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Speaker 1: Hey, guys, Saga and Crystal here.

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Speaker 2: Okay, so we've got Republicans realize they've got a problem

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Speaker 2: with healthcare, and you know, there's two things that happen.

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Speaker 2: Number One, Democrats with their shutdown strategy did focus a

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Speaker 2: lot of attention on healthcare, and I think that made

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Speaker 2: things more politically difficult for Republicans. Number two is again

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Speaker 2: just reality, like healthcare is really expensive. People are aware

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Speaker 2: now they've gotten their notices if they're part of the

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Speaker 2: you know, in the Obamacare marketplace, that their premiums are

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Speaker 2: going up. There was you know, a national discourse about

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Speaker 2: who's to bladed and to mention the party in the power,

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Speaker 2: you're just going to automatically sort of like lay the

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Speaker 2: blame at their feet, which is entirely appropriate. And so

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Speaker 2: Republicans are now scrambling to try to figure out something

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Speaker 2: that they can do, but they have zero consensus amongst

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Speaker 2: them about what that may look like. They really are

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Speaker 2: in the wilderness when it comes to healthcare. You guys

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Speaker 2: may recall Trump a couple weeks back, and I'm going

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Speaker 2: to announce a plan. Then there was a freak out

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Speaker 2: from the Republican caucus. They didn't like what he was

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Speaker 2: going to propose. That got pulled. We haven't heard anymore

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Speaker 2: about that since then. So yesterday they had a Republican

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Speaker 2: conference meeting where they laid out some of the potential

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Speaker 2: options that they may put together and put to a

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Speaker 2: vote next week in the House.

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Speaker 3: With regard to healthcare, let's put.

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Speaker 2: See one up on the screen, and I'm just going

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Speaker 2: to read these to you. Some of them are just

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Speaker 2: downright versical, and others are pathetic. Some of them, you know,

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Speaker 2: are fine things that may have some modest impact, but

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Speaker 2: it's certainly not going to be any sort of saving grace.

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Speaker 2: We've got association health plans. That's basically where you can

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Speaker 2: sort of like get a group together and create your

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Speaker 2: own pool to buy healthcare plans, which is something that

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Speaker 2: already happens, by the way, but I guess the idea

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Speaker 2: here is to make that easier to happen. Choice accounts,

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Speaker 2: health savings accounts, cost sharing reductions.

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Speaker 3: I don't really know what that means.

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Speaker 2: Codify Trump administration rules to fix the Unaffordable Care Act

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Speaker 2: PBM reform, that'd be a good thing to do.

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Speaker 3: This one.

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Speaker 2: I really love innovation, okay, price transparency, which again is

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Speaker 2: something that you know, okay, fine, but that's not really

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Speaker 2: going to solve the problem, like, oh, I have transparency

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Speaker 2: around the fact that I'm getting price couged, cite neutrality,

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Speaker 2: and provider owned hospitals. So this is sort of a

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Speaker 2: grab bag of ideas, none of which has been completely

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Speaker 2: you know, backed by the entire Republican Conference. And one

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Speaker 2: thing you'll notice here that is not on this slide

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Speaker 2: is extending the Obamacare subsidies. So the immediate source of pain,

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Speaker 2: which is these skyrocketing Obamacare prices marketplace prices because the

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Speaker 2: subsidies expire at the end of the year that is

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Speaker 2: actually not on the table. And even with these sort

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Speaker 2: of like market based reforms, which again could have maybe

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Speaker 2: a small modest impact, but is not going to be

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Speaker 2: any of them a saving grace. Even with these, there

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Speaker 2: is no real consensus within the Republican caucus, so they

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Speaker 2: really are sort of a drift.

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Speaker 3: On this one.

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Speaker 2: Let me put the next one up on the screen.

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Speaker 2: This is from Politico. They say the House gup erupts

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Speaker 2: over healthcare as leaders hunt for a plan. House Republican

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Speaker 2: leaders presented no firm planned Tuesday for advancing healthcare legislation

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Speaker 2: as anxiety rises. Instead, Speaker Mike Johnson presented attendees of

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Speaker 2: a closed door conference meeting with this list of ten

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Speaker 2: possible policies that could get votes in the coming weeks

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Speaker 2: or months. Some were specific, like an expansion of health

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Speaker 2: savings accounts, others were vague, like innovation. The list did

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Speaker 2: not include an extension of the expiring tax credits. The

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Speaker 2: presentation was followed by a heated discussion over the pathward

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Speaker 2: on healthcare for the party, and vulnerable members, including Representative

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Speaker 2: Jen Kiggins, stood up to warn against the political fallout

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Speaker 2: of failing to extend the expiring subsidies. Some later express

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Speaker 2: their dismay at how poorly they believe GOP leaders have

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Speaker 2: handled the topic. Quote, there was a general uneasiness because

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Speaker 2: nothing is coming together. Another one says we wasted so

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Speaker 2: much time. That was a conservative Republican also lamenting the

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Speaker 2: lack of a unified GOP health plan. Representative Ralph Norman

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Speaker 2: was willing to go on the record to say there

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Speaker 2: was no consensus. So that's sort of the state of

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Speaker 2: play from the Republicans in the Senate.

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Speaker 3: So that's the House, okay.

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Speaker 2: Mike Johnson is saying we're going to do some sort

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Speaker 2: of a vote on something next week. We'll find out

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Speaker 2: what that something is, but apparently does not include extension

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Speaker 2: of the Obamacare subsidies. In the Senate, John Thune is

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Speaker 2: planning to bring forward two separate plans. One that is

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Speaker 2: Republican led, which includes some Obamacare extension but for a

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Speaker 2: limited time and with some changes, et cetera. One that

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Speaker 2: is Democratic led, and with the expectation that neither one

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Speaker 2: of them is going to pass, so sort of a

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Speaker 2: show vote to pretendly, oh, we're doing something, We're having

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Speaker 2: votes on healthcare. Literally, no one is going to care

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Speaker 2: that you took a vote on something that is not

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Speaker 2: going to possibate.

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Speaker 1: It is genuinely crazy. I mean, do you remember when

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Speaker 1: Obamacare that was nine it's twenty twenty five. Actually it

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Speaker 1: was December o nine, right, that famous Christmas vote or

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Speaker 1: all that's right. So we're coming up on sixteen years

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Speaker 1: since this has been in the national conversation, and every

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Speaker 1: time we're going to repeal and replace Obamacare. Okay, Obamacare sucks,

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Speaker 1: I use, it's horrible, it's awful. Most people who are

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Speaker 1: on it don't like it very much, and they think

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Speaker 1: that it's very expensive. You're going to replace it though

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Speaker 1: with what? And they still don't have the what answer.

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Speaker 1: And in the interim six years, we have had multi

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Speaker 1: digit or sorry, multi three digit inflation in health care costs,

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Speaker 1: lowering of life expectancy, massive increase in obesity, huge increase

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Speaker 1: in comorbidities for people who are dying in hospitals, not

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Speaker 1: to mention the pandemic where the healthcare system and the

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Speaker 1: scientific community collapse its entire trust. Like, you cannot say

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Speaker 1: that this has been a victory over the last years,

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Speaker 1: and yet when they're in power, every single time we're

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Speaker 1: going to repeal and replace and still replace with what

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Speaker 1: and even this Trump proposal see three. Put that on

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Speaker 1: the screen. Honestly, he's backing this fifteen hundred dollars checks

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Speaker 1: for healthcare. Now, you know, let's put this all into perspective.

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Speaker 1: So I'm on Obamacare, all right. My healthcare went from

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Speaker 1: or went up by seventeen percent, which is a little

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Speaker 1: I think it's actually be about more than the fifteen hundred.

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Speaker 1: So if this word happen, this fifteen hundred dollars check,

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Speaker 1: it would cover the increase in my healthcare premium, which

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Speaker 1: would mean I would get to pay exactly what I

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Speaker 1: got to pay last. I mean, I guess that's a victory.

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Speaker 1: But for anybody else out there, I have a ten

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Speaker 1: thousand dollars deductible most people who are out there, or sorry,

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Speaker 1: fourteen thousand, five hundred dollars deductible. Most people are out

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Speaker 1: there who are on employer healthcare, they're deductibles probably in

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Speaker 1: the fifteen sixteen hundred range. So it wouldn't be horrible,

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Speaker 1: I guess, to cover some of their deductible, but it

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Speaker 1: would not increase or it would not cover their average

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Speaker 1: increase in premium. And this gets to costs. At the

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Speaker 1: end of the day, we have to deal with costs.

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Speaker 1: We can do that in a variety of ways. We

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Speaker 1: do price controls. I'm hugely supportive of HSA's I have

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Speaker 1: an HSA. I highly recommend people use it. But even

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Speaker 1: with HSA, let's say you average on you know s

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Speaker 1: and P five hundred returns. If health care inflation is

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Speaker 1: seventeen eighteen percent, you're still going to lose. And if

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Speaker 1: we don't do something about the costs and the system,

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Speaker 1: nothing can be done. But each individual part of the

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Speaker 1: cost structure is highly influential here in Washington at a

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Speaker 1: bipartisan level. So we talked about PBMs. We've been talking

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Speaker 1: about PBM reform for a decade. Yeah, still doesn't happen.

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Speaker 3: Yeah, And actually that was the build up. Remember Elon knew, Yes,

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Speaker 3: the PBM exactly.

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Speaker 2: Elon didn't like something about it, and so that got

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Speaker 2: killed and then the PBM part.

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Speaker 1: So PBM reform we've been talking about for a decade,

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Speaker 1: it's never happened. We have talked about Democrats have even

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Speaker 1: talked about public option. Not even that ever happened. We

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Speaker 1: talked about medicare being able to negotiate with drug prices

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Speaker 1: that didn't happen. We were allowed to negotiate the ten

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Speaker 1: biggest stri and by the way, even that is dragging on.

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Speaker 1: It's been kind of a nightmare. Trump is trying to

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Speaker 1: do trump RX with direct dozempic. I'm fine with that.

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Speaker 1: I support that, but that's one drug that's not like

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Speaker 1: the biggest problem in the healthcare costs have gone astronomical.

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Speaker 1: Doctors who watch Breaking points always hate it when I

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Speaker 1: say it, it's empirically true. They make way too much money.

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Speaker 1: The reason why they make so much money is because

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Speaker 1: they have way too much debt. So I don't even

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Speaker 1: hate on them. You have to make three hundred k

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Speaker 1: to pay off three hundred k well in debt.

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Speaker 2: And the biggest growth in costs in the healthcare system

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Speaker 2: over I don't know how the past number of decades

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Speaker 2: administrators administrative costs and I mean, look, that is the

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Speaker 2: problem with like a private for profit healthcare system is

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Speaker 2: if you have right all of this like complicated insurance, Okay,

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Speaker 2: is this covered? Is that covered? Or are we going

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Speaker 2: to cover this? What's your deductible? Are you eligible all

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Speaker 2: of that? Then yeah, your hospital is going to require

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Speaker 2: all kinds of paper pushers to deal with the insurance

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Speaker 2: companies which are there on the other side with their

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Speaker 2: own mass army of paper pushes, to try to figure

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Speaker 2: out how they can deny as much coverage as possible.

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Speaker 2: And so that's why you have these constantly ballooning administrative costs,

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Speaker 2: because it's required to navigate the absolute morass and disaster

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Speaker 2: of our healthcare system. I did want to point out

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Speaker 2: one thing about that President Trump plan, which is you

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Speaker 2: would not actually get fifteen hundred, you would get one

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Speaker 2: thousand because only people over the age of fifty get

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Speaker 2: the fuck out of hundred.

