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Speaker 1: Hey, everybody, Welcome to the Buck Sexton Show. Kara Frederick

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Speaker 1: is with us now. She's formerly of Facebook, currently with

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Speaker 1: the Heritage Foundation, where she's Director of Tech Policy. We're

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Speaker 1: going to talk to her about AI, about government snooping

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Speaker 1: on social media platforms, and all kinds of good stuff

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Speaker 1: or bad stuff, but interesting stuff. Kara, welcome, Thanks for

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Speaker 1: having me. So let's start with Elon Musk recently spoke

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Speaker 1: to Tucker Carlson on Fox Tree Saw it, and he said,

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Speaker 1: not only was he Elon flabbergasted by how much access

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Speaker 1: the government had to the communications and just the general

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Speaker 1: stuff going on at the social media platform Twitter, I'm

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Speaker 1: sure as true of other places, but that d ms

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Speaker 1: were easily accessible by the government to What did you

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Speaker 1: think of that one?

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Speaker 2: Yeah, so this, you know, honestly, it's not surprising. I mean,

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Speaker 2: especially given the backgrounds that you and I had there.

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Speaker 2: We do know, first and foremost there are legitimate reasons,

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Speaker 2: or at least there were legitimate reasons to you know,

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Speaker 2: work with some federal entities when it comes to things

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Speaker 2: like child sexual abuse material, you.

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Speaker 3: Know, sniffing out the bad guys.

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Speaker 2: I worked on foreign Islamic terrorism when I was working

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Speaker 2: for a big tech platform and before. So, you know,

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Speaker 2: there are times when you want the government that is

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Speaker 2: ostensibly there to protect the security of Americans to maybe

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Speaker 2: have some sort of access.

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Speaker 3: To these internal communications.

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Speaker 2: Unfortunately, over the past two to three years, we've seen

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Speaker 2: that there's been a abject dereliction of duty when it

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Speaker 2: comes to the you know, number one, keeping United citizens

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Speaker 2: safe and number two really prioritizing the the security of Americans,

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Speaker 2: especially from external hostile forces, and instead looking inward at

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Speaker 2: the American populace and inflating the definition of domestic extremists

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Speaker 2: and terrorism and whatnot. So, in my mind, the government

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Speaker 2: has lost our trust and deservedly so, and they have

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Speaker 2: been misprioritizing when they should be looking at real actual threats.

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Speaker 3: Then okay, maybe we can think about.

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Speaker 2: Some of these surveillance capabilities, but that's all gone, that's

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Speaker 2: been washed away, given especially what Elon exposed in the

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Speaker 2: Twitter files, and it is very telling. I think that

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Speaker 2: we at the Heritage Foundation we published a report in

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Speaker 2: February of twenty twenty two, and we said there's a

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Speaker 2: symbiotic relationship between these big tech companies and the government.

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Speaker 2: It goes as far as collusion and any sort of

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Speaker 2: collusion between government actors and these big tech companies to

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Speaker 2: silence the speech of American should be prohibited. And people

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Speaker 2: were like, you are, oh, come on, You're just a

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Speaker 2: fear monger.

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Speaker 3: What are you doing? But we've been proven right.

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Speaker 2: So the fact that the government had full access to

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Speaker 2: dms and Twitter, again, we knew Twitter dms a long

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Speaker 2: time ago were probably compromised, and this just proves it.

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Speaker 2: And frankly, this proves a Heritage Foundation right yet again.

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Speaker 1: So you worked at Facebook, I would assume, and tell

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Speaker 1: me if it's an incorrect assumption that the very very

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Speaker 1: cozy and all too close relationship between Twitter pre Elon

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Speaker 1: and the government and as he described it, effectively it

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Speaker 1: was a bloated activist organization Twitter masquerading as a social

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Speaker 1: media platform. I have to assume, and you tell me

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Speaker 1: if it's not right that the politics of Facebook, Google,

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Speaker 1: all the rest are basically the same and perhaps even worse.

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Speaker 2: I would say post twenty sixteen Trump elections, that's when

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Speaker 2: everything became exacerbated. So you have, you know, a cadre

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Speaker 2: of us who went in before that election. I did

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Speaker 2: there in twenty sixteen, early twenty sixteen, and you know,

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Speaker 2: we we were there to solve these big problems like

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Speaker 2: the foreign as long terrorism issue, and you had really

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Speaker 2: talented people, a lot of time patriots. I came right

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Speaker 2: out of a naval special Warfare command where I had

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Speaker 2: been doing counter terrorism analysis as a targeter, and I

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Speaker 2: went right into Facebook sort of thinking that, you know,

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Speaker 2: we were going to do the same thing. We were

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Speaker 2: going to protect Americans and their users from this you know,

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Speaker 2: foreign Islamic menace, which was taking the form of ISIS

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Speaker 2: at the time. And then I would say in twenty sixteen,

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Speaker 2: something really changed, and it was Trump's election, and they

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Speaker 2: just went hysterical. And the people that they started recruiting

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Speaker 2: post election, you know a lot of them went in

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Speaker 2: sort of thinking that they had a mandate to stifle

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Speaker 2: the conservative voices i'd say in America. You know, prior

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Speaker 2: to that, we there were some interesting i would say

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Speaker 2: data points when it came to the way that we

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Speaker 2: did our analysis, for instance.

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Speaker 3: And this is no secret.

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Speaker 2: Now these companies have been using organizations and goos like

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Speaker 2: the Southern Poverty Law Center to actually help them formulate

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Speaker 2: their policies and help them formulate the way that they

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Speaker 2: treated specific actors on these platforms, and they thought nothing

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Speaker 2: of it. They thought, Okay, SBLC is an honest broker.

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Speaker 2: So I think that part of it is ignorance. Part

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Speaker 2: of it is, you know, the sea that they swim

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Speaker 2: in in the Bay Area. And then the other part

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Speaker 2: of it is the twenty sixteen election just really unhinging

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Speaker 2: people and making them really come in with mission oriented

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Speaker 2: to sort of stifle the voices of the people that

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Speaker 2: they disagreed with.

