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

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Speaker 2: All right, let's go ahead and move on to this

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Speaker 2: whistleblower who spoke to George Sheridan over at Status Kou

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Speaker 2: who used to work at United Healthcare for the CEO

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Speaker 2: of United Healthcare, was shot and killed in New York City,

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Speaker 2: sparking a whole national conversation about the health insurance industry

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Speaker 2: and healthcare and this vigil ante killing, etc. So I

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Speaker 2: want to be careful about characterizing her claims, and you

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Speaker 2: can listen here. She worked there, She did not work

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Speaker 2: directly in the process of denying claims.

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Speaker 4: But she claims that.

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Speaker 2: She knew multiples, she worked closely with MULTIP employees who did,

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Speaker 2: and that they felt like they had come under pressure

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Speaker 2: to deny a certain number of claims. So denying people

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Speaker 2: care that you know, their doctors are saying that they

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Speaker 2: need in order to meet some sort of effectively like

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Speaker 2: monthly quota system. So let's take a listen to what

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Speaker 2: she had to say.

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Speaker 5: I would hear people, you know, on break time or whatever,

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Speaker 5: when we would all kind of come together in this

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Speaker 5: kitchen clutch, and people would be talking about what they

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Speaker 5: were told to do their job. It wasn't my job.

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Speaker 5: So I can't say one hundred percent, yes, we have

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Speaker 5: to deny so many claims per month to meet our shareholders' expectations.

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Speaker 5: But I do know that people were complaining because you know,

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Speaker 5: they had to meet a certain amount of claim denials.

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Speaker 6: So people were telling you that there was a quota

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Speaker 6: for how many denials yep, which is kind of like

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Speaker 6: less deadly. But you know, cops, towards the end of

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Speaker 6: the month, do you have a quota how many tickets

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Speaker 6: you have to give out?

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Speaker 5: Right?

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Speaker 2: So you know this is a claim that needs more reporting,

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Speaker 2: more verification, but I think worth putting out there because

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Speaker 2: this is someone who used to work at the company

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Speaker 2: has no reason we know of, at least to lie

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Speaker 2: about what she heard that was going on there. There

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Speaker 2: was also let's put this up on the screen. There

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Speaker 2: was someone online who said, listen, I've heard the same thing.

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Speaker 2: My mom used to work for a different insurance company.

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Speaker 2: I remember her coming home crying because she got in

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Speaker 2: trouble for not denying enough claims. She got fired for

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Speaker 2: not meeting quota after a couple of months. I would

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Speaker 2: also say Wendell Potter, who was a former health insurance

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Speaker 2: executive who left the industry has become a whistleblower, has

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Speaker 2: also spoken to these type of behavior, and just you

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Speaker 2: know the pressure that exists throughout these companies where if

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Speaker 2: you're going to make as much profit as possible, you

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Speaker 2: are inherently incentivized to deny as many claims as possible,

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Speaker 2: so you're having to pay out less, but of course

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Speaker 2: still taking in the same amount in terms of premiums.

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Speaker 2: We also know Sager based on the data that's been

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Speaker 2: reported out the United Health Group. United Healthcare was particularly

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Speaker 2: bad about the number of claims that they would deny.

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Speaker 2: They had the highest rate among the major insurers. We

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Speaker 2: also know they were being sued for implementing this AI

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Speaker 2: algorithm to deny claims that had, according to the claims

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Speaker 2: in the lawsuit, a ninety percent error rate. So, like

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Speaker 2: I said, this is a claim that needs more reporting

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Speaker 2: and more verification. But it would not be shocking to

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Speaker 2: learn that there was some either official or unofficial quota

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Speaker 2: type system within this insurance No.

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Speaker 1: Yeah, definitely not. And look, I don't know.

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Speaker 3: I mean, in terms of the insurance industry, things are

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Speaker 3: very much up in the air right now, just because

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Speaker 3: of the Trump administration that's coming in in which direction

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Speaker 3: that they decide to take things, And you know, you

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Speaker 3: could see, you could foresee a situation where they try

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Speaker 3: to mess with pre existing conditions.

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Speaker 1: Or the healthcare marketplace.

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Speaker 3: The individual mandate is already gone, so that's not the

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Speaker 3: big change that they might want to do, But there's

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Speaker 3: a lot of things on the edge is that they

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Speaker 3: could change in terms of how the insurance marketplace themselves

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Speaker 3: would work.

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Speaker 1: Literally going through this right now.

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Speaker 3: Shout out to open enrollment, it's a pain in the ass,

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Speaker 3: you know, it's like every American, it's justlievable, unbelievable.

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Speaker 1: I remember I had a.

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Speaker 3: Deductible of like eight thousand dollars. I think it was

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Speaker 3: like eight years ago, and everyone thought it was crazy.

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Speaker 1: And I was like, I'm a young guy.

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Speaker 3: So now when you go in you shop for insurance,

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Speaker 3: you see deductibles that are fifteen grand, like fifteen thousand dollars,

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Speaker 3: and you're still paying hundreds of dollars per month, you know,

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Speaker 3: and add kids onto that. Good luck man, and you

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Speaker 3: can just see how nightmarish it is. And this is

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Speaker 3: what I was talking about with the healthcare system. Most

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Speaker 3: people like their healthcare in terms of employer based. They

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Speaker 3: don't like the health insurance companies, but they like their

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Speaker 3: healthcare like employee provided healthcare.

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Speaker 1: But there are thirty to forty million.

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Speaker 3: People including the self employed like yours, yours truly and

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Speaker 3: you where we have to go out there and shop

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Speaker 3: in the marketplace, and that is where the nightmare situation

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Speaker 3: really starts to happen. Yes, you don't get the benefit

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Speaker 3: of themmployer subsidies or usually get a lower deductible plan

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Speaker 3: in those situations like that too, and that's when people

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Speaker 3: really get screwed.

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Speaker 2: Yeah, absolutely, it's also been you know, the political response

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Speaker 2: here has also been really interesting. We mentioned with Joss

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Speaker 2: Stin I just want to repeat this again that in

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Speaker 2: the CR there actually is one good provision that it's

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Speaker 2: not going to solve our healthcare woes, but it would

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Speaker 2: modestly improve them, which is to rain in these pharmacy

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Speaker 2: benefit managers, which they are these totally, as far as

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Speaker 2: I can tell, totally unnecessary middlemen that control what prescription

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Speaker 2: drugs are covered by health insurance agencies. And so they

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Speaker 2: strike these deals in between the pharmacies and the health

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Speaker 2: insurers and they take a cut of these rebates for themselves. Again,

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Speaker 2: it's just a sort of unnecessary middleman that helps to

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Speaker 2: drive up the cost of your prescription drugs. And so

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Speaker 2: there is a provision in the CR that would rain

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Speaker 2: them in. This is something that has bipartisan support, something

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Speaker 2: Josh Holly and Elizabeth Warren have worked together on in

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Speaker 2: the context of the Senate. So there are some small

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Speaker 2: reforms that have the potential to move forward. The other

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Speaker 2: thing Soccer that's in here that is funny and telling

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Speaker 2: is that the previously members of Congress were they are

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Speaker 2: right now required by law to shop for their health

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Speaker 2: insurance on the same health insurance exchanges though Bombcare exchanges.

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Speaker 4: That you and I have to shout for out insurance on.

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Speaker 1: Right and have no other options.

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Speaker 2: By the way, they stuck a little provision into the

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Speaker 2: CR to get them out of having to do that

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Speaker 2: and be able to participate in whatever the federal government

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Speaker 2: employee system is, et cetera. So you know, they are

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Speaker 2: well aware of some of the problems in the current system,

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Speaker 2: in spite of the fact that they apparently don't want to.

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Speaker 4: Fix that for everyone, just for themselves.

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Speaker 2: They want to better their own situation here, but not

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Speaker 2: necessarily yours or ours.

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Speaker 1: You don't need Congress enough, you know, and everyone.

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Speaker 3: I mean, there's been a lot of discourse right now

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Speaker 3: because they tried to put a pay raise in there,

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Speaker 3: the cost of living pay raise. We're like, well, we

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Speaker 3: got to pay our members of Congress enough, and like theoretically,

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Speaker 3: like maybe I could get there, but you know, in

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Speaker 3: the in the current introim, no, like no, not in

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Speaker 3: a time when you guys are multimos vast majority of

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Speaker 3: you what ninety percent they're making.

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Speaker 4: Most of their money on the insider trading, That's.

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Speaker 3: What I mean, over ninety percent of these people are

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Speaker 3: net worth over one million. You know, a lot of

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Speaker 3: them are extraordinarily wealthy in their own right.

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Speaker 1: It's like, no, we shouldn't pay you more.

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Speaker 4: Well, here's my thing.

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Speaker 3: You're trying to exhibit yourself from Obamacare and all this

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Speaker 3: other stuff.

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Speaker 1: No, no, no, no, and not happen here.

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Speaker 2: Here's my thing is I am totally willing to strike

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Speaker 2: a deal with Congress. You ban stock training trading, and

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Speaker 2: we will give you a giant, a big race, not

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Speaker 2: just this was going to be like a.

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Speaker 4: Few thousand dollars or whatever.

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Speaker 2: We'll give you a big race if you just ban

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Speaker 2: inside of your insider stock drink you're stock trading at all.

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Speaker 2: But I'm not going to hold my breath on that one.

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Speaker 2: The other political response you put this up on the

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Speaker 2: screen Ken Klippenstein, who continues to do great work on

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Speaker 2: reporting out the story. Kavy Hogel, Governor of New York,

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Speaker 2: and incredibly like she's like the definition of you know, middling.

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Speaker 2: This woman she wants to create a crisis hotline for CEOs.

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Speaker 2: So her response isn't hey, you know, clearly there's a

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Speaker 2: problem with the health insurance industry based on the way

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Speaker 2: that people are reacting to this cold blooded murder in

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Speaker 2: the streets of New York City. No, no, no, let

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Speaker 2: me make sure that CEOs have a special hotline exclusively

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Speaker 2: for them to report perceived threats. This is the same

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Speaker 2: woman that Ken report it on, and we brought you

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Speaker 2: this information here as well, who convened one hundred and

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Speaker 2: seventy five CEOs and other corporate representatives as well as

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Speaker 2: homeland security and counter terror officials discuss how to share

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Speaker 2: intelligence with corporate security. So you know her, you see

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Speaker 2: whose priority, who she prioritizes in terms of making sure

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Speaker 2: their lives are smooth and that they feel total comfort

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Speaker 2: and you know, are fully protected by the state. And

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Speaker 2: then you know the other thing that Ken notes here

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Speaker 2: and Ryan and Emily covered the fact that they they

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Speaker 2: charged Luigi Mangioni with terrorism, which was not unexpected. And listen,

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Speaker 2: I mean, I think he did intend to cause terror,

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Speaker 2: so on that. From that perspective, I understand the charge.

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Speaker 2: But I'm also concerned because between that between the arrest

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Speaker 2: of this woman in Florida for just like saying something

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Speaker 2: mean to health insurance representative and Seb Gorka, who's the

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Speaker 2: incoming terrors are is out now comparing people who have been,

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Speaker 2: you know, expressing their upset at the health insurance industry,

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Speaker 2: and some going so far as to sort of lionized

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Speaker 2: Luigi Mangioti as some sort of folk hero, characterizing them

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Speaker 2: as a sort of domestic terrorist akin to the Weather

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Speaker 2: Underground in comments that he made to Newsmax. So, you know,

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Speaker 2: the concern is that this is one more justification for

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Speaker 2: the US government to use to surveil and violate civil liberties,

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Speaker 2: and you know, have a whole new group of quote

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Speaker 2: unquote anti corporate extremists that they are using to quash

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Speaker 2: free speech and censor and surveill, et cetera.

