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Speaker 1: You're listening to The Buck Sexton Show podcast, make sure

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Speaker 1: you subscribe to the podcast on the iHeartRadio app or

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Speaker 1: wherever you get your podcasts.

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Speaker 2: Hey, everybody, Welcome to the Buck Sexton Show. Kara Frederick

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Speaker 2: is with us now. She's formerly of Facebook, currently with

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Speaker 2: the Heritage Foundation, where she's Director of Tech Policy. We're

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Speaker 2: going to talk to her about AI, about government snooping

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Speaker 2: on social media platforms, and all kinds of good stuff

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Speaker 2: or bad stuff, but interesting stuff. Kara, welcome, Thanks for

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Speaker 2: having me. So let's start with Elon Musk recently spoke

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Speaker 2: to Tucker Carlson on Fox Tree saw it, and he said,

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Speaker 2: not only was he Elon flabbergasted by how much access

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Speaker 2: the government had to the communications and just the general

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Speaker 2: stuff going on at the social media platform Twitter, I'm

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Speaker 2: sure as true of other places, but that dms were

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Speaker 2: easily accessible by the government to What do you think

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Speaker 2: of that one?

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Speaker 3: Yeah, so this, you know, honestly, it's not surprising. I mean,

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Speaker 3: especially given the backgrounds that you and I had there.

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Speaker 3: We do know, first and foremost there are legitimate reasons,

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Speaker 3: or at least there were legitimate reasons to you know,

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Speaker 3: work with some federal entities when it comes to things

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Speaker 3: like child sexual abuse material, you know.

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Speaker 1: Sniffing out the bad guys.

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Speaker 3: I worked on foreign Islamic terrorism when I was working

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Speaker 3: for a big tech platform and before, so you know,

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Speaker 3: there are times when you want the government that is

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Speaker 3: ostensibly there to protect the security of Americans to maybe

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Speaker 3: have some sort of access to these internal communications. Unfortunately,

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Speaker 3: over the past two to three years, we've seen that

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Speaker 3: there's been a abject dereliction of duty when it comes

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Speaker 3: to the you know, number one, keeping United citizens safe

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Speaker 3: and number two really prioritizing the the security of Americans,

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Speaker 3: especially from external hostile forces, and instead looking inward at

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Speaker 3: the American populace and inflating the definition of domestic extremists

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Speaker 3: and terrorism and whatnot. So, in my mind, the government

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Speaker 3: has lost our trust and deservedly so, and they have

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Speaker 3: been misprioritizing when they should be looking at real, actual threats.

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Speaker 1: Then okay, maybe we can think about.

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Speaker 3: Some of these surveillance capabilities, but that's all gone, that's

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Speaker 3: been washed away, given especially what Elon exposed in the

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Speaker 3: Twitter files, and it is very telling. I think that

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Speaker 3: we at the Heritage Foundation, we published a report in

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Speaker 3: February of twenty twenty two, and we said there's a

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Speaker 3: symbiotic relationship between these big tech companies and the government.

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Speaker 3: It goes as far as collusion and any sort of

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Speaker 3: collusion between government actors and these big tech companies to

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Speaker 3: silence the speech of Americans should be prohibited. And people

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Speaker 3: were like, you are, Oh, come on, You're just a

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Speaker 3: fear monger.

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Speaker 1: What are you doing. But we've been I've proven right.

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Speaker 3: So the fact that the government had full access to

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Speaker 3: dms and Twitter. Again, we knew Twitter dms a long

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Speaker 3: time ago were probably compromised, and this just proves it.

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Speaker 3: And frankly, this proves a Heritage Foundation right yet again.

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Speaker 2: So you worked at Facebook, I would assume, and tell

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Speaker 2: me if it's an incorrect assumption that the very very

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Speaker 2: cozy and all too close relationship between Twitter pre Elon

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Speaker 2: and the government and as he described it, effectively, it

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Speaker 2: was a bloated activist organization Twitter masquerading as a social

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Speaker 2: media platform. I have to assume, and you tell me

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Speaker 2: if it's not right that the politics of Facebook, Google,

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Speaker 2: all the rest are basically the same and perhaps even worse.

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Speaker 3: I would say, post twenty sixteen Trump elections, that's when

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Speaker 3: everything became exacerbated. So you have you know, a cadre

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Speaker 3: of US who went in before that election. I did

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Speaker 3: there in twenty six sixteen, early twenty sixteen, and you know,

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Speaker 3: we we were there to solve these big problems like

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Speaker 3: the foreign as long terrorism issue, and you had really

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Speaker 3: talented people, a lot of time patriots. I came right

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Speaker 3: out of a naval special warfare command where I had

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Speaker 3: been doing counter terrorism analysis as a targeter, and I

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Speaker 3: went right into Facebook sort of thinking that, you know,

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Speaker 3: we were going to do the same thing. We were

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Speaker 3: going to protect Americans and their users from this you know,

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Speaker 3: foreign Islamic menace, which was taking the form of ISIS

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Speaker 3: at the time. And then I would say in twenty sixteen,

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Speaker 3: something really changed and it was Trump's election, and they

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Speaker 3: just went hysterical. And the people that they started recruiting

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Speaker 3: post election, you know a lot of them went in

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Speaker 3: sort of thinking that they had a mandate to stifle

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Speaker 3: the conservative voices i'd say in America. You know, prior

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Speaker 3: to that, we there were some interesting i would say

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Speaker 3: data points when it came to the way that we

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Speaker 3: did our analysis, for instance, and this is no secret.

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Speaker 3: Now these companies have been using organizations and goos like

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Speaker 3: the Southern Poverty Law Center to actually help them formulate

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Speaker 3: their policies and help them formulate the way that they

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Speaker 3: treated specific actors on these platforms, and they thought nothing

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Speaker 3: of it. They thought, Okay, SBLC is an honest broker.

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Speaker 3: So I think that part of it is ignorance. Part

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Speaker 3: of it is, you know, the sea that they swim

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Speaker 3: in in the Bay Area, and then the other part

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Speaker 3: of it is the twenty sixteen election just really unhinging

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Speaker 3: people and making them really come in with mission oriented

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Speaker 3: to sort of stifle the voices of the people that

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Speaker 3: they disagreed with.