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Speaker 1: Is it fixable at these places? Like, how would you

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Speaker 1: actually if you were able to get you know, Zuckerberg

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Speaker 1: and his top lieutenants in a room and they were

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Speaker 1: willing to listen to reason. I mean, you know, I

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Speaker 1: think Facebook has just turned. I'm amazed that there's still

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Speaker 1: apparently as many people using it in America as there are.

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Speaker 1: I find it to be like an unwieldy trash heap

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Speaker 1: of nonsense, Like it's really hard to even figure out

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Speaker 1: where anything is anymore, or they've it all looks like

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Speaker 1: it was made by a bunch of of you know,

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Speaker 1: computer engineers, And I don't mean that in a good way,

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Speaker 1: Like it looks like it's just this kind of cobbled

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Speaker 1: together thing and all of the original just the ease,

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Speaker 1: and it's just they're throwing all this. I think it's

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Speaker 1: turned into a bad user user experience and user interface.

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Speaker 1: But anyway, forget about fixing that for a second. If

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Speaker 1: you were just trying to fix the fact that Facebook

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Speaker 1: is not fair to half of the country politically, is

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Speaker 1: it possible or do we just have to build our

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Speaker 1: own build our own thing. By the way, build your

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Speaker 1: own Facebook doesn't sound as crazy. Maybe it's buy your

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Speaker 1: own Facebook that's more expensive than Twitter. Have to call

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Speaker 1: elon on that one.

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Speaker 2: Yeah, you know, I will say personally, and when I

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Speaker 2: was in the company, I thought Mark Zuckerberg's instincts were

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Speaker 2: more libertarian than you know, more leftists. So and that

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Speaker 2: is sort of the old guard of the builders.

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Speaker 3: In Silicon Valley.

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Speaker 2: You know, these are engineers, and they think like engineers,

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Speaker 2: they think like programmers. They want to solve problems and

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Speaker 2: fix things unfortuntion only. I do think that he's he's

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Speaker 2: been there for so long and he's been frankly led

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Speaker 2: astray by others in the C suite and that other

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Speaker 2: sort of layer of upper management as well, who are

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Speaker 2: very concerned with pr fires and clearly, we know that

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Speaker 2: public relations only goes one way, and that's against conservatives

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Speaker 2: at this point. So you look at that and sort

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Speaker 2: of see how he's been co opted when his instincts

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Speaker 2: were maybe initially good. I remember a speech that he

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Speaker 2: gave in October of twenty nineteen at Georgetown University where

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Speaker 2: he said Facebook had to be the alternative to an

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Speaker 2: authoritarian China, which was propagating its digital platforms here in

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Speaker 2: the US. You know what happened to that that that

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Speaker 2: gave me a little bit of hope. But since he

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Speaker 2: since spent four hundred million dollars pushing certain Democrats in

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Speaker 2: elections under the auspices of these get out the vote measures,

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Speaker 2: but we know that they were in blue states going

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Speaker 2: to Democrat activist organizations who were then pushing only getting

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Speaker 2: out the vote for Demo. So unfortunately, I think those

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Speaker 2: instincts that he previously had have been quashed, and I

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Speaker 2: don't think there's any coming back from it. I do

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Speaker 2: think it is too far gone at this point.

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Speaker 1: Let's come back in a second and talk about TikTok.

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Speaker 1: Since you mentioned communists China, we'll get into that in

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Speaker 1: every night. All right, So TikTok, I gotta tell you.

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Speaker 1: I mean, I've I think that YouTube is much more

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Speaker 1: concerning for American freedom and everything else than TikTok. And

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Speaker 1: I've been saying this more and more people are saying

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Speaker 1: it now. It seemed to be about a month ago,

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Speaker 1: and it's like, oh, TikTok. I'm sitting here. I'm like,

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Speaker 1: what about the social media companies? They are already throwing

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Speaker 1: elections anyway, We've put that aside for a second. I mean,

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Speaker 1: how worried are you? Let's just look at TikTok. And

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Speaker 1: I won't do the you know, the what about with YouTube, Google,

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Speaker 1: Facebook or Instagram all that stuff, although I could. How

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Speaker 1: bad is TikTok really? How big a problem is it?

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Speaker 3: Yeah?

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Speaker 2: I think TikTok falls in to three problem buckets, I

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Speaker 2: would say, And the first and foremost is the most

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Speaker 2: obvious one. It's that Byte Dance as its parents parent company.

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Speaker 2: It is headquartered in Beijing and therefore subject to the

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Speaker 2: laws and policies of the People's Republic of China. One

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Speaker 2: of the laws that we like to talk about is

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Speaker 2: the twenty seventeen National Intelligence Law, which effectively compels private

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Speaker 2: companies to do the work of the state. So if

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Speaker 2: the Chinese state, the CCP, the Chinese Communist Party, decides

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Speaker 2: that they want specific data, they want access to this,

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Speaker 2: they want access to that, then by virtue of this law,

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Speaker 2: Byte Dance has to comply. And this is not my

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Speaker 2: original phrasing, but I think it illustrates the problem pretty well.

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Speaker 2: China doesn't have rule of law, they have rule by law.

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Speaker 2: So this is something if you're you know, the Bite

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Speaker 2: Dance CEO, you're kind of powerless to resist at this point,

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Speaker 2: and why would you. When you look at byte Dance,

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Speaker 2: they have one of three of their board members of

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Speaker 2: the main domestic subsidiary of Byte Dance is a card

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Speaker 2: carrying Chinese Communist government official. If you look at like

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Speaker 2: some of the good reporting that Forbes has done, they

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Speaker 2: scoured LinkedIn and found that three hundred plus profiles had

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Speaker 2: either current or former links of these current Byte Dance

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Speaker 2: employees to a Chinese propaganda arm to Chinese state media.

