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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 Unbust Creative. I'm Bridge Tad and this

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Speaker 1: is There Are No Girls on the Internet. Welcome to

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Speaker 1: another edition of our weekly news roundup where we get

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Speaker 1: into all the stories from the Internet that y'all might

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Speaker 1: have missed so you don't have to. And I am

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Speaker 1: so thrilled to be joined by honestly one of my

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Speaker 1: favorite podcasters out there, Ed Zechran of the Better Offline podcast,

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Speaker 1: where you've just started a three part series on business idiots.

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Speaker 1: Tell me about this series.

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Speaker 2: Well, I've noticed throughout my whole life that there have

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Speaker 2: been these people throughout the rungs of power who don't

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Speaker 2: seem to do jobs, and on top of that, don't

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Speaker 2: seem to understand jobs. And it's all come to a head.

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Speaker 2: It's taken a few years. It's started with remote work,

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Speaker 2: it went to crypto, the metaverse AI, all these people

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Speaker 2: with all of this money who don't seem to know

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Speaker 2: a single fucking thing about anything. And then I went

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Speaker 2: and looked back and I kind of went, oh, it

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Speaker 2: traces directly to neoliberal thinking. Milton Friedman, Thatcher, Reagan and

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Speaker 2: all that good stuff. So it's a three part saga.

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Speaker 2: It's the longest series I've done in the show. People

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Speaker 2: seem to really like it. I wasn't sure how they'd

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Speaker 2: take it, and I just built a studio out in

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Speaker 2: my place in Vegas and I recorded it there. So

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Speaker 2: it's a bit totally different people used to because before

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Speaker 2: this point I had this sound chamber thingy where I

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Speaker 2: just stood in it and I just yelled for an hour.

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Speaker 2: This is much more chilled out me speaking like an

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Speaker 2: almost like sitting by a campfire or a dumpster fire

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Speaker 2: in this case.

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Speaker 1: That's something that I've always liked about your podcast is

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Speaker 1: that you can often hear the passion in your in

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Speaker 1: what you're saying, and so I'll be interested. This is

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Speaker 1: this will be an interesting change. I also record from

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Speaker 1: a I used to record from a soundproofed little box

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Speaker 1: in my apartment.

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Speaker 2: Yeah, yeah, yeah, that's exactly.

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Speaker 1: It can really let loose in those little boxes.

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Speaker 2: But I quite like the city. I'm just as pissy.

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Speaker 2: It's not like I'm any It's not like I'm any

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Speaker 2: less angry, but I'm a lot more comfortable. I think

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Speaker 2: that's what it is like. I don't have to stand up,

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Speaker 2: which I actually like for diction reasons, like for projection,

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Speaker 2: but I can handle it sing down. It's just it

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Speaker 2: is more relaxed and more I really like it, and

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Speaker 2: people seem really happy with it, which is good because

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Speaker 2: usually they just send me pictures of knives.

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Speaker 1: Okay, I have to ask you about this piece in

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Speaker 1: your newsletter, which is terrific. Where's ther ed at? You

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Speaker 1: write so beautifully about what the Internet and technology has

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Speaker 1: meant for you personally and how you really would not

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Speaker 1: be the person that you are today if not for

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Speaker 1: technology in the Internet. And that's kind of one of

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Speaker 1: the guiding principles of our show too, is that the

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Speaker 1: Internet has really been at the forefront of who I am,

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Speaker 1: how my younger self sort of learned how to show

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Speaker 1: up in the world and become a self actualized person.

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Speaker 1: But do we still have an Internet landscape where that

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Speaker 1: kind of self discovery and self actualization is genuinely possible.

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Speaker 2: Yes, it's just that their land minds everywhere now there

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Speaker 2: are Before what it was was when I think, I

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Speaker 2: think with similar ages. So it was before a little

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Speaker 2: more open and a little more disconnected. It was just

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Speaker 2: it was more randomized and it didn't feel like there

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Speaker 2: were so many warring incentives other than those of just

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Speaker 2: individual users or admins on forums. Now you have every

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Speaker 2: step of the way a different incentive, or multiple ones

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Speaker 2: just bashing into each other or trying to have sex

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Speaker 2: on your computer. Like you'll go on a website on

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Speaker 2: your phone and your phone will be one hundred and

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Speaker 2: fifty degrees within two seconds because there's twenty one different

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Speaker 2: ad trackers. But then even with I feel like algorithms,

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Speaker 2: mentioning algorithms is almost cliche at this point because it's

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Speaker 2: so obvious. But even then, you've got people on forums

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Speaker 2: who are actively trying to scam you. And the large

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Speaker 2: problem is, ironically that while there are all these warring incentives,

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Speaker 2: the actual all people who run the Internet have never

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Speaker 2: had any responsibility for it. They've made all the money

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Speaker 2: off of it. But it's not like Google or Facebook,

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Speaker 2: and indeed both have kind of peeled back layers of

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Speaker 2: protecting people from spam and scams. It's not like they've

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Speaker 2: ever thought we should make sure this is good. They

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Speaker 2: did for a minute, I think they did for like

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Speaker 2: the first ten years of Google, maybe the first five

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Speaker 2: six years of Facebook, but they then went wholl whoa whoaah,

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Speaker 2: this isn't going to grow forever. We need to fuck

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Speaker 2: with it, we need to make it worse. But I

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Speaker 2: do think that that self discovery is still there. YouTube,

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Speaker 2: even though it's a complete nightmare with the algorithm, is incredible.

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Speaker 2: It really is still incredible. It fuckings. I wish anyone

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Speaker 2: other than Google owned it, Lockheed Martin Hanoi, perhaps Wow,

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Speaker 2: like a more ethical company than Google. Yeah, at least

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Speaker 2: we know what they're doing. I think the YouTube still

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Speaker 2: has this incredible educational side, and you have all of

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Speaker 2: these incredible niches on there. You have guys making covers

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Speaker 2: of songs in the styles of other bands. You have

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Speaker 2: people I saw someone making like a wood ice cream

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Speaker 2: on there the other day, Like, there's still this magic there.

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Speaker 2: The magic at the end isn't dead. And I think

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Speaker 2: that the problem is is that to get there you

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Speaker 2: have to go through like bobed wire or bobbed why.

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Speaker 2: You don't realize is sharp until it's cutting your throat.

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Speaker 1: The thing that I love so much about this vision

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Speaker 1: of the Internet that you lay out is that you're

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Speaker 1: able to make these business decisions that tech leaders have

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Speaker 1: made hyper personal for people. And I don't know it

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Speaker 1: was almost like a rallying cry of like yeah, like

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Speaker 1: never forget how these fucks ruined the like tried to

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Speaker 1: ruin the Internet to make money, Like like never forget

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Speaker 1: that we had something that was kind of good and

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Speaker 1: people decided what if we fucked with it to make money?

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Speaker 2: And that's the thing. It's these people, their power has

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Speaker 2: come from their relative anonymity. Well, we know who's sachur

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Speaker 2: in the Della and Sam Moltman and Sunda Pashai and

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Speaker 2: all of these people, we know who they are. They

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Speaker 2: have mostly just got glossy, they get glossy profiles, and

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Speaker 2: they're mostly unknown, when in fact I think they're turns

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Speaker 2: about to war criminals. We know who Mark Zuckerberg is,

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Speaker 2: But I don't think people realize Mark Zuckerberg's bad thing

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Speaker 2: he did was not just his outright sexism or stealing

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Speaker 2: the website from the Riverboat Twins the Winklebot bosses. It's

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Speaker 2: the fact that it's not just a wiged people that

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Speaker 2: he's led to getting killed. It's not all of that.

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Speaker 2: It's the fact that at its core, Facebook is a

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Speaker 2: tool that's used to hurt people. Now, it is used

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Speaker 2: to manipulate and twist them. It does not have to

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Speaker 2: be that way, literally doesn't. Because Mark Zuckerbet has complete

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Speaker 2: controlled the company. He could tomorrow turn off all monetization

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Speaker 2: on Facebook. The stock would crash, but there's nothing anyone

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Speaker 2: could do. They couldn't fire him. He controls the whole company.

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Speaker 2: He chooses this. And I think what people don't realize

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Speaker 2: is they kind of ambiently know these people suck. They

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Speaker 2: don't know what they're doing and why they're doing it

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Speaker 2: and the intentions behind it. And I think that educating

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Speaker 2: them on that is important because the more that that happens,

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Speaker 2: the more that people realize this is deliberate, the less

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Speaker 2: likely they are to get such a free ride in

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Speaker 2: the press. And they only care about the press. The

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Speaker 2: press is such a powerful thing with them because they've

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Speaker 2: got all the money in the world now they just

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Speaker 2: need They just have their names, They just have their legacy.

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Speaker 2: So fuck that legacy. I'll piss on it.

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Speaker 1: Piss on it, you do it on your podcast beautifully eloquently. Okay, Well,

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Speaker 1: because I'm talking to another tech podcaster, I kind of

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Speaker 1: have to start with my dessert a little bit because

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Speaker 1: a podcast that we piss on a lot on this

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Speaker 1: podcast is the All In Podcast, And I mean your

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Speaker 1: face just there really says it.

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Speaker 2: All and need a show.

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Speaker 1: Okay, So last week, obviously we talked about Musk and

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Speaker 1: Trump's big breakup flight update there, which is that Musk

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Speaker 1: did tweet that he regretted some of what he said

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Speaker 1: about Trump. Everybody was talking about this. But the All

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Speaker 1: In Podcast, which you might say, is hosted by a

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Speaker 1: group of business idiots. You know, it's one of the

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Speaker 1: biggest tech podcasts in the world. It's like four Silicon

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Speaker 1: Valley invester types, one of whom David sax Is Trump's

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Speaker 1: like AI and cryptos are. They were notably silent, and

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Speaker 1: all their listeners were like, what's up? Like why are

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Speaker 1: they not putting out an episode on this? Like where's

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Speaker 1: the episode?

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Speaker 2: I do love that though, I do love that their

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Speaker 2: listeners to this day continue to win the Fell for

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Speaker 2: It Again award. Yeah, it's like, I don't get it.

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Speaker 2: Why are these guys not being honest with me as

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Speaker 2: they've done like hundreds of craven obviously like very much

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Speaker 2: corrupt things, but they are business idiots. By the way,

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Speaker 2: David Sach's famous for selling fucking Yama to Microsoft, an

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Speaker 2: internal message to Shamath is actually evil. Shamav is one

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Speaker 2: of the original people who built the growth team at

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Speaker 2: Facebook under Sheryl Samberg, Naomi gLite, Jacob Yakob Olivan I

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Speaker 2: think his name is Javier Olivan, possibly Las Backstrom as

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Speaker 2: well as part of that all of the original shit

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Speaker 2: heels that made Facebook how bad it is today, Well

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Speaker 2: thanks to Shamav. But yeah, these what are those freaks

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Speaker 2: up to now? They just they're not talking about the

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Speaker 2: Elon breakup because they don't know which which daddy is

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Speaker 2: going to kiss them that week, Like, I don't really

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Speaker 2: know what they're doing.

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Speaker 1: No, And I have to say, it made me really

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Speaker 1: appreciate what you do what I do, that that we

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Speaker 1: have actual press and podcasts that get to talk about

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Speaker 1: what's happening, get to be actually critical of technology. And

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Speaker 1: I guess it just made me realize how much tech

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Speaker 1: press fails. It fails their audience, that fails as listeners.

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Speaker 2: I'm like, And that's the thing though, I think the

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Speaker 2: all in podcasts are genuinely harmful. I think they're bad

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Speaker 2: for society. I think they've done horrible things. I think

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Speaker 2: they're tiny in the comparison to the harm that Kevin

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Speaker 2: Ruce and Casey Newton do with hard Fork tell me more.

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Speaker 2: I think hard Fork. I think hard Fork gives people

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Speaker 2: permission to support the powerful. Casey Newton's boyfriend works at Anthropic.

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Speaker 2: They've had Dario m a Day or Warrio as I

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Speaker 2: call him, the CEO of Anthropic on their three times

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Speaker 2: they've had multiple different Anthropic and they disclosed that Casey's

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Speaker 2: boyfriend works there. The fuck is going on there? Every time?

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Speaker 2: I'm bringing this up with everyone because it's insane, It's

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Speaker 2: totally disgusting, and we have The New York Times pretending

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Speaker 2: like this is somehow okay. They are in the pocket

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Speaker 2: of the AI companies they regularly put Bruce himself is

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Speaker 2: one of the more noxious and genuinely bad for the

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Speaker 2: world people who he did a whole thing on a

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Speaker 2: company that claimed they were excited to replace jobs. They

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Speaker 2: were just going to replace humans in jobs. But when

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Speaker 2: you looked at the articles, it just went out, never

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Speaker 2: built anything, Remember built a single fucking thing. They were like, Oh,

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Speaker 2: We're gonna do a working environment to train models, and

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Speaker 2: Cabri Ruster is like, goddamn, I'm so scared. I'm so scared.

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Speaker 2: He did a piece about Agi. I said this on

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Speaker 2: Blue Sky. It's like it's like one hundred millionaires or

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Speaker 2: billionaires decided they were going to track down and capture

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Speaker 2: Santa Claus. Agi is impossible. It does not exist with

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Speaker 2: the day. We do not even have the beginning of

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Speaker 2: understanding of the intelligence of human beings. So, yeah, all

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Speaker 2: in sucks. All In's awful, noxious, terrible, poisonous. But hardfork

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Speaker 2: is just as damaging and quite frankly, Neili Patel over

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Speaker 2: at the Verge fucking speaking with Brian Chesky and Task

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Speaker 2: Rabbit CEO unbelievable. Sunda Pishai goes on there and he

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Speaker 2: gives him one hundred and fifty word questions. No, it's

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Speaker 2: not just all In. It's really convenient. Actually, I'm not

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Speaker 2: saying you're doing this to just say all In is

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Speaker 2: the problem. No, the problem is is that we have

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Speaker 2: people like I run a PR firm. I don't do

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Speaker 2: anything with anything related to my clients on the podcast. Ever,

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Speaker 2: well News that same deal just because to keep them file.

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Speaker 2: We have the some of the biggest tech podcasts which

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Speaker 2: are kissing up to Brian Shesky, They are kissing up

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Speaker 2: to every AI founder. They are literally co hosted by

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Speaker 2: someone who is going out with someone from the second

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Speaker 2: largest AI company. This is our tech media, this is

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Speaker 2: our tech podcasting. It's said disgrace when I a part

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Speaker 2: time blogger and podcaster who runs a PR firm and

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Speaker 2: the most ethical of them all. That's insane. What is

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Speaker 2: going on? How is this the world we live in?

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Speaker 2: The answer is business idiots. It's because the people that

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Speaker 2: control these publications do not actually connect with the means

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Speaker 2: of production and have any or have any real interest

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Speaker 2: or fundamental understanding of the things they're talking about. With

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Speaker 2: the exception of Neli Pateel, I actually think he knows better.

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Speaker 2: I just think he likes he wants to be friends

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Speaker 2: with them.

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Speaker 1: Oh that's even sadder. Something to me is like, these

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Speaker 1: people are all so wealthy, and at certain point, what's

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Speaker 1: the point of having fu money if you don't use

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Speaker 1: it to say as well anybody.

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Speaker 2: I don't think Ruth Newton and Neli Patel are like,

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Speaker 2: fuck you money. I have no idea about the oil

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Speaker 2: in guys months definitely read oh yeah.

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Speaker 1: I think I think they're like, I mean, what what

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Speaker 1: constitutes fuck you money?

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Speaker 2: To me?

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Speaker 1: Somebody who has none of it? I think yeah, less

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Speaker 1: than what they're.

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Speaker 2: Thinking, like one hundred dollars, seven hundred dollars. No, it's

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Speaker 2: like a hundred million, like like there go, I think

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Speaker 2: that's a good solid Like I can't think of even

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Speaker 2: how I'd spend half of it money, like yeah, other

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Speaker 2: than to create the all out podcast and then just

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Speaker 2: like ruin their seo just like just like just like

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Speaker 2: do nothing other than like dark seo operations to make

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Speaker 2: it impossible to find this.

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Speaker 1: What a dream?

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Speaker 2: Actually? Lex Fridman. Lex Fridman is the most insane one.

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Speaker 2: He's the top of the Tech podcast despite not covering tech,

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Speaker 2: and he is the worst speaking person ever. He is

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Speaker 2: so bad at talking.

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Speaker 1: I am blocked by him on Twitter for I have

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Speaker 1: no idea why.

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Speaker 2: I mean, I love him because he goes like he

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Speaker 2: was asking Donald Trump a question. He was like, bolitics,

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Speaker 2: he is a dead game and I was like, there there,

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Speaker 2: how do you win at that game? And it's just

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Speaker 2: like May, May, did you not practice speaking before? Is

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Speaker 2: this your first time? Is this your first It's like

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Speaker 2: that is the Lex Fridman podcast. It's just his first rodeo.

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Speaker 2: Every time he's like the guy from Memento, but it's

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Speaker 2: with speaking. Welcome to Lex podcast.

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Speaker 1: Oh my god, your Lex Treadman is so good.

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Speaker 2: It's it's I have all the impressions down. I really

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Speaker 2: want to go on Lex Fridman three hours with me

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Speaker 2: and Lex. I think I could have some real fun.

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Speaker 1: Oh my god, from your lips to Lex's ears. Maybe

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Speaker 1: hot unblock you and then you can just ask.

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Speaker 2: What you would do podcast on line? You you you right,

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Speaker 2: keyboard and every I have tried the best one though,

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Speaker 2: by far. Did you see the Lex Friedman Destiny debate?