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Speaker 4: So that's something to keep an eye on.

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Speaker 3: Terrorism laws are mostly unnecessary. They're like hate crime statues.

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Speaker 3: It's they're just like socially acceptable ways to smack even

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Speaker 3: more years onto people when we have perfectly good.

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Speaker 1: Laws on the books already.

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Speaker 4: Like they're political and arbitrary.

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Speaker 1: No, that's what I mean. Yeah, And then also you

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Speaker 1: get to enforcement.

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Speaker 3: It's like, well, if you kills somebody because they're white

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Speaker 3: and not because they're black, it's like, what do you

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Speaker 3: still hit them with the hate crime statue? That's literally

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Speaker 3: happened before, by the way, and of course there's always

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Speaker 3: a big debate.

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Speaker 1: It's a prosecutorial discretion, et cetera.

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Speaker 3: That's why I just think it's stupid, because it becomes

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Speaker 3: political and we just use them as as enhancements instead

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Speaker 3: of we have perfectly good laws on the books in

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Speaker 3: every state against murder. Yeah, that's that's all you need,

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Speaker 3: you know, to lock somebody up. If you want to

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Speaker 3: consider all that other stuff, you can take it in

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Speaker 3: for sentencing or for you know, probation or whatever. But yeah,

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Speaker 3: I just think it's crazy because they do this to

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Speaker 3: circumvent going around a normal murder charge gets somebody with

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Speaker 3: even more mandatory time or I don't know the exact

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Speaker 3: in and ounce, but this was all passed usually in

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Speaker 3: the post nine to eleven hysteria era, and we have

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Speaker 3: enough now to know that this stuff is just grossly

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Speaker 3: like unconstitutional, deprives people of civil liberties, and it's just

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Speaker 3: a socially acceptable way of just hitting people with political charges,

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Speaker 3: none of which you even need. And this is not

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Speaker 3: a defense of Louisian man gionn prosecute him for murder, Okay,

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Speaker 3: first degree murder you can easily do that money.

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Speaker 1: Yeah, exactly, we'll probably still spend life in prison. So

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Speaker 1: what's the point.

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Speaker 2: Because you could because here's the thing, you could make

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Speaker 2: the case that basically any murder is terrorism. And you know,

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Speaker 2: they have really stretched these definitions in the past, So

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Speaker 2: you're absolutely correct, it's unnecessary to have these laws on

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Speaker 2: the books whatsoever. And you know, we've seen the way

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Speaker 2: that these panics over quote unquote domestic extremism have been

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Speaker 2: used again the American public and to gravely violate people's

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Speaker 2: civil liberties. We certainly saw it during the War on Terror.

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Speaker 2: We've covered some of those cases here where you know, basically,

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Speaker 2: if you didn't have the FBI, the deep state in

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Speaker 2: there actively radicalizing people leading them up to that, Hey,

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Speaker 2: let's let's do this plot together.

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Speaker 4: Here's the money to be able to affect.

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Speaker 2: Why don't you buy a ticket to you know, go

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Speaker 2: fight fight the jihads here and then then so they

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Speaker 2: radicalize these people, they set them up, and then they

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Speaker 2: swoop in, Oh, look we've disrupted this grand plot. So

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Speaker 2: you saw that during the War on Terror, and then

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Speaker 2: we saw some really crazy stuff like with that Gretchen

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Speaker 2: Whitmer kidnapping quote unquote kidnapping plot, which also you know, similarly,

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Speaker 2: the FEDS were deeply involved in trying to facilitate and

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Speaker 2: radicalize and set up these these individuals.

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Speaker 1: Oh, you know the jan six people got hit with terrorism,

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Speaker 1: remember that.

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Speaker 2: No, No, that's actually one of the things people have

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Speaker 2: been pointing out is none of them have been hit

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Speaker 2: with terrorism, whereas these you know, where's luij Mangione is.

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Speaker 2: So again it just shows you it's very you can

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Speaker 2: just sort of pick and choose who and what you

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Speaker 2: consider to be quote unquote terrorism versus you know, the

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Speaker 2: January six people were charged with other things that were

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Speaker 2: laws on them.

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Speaker 1: It was illegal that shealt with that's legal entry.

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Speaker 3: That's why there was a debate, I just because I

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Speaker 3: thought that they had Yeah, it had been.

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Speaker 2: Some because that's one of the things people are pointing out.

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Speaker 2: It's like, oh, you're going to charge Luigi, but you

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Speaker 2: didn't charge any of the January six ers, And you know, clearly,

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Speaker 2: like whatever you think of them, that was clearly their

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Speaker 2: political aims. That were very clear in their in their

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Speaker 2: goals of what they were doing that day. So in

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Speaker 2: any case, that's a little bit of what's going on there.

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Speaker 2: The last thing that people were taking note of and

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Speaker 2: I don't want to overstate this, but I think it's interesting.

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Speaker 2: So the Economist has a running tracker of what Americans

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Speaker 2: say their top issues are. And in the wake of

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Speaker 2: this debate, after Luigian Mangioni allegedly shot and killed Brian Thompson,

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Speaker 2: though you know, Mac and Ryan say, actually he was

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Speaker 2: here in the studio during those hours, so and actually

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Speaker 2: said that while he was back in the control room,

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Speaker 2: he told him that he's allergic to McDonald's.

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Speaker 4: So there's a lot of pieces here that don't have it.

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Speaker 2: But in any case, in the wake of that murder,

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Speaker 2: healthcare has shot up to be the number two issue,

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Speaker 2: surpassing immigration in the Economists ongoing tracker. Inflation still number one.

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Speaker 2: I don't want to overstate the case because it actually

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Speaker 2: already was a pretty significant issue, so it only spiked

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Speaker 2: up a little bit.

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Speaker 4: We could put the chart up on the screen. You

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Speaker 4: can see for yourself.

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Speaker 2: This is the three month tracking, and this is a

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Speaker 2: little bit difficult to see. The top line is inflation. Okay,

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Speaker 2: inflation and prices. Twenty three point five percent say this

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Speaker 2: is my most important issue. And this is the type

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Speaker 2: of poll where you could only pick one. Some poles

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Speaker 2: are like, you can pick multiple. Okay, So twenty three

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Speaker 2: point five percent of people say that is my number

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Speaker 2: one issue. You now have healthcare jumping up. It's that

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Speaker 2: purple line to be just above immigration and jobs in

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Speaker 2: the economy as the second most important issue. So clearly

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Speaker 2: the conversation around this has, you know, has sparked a

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Speaker 2: lot of interest in American people in some reform, and

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Speaker 2: this is a ball that the Democratic Party has completely dropped.

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Speaker 2: There was almost no talk of significant health care reform

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Speaker 2: in this last presidential election really from either side. But

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Speaker 2: you know, historically it's been Democrats who have been trying

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Speaker 2: to push this issue forward far far, fall from back

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Speaker 2: when Bernie Sanders put Medicare for all on the table

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Speaker 2: and made it a national debate and where this was

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Speaker 2: an ongoing conversation. Kamala Harris back to medicare for all

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Speaker 2: allegedly back in the twenty twenty primary before donors got

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Speaker 2: mad at her and she ran away from that position

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Speaker 2: full steam, never to be embraced again. So in any case,

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Speaker 2: that's where the political debate is there.

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Speaker 1: It is all right.

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Speaker 2: So at the same time, this was an interesting story. Yeah,

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Speaker 2: both of us were interested in this one. So obviously

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Speaker 2: Supreme Court struck down firmative action. We've got new numbers.

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Speaker 2: I'll show you that in a minute. Of minority enrollment,

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Speaker 2: especially into elite law schools like Harvard Law. But some

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Speaker 2: of these ellege universities have been embroiled in a lawsuit

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Speaker 2: alleging that they have basically ripped off their students the

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Speaker 2: tune of hundreds of millions of dollars and that they

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Speaker 2: have violated an agreement that they had with the federal

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Speaker 2: government that allowed them to legally violate antitrust law. And

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Speaker 2: without getting too deep in the weeds of this, effectively,

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Speaker 2: they had to agree that their admissions would be one

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Speaker 2: hundred percent need blind, meaning that they would not factor

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Speaker 2: in a student's wealth income family wealth income at all

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Speaker 2: in their admissions, and in exchange for that agreement, they

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Speaker 2: would then not be subject to federal antitrust regulation. So

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Speaker 2: this has been an ongoing lawsuit as part of this

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Speaker 2: discovery process, and in one of the new filings, we

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Speaker 2: are learning some very interesting details about how not need

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Speaker 2: blind these universities were and how much they privileged the

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Speaker 2: admissions of high net worth families. The students, you know,

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Speaker 2: coming from high net worth families who were well connected

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Speaker 2: to let's say the president of the university or other

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Speaker 2: influential people, or given massive gifts to the school. And

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Speaker 2: let me just go ahead and put this article up

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Speaker 2: on the screen, and I want to read you some

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Speaker 2: of this because the details here. Listen, this is not

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Speaker 2: a shock to any of us, but seeing the details

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Speaker 2: out it really is something else. So the headline is

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Speaker 2: Suite accuses Georgetown penn An Mit of admissions based on wealth.

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Speaker 2: The schools were accused of giving special treatment to wealthy

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Speaker 2: students who might not otherwise have been admitted, so they say,

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Speaker 2: for years, Georgetown University's longtime president flagged eighty students to

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Speaker 2: be added to a special admissions list, but not apparently

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Speaker 2: for their academic or athletic prowess. Documents in a new

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Speaker 2: lawsuit claim those on the president's list were virtually assured

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Speaker 2: of admissions simply because of their family's wealth and donation potential.

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Speaker 2: According to a filed on Money Monday. In this long

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Speaker 2: running lawsuit at MIT, two children recommended by a wealthy

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Speaker 2: banker with ties to a university board member got special treatment,

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Speaker 2: according to the documents, and a deposition, the school's director

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Speaker 2: of admissions that the two children who appeared on a

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Speaker 2: quote cases of interest list were among those who quote,

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Speaker 2: we would really have not otherwise admitted. At the University

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Speaker 2: of Pennsylvania, some students designated BSI or bonafide special interest

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Speaker 2: have a dramatically higher rate of admission than other applicants,

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Speaker 2: according to expert testimony. Penn's former Associate Dean of Admissions,

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Speaker 2: Sarah Harberson, testified last year in a deposition that a

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Speaker 2: BSI tag meant the student's family was a big donor

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Speaker 2: or had connection to the board. Those students quote, were

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Speaker 2: untouchable and they would get in almost one hundred percent

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Speaker 2: of the time, according to this former Associate dean of admissions.

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Speaker 2: She also said the admissions office was powerless to deny

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Speaker 2: the student even if the student was incredibly weak, even

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Speaker 2: if the student had.

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Speaker 4: A major issue in the application.

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Speaker 2: So this is the affirmative action that has undoubtedly continued

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Speaker 2: even in the wake of race based affirmative action being

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Speaker 2: overturned at the Supreme Court. Where if you come from money,

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Speaker 2: if your parents are well connected to the board, well

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Speaker 2: connected to the president, if they've made big donations to

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Speaker 2: the school or the school even thinks that they can

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Speaker 2: make big donations in the future. You are put on

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Speaker 2: one of these special lists in some instances, and you

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Speaker 2: are on a glide path to get into these elite

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Speaker 2: institutions that helped you set you up for life, regardless

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Speaker 2: of whether or not you actually merit being there and

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Speaker 2: seeing it put plane like this is really quite a strong.