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Speaker 2: So you have active and former members of the Chinese

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Speaker 2: Communist Party, particularly in the information realm, working in byte dance.

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Speaker 3: And there's so.

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Speaker 2: Many other data points that I could talk about, but

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Speaker 2: that's the first one. Byte dance, very close links and

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Speaker 2: infiltration frankly by the CCP. They have an internal committee.

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Speaker 2: A DOJ report in September twenty twenty coming out of

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Speaker 2: the Trump administration assessed as much. And we know that

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Speaker 2: they are deeply involved in the inner workings of TikTok

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Speaker 2: as well. So that's one thing, and again just tip

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Speaker 2: of the iceberg. We can go into just really how

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Speaker 2: odious some of those connections are when it comes to

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Speaker 2: the connection with American data as well, but I'll table

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Speaker 2: that for now. And then number two there's that influence

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Speaker 2: campaign aspect, and you know, we talk about the manipulation

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Speaker 2: of the information environment. This is something that we were

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Speaker 2: dealing with in the intelligence community and especially in big

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Speaker 2: tech companies and looking at it now and what we've

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Speaker 2: seen is pro CCP narratives pushed on these platforms. We've

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Speaker 2: seen an actual Chinese state account come to TikTok and say,

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Speaker 2: how can we push our information and we've seen information

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Speaker 2: from those accounts pushed and not labeled as state media

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Speaker 2: as well, So we know that they're trying to do

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Speaker 2: the proverbial sewing of discord, such as promoting Democratic candidates

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Speaker 2: in the twenty twenty two midterm election to the detriment

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Speaker 2: of Republican candidates. They're trying to push stories about abortion

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Speaker 2: and incendiary things to help so dissent among the American populations,

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Speaker 2: something everyone always accused sort of the.

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Speaker 3: Russians of doing.

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Speaker 2: So that's another aspect of that information environment manipulation. And

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Speaker 2: then third, you have the kids. You know what it's

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Speaker 2: doing to children, and we know that TikTok in particular

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Speaker 2: with the four U algorithm just lights a fire under

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Speaker 2: these social contagions. We know that they're in bed with

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Speaker 2: the transgender lobby, featuring you know, prominent transgender activists, prominent

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Speaker 2: LGBTQ activists all over their websites raising awareness. They're very,

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Speaker 2: very open about doing this, and there are pediatric hospitals

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Speaker 2: that are reporting actual physical manifestations coming out in patients

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Speaker 2: because they use TikTok like things called TikTok tics, which

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Speaker 2: most researchers are classifying as you know, pure social contagion movements.

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Speaker 2: So you know, and this is again something that TikTok

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Speaker 2: is very very efficient at that it doesn't necessarily distinguish

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Speaker 2: it wholesale from the instagrams of the world. If you

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Speaker 2: want to talk about aboutism. We know their staffs on

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Speaker 2: that in terms of mental health harms. But TikTok is poised,

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Speaker 2: especially because, as Christopher Ray says, directory of the FBI

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Speaker 2: China controls the algorithm. That's even more problematic when they're

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Speaker 2: feeding our kids this poison.

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Speaker 1: But how much of the algorithm is the kids click

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Speaker 1: on the things they click on and then it's reflected

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Speaker 1: back to them, right, like, I mean not you know,

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Speaker 1: I asked this because remember Russia, remember Russia collusion. Back

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Speaker 1: in twenty sixteen, they're saying, oh, Hillary Clinton lost because

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Speaker 1: Trump worked with Russia. And then they talked about the

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Speaker 1: Facebook ads. I think there was like one hundred thousand

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Speaker 1: dollars that were spent on these bogus you know, or

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Speaker 1: Russian backed or whatever Facebook ads. And when you look

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Speaker 1: at the ads, I mean a lot of them were ridiculous.

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Speaker 1: I mean it looked like a guy named Yuri, you know,

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Speaker 1: working in like sub basement c of some you know

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Speaker 1: fsb outpost you know, on the on the in the

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Speaker 1: outer ring of Moscow was like looking at a you know,

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Speaker 1: little dictionary in English. I mean it was preposterous, right,

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Speaker 1: I mean their understanding of US politics beyond lock her

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Speaker 1: up for Hillary Clinton, which you know they got that right,

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Speaker 1: but their understanding was very weak. I mean the idea

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Speaker 1: that that I just hear all these people saying, oh,

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Speaker 1: my gosh, you know, the Chinese are gonna brainwash our

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Speaker 1: kids and make them lazy or whatever. I look at them, like,

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Speaker 1: what do you guys think Disney's door. I just this

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Speaker 1: is the part of it that there's so much more

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Speaker 1: upset about what's going on with the I understand the

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Speaker 1: spying thing and like if they can suck up your

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Speaker 1: information and do create that that's a separate issue, but

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Speaker 1: it sounds to me like there's also just a content

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Speaker 1: component of this, and I I don't under I just

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Speaker 1: feel like I feel like TikTok is the shiny object

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Speaker 1: where people in politics and in power get to pretend

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Speaker 1: that they're doing something that's like, oh, we're protecting the

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Speaker 1: kids from the bad influences online. They're protecting them from

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Speaker 1: one of dozens of major and endless minor influences online

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Speaker 1: that are all being pushed by the Democrat Party anyway,

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Speaker 1: like transgenderism. Beijing's not pushing transgenderism via TikTok on kids,

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Speaker 1: Democrat Disney is pushing transgenderism on kids.

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Speaker 2: Well, number one, you know, you're right, But number two,

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Speaker 2: 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.