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Speaker 2: So he debated this horrible fucking piece of shit. Sorry no,

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Speaker 2: he hosted a debate between Destiny's terrible far right twitch

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Speaker 2: streamer and Norman Finkelstein, and it was like three I

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Speaker 2: could not watch more than ten minutes of it. But

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Speaker 2: what kept happening was Norman Finkelstein, who is like a

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Speaker 2: has some views that and some not. I don't really

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Speaker 2: know too much about him. I don't want to get

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Speaker 2: into it, but it would be like Destiny going like

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Speaker 2: he talks, he talks like the road Runner, and then

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Speaker 2: Norman fincal Stein would just go miss the for really,

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Speaker 2: your your words and your Wikipedia they touching me on

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Speaker 2: every day. It's like Satan is pissing in my ears

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Speaker 2: and he gets his name wrong every time. That was great.

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Speaker 2: Lex Fridman just sat there the whole time, probably being like,

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Speaker 2: who are these people? Why are they in my house?

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Speaker 2: Why do they What is this in front of your foot?

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Speaker 2: It's a microphone, lex microL God. I want to go

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Speaker 2: on his pod so badly. Oh, I want to go

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Speaker 2: on his pod so badly. I want to go on

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Speaker 2: Lex Fridman. He'll never have me. He knows, he knows

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Speaker 2: what's coming now, he knows I'm turning up in full

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Speaker 2: joker makeup like it's also.

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Speaker 1: They're like four hours long. I don't know whoever I am.

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Speaker 3: I can hang for three hours, four hours, put me in.

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Speaker 3: I'm ready the eleven hours I'll be. I'll be that

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Speaker 3: just like on next legs we're doing that else? What

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Speaker 3: else do you want to talk about?

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Speaker 2: Legs? I got nothing on I had. I had fifteen

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Speaker 2: hours of sleep in pre operation for this. I've been preparing,

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Speaker 2: I've been training. He's like really like he does like

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Speaker 2: MMA or something.

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Speaker 1: They all do some kind of a physical fitness activity

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Speaker 1: as their hobby, but you can tell they're like never

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Speaker 1: normal about it. They never are doing it in a

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Speaker 1: chill way. It's always in some weird way.

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Speaker 2: They all do like really joyless working out as well.

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Speaker 2: You can tell that they're just every fucking time they

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Speaker 2: do it. It's like they're either in pain or they're

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Speaker 2: taking like nineteen different substances. Well, none of where they

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Speaker 2: drink like they probably drink the the butter coffee, yeah,

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Speaker 2: I proof coffee, real pervert mixtures. Yeah.

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Speaker 1: These are the people that we want in charge of

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Speaker 1: our like tech ecosystem.

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Speaker 2: These are the people that could speak for everyone, normal

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Speaker 2: normal dudes. More.

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Speaker 4: After a quick break, let's get right back into it.

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Speaker 1: Earlier, you mentioned some I don't know, we'll say, false

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Speaker 1: claims that are being made about AI. So let's get

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Speaker 1: into some of these stories. You probably saw this, and

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Speaker 1: I don't think I have to tell you, but did

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Speaker 1: you know that Meta's chatbots are not actually real people?

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Speaker 1: They didn't. If a chatbot is telling you it went

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Speaker 1: to grad school to learn about therapy, it's probably not

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Speaker 1: telling you the truth because it's not a person.

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Speaker 2: I'm shocked.

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Speaker 1: So Democratic Senators Corey Booker, Peter Welsh, Adam Schiff, and

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Speaker 1: Alex Padilla are asking Meta to investigate what they call

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Speaker 1: latant deception from its chatbots who basically lie about being

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Speaker 1: licensed therapist. This coming from exclusive reporting from one of

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Speaker 1: my favorite journalists, Samantha Cole over at for for a

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Speaker 1: Media shout out to Smantha Cole. So basically what happened

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Speaker 1: is that four or four initially reported that metas chatbots

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Speaker 1: were creating the false impression that their licensed therapist. So

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Speaker 1: these senators sent Meta a letter are basically saying like,

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Speaker 1: please stop doing that. The letter says that Meta is

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Speaker 1: deceiving users who seek mental health support from its AI

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Speaker 1: generated chatbots. So basically, when you would ask the chatbot

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Speaker 1: like what they what qualifies you to give me mental

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Speaker 1: health advice? They would say very specific things. They would say, oh,

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Speaker 1: I got my doctorate in psychology from an American Psychological Association,

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Speaker 1: a credited program, and it's had ten years experience, and

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Speaker 1: they would even give out specific license numbers. So it's

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Speaker 1: like very specific, untrue claims. It should go without saying

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Speaker 1: for listeners that these at thoughts these AI bots probably

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Speaker 1: did not go to college because they're not human and

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Speaker 1: obviously they didn't go to college. It does seem like

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Speaker 1: Meta might have tweaked this because according to froll four.

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Speaker 1: Now when you ask like what gives you the qualifications,

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Speaker 1: the chatbot will say like, oh, well, I'm not a

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Speaker 1: licensed therapist, but I am trained to help, which I

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Speaker 1: guess is like a little bit more accurate.

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Speaker 2: Well, did you see the story about that you could

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Speaker 2: get them like they had things where they'll talk to

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Speaker 2: children about having sex. Yes, Jeff Orwar it's the goat

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Speaker 2: over a Wall Street Journal with that one. I mean,

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Speaker 2: this is all a fucking result of something I've been

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Speaker 2: saying for a while. Hey, when we don't regulate any

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Speaker 2: tech ever, perhaps they're going to do whatever they fucking

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Speaker 2: want without restraint. I don't know, just an idea, just

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Speaker 2: the thought, just a little little nugget in my head

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Speaker 2: saying like maybe if we don't stop them from doing anything,

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Speaker 2: and in fact encourage them at all times, maybe they'll

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Speaker 2: think that they can do anything and then do it.

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Speaker 2: And then we're like, hey, here's a strongly worded letter

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Speaker 2: fuck you.

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Speaker 1: Yeah boo. It makes me sad that that's like the

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Speaker 1: best we can do. Is like these dems sent a

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Speaker 1: letter that was like, hey, can y'all stop? This is

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Speaker 1: actually not cool. We would love it if you stops.

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Speaker 2: Hmm, I don't like it. Please stop fucking just like

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Speaker 2: at this point they should just stop punishing. There must

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Speaker 2: be more than a government of any kind of even

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Speaker 2: people in the government it's currently being trodden on could

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Speaker 2: do more than this. But I think right now the

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Speaker 2: Dems are just like, fuck, who do we kiss up to? Now?

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Speaker 1: Yeah?

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Speaker 2: Oh, we can't kiss there? Or asks who's asked? Can

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Speaker 2: we kiss the people that vote for us? Ew? No, never,

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Speaker 2: oh no, we need to find a powerful person. Is

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Speaker 2: there any more powerful? Maybe Mark Cuban? We kiss Mark

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Speaker 2: cube It's just fucking pathetic. This should like have the

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Speaker 2: child sex thing, should have had someone put in jail

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Speaker 2: like that is like, if you make a robot to

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Speaker 2: fuck children, you should be in jail like a pedophile.

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Speaker 2: I don't know, it feels pretty fucking simple to me.

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Speaker 2: What shocks me is that this is like a radical

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Speaker 2: statement within that oh that people.

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Speaker 1: Who build robots that have sex with children should face

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Speaker 1: some sort of yeah, or try should face some sort

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Speaker 1: of meaningful coquen, Yeah, there should be a consequence.

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Speaker 2: And people are just like, yeah, that's crazy.

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Speaker 1: I agree with you completely, and we kind of beat

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Speaker 1: this from on the show all the time, Where do

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Speaker 1: you think it comes from that? In any other context

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Speaker 1: somebody that did that, would you would be like, well,

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Speaker 1: obviously that person can't be out on the street making

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Speaker 1: business decisions. What makes people kind of shrug their shoulders

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Speaker 1: and say, well, it's business, what are you going to do?

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Speaker 2: Tech and money? It's because people, because the twenty years

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Speaker 2: of the tech and business media treating businesses like they're

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Speaker 2: special has led to people treating businesses and tech like

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Speaker 2: they're special. That's it. No regulation means that we have

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Speaker 2: no way of controlling these people. We had one shot

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Speaker 2: with Lina Khan and that shot is gone. We will

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Speaker 2: happen in the future, I have hope, But right now

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Speaker 2: we are getting the consequences of unrestrained tech. And yeah,

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Speaker 2: you can say unrestrained capitalism. The reason I don't say

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Speaker 2: that people are like, oh, you should just talk about

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Speaker 2: capitalism is people have done a shit ton of that.

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Speaker 2: But right now, when you look at the way the

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Speaker 2: media discusses technology, it's an alarming lack of ignorance, but

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Speaker 2: also alarming lack of like morals, Like the fundamental tech

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Speaker 2: behind most of the growth and not the real growth

431
00:21:57,200 --> 00:22:00,959
Speaker 2: is in like the theoretical growth. The market inspired vibes

432
00:22:01,320 --> 00:22:04,800
Speaker 2: is based on a technology based on stealing, environmental destruction

433
00:22:05,280 --> 00:22:11,080
Speaker 2: and just unrestrained communication, just completely insane stuff. You can

434
00:22:11,200 --> 00:22:14,560
Speaker 2: have any conversation with these spots and people say, well,

435
00:22:14,560 --> 00:22:17,720
Speaker 2: you can't restrict that, because then we'd lose freedoms. Yeah, like,

436
00:22:17,760 --> 00:22:21,240
Speaker 2: we're not losing any right now. And on top of that,

437
00:22:21,720 --> 00:22:25,919
Speaker 2: what are we saving. Well, they could do this, so

438
00:22:26,000 --> 00:22:29,040
Speaker 2: we just let them do anything just in case we

439
00:22:29,119 --> 00:22:32,240
Speaker 2: don't try. We don't try and stop them. It is

440
00:22:32,280 --> 00:22:36,560
Speaker 2: impossible to stop large language models existing anymore. It is

441
00:22:36,640 --> 00:22:39,359
Speaker 2: possible to punish those who put them out there in

442
00:22:39,400 --> 00:22:43,200
Speaker 2: an unrestrained fashion. If someone chooses to they need the

443
00:22:43,240 --> 00:22:46,160
Speaker 2: AI bubble to pop first, then they In fact, I'd

444
00:22:46,200 --> 00:22:48,080
Speaker 2: love them too. I think they should all be punished.

445
00:22:49,000 --> 00:22:51,320
Speaker 1: Do you see this bubble popping? Because I was reading

446
00:22:51,359 --> 00:22:54,639
Speaker 1: earlier about how like this new Apple research paper are

447
00:22:54,640 --> 00:22:58,840
Speaker 1: basically debunking these claims that like oh LMS, they can

448
00:22:58,880 --> 00:23:02,880
Speaker 1: perform se reasoning like like, I feel like people who

449
00:23:02,880 --> 00:23:06,960
Speaker 1: make money from AI are lying to us about AI's capabilities,

450
00:23:07,040 --> 00:23:09,600
Speaker 1: lying to us about AGI. Sam Altman was just lying

451
00:23:09,600 --> 00:23:13,280
Speaker 1: about the environmental impact of AI earlier this week. Do

452
00:23:13,320 --> 00:23:14,760
Speaker 1: you think we're getting to a point where it's like

453
00:23:15,080 --> 00:23:18,160
Speaker 1: the lies and the reality might start meeting each other

454
00:23:18,160 --> 00:23:18,600
Speaker 1: a little bit.

455
00:23:19,160 --> 00:23:26,159
Speaker 2: Nope, emphatic, Well, no, nothing has changed in the last year. Nothing.

456
00:23:26,480 --> 00:23:28,919
Speaker 2: The paper you're talking about was about reasoning models. So

457
00:23:29,280 --> 00:23:32,760
Speaker 2: the only trick they've had in the last three years, frankly,

458
00:23:33,200 --> 00:23:35,119
Speaker 2: they had GPT four. Oh that was the big thing

459
00:23:35,160 --> 00:23:38,680
Speaker 2: with multimodal stuff, so we could see it can't think,

460
00:23:38,720 --> 00:23:41,800
Speaker 2: it can't see, it doesn't exist. It can take in

461
00:23:41,920 --> 00:23:45,439
Speaker 2: video and look at it, can see visual data and

462
00:23:45,480 --> 00:23:47,720
Speaker 2: say that. I have a reaction to that. Lax language

463
00:23:47,720 --> 00:23:49,560
Speaker 2: models are kind of cool, Like what they could do

464
00:23:49,640 --> 00:23:52,359
Speaker 2: is kind of interesting. We're denying that would be silly.

465
00:23:52,920 --> 00:23:54,960
Speaker 2: They can't do much more than they can today. And

466
00:23:54,960 --> 00:23:57,719
Speaker 2: that paper talked about how reasoning models don't fucking reason.

467
00:23:58,359 --> 00:24:01,040
Speaker 2: They don't have a concept of reasoning. But really, I

468
00:24:01,119 --> 00:24:04,560
Speaker 2: like to make this real simple. What's the product? What

469
00:24:04,760 --> 00:24:07,080
Speaker 2: is it? Where's the product? What is the thing that

470
00:24:07,119 --> 00:24:09,320
Speaker 2: we're all meant to be replaced by? Even you're having

471
00:24:09,400 --> 00:24:12,640
Speaker 2: art directors and copywriters replaced with it. You're having screen

472
00:24:13,480 --> 00:24:16,840
Speaker 2: screenwriters who are having their work looked at by AI.

473
00:24:17,160 --> 00:24:19,480
Speaker 2: It's kind of like what happened many years ago with

474
00:24:19,640 --> 00:24:22,360
Speaker 2: HR with resumes. They found a way to do that

475
00:24:22,440 --> 00:24:25,919
Speaker 2: but with words. But other than that, where's the fucking beef,

476
00:24:26,000 --> 00:24:29,080
Speaker 2: where's the product? Where is it? Agents don't exist. Every

477
00:24:29,080 --> 00:24:32,040
Speaker 2: time you see someone see agent, shoot them. Sorry, I

478
00:24:32,080 --> 00:24:35,159
Speaker 2: mean every time you see someone say agent, they are

479
00:24:35,240 --> 00:24:38,360
Speaker 2: lying because they're trying to say agent and make you think, oh,

480
00:24:38,440 --> 00:24:42,880
Speaker 2: autonomous thing. Autonomous And what they mean is chatbot, chatbot

481
00:24:42,920 --> 00:24:45,240
Speaker 2: that can sometimes connect to other systems and can't even

482
00:24:45,320 --> 00:24:49,240
Speaker 2: fucking do that. That's the crazy thing. That's the craziest thing.

483
00:24:49,280 --> 00:24:52,480
Speaker 2: That's why I sound so crazy, because you look outside.

484
00:24:52,520 --> 00:24:55,119
Speaker 2: You go and look on THEO and CNBC, you go

485
00:24:55,160 --> 00:24:57,199
Speaker 2: look at tech Crunch, you go look at all these publications,

486
00:24:57,240 --> 00:24:59,639
Speaker 2: and you see them going agents. Agents. Agents aren't the

487
00:24:59,680 --> 00:25:02,440
Speaker 2: agent coming, They're not coming. They don't work. There was

488
00:25:02,480 --> 00:25:05,000
Speaker 2: a study that came out of Salesforce I believe, that

489
00:25:05,080 --> 00:25:08,840
Speaker 2: said that agents fall apart on multi step queries. You

490
00:25:08,840 --> 00:25:12,359
Speaker 2: may think, what does multi step mean? Multi step means

491
00:25:12,560 --> 00:25:16,480
Speaker 2: more than one action. You know, tends to be what

492
00:25:16,600 --> 00:25:19,920
Speaker 2: happens when you do something. Yeah, and they can't fucking

493
00:25:20,000 --> 00:25:22,960
Speaker 2: do it. They fall apart. The reasoning paper talked about

494
00:25:22,960 --> 00:25:25,200
Speaker 2: how these things fall apart if you make them think

495
00:25:25,280 --> 00:25:25,760
Speaker 2: too much.

496
00:25:25,960 --> 00:25:28,359
Speaker 1: Yeah. I loved how it said it breaks down when

497
00:25:28,359 --> 00:25:30,280
Speaker 1: the face with an unfamiliar problem, And I was like,

498
00:25:30,280 --> 00:25:33,280
Speaker 1: oh my god, just like me, Like I feel the same.

499
00:25:33,200 --> 00:25:37,440
Speaker 2: Been there, but yeah, no one has built the economy

500
00:25:37,480 --> 00:25:38,000
Speaker 2: on my back.

501
00:25:38,440 --> 00:25:40,760
Speaker 1: Yeah like no, yeah, no one is saying I'm going

502
00:25:40,800 --> 00:25:43,880
Speaker 1: to like change the economy because I break down when

503
00:25:43,880 --> 00:25:45,920
Speaker 1: faced with unfamiliar our novel problems.

504
00:25:46,280 --> 00:25:50,280
Speaker 2: I'm but also, let's get simple again. Where's the product?

505
00:25:50,680 --> 00:25:54,480
Speaker 2: Where is it? Because that's my proof. There's no fucking product.

506
00:25:55,040 --> 00:25:57,320
Speaker 2: Everything is the same thing we were talking about six

507
00:25:57,359 --> 00:26:00,720
Speaker 2: months ago, twelve months ago. Oh agency no, they're not.

508
00:26:00,800 --> 00:26:04,320
Speaker 2: Stop lying to me like that's the thing you actually like.

509
00:26:04,400 --> 00:26:06,439
Speaker 2: People love to come to me and say, well, actually

510
00:26:06,720 --> 00:26:09,040
Speaker 2: the Microsoft's called I know. It's like, no, they don't.

511
00:26:09,240 --> 00:26:10,960
Speaker 2: They do, but it doesn't do much more than it

512
00:26:11,000 --> 00:26:13,520
Speaker 2: did a year ago. The only way Microsoft is able

513
00:26:13,560 --> 00:26:15,040
Speaker 2: to make and Google is able to make money on

514
00:26:15,200 --> 00:26:18,359
Speaker 2: AI is by forcing people to pay for it. Forcing

515
00:26:18,440 --> 00:26:21,440
Speaker 2: people to pay for Google Google Workspaces. They raised the price,

516
00:26:21,440 --> 00:26:23,560
Speaker 2: they've raised it months ago, but they only just emailed

517
00:26:23,600 --> 00:26:26,720
Speaker 2: everyone about it. People quite pissed off. And that's the thing.

