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Speaker 1: Cable news is ripping us apart, dividing the nation, making

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Speaker 1: it impossible to function as a society and to know

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Speaker 1: what is true and what is false. The good news

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Speaker 1: is that they're failing and they know it. That is

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Speaker 1: why we're building something new. Be part of creating a new, better, healthier,

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Speaker 1: member today at breakingpoints dot com. Your hard earned money

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Speaker 1: is going to help us build for the midterms and

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Speaker 1: the upcoming presidential election so we can provide unparalleled coverage

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Speaker 1: of what is sure to be one of the most

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Speaker 1: pivotal moments in American history. So what are you waiting for?

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Speaker 1: Go to Breakingpoints dot com to help us out. It's

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Speaker 1: time now for our weekly partnership segment segment with the Lever,

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Speaker 1: and we have some great reporting to bring you this

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Speaker 1: week from Andrew Prez. Great to see you, Andrew, Thank you.

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Speaker 1: Thanks for having me. Of course, So I'll set up

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Speaker 1: a little bit of the backstory here and then i

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Speaker 1: want you to dig into what you found. So the

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Speaker 1: audience probably remember there was a big story that broke

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Speaker 1: about shortly after the jobs decision. A ten year old

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Speaker 1: child who had been raped in Ohio who had to

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Speaker 1: travel across state lines into Indiana in order to receive

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Speaker 1: the care that she needed an abortion in that state,

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Speaker 1: which I think at this point Indiana has now passed

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Speaker 1: laws that probably make that not possible, but we'll put

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Speaker 1: that aside. There was immediately some questioning of this claim,

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Speaker 1: especially in right wing circles, but this became sort of

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Speaker 1: a mainstream questioning when Glenn Kessler, who is the fact

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Speaker 1: checker at the Washington Post, wrote a whole column calling

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Speaker 1: into doubt whether this account was actually correct. You have

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Speaker 1: found some decil now. The account did ultimately turn out

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Speaker 1: to be correct. The man who raped her, or at

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Speaker 1: least allegedly raped her, has been arrested, and it has

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Speaker 1: been confirmed at this point, so they had to backtrack

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Speaker 1: and essentially retract. His column had corrections to it. But

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Speaker 1: you actually found that from the beginning he was not

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Speaker 1: being straightforward about the information that he had. Can you

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Speaker 1: lay it that out for us, Andrew, and we have

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Speaker 1: your tear shit. Let's go and put that up on

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Speaker 1: the screen too. The headline here is emails raised questions

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Speaker 1: about Washington Post fact checker. The Post corrected Glenn Kessler's

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Speaker 1: column questioning reporting about a child rate victim shortly after

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Speaker 1: the lever obtain emails showing inaccuracy, inaccuracies, dig into what

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Speaker 1: you found here, Andrew Sure. So you know this week

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Speaker 1: we fact checked the fact checker and so Glenn Kessler's

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Speaker 1: original originally claimed that none of the county officials he

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Speaker 1: had contacted had ever heard of this this child's case

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Speaker 1: in their area. When when you know, when the news

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Speaker 1: broke that a man had been arrested for committing the crime,

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Speaker 1: Glenn updated his column to say that, unlike similar Ohio

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Speaker 1: county agencies they had contacted, Franklin County officials did not

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Speaker 1: offer a response. So we we actually went to Franklin

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Speaker 1: County officials and asked for their correspondence with with Kesseler.

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Speaker 1: And you know what we found was that his his

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Speaker 1: statements about that just weren't we're not accurate. You know,

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Speaker 1: they actually had responded to him pretty quickly before his

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Speaker 1: column was published, and they told him they wouldn't be

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Speaker 1: able to contact on sorry, they wouldn't be able to

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Speaker 1: discuss specific cases because of confidentiality restrictions under Ohio law.

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Speaker 1: And so, you know, his his the impression that he

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Speaker 1: gave in the piece was wrong. We reached out to

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Speaker 1: the Post, both to him and a spokesperson. They corrected

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Speaker 1: the piece. But you know, I do think there is

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Speaker 1: an open question of, you know, why this column was

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Speaker 1: ever published, you know, when it fed into this conservative

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Speaker 1: media firestorm, you know about a case that you know,

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Speaker 1: it turns out was fully accurate. But you know that

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Speaker 1: he was never going to be able to raise any

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Speaker 1: significant questions through this reporting process. It seemed like all

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Speaker 1: he did was contact a few child services agencies and

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Speaker 1: the governor's spokesperson and asked like, hey, have you heard

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Speaker 1: of this? That's not you know that that doesn't really

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Speaker 1: pass any kind of muster compared to like the action

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Speaker 1: reporting that went into the Indianapolis Star story. Yeah, well,

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Speaker 1: and it's two very different things saying we can't comment

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Speaker 1: on any cases we have versus no, we've never heard

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Speaker 1: of this. I mean, that's just a totally different representation

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Speaker 1: of the facts. Ultimately, yeah, one hundred percent. And in fact,

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Speaker 1: we reached out to another county and they told him

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Speaker 1: they had not heard of this case, but they also

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Speaker 1: said they wouldn't be able to discuss it anyway. So

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Speaker 1: there is like a distinction between how the agencies responded.

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Speaker 1: But you know this, we know the Franklin Agency actually

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Speaker 1: had told him they wouldn't be able to discuss specific cases.

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Speaker 1: That's the agency that actually made their criminal referral to

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Speaker 1: the police that led to the arrest here. Has there

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Speaker 1: been any sort of additional accountability or disciplining of Kessler

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Speaker 1: from the Post since he got this story wrong in

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Speaker 1: a really significant way, Yeah, not that we can tell.

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Speaker 1: You know, they updated the post, they issued a correct

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Speaker 1: on it. But you know, he's he's still fact checking.

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Speaker 1: He's he's you know, fact checking the news still today.

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Speaker 1: And you know, there's been no kind of like apology

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Speaker 1: here at all. You know, there were there were other

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Speaker 1: kind of significant issues in his piece too, Like he

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Speaker 1: had originally written that that it's it's really rare for

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Speaker 1: you know, people under the age of fifteen to get

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Speaker 1: abortions in Ohio, you know, by and he said it

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Speaker 1: happens like fifty times a year, which you know literally

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Speaker 1: means like once a week. It's not rare. It's not

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Speaker 1: rare at all. And you know, if you if you

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Speaker 1: were to broaden that out like over the you know,

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Speaker 1: throughout the nation, you have to imagine that the numbers

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Speaker 1: are substantially higher. So, you know, I think there's an

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Speaker 1: open question as to how this you know, piece was conceived,

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Speaker 1: why it happened, why it was written the way they did,

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Speaker 1: and you know what they what they thought they were

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Speaker 1: going to be able to prove through you know, through

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Speaker 1: the process that he undertook. Yeah, I guess that's the

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Speaker 1: other question is it's not like he really got any

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Speaker 1: sort of reporting that actually undercut the story. So you know,

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Speaker 1: there's a question of sort of the editing process too,

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Speaker 1: of who allowed this to go forward when there really

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Speaker 1: was nothing here substantial that you could hang your hat

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Speaker 1: on to undercut the initial reporting. Yeah. Yeah, it seems

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Speaker 1: like his central complaint was that the story was based

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Speaker 1: on the word of one doctor, the doctor who had

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Speaker 1: performed the abortion, and he was sort of saying it

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Speaker 1: like that, she claims to have done this, and then

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Speaker 1: you know, we saw that in emails that she she

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Speaker 1: claims to have performed the procedure. And then you know,

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Speaker 1: he mentioned in an exchange with Nieman lab that you

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Speaker 1: know that it was that the only source here was

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Speaker 1: an activist in one side of the debate. So you know,

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Speaker 1: it clearly betrays like some type of bias towards you know,

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Speaker 1: I guess abortion providers, and you know, I think I

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Speaker 1: do think that you know that the standards that natorial

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Speaker 1: standards here like would have probably flown at any paper. Right,

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Speaker 1: You're talking about a story that was based on a

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Speaker 1: doctor's word, like who was on the record by a

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Speaker 1: name who really subjected themselves to a to a you know,

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Speaker 1: a pretty giant you know, media controversy just by doing that,

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Speaker 1: by going on the record sharing this story. Yeah, that

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Speaker 1: is really true. I mean, it would be quite something

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Speaker 1: for them, under their own name to have just invented

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Speaker 1: this out of whole cloth, which is what Kessler was

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Speaker 1: effectively suggesting here. The last part of this that I

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Speaker 1: think is interesting, Andrew, is were there any other outlets

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Speaker 1: that after Kessler's piece was basically you know, kind of

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Speaker 1: fell apart and wasn't correct that dug into the details

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Speaker 1: to see whether he was representing correctly the information that

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Speaker 1: he was received, since it ended up being directly rebutted

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Speaker 1: by you know, the fact that this rapist was arrested. Yeah,

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Speaker 1: not as far as I know, And I'm not sure

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Speaker 1: that anyone went through the steps of filing public records

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Speaker 1: requests to these agencies. But you know what, it took

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Speaker 1: me like five minutes, and it came back really quickly. Honestly,

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Speaker 1: it came back in like in a matter of a week. So,

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Speaker 1: you know, and we've gone to some of the other

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Speaker 1: county agencies. Now we haven't seen any record of any

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Speaker 1: other correspondence with those agencies. You know. We we broadened

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Speaker 1: our request to go from you know, from the beginning

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Speaker 1: of July to August ninth, and so you know, as

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Speaker 1: far as I know, we're the only people that have

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Speaker 1: been digging into this at all. Well, I think that

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Speaker 1: says a lot as well as why we are so

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Speaker 1: proud to partner with you guys, and why for those

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Speaker 1: of you who are able to support the lever you

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Speaker 1: absolutely should do so, because you are focusing on stories

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Speaker 1: that the corporate media is not paying attention to. Andrew,

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Speaker 1: it's always great to see you. Thank you so much

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Speaker 1: for being with us today. Thank you, thanks for having

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Speaker 1: me our pleasure. Very interesting moment when Senator Rick Scott

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Speaker 1: was pressed on the quality of Republicans Senate candidates. Now

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Speaker 1: keep in mind this, what's the guy who is actually

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Speaker 1: the chair of the NRSC that is the National Republican

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Speaker 1: Senatorial Committee. They are the ones charged with trying to

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Speaker 1: get Republicans into the Senate and win back their majority.