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Speaker 2: If China is so intimately involved in the algorithm, which

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Speaker 2: it says it won't. If there's a forced investiture to

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Speaker 2: an American company of TikTok, it said, there's no way

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Speaker 2: we're giving up the algorithm. So in my mind that means, yeah,

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Speaker 2: they want to retain control of it. There's a commercial

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Speaker 2: element to it because it is really good, but there's

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Speaker 2: also that information environment manipulation potential there because they they

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Speaker 2: want to keep it in Chinese hands so badly, and

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Speaker 2: they've said as much, which flies in the face of

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Speaker 2: a lot of the assurances that these TikTok executives are

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Speaker 2: providing to Congress members with their you know, Project Texas potential,

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Speaker 2: you know, mollification of our representatives.

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Speaker 3: But so I also I do want to address that.

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Speaker 2: I think that when you have over sixty seven percent

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Speaker 2: of American teenagers as of last year on a particular platform,

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Speaker 2: and you have thirty percent, according to a pupil in

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Speaker 2: twenty twenty of preteens nine to eleven year olds on

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Speaker 2: a specific platform, and we have new data coming out

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Speaker 2: of the UK saying a decent percentage of toddlers are

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Speaker 2: now exposed to this content, then you.

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Speaker 3: Have a problem.

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Speaker 2: Then you have a the fact that you know, number one,

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Speaker 2: all of these children are on it to a much

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Speaker 2: greater degree.

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Speaker 3: Than you know.

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Speaker 2: Facebook, as you talked about is hemorrhaging users, especially in

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Speaker 2: this demographic Instagram as hemorrhaging users as well. Then that

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Speaker 2: becomes a source of information, and we know that they're

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Speaker 2: getting it for their news now as well. They're not

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Speaker 2: just looking at those cute videos. They're getting it to

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Speaker 2: be informed about the world. Top Google executives said as

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Speaker 2: much as well. In a tech conference. He said, when

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Speaker 2: people young kids want their news, they go to TikTok,

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Speaker 2: and Google is very much aware of that, and you know,

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Speaker 2: keeping their antenna up. And then you have American adults

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Speaker 2: so looking at the kid stuff that matters a lot,

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Speaker 2: but American adults as well. So you know, in two

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Speaker 2: years the amount of American adults that get their news

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Speaker 2: from TikTok has tripled. That's problematic, I think from a

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Speaker 2: civic perspective, I see you want to say something, but

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Speaker 2: I want to say one more thing before I let

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Speaker 2: you talk.

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Speaker 1: I'm standing in the way of the train. By all means,

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Speaker 1: go ahead, sorry you were saying. So.

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Speaker 2: I think the last thing is a number of enterprising

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Speaker 2: journalists have taken it upon themselves to create their own

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Speaker 2: TikTok accounts. And what they do is they register as

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Speaker 2: users from around thirteen to fourteen, and they have found,

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Speaker 2: to a man, within minutes, they are fed content that

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Speaker 2: is composed of self harm content, suicidal content, eating disorder content,

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Speaker 2: especially if they're registering as girls versus registering as boys.

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Speaker 2: So we do know that they're is something that isn't

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Speaker 2: just responsive to shall we say what the children want? Granted,

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Speaker 2: the TikTok algorithm is based off your engagement, not necessarily

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Speaker 2: your network. So how long your eyes linger over a

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Speaker 2: specific video if you're interested in depression, yes, it's more

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Speaker 2: likely to feed you self harm content and suicidal content.

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Speaker 2: But this appears to be pretty uniform across the board

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Speaker 2: for a lot of these journalists experimenting anyway. So there's

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Speaker 2: something in TikTok that is particularly I would say nefarious

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Speaker 2: when it comes to our children and the noxious content

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Speaker 2: they're being fed.

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Speaker 1: I mean, do you ever go on TikTok because I

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Speaker 1: gotta say, some of those shuffle dance moves very catchy.

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

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Speaker 3: I ever go on TikTok.

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Speaker 1: We get this. This is where like I get to

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Speaker 1: point at all the other people in America, usually especially

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Speaker 1: the Democrats, but I get to call them comedies and

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Speaker 1: have fun with it. Apparently on this one, I've got

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Speaker 1: like a soft spot for communist China. I think TikTok

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Speaker 1: it's super entertaining. I gotta tell you, it's all for me.

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Speaker 1: It's yeah, it's it's how to how to sear like

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Speaker 1: the perfect steak and different red meat. I follow this

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Speaker 1: like Max the meat guy who makes like wagu and

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Speaker 1: briskets and all these things. Uh, guys who know how

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Speaker 1: to make like tomahawks out of the stuff you find

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Speaker 1: in your backyard like this is, and of course cute

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Speaker 1: bulldog videos, like like, I just like, what is this?

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Speaker 1: How is this supposedly in some way doing anything that

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Speaker 1: is uh, you know, going to damage me? But that again,

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Speaker 1: I'm an adult and things are different, and you know

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Speaker 1: that can be a little bit of a challenge. And yeah,

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Speaker 1: so I got to get to the Oxford Gold group

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Speaker 1: here for a second, and we come back. We're going

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Speaker 1: to talk about AI because I think its kra Frederick

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Speaker 1: has to explain whether or not Skynet is going to

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Speaker 1: become self aware and cause the nuclear war that James

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Speaker 1: Cameron warned us about in Terminadoor one and two and

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Speaker 1: probably the other, but no one saw the other ones

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Speaker 1: because the other Terminator movie sucked, So you know, there

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Speaker 1: was a she knows it's true. There was a recent

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Speaker 1: banking collapse, the nation's largest collapse of a financial institution

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Speaker 1: since two thousand and eight. So stuff can go bad

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Speaker 1: real fast, you know this, And fiat currency is pretty

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Speaker 1: imperfect because it's a situation now we have inflation. We

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Speaker 1: also have thirty few trillion dollars a debt. How about

397
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Speaker 1: using gold and silver as a protection for your portfolio.