518
00:26:27,600 --> 00:26:30,399
Speaker 2: That's what really is cooking my brain right now, is

519
00:26:30,480 --> 00:26:32,359
Speaker 2: I still have people in the take media. You say, well,

520
00:26:32,359 --> 00:26:35,640
Speaker 2: AI's here, AI's the biggest, most smartest thing ever. Look

521
00:26:35,680 --> 00:26:38,159
Speaker 2: at coding. Yeah, I looked at coding. I had a

522
00:26:38,160 --> 00:26:40,080
Speaker 2: guy called Carl Brown from the Internet of Bugs on

523
00:26:40,080 --> 00:26:43,000
Speaker 2: the other day. The whole thing is people think that

524
00:26:43,119 --> 00:26:46,600
Speaker 2: software engineers just code. They think that that's their whole job.

525
00:26:47,920 --> 00:26:49,879
Speaker 2: They think that that's all they do. On top of that,

526
00:26:49,920 --> 00:26:52,280
Speaker 2: the code that's written by these things is not reliable

527
00:26:52,400 --> 00:26:55,800
Speaker 2: enough to just sink into production. It may speed up

528
00:26:55,840 --> 00:26:58,640
Speaker 2: some workflows, but even then, what workflows does it speed up?

529
00:26:58,720 --> 00:27:01,479
Speaker 2: And it's pumping the internet and software companies full of

530
00:27:01,760 --> 00:27:04,800
Speaker 2: shitty software, and on top of that, none of it

531
00:27:04,840 --> 00:27:07,240
Speaker 2: makes any money. It loses so much money. It loses

532
00:27:07,280 --> 00:27:09,560
Speaker 2: billions and billions of dollars and it makes no money.

533
00:27:09,640 --> 00:27:12,320
Speaker 2: Microsoft is going to make thirteen billion dollars in twenty

534
00:27:12,320 --> 00:27:14,480
Speaker 2: twenty five. You may think that sounds like a lot,

535
00:27:14,480 --> 00:27:17,280
Speaker 2: but they've spent hundreds of billions of dollars in capital

536
00:27:17,280 --> 00:27:21,879
Speaker 2: expenditures in the last three years. It's a fucking fast.

537
00:27:21,960 --> 00:27:24,440
Speaker 2: It pisses me off every time I think about it,

538
00:27:24,840 --> 00:27:27,800
Speaker 2: and people are still to this day trying to smuggbeck

539
00:27:28,400 --> 00:27:31,200
Speaker 2: did you see and it's usually something that doesn't exist.

540
00:27:31,720 --> 00:27:34,440
Speaker 2: It's like, ed, check out this, we made this new

541
00:27:34,480 --> 00:27:37,600
Speaker 2: life form and it is just the Ninja Turtles movie,

542
00:27:38,400 --> 00:27:41,520
Speaker 2: and it's just what the fuck I'm it. It's like

543
00:27:41,720 --> 00:27:45,760
Speaker 2: arguing with a child, except children have more fundamentally sound logic.

544
00:27:47,720 --> 00:27:49,840
Speaker 2: Every day, every day with this bollocks.

545
00:27:50,040 --> 00:27:52,720
Speaker 1: I remember going to South Bay, Southwest a few years

546
00:27:52,760 --> 00:27:56,160
Speaker 1: ago and the thing everybody was talking about was NFTs.

547
00:27:56,160 --> 00:27:58,960
Speaker 1: It was like nftis everywhere, and then going the following

548
00:27:59,040 --> 00:28:01,159
Speaker 1: year and it was like, we never did that. That

549
00:28:01,560 --> 00:28:04,560
Speaker 1: what you're talking about NFT. It's like how quickly this was.

550
00:28:04,600 --> 00:28:07,200
Speaker 1: We were being told this was like the big thing,

551
00:28:07,359 --> 00:28:07,760
Speaker 1: this is.

552
00:28:07,680 --> 00:28:09,840
Speaker 2: The big thing, but they couldn't work out how to

553
00:28:09,880 --> 00:28:14,040
Speaker 2: do that NFTs with. If Google and Face and Meta

554
00:28:14,080 --> 00:28:16,919
Speaker 2: and all them have actually found the NFT thing that

555
00:28:17,240 --> 00:28:19,960
Speaker 2: more than one person would buy, they would have done

556
00:28:20,040 --> 00:28:23,840
Speaker 2: NFTs if they thought selling, buying and selling pigeons was

557
00:28:23,880 --> 00:28:26,920
Speaker 2: the next growth market. Every some darpish I would be saying,

558
00:28:26,920 --> 00:28:32,359
Speaker 2: you cannot underestimate the pigeon fucking Jensen Wong of Invidea

559
00:28:32,440 --> 00:28:35,920
Speaker 2: would be breeding pigeons right now. In fact, that's statuely

560
00:28:35,920 --> 00:28:39,400
Speaker 2: the most insane thing so our economy. The US stock

561
00:28:39,440 --> 00:28:41,800
Speaker 2: market is about thirty five percent made up of the

562
00:28:41,800 --> 00:28:46,840
Speaker 2: Magnificent seven stocks. And I always fucked this up as Tesla, Google, Microsoft,

563
00:28:47,040 --> 00:28:52,120
Speaker 2: Meta in Video, someone else Microsoft.

564
00:28:53,080 --> 00:28:55,000
Speaker 1: I think you said Microsoft, but we'll let it fine.

565
00:28:55,360 --> 00:28:59,800
Speaker 2: Nevertheless, whoever they are, nineteen percent of that is in video.

566
00:29:00,240 --> 00:29:03,200
Speaker 2: In Video's entire I think like eighty eight percent of

567
00:29:03,240 --> 00:29:06,440
Speaker 2: their revenue is based on selling GPUs for AI. What

568
00:29:06,480 --> 00:29:11,040
Speaker 2: do you think happens when that stops happening? And also

569
00:29:11,120 --> 00:29:13,320
Speaker 2: people are saying, well, well, they're growing so much quarter

570
00:29:13,360 --> 00:29:16,360
Speaker 2: over quarter. Are you fucking telling me that in a

571
00:29:16,440 --> 00:29:18,600
Speaker 2: year's time, based on this growth rate, this will have

572
00:29:18,680 --> 00:29:21,080
Speaker 2: to happen and Video will be selling one hundred and

573
00:29:21,240 --> 00:29:25,880
Speaker 2: something billion dollars of GPUs a quarter, which means that

574
00:29:26,080 --> 00:29:28,280
Speaker 2: all of the rest of the companies in the Magnificent

575
00:29:28,280 --> 00:29:31,720
Speaker 2: seven will be sending them one hundred billion dollars a quarter.

576
00:29:32,000 --> 00:29:34,440
Speaker 2: Is that going to happen? So everyone's going to raise

577
00:29:34,480 --> 00:29:37,360
Speaker 2: their capital expenditures. I'm the crazy one for saying this

578
00:29:37,400 --> 00:29:40,800
Speaker 2: isn't going to happen. Anyway, when I'm right, people are

579
00:29:40,800 --> 00:29:41,560
Speaker 2: going to eat ship.

580
00:29:42,120 --> 00:29:43,920
Speaker 1: Well, I do have a question. I mean, it kind

581
00:29:43,920 --> 00:29:45,720
Speaker 1: of relates back to what we were talking about with

582
00:29:45,880 --> 00:29:50,560
Speaker 1: the tech podcast landscape. It's why do you think I mean,

583
00:29:50,640 --> 00:29:53,840
Speaker 1: is it just money and influence and proximity to power

584
00:29:53,880 --> 00:29:57,560
Speaker 1: that keeps the people who ostensibly should be telling this

585
00:29:57,640 --> 00:29:59,560
Speaker 1: story telling the truth about these things that you're that

586
00:29:59,560 --> 00:30:02,400
Speaker 1: you're talking about from doing cell Like, why are so

587
00:30:02,520 --> 00:30:04,120
Speaker 1: few voices saying these things?

588
00:30:04,440 --> 00:30:06,440
Speaker 2: I think there's a few things. One, I think people

589
00:30:06,440 --> 00:30:10,880
Speaker 2: are way too differential to the powerful. They are also undertrained,

590
00:30:11,000 --> 00:30:14,320
Speaker 2: under mentored. They are not rewarded for doing a good job.

591
00:30:14,360 --> 00:30:17,240
Speaker 2: They are rewarded for keeping the status quo. The status

592
00:30:17,320 --> 00:30:20,440
Speaker 2: quo is AI. So everyone's talking about AI. They are

593
00:30:21,000 --> 00:30:25,120
Speaker 2: as massed heads across all publications almost they are told

594
00:30:25,240 --> 00:30:27,800
Speaker 2: don't harsh the flow. They're not told those exact words.

595
00:30:27,800 --> 00:30:30,120
Speaker 2: But they can't sit there and be like, hey, it's

596
00:30:30,160 --> 00:30:34,120
Speaker 2: all this bullshit is all they could like, all these

597
00:30:34,120 --> 00:30:35,800
Speaker 2: companies are making more money than ever, but the things

598
00:30:35,840 --> 00:30:38,560
Speaker 2: they're talking about allies. They can't say that. They have

599
00:30:38,600 --> 00:30:40,440
Speaker 2: to be like, well, why are they talking about AI?

600
00:30:40,600 --> 00:30:42,920
Speaker 2: They can't be lying to us we can't start there,

601
00:30:43,240 --> 00:30:46,960
Speaker 2: We couldn't possibly suggest that. So there's this natural deference

602
00:30:47,040 --> 00:30:50,640
Speaker 2: that everything starts with. But really just basic education of

603
00:30:50,720 --> 00:30:53,480
Speaker 2: finance and business is just not fucking there. In most

604
00:30:53,480 --> 00:30:55,160
Speaker 2: of the tech press, there are some that are good

605
00:30:55,160 --> 00:30:58,240
Speaker 2: at it, a few of them, very few of them.

606
00:30:58,520 --> 00:31:00,840
Speaker 2: Kevin Russ is actually one of them. He knows better.

607
00:31:00,840 --> 00:31:03,880
Speaker 2: The fact that he chooses to do this is truly disgusting.

608
00:31:03,920 --> 00:31:06,080
Speaker 2: Same with Casey Newton. Casey Newton's one of the best

609
00:31:06,120 --> 00:31:09,040
Speaker 2: business journalists, or he used to be before he did

610
00:31:09,040 --> 00:31:12,960
Speaker 2: whatever this is. And you've got people out tomorrow at CNN,

611
00:31:13,120 --> 00:31:15,720
Speaker 2: one of the best business writers out there. She's saying

612
00:31:15,760 --> 00:31:17,600
Speaker 2: everything I'm saying. She sending it a little bit better

613
00:31:17,600 --> 00:31:19,800
Speaker 2: in some cases been on the show already. There are

614
00:31:19,840 --> 00:31:23,160
Speaker 2: people saying this. It's just for the most part, and

615
00:31:23,240 --> 00:31:24,880
Speaker 2: one wants to harsh the flow. No one wants to

616
00:31:24,920 --> 00:31:27,440
Speaker 2: lose access. And they are an alarming amount of people

617
00:31:27,440 --> 00:31:29,840
Speaker 2: in the ipatow who want to be friends with the

618
00:31:29,880 --> 00:31:33,520
Speaker 2: rock stars. If you've ever seen Almost Famous, Yeah that

619
00:31:33,520 --> 00:31:36,520
Speaker 2: that wonderful scene with Buster Scrugs the.

620
00:31:36,560 --> 00:31:41,560
Speaker 1: Case, Yeah, yes, well no.

621
00:31:43,160 --> 00:31:45,920
Speaker 2: Leicester Bags, Buster Scruggs, I've got Will that's what I'm

622
00:31:45,920 --> 00:31:48,720
Speaker 2: calling myself. I don't know.

623
00:31:48,800 --> 00:31:49,680
Speaker 1: That's someone though.

624
00:31:49,920 --> 00:31:52,600
Speaker 2: Let's someone though. Let my brain working in both brain cells,

625
00:31:52,680 --> 00:31:55,560
Speaker 2: dueling to the death. There, No, it's you, don't. You

626
00:31:55,560 --> 00:31:57,440
Speaker 2: don't make friends with the rock stars. And people want

627
00:31:57,480 --> 00:31:58,800
Speaker 2: to be friends with the rock stars. They want to

628
00:31:58,800 --> 00:32:00,600
Speaker 2: be the first one to get to hear the latest

629
00:32:00,600 --> 00:32:02,560
Speaker 2: thing that they have to think. I know that sounds

630
00:32:02,600 --> 00:32:07,040
Speaker 2: very like nineties cliche paranoia, but look at what's happening

631
00:32:07,080 --> 00:32:09,080
Speaker 2: and tell me otherwise the fact that you And it

632
00:32:09,080 --> 00:32:10,880
Speaker 2: gets back to the business idiot thing though, because you

633
00:32:10,960 --> 00:32:14,400
Speaker 2: have this editorial substrate, these editors that are just like

634
00:32:14,600 --> 00:32:16,920
Speaker 2: they haven't no fucking shit, but they know what the

635
00:32:16,920 --> 00:32:19,520
Speaker 2: powerful say generally is right, even if it's not, even

636
00:32:19,520 --> 00:32:22,440
Speaker 2: if it hasn't been right. They just they want it

637
00:32:22,480 --> 00:32:24,479
Speaker 2: to be right. And it's much easier to sit there

638
00:32:24,480 --> 00:32:25,960
Speaker 2: and be like, hey, it's going to be big now,

639
00:32:26,320 --> 00:32:29,040
Speaker 2: especially when the whole economy is doing it. You might think,

640
00:32:29,120 --> 00:32:31,320
Speaker 2: I don't know, as a journalist though, when the whole

641
00:32:31,320 --> 00:32:33,480
Speaker 2: economy is based on something kind of flimsy, maybe you

642
00:32:33,520 --> 00:32:35,800
Speaker 2: want to tell people about it. No, oh no, keep

643
00:32:36,000 --> 00:32:39,280
Speaker 2: got to keep out at Disneyland open and the thing

644
00:32:39,400 --> 00:32:43,520
Speaker 2: is they are gambled. Honestly, both me and them are gambling.

645
00:32:44,080 --> 00:32:46,720
Speaker 2: We're gambling. I'm not gambling. I'm basing it on logic.

646
00:32:46,760 --> 00:32:49,360
Speaker 2: But they are betting that they are right and that

647
00:32:49,400 --> 00:32:51,880
Speaker 2: the powerful will prevail with AI, that AI will suddenly

648
00:32:51,960 --> 00:32:54,240
Speaker 2: turn into this thing that does not exist, that AI

649
00:32:54,280 --> 00:32:56,600
Speaker 2: will become despite the fact that agents don't do what

650
00:32:56,640 --> 00:32:58,600
Speaker 2: they're meant to, that large language models are terrible at

651
00:32:58,600 --> 00:33:01,240
Speaker 2: taking distinct actions that large which models are unreliable and

652
00:33:01,240 --> 00:33:03,360
Speaker 2: hallucinat And on top of it, none of this shit

653
00:33:03,440 --> 00:33:05,480
Speaker 2: makes money. It's horribly unprofitable and people don't want to

654
00:33:05,480 --> 00:33:07,440
Speaker 2: pay for it. They think that all of that will

655
00:33:07,440 --> 00:33:10,440
Speaker 2: get fixed because number is going up right now because

656
00:33:11,040 --> 00:33:14,640
Speaker 2: n Video keeps selling GPUs. That is the only real

657
00:33:14,760 --> 00:33:17,680
Speaker 2: metric that is keeping this bullshit alive, because it's sure

658
00:33:17,720 --> 00:33:21,880
Speaker 2: asn't fucking business. There is an analyst that something beck

659
00:33:22,360 --> 00:33:25,000
Speaker 2: I think. Laura brann at Yah, who Finance wrote this up.

660
00:33:25,200 --> 00:33:27,760
Speaker 2: He said that he estimates Amazon will make five billion

661
00:33:27,800 --> 00:33:31,800
Speaker 2: dollars in AI revenue this year, not profit revenue. They

662
00:33:31,880 --> 00:33:34,000
Speaker 2: spent one hundred and five billion dollars or planned to

663
00:33:34,280 --> 00:33:37,160
Speaker 2: on capital expenditures in twenty twenty five. The fuck is

664
00:33:37,200 --> 00:33:39,720
Speaker 2: going on? What is going on? Why are we hearing

665
00:33:39,720 --> 00:33:42,160
Speaker 2: this and being like sounds good to me? Or it's

666
00:33:42,200 --> 00:33:45,800
Speaker 2: the early days. It's the early days. No, it is not.

667
00:33:46,000 --> 00:33:50,120
Speaker 2: We are three years in hundreds of billions in capital expenditures,

668
00:33:50,120 --> 00:33:52,960
Speaker 2: more money than has ever gone into any movement ever

669
00:33:53,120 --> 00:33:56,120
Speaker 2: in the tech industry's history, has gone into generative AI,

670
00:33:56,480 --> 00:33:58,520
Speaker 2: and we are where we were in twenty twenty three,

671
00:33:58,560 --> 00:34:01,080
Speaker 2: and people need to wait the fuck up. It's embarrassing.

