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Speaker 1: It's sponsorship time. But you know what, it's really great

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Speaker 1: when you get a sponsor that you already use. And

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Speaker 1: guess what. Quint's is something that in the Todd household

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Speaker 1: we already go to. Why do we go to Quint's

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Speaker 1: Because it's a place you go where you can get

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Speaker 1: some really nice clothes without the really expensive prices. And

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Speaker 1: one of the things I've been going through is I've

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Speaker 1: transitioned from being mister cot and ty guy to wanting

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Speaker 1: a little more casual but to look nice doing it.

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Speaker 1: Is I've become mister quarter zip guy. Well guess what.

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Speaker 1: Guess he's got amazing amounts of quarter zips. It is Quints.

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Speaker 1: I have gotten quite a few already from there. The

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Speaker 1: stuff's really nice. They have Mongolian cashmere sweaters for fifty dollars.

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Speaker 1: I just know, hey, cashmere, that's pretty good. You don't

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Speaker 1: normally get that for fifty bucks or less. Italian wool

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Speaker 1: coats that look and feel like designer the stuff. I'll

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Speaker 1: be honest, right, you look at it online, you think, okay,

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Speaker 1: is this really as nice as it looks? Well, when

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Speaker 1: I got it, I was like, oh, this is real quality.

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Speaker 1: So yeah, I'm going to end up making sure I

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Speaker 1: take it to my dry cleaner so I don't screw

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Speaker 1: sixty five day returns quints dot com slash chuck. Use

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Speaker 1: that code. Hello, They're happy Thursday and welcome to another

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Speaker 1: episode of the Chuck Podcast. Our third episode of the week,

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Speaker 1: which means this is our big weekend episode of a

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Speaker 1: terrific guest on a topic that I think is getting

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Speaker 1: more and more attention. And conversation and it's about screen

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Speaker 1: time and our kids. Interviewing Julie shell Foe. She is

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Speaker 1: the founder, former journalist, but the founder of an organization

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Speaker 1: called Mama Mothers Against Media Addiction. It is modeled after

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Speaker 1: Mad Mothers Against Drunk Driving and it is simply that

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Speaker 1: just trying to focus on getting screens out of schools,

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Speaker 1: not just you know, cell phone bands and schools, which

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Speaker 1: of course is you know, when you think about how

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Speaker 1: hard it is to get our two parties to agree

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Speaker 1: on anything about the one thing this year where there

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Speaker 1: has been bipartisan agreement on a policy idea, it has

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Speaker 1: been cell phone bands and schools, and the early metrics

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Speaker 1: in the places that implemented it first, you're already seeing

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Speaker 1: at least some evidence of teachers saying students are paying

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Speaker 1: more attention and test scores being up, and you know,

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Speaker 1: we'll see again it's it's it's a it's a small

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Speaker 1: sample size of the of the early adopters of this,

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Speaker 1: but it goes further than just phones and schools. As

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Speaker 1: I said, you know, this has to do with iPads

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Speaker 1: and you know, using devices and you know, what's the line.

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Speaker 1: And in this case, Julie makes a pretty uh, a

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Speaker 1: pretty credible case as to why even using screens, you know,

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Speaker 1: using school iPads, that there's got to be an age

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Speaker 1: where we don't do it until, you know, maybe it's,

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Speaker 1: you know, ninth grade, maybe it you know, you don't

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Speaker 1: get your first device as a teaching tool until eighth

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Speaker 1: or ninth grade, something like that. But to at least

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Speaker 1: have some sort of marker here, we've seen Ram Emmanuel

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Speaker 1: became the first major potential presidential candidate in twenty twenty

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Speaker 1: eight to call for a you know, a nationwide band,

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Speaker 1: which is what of course Australia is trying to implement,

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Speaker 1: I believe in just the last couple of days. So

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Speaker 1: it is the relationship with tech and our lives in

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Speaker 1: many ways I think is going to continue to be.

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Speaker 1: I mean, look, the economy is obviously first and foremost

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Speaker 1: in any campaign year. Healthcare is always a part of that.

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Speaker 1: But after those two issues, the impact of tech on

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Speaker 1: all of our lives, whether it's screen time and our kids,

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Speaker 1: whether it's AI and the and the fear of AI

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Speaker 1: job displacement, the the you know, the impact of Silicon

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Speaker 1: Valley and big tech in our lives is something it's

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Speaker 1: clear more Americans want more control over this. There's this

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Speaker 1: feeling that you don't you know when you the reason

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Speaker 1: I think you're seeing this growing bipartisan sort of revolt

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Speaker 1: and pushback. I mean, even look at the pushback that's

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Speaker 1: taking place inside the Republican Party where Donald Trump is

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Speaker 1: saying he wants to sign an executive order that prevents

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Speaker 1: any state from trying to put regulation on the AI industry.

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Speaker 1: And immediately, you know, they tried to put it in

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Speaker 1: the government. You know, they try to get it in

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Speaker 1: the bill to reopen the government, and there wasn't the

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Speaker 1: votes for that, and there was a total revolt on that.

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Speaker 1: But even on this, I mean, you have Ron Dea Santis,

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Speaker 1: the governor of Florida, unveiled He's already unveiled his own

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Speaker 1: what he calls here, I'm reading it here a citizen's

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Speaker 1: Bill of Rights for artificial intelligence. And the proposals he's

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Speaker 1: presenting are the types of things that a lot of

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Speaker 1: lawmakers left and right would like to be talking about,

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Speaker 1: including protecting consumers from from anxiety inducing things like deep fakes,

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Speaker 1: data collections, chat box, it's engaging with minors, chatbots posing

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Speaker 1: as mental health experts. You know how to deal with

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Speaker 1: fraudulent use of our names and likeness. You have the

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Speaker 1: revolt of people thinking that these data centers are the

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Speaker 1: reasons why our electricity bills are spiking all over the country.

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Speaker 1: There certainly appears to be a direct correlation, for instance,

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Speaker 1: in the state I live in Virginia. So it is

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Speaker 1: you know, I think it is worth noting how much

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Speaker 1: this is more than just you know, in some ways,

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Speaker 1: sometimes I bring it up as very singular issues, you know,

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Speaker 1: when it comes to well, what are we going to

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Speaker 1: do about AI job displacement, what are we going to

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Speaker 1: do about these data centers? Or what are we going

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Speaker 1: to do about you know, kids in schools. There is

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Speaker 1: a larger sort of tech in our lives, right, and

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Speaker 1: there is a growing feeling that we have less and

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Speaker 1: less choice of how do we how tech interacts with us.

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Speaker 1: And I think, you know, that's it's almost a universal

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Speaker 1: sort of political complaint, right. You will get a revolt

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Speaker 1: of the masses when there is a feeling that control

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Speaker 1: that they used to have they no longer have, right,

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Speaker 1: control that we all used to have. We feel as

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Speaker 1: if we no longer have I mean, you know, I

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Speaker 1: go back take the there's a there's a there's a

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Speaker 1: movement to try to sort of flip the script on

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Speaker 1: the Internet. Right, instead of us having to agree to

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Speaker 1: terms and conditions to use apps that big tech asks

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Speaker 1: us to click yes on that, it's the other way

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Speaker 1: around that when they want access to our data, they

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Speaker 1: have to ask us permission. Right. It's a little bit

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Speaker 1: what the Europeans have been trying to do and their

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Speaker 1: ability to regulate interactions between social media or AI and

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Speaker 1: the general public. But this is, you know, none of

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Speaker 1: this is unpopular, right, And it is interesting to watch

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Speaker 1: the Trump administration wanting to barrel through. They've gone they

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Speaker 1: have sort of are they they want government and AI

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Speaker 1: to be fused together. They bought they accept the premise

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Speaker 1: that America has to win the AI race. Right. I

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Speaker 1: think a lot of the AI tech founders have convinced

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Speaker 1: the Trump administration that if they attempt any restrictions, China

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Speaker 1: is going to win the I race. And it's it's

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Speaker 1: like it's the plot of the most recent Mission Impossible. Right,

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Speaker 1: whoever gets to the finish line first suddenly controls the globe.

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Speaker 1: Right is sort of the fear I think that has

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Speaker 1: been presented to the Trump administration and may explain why

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Speaker 1: they're going along with this idea. And of course I

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Speaker 1: think there's a reason there's a lot of us that

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Speaker 1: are very skeptical of just racing down this road. You know,

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Speaker 1: if you like the way the tech companies introduce social

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Speaker 1: media into society, then by all means, let them go

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Speaker 1: unrestricted introducing artificial intelligence into society. But if we look

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Speaker 1: at what social media has done, and we look at

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Speaker 1: it as a failure, not a success. And that's the thing,

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Speaker 1: is social media been a success or a failure? If

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Speaker 1: in some ways there are successful elements to it, there

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Speaker 1: are plenty of people that have made money off of it.

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Speaker 1: It has certainly allowed more access to influence and attention

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Speaker 1: from around the world, but it is destroyed truth. Right.

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Speaker 1: Why do we have so much skepticism about truth where

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Speaker 1: we're having a debate about what is true and what

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Speaker 1: isn't could arguably blame the algorithms and social media. And

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Speaker 1: so to me, it's not surprising that you have an

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Speaker 1: electorate that it won't take much to fire up against

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Speaker 1: the forces of Silicon Valley, because when you start to

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Speaker 1: say do you want to have artificial intelligence introduced into

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Speaker 1: society in a similar way that social media was. Or

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Speaker 1: do you want more guard rails? I promise you you

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Speaker 1: probably get seventy to eighty percent supporting that. No, there

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Speaker 1: better be more guard rails as we introduce artificial intelligence

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Speaker 1: into society than there was during social media. And so

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Speaker 1: you put all this together and you can start to

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Speaker 1: see and I still think this is not going you know,

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Speaker 1: I think these will be debate talking points in twenty

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Speaker 1: twenty six. I think you will certainly see various candidates

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Speaker 1: test driving some of these things. A citizen bill of

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Speaker 1: rights on when it comes to the implementation of artificial

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Speaker 1: intelligence will certainly, I think pop up in governor's races

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Speaker 1: in particular, and this cycle will have is the big

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Speaker 1: cycle for governor's races. I do think it'll be really

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Speaker 1: the presidential campaign that'll make this more of a national issue, right,

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Speaker 1: because that's when you're gonna you know, there'll be when

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Speaker 1: it comes to the job displacement issue, I think that

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Speaker 1: is what will feel more front center. And then of

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Speaker 1: course we want to see what is the state of

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Speaker 1: this economy. Right, this is an economy that is not

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Speaker 1: a good economy, but it is being essentially rescued right

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Speaker 1: now by AI investment. Right, you have a whole bunch

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Speaker 1: of you know, I talked to one smart observer of

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Speaker 1: the financial and a trained economist who said to me today,

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Speaker 1: actually that we're already in a recession. We just haven't

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Speaker 1: you know, the numbers just aren't out yet, and it won't.

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Speaker 1: You know, it looks like productivity is doing okay because

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Speaker 1: of just how much investment the sort of the six

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Speaker 1: or seven largest companies in the world are making into

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Speaker 1: artificial intelligence. It's essentially up and up America's GDP. It's

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Speaker 1: sort of propping all these things up. So it's masking

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Speaker 1: what is likely a recession for half the country. Right

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Speaker 1: there are parts of the you know, there's certainly the

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Speaker 1: one percent and those with some savings and some money

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Speaker 1: in the stock demartment. They are they are not experiencing

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Speaker 1: the recession. But for the for the other half of America,

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Speaker 1: they are experiencing recession. Right there. They have costs or

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Speaker 1: too much. Right, you have an affordability issue on health care,

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Speaker 1: and affordability issue and groceries, and affordability issue on housing.

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Speaker 1: These are things that you just can't skip. You can't

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Speaker 1: pinch pennies on right, you might be able to you

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Speaker 1: might be able to skip some things and try to

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Speaker 1: save money in certain places, it's not like you can

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Speaker 1: delay paying a health insurance premium and and risk to

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Speaker 1: go without health care. So this affordability crisis and this

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Speaker 1: recessionary feel that those that don't have that are paycheck

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Speaker 1: to paycheck, that don't have a bunch of savings in

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Speaker 1: the stock market is pretty acute, which transitions me pretty

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Speaker 1: well to what was, you know, another debacle by Trump

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Speaker 1: and this economy and his rally in Pennsylvania. Every time

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Speaker 1: this administration over the last couple of weeks, frankly, it's

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Speaker 1: been about two or three months that they try to

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Speaker 1: get you know, it's clear his own staff realizes they

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Speaker 1: have at least they have a political problem, if not

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Speaker 1: a real problem. Right, they know they have a political

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Speaker 1: problem messaging on the economy. And how do I know

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Speaker 1: that they all know? Because JD. Vance on Wednesday morning

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Speaker 1: basically was desperately trying to re explain Trump's comments from

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Speaker 1: Pennsylvania where he started calling it a hoax and then

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Speaker 1: he started, I mean to me, he went down this

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Speaker 1: road again where okay, well, you know, you know, your

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Speaker 1: little girl doesn't need thirty seven dollars, they can handle one.

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Speaker 1: Your child doesn't need fifty pencils, they can just have

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Speaker 1: one or two. It is such a tone deaf Let

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Speaker 1: them eat cake, mindset. Right, It's as if we have

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Speaker 1: President Marie Antoinette here, and it really rings as super

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Speaker 1: out of touch. And it's clear you of people like

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Speaker 1: Jade Vance realizing how bad this is. So he tweets

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Speaker 1: this morning, which is just the fun. I'm gonna read

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Speaker 1: his tweet, and then Joe Biden's insane policies left American

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Speaker 1: families unable to afford a decent living in their own country.

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Speaker 1: Through tax cuts, better paying jobs, and investment in American industry,

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Speaker 1: prisoner Trump is making America affordable again for working families,

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Speaker 1: one step at a time. Right. They're trying so hard

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Speaker 1: to explain this economy, but they're making the same mistake

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Speaker 1: that the Biden White House made, which is, hey, you

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Speaker 1: you feel like this economy and good, You're wrong. This

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Speaker 1: economy is doing We're doing really well, and it's healing,

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Speaker 1: and this is happening, and this is happening. You know.

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Speaker 1: In Biden's case, he kept leaning on, look at all

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Speaker 1: the new jobs that are being created, look at all

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Speaker 1: the money that's being invested. Well, in some ways, Trump

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Speaker 1: is trying to make the Trump and Vance are trying

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Speaker 1: to make the same case. Hey, you know, we're spending

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Speaker 1: all this money and we cut your taxes, and oh,

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Speaker 1: by the way, look at the stock market and look

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Speaker 1: at all these deals that we're doing, but pay no

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Speaker 1: attention to how tariffs are increasing your every day your

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Speaker 1: everyday bills. So the trumpet, the Trump White House is

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Speaker 1: walking into appears to be the same trap as Biden there,

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Speaker 1: you know, want to tell the public, don't don't believe

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Speaker 1: what you're feeling. That's essentially right. It was essentially the

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Speaker 1: message that Biden was communicating on the economy, that Harris

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Speaker 1: was communicating on the economy, until they realized now they

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Speaker 1: had to be they had to sort of do a

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Speaker 1: little I feel your pain. You know, Vance here trying

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Speaker 1: to do a little bit of I feel your pain.

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Speaker 1: But Trump wasn't going to go there under and if anything,

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Speaker 1: he sounded dismissive and defensive. And that's just you know,

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Speaker 1: this we've we've already seen in you know, another week,

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Speaker 1: another election that indicates that Republicans are troubled there's a

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Speaker 1: reason results matter more than promises, just like there's a

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Speaker 1: reason Morgan and Morgan is America's largest injury law firm.

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Speaker 1: For the last thirty five years, they've recovered twenty five

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Speaker 1: billion dollars for more than half a million clients. It

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Speaker 1: includes cases where insurance companies offered next to nothing, just

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Speaker 1: hoping to get away with paying as little as possible.

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Speaker 1: Morgan and Morgan fought back ended up winning millions. In fact,

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Speaker 1: in Pennsylvania, one client was awarded twenty six million dollars,

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Speaker 1: which was a staggering forty times the amount that the

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Speaker 1: insurance company originally offered. That original offer six hundred and

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Speaker 1: fifty thousand dollars twenty six million, six hundred and fifty

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Speaker 1: thousand dollars. So with more than a thousand lawyers across

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Speaker 1: the country, they know how to deliver for everyday people.

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Speaker 1: If you're injured, you need a lawyer. You need somebody

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Speaker 1: to get your back. Check out for the People dot com,

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Speaker 1: Slash podcast or Dow Pound Law Pound five to two

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Speaker 1: firms are not the same, So check out Morgan and Morgan.

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Speaker 1: Their fee is free unless they win special elections and

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Speaker 1: runoffs that took place on Tuesday. The most prominent race

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Speaker 1: was the City of Miami mayor. And for those of

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Speaker 1: you are wondering, Miami Dade County elects a county mayor.

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Speaker 1: That is a very powerful position. It's a strong mayor.

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Speaker 1: It's a huge budget. Miami Dade County's budget is bigger

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Speaker 1: than many states, you know, probably nearly half the states.

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Speaker 1: It is that large and complicated a county, the City

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Speaker 1: of Miami and the City of Miami mayor. City of

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Speaker 1: Miami is a is one of thirty plus incorporated cities

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Speaker 1: within Miami Dade County, but it does have the brand

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Speaker 1: City of Miami, so is the Mayor of Miami, you know,

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Speaker 1: when which is for those you know, there's the mayor

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Speaker 1: of Miami Dade County and then there's the mayor of

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Speaker 1: the City of Miami. So this was a runoff for

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Speaker 1: the mayor of the City of Miami. Now in the

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Speaker 1: City of Miami, it's it is there is a strong,

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Speaker 1: large Cuban American population, and you've had nearly a generation

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Speaker 1: of Cuban Republicans who have won that mayor's rate won

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Speaker 1: that office over and over by the way the office itself,

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Speaker 1: it's considered a weak mayor position. It's a part time job.

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Speaker 1: If you recall, the previous mayor of Francis Suarez had

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Speaker 1: all sorts of jobs, private sector jobs that he was

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Speaker 1: that he held while simultaneously being mayor. He got a

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Speaker 1: little brazen about it, so it gathered a lot more attention.

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Speaker 1: But it's considered a part time job, and it is

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Speaker 1: allowed to have other jobs. But the point is is

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Speaker 1: that that's how quote unquote small the job is. And it's

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Speaker 1: and like I said, it's a weak mare system, which

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Speaker 1: means you're just you've got the title, but you're essentially no.

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Speaker 1: No had the same amount of power as a city

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Speaker 1: council person that's elected. So but symbolically it's a big deal.

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Speaker 1: And it's the first time Democrats have won the mayor's

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Speaker 1: race in over thirty years. Now, it is worth noting

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Speaker 1: Kamala Harris actually carried the city of Miami even as

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Speaker 1: Donald Trump carried the county of Miami Dade, which in

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Speaker 1: itself was a surprising development. So but the margin that

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Speaker 1: the Democrat won this race and the fact that it

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Speaker 1: was the first time in thirty years, and oh, by

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Speaker 1: the way, there was a special election that no one

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Speaker 1: was paying attention to in a state house seat in

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Speaker 1: Georgia that flipped a twenty plus point Trump district that flipped.

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Speaker 1: We're starting it's the same pattern. We'sa on Tennessee seven

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Speaker 1: and that special same pattern we saw in twenty twenty five.

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Speaker 1: There's somewhere between a ten to fifteen to in some

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Speaker 1: places twenty point overperformance by Democrats. Now, why is this happening? One,

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Speaker 1: you have fired up Democratic voters. Two Democratic voters in general.

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Speaker 1: Are Democrats now have the more reliable voters the voters

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Speaker 1: you know we're divided on education. It's another way of saying,

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Speaker 1: you know, who's paying attention to the minutia more so?

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Speaker 1: And who's who the voters that parachute in for presidential elections. Well,

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Speaker 1: the Democrats now have the line's share of the voters

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Speaker 1: that are paying attention to all political minutia versus Republicans

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Speaker 1: are the ones that we'll see it turnout spike now

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Speaker 1: in presidential years. It's a total reverse of what it was.

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Speaker 1: What it was for the eighties, nineties and even the

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Speaker 1: first part of the twenty first century. But we have

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Speaker 1: now seen this is as as sort of Trump created

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Speaker 1: a realignment of the party the way he did. The

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Speaker 1: less reliable voter who used to be more likely voting

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Speaker 1: Democrat than Republican. Now that less likely voter more likely

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Speaker 1: votes Republican the Democrat. Which is why Democrats are overperforming

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Speaker 1: in all these special elections, all these non presidential elections,

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Speaker 1: whether it was the Supreme Court race in Wisconsin right

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Speaker 1: after Trump got elected, or it's what we just saw

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Speaker 1: earlier this week in the city of Miami or in

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Speaker 1: the state of Georgia, the everyday regular voter is just

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Speaker 1: showing up and that it is benefiting Democrats. Then you

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Speaker 1: throw in what is a sort of a growing demoralized

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Speaker 1: base of the Republican Party. Right, it's best expressed of

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Speaker 1: watching what's happening in the House, where you have more

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Speaker 1: and more Republicans getting cranky about not having any plan

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Speaker 1: on healthcare. Apparently Mike Johnson seemed to hint that, oh,

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Speaker 1: you know, we're going to have a healthcare plan and

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Speaker 1: really soon, and you're like, you are, you've had let's see,

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Speaker 1: when did Obamacare pass. Obamacare passed in March of twenty ten,

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Speaker 1: so they've had Republicans have had fifteen years to come

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Speaker 1: up with a healthcare plan alternative, and what they just

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Speaker 1: in another week they'll finally have one. Let's just say,

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Speaker 1: there's quite a bit of skepticism on that, and it's

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Speaker 1: deserved skepticism. And at this point, it doesn't look like

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Speaker 1: they're going to get a healthcare compromise before the end

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Speaker 1: of the year. But I'll be honest, I think it's

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Speaker 1: gonna come. I told you I think in the earlier

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Speaker 1: episode earlier this week, the outlines of a potential compromise

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Speaker 1: that will probably be more to the liking of Democrats

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Speaker 1: and Republicans is coming together in the House, and at

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Speaker 1: some point it will take Donald Trump just saying go

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Speaker 1: ahead and pass it. I'll support it, and I'll give

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Speaker 1: you cover. That's still but we are now to the

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Speaker 1: point where it looks like these are going to expire

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Speaker 1: and they're going to have to retroactively end up giving

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Speaker 1: giving people the subsidies back. I am, you know, maybe

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Speaker 1: I'll be eating crow on this, but I'm still convinced

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Speaker 1: that something's going to pass. These subsidies are going to

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Speaker 1: get extended a year or two. I think the only

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Speaker 1: thing Republicans have effectively said no to is the full

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Speaker 1: as a three year extension, which is technically what Democrats

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Speaker 1: have been asking for. But somewhere in the in the

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Speaker 1: two year range one to two year range, it is coming.

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Speaker 1: It's just at what point does Trump cry uncle? And

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Speaker 1: at this point it's not clear that he's ready to

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Speaker 1: cry uncle just yet. Before I get to the interview

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Speaker 1: with Julie Chelfo and Mothers Against Media Addiction and Mama

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Speaker 1: as the acronym spells, I want to just weigh in

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Speaker 1: on two things that are sort of business business see

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Speaker 1: issues that I'm actually particularly with one issue, I want

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Speaker 1: to hear from you. I want to hear from the

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Speaker 1: listeners and viewers on two things. One do you pay

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Speaker 1: attention to the prediction markets i e. Polymarket or Calshi. Two?

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Speaker 1: If you do, what is it that you pay attention to?

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Speaker 1: Is cultural predictions? Is it sports predictions? Is it political predictions? Three?

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Speaker 1: Do you participate? You know so, I will make a

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Speaker 1: full confession. I participated for the very first time on

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Speaker 1: the Calshi markets over the weekend, and it had to

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Speaker 1: do with the I was doing it simply to see

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Speaker 1: if anybody was leaking the information of the college football

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Speaker 1: playoff bracket So on Saturday, I sort of for the

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Speaker 1: first time, you know, I signed up for a Calshi account,

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Speaker 1: and I bought shares of Miami making the playoff, and

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Speaker 1: I bought shares of twenty five cents, which really meant that,

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Speaker 1: you know, essentially what the market was saying, they have

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Speaker 1: they have a one and four shot of making the

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Speaker 1: playoff or so. In the prediction markets, it was said

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Speaker 1: Notre Dame was in the ninety nineties, it was like

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Speaker 1: ninety two ninety three percent tile and Alabama was in

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Speaker 1: the mid seventies seventy four or seventy five percentile. So

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Speaker 1: I did it because I was mostly curious to see

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Speaker 1: if this was going to leak and we were going

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Speaker 1: to see people try to manipulate the prediction markets, like

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Speaker 1: we saw with the Nobel Peace Prize, where it turned

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Speaker 1: out the information got out. I think it wasn't an

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Speaker 1: official leak. It was I think somebody hacked into a

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Speaker 1: website and saw a website preview before folks knew, and

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Speaker 1: all of a sudden, you saw the prediction markets move

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Speaker 1: as if they knew something. As it turned out, they

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Speaker 1: did know something. And I just wanted to see if

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Speaker 1: somebody was going to do that. So I was as

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Speaker 1: I was waiting for the college football playoff, I was

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Speaker 1: watching this happen and you would see a lot of volatility,

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Speaker 1: but it wasn't a hundred percent, you know. It was

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Speaker 1: like now people think they know, but they don't know. Right,

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Speaker 1: you could see how that works. It gave me some reassurance.

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Speaker 1: I was glad to see it. You know that something

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Speaker 1: you know, whether it leaking or somebody wasn't trying to

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Speaker 1: gain the system. And obviously I ended up making a

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Speaker 1: couple of bucks on how that worked. I actually was

430
00:26:31,440 --> 00:26:35,679
Speaker 1: monitoring it was I was I assumed that Miami's that

431
00:26:36,040 --> 00:26:38,720
Speaker 1: the share price of the Miami prediction was going to

432
00:26:39,240 --> 00:26:41,800
Speaker 1: go up sooner than it did, Like it didn't. Really.