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Speaker 1: Have a little gold and silver on hand just in

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Speaker 1: case I've got gold and silver right here with me

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Speaker 1: in the radio studio. Now is the time. Don't wait

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00:21:33,200 --> 00:21:35,600
Speaker 1: because if a crisis hits, you're gonna need to have

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Speaker 1: it on hand. And also those prices of gold and

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Speaker 1: silver are gonna go way up. So now's the time

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Speaker 1: to call my friends at the Oxford Gold Group. Securing

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Speaker 1: your I array or four oh one K with real

406
00:21:45,760 --> 00:21:47,840
Speaker 1: gold and silver, by the way, is also a fantastic

407
00:21:48,160 --> 00:21:52,000
Speaker 1: portfolio protection plan. All you have to do is call

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00:21:52,080 --> 00:21:55,040
Speaker 1: the Oxford Gold Group. You can own real precious metals

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Speaker 1: just like I do. Call the Oxford Gold Group at

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Speaker 1: eight three three four zero four Gold eight three four

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Speaker 1: zero four g O L d okay so ms Kara

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Speaker 1: Frederick who is on the task force I believe at

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Speaker 1: the Heritage Foundation for dealing with AI related matters, So

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Speaker 1: you would you would have some insights into this. I

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Speaker 1: I think that the AI is and look is Elon

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Speaker 1: Musk both wealthier and smarter than me. Yes, but I

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Speaker 1: think all this stuff about how the world is going

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Speaker 1: to end because of AI is pretty crazy. Like I'm

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Speaker 1: looking at this, I'm like, how does this even happen?

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Speaker 1: Everyone's getting all freaked out. Do I I'm usually actually no,

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Speaker 1: I usually tell people everything's gonna be okay, because most

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Speaker 1: hysteria is just people wanting attention. But is AI really

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Speaker 1: I know, it's it's a big deal and it's gonna

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Speaker 1: matter a lot. I'm not saying it doesn't matter a lot.

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Speaker 1: But as a threat, what is the threat from AI?

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Speaker 1: That's the part of this that I still haven't No

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Speaker 1: one's really been able to explain to me. It's like, oh,

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Speaker 1: look at going to hurt our democracy because of the disinformation.

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Speaker 1: Watch CNN, look what they.

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Speaker 3: Do, fair point.

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Speaker 2: And I do think you stand in good company, uh

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Speaker 2: with with some of the skeptics, you know. One of

433
00:23:12,480 --> 00:23:14,800
Speaker 2: the things, as you know, in the intel community, though,

434
00:23:14,800 --> 00:23:17,920
Speaker 2: there was always that person who was like everyone everyone

435
00:23:18,240 --> 00:23:21,879
Speaker 2: China isn't ten feet tall, right, everyone, everyone al Kaita

436
00:23:22,040 --> 00:23:25,560
Speaker 2: is not looking at external operations, and they like are

437
00:23:25,600 --> 00:23:27,080
Speaker 2: the naysayers and everyone.

438
00:23:26,920 --> 00:23:30,560
Speaker 1: Kind of their whole thing is is they're the uh

439
00:23:30,920 --> 00:23:33,560
Speaker 1: you know, they're they're playing the role of what's the

440
00:23:33,600 --> 00:23:35,400
Speaker 1: what's the word we're looking for here? You know, when

441
00:23:35,400 --> 00:23:39,480
Speaker 1: you're just being in opposition, to be in opposition, I

442
00:23:39,480 --> 00:23:43,359
Speaker 1: forget what the word is, you know what I'm saying contrarian.

443
00:23:44,000 --> 00:23:45,600
Speaker 1: Thank you. They're playing the contrarian.

444
00:23:45,680 --> 00:23:49,480
Speaker 2: Yes, yes, yes, So so you know there there's that

445
00:23:49,680 --> 00:23:52,399
Speaker 2: aspect of you know, the AI community as well. But

446
00:23:52,680 --> 00:23:57,320
Speaker 2: I do think there's there there's reason to be worried.

447
00:23:57,560 --> 00:24:01,560
Speaker 2: And I like to quote, you know, two authoritarians on

448
00:24:01,600 --> 00:24:04,920
Speaker 2: this matter, and I thought this was commonplace, but apparently

449
00:24:04,960 --> 00:24:07,280
Speaker 2: not many people know that. Putin a few years ago

450
00:24:07,400 --> 00:24:10,040
Speaker 2: said whoever is going to lead an AI is going

451
00:24:10,080 --> 00:24:10,959
Speaker 2: to rule the world.

452
00:24:11,880 --> 00:24:14,359
Speaker 3: I think that that's the significant.

453
00:24:15,520 --> 00:24:18,600
Speaker 2: Discussion to be had, especially when Putin's saying that, and

454
00:24:18,640 --> 00:24:21,840
Speaker 2: then she and China says, you know, we want to

455
00:24:22,000 --> 00:24:25,040
Speaker 2: dominate in AI by twenty thirty. So you know, if

456
00:24:25,080 --> 00:24:28,480
Speaker 2: our adversaries have their eye on this and they see

457
00:24:28,480 --> 00:24:33,840
Speaker 2: it as some geopolitical strategic key, then I think that

458
00:24:33,920 --> 00:24:37,359
Speaker 2: it's important to sort of pay attention. And the reason

459
00:24:37,440 --> 00:24:40,960
Speaker 2: why I think they know harnessing these technologies is really

460
00:24:40,960 --> 00:24:43,840
Speaker 2: going to catapult them to the front of the line

461
00:24:43,880 --> 00:24:49,120
Speaker 2: of global dominance is is because they're doing things at

462
00:24:49,200 --> 00:24:52,800
Speaker 2: machine speed tends to be better than doing things at

463
00:24:52,880 --> 00:24:55,160
Speaker 2: human speed. And when you're coming when you're talking about

464
00:24:55,160 --> 00:24:58,960
Speaker 2: war fighting, when you're talking about things like even you know,

465
00:24:59,119 --> 00:25:02,000
Speaker 2: the stuff that the Intel pukes like us used to do,

466
00:25:02,440 --> 00:25:05,680
Speaker 2: that computer vision algorithms can do better, you know, instead of.