672
00:34:01,280 --> 00:34:04,120
Speaker 2: It's very embarrassing. And when this ship bursts, and it

673
00:34:04,200 --> 00:34:08,560
Speaker 2: has to, it has to because putting aside all the technology,

674
00:34:08,680 --> 00:34:11,440
Speaker 2: you really think people are gonna buy that? I think

675
00:34:11,480 --> 00:34:15,080
Speaker 2: it was like forty something billion dollars of GPU's last quarter,

676
00:34:15,400 --> 00:34:17,840
Speaker 2: So next one they're gonna be forty eight, I guess,

677
00:34:17,840 --> 00:34:19,279
Speaker 2: and then in a year it's gonna be what like

678
00:34:20,000 --> 00:34:23,239
Speaker 2: eighty something. Is that gonna happen? Is that gonna keep

679
00:34:23,280 --> 00:34:26,400
Speaker 2: happening or is it gonna slow down? Because they also

680
00:34:26,480 --> 00:34:28,719
Speaker 2: don't have anywhere to install them right now because they

681
00:34:28,719 --> 00:34:32,320
Speaker 2: assents don't pop up like weeds. No one looks at reality.

682
00:34:32,400 --> 00:34:35,239
Speaker 2: It drives me insane. But Robert and Sophie let me

683
00:34:35,320 --> 00:34:36,760
Speaker 2: yell every week, so I may.

684
00:34:36,960 --> 00:34:39,840
Speaker 1: Robert and Sophie of Cool Zone Media friends of the show,

685
00:34:39,960 --> 00:34:43,000
Speaker 1: and I'm gonna say friends irl. We actually did a

686
00:34:43,000 --> 00:34:46,040
Speaker 1: mini series collaboration with them about online harassment called Internet

687
00:34:46,080 --> 00:34:49,280
Speaker 1: Hate Machine. We love Robert and Sophie at this podcast.

688
00:34:49,440 --> 00:34:51,840
Speaker 2: No, those two encourage me constantly, Robin Sophie ever it

689
00:34:51,880 --> 00:34:54,600
Speaker 2: calls out, and they're fantastic. They all they're very supportive.

690
00:34:54,680 --> 00:34:58,000
Speaker 2: They fucking see it and it's just I'm ranting. I

691
00:34:58,040 --> 00:34:58,600
Speaker 2: realize them.

692
00:34:59,120 --> 00:35:02,439
Speaker 1: No, I love it. It's it's honestly nice to talk

693
00:35:02,440 --> 00:35:06,879
Speaker 1: to somebody who rants about this shit as much as

694
00:35:06,920 --> 00:35:09,120
Speaker 1: I do. I guess because it is. It is very

695
00:35:09,160 --> 00:35:11,799
Speaker 1: frustrating to sort of tell the same story over and

696
00:35:11,800 --> 00:35:13,759
Speaker 1: over again and feel like no one's listening. You were

697
00:35:13,800 --> 00:35:17,759
Speaker 1: talking about the sort of connection of business reporting. Did

698
00:35:17,760 --> 00:35:20,120
Speaker 1: you see that piece in the Wall Street Journal this

699
00:35:20,160 --> 00:35:24,160
Speaker 1: week that really shed light on X's new strategy to

700
00:35:24,280 --> 00:35:27,640
Speaker 1: woo back advertisers, which is essentially just yeah, just like

701
00:35:27,680 --> 00:35:30,239
Speaker 1: threats and extortion and like threats of lawsuits.

702
00:35:30,239 --> 00:35:32,600
Speaker 2: So for fucking cool. Yeah, very cool.

703
00:35:32,600 --> 00:35:34,799
Speaker 1: I mean, like it reminds if you've ever seen one

704
00:35:34,800 --> 00:35:38,160
Speaker 1: of my favorite movies, Goodfellas, it's what mobster Henry Hill

705
00:35:38,239 --> 00:35:41,240
Speaker 1: calls like real grease ball shit, Like this is real,

706
00:35:41,520 --> 00:35:43,880
Speaker 1: like mobster threat of extortion.

707
00:35:44,160 --> 00:35:47,240
Speaker 2: Sure, Verizon should have just been like, go fuck yourself,

708
00:35:47,320 --> 00:35:48,320
Speaker 2: Elon exactly.

709
00:35:48,360 --> 00:35:51,080
Speaker 1: So that's what I found very shocking is that it's

710
00:35:51,200 --> 00:35:54,759
Speaker 1: kind of working so essentially for folks who did not

711
00:35:54,800 --> 00:35:58,279
Speaker 1: see that piece. The Wall Street Journal reported that their

712
00:35:58,320 --> 00:36:02,080
Speaker 1: new strategy to woo back advertisers is to threaten them

713
00:36:02,120 --> 00:36:04,680
Speaker 1: with lawsuits, and so it kind of works. Like Verizon,

714
00:36:04,719 --> 00:36:07,640
Speaker 1: which had not advertised on X since twenty twenty, pledged

715
00:36:07,680 --> 00:36:10,440
Speaker 1: to spend at least ten million dollars this year and

716
00:36:10,480 --> 00:36:12,600
Speaker 1: if they didn't, X was going to sue them. The

717
00:36:12,600 --> 00:36:15,319
Speaker 1: same with Ralph Lauren, the fashion brand, they agreed to

718
00:36:15,320 --> 00:36:17,440
Speaker 1: start spending more money and ads on X because of

719
00:36:17,440 --> 00:36:19,759
Speaker 1: the threat of lawsuit. The Law Street Journal said that

720
00:36:19,760 --> 00:36:23,040
Speaker 1: at least six companies had either received lawsuit threats or

721
00:36:23,040 --> 00:36:26,160
Speaker 1: were motivated in part by pressure tactics and have struck

722
00:36:26,200 --> 00:36:29,879
Speaker 1: ad deals with X. And yeah, I mean, I part

723
00:36:29,880 --> 00:36:32,400
Speaker 1: of me kind of gets it, because who if you're

724
00:36:32,480 --> 00:36:34,239
Speaker 1: running a business, who wants to be tied up in

725
00:36:34,280 --> 00:36:36,319
Speaker 1: a lawsuit with somebody like Elon musk Oh.

726
00:36:36,360 --> 00:36:39,919
Speaker 2: This this is quite literally, this is quite literally a

727
00:36:40,040 --> 00:36:43,279
Speaker 2: nambas game. They went, this will be cheaper, It's be cheap.

728
00:36:43,440 --> 00:36:45,640
Speaker 2: They're probably not wrong, and they are right.

729
00:36:45,960 --> 00:36:51,280
Speaker 1: Yeah, it just is interesting that some of this kind

730
00:36:51,280 --> 00:36:55,640
Speaker 1: of sounds like straight up extortion one of the things. Yeah,

731
00:36:55,680 --> 00:36:59,440
Speaker 1: I mean, and I also feel like, how embarrassing is

732
00:36:59,480 --> 00:37:02,279
Speaker 1: it that you would have to extort these brands to

733
00:37:02,480 --> 00:37:05,080
Speaker 1: want to do business with you, these strong arm tactics.

734
00:37:05,160 --> 00:37:07,879
Speaker 2: This's embarrassing. But here's the real simple point that goes

735
00:37:07,920 --> 00:37:09,560
Speaker 2: back to what we were just talking about. If we

736
00:37:09,600 --> 00:37:11,600
Speaker 2: had a functional business press would be able to fucking

737
00:37:11,640 --> 00:37:14,120
Speaker 2: shame these companies. The business press should say, you got

738
00:37:14,120 --> 00:37:17,400
Speaker 2: extorted by Elon Musk, what are you doing? They should

739
00:37:17,440 --> 00:37:21,200
Speaker 2: describe this story as extortion, But they're afraid of getting sued.

740
00:37:21,680 --> 00:37:25,080
Speaker 2: Now what like this is the thing right now? The

741
00:37:25,120 --> 00:37:27,920
Speaker 2: problem is that the right wig is willing to throw

742
00:37:27,960 --> 00:37:30,000
Speaker 2: a bunch of money at shit. There are rich liberals.

743
00:37:30,000 --> 00:37:32,400
Speaker 2: They just don't fucking care enough. They don't think they'll

744
00:37:32,400 --> 00:37:37,319
Speaker 2: be sent anywhere. It's depressing. I mean, the idea of

745
00:37:37,640 --> 00:37:40,000
Speaker 2: like praying for a rich billionaire to save us is

746
00:37:40,080 --> 00:37:43,640
Speaker 2: kind of fucking ridiculous. But none of these companies had

747
00:37:43,680 --> 00:37:45,880
Speaker 2: to do this, they could have all rolled over. They

748
00:37:45,920 --> 00:37:47,680
Speaker 2: could have rolled over. They could have also made it

749
00:37:47,719 --> 00:37:49,880
Speaker 2: difficult if one of them had made it difficult. I

750
00:37:49,920 --> 00:37:53,200
Speaker 2: don't think they're going to super Horizon. Riison could actually

751
00:37:53,200 --> 00:37:56,239
Speaker 2: materially harm Elon Musks, All of them could. It's just

752
00:37:56,280 --> 00:37:58,400
Speaker 2: that they go, nah, it's not worth the fucking problem.

753
00:37:58,480 --> 00:38:01,359
Speaker 2: Ten million, that's we could and they can probably eat

754
00:38:01,360 --> 00:38:04,279
Speaker 2: the duct the off of taxes somehow. It's just it's

755
00:38:04,360 --> 00:38:07,000
Speaker 2: very it's very depressing, very very depressing.

756
00:38:07,160 --> 00:38:09,200
Speaker 1: It is. And I think when I look at what's

757
00:38:09,239 --> 00:38:13,279
Speaker 1: happening with civil society organizations that are find themselves in

758
00:38:13,320 --> 00:38:16,759
Speaker 1: the same arena with the Musk, like Media Matters, a

759
00:38:16,840 --> 00:38:20,320
Speaker 1: media watchdog group, who Musk is suming like they're engaged

760
00:38:20,360 --> 00:38:23,520
Speaker 1: in this big back and forth lawsuit and he assuming

761
00:38:23,520 --> 00:38:26,920
Speaker 1: them for basically doing their jobs producing research into how

762
00:38:27,200 --> 00:38:30,520
Speaker 1: X is moderated. There are organizations that don't have the

763
00:38:30,560 --> 00:38:33,000
Speaker 1: money to be like will We're not going to back down.

764
00:38:33,080 --> 00:38:35,480
Speaker 1: And then those are the organizations that are actually standing

765
00:38:35,560 --> 00:38:37,440
Speaker 1: up and being like, we're gonna fight this. This is

766
00:38:37,480 --> 00:38:40,000
Speaker 1: bully tactics. We're not gonna, you know, roll over. It's

767
00:38:40,000 --> 00:38:43,200
Speaker 1: interesting that the companies that have money and power roll

768
00:38:43,200 --> 00:38:47,960
Speaker 1: over immediately, Displayball immediately. And the organizations that don't, those

769
00:38:47,960 --> 00:38:50,040
Speaker 1: are the ones who have to like take the pressure

770
00:38:50,080 --> 00:38:51,480
Speaker 1: and be like, yeah, we have to stand up to

771
00:38:51,520 --> 00:38:52,160
Speaker 1: him again.

772
00:38:52,560 --> 00:38:57,920
Speaker 2: Functional business press there's never been, or at least if

773
00:38:57,920 --> 00:39:00,600
Speaker 2: that has been, is very been, very weak, any real

774
00:39:00,640 --> 00:39:03,840
Speaker 2: corporate accountability in the media. There is reporting that's quite excellent.

775
00:39:03,880 --> 00:39:06,080
Speaker 2: Look at the Wall Street Journal, Jeff Horwitz, part of

776
00:39:06,080 --> 00:39:08,479
Speaker 2: the team he did the Facebook file. Is incredible reporting there.

777
00:39:08,560 --> 00:39:10,600
Speaker 2: But there's not people just saying, hey, this fucking sucks.

778
00:39:10,600 --> 00:39:12,040
Speaker 2: Fuck you, what are you doing with a large enough

779
00:39:12,040 --> 00:39:14,360
Speaker 2: platforms what I'm trying to build Because the thing is,

780
00:39:14,520 --> 00:39:17,520
Speaker 2: these companies have more money than God. All they have

781
00:39:17,640 --> 00:39:20,640
Speaker 2: is their names. That's why Verizon did this, because Verisen went,

782
00:39:21,320 --> 00:39:23,560
Speaker 2: we won't get that much blowback in the press, but

783
00:39:23,600 --> 00:39:27,000
Speaker 2: we'll get a ton of easy riding with the markets.

784
00:39:27,040 --> 00:39:29,520
Speaker 2: The markets will love this, and they probably did. It's

785
00:39:29,560 --> 00:39:32,880
Speaker 2: ten million dollars, which is nothing to Verizon. It just sucks,

786
00:39:32,880 --> 00:39:34,279
Speaker 2: and I know that there's far more than could. They

787
00:39:34,320 --> 00:39:36,600
Speaker 2: should be government, like a government of some kind that

788
00:39:36,920 --> 00:39:42,920
Speaker 2: stops extortion in some way, but lacking that, a functional

789
00:39:43,000 --> 00:39:45,200
Speaker 2: business media would say, and it used to exist, you

790
00:39:45,239 --> 00:39:46,920
Speaker 2: go and looking like the two thousands. They used to

791
00:39:47,000 --> 00:39:51,440
Speaker 2: rip the shit out of people. It's awesome, but that

792
00:39:51,640 --> 00:39:55,200
Speaker 2: just it went away. And I think it's really easy

793
00:39:55,200 --> 00:39:59,000
Speaker 2: to say, well, they're protecting advertisers. It's really easy to say, well,

794
00:39:59,160 --> 00:40:02,600
Speaker 2: we don't want to piss off the advertisers. We couldn't possibly. No,

795
00:40:03,400 --> 00:40:05,800
Speaker 2: it's fast, impler. They want them to win. They support

796
00:40:05,840 --> 00:40:08,160
Speaker 2: the powerful. What they like the companies. They want to

797
00:40:08,200 --> 00:40:09,759
Speaker 2: keep saying nice things about them. They don't want to

798
00:40:09,760 --> 00:40:12,320
Speaker 2: say mean things about them, because then the companies wouldn't

799
00:40:12,360 --> 00:40:18,200
Speaker 2: like them. It's really fucking sad. People love companies, not

800
00:40:18,360 --> 00:40:21,799
Speaker 2: regular people. The media, yeah, they want to be the

801
00:40:21,800 --> 00:40:24,120
Speaker 2: ones that tell you the story of the powerful far

802
00:40:24,239 --> 00:40:31,399
Speaker 2: more than they want to hold anyone accountable more.

803
00:40:31,400 --> 00:40:44,440
Speaker 4: After a quick break, let's get right back into it.

804
00:40:47,960 --> 00:40:50,640
Speaker 1: This is gonna sound like a very weird analogy, but

805
00:40:51,280 --> 00:40:52,759
Speaker 1: one of the places that I sort of got my

806
00:40:52,800 --> 00:40:56,720
Speaker 1: start in media was celebrity journalism, and right, it reminds

807
00:40:56,800 --> 00:40:59,279
Speaker 1: me so much of this where is that it is

808
00:40:59,320 --> 00:41:01,800
Speaker 1: exactly that I mean it is. You don't you don't

809
00:41:01,840 --> 00:41:03,960
Speaker 1: want this celebrity to be mad at you because you

810
00:41:04,000 --> 00:41:06,160
Speaker 1: want to get the exclusive, you want to get the pictures,

811
00:41:06,160 --> 00:41:08,680
Speaker 1: you want the access, so all you can say it's

812
00:41:08,760 --> 00:41:11,600
Speaker 1: fluffy bullshit about the celebrity. It's like why celebrity journalism

813
00:41:12,040 --> 00:41:14,000
Speaker 1: It is essentially dead, but it used to be like

814
00:41:14,080 --> 00:41:15,040
Speaker 1: juicy and good.

815
00:41:15,360 --> 00:41:17,960
Speaker 2: Well I think it still exists in England sadly because

816
00:41:18,000 --> 00:41:21,040
Speaker 2: the way they do celebrity journalism is that they commit crime.

817
00:41:21,239 --> 00:41:23,760
Speaker 1: It's they're like going through like a runaway garbage.

818
00:41:23,880 --> 00:41:28,120
Speaker 2: They do like missus doubt fire situation to get one story.

819
00:41:28,160 --> 00:41:32,040
Speaker 2: It takes them seven years. They become your child's best

820
00:41:32,040 --> 00:41:34,960
Speaker 2: friend as a means of stealing your mobile phone. No,

821
00:41:35,200 --> 00:41:38,319
Speaker 2: it's it is like journal gossip journalism, except it's not

822
00:41:38,360 --> 00:41:42,200
Speaker 2: even good gossip journalism. Good gossip journalism still exists. I

823
00:41:42,239 --> 00:41:47,080
Speaker 2: say good in a qualitative sense. Morally pretty horrifying, but

824
00:41:47,400 --> 00:41:50,360
Speaker 2: it's it is everyone wants to be friends with the

825
00:41:50,440 --> 00:41:51,920
Speaker 2: rock stars. They want to be the first one to

826
00:41:51,960 --> 00:41:55,120
Speaker 2: talk positively about something, because people love reading positive news.

827
00:41:55,160 --> 00:41:57,200
Speaker 2: Not that you give them anything else, not that you

828
00:41:57,280 --> 00:42:00,440
Speaker 2: ever talk to your readers or see what they think.

829
00:42:00,719 --> 00:42:02,080
Speaker 2: One of my favorite things to do, by the way,

830
00:42:02,120 --> 00:42:04,120
Speaker 2: go on the verge and go and read any AI story.