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Speaker 1: Let's listen to what he had to say about this.

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Speaker 1: Candidates in a local radio interview in July, you talked

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Speaker 1: a lot about your business as an executive, and you

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Speaker 1: said we should start electing people that we would hire.

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Speaker 1: In Georgia, Herschel Walker, Republican Senate candidate, has lied about

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Speaker 1: the number of children he has about his business dealings.

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Speaker 1: His ex wife said he held a gun to her

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Speaker 1: head and said, I'm going to blow your effing brains out.

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Speaker 1: In Arizona, the candidate Blake Masters, called the unibomber an

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Speaker 1: underrated thinker. He said that Al Qaeda doesn't actually pose

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Speaker 1: substantial threat to Americans. I mean, I've got a list

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Speaker 1: of candidates here who've had some and said some pretty

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Speaker 1: troubling things. Would you hire these people to work for you? Well,

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Speaker 1: you'd go through each person, and but I'm not the

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Speaker 1: one doing it. It's the voters of those states are

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Speaker 1: doing it. The voters those states are going to make

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Speaker 1: a choice. You're trying to help Senate Republicans and lead

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Speaker 1: them to victory. These are your candidates. So, you know,

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Speaker 1: Margaret as remember the voters in Arizona choose who they're

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Speaker 1: gonna they're going to vote and what they're going to choose.

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Speaker 1: They're going to choose between Blake Master and Mark Kelly.

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Speaker 1: Mark Kelly has voted to keep the border open. He's

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Speaker 1: never voted for border security. He's voted for the tax increases.

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Speaker 1: He's voted for cutting medicare you know he's he's voted

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Speaker 1: with Chuck Schumer and with Joe Biden. Basically one hundred

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Speaker 1: percent of the time, warn't axect the same problem. This

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Speaker 1: election is going to be about Joe Biden. And so

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Speaker 1: this election is going to be about all the bad

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Speaker 1: things that have happened. This is the fact that we're

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Speaker 1: going into recession, the fact that you know, inflations of

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Speaker 1: nine percent, the fact that gas prices are up two dollars,

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Speaker 1: all these things, that's what people are looking at. These

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Speaker 1: These are your Senate Republican candidates. These are your candidates,

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Speaker 1: and the voting each of these states, the voters these

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Speaker 1: states are going to decide if they're going to hire.

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Speaker 1: Now I get to vote, I get to vote in Florida,

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Speaker 1: and that's how I think about it. But the voters

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Speaker 1: in those states will choose in those states who they want.

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Speaker 1: It's a choice between two people. But there look, all

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Speaker 1: the Democrat nominees are a Biden clones. Yeah you don't.

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Speaker 1: By the way, they won't be. But if you are,

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Speaker 1: you would acknowledge, you would acknowledge that if somebody went

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Speaker 1: in for an interview for private corporation, these things would

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Speaker 1: come up as red flags to hr so when asked repeatedly,

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Speaker 1: he will not defend his own candidates. And again, this

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Speaker 1: is the guy who's the chair of the n r SC.

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Speaker 1: That's pretty That was That was weird. I'm just going

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Speaker 1: to say, I think Blake Masters is a far better

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Speaker 1: I don't agree with everything Blake, but I only use

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Speaker 1: it comparable in any way Walker, who is genuine moron

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Speaker 1: and like beyond that personal life candidate quality, doesn't actually

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Speaker 1: appear to be able to speak a coherent sentence at

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Speaker 1: the national level. Also basically just nominated because Trump was like,

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Speaker 1: I love this guy and he's famous and thinks that

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Speaker 1: it validates that theory even though he has no apparent

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Speaker 1: like political ideology whatsoever. Yeah, and he's so all over

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Speaker 1: the terrible interviews. That's actually an interesting question of which one,

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Speaker 1: because Masters has an ideology and it's terrible, so like

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Speaker 1: which one is actually which one is actually worse? Oh,

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Speaker 1: he's definitely smarter. I'm just saying in terms of what

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Speaker 1: who would be more damaging, who would be who would

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Speaker 1: you rather have in the Senate, It's not totally clear

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Speaker 1: to me because Masters actually has an ideology that I

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Speaker 1: think is really I mean, he has a very sort

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Speaker 1: of radical libertarian Peterteel ideology. One is privatize their security

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Speaker 1: and all them access to contraceptions and all of that

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Speaker 1: being said, it is amazing to watch Rick Scott refusing

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Speaker 1: to outward like actively defend any of them and actively

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Speaker 1: promote any of them, or say that he would hire

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Speaker 1: any of them. Instead, he immediately pivots to like, well,

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Speaker 1: this is going to be about Biden, and these Democratic

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Speaker 1: candidates are all Biden closes. Okay, that wasn't that wasn't

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Speaker 1: the question, And he very much they are very much

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Speaker 1: hoping that the election is about Biden, is about inflation,

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Speaker 1: and are you likely to be correct about that? But

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Speaker 1: as I've been saying, the more that you zoom into

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Speaker 1: these individual races and individual candidates and where you're like,

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Speaker 1: I'll have some real problems on your hands here, because

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Speaker 1: it just came out this week the Trump back Michigan

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Speaker 1: Attorney general candidate was allegedly involved in a voting system

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Speaker 1: breach in Michigan. So these are the types of people's people.

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Speaker 1: And you've had situations plenty of times in the past.

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Speaker 1: We always talked about Toddy and Richard Murdoch, like Sharon Angele,

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Speaker 1: Christine O'Donnell, of these candidates that are fringe and outside

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Speaker 1: the mainstream. It's very unusual to have, I mean, in

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Speaker 1: race after race after you really have a national movement

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Speaker 1: of these canidates who are outside of the mainstream, both

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Speaker 1: on sort of like stop the steel January sixth stuff,

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Speaker 1: but also on abortion, also on gay marriage and a

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Speaker 1: range of other issue access to contraception, and range of

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Speaker 1: other issues. So it's a very unusual situation when I

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Speaker 1: haven't that I can't quite say that we've really seen before.

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Speaker 1: I've never understood how Rick Scott got elected to anything.

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Speaker 1: I'm gonna be honest with you, He's always just been

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Speaker 1: one of the most first of all, his ideology is

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Speaker 1: as close to like a true con as possible. Also,

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Speaker 1: even in terms of his business career, there's a lot

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Speaker 1: of questions how it exactly he made that ninety million,

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Speaker 1: which is his net worth. I don't get how he

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Speaker 1: was elected governor or senator. I've never truly understood it.

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Speaker 1: And then I don't know why he was put in

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Speaker 1: place of the national effort to elect Republicans whenever his

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Speaker 1: plan to cut Social Security and to privatize Medicare or

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Speaker 1: whatever then became the central talking point of the Biden administration,

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Speaker 1: which was then repudiated by Mitch McConnell because of how

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Speaker 1: stupid the entire thing was. I mean, in fact, what

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Speaker 1: he just said is he said the quiet part out loud.

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Speaker 1: But I mean, who's dumb enough to actually say that?

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Speaker 1: You're not even supposed to do that. So anyway, I've

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Speaker 1: never understood that. It's not a surprise that he's flailing.

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Speaker 1: And it also does show you these people have no power,

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Speaker 1: you know, the idea of the national Republicans have any power.

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Speaker 1: It's all Trump. Trump gets to select these people. You

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Speaker 1: have nothing to do with it. Ye yeah, basically in

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Speaker 1: a money doling a cash machine to whoever Trump picks.