433
00:26:42,160 --> 00:26:46,160
Speaker 1: It's clear the prediction markets did not believe Miami was

434
00:26:46,160 --> 00:26:48,040
Speaker 1: was going to get in, and that to me is

435
00:26:48,080 --> 00:26:51,200
Speaker 1: a reflection of what the conversation was. Right, If if

436
00:26:51,240 --> 00:26:54,680
Speaker 1: nobody has information, then they're basing it on sort of

437
00:26:54,720 --> 00:26:59,880
Speaker 1: the zeitgeist. Right. But I bring this up because recently

438
00:27:00,040 --> 00:27:03,639
Speaker 1: CNN signed to deal with Calshi where they're going to

439
00:27:03,760 --> 00:27:10,479
Speaker 1: in some form it start including or noting what the

440
00:27:10,520 --> 00:27:13,480
Speaker 1: prediction markets are saying about topics they might be covering.

441
00:27:13,520 --> 00:27:20,320
Speaker 1: So for instance, yesterday we saw the Federal Reserve cut

442
00:27:20,320 --> 00:27:23,560
Speaker 1: interest rates. Well, that was a market you could bet

443
00:27:23,600 --> 00:27:26,520
Speaker 1: on or you could you know, prediction marketing are and

444
00:27:26,600 --> 00:27:29,919
Speaker 1: it was it was in the seventies over the last

445
00:27:30,320 --> 00:27:32,199
Speaker 1: you know, a few days, and then it got up

446
00:27:32,240 --> 00:27:38,960
Speaker 1: today eighty five, ninety, et cetera. But as much as

447
00:27:39,000 --> 00:27:43,280
Speaker 1: I do enjoy sports gambling, okay, you've heard me reference

448
00:27:43,320 --> 00:27:46,160
Speaker 1: it before, some of you know I do football picks

449
00:27:46,200 --> 00:27:51,080
Speaker 1: for Tony Kornheiser on his podcast, I fully confess to

450
00:27:51,280 --> 00:27:57,080
Speaker 1: enjoying it, but I've been extraordinarily uncomfortable with the idea

451
00:27:57,200 --> 00:28:01,640
Speaker 1: of betting on politics or betting on elections. I haven't

452
00:28:01,680 --> 00:28:04,639
Speaker 1: ruled it. I'm not I'm not trying to be, you know,

453
00:28:04,760 --> 00:28:07,840
Speaker 1: sort of a school barm about this, but it is

454
00:28:11,359 --> 00:28:17,080
Speaker 1: I'm not yet comfortable with it. And I'm curious what

455
00:28:17,960 --> 00:28:21,480
Speaker 1: you guys think, and I'm asking you send me, send questions,

456
00:28:21,480 --> 00:28:25,080
Speaker 1: put some comments in the comment section, or go ahead

457
00:28:25,119 --> 00:28:28,160
Speaker 1: and send us a send us comment via ask Chuck,

458
00:28:28,160 --> 00:28:33,479
Speaker 1: and we'll we'll dive into it next week. You know,

459
00:28:33,600 --> 00:28:36,520
Speaker 1: there's there's a lot of fear out there that do

460
00:28:36,560 --> 00:28:39,920
Speaker 1: you you know, at what point I believe in the

461
00:28:39,920 --> 00:28:42,600
Speaker 1: wisdom of crowds and at the same time I don't

462
00:28:42,600 --> 00:28:48,080
Speaker 1: want to see crowds manipulate an outcome, right, And you

463
00:28:48,120 --> 00:28:50,760
Speaker 1: know what is that line? You know, what should be

464
00:28:50,840 --> 00:28:55,760
Speaker 1: something that is okay, it's okay to that, and what

465
00:28:55,840 --> 00:28:58,320
Speaker 1: is something that is should we really be betting on this?

466
00:29:01,320 --> 00:29:03,720
Speaker 1: You know, I'll give you an example. I'm gonna let

467
00:29:03,800 --> 00:29:07,040
Speaker 1: me put up a pull up a few a few

468
00:29:07,840 --> 00:29:10,720
Speaker 1: things that they offer up that I some things that

469
00:29:10,800 --> 00:29:17,520
Speaker 1: make sense and others that don't. So, for instance, Calsh

470
00:29:18,400 --> 00:29:22,120
Speaker 1: will allow you to bet on what the Federal reserves

471
00:29:22,120 --> 00:29:25,080
Speaker 1: here J. Powell will say during his December press conference,

472
00:29:25,560 --> 00:29:31,520
Speaker 1: and the choices are, will he attribute terror inflation to

473
00:29:31,560 --> 00:29:40,800
Speaker 1: the tariffs? Will he says if he says dot plot

474
00:29:40,880 --> 00:29:44,360
Speaker 1: at his December twenty twenty five posts, you know, meeting

475
00:29:44,440 --> 00:29:51,280
Speaker 1: of the Federal Reserve, that would be somehow a win here.

476
00:29:54,440 --> 00:30:00,680
Speaker 1: If if he uses if he refers to the ADP report,

477
00:30:00,720 --> 00:30:06,040
Speaker 1: apparently that would somehow be something you could win a

478
00:30:06,080 --> 00:30:08,040
Speaker 1: bet on it. There's sort of like it's one thing

479
00:30:08,080 --> 00:30:10,040
Speaker 1: to bet on whether there'll be an interest rate cut,

480
00:30:10,080 --> 00:30:14,600
Speaker 1: which again you know I'm uncomfor, you know, the State

481
00:30:14,640 --> 00:30:16,360
Speaker 1: of Virginia. Let me give you an example. The State

482
00:30:16,400 --> 00:30:20,440
Speaker 1: of Virginia does not allow in its sports mobile app

483
00:30:20,960 --> 00:30:24,200
Speaker 1: for people to gamble on who might win an award.

484
00:30:24,320 --> 00:30:26,320
Speaker 1: Like if I wanted to gamble on the on who

485
00:30:26,400 --> 00:30:29,880
Speaker 1: wins the Heisman Trophy, State of Virginia wouldn't do that.

486
00:30:30,040 --> 00:30:35,200
Speaker 1: They are very strict. And how what they approved for gambling,

487
00:30:35,240 --> 00:30:38,360
Speaker 1: and that was games of chance, right, And and a

488
00:30:39,040 --> 00:30:42,600
Speaker 1: sporting event in some ways is something that isn't knowable

489
00:30:42,640 --> 00:30:46,560
Speaker 1: beforehand versus in theory there is somebody that will know

490
00:30:46,720 --> 00:30:50,200
Speaker 1: the answer to who won the Heisman Trophy before it

491
00:30:50,280 --> 00:30:53,600
Speaker 1: is announced, versus an actual sporting event that we're all

492
00:30:53,640 --> 00:30:59,440
Speaker 1: watching in real time. Right, And so they have decided

493
00:30:59,520 --> 00:31:06,719
Speaker 1: not to legalized bets on outcomes that some committee or

494
00:31:07,560 --> 00:31:11,640
Speaker 1: group of people may know in advance. And that's I

495
00:31:11,680 --> 00:31:15,320
Speaker 1: think the line here. You got to ask yourself, like, look,

496
00:31:15,360 --> 00:31:17,360
Speaker 1: you can bet on who will be the Democratic nominee,

497
00:31:17,440 --> 00:31:19,280
Speaker 1: or excuse me, buy shares of who you think will

498
00:31:19,280 --> 00:31:21,640
Speaker 1: be the Democratic nominee. You can buy shares of who

499
00:31:21,680 --> 00:31:24,600
Speaker 1: you think Trump will nominate to be Fed chair. Okay,

500
00:31:25,640 --> 00:31:30,080
Speaker 1: but how do we know somebody within Trump's circle isn't

501
00:31:30,120 --> 00:31:32,280
Speaker 1: gaming this and isn't going to you know, try to

502
00:31:32,280 --> 00:31:35,600
Speaker 1: make money on this with quote insider information. Right in

503
00:31:35,680 --> 00:31:41,160
Speaker 1: the actual financial markets, it is illegal to insider trade, right,

504
00:31:41,200 --> 00:31:43,960
Speaker 1: And there's no doubt there'll be some Certainly the rules

505
00:31:43,960 --> 00:31:47,160
Speaker 1: about insider trading will apply to this if it's ever

506
00:31:47,560 --> 00:31:51,160
Speaker 1: if it's ever shown. But then there are things like

507
00:31:51,400 --> 00:31:54,120
Speaker 1: when will Trump announce his new chair the Federal Reserve?

508
00:31:54,560 --> 00:31:56,640
Speaker 1: So this is something that you can Will it be

509
00:31:56,640 --> 00:31:59,760
Speaker 1: before December twentieth, well, that's sixth sense. Will it be

510
00:31:59,760 --> 00:32:02,160
Speaker 1: before or the end of the calendar year? That's twenty

511
00:32:02,240 --> 00:32:05,200
Speaker 1: five percent believe that Will it be before January fifteenth

512
00:32:05,240 --> 00:32:08,000
Speaker 1: another seventy two percent? And then you know, I guess

513
00:32:08,000 --> 00:32:10,880
Speaker 1: everybody would get their money back if none of those hit,

514
00:32:10,960 --> 00:32:14,440
Speaker 1: or everybody loses if none of those hit. But if

515
00:32:14,480 --> 00:32:18,880
Speaker 1: you're you know, if you already know the answer to this,

516
00:32:19,240 --> 00:32:22,800
Speaker 1: or you're long on something and you've got access to Trump,

517
00:32:23,880 --> 00:32:26,280
Speaker 1: and you know what, if we have a president who's

518
00:32:26,320 --> 00:32:33,120
Speaker 1: extraordinarily transactional and who's open to somebody financially lobbying them,

519
00:32:33,280 --> 00:32:37,640
Speaker 1: which is a more polite way of saying, Brian, And

520
00:32:38,400 --> 00:32:40,760
Speaker 1: you know, I'm not saying I have no idea whether

521
00:32:40,800 --> 00:32:42,760
Speaker 1: we would ever have an American president who would be

522
00:32:42,840 --> 00:32:48,400
Speaker 1: transactional like that on things like pardons or whatever. But

523
00:32:48,560 --> 00:32:51,280
Speaker 1: that's the part of this that I will admit gives

524
00:32:51,360 --> 00:32:58,160
Speaker 1: me some pause. There's some parts of this that feels

525
00:32:58,200 --> 00:33:03,040
Speaker 1: like the Democratic nominee in Texas that looking at those markets,

526
00:33:03,080 --> 00:33:05,719
Speaker 1: that's interesting of interest to me. In case you're wondering

527
00:33:06,080 --> 00:33:11,320
Speaker 1: right now, Tall Rico is fifty six cents and Jasmine

528
00:33:11,320 --> 00:33:15,239
Speaker 1: Crockett is at forty two cents. So that's not what

529
00:33:15,240 --> 00:33:19,840
Speaker 1: the polling says right now, but this is what betters

530
00:33:19,960 --> 00:33:23,400
Speaker 1: or predictors, I guess you would say, believe is going

531
00:33:23,520 --> 00:33:26,720
Speaker 1: to be the outcome of that. And so look, they've

532
00:33:26,720 --> 00:33:28,720
Speaker 1: been betting on elections in the UK, you could bet

533
00:33:28,720 --> 00:33:34,080
Speaker 1: on American elections and overseas markets from before. But I

534
00:33:34,080 --> 00:33:35,560
Speaker 1: guess what I'm getting at here is if we're going

535
00:33:35,600 --> 00:33:39,360
Speaker 1: to go down this road, there probably needs to be

536
00:33:39,400 --> 00:33:42,360
Speaker 1: a bit more regulation around it. There probably needs to

537
00:33:42,400 --> 00:33:45,440
Speaker 1: be sort of a line drawn in the sand that is, hey,

538
00:33:45,480 --> 00:33:51,880
Speaker 1: this is a that's a manipulation. That's too that's too

539
00:33:51,920 --> 00:33:54,800
Speaker 1: much of a thing that can be manipulated. So take

540
00:33:54,840 --> 00:33:57,840
Speaker 1: this question that you can bet on the following on

541
00:33:57,920 --> 00:34:01,080
Speaker 1: Calshi who will visit mar A Lago BEFO twenty six

542
00:34:03,680 --> 00:34:07,040
Speaker 1: and you can just and there's all sorts of choices

543
00:34:07,040 --> 00:34:11,440
Speaker 1: that you can buy shares of Benjamin Nett Yahoo. That's

544
00:34:11,719 --> 00:34:15,960
Speaker 1: according to Kooshi that that's the leading prediction on that

545
00:34:16,000 --> 00:34:20,040
Speaker 1: one at eighty seven cents out of a dollar. Mbs

546
00:34:20,160 --> 00:34:22,560
Speaker 1: of Saudi Arabia that's at thirteen, he's second on the list.

547
00:34:22,600 --> 00:34:25,600
Speaker 1: Zelenski's at ten cents. He's third on the list. Sam

548
00:34:25,640 --> 00:34:29,320
Speaker 1: Altman's at seven, Brittany Mahomes is at five, Jamie Diamond

549
00:34:29,360 --> 00:34:32,840
Speaker 1: is at five, Jerome Pals at four, Kim Kardashian is

550
00:34:32,880 --> 00:34:36,719
Speaker 1: at two. You know, it's kind of silly, right, But

551
00:34:36,880 --> 00:34:40,960
Speaker 1: here's something else. There was a huge spike for net

552
00:34:41,080 --> 00:34:44,640
Speaker 1: Yahoo over the last day or two, and I think

553
00:34:44,640 --> 00:34:47,080
Speaker 1: there's an assumption that he's going to somehow make his

554
00:34:47,120 --> 00:34:50,440
Speaker 1: way out here. He's desperate for Trump to continue to

555
00:34:50,520 --> 00:34:52,640
Speaker 1: lobby for a pardon on his behalf with the is

556
00:34:53,080 --> 00:34:55,400
Speaker 1: with the President of Israel, so there may be some

557
00:34:55,480 --> 00:34:58,120
Speaker 1: motivation there. But it's one of those things that feels

558
00:34:58,120 --> 00:35:02,200
Speaker 1: like somebody already knows the answer, but the public didn't

559
00:35:02,239 --> 00:35:07,839
Speaker 1: see it. So we have a betting market on it, right. Look,

560
00:35:08,400 --> 00:35:12,040
Speaker 1: we're a free country. If you choose to do this

561
00:35:12,120 --> 00:35:14,360
Speaker 1: with your money, it's your choice to do this with

562
00:35:14,400 --> 00:35:17,160
Speaker 1: the money. The question I have is how comfortable are

563
00:35:17,200 --> 00:35:21,840
Speaker 1: you with news organizations incorporating this into their coverage, which

564
00:35:21,880 --> 00:35:27,960
Speaker 1: is a decision seeing in may. You know, we already

565
00:35:28,160 --> 00:35:30,440
Speaker 1: have a lot of people that I think correctly complain

566
00:35:30,520 --> 00:35:33,799
Speaker 1: that our campaign coverage is always focused on the polls, right,

567
00:35:33,840 --> 00:35:38,480
Speaker 1: that it's horse race coverage. Will this make all political

568
00:35:38,520 --> 00:35:41,560
Speaker 1: and policy coverage horse race coverage? You know? Are we

569
00:35:41,640 --> 00:35:46,480
Speaker 1: going to see stories about the debate over extending healthcare

570
00:35:46,520 --> 00:35:50,360
Speaker 1: subsidies and oh, by the way, the prediction markets, well,

571
00:35:50,520 --> 00:35:52,799
Speaker 1: they think there's going to be a deal, and that

572
00:35:52,840 --> 00:35:55,680
Speaker 1: there'll be a deal to extend these subsidies sometime by

573
00:35:55,719 --> 00:35:58,280
Speaker 1: the end of January. There may not be any reporting

574
00:35:58,280 --> 00:36:01,000
Speaker 1: that says that it may be true, it may not

575
00:36:01,080 --> 00:36:08,120
Speaker 1: be true. Is that stuff worth incorporating in reporting if

576
00:36:08,160 --> 00:36:11,040
Speaker 1: you're seeing it and look, in fairness to them, let's

577
00:36:11,040 --> 00:36:13,600
Speaker 1: see how they use it, let's say how they incorporate it.

578
00:36:14,520 --> 00:36:17,640
Speaker 1: But again, I do this more as you can hear

579
00:36:17,680 --> 00:36:21,400
Speaker 1: in my voice as somebody who again I am I

580
00:36:21,400 --> 00:36:24,279
Speaker 1: am I'm a bit libertarian on the vices, right. You

581
00:36:24,320 --> 00:36:29,200
Speaker 1: want to legalize cannabis, you want to, you know, put

582
00:36:29,200 --> 00:36:33,760
Speaker 1: a high age element on it, legalize other vices. I'm

583
00:36:33,920 --> 00:36:38,799
Speaker 1: gambling things like that. I'm I'm you know, that's your business, right,

584
00:36:38,880 --> 00:36:43,560
Speaker 1: I'm I am, I am. I am an empathetic libertarian here.

585
00:36:44,239 --> 00:36:49,680
Speaker 1: The question is whether our political debate, our policy debates,

586
00:36:49,719 --> 00:36:54,279
Speaker 1: are they going to be enhanced or harmed by the

587
00:36:54,360 --> 00:37:04,560
Speaker 1: casinification of news. So obviously you can hear my hesitance

588
00:37:04,560 --> 00:37:09,080
Speaker 1: on this, you can hear my concern on this, but

589
00:37:09,160 --> 00:37:12,880
Speaker 1: I'm not unpersuadable on this. So that's why I am

590
00:37:13,000 --> 00:37:14,680
Speaker 1: this is one of those I'd love to hear what

591
00:37:15,440 --> 00:37:17,400
Speaker 1: many of you think on this, whether any of you

592
00:37:17,440 --> 00:37:19,960
Speaker 1: have enough of an opinion to express it to me

593
00:37:21,920 --> 00:37:23,120
Speaker 1: if you don't want to put your name on and

594
00:37:23,160 --> 00:37:25,239
Speaker 1: I get that too, by the way, on this, so

595
00:37:25,760 --> 00:37:28,239
Speaker 1: please do shoot me an ope on this. Where are

596
00:37:28,239 --> 00:37:31,000
Speaker 1: you on these prediction markets? You know, I am a

597
00:37:31,040 --> 00:37:34,440
Speaker 1: gawker of them. I'm fascinated to see a lot of

598
00:37:34,480 --> 00:37:38,040
Speaker 1: times it's just representative of what the zeitgeist is already saying,

599
00:37:38,160 --> 00:37:40,920
Speaker 1: or what the polling is indicating, or what reporting has

600
00:37:40,960 --> 00:37:47,239
Speaker 1: already been out there. So you know, for instance, they've

601
00:37:47,239 --> 00:37:49,960
Speaker 1: got a market of which world leaders will be out

602
00:37:50,000 --> 00:37:52,360
Speaker 1: by the end of the year. Which world leader do

603
00:37:52,400 --> 00:37:54,360
Speaker 1: you think is number one on their list? If you

604
00:37:54,400 --> 00:37:57,880
Speaker 1: said Nicholas Maduro, you'd be right, right, Well, that's common sense,

605
00:37:58,600 --> 00:38:02,200
Speaker 1: But there's actually it's only trading at eleven cents, which

606
00:38:02,280 --> 00:38:06,239
Speaker 1: means it's essentially the prediction markets believe there's only a

607
00:38:06,239 --> 00:38:09,920
Speaker 1: one in essentially a one and ten chance that Maduro

608
00:38:10,040 --> 00:38:15,919
Speaker 1: is out before the end of this calendar year. Does

609
00:38:15,960 --> 00:38:18,879
Speaker 1: that matter to you? Does that at all? And here's

610
00:38:18,880 --> 00:38:22,120
Speaker 1: the thing. It may have no impact whatsoever, But the

611
00:38:22,160 --> 00:38:24,759
Speaker 1: minute I reported that, have I planted a seat in

612
00:38:24,800 --> 00:38:29,000
Speaker 1: your head and somehow created a form of sort of

613
00:38:29,440 --> 00:38:34,719
Speaker 1: news consumption bias, if you will, right, So anyway, it's

614
00:38:35,360 --> 00:38:41,920
Speaker 1: that's the beauty of this format is is we can

615
00:38:42,000 --> 00:38:44,880
Speaker 1: sort of throw out this idea. I'm not going to

616
00:38:44,920 --> 00:38:50,560
Speaker 1: sit here and pretend that i have concerns, I'm intrigued,

617
00:38:52,400 --> 00:38:55,439
Speaker 1: and I'm wrapped and it's and I have my own

618
00:38:55,480 --> 00:38:58,719
Speaker 1: hesitations and all of those things. So I'd love for

619
00:38:58,760 --> 00:39:01,799
Speaker 1: you to share that. So with that, I'm gonna sneak

620
00:39:01,800 --> 00:39:05,640
Speaker 1: it a break when we come back my conversation with

621
00:39:05,719 --> 00:39:09,080
Speaker 1: the founder of Mama Mothers Against Media Addiction, and then

622
00:39:09,120 --> 00:39:11,719
Speaker 1: after the interview, we'll do a little last chuck and

623
00:39:11,840 --> 00:39:13,960
Speaker 1: I'll do a little bit of my weekend sport preview.

624
00:39:16,400 --> 00:39:19,040
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648
00:40:38,280 --> 00:40:42,759
Speaker 1: So joining me today under of something called MAMA. That's

649
00:40:42,800 --> 00:40:48,000
Speaker 1: the acronym. It is Mother's against Media Addiction. The founder

650
00:40:48,080 --> 00:40:52,360
Speaker 1: is Julia excuse me, Julie Sheelfell. She's a former New

651
00:40:52,440 --> 00:40:56,640
Speaker 1: York Times reporter has become a bit of I think,

652
00:40:56,680 --> 00:41:02,480
Speaker 1: a media ecologist of sorts. And look you as many

653
00:41:02,520 --> 00:41:04,319
Speaker 1: of you know, many of my listeners here know that

654
00:41:04,800 --> 00:41:07,319
Speaker 1: trying to figure out how we rebuild trust in the

655
00:41:07,360 --> 00:41:11,239
Speaker 1: information ecosystem, how can we clean up the information ecosystem?

656
00:41:11,560 --> 00:41:14,160
Speaker 1: And one of the more remarkable things in this polarized

657
00:41:14,239 --> 00:41:17,680
Speaker 1: environment is that what's been interesting is that the only

658
00:41:17,719 --> 00:41:20,160
Speaker 1: time we can find areas of agreement is when it

659
00:41:20,200 --> 00:41:23,960
Speaker 1: is about protecting our kids online social media. And so

660
00:41:24,480 --> 00:41:27,640
Speaker 1: as we try to figure out how big tech is

661
00:41:27,680 --> 00:41:32,280
Speaker 1: going to build AI and will they do it safely

662
00:41:32,480 --> 00:41:37,080
Speaker 1: or not? Given the experience with social media, I figured

663
00:41:37,080 --> 00:41:38,960
Speaker 1: this would be a really good conversation because I want

664
00:41:39,000 --> 00:41:42,759
Speaker 1: to learn more about mama. So, Julie, welcome to the podcast.

665
00:41:43,480 --> 00:41:45,400
Speaker 2: Hi Chuck, thank you so much for having me. It's

666
00:41:45,400 --> 00:41:46,279
Speaker 2: a pleasure to be here.

667
00:41:46,760 --> 00:41:50,439
Speaker 1: So let's start with you know this, you know look,

668
00:41:50,640 --> 00:41:53,640
Speaker 1: I'm in I just got out of an hour long

669
00:41:53,719 --> 00:41:58,160
Speaker 1: meeting in a nonprofit group I'm involved with called Trust

670
00:41:58,160 --> 00:42:00,960
Speaker 1: in Media and where it's just something like what can

671
00:42:01,000 --> 00:42:05,440
Speaker 1: we do to repair and rebuild trust in all sorts

672
00:42:05,440 --> 00:42:08,759
Speaker 1: of institutions right sort of the information ecosystem that was

673
00:42:08,800 --> 00:42:10,960
Speaker 1: sort of at the at the heart of the What's

674
00:42:11,000 --> 00:42:13,960
Speaker 1: interesting about this organization that I'm working with is that

675
00:42:14,000 --> 00:42:17,560
Speaker 1: it includes folks in the national security space and and

676
00:42:18,160 --> 00:42:20,960
Speaker 1: folks in the business space, and in health and in

677
00:42:21,040 --> 00:42:26,400
Speaker 1: sports as well as news and politics. And you're tackling

678
00:42:26,520 --> 00:42:29,120
Speaker 1: this from a from a youth space, and as we

679
00:42:29,840 --> 00:42:34,040
Speaker 1: transition from fearing social media to fearing AI. This is

680
00:42:34,200 --> 00:42:36,160
Speaker 1: I think a pretty relevant conversation. So I want to

681
00:42:36,160 --> 00:42:41,600
Speaker 1: start with what motivated you to do this? I think

682
00:42:41,719 --> 00:42:47,560
Speaker 1: it's obvious in some cases, but are you impressed without

683
00:42:47,640 --> 00:42:50,520
Speaker 1: quickly we actually have come together on the issue of

684
00:42:50,560 --> 00:42:53,759
Speaker 1: at least our phones and schools.

685
00:42:54,520 --> 00:42:58,359
Speaker 2: Well, okay, so there were like three questions in that's

686
00:42:58,400 --> 00:43:04,839
Speaker 2: what I did. I'm gonna work backwards. So so I

687
00:43:05,000 --> 00:43:07,760
Speaker 2: was moved to start Mama for a couple reasons.

688
00:43:09,200 --> 00:43:11,480
Speaker 1: One is that did you start it?

689
00:43:11,480 --> 00:43:15,640
Speaker 2: By the way, So I incorporated her in twenty twenty three.