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Speaker 2: Is it fixable at these places? Like how would you

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Speaker 2: actually if you were able to get you know, Zuckerberg

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Speaker 2: and his top lieutenants in a room and they were

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Speaker 2: willing to listen to reason. I mean, you know, I

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Speaker 2: think Facebook has just turned I'm a man that there's

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Speaker 2: still apparently as many people using it in America as

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Speaker 2: there are. I find it to be like an unwieldy

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Speaker 2: trash heap of nonsense, Like it's really hard to even

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Speaker 2: figure out where anything is anymore. They've it all looks

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Speaker 2: like it was made by a bunch of you know,

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Speaker 2: computer engineers. And I don't mean that in a good way,

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Speaker 2: Like it looks like it's just this kind of cobbled

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Speaker 2: together thing and all of the original just the ease,

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Speaker 2: and it's just they're throwing all this. I think it's

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Speaker 2: turned into a bad user user experience and user interface.

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Speaker 2: But anyway, forget about fixing that for a second. If

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Speaker 2: you were just trying to fix the fact that Facebook

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Speaker 2: is not fair to half of the country politically, is

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Speaker 2: it possible or do we just have to build our

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Speaker 2: own build our own thing. By the way, build your

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Speaker 2: own Facebook doesn't sound as crazy. Maybe it's buy your

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Speaker 2: own Facebook that's more expensive than Twitter. Not to call

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Speaker 2: elon on that one.

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Speaker 3: Yeah, you know, I will say personally, and when I

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Speaker 3: was in the company, I thought Mark Zuckerberg's instincts were

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Speaker 3: more libertarian than you know, more leftists. So and that

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Speaker 3: is sort of the old guard of the builders.

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Speaker 1: In Silicon Valley.

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Speaker 3: You know, these are engineers, and they think like engineers,

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Speaker 3: they think like programmers. They want to solve problems and

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Speaker 3: fix things. Unfortunately, I do think that he's he's been

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Speaker 3: there for so long and he's been frankly led astray

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Speaker 3: by others in the C suite and that other sort

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Speaker 3: of layer of upper management as well, who are very

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Speaker 3: concerned with pr fires. And clearly we know that public

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Speaker 3: relations only goes one way, and that's against Conservatives at

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Speaker 3: this point. So you look at that and sort of

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Speaker 3: see how he's been co opted when his instincts were

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Speaker 3: maybe initially good. I remember a speech that he gave

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Speaker 3: in October of twenty nineteen at Georgetown University where he

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Speaker 3: said Facebook had to be the alternative to an authoritarian

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Speaker 3: China which was propagating its digital platforms here in the US.

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Speaker 3: You know what happened to that that that gave me

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Speaker 3: a little bit of hope. But since he since spent

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Speaker 3: four hundred million dollars pushing certain Democrats in elections under

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Speaker 3: the auspices of these out the vote measures, but we

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Speaker 3: know that they were in blue states going to Democrat

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Speaker 3: activist organizations who were then pushing only getting out the

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Speaker 3: vote for Democrats. So unfortunately, I think those instincts that

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Speaker 3: he previously had have been quashed, and I don't think

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Speaker 3: there's any coming back from it. I do think it

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Speaker 3: is too far gone at this point.

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Speaker 2: Let's come back in a second, and talk about TikTok.

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Speaker 2: Since you mentioned communist China, we'll get into that in

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Speaker 2: All right. So TikTok, I gotta tell you. I mean,

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Speaker 2: I've I think that YouTube is much more concerning for

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Speaker 2: American freedom and everything else than TikTok. And I've been

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Speaker 2: saying this more and more people are saying it now.

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Speaker 2: It seemed to be about a month ago and it

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Speaker 2: was like, oh, TikTok, I'm sitting here. I'm like, what

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Speaker 2: about the social media companies? They are already throwing elections anyway,

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Speaker 2: We've put that aside for a second. I mean, how

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Speaker 2: worried are you? Let's just look at TikTok. And I

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Speaker 2: won't do the you know, the what about with YouTube, Google,

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Speaker 2: Facebook or Instagram all that stuff, although I could. How

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Speaker 2: bad is TikTok really? How big a problem is it?

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Speaker 1: Yeah?

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Speaker 3: I think TikTok falls into three problem buckets. I would say,

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Speaker 3: and The first and foremost is the most obvious one.

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Speaker 3: It's that Byte Dances its parents parent company. It is

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Speaker 3: headquartered in Beijing and therefore subject to the laws and

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Speaker 3: policies of the People's Republic of China. One of the

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Speaker 3: laws that we like to talk about is the twenty

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Speaker 3: seventeen National Intelligence Law, which effectively compels private companies to

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Speaker 3: do the work of the state. So if the Chinese state,

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Speaker 3: the CCP, the Chinese Communist Party, decides that they want

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Speaker 3: specific data, they want access to this, they want access

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Speaker 3: to that, then by virtue of this law, Byte Dance

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Speaker 3: has to comply. And this is not my original phrasing,

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Speaker 3: but I think it illustrates the problem pretty well. China

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Speaker 3: doesn't have rule of law, they have rule by law.

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Speaker 3: This if this is something if you're you know, the

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Speaker 3: Bite Dance CEO, you're kind of powerless to resist at

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Speaker 3: this point, and why would you. When you look at

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Speaker 3: Byte Dance, they have one of three of their board

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Speaker 3: members of the main domestic subsidiary of Byte Dance is

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Speaker 3: a card carrying Chinese Communist government official. If you look

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Speaker 3: at like some of the good reporting that Forbes has done,

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Speaker 3: they scoured LinkedIn and found that three hundred plus profiles

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Speaker 3: had either current or former links of these current byte

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Speaker 3: Dance employees to a Chinese propaganda arm, to Chinese state media.

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Speaker 3: So you have active and former members of the Chinese

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Speaker 3: Communist Party, particularly in the information realm, working in byte Dance.

