing this for a very

505
00:27:38,880 --> 00:27:42,800
Speaker 1: long time without AI, Like before AI was super ubiquitous.

506
00:27:42,920 --> 00:27:45,520
Speaker 1: A few years ago, there was an image circulating online

507
00:27:45,560 --> 00:27:49,760
Speaker 1: of a woman topless in her bathtub vaping, and it's

508
00:27:49,760 --> 00:27:52,520
Speaker 1: a picture of her feet, but in the picture you

509
00:27:52,560 --> 00:27:56,040
Speaker 1: can see her faucet, and like, there's a blurry reflected

510
00:27:56,080 --> 00:27:59,360
Speaker 1: image of this woman topless if you look close enough,

511
00:27:59,359 --> 00:28:01,960
Speaker 1: because you know how creeps will zoom in on a

512
00:28:01,960 --> 00:28:04,080
Speaker 1: photo if I think there's some chance of nudity in it,

513
00:28:04,400 --> 00:28:07,080
Speaker 1: if you zoom in enough, you can see a topless

514
00:28:07,160 --> 00:28:10,000
Speaker 1: naked woman in the reflection of the faucet in this picture.

515
00:28:10,240 --> 00:28:13,200
Speaker 1: And so this picture was making the rounds on social media,

516
00:28:13,240 --> 00:28:16,720
Speaker 1: with people saying that it was AOC. However, in reality,

517
00:28:16,760 --> 00:28:19,200
Speaker 1: I think it was actually Sidney Leathers. If you don't

518
00:28:19,200 --> 00:28:21,720
Speaker 1: know who Sidney Leathers is, Sidney Leathers was the woman

519
00:28:21,920 --> 00:28:26,560
Speaker 1: who was involved in Anthony Wiener's the first iteration of

520
00:28:26,600 --> 00:28:29,080
Speaker 1: Anthony Wiener's text scandal. Do you remember that?

521
00:28:29,520 --> 00:28:31,400
Speaker 3: Ah? Yes, the leather Wiener scandal.

522
00:28:31,800 --> 00:28:35,000
Speaker 1: Yes, exactly, the Leather Wiener scandal. So the image was

523
00:28:35,040 --> 00:28:37,520
Speaker 1: actually her but they were like, oh, this is a

524
00:28:37,520 --> 00:28:41,040
Speaker 1: picture of AOC topless and naked in her tub, even

525
00:28:41,080 --> 00:28:44,080
Speaker 1: though like it was barely a picture of Sidney Leathers

526
00:28:44,080 --> 00:28:46,200
Speaker 1: topless and naked in her tub, Like if you're zooming

527
00:28:46,280 --> 00:28:49,120
Speaker 1: in and like enhancing and enhancing and enhancing just to

528
00:28:49,160 --> 00:28:52,600
Speaker 1: see a new topless woman. Debatable on whether that is

529
00:28:52,600 --> 00:28:55,800
Speaker 1: a top like, come on, you know that's really on you, Like,

530
00:28:55,840 --> 00:28:57,680
Speaker 1: don't put that on her? That makes sense to you,

531
00:28:57,720 --> 00:29:00,239
Speaker 1: know what I'm saying, Yeah, that makes sense. So this

532
00:29:00,280 --> 00:29:03,040
Speaker 1: is what's called a cheap fake, which is exactly what

533
00:29:03,080 --> 00:29:07,200
Speaker 1: it sounds like, a cheaply manipulated piece of content. Maybe

534
00:29:07,200 --> 00:29:10,720
Speaker 1: it's as simple as something as like mislabeling what that

535
00:29:10,760 --> 00:29:14,160
Speaker 1: content is, so saying, oh, this is AOC, not Sydney Leathers.

536
00:29:14,560 --> 00:29:17,920
Speaker 1: That is how like low budget and cheap cheap fakes

537
00:29:18,000 --> 00:29:22,640
Speaker 1: can be. So it's not always AI, right, So AI

538
00:29:22,960 --> 00:29:25,600
Speaker 1: is not the reason why people are creating this kind

539
00:29:25,680 --> 00:29:30,640
Speaker 1: of misleading content. However, AI makes it so easy and

540
00:29:30,680 --> 00:29:33,640
Speaker 1: so fast and so cheap for anybody to do it right.

541
00:29:34,000 --> 00:29:37,640
Speaker 1: It might have taken some effort to like make a

542
00:29:38,640 --> 00:29:41,080
Speaker 1: believable cheap fake of a girl in your high school

543
00:29:41,080 --> 00:29:43,360
Speaker 1: class who you want to sexually humiliate. But with AI,

544
00:29:43,640 --> 00:29:47,480
Speaker 1: anybody can do it easily. Francesca Manny, the high school

545
00:29:47,600 --> 00:29:50,720
Speaker 1: student who was targeted by these deep stakes, I really

546
00:29:50,720 --> 00:29:53,280
Speaker 1: got to give it to her because, like she worked

547
00:29:53,280 --> 00:29:56,440
Speaker 1: with this lawmaker and advocated and talked about her experience

548
00:29:56,600 --> 00:29:59,400
Speaker 1: to help raise awareness about this law. She says, the

549
00:29:59,440 --> 00:30:02,400
Speaker 1: issue is pret black and white. No kid, teen or

550
00:30:02,440 --> 00:30:05,000
Speaker 1: woman should ever have to experience what I went through.

551
00:30:05,040 --> 00:30:08,440
Speaker 1: I felt sad and helpless. I'm here standing up and

552
00:30:08,480 --> 00:30:11,080
Speaker 1: shouting for change, fighting for laws so no one else

553
00:30:11,080 --> 00:30:13,120
Speaker 1: has to feel as lost and powerless as I did.

554
00:30:13,120 --> 00:30:17,200
Speaker 1: On October twentieth. The glaring lack of laws speaks volumes,

555
00:30:17,600 --> 00:30:20,959
Speaker 1: and when you have a high school student having to

556
00:30:21,680 --> 00:30:26,680
Speaker 1: advocate to protect people in this way, something is really wrong.

557
00:30:26,800 --> 00:30:31,200
Speaker 1: Like I give it up to Francesca. She sounds amazing. Francesca,

558
00:30:31,240 --> 00:30:33,640
Speaker 1: if you're listening, come to the show anytime. We support you.

559
00:30:34,000 --> 00:30:36,640
Speaker 1: But she should be like getting ready for her prom

560
00:30:36,680 --> 00:30:39,320
Speaker 1: and hanging out with her friends and reading magazines and

561
00:30:39,360 --> 00:30:42,200
Speaker 1: playing supports and like doing things that high schoolers are

562
00:30:42,200 --> 00:30:44,240
Speaker 1: supposed to be doing. It kind of breaks my heart

563
00:30:44,640 --> 00:30:47,600
Speaker 1: that she has to be working, because this is work

564
00:30:47,920 --> 00:30:51,320
Speaker 1: in this way to advocate for a system where kids

565
00:30:51,360 --> 00:30:53,560
Speaker 1: like her aren't harmed like this, like this should not

566
00:30:53,640 --> 00:30:57,120
Speaker 1: be on her, a child, To ask adults whose job

567
00:30:57,240 --> 00:30:59,680
Speaker 1: it is to keep her people like her safe, who

568
00:30:59,840 --> 00:31:03,400
Speaker 1: just done fuck all? It infuriates me, She says. Our

569
00:31:03,520 --> 00:31:06,360
Speaker 1: voices are our secret weapon, and our words are like

570
00:31:06,520 --> 00:31:09,680
Speaker 1: power ups in Fortnite. My mom and I are advocating

571
00:31:09,680 --> 00:31:12,080
Speaker 1: to create a world we're being safe. Isn't just a hope,

572
00:31:12,120 --> 00:31:13,680
Speaker 1: it's a reality for everyone.

573
00:31:14,120 --> 00:31:14,360
Speaker 3: Oof.

574
00:31:14,560 --> 00:31:18,120
Speaker 1: Francesca girl, thank you for what you're doing. I am

575
00:31:18,160 --> 00:31:22,040
Speaker 1: so sorry that we have failed you. Yeah. I mean,

576
00:31:22,680 --> 00:31:27,800
Speaker 1: it's an issue that really pisses me off because it's

577
00:31:27,840 --> 00:31:32,240
Speaker 1: just so fucked up, and she really shouldn't have to

578
00:31:32,240 --> 00:31:32,800
Speaker 1: be doing this.

579
00:31:33,240 --> 00:31:35,320
Speaker 3: And it also seems like it should be a no

580
00:31:35,480 --> 00:31:39,520
Speaker 3: brainer that there should be some action on this bill

581
00:31:39,800 --> 00:31:42,320
Speaker 3: that was introduced in December of twenty twenty two. It's

582
00:31:42,320 --> 00:31:47,520
Speaker 3: been over a year and no action. Like everybody's constantly

583
00:31:48,560 --> 00:31:51,080
Speaker 3: yelling their heads off that we need to save the children.

584
00:31:51,720 --> 00:31:57,120
Speaker 3: Here is a bill that would help protect children from

585
00:31:57,960 --> 00:32:03,240
Speaker 3: what I would imagine nearly all reasonable people would agree

586
00:32:03,440 --> 00:32:09,120
Speaker 3: is like a harmful, bad thing. So the lack of

587
00:32:09,400 --> 00:32:14,360
Speaker 3: action on this bill is like puzzling and infuriating. Hopefully

588
00:32:14,960 --> 00:32:16,360
Speaker 3: we'll see some action on it.

589
00:32:16,680 --> 00:32:19,719
Speaker 1: Agreed. So I did want to talk briefly about this

590
00:32:19,800 --> 00:32:23,800
Speaker 1: open Ai story. Open ai the company behind chat Gypt.

591
00:32:24,000 --> 00:32:27,920
Speaker 1: We did a whole episode about their CEO, Sam Altman,

592
00:32:28,040 --> 00:32:30,960
Speaker 1: being fired and then rehired and all of that. Well,

593
00:32:31,000 --> 00:32:33,960
Speaker 1: they quietly changed it their terms of service to remove

594
00:32:34,040 --> 00:32:37,000
Speaker 1: language forbidding the use of its technology for military purposes.