467
00:25:06,040 --> 00:25:07,280
Speaker 3: What project may even tried to do.

468
00:25:07,359 --> 00:25:09,280
Speaker 2: Instead of a human being sitting in front of an

469
00:25:09,440 --> 00:25:12,399
Speaker 2: FMV screen, you know, saying that's a labeling a truck,

470
00:25:12,640 --> 00:25:16,760
Speaker 2: labeling a rock, labeling a car, you have machines able

471
00:25:16,800 --> 00:25:20,760
Speaker 2: to do that. And then when machines can make decisions,

472
00:25:21,119 --> 00:25:24,480
Speaker 2: and again, you know, the autonomous weapons that we have

473
00:25:24,520 --> 00:25:27,080
Speaker 2: are mostly semi autonomous, so there's always a human in

474
00:25:27,119 --> 00:25:27,360
Speaker 2: the loop.

475
00:25:27,400 --> 00:25:27,840
Speaker 3: At this point.

476
00:25:27,920 --> 00:25:30,119
Speaker 2: There's a lot of debate and discussion about that. But

477
00:25:30,160 --> 00:25:32,040
Speaker 2: when you get to the point that a machine is

478
00:25:32,119 --> 00:25:37,880
Speaker 2: basically cutting out all of that analytical rigor that can

479
00:25:37,920 --> 00:25:40,600
Speaker 2: be applied elsewhere to do things only humans can do,

480
00:25:41,000 --> 00:25:44,040
Speaker 2: that's going to give the war fighter a massive advantage.

481
00:25:44,080 --> 00:25:47,359
Speaker 2: So if you have a computer vision algorithm determining that

482
00:25:47,359 --> 00:25:48,919
Speaker 2: that's a rock and that's a tree, and that you

483
00:25:48,920 --> 00:25:53,760
Speaker 2: shouldn't you know, hit it with a with a kinetic strike,

484
00:25:54,040 --> 00:25:56,159
Speaker 2: then that's going to be better if you have another

485
00:25:56,240 --> 00:25:59,240
Speaker 2: analyst sort of doing things like determining if there's a

486
00:25:59,240 --> 00:26:03,040
Speaker 2: positive identify vocation of that actual terrorist actor which would

487
00:26:03,119 --> 00:26:05,640
Speaker 2: cause that hell fire to rain down. So I think

488
00:26:05,680 --> 00:26:09,439
Speaker 2: that you know, it's offering specific advantages because of that

489
00:26:09,600 --> 00:26:12,600
Speaker 2: machine speed vice human speed. People talk a lot about

490
00:26:12,680 --> 00:26:15,560
Speaker 2: drone swarms too, so instead of you know, training up

491
00:26:15,600 --> 00:26:18,880
Speaker 2: a human pilot necessarily these you know, what my old

492
00:26:18,920 --> 00:26:20,840
Speaker 2: boss at the Center for an American Security used to

493
00:26:20,880 --> 00:26:22,960
Speaker 2: say is that, you know, the human being sort of

494
00:26:22,960 --> 00:26:25,680
Speaker 2: acts as the quarterback and you let your your smaller,

495
00:26:25,760 --> 00:26:29,320
Speaker 2: you're cheaper systems do some of the other work for you,

496
00:26:29,440 --> 00:26:31,240
Speaker 2: so you're not sort of wasting a human being in

497
00:26:31,240 --> 00:26:33,720
Speaker 2: the human capital on that kind of thing too. So

498
00:26:34,040 --> 00:26:36,720
Speaker 2: I think those are just two examples of what AI

499
00:26:37,240 --> 00:26:39,800
Speaker 2: can help accomplish in war.

500
00:26:40,440 --> 00:26:43,080
Speaker 3: And we know that she's on a war footing.

501
00:26:43,520 --> 00:26:46,160
Speaker 2: We know that Putin is currently at war right now too,

502
00:26:46,200 --> 00:26:48,879
Speaker 2: so so that's another thing. The information environment, like what

503
00:26:48,920 --> 00:26:51,920
Speaker 2: you were talking about before, that's a whole nother kettle

504
00:26:51,920 --> 00:26:53,960
Speaker 2: of worms, and we can talk about that all day

505
00:26:54,000 --> 00:26:55,960
Speaker 2: as well. Well.

506
00:26:56,920 --> 00:26:59,560
Speaker 1: I don't know, it seems to me like if there's

507
00:26:59,560 --> 00:27:01,520
Speaker 1: a little more MA that can clean up after me

508
00:27:01,880 --> 00:27:05,240
Speaker 1: and tell me how great I am all the time

509
00:27:05,400 --> 00:27:09,680
Speaker 1: and you know, not ask any questions beyond that, sounds good.

510
00:27:09,840 --> 00:27:11,760
Speaker 1: I'm not that worried about it, like turning into the

511
00:27:11,840 --> 00:27:14,840
Speaker 1: terminator and deciding that you know, it has feelings too,

512
00:27:14,880 --> 00:27:16,800
Speaker 1: and it's self aware at all. This I don't know.

513
00:27:16,840 --> 00:27:19,840
Speaker 1: I mean, I guess I do think it's it's definitely

514
00:27:19,840 --> 00:27:22,240
Speaker 1: going to be interesting for high school kids who want

515
00:27:22,240 --> 00:27:25,320
Speaker 1: to have some program that can easily write their you know,

516
00:27:25,600 --> 00:27:28,320
Speaker 1: kind of B minus level term paper for them that

517
00:27:28,840 --> 00:27:32,960
Speaker 1: we know it can do pretty quickly. But friends, you

518
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Speaker 1: So caro, when you're not trying to save the Internet

540
00:28:41,880 --> 00:28:45,280
Speaker 1: for the purposes of freedom, humanity and world peace and

541
00:28:45,280 --> 00:28:47,320
Speaker 1: all that stuff, what else? What do we need to

542
00:28:47,320 --> 00:28:50,440
Speaker 1: with care? Frederick? You were a Navy intel analyst. Is

543
00:28:50,440 --> 00:28:52,320
Speaker 1: that what I'm getting? Because you said vice in a

544
00:28:52,360 --> 00:28:56,280
Speaker 1: way only people from the intelligence community ever say vice

545
00:28:56,480 --> 00:28:59,080
Speaker 1: like that, meaning instead, do you know that no one

546
00:28:59,120 --> 00:29:01,600
Speaker 1: else You'll never come across anybody who did not work

547
00:29:01,640 --> 00:29:03,960
Speaker 1: in the IC who will be like, well, I think

548
00:29:03,960 --> 00:29:06,200
Speaker 1: that's a good idea of vice. This other idea you're like, wait,

549
00:29:06,240 --> 00:29:08,280
Speaker 1: what were the only ones who do that?