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Speaker 1: You're going to burst a blood vessel in my brain.

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Speaker 2: Eighteen to forty nine year olds only get one thousand.

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Speaker 2: So I'm sorry, Sagart, you will only be get so

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Speaker 2: it will actually cover this increase in the preview.

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Speaker 1: You were going to set me on five. So the

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Speaker 1: boomers get the bailout and Gen X Actually yeah, so

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Speaker 1: the the aging gen X and boomers are the ones

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Speaker 1: who get the fifteen hundred dollars bailout. But those of

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Speaker 1: us who are just parents of young childrenyeah, fuck them right,

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Speaker 1: You're not doing anything for the I don't even have words.

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Speaker 1: I either agree. That makes it. Yeah, that's America, Okay,

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Speaker 1: total boomer luxury communism. Look it up. One of my

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Speaker 1: buddies just wrote a whole piece about it in the

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Speaker 1: American mind that that's what we have now in this country.

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Speaker 1: It's amazing. And look just I mean, the healthcare system

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Speaker 1: as it is, it cannot stand, and yet it does.

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Speaker 1: It's seventeen to twenty percent of GDP. That's part of

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Speaker 1: the reason why, you know, even replacing it and cutting

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Speaker 1: costs it would cause a recession. The number of administrators

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Speaker 1: and costs and price that is all tied up into

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Speaker 1: pharmaceutic I mean, go take a drive, man, you know,

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Speaker 1: if you go on into the outer boroughs of some

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Speaker 1: bigger cities. I was in Philadelphia recently. You drive past

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Speaker 1: drug company hospital, drug company hospital here, like, oh man,

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Speaker 1: I mean, it's a Titanic empire that rules this country.

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Speaker 3: Right, yeah.

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Speaker 2: No. And I see a lot of candidates who are

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Speaker 2: leaning more and more heavily into you know, medicare for all,

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Speaker 2: and it's increasingly once again on the table as a

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Speaker 2: litmus test. You see some people like Chris van Holland

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Speaker 2: who's now come on board. This is what voters are

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Speaker 2: demanding in a lot of these primaries as well.

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Speaker 3: So it's definitely.

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Speaker 2: Like healthcare politics are back, and the pain is so undeniable,

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Speaker 2: and I think everybody sees what you see this that

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Speaker 2: this is literally unsustainable, like we are going to enter

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Speaker 2: a place where people just healthy people in particular, and

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Speaker 2: younger people in particularly, just like I, it does not

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Speaker 2: make sense.

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Speaker 3: For me to pot said it does not have a

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Speaker 3: chance for me to payos.

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Speaker 1: It's no chance.

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Speaker 3: And then once they exit, then guess what.

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Speaker 2: The prices go up even more because they're helping to

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Speaker 2: subsidize the you know, old typically older, sicker populations, and

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Speaker 2: that's how you end up in this desk spiral. So

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Speaker 2: these little you know, quote unquote innovation or choice accounts,

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Speaker 2: whatever that is, is not going to fix the absolute

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Speaker 2: disastrous mess.

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Speaker 3: And there are things that you know, especially when Trump.

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Speaker 2: Is less sort of like ideologically committed to we must

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Speaker 2: have a free market solution, there are things that Republicans

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Speaker 2: could suggest that would be better that they should in

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Speaker 2: my opinion, they should get on board with a public option.

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Speaker 2: I mean that still is like market based, if that's

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Speaker 2: what you're.

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Speaker 3: Hung up on.

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Speaker 2: But I think since that has the taint of like

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Speaker 2: Joe Biden talked about it. They will never get on

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Speaker 2: board with that.

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Speaker 3: But you know, there are.

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Speaker 2: Things you could do, in my opinion, that would be

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Speaker 2: inferior to Medicare for all, but wouldn't be short of that.

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Speaker 2: But they're just they haven't thought about it. They are

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Speaker 2: so ideologically and partisan driven that they're just hoping everybody

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Speaker 2: forgets about this conversation and moves on to something else.

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Speaker 3: Is basically their plan.

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Speaker 1: Yep, that's right. Let's go on to AI. This man,

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Speaker 1: this latest one. This is some dark, dark stuff. Sam Altman,

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Speaker 1: in a recent interview, was asked about chat shept and

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Speaker 1: whether he uses it to raise his baby. Let's take a.

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Speaker 4: Listen, and do you use chat rept when raising your baby?

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Speaker 4: I do.

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Speaker 5: I mean, I feel kind of bad about it because

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Speaker 5: we have this like genius level at everything intelligence sitting

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Speaker 5: there like waiting to unravel the mysteries of humanity, and

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Speaker 5: I'm like, why does my kid stop dropping his pizza

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Speaker 5: on the floor and laughing? Yeah, you know, And so

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Speaker 5: I feel like I'm not asking a good enough question,

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Speaker 5: but it is. I don't I cannot imagine having gone

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Speaker 5: through that, like figuring out how to raise a newborn

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Speaker 5: without chati. Clearly people did it for a long time,

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Speaker 5: no problem.

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Speaker 4: Yes, but.

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Speaker 1: Yeah, we did it for a long time. We don't

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Speaker 1: need chatgypt. Actually, in fact, I'll tell you a story

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Speaker 1: because I I've done this. You will try to try

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Speaker 1: to ask it some sort of developmental question. Routinely gets

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Speaker 1: things wrong. Ask it to calculate medicinal dosage. The math

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Speaker 1: is totally wrong. This literally happened to me.

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Speaker 2: Actually, definitely do not rely on what sort of medicine,

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Speaker 2: just just amount you should get.

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Speaker 1: Yeah, here's here's my theory. They're good at math, right

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Speaker 1: X weight? All this put it in that wrong. The

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Speaker 1: only reason I know this is I go, this isn't right. Yeah,

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Speaker 1: And so then I went I had to go to

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Speaker 1: the charts or whatever a little bit more research. What

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Speaker 1: if somebody had done that. Imagine if somebody had done that.

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Speaker 1: That's this is my one time just thinking, hey, it's

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Speaker 1: good at math. At the very least we could trust math.

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Speaker 1: Now imagine in trying to use it for developmental purposes

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Speaker 1: or any other this like this is dangerous now beyond

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Speaker 1: even just saying like in my example, I'm just trying

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Speaker 1: to use it for math, but imagine outsourcing like genuine

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Speaker 1: advice and you know, getting tips or something like how

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Speaker 1: to introduce solids? What if chatchypt realies? You can tell

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Speaker 1: I'm drawing from my own life, right, how to introduce

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Speaker 1: There's a lot of different schools of thought. Right, there's

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Speaker 1: a lot of what if it says you should do

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Speaker 1: it this way? What if it's the wrong way? The

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Speaker 1: actually there is no right or wrong way. The point

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Speaker 1: is it should be up to you and you should

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Speaker 1: go and do some research, read some books. There's a

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Speaker 1: baby weading, there's pure I mean, there's all different schools

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Speaker 1: of thought, right, and it's there. There's no way it

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Speaker 1: could ever capture the reality of what that is like.

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Speaker 1: And it's truly, in my opinion, it's so dystopian because

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Speaker 1: it is outsourcing like the individuality and I mean not

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Speaker 1: even just like the cool part about getting to be

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Speaker 1: a part making these decisions, but also the human part

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Speaker 1: of it of like asking your mother or your friends

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Speaker 1: or your family and trying to crowdsource that altogether and

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Speaker 1: then your doctor anybody to make an informed decision with

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Speaker 1: yourself instead of outsourcing you know, your decision making. And

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Speaker 1: that's what's happening at scale. And that's why I really worry.

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Speaker 1: If you're in college and you're one hundred percent use

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Speaker 1: to outsourcing everything to chat CHEAPT, then in ten years

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Speaker 1: from now, when you're having a baby, it will be

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Speaker 1: the most natural thing in the world to outsource that

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Speaker 1: to an AI. Yeah, and that is like again, I mean,

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Speaker 1: these are a moral If you ask chat ept whether

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Speaker 1: you should an abort and a down syndrome baby based

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Speaker 1: on costs, they'll probably tell you yes. Right, think about that.

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Speaker 1: There's no there, there's nobody who there's nothing programmed within

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Speaker 1: these things to really give you any morals. They you know,

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Speaker 1: every especially if you set the parameters. And then with psychosis,

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Speaker 1: the most recent chat GPT is you can decide its

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Speaker 1: personality on how right. So now it's I want a

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Speaker 1: persnickety in a laughable chat ChiPT, I want the default

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Speaker 1: chat GPT. Well, which one's gonna give you different advice

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Speaker 1: on a kit? I know I sound pedantic, but like

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Speaker 1: this stuff is real and when the CEO is out

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Speaker 1: here recommending its usage, then that should come with liability.

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Speaker 1: What do doctors and all of them tell you never believe

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Speaker 1: everything that you read on the internet and here, this

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Speaker 1: guy is like, oh, I use it to raise my

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Speaker 1: own bait. I mean, these are wildly powerful. They want

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Speaker 1: you to, by the way, so that they can sell

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Speaker 1: you baby products inside of the chat bill. Well, here's

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Speaker 1: our favorite baby formula based on way you recommend it.

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Speaker 1: Go ahead and buy it here in the chat gpt app.

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Speaker 1: I mean, can you imagine anything more dystopian? Man? And

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Speaker 1: Google already does this to a limited extent, but this

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Speaker 1: is way worse.

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Speaker 2: And yeah, the errors, that's one thing that's like an

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Speaker 2: acute like you know, you could actually that receiving advice.

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Speaker 2: That's genuinely dangerous. But to me, the most more dystopian

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Speaker 2: part is just the assault on our humanity, you know.

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Speaker 2: I mean, parenting is like such a just sort of

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Speaker 2: essential beautiful like the essence of being human, right, the

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Speaker 2: essence of the project of humanity. And when you say

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Speaker 2: something like I can't even imagine raising a newborn without

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Speaker 2: chat GPT, I don't know. I mean this is what

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Speaker 2: I worry about on a sort of like philosophical level

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Speaker 2: with these products in general, how much of our humanity

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Speaker 2: is being like sucked up and colonized, how much of

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Speaker 2: our mind share is being sucked up and colonized by

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Speaker 2: these products, where yeah, instead of going to your like,

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Speaker 2: let me call mom and see what she said, you

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Speaker 2: know when when I was a baby, Like how did

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Speaker 2: this go for her? Instead you're asking this robot and

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Speaker 2: that's you know, it's one small example, but it takes

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Speaker 2: you that much further away from like the human connections

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Speaker 2: that we already are losing thanks again to these like

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Speaker 2: tech oligarchs and their their social media the experiments. The

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Speaker 2: other thing about it that it made me think of

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Speaker 2: is a lot of these guys that developed Sam Altman

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Speaker 2: didn't develop a social media product, but like Mark Zuckerberg

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Speaker 2: or whatever, a lot of these guys, if you ask

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Speaker 2: them about their kids and their social media use, oh

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Speaker 2: don't they are very careful about screen time for their

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Speaker 2: own kids, even as they are developing products that are

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Speaker 2: designed with you know, millions and millions of dollars to

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Speaker 2: make them as addictive as possible for your kids. And

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Speaker 2: so it's interesting to me that with this because they

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Speaker 2: are such almost like religious believers, I mean truly, I

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Speaker 2: think religious is the right word to use around what

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Speaker 2: these guys think about AI and its promise and what

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Speaker 2: it's going to be they think they're inventing God in

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Speaker 2: a machine.