595
00:32:37,440 --> 00:32:40,120
Speaker 1: The new terms of service still forbids the technology being

596
00:32:40,200 --> 00:32:43,600
Speaker 1: used to make weapons specifically, but other military or like

597
00:32:43,720 --> 00:32:47,560
Speaker 1: military adjacent purposes are now fair game. This change follows

598
00:32:47,600 --> 00:32:50,400
Speaker 1: a newly announced collaboration between open ai and the US

599
00:32:50,440 --> 00:32:54,360
Speaker 1: Department of Defense. So this policy change really continues this

600
00:32:54,440 --> 00:32:57,800
Speaker 1: trend of Sam Altman and open Ai as they embrace

601
00:32:57,920 --> 00:33:01,920
Speaker 1: this profit making potential of relaxing the company's sort of

602
00:33:01,960 --> 00:33:05,600
Speaker 1: original lofty ideas. I think initially open ai was meant

603
00:33:05,640 --> 00:33:07,920
Speaker 1: to be a nonprofit and they had this sort of

604
00:33:08,000 --> 00:33:13,520
Speaker 1: like at least stated, kind of like altruistic sounding ish goal.

605
00:33:13,840 --> 00:33:15,760
Speaker 1: Now they're like, no, we're trying to make money. We're

606
00:33:15,800 --> 00:33:17,920
Speaker 1: trying to get paid, y'all. Like that's what it is.

607
00:33:18,000 --> 00:33:20,440
Speaker 1: Let's just call it what it is so. Just two

608
00:33:20,440 --> 00:33:23,280
Speaker 1: months ago, we watched the internet pretty much melt down

609
00:33:23,320 --> 00:33:25,880
Speaker 1: with a nonprofit board of open ai, which a Wired

610
00:33:25,960 --> 00:33:28,560
Speaker 1: article from the summer describes as responsible for making sure

611
00:33:28,600 --> 00:33:31,320
Speaker 1: that the drive for revenue and profits will not overwhelm

612
00:33:31,720 --> 00:33:36,160
Speaker 1: open AI's original idea, fired Altman for allegedly not staying

613
00:33:36,160 --> 00:33:40,080
Speaker 1: true to its idealistic mission. The firing didn't take Within days,

614
00:33:40,280 --> 00:33:42,360
Speaker 1: he was back and many of the other board members

615
00:33:42,400 --> 00:33:45,400
Speaker 1: were out. We're really witnessing this evolution where we see

616
00:33:45,440 --> 00:33:48,960
Speaker 1: open ai going from this organization that has this ethos

617
00:33:49,000 --> 00:33:52,960
Speaker 1: of like free and open and non military to exclusive

618
00:33:53,160 --> 00:33:57,440
Speaker 1: enterprise licenses and yes military. It kind of reminds me

619
00:33:57,600 --> 00:33:59,960
Speaker 1: of not to keep picking on Google in this episode,

620
00:34:00,040 --> 00:34:02,960
Speaker 1: but like it's like every every story has been like

621
00:34:03,040 --> 00:34:05,480
Speaker 1: you know what, fuck Google, But like, do you know

622
00:34:05,480 --> 00:34:08,480
Speaker 1: how Google they used to have that motto don't be

623
00:34:08,600 --> 00:34:12,040
Speaker 1: evil and then soon thereafter they were like, yeah, we

624
00:34:12,120 --> 00:34:15,000
Speaker 1: make death tech. We make technology that is used to kill,

625
00:34:15,239 --> 00:34:19,000
Speaker 1: and what about it. I'm not saying Google's evil, but

626
00:34:19,080 --> 00:34:21,759
Speaker 1: like to go from don't be evil to like, we

627
00:34:21,880 --> 00:34:25,480
Speaker 1: literally make weapons that have and do kill people, it's

628
00:34:25,520 --> 00:34:26,240
Speaker 1: a big jump.

629
00:34:26,480 --> 00:34:28,480
Speaker 3: Yeah, it's a little squishy here, now.

630
00:34:28,520 --> 00:34:31,800
Speaker 1: So I will say open AI's collaboration with the Department

631
00:34:31,800 --> 00:34:34,520
Speaker 1: of Defense. It does sound like they are stopping short

632
00:34:35,160 --> 00:34:39,560
Speaker 1: of using their AI tech to build weapons in this case, however,

633
00:34:39,920 --> 00:34:42,240
Speaker 1: it does sound like this week they moved a step

634
00:34:42,320 --> 00:34:45,400
Speaker 1: closer to having their technology be used to build weapons.

635
00:34:45,640 --> 00:34:47,480
Speaker 1: We already know that AI is being used by the

636
00:34:47,560 --> 00:34:51,239
Speaker 1: Israeli military to identify targets for bombings in Gaza. They

637
00:34:51,320 --> 00:34:55,040
Speaker 1: use a system called the Gospel, which I'll just move on.

638
00:34:55,080 --> 00:34:56,360
Speaker 1: I have a lot to say about that name, but

639
00:34:56,400 --> 00:34:59,200
Speaker 1: we'll just move on, which the Israeli military has said

640
00:34:59,239 --> 00:35:03,200
Speaker 1: allows them to identify enemy combatants and equipment while reducing

641
00:35:03,280 --> 00:35:07,759
Speaker 1: civilian casualties. That's what they're saying. However, a fact that

642
00:35:07,800 --> 00:35:09,920
Speaker 1: we just know to be true about AI is that

643
00:35:09,960 --> 00:35:13,520
Speaker 1: it is super janky and gets things wrong consistently all

644
00:35:13,520 --> 00:35:15,160
Speaker 1: the time. And so if you're saying like, oh, no, no,

645
00:35:15,880 --> 00:35:18,160
Speaker 1: we're actually doing a good thing by using this AI

646
00:35:18,360 --> 00:35:21,759
Speaker 1: because it helps us reduce civilian casualties, and we know

647
00:35:22,000 --> 00:35:25,440
Speaker 1: with certainty because of the AI technology that the people

648
00:35:25,480 --> 00:35:29,520
Speaker 1: that we bomb are actually enemy combatants. Don't worry if

649
00:35:29,640 --> 00:35:31,760
Speaker 1: what you're if the lynchpin of that is the AI

650
00:35:31,880 --> 00:35:35,520
Speaker 1: technology being effective. We know it's not effective, so like

651
00:35:35,600 --> 00:35:37,839
Speaker 1: that doesn't really work, and it really does sound like

652
00:35:38,040 --> 00:35:41,319
Speaker 1: this whole system is likely kind of being used to

653
00:35:41,480 --> 00:35:47,960
Speaker 1: justify killing civilians because AI identified them as combatants. So

654
00:35:48,120 --> 00:35:51,279
Speaker 1: this whole situation is yet another reminder that technology is

655
00:35:51,320 --> 00:35:54,000
Speaker 1: not neutral and that the decisions made by people who

656
00:35:54,000 --> 00:35:59,120
Speaker 1: make technology have huge implications. And with all war and conflict,

657
00:35:59,200 --> 00:36:02,719
Speaker 1: it really is a gender justice issue. Okay, so we

658
00:36:02,800 --> 00:36:07,560
Speaker 1: have to talk about this kind of funky AI racing

659
00:36:07,680 --> 00:36:11,439
Speaker 1: ambassador thing. So this week the Formula E motorsports team

660
00:36:11,920 --> 00:36:17,800
Speaker 1: Mahindra launched Ava Beyond Reality, an AI generated female presenting

661
00:36:18,120 --> 00:36:20,920
Speaker 1: AI ambassador for the team. So, as far as I

662
00:36:20,960 --> 00:36:25,040
Speaker 1: can tell, Ava was an existing AI influencer and she

663
00:36:25,160 --> 00:36:27,799
Speaker 1: was going to be used to publicize this racing team.

664
00:36:28,200 --> 00:36:34,960
Speaker 1: The team's fans hated it, like clear loud, instant negative feedback.

665
00:36:35,160 --> 00:36:37,200
Speaker 1: They hated it so much that the entire thing was

666
00:36:37,239 --> 00:36:40,240
Speaker 1: basically scrubbed from the Internet in less than forty eight hours,

667
00:36:40,320 --> 00:36:43,520
Speaker 1: according to Car and Driver magazine. So the racing company

668
00:36:44,320 --> 00:36:46,799
Speaker 1: did kind of release an apology on Instagram. They said,

669
00:36:47,239 --> 00:36:50,440
Speaker 1: nurturing diversity, inclusion and innovation is at the heart of

670
00:36:50,480 --> 00:36:53,680
Speaker 1: Mahindra Racing. Our AI influencer program was designed with this

671
00:36:53,719 --> 00:36:58,160
Speaker 1: innovation in mind. Your comments hold tremendous value. We listened, understood,

672
00:36:58,200 --> 00:37:01,800
Speaker 1: and decided to discontinue the project, like grand opening grand

673
00:37:01,840 --> 00:37:05,239
Speaker 1: closing for their AI lay the Ambassador. So I think

674
00:37:05,280 --> 00:37:07,840
Speaker 1: one of the reasons why this got such negative feedback

675
00:37:07,960 --> 00:37:11,160
Speaker 1: is that there are real, live human women in racing,

676
00:37:11,360 --> 00:37:14,400
Speaker 1: but these women are often sidelined and marginalized in the sport.

677
00:37:14,680 --> 00:37:17,239
Speaker 1: According to Car and Driver, fewer than five percent of

678
00:37:17,320 --> 00:37:20,319
Speaker 1: elite level pilots identify as women, So it sort of

679
00:37:20,360 --> 00:37:25,080
Speaker 1: seems like this team would rather create an AI generated

680
00:37:25,440 --> 00:37:29,600
Speaker 1: fake woman than actually amplify a real, living and breathing

681
00:37:29,680 --> 00:37:33,000
Speaker 1: human woman who does exist in the sport. What's really

682
00:37:33,040 --> 00:37:35,279
Speaker 1: interesting is that Car and Driver did a deep dive

683
00:37:35,320 --> 00:37:38,880
Speaker 1: into this AI influencer social footprint and they found that

684
00:37:38,920 --> 00:37:41,880
Speaker 1: she posts about things like shoes and self care. So

685
00:37:42,280 --> 00:37:46,120
Speaker 1: Car and Driver suspects that this AI ambassador was actually

686
00:37:46,120 --> 00:37:50,120
Speaker 1: like a clunky marketing attempt to appeal to women, like

687
00:37:50,480 --> 00:37:54,080
Speaker 1: sometimes when people are using AI, like the story that

688
00:37:54,120 --> 00:37:57,080
Speaker 1: we talked about with the tech conference that was using

689
00:37:57,160 --> 00:38:02,400
Speaker 1: a fake like fake AI ambassador for their tech conference

690
00:38:02,960 --> 00:38:04,640
Speaker 1: It's clear to me that that was an attempt idea

691
00:38:05,000 --> 00:38:06,880
Speaker 1: to appeal to men, right, Just a lot of the

692
00:38:06,880 --> 00:38:09,560
Speaker 1: way that she presented was like, this is about men.