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Speaker 3: And there's so many other data points that I could

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Speaker 3: talk about, but that's the first one. Byte Dance very

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Speaker 3: close links and infiltration frankly by the CCP. They have

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Speaker 3: an internal committee, a DOJ report in September twenty twenty

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Speaker 3: coming out of the Trump administration assessed as much, and

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Speaker 3: we know that they are deeply involved in the inner

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Speaker 3: workings of TikTok as well.

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Speaker 1: So that's one thing, and again just tip of the iceberg.

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Speaker 3: We can go into just really how odious some of

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Speaker 3: those connections are when it comes to the connection with

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Speaker 3: American data as well, but I'll table that for now.

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Speaker 3: And then number two there's that influence campaign aspect, and

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Speaker 3: you know, we talk about the manipulation of the information environment.

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Speaker 3: This is something that we were dealing with in the

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Speaker 3: intelligence community and especially in big tech companies and looking

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Speaker 3: at it now and what we've seen is pro CCP

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Speaker 3: narratives pushed on these platforms. We've seen an actual Chinese

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Speaker 3: state account come to TikTok and say, how can we

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Speaker 3: push our information? And we've seen information from those accounts

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Speaker 3: pushed and not labeled as state media as well. So

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Speaker 3: we know that they're trying to do the proverbial sowing

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Speaker 3: of discord, such as promoting Democratic candidates in the twenty

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Speaker 3: twenty two midterm election to the detriment of Republican candidates.

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Speaker 3: They're trying to push stories about abortion and incendiary things

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Speaker 3: to help so descent among the American populations, something everyone

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Speaker 3: always accused sort.

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Speaker 1: Of the Russians of doing.

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Speaker 3: So that's another aspect of that information environment manipulation.

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Speaker 1: And then third, you have the kids. You know what

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Speaker 1: it's doing to children.

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Speaker 3: And we know that TikTok, in particular with the four

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Speaker 3: U algorithm, just lights a.

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Speaker 1: Fire under these social contagions.

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Speaker 3: We know that they're in bed with the transgender lobby,

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Speaker 3: featuring you know, prominent transgender activists, prominent LGBTQ activists all

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Speaker 3: over their websites raising awareness. They're very very open about

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Speaker 3: doing this, and there are pediatric hospitals that are reporting

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Speaker 3: actual physical manifestations coming out in patients because they use

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Speaker 3: TikTok like things called TikTok tics, which most researchers are

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Speaker 3: classifying as you know, pure social contagion movements. So you know,

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Speaker 3: and this is again something that TikTok is very very

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Speaker 3: efficient at that it doesn't necessarily distinguish it wholesale from

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Speaker 3: the instagrams of the world. If you want to talk

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Speaker 3: about what about is them. We know their staffs on

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Speaker 3: that in terms of mental health harms. But TikTok is poised,

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Speaker 3: especially because, as Christopher Ray says, directory of the FBI

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Speaker 3: China controls the algorithm. That's even more problematic when they're

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Speaker 3: feeding our kids this poison.

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Speaker 2: But how much of the algorithm is the kids click

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Speaker 2: on the things they click on and then it's reflected

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Speaker 2: back to them, right, Like you know, I asked this

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Speaker 2: because remember Russia, remember Russia collusion. Back in twenty sixteen,

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Speaker 2: they're saying, oh, Hillary Clinton lost because Trump worked with Russia.

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Speaker 2: And then they talked about the Facebook ads. I think

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Speaker 2: there was like one hundred thousand dollars that were spent

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Speaker 2: on these bogus you know, or Russian backed or whatever

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Speaker 2: Facebook ads. And when you look at the ads, I

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Speaker 2: mean a lot of them were ridiculous. I mean it

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Speaker 2: looked like a guy named Yuri, you know know, working

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Speaker 2: in like sub basement c of some you know fsb

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Speaker 2: outpost you know, on the on the in the outer

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Speaker 2: ring of Moscow was like looking at a you know,

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Speaker 2: a little dictionary in English. I mean it was preposterous, right,

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Speaker 2: I mean their understanding of US politics beyond lock her

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Speaker 2: up for Hillary Clinton, which you know they got that right,

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Speaker 2: but their understanding was very weak. I mean the idea

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Speaker 2: that that I just hear all these people saying, oh

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Speaker 2: my gosh, you know, the Chinese are going to brainwash

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Speaker 2: our kids and make them lazy or whatever. I look

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Speaker 2: at them, like, what do you guys think Disney's door.

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Speaker 2: I just this is the part of it that there's

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Speaker 2: so much more upset about what's going on with the

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Speaker 2: I understand the spying thing, and like if they can

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Speaker 2: suck up your information and do create that that's a

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Speaker 2: separate issue, but it sounds to me like there's also

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Speaker 2: just a content component of this, and I I don't

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Speaker 2: under I just feel like I feel like TikTok is

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Speaker 2: the shiny object where people in politics and in power

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Speaker 2: get to pretend that they're doing something that's like, oh,

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Speaker 2: we're protecting the kids from the bad influences online. They're

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Speaker 2: protecting them from one of dozens of major and endless

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Speaker 2: minor influences online that are all being pushed by the

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Speaker 2: Democrat Party anyway, like transgenderism. Beijing's not pushing transgenderism via

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Speaker 2: TikTok on kids. The Democrat Disney is pushing transgenderism on kids.

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Speaker 3: Well, number one, you know, you're right, But number two,

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Speaker 3: we also don't know that Beijing isn't pushing this stuff,

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Speaker 3: and that's you know, part of the problem too. If

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Speaker 3: China is so intimately involved in the algorithm, which it

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Speaker 3: says it won't. If there's a forced investiture to an

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Speaker 3: American company of TikTok, it said, there's no way we're

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Speaker 3: giving up the algorithm. So in my mind, that means, yeah,

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Speaker 3: they want to retain control of it. There's a commercial

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Speaker 3: element to it because it is really good, but there's

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Speaker 3: also that information environment of manipulation potential there because they

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Speaker 3: want to keep it in Chinese hands so badly, and

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Speaker 3: they've said as much, which flies in the face of

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Speaker 3: a lot of the assurances that these TikTok executives are

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Speaker 3: providing to Congress members with their you know, Project Texas potential,

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Speaker 3: you know, mollification of our representatives.