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Speaker 1: So anyway, it's interesting from that point of view too. Yep,

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Speaker 1: and he is. He has definitely single handedly Trump handicapped

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Speaker 1: their chances by propping up ozz herschel Walker. You know,

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Speaker 1: I don't know that Masters is any better or worse

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Speaker 1: than the other people he was against, but he really

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Speaker 1: separated from the pack after the Trump endorsement. Jade Vance

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Speaker 1: is not doing well in Ohio. Now. I think he'll

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Speaker 1: ultimately win in that state, but he hasn't been fundraising,

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Speaker 1: he hasn't been apparently on the ground doing the work,

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Speaker 1: and some of the polls have ryan up by a

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Speaker 1: couple percentage points. Again, I think Vans will ultimately win,

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Speaker 1: but it is at least he had the good sense

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Speaker 1: Trump ultimately not to wait in directly for Eric Bryton's

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Speaker 1: because that would have really well. He waited in for Eric,

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Speaker 1: so he was okay. Yes. Indeed, wanted to update you

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Speaker 1: guys on a story that we covered here previously, which

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Speaker 1: was the Democratic Party conspiring and using some dirty and

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Speaker 1: also potentially illegal tricks to get a Green Party candidate

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Speaker 1: kicked off the ballot in the Senate race in North Carolina.

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Speaker 1: So that Green Party candidate is Matthew ho He is

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Speaker 1: a veteran. He had collected all the signatures and done

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Speaker 1: everything he needed to do to get on the ballot there,

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Speaker 1: and then the Democratic Party came in and they did

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Speaker 1: a couple of things. So the first thing they did

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Speaker 1: is they took all the signatures on his petitions and

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Speaker 1: they called through because they have the voter files, so

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Speaker 1: they have a lot of this data and contact information

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Speaker 1: in their records, called through to try to persuade all

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Speaker 1: of these signatories to take their name off the ballot.

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Speaker 1: Now that's sort of gross and anti democratic, but it's

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Speaker 1: not illegal. But they were also caught pretending to be

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Speaker 1: with the Green Party to put add pressure on those

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Speaker 1: people to pull their names off of the petitions. That

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Speaker 1: didn't work out. They didn't get that many people to

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Speaker 1: agree with them, so it looked like Matthew Hoe was

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Speaker 1: going to be on the ballot as a Green Party candidate.

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Speaker 1: He had way more than enough signatures on the petitions.

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Speaker 1: Then the State Board of Elections, which is a split

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Speaker 1: between Republicans and Democrats, and Democrats have the majority in

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Speaker 1: North Carolina, meets and the Democrats vote in a partisan

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Speaker 1: block to still block him from the ballot because they

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Speaker 1: say there might be fraud. They didn't prove that there

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Speaker 1: was fraud in the signature gathering, which is incumbent upon

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Speaker 1: them to do. You have to show this one is

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Speaker 1: not legitimate, that one is not legitimate. Specifically, they said,

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Speaker 1: we think generally there are enough questions here that we're

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Speaker 1: just going to kick you off the ballot. Okay, So

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Speaker 1: that's where things stood. Previously, Matthew Hoe and the Green

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Speaker 1: Party went to court to try to regain ballot access,

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Speaker 1: and lo and behold, a federal judge agreed with the

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Speaker 1: Green Party, says breaking. This is from Matthew Hover, Senate.

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Speaker 1: A federal judge just ordered the North Carolina State Board

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Speaker 1: of Elections to place the Green Party and our campaign

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Speaker 1: on the ballot. We won against the Democratic Party establishment

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Speaker 1: scheme to sabotage our campaign for working people. Now let's

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Speaker 1: win this race and make history. Apparently, the Dems are

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Speaker 1: still filing another lawsuit to try to block the Green

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Speaker 1: Party from the ballot, in one particular in Wait County,

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Speaker 1: which I think is a sizeable county correct in the state.

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Speaker 1: So the legal battles are not over. But the fact

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Speaker 1: that a federal judge agreed with the Green Party here

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Speaker 1: and reinstated them on the ballot. Just shows you how

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Speaker 1: outrageous and how ultimately illegal the techniques were that we're

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Speaker 1: being used here to try to block people from having

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Speaker 1: another choice on the Bellet just beat them on the ballot,

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Speaker 1: Go ahead. I don't get it, Like all of the

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Speaker 1: d democracy just Green Party people are on the ballots

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Speaker 1: all the time, but they rarely win. Okay, there's zero

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Speaker 1: evidence actually to show even that the Green Party had

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Speaker 1: any effect in twenty sixteen. You know a lot of

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Speaker 1: the cope. You know, if you remember everybody blamed like

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Speaker 1: Ralph Nader for swinging the election for It's like, no,

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Speaker 1: people didn't like al Gore, so they voted for Ralph Nader, right,

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Speaker 1: and then you know, whatever the vote total was in Florida.

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Speaker 1: I'm just saying, like to the extent that you blame

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Speaker 1: people for voting for them. I mean, actually, at George

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Speaker 1: hw Bush was once asked about Ross Paro, I want

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Speaker 1: to say, in the two thousands, and he just said,

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Speaker 1: I don't like him, and I think he cost me

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Speaker 1: the election because ultimately Clinton only won the election with

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Speaker 1: like forty two percent or whatever of the popular vote,

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Speaker 1: because I think Paro got like fifteen to twenty. No,

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Speaker 1: George HW. Bush. People just didn't like you, so they

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Speaker 1: didn't vote for you. You You can't blame Ross Perot for that.

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Speaker 1: Maybe you shouldn't have you know, drafted NAFTA. That might

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Speaker 1: have had something to do with it. So anyway, these

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Speaker 1: things drive me crazy. And any attempt by any major

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Speaker 1: party to try and snuff people out at the ballot

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Speaker 1: level and more is almost always, you know, trying to

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Speaker 1: rig the election in their favor. I think that's what

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Speaker 1: they're doing here, Yeah, I mean, and it's gross, Like

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Speaker 1: they clearly are afraid that the race is already thinks

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Speaker 1: lean Republican, but they've got somewhat of a shot in

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Speaker 1: North Carolina to pick up that Senate seed, and they

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Speaker 1: feel like, oh, well, if this Green Party candidate is

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Speaker 1: on the ballot, they're going to take voters from us disproportionately,

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Speaker 1: and so that's going to be a hindrance to us.

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Speaker 1: First of all, there isn't actually, I mean, it's never

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Speaker 1: it's not clear that these are people who would have

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Speaker 1: voted Democrat. Otherwise they may not have voted, they may

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Speaker 1: have voted for the Republican. Like that's one thing, But

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Speaker 1: the other thing is more to the point people should

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Speaker 1: be able to have choices. You have to go out

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Speaker 1: and you have to make the case. You have to

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Speaker 1: prove to them why they should vote the Democrat instead

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Speaker 1: of voting for the Green Party candidate, rather than using

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Speaker 1: sabotage and illegal tricks to try to kick people off

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Speaker 1: the ballot. So a small victory for small d democracy here.

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Speaker 1: So we had a slew of interesting poll numbers come

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Speaker 1: down this week, and to be honest with you, we

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Speaker 1: had them all in the show ready to go, and

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Speaker 1: then there was a little FBI raid situation that we

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Speaker 1: felt we needed to cover. So this got pushed down,

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Speaker 1: but it still was import and so a couple pieces here.

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Speaker 1: First of all, Nate Silver's five thirty eight model for

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Speaker 1: the first time has Dems significantly favored to keep control

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Speaker 1: of the Senate sixty forty shot. And you know he

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Speaker 1: has different models. So in his most elaborate model at

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Speaker 1: sixty forty, in the quote unquote classic model, which doesn't

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Speaker 1: take into effect the I think like analyst views, it's

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Speaker 1: even more at seventy one twenty nine. In the light model,

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Speaker 1: which just looks at poles, it's seventy seven to twenty three.

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Speaker 1: But you know this is a significant shift over the

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Speaker 1: course of the summer post dubs. However, there are so

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Speaker 1: many other numbers and indicators that go in the other

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Speaker 1: direction that it really is a very difficult landscape to part.

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Speaker 1: So let's put this up on the screen. You have

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Speaker 1: sixty plus percent of Americans disapproving of Biden's handling of inflation,

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Speaker 1: which is he's underwater twenty nine to sixty nine, immigration

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Speaker 1: thirty four to sixty four, economic recovery thirty seven to

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Speaker 1: sixty two, crime thirty to sixty one, gas prices thirty

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Speaker 1: four to sixty five, taxes thirty seven to sixty one,

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Speaker 1: and gun violence thirty five sixty four. But let's just

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Speaker 1: focus in. I mean, inflation is obviously the most important

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Speaker 1: one there, and for him to be underwater by such

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Speaker 1: a significant margin, and we know how poor his approval

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Speaker 1: ratings have been. Normally, you would look at those numbers

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Speaker 1: and you'd say it's a lock. Republicans are going to

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Speaker 1: have a historic, massive wave. And yet, because you have

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Speaker 1: these complicating factors of number one, the Dobbs decision Roe

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Speaker 1: versus Way being overturned, and number two, the fact that

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Speaker 1: Republicans have gone out of their way to nominate not

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Speaker 1: the greatest in the world candidates, you have a much

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Speaker 1: more Democrats have a much better shot than they would otherwise.