693
00:38:10,239 --> 00:38:14,319
Speaker 1: This AI racing ambassador, according to Car and Driver, might

694
00:38:14,360 --> 00:38:17,840
Speaker 1: have actually been an attempt to market to women. The

695
00:38:17,880 --> 00:38:21,920
Speaker 1: piece reads the photographs, shoes, clothing, selfies seemed drawn from

696
00:38:21,960 --> 00:38:25,239
Speaker 1: the same kind of influencer style sponsored content theme so

697
00:38:25,400 --> 00:38:28,319
Speaker 1: common from accounts that advertise toward women, in this case,

698
00:38:28,400 --> 00:38:32,640
Speaker 1: paired with your standard AI girlfriend imagery that's from Hazel Southwell,

699
00:38:32,880 --> 00:38:36,840
Speaker 1: longtime Formula E correspondent, and yet the text that accompanies

700
00:38:36,880 --> 00:38:39,160
Speaker 1: them doesn't appeal to women at all. It feels very

701
00:38:39,280 --> 00:38:42,440
Speaker 1: much like some type of outside marketing agency deal gone

702
00:38:42,520 --> 00:38:43,520
Speaker 1: terribly wrong.

703
00:38:44,080 --> 00:38:48,719
Speaker 3: That is an interesting distinction that like for that tech

704
00:38:48,760 --> 00:38:53,600
Speaker 3: conference it was an AI generated woman who I guess

705
00:38:53,640 --> 00:38:55,640
Speaker 3: it wasn't an AI generated woman, right, It was like

706
00:38:55,680 --> 00:38:58,279
Speaker 3: a like you said, like a cheap fake where they

707
00:38:59,120 --> 00:39:00,800
Speaker 3: had a model an actor.

708
00:39:02,000 --> 00:39:06,360
Speaker 1: Honestly, I'm not even comfortable saying that, like have I

709
00:39:06,400 --> 00:39:07,920
Speaker 1: have not. I have yet to get to the bottom

710
00:39:07,920 --> 00:39:09,320
Speaker 1: of what's going on there. People should listen to that

711
00:39:09,360 --> 00:39:10,759
Speaker 1: episode because it is if you haven't listened to it

712
00:39:10,800 --> 00:39:12,520
Speaker 1: because it is wild, will put it in the show notes.

713
00:39:12,520 --> 00:39:16,160
Speaker 1: But like, I'm not even comfortable saying in every instance

714
00:39:16,280 --> 00:39:19,360
Speaker 1: it was a human woman. In some cases, I believe

715
00:39:19,400 --> 00:39:21,080
Speaker 1: it was AI generated and this guy just thought we

716
00:39:21,120 --> 00:39:24,920
Speaker 1: wouldn't notice. In some cases, I think it's a handful

717
00:39:24,960 --> 00:39:28,520
Speaker 1: of women who kind of look alike. I honestly wouldn't

718
00:39:28,520 --> 00:39:31,879
Speaker 1: even feel comfortable guessing what's going on there.

719
00:39:32,960 --> 00:39:34,880
Speaker 3: But I guess the point that I tried to reinforce

720
00:39:34,920 --> 00:39:39,200
Speaker 3: there is that, like it was a don't know, disingenuous

721
00:39:39,280 --> 00:39:43,359
Speaker 3: representation of a woman, yes, meant to appeal to men,

722
00:39:43,480 --> 00:39:49,080
Speaker 3: like you said, and here it's a disingenuous representation of

723
00:39:49,120 --> 00:39:53,440
Speaker 3: a woman meant to appeal to women. And it is

724
00:39:53,520 --> 00:39:56,760
Speaker 3: nice to think about all the diversity that's out there

725
00:39:56,920 --> 00:39:59,640
Speaker 3: in disingenuous representations of women.

726
00:40:00,400 --> 00:40:04,719
Speaker 1: Everybody can be targeted. It's it's kind of conclusive when

727
00:40:04,719 --> 00:40:07,680
Speaker 1: you think about it, Like anybody can be targeted for

728
00:40:08,280 --> 00:40:12,560
Speaker 1: a disingenuous AI bought campaign, doesn't matter who you are.

729
00:40:13,320 --> 00:40:15,239
Speaker 3: Yeah, truly is a meritocracy.

730
00:40:16,560 --> 00:40:18,759
Speaker 1: So I will say, like, the thing that really kind

731
00:40:18,760 --> 00:40:21,600
Speaker 1: of sticks in my crawl about this is that it

732
00:40:21,640 --> 00:40:24,080
Speaker 1: does actually sound like this team has a history of

733
00:40:24,120 --> 00:40:26,239
Speaker 1: like trying to do good things and like trying to

734
00:40:26,239 --> 00:40:29,359
Speaker 1: be pretty inclusive. They've done campaigns on the past where

735
00:40:29,360 --> 00:40:32,239
Speaker 1: they highlight like actual human women in the sport, so

736
00:40:32,320 --> 00:40:35,040
Speaker 1: it seems like they didn't even really have to do this.

737
00:40:35,040 --> 00:40:37,759
Speaker 1: This script with the AI woman, this point from the

738
00:40:37,800 --> 00:40:40,720
Speaker 1: car and Driver piece really nails it. The entire Eva

739
00:40:40,840 --> 00:40:43,560
Speaker 1: Rose debacle waves a big red flag at situations where

740
00:40:43,560 --> 00:40:46,440
Speaker 1: inclusion and diversity are swept up by a marketing machine

741
00:40:46,680 --> 00:40:50,200
Speaker 1: intent on piggybacking on the latest tech trends without consideration

742
00:40:50,320 --> 00:40:52,719
Speaker 1: for the human dynamics that make up the sport. It's

743
00:40:52,800 --> 00:40:55,640
Speaker 1: encouraging that the backlash was widespread enough to make Mahindra

744
00:40:55,680 --> 00:40:58,200
Speaker 1: rethink Eva Rose. And we can only hope at the

745
00:40:58,239 --> 00:41:01,600
Speaker 1: next company that thinks programming diversity is better than hiring

746
00:41:01,640 --> 00:41:04,840
Speaker 1: diverse employees takes note. And that to me is really

747
00:41:04,960 --> 00:41:07,680
Speaker 1: the point. Like it's a silly story, yes, but that

748
00:41:07,800 --> 00:41:09,719
Speaker 1: is like the nugget of saying that we should really

749
00:41:09,760 --> 00:41:13,000
Speaker 1: be taking away is that when companies are like, oh,

750
00:41:13,000 --> 00:41:15,680
Speaker 1: we want to do something that involves a woman or

751
00:41:15,680 --> 00:41:19,440
Speaker 1: a person of color or somebody to highlight if you

752
00:41:19,600 --> 00:41:24,319
Speaker 1: use AI, that is a gig that an actual marginalized

753
00:41:24,400 --> 00:41:26,920
Speaker 1: human does not book and does not get paid for.

754
00:41:27,320 --> 00:41:29,120
Speaker 1: We did an episode about this a while back. But

755
00:41:29,960 --> 00:41:33,760
Speaker 1: a lot of these AI influencers, even if they're women,

756
00:41:33,960 --> 00:41:35,680
Speaker 1: Like there's there's one that's really famous, it's like a

757
00:41:36,160 --> 00:41:39,480
Speaker 1: black woman model that is run by a white man,

758
00:41:39,560 --> 00:41:42,319
Speaker 1: and so it's it's a lot of times these when

759
00:41:42,360 --> 00:41:45,160
Speaker 1: you book an AI for to be an influencer or

760
00:41:45,239 --> 00:41:48,080
Speaker 1: talent in some way, that is a gig, that is

761
00:41:48,200 --> 00:41:51,440
Speaker 1: money that a human person from that background is not getting.

762
00:41:51,480 --> 00:41:55,400
Speaker 1: And so particularly in situations like this one, when human

763
00:41:55,440 --> 00:41:59,440
Speaker 1: women already feel marginalized, to use a fake woman as

764
00:41:59,480 --> 00:42:02,279
Speaker 1: an ambassad or rather than amplifying one of those real

765
00:42:02,400 --> 00:42:05,760
Speaker 1: human women who feel sidelined sometimes in this sport doesn't

766
00:42:05,800 --> 00:42:08,640
Speaker 1: sit right with me, right, Like, actual inclusion has to

767
00:42:08,680 --> 00:42:12,400
Speaker 1: involve actual humans from the identity that you're trying to include.

768
00:42:12,719 --> 00:42:15,160
Speaker 1: And it's one of my worries about AI more generally

769
00:42:15,200 --> 00:42:17,600
Speaker 1: that it will just be used to further displace and

770
00:42:17,680 --> 00:42:20,960
Speaker 1: marginalize people who are already marginalized. Like you don't get

771
00:42:20,960 --> 00:42:23,080
Speaker 1: credit for working with a woman in a male dominated

772
00:42:23,120 --> 00:42:26,040
Speaker 1: space if that woman is not even real, if she's AI.

773
00:42:31,320 --> 00:42:31,799
Speaker 2: More, after a.

774
00:42:31,840 --> 00:42:44,400
Speaker 4: Quick break, let's get right back into it.

775
00:42:47,800 --> 00:42:52,879
Speaker 1: So bad news keeps on coming for media. Yesterday, Conde Nast,

776
00:42:53,040 --> 00:42:55,800
Speaker 1: the parent company of the music criticism site Pitchfork, announced

777
00:42:56,120 --> 00:42:59,080
Speaker 1: layoffs and that the site would be folded into the

778
00:42:59,160 --> 00:43:03,160
Speaker 1: Conde Nast out let GQ. Conde nas executive Anna Wintur

779
00:43:03,440 --> 00:43:06,960
Speaker 1: allegedly delivered the news of the mass layoffs while at

780
00:43:07,000 --> 00:43:11,480
Speaker 1: a conference room table without even removing her signature sunglasses

781
00:43:12,000 --> 00:43:16,120
Speaker 1: ouch no when tour described it as quote the best

782
00:43:16,160 --> 00:43:18,360
Speaker 1: path for the brand so that our coverage of music

783
00:43:18,400 --> 00:43:21,160
Speaker 1: can continue to thrive within the company. That kind of

784
00:43:21,200 --> 00:43:24,880
Speaker 1: makes it sound like Pitchfork must have not been successful. However,

785
00:43:25,160 --> 00:43:29,000
Speaker 1: Claire Willett, Conde Nass audience analytics person, said that this

786
00:43:29,120 --> 00:43:32,000
Speaker 1: was actually not the case, saying on Twitter, Pitchfork has

787
00:43:32,000 --> 00:43:34,600
Speaker 1: the highest daily sight visitors of any of our titles.