550
00:29:09,840 --> 00:29:11,760
Speaker 3: No way? Oh man, I'm about habits.

551
00:29:12,000 --> 00:29:14,840
Speaker 1: Oh I'll give you more. I'll give you more optics.

552
00:29:15,360 --> 00:29:17,920
Speaker 1: Only people from DC talk about well from this optic

553
00:29:18,000 --> 00:29:20,560
Speaker 1: or from that optic or whatever. There there are some

554
00:29:20,720 --> 00:29:24,000
Speaker 1: things that the the Intel people in particular, we have

555
00:29:24,120 --> 00:29:27,560
Speaker 1: this weird nerd vernacular. And I caught you doing some

556
00:29:27,640 --> 00:29:30,640
Speaker 1: nerd vernacular during this podcast. So at least I know

557
00:29:30,680 --> 00:29:32,680
Speaker 1: you're the real deal. At least I know that you

558
00:29:32,720 --> 00:29:36,200
Speaker 1: know you were pouring over those reports eight cups of

559
00:29:36,200 --> 00:29:36,960
Speaker 1: coffee deep.

560
00:29:39,000 --> 00:29:41,760
Speaker 2: Yeah, it's funny, you know, get Smart with Steve Carell

561
00:29:41,800 --> 00:29:42,600
Speaker 2: and Anne Hathaway.

562
00:29:42,760 --> 00:29:43,800
Speaker 3: It's a it's a movie.

563
00:29:43,800 --> 00:29:46,400
Speaker 2: It's like a I don't know, spy caper and you

564
00:29:46,520 --> 00:29:49,320
Speaker 2: have the Intel analyst who like finally gets his chance

565
00:29:49,360 --> 00:29:52,120
Speaker 2: to like be an operations officer in the field and

566
00:29:52,360 --> 00:29:56,360
Speaker 2: he's you know, talking to some operator types and he's like, wait, did.

567
00:29:56,400 --> 00:29:59,680
Speaker 3: Nobody read my report? Like that was me. I was like,

568
00:29:59,720 --> 00:30:01,680
Speaker 3: didn't nobody read my report?

569
00:30:02,440 --> 00:30:05,200
Speaker 2: That person who would would go out with the guys

570
00:30:05,240 --> 00:30:08,800
Speaker 2: and special operations forces and sort of be like, Hey, everybody,

571
00:30:08,880 --> 00:30:10,240
Speaker 2: this is what we should be thinking about.

572
00:30:10,360 --> 00:30:11,800
Speaker 3: Here's the target. You should go there.

573
00:30:12,240 --> 00:30:15,920
Speaker 2: Uh So I was always a civilian, a civilian intelligence officer,

574
00:30:16,000 --> 00:30:18,080
Speaker 2: and we called them targeters. When I was working with

575
00:30:18,200 --> 00:30:21,440
Speaker 2: Naval Special Warfare development group. But that was yeah, that

576
00:30:21,520 --> 00:30:23,720
Speaker 2: was my job. I was an al Qaida analyst first

577
00:30:23,760 --> 00:30:25,640
Speaker 2: and foremost and all sorcers.

578
00:30:25,680 --> 00:30:30,360
Speaker 1: So what they did was they like original original gangster

579
00:30:30,720 --> 00:30:33,920
Speaker 1: old school al Qaeda or the uh a q I.

580
00:30:33,960 --> 00:30:36,320
Speaker 1: Because I was a q I, Oh you were, They

581
00:30:36,320 --> 00:30:37,920
Speaker 1: brought me in to do C D C a q

582
00:30:38,120 --> 00:30:39,880
Speaker 1: I and then I got moved to O I uh

583
00:30:40,000 --> 00:30:42,520
Speaker 1: O I A and and uh and a q I

584
00:30:42,640 --> 00:30:44,840
Speaker 1: basically so no way.

585
00:30:44,800 --> 00:30:45,800
Speaker 3: Yeah, no, I was.

586
00:30:46,040 --> 00:30:49,440
Speaker 2: I was looking at guys over yeah, the original guys,

587
00:30:50,040 --> 00:30:52,080
Speaker 2: some of the guys and hiding and Iran for a

588
00:30:52,120 --> 00:30:54,000
Speaker 2: little bit that is now in the open.

589
00:30:54,320 --> 00:30:55,680
Speaker 3: Uh and uh. Yeah.

590
00:30:55,680 --> 00:30:58,240
Speaker 2: When I deployed a couple of times, that was my

591
00:30:58,880 --> 00:31:01,360
Speaker 2: That was my thing, looking at a kind of external

592
00:31:01,480 --> 00:31:03,440
Speaker 2: operations operatives.

593
00:31:03,600 --> 00:31:08,160
Speaker 1: So what did you think of of Thirteen Hours the movie,

594
00:31:08,200 --> 00:31:09,720
Speaker 1: by the way, because I always thought it was so

595
00:31:09,760 --> 00:31:14,560
Speaker 1: funny that the case officers in that were like so smug,

596
00:31:16,360 --> 00:31:19,080
Speaker 1: which is just great. It was just great. The analyst

597
00:31:19,120 --> 00:31:20,840
Speaker 1: were all sitting there like, yeah, maybe we were just

598
00:31:20,880 --> 00:31:23,480
Speaker 1: back at headquarters making coffee, but like at least we

599
00:31:23,560 --> 00:31:26,000
Speaker 1: respected the paramilitary guys.