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Speaker 1: But so I also I do want to address that.

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Speaker 3: I think that when you have over sixty seven percent

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Speaker 3: of American teenagers as of last year on a particular platform,

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Speaker 3: and you have thirty percent, according to a pupil in

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Speaker 3: twenty twenty of preteens nine to eleven year olds on

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Speaker 3: a specific platform, and we have new data coming out

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Speaker 3: of the UK saying a decent percentage of toddlers are

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Speaker 3: now exposed to this content, then you.

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Speaker 1: Have a problem.

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Speaker 3: Then you have a the fact that you know, number one,

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Speaker 3: all of these children are on it to a much

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Speaker 3: greater degree than you know, Facebook, as you talked about,

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Speaker 3: is hemorrhaging users, especially in this demographic Instagram as hemorrhaging

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Speaker 3: users as well. Then then that becomes a source of information,

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Speaker 3: and we know that they're getting it for their news

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Speaker 3: now as well. They're not just looking at those cute videos.

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Speaker 3: They're they're getting it to be informed about the world.

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Speaker 3: Top Google executives said as much as well. In a

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Speaker 3: tech conference. He said, when people young kids want their news,

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Speaker 3: they go to TikTok and Google is very much aware

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Speaker 3: of that, and you know, keeping their antenna up. And

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Speaker 3: then you have American adults, so looking at the kid

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Speaker 3: stuff that matters a lot, but American adults as well.

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Speaker 3: So you know, in two years the amount of American

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Speaker 3: adults that get their news from TikTok has tripled. That's problematic,

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Speaker 3: I think from a civic perspective. I see you want

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Speaker 3: to say something, but I want to say one more

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Speaker 3: thing before I let you talk.

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Speaker 2: I'm standing in the way of the train. By all means,

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Speaker 2: go ahead, Sorry you were saying yeah.

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Speaker 3: So I think the last thing is a number of

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Speaker 3: enterprising journalists have taken it upon themselves to create their

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Speaker 3: own TikTok accounts. And what they do is they register

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Speaker 3: as users from around thirteen to fourteen, and they have found,

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Speaker 3: to a man, within minutes, they are fed content that

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Speaker 3: is composed of self harm content, suicidal content, eating disorder content,

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Speaker 3: especially if they're registering as girls versus registering as boys.

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Speaker 3: So we do know that there is something that isn't

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Speaker 3: just responsive to shall we say.

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Speaker 1: What the children want?

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Speaker 3: Granted, the TikTok algorithm is based off your engagement, not

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Speaker 3: necessarily your network, So how long your eyes linger over

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Speaker 3: a specific video if you're interested in depression, Yes, it's

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Speaker 3: more likely to feed you self harm content and suicidal content.

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Speaker 3: But this appears to be pretty uniform across the board

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Speaker 3: for a lot of these journalists experimenting anyway. So there's

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Speaker 3: something in TikTok that is particularly I would say nefarious

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Speaker 3: when it comes to our children and the noxious content

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Speaker 3: they're being fed.

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Speaker 2: I mean, do you ever go on TikTok because I

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Speaker 2: gotta say, some of those shuffle dance moves very catchy.

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Speaker 1: Fuck.

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Speaker 3: No, I am not going on TikTok, nor will I

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Speaker 3: ever go on Tiktokay.

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Speaker 2: This is where I get to point it out all

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Speaker 2: the other people in America, usually especially the Democrats, but

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Speaker 2: I get to call them comedies and have fun with it.

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Speaker 2: Apparently on this one, I've got like a soft spot

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Speaker 2: for communist China. So I'm like, I think TikTok is

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Speaker 2: super entertaining. I gotta tell you it's all for me.

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Speaker 2: It's yeah, it's it's how to how to sear like

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00:20:27,697 --> 00:20:30,577
Speaker 2: the perfect steak and different red meat. I follow this

377
00:20:30,737 --> 00:20:34,177
Speaker 2: like Max the meat guy who makes like Wagu and

378
00:20:34,257 --> 00:20:37,137
Speaker 2: briskets and all these things. Uh, guys who know how

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00:20:37,137 --> 00:20:40,657
Speaker 2: to make like tomahawks out of the stuff you find

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Speaker 2: in your backyard like this is and of course cute

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Speaker 2: pulldog videos like like, I was like, what is this?

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Speaker 2: How is this supposedly in some way doing anything that

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Speaker 2: is uh, you know, going to damage me? But then again,

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Speaker 2: I'm an adult and things are different, and you know

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Speaker 2: that can be a little bit of a challenge. And yeah,

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Speaker 2: so I got to get to the Oxford Gold Group

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Speaker 2: here for a second, and we come back. We're going

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Speaker 2: to talk about AI because I think its kra Frederick

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Speaker 2: has to plane whether or not Skynet is going to

390
00:21:12,977 --> 00:21:15,977
Speaker 2: become self aware and cause the nuclear war that James

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Speaker 2: Cameron warned us about in Terminator one and two and

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Speaker 2: probably the other ones, but no one saw the other

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Speaker 2: ones because the other Terminator movie sucked, So you know,

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Speaker 2: there was a she knows it's true. There was a

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Speaker 2: recent banking collapse, the nation's largest collapse of a financial

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Speaker 2: institution since two thousand and eight. So stuff can go

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Speaker 2: bad real fast, you know this, And Fiat currency is

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Speaker 2: pretty imperfect because it's a situation. Now we have inflation.

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Speaker 2: We also have thirty two trillion dollars a debt. How

400
00:21:42,017 --> 00:21:46,337
Speaker 2: about using gold and silver as a protection for your portfolio.

401
00:21:46,377 --> 00:21:48,297
Speaker 2: Have a little gold and silver on hand just in case.