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Speaker 1: It's amazing. I love it.

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Speaker 3: It's just like the original affirmative action lawsuit. I'm so

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Speaker 3: happy to actually And by the way, I met plenty

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Speaker 3: of these folks in school, or remember them, well, some

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Speaker 3: of them the daughters of kings and queen. We won't

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Speaker 3: mention any exact countries, shall we, But let's just say

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Speaker 3: their academic prowess was never particularly provative or impressive when

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Speaker 3: they did show up to class. What's funny is that

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00:20:11,760 --> 00:20:15,320
Speaker 3: when you pair it, you can see a complete freak

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Speaker 3: out now of modern academia because these people they don't

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Speaker 3: bring in a million two million, We're talking hundreds of millions.

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Speaker 3: Like one of the things that you learn from the

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Speaker 3: Varsity Blues case is that being worth a cool twenty

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Speaker 3: thirty fifty million.

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Speaker 1: They're like, that's a joke. That's nothing. You can't buy

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Speaker 1: your way into Harvard with that.

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Speaker 3: That's why they needed the varsity coach to get them

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Speaker 3: and the way you buy your way in and the

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Speaker 3: kids that they're talking about here, we're talking about ten fifty,

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Speaker 3: twenty fifty, you know, one hundred million dollar donations to

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Speaker 3: some of these universities.

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Speaker 1: That's why they do it now.

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Speaker 3: It's an existential threat to the universities, which is why

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Speaker 3: they've been protecting this stuff for all time. Because we're

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Speaker 3: talking about millions per year in annim that many, you know,

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Speaker 3: alumni will donate just to increase their chance of admission

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Speaker 3: by for their kids by what five percent, six percent

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Speaker 3: something like that. So you see that together, combined with

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Speaker 3: now the affirmative action hit that these places are taking already,

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Speaker 3: of which the DEI freak out on, this is huge.

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

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Speaker 3: This is the new admissions data from Harvard Law, but

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Speaker 3: it includes MIT, Pennsylvania and a few others and says

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Speaker 3: black student enrollment and Harvard Law drops by more than

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Speaker 3: half to the number not seen since the nineteen sixties.

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Speaker 3: Not just so happens when affirmative action was invented. By

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Speaker 3: the way, just three point four percent of the class.

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Speaker 3: But what's astonishing is lo and behold the Asian number skyrockets,

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Speaker 3: which is exactly what the people for the Students for

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Speaker 3: Fair Admission lawsuits were able to prove at the time,

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Speaker 3: which is this was a systematic racial discrimination campaign in

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Speaker 3: the face of all of this talk about DEI and others. So,

439
00:21:58,720 --> 00:22:01,040
Speaker 3: and of course the fairest critique was, well what about

440
00:22:01,160 --> 00:22:01,760
Speaker 3: you know, what was.

441
00:22:01,720 --> 00:22:04,159
Speaker 1: It, legacy admissions. It's like, well, great, let's get rid

442
00:22:04,200 --> 00:22:04,640
Speaker 1: of them both.

443
00:22:04,880 --> 00:22:07,160
Speaker 3: So now, if we have a five year time span

444
00:22:07,600 --> 00:22:10,440
Speaker 3: to get rid of a racial based affirmative action and

445
00:22:10,920 --> 00:22:14,280
Speaker 3: legacy admissions, we will have one of the most meritocratic

446
00:22:14,320 --> 00:22:18,320
Speaker 3: higher education systems that probably in the history of the

447
00:22:18,400 --> 00:22:20,560
Speaker 3: United States, because even in the early sixties you had

448
00:22:20,600 --> 00:22:24,200
Speaker 3: all that crazy stuff with anti semitism and racism, etc.

449
00:22:24,560 --> 00:22:26,199
Speaker 1: So this is a very very positive outcome.

450
00:22:27,240 --> 00:22:30,840
Speaker 3: Another reason why it matters is because as this starts

451
00:22:30,840 --> 00:22:33,760
Speaker 3: to trickle down and already we have race based admissions

452
00:22:33,800 --> 00:22:37,840
Speaker 3: that's been nuked on the on the admissions level for universities.

453
00:22:38,119 --> 00:22:41,240
Speaker 3: Is as we have this filter down to state and

454
00:22:41,760 --> 00:22:45,320
Speaker 3: state colleges that are publicly funded, it becomes even more important.

455
00:22:45,560 --> 00:22:47,879
Speaker 3: And you have it so that the actual public funded

456
00:22:47,960 --> 00:22:50,840
Speaker 3: universities the best single way to go from middle class

457
00:22:50,920 --> 00:22:53,199
Speaker 3: upper middle class, or from lower class to middle class,

458
00:22:53,359 --> 00:22:56,239
Speaker 3: and you remove some of these admissions practices, then we

459
00:22:56,320 --> 00:22:59,600
Speaker 3: actually can really set things set things straight. Next we

460
00:22:59,640 --> 00:23:01,440
Speaker 3: have to do tuition, but we'll get there.

461
00:23:01,880 --> 00:23:04,320
Speaker 2: I mean, my feelings about this a little more complicated

462
00:23:04,359 --> 00:23:07,040
Speaker 2: than yours, because when I say, like, oh, the number

463
00:23:07,040 --> 00:23:09,320
Speaker 2: of black students and Harvard dropped to levels of the sixties,

464
00:23:09,400 --> 00:23:14,600
Speaker 2: I'm not like, yay. But but my opposition, my opposition

465
00:23:14,760 --> 00:23:19,440
Speaker 2: to affirmative action has been that this is it's fundamentally

466
00:23:19,680 --> 00:23:23,960
Speaker 2: a neoliberal policy that says we're not going to change

467
00:23:23,960 --> 00:23:25,480
Speaker 2: the distribution of wealth.

468
00:23:25,720 --> 00:23:27,600
Speaker 4: And by the way, if you look within.

469
00:23:27,720 --> 00:23:30,719
Speaker 2: Racial groups, you have a very similar level of like

470
00:23:30,920 --> 00:23:35,600
Speaker 2: mass inequality within those groups, and primarily affirmative action that

471
00:23:35,760 --> 00:23:40,280
Speaker 2: has benefited like upper middle class or wealthier individuals within

472
00:23:40,359 --> 00:23:43,840
Speaker 2: those minority groups. So we're not going to change the

473
00:23:43,920 --> 00:23:48,840
Speaker 2: distribution of wealth. We're just going to diversify this rarefied

474
00:23:49,200 --> 00:23:53,600
Speaker 2: few and not deal with these larger structural issues and

475
00:23:53,640 --> 00:23:55,720
Speaker 2: make it so that you know, even if you don't

476
00:23:55,720 --> 00:23:58,000
Speaker 2: go to Harvard and you don't go to the top school.

477
00:23:58,040 --> 00:24:00,400
Speaker 2: Even if you don't go to college at all, you're

478
00:24:00,400 --> 00:24:03,080
Speaker 2: going to be able to have a stable middle class

479
00:24:03,359 --> 00:24:05,399
Speaker 2: or working class life. You're going to be able to

480
00:24:05,400 --> 00:24:06,640
Speaker 2: have a home, You're going to be able to car,

481
00:24:06,640 --> 00:24:08,560
Speaker 2: You're going to be able to afford to, you know,

482
00:24:08,600 --> 00:24:11,000
Speaker 2: a family, You're going to have health insurance or health

483
00:24:11,040 --> 00:24:13,639
Speaker 2: care and not have to have health insurance, but actually

484
00:24:13,680 --> 00:24:16,680
Speaker 2: just get the care that you need. And so rather

485
00:24:16,720 --> 00:24:18,879
Speaker 2: than dealing with this this is sort of like you know,

486
00:24:19,000 --> 00:24:24,000
Speaker 2: a band aid on white liberals concerns that about the

487
00:24:24,320 --> 00:24:28,600
Speaker 2: racial inequity that genuinely exists that they see in the world. So,

488
00:24:29,000 --> 00:24:31,840
Speaker 2: you know, that's why I've been I haven't been a

489
00:24:31,880 --> 00:24:35,359
Speaker 2: supporter of affirmative action, but I think it does underscore.

490
00:24:35,760 --> 00:24:39,240
Speaker 2: You know, our education system starting at pre K is

491
00:24:39,640 --> 00:24:42,640
Speaker 2: wildly unequal, very much dependent in spite of it being

492
00:24:42,640 --> 00:24:46,120
Speaker 2: a public school system, very much dependent on what zip

493
00:24:46,119 --> 00:24:48,320
Speaker 2: code you happen to be born into. What is the

494
00:24:48,359 --> 00:24:50,800
Speaker 2: wealth of that zip code. It determines how much funding

495
00:24:50,840 --> 00:24:53,440
Speaker 2: goes into that education system. We have some really great

496
00:24:53,600 --> 00:24:55,679
Speaker 2: public schools. In fact, there's one, you know, just a

497
00:24:55,680 --> 00:24:58,359
Speaker 2: few miles down the road here in Alexandria. That's one

498
00:24:58,359 --> 00:25:01,600
Speaker 2: of the top high schools in the entire country where

499
00:25:01,640 --> 00:25:04,720
Speaker 2: you're sure to get a fantastic education, and you have

500
00:25:04,840 --> 00:25:08,400
Speaker 2: some that are completely and utterly failing. Not to mention,

501
00:25:08,880 --> 00:25:12,560
Speaker 2: you know, the more that we have this mass class inequality,

502
00:25:12,880 --> 00:25:14,760
Speaker 2: and I think we truly are in a second Gilded

503
00:25:14,760 --> 00:25:16,639
Speaker 2: Age now. Even if you look at you know, Elon

504
00:25:16,720 --> 00:25:19,520
Speaker 2: Musk and him basically taking over the government, and the

505
00:25:19,560 --> 00:25:21,920
Speaker 2: way that the rewards are just constantly rigged to flow

506
00:25:21,960 --> 00:25:25,119
Speaker 2: to the top, you you know, you have to deal

507
00:25:25,160 --> 00:25:29,240
Speaker 2: with that structure in order to You can't jerry rig

508
00:25:29,280 --> 00:25:30,760
Speaker 2: it at the end and say all right, well, we

509
00:25:30,760 --> 00:25:32,359
Speaker 2: screwed you all the way up here, but we're going

510
00:25:32,400 --> 00:25:35,199
Speaker 2: to give you a few token goodies here once you

511
00:25:35,240 --> 00:25:36,959
Speaker 2: get to the top of the food chain and make

512
00:25:37,040 --> 00:25:39,159
Speaker 2: us all feel better about the fact that you have

513
00:25:40,080 --> 00:25:44,000
Speaker 2: more black first year law students at Harvard. The one

514
00:25:44,040 --> 00:25:45,760
Speaker 2: other thing I wanted to note is, you know, this

515
00:25:45,840 --> 00:25:47,520
Speaker 2: didn't just look at Harvard Law. It did look at

516
00:25:47,520 --> 00:25:50,960
Speaker 2: other law schools. It was not a universal trend across

517
00:25:51,000 --> 00:25:53,920
Speaker 2: the board. There were a few schools where black first

518
00:25:53,960 --> 00:25:57,040
Speaker 2: year law students actually did increase. I think Stanford maybe

519
00:25:57,119 --> 00:25:59,800
Speaker 2: was one of those, but most of them saw some

520
00:26:00,160 --> 00:26:04,399
Speaker 2: of a decline. Harvard and un C were two that

521
00:26:04,440 --> 00:26:06,920
Speaker 2: saw the most severe decline because they were specifically named

522
00:26:06,920 --> 00:26:07,600
Speaker 2: in the lawsuits.