831
00:42:04,160 --> 00:42:06,319
Speaker 2: Read the comments the comments they don't like gay are

832
00:42:06,360 --> 00:42:08,640
Speaker 2: then they're not enjoying this. It's almost as if real

833
00:42:08,719 --> 00:42:11,239
Speaker 2: people are really disgusted by this stuff. But it is

834
00:42:11,280 --> 00:42:14,680
Speaker 2: gossip journalism. It's you will talk to reports and you

835
00:42:14,719 --> 00:42:16,600
Speaker 2: will say to them, hey, so you're covering like AI

836
00:42:16,719 --> 00:42:19,680
Speaker 2: for example, and well you write about open AI and

837
00:42:19,680 --> 00:42:22,160
Speaker 2: you seem to know all of the guys' names and

838
00:42:22,200 --> 00:42:25,360
Speaker 2: all of their histories. What about the finance? They lost

839
00:42:25,400 --> 00:42:27,840
Speaker 2: five billion dollars in twenty twenty four. What do you

840
00:42:27,840 --> 00:42:29,360
Speaker 2: think of that? They go, oh, I don't deal with

841
00:42:29,400 --> 00:42:32,960
Speaker 2: the numbers. Oh, I'm not a finance journalist. Can you

842
00:42:33,040 --> 00:42:37,399
Speaker 2: fucking count? And they can, but they just they don't

843
00:42:37,400 --> 00:42:41,000
Speaker 2: want to touch it. Because when you start thinking, ah,

844
00:42:41,160 --> 00:42:43,600
Speaker 2: this company open Ai, they seem to burn billions of

845
00:42:43,640 --> 00:42:46,040
Speaker 2: dollars and they keep they don't have enough, they don't

846
00:42:46,040 --> 00:42:48,920
Speaker 2: bring in more, They lose more money than they bring in,

847
00:42:48,920 --> 00:42:51,440
Speaker 2: and they need more money than anyone's ever had before.

848
00:42:52,400 --> 00:42:54,400
Speaker 2: Is that a statele No need to think about that.

849
00:42:54,440 --> 00:42:57,320
Speaker 2: They released a new model type it up. Send Sam

850
00:42:57,320 --> 00:43:00,200
Speaker 2: Alton made a blog send because they want to be

851
00:43:00,239 --> 00:43:02,239
Speaker 2: the they want to be part of the party. And

852
00:43:02,280 --> 00:43:04,279
Speaker 2: then there's this bullshit where they're like, oh, yeah, well

853
00:43:04,320 --> 00:43:06,520
Speaker 2: it's clicks. You want to get clicks. But the readers

854
00:43:06,520 --> 00:43:10,239
Speaker 2: want this, The readers want what you give them. You

855
00:43:10,320 --> 00:43:13,520
Speaker 2: can sure readers might not like you if you don't

856
00:43:13,560 --> 00:43:15,360
Speaker 2: cover the most popular thing, but you don't have to

857
00:43:15,400 --> 00:43:17,360
Speaker 2: cover it in this tripe way.

858
00:43:17,560 --> 00:43:20,320
Speaker 1: No, you don't have to be farning like that's I

859
00:43:20,320 --> 00:43:23,080
Speaker 1: don't think anybody's any reader is demanding that.

860
00:43:23,440 --> 00:43:25,359
Speaker 2: I think that there are some that do, and they're

861
00:43:25,480 --> 00:43:27,680
Speaker 2: very loud, and people are stupid. They're like, oh well,

862
00:43:27,719 --> 00:43:30,880
Speaker 2: people in the valley say fuck them, fucking asshot. Fuck you.

863
00:43:31,440 --> 00:43:34,120
Speaker 2: If you're an investor or an engineer in the valley

864
00:43:34,160 --> 00:43:36,240
Speaker 2: and you're like, no, you need to talk positively about AI.

865
00:43:36,320 --> 00:43:38,759
Speaker 2: Why do we have to prove ourselves to you? You're

866
00:43:38,760 --> 00:43:41,720
Speaker 2: selling to me. You sell to me. I am the buyer.

867
00:43:41,920 --> 00:43:44,040
Speaker 2: I am not. You are not the buyer. I don't

868
00:43:44,080 --> 00:43:46,600
Speaker 2: have to prove myself to Oh you don't understand it?

869
00:43:46,680 --> 00:43:49,239
Speaker 2: Fuck you. Also, if I don't understand it, you're doing

870
00:43:49,239 --> 00:43:52,160
Speaker 2: a poor job explaining it. It's not my fucking fault.

871
00:43:52,280 --> 00:43:58,799
Speaker 2: Chat GPT is changing everything. How how instead reporters just

872
00:43:58,840 --> 00:44:02,040
Speaker 2: go chat? GPT is changing everything. It is growing faster

873
00:44:02,080 --> 00:44:07,120
Speaker 2: than anything before. They're completely right, So did the coronavirus Like,

874
00:44:08,040 --> 00:44:11,200
Speaker 2: I'm just like, look, we could. We took the coronavirus

875
00:44:11,239 --> 00:44:16,080
Speaker 2: seriously in a very different way. It's just it's frustrating

876
00:44:16,120 --> 00:44:19,160
Speaker 2: because I was not trained in economics. I didn't go

877
00:44:19,200 --> 00:44:21,239
Speaker 2: to a well known school. I didn't get great I

878
00:44:21,280 --> 00:44:23,440
Speaker 2: got decent grades in university, but like I got terrible

879
00:44:23,440 --> 00:44:26,440
Speaker 2: grades in high school. I'm not like, I don't consider

880
00:44:26,440 --> 00:44:29,160
Speaker 2: myself a super smart person, but this stuff is out there.

881
00:44:29,239 --> 00:44:32,439
Speaker 2: It's obvious. It's very obvious. Even if you load chat

882
00:44:32,480 --> 00:44:34,880
Speaker 2: GPT try and make it do your job. It don't work.

883
00:44:35,239 --> 00:44:37,680
Speaker 2: Don't do it. I have a spreadsheet heavy job. I

884
00:44:37,680 --> 00:44:40,319
Speaker 2: would love a computer to do it for me. It refuses.

885
00:44:40,520 --> 00:44:42,279
Speaker 1: It's funny that you mentioned this. It took a lot

886
00:44:42,280 --> 00:44:44,239
Speaker 1: of time, and it's trying to get CHATTYBT to do

887
00:44:44,239 --> 00:44:46,719
Speaker 1: a spreadsheet related task and it was just a waste

888
00:44:46,719 --> 00:44:48,440
Speaker 1: of four hours. I should have done it myself.

889
00:44:48,680 --> 00:44:50,400
Speaker 2: My favorite one I did recently. So this is a

890
00:44:50,400 --> 00:44:53,680
Speaker 2: company called Manus m A and us I called Manus.

891
00:44:54,680 --> 00:44:57,560
Speaker 2: That's what I'm calling them now, Manus. So they claimed

892
00:44:57,600 --> 00:45:00,160
Speaker 2: to be like the first agentic platform, and so what

893
00:45:00,160 --> 00:45:02,040
Speaker 2: you do is I asked them to be like, get

894
00:45:02,040 --> 00:45:06,200
Speaker 2: me a list of every article that has run mentioning

895
00:45:06,239 --> 00:45:09,520
Speaker 2: me in the last two years. It took ten minutes,

896
00:45:10,400 --> 00:45:13,680
Speaker 2: and every single step it coded Python to do. You

897
00:45:13,719 --> 00:45:16,200
Speaker 2: could see it just like burning resources. And it came

898
00:45:16,239 --> 00:45:19,640
Speaker 2: back with ten articles. Oh god, there is over one

899
00:45:19,719 --> 00:45:21,919
Speaker 2: hundred in the last two years, and I'm okay, there's more.

900
00:45:22,040 --> 00:45:26,040
Speaker 2: You've missed a lot. Came back with another nine. It's

901
00:45:26,080 --> 00:45:30,359
Speaker 2: just like, I love living in the future. But that's

902
00:45:30,400 --> 00:45:32,480
Speaker 2: the insane thing. If I was an investor and I

903
00:45:32,520 --> 00:45:35,440
Speaker 2: saw this, I'd be freaked the fuck out. I'd be like,

904
00:45:35,520 --> 00:45:39,680
Speaker 2: oh boy, we're all doing this. It's like finding out

905
00:45:39,719 --> 00:45:44,600
Speaker 2: everyone at the party but you shit themselves.

906
00:45:45,080 --> 00:45:47,080
Speaker 1: That's a good analogy, it really is.

907
00:45:47,400 --> 00:45:51,879
Speaker 2: And you're just like, do what I should. I there

908
00:45:52,480 --> 00:45:54,719
Speaker 2: is everyone else doing this? Is that good? And they

909
00:45:54,880 --> 00:45:57,359
Speaker 2: were like I love it, and you're like, I'm not good,

910
00:45:57,520 --> 00:46:00,879
Speaker 2: but I don't know. I'm just I'm so tired of it.

911
00:46:01,000 --> 00:46:03,480
Speaker 2: But I do think I will prevail. I don't own

912
00:46:03,480 --> 00:46:05,759
Speaker 2: any stocks, by the way. That's my other thing. That's

913
00:46:05,800 --> 00:46:07,359
Speaker 2: the other thing. People love to be like, oh, you're

914
00:46:07,400 --> 00:46:09,680
Speaker 2: just doing this because I've got no, I've got nothing,

915
00:46:10,680 --> 00:46:14,000
Speaker 2: nothing of late no. And that's that's the scariest thing

916
00:46:14,040 --> 00:46:15,560
Speaker 2: I'm doing this just the level of the game.

917
00:46:17,080 --> 00:46:19,400
Speaker 1: Well, And I have to tell you one thing that

918
00:46:19,480 --> 00:46:23,000
Speaker 1: AI has been doing kind of sex successfully is really

919
00:46:23,120 --> 00:46:27,240
Speaker 1: polluting our discourse with like fake social media images because

920
00:46:27,840 --> 00:46:31,000
Speaker 1: out of LA, I have to say, like so many

921
00:46:31,120 --> 00:46:35,040
Speaker 1: of the obviously AI generated images showing LA this past

922
00:46:35,040 --> 00:46:40,320
Speaker 1: week as like a flaming, burnt out healthscape of urban wreckage,

923
00:46:40,760 --> 00:46:44,200
Speaker 1: which is pretty different from the actual photos I've seen

924
00:46:44,400 --> 00:46:47,839
Speaker 1: of folks protesting. And I'm from DC. I remember very

925
00:46:47,840 --> 00:46:50,120
Speaker 1: clearly in twenty twenty people when I would be like, oh,

926
00:46:50,160 --> 00:46:51,640
Speaker 1: live in DC, people would be like, oh, I've seen

927
00:46:51,719 --> 00:46:54,799
Speaker 1: pictures online of like isn't DC just on fire now?

928
00:46:54,920 --> 00:46:58,960
Speaker 1: Like so sorry about your city? And I do, I mean,

929
00:46:58,960 --> 00:47:01,160
Speaker 1: I want to talk a bit about what's happening in LA,

930
00:47:01,280 --> 00:47:05,239
Speaker 1: because it has been a whirlwind of disinformation, both AI

931
00:47:05,360 --> 00:47:07,439
Speaker 1: generated and not. Like one of the images I saw

932
00:47:07,600 --> 00:47:10,120
Speaker 1: was a still image from the movie Blue Thunder, the

933
00:47:10,160 --> 00:47:13,399
Speaker 1: action movie. Like the way people will slip in these

934
00:47:13,440 --> 00:47:15,960
Speaker 1: images where it's like I know this image that's not.

935
00:47:16,080 --> 00:47:18,960
Speaker 2: LA, and I think that's more prevalent than the AI.

936
00:47:18,880 --> 00:47:22,560
Speaker 1: Slaw totally a thousand percent. One of the images that

937
00:47:22,840 --> 00:47:26,120
Speaker 1: is that I see a lot is this picture of

938
00:47:26,160 --> 00:47:30,640
Speaker 1: a palette of bricks where it resurfaced in that protest

939
00:47:30,680 --> 00:47:34,319
Speaker 1: in LA where someone posted it to x saying it's

940
00:47:34,320 --> 00:47:39,520
Speaker 1: civil war. Democrat militants are flooding the city with palettes

941
00:47:39,520 --> 00:47:42,680
Speaker 1: of bricks. So the image is actually it's not AI generated,

942
00:47:42,719 --> 00:47:44,680
Speaker 1: it's a real picture of bricks, but the bricks are

943
00:47:44,680 --> 00:47:48,560
Speaker 1: from like a materials wholesaler based in Malaysia. But that

944
00:47:48,800 --> 00:47:52,479
Speaker 1: same exact picture was floating around DC in twenty twenty,

945
00:47:52,480 --> 00:47:54,840
Speaker 1: and it was supposed to be evidence that like paid

946
00:47:54,920 --> 00:47:58,640
Speaker 1: protesters and George Sorows are floating into this into Democrat

947
00:47:58,680 --> 00:48:02,560
Speaker 1: cities to cause violence. And I think that you're so

948
00:48:02,920 --> 00:48:06,080
Speaker 1: right that even though there are these AI generated images

949
00:48:06,120 --> 00:48:08,279
Speaker 1: that are meant to create a certain narrative about what's

950
00:48:08,320 --> 00:48:11,160
Speaker 1: going on in cities when there's protests, I do think

951
00:48:11,200 --> 00:48:14,600
Speaker 1: it's more like cheap fakes where the image is real,

952
00:48:14,719 --> 00:48:18,040
Speaker 1: but just the context around it is not correct. Yeah,

953
00:48:18,040 --> 00:48:18,359
Speaker 1: And I.

954
00:48:18,320 --> 00:48:22,120
Speaker 2: Think that there's also a bigger problem, which is how

955
00:48:22,160 --> 00:48:25,440
Speaker 2: can I put this people who are not racists, people

956
00:48:25,480 --> 00:48:28,680
Speaker 2: not on the right wing have no unity or solidarity

957
00:48:28,719 --> 00:48:31,520
Speaker 2: behind any given message. I think that there is a

958
00:48:31,640 --> 00:48:34,480
Speaker 2: large part of them that will throw anyone under the

959
00:48:34,480 --> 00:48:36,480
Speaker 2: bus for their own safety. And on top of that,

960
00:48:37,239 --> 00:48:40,360
Speaker 2: I think we're way too attached to this concept of objectivity.

961
00:48:40,640 --> 00:48:42,760
Speaker 2: I think people are too scared to say the truth,

962
00:48:43,040 --> 00:48:47,280
Speaker 2: such as, I don't know they're marching fucking soldiers against

963
00:48:47,560 --> 00:48:51,359
Speaker 2: US citizens. That's fascism. Seems like a fairly obvious one.

964
00:48:51,360 --> 00:48:53,800
Speaker 2: But the New York Times is like, men with guns

965
00:48:53,840 --> 00:48:58,239
Speaker 2: approach people something on fire.

966
00:48:58,600 --> 00:49:00,319
Speaker 1: So the New York Times have got to be one

967
00:49:00,320 --> 00:49:02,560
Speaker 1: of the biggest offenders. They're so passive.

968
00:49:03,200 --> 00:49:05,480
Speaker 2: It's them, but it's also the people in power. It's

969
00:49:05,520 --> 00:49:08,440
Speaker 2: the Democrat senators are like, we will hold them accountable

970
00:49:08,560 --> 00:49:11,759
Speaker 2: how I don't fucking know. But people are like, oh,

971
00:49:11,840 --> 00:49:16,439
Speaker 2: we needed Joe Rogan have left. We need to put

972
00:49:16,640 --> 00:49:19,319
Speaker 2: tens of millions of dollars into politics that talk about

973
00:49:19,360 --> 00:49:22,680
Speaker 2: podcasts that aren't pod save America? How about that? How

974
00:49:22,680 --> 00:49:25,600
Speaker 2: about we need to we need to put my idea

975
00:49:25,600 --> 00:49:27,520
Speaker 2: and have a newslet are going out, but there's on Monday.

976
00:49:27,520 --> 00:49:29,799
Speaker 2: It's like, I don't know. Look at what the right

977
00:49:29,920 --> 00:49:31,480
Speaker 2: or even the center right has look at like the

978
00:49:31,520 --> 00:49:34,719
Speaker 2: Barstool Media, for example, there are parts of Barstol's like

979
00:49:34,719 --> 00:49:38,239
Speaker 2: a racism chart. You've got the Fortnite, Yeah, but you've

980
00:49:38,239 --> 00:49:39,920
Speaker 2: got things like the Yak which are kind of like

981
00:49:40,600 --> 00:49:43,360
Speaker 2: unclear about their goals or anything. But it's just five

982
00:49:43,400 --> 00:49:45,520
Speaker 2: guys just sitting around being like a thats whats and

983
00:49:45,560 --> 00:49:47,719
Speaker 2: like they seem to like each other. And people are like,

984
00:49:47,760 --> 00:49:49,359
Speaker 2: how the fuck do they do it? How do they

985
00:49:49,360 --> 00:49:51,560
Speaker 2: build these things? It's like they found five people who

986
00:49:51,560 --> 00:49:53,200
Speaker 2: are entertaining to watch, and they gave them a lot

987
00:49:53,200 --> 00:49:56,440
Speaker 2: of money and build a build big fucking studio that

988
00:49:56,600 --> 00:49:59,239
Speaker 2: looks good and they stream regularly and they give them

989
00:49:59,239 --> 00:50:02,320
Speaker 2: lots of money and lots of promotion. How did it

990
00:50:02,440 --> 00:50:07,160
Speaker 2: possibly work? Here's my idea Western Kabuki. They're a podcast

991
00:50:07,239 --> 00:50:09,719
Speaker 2: with June Juniper, trans trans woman.

992
00:50:09,960 --> 00:50:13,160
Speaker 1: I've better guess down the show.

993
00:50:13,120 --> 00:50:16,720
Speaker 2: I love w K point is, find them or someone

994
00:50:16,800 --> 00:50:19,200
Speaker 2: like them and give them like five to ten million dollars,

995
00:50:19,280 --> 00:50:22,320
Speaker 2: build a studio, give them national production, give them fucking

996
00:50:22,360 --> 00:50:26,560
Speaker 2: advertising budgets, some radio and TV really push it. Those

997
00:50:26,640 --> 00:50:29,200
Speaker 2: people will begin to move. People left. The reason that

998
00:50:29,239 --> 00:50:31,719
Speaker 2: people are so freaked out by fucking trans people other

999
00:50:31,760 --> 00:50:36,800
Speaker 2: than being incurious morons is they're not used to seeing them.