690
00:43:16,000 --> 00:43:19,399
Speaker 2: We only announced ourselves publicly early last year, so we're

691
00:43:19,400 --> 00:43:23,000
Speaker 2: not even two years old yet. But a few years

692
00:43:23,040 --> 00:43:27,680
Speaker 2: before that, I gave a ted talk because I was

693
00:43:27,719 --> 00:43:32,520
Speaker 2: so deeply concerned about erosion of trust and information and

694
00:43:32,560 --> 00:43:36,040
Speaker 2: what was happening in our media environments. And having spent

695
00:43:36,120 --> 00:43:39,520
Speaker 2: my career in professional newsrooms. I was at the New

696
00:43:39,600 --> 00:43:42,040
Speaker 2: York Times and before that I was at Newsweek. I

697
00:43:42,120 --> 00:43:46,640
Speaker 2: sort of watched the rise of digital media and how

698
00:43:46,719 --> 00:43:50,440
Speaker 2: these legacy news organizations responded to it. And what was

699
00:43:50,560 --> 00:43:53,200
Speaker 2: very clear to me, what was very unfortunate is that

700
00:43:53,280 --> 00:43:58,160
Speaker 2: even though journalists are really good at understanding and vetting

701
00:43:58,200 --> 00:44:02,319
Speaker 2: information quality. They weren't trained in media literacy, and they

702
00:44:02,360 --> 00:44:06,799
Speaker 2: didn't understand how what they publish and how what they

703
00:44:06,800 --> 00:44:11,880
Speaker 2: communicated communicated. But they were communicating online and on television

704
00:44:12,200 --> 00:44:15,480
Speaker 2: was helping shape the information environment. And so you know,

705
00:44:15,800 --> 00:44:18,279
Speaker 2: the question about regaining trust is a big one, and

706
00:44:18,360 --> 00:44:20,279
Speaker 2: I hope we can talk about it more later in

707
00:44:20,320 --> 00:44:24,560
Speaker 2: the show. But specifically for Mama, I was reporting a

708
00:44:24,600 --> 00:44:28,160
Speaker 2: lot on youth mental health and suicide. And it was

709
00:44:28,239 --> 00:44:32,480
Speaker 2: about a decade ago that we saw a terrible increase

710
00:44:32,600 --> 00:44:36,400
Speaker 2: in suicide rates among American adolescents. And I reported on

711
00:44:36,440 --> 00:44:40,120
Speaker 2: that and it was manifestly obvious that social media was

712
00:44:40,160 --> 00:44:43,000
Speaker 2: at the heart of that problem. And then, as sad

713
00:44:43,000 --> 00:44:47,399
Speaker 2: as that story was, we saw suicide rates go up

714
00:44:47,800 --> 00:44:50,320
Speaker 2: not just in teens, but in tweens, which your children

715
00:44:50,320 --> 00:44:52,680
Speaker 2: as young as nine to ten. Now I'm a mom,

716
00:44:52,760 --> 00:44:57,440
Speaker 2: I have three sons, and reporting that story really shook

717
00:44:57,520 --> 00:45:00,560
Speaker 2: me to my core. When you have nine ten year olds,

718
00:45:00,680 --> 00:45:04,400
Speaker 2: ten year old children who want to die, something is

719
00:45:04,440 --> 00:45:08,160
Speaker 2: profoundly messed up. Because I run Mama, I won't use

720
00:45:08,200 --> 00:45:10,520
Speaker 2: the crude language that's really in my head about that

721
00:45:10,560 --> 00:45:14,319
Speaker 2: how messed up that is, But I realized something had

722
00:45:14,320 --> 00:45:16,880
Speaker 2: to be done. And what has to be done is

723
00:45:16,920 --> 00:45:20,160
Speaker 2: that this information environment has to be brought under control

724
00:45:20,239 --> 00:45:21,440
Speaker 2: so that it's safe for children.

725
00:45:22,760 --> 00:45:26,400
Speaker 1: And that's you know, I look at this movement right

726
00:45:26,520 --> 00:45:30,160
Speaker 1: of what we've seen about trying to at least take

727
00:45:30,719 --> 00:45:34,480
Speaker 1: bones out of classrooms, and you know, when I see

728
00:45:34,480 --> 00:45:35,840
Speaker 1: how hard it is to get to the left and

729
00:45:35,880 --> 00:45:37,480
Speaker 1: the right to agree on anything, and it is the

730
00:45:37,600 --> 00:45:41,120
Speaker 1: one thing, whether it's a super liberal state legislature, super

731
00:45:41,120 --> 00:45:44,600
Speaker 1: conservative state legislature, this stuff has made it through. This

732
00:45:44,719 --> 00:45:48,120
Speaker 1: is the one place it has made it through. I

733
00:45:48,200 --> 00:45:52,319
Speaker 1: fear we're too late on social media, but maybe this

734
00:45:52,440 --> 00:45:53,680
Speaker 1: gets us there on AI.

735
00:45:54,600 --> 00:45:57,840
Speaker 2: So I don't think we're too late on social media

736
00:45:58,080 --> 00:46:03,040
Speaker 2: because every day a child is growing up and being

737
00:46:03,120 --> 00:46:07,200
Speaker 2: exposed to things through these platforms, and every day that

738
00:46:07,239 --> 00:46:12,160
Speaker 2: we allow a handful of companies to share whatever they

739
00:46:12,200 --> 00:46:16,200
Speaker 2: want with whoever they want, under any circumstances, with no

740
00:46:16,360 --> 00:46:20,240
Speaker 2: regulation is a day that we are leaving our children

741
00:46:20,360 --> 00:46:25,080
Speaker 2: vulnerable to terrible harms. When I was reporting my stories,

742
00:46:25,239 --> 00:46:28,000
Speaker 2: we didn't have data about the amount of suicide and

743
00:46:28,040 --> 00:46:32,399
Speaker 2: self harm content. Meta released data on it just last year,

744
00:46:32,440 --> 00:46:36,560
Speaker 2: and according to Meta's own report, there were forty eight

745
00:46:36,760 --> 00:46:40,200
Speaker 2: million separate pieces of suicide and self harm content on

746
00:46:40,239 --> 00:46:43,360
Speaker 2: their platforms in the previous year, and that's just the

747
00:46:43,400 --> 00:46:46,960
Speaker 2: pieces that they'll acknowledge. There's probably a lot more than that. So,

748
00:46:47,320 --> 00:46:50,680
Speaker 2: you know, as long as we've had mass media, beginning

749
00:46:50,680 --> 00:46:54,799
Speaker 2: in the nineteen thirties with radio, our government has regulated it.

750
00:46:54,800 --> 00:46:56,719
Speaker 2: It is said, there need to be limits, there need

751
00:46:56,760 --> 00:47:00,440
Speaker 2: to be standards. It's not censoring free speech. It's just

752
00:47:00,560 --> 00:47:05,160
Speaker 2: recognizing that not all content is appropriate in all places

753
00:47:05,160 --> 00:47:08,160
Speaker 2: for all audiences. I don't think we want a world.

754
00:47:08,200 --> 00:47:12,400
Speaker 2: You know, if we allowed every single course vulgar X

755
00:47:12,520 --> 00:47:18,320
Speaker 2: rated activity on our regular television channels and on the radio,

756
00:47:18,520 --> 00:47:20,560
Speaker 2: you know what kind of world would we have. So

757
00:47:20,600 --> 00:47:21,600
Speaker 2: I'm not exact.

758
00:47:21,480 --> 00:47:23,960
Speaker 1: Way that world does exist. It's the Internet. I mean,

759
00:47:24,239 --> 00:47:30,640
Speaker 1: we've let this happen on the Internet and nobody wanted this, right, Like,

760
00:47:30,800 --> 00:47:33,920
Speaker 1: we know we don't like this, but we can't agree

761
00:47:33,920 --> 00:47:35,000
Speaker 1: on how to stop this.

762
00:47:35,640 --> 00:47:39,200
Speaker 2: Well, I think there is actually pretty wide consensus on

763
00:47:39,320 --> 00:47:41,759
Speaker 2: how to stop it. I think we also are just

764
00:47:41,840 --> 00:47:46,239
Speaker 2: facing an industry that has unlimited amounts of money and

765
00:47:46,280 --> 00:47:50,560
Speaker 2: they are spending on godly amounts of money on lobbying

766
00:47:51,120 --> 00:47:53,960
Speaker 2: Our friends in an organization called Issue one have been

767
00:47:54,040 --> 00:47:58,080
Speaker 2: tracking this and they have found that the tech big

768
00:47:58,120 --> 00:48:02,560
Speaker 2: tech industry has one one lobbyist for every two lawmakers

769
00:48:02,600 --> 00:48:07,280
Speaker 2: in Washington, and Meta alone has won one lobbyist for

770
00:48:07,440 --> 00:48:11,480
Speaker 2: every seven lawmakers. So you know what's happening is, even

771
00:48:11,520 --> 00:48:17,120
Speaker 2: though there's wide agreement among most lawmakers and most parents

772
00:48:17,239 --> 00:48:21,320
Speaker 2: and most citizens, you have big tech spending on godly

773
00:48:21,480 --> 00:48:24,160
Speaker 2: and holy amounts of money and getting just a few

774
00:48:24,200 --> 00:48:27,600
Speaker 2: people who are really messing up legislation. I mean, one

775
00:48:27,640 --> 00:48:31,120
Speaker 2: example is the Kids Online Safety Act, which passed last

776
00:48:31,200 --> 00:48:33,480
Speaker 2: year in the Senate by a vote of ninety one

777
00:48:33,480 --> 00:48:36,680
Speaker 2: to three, which like, when does that happen? And over

778
00:48:36,760 --> 00:48:39,440
Speaker 2: in the House, Speaker Johnson refused to bring it for

779
00:48:39,480 --> 00:48:43,080
Speaker 2: a vote. Steve Scalise says it wasn't good legislation and

780
00:48:43,280 --> 00:48:46,000
Speaker 2: nobody could understand why. And then it was announced that

781
00:48:46,080 --> 00:48:49,880
Speaker 2: Meta is building a twenty eight billion dollar AI data

782
00:48:49,920 --> 00:48:53,040
Speaker 2: processing plant in the state which you know, I don't know.

783
00:48:53,080 --> 00:48:54,439
Speaker 2: Does that have something to do with it?

784
00:48:54,520 --> 00:48:57,399
Speaker 1: Maybe in the state of I assume you mean state

785
00:48:57,400 --> 00:48:58,080
Speaker 1: of Louisiana.

786
00:48:58,160 --> 00:48:59,120
Speaker 2: The state of Louisiana.

787
00:48:59,160 --> 00:49:02,680
Speaker 1: Yep, Johnson and and Scalize, Yeah, no, and we're seeing

788
00:49:02,719 --> 00:49:06,879
Speaker 1: the same thing with AI. I mean, take the take

789
00:49:06,920 --> 00:49:10,960
Speaker 1: the issue of this moratorium from states being able to

790
00:49:10,960 --> 00:49:16,160
Speaker 1: regulate AI that I do think is become I think

791
00:49:16,160 --> 00:49:20,320
Speaker 1: it's now too toxic to support. I think we'll find.

792
00:49:20,120 --> 00:49:22,439
Speaker 2: Out, right I well, I hope you're right.

793
00:49:22,840 --> 00:49:25,160
Speaker 1: Yeah, I mean we're going to find out and maybe

794
00:49:25,320 --> 00:49:28,160
Speaker 1: unfortunately this gets can't tell you how many pieces of

795
00:49:28,280 --> 00:49:31,800
Speaker 1: really harmful legislation gets snuck in in the month of December.

796
00:49:32,760 --> 00:49:37,680
Speaker 1: It's historically because everybody's in holiday mode. It'll just can

797
00:49:37,840 --> 00:49:40,759
Speaker 1: can sometimes get So I think we are in the

798
00:49:40,800 --> 00:49:42,839
Speaker 1: month of December when we're taping this and when this

799
00:49:42,880 --> 00:49:45,200
Speaker 1: is when people are listening to this, So there's always

800
00:49:45,200 --> 00:49:48,160
Speaker 1: a chance this is something that gets snuck in. But

801
00:49:48,280 --> 00:49:52,759
Speaker 1: it does seem as if there's enough opposition. But I

802
00:49:52,800 --> 00:49:53,839
Speaker 1: know you're working on this.

803
00:49:53,960 --> 00:49:57,440
Speaker 2: I mean, our members have been sending thousands of letters.

804
00:49:57,520 --> 00:50:00,399
Speaker 2: And for those of your listeners who aren't familiar with this,

805
00:50:01,560 --> 00:50:06,000
Speaker 2: the tech industry is trying to get this sweetheart deal

806
00:50:06,080 --> 00:50:09,560
Speaker 2: passed where they would pass a federal law saying there

807
00:50:09,560 --> 00:50:12,840
Speaker 2: can be no state regulation of AI. And this is

808
00:50:12,880 --> 00:50:16,880
Speaker 2: such a profound violation of states' rights and their fundamental

809
00:50:16,920 --> 00:50:20,840
Speaker 2: ability to keep their citizens safe that we had forty four.

810
00:50:20,920 --> 00:50:24,560
Speaker 2: I think attorneys general send a letter to the Senate

811
00:50:24,600 --> 00:50:26,600
Speaker 2: when this was being considered earlier this year in the

812
00:50:26,600 --> 00:50:30,000
Speaker 2: Big Beautiful Bill, saying this is outrageous. You can't do this.

813
00:50:30,160 --> 00:50:32,680
Speaker 2: Hundreds of lawmakers of both parties, so you can't do this,

814
00:50:32,760 --> 00:50:34,799
Speaker 2: And it came out of the Big Beautiful Bill. Now

815
00:50:34,840 --> 00:50:38,600
Speaker 2: they're trying to sneak it back into the Defense Reauthorization Act.

816
00:50:38,880 --> 00:50:41,839
Speaker 2: And we're also hearing there's the possibility of an executive order.

817
00:50:42,239 --> 00:50:45,319
Speaker 2: So you know, the way the draft was worded, it's

818
00:50:45,360 --> 00:50:48,480
Speaker 2: so vague. It would not only prohibit states from regulating AI,

819
00:50:48,560 --> 00:50:51,360
Speaker 2: it would prohibit them from regulating social media. So nobody

820
00:50:51,400 --> 00:50:54,319
Speaker 2: wants this, and it would just be a real boon

821
00:50:54,440 --> 00:50:56,880
Speaker 2: to a handful of billionaires who own these companies.

822
00:50:57,480 --> 00:51:00,279
Speaker 1: Well, I'm glad you brought up the executive order because

823
00:51:00,280 --> 00:51:02,640
Speaker 1: it does seem as if that was that was going

824
00:51:02,719 --> 00:51:06,200
Speaker 1: to be the tech community's last resort. And that does

825
00:51:06,239 --> 00:51:07,960
Speaker 1: look like that's going to happen, doesn't it.

826
00:51:07,960 --> 00:51:11,839
Speaker 2: That's right, you know, I hope it doesn't. I do

827
00:51:11,960 --> 00:51:15,080
Speaker 2: think even if it does happen, it's so problematic that

828
00:51:15,160 --> 00:51:17,640
Speaker 2: it's unlikely to be enforced right away.

829
00:51:17,400 --> 00:51:19,160
Speaker 1: But I mean, I don't think it's by the way,

830
00:51:19,160 --> 00:51:21,360
Speaker 1: I don't think it's enforceable, and I think there'll be

831
00:51:21,400 --> 00:51:24,279
Speaker 1: some states that basically like, let's go, we're going to

832
00:51:24,400 --> 00:51:26,319
Speaker 1: we're going to do some regulate Let go ahead and

833
00:51:26,360 --> 00:51:27,799
Speaker 1: try to stop us. Yeah.

834
00:51:28,239 --> 00:51:31,400
Speaker 2: Yeah, I mean, how like, in what other industry do

835
00:51:31,480 --> 00:51:33,600
Speaker 2: we say, Okay, you can just do whatever you want.

836
00:51:33,680 --> 00:51:35,680
Speaker 2: You know, we regulate our food to make sure it's safe.

837
00:51:35,680 --> 00:51:38,319
Speaker 2: We regulate our vehicles to make sure they're safe. We

838
00:51:38,360 --> 00:51:40,719
Speaker 2: don't want to live in a world where companies are

839
00:51:40,760 --> 00:51:44,240
Speaker 2: not required to test cars and make sure the breaks

840
00:51:44,280 --> 00:51:45,440
Speaker 2: work before you get them.

841
00:51:45,840 --> 00:51:49,040
Speaker 1: Well, this gets into something that I think is one

842
00:51:49,080 --> 00:51:53,920
Speaker 1: of those do you know one of the ways the

843
00:51:54,000 --> 00:51:57,000
Speaker 1: advocates of low regulation right now in AI, one of

844
00:51:57,040 --> 00:51:59,239
Speaker 1: the arguments they make is, hey, we did this with

845
00:51:59,280 --> 00:52:02,800
Speaker 1: the internet. We have very little regulation. And my retort

846
00:52:02,880 --> 00:52:05,960
Speaker 1: always is, and how did that go? Right? Like, we

847
00:52:06,880 --> 00:52:09,440
Speaker 1: decided to have a hands off approach on social media

848
00:52:11,200 --> 00:52:14,560
Speaker 1: and that turned out to be a colossal mistake. Now

849
00:52:14,600 --> 00:52:17,560
Speaker 1: I happen to believe social media, I mean, here's the problem.

850
00:52:17,600 --> 00:52:19,560
Speaker 1: I think this has been a disaster. I think it

851
00:52:20,040 --> 00:52:23,680
Speaker 1: something that destroys the information ecosystem, that destroys trust, that

852
00:52:24,640 --> 00:52:28,640
Speaker 1: breaks up families. This is not a successful business, and

853
00:52:28,719 --> 00:52:33,080
Speaker 1: yet we want to create the same regulatory environment to

854
00:52:33,160 --> 00:52:35,560
Speaker 1: allow AI to thrive, and we think that's going to

855
00:52:35,640 --> 00:52:38,359
Speaker 1: be good because of the experience with social media. And

856
00:52:38,400 --> 00:52:41,600
Speaker 1: I do think this is why there's more public and

857
00:52:42,440 --> 00:52:45,160
Speaker 1: there's more bipartisan pushback on this because I think if

858
00:52:45,200 --> 00:52:49,279
Speaker 1: you frame the question of do you want the tech

859
00:52:49,320 --> 00:52:54,480
Speaker 1: companies to have this to use this same lack of

860
00:52:54,560 --> 00:52:56,359
Speaker 1: rules of the game to build AI as they build

861
00:52:56,400 --> 00:52:58,719
Speaker 1: social media? Do you trust the same people that build

862
00:52:58,760 --> 00:53:01,319
Speaker 1: social media to build AI? I think the answers no.

863
00:53:02,440 --> 00:53:06,920
Speaker 2: Well, Chuck, it's not a successful business for families, for society,

864
00:53:07,080 --> 00:53:10,520
Speaker 2: for democracy, for children, but it is for shareholders and

865
00:53:10,600 --> 00:53:13,000
Speaker 2: for a handful of people who own these companies. And

866
00:53:13,040 --> 00:53:17,400
Speaker 2: what we're seeing is this really unprecedented alliance between a

867
00:53:17,440 --> 00:53:20,680
Speaker 2: few people at the highest levels of government and the

868
00:53:20,719 --> 00:53:24,960
Speaker 2: folks who own these companies. Now, everybody who has children

869
00:53:25,520 --> 00:53:28,520
Speaker 2: says that's not what's most important. You know, I have

870
00:53:28,600 --> 00:53:32,280
Speaker 2: no problem with people making money and building a successful company,

871
00:53:32,320 --> 00:53:33,960
Speaker 2: but you should not be able to do it on

872
00:53:34,000 --> 00:53:36,680
Speaker 2: the backs of children, So you know, that is why

873
00:53:36,719 --> 00:53:40,239
Speaker 2: Mama is working. We started, you know, hoping that we

874
00:53:40,280 --> 00:53:42,839
Speaker 2: could have six chapters by the end of the first year.

875
00:53:42,960 --> 00:53:45,520
Speaker 2: We were sort of inspired by mothers against drunk driving

876
00:53:45,560 --> 00:53:48,560
Speaker 2: and the way they had chapters around the country. Instead,

877
00:53:48,600 --> 00:53:51,920
Speaker 2: we're up to nearly forty chapters in twenty two states.

878
00:53:52,719 --> 00:53:55,760
Speaker 2: Our members are working in their homes, in their communities,

879
00:53:55,880 --> 00:54:01,560
Speaker 2: in their schools, and with policymakers to advance changes, just

880
00:54:01,600 --> 00:54:03,399
Speaker 2: like we had to make big changes once we learned

881
00:54:03,400 --> 00:54:06,720
Speaker 2: about the harms of big tobacco, saying okay, it shouldn't

882
00:54:06,760 --> 00:54:10,000
Speaker 2: be normative that we give this to young children. It

883
00:54:10,000 --> 00:54:13,600
Speaker 2: shouldn't be okay that we load it full of chemicals

884
00:54:13,680 --> 00:54:17,680
Speaker 2: that can cause cancer. And we have to ensure that

885
00:54:17,719 --> 00:54:19,839
Speaker 2: we're both using it in a safe way and that

886
00:54:19,880 --> 00:54:23,759
Speaker 2: the manufacturers of these products are held to basic safety standards.

887
00:54:25,200 --> 00:54:27,040
Speaker 1: Glad you brought up tobacco. I was actually that was

888
00:54:27,040 --> 00:54:28,520
Speaker 1: the next way I was going to ask, which is

889
00:54:28,800 --> 00:54:34,880
Speaker 1: what lessons are there to take away from the essentially

890
00:54:35,560 --> 00:54:37,719
Speaker 1: what was a thirty year fight, but it was the

891
00:54:37,800 --> 00:54:42,600
Speaker 1: public one. Right tobacco. You know, it's not gone, but

892
00:54:42,719 --> 00:54:46,920
Speaker 1: it is it is now where it should be available

893
00:54:46,920 --> 00:54:50,440
Speaker 1: to those who want it for adults and adults only.

894
00:54:51,440 --> 00:54:54,200
Speaker 2: It's a great example, and I think this fight is

895
00:54:54,280 --> 00:54:57,480
Speaker 2: a lot like big tobacco in some ways, and there

896
00:54:57,520 --> 00:55:02,080
Speaker 2: are a few differences. And what's similar is that big

897
00:55:02,200 --> 00:55:05,360
Speaker 2: tech and social media products and technology in general is

898
00:55:05,400 --> 00:55:09,040
Speaker 2: so embedded in our society that in order to shift

899
00:55:09,080 --> 00:55:11,840
Speaker 2: how we use it, to shift how we regulate it,

900
00:55:11,840 --> 00:55:15,120
Speaker 2: it's going to take change at multiple levels. So, just

901
00:55:15,200 --> 00:55:17,920
Speaker 2: like with big tobacco, we need culture change. We need

902
00:55:17,960 --> 00:55:20,800
Speaker 2: a lot of education so that people think about it differently.

903
00:55:22,000 --> 00:55:25,120
Speaker 2: We had to go back to Hollywood, you know, whereas

904
00:55:25,160 --> 00:55:28,160
Speaker 2: tobacco companies had paid them to write scripts where the

905
00:55:28,200 --> 00:55:30,880
Speaker 2: sexy leading men and women were smokers. We had to

906
00:55:31,200 --> 00:55:34,000
Speaker 2: convince them to make sure that the sexy men and

907
00:55:34,040 --> 00:55:37,759
Speaker 2: women leading you know, stars were not smoking. We had

908
00:55:37,760 --> 00:55:40,200
Speaker 2: to get rid of Joe Camel. So there had to

909
00:55:40,200 --> 00:55:42,080
Speaker 2: be a lot of public education. And then there were

910
00:55:42,120 --> 00:55:45,479
Speaker 2: also lawsuits, and we're seeing with organizations like the Social

911
00:55:45,520 --> 00:55:49,640
Speaker 2: Media Victims Law Center and other lawmakers where they are

912
00:55:49,719 --> 00:55:52,440
Speaker 2: suing these companies because the companies are claiming it's not

913
00:55:52,480 --> 00:55:55,120
Speaker 2: their fault that children are harmed. But now the courts

914
00:55:55,120 --> 00:55:59,640
Speaker 2: are hearing cases where for example, you know, and apologies

915
00:55:59,680 --> 00:56:02,560
Speaker 2: to talk about a difficult subject in listeners, just like

916
00:56:02,600 --> 00:56:05,080
Speaker 2: a kind of a trigger warning. But you know, I've

917
00:56:05,080 --> 00:56:09,400
Speaker 2: met families whose children were sent, affirmatively sent by Instagram

918
00:56:09,800 --> 00:56:13,840
Speaker 2: of videos of someone hanging themselves, and then their child replicated.

919
00:56:13,920 --> 00:56:16,920
Speaker 2: We had a little girl in Pennsylvania who tried the

920
00:56:17,040 --> 00:56:20,160
Speaker 2: choking challenge after the platform sent it to her, And

921
00:56:20,200 --> 00:56:22,239
Speaker 2: so courts are now hearing that. And that's just like

922
00:56:22,280 --> 00:56:23,120
Speaker 2: what happened with Big Toma.

923
00:56:23,239 --> 00:56:25,520
Speaker 1: Well, this is this whole thing was sexual section two thirty.

924
00:56:25,600 --> 00:56:27,600
Speaker 1: You know, I've had this argument, I'm not a lawyer.

925
00:56:27,719 --> 00:56:30,840
Speaker 1: I played one on I've played one on television. Was

926
00:56:31,040 --> 00:56:34,719
Speaker 1: how I joke And basically every reporter over time sort

927
00:56:34,719 --> 00:56:36,719
Speaker 1: of starts to think like a lawyer at times. Right,

928
00:56:37,320 --> 00:56:42,120
Speaker 1: And I don't understand how Section two thirty even applies

929
00:56:42,160 --> 00:56:47,120
Speaker 1: that once a tech company creates an algorithm, they're now

930
00:56:47,160 --> 00:56:49,920
Speaker 1: a publisher. If they choose not to be a publisher,

931
00:56:50,360 --> 00:56:53,239
Speaker 1: then they don't have any liability. But the minute they

932
00:56:53,280 --> 00:56:57,040
Speaker 1: created an algorithm, as you point out, they sent the video,

933
00:56:58,200 --> 00:57:01,080
Speaker 1: they are It's no different than in our old newspaper

934
00:57:01,160 --> 00:57:04,880
Speaker 1: editors deciding what goes what goes on the above the

935
00:57:04,920 --> 00:57:07,759
Speaker 1: fold that people can see in the news box where

936
00:57:07,800 --> 00:57:10,040
Speaker 1: you you still when were you still? Sorry for those

937
00:57:10,080 --> 00:57:11,839
Speaker 1: of that don't remember this, we need to go it.

938
00:57:11,960 --> 00:57:14,399
Speaker 1: Actually put a quarter in and get your newspaper out

939
00:57:14,400 --> 00:57:17,640
Speaker 1: of a box and went quote unquote below the fall.