523
00:26:07,680 --> 00:26:09,480
Speaker 4: Yes, so I think they felt.

524
00:26:09,240 --> 00:26:15,439
Speaker 2: Particularly like, you know, they needed to really aggressively go

525
00:26:15,520 --> 00:26:18,520
Speaker 2: in the opposite direction. So you know, the differences in

526
00:26:18,520 --> 00:26:20,520
Speaker 2: the data here are also worth noting.

527
00:26:20,320 --> 00:26:22,600
Speaker 3: Right, So I mean, look, I'm not saying ya, I'm

528
00:26:22,600 --> 00:26:26,240
Speaker 3: saying ya to the idea they have more merit based admission.

529
00:26:26,320 --> 00:26:28,480
Speaker 3: That's what I would say is good. And so if

530
00:26:28,520 --> 00:26:31,440
Speaker 3: things drop dramatically, that tells you something about how things

531
00:26:31,520 --> 00:26:33,800
Speaker 3: used to operate there, and it gives Look, there was

532
00:26:33,840 --> 00:26:34,800
Speaker 3: also lack of trust.

533
00:26:34,880 --> 00:26:38,840
Speaker 2: It was unfair to discriminate against asiancudents in particular, you know,

534
00:26:39,040 --> 00:26:40,080
Speaker 2: is another piece of them.

535
00:26:40,160 --> 00:26:43,520
Speaker 3: They're literally penalized for being like for having higher test

536
00:26:43,520 --> 00:26:47,159
Speaker 3: scores and for having higher GPAs. That's insane in the

537
00:26:47,200 --> 00:26:50,840
Speaker 3: profession of law, especially in medicine or at MIT any

538
00:26:50,880 --> 00:26:54,000
Speaker 3: of these other places which there was also significant problems

539
00:26:54,240 --> 00:26:54,800
Speaker 3: that are there.

540
00:26:54,880 --> 00:26:55,080
Speaker 5: You know.

541
00:26:55,160 --> 00:26:57,040
Speaker 3: The other thing that you see here is actually black

542
00:26:57,080 --> 00:27:00,399
Speaker 3: student and Hispanic enrollment went up at the state college level,

543
00:27:00,440 --> 00:27:02,840
Speaker 3: which tells you what which is that's a very good thing,

544
00:27:03,160 --> 00:27:06,480
Speaker 3: isn't it, Because you can see that having access and

545
00:27:06,520 --> 00:27:10,119
Speaker 3: having a merit based administry, merit based admission system to

546
00:27:10,320 --> 00:27:14,960
Speaker 3: more accessible quote unquote colleges and others, means that everybody

547
00:27:15,000 --> 00:27:17,720
Speaker 3: can have a fair shot, which is which is allegedly

548
00:27:17,800 --> 00:27:19,879
Speaker 3: you know what the country is founded on and what

549
00:27:19,960 --> 00:27:22,439
Speaker 3: we want. So it's very important for what you just

550
00:27:22,480 --> 00:27:25,080
Speaker 3: said to understand about the wealth gap and return it.

551
00:27:25,160 --> 00:27:27,119
Speaker 3: We've talked a lot about this a lot, but everyone

552
00:27:27,119 --> 00:27:28,879
Speaker 3: talks about, Oh, if you look at the white black

553
00:27:29,200 --> 00:27:32,560
Speaker 3: wealth gap, those statistics are bullshit and completely fake. The

554
00:27:32,680 --> 00:27:35,800
Speaker 3: vast majority of the quote unquote wealth gap between whites

555
00:27:35,800 --> 00:27:38,040
Speaker 3: and blacks is in the top ten percent of whites,

556
00:27:38,040 --> 00:27:40,200
Speaker 3: in the top ten percent of blacks because there are

557
00:27:40,240 --> 00:27:43,480
Speaker 3: way more one hundred billionaires people like Elon and others

558
00:27:43,640 --> 00:27:46,440
Speaker 3: as opposed to you know, I don't even know Oprah

559
00:27:46,440 --> 00:27:49,320
Speaker 3: and a few others. If you take the median, the

560
00:27:49,400 --> 00:27:52,679
Speaker 3: wealth gap is not nearly as pronounced as what it is.

561
00:27:52,720 --> 00:27:54,840
Speaker 1: People use the aggregate data.

562
00:27:54,880 --> 00:27:57,600
Speaker 3: This is something what's Matt Bruneg has talked quite a

563
00:27:57,600 --> 00:27:59,560
Speaker 3: bit about this. He has some decent charts from a

564
00:27:59,600 --> 00:28:01,800
Speaker 3: few years ago that I remember cribbing a lot of

565
00:28:01,800 --> 00:28:05,399
Speaker 3: these stats. From a point on this is that if

566
00:28:05,480 --> 00:28:10,239
Speaker 3: you focus on median and middle class equality, then you

567
00:28:10,280 --> 00:28:14,080
Speaker 3: actually come much better or much closer to restoring the

568
00:28:14,160 --> 00:28:17,600
Speaker 3: quote unquote wealth gap that actually matters, and that's the

569
00:28:17,640 --> 00:28:21,000
Speaker 3: one of between rich and between poor. But of course

570
00:28:21,040 --> 00:28:24,120
Speaker 3: that's very inconvenient, and it's much easier, as you said,

571
00:28:24,200 --> 00:28:28,159
Speaker 3: is to diversify the top echelons. I mean, my favorite

572
00:28:28,200 --> 00:28:31,280
Speaker 3: example is the Nasdaq, where you're not allowed to go

573
00:28:31,359 --> 00:28:34,560
Speaker 3: public unless you have a person of color on your board.

574
00:28:34,760 --> 00:28:37,639
Speaker 3: Nothing to do with your business practices or any of

575
00:28:37,680 --> 00:28:39,440
Speaker 3: these others. You literally have to have a woman and

576
00:28:39,480 --> 00:28:41,920
Speaker 3: a person of color. It's like, that's what we've decided

577
00:28:41,920 --> 00:28:43,719
Speaker 3: to change at the highest echelons.

578
00:28:43,880 --> 00:28:48,440
Speaker 2: So the white families on average hold around eight times

579
00:28:48,480 --> 00:28:51,560
Speaker 2: more wealth than black families, so if you are comparing

580
00:28:51,600 --> 00:28:56,120
Speaker 2: on a racial basis, there is a significant difference. My

581
00:28:56,280 --> 00:28:59,320
Speaker 2: point is that what we really need to do is

582
00:29:00,080 --> 00:29:03,040
Speaker 2: there needs to be a better sharing of the pie.

583
00:29:03,200 --> 00:29:06,080
Speaker 2: You should not have it be so concentrated at the top,

584
00:29:06,320 --> 00:29:10,680
Speaker 2: where the only goal is to just diversify the highest

585
00:29:10,680 --> 00:29:15,960
Speaker 2: heights and keep everybody scrapping amongst themselves for who can

586
00:29:16,160 --> 00:29:18,520
Speaker 2: make it into the upper middle class, the wealthy, and

587
00:29:18,760 --> 00:29:22,800
Speaker 2: the top. That is my point, because I don't want

588
00:29:22,800 --> 00:29:26,760
Speaker 2: to deny that there continues to be discrimination, that historic discrimination,

589
00:29:27,040 --> 00:29:30,280
Speaker 2: especially housing, which is such a key building block of wealth,

590
00:29:30,800 --> 00:29:33,960
Speaker 2: continues to you know, haunt this country and continue to

591
00:29:34,040 --> 00:29:38,920
Speaker 2: drive a significant wealth gap between black and white. So

592
00:29:38,960 --> 00:29:41,120
Speaker 2: it's not my goal to deny that. What I was

593
00:29:41,160 --> 00:29:44,040
Speaker 2: pointing out is if you look within any particular racial group,

594
00:29:44,080 --> 00:29:47,480
Speaker 2: you see a similar distribution of wealth in terms of

595
00:29:47,520 --> 00:29:50,000
Speaker 2: the gap between the haves and the haves. Now that's

596
00:29:50,080 --> 00:29:52,760
Speaker 2: what I want to close. That's what I'm committed to.

597
00:29:53,040 --> 00:29:56,640
Speaker 2: And you know, there is no there is no indication

598
00:29:56,720 --> 00:29:59,680
Speaker 2: that affirmative action was a part of helping to close

599
00:30:00,160 --> 00:30:02,800
Speaker 2: that wealth gap between you know, the top one percent,

600
00:30:02,880 --> 00:30:05,000
Speaker 2: the top point one percent, and everyone else.

601
00:30:05,040 --> 00:30:06,560
Speaker 4: And those are the policies that I'm interested in.

602
00:30:07,000 --> 00:30:10,120
Speaker 3: Absolutely, I don't disagree with that. I just say the average.

603
00:30:10,160 --> 00:30:12,920
Speaker 3: Remember that average includes people like Elon. You have to

604
00:30:12,960 --> 00:30:15,720
Speaker 3: look at income quintile of the medium.

605
00:30:16,160 --> 00:30:17,280
Speaker 4: Should they just be thrown out?

606
00:30:17,560 --> 00:30:20,560
Speaker 3: Yeah, well yes, actually, because it's ridiculous, you know, to

607
00:30:20,600 --> 00:30:23,560
Speaker 3: skew things by including people who are worth hundreds of

608
00:30:23,560 --> 00:30:26,480
Speaker 3: millions or hundreds of billions of dollars in the overall things.

609
00:30:26,480 --> 00:30:28,040
Speaker 2: Well, but I mean, that's kind of a core part

610
00:30:28,040 --> 00:30:29,760
Speaker 2: of the problem. Though I agree with a lot people

611
00:30:29,760 --> 00:30:31,840
Speaker 2: that have hundreds of billions of dollars.

612
00:30:31,840 --> 00:30:34,560
Speaker 3: When that are separate out the top ten percent, it's

613
00:30:34,600 --> 00:30:37,480
Speaker 3: not even close to eight times. It's it's complicated in

614
00:30:37,600 --> 00:30:39,320
Speaker 3: terms of what it actually isn't I'm not going to

615
00:30:39,360 --> 00:30:41,760
Speaker 3: deny that the gap isn't there, but it's not eight

616
00:30:41,800 --> 00:30:44,040
Speaker 3: times for what it is. I'm trying to find the

617
00:30:44,040 --> 00:30:47,040
Speaker 3: Matt Brunneck statistics that I was criving this from from

618
00:30:47,040 --> 00:30:50,200
Speaker 3: a few years ago. But the point stands that that's

619
00:30:50,240 --> 00:30:52,040
Speaker 3: part of the reason why average is actually not a

620
00:30:52,080 --> 00:30:52,640
Speaker 3: good statistic.

621
00:30:53,000 --> 00:30:55,880
Speaker 2: The median wealth for a white family was two hundred

622
00:30:55,880 --> 00:30:58,640
Speaker 2: and eighty five and twenty twenty two, the median wealth

623
00:30:58,640 --> 00:31:01,120
Speaker 2: for a black family was forty five thousand dollars.