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Speaker 3: That's what they think.

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Speaker 2: They have this religious devotion to it where it has

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Speaker 2: they do incorporate it all the way into their lives,

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Speaker 2: including in you know, something as essential and something so

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Speaker 2: important as raising your baby for you know, a young age.

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Speaker 2: So I thought that was an interesting sort of revelation too,

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Speaker 2: about how he is thinking about his own products.

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Speaker 1: One hundred percent. And I just think, you know, he's like, oh,

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Speaker 1: we have a grand super intelligence and all that. You know,

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Speaker 1: anybody who if you're trying to do this intelligence is

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Speaker 1: not really you know, there's no intelligent way, like I said, necessarily,

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Speaker 1: the only intelligence that you can use is to think

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Speaker 1: about it, be intentional, and then actually look at your

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Speaker 1: specific set of circumstances. Just my opinion. But this also

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Speaker 1: translates now to the Pentagon, where Pete Hegseth has now

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Speaker 1: rolled out Google's Gemini AI into the hands of every

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Speaker 1: American service member and encouraging them to use it. Let's

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Speaker 1: take a listen.

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Speaker 6: The future of American warfare is here, and it's spelled AI.

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Speaker 6: As technologies advanced, so do our adversaries. But here at

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Speaker 6: the War Department, we are not sitting idly by. Under

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Speaker 6: the leadership of President Trump, America will lead the charge

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Speaker 6: on this technology transformation by revolutionizing the way we win.

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Speaker 6: And that's why today we are unleashing Genai dot Mill.

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Speaker 6: This platform puts the world's most powerful frontier AI models,

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Speaker 6: starting with Google Gemini, directly into the hands of every

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Speaker 6: American warrior at the click of a button. AI models

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Speaker 6: on Genai can be utilized to conduct deep research, format documents,

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Speaker 6: and even analyze video or imagery at unprecedented speed. Building

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Speaker 6: on the great work of Undersecretary Emil Michael and his team,

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Speaker 6: we will continue to aggressively feel the world's best technology

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Speaker 6: to make our fighting force more lethal than ever before.

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Speaker 6: And all of it is American made. The possibilities with

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Speaker 6: AI are endless, so you can see from there.

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Speaker 1: I mean, what is mystifying to me is why are

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Speaker 1: they're going so hard at promoting said Jenai. They have

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Speaker 1: posters all over the penicy encouraging individual soldiers and others

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Speaker 1: to use it at their routine task. I mean, I

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Speaker 1: just gave an example about how it chat rept or

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Speaker 1: failed at math room, which by the way, is its

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Speaker 1: most basic function. No, that's what you should rely If

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Speaker 1: you rely on computers for anything, you know, asking it

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Speaker 1: advice or any of that is absolutely stupid, even at

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Speaker 1: the current form. But you should maybe rely on it

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Speaker 1: for math. Now they're relying on it for math and

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Speaker 1: potential errors and reasoning fallacies or logical problems in the

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Speaker 1: most high stakes environment like what about weapons planning, manufacturing ammunition.

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Speaker 1: I can't even get chat reipt, like I said, to

435
00:21:40,680 --> 00:21:44,399
Speaker 1: calculate basic dosage instructions properly. How are they going to

436
00:21:44,480 --> 00:21:48,959
Speaker 1: use multicomplex processes by rolling it into the critical supply

437
00:21:49,080 --> 00:21:52,280
Speaker 1: chain and others to calculate, let's say, how many weapons

438
00:21:52,320 --> 00:21:54,919
Speaker 1: that they need here or there without double checking? And

439
00:21:54,920 --> 00:21:59,320
Speaker 1: then look, maybe that's even a quote good use. Do

440
00:21:59,359 --> 00:22:03,160
Speaker 1: we really have confidence that they have put in you know, guardrails,

441
00:22:03,200 --> 00:22:06,280
Speaker 1: even security, cybersecurity to make sure that nobody is hacking

442
00:22:06,280 --> 00:22:09,840
Speaker 1: into it? And then to what end is this, like

443
00:22:10,000 --> 00:22:14,560
Speaker 1: AI military thing even happening. I mean, look, the terminator

444
00:22:14,600 --> 00:22:16,760
Speaker 1: and all of that is science fiction, but there is

445
00:22:16,800 --> 00:22:19,919
Speaker 1: an element to this when we're becoming obsessed with autonomous

446
00:22:20,000 --> 00:22:23,720
Speaker 1: drones and AI and putting it together like the vast

447
00:22:23,760 --> 00:22:26,920
Speaker 1: amount of power considering how I mean, look, Israel used

448
00:22:26,920 --> 00:22:28,960
Speaker 1: it all the time. We covered a couple of segments

449
00:22:28,960 --> 00:22:32,040
Speaker 1: on this, those like autonomous drones and AI training ground, Like,

450
00:22:32,320 --> 00:22:34,639
Speaker 1: how are we going, you know, the future of warfare.

451
00:22:34,680 --> 00:22:37,920
Speaker 1: They're obsessed with marrying these two types of things. Will

452
00:22:37,920 --> 00:22:40,200
Speaker 1: that be used against us? Is that part of the plan?

453
00:22:40,560 --> 00:22:42,159
Speaker 1: How do you what you know, what do you have

454
00:22:42,280 --> 00:22:44,600
Speaker 1: programmed inside of that to make sure it's never going

455
00:22:44,680 --> 00:22:46,920
Speaker 1: to happen. I just don't think any of these questions

456
00:22:46,960 --> 00:22:49,080
Speaker 1: were asked at all at all. They're just like, yeah,

457
00:22:49,160 --> 00:22:51,280
Speaker 1: everybody use it, and let's just see what happens.

458
00:22:51,400 --> 00:22:54,639
Speaker 3: Completely not it's dangerous constantly.

459
00:22:54,160 --> 00:22:56,919
Speaker 2: And that's this whole administration's approach to AI is like,

460
00:22:57,040 --> 00:22:59,520
Speaker 2: let's just push as hard as we possibly can. Let's

461
00:22:59,520 --> 00:23:01,800
Speaker 2: just roll it off out off to the races. What

462
00:23:01,840 --> 00:23:04,159
Speaker 2: are the consequences? We don't really know, but we're like

463
00:23:04,200 --> 00:23:05,719
Speaker 2: hoping and praying it's going to be great and it's

464
00:23:05,720 --> 00:23:06,440
Speaker 2: going to be amazing.

465
00:23:06,960 --> 00:23:08,240
Speaker 3: And then you know, to see.

466
00:23:08,040 --> 00:23:11,560
Speaker 2: That applied specifically at the Department of War, where you

467
00:23:11,640 --> 00:23:15,720
Speaker 2: have all these like ethical and accuracy questions, and again

468
00:23:15,800 --> 00:23:19,360
Speaker 2: the like the more removed the human being and their

469
00:23:19,480 --> 00:23:22,840
Speaker 2: natural revulsion to killing other human beings, the more removed

470
00:23:22,880 --> 00:23:25,399
Speaker 2: they are from that process, you know, the easier it

471
00:23:25,440 --> 00:23:29,119
Speaker 2: becomes to commit atrocities and barbarism. I mean, you can

472
00:23:29,200 --> 00:23:32,080
Speaker 2: even you know, if you think about the the initial

473
00:23:32,119 --> 00:23:33,960
Speaker 2: boat strike right where they had to do the double

474
00:23:34,040 --> 00:23:37,439
Speaker 2: chap strike. Okay, so you have the initial strike, they

475
00:23:37,520 --> 00:23:40,280
Speaker 2: see that there's the two survivors, and then over forty

476
00:23:40,320 --> 00:23:42,600
Speaker 2: minutes they watch them like struggling for their lives, and

477
00:23:42,640 --> 00:23:46,639
Speaker 2: they decide to kill them with the second strike. You know,

478
00:23:46,760 --> 00:23:49,359
Speaker 2: if you had had to be like that guy boarding

479
00:23:49,359 --> 00:23:51,119
Speaker 2: the ship and actually shooting them in the head with

480
00:23:51,160 --> 00:23:52,960
Speaker 2: a pistol or whatever, I don't think they would have

481
00:23:53,000 --> 00:23:55,000
Speaker 2: made the same decision. Like, if you had to be

482
00:23:55,080 --> 00:23:57,880
Speaker 2: that up close and personal with the killing, it would

483
00:23:57,880 --> 00:24:00,159
Speaker 2: have been much clearer to them, like, of course this

484
00:24:00,280 --> 00:24:02,679
Speaker 2: is wrong. These are shipwrecked individuals, Like this is the

485
00:24:02,720 --> 00:24:06,480
Speaker 2: textbook definition of what you don't what is utterly dishonorable

486
00:24:06,520 --> 00:24:09,479
Speaker 2: and atrocious to do in wartime. So the more you

487
00:24:09,480 --> 00:24:13,840
Speaker 2: get removed from that basic humanity, the more dystopian it becomes.

488
00:24:13,840 --> 00:24:14,919
Speaker 3: I mean, there's a reason.

489
00:24:14,640 --> 00:24:18,480
Speaker 2: Why sci fi writers reach for these utterly dystopian scenarios.

490
00:24:18,760 --> 00:24:22,080
Speaker 2: And now we are really at the precipice of them

491
00:24:22,160 --> 00:24:25,960
Speaker 2: being easily realizable, not to mention, you know, we already

492
00:24:26,000 --> 00:24:28,600
Speaker 2: have all these issues with AI and deceptive behavior and

493
00:24:28,640 --> 00:24:30,520
Speaker 2: all these sorts of things that are on account before

494
00:24:30,560 --> 00:24:32,639
Speaker 2: they think they patch it up, but they're not really sure.

495
00:24:32,720 --> 00:24:33,119
Speaker 3: I don't know.

496
00:24:33,200 --> 00:24:35,840
Speaker 2: We've seen we've also gotten a glimpse of what AI

497
00:24:36,040 --> 00:24:38,359
Speaker 2: unleashed on the battlefield looks like in Gaza, which has

498
00:24:38,400 --> 00:24:39,920
Speaker 2: been a test case for a lot of these things,

499
00:24:39,960 --> 00:24:42,399
Speaker 2: where they generated all of these targets, did not do

500
00:24:42,480 --> 00:24:45,000
Speaker 2: any check whether these were you know, legitimate targets, didn't

501
00:24:45,000 --> 00:24:47,960
Speaker 2: really care and just went out and mass murdered people

502
00:24:48,040 --> 00:24:50,000
Speaker 2: or were able to track people down to their homes

503
00:24:50,040 --> 00:24:52,560
Speaker 2: so that they would be able to inflict more casualties

504
00:24:52,600 --> 00:24:57,040
Speaker 2: among low level soldiers and their families. So I think

505
00:24:57,040 --> 00:24:59,720
Speaker 2: we've already got examples of the dystopian direction that this

506
00:24:59,800 --> 00:25:03,760
Speaker 2: is all heading, and Soccer maybe can explain this next one.

507
00:25:03,800 --> 00:25:07,879
Speaker 2: Meta is a pivot from their Their original idea was

508
00:25:07,880 --> 00:25:10,640
Speaker 2: sort of like framed in this very like altruistic way,

509
00:25:10,680 --> 00:25:12,600
Speaker 2: like we're going to create an LL and it's going

510
00:25:12,640 --> 00:25:15,399
Speaker 2: to be completely open source. This is sort of like

511
00:25:15,600 --> 00:25:18,960
Speaker 2: you know, idealistic anarchist type approach.