940
00:57:17,960 --> 00:57:21,520
Speaker 1: It's a choice. A algorithm is a choice that the

941
00:57:21,560 --> 00:57:24,000
Speaker 1: tech company makes. I don't see how Section two thirty

942
00:57:24,000 --> 00:57:24,760
Speaker 1: applies at all.

943
00:57:25,240 --> 00:57:27,880
Speaker 2: Well, I think instead of calling at social media, we

944
00:57:27,920 --> 00:57:30,680
Speaker 2: should be calling at mass media publishing, because that's what

945
00:57:30,800 --> 00:57:34,600
Speaker 2: it is. And so when you hear the tech lobby saying, well,

946
00:57:34,640 --> 00:57:38,600
Speaker 2: this is you know, First Amendment. The First Amendment guarantees

947
00:57:38,760 --> 00:57:41,240
Speaker 2: us the right to free speech. It doesn't guarantee you

948
00:57:41,280 --> 00:57:43,760
Speaker 2: the right to publish speech. It doesn't guarantee you the

949
00:57:43,840 --> 00:57:46,520
Speaker 2: right to broadcast speech, and it doesn't guarantee you the

950
00:57:46,600 --> 00:57:50,280
Speaker 2: right to mass mediated speech. So for the last century

951
00:57:50,360 --> 00:57:53,720
Speaker 2: we've had all kinds of laws and restrictions and accountability

952
00:57:54,000 --> 00:57:58,280
Speaker 2: for publishers, for broadcasters, and we allow social media to

953
00:57:58,280 --> 00:58:02,120
Speaker 2: come in and turn anybody and everybody into a publisher

954
00:58:02,200 --> 00:58:05,600
Speaker 2: without any accountability. So, you know, in terms of your

955
00:58:05,720 --> 00:58:09,840
Speaker 2: opening question about how do we get the information ecosystem

956
00:58:09,920 --> 00:58:13,120
Speaker 2: back on track. I think that it's pretty simple. We

957
00:58:13,280 --> 00:58:16,920
Speaker 2: have to pass some regulation that holds people accountable for

958
00:58:16,960 --> 00:58:21,360
Speaker 2: what they publish. And again, advertisers supported speech is very

959
00:58:21,400 --> 00:58:25,680
Speaker 2: different than First Amendment speech, and social media is supported

960
00:58:25,680 --> 00:58:28,600
Speaker 2: by advertising, so that's another legal aspect of it that

961
00:58:28,960 --> 00:58:30,360
Speaker 2: I think needs to be looked at.

962
00:58:32,240 --> 00:58:35,920
Speaker 1: Having good life insurance is incredibly important. I know from

963
00:58:35,960 --> 00:58:39,040
Speaker 1: personal experience. I was sixteen when my father passed away.

964
00:58:39,040 --> 00:58:41,440
Speaker 1: We didn't have any money. He didn't leave us in

965
00:58:41,480 --> 00:58:45,880
Speaker 1: the best shape. My mother, single mother, now widow. Myself

966
00:58:45,960 --> 00:58:47,760
Speaker 1: sixteen trying to figure out how am I going to

967
00:58:47,760 --> 00:58:50,920
Speaker 1: pay for college and lo and behold, my dad had

968
00:58:51,000 --> 00:58:54,200
Speaker 1: one life insurance policy that we found wasn't a lot,

969
00:58:54,520 --> 00:58:57,240
Speaker 1: but it was important at the time, and it's why

970
00:58:57,360 --> 00:59:00,800
Speaker 1: I was able to go to college. Did he know

971
00:59:00,880 --> 00:59:04,880
Speaker 1: how important that would be in that moment? Well, guess what.

972
00:59:05,200 --> 00:59:07,720
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973
00:59:08,120 --> 00:59:10,480
Speaker 1: They can provide you with peace of mind knowing your

974
00:59:10,520 --> 00:59:13,800
Speaker 1: family is protected even if the worst comes to pass.

975
00:59:14,200 --> 00:59:17,440
Speaker 1: Ethos is an online platform that makes getting life insurance

976
00:59:17,480 --> 00:59:20,560
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977
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978
00:59:24,240 --> 00:59:28,440
Speaker 1: one hundred percent online. There's no medical exam require you

979
00:59:28,520 --> 00:59:31,000
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980
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981
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Speaker 1: can get same day coverage without ever leaving your home.

982
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Speaker 1: You can get up to three million dollars in coverage,

983
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Speaker 1: and some policies start as low as two dollars a

984
00:59:41,240 --> 00:59:44,520
Speaker 1: day that would be billed monthly. As of March twenty

985
00:59:44,560 --> 00:59:47,560
Speaker 1: twenty five, Business Insider named Ethos the number one no

986
00:59:47,640 --> 00:59:52,080
Speaker 1: medical exam instant life insurance provider. So protect your family

987
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988
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Speaker 1: ethos dot com slash chuck. So again, that's Ethos dot

989
00:59:59,640 --> 01:00:04,280
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990
01:00:04,320 --> 01:00:07,640
Speaker 1: themselves may vary as well, but trust me, life insurance

991
01:00:07,680 --> 01:00:10,600
Speaker 1: is something you should really think about, especially if you've

992
01:00:10,600 --> 01:00:16,240
Speaker 1: got a growing family. Oh, it used to drive me

993
01:00:16,320 --> 01:00:20,240
Speaker 1: crazy during the during sort of when we were more

994
01:00:20,280 --> 01:00:22,720
Speaker 1: talking about what when Facebook was trying to deny any

995
01:00:22,720 --> 01:00:26,240
Speaker 1: culpability during the during the Russian influence operation, and it

996
01:00:26,320 --> 01:00:29,200
Speaker 1: was sort of like or you know, putting up advertisements

997
01:00:29,240 --> 01:00:32,960
Speaker 1: that were totally miss misleading. When I was at Meet

998
01:00:33,000 --> 01:00:36,200
Speaker 1: the Press, if we aired an ad that we knew

999
01:00:36,280 --> 01:00:43,040
Speaker 1: was misleading, We as the broadcaster, shared liability with the advertiser.

1000
01:00:43,840 --> 01:00:45,960
Speaker 1: In fact, in some ways we were we'd be held

1001
01:00:46,160 --> 01:00:49,200
Speaker 1: more liable than the advertiser themselves. That is not how

1002
01:00:49,240 --> 01:00:53,280
Speaker 1: it works on these platforms. Meta is you know, somebody

1003
01:00:53,320 --> 01:00:58,080
Speaker 1: advertises in Instagram with a with a misleading ad. You

1004
01:00:58,120 --> 01:00:59,920
Speaker 1: don't get to sue Meta for that. You only get

1005
01:01:00,080 --> 01:01:03,520
Speaker 1: to sue I guess the company itself if you want

1006
01:01:03,520 --> 01:01:06,280
Speaker 1: to do it. So there's that has to change. And

1007
01:01:06,320 --> 01:01:08,440
Speaker 1: I and it's one of those where I don't understand.

1008
01:01:08,760 --> 01:01:10,960
Speaker 1: I don't understand. As you said, the laws that are

1009
01:01:10,960 --> 01:01:13,920
Speaker 1: already on the book should already be applied like this idea.

1010
01:01:13,920 --> 01:01:15,800
Speaker 1: They have to be you know, they carved out this

1011
01:01:15,880 --> 01:01:18,480
Speaker 1: separate and I think that's the mistake that was made.

1012
01:01:19,160 --> 01:01:21,320
Speaker 2: I think that was a mistake that's made. I think

1013
01:01:21,360 --> 01:01:23,840
Speaker 2: that you know, when some of these technologies were introduced,

1014
01:01:23,880 --> 01:01:26,440
Speaker 2: there were folks in Congress who didn't quite understand it.

1015
01:01:26,760 --> 01:01:28,840
Speaker 2: You know, as you know, we haven't passed a single

1016
01:01:28,880 --> 01:01:32,720
Speaker 2: regulation federal regulation on social media since nineteen ninety eight,

1017
01:01:32,760 --> 01:01:34,000
Speaker 2: which is before social.

1018
01:01:33,760 --> 01:01:35,720
Speaker 1: Media was no social media, so.

1019
01:01:35,800 --> 01:01:39,800
Speaker 2: You know, we are definitely behind and making sure these

1020
01:01:39,800 --> 01:01:42,440
Speaker 2: products are safe. And we're seeing other countries take the

1021
01:01:42,520 --> 01:01:44,919
Speaker 2: lead in protecting childhood.

1022
01:01:44,400 --> 01:01:48,600
Speaker 1: Right yep, So let's talk about the core goal of

1023
01:01:48,640 --> 01:01:53,800
Speaker 1: your group, right it's media addiction. And you know, I

1024
01:01:54,120 --> 01:01:56,600
Speaker 1: started out by sort of trying to be hopeful and

1025
01:01:56,680 --> 01:01:59,080
Speaker 1: noting that, Hey, look, the one place where politicians seem

1026
01:01:59,120 --> 01:02:01,320
Speaker 1: to find agreement on this issue, like how do we

1027
01:02:01,440 --> 01:02:04,920
Speaker 1: protect our children? And I've been heartened by all these

1028
01:02:05,040 --> 01:02:09,000
Speaker 1: laws passed in the States on self abandoned schools. But

1029
01:02:09,000 --> 01:02:11,080
Speaker 1: do you know what a lot of schools proactively do.

1030
01:02:12,320 --> 01:02:16,480
Speaker 1: I proactively give an iPad to every student that comes

1031
01:02:16,520 --> 01:02:19,040
Speaker 1: in the classroom. In fact, there is a movement in

1032
01:02:19,080 --> 01:02:23,600
Speaker 1: my neighborhood. It's a very small movement, but that's how

1033
01:02:23,680 --> 01:02:27,080
Speaker 1: these movements start, right neighbor to neighbor, going, Hey, tell

1034
01:02:27,200 --> 01:02:30,960
Speaker 1: Arlington schools stop giving you know, teach out of books,

1035
01:02:31,040 --> 01:02:31,840
Speaker 1: not iPads.

1036
01:02:32,000 --> 01:02:36,280
Speaker 2: It's not just in your district, Chuck. We're seeing this nationwide.

1037
01:02:36,440 --> 01:02:39,680
Speaker 2: You know, Mama, Chapter leaders across the country have been

1038
01:02:39,720 --> 01:02:44,520
Speaker 2: among the most vocal in their community to say to teachers,

1039
01:02:44,520 --> 01:02:48,360
Speaker 2: say to schools, this is not what we want teachers,

1040
01:02:48,560 --> 01:02:52,600
Speaker 2: and schools have sort of too quickly. I think bought

1041
01:02:52,640 --> 01:02:55,840
Speaker 2: into this idea that if it's technological, it must be better.

1042
01:02:56,400 --> 01:02:59,880
Speaker 2: And we are now seeing the lowest reading in math

1043
01:03:00,040 --> 01:03:04,120
Speaker 2: scores in our lowest performing children than we've seen since

1044
01:03:04,160 --> 01:03:07,520
Speaker 2: the United States started measuring that back in the seventies.

1045
01:03:07,880 --> 01:03:10,480
Speaker 2: So there's just been a terrible decline in reading in

1046
01:03:10,560 --> 01:03:16,360
Speaker 2: math performance that matches up exactly to the introduction of

1047
01:03:17,440 --> 01:03:21,840
Speaker 2: tablets and laptops into classrooms. At MAMA, we're not anti tech.

1048
01:03:22,240 --> 01:03:24,320
Speaker 2: We think tech can be fun and tech can be helpful.

1049
01:03:24,600 --> 01:03:27,959
Speaker 2: We just don't think it should replace real life experiences

1050
01:03:28,000 --> 01:03:31,600
Speaker 2: and interaction. And there's actually centuries of evidence that show

1051
01:03:31,680 --> 01:03:36,400
Speaker 2: embodied learning experiences are critical for memory consolidation in the mind.

1052
01:03:36,520 --> 01:03:39,680
Speaker 2: That's how children learn by doing, so we know that

1053
01:03:39,720 --> 01:03:42,760
Speaker 2: physically holding a book is affecting the mind in a

1054
01:03:42,840 --> 01:03:46,200
Speaker 2: much different way than looking at it on a screen. So,

1055
01:03:46,560 --> 01:03:48,400
Speaker 2: you know, we have a three part mission at MAMA.

1056
01:03:48,640 --> 01:03:51,920
Speaker 2: It's parent education, it's getting phones out of school so

1057
01:03:52,040 --> 01:03:55,280
Speaker 2: children can learn, and it's demanding safeguards that our lawmakers

1058
01:03:55,320 --> 01:03:57,720
Speaker 2: act to ensure these products if they're going to be

1059
01:03:57,720 --> 01:03:59,200
Speaker 2: out there, that they're safer kids.

1060
01:04:00,240 --> 01:04:03,280
Speaker 1: You know, So, do you think there's a you know,

1061
01:04:04,480 --> 01:04:10,800
Speaker 1: you think there's a an age where you don't even

1062
01:04:10,920 --> 01:04:14,880
Speaker 1: you know, you say, okay, look because now you have

1063
01:04:14,920 --> 01:04:20,160
Speaker 1: these like smartboards instead of chalkboards right where you're able

1064
01:04:20,200 --> 01:04:22,400
Speaker 1: to do and that to me makes sense. You want

1065
01:04:22,440 --> 01:04:25,520
Speaker 1: to have these smart boards. And you know, the upside

1066
01:04:25,520 --> 01:04:28,080
Speaker 1: about having a textbook online is that it's always up

1067
01:04:28,080 --> 01:04:30,240
Speaker 1: to date. It's always current, right, it's never out of

1068
01:04:30,760 --> 01:04:32,680
Speaker 1: it's never now to date. You can quickly without having

1069
01:04:32,680 --> 01:04:37,200
Speaker 1: to buy new textbooks. So there are reasons school districts

1070
01:04:37,240 --> 01:04:39,480
Speaker 1: want to save money, right, which is to have some

1071
01:04:39,600 --> 01:04:44,400
Speaker 1: of this stuff available. What's the Is there a regulatory

1072
01:04:44,480 --> 01:04:47,520
Speaker 1: line we can create in legislation that says, okay, you know,

1073
01:04:47,520 --> 01:04:53,600
Speaker 1: we're not introducing any tech into classrooms until sixth grade?

1074
01:04:53,760 --> 01:04:56,360
Speaker 1: Is that is? That is? Is there enough studies to

1075
01:04:56,440 --> 01:04:59,600
Speaker 1: support a hard and fast line like that.

1076
01:05:00,360 --> 01:05:04,560
Speaker 2: Well, so there's a lot of studies that support the

1077
01:05:04,600 --> 01:05:08,960
Speaker 2: delaying of technology and media for children as long as possible.

1078
01:05:09,200 --> 01:05:12,520
Speaker 2: The American Academy of Pediatric has for many, many years

1079
01:05:12,800 --> 01:05:18,000
Speaker 2: recommended the most minimal amount of screens for your infant

1080
01:05:18,040 --> 01:05:21,400
Speaker 2: and toddler as possible. If your child's under two, they

1081
01:05:21,440 --> 01:05:24,240
Speaker 2: should not be exposed to a screen. If every once

1082
01:05:24,280 --> 01:05:26,120
Speaker 2: in a while, you let your toddler talk to their

1083
01:05:26,160 --> 01:05:29,200
Speaker 2: grandparents on FaceTime or Skype. I don't think it's such

1084
01:05:29,240 --> 01:05:32,960
Speaker 2: a big deal, but nobody should be parking their infant

1085
01:05:33,040 --> 01:05:35,960
Speaker 2: in front of a screen for entertainment because we know

1086
01:05:36,440 --> 01:05:39,880
Speaker 2: it affects their brain development. And there is a researcher

1087
01:05:39,920 --> 01:05:44,240
Speaker 2: who's been taking scans of preschoolers and finding that the

1088
01:05:44,240 --> 01:05:47,800
Speaker 2: ones who had screens have actual less white matter in

1089
01:05:47,840 --> 01:05:51,120
Speaker 2: their brains. In terms of whether we could make a rule,

1090
01:05:51,360 --> 01:05:54,000
Speaker 2: you know, we live in a country where education is

1091
01:05:54,000 --> 01:05:57,640
Speaker 2: decided state by state, and so every state does it differently.

1092
01:05:58,600 --> 01:06:01,720
Speaker 2: My recommendation again would be to delay these things as

1093
01:06:01,800 --> 01:06:04,200
Speaker 2: long as possible. Now there's a lot of parents out

1094
01:06:04,200 --> 01:06:06,400
Speaker 2: there saying we have to have our kids ready to

1095
01:06:06,520 --> 01:06:09,560
Speaker 2: compete in a technological world, and I agree with that.

1096
01:06:10,160 --> 01:06:13,520
Speaker 2: But the question I always ask is, you know, if

1097
01:06:13,520 --> 01:06:16,560
Speaker 2: you want your child to be a safe driver, for example,

1098
01:06:16,840 --> 01:06:19,040
Speaker 2: And we all agree that we want our children, once

1099
01:06:19,040 --> 01:06:21,400
Speaker 2: they learn to drive, to be safe on the roads,

1100
01:06:21,640 --> 01:06:24,040
Speaker 2: But none of us think they should start learning to

1101
01:06:24,120 --> 01:06:27,040
Speaker 2: drive at age seven, or age eight or age nine,

1102
01:06:27,080 --> 01:06:29,520
Speaker 2: because their brains and their bodies are not ready for that.

1103
01:06:30,000 --> 01:06:32,680
Speaker 2: And so for children, what we think is important is

1104
01:06:32,720 --> 01:06:37,000
Speaker 2: that the foundational skills they need to be competent learners

1105
01:06:37,480 --> 01:06:41,200
Speaker 2: are there before we introduce technology, and then we introduce

1106
01:06:41,280 --> 01:06:44,640
Speaker 2: technology in a limited way so that they can learn

1107
01:06:44,680 --> 01:06:47,200
Speaker 2: the skills they need, but that it doesn't overtake all

1108
01:06:47,240 --> 01:06:48,160
Speaker 2: of their other learning.

1109
01:06:49,080 --> 01:06:52,520
Speaker 1: You know, it's interesting. I was at a Texas Tribune

1110
01:06:52,520 --> 01:06:55,720
Speaker 1: festival a couple of weeks ago and saw a conversation

1111
01:06:55,960 --> 01:06:58,960
Speaker 1: with the new president of SMU, who used to be

1112
01:06:59,000 --> 01:07:04,040
Speaker 1: the president of UTI, and they were talking about, you know,

1113
01:07:04,080 --> 01:07:05,960
Speaker 1: over the last ten years, there had been this shift

1114
01:07:06,000 --> 01:07:09,520
Speaker 1: away from kids majoring in sort of the liberal arts, right,

1115
01:07:09,640 --> 01:07:12,720
Speaker 1: majoring in English. In fact, you have universities dropping that

1116
01:07:12,920 --> 01:07:17,760
Speaker 1: as a major, right that there's more specificity that students

1117
01:07:17,760 --> 01:07:21,120
Speaker 1: were leaning towards. And his thesis, his theory and what

1118
01:07:21,200 --> 01:07:24,240
Speaker 1: he was going to bet on is that in the

1119
01:07:24,280 --> 01:07:26,560
Speaker 1: next ten years that we were going to see a

1120
01:07:26,640 --> 01:07:30,040
Speaker 1: turn back to the liberal arts. And in fact, he

1121
01:07:30,200 --> 01:07:36,400
Speaker 1: thinks his job's going to be how to help students

1122
01:07:36,440 --> 01:07:39,320
Speaker 1: when they come to college that college, that college may

1123
01:07:39,440 --> 01:07:45,480
Speaker 1: turn into how to learn because we're in some ways,

1124
01:07:45,920 --> 01:07:49,280
Speaker 1: because our kids are being raised on screens, they know how,

1125
01:07:49,400 --> 01:07:52,560
Speaker 1: they know, they know where to find information. They don't

1126
01:07:52,600 --> 01:07:56,640
Speaker 1: know how to how to how to create the information right,

1127
01:07:56,680 --> 01:08:00,040
Speaker 1: they don't know how to confine the information if the

1128
01:08:00,040 --> 01:08:04,520
Speaker 1: power goes out type of issue, and that that's in

1129
01:08:04,560 --> 01:08:07,280
Speaker 1: some ways that might be a role that undergraduate universities

1130
01:08:07,280 --> 01:08:09,360
Speaker 1: are going to be playing again. So that was an

1131
01:08:09,400 --> 01:08:12,080
Speaker 1: interesting thesis he had. What do you make of that?

1132
01:08:12,560 --> 01:08:15,520
Speaker 2: I mean, that is a very interesting thesis, and it's

1133
01:08:15,560 --> 01:08:19,400
Speaker 2: something I think about all the time. You know, I worry,

1134
01:08:19,439 --> 01:08:22,960
Speaker 2: for example, when you see doctors using their phones to

1135
01:08:23,000 --> 01:08:26,519
Speaker 2: look up everything. You know, I want my surgeon if

1136
01:08:26,560 --> 01:08:29,040
Speaker 2: I'm going in for brain surgery, I want my surgeon

1137
01:08:29,080 --> 01:08:31,080
Speaker 2: to know how to do the surgery even if the

1138
01:08:31,120 --> 01:08:34,960
Speaker 2: power goes out. You know, you want you want our

1139
01:08:35,960 --> 01:08:39,280
Speaker 2: experts to have the skills and the training that they need.

1140
01:08:39,720 --> 01:08:42,360
Speaker 2: You know, it's an interesting hypothesis. I think that a

1141
01:08:42,400 --> 01:08:45,479
Speaker 2: lot of learning, though, happens in early childhood, and there

1142
01:08:45,479 --> 01:08:48,880
Speaker 2: are certain foundational building blocks. We know, if by a

1143
01:08:48,920 --> 01:08:51,880
Speaker 2: certain age you're not reading at a certain level, it's

1144
01:08:52,000 --> 01:08:54,599
Speaker 2: very unlikely that you'll get there in your twenties. Right.

1145
01:08:54,720 --> 01:08:57,960
Speaker 2: That is why in this country we long ago established

1146
01:08:58,320 --> 01:09:00,840
Speaker 2: had start in zero to three, because there was so

1147
01:09:00,920 --> 01:09:04,240
Speaker 2: much evidence that what happens during early childhood really affects

1148
01:09:04,280 --> 01:09:08,120
Speaker 2: your lifelong success in learning and even other things like

1149
01:09:08,160 --> 01:09:15,200
Speaker 2: your economic you know, status, your your job, you know, availability,

1150
01:09:15,240 --> 01:09:19,280
Speaker 2: all those kinds of things. So you know, we we're

1151
01:09:19,320 --> 01:09:23,120
Speaker 2: pushing to make sure. You know, look, I'm an American.

1152
01:09:23,320 --> 01:09:27,000
Speaker 2: I want us to win the AI race, but we

1153
01:09:27,160 --> 01:09:30,200
Speaker 2: need to make sure that these technologies are developed in

1154
01:09:30,280 --> 01:09:33,519
Speaker 2: ways that are all so safe and responsible. And I

1155
01:09:33,560 --> 01:09:37,000
Speaker 2: have every confidence that our technology leaders can do both

1156
01:09:37,040 --> 01:09:39,360
Speaker 2: of those things right. There's no reason we need to

1157
01:09:39,360 --> 01:09:42,720
Speaker 2: just say, okay, let's have no legislation and let's just

1158
01:09:42,800 --> 01:09:44,920
Speaker 2: let them do whatever they want. You know, we've already

1159
01:09:44,960 --> 01:09:48,240
Speaker 2: seen I don't know if you caught this in the news,

1160
01:09:48,240 --> 01:09:50,200
Speaker 2: but there was a teddy Bear with AI in it

1161
01:09:50,240 --> 01:09:53,919
Speaker 2: that just had to be recalled because if a child

1162
01:09:54,080 --> 01:09:56,120
Speaker 2: asked the teddy Bear would tell them where they could

1163
01:09:56,120 --> 01:09:59,200
Speaker 2: find knives matches. If you ask the teddy Bear a

1164
01:09:59,280 --> 01:10:01,960
Speaker 2: sex question and suddenly they lead you in, you know,

1165
01:10:01,960 --> 01:10:05,080
Speaker 2: it would lead you into a conversation about like fringe.

1166
01:10:05,400 --> 01:10:08,960
Speaker 2: You know, sex spetishes like these are not products that

1167
01:10:09,000 --> 01:10:11,439
Speaker 2: are safe for kids, and we should not make them

1168
01:10:11,520 --> 01:10:14,080
Speaker 2: available to kids until we know they're safe.

1169
01:10:14,120 --> 01:10:21,760
Speaker 1: I'm curious what you thought of of mister Altman admitting

1170
01:10:21,840 --> 01:10:24,760
Speaker 1: that he was surprised at how many people chose to

1171
01:10:24,920 --> 01:10:32,640
Speaker 1: use these these in this case chat ChiPT as a

1172
01:10:32,720 --> 01:10:35,839
Speaker 1: sort of therapist, and that he didn't see that coming.

1173
01:10:36,439 --> 01:10:40,120
Speaker 1: And I'm thinking, in your we've put you in charge

1174
01:10:40,160 --> 01:10:40,760
Speaker 1: of building this.

1175
01:10:42,200 --> 01:10:44,160
Speaker 2: You know, I have the same feeling about him that

1176
01:10:44,240 --> 01:10:47,840
Speaker 2: I did about Mark Zuckerberg. I mean, I think that

1177
01:10:47,840 --> 01:10:52,160
Speaker 2: that these guys didn't read enough poetry in college.

1178
01:10:52,439 --> 01:10:54,160
Speaker 1: I think I've got a real I have a real

1179
01:10:54,200 --> 01:10:56,719
Speaker 1: cynical view of these guys. These guys were the people

1180
01:10:56,720 --> 01:10:59,120
Speaker 1: that never knew how to be friends in real life,

1181
01:10:59,680 --> 01:11:03,160
Speaker 1: are constantly looking. And you know, I joke that. You

1182
01:11:03,160 --> 01:11:07,160
Speaker 1: know that Zuckerberg had a hard time meeting girls in college,

1183
01:11:07,160 --> 01:11:11,280
Speaker 1: so he thought he could hack his way into finding

1184
01:11:11,320 --> 01:11:14,080
Speaker 1: a computer algorithm to match him up with people that

1185
01:11:14,240 --> 01:11:16,200
Speaker 1: might be more interested in him.