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Speaker 1: The last thing I'll say is that, you know, some

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Speaker 1: of these people who just express displeasure, that's the word,

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Speaker 1: with Biden are critiquing from the left. So they're not

431
00:22:52,160 --> 00:22:55,119
Speaker 1: like considering voting for Republicans. They're unhappy with Biden. But

432
00:22:55,119 --> 00:22:57,200
Speaker 1: that's part of why so many of the Democratic candidates

433
00:22:57,240 --> 00:22:59,840
Speaker 1: are able to outperform where the president is. Yeah, no,

434
00:23:00,280 --> 00:23:02,720
Speaker 1: this is the thing. There's a lot of confounding variables.

435
00:23:02,760 --> 00:23:04,960
Speaker 1: So on the one hand, Biden is tremendously unpopular. On

436
00:23:05,000 --> 00:23:07,400
Speaker 1: the other hand, Biden is no longer becoming the defining

437
00:23:07,440 --> 00:23:10,960
Speaker 1: issue of American politics, which he previously was before abortion

438
00:23:11,040 --> 00:23:14,120
Speaker 1: and especially before the Trump FBI raids. So even though

439
00:23:14,160 --> 00:23:19,800
Speaker 1: he's trailing massively on inflation, immigration, economic recovery, crime, gas taxes,

440
00:23:19,840 --> 00:23:23,560
Speaker 1: and guns, it none of those currently, or at least

441
00:23:23,600 --> 00:23:26,240
Speaker 1: inflation might be still number one. But if abortion has

442
00:23:26,359 --> 00:23:28,879
Speaker 1: energized enough Democrats to come out and vote at the

443
00:23:29,000 --> 00:23:31,040
Speaker 1: very least throws something in there. And then if you

444
00:23:31,080 --> 00:23:33,400
Speaker 1: take Trump and you superimpose him on all of that,

445
00:23:33,640 --> 00:23:37,160
Speaker 1: it just scrambles everything. So the special election results tell

446
00:23:37,240 --> 00:23:40,280
Speaker 1: us more than anything. Same with Kansas, it's how actual

447
00:23:40,320 --> 00:23:43,240
Speaker 1: people are voting, and all of that does indicate that

448
00:23:43,280 --> 00:23:45,679
Speaker 1: we're just living in a different world. So it's a

449
00:23:45,720 --> 00:23:48,840
Speaker 1: weird time, honestly, because usually this is the one to

450
00:23:48,880 --> 00:23:52,080
Speaker 1: one relationship. But you know, whenever you have such hot

451
00:23:52,080 --> 00:23:55,200
Speaker 1: button issues that kind of go above even the most

452
00:23:55,440 --> 00:23:57,760
Speaker 1: kitchen table ones, it can change a lot on the

453
00:23:57,760 --> 00:24:00,960
Speaker 1: down ballot race well, because there's a difference between what

454
00:24:01,000 --> 00:24:03,359
Speaker 1: people say is the most important to them and what

455
00:24:03,680 --> 00:24:06,480
Speaker 1: really motivated Yea to go out. It's hard to get

456
00:24:06,520 --> 00:24:08,560
Speaker 1: at that in a poll. But what you see in

457
00:24:08,600 --> 00:24:12,480
Speaker 1: the actual election results is that abortion has been highly

458
00:24:12,520 --> 00:24:16,359
Speaker 1: motivating for the Democratic base, which frankly was very apathetic before.

459
00:24:16,480 --> 00:24:19,320
Speaker 1: The Republican energy level has always been there, and that

460
00:24:19,359 --> 00:24:22,960
Speaker 1: makes sense. They are upset the Biden's president, They're upset

461
00:24:22,960 --> 00:24:25,280
Speaker 1: Democrats have control of the House in the Senate. They're

462
00:24:25,320 --> 00:24:29,120
Speaker 1: motivated to come out and change that reality. Democratic base

463
00:24:29,200 --> 00:24:31,080
Speaker 1: was kind of like, ah, that's by the thing, isn't

464
00:24:31,119 --> 00:24:32,600
Speaker 1: all what I thought I was going to be. I'm

465
00:24:32,640 --> 00:24:34,880
Speaker 1: not that happy. I feel that, you know, I feel

466
00:24:34,920 --> 00:24:37,760
Speaker 1: that stress in terms of my household budget and prices

467
00:24:37,800 --> 00:24:42,160
Speaker 1: going up. I'm worried about the economy. Abortion really gave

468
00:24:42,240 --> 00:24:46,399
Speaker 1: them a galvanizing issue to get their voters to see

469
00:24:46,400 --> 00:24:49,560
Speaker 1: that there is something very clear and tangible at stake

470
00:24:49,680 --> 00:24:52,760
Speaker 1: in these elections. And then I agree with you if Trump,

471
00:24:52,840 --> 00:24:56,000
Speaker 1: especially if he goes ahead and announces for his next

472
00:24:56,440 --> 00:25:00,800
Speaker 1: his re election campaign, that is another issue that Republicans

473
00:25:00,840 --> 00:25:02,760
Speaker 1: really don't want on that they don't want chump on

474
00:25:02,800 --> 00:25:05,399
Speaker 1: the ballot in the midterms. They just wanted this to

475
00:25:05,440 --> 00:25:08,760
Speaker 1: be focused on Biden. That that strategy has already been

476
00:25:08,880 --> 00:25:11,359
Speaker 1: somewhat confounded. And you know, they also have done it

477
00:25:11,400 --> 00:25:14,480
Speaker 1: to themselves, Like we talked early on about how they

478
00:25:14,520 --> 00:25:16,679
Speaker 1: decided they were not going to put out an agenda.

479
00:25:16,960 --> 00:25:19,439
Speaker 1: They were just going to basically sit back and be like,

480
00:25:19,480 --> 00:25:22,199
Speaker 1: we're not them. That also leaves them a little more

481
00:25:22,320 --> 00:25:25,080
Speaker 1: vulnerable because what ultimately are you voting for? You know,

482
00:25:25,160 --> 00:25:27,200
Speaker 1: I mean they can say inflation sucks, et cetera, et cetera,

483
00:25:27,240 --> 00:25:28,679
Speaker 1: but like they don't have a plan to deal with it.

484
00:25:28,720 --> 00:25:30,760
Speaker 1: They want to push the Fed to do more and

485
00:25:30,840 --> 00:25:33,400
Speaker 1: cut more spending and put you know, and make things

486
00:25:33,440 --> 00:25:36,920
Speaker 1: more painful. So the fact that they didn't really come

487
00:25:37,000 --> 00:25:39,280
Speaker 1: up with an affirmative agenda also, I think makes them

488
00:25:39,280 --> 00:25:41,720
Speaker 1: a little more vulnerable. Right now, I think you're absolutely right,

489
00:25:41,800 --> 00:25:43,919
Speaker 1: And yeah, I think it's going to be a problem,

490
00:25:44,000 --> 00:25:47,359
Speaker 1: especially with these confounding variables. But it's interesting. Nonetheless, it

491
00:25:47,400 --> 00:25:52,400
Speaker 1: is interesting, all right, guys, more for reall lator. All right,

492
00:25:52,440 --> 00:25:55,879
Speaker 1: So I have been tracking this crazy story that unfolded

493
00:25:56,000 --> 00:25:59,800
Speaker 1: at a Starbucks down in South Carolina. It's actually very

494
00:26:00,280 --> 00:26:03,040
Speaker 1: to where I started college at Clemson University, so I

495
00:26:03,080 --> 00:26:06,040
Speaker 1: was watching this with great interest. A number of workers

496
00:26:06,119 --> 00:26:10,639
Speaker 1: approached their manager and collectively asked that manager for a

497
00:26:10,680 --> 00:26:16,600
Speaker 1: pay raise and other improvements in working conditioners, conditions, conditioners.