624
00:31:01,160 --> 00:31:04,160
Speaker 3: So that's what five domes, right, It's a large gap. Yeah, yeah, yeah,

625
00:31:04,160 --> 00:31:06,680
Speaker 3: that's true. That's definitely true. A lot of that also

626
00:31:06,720 --> 00:31:08,840
Speaker 3: comes back to housing, like you just said. But the

627
00:31:08,880 --> 00:31:11,719
Speaker 3: part of the problem too is even in terms of housing,

628
00:31:11,800 --> 00:31:14,720
Speaker 3: is there is that big skew for working class, for

629
00:31:15,400 --> 00:31:20,200
Speaker 3: within whites in terms of generational access to housing. Yeah,

630
00:31:20,240 --> 00:31:22,200
Speaker 3: obviously that was a big problem with redlining, et cetera.

631
00:31:22,360 --> 00:31:24,600
Speaker 3: That's one thing I will give them. It's true in

632
00:31:24,680 --> 00:31:27,760
Speaker 3: terms of denying wealth. There's also though, there was a

633
00:31:27,760 --> 00:31:29,840
Speaker 3: big problem after two thousand and eight. Matt Stoler has

634
00:31:29,840 --> 00:31:32,600
Speaker 3: talked a lot about this in terms of where bailout

635
00:31:32,680 --> 00:31:36,040
Speaker 3: disparity happened with a lot of people's access to housing

636
00:31:36,080 --> 00:31:39,959
Speaker 3: because it was specifically Black and Hispanic households who lost

637
00:31:40,040 --> 00:31:42,760
Speaker 3: access to a lot of those loans were most susceptible

638
00:31:42,800 --> 00:31:45,760
Speaker 3: to the subprime crisis lost access to their houses, and

639
00:31:45,800 --> 00:31:49,480
Speaker 3: then after the real estate explosion of twenty ten forward,

640
00:31:49,680 --> 00:31:52,000
Speaker 3: that increased the widening of the gap as well. So

641
00:31:52,200 --> 00:31:55,600
Speaker 3: that gets to a lot of Barack Obama discourse, et cetera,

642
00:31:55,680 --> 00:31:58,240
Speaker 3: which I know that he's been excellent on. So anyway,

643
00:31:58,280 --> 00:32:00,880
Speaker 3: it's an interesting conversation in terms of how he actually

644
00:32:00,880 --> 00:32:03,720
Speaker 3: moved forward in the country. And it's one where a

645
00:32:03,720 --> 00:32:07,560
Speaker 3: lot of the people who were posting about affirmative action

646
00:32:08,320 --> 00:32:11,320
Speaker 3: always were very conveniently ignoring. I was always one of

647
00:32:11,320 --> 00:32:13,120
Speaker 3: those who was like, yeah, let's ban it, get rid

648
00:32:13,120 --> 00:32:15,080
Speaker 3: of it, you know, I think getting rid of legacy

649
00:32:15,120 --> 00:32:17,960
Speaker 3: admissions is extraordinarily important to make sure that there's not

650
00:32:18,000 --> 00:32:22,360
Speaker 3: a continued aristocracy in the country, because everybody knows that

651
00:32:22,520 --> 00:32:24,480
Speaker 3: if you just have somebody write your letter or whatever

652
00:32:24,680 --> 00:32:26,800
Speaker 3: at one of these universities, you go from a five

653
00:32:26,800 --> 00:32:28,920
Speaker 3: percent admissions rate to fifty and then if you donate

654
00:32:29,000 --> 00:32:30,400
Speaker 3: some money, you go up to like seventy five.

655
00:32:30,480 --> 00:32:31,800
Speaker 1: Yeah, which is as sweet as it gets.

656
00:32:32,200 --> 00:32:34,360
Speaker 2: The details here of like, oh, if you're on this list,

657
00:32:34,640 --> 00:32:36,920
Speaker 2: you just get in period.

658
00:32:36,600 --> 00:32:38,800
Speaker 4: Whether you deserve to be there or not, must be nice.

659
00:32:38,920 --> 00:32:41,800
Speaker 2: Let's really focus on ending that type of affirmative action

660
00:32:44,560 --> 00:32:48,480
Speaker 2: after Timothy Shallomey recently went on the Theovon podcast and

661
00:32:48,560 --> 00:32:53,440
Speaker 2: they both were had fawning praise for Senator Bernie Sanders, which,

662
00:32:53,440 --> 00:32:54,400
Speaker 2: of course I love to see.

663
00:32:54,520 --> 00:32:55,640
Speaker 4: Let's go and take a listen to that.

664
00:32:56,000 --> 00:32:57,640
Speaker 1: Yeah, we had Bernie Sanders on him. He was saying

665
00:32:57,680 --> 00:33:00,320
Speaker 1: that he said Pete Seeger was one of his favorite.

666
00:33:00,120 --> 00:33:03,640
Speaker 7: Scouet McNay could play Bernie Sanders in a biopic, right, Yeah,

667
00:33:03,760 --> 00:33:05,560
Speaker 7: totally could. Bernie has an aged.

668
00:33:05,920 --> 00:33:06,959
Speaker 1: Now, Bernie still looks at same.

669
00:33:07,040 --> 00:33:11,000
Speaker 7: He's the best he's in the last Yeah, he's like

670
00:33:11,040 --> 00:33:11,800
Speaker 7: a real folk hero.

671
00:33:13,000 --> 00:33:13,480
Speaker 1: Great point.

672
00:33:13,560 --> 00:33:15,840
Speaker 7: Yeah, Bernie is is he is a folk hero. Yeah,

673
00:33:15,840 --> 00:33:17,880
Speaker 7: he's folk music. Mitchell Lama. You know about Mitchell Lama.

674
00:33:18,000 --> 00:33:20,560
Speaker 7: Mitchell Yeah, the Five the Restaurant Stars or no, no, no, no no.

675
00:33:20,960 --> 00:33:24,040
Speaker 7: Mitchellama is like, uh, there's like two vert to my

676
00:33:24,120 --> 00:33:26,360
Speaker 7: understated is two versions of like good arts housing. Here

677
00:33:26,360 --> 00:33:28,400
Speaker 7: you got section eight that means you're paying like under

678
00:33:28,440 --> 00:33:29,640
Speaker 7: eight hundred bucks. Mitchell Lama.

679
00:33:30,680 --> 00:33:32,560
Speaker 1: Yeah, oh that damn Mitchell Lama.

680
00:33:32,600 --> 00:33:37,760
Speaker 7: Brother absolutely, Oh that's me baby moderate Mitchellama. Program FRIDS,

681
00:33:37,760 --> 00:33:40,640
Speaker 7: Affordable Rental and cooperative housing and motterate and middle income families.

682
00:33:41,120 --> 00:33:43,200
Speaker 2: Also give you a little taste there of them busting

683
00:33:43,200 --> 00:33:44,600
Speaker 2: out the housing policy discourse.

684
00:33:44,720 --> 00:33:47,240
Speaker 3: Yes, Mitchell Lama, which apparently Shala May I think he

685
00:33:47,320 --> 00:33:47,760
Speaker 3: was raised in.

686
00:33:47,840 --> 00:33:51,440
Speaker 1: That's what he talked about. They're on the podcast. I

687
00:33:51,480 --> 00:33:51,800
Speaker 1: don't know.

688
00:33:52,120 --> 00:33:54,960
Speaker 3: I've seen this passed around this whole like, oh, so

689
00:33:55,040 --> 00:33:56,920
Speaker 3: I saw somebody respond to this and like this shows

690
00:33:56,920 --> 00:33:58,560
Speaker 3: that Bernie Sanders would have won.

691
00:33:58,640 --> 00:34:00,040
Speaker 1: And I was like, because.

692
00:34:00,080 --> 00:34:03,320
Speaker 3: Shallow May likes Bernie Sanders. I mean, Kamala had plenty

693
00:34:03,360 --> 00:34:07,480
Speaker 3: of uh of celebrity endorsements last time I checked. Also,

694
00:34:07,520 --> 00:34:09,919
Speaker 3: if I remember, there were a bunch of celebrit who

695
00:34:09,920 --> 00:34:12,400
Speaker 3: backed Bernie in twenty twenty or even in twenty sixteen.

696
00:34:12,440 --> 00:34:13,200
Speaker 1: What what's her name?

697
00:34:13,760 --> 00:34:19,400
Speaker 3: Uh, the supermodel Radakowski Emi, Emily Radakowski, if I recall,

698
00:34:20,360 --> 00:34:21,720
Speaker 3: there were definitely a few others.

699
00:34:21,800 --> 00:34:24,440
Speaker 1: I think Cardi b I think it was it.

700
00:34:24,239 --> 00:34:25,480
Speaker 4: Wasn't so much about Shada.

701
00:34:25,640 --> 00:34:29,440
Speaker 3: Obviously it's simplistic to be like this bruise, but I'm

702
00:34:29,440 --> 00:34:30,600
Speaker 3: just saying very viral.

703
00:34:32,280 --> 00:34:34,640
Speaker 2: But no, no, I think it's about shallow May Backingham.

704
00:34:34,680 --> 00:34:37,799
Speaker 2: I think it's about Bernie was capable of going on

705
00:34:37,840 --> 00:34:41,439
Speaker 2: the Theovon podcast and Theovon being like, I love this guy.

706
00:34:41,560 --> 00:34:42,600
Speaker 4: Yeah, guy's great, he's.

707
00:34:42,520 --> 00:34:44,400
Speaker 1: A phone here team too. He still didn't win.

708
00:34:44,520 --> 00:34:45,320
Speaker 4: Yeah, he got screwed.

709
00:34:45,400 --> 00:34:47,319
Speaker 1: Yeah, and it was but the only reason.

710
00:34:47,719 --> 00:34:50,000
Speaker 2: But we're also talking about, you know, a general election

711
00:34:50,200 --> 00:34:52,960
Speaker 2: versus a primary election. Obviously, one of the big learnings

712
00:34:52,960 --> 00:34:58,160
Speaker 2: out of this election was, oh that like bro podcast comedy,

713
00:34:58,480 --> 00:35:02,560
Speaker 2: that whole sphere out to be really important, and you know,

714
00:35:02,600 --> 00:35:06,120
Speaker 2: there's a whole I think important conversation about number one

715
00:35:06,200 --> 00:35:08,880
Speaker 2: KMA even Okay, I never bought that she should have

716
00:35:08,920 --> 00:35:11,080
Speaker 2: gone on Brogan because I just don't think she would

717
00:35:11,080 --> 00:35:13,280
Speaker 2: have done well there. So there's number one, the problem

718
00:35:13,320 --> 00:35:15,760
Speaker 2: of just like having a candidate that you would want

719
00:35:15,800 --> 00:35:19,200
Speaker 2: to see in any one of these venues. But it

720
00:35:19,239 --> 00:35:22,520
Speaker 2: recalls the fact that back in twenty sixteen and in

721
00:35:22,560 --> 00:35:26,200
Speaker 2: twenty twenty. But I think especially in twenty sixteen, Bernie

722
00:35:26,280 --> 00:35:30,000
Speaker 2: had huge support among the bros, among the Latinos, among

723
00:35:30,040 --> 00:35:32,239
Speaker 2: the working class, all the groups that have fled the

724
00:35:32,239 --> 00:35:36,240
Speaker 2: Democratic Party the fastest were his strongest basis of support.

725
00:35:36,480 --> 00:35:37,480
Speaker 4: And he was smeared for that.