1186
01:11:16,439 --> 01:11:18,680
Speaker 2: Well that's how Facebook started, right. It was like a

1187
01:11:18,800 --> 01:11:21,280
Speaker 2: rate a girl if she's hot, hot or not.

1188
01:11:21,360 --> 01:11:23,080
Speaker 1: That's what he was looking for. He was trying to

1189
01:11:23,120 --> 01:11:26,240
Speaker 1: meet girls. I mean, not an unusual thing for a

1190
01:11:26,320 --> 01:11:28,840
Speaker 1: nineteen year old. I'm not going to you know, a

1191
01:11:28,920 --> 01:11:31,320
Speaker 1: lot of nineteen year olds boys and girls don't know

1192
01:11:31,360 --> 01:11:35,760
Speaker 1: how to do this. So I empathize in theory, but

1193
01:11:36,000 --> 01:11:40,080
Speaker 1: I do think the entire the leadership of Silicon Valley

1194
01:11:40,720 --> 01:11:44,720
Speaker 1: are some people that didn't grow up the way a

1195
01:11:44,760 --> 01:11:46,200
Speaker 1: lot of other people grew up.

1196
01:11:46,720 --> 01:11:49,520
Speaker 2: I mean, I'll give you one more example. G Nome Shazir,

1197
01:11:49,840 --> 01:11:55,000
Speaker 2: who founded character Ai, was interviewed in a podcast a

1198
01:11:55,040 --> 01:11:58,880
Speaker 2: couple of years ago and they asked him about character

1199
01:11:58,920 --> 01:12:01,639
Speaker 2: AI and he said, well, give you my humorous VC pitch.

1200
01:12:02,040 --> 01:12:05,680
Speaker 2: He said, you know, we know child's out walking with

1201
01:12:05,720 --> 01:12:08,720
Speaker 2: their parents. They're holding hands, they're asking questions, and the

1202
01:12:08,760 --> 01:12:11,880
Speaker 2: parents giving the child a lot of information. But they're

1203
01:12:11,880 --> 01:12:15,240
Speaker 2: not just giving them facts. They're also you know, they're

1204
01:12:15,280 --> 01:12:18,400
Speaker 2: their friend. They're giving them emotional support. They said, that's

1205
01:12:18,439 --> 01:12:20,280
Speaker 2: what we want character ai to do. We don't want

1206
01:12:20,280 --> 01:12:23,720
Speaker 2: to replace Google. We want to replace your mom. And

1207
01:12:23,800 --> 01:12:27,720
Speaker 2: he said this, Oh my god. And then and then

1208
01:12:27,800 --> 01:12:31,519
Speaker 2: we've seen all of these lawsuits now because character AI

1209
01:12:31,920 --> 01:12:35,640
Speaker 2: when people have turned to it for emotional support or

1210
01:12:35,760 --> 01:12:39,200
Speaker 2: because they are depressed or they are struggling, rather than

1211
01:12:39,280 --> 01:12:45,160
Speaker 2: direct them to get help, rather than stopping, the AI

1212
01:12:45,360 --> 01:12:50,280
Speaker 2: program is providing detailed instructions and encouragement. In some cases

1213
01:12:50,640 --> 01:12:53,280
Speaker 2: for someone to end their own life. So I think,

1214
01:12:53,320 --> 01:12:56,240
Speaker 2: to that company's credit, they have now announced that it

1215
01:12:56,240 --> 01:12:59,320
Speaker 2: should not be used by anyone under eighteen. But again

1216
01:12:59,520 --> 01:13:03,479
Speaker 2: the question remains, why didn't they consider this consequence when

1217
01:13:03,520 --> 01:13:07,719
Speaker 2: they created the product? And so again that is why, Mama,

1218
01:13:07,720 --> 01:13:12,080
Speaker 2: we're so focused right now in ensuring that our lawmakers

1219
01:13:12,160 --> 01:13:16,559
Speaker 2: take action to require these companies to be accountable for

1220
01:13:16,600 --> 01:13:19,800
Speaker 2: the products that they make, that there's transparency so we

1221
01:13:19,960 --> 01:13:23,120
Speaker 2: know what's happening at these companies, because unless you require

1222
01:13:23,160 --> 01:13:26,240
Speaker 2: them to provide this data, you know researchers can't even

1223
01:13:26,439 --> 01:13:29,400
Speaker 2: study it, and that there's responsibility and that when they

1224
01:13:29,600 --> 01:13:32,639
Speaker 2: design them in such a way that it's inflicting harm

1225
01:13:32,760 --> 01:13:36,360
Speaker 2: on our children at scale, that they are held responsible

1226
01:13:36,400 --> 01:13:39,439
Speaker 2: for that. And we are seeing multiple lawsuits now from

1227
01:13:39,800 --> 01:13:42,800
Speaker 2: school districts throughout the country who are saying, you know what,

1228
01:13:42,920 --> 01:13:45,400
Speaker 2: you have to reimburse us for the tens of millions

1229
01:13:45,400 --> 01:13:51,400
Speaker 2: of dollars it's costing us for therapists, for emergency psychiatric beds,

1230
01:13:51,439 --> 01:13:53,880
Speaker 2: for all of these costs that have been borne by

1231
01:13:54,000 --> 01:13:58,400
Speaker 2: Americans and our communities that are really the cause that

1232
01:13:58,560 --> 01:14:01,320
Speaker 2: been caused by these tech company products.

1233
01:14:01,880 --> 01:14:04,880
Speaker 1: I want to go back to the school issue, you know,

1234
01:14:04,960 --> 01:14:08,400
Speaker 1: one of the fears. You know, I think about the

1235
01:14:08,439 --> 01:14:11,240
Speaker 1: following right with the advent of AI over the last

1236
01:14:11,680 --> 01:14:16,599
Speaker 1: twenty years, the focus among the many parents. My kids

1237
01:14:16,600 --> 01:14:19,600
Speaker 1: are now eighteen and twenty one, so I remember, and

1238
01:14:19,640 --> 01:14:22,040
Speaker 1: there was always, you know, every conversation with every parent

1239
01:14:22,040 --> 01:14:26,200
Speaker 1: of you know, in my cohort was always boy, you know, hey,

1240
01:14:26,960 --> 01:14:29,000
Speaker 1: they got to learn coding, and oh, but they better

1241
01:14:29,040 --> 01:14:32,080
Speaker 1: learn coding, and that's what's you know, and stem and

1242
01:14:32,160 --> 01:14:33,759
Speaker 1: all of this stuff. And then all of a sudden

1243
01:14:34,479 --> 01:14:36,519
Speaker 1: we realize, oh, no, coding is not going to be

1244
01:14:36,560 --> 01:14:39,720
Speaker 1: a life skill. This isn't this isn't going to be

1245
01:14:39,760 --> 01:14:42,240
Speaker 1: something you need, and it isn't going to help you

1246
01:14:42,280 --> 01:14:44,640
Speaker 1: get another job. In fact, that is going to be

1247
01:14:44,680 --> 01:14:49,080
Speaker 1: replaced by a robot. So that's that's a that's a

1248
01:14:49,160 --> 01:14:53,479
Speaker 1: no longer necessary. That's like teaching somebody how to dig

1249
01:14:53,520 --> 01:14:57,800
Speaker 1: out an ice block. We don't we don't refrigerate with

1250
01:14:57,880 --> 01:15:01,040
Speaker 1: ice blocks anymore. That is no longer an industry that

1251
01:15:01,160 --> 01:15:05,439
Speaker 1: is necessary. The biggest fear I have now moving from

1252
01:15:05,520 --> 01:15:09,479
Speaker 1: parent to grandparent. Right, I'm not there yet, but I

1253
01:15:09,600 --> 01:15:13,800
Speaker 1: have nieces and nephews who are having kids. Now, and

1254
01:15:13,840 --> 01:15:18,800
Speaker 1: that is they don't you and I thought think we

1255
01:15:18,880 --> 01:15:21,160
Speaker 1: have an idea of the world our kids are going

1256
01:15:21,240 --> 01:15:25,360
Speaker 1: to be living in. I think that's harder and harder

1257
01:15:25,360 --> 01:15:28,960
Speaker 1: to visualize, and I think it's paralyzing parents and trying

1258
01:15:28,960 --> 01:15:32,240
Speaker 1: to figure out what it is that they should be

1259
01:15:32,320 --> 01:15:35,000
Speaker 1: asking the schools to be teaching their kids to prepare

1260
01:15:35,040 --> 01:15:39,800
Speaker 1: them for this next generation of jobs or this next

1261
01:15:39,840 --> 01:15:42,479
Speaker 1: generation of society, because I don't think any of us

1262
01:15:42,800 --> 01:15:45,200
Speaker 1: have the first clue of what it's going to look like.

1263
01:15:45,880 --> 01:15:49,200
Speaker 1: And I don't know how that factors into what you're

1264
01:15:49,240 --> 01:15:52,559
Speaker 1: working on, but it's to me part of the of

1265
01:15:52,600 --> 01:15:55,799
Speaker 1: the fear factor that has allowed so much technology into

1266
01:15:55,840 --> 01:15:57,040
Speaker 1: the school systems.

1267
01:15:57,880 --> 01:16:03,200
Speaker 2: So the way we describe our work at MAMA is

1268
01:16:03,240 --> 01:16:07,000
Speaker 2: that where a grassroots movement of parents and allies fighting

1269
01:16:07,080 --> 01:16:10,519
Speaker 2: back against media addiction and creating a world where real

1270
01:16:10,600 --> 01:16:14,040
Speaker 2: life experiences and interactions remain at the heart of a

1271
01:16:14,080 --> 01:16:17,960
Speaker 2: healthy childhood. And the reason for that is because tech

1272
01:16:18,080 --> 01:16:21,320
Speaker 2: is here, it's not going anywhere. We are going to

1273
01:16:21,439 --> 01:16:25,120
Speaker 2: have a lot of technology available to us, and it's

1274
01:16:25,160 --> 01:16:27,680
Speaker 2: going to help us solve a lot of problems. But

1275
01:16:27,760 --> 01:16:30,960
Speaker 2: there are many things about being human that we don't

1276
01:16:31,000 --> 01:16:34,080
Speaker 2: want to change, and it should always be part of

1277
01:16:34,080 --> 01:16:36,880
Speaker 2: being human. I don't want a robot to hold my

1278
01:16:37,000 --> 01:16:39,719
Speaker 2: toddler or my grandbaby. I want to hold that child.

1279
01:16:40,080 --> 01:16:43,200
Speaker 2: And we don't think that we should just blindly say, okay,

1280
01:16:43,240 --> 01:16:47,760
Speaker 2: tech is going to replace everything. We know that, and

1281
01:16:48,040 --> 01:16:51,879
Speaker 2: there's abundant evidence about the role of parents and adults

1282
01:16:51,880 --> 01:16:55,200
Speaker 2: in children's lives and about the role of embodied experiences

1283
01:16:55,240 --> 01:16:59,280
Speaker 2: in children's learning. And we also know that there are many, many,

1284
01:16:59,320 --> 01:17:02,920
Speaker 2: many jobs that technology is not good for. I don't

1285
01:17:02,920 --> 01:17:05,160
Speaker 2: know if you saw that video that was making its

1286
01:17:05,280 --> 01:17:09,160
Speaker 2: way across social media last week, but there was a

1287
01:17:09,200 --> 01:17:11,920
Speaker 2: new AI robot. It was presented at a conference, and

1288
01:17:11,960 --> 01:17:13,840
Speaker 2: as it got across the stage, you know it was

1289
01:17:13,880 --> 01:17:16,920
Speaker 2: it looked really cool until it fell down because they

1290
01:17:16,920 --> 01:17:19,800
Speaker 2: haven't even figured out balance yet. So, you know, over

1291
01:17:19,880 --> 01:17:23,639
Speaker 2: and over again, for more than a century, we've heard

1292
01:17:23,720 --> 01:17:28,000
Speaker 2: tech companies promise that their product is going to revolutionize everything,

1293
01:17:28,360 --> 01:17:31,800
Speaker 2: and some products do and they you know, a smartphone

1294
01:17:31,840 --> 01:17:34,360
Speaker 2: has changed a lot of things. Has it changed the

1295
01:17:34,360 --> 01:17:37,040
Speaker 2: way we eat food, Has it changed the way we

1296
01:17:37,120 --> 01:17:40,000
Speaker 2: nurture our babies, has it changed the way you know,

1297
01:17:40,200 --> 01:17:43,679
Speaker 2: our economy is run, not exactly. There are some changes

1298
01:17:43,680 --> 01:17:47,280
Speaker 2: in our payments, right, but absolutely everything is not going

1299
01:17:47,360 --> 01:17:50,400
Speaker 2: to change. So when it comes to thinking about the future,

1300
01:17:50,800 --> 01:17:53,640
Speaker 2: you know, what I want from my children is to

1301
01:17:53,720 --> 01:17:57,639
Speaker 2: have the same thing that my grandparents wanted for their kids.

1302
01:17:57,880 --> 01:18:00,720
Speaker 2: It's the same thing I want for my future grandchildren.

1303
01:18:01,000 --> 01:18:03,400
Speaker 2: I want them to grow up healthy. I want them

1304
01:18:03,439 --> 01:18:06,240
Speaker 2: to grow up confident and capable. I want them to

1305
01:18:06,280 --> 01:18:09,040
Speaker 2: have a set of skills so that they are resilient

1306
01:18:09,080 --> 01:18:12,320
Speaker 2: and can adapt because nobody knows exactly what the workplace

1307
01:18:12,400 --> 01:18:17,439
Speaker 2: is going to need, right, but we're always gonna need trades,

1308
01:18:17,640 --> 01:18:20,640
Speaker 2: We're always going to need skilled workers, We're always going

1309
01:18:20,720 --> 01:18:25,000
Speaker 2: to need caretakers, and all of these professions that hold

1310
01:18:25,120 --> 01:18:27,719
Speaker 2: up what it means to have a society. The tech

1311
01:18:27,760 --> 01:18:30,280
Speaker 2: workers and the folks that the machines are a small

1312
01:18:30,320 --> 01:18:32,880
Speaker 2: part of that. And I think we also just don't

1313
01:18:32,920 --> 01:18:35,280
Speaker 2: want to give it all over to the machines. You know,

1314
01:18:35,320 --> 01:18:38,760
Speaker 2: the Luddites happened because those folks like didn't want to

1315
01:18:38,800 --> 01:18:41,200
Speaker 2: lose their jobs. And now we're at a point where

1316
01:18:41,240 --> 01:18:43,920
Speaker 2: many many more types of work can be replaced, and

1317
01:18:44,000 --> 01:18:45,840
Speaker 2: we have to decide is that what we want? Do

1318
01:18:45,880 --> 01:18:48,599
Speaker 2: we want actors to not you know, have jobs anymore.

1319
01:18:48,640 --> 01:18:51,960
Speaker 2: Do we want everything to be robots? And I don't

1320
01:18:52,000 --> 01:18:53,800
Speaker 2: think we do. I don't think anybody wants to take

1321
01:18:53,800 --> 01:18:55,960
Speaker 2: their kindergartener to class and have them taught by a

1322
01:18:56,040 --> 01:18:56,799
Speaker 2: robot teacher.

1323
01:19:05,920 --> 01:19:08,280
Speaker 1: This is where I'm weirdly optimistic. Like you know, the

1324
01:19:08,360 --> 01:19:12,960
Speaker 1: human species is pretty adaptable and has survived quite a

1325
01:19:13,000 --> 01:19:16,080
Speaker 1: few challenges over the last few million years. I have

1326
01:19:16,080 --> 01:19:18,000
Speaker 1: a feeling we're not going to let ourselves be replaced

1327
01:19:18,000 --> 01:19:21,400
Speaker 1: by robots. I just wonder if we know we need it,

1328
01:19:21,560 --> 01:19:23,160
Speaker 1: when we're ready to start fighting.

1329
01:19:22,880 --> 01:19:26,559
Speaker 2: Back, Well, it's time. I mean, the time is here, right.

1330
01:19:26,720 --> 01:19:29,320
Speaker 1: And that's where I want to get to. So okay,

1331
01:19:29,400 --> 01:19:34,559
Speaker 1: So you're you are trying to essentially become a political force,

1332
01:19:34,880 --> 01:19:37,880
Speaker 1: not left or right, just a force. You know, we're

1333
01:19:37,920 --> 01:19:44,200
Speaker 1: not exactly advocating an advocacy. What are you working on

1334
01:19:44,280 --> 01:19:46,559
Speaker 1: in the next six months? I know in the federal

1335
01:19:46,640 --> 01:19:49,160
Speaker 1: level we talked a little bit about it. It's the

1336
01:19:49,640 --> 01:19:55,439
Speaker 1: stopping this AI moratorium, AI regulatory moratorium. What are some

1337
01:19:56,200 --> 01:19:59,559
Speaker 1: near term activities in the States that you're working on

1338
01:19:59,640 --> 01:20:03,479
Speaker 1: as well? Well? And are you going to you know,

1339
01:20:03,600 --> 01:20:08,439
Speaker 1: try to do candidate questionnaires or try to do things

1340
01:20:08,560 --> 01:20:11,479
Speaker 1: like that, or are you not in that space just yet?

1341
01:20:11,840 --> 01:20:13,880
Speaker 2: So we're not in that space just yet. We're a five,

1342
01:20:13,920 --> 01:20:16,200
Speaker 2: A one, C three, we're not a C four. You know.

1343
01:20:16,520 --> 01:20:20,559
Speaker 2: Working on demanding safeguards is one small part of what

1344
01:20:20,600 --> 01:20:24,440
Speaker 2: we do. We also work really hard on our communities

1345
01:20:24,560 --> 01:20:28,920
Speaker 2: to educate parents on why they don't have to rush

1346
01:20:28,920 --> 01:20:32,160
Speaker 2: to give their kids a phone. Our wonderful chapter leader

1347
01:20:32,280 --> 01:20:36,880
Speaker 2: in Pittsburgh, you know, talked with the members of that

1348
01:20:37,000 --> 01:20:39,360
Speaker 2: chapter and they really wanted their children to be able

1349
01:20:39,439 --> 01:20:42,559
Speaker 2: to go to school without phones. But some parents were worried,

1350
01:20:42,560 --> 01:20:45,040
Speaker 2: what about an emergency? And they went and they talked

1351
01:20:45,040 --> 01:20:47,559
Speaker 2: to local shop owners and they said, you know, in

1352
01:20:47,600 --> 01:20:49,840
Speaker 2: an emergency, can my child come in and use the phone?

1353
01:20:50,200 --> 01:20:52,640
Speaker 2: And the store owners were like, of course, and they

1354
01:20:52,800 --> 01:20:54,679
Speaker 2: put little stickers in the window so the kids would

1355
01:20:54,680 --> 01:20:57,679
Speaker 2: even know they were welcome there. Right, So Mama Mama

1356
01:20:57,720 --> 01:21:01,320
Speaker 2: works in communities, and then we also work with schools

1357
01:21:01,520 --> 01:21:04,400
Speaker 2: because this is a huge issue now, not just the phones,

1358
01:21:04,439 --> 01:21:07,360
Speaker 2: but what people are calling ed tech. So that is

1359
01:21:07,400 --> 01:21:12,599
Speaker 2: the giving of tablets to kindergarteners, the giving of laptop

1360
01:21:12,680 --> 01:21:15,439
Speaker 2: to first graders, and all over the country we're hearing

1361
01:21:15,479 --> 01:21:18,280
Speaker 2: from parents that they're at war with their schools because

1362
01:21:18,280 --> 01:21:20,120
Speaker 2: they don't want their children to come home and have

1363
01:21:20,200 --> 01:21:22,719
Speaker 2: these devices. They don't want their children to have access

1364
01:21:22,720 --> 01:21:25,000
Speaker 2: to YouTube where they're going to be watching videos over

1365
01:21:25,040 --> 01:21:27,320
Speaker 2: and over, and the platform is designed to keep them

1366
01:21:27,320 --> 01:21:30,080
Speaker 2: on there as long as possible. And then in terms

1367
01:21:30,160 --> 01:21:34,200
Speaker 2: of legislation, we are very excited about all of the

1368
01:21:34,280 --> 01:21:37,920
Speaker 2: lawmakers that introduce bills to try to require products to

1369
01:21:37,960 --> 01:21:41,280
Speaker 2: be safer. In the last legislative session we saw close

1370
01:21:41,320 --> 01:21:45,080
Speaker 2: to four hundred bills introduced. There were a lot, but

1371
01:21:45,160 --> 01:21:48,759
Speaker 2: we've seen success in everything from bell to bell phone

1372
01:21:48,800 --> 01:21:52,280
Speaker 2: bands to social media warning labels pass. And we're most

1373
01:21:52,320 --> 01:21:56,519
Speaker 2: excited about legislation like something called the Age Appropriate Design

1374
01:21:56,560 --> 01:21:59,040
Speaker 2: Code or the Kids Code, And this is law that

1375
01:21:59,200 --> 01:22:04,000
Speaker 2: requires companies that make digital products to be used by

1376
01:22:04,120 --> 01:22:07,040
Speaker 2: children to show what's called a duty of care to

1377
01:22:07,160 --> 01:22:10,479
Speaker 2: children in making them safe by design, so if the kids,

1378
01:22:10,560 --> 01:22:12,000
Speaker 2: if a kid's going to go on there, then it

1379
01:22:12,000 --> 01:22:14,679
Speaker 2: should be designed in a safe way. And that means

1380
01:22:14,720 --> 01:22:18,240
Speaker 2: doing things like putting privacy settings at the highest by default,

1381
01:22:18,320 --> 01:22:20,920
Speaker 2: not the lowest, so that every American parent doesn't have

1382
01:22:20,960 --> 01:22:22,760
Speaker 2: to go in there and figure out how to how

1383
01:22:22,760 --> 01:22:25,160
Speaker 2: to make sure a stranger can't contact their child. But

1384
01:22:25,160 --> 01:22:28,639
Speaker 2: it's set that way from the beginning. So Nebraska and Vermont,

1385
01:22:28,680 --> 01:22:31,360
Speaker 2: for example, this year passed the Age Appropriate Design Code.

1386
01:22:31,640 --> 01:22:34,680
Speaker 1: By the way, that's that just shows you the ideological

1387
01:22:35,080 --> 01:22:38,759
Speaker 1: breadth if you will, that's there, right. You know, Vermont

1388
01:22:38,880 --> 01:22:41,320
Speaker 1: is Vermont the hom of Bernie Sanders. Well guess what

1389
01:22:41,439 --> 01:22:45,719
Speaker 1: Nebraska is. They root for a team they call Big Red.

1390
01:22:45,760 --> 01:22:49,559
Speaker 1: And it isn't just because Nebraska's colors are red. Yep.

1391
01:22:50,040 --> 01:22:53,479
Speaker 2: And you know, we're also seeing some interesting legislation now

1392
01:22:53,520 --> 01:22:59,960
Speaker 2: being introduced about AI. So in October, Senators Mark Warner

1393
01:23:00,040 --> 01:23:04,360
Speaker 2: from Virginia, Josh Holly from Missouri, Dick Blumenthal from Connecticut,

1394
01:23:04,520 --> 01:23:08,360
Speaker 2: Chris Murphy from Connecticut, and Katie Britt from Alabama introduced

1395
01:23:08,400 --> 01:23:11,840
Speaker 2: this bipartisan bill that would ban miners from using AI

1396
01:23:12,000 --> 01:23:15,240
Speaker 2: chatbot companions. And it would have an incredible impact on

1397
01:23:15,280 --> 01:23:17,880
Speaker 2: the safety of our children. So you know, that's the

1398
01:23:17,960 --> 01:23:21,120
Speaker 2: kind of thing. I mean, who who wouldn't support that?

1399
01:23:21,160 --> 01:23:23,559
Speaker 2: I mean you have to ask who wouldn't support keeping

1400
01:23:23,640 --> 01:23:25,759
Speaker 2: kids safe? Do you?

1401
01:23:25,760 --> 01:23:28,040
Speaker 1: You know, do you plan on having sort of Mama

1402
01:23:28,320 --> 01:23:30,639
Speaker 1: seal of approsals? You know, meaning like, if you want

1403
01:23:30,680 --> 01:23:34,640
Speaker 1: to see what are what are? What are you know?

1404
01:23:34,720 --> 01:23:38,080
Speaker 1: Could I, you know, maybe this a little early my

1405
01:23:38,240 --> 01:23:41,360
Speaker 1: next holiday season, Will I go on Mama's website and

1406
01:23:41,400 --> 01:23:44,360
Speaker 1: be able to see these are products that you should

1407
01:23:44,400 --> 01:23:47,760
Speaker 1: feel comfortable, that have some tech in him, that are

1408
01:23:47,520 --> 01:23:48,479
Speaker 1: that are safe.

1409
01:23:49,560 --> 01:23:52,559
Speaker 2: Chuck, I don't want to promise that to your listeners.

1410
01:23:52,600 --> 01:23:55,160
Speaker 2: It's definitely on our to do list. We're moving as

1411
01:23:55,200 --> 01:23:58,600
Speaker 2: quickly as we can. As I mentioned, you know, we

1412
01:23:58,680 --> 01:24:02,400
Speaker 2: had this ambitious goal of having six chapters, and the

1413
01:24:02,479 --> 01:24:05,439
Speaker 2: demand is just completely overwhelming. Right after we launched, we

1414
01:24:05,439 --> 01:24:08,559
Speaker 2: got requests from all fifty states and all over the globe,

1415
01:24:08,720 --> 01:24:12,639
Speaker 2: like every continent except Antarctica. So you know, we are

1416
01:24:12,840 --> 01:24:16,639
Speaker 2: very grateful that we receive some funding this year from

1417
01:24:16,680 --> 01:24:22,800
Speaker 2: the Rockefeller Foundation, and we are primarily funded by individual

1418
01:24:22,840 --> 01:24:25,840
Speaker 2: donors and family foundations. There's a lot of folks who've

1419
01:24:25,840 --> 01:24:27,960
Speaker 2: come together to make this work happen, and we're growing

1420
01:24:27,960 --> 01:24:30,360
Speaker 2: as quickly as we can. But yes, I do think

1421
01:24:30,400 --> 01:24:33,160
Speaker 2: eventually that is something we would like to provide.

1422
01:24:33,360 --> 01:24:35,000
Speaker 1: You know, it's interesting you just talked about the world.