402
00:21:48,337 --> 00:21:50,217
Speaker 2: I've got gold and silver right here with me in

403
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Speaker 2: the radio studio. Now is the time. Don't wait, because

404
00:21:54,097 --> 00:21:55,857
Speaker 2: if a crisis hits, you're gonna need to have it

405
00:21:55,897 --> 00:21:57,897
Speaker 2: on hand. And also those prices of gold and silver

406
00:21:57,937 --> 00:22:01,177
Speaker 2: are going to go way up. So now's the time

407
00:22:01,337 --> 00:22:04,137
Speaker 2: to call my friends at the Oxford Gold Group. Securing

408
00:22:04,137 --> 00:22:05,857
Speaker 2: your I array or four to one K with real

409
00:22:05,897 --> 00:22:07,977
Speaker 2: gold and silver, by the way, is also a fantastic

410
00:22:08,337 --> 00:22:12,137
Speaker 2: portfolio protection plan. All you have to do is call

411
00:22:12,217 --> 00:22:15,177
Speaker 2: the Oxford Gold Group. You can own real precious metals

412
00:22:15,217 --> 00:22:17,457
Speaker 2: just like I do. Call the Oxford Gold Group at

413
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Speaker 2: eight three three four zero four gold A three three

414
00:22:21,017 --> 00:22:27,417
Speaker 2: four zero four g O L d Okay so ms

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Speaker 2: Kara Frederick who is on the task force I believe

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Speaker 2: at the Heritage Foundation for dealing with AI related matters,

417
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Speaker 2: So you would you would have some insights into this.

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Speaker 2: I I think that the AI is and look is

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Speaker 2: Elon Musk, both wealthier and smarter than me. Yes, but

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Speaker 2: I think all this stuff about how the world is

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Speaker 2: going to end because of AI is pretty crazy. Like

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Speaker 2: I'm looking at this, I'm like, how does this even happen?

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Speaker 2: Everyone's getting all freaked out? Do I I'm usually actually no,

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Speaker 2: I usually tell people everything's gonna be okay, because most

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Speaker 2: hysteria is just people wanting attention. But is AI really?

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Speaker 2: I know it's a big deal and it's gonna matter all.

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Speaker 2: I'm not saying it doesn't matter a lot, but as

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Speaker 2: a threat, what is the threat from AI? That's the

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Speaker 2: part of this that I still haven't No one's really

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Speaker 2: been able to explain to me. It's like, oh, like

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Speaker 2: it's going to hurt our democracy because of the disinformation.

432
00:23:23,217 --> 00:23:24,817
Speaker 2: Watch CNN, look what they.

433
00:23:24,697 --> 00:23:26,657
Speaker 1: Do, fair point.

434
00:23:26,977 --> 00:23:29,537
Speaker 3: And I do think you stand in good company with

435
00:23:29,537 --> 00:23:30,657
Speaker 3: with some of the skeptics.

436
00:23:31,577 --> 00:23:31,817
Speaker 2: You know.

437
00:23:32,337 --> 00:23:34,937
Speaker 3: One of the things, as you know, in the Intel community, though,

438
00:23:34,937 --> 00:23:38,057
Speaker 3: there was always that person who was like everyone everyone

439
00:23:38,377 --> 00:23:42,017
Speaker 3: China isn't ten feet tall, right, everyone everyone al Kaita

440
00:23:42,177 --> 00:23:45,697
Speaker 3: is not looking at external operations, and they like are

441
00:23:45,737 --> 00:23:47,457
Speaker 3: the naysayers and everyone kind.

442
00:23:47,257 --> 00:23:51,257
Speaker 2: Of their whole thing is is they're the uh you know,

443
00:23:51,297 --> 00:23:54,017
Speaker 2: they're they're playing the role of what's the what's the

444
00:23:54,097 --> 00:23:55,817
Speaker 2: word we're looking for here. You know, when you're just

445
00:23:55,897 --> 00:23:59,977
Speaker 2: being in opposition, to be in opposition, I forget what

446
00:24:00,017 --> 00:24:04,497
Speaker 2: the word is. You know what I'm saying, contrarian. Thank you.

447
00:24:04,537 --> 00:24:05,737
Speaker 2: They're playing the contrarian.

448
00:24:05,817 --> 00:24:10,057
Speaker 3: Yes, yes, yes, so so you know there's that aspect

449
00:24:10,297 --> 00:24:12,897
Speaker 3: of you know, the AI community as well. But I

450
00:24:12,937 --> 00:24:17,937
Speaker 3: do think there's there there's reason to be worried. And

451
00:24:18,337 --> 00:24:22,257
Speaker 3: I like to quote, you know, two authoritarians on this matter.

452
00:24:22,337 --> 00:24:25,457
Speaker 3: And I thought this was commonplace, but apparently not. Many

453
00:24:25,497 --> 00:24:28,337
Speaker 3: people know that Putin a few years ago said whoever

454
00:24:28,457 --> 00:24:30,617
Speaker 3: is going to lead an AI is going to rule

455
00:24:30,657 --> 00:24:36,417
Speaker 3: the world. I think that that's the significant discussion to

456
00:24:36,777 --> 00:24:39,457
Speaker 3: be had, especially when Putin's saying that, and then she

457
00:24:39,657 --> 00:24:42,657
Speaker 3: and China says, you know, we want to dominate in

458
00:24:42,777 --> 00:24:46,177
Speaker 3: AI by twenty thirty. So, you know, if our adversaries

459
00:24:46,257 --> 00:24:48,977
Speaker 3: have their eye on this and they see it as

460
00:24:49,017 --> 00:24:54,617
Speaker 3: some geopolitical strategic key, then I think that it's important

461
00:24:54,617 --> 00:24:58,217
Speaker 3: to sort of pay attention. And the reason why I

462
00:24:58,617 --> 00:25:01,377
Speaker 3: think they know harnessing these technologies is really going to

463
00:25:01,897 --> 00:25:04,057
Speaker 3: uh catapult them to the front of the line of

464
00:25:04,377 --> 00:25:10,257
Speaker 3: global dominance is is because they're doing things at machine speed.

465
00:25:10,617 --> 00:25:13,537
Speaker 3: Tends to be better than doing things that human speed.