1000
00:50:37,480 --> 00:50:40,080
Speaker 2: You show a trans person just fucking existing and talking

1001
00:50:40,120 --> 00:50:42,160
Speaker 2: and having fun, people gotta go, oh, I as seen

1002
00:50:42,200 --> 00:50:45,520
Speaker 2: one of them before. That's a regular person. They just

1003
00:50:45,560 --> 00:50:48,600
Speaker 2: happen to be trans. You normalize this stuff by making

1004
00:50:48,600 --> 00:50:51,080
Speaker 2: it normal. And you want to have a Joe Rogan

1005
00:50:51,160 --> 00:50:53,920
Speaker 2: of the left, heavily fund someone with those principles and

1006
00:50:53,920 --> 00:50:57,960
Speaker 2: then let them cook, let them build a network. Instead,

1007
00:50:58,000 --> 00:51:00,160
Speaker 2: everyone wants to go and try, and they're like, what

1008
00:51:00,239 --> 00:51:03,680
Speaker 2: if we were gonna here's a pitch deck. We need

1009
00:51:03,680 --> 00:51:06,240
Speaker 2: four million dollars for data. Did you see the Democrat

1010
00:51:06,280 --> 00:51:06,680
Speaker 2: pitch deck?

1011
00:51:06,760 --> 00:51:09,000
Speaker 1: By the way, I did see that pitch deck. It

1012
00:51:09,080 --> 00:51:11,640
Speaker 1: came from a group called the Speaking with American Men Project,

1013
00:51:11,680 --> 00:51:13,680
Speaker 1: and they said they were willing to spend millions of

1014
00:51:13,719 --> 00:51:17,160
Speaker 1: dollars up to twenty million dollars to better understand how

1015
00:51:17,200 --> 00:51:20,920
Speaker 1: to appeal to the modern man. Well meaning, sure, but

1016
00:51:21,040 --> 00:51:22,200
Speaker 1: let's just say I don't think it.

1017
00:51:22,120 --> 00:51:25,600
Speaker 2: Was well received. So fucking funny. I love like, they

1018
00:51:25,640 --> 00:51:28,120
Speaker 2: need two to four million dollars just for data. If

1019
00:51:28,160 --> 00:51:30,160
Speaker 2: you put that in a pitch deck, they should kill you.

1020
00:51:31,520 --> 00:51:37,640
Speaker 1: Just straight execution, no trial, poison, but I mean it

1021
00:51:37,680 --> 00:51:39,920
Speaker 1: brings it. It really raises your question of like, what

1022
00:51:39,920 --> 00:51:42,279
Speaker 1: are we doing here? What are we doing here? Like

1023
00:51:42,360 --> 00:51:46,480
Speaker 1: it is. And I also think to your point about

1024
00:51:47,040 --> 00:51:49,560
Speaker 1: sort of not needing the Joe Rogan and the left,

1025
00:51:49,719 --> 00:51:51,880
Speaker 1: there's only a certain amount of people who are going

1026
00:51:51,920 --> 00:51:53,799
Speaker 1: to sit down and listen to a political podcast, right,

1027
00:51:53,840 --> 00:51:55,719
Speaker 1: and so like you really need people who are like

1028
00:51:56,560 --> 00:52:00,600
Speaker 1: doing sports content or makeup content, beauty content, muck bangs,

1029
00:52:00,680 --> 00:52:03,520
Speaker 1: whatever it is. Like wherever people are already at, I

1030
00:52:03,520 --> 00:52:05,279
Speaker 1: think that's where you have to meet them. And I

1031
00:52:05,320 --> 00:52:07,440
Speaker 1: also think that we're good at that on the left always.

1032
00:52:07,480 --> 00:52:10,399
Speaker 1: I think that we, yeah, we want to spend four

1033
00:52:10,440 --> 00:52:14,400
Speaker 1: million dollars just researching the issue as opposed to actually

1034
00:52:14,400 --> 00:52:16,760
Speaker 1: doing anything that's going to be meaningful or useful.

1035
00:52:17,520 --> 00:52:20,600
Speaker 2: I think it's that. And also Joe Rogan is just

1036
00:52:20,640 --> 00:52:23,120
Speaker 2: an app, like just a big click and that they're

1037
00:52:23,120 --> 00:52:25,560
Speaker 2: a little throat clearing. Joe Rogan's horrible. The people who

1038
00:52:25,600 --> 00:52:27,680
Speaker 2: hasn't a horrible but if you actually listen to him.

1039
00:52:28,480 --> 00:52:30,879
Speaker 2: There's the same with THEO Voon. It's like you never

1040
00:52:30,920 --> 00:52:32,800
Speaker 2: sit there and go like, God, this guy's really talking

1041
00:52:32,880 --> 00:52:35,319
Speaker 2: down to me. You don't sit there and think, fuck,

1042
00:52:35,360 --> 00:52:38,240
Speaker 2: I wish you'd ask. I think that people in general

1043
00:52:38,480 --> 00:52:41,200
Speaker 2: really do not understand that we as human beings regularly

1044
00:52:41,239 --> 00:52:44,279
Speaker 2: forget things and regularly don't understand things. We regularly are

1045
00:52:44,320 --> 00:52:47,719
Speaker 2: just like, fuck, what's a how does stop? What is

1046
00:52:47,760 --> 00:52:50,839
Speaker 2: a stock? Like like a really obvious thing? And Joe

1047
00:52:50,880 --> 00:52:54,839
Speaker 2: rogen goes like computer interesting, Jamie brooking up a picture

1048
00:52:54,840 --> 00:52:56,919
Speaker 2: of a care Yeah, yeah, it's just like you don't

1049
00:52:56,920 --> 00:52:59,080
Speaker 2: get the sense that he knows anything, but he makes

1050
00:52:59,280 --> 00:53:01,719
Speaker 2: he asks the questions and he gets decent answers. He's

1051
00:53:01,719 --> 00:53:04,399
Speaker 2: able to keep him going for three hours somehow, and

1052
00:53:04,440 --> 00:53:06,920
Speaker 2: he is affable and friendly and happy to see them.

1053
00:53:06,920 --> 00:53:08,920
Speaker 2: There's I don't know if you call it charm, but

1054
00:53:08,960 --> 00:53:12,400
Speaker 2: it's at least they seem to like enjoy each other's proximity.

1055
00:53:12,719 --> 00:53:17,680
Speaker 2: It is it becomes political, but it is not inherently political.

1056
00:53:17,880 --> 00:53:19,920
Speaker 2: So people get all tied up, and how do we

1057
00:53:20,000 --> 00:53:23,239
Speaker 2: recreate that by getting someone who's a good interviewer and

1058
00:53:23,280 --> 00:53:25,239
Speaker 2: giving them lots of money and also look at his

1059
00:53:25,280 --> 00:53:27,799
Speaker 2: fuck it? Same with theovon. Look at their production. Look

1060
00:53:27,800 --> 00:53:29,920
Speaker 2: how good that production is. Look how nice it is?

1061
00:53:30,640 --> 00:53:33,480
Speaker 2: Why don't you build that? Oh? What pod? Save? America.

1062
00:53:33,520 --> 00:53:37,640
Speaker 2: Fuck pod Save America. Fuck them. I'm sorry. We mean

1063
00:53:37,920 --> 00:53:41,520
Speaker 2: every fucking time, every time it feels like nothing's happening.

1064
00:53:41,680 --> 00:53:45,319
Speaker 2: It's so condescending. Did you see the David Cross John

1065
00:53:45,360 --> 00:53:48,439
Speaker 2: Favreau interview? Now that was my foot, So David Cross

1066
00:53:48,440 --> 00:53:51,919
Speaker 2: said Jon Favreau on his podcast, and so he goes

1067
00:53:51,960 --> 00:53:54,000
Speaker 2: through his book and there's like the three of them

1068
00:53:54,000 --> 00:53:55,600
Speaker 2: goes through and there's the guy because I don't remember

1069
00:53:56,000 --> 00:53:58,680
Speaker 2: Johnny's and David Cross just like you got Jon Favreau

1070
00:53:58,920 --> 00:54:01,640
Speaker 2: and John Down and John Fevau has no idea what

1071
00:54:01,640 --> 00:54:04,319
Speaker 2: the fuck to do. And it's just like, yeah, if

1072
00:54:04,320 --> 00:54:06,279
Speaker 2: you if your big book that you're releasing for your

1073
00:54:06,360 --> 00:54:08,080
Speaker 2: very important podcasts that you care about a lot is

1074
00:54:08,120 --> 00:54:10,239
Speaker 2: ghost written, how am I meant to think you care?

1075
00:54:10,560 --> 00:54:11,360
Speaker 1: Yeah?

1076
00:54:11,400 --> 00:54:13,600
Speaker 2: How am I meant to How is anyone meant to equate?

1077
00:54:13,719 --> 00:54:13,879
Speaker 1: Oh?

1078
00:54:13,880 --> 00:54:17,320
Speaker 2: How do we get young men? Oh? Young? We don't

1079
00:54:17,360 --> 00:54:19,759
Speaker 2: have anything like that that has the budgets of a

1080
00:54:19,760 --> 00:54:24,080
Speaker 2: Theo Vonne or the Yak or anything barstool. Look at Espianation,

1081
00:54:24,160 --> 00:54:26,880
Speaker 2: probably the closest we've ever got. And Vox just mostly

1082
00:54:26,920 --> 00:54:29,399
Speaker 2: enjoys taking money away from them. And that's the thing.

1083
00:54:29,960 --> 00:54:33,120
Speaker 2: These organizations don't exist because no one wants to fund them.

1084
00:54:33,440 --> 00:54:35,960
Speaker 2: They don't they want to. Joe Rogan have left despite

1085
00:54:36,000 --> 00:54:37,680
Speaker 2: realizing that it took years and years to buil Joe

1086
00:54:37,760 --> 00:54:39,600
Speaker 2: Rogan and a shit ton of money that he had

1087
00:54:39,600 --> 00:54:42,560
Speaker 2: and other people gave him. If you actually fund these

1088
00:54:42,600 --> 00:54:45,920
Speaker 2: things and put money behind them in all of these cases,

1089
00:54:46,520 --> 00:54:48,840
Speaker 2: I don't know if they are sincere, but they certainly

1090
00:54:48,880 --> 00:54:52,600
Speaker 2: fucking come off that way. It's probably fake. They're very

1091
00:54:52,640 --> 00:54:55,320
Speaker 2: good at lying. I think they're probably sincere towards the beginning.

1092
00:54:55,760 --> 00:54:58,560
Speaker 2: What if we had a very well funded kind of

1093
00:54:58,640 --> 00:55:01,480
Speaker 2: like leftist and the term leftist is even a problem

1094
00:55:01,520 --> 00:55:04,759
Speaker 2: because it's like, well, what is a leftist? Miserable little

1095
00:55:04,760 --> 00:55:06,840
Speaker 2: pile of secrets? I guess, like, I don't fucking know.

1096
00:55:07,280 --> 00:55:09,680
Speaker 2: How about you get together a diverse group of people

1097
00:55:09,680 --> 00:55:11,719
Speaker 2: who enjoys being around each other and they chat fucking

1098
00:55:11,760 --> 00:55:14,759
Speaker 2: shit in a really glossy, good video forward thing with

1099
00:55:14,800 --> 00:55:16,839
Speaker 2: a good social team that knows how to cut them.

1100
00:55:17,040 --> 00:55:19,680
Speaker 2: They do fun things. I choose the Yak because it's

1101
00:55:19,719 --> 00:55:24,160
Speaker 2: like the weirdly centristy cart like it. They just they

1102
00:55:24,200 --> 00:55:26,800
Speaker 2: just kind of enjoy like big cat in them. It's fine, fine,

1103
00:55:26,840 --> 00:55:30,000
Speaker 2: to watch like, I don't really love it. It's just

1104
00:55:30,520 --> 00:55:34,440
Speaker 2: I'm frustrated because the solutions are actually really obvious solutions

1105
00:55:34,440 --> 00:55:37,160
Speaker 2: the wrong word. The starting points are very obvious.

1106
00:55:37,400 --> 00:55:39,600
Speaker 1: You don't need four million dollars to figure them out.

1107
00:55:40,040 --> 00:55:44,719
Speaker 2: Actually I disagree for something else. Yes, you don't need research,

1108
00:55:45,040 --> 00:55:49,279
Speaker 2: you need money. Shove money into fucking million dollars of

1109
00:55:49,280 --> 00:55:53,480
Speaker 2: advertising budget a year minimum, just fucking create magic. Do

1110
00:55:53,520 --> 00:55:56,920
Speaker 2: you think all of the top podcasts on the podcast

1111
00:55:57,040 --> 00:55:59,239
Speaker 2: charts are there because just because they're good or is

1112
00:55:59,280 --> 00:56:01,280
Speaker 2: it because they're famous people or had a bunch of money.

1113
00:56:01,800 --> 00:56:04,000
Speaker 2: It's really easy to do this. You could do this

1114
00:56:04,040 --> 00:56:06,920
Speaker 2: tomorrow if you really fucking wanted to. You could choose

1115
00:56:06,960 --> 00:56:09,520
Speaker 2: someone and just be like, that person's gonna be it,

1116
00:56:09,760 --> 00:56:12,240
Speaker 2: and they're like, oh, who will be the who cares?

1117
00:56:12,680 --> 00:56:16,640
Speaker 2: Who actually cares? Start creating content like the Theovons, the Acts,

1118
00:56:16,640 --> 00:56:19,680
Speaker 2: the Joe Rogan's of the world that resembles people who

1119
00:56:19,719 --> 00:56:24,480
Speaker 2: aren't just white guys who there are tons of dating

1120
00:56:24,640 --> 00:56:27,839
Speaker 2: trans people, tons of entertaining black woman like yourself. It's

1121
00:56:27,840 --> 00:56:29,200
Speaker 2: just like there are tons of paper you could just

1122
00:56:29,200 --> 00:56:31,880
Speaker 2: fucking pick, put them together, throw the money. Well, if

1123
00:56:31,920 --> 00:56:34,920
Speaker 2: it doesn't work. Yeah, I agree, let's do nothing then, yeah,

1124
00:56:35,080 --> 00:56:38,799
Speaker 2: let's just sit here and let's pay another consultant that's

1125
00:56:38,800 --> 00:56:40,600
Speaker 2: a better Kinsey needs their body.

1126
00:56:40,640 --> 00:56:42,640
Speaker 1: Yeah, like a better way to spend the money. And

1127
00:56:44,080 --> 00:56:46,560
Speaker 1: having come up in some of these sort of democratic

1128
00:56:46,600 --> 00:56:50,200
Speaker 1: establishment circles, they love to be like, oh, we need

1129
00:56:50,239 --> 00:56:54,480
Speaker 1: to solve this problem, but we are not interested in sailing.

1130
00:56:54,560 --> 00:56:56,600
Speaker 1: We're not interested in spending a little bit of money.

1131
00:56:56,640 --> 00:56:58,919
Speaker 1: If it doesn't work, that's it. We can't try anything else.

1132
00:56:59,160 --> 00:57:00,680
Speaker 1: It's it's so off defeating.

1133
00:57:01,080 --> 00:57:06,000
Speaker 2: Also, it will work. I will. I'm sorry, I'm a dumbass.

1134
00:57:06,040 --> 00:57:09,960
Speaker 2: And at CS I propped I popped up like a

1135
00:57:10,000 --> 00:57:13,440
Speaker 2: five day long talk show at CS, and I cobbled

1136
00:57:13,440 --> 00:57:15,040
Speaker 2: it together as I went. It was some of the

1137
00:57:15,040 --> 00:57:17,840
Speaker 2: best fucking broad I'm not being arrogant. It was some

1138
00:57:17,880 --> 00:57:20,280
Speaker 2: of the best fucking broadcasts I've heard. And I made it,

1139
00:57:20,400 --> 00:57:22,040
Speaker 2: and I made it with no planning, And it was

1140
00:57:22,080 --> 00:57:24,520
Speaker 2: because I thought, I know these people are no questions

1141
00:57:24,560 --> 00:57:27,000
Speaker 2: to ask them. I'm not even positioning myself in any way.

1142
00:57:27,000 --> 00:57:29,960
Speaker 2: I'm saying this took I think the combined cost of

1143
00:57:30,000 --> 00:57:32,480
Speaker 2: that just to to say some stuff like sixteen thousand

1144
00:57:32,520 --> 00:57:36,880
Speaker 2: dollars total that was with a gear equipment, hotel rooms,

1145
00:57:36,880 --> 00:57:39,520
Speaker 2: and that was because it was the wow cs Like

1146
00:57:39,560 --> 00:57:42,120
Speaker 2: that's the thing, like you can and I budgeted that myself,

1147
00:57:42,160 --> 00:57:45,480
Speaker 2: by the way, But that's the thing. It isn't actually

1148
00:57:45,480 --> 00:57:47,160
Speaker 2: that expensive to do these things, and if you put

1149
00:57:47,240 --> 00:57:50,040
Speaker 2: real money into it, you could do something fucking incredible

1150
00:57:50,040 --> 00:57:52,240
Speaker 2: and you could absolutely. Do you think people want to

1151
00:57:52,280 --> 00:57:53,280
Speaker 2: hear your Rogan interview?

1152
00:57:53,360 --> 00:57:57,080
Speaker 1: These people got No, he's not a good, very good interviewer.

1153
00:57:57,200 --> 00:57:58,680
Speaker 2: Like I don't know if I agree.

1154
00:57:58,960 --> 00:57:59,480
Speaker 1: Tell me more.