512
00:25:18,880 --> 00:25:22,439
Speaker 1: To well more so the theory, but it wasn't altruistic.

513
00:25:22,560 --> 00:25:23,400
Speaker 1: They sold it that way.

514
00:25:23,520 --> 00:25:25,520
Speaker 3: Yeah, that's that's what I'm saying. That's how they sold it, right.

515
00:25:25,560 --> 00:25:27,280
Speaker 1: So the reason they did that is because they wanted

516
00:25:27,320 --> 00:25:29,600
Speaker 1: other people to use their model, so that people would

517
00:25:29,600 --> 00:25:31,760
Speaker 1: stack and that they would own the tech a while

518
00:25:31,760 --> 00:25:34,439
Speaker 1: they continued. Now what they've decided to do, let's put

519
00:25:34,440 --> 00:25:37,040
Speaker 1: the next one please on the screen. D three is

520
00:25:37,080 --> 00:25:42,119
Speaker 1: they're explicitly pivoting away from open source, specifically into a

521
00:25:42,160 --> 00:25:45,560
Speaker 1: money making AI model. Zuckerberg quote, months into building the

522
00:25:45,600 --> 00:25:49,239
Speaker 1: priciest teams in technology history, is getting personally involved and

523
00:25:49,280 --> 00:25:51,480
Speaker 1: pivoting the company's focus to an AI model it can

524
00:25:51,520 --> 00:25:54,879
Speaker 1: make money off of. One new model, code named Avocado,

525
00:25:55,840 --> 00:25:58,719
Speaker 1: is expected to debut next spring. Will be launched as

526
00:25:58,760 --> 00:26:01,359
Speaker 1: a closed model, one that will be tightly controlled and

527
00:26:01,359 --> 00:26:04,520
Speaker 1: that Meta will sell access to the move, which aligns

528
00:26:04,520 --> 00:26:06,760
Speaker 1: with rivals Google and Open Ai do with their models,

529
00:26:06,760 --> 00:26:09,560
Speaker 1: would mark the biggest departure from the open source strategy

530
00:26:09,560 --> 00:26:12,840
Speaker 1: that Meta has now touted for years. Dramatically shifted earlier

531
00:26:12,840 --> 00:26:15,640
Speaker 1: this year after the company released Lama for an open

532
00:26:15,680 --> 00:26:19,400
Speaker 1: source model that disappointed Silicon Valley and Zuckerberg. Now, Zuckerberg

533
00:26:19,440 --> 00:26:22,240
Speaker 1: now spends much of his time and energy working closely

534
00:26:22,280 --> 00:26:25,520
Speaker 1: with new hires in a group called TBD Labs, and

535
00:26:25,560 --> 00:26:28,520
Speaker 1: it is using quote several third party models to train

536
00:26:28,640 --> 00:26:32,280
Speaker 1: the process for Avocado, distilling all of its rival models

537
00:26:32,280 --> 00:26:35,040
Speaker 1: down and then training off of that. So what that

538
00:26:35,119 --> 00:26:37,840
Speaker 1: means is that he's basically going in the same direction

539
00:26:38,119 --> 00:26:41,400
Speaker 1: as everybody else, as Google, as open ai, and abandoning

540
00:26:41,480 --> 00:26:43,720
Speaker 1: even the pretense of open source. We just want to

541
00:26:43,800 --> 00:26:46,520
Speaker 1: highlight this because the industry is still moving all in

542
00:26:46,560 --> 00:26:50,800
Speaker 1: a completely profit driven direction and one that abandons any like,

543
00:26:51,160 --> 00:26:54,360
Speaker 1: you know, original claims about how they're going to cure cancer.

544
00:26:54,720 --> 00:26:57,159
Speaker 1: Now it's just all about making money. It's all about

545
00:26:57,280 --> 00:27:00,520
Speaker 1: enterprise making money. That's it, okay, And it comes to

546
00:27:00,600 --> 00:27:02,439
Speaker 1: the cost. Let's put the next one up on the screen.

547
00:27:03,119 --> 00:27:06,320
Speaker 1: It's about data centers. From the lever they show quote

548
00:27:06,400 --> 00:27:10,719
Speaker 1: data center boom risk blackouts. According to a new watchdog report,

549
00:27:10,800 --> 00:27:13,720
Speaker 1: data center construction should be put on hold until grid

550
00:27:13,800 --> 00:27:18,119
Speaker 1: operators can ensure reliability. A regional energy monitor has argued,

551
00:27:18,160 --> 00:27:21,679
Speaker 1: and that fits with what I had showed everybody in

552
00:27:21,720 --> 00:27:24,560
Speaker 1: our last show about the expected amount of data center

553
00:27:24,640 --> 00:27:27,480
Speaker 1: power compared to the amount of market are the amount

554
00:27:27,480 --> 00:27:30,000
Speaker 1: of power coming online by twenty thirty.

555
00:27:29,880 --> 00:27:32,800
Speaker 2: Well, and they focus in particular in what they describe

556
00:27:32,840 --> 00:27:35,600
Speaker 2: they say. The independent watchdog for the country's largest power

557
00:27:35,600 --> 00:27:39,560
Speaker 2: grid operator has issued a regulatory grenade asking the Federal

558
00:27:39,640 --> 00:27:43,920
Speaker 2: Garment to intervene amid PJM interconnections plans to power data

559
00:27:43,960 --> 00:27:46,560
Speaker 2: centers it knows it does not have the capacity for,

560
00:27:46,680 --> 00:27:49,480
Speaker 2: despite acknowledging the heightened risk of blackouts. This comes as

561
00:27:49,560 --> 00:27:52,640
Speaker 2: PGM as seen windfall profits from shouldering energy draining data

562
00:27:52,640 --> 00:27:55,239
Speaker 2: centers at a multi billion dollar cost to consumers. So

563
00:27:55,520 --> 00:27:57,840
Speaker 2: if you guys recall the charts that we put up

564
00:27:57,880 --> 00:28:02,160
Speaker 2: before that showed the different grid regions across the country,

565
00:28:02,480 --> 00:28:04,639
Speaker 2: the one that was under the most strain is actually

566
00:28:04,680 --> 00:28:07,040
Speaker 2: the one right here, and it's called PJMS.

567
00:28:07,080 --> 00:28:09,800
Speaker 3: What that regional grid is called.

568
00:28:09,600 --> 00:28:12,800
Speaker 2: It services some roughly I think thirteen different states or

569
00:28:12,840 --> 00:28:15,320
Speaker 2: parts of thirteen different states. It's like Virginia and the

570
00:28:15,359 --> 00:28:18,879
Speaker 2: mid Atlantic out to the Midwest, I think Michigan, parts

571
00:28:18,880 --> 00:28:20,000
Speaker 2: of Kentucky, et cetera.

572
00:28:20,119 --> 00:28:21,280
Speaker 3: So that's kind of the region.

573
00:28:21,720 --> 00:28:24,560
Speaker 2: And no surprise that this is the area that is

574
00:28:24,640 --> 00:28:27,520
Speaker 2: under the most strained right now, because Virginia is the

575
00:28:27,800 --> 00:28:31,320
Speaker 2: epicenter of this mass data center boom. I mean, if

576
00:28:31,320 --> 00:28:33,560
Speaker 2: you drive out towards Dulles, if you even go out

577
00:28:33,600 --> 00:28:36,400
Speaker 2: towards where I live in the rural areas, these things

578
00:28:36,400 --> 00:28:40,120
Speaker 2: are popping up everywhere, and they have not built out

579
00:28:40,200 --> 00:28:42,840
Speaker 2: the capacity to ensure that number one, I mean the

580
00:28:42,840 --> 00:28:45,240
Speaker 2: prices are going out. Number two, that there are not

581
00:28:45,360 --> 00:28:49,520
Speaker 2: going to be active blackouts, like we are very close

582
00:28:49,960 --> 00:28:53,000
Speaker 2: to that sort of a possibility. And of course, like

583
00:28:53,120 --> 00:28:55,000
Speaker 2: if there's going to be a choice about whether it's

584
00:28:55,120 --> 00:28:57,480
Speaker 2: ordinary consumers who are getting the power, whether it's these

585
00:28:57,520 --> 00:29:01,120
Speaker 2: giant data centers with their corporate titans of funneled millions

586
00:29:01,160 --> 00:29:04,920
Speaker 2: of dollars into the local politicians campaign coffers, guess who's

587
00:29:04,960 --> 00:29:07,640
Speaker 2: going to be chosen there? So we are you know,

588
00:29:08,000 --> 00:29:11,800
Speaker 2: this warning is really quite dire and extraordinary, given that

589
00:29:11,840 --> 00:29:15,120
Speaker 2: we are early in this data center buildout and already

590
00:29:15,400 --> 00:29:17,920
Speaker 2: we are straining the grid capacity that we have existing.

591
00:29:17,920 --> 00:29:21,320
Speaker 2: And of course there's nothing anything remotely approaching like a

592
00:29:21,320 --> 00:29:23,440
Speaker 2: buildout that would be sufficient to deal with all of

593
00:29:23,480 --> 00:29:24,240
Speaker 2: this exactly.

594
00:29:24,920 --> 00:29:26,760
Speaker 1: And all right, yeah, I mean you just put it

595
00:29:26,800 --> 00:29:28,840
Speaker 1: all together. Oh final thing, we had to put this

596
00:29:28,880 --> 00:29:30,120
Speaker 1: one in Defaul, Oh yeah, that's up on.

597
00:29:30,120 --> 00:29:31,080
Speaker 3: This is an interesting one.

598
00:29:31,120 --> 00:29:33,680
Speaker 1: So this is new for The Washington Post, their new

599
00:29:33,720 --> 00:29:39,280
Speaker 1: consumer audio offering, your personalized podcast AI podcast, only available

600
00:29:39,280 --> 00:29:41,880
Speaker 1: in the podcast app app. Users will be able to

601
00:29:41,960 --> 00:29:46,160
Speaker 1: shape their own briefing, select their topics, select their links,

602
00:29:46,440 --> 00:29:49,920
Speaker 1: pick their hosts, and then soon even ask questions using

603
00:29:49,960 --> 00:29:53,240
Speaker 1: our Ask the Post AI technology. So don't need the

604
00:29:53,440 --> 00:29:55,920
Speaker 1: don't need anybody doing it, don't need anybody doing the research.

605
00:29:56,160 --> 00:29:58,400
Speaker 1: It'll just AI all of it for you, and soon

606
00:29:58,560 --> 00:30:01,240
Speaker 1: you won't even have to listen to a show like ours.

607
00:30:01,240 --> 00:30:03,760
Speaker 1: You can just build your own. So, I mean, personally

608
00:30:03,840 --> 00:30:06,360
Speaker 1: kind of hoping it fails right out of selfish reasons.

609
00:30:06,360 --> 00:30:08,640
Speaker 1: I think it misunderstands why people listen to a podcast

610
00:30:08,880 --> 00:30:10,560
Speaker 1: in the first place. I don't think people want to

611
00:30:10,600 --> 00:30:12,040
Speaker 1: do a lot of their own work. That's kind of

612
00:30:12,040 --> 00:30:14,960
Speaker 1: what you pay editorial for, right, is to curate what

613
00:30:15,480 --> 00:30:18,160
Speaker 1: other people. That's kind of our offering, if you will.