466
00:25:13,577 --> 00:25:15,817
Speaker 3: And when you're coming when you're talking about war fighting,

467
00:25:16,377 --> 00:25:19,337
Speaker 3: when you're talking about things like even you know, the

468
00:25:19,377 --> 00:25:22,137
Speaker 3: stuff that the Intel pukes like us used to do,

469
00:25:22,577 --> 00:25:25,577
Speaker 3: that computer vision algorithms can do better, you know, instead

470
00:25:25,577 --> 00:25:27,857
Speaker 3: of what project may even tried to do. Instead of

471
00:25:27,857 --> 00:25:30,417
Speaker 3: a human being sitting in front of an FMV screen,

472
00:25:30,497 --> 00:25:33,537
Speaker 3: you know, saying that's labeling a truck, labeling a rock,

473
00:25:33,657 --> 00:25:37,377
Speaker 3: labeling a car, you have machines able to do that.

474
00:25:37,537 --> 00:25:42,217
Speaker 3: And then when machines can make decisions, and again, you know,

475
00:25:43,097 --> 00:25:45,977
Speaker 3: the autonomous weapons that we have are mostly semi autonomous,

476
00:25:45,977 --> 00:25:47,977
Speaker 3: so there's always a human in the loop. At this point,

477
00:25:48,057 --> 00:25:50,257
Speaker 3: there's a lot of debate and discussion about that. But

478
00:25:50,337 --> 00:25:52,177
Speaker 3: when you get to the point that a machine is

479
00:25:52,257 --> 00:25:58,017
Speaker 3: basically cutting out all of that analytical rigor that can

480
00:25:58,057 --> 00:26:00,737
Speaker 3: be applied elsewhere to do things only humans can do,

481
00:26:01,137 --> 00:26:04,177
Speaker 3: that's going to give the war fighter a massive advantage.

482
00:26:04,217 --> 00:26:07,497
Speaker 3: So if you have a computer vision algorithm determining that

483
00:26:07,497 --> 00:26:09,057
Speaker 3: that's a rock and that's a tree, and that you

484
00:26:09,097 --> 00:26:13,897
Speaker 3: shouldn't you know, hit it with a with a kinetic strike,

485
00:26:14,177 --> 00:26:16,337
Speaker 3: then that's going to be better. If you have another

486
00:26:16,377 --> 00:26:19,377
Speaker 3: analyst sort of doing things like determining if there's a

487
00:26:19,417 --> 00:26:23,497
Speaker 3: positive identification of that actual terrorist actor which would cause

488
00:26:23,537 --> 00:26:25,977
Speaker 3: that hell fire to rain down. So I think that

489
00:26:26,097 --> 00:26:30,137
Speaker 3: you know, it's offering specific advantages because of that machine

490
00:26:30,137 --> 00:26:33,137
Speaker 3: speed vice human speed. People talk a lot about drone

491
00:26:33,177 --> 00:26:35,817
Speaker 3: swarms too, so instead of you know, training up a

492
00:26:35,897 --> 00:26:39,297
Speaker 3: human pilot necessarily these you know, what my old boss

493
00:26:39,297 --> 00:26:41,217
Speaker 3: at the Center for an American Security used to say

494
00:26:41,337 --> 00:26:43,297
Speaker 3: is that, you know, the human being sort of acts

495
00:26:43,337 --> 00:26:46,057
Speaker 3: as the quarterback and you let your your smaller, you're

496
00:26:46,257 --> 00:26:49,457
Speaker 3: cheaper systems do some of the other work for you,

497
00:26:49,577 --> 00:26:51,377
Speaker 3: so you're not sort of wasting a human being in

498
00:26:51,377 --> 00:26:53,857
Speaker 3: the human capital on that kind of thing too. So

499
00:26:54,177 --> 00:26:56,857
Speaker 3: I think those are just two examples of what AI

500
00:26:57,377 --> 00:26:59,937
Speaker 3: can help accomplish in war.

501
00:27:00,577 --> 00:27:03,217
Speaker 1: And we know that she's on a war footing.

502
00:27:03,657 --> 00:27:06,297
Speaker 3: We know that Putin is currently at war right now too,

503
00:27:06,337 --> 00:27:09,017
Speaker 3: so so that's another thing. The information environment, like what

504
00:27:09,057 --> 00:27:12,057
Speaker 3: you were talking about before, that's a whole nother kettle

505
00:27:12,057 --> 00:27:14,097
Speaker 3: of worms, and we can talk about that all day

506
00:27:14,137 --> 00:27:14,577
Speaker 3: as well.

507
00:27:15,897 --> 00:27:19,537
Speaker 2: Oh, I don't know, it seems to me like if

508
00:27:19,577 --> 00:27:21,657
Speaker 2: there's a little machine that can clean up after me,

509
00:27:22,017 --> 00:27:25,377
Speaker 2: and tell me how great I am all the time,

510
00:27:25,537 --> 00:27:29,817
Speaker 2: and you know, not ask any questions beyond that sounds good.

511
00:27:29,977 --> 00:27:31,897
Speaker 2: I'm not that worried about it, like turning into the

512
00:27:31,977 --> 00:27:34,977
Speaker 2: terminator and deciding that you know, it has feelings too,

513
00:27:35,017 --> 00:27:36,937
Speaker 2: and it's self aware at all. This I don't know.

514
00:27:36,977 --> 00:27:39,977
Speaker 2: I mean, I guess I do think it's it's definitely

515
00:27:39,977 --> 00:27:42,377
Speaker 2: going to be interesting for high school kids who want

516
00:27:42,377 --> 00:27:45,457
Speaker 2: to have some program that can easily write there, you know,

517
00:27:45,737 --> 00:27:48,457
Speaker 2: kind of B minus level term paper for them that

518
00:27:48,977 --> 00:27:53,097
Speaker 2: we know it can do pretty quickly. But friends, you

519
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523
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524
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531
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536
00:28:44,337 --> 00:28:48,377
Speaker 2: Is that website choq dot com, Be sure to use

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00:28:48,457 --> 00:28:51,097
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538
00:28:51,137 --> 00:28:54,537
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539
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Speaker 2: b U c K for thirty five percent off. So, Caro,

540
00:28:59,337 --> 00:29:02,617
Speaker 2: when you're not trying to save the Internet for the

541
00:29:02,617 --> 00:29:05,977
Speaker 2: purposes of freedom, humanity and world peace and all that stuff,

542
00:29:05,977 --> 00:29:08,097
Speaker 2: what else? What do we need to have a Cara Fredder?