1423
01:24:35,520 --> 01:24:37,400
Speaker 1: I heard a stat and I wonder if you guys

1424
01:24:37,920 --> 01:24:40,760
Speaker 1: are now a Clearinghouse for some of these studies that

1425
01:24:40,800 --> 01:24:43,640
Speaker 1: there has not been a study around the world on

1426
01:24:44,800 --> 01:24:48,439
Speaker 1: social media usage or early phone usage that it hasn't

1427
01:24:48,439 --> 01:24:53,720
Speaker 1: mattered whether it's a rich country a poor country. It

1428
01:24:53,760 --> 01:24:58,280
Speaker 1: hasn't mattered what ethnicity or if it's a homogenous society

1429
01:24:58,360 --> 01:25:01,760
Speaker 1: or a multi ethnic society that this is. It is

1430
01:25:01,840 --> 01:25:06,760
Speaker 1: so clear that this tech at a young age has

1431
01:25:06,800 --> 01:25:08,320
Speaker 1: been harmful. Hard stop.

1432
01:25:09,760 --> 01:25:14,400
Speaker 2: You know, social media doesn't discriminate, and we have definitely

1433
01:25:14,439 --> 01:25:17,920
Speaker 2: seen that. You know, with the National Emergency and Youth

1434
01:25:18,600 --> 01:25:23,519
Speaker 2: Mental Health it cuts across race, class, gender, geography, you

1435
01:25:23,600 --> 01:25:29,040
Speaker 2: name it. There is additional concerns for vulnerable communities. We

1436
01:25:29,160 --> 01:25:34,160
Speaker 2: have seen. Unfortunately, the increase in suicide rates among Hispanic

1437
01:25:34,280 --> 01:25:37,920
Speaker 2: adolescents is even higher than in its white counterparts. The

1438
01:25:38,040 --> 01:25:42,720
Speaker 2: rate of increase in Black youth is high. So I

1439
01:25:42,760 --> 01:25:45,719
Speaker 2: think we're beginning to see a different kind of digital

1440
01:25:45,760 --> 01:25:50,320
Speaker 2: divide where low income families, and in part it might

1441
01:25:50,360 --> 01:25:52,160
Speaker 2: be because childcare is so expensive.

1442
01:25:52,160 --> 01:25:53,960
Speaker 1: And I'm just going to say, is that child is

1443
01:25:54,000 --> 01:25:57,519
Speaker 1: basically unfortunately we have tech. Nanny's right. You know, I

1444
01:25:57,560 --> 01:26:00,000
Speaker 1: was a latchkey kid. First thing I did when I

1445
01:26:00,040 --> 01:26:01,080
Speaker 1: had to get home was I had to call my

1446
01:26:01,080 --> 01:26:02,280
Speaker 1: mom at work to let her know I got in

1447
01:26:02,320 --> 01:26:03,760
Speaker 1: the house right, and you.

1448
01:26:03,680 --> 01:26:06,200
Speaker 2: Probably watched TV and that was fine, though, you know

1449
01:26:06,600 --> 01:26:06,840
Speaker 2: I did.

1450
01:26:06,840 --> 01:26:08,920
Speaker 1: Turn on TV. But what would happen now? Right? You

1451
01:26:09,120 --> 01:26:12,880
Speaker 1: whip on? You probably, you know, put the device on, right.

1452
01:26:13,080 --> 01:26:15,240
Speaker 2: I mean, I watch more Love Boat than any ten

1453
01:26:15,320 --> 01:26:17,599
Speaker 2: year old should have. But there was a limit to

1454
01:26:17,960 --> 01:26:19,360
Speaker 2: what kinds of things.

1455
01:26:19,240 --> 01:26:21,400
Speaker 1: My parents did. Weren't crazy about me watching love Boat

1456
01:26:21,479 --> 01:26:23,200
Speaker 1: or Three's Company, I remember exactly.

1457
01:26:25,080 --> 01:26:26,440
Speaker 2: We didn't even know.

1458
01:26:26,680 --> 01:26:30,479
Speaker 1: I'll see an old rerunning, Oh my god, like that

1459
01:26:30,640 --> 01:26:33,720
Speaker 1: is risque. I can't believe they let that on television. Right.

1460
01:26:33,800 --> 01:26:36,920
Speaker 2: Well, now, the average age of first exposure to pornography

1461
01:26:37,000 --> 01:26:40,719
Speaker 2: is twelve, okay, Chuck, twelve like you know, and look

1462
01:26:40,920 --> 01:26:44,240
Speaker 2: like I think as a parent, you know, I don't

1463
01:26:44,280 --> 01:26:48,559
Speaker 2: have crazy conservative ideas. I know that at some point

1464
01:26:48,680 --> 01:26:52,080
Speaker 2: my sons will probably be exposed to pornography in high school.

1465
01:26:52,439 --> 01:26:54,920
Speaker 2: But I don't think it should be mainline to them

1466
01:26:55,200 --> 01:26:58,679
Speaker 2: by a couple of companies that are making gobs of profit.

1467
01:26:58,800 --> 01:27:01,720
Speaker 2: If they can get people to stay on their platform.

1468
01:27:01,320 --> 01:27:04,200
Speaker 1: Longer, there's certain things that should be hard to come by,

1469
01:27:04,400 --> 01:27:08,639
Speaker 1: and they have to frankly in some ways you should

1470
01:27:08,640 --> 01:27:11,519
Speaker 1: feel a little bit of shame, because that's actually a

1471
01:27:11,560 --> 01:27:13,920
Speaker 1: good thing to have through life.

1472
01:27:14,120 --> 01:27:16,400
Speaker 2: Yeah. Well, you know, look, I don't think anybody would

1473
01:27:16,439 --> 01:27:23,400
Speaker 2: support allowing pornography at the checkout register at the supermarket,

1474
01:27:23,600 --> 01:27:26,200
Speaker 2: but we didn't. We don't have a law that forces

1475
01:27:26,280 --> 01:27:29,600
Speaker 2: people to, you know, put it high up behind the

1476
01:27:29,680 --> 01:27:31,679
Speaker 2: counter or wrap it in paper bags.

1477
01:27:31,720 --> 01:27:31,880
Speaker 1: You know.

1478
01:27:31,920 --> 01:27:34,559
Speaker 2: So custom is part of this and part of what's

1479
01:27:34,680 --> 01:27:39,679
Speaker 2: challenging in the digital eras that were you know, these companies, we're.

1480
01:27:39,560 --> 01:27:41,759
Speaker 1: Still making the customs. We don't really have a making

1481
01:27:41,800 --> 01:27:44,800
Speaker 1: the customs right, and I think that that's been that's

1482
01:27:44,840 --> 01:27:49,160
Speaker 1: what makes this feel so challenging. And because I think

1483
01:27:49,200 --> 01:27:51,960
Speaker 1: we all agree we've got to figure out how to

1484
01:27:52,000 --> 01:27:56,320
Speaker 1: slow down the adapt the adaptation of tech in kids' lives.

1485
01:27:57,520 --> 01:28:00,280
Speaker 1: But my god, is the toothpaste already out of too?

1486
01:28:01,200 --> 01:28:04,519
Speaker 2: Well. We have Mama's House Rules. If you go to

1487
01:28:04,680 --> 01:28:07,000
Speaker 2: we Are Mama dot org and you click on learn,

1488
01:28:07,160 --> 01:28:10,200
Speaker 2: you can scroll down and Mama's House Rules give you

1489
01:28:10,280 --> 01:28:13,479
Speaker 2: some ideas of how as a family you can manage it,

1490
01:28:13,600 --> 01:28:17,360
Speaker 2: limit it, put it in a container. Again, we're not

1491
01:28:17,800 --> 01:28:19,960
Speaker 2: saying there's going to be no tech in your life.

1492
01:28:20,400 --> 01:28:23,439
Speaker 2: But we're just saying, how do you have a life

1493
01:28:23,560 --> 01:28:27,280
Speaker 2: that's based on real life experiences and interaction, because once

1494
01:28:27,600 --> 01:28:31,519
Speaker 2: you or your child are spending too much time online,

1495
01:28:31,600 --> 01:28:34,920
Speaker 2: it just becomes so unhealthy. And you know we're all

1496
01:28:34,960 --> 01:28:37,400
Speaker 2: media addicted, right, it's not just our children, and what

1497
01:28:37,479 --> 01:28:40,560
Speaker 2: we model has a huge huge impact on them.

1498
01:28:41,960 --> 01:28:44,840
Speaker 1: Well, if I just went on your site, we are

1499
01:28:44,960 --> 01:28:48,880
Speaker 1: mama dot org. So that's the fairly quick and easy place.

1500
01:28:49,000 --> 01:28:54,360
Speaker 1: You mentioned one other thing about tobacco, about how those

1501
01:28:54,400 --> 01:28:57,439
Speaker 1: that were advocating against it had a went to Hollywood

1502
01:28:57,479 --> 01:29:00,280
Speaker 1: and said, hey, we need to redo this. Are you

1503
01:29:00,360 --> 01:29:02,800
Speaker 1: trying to do the same thing with Hollywood with tech?

1504
01:29:03,000 --> 01:29:06,400
Speaker 1: You know, you say, hey, you know, make it so

1505
01:29:06,439 --> 01:29:10,120
Speaker 1: that you know, everything that happens online is bad, the

1506
01:29:10,160 --> 01:29:13,160
Speaker 1: cool stuff is in person or something like that. How

1507
01:29:13,200 --> 01:29:16,040
Speaker 1: are you trying to influence culture in that front?

1508
01:29:16,600 --> 01:29:20,280
Speaker 2: So, you know, we've had some conversations with some people

1509
01:29:20,520 --> 01:29:23,320
Speaker 2: in the space, I think other you know, Joe Gordon

1510
01:29:23,400 --> 01:29:29,440
Speaker 2: Levitt has been a tremendous voice for healthy human relationships.

1511
01:29:29,479 --> 01:29:32,080
Speaker 2: I know he's a dad and that he again he's

1512
01:29:32,120 --> 01:29:35,120
Speaker 2: not anti tech. He just thinks like tech should be

1513
01:29:35,200 --> 01:29:38,479
Speaker 2: safe and that we shouldn't. He has a wonderful ted

1514
01:29:38,520 --> 01:29:41,599
Speaker 2: talk that you could check out about his own experience

1515
01:29:41,600 --> 01:29:44,720
Speaker 2: with social media and what he learned is unhealthy about it.

1516
01:29:45,479 --> 01:29:47,559
Speaker 2: I think that more and more people in Hollywood are

1517
01:29:47,600 --> 01:29:51,799
Speaker 2: waking up to this, especially after the actors the sag

1518
01:29:51,840 --> 01:29:55,719
Speaker 2: after strike last year. You know, so many studios had

1519
01:29:55,880 --> 01:30:00,679
Speaker 2: sort of suggested that they just pay actors and don't

1520
01:30:00,720 --> 01:30:02,320
Speaker 2: hire them again, and then they can just make an

1521
01:30:02,320 --> 01:30:04,920
Speaker 2: AI likeness. And I don't think anybody likes the idea

1522
01:30:05,040 --> 01:30:10,160
Speaker 2: of never working again, because you know, pixels can just

1523
01:30:10,240 --> 01:30:12,720
Speaker 2: replace them. And then I think Pixar in the last

1524
01:30:12,760 --> 01:30:16,320
Speaker 2: Toy Story movie, I think they actually had a digital

1525
01:30:16,400 --> 01:30:19,519
Speaker 2: device kind of be one of the negative characters. So

1526
01:30:19,520 --> 01:30:21,320
Speaker 2: maybe somebody over there is getting the message.

1527
01:30:21,680 --> 01:30:27,519
Speaker 1: Interesting, Julie, I am, you know, cheering you on. I'm

1528
01:30:27,520 --> 01:30:29,880
Speaker 1: glad to give you a platform. I want to stay

1529
01:30:29,880 --> 01:30:33,679
Speaker 1: in touch, want to continue to help spread the word.

1530
01:30:33,720 --> 01:30:37,400
Speaker 1: I mean, I think I think generally everybody gets it.

1531
01:30:37,560 --> 01:30:40,160
Speaker 1: I think there's a lot of paralysis out there, and

1532
01:30:40,200 --> 01:30:43,519
Speaker 1: I think what you've shown is that hey, you're not alone.

1533
01:30:44,280 --> 01:30:47,599
Speaker 2: You're not alone. And it's just been so moving and

1534
01:30:47,680 --> 01:30:51,679
Speaker 2: inspiring to see all these parents come forward We're looking

1535
01:30:51,720 --> 01:30:55,920
Speaker 2: for more chapter leaders and a couple of specific states.

1536
01:30:55,960 --> 01:30:59,960
Speaker 2: So if you have any listeners in Wisconsin or ten

1537
01:31:00,600 --> 01:31:04,000
Speaker 2: or Kentucky.

1538
01:31:02,479 --> 01:31:06,479
Speaker 1: Here you go and challenge accepted. Here we go. Come on,

1539
01:31:06,640 --> 01:31:09,400
Speaker 1: Kentucky and Tennessee and Wisconsin. I got listeners in all

1540
01:31:09,400 --> 01:31:13,320
Speaker 1: those states. We know this. Let's go, let's get this done.

1541
01:31:13,360 --> 01:31:15,679
Speaker 1: And I'd like to see you know who the best

1542
01:31:15,720 --> 01:31:19,640
Speaker 1: advocates for this could be today's college students. You know,

1543
01:31:19,720 --> 01:31:23,519
Speaker 1: I'd love to see you get college chapters. That was look,

1544
01:31:23,560 --> 01:31:26,439
Speaker 1: that was a big part of Matt They got some

1545
01:31:26,520 --> 01:31:27,720
Speaker 1: college chapters.

1546
01:31:27,320 --> 01:31:29,679
Speaker 2: And that that helped absolutely. We work with some youth

1547
01:31:29,760 --> 01:31:33,360
Speaker 2: led groups now that are just truly wonderful, and you know,

1548
01:31:33,840 --> 01:31:38,519
Speaker 2: I am both grateful for their advocacy and it breaks

1549
01:31:38,560 --> 01:31:41,440
Speaker 2: my heart that we have put kids in this situation

1550
01:31:41,560 --> 01:31:45,000
Speaker 2: where children have to go to Congress and testify about

1551
01:31:45,000 --> 01:31:47,920
Speaker 2: what happened to them and how they were groomed online

1552
01:31:48,040 --> 01:31:53,599
Speaker 2: or received eating disorder encouragement. You know, we're the parents.

1553
01:31:53,680 --> 01:31:55,240
Speaker 2: We have to make it safe for our kids.

1554
01:31:55,800 --> 01:31:57,960
Speaker 1: Julie, so much, so grateful to talk with you.

1555
01:31:58,040 --> 01:32:08,200
Speaker 2: Thank you you too, pleasure. Thanks, well, there you go.

1556
01:32:09,600 --> 01:32:14,760
Speaker 1: It's so funny the way I think many a parent thinks, right,

1557
01:32:14,800 --> 01:32:18,280
Speaker 1: when my kids were young, I'd have older friends of

1558
01:32:18,320 --> 01:32:20,559
Speaker 1: mine whose kids were already in college going, but I'm

1559
01:32:20,560 --> 01:32:22,599
Speaker 1: glad I don't have to raise kids in this environment.

1560
01:32:22,680 --> 01:32:24,559
Speaker 1: And it's like, well, you know, you know we all

1561
01:32:24,800 --> 01:32:28,439
Speaker 1: were you know, every you know, every parent you know,

1562
01:32:28,520 --> 01:32:31,080
Speaker 1: after their kids get through and they see what comes next,

1563
01:32:31,120 --> 01:32:32,679
Speaker 1: you think, oh, I'm glad I didn't have to deal

1564
01:32:32,680 --> 01:32:37,160
Speaker 1: with that right on that front. But there's no doubt

1565
01:32:37,240 --> 01:32:43,439
Speaker 1: this this UH, the relationship between when to introduce tech

1566
01:32:43,479 --> 01:32:46,320
Speaker 1: and we just haven't. You know, there's there's plenty of

1567
01:32:46,600 --> 01:32:50,519
Speaker 1: smart and there's probably more studies that need to be done,

1568
01:32:50,560 --> 01:32:53,639
Speaker 1: but so far every study indicates of just you know,

1569
01:32:53,720 --> 01:33:00,800
Speaker 1: how how damaging too much screen time has been to

1570
01:33:01,680 --> 01:33:05,240
Speaker 1: UH to kids. And clearly there's been some that it

1571
01:33:05,360 --> 01:33:10,320
Speaker 1: leads to some learning issues and certainly slows things down.

1572
01:33:10,360 --> 01:33:13,679
Speaker 1: So I hope you take a look at what she's

1573
01:33:13,760 --> 01:33:17,280
Speaker 1: up to UH. And and to my some of my

1574
01:33:17,320 --> 01:33:20,160
Speaker 1: libertarian friends out there, let I think there's a I

1575
01:33:20,200 --> 01:33:23,600
Speaker 1: think there is a a a fair compromise here, and

1576
01:33:23,640 --> 01:33:27,839
Speaker 1: I think it's it's you know, I don't think anybody

1577
01:33:27,920 --> 01:33:31,599
Speaker 1: is going it's it's either all or nothing. I think

1578
01:33:31,640 --> 01:33:35,479
Speaker 1: we have to come through data and decide what is

1579
01:33:35,520 --> 01:33:39,479
Speaker 1: what is what is the right time? Right we we

1580
01:33:39,520 --> 01:33:43,320
Speaker 1: wait until kids are fifteen or sixteen before we'll let

1581
01:33:43,400 --> 01:33:46,240
Speaker 1: them get behind a wheel, even as a restricted driver.

1582
01:33:47,960 --> 01:33:50,800
Speaker 1: You know, we've we didn't you know, we didn't have

1583
01:33:50,880 --> 01:33:53,560
Speaker 1: those laws immediately in place when cars got introduced, but

1584
01:33:53,600 --> 01:33:56,360
Speaker 1: it was something over time. And I think in this case,

1585
01:33:56,439 --> 01:33:58,680
Speaker 1: you know, we realize, oh cigarettes, there ought to be

1586
01:33:58,680 --> 01:34:01,200
Speaker 1: a minimum major requirement. Being able to buy alcohol. That

1587
01:34:01,240 --> 01:34:03,559
Speaker 1: will be a minimum major requirement. Being able to drive

1588
01:34:03,600 --> 01:34:09,000
Speaker 1: a car, that'll be a minimum major requirement. Social media.

1589
01:34:09,920 --> 01:34:13,160
Speaker 1: It definitely thinks it definitely feels like it's one of

1590
01:34:13,160 --> 01:34:17,439
Speaker 1: those that you know, falls in line with certainly what

1591
01:34:17,479 --> 01:34:19,720
Speaker 1: we've done in the past when we've identified something that

1592
01:34:19,760 --> 01:34:23,960
Speaker 1: we think can be more harmful to kids before getting

1593
01:34:24,000 --> 01:34:29,120
Speaker 1: exposed to something before their brains are fully formed. All right,

1594
01:34:29,240 --> 01:34:33,439
Speaker 1: let's get into Let's get into a few of the

1595
01:34:33,520 --> 01:34:39,880
Speaker 1: questions here as Chuck, this one comes from Austin. From Austin,

1596
01:34:40,720 --> 01:34:43,040
Speaker 1: he is not a first timer. I think we'll call

1597
01:34:43,120 --> 01:34:45,559
Speaker 1: him a long timer. Hey, sen say your interview with

1598
01:34:45,560 --> 01:34:48,080
Speaker 1: Sarah Esker blew my mind. All right, I had no

1599
01:34:48,160 --> 01:34:50,719
Speaker 1: idea that the original article, the first was about congressional

1600
01:34:50,760 --> 01:34:53,479
Speaker 1: representation and apportionment makes so much sense that our founders

1601
01:34:53,479 --> 01:34:57,640
Speaker 1: prioritize representation before anything else. Got me thinking, could this

1602
01:34:57,720 --> 01:34:59,840
Speaker 1: be the basis for a limited series or film? Most

1603
01:34:59,840 --> 01:35:02,519
Speaker 1: of Americans don't know this history, and dramatizing it could

1604
01:35:02,520 --> 01:35:04,920
Speaker 1: have make structural reform like a return to our roots.

1605
01:35:04,960 --> 01:35:10,960
Speaker 1: Man Austin, what a terrific idea. You know, you know,

1606
01:35:11,000 --> 01:35:14,720
Speaker 1: it's interesting, I assume and I hope you're watching the

1607
01:35:14,800 --> 01:35:22,519
Speaker 1: Ken Burns American Revolution series. It's fantastic and it's certainly

1608
01:35:22,560 --> 01:35:25,640
Speaker 1: also very you know, there's nothing harder than trying to

1609
01:35:25,640 --> 01:35:30,760
Speaker 1: put together a documentary like he does, essentially without any

1610
01:35:30,880 --> 01:35:35,080
Speaker 1: archival material, archive video, things like that, pictures, et cetera.

1611
01:35:36,320 --> 01:35:42,639
Speaker 1: It's not easy, but a dramatic a limited series on

1612
01:35:43,080 --> 01:35:48,320
Speaker 1: the essentially the fight over the Constitution and you know,

1613
01:35:48,600 --> 01:35:52,280
Speaker 1: the failure of the Articles of Confederation and sort of yes,

1614
01:35:52,600 --> 01:35:55,640
Speaker 1: and we could create some drama. You get some characters.

1615
01:35:56,040 --> 01:35:59,280
Speaker 1: Ben Franklin's a great character. Thomas Jefferson's a great character.

1616
01:35:59,400 --> 01:36:02,639
Speaker 1: John Adams is right. We know a little bit something

1617
01:36:02,680 --> 01:36:05,640
Speaker 1: about all of them. Some of them have been portrayed

1618
01:36:05,800 --> 01:36:10,040
Speaker 1: I mean the John Adams series and that that HBO

1619
01:36:10,120 --> 01:36:13,519
Speaker 1: put on some fifteen or so years ago with Paul

1620
01:36:13,520 --> 01:36:17,519
Speaker 1: Giamatti is just one that if you all consider yourself

1621
01:36:17,520 --> 01:36:22,120
Speaker 1: a history junkie, then you've already seen it. But I

1622
01:36:22,160 --> 01:36:24,639
Speaker 1: love this idea, Austin, and I think you're right because

1623
01:36:24,680 --> 01:36:26,560
Speaker 1: I think there's there was plenty of drama. There was

1624
01:36:26,640 --> 01:36:31,320
Speaker 1: plenty of debate, you know, and you can have different episodes.

1625
01:36:31,360 --> 01:36:34,439
Speaker 1: You know where the cliffhanger is, you know, you know

1626
01:36:34,479 --> 01:36:36,680
Speaker 1: there was a cliffhanger and what the official language was

1627
01:36:36,720 --> 01:36:38,400
Speaker 1: going to be, Right, there's a cliff you know, all

1628
01:36:38,520 --> 01:36:41,280
Speaker 1: these little cliffhangers about what were we going to be

1629
01:36:41,320 --> 01:36:46,599
Speaker 1: as a country, you know, all those things. I think

1630
01:36:46,640 --> 01:36:49,880
Speaker 1: there would be interest in this. And again we've seen this,

1631
01:36:50,280 --> 01:36:55,679
Speaker 1: you know, his historical dramas have done well. And look,

1632
01:36:56,240 --> 01:37:00,800
Speaker 1: I have a I believe that part of you know,

1633
01:37:01,080 --> 01:37:05,679
Speaker 1: I think we need to offer more ways for people

1634
01:37:05,680 --> 01:37:09,080
Speaker 1: to learn how our system works. Not everybody wants to

1635
01:37:09,080 --> 01:37:13,360
Speaker 1: read a book about it. Not everybody wants to read

1636
01:37:15,439 --> 01:37:18,960
Speaker 1: a textbook about it. Not everybody wants to listen to

1637
01:37:19,680 --> 01:37:23,479
Speaker 1: ten hour podcast about it. So whatever way you get

1638
01:37:23,520 --> 01:37:26,519
Speaker 1: that into the ecosystem and your idea is a good

1639
01:37:26,560 --> 01:37:32,280
Speaker 1: one of sort of dramatizing, you know, the fights over

1640
01:37:32,360 --> 01:37:36,200
Speaker 1: the founding and the fights that took place, so that

1641
01:37:36,240 --> 01:37:39,960
Speaker 1: people can see the compromises. You know that the final

1642
01:37:40,760 --> 01:37:44,519
Speaker 1: the decisions that were made were all compromises, right, it was.

1643
01:37:44,800 --> 01:37:49,880
Speaker 1: It was never designed to be perfect. So anyway, it's

1644
01:37:49,880 --> 01:37:54,679
Speaker 1: a it's a terrific idea. Hey Netflix, are you listening, Austin?

1645
01:37:54,720 --> 01:37:56,479
Speaker 1: And I would like to pitch this as a as

1646
01:37:56,479 --> 01:38:01,519
Speaker 1: a TV series. Austin, I promise if for some reason

1647
01:38:01,600 --> 01:38:04,679
Speaker 1: we get this opportunity, I'll hook you up. Don't worry.

1648
01:38:05,560 --> 01:38:08,800
Speaker 1: Next question comes from Michael Laurry says, Hey, Chuck, what

1649
01:38:08,880 --> 01:38:10,519
Speaker 1: were your best and worst movies of the year. I

1650
01:38:10,600 --> 01:38:13,280
Speaker 1: also love your thoughts on ken Burn's American Revolution series. Aha,

1651
01:38:13,320 --> 01:38:15,559
Speaker 1: I already gave you that one. What's your favorite of

1652
01:38:15,560 --> 01:38:17,800
Speaker 1: his work? And which upcoming projects are you most excited about?