498
00:26:16,600 --> 00:26:20,840
Speaker 1: Oh my god, the manager freaked out, filed a police report,

499
00:26:21,160 --> 00:26:25,360
Speaker 1: claimed that they were assaulted and accused these workers, who

500
00:26:25,400 --> 00:26:28,720
Speaker 1: are now unionized at this location, of kidnapping them and

501
00:26:29,440 --> 00:26:32,760
Speaker 1: more perfect union actually obtained the police report which goes

502
00:26:32,760 --> 00:26:35,680
Speaker 1: into the details of what this manager said that these

503
00:26:35,720 --> 00:26:37,199
Speaker 1: workers did. Let's go ahead and put this up on

504
00:26:37,240 --> 00:26:40,040
Speaker 1: the screen, they say, exclusive, We've obtained the police report

505
00:26:40,080 --> 00:26:43,400
Speaker 1: filed by Starbucks manager in South Carolina that falsely accused

506
00:26:43,440 --> 00:26:47,080
Speaker 1: union workers of kidnapping and assault workers demanded the Starbucks

507
00:26:47,080 --> 00:26:49,960
Speaker 1: give them the payrais that went to non union workers. Instead,

508
00:26:50,080 --> 00:26:54,600
Speaker 1: Starbucks suspended them. So this again is what the manager claimed,

509
00:26:54,600 --> 00:26:57,159
Speaker 1: that they were threatened and it was an assault and

510
00:26:57,200 --> 00:27:00,919
Speaker 1: it was kidnapping and all of this wellect Union also

511
00:27:01,080 --> 00:27:05,560
Speaker 1: obtained the audio of the whole incident, and none of

512
00:27:05,600 --> 00:27:10,880
Speaker 1: that happened. The audio shows the workers calmly asking for

513
00:27:10,960 --> 00:27:14,280
Speaker 1: a pay raise that Starbucks has, by the way, illegally

514
00:27:14,320 --> 00:27:17,600
Speaker 1: withheld because you can't, you know, you can't punish workers

515
00:27:17,640 --> 00:27:20,919
Speaker 1: just for being unionized. The manager then leaves the meeting,

516
00:27:21,240 --> 00:27:25,000
Speaker 1: not kidnapped, after six minutes with no incident. So that's

517
00:27:25,000 --> 00:27:27,439
Speaker 1: what really happened there, and these workers have been suspended

518
00:27:27,840 --> 00:27:30,920
Speaker 1: because of this whole situation when they were just exercising

519
00:27:30,960 --> 00:27:33,640
Speaker 1: their like collective rights to ask for something that they

520
00:27:33,760 --> 00:27:36,560
Speaker 1: are actually owed. Yeah, the police report and the audio,

521
00:27:36,640 --> 00:27:39,200
Speaker 1: especially as you pointed out, I mean, it shows clearly

522
00:27:39,480 --> 00:27:42,399
Speaker 1: six minutes they asked for a raise, and then the

523
00:27:42,440 --> 00:27:44,520
Speaker 1: guy left and then he called nine to one one

524
00:27:44,600 --> 00:27:50,359
Speaker 1: and said he'd been kidnapped. That's impossible. This is serious,

525
00:27:50,400 --> 00:27:53,040
Speaker 1: Like this is kidnapping and assault. Those are like, I mean,

526
00:27:53,080 --> 00:27:55,040
Speaker 1: if it's actual kidnapping, you know, I mean those are

527
00:27:55,160 --> 00:27:57,920
Speaker 1: that's a serious felony, that's in crime. Yes, and these

528
00:27:58,359 --> 00:28:00,680
Speaker 1: not only is suspended as serious, I mean this s

529
00:28:00,680 --> 00:28:03,520
Speaker 1: merchman of character. Honestly, they should sue them. That's really

530
00:28:03,520 --> 00:28:06,119
Speaker 1: what should happen here as a result for defamation of

531
00:28:06,200 --> 00:28:08,000
Speaker 1: character and for a false police report. You know, and

532
00:28:08,040 --> 00:28:10,439
Speaker 1: at the very least given that they have so much evidence.

533
00:28:10,440 --> 00:28:12,560
Speaker 1: So I don't know what this idiot was thinking, but yeah,

534
00:28:12,600 --> 00:28:15,240
Speaker 1: backfired mask. It's really something And you have to say, like,

535
00:28:15,359 --> 00:28:17,680
Speaker 1: first of all, workers were very smart to yeah yeah,

536
00:28:17,880 --> 00:28:20,840
Speaker 1: always have and stuff like this. Yeah, just say definitely,

537
00:28:20,960 --> 00:28:23,080
Speaker 1: so that they had the audio recording so that then

538
00:28:23,200 --> 00:28:25,760
Speaker 1: when the mandit was like I was assaulted and kidnapp

539
00:28:25,800 --> 00:28:27,960
Speaker 1: they could be like, oh really because here's the recording.

540
00:28:28,440 --> 00:28:31,000
Speaker 1: And then you know, kudos some more perfect union for

541
00:28:31,200 --> 00:28:33,560
Speaker 1: following the story. And there have been a number you know,

542
00:28:33,600 --> 00:28:36,360
Speaker 1: there's been a resurgence of labor reporting that I think

543
00:28:36,400 --> 00:28:39,440
Speaker 1: has made a huge difference in terms of really understanding

544
00:28:39,440 --> 00:28:42,080
Speaker 1: these issues and shedding light on this because even though

545
00:28:42,320 --> 00:28:44,920
Speaker 1: you know, I can't imagine that the assault charges are

546
00:28:44,960 --> 00:28:47,800
Speaker 1: going to go anywhere now, these are workers were still suspended.

547
00:28:48,160 --> 00:28:50,680
Speaker 1: So this also gives them some some grounds to be

548
00:28:50,720 --> 00:28:53,800
Speaker 1: able to try to get reinstated there and take this

549
00:28:53,880 --> 00:28:57,240
Speaker 1: ultimately to Starbucks and show that this was that this

550
00:28:57,360 --> 00:28:59,640
Speaker 1: was wrongful. Not that Starbucks higer ups will care, but

551
00:28:59,680 --> 00:29:02,080
Speaker 1: they would care about legal ramifications. And you have an

552
00:29:02,160 --> 00:29:05,760
Speaker 1: NLRB that actually bark backsworkers. Now totally agree, all right, guys,

553
00:29:05,760 --> 00:29:08,040
Speaker 1: more for y'all leader. We've been tracking the details of

554
00:29:08,080 --> 00:29:10,160
Speaker 1: this FBI case and everything, and one of the things

555
00:29:10,160 --> 00:29:14,120
Speaker 1: that actually struck me is you might forget you might remember. Actually,

556
00:29:14,360 --> 00:29:17,920
Speaker 1: I won't blame you've forgotten. Andrew McCabe, the disgraced former

557
00:29:17,960 --> 00:29:21,840
Speaker 1: deputy director of the FBI who himself nearly was prosecuted

558
00:29:21,840 --> 00:29:24,800
Speaker 1: for lying key to the Russia Gate comy investigation and

559
00:29:24,840 --> 00:29:27,840
Speaker 1: all of that. Well, even he on CNN because he

560
00:29:27,880 --> 00:29:31,520
Speaker 1: is now a CNN analyst, is saying there is it

561
00:29:31,520 --> 00:29:34,520
Speaker 1: would such an extraordinary move for this raid to go

562
00:29:34,600 --> 00:29:38,240
Speaker 1: forward that it must be about something else, otherwise it

563
00:29:38,280 --> 00:29:40,000
Speaker 1: would just be crazy. Let's take a listen to what

564
00:29:40,040 --> 00:29:43,920
Speaker 1: he says. I completely agree, like this is such a bold,

565
00:29:44,080 --> 00:29:47,400
Speaker 1: such a disruptive, such an aggressive mood. The idea that

566
00:29:47,440 --> 00:29:49,520
Speaker 1: they would do this simply because they weren't getting the

567
00:29:49,560 --> 00:29:52,680
Speaker 1: sort of compliance they were looking for out of securing

568
00:29:52,720 --> 00:29:55,040
Speaker 1: the room with the documents and things like that seems

569
00:29:55,280 --> 00:29:59,720
Speaker 1: really unimaginable to me. It seems like they must have.

570
00:30:00,000 --> 00:30:03,160
Speaker 1: I hope they had more than just that. I mean,

571
00:30:03,440 --> 00:30:06,440
Speaker 1: he's really sort of buying into this notion that this

572
00:30:06,520 --> 00:30:10,080
Speaker 1: must be some grand plot, that this is step one,

573
00:30:10,200 --> 00:30:11,720
Speaker 1: which you would think that the dock of the move

574
00:30:11,760 --> 00:30:14,680
Speaker 1: we're basically protextual as a way to go in and

575
00:30:14,720 --> 00:30:17,840
Speaker 1: search for the things that you really want. But the

576
00:30:17,880 --> 00:30:21,320
Speaker 1: reporting suggests that's just not the case. Like, I mean,

577
00:30:21,480 --> 00:30:25,400
Speaker 1: it looks like they really did have this myopic focus

578
00:30:25,600 --> 00:30:28,920
Speaker 1: on the documents, that it's not actually connected to January's.

579
00:30:29,320 --> 00:30:31,800
Speaker 1: There's an off chance they found something else that's relevant.

580
00:30:31,800 --> 00:30:35,000
Speaker 1: That's certainly possible, but it really looks from the reporting

581
00:30:35,160 --> 00:30:38,280
Speaker 1: like they weren't getting compliance, They felt they were being

582
00:30:38,320 --> 00:30:41,280
Speaker 1: lied to, They felt that he was still concealing documents.

583
00:30:41,600 --> 00:30:44,320
Speaker 1: You know, any normal citizen with that set of facts,

584
00:30:44,320 --> 00:30:46,920
Speaker 1: of course, the FBI would be at your door like that.

585
00:30:47,560 --> 00:30:50,880
Speaker 1: They thought they were being extra careful by like, oh,

586
00:30:50,920 --> 00:30:52,239
Speaker 1: we'll do it when he's out of town and then

587
00:30:52,280 --> 00:30:56,800
Speaker 1: it won't be a media circus. How I don't know

588
00:30:57,120 --> 00:30:58,880
Speaker 1: how they could have thought that that was the case.