788
00:43:34,840 --> 00:43:38,000
Speaker 1: Their higher consuming segments generate more unique page views by

789
00:43:38,040 --> 00:43:41,719
Speaker 1: volume than any title. This despite scant resourcing, especially from

790
00:43:41,719 --> 00:43:44,759
Speaker 1: corporate well placed in a post scale era, or at

791
00:43:44,840 --> 00:43:47,560
Speaker 1: least was so I should be clear that I don't

792
00:43:47,560 --> 00:43:50,480
Speaker 1: have insight into exactly what is going on here and

793
00:43:50,520 --> 00:43:53,480
Speaker 1: why this decision was made. If I was a betting woman,

794
00:43:53,800 --> 00:43:55,799
Speaker 1: I would say that it has something to do with

795
00:43:55,880 --> 00:43:59,560
Speaker 1: the recent unionization effort by Pitchfork staff, and it was

796
00:43:59,640 --> 00:44:02,160
Speaker 1: confirm that eight of the staff who were laid off

797
00:44:02,239 --> 00:44:06,360
Speaker 1: were union members. The United Musicians and Allied Workers Union

798
00:44:06,440 --> 00:44:09,799
Speaker 1: released a statement saying the mass layoffs at Pitchfork are

799
00:44:10,040 --> 00:44:14,480
Speaker 1: union busting. So I have my suspicions of what's going

800
00:44:14,520 --> 00:44:16,680
Speaker 1: on here, but I don't have any special insights, so

801
00:44:16,719 --> 00:44:17,560
Speaker 1: I can't really say.

802
00:44:18,160 --> 00:44:22,800
Speaker 3: Damn. I had read that Pitchfork was being folded into GQ,

803
00:44:23,160 --> 00:44:27,319
Speaker 3: and yeah, I just assumed it was because people weren't

804
00:44:27,360 --> 00:44:31,120
Speaker 3: reading it, weren't looking at it. But apparently that's not

805
00:44:31,200 --> 00:44:33,640
Speaker 3: the case. It's just union busting allegedly.

806
00:44:34,040 --> 00:44:37,240
Speaker 1: So let's talk about that, because I really really hate

807
00:44:37,640 --> 00:44:41,000
Speaker 1: that of all the titles that Conde Nast has, it

808
00:44:41,040 --> 00:44:44,880
Speaker 1: is folding Pitchfork into GQ specifically. Do you know what

809
00:44:44,960 --> 00:44:45,839
Speaker 1: GQ stands for?

810
00:44:46,080 --> 00:44:49,359
Speaker 3: Yeah, Gentlemen's Quarterly. It's like marketed for men.

811
00:44:49,800 --> 00:44:53,799
Speaker 1: Yeah, it is a men's publication, right, And so I think,

812
00:44:53,960 --> 00:44:56,800
Speaker 1: especially out of time, when you have so many people

813
00:44:56,800 --> 00:44:59,520
Speaker 1: who are not men. I'm thinking of big artists like

814
00:44:59,560 --> 00:45:02,279
Speaker 1: Taylor's so and Beyonce, artists like boy genists we have.

815
00:45:02,640 --> 00:45:05,640
Speaker 1: We're in the heyday of so many musicians who are

816
00:45:05,680 --> 00:45:09,160
Speaker 1: not men really being powerhouses and dominating and shaping the

817
00:45:09,239 --> 00:45:12,200
Speaker 1: music industry. So like, what are you trying to say

818
00:45:12,200 --> 00:45:16,840
Speaker 1: that music is a men's interest? You know, Conde Nast

819
00:45:16,960 --> 00:45:20,200
Speaker 1: has Vanity Fair, they have Wired why not fold it

820
00:45:20,239 --> 00:45:23,040
Speaker 1: into one of those those are also Conde Nast publications.

821
00:45:23,080 --> 00:45:25,120
Speaker 1: It feels pretty marginalizing to me.

822
00:45:25,440 --> 00:45:28,640
Speaker 3: And also I know several women who enjoy listening to music.

823
00:45:28,960 --> 00:45:34,320
Speaker 1: Music. It's not just for men anymore. So it does

824
00:45:34,640 --> 00:45:37,400
Speaker 1: seem like an end of an era for Pitchfork. Like

825
00:45:38,120 --> 00:45:41,680
Speaker 1: I feel strongly about this because I was definitely a

826
00:45:41,719 --> 00:45:45,120
Speaker 1: Pitchfork person, Like I was what you might consider a

827
00:45:45,200 --> 00:45:47,800
Speaker 1: hipster in the heyday of hipsters. I lived in Brooklyn,

828
00:45:47,920 --> 00:45:50,000
Speaker 1: I did all of that. Like, I was a person

829
00:45:50,040 --> 00:45:53,080
Speaker 1: who took the Pitchfork end of your lists very very seriously.

830
00:45:53,840 --> 00:45:56,719
Speaker 1: I used to sometimes obsess about the numerical ratings they

831
00:45:56,760 --> 00:46:00,000
Speaker 1: would give artists. Do you remember when Halsey accidentally praised

832
00:46:00,200 --> 00:46:02,480
Speaker 1: nine to eleven because they were trying to hate on Pitchfork.

833
00:46:02,719 --> 00:46:04,720
Speaker 3: Yeah, it was a little it was a little awkward.

834
00:46:05,080 --> 00:46:07,720
Speaker 1: Yeah, So for folks, it's one of my favorite Pitchfork Memories.

835
00:46:07,760 --> 00:46:11,320
Speaker 1: I think Pitchwork gave Halsey's album a not good review,

836
00:46:11,680 --> 00:46:15,160
Speaker 1: and Halsey was like, I just wish Pitchwork would explode.

837
00:46:15,600 --> 00:46:17,920
Speaker 1: And it turned out that Pitchfork happened to have an

838
00:46:17,920 --> 00:46:21,680
Speaker 1: office in the World Trade Center, so it sounded like

839
00:46:21,840 --> 00:46:25,480
Speaker 1: it sounded like Halsey was like praising nine to eleven

840
00:46:25,880 --> 00:46:28,080
Speaker 1: and yeah, It's just one of those things where was like,

841
00:46:28,400 --> 00:46:30,880
Speaker 1: who could have seen it coming? But loved that, loved

842
00:46:30,880 --> 00:46:31,400
Speaker 1: that tweet.

843
00:46:31,920 --> 00:46:32,080
Speaker 3: You know.

844
00:46:32,160 --> 00:46:35,920
Speaker 1: So I have a I have a history with Pitchfork.

845
00:46:35,920 --> 00:46:39,400
Speaker 1: I'm not gonna pretend like I'm unbiased here. I drove

846
00:46:39,760 --> 00:46:43,200
Speaker 1: from DC to Chicago to go to the Pitchfork Music Festival,

847
00:46:43,360 --> 00:46:47,520
Speaker 1: and I had a memorably unpleasant drug experience while listening

848
00:46:47,520 --> 00:46:50,000
Speaker 1: to the band Animal Collective. I will never forget it.

849
00:46:50,560 --> 00:46:53,440
Speaker 1: If folks want more detail, maybe I can go into

850
00:46:53,480 --> 00:46:56,399
Speaker 1: it on It's not that exciting. Aren't all like bad

851
00:46:56,520 --> 00:46:59,560
Speaker 1: drug trips the same in a kind of way. But

852
00:46:59,600 --> 00:47:03,560
Speaker 1: like so, Pitchfork definitely occupies a certain soft spot in

853
00:47:03,600 --> 00:47:04,760
Speaker 1: my media diet.

854
00:47:05,000 --> 00:47:08,960
Speaker 3: It's like a pillar of not just music journalism, but

855
00:47:09,000 --> 00:47:14,280
Speaker 3: like journalism in America, right, Like how many national publications

856
00:47:14,320 --> 00:47:18,960
Speaker 3: are there that like focus on music and culture the

857
00:47:19,000 --> 00:47:22,680
Speaker 3: way that they do, or I guess did. It's pretty

858
00:47:22,719 --> 00:47:26,720
Speaker 3: sad to see that just folded into a lifestyle brand,

859
00:47:26,880 --> 00:47:28,680
Speaker 3: and not just the lifestyle brand, but like a lifestyle

860
00:47:28,800 --> 00:47:30,919
Speaker 3: brand marketed to men.

861
00:47:31,520 --> 00:47:34,360
Speaker 1: And I think, you know, Pitchfork. When they first started,

862
00:47:34,480 --> 00:47:37,160
Speaker 1: they got a lot of heat for really only covering

863
00:47:37,200 --> 00:47:42,040
Speaker 1: like white indie male artists, but by the end they

864
00:47:42,080 --> 00:47:45,120
Speaker 1: were doing what I found to be like really inclusive coverage.

865
00:47:45,200 --> 00:47:47,640
Speaker 1: And yeah, it makes me sad to see them folded

866
00:47:47,680 --> 00:47:52,759
Speaker 1: into an explicitly a men's magazine. And I can only

867
00:47:52,800 --> 00:47:55,880
Speaker 1: imagine that whatever the next iteration of Pitchfork is is

868
00:47:55,920 --> 00:47:58,239
Speaker 1: going to be hollowed out. And so I think it's

869
00:47:58,239 --> 00:47:59,680
Speaker 1: an It feels like the end of an era of

870
00:47:59,680 --> 00:48:02,399
Speaker 1: a pitch but also an end of an era for

871
00:48:02,520 --> 00:48:06,319
Speaker 1: like media and criticism more generally. It feels like like

872
00:48:06,360 --> 00:48:09,080
Speaker 1: if you are not in media or like media adjacent,

873
00:48:09,400 --> 00:48:12,680
Speaker 1: I cannot stress to you enough how how tenuous it

874
00:48:12,719 --> 00:48:15,239
Speaker 1: feels right now, Like every day there is a new

875
00:48:15,320 --> 00:48:18,320
Speaker 1: round of layoffs announced in media and also tech. So

876
00:48:18,360 --> 00:48:21,400
Speaker 1: it also seems like it's one of those situations where

877
00:48:21,520 --> 00:48:24,919
Speaker 1: a lot of the big bets on digital media that

878
00:48:25,480 --> 00:48:27,600
Speaker 1: that like people with money made are just like not

879
00:48:27,640 --> 00:48:30,880
Speaker 1: coming to fruition because of their choices. Like it is

880
00:48:30,920 --> 00:48:34,319
Speaker 1: not like it's not rank and file staffers or like

881
00:48:34,360 --> 00:48:38,120
Speaker 1: writers or editorial who are causing this disruption. It is

882
00:48:38,200 --> 00:48:41,320
Speaker 1: decisions being made at the executive suit level. And I

883
00:48:41,360 --> 00:48:43,680
Speaker 1: think that's what is so infuriating, is like it is

884
00:48:43,719 --> 00:48:46,120
Speaker 1: not those people who lose their jobs and who are

885
00:48:46,200 --> 00:48:49,799
Speaker 1: laid off and have their careers disrupted. It is the

886
00:48:49,920 --> 00:48:52,840
Speaker 1: rank and file staffers who aren't making those decisions. And so,

887
00:48:53,680 --> 00:48:57,080
Speaker 1: you know, I just think it really says something grim

888
00:48:57,280 --> 00:48:59,880
Speaker 1: about the state of media that we're in right now.