1653
01:38:17,800 --> 01:38:20,559
Speaker 1: African American History, LBJA, Baseball, Stand Up the Mormons. Lastly,

1654
01:38:20,560 --> 01:38:23,000
Speaker 1: if Netflix is buying Warner Brothers, do you think David

1655
01:38:23,000 --> 01:38:25,959
Speaker 1: Ellison hurt his chances by criticizing the process and highlighting

1656
01:38:26,000 --> 01:38:32,200
Speaker 1: ties as a selling point. So I'm like a small

1657
01:38:32,240 --> 01:38:38,800
Speaker 1: confession about movies these days, I watch fewer movies than ever,

1658
01:38:39,240 --> 01:38:43,799
Speaker 1: and I think part of it is I am trapped

1659
01:38:43,840 --> 01:38:48,599
Speaker 1: in the joy of episodic television that we have right

1660
01:38:48,640 --> 01:38:50,839
Speaker 1: we are living, we are still living in the golden

1661
01:38:50,880 --> 01:38:55,880
Speaker 1: age of this, you know, whether it's man I am,

1662
01:38:56,120 --> 01:39:00,320
Speaker 1: I'm digging plorabis and I'm still curious where we're headed here,

1663
01:39:00,360 --> 01:39:03,760
Speaker 1: although I have a my latest theory on Pluribus is

1664
01:39:04,600 --> 01:39:08,800
Speaker 1: that it's a are you sure we want one collective

1665
01:39:09,200 --> 01:39:11,600
Speaker 1: intelligence about who we are? Right? I don't know. I

1666
01:39:11,640 --> 01:39:15,040
Speaker 1: felt like maybe maybe it's commentary on artificial intelligence. By

1667
01:39:15,080 --> 01:39:19,800
Speaker 1: the way, I'm an episode behind, so no spoilers on

1668
01:39:19,840 --> 01:39:25,720
Speaker 1: this front. And my other confession is when I travel,

1669
01:39:25,920 --> 01:39:28,600
Speaker 1: that's when I kind of junk out on movies. So

1670
01:39:32,400 --> 01:39:35,439
Speaker 1: I just watched it. I've been traveling this week. So

1671
01:39:35,479 --> 01:39:40,320
Speaker 1: I finally watched the Naked Gun reboots. Eh, you know,

1672
01:39:40,640 --> 01:39:45,280
Speaker 1: I there was some fun grace notes. I appreciated the effort,

1673
01:39:45,400 --> 01:39:48,720
Speaker 1: but there's a part of me that feels it's almost

1674
01:39:49,080 --> 01:39:54,240
Speaker 1: too far away, too long ago, like we lost an

1675
01:39:54,400 --> 01:39:58,440
Speaker 1: entire you know, that was arguably three pop culture generations

1676
01:39:58,479 --> 01:40:01,760
Speaker 1: ago that the last nake Gun movie came out. So

1677
01:40:04,000 --> 01:40:08,200
Speaker 1: I think, you know, it. It's a you know, it's

1678
01:40:08,200 --> 01:40:12,599
Speaker 1: interesting being in the later stages now of middle aged right,

1679
01:40:12,640 --> 01:40:15,880
Speaker 1: which is right, I've got grown kids. But you know,

1680
01:40:15,880 --> 01:40:19,639
Speaker 1: it's funny about the movie industry. There's two there's two

1681
01:40:20,800 --> 01:40:24,880
Speaker 1: demographic groups that actually go to movie theaters. Apparently one

1682
01:40:24,960 --> 01:40:30,679
Speaker 1: is teenagers, so that's that explains all the Marvel movies

1683
01:40:30,720 --> 01:40:33,439
Speaker 1: and all that other stuff. And the other is empty nesters,

1684
01:40:35,080 --> 01:40:38,120
Speaker 1: which is why you get right now we you know,

1685
01:40:38,880 --> 01:40:40,439
Speaker 1: you know, over the last fifteen years, it was a

1686
01:40:40,479 --> 01:40:43,799
Speaker 1: lot of baby boomer nostalgia. Now we're getting we're starting

1687
01:40:43,800 --> 01:40:50,160
Speaker 1: to get gen x nostalgia on this front. So as

1688
01:40:50,160 --> 01:40:55,080
Speaker 1: for my favorite ken burns, look, I've seen a lot

1689
01:40:55,120 --> 01:40:57,760
Speaker 1: of them. I enjoyed, you know, my I'm going to

1690
01:40:57,840 --> 01:40:59,840
Speaker 1: do a deep cut here. I enjoyed the Prohibition one.

1691
01:41:00,120 --> 01:41:01,880
Speaker 1: Is that was something I knew the least. It was

1692
01:41:01,920 --> 01:41:04,960
Speaker 1: one of those gaps for me, so it filled in

1693
01:41:05,120 --> 01:41:07,000
Speaker 1: a lot of gaps. So I really enjoyed that one.

1694
01:41:08,080 --> 01:41:12,360
Speaker 1: And Baseball, of course, was I think transformational. You know,

1695
01:41:12,400 --> 01:41:14,719
Speaker 1: it was in the Civil War, and obviously the Civil

1696
01:41:14,760 --> 01:41:19,920
Speaker 1: War was his essentially the masterpiece that gave him the

1697
01:41:20,000 --> 01:41:24,680
Speaker 1: currency to do all of this. Right, he is America's historian.

1698
01:41:25,640 --> 01:41:28,960
Speaker 1: And what I had really admire about what ken Burns

1699
01:41:28,960 --> 01:41:30,760
Speaker 1: has pulled off in the American Revolution. I mean, think

1700
01:41:30,760 --> 01:41:35,960
Speaker 1: about all, you know, if he had asked me, well,

1701
01:41:36,000 --> 01:41:37,599
Speaker 1: what do you think I'm going to do the American Revolution,

1702
01:41:37,640 --> 01:41:41,599
Speaker 1: I'd be like, oh, boy, right after these debates that

1703
01:41:41,680 --> 01:41:45,240
Speaker 1: went through sixteen nineteen versus seventeen seventy six, these competing

1704
01:41:45,240 --> 01:41:48,759
Speaker 1: commissions and all of this, and like, boy, good luck

1705
01:41:48,960 --> 01:41:52,080
Speaker 1: trying to make something that everybody can feel comfortable with.

1706
01:41:52,120 --> 01:41:55,439
Speaker 1: And lo and behold, he seemed to make something that

1707
01:41:56,360 --> 01:42:01,400
Speaker 1: you know he has not gotten is somehow avoided stepping

1708
01:42:01,439 --> 01:42:06,240
Speaker 1: on too many political land mindes. You know he's been

1709
01:42:06,920 --> 01:42:12,600
Speaker 1: I think it's been very straightforward, and I have found it,

1710
01:42:13,240 --> 01:42:15,920
Speaker 1: you know, I've certainly learned some things that I you know,

1711
01:42:16,000 --> 01:42:19,200
Speaker 1: you think you know, and there are more things that

1712
01:42:19,280 --> 01:42:23,519
Speaker 1: you learn there. So it's uh. I really admire his

1713
01:42:23,600 --> 01:42:27,759
Speaker 1: ability to have success with this in this polarized culture

1714
01:42:27,800 --> 01:42:30,960
Speaker 1: that we lived in. We had to give him some

1715
01:42:31,040 --> 01:42:33,080
Speaker 1: special award like, I don't know how you did it,

1716
01:42:33,120 --> 01:42:36,160
Speaker 1: but congratulations, you did something that we don't seem to

1717
01:42:36,160 --> 01:42:41,200
Speaker 1: have any politicians capable of doing. So I owe you

1718
01:42:41,280 --> 01:42:46,240
Speaker 1: a few more movies. I'll does do you care that

1719
01:42:46,280 --> 01:42:51,240
Speaker 1: I watched the Mission Impossible Final Reckoning movies? I will

1720
01:42:51,439 --> 01:42:55,880
Speaker 1: confess that, you know, I I it's a it's a

1721
01:42:56,680 --> 01:42:59,400
Speaker 1: I always I designate. Like I say, most of my

1722
01:42:59,439 --> 01:43:02,760
Speaker 1: movie watching now is when I'm want to be sort

1723
01:43:02,800 --> 01:43:06,160
Speaker 1: of if I'm on a long flight and I'm trying

1724
01:43:06,200 --> 01:43:10,960
Speaker 1: to pass the time. It's in fact, Mission and the

1725
01:43:10,960 --> 01:43:14,559
Speaker 1: Mission Impossible series in the Jurassic Park series, right, those

1726
01:43:14,600 --> 01:43:17,559
Speaker 1: are some I consider those guilty pleasures, meaning you know,

1727
01:43:17,760 --> 01:43:20,200
Speaker 1: I know you know what you're going to get. They

1728
01:43:20,240 --> 01:43:23,360
Speaker 1: are what they are. I find them to be the

1729
01:43:23,360 --> 01:43:29,400
Speaker 1: perfect airplane movies. So I did venture into both of those.

1730
01:43:29,439 --> 01:43:33,920
Speaker 1: But I owe you a better, Michael, I owe you

1731
01:43:33,960 --> 01:43:38,240
Speaker 1: a better a better list of movies I like this year,

1732
01:43:38,320 --> 01:43:41,920
Speaker 1: So cot me some slack on that, all right. Next

1733
01:43:42,000 --> 01:43:48,800
Speaker 1: question comes from Yavari from Finland. He says, hey, check

1734
01:43:48,840 --> 01:43:51,719
Speaker 1: listener from Finland here loving the longer episodes, keep them coming.

1735
01:43:52,200 --> 01:43:54,080
Speaker 1: The sentate primaries in Texas are turning out to be

1736
01:43:54,120 --> 01:43:55,880
Speaker 1: quite interesting. Oh yes they are. How bloody do you

1737
01:43:55,920 --> 01:43:58,479
Speaker 1: think they could get? For example, between Alread and Tolerico. Well,

1738
01:43:58,960 --> 01:44:02,080
Speaker 1: in fairness, I think I did not answer this question

1739
01:44:02,120 --> 01:44:04,040
Speaker 1: in a timely fashion, because as we now know, it's

1740
01:44:04,040 --> 01:44:06,800
Speaker 1: no longer all Read in toat Rico. It is tall

1741
01:44:06,920 --> 01:44:12,360
Speaker 1: Rico and Crockett. If you are a listener to every episode,

1742
01:44:12,400 --> 01:44:14,439
Speaker 1: you may have already heard my take on that. I'll

1743
01:44:14,439 --> 01:44:15,920
Speaker 1: give you that in a second, but let me finish

1744
01:44:15,960 --> 01:44:18,920
Speaker 1: your question. You noted between all Read and tall Rico,

1745
01:44:18,960 --> 01:44:20,519
Speaker 1: you saw neither one seems to be the type to

1746
01:44:20,600 --> 01:44:24,120
Speaker 1: knife each other that much, but she never know. Then

1747
01:44:24,160 --> 01:44:26,720
Speaker 1: on the Republican side, now that Hunt has jumped in

1748
01:44:26,800 --> 01:44:28,680
Speaker 1: the Republican side, is there a lane for him in

1749
01:44:28,720 --> 01:44:30,800
Speaker 1: your opinion? Also, could you give a brief outlook on

1750
01:44:30,800 --> 01:44:32,640
Speaker 1: how Montana looks for the Dems in twenty twenty six.

1751
01:44:32,680 --> 01:44:34,200
Speaker 1: I have a couple of friends there and they seem

1752
01:44:34,280 --> 01:44:39,000
Speaker 1: optimistic about Russell Cleveland seating Ryan Zinky. That's in a

1753
01:44:39,120 --> 01:44:43,519
Speaker 1: house race. I believe that zinc is running their kind

1754
01:44:43,560 --> 01:44:47,040
Speaker 1: regards Lea Fari let me start with the Texas thing.

1755
01:44:47,040 --> 01:44:50,120
Speaker 1: Like I said, I answered it before. Obviously, look, Jasmine

1756
01:44:50,120 --> 01:44:54,160
Speaker 1: Crockett essentially drove colinra al Read out of the race,

1757
01:44:55,040 --> 01:44:56,880
Speaker 1: and it was pretty clear three way primary was going

1758
01:44:56,920 --> 01:44:59,479
Speaker 1: to create a runoff that there was enough people that

1759
01:44:59,520 --> 01:45:02,479
Speaker 1: got to all read convinced him to run for something

1760
01:45:02,479 --> 01:45:04,320
Speaker 1: that's sure remnant of his old house seat there in

1761
01:45:04,320 --> 01:45:09,439
Speaker 1: North Texas, because you know, if Democrats Republicans are already with

1762
01:45:09,479 --> 01:45:12,839
Speaker 1: that three way raise, you know that's destined for a runoff,

1763
01:45:12,880 --> 01:45:15,680
Speaker 1: which means even more money of them throwing mud at

1764
01:45:15,680 --> 01:45:18,760
Speaker 1: each other, and it's an internal fight. You know, it

1765
01:45:18,760 --> 01:45:24,520
Speaker 1: did seem to be a self inflicted wound for Democrats

1766
01:45:24,560 --> 01:45:29,040
Speaker 1: to also have a large enough primary with prominent candidates

1767
01:45:29,040 --> 01:45:31,880
Speaker 1: that would guarantee a runoff on their end. You know why,

1768
01:45:32,600 --> 01:45:36,560
Speaker 1: you know, why not take advantage of the situation of Republicans.

1769
01:45:36,560 --> 01:45:38,920
Speaker 1: And so now you do have a situation where I

1770
01:45:38,920 --> 01:45:41,200
Speaker 1: all want to agree to get out. It's Jasmine Crockett and

1771
01:45:41,439 --> 01:45:44,720
Speaker 1: James Tollerico And as I implied history, I think this

1772
01:45:44,760 --> 01:45:46,479
Speaker 1: is going to be a race that is less about

1773
01:45:46,479 --> 01:45:51,280
Speaker 1: ideology and more about style. Right. You're essentially he says,

1774
01:45:51,439 --> 01:45:52,960
Speaker 1: you know, how much do you want a fighter and

1775
01:45:53,000 --> 01:45:55,479
Speaker 1: how much do you want to uniter? Right, You're going

1776
01:45:55,560 --> 01:45:58,559
Speaker 1: to have the pastor versus the fighter, right, and what

1777
01:45:58,760 --> 01:46:03,280
Speaker 1: is the mind set of the electorate? And if, like

1778
01:46:03,320 --> 01:46:06,280
Speaker 1: I said, I went pretty long on this and yesterday's

1779
01:46:06,320 --> 01:46:15,000
Speaker 1: episode so I'll keep this one shorter, But essentially, I

1780
01:46:19,080 --> 01:46:21,040
Speaker 1: think this is going to be a noisy primary on

1781
01:46:21,080 --> 01:46:22,720
Speaker 1: the Democratic side, But I don't know if it's going

1782
01:46:22,800 --> 01:46:26,160
Speaker 1: to be a nasty primary, right I think both could

1783
01:46:26,479 --> 01:46:30,200
Speaker 1: pay a penalty, you know, and how they go about it, right,

1784
01:46:30,240 --> 01:46:33,800
Speaker 1: Tyla Rico has a as a brand that if he

1785
01:46:34,160 --> 01:46:37,840
Speaker 1: gets too negative or sort of practices sort of conventional

1786
01:46:37,920 --> 01:46:42,280
Speaker 1: old school negative politics, that could boomerang on him because

1787
01:46:42,280 --> 01:46:45,439
Speaker 1: he's trying to be a different type of a different

1788
01:46:45,479 --> 01:46:48,439
Speaker 1: type of candidate, a different type of Democrat. And I

1789
01:46:48,439 --> 01:46:51,799
Speaker 1: think Crockett are already sort of you know, she's she's

1790
01:46:52,320 --> 01:46:58,320
Speaker 1: a bit polarizing, certainly in a general electorate, and she

1791
01:46:58,479 --> 01:47:01,080
Speaker 1: needs a United Democratic Party behind her if she wins

1792
01:47:00,840 --> 01:47:04,680
Speaker 1: this primary. So I definitely agree with your take that

1793
01:47:04,720 --> 01:47:06,920
Speaker 1: if it had been all read Tolrico and Crockett had

1794
01:47:06,920 --> 01:47:08,680
Speaker 1: decided not to run, that that would have been a

1795
01:47:09,360 --> 01:47:11,720
Speaker 1: that would have been a patty cake primary a little bit,

1796
01:47:11,840 --> 01:47:15,040
Speaker 1: you know, I think it would have been sock. The

1797
01:47:15,160 --> 01:47:18,600
Speaker 1: curiosity I have about the Democratic side of this is

1798
01:47:18,680 --> 01:47:23,360
Speaker 1: will outside groups sort of be the ones to throw

1799
01:47:23,400 --> 01:47:27,160
Speaker 1: all the insults? Right? Well, we see a bunch of

1800
01:47:27,160 --> 01:47:31,720
Speaker 1: outside Democratic money essentially try to damage Crockett, because I mean,

1801
01:47:32,280 --> 01:47:35,599
Speaker 1: that's what I think is most likely you do have

1802
01:47:36,680 --> 01:47:39,400
Speaker 1: You're not going to get the party leadership to admit

1803
01:47:39,439 --> 01:47:42,479
Speaker 1: that they prefer publicly Tolerico over Crockett, but their body

1804
01:47:42,560 --> 01:47:44,840
Speaker 1: language is going to come across that way. There's just

1805
01:47:44,880 --> 01:47:48,439
Speaker 1: a perception that she can't win the general. I do

1806
01:47:48,479 --> 01:47:52,160
Speaker 1: find this story. I'm not sure it's interesting that the

1807
01:47:52,240 --> 01:47:56,000
Speaker 1: NRSC decided to take a victory lap and say, hey,

1808
01:47:56,040 --> 01:47:58,200
Speaker 1: look what we did and try to take credit for

1809
01:47:58,280 --> 01:48:02,839
Speaker 1: talking Crockett into running. And of course noticed this story

1810
01:48:02,880 --> 01:48:06,040
Speaker 1: about you know how the NRSC is sort of taking

1811
01:48:06,040 --> 01:48:09,719
Speaker 1: credit for getting for essentially coaxing Crockett into the race.

1812
01:48:10,280 --> 01:48:12,920
Speaker 1: Meaning they did some early polling, they were curious to

1813
01:48:12,920 --> 01:48:15,600
Speaker 1: see if Crockett had any traction. They tested her and

1814
01:48:15,640 --> 01:48:18,640
Speaker 1: they realized that she was actually the best known, that

1815
01:48:18,720 --> 01:48:20,960
Speaker 1: she would do the best in a Democratic primary, just

1816
01:48:20,960 --> 01:48:23,440
Speaker 1: sort of implying that she had the highest name recognition

1817
01:48:23,479 --> 01:48:26,960
Speaker 1: of anybody that could possibly run. And you know, John

1818
01:48:27,000 --> 01:48:29,600
Speaker 1: Cornan is not hidden his belief that he thinks that

1819
01:48:29,920 --> 01:48:33,320
Speaker 1: that Crockett would be easier to defeat than anybody else.

1820
01:48:33,360 --> 01:48:36,519
Speaker 1: He faces problem for John Cornan is. I do not

1821
01:48:36,560 --> 01:48:40,640
Speaker 1: see a scenario where he's the nominee, and I'm mildly

1822
01:48:40,840 --> 01:48:44,720
Speaker 1: surprised he chose to run. You know, I had a

1823
01:48:44,760 --> 01:48:46,680
Speaker 1: thesis and I think I shared it, and so I

1824
01:48:46,680 --> 01:48:49,400
Speaker 1: will look, I was wrong about the following. I thought

1825
01:48:50,000 --> 01:48:53,240
Speaker 1: he certainly was behaving like somebody who was buying time

1826
01:48:53,320 --> 01:48:57,160
Speaker 1: from everybody, saying that he let me give me to

1827
01:48:57,360 --> 01:49:01,200
Speaker 1: the filing deadline to prove that I can win this thing. Well,

1828
01:49:01,240 --> 01:49:05,120
Speaker 1: then Wesley Hunt got in. It got complicated. But you know,

1829
01:49:05,160 --> 01:49:08,640
Speaker 1: while Cornan certainly has done a good job raising Paxton's

1830
01:49:08,680 --> 01:49:13,559
Speaker 1: negative was his early TV negative TV ads, it hasn't

1831
01:49:13,760 --> 01:49:19,000
Speaker 1: accrued to Cornan's benefit. And you know, there's there's certainly

1832
01:49:19,320 --> 01:49:23,000
Speaker 1: he just right, he feels like he's just a fish

1833
01:49:23,040 --> 01:49:25,799
Speaker 1: out of water. And where this Republican Party is these days,

1834
01:49:26,400 --> 01:49:28,880
Speaker 1: and it's as if the electorate knows that Cornan is

1835
01:49:28,920 --> 01:49:30,599
Speaker 1: not one of them. Right, he is getting a strong

1836
01:49:30,680 --> 01:49:35,200
Speaker 1: thirty thirty two percent, which is about what's left of

1837
01:49:35,280 --> 01:49:38,920
Speaker 1: the Bush wing of the Republican Party nationally, and it's

1838
01:49:38,960 --> 01:49:42,840
Speaker 1: about what's left inside the state of Texas. Now, if

1839
01:49:42,840 --> 01:49:46,320
Speaker 1: Cornyn were the general election of the Republican nominee there's

1840
01:49:46,360 --> 01:49:48,040
Speaker 1: no doubt. I think he's a favorite no matter who

1841
01:49:48,040 --> 01:49:50,960
Speaker 1: he would face, whether it was Tolerico or Crockett. There So,

1842
01:49:51,640 --> 01:49:53,800
Speaker 1: obviously the Republican primary is going to be scorched earth,

1843
01:49:54,439 --> 01:50:00,200
Speaker 1: just scorched earth. But you know, nothing's really moved. What

1844
01:50:00,240 --> 01:50:03,280
Speaker 1: I'm curious about what is going to move Cornan's numbers up.

1845
01:50:03,960 --> 01:50:07,719
Speaker 1: I know what everybody believes will move Paxton's numbers down.

1846
01:50:08,439 --> 01:50:12,160
Speaker 1: What moves Cornan's numbers up? And in some ways he

1847
01:50:12,240 --> 01:50:15,519
Speaker 1: may have made a mistake. And the NRSC is bragging

1848
01:50:15,560 --> 01:50:20,320
Speaker 1: about getting Jasmine Crockett in. But if Republican voters are

1849
01:50:20,320 --> 01:50:24,680
Speaker 1: convinced that anybody can beat Crockett, then they don't have

1850
01:50:24,760 --> 01:50:27,840
Speaker 1: to hold their nose and support Cornyan because he's quote

1851
01:50:27,880 --> 01:50:30,439
Speaker 1: unquote the only one that can prevent a Democrat from

1852
01:50:30,479 --> 01:50:34,439
Speaker 1: winning the Senate. See So I just find it interesting

1853
01:50:34,479 --> 01:50:37,880
Speaker 1: that the NRSC is meeting their chest when they actually

1854
01:50:38,880 --> 01:50:42,400
Speaker 1: that if Crockett is the center of attention in the

1855
01:50:42,400 --> 01:50:46,679
Speaker 1: Democratic primary, that actually may send the message to Republican

1856
01:50:46,720 --> 01:50:52,280
Speaker 1: primary voters that hey, Democrats are going to nominate somebody

1857
01:50:52,280 --> 01:50:54,840
Speaker 1: who can win a general election, so you don't have

1858
01:50:54,880 --> 01:51:00,519
Speaker 1: to worry about supporting the mushy moderate John Cornett. And

1859
01:51:00,560 --> 01:51:06,639
Speaker 1: there's another reason why Cornyn may regret that there is

1860
01:51:06,760 --> 01:51:10,800
Speaker 1: a competitive primary at all. In the Democratic primary, it's

1861
01:51:10,840 --> 01:51:14,640
Speaker 1: a you know, Democratic you know, if there wasn't a

1862
01:51:14,640 --> 01:51:16,920
Speaker 1: competitive Democratic primary vote, you would have seen some that

1863
01:51:17,000 --> 01:51:18,800
Speaker 1: might vote in a Democratic primary choose to vote in

1864
01:51:18,800 --> 01:51:21,559
Speaker 1: the Republican primary. There's a little bit of choice in Texas,

1865
01:51:22,720 --> 01:51:25,920
Speaker 1: and he may cost himself some potential sort of moderate

1866
01:51:25,920 --> 01:51:28,519
Speaker 1: Democratic voters who might have if there really wasn't a

1867
01:51:28,560 --> 01:51:35,960
Speaker 1: competitive Democratic primary, they may have done that. So, look,

1868
01:51:36,080 --> 01:51:38,760
Speaker 1: you are right to point out those Texas primaries. It's

1869
01:51:38,800 --> 01:51:44,519
Speaker 1: certainly why why we're already have identified the Texas primary

1870
01:51:44,600 --> 01:51:47,160
Speaker 1: night as the next night we go live with election

1871
01:51:47,280 --> 01:51:49,960
Speaker 1: results in twenty twenty six. That is going to be

1872
01:51:50,439 --> 01:51:53,599
Speaker 1: as consequential of a primary night as there is in

1873
01:51:53,680 --> 01:51:58,000
Speaker 1: deciding whether or not Democrats do have a serious chance

1874
01:51:58,120 --> 01:52:01,200
Speaker 1: at picking up the Senate. All right, I'm thinking one

1875
01:52:01,200 --> 01:52:04,080
Speaker 1: more question before we get into my little college football

1876
01:52:04,080 --> 01:52:07,280
Speaker 1: preview for the weekend. It comes from Brian from Sterling,

1877
01:52:07,360 --> 01:52:10,440
Speaker 1: Virginia says, love the Toddcast, especially the World War One discussions.

1878
01:52:10,720 --> 01:52:12,519
Speaker 1: Here's a favorite historical what if of mine? What if

1879
01:52:12,600 --> 01:52:15,520
Speaker 1: Arch Duke Ferdinand's driver hadn't taken a wrong turn, Sorryjevo,

1880
01:52:15,840 --> 01:52:18,080
Speaker 1: placing the air to the Austrio Hungarian throne right in

1881
01:52:18,080 --> 01:52:21,200
Speaker 1: front of Gabrielle prince Hip, the guy who shot him.

1882
01:52:21,479 --> 01:52:23,840
Speaker 1: Without that fateful moment, we might have avoided World War One,

1883
01:52:23,880 --> 01:52:26,040
Speaker 1: no rise of communism, no World War Two, no Cold War,

1884
01:52:26,040 --> 01:52:28,240
Speaker 1: no Holocaust, and a radically different Middle East. That single

1885
01:52:28,240 --> 01:52:31,320
Speaker 1: wrong term may have shaped the entire modern world. That's

1886
01:52:31,400 --> 01:52:36,920
Speaker 1: quite the what if I am. I am not a

1887
01:52:36,920 --> 01:52:39,680
Speaker 1: believer in that. I you know, I think we were

1888
01:52:39,720 --> 01:52:42,439
Speaker 1: on a hair trigger and I think that there were certainly,

1889
01:52:43,080 --> 01:52:44,880
Speaker 1: you know, we were in a Look think about the

1890
01:52:44,920 --> 01:52:49,640
Speaker 1: period we were in. We were hypernationalists around the globe.