614
00:30:18,200 --> 00:30:20,160
Speaker 1: But who knows, you know, maybe I'm wrong, I don't know,

615
00:30:20,760 --> 00:30:24,040
Speaker 1: but that's look, you know, I don't want it's not

616
00:30:24,080 --> 00:30:26,920
Speaker 1: just about us, because it's about humanity itself. Yeah, you know,

617
00:30:27,080 --> 00:30:29,960
Speaker 1: without without any of that, Are you really getting what

618
00:30:30,000 --> 00:30:31,280
Speaker 1: you need you know from the news.

619
00:30:31,400 --> 00:30:35,320
Speaker 2: Yeah, that's my question is like I'm hoping that people

620
00:30:35,400 --> 00:30:38,280
Speaker 2: are biased towards people. I think, so, you know what

621
00:30:38,360 --> 00:30:40,600
Speaker 2: I mean, So much of and this is like up

622
00:30:40,640 --> 00:30:42,760
Speaker 2: and you know, it's like a double edged sword, but

623
00:30:42,840 --> 00:30:46,280
Speaker 2: so much of the like podcast, YouTube space is like.

624
00:30:46,240 --> 00:30:47,720
Speaker 3: These parasocial relationships.

625
00:30:47,800 --> 00:30:51,000
Speaker 2: We're not just about the specific stories and content that

626
00:30:51,040 --> 00:30:53,440
Speaker 2: are chosen, but it's also about like the way I

627
00:30:53,480 --> 00:30:55,680
Speaker 2: feel about this person, whether I trust them, whether I

628
00:30:55,800 --> 00:30:58,200
Speaker 2: like them, whether I find them like entertaining to spend

629
00:30:58,200 --> 00:31:01,720
Speaker 2: some of my time with. And so I am still

630
00:31:01,760 --> 00:31:05,719
Speaker 2: hopeful that there is a bias, that people have a

631
00:31:05,800 --> 00:31:09,440
Speaker 2: bias towards other people, that they want other people to

632
00:31:09,480 --> 00:31:12,200
Speaker 2: be behind the art that they consume. They want to

633
00:31:12,280 --> 00:31:16,239
Speaker 2: feel like there is humanity embedded in the experiences that

634
00:31:16,280 --> 00:31:19,800
Speaker 2: they're having and the content that they are consuming. But

635
00:31:20,000 --> 00:31:21,320
Speaker 2: I don't know if that's true or not. I guess

636
00:31:21,320 --> 00:31:23,320
Speaker 2: we're going to find out.

637
00:31:24,600 --> 00:31:26,960
Speaker 1: All right, let's get to Epstein some great new reporting

638
00:31:27,040 --> 00:31:28,920
Speaker 1: over from drop site. Let's ahead and put this up

639
00:31:28,960 --> 00:31:32,960
Speaker 1: here on the screen. This is one of the longtime

640
00:31:33,600 --> 00:31:37,640
Speaker 1: major questions of Jeffrey Epstein is how did he get

641
00:31:37,840 --> 00:31:40,920
Speaker 1: all of this money? All roads seem to trace back

642
00:31:41,080 --> 00:31:45,240
Speaker 1: to les Wexner, the victorious, secret, seclusive billionaire who is

643
00:31:45,320 --> 00:31:49,280
Speaker 1: very pro Israel. Now we have learned from drop site

644
00:31:49,320 --> 00:31:51,040
Speaker 1: and from many of the emails which I was able

645
00:31:51,040 --> 00:31:54,400
Speaker 1: to read, also from myself, the primary source documents is

646
00:31:54,560 --> 00:31:59,520
Speaker 1: specifically about how he ran Leslie Wexner's pro Israel philanthropy

647
00:31:59,600 --> 00:32:04,280
Speaker 1: machine much more than less Wesner and the philanthropy later

648
00:32:04,320 --> 00:32:06,720
Speaker 1: on claims. So let me explain a little bit of that. Basically,

649
00:32:06,720 --> 00:32:10,720
Speaker 1: what they're able to report is that after his death,

650
00:32:11,160 --> 00:32:14,320
Speaker 1: and what we learned is that the Wexner Foundation, which

651
00:32:14,360 --> 00:32:18,000
Speaker 1: is a longtime supporter of Zionist politics and including paying

652
00:32:18,080 --> 00:32:21,240
Speaker 1: aud Barack millions of dollars the former Prime Minister of

653
00:32:21,280 --> 00:32:24,200
Speaker 1: Israel to author two reports, one of which he never

654
00:32:24,240 --> 00:32:29,760
Speaker 1: even finished, was directed largely by Epstein in such a

655
00:32:29,760 --> 00:32:33,520
Speaker 1: way that they were asking Epstein directly or Wexner in

656
00:32:33,560 --> 00:32:37,040
Speaker 1: the foundation. We're saying directly, quote, ask Jeffrey about how

657
00:32:37,080 --> 00:32:41,200
Speaker 1: to spend the money. Remember, there's all these questions from

658
00:32:41,600 --> 00:32:45,680
Speaker 1: Epstein himself and his relationship with Wexner. Wester signed power

659
00:32:45,680 --> 00:32:48,360
Speaker 1: of attorney over him to manage his money. He gifted

660
00:32:48,640 --> 00:32:52,120
Speaker 1: the famous mansion to him. Why would somebody who is

661
00:32:52,280 --> 00:32:56,560
Speaker 1: ultra wealthy give such power over their own estate and

662
00:32:56,640 --> 00:33:00,280
Speaker 1: billions of dollars over to their acquaintance. This is one

663
00:33:00,320 --> 00:33:03,000
Speaker 1: of the ultimate mysteries. You can have your own theories,

664
00:33:03,040 --> 00:33:06,200
Speaker 1: I certainly do, but nothing has ever really been confirmed

665
00:33:06,240 --> 00:33:08,680
Speaker 1: on the story. I think the central part of it, though,

666
00:33:08,960 --> 00:33:12,600
Speaker 1: is that the emails reveal how central he was to

667
00:33:12,760 --> 00:33:17,120
Speaker 1: dispersing money from Leslie Wesner's charity many on behalf of

668
00:33:17,200 --> 00:33:19,960
Speaker 1: Zionis and the pro Israel causes, and to the point

669
00:33:20,000 --> 00:33:24,240
Speaker 1: where you can actually see that Wexner claims later on

670
00:33:24,680 --> 00:33:27,960
Speaker 1: about how what les Wexner had claimed he was just

671
00:33:28,000 --> 00:33:30,120
Speaker 1: a whiz with money, that he had nothing on me,

672
00:33:30,400 --> 00:33:32,320
Speaker 1: and that it was a very normal relationship and he

673
00:33:32,360 --> 00:33:36,080
Speaker 1: severed his ties. And he also claimed that his control

674
00:33:36,120 --> 00:33:39,080
Speaker 1: over the foundation wasn't nearly as total as what we

675
00:33:39,160 --> 00:33:41,080
Speaker 1: now know. And so I encourage everybody to go and

676
00:33:41,120 --> 00:33:44,160
Speaker 1: read this report because it shows you very very clearly

677
00:33:44,400 --> 00:33:47,360
Speaker 1: how much control he actually had over the foundation, dispersing

678
00:33:47,360 --> 00:33:51,240
Speaker 1: these millions to Israeli Prime Minister, and the level of

679
00:33:51,240 --> 00:33:54,640
Speaker 1: trust that Wexner still had with Epstein for many, many

680
00:33:54,720 --> 00:33:57,160
Speaker 1: years after the relationship started in the nineteen nineties.

681
00:33:57,320 --> 00:34:00,080
Speaker 2: Yeah, I mean what you gather from these emails is

682
00:34:00,120 --> 00:34:03,880
Speaker 2: that Wexner truly did just hand over his entire financial life.

683
00:34:03,920 --> 00:34:06,640
Speaker 2: It's greazy to Epstein and both in terms of his

684
00:34:06,880 --> 00:34:09,799
Speaker 2: personal money. How much was going to go to the

685
00:34:09,880 --> 00:34:12,719
Speaker 2: foundation from his personal money, and then how much what

686
00:34:12,920 --> 00:34:15,440
Speaker 2: was going to be dispersed and to whom? And so

687
00:34:15,640 --> 00:34:19,040
Speaker 2: it's everything from you know, the investments, to you know,

688
00:34:19,120 --> 00:34:23,320
Speaker 2: the various pro Israel charities that were going to receive

689
00:34:23,600 --> 00:34:28,040
Speaker 2: significant donations. This foundation was one of the largest and

690
00:34:28,360 --> 00:34:32,880
Speaker 2: most prolific donors to Zionist causes in the entire United States.

691
00:34:33,080 --> 00:34:36,279
Speaker 2: So they were heavyweights in this space. And it was

692
00:34:36,320 --> 00:34:38,520
Speaker 2: all Jeffrey, I should say all, but it was largely,

693
00:34:38,640 --> 00:34:41,840
Speaker 2: it appears from these emails, largely Jeffrey Epstein, who was

694
00:34:41,880 --> 00:34:44,799
Speaker 2: directing that funding and packing the charities to which that

695
00:34:45,160 --> 00:34:46,000
Speaker 2: to where that.

696
00:34:45,920 --> 00:34:46,680
Speaker 3: Money was going to go.

697
00:34:47,160 --> 00:34:51,160
Speaker 2: This is completely contrary to how, of course Wexner portrayed

698
00:34:51,160 --> 00:34:55,560
Speaker 2: their relationship and completely contrary to how the Foundation portrayed

699
00:34:55,600 --> 00:34:58,000
Speaker 2: their relationship, where they said, oh, this guy, we barely

700
00:34:58,000 --> 00:35:00,400
Speaker 2: had anything to do with him, and you know, after conviction,

701
00:35:00,560 --> 00:35:02,640
Speaker 2: he was out, we were done. We had absolutely no

702
00:35:02,920 --> 00:35:06,160
Speaker 2: you know, absolutely no influence from him whatsoever. That was

703
00:35:06,200 --> 00:35:10,560
Speaker 2: all just dramatically untrue based on these emails. So this

704
00:35:10,640 --> 00:35:13,759
Speaker 2: is one incredibly important piece of the puzzle because the

705
00:35:13,840 --> 00:35:17,200
Speaker 2: amount of money that Epstein was getting just from this

706
00:35:17,440 --> 00:35:23,399
Speaker 2: one relationship was incredibly sizable and funded a significant part

707
00:35:23,719 --> 00:35:27,840
Speaker 2: of his lifestyle. But we also got this very important

708
00:35:29,200 --> 00:35:32,360
Speaker 2: reporting again from Bloomberg, which we've been talking about Jason Leepold.

709
00:35:32,400 --> 00:35:34,680
Speaker 2: He's been doing great work with these emails as well

710
00:35:35,160 --> 00:35:38,960
Speaker 2: about how his involvement with Wall Street you know, didn't

711
00:35:38,960 --> 00:35:43,120
Speaker 2: really skip a beat after his conviction. You know, they

712
00:35:43,320 --> 00:35:45,319
Speaker 2: took a look at emails of all these you know,

713
00:35:45,360 --> 00:35:48,640
Speaker 2: banker types, hedge fund hedge fund types messaging him own,

714
00:35:48,719 --> 00:35:51,120
Speaker 2: sorry about your conviction, You'll be fine, you know, Hey,

715
00:35:51,120 --> 00:35:52,920
Speaker 2: how about you want to invest in this? You want

716
00:35:52,960 --> 00:35:55,319
Speaker 2: to invest in that? And what you get from this

717
00:35:55,360 --> 00:35:57,600
Speaker 2: one Sager is really a sense that you know, as

718
00:35:57,600 --> 00:35:59,640
Speaker 2: long as he had money, they didn't really give a shit.