726
00:35:37,800 --> 00:35:41,080
Speaker 2: You know, he was the whole Bernie brow was meant

727
00:35:41,160 --> 00:35:44,360
Speaker 2: as a smear, and they were derided as being toxic,

728
00:35:44,400 --> 00:35:48,160
Speaker 2: et cetera, et cetera. Now it was always inaccurate to

729
00:35:48,200 --> 00:35:50,840
Speaker 2: paint his support as being completely male.

730
00:35:51,120 --> 00:35:52,440
Speaker 4: In fact, there were, you know.

731
00:35:52,880 --> 00:35:56,279
Speaker 2: Young women, young men overwhelmingly supported Bernie Sanders both in

732
00:35:56,320 --> 00:35:58,880
Speaker 2: twenty sixteen and twenty twenty. But I think it was

733
00:35:59,000 --> 00:36:03,359
Speaker 2: more The point here was that he's someone who Joe

734
00:36:03,440 --> 00:36:06,600
Speaker 2: Rogan did support, that this ecosystem did support.

735
00:36:06,960 --> 00:36:07,600
Speaker 4: Even though his.

736
00:36:07,600 --> 00:36:11,640
Speaker 2: Politics are obviously very different from Trump, he taps into

737
00:36:11,680 --> 00:36:15,560
Speaker 2: that similar desire for something different and a challenge to

738
00:36:16,239 --> 00:36:16,960
Speaker 2: the establishment.

739
00:36:17,080 --> 00:36:18,799
Speaker 4: I think Trump's challenge the stablishment was fake.

740
00:36:18,719 --> 00:36:20,400
Speaker 2: Blah blah blah, but there's no doubt that that is

741
00:36:20,400 --> 00:36:24,640
Speaker 2: how he is perceived and has successfully per positioned himself

742
00:36:24,680 --> 00:36:27,400
Speaker 2: as being the sort of anti system politician. And Bernie

743
00:36:27,440 --> 00:36:30,400
Speaker 2: Sanders fits that same model. And you know, if Democrats

744
00:36:30,400 --> 00:36:32,560
Speaker 2: are losing the bros here, you got a couple of bros.

745
00:36:32,320 --> 00:36:33,880
Speaker 4: They're like, oh my god, I love that guy. He's

746
00:36:33,920 --> 00:36:34,399
Speaker 4: a folk hero.

747
00:36:34,680 --> 00:36:37,560
Speaker 3: Yeah, I'm just I still am not so sure I

748
00:36:37,600 --> 00:36:41,440
Speaker 3: buy that analysis, just because with Bernie it was fundamentally

749
00:36:41,440 --> 00:36:45,520
Speaker 3: irreconcilable to the Democratic coalition, Like you had rich white

750
00:36:45,560 --> 00:36:48,920
Speaker 3: people who are was obsessed with race in twenty sixteen,

751
00:36:49,120 --> 00:36:51,719
Speaker 3: who yes, you know smeared ber Look, I agree the

752
00:36:51,760 --> 00:36:54,840
Speaker 3: Bernie Sanders bro thing was bullshit, but people ate it

753
00:36:54,920 --> 00:36:56,520
Speaker 3: up right. Well, that was kind in the midst of

754
00:36:56,600 --> 00:36:59,160
Speaker 3: Russia Gate and all this other crap. I mean, he

755
00:36:59,280 --> 00:37:01,920
Speaker 3: was them out of the coalition because they hate.

756
00:37:01,680 --> 00:37:04,760
Speaker 2: Them, No, they so if you looked at the polling

757
00:37:05,680 --> 00:37:09,560
Speaker 2: twenty twenty, we covered this extensively in every primary, including

758
00:37:09,560 --> 00:37:11,520
Speaker 2: the ones that he lost got blown out by Joe Biden,

759
00:37:11,520 --> 00:37:15,440
Speaker 2: like in Mississippi. If you ask people about his policy

760
00:37:15,719 --> 00:37:19,960
Speaker 2: platform and about their personal feelings about him, he won

761
00:37:20,000 --> 00:37:25,000
Speaker 2: on the policies he won on his approval rating. People

762
00:37:25,040 --> 00:37:28,880
Speaker 2: were convinced he couldn't beat Trump. They're convinced about that

763
00:37:28,920 --> 00:37:31,920
Speaker 2: in twenty sixteen, they're convinced about that in twenty twenty.

764
00:37:32,640 --> 00:37:35,600
Speaker 2: You know, we saw have seen how that ultimately worked out.

765
00:37:35,960 --> 00:37:38,680
Speaker 2: But they bought into the narrative that was fed to

766
00:37:38,680 --> 00:37:42,160
Speaker 2: them about by MSNBC and other places that you can't vote.

767
00:37:42,160 --> 00:37:44,120
Speaker 2: You may like this guy, you can't vote for him

768
00:37:44,400 --> 00:37:47,080
Speaker 2: because he's not the right person to go up against

769
00:37:47,120 --> 00:37:49,680
Speaker 2: Donald Trump. Now, listen, Ultimately we'll never know what would

770
00:37:49,719 --> 00:37:51,239
Speaker 2: have happened if he had been the nominee and he

771
00:37:51,360 --> 00:37:53,400
Speaker 2: you know, in twenty sixteen or in twenty twenty, and

772
00:37:53,400 --> 00:37:56,240
Speaker 2: how that ultimately would have gone. But I would submit

773
00:37:56,680 --> 00:38:00,040
Speaker 2: based on history, based on you know the fact that

774
00:38:00,040 --> 00:38:03,759
Speaker 2: that he sort of has a modern version of the

775
00:38:04,160 --> 00:38:09,760
Speaker 2: New Deal left populist approach to combating what you wouldn't

776
00:38:09,760 --> 00:38:13,000
Speaker 2: but I would characterize as a fascist appeal. That is

777
00:38:13,000 --> 00:38:14,960
Speaker 2: what has been successful in the past. And I think

778
00:38:15,000 --> 00:38:16,920
Speaker 2: given the groups that he had the most traction with,

779
00:38:17,280 --> 00:38:19,640
Speaker 2: there's certainly a good case to be made that he

780
00:38:19,680 --> 00:38:23,120
Speaker 2: would have been a much better candidate to go up

781
00:38:23,160 --> 00:38:28,280
Speaker 2: directly against trump Ism than this like lukewarm dover milk

782
00:38:28,320 --> 00:38:32,400
Speaker 2: toast neoliberalism that just promises to protect a system that

783
00:38:32,440 --> 00:38:34,440
Speaker 2: people hate and is fundamentally unjust.

784
00:38:34,600 --> 00:38:37,399
Speaker 3: Yeah, the problem with that is that Bernie himself then

785
00:38:37,600 --> 00:38:40,000
Speaker 3: decides to go along with it. He goes into Russiagate,

786
00:38:40,080 --> 00:38:42,880
Speaker 3: he goes into the wocism he runs on, adopts a

787
00:38:42,880 --> 00:38:44,920
Speaker 3: lot of the stuff into his campaign in twenty twenty.

788
00:38:44,960 --> 00:38:47,600
Speaker 3: He shifts a lot of his own positions, moves away

789
00:38:47,600 --> 00:38:50,480
Speaker 3: from what made him so independent on war, on so

790
00:38:50,560 --> 00:38:52,840
Speaker 3: much stuff. Right, He's been a huge disappointment, if anything,

791
00:38:52,840 --> 00:38:54,920
Speaker 3: to a lot of the people who originally joined his

792
00:38:55,000 --> 00:38:56,840
Speaker 3: movement in twenty sixteen.

793
00:38:56,880 --> 00:38:58,560
Speaker 1: And so I just think.

794
00:38:58,400 --> 00:39:01,160
Speaker 3: That that shows where the power or lies in the

795
00:39:01,200 --> 00:39:05,160
Speaker 3: Democratic Party. It's with these DEI academics, not now. Maybe

796
00:39:05,160 --> 00:39:07,480
Speaker 3: it's broken now. I don't think so, though, because what

797
00:39:07,600 --> 00:39:10,000
Speaker 3: I think is that now the only people who remain

798
00:39:10,440 --> 00:39:13,280
Speaker 3: committed Democrats in this country are rich white people. That's

799
00:39:13,320 --> 00:39:16,439
Speaker 3: basically it. The good news for I guess for you

800
00:39:16,840 --> 00:39:18,879
Speaker 3: is that you can convince these people of anything as

801
00:39:18,880 --> 00:39:20,080
Speaker 3: long as they think they'll win.

802
00:39:20,200 --> 00:39:21,440
Speaker 1: That's all they really care about.

803
00:39:21,680 --> 00:39:25,600
Speaker 3: But which direction of all of that will come from

804
00:39:25,680 --> 00:39:28,080
Speaker 3: the pages of the New York Times, the pages of

805
00:39:29,120 --> 00:39:31,880
Speaker 3: you know, like people like Jon Favreau and others for

806
00:39:32,000 --> 00:39:34,600
Speaker 3: what they think and continue to be told you got

807
00:39:34,640 --> 00:39:37,799
Speaker 3: to give people some agency. You know, Sure, it's MSNBC's fault,

808
00:39:37,840 --> 00:39:40,000
Speaker 3: but they trusted them in the first place, right, Like

809
00:39:40,000 --> 00:39:42,239
Speaker 3: they're the ones who bought their stuff and decided to

810
00:39:42,320 --> 00:39:45,120
Speaker 3: vote against them. So at a certain point, like, it's

811
00:39:45,160 --> 00:39:47,440
Speaker 3: not MSNBC's fault. They have a bunch of people who

812
00:39:47,440 --> 00:39:49,839
Speaker 3: are brainwashed enough or whatever to be able to want

813
00:39:49,880 --> 00:39:50,719
Speaker 3: to listen to them.

814
00:39:50,719 --> 00:39:52,560
Speaker 4: But who's doing the brainwashing here?

815
00:39:52,640 --> 00:39:54,000
Speaker 2: I mean, I think you had a lot of earnest

816
00:39:54,040 --> 00:39:57,360
Speaker 2: people who were deeply troubled by the threat that Trump

817
00:39:57,360 --> 00:40:00,680
Speaker 2: POWs and you know, we're looking for answers, and this

818
00:40:00,800 --> 00:40:03,000
Speaker 2: was the network that they, you know, had been had

819
00:40:03,040 --> 00:40:05,440
Speaker 2: been sort of primed to trust and think that was

820
00:40:05,680 --> 00:40:09,000
Speaker 2: not going to lead them astray. And you know, I

821
00:40:09,040 --> 00:40:12,560
Speaker 2: do think that there's a moment now of reckoning among

822
00:40:12,719 --> 00:40:19,000
Speaker 2: that liberal base, which is fleeing MSNBC in droves, which

823
00:40:19,080 --> 00:40:22,000
Speaker 2: has lost a lot of trust in The Washington Post

824
00:40:22,040 --> 00:40:25,239
Speaker 2: is another place that saw a huge subscriber exodus. You

825
00:40:25,360 --> 00:40:29,160
Speaker 2: see a huge search in a lot of like left

826
00:40:29,160 --> 00:40:32,440
Speaker 2: populace and my husband's in particular online where I think

827
00:40:32,480 --> 00:40:34,680
Speaker 2: there are a lot of liberal refugees who were like

828
00:40:34,880 --> 00:40:37,600
Speaker 2: this did not work, Like this line that we were

829
00:40:37,600 --> 00:40:41,319
Speaker 2: fed about how we had to abandon caring about any

830
00:40:41,360 --> 00:40:45,160
Speaker 2: issues in order to defeat Trump, Like this was bullshit.