600
00:31:27,240 --> 00:31:28,760
Speaker 3: Yeah, oh, I don't know.

601
00:31:28,840 --> 00:31:31,440
Speaker 2: I had a lot of OGA friends when I was

602
00:31:31,480 --> 00:31:36,160
Speaker 2: out there, so you know, you gotta keep those relationships warm.

603
00:31:36,640 --> 00:31:39,600
Speaker 3: So I was I liked your side of.

604
00:31:38,960 --> 00:31:43,360
Speaker 1: The you know, but did you notice in the movie

605
00:31:43,400 --> 00:31:45,520
Speaker 1: for some reason, just to really hammer it home, like

606
00:31:45,600 --> 00:31:50,000
Speaker 1: one of the American case officers in Thirteen Hours just

607
00:31:50,080 --> 00:31:52,440
Speaker 1: randomly has kind of like a French accent and he's

608
00:31:52,440 --> 00:31:55,480
Speaker 1: turning around to like he's turning around to you know,

609
00:31:55,520 --> 00:31:57,920
Speaker 1: what's his name, John Krasinsky, and like all these guys

610
00:31:58,000 --> 00:32:00,720
Speaker 1: were all, you know, jacked and these bad but he's

611
00:32:00,760 --> 00:32:03,480
Speaker 1: like he's like I had to do fancy things, dude,

612
00:32:03,520 --> 00:32:05,880
Speaker 1: Like I am so cool. They're like where did this?

613
00:32:06,240 --> 00:32:09,120
Speaker 1: Where did Why is is his name? Like like Jacques Cousteau,

614
00:32:09,280 --> 00:32:09,800
Speaker 1: Like where.

615
00:32:09,600 --> 00:32:10,120
Speaker 3: Did this guy?

616
00:32:10,400 --> 00:32:13,080
Speaker 1: He's an American? Like they just I love it because

617
00:32:13,200 --> 00:32:18,640
Speaker 1: those guys, uh, the the the GRS guys in the movie, like, oh,

618
00:32:18,680 --> 00:32:20,600
Speaker 1: it's funny the things they would say about the case officer.

619
00:32:20,680 --> 00:32:23,120
Speaker 1: I'm just sorry. I just was observing. I'm just observing.

620
00:32:23,920 --> 00:32:24,160
Speaker 3: Fuck.

621
00:32:24,280 --> 00:32:26,960
Speaker 2: Every time I left when we were forward, every time

622
00:32:27,000 --> 00:32:29,520
Speaker 2: I left the Tactical Operations Center, all the guys in

623
00:32:29,560 --> 00:32:30,320
Speaker 2: a chorus.

624
00:32:30,000 --> 00:32:34,320
Speaker 3: Would go beat it NERD. So yeah, it's a yeah,

625
00:32:34,400 --> 00:32:36,400
Speaker 3: but you guys were like, we.

626
00:32:36,440 --> 00:32:39,840
Speaker 1: Had, we had our reports and are our cool stuff,

627
00:32:40,040 --> 00:32:43,960
Speaker 1: you know in the meetings that we did too? So interesting?

628
00:32:44,040 --> 00:32:46,760
Speaker 1: All right, Well, Cara, where should people go to check

629
00:32:46,800 --> 00:32:48,560
Speaker 1: out your work and the stuff that you're up to

630
00:32:48,600 --> 00:32:49,560
Speaker 1: and all that good stuff.

631
00:32:50,160 --> 00:32:52,960
Speaker 2: Yeah, first and foremost, go to Heritage dot org. So

632
00:32:53,080 --> 00:32:55,920
Speaker 2: all of our work is on the Heritage website. I

633
00:32:56,000 --> 00:32:58,520
Speaker 2: direct the Tech Policy Center there and yeah, we're we're

634
00:32:58,560 --> 00:33:01,040
Speaker 2: looking at you know, five big lines of effort. So

635
00:33:01,240 --> 00:33:05,720
Speaker 2: go to that website to see what we're up to. Personally,

636
00:33:06,200 --> 00:33:08,160
Speaker 2: I'm again kind of in the belly of the beast.

637
00:33:08,240 --> 00:33:11,880
Speaker 2: I'm on Twitter Kara A. Frederick and on Instagram as

638
00:33:11,960 --> 00:33:14,920
Speaker 2: Kara fred with two DS, so you can check out

639
00:33:14,960 --> 00:33:15,280
Speaker 2: my work.

640
00:33:15,440 --> 00:33:17,880
Speaker 3: I do a lot of some personal but mostly and.

641
00:33:17,800 --> 00:33:19,600
Speaker 1: You got a tiny you got a tiny baby too,

642
00:33:19,680 --> 00:33:21,800
Speaker 1: right right now you're like a.

643
00:33:21,480 --> 00:33:24,600
Speaker 3: Yeah, yeah, yeah, yeah.

644
00:33:24,640 --> 00:33:27,120
Speaker 2: So she's an infant and I don't hear her, but

645
00:33:27,360 --> 00:33:29,200
Speaker 2: she's probably gonna start crying a little bit.

646
00:33:29,240 --> 00:33:31,720
Speaker 1: So this is I'm glad we didn't get the form

647
00:33:31,720 --> 00:33:33,360
Speaker 1: where you pretend you hear the infant crying so you

648
00:33:33,360 --> 00:33:35,800
Speaker 1: can end the interview early. So that's good. That means

649
00:33:35,800 --> 00:33:38,600
Speaker 1: we kept it moving here. Check out Kara stuff everybody.

650
00:33:38,680 --> 00:33:42,000
Speaker 1: Kara Frederick, thank you so much. Appreciate you joining us