889
00:49:00,000 --> 00:49:03,879
Speaker 1: There's a really good piece in Defector by Israel Darmalo

890
00:49:04,200 --> 00:49:07,480
Speaker 1: that put it really well Israel rights. Throughout the industry,

891
00:49:07,600 --> 00:49:10,400
Speaker 1: features in reporting and music reviews have taken a backseat

892
00:49:10,440 --> 00:49:13,360
Speaker 1: as companies push for more social media and video content.

893
00:49:13,600 --> 00:49:16,120
Speaker 1: What has filled the vacuum left behind by actual music

894
00:49:16,120 --> 00:49:19,919
Speaker 1: criticism is a loose collection of YouTubers and influencers who

895
00:49:19,960 --> 00:49:22,960
Speaker 1: feed slop to their younger audiences and fan communities that

896
00:49:23,040 --> 00:49:25,560
Speaker 1: engage with music solely through their obsession with a particular

897
00:49:25,600 --> 00:49:28,239
Speaker 1: pop act. This has all helped produce a mass of

898
00:49:28,320 --> 00:49:31,160
Speaker 1: music fans who don't understand the value of criticism and

899
00:49:31,280 --> 00:49:34,120
Speaker 1: outright to test being told that things they like might suck.

900
00:49:34,600 --> 00:49:37,800
Speaker 1: Even worse, it has helped destroy what scant opportunities remain

901
00:49:37,840 --> 00:49:40,840
Speaker 1: for obscure or up and coming musicians to find an audience.

902
00:49:41,080 --> 00:49:43,080
Speaker 1: It's harder than ever to make it big without a

903
00:49:43,120 --> 00:49:46,040
Speaker 1: co sign from Drake or Kayler. Swift and Stuffing one

904
00:49:46,080 --> 00:49:48,799
Speaker 1: of the few music publications left that swam against all

905
00:49:48,800 --> 00:49:52,560
Speaker 1: these currents into GQ stuffy environs, isn't going to help things.

906
00:49:52,800 --> 00:49:55,439
Speaker 1: So I agree with that so much, and I would

907
00:49:55,440 --> 00:49:58,200
Speaker 1: actually take this a step further more and more. I

908
00:49:58,239 --> 00:50:00,279
Speaker 1: think that this is the way of meeting. Yeah, Like,

909
00:50:00,360 --> 00:50:02,640
Speaker 1: this is the future of media, and it is making

910
00:50:02,800 --> 00:50:05,400
Speaker 1: everything worse. It is making us all less informed, It

911
00:50:05,440 --> 00:50:09,400
Speaker 1: is making us all less able to be savvy critics

912
00:50:09,440 --> 00:50:12,239
Speaker 1: of the media that we consume. Like right now, there

913
00:50:12,280 --> 00:50:16,640
Speaker 1: are literally people celebrating the demise of Pitchwork because Pitchwork

914
00:50:16,680 --> 00:50:19,680
Speaker 1: gave their favorite artist a bad rating like five years

915
00:50:19,680 --> 00:50:23,840
Speaker 1: ago or whatever. Right Like, when media criticism just becomes

916
00:50:23,840 --> 00:50:26,839
Speaker 1: a vehicle for stands to stay in their faves, we

917
00:50:26,880 --> 00:50:30,120
Speaker 1: lose something. We lose something valuable like, I know this

918
00:50:30,200 --> 00:50:33,560
Speaker 1: might sound super hoity toity when you compare it to

919
00:50:33,600 --> 00:50:35,399
Speaker 1: everything else going on in the world, but I do

920
00:50:35,440 --> 00:50:37,160
Speaker 1: think that it is connected. I think this is a

921
00:50:37,160 --> 00:50:41,520
Speaker 1: symptom of a larger rot happening in our society right now.

922
00:50:41,560 --> 00:50:45,040
Speaker 1: When it comes to technology and media, media criticism is

923
00:50:45,160 --> 00:50:48,440
Speaker 1: and has been the staple of a robust, healthy society,

924
00:50:48,719 --> 00:50:50,960
Speaker 1: and it has been that way since our earliest days.

925
00:50:51,320 --> 00:50:55,600
Speaker 1: If that is replaced by like thinly veiled ad copied

926
00:50:55,680 --> 00:50:58,360
Speaker 1: just another way to sell us crap, or like AI

927
00:50:58,600 --> 00:51:02,040
Speaker 1: generated listicles, or just like fan content, then we really

928
00:51:02,040 --> 00:51:04,360
Speaker 1: do lose something. Like I don't think this is a

929
00:51:04,880 --> 00:51:07,400
Speaker 1: I understand that this seems like a small story and

930
00:51:07,480 --> 00:51:09,359
Speaker 1: maybe it isn't a scale of things, but I think

931
00:51:09,400 --> 00:51:12,200
Speaker 1: that this is a symptom of a larger rot happening

932
00:51:12,239 --> 00:51:12,920
Speaker 1: in our society.

933
00:51:13,520 --> 00:51:18,279
Speaker 3: Yeah, preach, you know, And it's probably not coincidental that

934
00:51:18,680 --> 00:51:22,160
Speaker 3: valuable thing that is being lost that you know, like good,

935
00:51:22,520 --> 00:51:28,480
Speaker 3: high quality media criticism and being replaced by much lower

936
00:51:28,600 --> 00:51:33,240
Speaker 3: quality like fan content on you know, on YouTube or wherever,

937
00:51:34,719 --> 00:51:41,200
Speaker 3: is happening in parallel with what sounds like union busting

938
00:51:41,440 --> 00:51:45,960
Speaker 3: at Conde Nast, right where like professional journalists who are

939
00:51:45,960 --> 00:51:49,920
Speaker 3: putting out this high quality stuff. Sounds like we're either

940
00:51:50,080 --> 00:51:56,080
Speaker 3: acted or laid off because they were wanting to agitate

941
00:51:56,160 --> 00:52:00,840
Speaker 3: for better wages, better conditions or something. And I'm sure

942
00:52:00,880 --> 00:52:03,840
Speaker 3: that the people creating those YouTube videos, you know, the stands,

943
00:52:03,920 --> 00:52:11,080
Speaker 3: the whoever, uh, they're probably doing it for a lot

944
00:52:11,640 --> 00:52:15,600
Speaker 3: less compensation than those professional writers, if any compensation.

945
00:52:16,360 --> 00:52:19,239
Speaker 1: And I think I think you're so spot on. And

946
00:52:19,280 --> 00:52:23,440
Speaker 1: when you fold the threat of AI replacing journalists and

947
00:52:23,480 --> 00:52:27,200
Speaker 1: writers and people who make interesting stuff, like it's clear

948
00:52:27,239 --> 00:52:32,000
Speaker 1: to me that what suits, you know, executives thought they

949
00:52:32,160 --> 00:52:36,560
Speaker 1: had in Pitchfork was a brand and so what was important,

950
00:52:36,600 --> 00:52:39,920
Speaker 1: what was valuable was the brand name Pitchfork. What was

951
00:52:40,000 --> 00:52:43,240
Speaker 1: not adding value in their kind of myopic view of things,

952
00:52:43,600 --> 00:52:46,359
Speaker 1: was the writers who actually do the thing, the people

953
00:52:46,360 --> 00:52:48,040
Speaker 1: who write, the people who do the interviews, the people

954
00:52:48,040 --> 00:52:50,360
Speaker 1: who make the thing. They're like, oh, we can outsource

955
00:52:50,400 --> 00:52:53,600
Speaker 1: that with AI. That's not that's not you know, I

956
00:52:53,600 --> 00:52:55,840
Speaker 1: don't know this to be specifically true for Pitchfork, but

957
00:52:55,880 --> 00:52:58,920
Speaker 1: I think that that is the larger trend I'm seeing.

958
00:52:59,160 --> 00:53:01,840
Speaker 1: I'm seeing media go toward that the people who actually

959
00:53:01,880 --> 00:53:04,879
Speaker 1: make the thing that bring an audience. What they do

960
00:53:05,000 --> 00:53:08,000
Speaker 1: is replaceable. What they do can be replaced by AI

961
00:53:08,200 --> 00:53:10,080
Speaker 1: or somebody who's not making any money, or just some

962
00:53:10,200 --> 00:53:13,680
Speaker 1: teen on YouTube or whatever. And what's actually important, what

963
00:53:13,760 --> 00:53:16,200
Speaker 1: actually makes money, is the brand. And I don't think

964
00:53:16,239 --> 00:53:18,440
Speaker 1: that is going to work out. I don't think. I

965
00:53:18,480 --> 00:53:21,960
Speaker 1: think what actually puts butts in seats and eyeballs on

966
00:53:22,080 --> 00:53:26,040
Speaker 1: pages is good writing, is good content, is thoughtful writing.

967
00:53:26,320 --> 00:53:29,359
Speaker 1: And I think that And honestly, like I was, I

968
00:53:29,400 --> 00:53:30,960
Speaker 1: was like kind of gone a deep dive with this.