543
00:29:08,137 --> 00:29:11,137
Speaker 2: You were a Navy intel analyst. Is that what I'm getting?

544
00:29:11,137 --> 00:29:13,457
Speaker 2: Because you said vice in a way only people from

545
00:29:13,457 --> 00:29:18,217
Speaker 2: the intelligence community ever say vice like that, meaning instead

546
00:29:18,417 --> 00:29:20,497
Speaker 2: you know that no one else You'll never come across

547
00:29:20,497 --> 00:29:23,297
Speaker 2: anybody who did not work in the IC who will

548
00:29:23,337 --> 00:29:25,057
Speaker 2: be like, well, I think that's a good idea of vice.

549
00:29:25,097 --> 00:29:27,737
Speaker 2: This other idea you're like, wait, what, we're the only

550
00:29:27,777 --> 00:29:28,417
Speaker 2: ones who do that.

551
00:29:30,017 --> 00:29:31,897
Speaker 1: No way, Oh man about Habita.

552
00:29:32,457 --> 00:29:34,977
Speaker 2: Oh I'll give you more. I'll give you more optics.

553
00:29:35,497 --> 00:29:38,057
Speaker 2: Only people from DC talking about well from this optic

554
00:29:38,137 --> 00:29:40,697
Speaker 2: or from that optic or whatever. There there are some

555
00:29:40,857 --> 00:29:44,137
Speaker 2: things that the the Intel people in particular, we have

556
00:29:44,257 --> 00:29:47,697
Speaker 2: this weird nerd vernacular. And I caught you doing some

557
00:29:47,777 --> 00:29:50,777
Speaker 2: nerd vernacular during this podcast. So at least I know

558
00:29:50,817 --> 00:29:52,977
Speaker 2: you're the real deal. At least I know that you know,

559
00:29:53,417 --> 00:29:57,097
Speaker 2: you were pouring over those reports eight cups of coffee deep.

560
00:29:59,137 --> 00:30:01,897
Speaker 3: Yeah, it's funny, you know, get smart with Steve Carell

561
00:30:01,937 --> 00:30:04,257
Speaker 3: and Anne Hathaway. It's a it's a movie. It's like

562
00:30:04,297 --> 00:30:06,937
Speaker 3: a I don't know, spy caper. And you have the

563
00:30:06,977 --> 00:30:09,697
Speaker 3: Intel analyst who like finally gets this chance to like

564
00:30:09,737 --> 00:30:13,017
Speaker 3: be in operations officer in the field and he's you know,

565
00:30:13,097 --> 00:30:16,457
Speaker 3: talking to some operator types and he's like, wait, did

566
00:30:16,537 --> 00:30:18,097
Speaker 3: nobody read my report?

567
00:30:18,457 --> 00:30:20,897
Speaker 1: Like that was me. I was like, did nobody read

568
00:30:20,977 --> 00:30:21,577
Speaker 1: my report?

569
00:30:22,577 --> 00:30:25,337
Speaker 3: That person who would would go out with the guys

570
00:30:25,377 --> 00:30:28,017
Speaker 3: and special Operations forces and sort of be.

571
00:30:28,017 --> 00:30:30,377
Speaker 1: Like, hey, everybody, this is what we should be thinking about.

572
00:30:30,497 --> 00:30:31,937
Speaker 1: Here's the target. You should go there.

573
00:30:32,377 --> 00:30:36,057
Speaker 3: Uh So I was always a civilian, a civilian intelligence officer,

574
00:30:36,137 --> 00:30:38,217
Speaker 3: and we called him Targeters when I was working with

575
00:30:38,297 --> 00:30:39,737
Speaker 3: Naval Special Warfare Development Group.

576
00:30:39,817 --> 00:30:42,177
Speaker 1: But that was, yeah, that was my job.

577
00:30:42,457 --> 00:30:44,897
Speaker 3: I was an Al Qaida analyst first and foremost and

578
00:30:45,017 --> 00:30:45,777
Speaker 3: all sorcers.

579
00:30:45,817 --> 00:30:50,497
Speaker 2: So what they did was they like original original gangster

580
00:30:50,857 --> 00:30:54,337
Speaker 2: old school al Qaeda or the uh a Qi. Because

581
00:30:54,377 --> 00:30:56,737
Speaker 2: I was a q I, Oh you were, they brought

582
00:30:56,777 --> 00:30:58,697
Speaker 2: me in to do CDC a QI and then I

583
00:30:58,697 --> 00:31:01,097
Speaker 2: got moved to OI uh o I A and and

584
00:31:01,257 --> 00:31:05,417
Speaker 2: uh and a q I basically so no way, yeah.

585
00:31:05,297 --> 00:31:05,937
Speaker 1: No, I was.

586
00:31:06,177 --> 00:31:10,217
Speaker 3: I was looking at guys over yeah, the original guys.

587
00:31:10,217 --> 00:31:12,257
Speaker 3: Some of the guys been hiding in Iran for a

588
00:31:12,257 --> 00:31:15,817
Speaker 3: little bit that is now in the open. And yeah,

589
00:31:15,817 --> 00:31:18,417
Speaker 3: when I deployed a couple of times, that was my

590
00:31:19,017 --> 00:31:23,617
Speaker 3: that was my thing, looking at Al Kaida external operations operatives.