1155
00:58:00,240 --> 00:58:04,560
Speaker 2: I don't think he's a smart interviewer, But I think

1156
00:58:04,720 --> 00:58:08,240
Speaker 2: Rogan is able to ask these questions that put people

1157
00:58:08,240 --> 00:58:11,480
Speaker 2: at ease, and he gets detailed, aren't weird answers out

1158
00:58:11,520 --> 00:58:14,760
Speaker 2: of them. He is able to relax them, probably because

1159
00:58:14,800 --> 00:58:17,440
Speaker 2: like there's one brain cell just like pops on and

1160
00:58:17,480 --> 00:58:20,800
Speaker 2: on like a light bulb question and he's like yeah,

1161
00:58:20,800 --> 00:58:22,960
Speaker 2: I like like, oh, yeah, I saw the Grinch the

1162
00:58:23,000 --> 00:58:25,600
Speaker 2: other yeah, I knew a guy like that. It's just

1163
00:58:25,640 --> 00:58:29,439
Speaker 2: like Joe Rogan doesn't know a single guy, Like he's

1164
00:58:29,480 --> 00:58:31,560
Speaker 2: just he stays in that studio. But his studio is

1165
00:58:31,600 --> 00:58:36,320
Speaker 2: really nice. They get fun clips, timple, horrifying, horrifying, and

1166
00:58:36,360 --> 00:58:41,640
Speaker 2: he is just a gender setting for like literal entities. Again,

1167
00:58:41,880 --> 00:58:44,800
Speaker 2: huge amounts of money, tons of cash, and yes there's

1168
00:58:44,800 --> 00:58:47,919
Speaker 2: grifts there, but goddamn, put the money into it. Throw

1169
00:58:47,960 --> 00:58:49,959
Speaker 2: the money into it. You want to know why people

1170
00:58:50,080 --> 00:58:52,200
Speaker 2: don't really know about a lot of these issues. It's

1171
00:58:52,240 --> 00:58:55,880
Speaker 2: insane that I me a part timer and one of

1172
00:58:55,880 --> 00:58:58,120
Speaker 2: the first people to just be like, oh, fucking business

1173
00:58:58,200 --> 00:59:01,439
Speaker 2: people stupid. We have a look up these business people

1174
00:59:01,440 --> 00:59:04,480
Speaker 2: and they're just saying fucking stupid shit. I should not

1175
00:59:04,640 --> 00:59:08,480
Speaker 2: be dynamic for saying this, but it's proof, and I

1176
00:59:08,520 --> 00:59:11,280
Speaker 2: mean this in a positive way. There's tons of opportunity

1177
00:59:11,320 --> 00:59:14,200
Speaker 2: out there. There's so many ways, and if you bankroll

1178
00:59:14,240 --> 00:59:16,600
Speaker 2: these things and make them aggressive and fun and support

1179
00:59:16,640 --> 00:59:19,760
Speaker 2: them legally as well as giving them the social proof

1180
00:59:19,920 --> 00:59:22,760
Speaker 2: and the advertising budget, people are gonna want to listen

1181
00:59:22,800 --> 00:59:24,920
Speaker 2: to that they don't. You can make them positive. They

1182
00:59:24,960 --> 00:59:27,040
Speaker 2: don't even need to be about serious stuff. But shit,

1183
00:59:27,120 --> 00:59:29,640
Speaker 2: when labor issues come up, you say, yeah, we're pro worker.

1184
00:59:29,920 --> 00:59:33,280
Speaker 2: Oh I think workers might like that one. Well, why

1185
00:59:33,320 --> 00:59:36,040
Speaker 2: are we angry at people because the business idiots? There

1186
00:59:36,040 --> 00:59:38,280
Speaker 2: are things you can and it's not even something you

1187
00:59:38,320 --> 00:59:41,920
Speaker 2: need to do disingenuously. I choose Western kabuki because all

1188
00:59:41,960 --> 00:59:44,400
Speaker 2: of those people. I think if you tried Caleb Wilson,

1189
00:59:45,120 --> 00:59:48,000
Speaker 2: one of my closest friends, do a football podcast for him.

1190
00:59:48,040 --> 00:59:50,520
Speaker 2: I think if you tried to force Kayleb to hold

1191
00:59:50,520 --> 00:59:52,400
Speaker 2: an opinion that he didn't want, he would kill you.

1192
00:59:52,960 --> 00:59:55,480
Speaker 2: But that's what you want. But again, the Democrats would

1193
00:59:55,480 --> 00:59:57,800
Speaker 2: say that and be like whoa wlah, whohah, what do

1194
00:59:57,840 --> 00:59:59,040
Speaker 2: you mean you don't like John Fatirman.

1195
01:00:01,440 --> 01:00:04,120
Speaker 1: Yeah, they would find somewhere like this isn't this is

1196
01:00:04,160 --> 01:00:06,200
Speaker 1: too folks. He needs to be more scolding.

1197
01:00:06,280 --> 01:00:09,720
Speaker 2: They've got their purity politics except for like trans people.

1198
01:00:09,760 --> 01:00:13,360
Speaker 2: Then they're like, well, okay, we don't have beliefs around

1199
01:00:13,360 --> 01:00:13,960
Speaker 2: these parts.

1200
01:00:14,400 --> 01:00:15,120
Speaker 1: No, totally.

1201
01:00:19,960 --> 01:00:33,360
Speaker 4: Let's take a quick break at our back.

1202
01:00:39,040 --> 01:00:41,760
Speaker 1: I have a question you, So am I mistaken. You're

1203
01:00:41,840 --> 01:00:45,160
Speaker 1: like a sports guy, right, like you're into sports. Okay,

1204
01:00:45,200 --> 01:00:46,960
Speaker 1: I have a story that I want to ask you about.

1205
01:00:47,200 --> 01:00:51,960
Speaker 1: Do you know much about FanDuel I'm familiar? Okay, So

1206
01:00:52,080 --> 01:00:54,000
Speaker 1: I saw this story last week, and I don't know

1207
01:00:54,080 --> 01:00:56,160
Speaker 1: much about sports. Actually, this is the only sports that

1208
01:00:56,160 --> 01:00:58,280
Speaker 1: I know anything about, which is tracked and ran track

1209
01:00:58,320 --> 01:01:01,720
Speaker 1: in high school. But Gabby Thomas, she's this Harvard educated

1210
01:01:01,800 --> 01:01:04,320
Speaker 1: black track olympian. She won a gold medals in twenty

1211
01:01:04,360 --> 01:01:07,040
Speaker 1: twenty four during the Summer Olympics. She is awesome, we

1212
01:01:07,120 --> 01:01:12,400
Speaker 1: love her, but she experienced this like very scary hostile

1213
01:01:12,480 --> 01:01:17,080
Speaker 1: behavior from somebody who basically was trying to, according to them,

1214
01:01:17,800 --> 01:01:20,480
Speaker 1: throw her like have like get in her head to

1215
01:01:20,520 --> 01:01:23,560
Speaker 1: the point where she was not able to compete. So

1216
01:01:23,720 --> 01:01:27,480
Speaker 1: the exactly so last week she posted on X that

1217
01:01:27,520 --> 01:01:29,640
Speaker 1: a man followed her around the track and took pictures

1218
01:01:29,640 --> 01:01:32,480
Speaker 1: as she signed autographs for fans for mostly children, and

1219
01:01:32,720 --> 01:01:36,959
Speaker 1: shouted racist personal insults at her. So this dummy who

1220
01:01:37,000 --> 01:01:40,160
Speaker 1: did this also went on X and bragged, Yeah, I

1221
01:01:40,240 --> 01:01:42,400
Speaker 1: made Gabby Luz by heckling her and then it made

1222
01:01:42,440 --> 01:01:45,480
Speaker 1: my parlay win, and then posted a screenshot from the

1223
01:01:45,480 --> 01:01:48,440
Speaker 1: betting platform fan Duel. So when I saw this, I

1224
01:01:48,520 --> 01:01:51,440
Speaker 1: was like, well, I'm sure the betting platform would probably

1225
01:01:51,440 --> 01:01:53,920
Speaker 1: be very interested to know that one of their betters is,

1226
01:01:54,360 --> 01:01:56,720
Speaker 1: in his own words, like trying to influence the sport

1227
01:01:56,760 --> 01:02:00,200
Speaker 1: that he's gambling on. And Vanduel found him, boot him

1228
01:02:00,240 --> 01:02:02,200
Speaker 1: from the platform, and it was like, he's never going

1229
01:02:02,240 --> 01:02:05,240
Speaker 1: to be able to be on this platform again, they

1230
01:02:05,280 --> 01:02:08,920
Speaker 1: said FanDuel condemns in the strongest terms, abusive behavior directed

1231
01:02:08,960 --> 01:02:12,040
Speaker 1: toward athletes, threatening or harassing athletes is unacceptable.

1232
01:02:12,200 --> 01:02:12,720
Speaker 2: That's good.

1233
01:02:13,000 --> 01:02:15,200
Speaker 1: It is good. So I have to say, like, I

1234
01:02:15,200 --> 01:02:17,840
Speaker 1: look this guy up on Twitter, even though he deleted

1235
01:02:17,840 --> 01:02:20,520
Speaker 1: the tweet that was bragging about harassing Gabby. This is

1236
01:02:20,520 --> 01:02:24,880
Speaker 1: somebody who like clearly is very interested in sports betting.

1237
01:02:25,160 --> 01:02:28,040
Speaker 1: And I found this very alarming stat from ESPN. A

1238
01:02:28,080 --> 01:02:31,320
Speaker 1: study commission last year by the NCAA found that abuse

1239
01:02:31,400 --> 01:02:33,880
Speaker 1: by angry sports betters is one of the most common

1240
01:02:33,920 --> 01:02:37,160
Speaker 1: types of harassment college athletes received, making up at least

1241
01:02:37,160 --> 01:02:40,960
Speaker 1: twelve percent of publicly posted social media abuse. And I

1242
01:02:41,000 --> 01:02:42,920
Speaker 1: don't know it just really I don't think I had

1243
01:02:42,960 --> 01:02:45,920
Speaker 1: really checked in on how the rise and normalization of

1244
01:02:45,920 --> 01:02:50,640
Speaker 1: online spets online sports betting has like the way that

1245
01:02:50,640 --> 01:02:53,520
Speaker 1: that has kind of like shaped our online discourse, if

1246
01:02:53,520 --> 01:02:55,200
Speaker 1: it is in fact is linked to a rise on

1247
01:02:55,200 --> 01:02:57,440
Speaker 1: online harassment of athletes, like, I found that to be

1248
01:02:57,560 --> 01:02:59,840
Speaker 1: like actually genuinely kind of alarming.

1249
01:03:00,360 --> 01:03:03,720
Speaker 2: So Arif Hassan, who is the other co host of

1250
01:03:03,760 --> 01:03:06,160
Speaker 2: the sixty minute Trail the podcast Do with Western Kabooki's

1251
01:03:06,160 --> 01:03:08,919
Speaker 2: Caleb Wilson, did a great piece about this last year.

1252
01:03:09,080 --> 01:03:11,960
Speaker 2: I think all of the sport the sports gambling companies,

1253
01:03:11,960 --> 01:03:13,480
Speaker 2: I think all of them should be shut down. I

1254
01:03:13,480 --> 01:03:15,880
Speaker 2: think all of their executives should be put in prison forever.

1255
01:03:16,680 --> 01:03:18,520
Speaker 2: I think it is one of the most socially corrosive

1256
01:03:18,520 --> 01:03:20,200
Speaker 2: creations of all the time. I think. I live in

1257
01:03:20,280 --> 01:03:23,400
Speaker 2: Las Vegas. I believe that sport gambling should be if

1258
01:03:23,440 --> 01:03:25,960
Speaker 2: it is allowed to exist, which is a moral question

1259
01:03:26,160 --> 01:03:30,920
Speaker 2: to itself, it should be so heavily regulated. The companies

1260
01:03:30,920 --> 01:03:34,880
Speaker 2: like fanjil should be criminalized. Instead, they are growing like wildfire.

1261
01:03:35,560 --> 01:03:37,480
Speaker 2: I think that man in question should be in jail

1262
01:03:37,560 --> 01:03:40,400
Speaker 2: for a long time. I think that, in fact, if

1263
01:03:40,400 --> 01:03:42,560
Speaker 2: we had any two teeth, he should be done for

1264
01:03:42,760 --> 01:03:45,960
Speaker 2: attempting some kind of tortious interference. He should be like,

1265
01:03:46,160 --> 01:03:48,440
Speaker 2: make up legal fucking thing and stop pulling him for

1266
01:03:48,640 --> 01:03:52,000
Speaker 2: criminal proceedings, because that's how you actually make an example

1267
01:03:52,040 --> 01:03:54,080
Speaker 2: of people. It's horrible is to say, but if you

1268
01:03:54,120 --> 01:03:56,280
Speaker 2: go and chase a black woman, try to do a

1269
01:03:56,400 --> 01:03:58,240
Speaker 2: job because you want to make a little money, you

1270
01:03:58,240 --> 01:04:00,400
Speaker 2: should have to. You should get your life. I've kind

1271
01:04:00,440 --> 01:04:02,880
Speaker 2: of ruined by the legal process. You should even if

1272
01:04:02,920 --> 01:04:05,160
Speaker 2: you even if you win, it should be there should

1273
01:04:05,200 --> 01:04:10,000
Speaker 2: be a social punishment for such actions, not only for

1274
01:04:10,080 --> 01:04:11,960
Speaker 2: doing it to a person, but for a person of

1275
01:04:12,000 --> 01:04:15,680
Speaker 2: color within any industry already kind of like fighting against

1276
01:04:15,680 --> 01:04:20,360
Speaker 2: the racial horrors of America. You should they should be

1277
01:04:20,600 --> 01:04:23,280
Speaker 2: so scary to do that that you shouldn't do it. Instead,

1278
01:04:23,720 --> 01:04:25,120
Speaker 2: the black woman is the one that has to be

1279
01:04:25,160 --> 01:04:27,440
Speaker 2: scared and the guy, Oh no, I can't bet on fangir,

1280
01:04:27,560 --> 01:04:30,720
Speaker 2: but you can bet on DraftKings. I bet you can

1281
01:04:30,800 --> 01:04:34,439
Speaker 2: just go into any fucking casino. But there's never there's

1282
01:04:34,440 --> 01:04:37,680
Speaker 2: never actual real consequences because and even I don't even

1283
01:04:37,720 --> 01:04:39,800
Speaker 2: even looking up the story briefly, I don't see much

1284
01:04:39,840 --> 01:04:43,760
Speaker 2: horror around this. This should be like, I guess what

1285
01:04:43,800 --> 01:04:47,600
Speaker 2: should be front page news right now, the Gestapo or

1286
01:04:47,640 --> 01:04:51,400
Speaker 2: the like. It's just horrifying every day and new horror. Yeah,

1287
01:04:51,480 --> 01:04:53,520
Speaker 2: I stand by the thing about the sport the sports

1288
01:04:53,640 --> 01:04:55,680
Speaker 2: betting companies being yelled.

1289
01:04:55,960 --> 01:04:59,840
Speaker 1: Well, I'm psych it is wild that rather than facing consequences,

1290
01:05:00,040 --> 01:05:03,040
Speaker 1: what I think is like just an obvious social negative,

1291
01:05:03,480 --> 01:05:05,880
Speaker 1: these companies have been able to amass more and more power,

1292
01:05:05,880 --> 01:05:09,880
Speaker 1: They become more ubiquitous, and they're all over college campuses.

1293
01:05:09,920 --> 01:05:11,600
Speaker 1: And I don't have any data around this, but I

1294
01:05:11,640 --> 01:05:14,000
Speaker 1: can only imagine that the consequences for young men and

1295
01:05:14,040 --> 01:05:15,960
Speaker 1: boys is like not great, Well.

1296
01:05:15,840 --> 01:05:19,040
Speaker 2: Think about it. That's the other thing with the whole oh,

1297
01:05:19,080 --> 01:05:21,520
Speaker 2: what we're doing with young men. It's like, I don't know,

1298
01:05:21,920 --> 01:05:25,600
Speaker 2: maybe we shouldn't have legalized sports gambling and made it

1299
01:05:25,680 --> 01:05:29,280
Speaker 2: hyper masculine and also as the right wing sunk as

1300
01:05:29,360 --> 01:05:33,080
Speaker 2: much money as possible into big strong men looking strong

1301
01:05:33,120 --> 01:05:35,600
Speaker 2: and blaming women and people of color for everything, not

1302
01:05:35,760 --> 01:05:37,800
Speaker 2: spend money on everything and just sit back and go

1303
01:05:37,840 --> 01:05:42,640
Speaker 2: and isn't the world nice? I don't know the whole

1304
01:05:42,760 --> 01:05:44,800
Speaker 2: man thing as well, is really for and this is

1305
01:05:44,800 --> 01:05:47,840
Speaker 2: a very male dominated. The sports gambling thing is male dominated.

1306
01:05:47,880 --> 01:05:50,560
Speaker 2: You've got people that do YouTube channels about how you

1307
01:05:50,560 --> 01:05:52,960
Speaker 2: can scam your way through parlays. It's just like you

1308
01:05:53,040 --> 01:05:57,520
Speaker 2: have this media cluster around it that's growing as well.