589
00:30:58,880 --> 00:31:02,800
Speaker 1: I'm incredulous that they really that was their logic, that

590
00:31:02,840 --> 00:31:04,960
Speaker 1: this will be no big deal. But I actually think

591
00:31:05,000 --> 00:31:06,560
Speaker 1: they were in that much of a lebble. I think

592
00:31:06,720 --> 00:31:09,560
Speaker 1: that they actually believed, like, we could do this, focus

593
00:31:09,640 --> 00:31:10,960
Speaker 1: on this one thing and it's not going to be

594
00:31:11,000 --> 00:31:13,480
Speaker 1: a big thing. Oh absolutely. It's just so funny because

595
00:31:13,480 --> 00:31:16,600
Speaker 1: it's like even Andrew McCabe is like, man, this better

596
00:31:16,680 --> 00:31:18,800
Speaker 1: be big. It's just so aggressive. There has to be more,

597
00:31:18,800 --> 00:31:20,640
Speaker 1: and it's like, no, it actually people are actually even

598
00:31:20,640 --> 00:31:23,959
Speaker 1: more incompetent than you who are currently at the FBI.

599
00:31:24,040 --> 00:31:26,880
Speaker 1: So like even CNN saying there has to be more,

600
00:31:26,920 --> 00:31:29,360
Speaker 1: and if there isn't, it's going to be it's going

601
00:31:29,400 --> 00:31:32,080
Speaker 1: to be interesting for the fallout of this. So look,

602
00:31:32,080 --> 00:31:34,360
Speaker 1: we're going to continue to look at their reporting, continue

603
00:31:34,360 --> 00:31:36,440
Speaker 1: to look at the details, but all signs point to

604
00:31:36,480 --> 00:31:39,880
Speaker 1: an FBI that just simply underestimated what the ramifications of

605
00:31:39,960 --> 00:31:43,200
Speaker 1: this would be. Kind of hilarious, Yeah, amazing. All right,

606
00:31:43,280 --> 00:31:48,120
Speaker 1: we'll see you guys later. Hei there. Welcome to another

607
00:31:48,120 --> 00:31:51,360
Speaker 1: segment of fifty one forty nine on Breaking Points, where

608
00:31:51,400 --> 00:31:55,640
Speaker 1: we dive into different topics at the intersection of business, politics,

609
00:31:55,640 --> 00:31:58,560
Speaker 1: and society, and today I want to talk about how

610
00:31:58,680 --> 00:32:03,120
Speaker 1: big tech is quite running roughshod over privacy laws in

611
00:32:03,160 --> 00:32:07,160
Speaker 1: the United States. Now, what first sparked my interest in

612
00:32:07,200 --> 00:32:10,400
Speaker 1: doing this segment has been Amazon's recent block presser acquisitions,

613
00:32:10,400 --> 00:32:12,720
Speaker 1: not only in just the past few weeks alone, but

614
00:32:13,080 --> 00:32:16,120
Speaker 1: over the last few years. Because I think a lot

615
00:32:16,160 --> 00:32:19,200
Speaker 1: of us think of Amazon as this e commerce giant,

616
00:32:19,320 --> 00:32:23,360
Speaker 1: capable of instilling fear into any retailer with something as

617
00:32:23,360 --> 00:32:26,880
Speaker 1: simple as a press release, and they most certainly are that,

618
00:32:27,640 --> 00:32:31,600
Speaker 1: but they are now capable of so much more. Amazon

619
00:32:31,760 --> 00:32:35,040
Speaker 1: bought Whole Foods for a price that should qualify for

620
00:32:35,120 --> 00:32:40,280
Speaker 1: free shipping thirteen point seven billion dollars, turning Amazon buying

621
00:32:40,400 --> 00:32:44,240
Speaker 1: MGM Studios for eight point five billion bucks and acquiring

622
00:32:44,520 --> 00:32:48,280
Speaker 1: iconic film franchises like James Bond and Rocky Out. Amazon,

623
00:32:48,600 --> 00:32:51,479
Speaker 1: in a new deal buying its way into the pharmacy business.

624
00:32:51,480 --> 00:32:55,160
Speaker 1: It is buying Hillpack and online pharmacy that offers pre

625
00:32:55,280 --> 00:32:58,840
Speaker 1: sorted doses of medication. Is there. It's Amazon buying up

626
00:32:58,880 --> 00:33:02,520
Speaker 1: all the smart doorbell. Amazon will buy smart device maker

627
00:33:02,600 --> 00:33:06,600
Speaker 1: Ring for over one billion dollars. According to Reuter's Amazon

628
00:33:06,840 --> 00:33:09,840
Speaker 1: just announced that it is buying the primary care provider

629
00:33:10,040 --> 00:33:14,600
Speaker 1: One Medical. The deal is worth three point nine billion dollars.

630
00:33:14,840 --> 00:33:18,000
Speaker 1: So Amazon is acquiring roomba maker I Robot. It's an

631
00:33:18,000 --> 00:33:20,760
Speaker 1: all cash deal value at one point seven billion dollars.

632
00:33:20,760 --> 00:33:23,240
Speaker 1: The company announced all of this this morning. WHOA imagine

633
00:33:23,280 --> 00:33:27,040
Speaker 1: a company so powerful, so omnipresident in your life that

634
00:33:27,080 --> 00:33:30,040
Speaker 1: they not only know everything about your consumption habits, what

635
00:33:30,080 --> 00:33:32,960
Speaker 1: you buy through Amazon, but also what your typical diet

636
00:33:33,000 --> 00:33:36,080
Speaker 1: consists of through your purchases at Whole Foods, what types

637
00:33:36,120 --> 00:33:40,080
Speaker 1: of shows and entertainment you enjoy through MGM and Prime Video.

638
00:33:40,640 --> 00:33:44,760
Speaker 1: But maybe more intrusive, they can also surveil the intimate

639
00:33:44,800 --> 00:33:48,840
Speaker 1: goings on within your home, the perimeter, the movements inside

640
00:33:48,840 --> 00:33:52,080
Speaker 1: your home with ring devices, map the floor plan of

641
00:33:52,120 --> 00:33:54,920
Speaker 1: your home, figure out what furniture you have don't have

642
00:33:55,080 --> 00:33:58,280
Speaker 1: through their recent Ie Robot roomba acquisition, and of course,

643
00:33:58,800 --> 00:34:02,200
Speaker 1: listen in on your most private and intimate conversations at

644
00:34:02,240 --> 00:34:06,160
Speaker 1: home with Alexa enabled smart devices. And let's not forget

645
00:34:06,160 --> 00:34:09,760
Speaker 1: about their potential access to your health records through One Medical.

646
00:34:10,320 --> 00:34:13,280
Speaker 1: This is the Amazon of twenty twenty two, a company

647
00:34:13,320 --> 00:34:16,440
Speaker 1: capable of knowing you better than you know yourself, and

648
00:34:16,560 --> 00:34:20,120
Speaker 1: that is a little scary. Weaved an incredible year. The

649
00:34:20,160 --> 00:34:23,160
Speaker 1: team has invented a lot on behalf of customers and

650
00:34:23,239 --> 00:34:26,120
Speaker 1: I cannot wait to show you what we have So far.

651
00:34:26,600 --> 00:34:29,400
Speaker 1: Limp and his team have made Alexa compatible with more

652
00:34:29,440 --> 00:34:33,840
Speaker 1: than one hundred thousand products. Echo frames allow you to

653
00:34:33,840 --> 00:34:36,680
Speaker 1: get done, more around you and be more present in

654
00:34:36,719 --> 00:34:40,360
Speaker 1: the every day. Now, they're going to know more about

655
00:34:40,440 --> 00:34:45,239
Speaker 1: you than anyone knows. They're trying to move as intimately

656
00:34:45,280 --> 00:34:51,720
Speaker 1: as possible, as quietly as possible, into everyday life. Echo

657
00:34:51,760 --> 00:34:55,759
Speaker 1: Loop is a smart ring packed with ways to stay

658
00:34:55,800 --> 00:34:59,399
Speaker 1: on top of your day. Amazon wants to have the

659
00:34:59,680 --> 00:35:04,800
Speaker 1: entire your environment essentially miked. I like them start my

660
00:35:04,880 --> 00:35:08,879
Speaker 1: running playlist. They want your walk in the park, they

661
00:35:08,920 --> 00:35:12,239
Speaker 1: want your run down the city street. Nationwise teamed up

662
00:35:12,280 --> 00:35:15,360
Speaker 1: with Amazon to bring you the all new Echo Auto.

663
00:35:15,800 --> 00:35:17,759
Speaker 1: They want what you do in your car, they want

664
00:35:17,800 --> 00:35:21,359
Speaker 1: what you do in your home. Amazon smartenhing Alexa bake

665
00:35:21,480 --> 00:35:24,799
Speaker 1: for thirty minutes at three hundred and fifty degrees. All

666
00:35:24,880 --> 00:35:30,560
Speaker 1: these intimacies, all this insight is being integrated, analyzed and integrated.