719
00:35:59,800 --> 00:36:01,040
Speaker 3: Yeah, as long as he had money to.

720
00:36:01,000 --> 00:36:03,400
Speaker 2: Invest that they could that they could play with and

721
00:36:03,440 --> 00:36:06,200
Speaker 2: be involved in making money, then they were happy to

722
00:36:06,600 --> 00:36:09,920
Speaker 2: have his his involvement and the other thing that really

723
00:36:09,920 --> 00:36:12,719
Speaker 2: comes out of this piece is just what a son

724
00:36:12,800 --> 00:36:15,319
Speaker 2: of a bitch that he was, like, yes, totally like

725
00:36:15,680 --> 00:36:20,480
Speaker 2: in these different transactions, playing both sides, lying to both sides,

726
00:36:20,640 --> 00:36:22,880
Speaker 2: pretending like, I'm going to vote on your side in

727
00:36:22,880 --> 00:36:25,520
Speaker 2: this particular dispute with the other one, pretending he's going

728
00:36:25,560 --> 00:36:27,839
Speaker 2: to vote on that side. Then he picks his side.

729
00:36:27,880 --> 00:36:31,720
Speaker 2: Then afterwards he sues everyone involved. I mean just absolutely,

730
00:36:31,960 --> 00:36:35,279
Speaker 2: like ruthlessly nefarious, but in a way that obviously you know,

731
00:36:35,480 --> 00:36:37,840
Speaker 2: worked for him in terms of netting him lots of cash.

732
00:36:37,880 --> 00:36:40,160
Speaker 1: Absolutely, you know, I'm glad that so we had access

733
00:36:40,160 --> 00:36:42,480
Speaker 1: to those emails, but Bloomberg was the better, you know,

734
00:36:43,040 --> 00:36:45,000
Speaker 1: outlet to do it because they could get to all

735
00:36:45,040 --> 00:36:47,359
Speaker 1: of the Wall Street connections. Yeah, and you know it's

736
00:36:47,360 --> 00:36:49,719
Speaker 1: hard to decipher. I'm not a financier, Like, I don't

737
00:36:49,719 --> 00:36:52,200
Speaker 1: fully understand all of it, but they just show you

738
00:36:52,560 --> 00:36:55,759
Speaker 1: and piece together the network of how it existed at

739
00:36:55,800 --> 00:36:58,799
Speaker 1: that time. How even as he's under investigation, they don't care.

740
00:36:59,000 --> 00:37:02,000
Speaker 1: They're still willing to do business with you. And remember

741
00:37:02,160 --> 00:37:05,480
Speaker 1: all of this continued even after he pled guilty and

742
00:37:05,560 --> 00:37:07,840
Speaker 1: was a registered sex offender. They continued to take his

743
00:37:07,840 --> 00:37:10,440
Speaker 1: businesses hundreds of millions. And you have very very high

744
00:37:10,520 --> 00:37:13,440
Speaker 1: level Wall Street financiers who are all embroiled in this.

745
00:37:13,640 --> 00:37:15,680
Speaker 1: You have Leon Black, you have all of these others

746
00:37:15,800 --> 00:37:18,640
Speaker 1: paying him personally one hundred, one hundred and fifty million.

747
00:37:19,320 --> 00:37:22,440
Speaker 1: He got a billion dollars in suspicious activity reports that

748
00:37:22,520 --> 00:37:25,040
Speaker 1: continue to flow through all of the banks and the

749
00:37:25,080 --> 00:37:28,560
Speaker 1: Treasury Department that are filed after his death, over a billion.

750
00:37:28,640 --> 00:37:31,759
Speaker 1: Again wired to whom, wired for what? Wired where? For

751
00:37:31,800 --> 00:37:34,759
Speaker 1: what purpose? There's to which country? You know, I mean,

752
00:37:34,880 --> 00:37:38,440
Speaker 1: all of this points to a vast array of and

753
00:37:38,680 --> 00:37:41,360
Speaker 1: you know, I've discussed with this with Ryan at this

754
00:37:41,480 --> 00:37:44,480
Speaker 1: point to say he was not at the very least

755
00:37:44,480 --> 00:37:47,920
Speaker 1: an international finance here with connections to multiple intelligence agencies.

756
00:37:48,120 --> 00:37:51,200
Speaker 1: It's just not actual, it's not Factual's that was his core.

757
00:37:51,400 --> 00:37:53,799
Speaker 1: His job was to move money around. That's what he

758
00:37:53,840 --> 00:37:56,399
Speaker 1: was the ultimate expert at. This proves it. Yeah, as

759
00:37:56,400 --> 00:37:59,760
Speaker 1: you said, he was also extremely like bitchy in his emails,

760
00:38:00,080 --> 00:38:02,560
Speaker 1: being like I'll threaten you, I'll blackmail you, you know,

761
00:38:02,600 --> 00:38:05,799
Speaker 1: basically kind of hinting at various problems for people if

762
00:38:05,840 --> 00:38:08,440
Speaker 1: they didn't continue to do business with him. That was

763
00:38:08,560 --> 00:38:13,720
Speaker 1: very useful to the Coat duvoir or Mongolia, the Israeli government,

764
00:38:13,800 --> 00:38:16,240
Speaker 1: potentially the US, Russia. I mean, who knows, it was everybody.

765
00:38:16,280 --> 00:38:18,479
Speaker 1: He literally was a hatchet man. And yes he also

766
00:38:18,520 --> 00:38:22,000
Speaker 1: had his peccadillos, I guess, which were enabled to look

767
00:38:22,080 --> 00:38:26,080
Speaker 1: past and part of the entire story. And that's why

768
00:38:26,440 --> 00:38:28,640
Speaker 1: when all of this keeps coming together, you see that

769
00:38:28,760 --> 00:38:31,760
Speaker 1: Leslie Wexner, he didn't, you know, he didn't cut ties

770
00:38:32,200 --> 00:38:34,719
Speaker 1: with Jeffrey Epstein. He kind of knew what was going on. Yeah,

771
00:38:34,760 --> 00:38:37,040
Speaker 1: as the emails that they report what was happening, he

772
00:38:37,080 --> 00:38:38,120
Speaker 1: s could always be careful.

773
00:38:38,160 --> 00:38:40,000
Speaker 2: Oh yeah, let's put you two up on the screen

774
00:38:40,000 --> 00:38:42,160
Speaker 2: because we do have that image because I think this,

775
00:38:42,440 --> 00:38:45,080
Speaker 2: I mean, this tells you everything right here. So this

776
00:38:45,280 --> 00:38:49,520
Speaker 2: is Les Wexner. You start at the bottom, rights to Epstein.

777
00:38:49,920 --> 00:38:52,600
Speaker 2: Abigail told me the result, and we're referring here to

778
00:38:52,640 --> 00:38:55,719
Speaker 2: that quote unquote sweetheart deal. No, definitely sweetheart deal. All

779
00:38:55,760 --> 00:38:58,799
Speaker 2: I can say is I feel sorry you violated your

780
00:38:58,840 --> 00:39:01,960
Speaker 2: own number one rule, will always be careful. And then

781
00:39:02,000 --> 00:39:06,919
Speaker 2: Epstein replies, no excuse. So, I mean, does that tell

782
00:39:06,960 --> 00:39:07,960
Speaker 2: you that he knew.

783
00:39:07,760 --> 00:39:08,520
Speaker 3: What was going on?

784
00:39:08,640 --> 00:39:10,240
Speaker 1: And he you know, you can infer.

785
00:39:10,440 --> 00:39:13,560
Speaker 2: Yeah, you can infer some things, and certainly there was

786
00:39:13,560 --> 00:39:16,319
Speaker 2: no like, this is disgusting. I can't believe. I will

787
00:39:16,360 --> 00:39:18,880
Speaker 2: never be associated with you. No, none of that. Just

788
00:39:18,920 --> 00:39:20,959
Speaker 2: like I'm sorry, I feel bad for you. I guess

789
00:39:21,000 --> 00:39:24,080
Speaker 2: you weren't careful enough, you know, in the things that

790
00:39:24,120 --> 00:39:24,600
Speaker 2: you were doing.

791
00:39:24,800 --> 00:39:27,480
Speaker 1: So exactly right. All right, we got one Dav'd Rojas

792
00:39:27,480 --> 00:39:29,879
Speaker 1: standing by to talk Miami. Let's get to it.

793
00:39:30,120 --> 00:39:32,920
Speaker 2: So has some pretty interesting election results down in Miami

794
00:39:32,920 --> 00:39:36,839
Speaker 2: where Democrats were able to flip the mayoral race, which

795
00:39:36,960 --> 00:39:39,400
Speaker 2: has a lot of interesting potential political dynamics. So I'm

796
00:39:39,440 --> 00:39:41,839
Speaker 2: the perfect person to break all of this down for us.

797
00:39:41,880 --> 00:39:42,000
Speaker 7: One.

798
00:39:42,040 --> 00:39:45,080
Speaker 2: David Rojas is a calumnist for Compact Magazine's got a

799
00:39:45,080 --> 00:39:47,440
Speaker 2: new piece up there about exactly this. You can put

800
00:39:47,480 --> 00:39:49,960
Speaker 2: that up on the screen. The headline is how the

801
00:39:50,040 --> 00:39:53,799
Speaker 2: GOP lost Miami and he is also the author of

802
00:39:53,880 --> 00:39:57,839
Speaker 2: the fantastic substack Social Democracy with Populist Characteristics.

803
00:39:57,840 --> 00:39:59,680
Speaker 3: Exactly our kind of guy. Great to see you.

804
00:39:59,640 --> 00:40:01,920
Speaker 4: On, Thanks for having me back on guys.

805
00:40:02,040 --> 00:40:05,040
Speaker 3: Okay, so, how did the GOP lose Miami?

806
00:40:05,400 --> 00:40:09,040
Speaker 7: Uh? Well, there's, you know, like any election, there's both

807
00:40:09,080 --> 00:40:10,759
Speaker 7: local and national dynamics.

808
00:40:10,800 --> 00:40:12,840
Speaker 4: On the one hand, Oh wow, I was.

809
00:40:12,840 --> 00:40:17,799
Speaker 7: Actually really impressed just to see Miami voters rebuke like

810
00:40:17,840 --> 00:40:22,440
Speaker 7: the city establishment, which is extraordinary, extraordinarily corrupt, reaching new

811
00:40:22,480 --> 00:40:26,440
Speaker 7: heights under the current administration of Francis Suarez primer. For

812
00:40:26,480 --> 00:40:28,120
Speaker 7: those who are familiarly, Actually, I think I think you

813
00:40:28,120 --> 00:40:31,640
Speaker 7: guys covered this back when in twenty twenty one he

814
00:40:31,880 --> 00:40:35,640
Speaker 7: like during the twenty twenty one July protests in Cuba,

815
00:40:35,680 --> 00:40:37,840
Speaker 7: like got on top of a car or like a

816
00:40:37,920 --> 00:40:41,239
Speaker 7: van and called for air strikes on Cuba. God, you know,

817
00:40:41,320 --> 00:40:44,120
Speaker 7: he's this like Olive arc bro tech guy.

818
00:40:45,320 --> 00:40:46,240
Speaker 4: I once saw.

819
00:40:46,120 --> 00:40:49,919
Speaker 7: Him at like an event in Miami and he said, oh,

820
00:40:50,000 --> 00:40:52,720
Speaker 7: even my dad doesn't understand crypto, and he's a genius.

821
00:40:52,760 --> 00:40:56,080
Speaker 4: He went to Harvard so anyway.