1891
01:52:49,680 --> 01:52:51,880
Speaker 1: We were, And this is why I think we're in

1892
01:52:51,880 --> 01:52:55,080
Speaker 1: a similar period now, which is, you know, every time

1893
01:52:55,120 --> 01:52:58,360
Speaker 1: we've had a massive transition in our economy, you know,

1894
01:52:58,400 --> 01:53:01,160
Speaker 1: over the last one hundred and fifty years, right that

1895
01:53:01,280 --> 01:53:04,200
Speaker 1: was the advent, you know, when we were moving from

1896
01:53:04,240 --> 01:53:08,800
Speaker 1: an agrarian economy to an industrial economy. Globally, look at

1897
01:53:08,840 --> 01:53:13,040
Speaker 1: all that happened in the first fifteen years twenty years

1898
01:53:13,040 --> 01:53:16,600
Speaker 1: of the twentieth century. You had the Russian Revolution, we

1899
01:53:16,640 --> 01:53:21,280
Speaker 1: had all. I mean, there was disruption and voter revolt

1900
01:53:21,439 --> 01:53:25,400
Speaker 1: and people revolt and worker revolts all over the globe.

1901
01:53:26,320 --> 01:53:35,080
Speaker 1: So it was a very unstable time. So I as

1902
01:53:35,160 --> 01:53:38,400
Speaker 1: much as I mean I love the is, I love

1903
01:53:38,439 --> 01:53:41,280
Speaker 1: a good alternative history, a little good a little butterfly effect.

1904
01:53:41,400 --> 01:53:47,840
Speaker 1: I wish. I hope you're right. I'm don't think, but

1905
01:53:47,960 --> 01:53:50,599
Speaker 1: I look at it. I think there there we were

1906
01:53:50,600 --> 01:53:53,120
Speaker 1: in a the whole globe wasn't in a in an

1907
01:53:53,200 --> 01:53:55,240
Speaker 1: unsteady position. In fact, you could argue we're in a

1908
01:53:55,240 --> 01:53:58,559
Speaker 1: similar position now right where you know we were were.

1909
01:53:58,960 --> 01:54:04,960
Speaker 1: We were pursuing a very uh closed border. We were

1910
01:54:05,400 --> 01:54:09,160
Speaker 1: uh anti immigrant at that time, we were had high

1911
01:54:09,240 --> 01:54:12,800
Speaker 1: tariff rates, we were a bit nationalistic. Remember, we didn't

1912
01:54:12,800 --> 01:54:14,360
Speaker 1: want to get involved at all. What was going on

1913
01:54:14,439 --> 01:54:18,360
Speaker 1: in Europe. You know, Hey, that ain't happening here. That's

1914
01:54:18,400 --> 01:54:20,400
Speaker 1: why we're We've got two oceans between us and the

1915
01:54:20,479 --> 01:54:26,000
Speaker 1: other comments. So that's that would be my only pushback

1916
01:54:26,040 --> 01:54:28,000
Speaker 1: to your theory is that I think that we were

1917
01:54:28,160 --> 01:54:32,080
Speaker 1: that that that we were just the transition of the

1918
01:54:32,280 --> 01:54:35,440
Speaker 1: of the global economy from agrarian to industrial and the

1919
01:54:35,520 --> 01:54:38,600
Speaker 1: race to industrialization, and you had this you just had

1920
01:54:38,920 --> 01:54:42,160
Speaker 1: there was all sorts of tumult that was happening inside

1921
01:54:42,200 --> 01:54:45,640
Speaker 1: the Ottoman Empire. That was tumult, that was you know

1922
01:54:45,800 --> 01:54:47,960
Speaker 1: it sort of it was the big we were in

1923
01:54:47,960 --> 01:54:50,560
Speaker 1: the we were in the late stages of the British Empire,

1924
01:54:50,640 --> 01:54:54,520
Speaker 1: right that it was you know, uh, they were it

1925
01:54:54,560 --> 01:55:00,560
Speaker 1: was getting chipped away at and certainly we had you know,

1926
01:55:00,680 --> 01:55:04,920
Speaker 1: look at how the fact that that did trigger a

1927
01:55:05,000 --> 01:55:07,480
Speaker 1: global you know, the Great War if it will, I

1928
01:55:07,520 --> 01:55:14,600
Speaker 1: think tells you what a tinderbox the globe wash was

1929
01:55:14,640 --> 01:55:17,600
Speaker 1: at that time. And that's something that I think about

1930
01:55:17,600 --> 01:55:19,360
Speaker 1: in this current period. You know, we're going to get

1931
01:55:19,400 --> 01:55:21,120
Speaker 1: through this, and we're going to get through this, but

1932
01:55:21,200 --> 01:55:27,360
Speaker 1: this transformation of our economy has you know, rattled the globe.

1933
01:55:27,440 --> 01:55:31,280
Speaker 1: Right look at Brexit, then look at what we're doing,

1934
01:55:31,520 --> 01:55:36,760
Speaker 1: and look at sort of the shift and uh into

1935
01:55:36,920 --> 01:55:40,080
Speaker 1: nationalism here and what that is. Look what that is triggered.

1936
01:55:40,120 --> 01:55:42,520
Speaker 1: You know, when when the biggest economy in the world

1937
01:55:42,600 --> 01:55:46,320
Speaker 1: starts behaving in an isolationist, nationalistic sort of you know

1938
01:55:46,480 --> 01:55:49,560
Speaker 1: America first, Well, suddenly other countries are going to be

1939
01:55:49,680 --> 01:55:54,680
Speaker 1: Japan first, and Germany first, and Italy first, and and

1940
01:55:54,720 --> 01:55:59,560
Speaker 1: so on and so forth. And so I certainly hope

1941
01:55:59,560 --> 01:56:04,680
Speaker 1: we can do we can escape this without without the

1942
01:56:04,680 --> 01:56:07,280
Speaker 1: mass casualties that we had with World War One. But

1943
01:56:07,360 --> 01:56:11,360
Speaker 1: it's not as if we're in a stable time period

1944
01:56:12,040 --> 01:56:14,840
Speaker 1: in our globe's history at the moment right where you

1945
01:56:14,880 --> 01:56:19,600
Speaker 1: have a pretty active war taking place between one of

1946
01:56:19,640 --> 01:56:26,080
Speaker 1: the largest countries in the world, Russia versus Ukraine. Look

1947
01:56:26,120 --> 01:56:31,560
Speaker 1: at sort of the unsteadiness, what we may be instigating

1948
01:56:31,680 --> 01:56:38,720
Speaker 1: a military conflict in Venezuela. We're a bit on uh.

1949
01:56:39,240 --> 01:56:42,640
Speaker 1: There's certainly a lot of concern about potentially hot conflict

1950
01:56:42,640 --> 01:56:47,840
Speaker 1: with China over Taiwan. You've seen the rhetorical back and

1951
01:56:47,880 --> 01:56:50,920
Speaker 1: forth between the new Prime Minister of Japan in China.

1952
01:56:51,040 --> 01:56:56,600
Speaker 1: So it's a it's an interesting what if. But I

1953
01:56:56,920 --> 01:57:00,400
Speaker 1: think when you look at the when you look at

1954
01:57:00,520 --> 01:57:03,480
Speaker 1: sort of what was happening around the world at that time,

1955
01:57:03,560 --> 01:57:06,240
Speaker 1: why were we in this period where something like that

1956
01:57:06,280 --> 01:57:14,920
Speaker 1: could trigger all of this, I think you're underestimating what

1957
01:57:15,160 --> 01:57:19,000
Speaker 1: was a very I think a very unstable period that

1958
01:57:19,040 --> 01:57:28,560
Speaker 1: was taking place around the globe. This is the time

1959
01:57:28,600 --> 01:57:32,920
Speaker 1: where I've normally got this huge college football preview. We

1960
01:57:32,960 --> 01:57:37,040
Speaker 1: only have one game this weekend, but I always have

1961
01:57:37,080 --> 01:57:41,880
Speaker 1: good memories of this, because I've been to a couple

1962
01:57:41,960 --> 01:57:46,160
Speaker 1: of Army Navy games, and I'm sure if you've never

1963
01:57:46,200 --> 01:57:51,320
Speaker 1: been to an Army Navy game, you've missed out on something.

1964
01:57:51,800 --> 01:57:56,160
Speaker 1: And the first one I got to experience was was

1965
01:57:56,200 --> 01:57:59,600
Speaker 1: the first time I ever got to participate in a pool,

1966
01:58:00,120 --> 01:58:04,760
Speaker 1: presidential pool, if you will. The fact that I got

1967
01:58:04,800 --> 01:58:07,720
Speaker 1: to do it probably violated some probably was a secret

1968
01:58:07,760 --> 01:58:10,400
Speaker 1: service violation, but it is now thirty years thirty one

1969
01:58:10,440 --> 01:58:13,120
Speaker 1: years ago, so I think I can now it can

1970
01:58:13,200 --> 01:58:17,440
Speaker 1: be told. My late longtime friend von Vers, who some

1971
01:58:17,480 --> 01:58:20,960
Speaker 1: of you have heard me talk about before and I

1972
01:58:21,320 --> 01:58:24,960
Speaker 1: basically grew up in politics together working at the Hotline.

1973
01:58:27,000 --> 01:58:29,360
Speaker 1: We lost him a couple of years ago. I'm still

1974
01:58:29,640 --> 01:58:32,440
Speaker 1: still feels like yesterday. I miss him every day. I

1975
01:58:32,480 --> 01:58:37,720
Speaker 1: miss talking politics with him, and we worked together at

1976
01:58:37,760 --> 01:58:40,920
Speaker 1: the Hotline. We work together at NBC. But when he'd

1977
01:58:41,040 --> 01:58:42,880
Speaker 1: gone back and forth from the Hotline a couple of times,

1978
01:58:42,880 --> 01:58:45,240
Speaker 1: and he actually got a network job before I ever did,

1979
01:58:45,280 --> 01:58:52,280
Speaker 1: he went to CBS and he suddenly he got roped

1980
01:58:52,320 --> 01:58:54,600
Speaker 1: into he was working in their political bureau and they

1981
01:58:54,640 --> 01:58:56,920
Speaker 1: were like a whole bunch of people wanted the weekend

1982
01:58:56,960 --> 01:59:01,720
Speaker 1: off and from their White House Unity talked Vaughan into

1983
01:59:01,840 --> 01:59:08,360
Speaker 1: being the guy who ran the transmission pool uh for

1984
01:59:08,480 --> 01:59:10,920
Speaker 1: the White House Press Corps for the Army Navy game

1985
01:59:10,960 --> 01:59:13,480
Speaker 1: because the president was going at this president at the time,

1986
01:59:13,520 --> 01:59:16,640
Speaker 1: of course, was Bill Clinton. This year they were playing.

1987
01:59:16,680 --> 01:59:18,400
Speaker 1: That year, they were playing the Army Navy game at

1988
01:59:18,400 --> 01:59:23,320
Speaker 1: the old Veterans Stadium in Philadelphia. The reason I remember

1989
01:59:23,360 --> 01:59:26,200
Speaker 1: this is because they also said, Hey, if you want

1990
01:59:26,240 --> 01:59:28,240
Speaker 1: to bring a friend, we'll give you an extra an

1991
01:59:28,240 --> 01:59:30,920
Speaker 1: extra pass. So he says, hey, you want to come

1992
01:59:30,920 --> 01:59:32,880
Speaker 1: with me? And I was like yeah, So you know,

1993
01:59:32,960 --> 01:59:38,480
Speaker 1: we drive from DC to Philadelphia. And the only if

1994
01:59:38,520 --> 01:59:42,000
Speaker 1: I had brought a Phillips screwdriver with me, I pro

1995
01:59:42,640 --> 01:59:46,120
Speaker 1: there is a there's not a zero percent chance I

1996
01:59:46,120 --> 01:59:48,360
Speaker 1: would have walked away with a Mike with a framed

1997
01:59:48,440 --> 01:59:52,840
Speaker 1: Mike Schmidt jersey from the Phillies locker room. The White

1998
01:59:52,840 --> 01:59:57,880
Speaker 1: House transmission pool was in the actual Phillies clubhouse in

1999
01:59:58,000 --> 02:00:02,760
Speaker 1: Veterans Stadium, and I just was mesmerized by it, and

2000
02:00:02,760 --> 02:00:06,280
Speaker 1: we you know it is now. Of course, I've been

2001
02:00:06,320 --> 02:00:08,800
Speaker 1: through what a transmission pool is. I've been through all

2002
02:00:08,800 --> 02:00:11,360
Speaker 1: of those things and and it's kind of a it

2003
02:00:11,400 --> 02:00:15,760
Speaker 1: can It can be a lot of a lot of

2004
02:00:15,800 --> 02:00:17,760
Speaker 1: work and a lot of sitting around, and it can

2005
02:00:17,800 --> 02:00:21,560
Speaker 1: be a lonely thing, which is why many networks are like,

2006
02:00:21,600 --> 02:00:24,520
Speaker 1: you know, you should have some company with this. But

2007
02:00:25,280 --> 02:00:27,040
Speaker 1: it was the first time I got to experience Army

2008
02:00:27,120 --> 02:00:28,840
Speaker 1: Navy game, and I got to experience it sort of

2009
02:00:29,200 --> 02:00:31,960
Speaker 1: behind the scenes, and we watched the entire game on

2010
02:00:32,000 --> 02:00:35,360
Speaker 1: the on the sideline, and like almost every Army Navy game,

2011
02:00:35,400 --> 02:00:37,520
Speaker 1: it came down to the last drive of the game,

2012
02:00:40,000 --> 02:00:43,880
Speaker 1: and uh, it was it was just fantastic, the pageantry,

2013
02:00:44,560 --> 02:00:51,880
Speaker 1: the passion. Uh watching halftime when the Commander in Chief

2014
02:00:51,960 --> 02:00:54,840
Speaker 1: moves from one side, you know, sitting on the Army side,

2015
02:00:55,000 --> 02:00:57,520
Speaker 1: moving over to the Navy side, or if Navy was

2016
02:00:57,560 --> 02:00:59,520
Speaker 1: the home team, the Navy side moving over to the

2017
02:00:59,600 --> 02:01:02,480
Speaker 1: Army side, and there's this sort of ordinate sort of

2018
02:01:03,280 --> 02:01:09,080
Speaker 1: uh you know, you get the Navy, you get all

2019
02:01:09,120 --> 02:01:12,480
Speaker 1: the troops lining up and sort of lining and sort

2020
02:01:12,480 --> 02:01:15,160
Speaker 1: of saluting the Commander in chief as they go. When

2021
02:01:15,400 --> 02:01:17,200
Speaker 1: I've been to a couple of them, and some president

2022
02:01:17,240 --> 02:01:19,600
Speaker 1: doesn't always go. Every year, when the president doesn't go,

2023
02:01:19,640 --> 02:01:22,120
Speaker 1: it's usually the Secretary of Defense that goes. Sometimes the

2024
02:01:22,160 --> 02:01:24,400
Speaker 1: president and the Secretary of Defense goes. I imagine, we

2025
02:01:24,480 --> 02:01:28,080
Speaker 1: know Donald Trump loves himself a football game, and if

2026
02:01:28,080 --> 02:01:29,920
Speaker 1: he can officially go to a football game, he's going

2027
02:01:30,000 --> 02:01:33,520
Speaker 1: to go. So I'm sure we're going to uh have

2028
02:01:33,600 --> 02:01:36,840
Speaker 1: a Trump moment or two at this Army Navy game

2029
02:01:36,880 --> 02:01:38,640
Speaker 1: because he's not gonna he's not going to miss a

2030
02:01:38,640 --> 02:01:44,800
Speaker 1: moment to miss a moment. But uh, it's it's, uh,

2031
02:01:45,800 --> 02:01:50,680
Speaker 1: it's just one of those. It really is. Uh, it's

2032
02:01:50,720 --> 02:01:56,680
Speaker 1: it's probably the only it's probably the purest college football

2033
02:01:56,720 --> 02:01:58,560
Speaker 1: that we have left. Right if you if you're sort

2034
02:01:58,560 --> 02:02:01,760
Speaker 1: of burned out on nil and you're burned out on

2035
02:02:01,960 --> 02:02:05,560
Speaker 1: the conference realignment. Although both Army and Navy have participated

2036
02:02:05,600 --> 02:02:09,800
Speaker 1: in conference realignments, in fact they are now both members

2037
02:02:09,840 --> 02:02:11,680
Speaker 1: of conferences, there was a period where Army was an

2038
02:02:11,720 --> 02:02:14,960
Speaker 1: independent and they were sort of between conferences and things,

2039
02:02:15,520 --> 02:02:18,480
Speaker 1: and one of these days it's very possible. I'm really excited.

2040
02:02:18,480 --> 02:02:21,440
Speaker 1: Over the last couple of years we've actually seen Army

2041
02:02:21,440 --> 02:02:25,240
Speaker 1: and Navy higher coaches that want to innovate and saying, Okay,

2042
02:02:25,280 --> 02:02:27,320
Speaker 1: we have a we're going to have size limits of

2043
02:02:27,320 --> 02:02:29,720
Speaker 1: what we're going to have, but you know, which is

2044
02:02:29,720 --> 02:02:32,040
Speaker 1: why they run the triple option, but hey, why not

2045
02:02:32,160 --> 02:02:34,280
Speaker 1: throw out of the triple options some And we've seen

2046
02:02:34,800 --> 02:02:39,720
Speaker 1: much more dynamic offenses out of both both service academies,

2047
02:02:39,760 --> 02:02:42,960
Speaker 1: which has just been which has allowed them to pull

2048
02:02:43,000 --> 02:02:45,240
Speaker 1: a few upsets every now and then be a bit

2049
02:02:45,360 --> 02:02:49,840
Speaker 1: more sort of punch above their weight as programs. And

2050
02:02:50,480 --> 02:02:52,640
Speaker 1: you know, the only curiosity I have is I have

2051
02:02:52,720 --> 02:02:56,840
Speaker 1: no idea how how these football programs are going to

2052
02:02:59,160 --> 02:03:02,280
Speaker 1: how the how the NIL programs are going to work

2053
02:03:02,400 --> 02:03:04,280
Speaker 1: for Army and Navy, and whether there we're going to

2054
02:03:04,360 --> 02:03:07,000
Speaker 1: start to see collectives for Army and Navy. I imagine

2055
02:03:07,040 --> 02:03:11,600
Speaker 1: we won't. It feels like that that's a that that's

2056
02:03:11,800 --> 02:03:16,360
Speaker 1: that's a gray area that autumn that that folks ought

2057
02:03:16,400 --> 02:03:18,640
Speaker 1: to stay out of. But you know, I don't want

2058
02:03:18,640 --> 02:03:20,760
Speaker 1: to rule anything out that we'll start to see something

2059
02:03:21,880 --> 02:03:26,680
Speaker 1: on that front, but I I do. I'm a sucker

2060
02:03:26,720 --> 02:03:29,160
Speaker 1: for it. And if you ever get the chance to

2061
02:03:29,160 --> 02:03:33,000
Speaker 1: go in person, I would just say take the opportunity.

2062
02:03:33,240 --> 02:03:36,000
Speaker 1: Even if you don't consider yourself sort of a you know,

2063
02:03:36,080 --> 02:03:39,960
Speaker 1: a big sort of flag waving type person. I know

2064
02:03:40,040 --> 02:03:43,360
Speaker 1: that's not everybody's Cupetit doesn't make any less patriotic I'm not.

2065
02:03:43,600 --> 02:03:45,160
Speaker 1: I don't want to. I don't want to imply that

2066
02:03:45,240 --> 02:03:49,600
Speaker 1: at all, but I it is. And here's the other day.

2067
02:03:49,640 --> 02:03:53,640
Speaker 1: It's always a good game, and it's just an incredible atmosphere.

2068
02:03:56,880 --> 02:04:00,240
Speaker 1: And if you want to know, I've some of my

2069
02:04:00,320 --> 02:04:06,360
Speaker 1: closest friends and a former roommate. It's a Naval Academy grad.

2070
02:04:06,480 --> 02:04:12,840
Speaker 1: So I always root for Navy. But I just say

2071
02:04:12,840 --> 02:04:14,800
Speaker 1: go Navy. I don't say beat Army, because you know

2072
02:04:15,600 --> 02:04:19,240
Speaker 1: I don't have I I'm not anti Army on this one,

2073
02:04:19,720 --> 02:04:22,040
Speaker 1: but I do always come down on the Navy side.

2074
02:04:22,080 --> 02:04:25,600
Speaker 1: So go Navy. I hope you do well. But what

2075
02:04:25,680 --> 02:04:27,920
Speaker 1: I most want is just a great game, because when

2076
02:04:27,960 --> 02:04:30,560
Speaker 1: the Army Navy game is competitive, it's a lot of

2077
02:04:30,560 --> 02:04:33,839
Speaker 1: fun to watch, especially when it's the only college football

2078
02:04:33,840 --> 02:04:37,040
Speaker 1: game we have this weekend. Although there is the FCS playoffs,

2079
02:04:37,240 --> 02:04:42,680
Speaker 1: I'm not in denial on that. One of the things

2080
02:04:42,720 --> 02:04:44,600
Speaker 1: I promise is that I might dabble a little bit

2081
02:04:45,520 --> 02:04:47,960
Speaker 1: in NFL. I know I do a little bit of

2082
02:04:48,000 --> 02:04:53,120
Speaker 1: that with Tony Kohneiser, but I'll be honest with you

2083
02:04:53,200 --> 02:04:56,120
Speaker 1: until Miami. As long as Miami is alive in the

2084
02:04:56,160 --> 02:04:59,160
Speaker 1: college football playoff, I'm going to be pretty pretty focused

2085
02:04:59,200 --> 02:05:02,760
Speaker 1: on that. But as a Packer fan, I'm starting to

2086
02:05:02,880 --> 02:05:06,760
Speaker 1: feel as if i'm, you know, the beginning of this season,

2087
02:05:06,800 --> 02:05:09,760
Speaker 1: after the first couple of weeks, first two games, Miami

2088
02:05:09,760 --> 02:05:16,400
Speaker 1: beats Notre Dame in South Florida by pretty significant margins,

2089
02:05:16,440 --> 02:05:19,080
Speaker 1: and they looked like a really good football team, and

2090
02:05:19,320 --> 02:05:21,840
Speaker 1: was you know, going to be championship caliber? First two

2091
02:05:21,880 --> 02:05:25,680
Speaker 1: games in the NFL season, Packers beat the Lions and

2092
02:05:25,720 --> 02:05:28,360
Speaker 1: the Commanders back to back. At the time when Commanders

2093
02:05:28,360 --> 02:05:29,680
Speaker 1: we thought, hey, you know, they just were in the

2094
02:05:29,760 --> 02:05:33,360
Speaker 1: NFC title game the year before. They looked like a juggernaut.

2095
02:05:33,480 --> 02:05:36,080
Speaker 1: Then they had kind of a mid season dip. Miami

2096
02:05:36,120 --> 02:05:38,720
Speaker 1: had kind of a mid season dip. And now both

2097
02:05:38,760 --> 02:05:42,080
Speaker 1: my teams are starting to starting to look like they

2098
02:05:42,080 --> 02:05:44,920
Speaker 1: at least they can compete for the highest title of

2099
02:05:44,960 --> 02:05:48,480
Speaker 1: the season. So to say I'm a bit giddy about

2100
02:05:48,560 --> 02:05:51,480
Speaker 1: my two football teams as an understatement, and I just

2101
02:05:51,560 --> 02:05:54,960
Speaker 1: thank them in this in what can be when the

2102
02:05:55,040 --> 02:05:57,480
Speaker 1: day job and the topics of what I have to

2103
02:05:57,520 --> 02:06:00,400
Speaker 1: cover for the day job can be anxiety and doucing

2104
02:06:00,480 --> 02:06:04,920
Speaker 1: and sometimes demoralizing and sometimes depressing. It's always nice when

2105
02:06:04,960 --> 02:06:09,320
Speaker 1: you're when your sports teams can can provide a little

2106
02:06:09,320 --> 02:06:11,840
Speaker 1: bit of a little bit of happiness. But of course,

2107
02:06:12,080 --> 02:06:15,760
Speaker 1: as we all dedicated sports fans know, when your team,

2108
02:06:15,800 --> 02:06:19,640
Speaker 1: when you suddenly have expectations for your team, you don't

2109
02:06:19,760 --> 02:06:22,600
Speaker 1: enjoy any part of the game until you've survived winning

2110
02:06:22,640 --> 02:06:25,560
Speaker 1: that game. I can tell you I barely enjoyed any

2111
02:06:25,600 --> 02:06:28,240
Speaker 1: part of that Packers Bears game, but I was but

2112
02:06:28,360 --> 02:06:30,960
Speaker 1: after we won, then I was really enjoying the fact

2113
02:06:31,000 --> 02:06:35,320
Speaker 1: that we that that the Packers won. So to say that,

2114
02:06:35,360 --> 02:06:40,920
Speaker 1: I'm I'm I'm feeling pretty uh pretty excited about about

2115
02:06:40,760 --> 02:06:44,680
Speaker 1: what could be coming over the next few weeks. I

2116
02:06:44,680 --> 02:06:46,400
Speaker 1: don't want to jinx it, and for all I know,

2117
02:06:47,080 --> 02:06:50,440
Speaker 1: it could come crashing down for both teams in a heartbeat.

2118
02:06:50,520 --> 02:06:55,560
Speaker 1: But you know, when you have a pretty good defensive line,

2119
02:06:55,560 --> 02:06:58,760
Speaker 1: it's amazing how far your teams can go. And both

2120
02:06:58,800 --> 02:07:02,839
Speaker 1: the Packers and the and the Hurricanes have themselves pretty

2121
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Speaker 1: elite defensive lines. And if you to me, that's the

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Speaker 1: key to winning championships a disruptive defense. And both my

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Speaker 1: Hurricanes and my Packers have disruptive defenses. All Right, With that,

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Speaker 1: with my sports biases out of the way, I will

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Speaker 1: call it a week. I will see you next week.

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Speaker 1: Enjoy the weekend, be safe out there, and until we

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Speaker 1: upload again, h