969
00:53:31,360 --> 00:53:35,359
Speaker 1: Claire Willet, the audience analytics person from Conde Nast, When

970
00:53:35,400 --> 00:53:39,720
Speaker 1: someone when Claire was like, oh, Pitchfork actually was driving

971
00:53:39,760 --> 00:53:42,400
Speaker 1: lots of engagement. They're doing fine, someone was like, so

972
00:53:42,440 --> 00:53:47,040
Speaker 1: then why do this? She basically was like, because rich

973
00:53:47,080 --> 00:53:51,319
Speaker 1: people don't read. Because rich people buy things, they kind

974
00:53:51,360 --> 00:53:54,400
Speaker 1: of hate the things that they acquire. They don't understand

975
00:53:54,400 --> 00:53:56,840
Speaker 1: the things that they acquire, and they don't the only

976
00:53:57,640 --> 00:54:00,480
Speaker 1: way that they have to engage with the the things

977
00:54:00,480 --> 00:54:03,879
Speaker 1: that they get ownership over is to crush them, because

978
00:54:03,880 --> 00:54:06,920
Speaker 1: they don't understand them, and they like have a weird

979
00:54:07,120 --> 00:54:09,879
Speaker 1: kind of disdain for these things that they buy. And

980
00:54:09,920 --> 00:54:11,840
Speaker 1: we've seen that time and time again here in the

981
00:54:11,880 --> 00:54:15,840
Speaker 1: DMV area where I live, a wealthy person just acquired

982
00:54:15,880 --> 00:54:19,440
Speaker 1: the newspaper, the Baltimore Sun, and first day in the office,

983
00:54:19,520 --> 00:54:21,480
Speaker 1: people are meeting the new owner for the first time,

984
00:54:21,800 --> 00:54:24,239
Speaker 1: and what does he do stand in front of her

985
00:54:24,320 --> 00:54:27,640
Speaker 1: room and denigrate the people who now work for him,

986
00:54:27,640 --> 00:54:30,040
Speaker 1: being like, all of you don't know what you're doing,

987
00:54:30,120 --> 00:54:32,240
Speaker 1: You don't know how to write. We should be doing

988
00:54:32,360 --> 00:54:36,600
Speaker 1: more social media, more listicals, more engage in content. What

989
00:54:36,640 --> 00:54:40,320
Speaker 1: you're doing is worthless and valueless to me. This company

990
00:54:40,320 --> 00:54:42,720
Speaker 1: that I just bought, and so I think that she's

991
00:54:42,719 --> 00:54:46,160
Speaker 1: onto something that there is a deep, deep disdain when

992
00:54:46,239 --> 00:54:50,160
Speaker 1: wealthy people acquire things that people like I was like, like,

993
00:54:50,280 --> 00:54:52,279
Speaker 1: I don't respect it, and I almost kind of like

994
00:54:52,400 --> 00:54:54,520
Speaker 1: weirdly hate this thing I just acquired.

995
00:54:55,000 --> 00:54:59,000
Speaker 3: Yeah, And also I think those wealthy people often view

996
00:54:59,040 --> 00:55:02,279
Speaker 3: it as, you know, very much a thing, not like

997
00:55:03,400 --> 00:55:07,799
Speaker 3: any sort of organization or community of you know, people

998
00:55:07,840 --> 00:55:09,760
Speaker 3: who actually do the work of making the thing happen.

999
00:55:10,200 --> 00:55:13,440
Speaker 3: And when those people start to agitate and want a

1000
00:55:13,480 --> 00:55:17,879
Speaker 3: little bit of power through advocating for reunion, that is

1001
00:55:18,000 --> 00:55:25,319
Speaker 3: like so threatening. And you know, we've definitely seen executives

1002
00:55:25,360 --> 00:55:30,000
Speaker 3: willing to like take huge losses just so that they

1003
00:55:30,080 --> 00:55:34,239
Speaker 3: don't have to negotiate with the union about anything, right,

1004
00:55:34,360 --> 00:55:36,440
Speaker 3: like just wanting absolute control.

1005
00:55:36,960 --> 00:55:39,920
Speaker 1: I saw this tweet from John Frankensteiner that I really appreciated.

1006
00:55:40,360 --> 00:55:43,480
Speaker 1: Every industry has been taken over by a guy that's like, oh,

1007
00:55:43,560 --> 00:55:45,960
Speaker 1: that thing that's made life a little less unbearable for

1008
00:55:46,040 --> 00:55:49,440
Speaker 1: multiple generations. I'm shutting it down to increase profits by

1009
00:55:49,480 --> 00:55:53,120
Speaker 1: two percent for a single quarter, and that's done. That

1010
00:55:53,200 --> 00:55:56,480
Speaker 1: say it all. So this last story. In doing the

1011
00:55:56,560 --> 00:55:59,120
Speaker 1: research for this story, I found out that I actually

1012
00:55:59,280 --> 00:56:01,640
Speaker 1: might be on the wrong side of history on this one.

1013
00:56:01,719 --> 00:56:04,839
Speaker 1: So I'm curious for folcused thoughts. I was also had

1014
00:56:04,880 --> 00:56:07,920
Speaker 1: to do a story about how our grand experiment with

1015
00:56:08,000 --> 00:56:11,799
Speaker 1: those electronic self kiosks and stores could be coming to

1016
00:56:11,840 --> 00:56:13,960
Speaker 1: an end, and Mike You and I were talking about

1017
00:56:13,960 --> 00:56:15,520
Speaker 1: it and I was like, oh my god, I hate

1018
00:56:15,560 --> 00:56:19,640
Speaker 1: those self checkout kiosks at Target, and you were like, oh, those,

1019
00:56:19,800 --> 00:56:20,520
Speaker 1: I love those.

1020
00:56:21,160 --> 00:56:25,840
Speaker 3: It's true. I mean they are often maddening, and I've definitely,

1021
00:56:25,920 --> 00:56:30,120
Speaker 3: like occasionally had some like pretty negative interactions with the

1022
00:56:30,520 --> 00:56:33,600
Speaker 3: poor staff person whose job is to like stand there

1023
00:56:33,600 --> 00:56:38,399
Speaker 3: and tend to those machines, not like, I was never

1024
00:56:38,480 --> 00:56:43,880
Speaker 3: mean to them, but you know, communicating my frustration with

1025
00:56:44,160 --> 00:56:47,399
Speaker 3: their the janky machines. So it's I understand why people

1026
00:56:47,480 --> 00:56:49,759
Speaker 3: might not like them, but most of the time they

1027
00:56:49,800 --> 00:56:53,799
Speaker 3: actually work pretty well and allow me to just like

1028
00:56:53,880 --> 00:56:56,680
Speaker 3: get through there, get out work quickly.

1029
00:56:57,480 --> 00:56:59,160
Speaker 1: How would you feel if I told you that sixty

1030
00:56:59,200 --> 00:57:01,560
Speaker 1: seventy percent of con say that they've had self checkout

1031
00:57:01,560 --> 00:57:03,879
Speaker 1: machines fail. Would that make you think that they were

1032
00:57:03,920 --> 00:57:04,439
Speaker 1: working well?

1033
00:57:04,520 --> 00:57:04,680
Speaker 2: Is that?

1034
00:57:04,880 --> 00:57:07,040
Speaker 1: Is that a Is that a stat that fills you

1035
00:57:07,080 --> 00:57:07,800
Speaker 1: with confidence?

1036
00:57:08,400 --> 00:57:11,160
Speaker 3: I mean, if anything, that sounds like an underreporting, right,

1037
00:57:11,280 --> 00:57:14,839
Speaker 3: Like I'd say, among people who use them with any frequency,

1038
00:57:15,360 --> 00:57:17,800
Speaker 3: nearly one hundred percent of people have experienced the case

1039
00:57:17,840 --> 00:57:20,640
Speaker 3: where they fail at one point or another, but most

1040
00:57:20,720 --> 00:57:24,400
Speaker 3: of the time they actually work okay.

1041
00:57:24,440 --> 00:57:26,760
Speaker 1: So I don't know if I am out of step

1042
00:57:26,800 --> 00:57:28,960
Speaker 1: with the way that everybody feels on this. I thought

1043
00:57:28,960 --> 00:57:30,800
Speaker 1: we were all. I thought we all I was like,

1044
00:57:30,840 --> 00:57:32,600
Speaker 1: we have to do this story because don't we all

1045
00:57:32,600 --> 00:57:35,560
Speaker 1: hate these? Now I'm like, maybe I'm at maybe I'm

1046
00:57:35,600 --> 00:57:37,440
Speaker 1: on the wrong side of history on this one, and

1047
00:57:37,520 --> 00:57:39,800
Speaker 1: you know what, I've been there before. I'll you know,

1048
00:57:39,880 --> 00:57:42,120
Speaker 1: if I'm the old if I'm the only hater of

1049
00:57:42,160 --> 00:57:45,320
Speaker 1: these I will I'll stand on that. So I hate

1050
00:57:45,320 --> 00:57:49,760
Speaker 1: the self check kiosks, and apparently retail establishments might actually

1051
00:57:49,800 --> 00:57:52,520
Speaker 1: be on my side too. According to Gizmoto and a

1052
00:57:52,560 --> 00:57:55,920
Speaker 1: report from BBC, stores across the country are reversing course

1053
00:57:56,000 --> 00:57:59,280
Speaker 1: on these machines, and the consensus is growing among analysts

1054
00:57:59,280 --> 00:58:02,320
Speaker 1: and insider that self checkout has been a disaster for

1055
00:58:02,360 --> 00:58:05,880
Speaker 1: consumers and retailers alike. I totally agree, one hundred percent.

1056
00:58:05,960 --> 00:58:08,960
Speaker 1: Get them out of there. So the machines are probably

1057
00:58:09,000 --> 00:58:12,120
Speaker 1: like not going away anytime soon, but they might be

1058
00:58:12,360 --> 00:58:15,480
Speaker 1: less a part of like the retail shopping experience, Like

1059
00:58:15,800 --> 00:58:18,520
Speaker 1: at certain Target stores, they might start limiting the amount

1060
00:58:18,560 --> 00:58:20,920
Speaker 1: of items that you can check out at a self checkout.

1061
00:58:20,960 --> 00:58:23,160
Speaker 1: I think it's like ten of them or less. Dollar

1062
00:58:23,280 --> 00:58:26,280
Speaker 1: General really bet big on self checkout, but at their

1063
00:58:26,320 --> 00:58:28,880
Speaker 1: earning call last month, their CEO said the store is

1064
00:58:28,920 --> 00:58:32,280
Speaker 1: planning to increase the number of employees and stores, particularly

1065
00:58:32,320 --> 00:58:34,800
Speaker 1: in the checkout area. So like a big reversal of

1066
00:58:35,360 --> 00:58:38,320
Speaker 1: relying on self checkout. So there are so many reasons

1067
00:58:38,320 --> 00:58:41,240
Speaker 1: to hate self checkout. I don't know how somebody who

1068
00:58:41,360 --> 00:58:44,680
Speaker 1: likes it contends with these like very clear reasons why

1069
00:58:44,760 --> 00:58:47,120
Speaker 1: they don't work and why they're awful. Like, I don't

1070
00:58:47,120 --> 00:58:49,320
Speaker 1: see how a reasonable person could disagree with this evidence,

1071
00:58:49,360 --> 00:58:50,840
Speaker 1: So like you'll have to let me know.

1072
00:58:51,400 --> 00:58:55,680
Speaker 3: So when you go to target, you go queue up

1073
00:58:55,840 --> 00:59:00,080
Speaker 3: in like align with one of the humans and just

1074
00:59:00,120 --> 00:59:03,000
Speaker 3: like watch people in this self checkout line like go

1075
00:59:03,120 --> 00:59:05,560
Speaker 3: through and leave the store while you're still standing there.