831
00:40:45,360 --> 00:40:46,239
Speaker 4: This failed, and.

832
00:40:46,200 --> 00:40:47,799
Speaker 2: We were led astray, and we were lied to when

833
00:40:47,800 --> 00:40:51,120
Speaker 2: we were manipulated. So there is an opening there. Now

834
00:40:51,239 --> 00:40:54,319
Speaker 2: I have been very upfront about I think it's much

835
00:40:54,360 --> 00:40:56,680
Speaker 2: more likely the Democratic Party just does like a trum

836
00:40:57,160 --> 00:41:00,319
Speaker 2: trump Ism light, that they pull a Bill Clinton that

837
00:41:00,360 --> 00:41:03,520
Speaker 2: they you know, sort of rather than having a direct

838
00:41:03,560 --> 00:41:06,960
Speaker 2: rebuttal to trump Ism. Instead they go in the direction

839
00:41:07,000 --> 00:41:09,960
Speaker 2: of will just like be a a kinder, gentler version

840
00:41:10,000 --> 00:41:12,279
Speaker 2: of that. I think that's the most likely outcome, But

841
00:41:12,800 --> 00:41:15,480
Speaker 2: there is a possibility here that didn't exist before.

842
00:41:15,880 --> 00:41:16,600
Speaker 4: Another thing that.

843
00:41:16,560 --> 00:41:18,640
Speaker 2: I would say is, you know, it would be a

844
00:41:18,680 --> 00:41:22,239
Speaker 2: mistake to think that these political coalitions, and this is

845
00:41:22,280 --> 00:41:25,200
Speaker 2: the mistake Democrats made in the past, are immovable and

846
00:41:25,320 --> 00:41:30,200
Speaker 2: unchangeable and locked into place. There is a lot of

847
00:41:30,400 --> 00:41:34,760
Speaker 2: I think one thing we've learned is people are continually adjusting, evaluating,

848
00:41:35,040 --> 00:41:38,680
Speaker 2: shifting loyalties, even in this election, Like you know, it's

849
00:41:38,680 --> 00:41:40,840
Speaker 2: not like Trump. Trump didn't even get fifty percent of

850
00:41:40,840 --> 00:41:44,879
Speaker 2: the vote, and plenty of swing state Democrats did win.

851
00:41:45,800 --> 00:41:49,280
Speaker 2: You saw candidates who outperformed like Cherry Brown. He loses,

852
00:41:49,280 --> 00:41:51,360
Speaker 2: but he outperformed the top that take it by twelve points.

853
00:41:52,280 --> 00:41:54,880
Speaker 2: You see candidates who are able to stitch together a

854
00:41:54,920 --> 00:41:57,560
Speaker 2: somewhat different coalition hold on to more of that historic

855
00:41:57,600 --> 00:42:01,000
Speaker 2: Democratic working class base. So well, I wouldn't treat people

856
00:42:01,000 --> 00:42:02,920
Speaker 2: as like, well, they're just this is how they are

857
00:42:02,960 --> 00:42:04,839
Speaker 2: now and that's it, and the coalitions are never going

858
00:42:04,880 --> 00:42:07,719
Speaker 2: to change again, because that obviously over some period of

859
00:42:07,719 --> 00:42:08,200
Speaker 2: time is not.

860
00:42:08,120 --> 00:42:08,719
Speaker 4: Going to be the case.

861
00:42:08,800 --> 00:42:11,200
Speaker 3: My theory is that if Democrats win, it will have

862
00:42:11,239 --> 00:42:13,200
Speaker 3: nothing to do with Democrats in the next two to

863
00:42:13,239 --> 00:42:15,040
Speaker 3: four years because of what's.

864
00:42:14,880 --> 00:42:16,960
Speaker 1: What's the shit that's happening right now?

865
00:42:17,000 --> 00:42:20,560
Speaker 3: A governments shut down, you try and cut social Security?

866
00:42:20,320 --> 00:42:20,880
Speaker 1: Okay, okay.

867
00:42:21,000 --> 00:42:23,480
Speaker 3: In twenty eighteen, do you think that the twenty eighteen

868
00:42:23,520 --> 00:42:26,960
Speaker 3: Democratic victories were because of Democrats or because of Trump's

869
00:42:27,239 --> 00:42:28,480
Speaker 3: trying to repeal Obamacare.

870
00:42:28,719 --> 00:42:29,960
Speaker 1: I think it's because of Obamacare.

871
00:42:30,000 --> 00:42:31,160
Speaker 3: I don't think I had a damn thing to do

872
00:42:31,520 --> 00:42:33,360
Speaker 3: with the Democratic Party, And I think here.

873
00:42:33,280 --> 00:42:35,200
Speaker 2: I think twenty twenty, I would say the same thing.

874
00:42:35,239 --> 00:42:37,000
Speaker 2: I don't think that Joe Biden's victory had anything to

875
00:42:37,040 --> 00:42:38,960
Speaker 2: do with you. It was look at it, it was

876
00:42:39,040 --> 00:42:42,560
Speaker 2: because people didn't hate him. Then they felt fondly towards him.

877
00:42:42,600 --> 00:42:44,680
Speaker 2: Even now I think they feel more fondly towards him

878
00:42:44,680 --> 00:42:45,120
Speaker 2: than it's.

879
00:42:45,000 --> 00:42:48,120
Speaker 1: Really justify, which is the lowest of all time.

880
00:42:48,280 --> 00:42:51,279
Speaker 2: But he you know, I think he is able to

881
00:42:51,560 --> 00:42:54,680
Speaker 2: barely egan a win because of Trump's faults. I think

882
00:42:54,719 --> 00:42:57,160
Speaker 2: you're one hundred percent correct about that. But yeah, look,

883
00:42:57,280 --> 00:43:00,040
Speaker 2: Elon is like running the government now. He's in a

884
00:43:00,040 --> 00:43:04,440
Speaker 2: already minded like you know, iin Randy and hobbyer Malay

885
00:43:04,560 --> 00:43:08,400
Speaker 2: type who wants to slash social services. So they go

886
00:43:08,480 --> 00:43:12,359
Speaker 2: in that direction. And there's tons of ambient chaos, and

887
00:43:12,480 --> 00:43:14,640
Speaker 2: the Middle East is still a disaster, all of these

888
00:43:14,640 --> 00:43:17,279
Speaker 2: things going on in the world, and you know, you

889
00:43:17,280 --> 00:43:20,080
Speaker 2: could imagine Democrats running like a union leader, like a

890
00:43:20,120 --> 00:43:25,680
Speaker 2: Sean Fain. And if Trump has sort of abandoned his

891
00:43:25,880 --> 00:43:29,960
Speaker 2: pledges on leaving social Security in place, leaving me to

892
00:43:30,000 --> 00:43:33,520
Speaker 2: karaen place, he has not created the you know, the

893
00:43:33,640 --> 00:43:36,319
Speaker 2: utopia that he promised to people. Yeah, you could see

894
00:43:36,320 --> 00:43:39,279
Speaker 2: those coalitions shifting again. It's not impossible to imagine again.

895
00:43:39,280 --> 00:43:41,399
Speaker 2: I think it's an uphill climb, and I think you're right.

896
00:43:41,520 --> 00:43:43,920
Speaker 2: Probably a Democratic success is most likely to come just

897
00:43:43,920 --> 00:43:45,480
Speaker 2: because Trump is a disaster.

898
00:43:45,719 --> 00:43:48,480
Speaker 4: But nothing is written in stone. You never know that.

899
00:43:48,640 --> 00:43:51,319
Speaker 2: You have seen some interesting people like David Brooks and

900
00:43:51,360 --> 00:43:53,600
Speaker 2: others be like, you know, maybe Bernie Sanders had a boy.

901
00:43:53,719 --> 00:43:55,560
Speaker 3: I just think it's too late at this point, but

902
00:43:55,680 --> 00:43:57,360
Speaker 3: I listen, you know it could be wrong. What do

903
00:43:57,400 --> 00:43:59,560
Speaker 3: you mean it's to though it's too late, you can't

904
00:43:59,560 --> 00:44:01,360
Speaker 3: abandon and all of that. Like, these people are just

905
00:44:01,400 --> 00:44:03,840
Speaker 3: Republicans in terms, not even Republicans.

906
00:44:03,280 --> 00:44:08,239
Speaker 2: But anti lest That just is like, that's just to

907
00:44:08,360 --> 00:44:12,680
Speaker 2: me as foolhardy a statement, as when Democrats said, these

908
00:44:12,719 --> 00:44:15,480
Speaker 2: people are just they voted for baracka problem, They're just Democrats. Now,

909
00:44:15,480 --> 00:44:17,359
Speaker 2: young people are always going to be Democrats. Latinos are

910
00:44:17,400 --> 00:44:18,839
Speaker 2: just going to continue to fall more and more into

911
00:44:18,840 --> 00:44:21,080
Speaker 2: the Democratic camp. Black people are always going to vote

912
00:44:21,120 --> 00:44:22,880
Speaker 2: like close to one hundred percent for Democrats.

913
00:44:23,280 --> 00:44:24,400
Speaker 4: People change their minds.

914
00:44:24,440 --> 00:44:25,520
Speaker 1: No, I'm talking about time.

915
00:44:25,480 --> 00:44:27,120
Speaker 4: And Trump is a very unique figure too.

916
00:44:27,239 --> 00:44:27,479
Speaker 1: Yeah.

917
00:44:27,880 --> 00:44:30,200
Speaker 3: What I mean by that is a time horizon picture.

918
00:44:30,239 --> 00:44:32,440
Speaker 3: So I think are we talking about twelve years or

919
00:44:32,520 --> 00:44:33,880
Speaker 3: talking about four years? I would say in the next

920
00:44:33,880 --> 00:44:36,040
Speaker 3: four years, I would say they're pretty solidly Republicans. So

921
00:44:36,080 --> 00:44:38,719
Speaker 3: the so called Reagan Democrats, right, the Reagan Democrats who

922
00:44:38,760 --> 00:44:41,719
Speaker 3: eventually then voted for Bill Clinton twelve years ago. Yeah,

923
00:44:41,719 --> 00:44:44,879
Speaker 3: it takes a while, but you know, with the Obama people.

924
00:44:44,600 --> 00:44:45,319
Speaker 1: It took eight years.

925
00:44:45,360 --> 00:44:47,360
Speaker 3: It took twelve years actually for some of these people

926
00:44:47,520 --> 00:44:49,320
Speaker 3: to flip around. I'm not saying they're set in stone,

927
00:44:49,360 --> 00:44:51,919
Speaker 3: but I don't think they're rapidly going to shift right

928
00:44:51,960 --> 00:44:55,360
Speaker 3: back esthetically, especially with where the Democratic Party is today.

929
00:44:55,400 --> 00:44:57,080
Speaker 3: So do I think that all of them are going

930
00:44:57,120 --> 00:44:59,480
Speaker 3: to rapidly shift their vote in four years. No, I

931
00:44:59,480 --> 00:45:03,319
Speaker 3: don't think so, especially the so called ro Coalition and

932
00:45:03,360 --> 00:45:05,720
Speaker 3: all of that. They have too much, there's too much appeal,

933
00:45:05,719 --> 00:45:08,240
Speaker 3: there's too much lack of institutional trust that just doesn't

934
00:45:08,280 --> 00:45:11,680
Speaker 3: mesh with where Democrats are. I mean, there's still the Institute,

935
00:45:11,719 --> 00:45:14,200
Speaker 3: the Party of Institutions, still the Party of the New

936
00:45:14,239 --> 00:45:16,160
Speaker 3: York Times and all those others. Yes, they can turn

937
00:45:16,200 --> 00:45:19,000
Speaker 3: off MSNBC. I think they'll be back. They always are.