822
00:40:56,160 --> 00:40:59,799
Speaker 2: But he really strongly embraced crypto. Yeah, and embraced my

823
00:41:00,400 --> 00:41:02,520
Speaker 2: as this like you know, I mean a Miami. Part

824
00:41:02,560 --> 00:41:04,600
Speaker 2: of why I think this is interesting nationally is because

825
00:41:04,600 --> 00:41:07,920
Speaker 2: Miami has become the symbol of like the Republican realignment

826
00:41:07,960 --> 00:41:10,120
Speaker 2: with Latinos. And also you had all of these like

827
00:41:10,160 --> 00:41:12,959
Speaker 2: Republican elites and tech guys who were setting up shop

828
00:41:13,000 --> 00:41:15,759
Speaker 2: there as well. So so you know, this part of

829
00:41:15,760 --> 00:41:19,600
Speaker 2: what makes this really noteworthy from a national perspective, totally.

830
00:41:19,200 --> 00:41:19,719
Speaker 4: Totally yeah.

831
00:41:19,760 --> 00:41:23,480
Speaker 7: I mean the city had been growing exponentially, but in

832
00:41:23,560 --> 00:41:26,000
Speaker 7: recent years, you know, all the people that have come

833
00:41:26,040 --> 00:41:27,839
Speaker 7: to Florida and especially South Florida, a lot of people

834
00:41:27,880 --> 00:41:30,600
Speaker 7: are leaving that because the cost of living has gotten

835
00:41:30,640 --> 00:41:33,279
Speaker 7: so out of control. I mean, by some metrics, the

836
00:41:33,320 --> 00:41:36,920
Speaker 7: Miami metro area is the most unaffordable in the country. Wow,

837
00:41:37,520 --> 00:41:41,359
Speaker 7: Like average rent has like skyrocketed like eighty percent since

838
00:41:41,400 --> 00:41:42,040
Speaker 7: the pandemic.

839
00:41:42,480 --> 00:41:45,040
Speaker 4: So it's it's it's really grim.

840
00:41:45,520 --> 00:41:50,280
Speaker 7: And yeah, with Suarez like just cartoonish levels of corruption.

841
00:41:50,640 --> 00:41:53,520
Speaker 7: I mean really you can barely even say that he's mayor.

842
00:41:53,560 --> 00:41:56,160
Speaker 7: He's spent most of his time abroad lobbying on behalf

843
00:41:56,200 --> 00:41:59,960
Speaker 7: of Saudi Arabia, and he's been an offense since twenty seventeen.

844
00:42:00,080 --> 00:42:02,920
Speaker 7: So after the Koshogi killing, he's done a lot of

845
00:42:02,960 --> 00:42:05,839
Speaker 7: pr for the kingdom to kind of rehability hit them.

846
00:42:05,840 --> 00:42:10,359
Speaker 7: And actually he's currently under consideration, reportedly as next ambassador

847
00:42:10,400 --> 00:42:11,600
Speaker 7: to Saudi Arabia or.

848
00:42:13,360 --> 00:42:13,720
Speaker 3: Perfect.

849
00:42:13,880 --> 00:42:18,080
Speaker 7: So there's that and then the national aspect, which is that,

850
00:42:18,400 --> 00:42:21,439
Speaker 7: you know, Miami is a city which is like more

851
00:42:21,480 --> 00:42:25,400
Speaker 7: than seventy percent Latina, mostly Cuban American, but you know,

852
00:42:25,440 --> 00:42:29,680
Speaker 7: there's also South Americans and Latinos of Caribbean descent, et cetera,

853
00:42:29,800 --> 00:42:35,919
Speaker 7: Central America, even Mexicans as well. And well, yeah, Latinos are,

854
00:42:36,400 --> 00:42:38,000
Speaker 7: like the rest of the country, are really unhappy about

855
00:42:38,040 --> 00:42:40,359
Speaker 7: the economy. If anything, things have gotten worse under Trump.

856
00:42:40,800 --> 00:42:45,920
Speaker 7: But also Latinos are obviously more disproportionately affected by the

857
00:42:46,000 --> 00:42:49,799
Speaker 7: administration's immigration policies, which a lot of people down here,

858
00:42:49,920 --> 00:42:51,880
Speaker 7: especially it's kind of new to them, the Cuban Americans,

859
00:42:51,960 --> 00:42:54,800
Speaker 7: especially because they kind of got a pass by authorities

860
00:42:54,800 --> 00:42:58,600
Speaker 7: and especially state authorities for decades for lots of reasons.

861
00:42:58,880 --> 00:43:00,719
Speaker 1: Yeah, wet foot drive, food and all that. They had

862
00:43:00,760 --> 00:43:05,280
Speaker 1: all of their special little carve outs there just for themselves.

863
00:43:05,280 --> 00:43:08,120
Speaker 1: Can you break down some of the map results on

864
00:43:08,440 --> 00:43:11,120
Speaker 1: F one. I'm just curious if there was any regional

865
00:43:11,160 --> 00:43:14,279
Speaker 1: stuff that you can point out to us, specifically in

866
00:43:14,360 --> 00:43:19,080
Speaker 1: the mayoral runoff election and where some bigger swings happen

867
00:43:19,160 --> 00:43:22,000
Speaker 1: that we should take note of. Maybe in terms of demographics.

868
00:43:22,640 --> 00:43:25,920
Speaker 7: Yeah, that's a great question. So that western part of

869
00:43:25,960 --> 00:43:29,320
Speaker 7: the city is more Latino, especially Cuban American. That's like

870
00:43:29,360 --> 00:43:32,080
Speaker 7: Little Havana. The northern part of the city is more Haitian,

871
00:43:32,160 --> 00:43:38,520
Speaker 7: so that makes perfect sense. Miami's incredibly segregated, including the county,

872
00:43:38,640 --> 00:43:42,479
Speaker 7: so a lot of these enclaves are disproportionately one kind

873
00:43:42,520 --> 00:43:46,960
Speaker 7: of immigrant community. But even still, yeah, like I said,

874
00:43:47,040 --> 00:43:50,160
Speaker 7: the like seventy percent of the city is Latino. There

875
00:43:50,200 --> 00:43:54,839
Speaker 7: were these huge swings, like twenty point swings of Latinos

876
00:43:54,880 --> 00:43:59,399
Speaker 7: towards the Democrats. And another thing, the cope from Republicans

877
00:43:59,440 --> 00:44:01,440
Speaker 7: is that, oh was really low. It was like twenty

878
00:44:01,440 --> 00:44:03,279
Speaker 7: one percent, which yeah, sure is really low.

879
00:44:03,600 --> 00:44:06,280
Speaker 4: But for a mayoral election, and especially in my mayor

880
00:44:06,480 --> 00:44:08,719
Speaker 4: election in Miami, this is is pretty good.

881
00:44:08,840 --> 00:44:11,400
Speaker 7: Like it's about thirty percent more than like the previous

882
00:44:11,440 --> 00:44:14,719
Speaker 7: three or four elections to give an idea. Yes, Suarez

883
00:44:14,760 --> 00:44:16,920
Speaker 7: who won two times, he got like seventy percent of

884
00:44:16,920 --> 00:44:20,120
Speaker 7: the vote and turn out was like fifteen percent, so

885
00:44:20,280 --> 00:44:25,640
Speaker 7: it's still pretty significant. And again Higgins, yeah, she's the

886
00:44:25,680 --> 00:44:28,320
Speaker 7: first Democrat to win in like at least two decades,

887
00:44:28,360 --> 00:44:30,759
Speaker 7: and the first non Cuban to win. It's funny, you know,

888
00:44:30,840 --> 00:44:34,399
Speaker 7: she is a Democrat, and like the Cubans would call

889
00:44:34,400 --> 00:44:38,160
Speaker 7: her La gringa, you know, talk about the Republicans, but

890
00:44:38,239 --> 00:44:41,040
Speaker 7: talk about you know all the you know, the talking

891
00:44:41,080 --> 00:44:42,960
Speaker 7: points for Republicans, we're losing our country.

892
00:44:42,960 --> 00:44:46,000
Speaker 4: Well, look at that. It's the Democrat that's the same.

893
00:44:46,120 --> 00:44:48,120
Speaker 1: It's a shame to see a white lady be elected

894
00:44:48,160 --> 00:44:49,200
Speaker 1: by Cubans.

895
00:44:48,800 --> 00:44:48,960
Speaker 6: You know.

896
00:44:50,920 --> 00:44:52,680
Speaker 2: So you write and your piece, do you say the

897
00:44:52,719 --> 00:44:55,520
Speaker 2: White House is ongoing regime change efforts in Venezuela can

898
00:44:55,520 --> 00:44:57,400
Speaker 2: be right in part as an attempt to retain the

899
00:44:57,480 --> 00:45:01,319
Speaker 2: loyalty of these disaffected supporters. Cuclon to say it's likely

900
00:45:01,360 --> 00:45:03,320
Speaker 2: to work to an extent. Republicans should be able to

901
00:45:03,360 --> 00:45:06,040
Speaker 2: keep a critical mass of Florida Latinos and especially Cuban

902
00:45:06,080 --> 00:45:09,640
Speaker 2: Americans in the fold in the twenty twenty six midterms.

903
00:45:10,000 --> 00:45:11,920
Speaker 2: Just expand on that, especially since we just had the

904
00:45:11,960 --> 00:45:14,200
Speaker 2: news about this, which we covered earlier in the show.

905
00:45:14,200 --> 00:45:16,480
Speaker 2: But I'm interested in your thoughts on as well, that

906
00:45:16,560 --> 00:45:18,360
Speaker 2: we seize this massive oil tanker.

907
00:45:19,440 --> 00:45:21,719
Speaker 7: Yeah, I don't think that's a coincidence. I mean, it

908
00:45:21,760 --> 00:45:26,640
Speaker 7: came the day after the election, So just for contexts,

909
00:45:26,800 --> 00:45:30,160
Speaker 7: Venezuela for decades, since Chaves came into power in nineteen

910
00:45:30,200 --> 00:45:34,960
Speaker 7: ninety nine, has sent billions in oil to Cuba. And

911
00:45:35,080 --> 00:45:38,720
Speaker 7: that's that's really largely how the regime has stayed afloat

912
00:45:38,719 --> 00:45:40,560
Speaker 7: for a long time, and there's still sending a lot

913
00:45:40,560 --> 00:45:41,560
Speaker 7: of oil even though.

914
00:45:41,440 --> 00:45:44,160
Speaker 4: Oil production in Venezuela has cratered.

915
00:45:44,200 --> 00:45:48,759
Speaker 7: So that's a big talking point down here among the

916
00:45:48,800 --> 00:45:49,560
Speaker 7: Cuban Americans.

917
00:45:49,600 --> 00:45:52,040
Speaker 4: They'll say the oil that they're sending.

918
00:45:52,320 --> 00:45:54,640
Speaker 7: And also Mexico in recent years has been sending them

919
00:45:55,239 --> 00:45:57,839
Speaker 7: some oil, so they've wanted to crack down on that.

920
00:45:58,920 --> 00:46:03,760
Speaker 7: So I yeah, I mean more broadly, like the strikes

921
00:46:03,760 --> 00:46:07,120
Speaker 7: in the Caribbean and the regime change efforts in Venezuela.