1076
00:59:05,960 --> 00:59:09,120
Speaker 1: Well, we go to the same target, so you and

1077
00:59:09,200 --> 00:59:11,520
Speaker 1: I both know that the self checkout line at that

1078
00:59:11,560 --> 00:59:14,760
Speaker 1: particular target it's janky as hell. First of all, if

1079
00:59:15,000 --> 00:59:17,160
Speaker 1: you're ever buying something that you have to show your

1080
00:59:17,200 --> 00:59:21,000
Speaker 1: ID for like an allergy medicine here or like alcohol,

1081
00:59:21,320 --> 00:59:23,360
Speaker 1: there's never a person in there. So it's like it's

1082
00:59:23,400 --> 00:59:25,880
Speaker 1: not really you're describing like a check a self checkout

1083
00:59:25,920 --> 00:59:29,400
Speaker 1: experience where folks are breezing through. I've never seen anybody

1084
00:59:29,400 --> 00:59:31,640
Speaker 1: breathe through the self checkout at that target, and you

1085
00:59:31,840 --> 00:59:33,880
Speaker 1: know it, this is this is you are. I feel

1086
00:59:33,880 --> 00:59:37,520
Speaker 1: like you're being disingenuous to support your your love of

1087
00:59:37,600 --> 00:59:39,520
Speaker 1: these robot kiosks.

1088
00:59:40,080 --> 00:59:45,040
Speaker 3: Not the case. I mean. I have watched people stand

1089
00:59:45,120 --> 00:59:49,280
Speaker 3: in line for one of the human cashiers, meet each other,

1090
00:59:49,400 --> 00:59:54,280
Speaker 3: fall in love, have children pass away, those children grow

1091
00:59:54,400 --> 01:00:00,000
Speaker 3: up to eventually check out with the items that they're

1092
01:00:00,200 --> 01:00:03,240
Speaker 3: parents had brought into the line. Like that's the pace

1093
01:00:03,400 --> 01:00:05,520
Speaker 3: at which those human checkout lines go.

1094
01:00:05,760 --> 01:00:09,760
Speaker 1: I'm not saying the humans are faster. I'm saying this

1095
01:00:09,840 --> 01:00:11,880
Speaker 1: is this is my take, and I again, I want

1096
01:00:11,880 --> 01:00:14,360
Speaker 1: to know what people think. I'm saying that, Like, if

1097
01:00:14,360 --> 01:00:17,600
Speaker 1: it's gonna be slow with a human cashier or slow

1098
01:00:17,680 --> 01:00:20,120
Speaker 1: with a robot cashier, I would go slow with the

1099
01:00:20,200 --> 01:00:23,080
Speaker 1: human every time, because at least it's like not beeping

1100
01:00:23,080 --> 01:00:25,360
Speaker 1: at me. At least it's it's it's a human communicating

1101
01:00:25,400 --> 01:00:28,160
Speaker 1: with me in words and I can like express myself

1102
01:00:28,160 --> 01:00:31,560
Speaker 1: to them. And also another thing that I feel about

1103
01:00:31,560 --> 01:00:34,080
Speaker 1: these kiosks is that when you have a in my

1104
01:00:34,240 --> 01:00:36,400
Speaker 1: in my grocery store, the giant that I go to,

1105
01:00:36,840 --> 01:00:40,800
Speaker 1: they're kiosks routinely mess up and I feel like, when

1106
01:00:40,840 --> 01:00:43,240
Speaker 1: I have a mess up, it's always the kiosk, it's

1107
01:00:43,240 --> 01:00:45,840
Speaker 1: never me. But when the person has to come over

1108
01:00:45,920 --> 01:00:48,280
Speaker 1: to like fix it or whatever, I feel like the

1109
01:00:48,400 --> 01:00:51,600
Speaker 1: look they're giving me is like, oh, this stupid boom,

1110
01:00:51,600 --> 01:00:53,880
Speaker 1: this stupid old lady doesn't know how to use the

1111
01:00:53,880 --> 01:00:56,080
Speaker 1: self checkout and is like messing it up. There's no

1112
01:00:56,160 --> 01:00:58,480
Speaker 1: way to convey. It's like no, no, no, your technology

1113
01:00:58,520 --> 01:01:01,360
Speaker 1: has failed, not me. I feel very shamed. I feel

1114
01:01:01,400 --> 01:01:04,320
Speaker 1: like other shoppers are like, look who was to be

1115
01:01:04,400 --> 01:01:07,280
Speaker 1: hollowed for a self checkout and broken and a human

1116
01:01:07,360 --> 01:01:09,600
Speaker 1: had to come over to fix it. It's not a

1117
01:01:09,640 --> 01:01:10,560
Speaker 1: pleasant experience.

1118
01:01:11,840 --> 01:01:14,880
Speaker 3: I mean it's it's not. But uh, you know they're

1119
01:01:15,000 --> 01:01:17,120
Speaker 3: nice at that Giant, right, there's like that one guy

1120
01:01:17,200 --> 01:01:20,080
Speaker 3: who is nice. He like he gets it. There's that

1121
01:01:20,280 --> 01:01:22,880
Speaker 3: older woman, she gets it, she understands their janky. But

1122
01:01:22,920 --> 01:01:26,080
Speaker 3: I think we're like reinforcing this false dichotomy that it's

1123
01:01:26,320 --> 01:01:30,760
Speaker 3: it's either humans or robots. Uh. Like I think that's

1124
01:01:30,800 --> 01:01:34,280
Speaker 3: what uh Dollar General was trying to go for, Like, oh,

1125
01:01:34,280 --> 01:01:36,160
Speaker 3: we can get rid of all of our staff and

1126
01:01:36,200 --> 01:01:38,720
Speaker 3: we'll just have these machines, will be like one human

1127
01:01:38,880 --> 01:01:42,480
Speaker 3: in the store who does everything. And I feel like

1128
01:01:42,600 --> 01:01:44,480
Speaker 3: that's what doesn't work, right, Like, even when you have

1129
01:01:44,480 --> 01:01:48,920
Speaker 3: the self checkout machines, there need to be staff there

1130
01:01:48,960 --> 01:01:50,919
Speaker 3: tending them to like check your ID if you're trying

1131
01:01:50,920 --> 01:01:55,680
Speaker 3: to buy alcohol or you know, clear clear the machine

1132
01:01:55,720 --> 01:01:59,960
Speaker 3: if it has experiences an error or something. The top

1133
01:02:00,360 --> 01:02:04,680
Speaker 3: when the machines are most frustrating is when there's not

1134
01:02:04,840 --> 01:02:07,760
Speaker 3: a human there, or there's just like two teen humans

1135
01:02:08,040 --> 01:02:10,120
Speaker 3: more interested in flirting with each other than like helping

1136
01:02:10,160 --> 01:02:13,520
Speaker 3: me check out. But when there's like, you know, either

1137
01:02:13,560 --> 01:02:17,000
Speaker 3: of those two two older folks at Giant who like

1138
01:02:17,160 --> 01:02:20,400
Speaker 3: know the drill and are on it, it's a perfectly

1139
01:02:20,440 --> 01:02:21,560
Speaker 3: reasonable experience.

1140
01:02:22,120 --> 01:02:25,720
Speaker 1: Mike, listen to yourself. The robots only were if there's

1141
01:02:25,760 --> 01:02:29,000
Speaker 1: a human there. In the scenario that you just described,

1142
01:02:29,200 --> 01:02:31,360
Speaker 1: there needs to be a human. That's what I'm saying.

1143
01:02:31,640 --> 01:02:35,080
Speaker 1: Why have the robot kiosks if there needs to be

1144
01:02:35,120 --> 01:02:37,520
Speaker 1: a human there to make sure that they're working. Just

1145
01:02:37,600 --> 01:02:38,360
Speaker 1: have the human.

1146
01:02:38,960 --> 01:02:45,440
Speaker 3: But one human can tend I don't know, like six machines.

1147
01:02:45,520 --> 01:02:50,720
Speaker 3: Let's say, right, so, like so that one human can

1148
01:02:50,760 --> 01:02:53,600
Speaker 3: have six people going through at the same rate that

1149
01:02:54,480 --> 01:02:58,840
Speaker 3: a human cashier who is like manually scanning the things

1150
01:02:59,000 --> 01:02:59,919
Speaker 3: only gets one through.

1151
01:03:00,560 --> 01:03:04,400
Speaker 1: Well, then hire more humans at the stores. And here's

1152
01:03:04,440 --> 01:03:06,040
Speaker 1: some This is not just me talking out of my ass.

1153
01:03:06,040 --> 01:03:08,640
Speaker 1: I actually do have an outline with evidence here. Let

1154
01:03:08,680 --> 01:03:10,240
Speaker 1: me let me just like hitch you with some facts.

1155
01:03:10,400 --> 01:03:13,840
Speaker 1: So a big reason why these kiosks to me don't

1156
01:03:13,840 --> 01:03:16,440
Speaker 1: work is that they make everything more expensive. Not only

1157
01:03:16,480 --> 01:03:20,160
Speaker 1: do self checkout machines allegedly double theft rates, they actually

1158
01:03:20,200 --> 01:03:23,760
Speaker 1: increase labor costs because employees who get taken away from

1159
01:03:23,760 --> 01:03:26,800
Speaker 1: their other duties to help customers deal with those chios

1160
01:03:26,920 --> 01:03:29,920
Speaker 1: and errors and blah blah blah overall, some analyst said,

1161
01:03:29,960 --> 01:03:33,560
Speaker 1: the machines increase costs overall. So I don't think it's

1162
01:03:33,560 --> 01:03:36,640
Speaker 1: a good use of a human's time to service a robot,

1163
01:03:36,720 --> 01:03:38,840
Speaker 1: so that at that point they're not like you're paying

1164
01:03:38,840 --> 01:03:42,920
Speaker 1: a human. Yeah, it's less human staff, but you're paying

1165
01:03:42,960 --> 01:03:45,400
Speaker 1: that human not to engage with the customer, but to

1166
01:03:45,440 --> 01:03:48,040
Speaker 1: engage with a robot that system. It doesn't make any

1167
01:03:48,080 --> 01:03:51,080
Speaker 1: sense to me just have like things are going fine

1168
01:03:51,200 --> 01:03:52,360
Speaker 1: with human cashiers.

1169
01:03:52,400 --> 01:03:54,400
Speaker 3: You know. It's like we were talking about in one

1170
01:03:54,440 --> 01:03:58,920
Speaker 3: of the earlier segments that like, you know, technology isn't neutral, right,

1171
01:03:58,960 --> 01:04:01,400
Speaker 3: It's not just some objective thing that exists in the world.