667
00:35:31,280 --> 00:35:37,000
Speaker 1: Alexa alarm off. That is an extraordinary kind of power

668
00:35:37,440 --> 00:35:40,160
Speaker 1: that has never before existed. Now, I have to say,

669
00:35:40,160 --> 00:35:43,560
Speaker 1: some folks find privacy not to be that big a deal,

670
00:35:44,160 --> 00:35:46,920
Speaker 1: but many others are much more alarmed. It all kind

671
00:35:46,920 --> 00:35:50,360
Speaker 1: of depends on your own personal comfort level with trading

672
00:35:50,400 --> 00:35:54,200
Speaker 1: privacy for convenience, and everyone's position on this sliding scale

673
00:35:54,280 --> 00:35:56,760
Speaker 1: is different. But taking a look at this Pew research

674
00:35:56,800 --> 00:35:59,960
Speaker 1: survey conducted in twenty nineteen, eighty one percent of Americans

675
00:36:00,080 --> 00:36:03,320
Speaker 1: feel that the potential risks of companies collecting data about

676
00:36:03,320 --> 00:36:06,479
Speaker 1: them outweigh the benefits, and seventy nine percent are either

677
00:36:06,680 --> 00:36:10,240
Speaker 1: very or somewhat concerned about how companies use the data collected.

678
00:36:10,880 --> 00:36:14,040
Speaker 1: But regardless of your own personal views on privacy, the

679
00:36:14,120 --> 00:36:17,880
Speaker 1: reality is that Amazon has evolved from disrupting book sales

680
00:36:17,920 --> 00:36:20,840
Speaker 1: to now, as Glenn Greenwald wrote back in twenty nineteen,

681
00:36:20,960 --> 00:36:24,400
Speaker 1: quote a critical partner for the US government in building

682
00:36:24,440 --> 00:36:29,319
Speaker 1: an ever more invasive, militarized, and sprawling surveillance state. To

683
00:36:29,400 --> 00:36:34,400
Speaker 1: keep Amazon running, Bezos had developed an unprecedented digital infrastructure.

684
00:36:35,400 --> 00:36:38,080
Speaker 1: He realized he could rent parts of it out, not

685
00:36:38,200 --> 00:36:42,720
Speaker 1: just to businesses, but also to the government. Nobody hangs

686
00:36:42,719 --> 00:36:48,080
Speaker 1: out in Washington, DC just to go to the free museums.

687
00:36:48,480 --> 00:36:51,080
Speaker 1: You buy a home in Washington, you buy a newspaper

688
00:36:51,080 --> 00:36:54,840
Speaker 1: in Washington, because it is the most influential city in

689
00:36:54,880 --> 00:36:58,120
Speaker 1: the world, and you want to lay your hands on

690
00:36:58,160 --> 00:37:02,360
Speaker 1: that power team. He got a major boost when it

691
00:37:02,440 --> 00:37:05,880
Speaker 1: was revealed that Amazon Web Services had designed a computing

692
00:37:05,920 --> 00:37:11,120
Speaker 1: cloud for the CIA. Amazon Web Services was awarded a

693
00:37:11,160 --> 00:37:14,360
Speaker 1: ten year contract for six hundred million dollars. Amazon is

694
00:37:14,360 --> 00:37:17,399
Speaker 1: helping the CIA build a secure cloud computer network. The

695
00:37:17,440 --> 00:37:20,000
Speaker 1: CIA contract was probably one of the best things that

696
00:37:20,120 --> 00:37:24,239
Speaker 1: happened to Amazon's cloud business. It lifted all doubts about

697
00:37:24,280 --> 00:37:26,880
Speaker 1: the security of the cloud and on whether you could

698
00:37:27,000 --> 00:37:31,319
Speaker 1: trust Amazon with your most precious data. The message to

699
00:37:31,400 --> 00:37:34,760
Speaker 1: the world is if the CIA trusts Amazon with its data,

700
00:37:34,840 --> 00:37:37,839
Speaker 1: and maybe other companies and government institutions can as well.

701
00:37:38,480 --> 00:37:44,040
Speaker 1: Introducing Amazon Recognition, video recognition allows you to pass an

702
00:37:44,040 --> 00:37:46,879
Speaker 1: image shot. You can say, do these two phases match,

703
00:37:46,960 --> 00:37:51,280
Speaker 1: which is incredibly useful, useful for applications in the security space.

704
00:37:51,280 --> 00:37:54,080
Speaker 1: You can imagine unlocking your After Amazon rolled out a

705
00:37:54,120 --> 00:37:59,160
Speaker 1: facial recognition tool, it marketed it to law enforcement recognize

706
00:37:59,160 --> 00:38:02,320
Speaker 1: and track person of interest from a collection of tens

707
00:38:02,320 --> 00:38:07,080
Speaker 1: of millions of faces. Police we've spoken to say, it's

708
00:38:07,080 --> 00:38:10,400
Speaker 1: a valuable tool to identify suspects quickly. Here's to be

709
00:38:10,440 --> 00:38:12,160
Speaker 1: a match, but I'm going to make sure I look

710
00:38:12,200 --> 00:38:15,160
Speaker 1: at it all. And while Amazon has offered guidelines for

711
00:38:15,239 --> 00:38:17,720
Speaker 1: how it should be used, there are a few laws

712
00:38:17,760 --> 00:38:20,360
Speaker 1: governing the use of this technology, and that's kind of

713
00:38:20,360 --> 00:38:24,680
Speaker 1: the problem. Unlike Europe, which has this uniform, comprehensive privacy law,

714
00:38:24,840 --> 00:38:26,839
Speaker 1: the US is kind of like the Wild West when

715
00:38:26,840 --> 00:38:30,360
Speaker 1: it comes to protecting an individual's private data. The US

716
00:38:30,760 --> 00:38:33,560
Speaker 1: doesn't have a singular law that covers the privacy of

717
00:38:33,600 --> 00:38:36,080
Speaker 1: all types of data. Instead, it has this mix of

718
00:38:36,160 --> 00:38:40,359
Speaker 1: laws that go by acronyms like HIPPA, Furpa, coppa, etc.

719
00:38:41,080 --> 00:38:44,720
Speaker 1: And because such patchwork of privacy laws are extremely narrow

720
00:38:44,760 --> 00:38:48,719
Speaker 1: focused and oftentimes outdated as well, it creates a situation

721
00:38:48,840 --> 00:38:51,719
Speaker 1: where you might think your private data is protected, but

722
00:38:52,080 --> 00:38:54,879
Speaker 1: it's really not. This is reporting from the New York

723
00:38:54,880 --> 00:38:58,600
Speaker 1: Times quote the Health Insurance Portability and Accountability Act HIPPA

724
00:38:58,800 --> 00:39:02,360
Speaker 1: has little to do with privacy and covers only communication

725
00:39:02,440 --> 00:39:07,200
Speaker 1: between you and covered entities, which include doctors, hospitals, pharmacies, insurers,

726
00:39:07,239 --> 00:39:10,960
Speaker 1: and other similar businesses. People tend to think HIPPA covers

727
00:39:11,000 --> 00:39:14,640
Speaker 1: all health data, but it doesn't Your fitbit data isn't protected,

728
00:39:14,680 --> 00:39:17,720
Speaker 1: for example, Nor does the law restrict who can ask

729
00:39:17,760 --> 00:39:21,360
Speaker 1: for your COVID nineteen vaccination status. Right now, the strongest

730
00:39:21,400 --> 00:39:24,719
Speaker 1: privacy laws passed in the US is in California, with

731
00:39:25,160 --> 00:39:29,520
Speaker 1: regulations somewhat mimicking the protections given to citizens in Europe.

732
00:39:29,840 --> 00:39:35,080
Speaker 1: California's Consumer Privacy Act gives Golden State residents a slew

733
00:39:35,160 --> 00:39:39,120
Speaker 1: of new rights related to user data. New regulations aim

734
00:39:39,239 --> 00:39:43,000
Speaker 1: to provide more control over the personal information that companies

735
00:39:43,160 --> 00:39:47,960
Speaker 1: regularly collect and sell. The Act gives Californians the right

736
00:39:48,120 --> 00:39:51,560
Speaker 1: to ask tech companies what kind of data they're collecting

737
00:39:51,560 --> 00:39:54,760
Speaker 1: on them. They can request for that data to be deleted,

738
00:39:54,880 --> 00:39:57,600
Speaker 1: and they can also tell companies don't sell that data

739
00:39:57,640 --> 00:40:00,600
Speaker 1: to other people and make money off of it. California

740
00:40:00,680 --> 00:40:03,480
Speaker 1: is home to some major tech companies, of course, how

741
00:40:03,520 --> 00:40:06,040
Speaker 1: are they reacting to this new law and will they

742
00:40:06,040 --> 00:40:11,320
Speaker 1: comply with it? Pretty much all tech companies are scrambling

743
00:40:11,360 --> 00:40:15,799
Speaker 1: and grappling with exactly how they're going to be enabling

744
00:40:16,000 --> 00:40:19,879
Speaker 1: themselves to comply with this law. Other states may follow suit.