591
00:31:23,737 --> 00:31:28,297
Speaker 2: So what did you think of Thirteen Hours the movie,

592
00:31:28,337 --> 00:31:29,857
Speaker 2: by the way, because I always thought it was so

593
00:31:29,897 --> 00:31:34,697
Speaker 2: funny that the case officers in that were like so smug,

594
00:31:36,497 --> 00:31:39,217
Speaker 2: which is just great. It was just great. The analyst

595
00:31:39,257 --> 00:31:40,937
Speaker 2: were all sitting there like yeah, maybe we were just

596
00:31:41,017 --> 00:31:43,617
Speaker 2: back at headquarters making coffee, but like at least we

597
00:31:43,697 --> 00:31:46,177
Speaker 2: respected the paramilitary guys.

598
00:31:47,377 --> 00:31:48,897
Speaker 1: Yeah, oh don't. I don't know.

599
00:31:48,977 --> 00:31:51,577
Speaker 3: I had a lot of OGA friends when I was

600
00:31:51,617 --> 00:31:54,817
Speaker 3: out there, so you know, you got to you gotta.

601
00:31:54,697 --> 00:31:58,697
Speaker 1: Keep those relationships warm. So I was I liked your

602
00:31:58,737 --> 00:31:59,177
Speaker 1: side of the.

603
00:32:01,537 --> 00:32:03,657
Speaker 2: You know, but did you notice in the movie for

604
00:32:03,657 --> 00:32:05,977
Speaker 2: some reason, just to really hammer it home, like one

605
00:32:05,977 --> 00:32:10,577
Speaker 2: of the American case officers in Thirteen Hours just randomly

606
00:32:10,577 --> 00:32:12,897
Speaker 2: has kind of like a French accent and he's turning

607
00:32:12,937 --> 00:32:15,617
Speaker 2: around to like he's turning around to uh, you know,

608
00:32:15,657 --> 00:32:18,057
Speaker 2: what's his name? John Krasinsky, And like all these guys

609
00:32:18,137 --> 00:32:20,657
Speaker 2: were all, you know, jacked and these badasses there and

610
00:32:20,697 --> 00:32:23,617
Speaker 2: he's like he's like, IDA had to do fancy things, dude,

611
00:32:23,697 --> 00:32:26,017
Speaker 2: Like I am so cool. They're like where did this?

612
00:32:26,377 --> 00:32:29,217
Speaker 2: Where did Why is is his name? Like like Jacques Couston,

613
00:32:29,417 --> 00:32:31,617
Speaker 2: Like where did this guy? He's an Emeritan? Like they

614
00:32:31,737 --> 00:32:34,417
Speaker 2: just I love it because those guys.

615
00:32:34,417 --> 00:32:35,857
Speaker 1: Uh, the the the.

616
00:32:35,737 --> 00:32:39,377
Speaker 2: GRS guys in the movie, like, oh, it's funny the

617
00:32:39,497 --> 00:32:41,257
Speaker 2: things they would say about the case officer. I'm just sorry.

618
00:32:41,377 --> 00:32:43,257
Speaker 2: I just was observing. I'm just observing.

619
00:32:44,057 --> 00:32:44,297
Speaker 1: Fuck.

620
00:32:44,417 --> 00:32:47,097
Speaker 3: Every time I left when we were forward, every time

621
00:32:47,137 --> 00:32:48,857
Speaker 3: I left the tactical Operations Center.

622
00:32:48,977 --> 00:32:51,777
Speaker 1: All the guys in a chorus would go beat it NERD.

623
00:32:52,537 --> 00:32:55,377
Speaker 1: So yeah, it's a yeah, but you guys were like.

624
00:32:56,457 --> 00:32:59,977
Speaker 2: He had, we had our reports and are our cool stuff,

625
00:33:00,217 --> 00:33:04,097
Speaker 2: you know in the meetings that we did too? So interesting?

626
00:33:04,177 --> 00:33:06,897
Speaker 2: All right? Well, Cara, where should people go to check

627
00:33:06,937 --> 00:33:08,697
Speaker 2: out your work and the stuff that you're up to

628
00:33:08,777 --> 00:33:09,697
Speaker 2: and all that good stuff?

629
00:33:10,297 --> 00:33:13,097
Speaker 3: Yeah, first and foremost, go to Heritage dot org. So

630
00:33:13,217 --> 00:33:16,097
Speaker 3: all of our work is on the Heritage website. I

631
00:33:16,137 --> 00:33:18,657
Speaker 3: direct the Tech Policy Center there and yeah, we're we're

632
00:33:18,697 --> 00:33:21,217
Speaker 3: looking at you know, five big lines of efforts. So

633
00:33:21,377 --> 00:33:25,857
Speaker 3: go to that website to see what we're up to. Personally,

634
00:33:26,337 --> 00:33:28,297
Speaker 3: I'm again kind of in the belly of the beast.

635
00:33:28,377 --> 00:33:32,017
Speaker 3: I'm on Twitter Kara A Frederick and on Instagram as

636
00:33:32,097 --> 00:33:35,417
Speaker 3: Karafred with two DS, so you can check out my work.

637
00:33:35,577 --> 00:33:38,137
Speaker 1: I do a lot of some personal but mostly.

638
00:33:37,857 --> 00:33:39,737
Speaker 2: And you got a tiny you got a tiny baby too,

639
00:33:39,817 --> 00:33:42,337
Speaker 2: right right now you're like a yeah.

640
00:33:42,057 --> 00:33:44,737
Speaker 1: Yeah, yeah, yeah.

641
00:33:44,777 --> 00:33:47,257
Speaker 3: So she's an infant and I don't hear her, but

642
00:33:47,497 --> 00:33:49,337
Speaker 3: she's probably gonna start crying a little bit.

643
00:33:49,377 --> 00:33:51,857
Speaker 2: So this is I'm glad we didn't get the form

644
00:33:51,857 --> 00:33:53,497
Speaker 2: where you pretend you hear the infant crying so you

645
00:33:53,497 --> 00:33:55,937
Speaker 2: can end the interview early. So that's good. That means

646
00:33:55,937 --> 00:33:58,777
Speaker 2: we kept it moving here. Check out Karra stuff everybody.

647
00:33:58,817 --> 00:34:02,137
Speaker 2: Kara Frederick, thank you so much. Appreciate you joining us