1309
01:05:57,920 --> 01:06:00,520
Speaker 2: It's just proof that a lot of modern bisiness doesn't

1310
01:06:00,560 --> 01:06:03,520
Speaker 2: have any morals at all, and we don't have governments

1311
01:06:03,600 --> 01:06:06,440
Speaker 2: that believe, thanks to Ronald Reagan and his ilk that

1312
01:06:06,480 --> 01:06:09,120
Speaker 2: we don't have like regulation or things that would stop

1313
01:06:09,160 --> 01:06:11,760
Speaker 2: businesses from growing rapaciously. And I mean, my whole bit

1314
01:06:11,880 --> 01:06:14,760
Speaker 2: is that growth is behind every fucking problem. We have

1315
01:06:15,600 --> 01:06:18,800
Speaker 2: the desperation for growth, and everything is fucking up everything,

1316
01:06:19,760 --> 01:06:21,760
Speaker 2: and sports gambling is it too. But it goes to

1317
01:06:21,880 --> 01:06:24,160
Speaker 2: Joe Rogan of the Left thing, it's like they grew

1318
01:06:24,200 --> 01:06:28,280
Speaker 2: into an opportunity. I don't think that. I think it's

1319
01:06:28,280 --> 01:06:31,520
Speaker 2: an easy way out to blame right wing podcasts for

1320
01:06:31,560 --> 01:06:34,120
Speaker 2: turning men against the Democrats when the Democrats just offered

1321
01:06:34,160 --> 01:06:38,120
Speaker 2: nothing to anyone ever, and in fact through everyone under

1322
01:06:38,160 --> 01:06:40,440
Speaker 2: the bus. And now they're throwing young men under the bus,

1323
01:06:40,520 --> 01:06:43,439
Speaker 2: which I mean, you can do that. Maybe it's right.

1324
01:06:44,120 --> 01:06:45,960
Speaker 2: Oh no, but you're not. You don't seem to be

1325
01:06:45,960 --> 01:06:51,000
Speaker 2: trying to appeal to any voter right now, just going like, ah,

1326
01:06:49,840 --> 01:06:53,560
Speaker 2: what did you see that? That's terrible. Someone should do something.

1327
01:06:53,600 --> 01:06:56,640
Speaker 2: You're a senator, I know, but what could I possibly do?

1328
01:06:58,280 --> 01:07:03,360
Speaker 2: I attend a ten sentate hearing's fuck, no, I got nothing.

1329
01:07:03,560 --> 01:07:07,000
Speaker 2: I'm not busy. I just don't want to go strongly

1330
01:07:07,080 --> 01:07:11,520
Speaker 2: worded letter. You know, it's the tool in their toolbox. Yes,

1331
01:07:12,480 --> 01:07:17,000
Speaker 2: you know, right wing people love to read killing me.

1332
01:07:18,200 --> 01:07:23,000
Speaker 1: Oh ed, this is honestly talking to you about these stories,

1333
01:07:23,280 --> 01:07:25,080
Speaker 1: is I don't know. This is a different kind of

1334
01:07:25,080 --> 01:07:28,479
Speaker 1: episodes than you usually do. Because I feel like I no,

1335
01:07:28,640 --> 01:07:32,040
Speaker 1: I do not apologize. This is exactly why I wanted

1336
01:07:32,080 --> 01:07:33,720
Speaker 1: to have you on. And one of the reasons I

1337
01:07:33,720 --> 01:07:37,080
Speaker 1: wanted to have you on is literally, whenever AI comes up,

1338
01:07:37,520 --> 01:07:40,400
Speaker 1: even tangentially in any episode we do, someone in the

1339
01:07:40,400 --> 01:07:42,880
Speaker 1: Spotify comments will be like, you should talk to Ed.

1340
01:07:43,080 --> 01:07:46,160
Speaker 1: Like this is like a hand like there are a

1341
01:07:46,200 --> 01:07:48,600
Speaker 1: handful of people who are like, whenever anything comes up,

1342
01:07:48,640 --> 01:07:52,040
Speaker 1: like listen to Ed, talk to Ed. So thank you for.

1343
01:07:51,600 --> 01:07:55,280
Speaker 2: For and also to those listeners. Bridget supported bat off

1344
01:07:55,280 --> 01:07:58,440
Speaker 2: line from the literal beginning, not just should talk to it.

1345
01:07:58,520 --> 01:08:02,160
Speaker 2: Bridget has been that very early. She deserves tons of credit.

1346
01:08:02,240 --> 01:08:04,920
Speaker 2: Like it's not like she has been behind, She's been ahead.

1347
01:08:05,280 --> 01:08:08,680
Speaker 1: Oh well, you know, I I remember when you when

1348
01:08:08,800 --> 01:08:10,200
Speaker 1: y'all and out you were doing the show. I was

1349
01:08:10,560 --> 01:08:13,800
Speaker 1: genuinely so excited exactly because of what you were just

1350
01:08:13,840 --> 01:08:16,439
Speaker 1: talking about. How especially in the podcast space, there are

1351
01:08:16,479 --> 01:08:19,599
Speaker 1: just so few people who are having these conversations who

1352
01:08:19,640 --> 01:08:23,080
Speaker 1: really want to kind of shed some light on all

1353
01:08:23,160 --> 01:08:27,599
Speaker 1: the ego and lies and bullshit and bullshit and scams

1354
01:08:27,800 --> 01:08:30,160
Speaker 1: that I feel like has become so normalized in the space.

1355
01:08:30,200 --> 01:08:33,719
Speaker 1: They're They're like, I could probably count on two hands

1356
01:08:33,760 --> 01:08:35,560
Speaker 1: how many voices I think are out there in the

1357
01:08:35,920 --> 01:08:38,080
Speaker 1: podcast space, specifically who are doing that, and there needs

1358
01:08:38,080 --> 01:08:38,519
Speaker 1: to be more.

1359
01:08:39,040 --> 01:08:41,880
Speaker 2: And also I don't think enough of them love technology,

1360
01:08:41,920 --> 01:08:44,080
Speaker 2: because that's the fundamental thing. I'm pissed off because they

1361
01:08:44,120 --> 01:08:46,920
Speaker 2: took something from us. I'm pissed off because I like

1362
01:08:47,000 --> 01:08:48,760
Speaker 2: the computer and I love to pay like you. The

1363
01:08:49,080 --> 01:08:51,120
Speaker 2: whole cool so media thing is the result of the Internet.

1364
01:08:51,160 --> 01:08:52,840
Speaker 2: My whole following is the Internet. My life's the Internet.

1365
01:08:52,840 --> 01:08:55,719
Speaker 2: And the drill crying tweet if you've seen that one, yeah,

1366
01:08:56,000 --> 01:08:58,520
Speaker 2: now get the fuck out of my office like it's

1367
01:08:59,080 --> 01:09:01,040
Speaker 2: And I think that that is missing from a lot

1368
01:09:01,080 --> 01:09:04,559
Speaker 2: of tech cynics as well. It's just that they want

1369
01:09:04,600 --> 01:09:07,040
Speaker 2: something to be mad at, rather than they're mad that

1370
01:09:07,080 --> 01:09:09,679
Speaker 2: something was taken. Also, if all this money and power

1371
01:09:09,720 --> 01:09:12,280
Speaker 2: went somewhere good, imagine how much better the world could be.

1372
01:09:12,479 --> 01:09:16,240
Speaker 2: I don't even mean that in the vague, vacuous, nonprofit

1373
01:09:16,800 --> 01:09:20,000
Speaker 2: flavored Oh the world be good. It'd be more fun,

1374
01:09:20,000 --> 01:09:22,879
Speaker 2: they'd be more exciting things. We could have more solidary,

1375
01:09:22,920 --> 01:09:24,439
Speaker 2: we could love each other more. They could be more

1376
01:09:24,439 --> 01:09:27,120
Speaker 2: interesting and fun, art made. They could be more support

1377
01:09:27,200 --> 01:09:32,160
Speaker 2: for social issues instead it's just this fucking smudge on

1378
01:09:32,200 --> 01:09:35,160
Speaker 2: the world right now when nothing's happening, hundreds of billions

1379
01:09:35,200 --> 01:09:38,200
Speaker 2: of dollars going into fucking nothing. It's depressing, but it

1380
01:09:38,240 --> 01:09:40,880
Speaker 2: can be better, and it gets better by taking their names.

1381
01:09:41,520 --> 01:09:44,280
Speaker 2: Don't let them, don't let make Google Search hate them.

1382
01:09:44,400 --> 01:09:46,479
Speaker 2: I fucked that part of Google Search. That's one of

1383
01:09:46,560 --> 01:09:47,519
Speaker 2: my proudest moments.

1384
01:09:47,880 --> 01:09:49,680
Speaker 1: How th tell me so?

1385
01:09:49,720 --> 01:09:52,040
Speaker 2: I did an episode last year called the Man who

1386
01:09:52,120 --> 01:09:54,479
Speaker 2: Killed Google Search or the Man that destroyed Google Search.

1387
01:09:55,080 --> 01:09:57,439
Speaker 2: And there's a guy called Propagard Ragavan. I found his

1388
01:09:57,520 --> 01:10:02,000
Speaker 2: details in the Department of Justice Anti Antitrust trial. They

1389
01:10:02,000 --> 01:10:04,160
Speaker 2: had all these emails and I basically found the story

1390
01:10:04,160 --> 01:10:05,519
Speaker 2: of this guy who was the head of ads called

1391
01:10:05,520 --> 01:10:08,799
Speaker 2: probably gol Ragavan, who wrapped fucked this guy Ben Gomes

1392
01:10:09,040 --> 01:10:12,080
Speaker 2: pushed him out, and literally the emails are like Ben

1393
01:10:12,120 --> 01:10:14,840
Speaker 2: Gomes or Shashi Fakher. I think one of the engineers goes.

1394
01:10:15,080 --> 01:10:17,320
Speaker 2: I'm just worried that all Google cares about his growth,

1395
01:10:17,640 --> 01:10:19,559
Speaker 2: to the point that when I read that, I had

1396
01:10:19,560 --> 01:10:21,240
Speaker 2: to go and check the u URL to make sure

1397
01:10:21,280 --> 01:10:25,000
Speaker 2: I was not being pranked. But it was my biggest story.

1398
01:10:25,080 --> 01:10:27,799
Speaker 2: And now Proba Ragavan has been given a technical title.

1399
01:10:27,880 --> 01:10:30,799
Speaker 2: He's like the chief technologist. He was the head of everything,

1400
01:10:31,000 --> 01:10:34,840
Speaker 2: and that he's the chief technologist. They put him out back,

1401
01:10:35,120 --> 01:10:36,800
Speaker 2: they put him on, they put him in an office

1402
01:10:36,840 --> 01:10:38,200
Speaker 2: without a door handle inside.

1403
01:10:38,800 --> 01:10:39,799
Speaker 1: He's still in that office.

1404
01:10:40,120 --> 01:10:42,280
Speaker 2: Yeah, eight hours a day. All right, Propa guy, you

1405
01:10:42,280 --> 01:10:45,320
Speaker 2: can leave now you're stock still vesting, mate, but don't

1406
01:10:45,320 --> 01:10:48,120
Speaker 2: talk to anyone. Don't plug in your computer. Piece of shit.

1407
01:10:48,920 --> 01:10:51,000
Speaker 2: I'd love to meet that guy. But he hates me

1408
01:10:51,439 --> 01:10:52,080
Speaker 2: and he's hard.

1409
01:10:52,360 --> 01:10:54,760
Speaker 1: That's incredible. I mean you said something. I mean, I

1410
01:10:55,560 --> 01:10:58,160
Speaker 1: like you. I love technology. The reason why I make

1411
01:10:58,200 --> 01:11:00,960
Speaker 1: a tech podcast is because I I love tech. I

1412
01:11:01,000 --> 01:11:03,920
Speaker 1: want it to be better. I want technology to be

1413
01:11:04,000 --> 01:11:07,519
Speaker 1: something that feels good and free again, Like I like,

1414
01:11:07,800 --> 01:11:11,040
Speaker 1: genuinely remember how it felt to stumble upon weird corners

1415
01:11:11,040 --> 01:11:13,000
Speaker 1: of the Internet that you could just tell were labors

1416
01:11:13,000 --> 01:11:16,000
Speaker 1: of love, like weird flash sites and shit like that.

1417
01:11:16,280 --> 01:11:19,120
Speaker 1: And I mean, you can really tell when someone who

1418
01:11:19,200 --> 01:11:21,320
Speaker 1: is talking about the Internet, who does so for a living,

1419
01:11:21,760 --> 01:11:25,479
Speaker 1: doesn't come from a place of loving tech and like

1420
01:11:25,560 --> 01:11:29,040
Speaker 1: being crazy about computers and crazy. Yeah, I do think

1421
01:11:29,080 --> 01:11:31,960
Speaker 1: it's like cranks who hate everything. They don't actually want

1422
01:11:32,000 --> 01:11:33,840
Speaker 1: it to be good. They want something to complain about,

1423
01:11:34,200 --> 01:11:35,360
Speaker 1: or or.

1424
01:11:36,280 --> 01:11:39,559
Speaker 2: They don't love technology, they love the tech industry. It's

1425
01:11:39,560 --> 01:11:42,880
Speaker 2: is a sick position that is gossip journalist, and I

1426
01:11:42,920 --> 01:11:46,240
Speaker 2: think that's the majority of them. It's scary and I'd

1427
01:11:46,240 --> 01:11:48,000
Speaker 2: love to be proven wrong well ed.

1428
01:11:48,400 --> 01:11:52,040
Speaker 1: Where can folks listen to better offline? Where can folks

1429
01:11:52,040 --> 01:11:52,920
Speaker 1: read the newsletter?

1430
01:11:53,479 --> 01:11:56,920
Speaker 2: Betrofline dot com. It's I bought that website for a reason.

1431
01:11:57,439 --> 01:11:59,679
Speaker 2: Betrofline dot com. You can find all my crap there.

1432
01:12:00,040 --> 01:12:02,240
Speaker 2: You click the links, you're find my profile, you find

1433
01:12:02,320 --> 01:12:05,840
Speaker 2: my everything, and that you has the big skull on it.

1434
01:12:05,880 --> 01:12:07,320
Speaker 2: That's how you know you're in the right place. And

1435
01:12:07,320 --> 01:12:09,200
Speaker 2: we're selling merch now, which I'm so happy with.

1436
01:12:09,320 --> 01:12:10,920
Speaker 1: I was just gonna say the merch is good.

1437
01:12:11,320 --> 01:12:14,720
Speaker 2: It's banging. It's so good. I don't the only reason

1438
01:12:14,880 --> 01:12:16,920
Speaker 2: I'm in Las Vegas, it's really hort otherwise be wearing

1439
01:12:16,920 --> 01:12:19,200
Speaker 2: my hoodie like it's fucking like the Zip Hop hoodies

1440
01:12:19,240 --> 01:12:21,040
Speaker 2: are fucking banging. I love it. I love that we

1441
01:12:21,080 --> 01:12:22,640
Speaker 2: have good merch. I wouldn't accept it. Were gonna do

1442
01:12:22,760 --> 01:12:23,760
Speaker 2: challenge coins?

1443
01:12:25,280 --> 01:12:27,759
Speaker 1: Yeah, is that your cat in the background, by the way.

1444
01:12:27,640 --> 01:12:30,000
Speaker 2: Yes, been trying to keep him from jumping up. It

1445
01:12:30,040 --> 01:12:32,240
Speaker 2: pisses me off. When I'm trying to do stuff, people

1446
01:12:32,280 --> 01:12:33,800
Speaker 2: are like, oh I cat, I'm like, get the fuck

1447
01:12:33,880 --> 01:12:36,479
Speaker 2: out of my camera. God God, I love him daily,

1448
01:12:36,520 --> 01:12:37,599
Speaker 2: but he's a pain in the ask.

1449
01:12:37,800 --> 01:12:41,960
Speaker 1: Wait, what is his name? Howe Howell? While Ed and Howell,

1450
01:12:42,160 --> 01:12:44,479
Speaker 1: thank you both for being here. Thank you for all

1451
01:12:44,520 --> 01:12:47,040
Speaker 1: of your work. If you want to follow me around

1452
01:12:47,080 --> 01:12:50,360
Speaker 1: the internet, I'm on TikTok at Bridget Murrie, I'm on

1453
01:12:50,560 --> 01:12:52,519
Speaker 1: Instagram at Bridget ren DC, and we're on YouTube. But

1454
01:12:52,520 --> 01:12:54,360
Speaker 1: there are No Girls on the Internet. Thank you much

1455
01:12:54,400 --> 01:12:55,920
Speaker 1: so much for being her Ed. We will see you

1456
01:12:55,960 --> 01:12:56,599
Speaker 1: on the internet.

1457
01:12:56,720 --> 01:12:57,080
Speaker 2: Thank you.

1458
01:13:06,439 --> 01:13:08,559
Speaker 1: If you're looking for ways to support the show, check

1459
01:13:08,560 --> 01:13:11,200
Speaker 1: out our merch store at tangody dot com slash store.

1460
01:13:12,400 --> 01:13:14,400
Speaker 1: Got a story about an interesting thing in tech, or

1461
01:13:14,520 --> 01:13:16,360
Speaker 1: just want to say hi, You can reach us at

1462
01:13:16,360 --> 01:13:19,080
Speaker 1: Hello at teangody dot com. You can also find transcripts

1463
01:13:19,080 --> 01:13:21,519
Speaker 1: for today's episode at TENG Goody dot com. There Are

1464
01:13:21,520 --> 01:13:23,479
Speaker 1: No Girls on the Internet was created by me Bridget

1465
01:13:23,479 --> 01:13:27,120
Speaker 1: tod It's a production of iHeartRadio and Unboss Creative, edited

1466
01:13:27,120 --> 01:13:30,920
Speaker 1: by Joey pat Jonathan Strickland as our executive producer. Tari

1467
01:13:31,000 --> 01:13:34,080
Speaker 1: Harrison is our producer and sound engineer. Michael Almado is

1468
01:13:34,120 --> 01:13:37,120
Speaker 1: our contributing producer. I'm your host, Bridget Todd. If you

1469
01:13:37,120 --> 01:13:38,840
Speaker 1: want to help us grow, rate and review us.

1470
01:13:38,760 --> 01:13:39,720
Speaker 4: On Apple Podcasts.

1471
01:13:40,479 --> 01:13:43,320
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1472
01:13:43,320 --> 01:13:49,680
Speaker 1: Apple Podcasts, or wherever you get your podcasts