922
00:46:07,760 --> 00:46:10,960
Speaker 7: Like people down here extremely we talked about this for before,

923
00:46:11,080 --> 00:46:17,120
Speaker 7: Neo Conservative especially, Yeah, the Cuban Americans, Nicaraguans and Venezuela

924
00:46:17,239 --> 00:46:20,080
Speaker 7: Cuban Americans yet voted by like around seventy percent for

925
00:46:20,160 --> 00:46:23,200
Speaker 7: Trump and Venezuelan is actually was surprised, is around like

926
00:46:23,239 --> 00:46:24,240
Speaker 7: fifty something percent.

927
00:46:24,280 --> 00:46:26,240
Speaker 4: Democrats did better than I expected.

928
00:46:26,280 --> 00:46:29,600
Speaker 7: Couldn't find anything on Nicaraguans, but so you know, they

929
00:46:29,640 --> 00:46:32,400
Speaker 7: believe in like this global cabal of communism, and on

930
00:46:32,440 --> 00:46:37,840
Speaker 7: a regional level globally, it's like you know, Russia, China, Iran, Venezuela, Cuba.

931
00:46:37,920 --> 00:46:41,839
Speaker 7: Regionally it's the troit cv terror Venezuela, Cuba, Nicaragua.

932
00:46:42,640 --> 00:46:42,839
Speaker 1: Uh.

933
00:46:42,880 --> 00:46:45,120
Speaker 7: And so their view is that like, if one of these,

934
00:46:45,239 --> 00:46:48,359
Speaker 7: especially like the biggest one, Venezuela or Cuba, go down,

935
00:46:48,400 --> 00:46:50,840
Speaker 7: then like all the like kind of reverse Domino theory,

936
00:46:50,840 --> 00:46:54,200
Speaker 7: the rest will go down with them. So in the

937
00:46:54,360 --> 00:46:58,920
Speaker 7: in his first term, a lot of Latino South Flordians

938
00:46:58,920 --> 00:47:02,000
Speaker 7: were really happy with Trump, the guideos stuff in twenty nineteen,

939
00:47:02,840 --> 00:47:06,759
Speaker 7: et cetera. And you talk to Venezuelans and Cubans now

940
00:47:06,800 --> 00:47:09,160
Speaker 7: and they might say, like, yeah, I mean, honestly, this

941
00:47:09,719 --> 00:47:14,319
Speaker 7: deportation stuff and immigration stuff has gone too far. But

942
00:47:14,440 --> 00:47:18,520
Speaker 7: they're really happy with the foreign policy, especially Rubio as secretary.

943
00:47:18,040 --> 00:47:21,839
Speaker 1: Of course, yeah, former senator. They're great. They're great representative.

944
00:47:21,920 --> 00:47:24,680
Speaker 1: And so you know, like you said, there's national characteristics,

945
00:47:24,719 --> 00:47:28,680
Speaker 1: there's local stuff that is all going on. Nobody's expecting

946
00:47:28,680 --> 00:47:32,920
Speaker 1: Florida to become a blue state anytime soon, but in general,

947
00:47:33,160 --> 00:47:34,720
Speaker 1: what should we look for, Like, we've got the Florida

948
00:47:34,760 --> 00:47:38,759
Speaker 1: GOP primary going on right now, fishback Byron Donald's, you've

949
00:47:38,760 --> 00:47:41,640
Speaker 1: still got DeSantis there at the top. Anything that you've

950
00:47:41,640 --> 00:47:43,120
Speaker 1: got your eye on for our audience.

951
00:47:44,200 --> 00:47:47,239
Speaker 7: Yeah, So yeah, I said this in the piece, It's

952
00:47:47,239 --> 00:47:50,759
Speaker 7: hard to know and this think of an idea about

953
00:47:50,760 --> 00:47:53,960
Speaker 7: how contradictory things are down here.

954
00:47:54,120 --> 00:47:55,040
Speaker 4: So on like the one.

955
00:47:55,200 --> 00:47:59,120
Speaker 7: On one level, there was a lot of consternation about

956
00:47:59,440 --> 00:48:04,000
Speaker 7: the build what the attempts to build a Trump's presidential

957
00:48:04,040 --> 00:48:07,120
Speaker 7: library in Miami. And the thing is the land is

958
00:48:07,120 --> 00:48:09,359
Speaker 7: owned by Miami Dade College, so the city there's been

959
00:48:09,400 --> 00:48:13,200
Speaker 7: a lot of shady dealings about them, like trying to

960
00:48:13,200 --> 00:48:15,640
Speaker 7: like give the land away for free. And there's fears

961
00:48:15,680 --> 00:48:17,799
Speaker 7: that it would be one thing if it was just

962
00:48:17,880 --> 00:48:20,399
Speaker 7: the library, but there's fears that you know, they'll want

963
00:48:20,400 --> 00:48:23,319
Speaker 7: to build like a library slash hotel and casino, which

964
00:48:23,560 --> 00:48:25,760
Speaker 7: would be really grotesque. There's been a lot of local

965
00:48:25,800 --> 00:48:30,279
Speaker 7: opposition towards that. At the same time, and going back

966
00:48:30,320 --> 00:48:33,200
Speaker 7: to what we were just talking about. If you guys

967
00:48:33,239 --> 00:48:36,840
Speaker 7: are familiar with the Miami suburb of Yaleo, which is

968
00:48:36,880 --> 00:48:40,759
Speaker 7: like ninety percent Cuban American, they just like inaugurated a

969
00:48:40,800 --> 00:48:44,880
Speaker 7: twelve foot statue bronze statue of Trump Trump the Fighter

970
00:48:45,360 --> 00:48:45,920
Speaker 7: it's called.

971
00:48:46,000 --> 00:48:49,759
Speaker 4: So it's there's a funny mix of things down here.

972
00:48:49,840 --> 00:48:54,040
Speaker 7: Yeah, there is, on the one hand, like a consternation

973
00:48:54,200 --> 00:48:56,920
Speaker 7: over like the immigration policies. On the other hand, they

974
00:48:56,960 --> 00:49:01,200
Speaker 7: love the regime change stuff. I think given these results

975
00:49:01,239 --> 00:49:05,040
Speaker 7: from Higgins, it's possible. I mean, I think there's no

976
00:49:05,120 --> 00:49:09,799
Speaker 7: chance in hell that Democrats really will be that successful

977
00:49:09,960 --> 00:49:14,240
Speaker 7: in Florida. I mean, like Val Deming's in twenty twenty

978
00:49:14,239 --> 00:49:16,680
Speaker 7: two was about as good a candidate I think as

979
00:49:16,680 --> 00:49:18,960
Speaker 7: Democrats could have a few.

980
00:49:18,840 --> 00:49:20,640
Speaker 4: Years ago, and they still got killed.

981
00:49:20,920 --> 00:49:24,640
Speaker 7: Now the environment has changed a lot, So it could

982
00:49:24,719 --> 00:49:29,000
Speaker 7: be that maybe Democrats managed to make some game like

983
00:49:29,200 --> 00:49:33,439
Speaker 7: moderate gains because of the fact that maybe some neo

984
00:49:33,520 --> 00:49:36,040
Speaker 7: conservative Latinos are like, you know what, we love Trump,

985
00:49:36,200 --> 00:49:39,360
Speaker 7: but yah, maybe we don't want to support Republicans on

986
00:49:39,400 --> 00:49:40,320
Speaker 7: like domestic issues.

987
00:49:40,360 --> 00:49:42,759
Speaker 4: We'll stay home. Hard to know, We'll have to see.

988
00:49:43,040 --> 00:49:45,960
Speaker 2: And how extractable do you think any of this is?

989
00:49:46,000 --> 00:49:49,239
Speaker 2: Because the South Florida Latino community is fairly unique in

990
00:49:49,320 --> 00:49:52,400
Speaker 2: terms of the American context. But you know, look at

991
00:49:52,400 --> 00:49:56,680
Speaker 2: the mayoral results. It's roughly a twenty point shift in

992
00:49:56,680 --> 00:49:59,239
Speaker 2: the direction of Democrats. Kamala did win Miami, but it

993
00:49:59,280 --> 00:50:01,280
Speaker 2: was by like less than a point. It was basically

994
00:50:02,000 --> 00:50:05,160
Speaker 2: very very close. So and then Higgins is able to

995
00:50:05,160 --> 00:50:08,880
Speaker 2: win by what nineteen so huge shift, huge over performance

996
00:50:08,960 --> 00:50:12,000
Speaker 2: for Democrats in Miami. You know, how much can we

997
00:50:12,040 --> 00:50:15,839
Speaker 2: extrapolate that to other Latino communities and other communities in

998
00:50:15,880 --> 00:50:17,080
Speaker 2: general across the country.

999
00:50:17,960 --> 00:50:20,960
Speaker 7: Yeah, I would say that outside of Florida, and especially

1000
00:50:21,000 --> 00:50:23,640
Speaker 7: South Florida, because for instance, there are differences within the state.

1001
00:50:24,040 --> 00:50:28,040
Speaker 7: Orlando is much more disproportionately Puerto Rican which tend to

1002
00:50:28,080 --> 00:50:32,080
Speaker 7: swing more towards Democrats, though though they Trump did perform

1003
00:50:32,120 --> 00:50:34,879
Speaker 7: pretty well with them in twenty twenty four. I would

1004
00:50:34,880 --> 00:50:39,080
Speaker 7: say that elsewhere in the country, Republicans are are gonna

1005
00:50:39,120 --> 00:50:42,200
Speaker 7: get killed with Latinos. I mean, we already saw that

1006
00:50:42,280 --> 00:50:47,040
Speaker 7: in New Jersey and in Virginia, Like I mean, unless

1007
00:50:47,040 --> 00:50:50,160
Speaker 7: the economy improves or they decide to start doing things

1008
00:50:50,239 --> 00:50:54,160
Speaker 7: differently with immigration, and that's extremely unlikely. A lot of

1009
00:50:54,239 --> 00:50:57,759
Speaker 7: Latinos here, but also especially in border states, and I

1010
00:50:58,080 --> 00:51:01,200
Speaker 7: guess you could call Florida border states. Well, like they

1011
00:51:01,239 --> 00:51:05,600
Speaker 7: were not happy with the situation under Biden. You know

1012
00:51:05,640 --> 00:51:07,160
Speaker 7: a lot of people would think, oh, you know, like

1013
00:51:07,200 --> 00:51:09,760
Speaker 7: there's millions of people now and jumping the line, defrauding

1014
00:51:09,760 --> 00:51:10,720
Speaker 7: the asylum system.

1015
00:51:11,160 --> 00:51:12,480
Speaker 4: But that's one thing.

1016
00:51:12,520 --> 00:51:15,399
Speaker 7: Another thing is that like, uh, oh, this has gone

1017
00:51:15,440 --> 00:51:18,320
Speaker 7: too far they're deporting people without process, they're reporting people

1018
00:51:18,400 --> 00:51:21,960
Speaker 7: the maximum security prisons in Africa from countries they don't

1019
00:51:22,000 --> 00:51:25,919
Speaker 7: belong to. There has to be a more sensible middle ground,

1020
00:51:25,960 --> 00:51:28,719
Speaker 7: and I think that doesn't even just apply to Latinos,

1021
00:51:28,760 --> 00:51:30,080
Speaker 7: but just Americans as a whole.

1022
00:51:30,200 --> 00:51:33,240
Speaker 1: Very interesting man. Thank you as always one. We appreciate

1023
00:51:33,239 --> 00:51:35,560
Speaker 1: your analysis. Good to see Bro. Thank you guys so

1024
00:51:35,640 --> 00:51:38,359
Speaker 1: much for watching. We appreciate you. Friday Show tomorrow, See

1025
00:51:38,400 --> 00:51:39,040
Speaker 1: you all then,