745
00:40:19,960 --> 00:40:23,800
Speaker 1: We've seen other states follow California's lead. And regulation before

746
00:40:23,840 --> 00:40:27,160
Speaker 1: and there are nine other states currently considering privacy regulations

747
00:40:27,200 --> 00:40:30,120
Speaker 1: like this. Yep, that's right. Other states are indeed wanting

748
00:40:30,160 --> 00:40:33,200
Speaker 1: to follow suits. So big tech firms are working actively,

749
00:40:33,280 --> 00:40:38,200
Speaker 1: albeit quietly, to prevent the passing of similar laws in

750
00:40:38,320 --> 00:40:41,720
Speaker 1: other states, or at the very least weakening the new laws.

751
00:40:42,160 --> 00:40:46,400
Speaker 1: This is reporting from the Register Quote. Amazon, Apple, Google, Meta,

752
00:40:46,480 --> 00:40:50,560
Speaker 1: and Microsoft often support privacy and public statements, but behind

753
00:40:50,600 --> 00:40:53,480
Speaker 1: the scenes, they've been working through some common organizations to

754
00:40:53,520 --> 00:40:57,040
Speaker 1: weaken or kill privacy legislation in the United States. That's

755
00:40:57,040 --> 00:40:59,719
Speaker 1: according to a report this week from news nonprofit The

756
00:40:59,719 --> 00:41:03,200
Speaker 1: Market Up, which said the corporation's hire lobbyists from the

757
00:41:03,280 --> 00:41:06,400
Speaker 1: same few groups and law firms to defang or drowned

758
00:41:06,440 --> 00:41:09,960
Speaker 1: state privacy bills. The report examined thirty one states where

759
00:41:10,000 --> 00:41:14,000
Speaker 1: state legislators were considering privacy legislation and identified four hundred

760
00:41:14,000 --> 00:41:17,440
Speaker 1: and forty five lobbyists and lobbying firms working on behalf

761
00:41:17,440 --> 00:41:21,640
Speaker 1: of Amazon, Apple, Google, Meta, and Microsoft, along with industry

762
00:41:21,680 --> 00:41:25,360
Speaker 1: groups like tech net and the State Privacy and Security Coalition.

763
00:41:25,760 --> 00:41:29,279
Speaker 1: Setting a few examples, a Connecticut data privacy bill died

764
00:41:29,360 --> 00:41:32,920
Speaker 1: last year after lobbyists weighed in against it, though the

765
00:41:32,920 --> 00:41:36,400
Speaker 1: state did pass SB six, an Act concerning personal data

766
00:41:36,480 --> 00:41:40,080
Speaker 1: privacy and online monitoring, in April. The Washington Privacy Act

767
00:41:40,120 --> 00:41:42,719
Speaker 1: collapsed for the third time last year, so did the

768
00:41:42,719 --> 00:41:47,360
Speaker 1: Oklahoma Computer Data Privacy Act and similar privacy legislation in Florida.

769
00:41:47,840 --> 00:41:51,600
Speaker 1: In some cases, the companies lobbying state lawmakers actually draft

770
00:41:51,640 --> 00:41:54,480
Speaker 1: the bills that will later be passed to regulate them.

771
00:41:54,800 --> 00:41:57,399
Speaker 1: That's what happened with the first version of the twenty

772
00:41:57,400 --> 00:42:01,080
Speaker 1: twenty one Virginia Consumer Data Protection Act, which is penned

773
00:42:01,080 --> 00:42:04,000
Speaker 1: by an Amazon lobbyists. Yeah, unfortunately, this is not a

774
00:42:04,040 --> 00:42:07,520
Speaker 1: novel situation in the US where companies are hiring lobbyists

775
00:42:07,520 --> 00:42:11,000
Speaker 1: to write legislation on their behalf so just to recap

776
00:42:11,680 --> 00:42:15,120
Speaker 1: more stringent privacy laws would of course affect the business

777
00:42:15,120 --> 00:42:17,520
Speaker 1: model of Amazon and other big tech firms, And once

778
00:42:17,520 --> 00:42:21,440
Speaker 1: they've realized that public opinion has now turned strongly against

779
00:42:21,480 --> 00:42:25,080
Speaker 1: them in favor of more privacy protection for individual citizens,

780
00:42:25,680 --> 00:42:29,480
Speaker 1: they all have now undertaken huge efforts to quietly craft

781
00:42:29,560 --> 00:42:33,280
Speaker 1: legislation that claim to protect consumer privacy, but in reality

782
00:42:34,239 --> 00:42:37,080
Speaker 1: the laws are actually quite watered down and flimsy. But

783
00:42:37,160 --> 00:42:39,480
Speaker 1: in practice, what will almost certainly be. The case is

784
00:42:39,480 --> 00:42:43,080
Speaker 1: that companies like Amazon, Google, Meta, even Apple, who are

785
00:42:43,160 --> 00:42:47,040
Speaker 1: notoriously more careful with their customers data, will continue to

786
00:42:47,239 --> 00:42:51,760
Speaker 1: publicly preach consumer privacy while behind the scenes their business

787
00:42:51,800 --> 00:42:55,440
Speaker 1: practices will necessarily force them to push the boundaries of

788
00:42:55,480 --> 00:42:58,480
Speaker 1: what the law and what the public will accept in

789
00:42:58,560 --> 00:43:01,279
Speaker 1: terms of data gathering and service. And the point I

790
00:43:01,280 --> 00:43:03,840
Speaker 1: want to stress is that privacy is something that once

791
00:43:03,920 --> 00:43:07,880
Speaker 1: given up, you typically don't get it back. Here's what

792
00:43:07,960 --> 00:43:10,560
Speaker 1: Tim Cook's CEO of Apple, has said about privacy and

793
00:43:10,600 --> 00:43:14,520
Speaker 1: response to allowing government agencies access to private data. Quote,

794
00:43:14,520 --> 00:43:16,800
Speaker 1: if you put a key under the mat for the cops,

795
00:43:16,880 --> 00:43:19,879
Speaker 1: a burglar can find it too. Criminals are using every

796
00:43:19,920 --> 00:43:23,080
Speaker 1: technology tool at their disposal to hack into people's accounts.

797
00:43:23,400 --> 00:43:25,800
Speaker 1: If they know there's a key hidden somewhere, they won't

798
00:43:25,800 --> 00:43:28,480
Speaker 1: stop until they find it. Now. While I have my

799
00:43:28,680 --> 00:43:31,200
Speaker 1: own qualms with Apple and not entirely sold on the

800
00:43:31,320 --> 00:43:34,680
Speaker 1: genuineness of their privacy practices, I do think that his

801
00:43:34,840 --> 00:43:37,279
Speaker 1: thoughts here are on point in that we ought to

802
00:43:37,320 --> 00:43:40,359
Speaker 1: think about this issue not just as a trade off

803
00:43:40,400 --> 00:43:46,480
Speaker 1: between convenience and privacy, but rather against potential tyranny. Democracy

804
00:43:46,560 --> 00:43:49,480
Speaker 1: is something that's very fragile and a tax that democracy

805
00:43:49,560 --> 00:43:53,040
Speaker 1: can manifest not just within the government, but in many

806
00:43:53,040 --> 00:43:57,120
Speaker 1: different ways. Private power in the form of abusive multinational

807
00:43:57,160 --> 00:44:01,360
Speaker 1: tech conglomerates, companies whose leaders have a do vast amounts

808
00:44:01,400 --> 00:44:04,759
Speaker 1: of power without ever receiving a single vote, coupled with

809
00:44:04,800 --> 00:44:08,880
Speaker 1: a political system that is becoming increasingly incapable of checking

810
00:44:08,880 --> 00:44:12,839
Speaker 1: such power is a deeply concerning development for our democracy.

811
00:44:13,440 --> 00:44:17,080
Speaker 1: And such change isn't going to come from the inside.

812
00:44:17,320 --> 00:44:20,360
Speaker 1: Privacy if we deem it important enough to protect, I

813
00:44:20,360 --> 00:44:22,080
Speaker 1: think we'll have to come from us. I think we

814
00:44:22,160 --> 00:44:26,000
Speaker 1: all have agency in protecting our own privacy, and we

815
00:44:26,040 --> 00:44:29,480
Speaker 1: should own as much of it as possible. Thank you

816
00:44:29,520 --> 00:44:31,760
Speaker 1: so much for watching. I hope you found this segment

817
00:44:31,840 --> 00:44:35,439
Speaker 1: to be helpful. If so, you can support my work

818
00:44:35,480 --> 00:44:37,799
Speaker 1: by checking out my other videos and subscribing to my

819
00:44:37,920 --> 00:44:40,160
Speaker 1: channel fifty one to forty nine with James Lee, where

820
00:44:40,200 --> 00:44:44,200
Speaker 1: I dive into other topics related to business, politics and society.

821
00:44:44,480 --> 00:44:47,640
Speaker 1: The link will be in the description below. As always,

822
00:44:48,000 --> 00:44:50,120
Speaker 1: remember to subscribe to Breaking Points and thank you for

823
00:44:50,160 --> 00:44:50,840
Speaker 1: your time today.