1172
01:04:01,480 --> 01:04:05,400
Speaker 3: It is an extension a tool of the humans who

1173
01:04:05,720 --> 01:04:10,080
Speaker 3: are employing it. Employing is not the right word to

1174
01:04:10,160 --> 01:04:14,040
Speaker 3: use here, but like the humans who are determining how

1175
01:04:14,080 --> 01:04:18,919
Speaker 3: it works, determining the systems in which it works. Yeah,

1176
01:04:18,960 --> 01:04:21,040
Speaker 3: they should hire more humans to make them work, but like,

1177
01:04:22,320 --> 01:04:25,120
Speaker 3: I don't know, I think it could be a useful tool, right,

1178
01:04:25,160 --> 01:04:29,600
Speaker 3: Like the scanner gun that they use, that's a useful tool, right, Like,

1179
01:04:30,200 --> 01:04:33,280
Speaker 3: I mean, they hear what you're saying, but I feel

1180
01:04:33,280 --> 01:04:33,880
Speaker 3: like they work.

1181
01:04:34,800 --> 01:04:38,960
Speaker 1: Well, I'll say this, I might be in the minority here.

1182
01:04:39,360 --> 01:04:42,280
Speaker 1: Sixty percent of consumers say they prefer self checkouts as

1183
01:04:42,280 --> 01:04:45,080
Speaker 1: of twenty twenty one. I'm not sure how that squares

1184
01:04:45,080 --> 01:04:46,760
Speaker 1: with the sixty seven percent of them that have had

1185
01:04:46,760 --> 01:04:49,040
Speaker 1: those machines not work. I want to I do want

1186
01:04:49,080 --> 01:04:52,520
Speaker 1: to say, like, well, I'll say this, there are probably

1187
01:04:52,520 --> 01:04:54,960
Speaker 1: people listening who have all kinds of reasons to prefer

1188
01:04:55,040 --> 01:04:58,640
Speaker 1: self checkout, Like maybe I definitely I'm not saying they

1189
01:04:58,640 --> 01:05:01,400
Speaker 1: should be phased out entirely, Like I don't think they

1190
01:05:01,400 --> 01:05:04,960
Speaker 1: should be given the real estate that they have in

1191
01:05:05,000 --> 01:05:07,840
Speaker 1: retail stores. Like I think if you're somebody who, for

1192
01:05:07,920 --> 01:05:09,840
Speaker 1: whatever reason you might have where you're like I don't

1193
01:05:09,840 --> 01:05:11,640
Speaker 1: really want to have to engage with a human today

1194
01:05:12,440 --> 01:05:14,640
Speaker 1: the hungover line or whatever where you can just scan

1195
01:05:14,720 --> 01:05:17,200
Speaker 1: your stuff and go, you probably will have to engage

1196
01:05:17,200 --> 01:05:19,840
Speaker 1: with a human when it invariably fails. But Okay, all

1197
01:05:19,840 --> 01:05:23,439
Speaker 1: I am saying is that the massive investment in that cause,

1198
01:05:23,480 --> 01:05:26,880
Speaker 1: Like when you go to Giant, it's like, I think

1199
01:05:26,880 --> 01:05:29,840
Speaker 1: the Target is a good example. They have more self

1200
01:05:29,920 --> 01:05:33,000
Speaker 1: checkout than they have human checkout. I think that is ridiculous.

1201
01:05:33,640 --> 01:05:35,680
Speaker 3: Definitely, we can all agree that humans need to be

1202
01:05:35,720 --> 01:05:38,520
Speaker 3: a big part of the equation, and when there aren't

1203
01:05:38,600 --> 01:05:41,080
Speaker 3: enough humans, that's when things break down.

1204
01:05:41,360 --> 01:05:45,200
Speaker 1: We can hold it there. Honestly, I want to hear

1205
01:05:45,560 --> 01:05:49,000
Speaker 1: folks is experience. You don't just have to like agree

1206
01:05:49,040 --> 01:05:51,480
Speaker 1: with me, because I'm getting the sense that I am

1207
01:05:51,480 --> 01:05:54,280
Speaker 1: on the wrong. I am the minority here, and that's fine.

1208
01:05:54,720 --> 01:05:56,160
Speaker 1: If I have to be the only person who can

1209
01:05:56,200 --> 01:05:59,320
Speaker 1: see the truth, I will do that. But let me

1210
01:05:59,440 --> 01:06:02,120
Speaker 1: know what y'all think about self checkout. I really want

1211
01:06:02,120 --> 01:06:04,840
Speaker 1: to know. If I'm wrong, show me the evidence. I

1212
01:06:04,840 --> 01:06:06,720
Speaker 1: am willing to update my opinions.

1213
01:06:07,040 --> 01:06:08,840
Speaker 3: Yeah, I look forward to hearing what people have to say.

1214
01:06:09,440 --> 01:06:11,760
Speaker 1: Mike, thank you so much for being here. As always.

1215
01:06:11,840 --> 01:06:13,840
Speaker 1: I guess I'll see you at that janky ass Target

1216
01:06:13,880 --> 01:06:17,120
Speaker 1: self checkout line trying to buy wine.

1217
01:06:17,800 --> 01:06:20,880
Speaker 3: Yeah, I'll wave to you while you're setting up camp

1218
01:06:20,920 --> 01:06:24,640
Speaker 3: over in the human cashier line. I'll bring you some water,

1219
01:06:24,800 --> 01:06:26,600
Speaker 3: maybe some magazines.

1220
01:06:26,360 --> 01:06:28,800
Speaker 1: And listeners, thank you for joining us. So there's another

1221
01:06:28,880 --> 01:06:31,000
Speaker 1: story that we didn't have time to get into that

1222
01:06:31,200 --> 01:06:35,560
Speaker 1: former Facebook CEO Cheryl Sandberg is leaving Facebook's board. So

1223
01:06:35,720 --> 01:06:38,840
Speaker 1: I figured it was a perfect excuse to tell kind

1224
01:06:38,880 --> 01:06:41,000
Speaker 1: of a wild story about the time that I actually

1225
01:06:41,080 --> 01:06:45,160
Speaker 1: met Cheryl Sandberg irl and how she basically crushed my

1226
01:06:45,280 --> 01:06:48,160
Speaker 1: faith in feminism. So check it out ad free on

1227
01:06:48,240 --> 01:06:51,560
Speaker 1: the Patreon at tangoti dot com slash Patreon. So we'll

1228
01:06:51,560 --> 01:06:56,680
Speaker 1: see you there and see you on the internet. Okay,

1229
01:06:56,760 --> 01:06:59,680
Speaker 1: so this is Future bridget here. I'm here with producer Mike.

1230
01:07:00,040 --> 01:07:04,520
Speaker 1: You were qaing this episode about self checkouts. It got

1231
01:07:04,600 --> 01:07:08,000
Speaker 1: very heated. We both have strong feelings. A gauntlet was thrown.

1232
01:07:08,840 --> 01:07:11,400
Speaker 1: Mike and I have decided we are going to go

1233
01:07:11,440 --> 01:07:13,760
Speaker 1: to the supermarket. We're going to get the same amount

1234
01:07:13,760 --> 01:07:16,760
Speaker 1: of stuff we are gonna get in self checkout, and

1235
01:07:17,120 --> 01:07:19,960
Speaker 1: I'm you're gonna do self checkout. I'm gonna do human checkout.

1236
01:07:20,280 --> 01:07:22,880
Speaker 1: I guarantee you. I will be out of there faster

1237
01:07:22,960 --> 01:07:24,440
Speaker 1: and more efficiently. I guarantee it.

1238
01:07:24,480 --> 01:07:26,160
Speaker 3: And we're gonna go to multiple stores. We're gonna go

1239
01:07:26,200 --> 01:07:28,320
Speaker 3: to Giant, We're gonna go to Target, We're gonna find

1240
01:07:28,320 --> 01:07:30,680
Speaker 3: a third store, maybe like Safeway down by Adams Morgan

1241
01:07:30,800 --> 01:07:31,600
Speaker 3: or something fine.

1242
01:07:31,720 --> 01:07:35,440
Speaker 1: I guarantee I will have a more pleasant experience, a

1243
01:07:35,480 --> 01:07:38,959
Speaker 1: more efficient experience, and a faster experience. There's just no way, Mike,

1244
01:07:40,840 --> 01:07:41,320
Speaker 1: let's go.

1245
01:07:41,440 --> 01:07:43,360
Speaker 3: I don't know how I ended up the like anti

1246
01:07:43,480 --> 01:07:44,080
Speaker 3: John Henry.

1247
01:07:46,920 --> 01:07:49,480
Speaker 1: Okay, listeners, you heard it here first. This is our

1248
01:07:49,520 --> 01:07:52,640
Speaker 1: first ever there Are No Girls on the Internet challenge.

1249
01:07:53,040 --> 01:07:55,200
Speaker 1: Y'all already know I'm about to win. I am so

1250
01:07:55,360 --> 01:07:56,800
Speaker 1: confident that I'm going to win.

1251
01:07:57,880 --> 01:07:59,080
Speaker 3: Let's go right now.

1252
01:08:00,280 --> 01:08:02,800
Speaker 1: Let me finish the episode and I'll get my fucking shoes.

1253
01:08:04,080 --> 01:08:06,200
Speaker 1: If you're looking for ways to support the show, check

1254
01:08:06,200 --> 01:08:08,800
Speaker 1: out our March store at tangody dot com, slash store.

1255
01:08:10,000 --> 01:08:12,040
Speaker 1: Got a story about an interesting thing in tech, or

1256
01:08:12,120 --> 01:08:14,000
Speaker 1: just want to say hi? You can reach us at

1257
01:08:14,000 --> 01:08:16,719
Speaker 1: Hello at tegody dot com. You can also find transcripts

1258
01:08:16,720 --> 01:08:19,120
Speaker 1: for today's episode at TENG Goody dot com. There Are

1259
01:08:19,120 --> 01:08:21,120
Speaker 1: No Girls on the Internet was created by me Bridget

1260
01:08:21,120 --> 01:08:24,719
Speaker 1: tod It's a production of iHeartRadio and Unboss Creative, edited

1261
01:08:24,760 --> 01:08:28,559
Speaker 1: by Joey pat Jonathan Strickland is our executive producer. Tari

1262
01:08:28,600 --> 01:08:31,720
Speaker 1: Harrison is our producer and sound engineer. Michael Almado is

1263
01:08:31,720 --> 01:08:34,720
Speaker 1: our contributing producer. I'm your host, Bridget Todd. If you

1264
01:08:34,760 --> 01:08:36,479
Speaker 1: want to help us grow, rate and review us on

1265
01:08:36,479 --> 01:08:40,120
Speaker 1: Apple Podcasts. For more podcasts from iHeartRadio, check out the

1266
01:08:40,120 --> 01:08:43,320
Speaker 1: iHeartRadio app, Apple Podcasts, or wherever you get your podcasts