938
00:45:19,640 --> 00:45:22,400
Speaker 3: I think that they'll come back to some bullshit Russia

939
00:45:22,480 --> 00:45:25,520
Speaker 3: Gate or whatever. Immigration will be a great test. Will

940
00:45:25,640 --> 00:45:28,640
Speaker 3: Wilson will right back to AOC crying at the gates

941
00:45:28,960 --> 00:45:32,040
Speaker 3: and all these other things. So I predict that actually

942
00:45:32,080 --> 00:45:35,520
Speaker 3: that will make people more hardened, especially when the next

943
00:45:35,560 --> 00:45:38,160
Speaker 3: media reaction is But I could be totally wrong. You know,

944
00:45:38,200 --> 00:45:41,319
Speaker 3: you could have the other so called asteroid austerity and

945
00:45:41,360 --> 00:45:43,839
Speaker 3: all these other folks. But you know THEO Vaughn. Look,

946
00:45:43,880 --> 00:45:46,400
Speaker 3: he may say he likes Bernie, he loves RFK Junior.

947
00:45:46,440 --> 00:45:47,719
Speaker 3: You know, he's the first person who has.

948
00:45:48,200 --> 00:45:52,360
Speaker 2: Rump on anti system, yes politicians, right, yeah, And I

949
00:45:52,400 --> 00:45:55,719
Speaker 2: mean the other thing is Trump is assuming that they

950
00:45:55,719 --> 00:45:58,480
Speaker 2: don't try to pull some whatever, is not going to

951
00:45:58,520 --> 00:45:59,240
Speaker 2: be running again.

952
00:45:59,640 --> 00:46:01,400
Speaker 4: And I do think he is this sort.

953
00:46:01,239 --> 00:46:05,760
Speaker 2: Of very unique figure and there is no proven ability

954
00:46:05,800 --> 00:46:06,160
Speaker 2: in this way.

955
00:46:06,200 --> 00:46:07,160
Speaker 4: He is like Obama.

956
00:46:07,600 --> 00:46:11,200
Speaker 2: There is no proven track record of another Republican really

957
00:46:11,200 --> 00:46:14,920
Speaker 2: being able to position themselves the way that he has.

958
00:46:15,320 --> 00:46:20,000
Speaker 2: So you know, whoever runs next, very likely JD is

959
00:46:20,080 --> 00:46:22,879
Speaker 2: going to be coming off of this sort of who

960
00:46:22,920 --> 00:46:24,560
Speaker 2: knows how the next four years is going to go,

961
00:46:24,640 --> 00:46:27,200
Speaker 2: but probably people are going to be dissatisfied because people

962
00:46:27,239 --> 00:46:30,640
Speaker 2: are really dissatisfied, and you're going to have to own

963
00:46:30,680 --> 00:46:32,680
Speaker 2: that you're going to be in the position of having

964
00:46:32,719 --> 00:46:37,160
Speaker 2: to defend an unpopular four years of administration. Now you are

965
00:46:37,160 --> 00:46:41,160
Speaker 2: going to be the system pro system politician. And so yeah,

966
00:46:41,160 --> 00:46:44,400
Speaker 2: if Democrats want to not just be able to coast

967
00:46:44,440 --> 00:46:47,120
Speaker 2: off of Okay, those guys are unpopular now, if they

968
00:46:47,160 --> 00:46:49,160
Speaker 2: want to be able to actually win and hold on

969
00:46:49,239 --> 00:46:52,799
Speaker 2: to power and build durable and sustainable majorities, they're going

970
00:46:52,880 --> 00:46:54,120
Speaker 2: to have to do something different.

971
00:46:54,160 --> 00:46:55,759
Speaker 4: Am I particularly hopeful they're going to do that.

972
00:46:55,920 --> 00:46:58,160
Speaker 2: No, but there is an opening, there's a possibility, and

973
00:46:58,239 --> 00:47:00,400
Speaker 2: at least some people in the party are grappled with that.

974
00:47:00,480 --> 00:47:03,840
Speaker 2: And I think most importantly, large chunks of the Democratic

975
00:47:03,880 --> 00:47:07,000
Speaker 2: base have been shaken out of this view that they've

976
00:47:07,040 --> 00:47:09,880
Speaker 2: just got to line up behind Pete or Kamala or

977
00:47:09,960 --> 00:47:12,000
Speaker 2: Gavin or whoever the hell they line up and tell

978
00:47:12,040 --> 00:47:13,960
Speaker 2: them next is like, you must vote for this person

979
00:47:14,000 --> 00:47:14,560
Speaker 2: because that's.

980
00:47:14,400 --> 00:47:15,200
Speaker 4: The only way to win.

981
00:47:15,280 --> 00:47:18,839
Speaker 2: I don't think the most hopeful thing is that the

982
00:47:18,960 --> 00:47:21,480
Speaker 2: MSNBC's of the world are never going to have the

983
00:47:21,520 --> 00:47:24,400
Speaker 2: power over the Democratic base that they did in twenty

984
00:47:24,440 --> 00:47:27,520
Speaker 2: sixteen and in twenty twenty. And that's the only thing

985
00:47:27,560 --> 00:47:30,680
Speaker 2: that really creates like a different possibility, a different outcome.

986
00:47:30,800 --> 00:47:33,200
Speaker 1: I don't know. I still think the Pete syop is strong.

987
00:47:33,520 --> 00:47:35,719
Speaker 3: I don't like saying that, but I think that the

988
00:47:35,760 --> 00:47:39,120
Speaker 3: Pete the Buddhaje edge sy op. Imagine his Senate run

989
00:47:39,480 --> 00:47:42,080
Speaker 3: in two years. You could see it, you know. Can

990
00:47:42,080 --> 00:47:45,160
Speaker 3: you see in Michigan he's already working. Him and Chastin

991
00:47:45,239 --> 00:47:48,360
Speaker 3: have moved there, allegedly for childcare purposes. I think we

992
00:47:48,400 --> 00:47:50,720
Speaker 3: all know that that's fake. It's a nice swing state.

993
00:47:50,760 --> 00:47:54,600
Speaker 3: He can get the veneer of a swing state politician.

994
00:47:54,800 --> 00:47:57,480
Speaker 3: He's got what was that place where he raised money

995
00:47:57,719 --> 00:48:00,400
Speaker 3: in California, Cave.

996
00:48:00,200 --> 00:48:01,800
Speaker 1: Wine Cave Pete he never left.

997
00:48:01,840 --> 00:48:04,719
Speaker 2: Oh, it's very like, don't get me wrong, very possible.

998
00:48:04,840 --> 00:48:08,640
Speaker 2: I love like him, We love on Fox News and whatever.

999
00:48:08,840 --> 00:48:10,520
Speaker 4: So maybe, and he's making the rounds.

1000
00:48:10,520 --> 00:48:12,560
Speaker 3: I'm not sure if you've seen this breakfast club. He's

1001
00:48:12,600 --> 00:48:15,399
Speaker 3: everywhere right now. Yeah, tell you're talking about Actually, here's

1002
00:48:15,440 --> 00:48:19,080
Speaker 3: why he was the greatest transportation secretary in history. As

1003
00:48:19,120 --> 00:48:21,640
Speaker 3: we all have sky high airfare and all this other

1004
00:48:21,719 --> 00:48:23,080
Speaker 3: bullshit that we have to deal with.

1005
00:48:22,960 --> 00:48:27,040
Speaker 2: Listen, I think that is most the most likely outcome.

1006
00:48:27,280 --> 00:48:30,799
Speaker 2: I don't deny that some Pete Gavin whoever is the

1007
00:48:30,840 --> 00:48:34,239
Speaker 2: next Democratic nominee, that is the most likely outcome. But

1008
00:48:34,239 --> 00:48:36,759
Speaker 2: but the fact that you don't have that iron grip

1009
00:48:36,800 --> 00:48:40,480
Speaker 2: of the liberal institutions helps create a little bit of possibility.

1010
00:48:40,040 --> 00:48:43,359
Speaker 3: Hopefully in Shalla as they were saying, Okay, everybody, thank

1011
00:48:43,400 --> 00:48:45,879
Speaker 3: you so much for watching our last show of the year.

1012
00:48:46,320 --> 00:48:50,480
Speaker 3: We love you guys so much. Merry Christmas, Happy holidays.

1013
00:48:50,760 --> 00:48:53,759
Speaker 3: I guess, well, maybe we'll be around if there's something crazy.

1014
00:48:53,480 --> 00:48:55,160
Speaker 1: Crazy breaking, and I'm going to keep my eye out.

1015
00:48:55,160 --> 00:48:57,440
Speaker 2: We'll keep our eyes with the government shutdown, in particular,

1016
00:48:57,640 --> 00:48:59,960
Speaker 2: I'll be around more so than you so I can

1017
00:49:00,239 --> 00:49:00,920
Speaker 2: some updates.

1018
00:49:00,960 --> 00:49:02,040
Speaker 4: You guys will have to put up with me.

1019
00:49:04,160 --> 00:49:06,600
Speaker 1: I'm on strict orders. I'm on strict orders.

1020
00:49:06,640 --> 00:49:08,879
Speaker 2: We also don't want to give our producers and our team,

1021
00:49:09,200 --> 00:49:11,719
Speaker 2: you know, break, and so we're going to try to,

1022
00:49:11,880 --> 00:49:14,479
Speaker 2: you know, just lay off the concept. But that doesn't

1023
00:49:14,520 --> 00:49:16,880
Speaker 2: mean there's gotten not going to be anything that is posting.

1024
00:49:16,920 --> 00:49:19,040
Speaker 2: I'm actually gonna have a conversation with Matt Burning today

1025
00:49:19,040 --> 00:49:20,680
Speaker 2: that's going to go up on the channel about healthcare

1026
00:49:20,719 --> 00:49:23,080
Speaker 2: that should be was name checked in this very show.

1027
00:49:22,960 --> 00:49:25,120
Speaker 3: About that well thing will tell me to send me

1028
00:49:25,120 --> 00:49:27,880
Speaker 3: that post because I can't, okay.

1029
00:49:27,040 --> 00:49:28,359
Speaker 4: So we'll have that up.

1030
00:49:28,400 --> 00:49:31,120
Speaker 2: But then also we've put together some of the segments

1031
00:49:31,160 --> 00:49:34,120
Speaker 2: and crazy moments throughound the year that you guys responded

1032
00:49:34,320 --> 00:49:37,239
Speaker 2: most to, and we've done a little you know, some

1033
00:49:37,360 --> 00:49:40,080
Speaker 2: introductions to those, and it's just a crazy When we

1034
00:49:40,080 --> 00:49:42,200
Speaker 2: were recording, it was a crazy trip down memory lane,

1035
00:49:42,239 --> 00:49:45,440
Speaker 2: like the Biden drop down and the RFK brainworms and

1036
00:49:45,520 --> 00:49:48,160
Speaker 2: all of these wild things that unfolded. So that will

1037
00:49:48,200 --> 00:49:50,400
Speaker 2: also be going up on the channel, so you can

1038
00:49:50,440 --> 00:49:51,680
Speaker 2: do a little trip down memory lane.

1039
00:49:51,760 --> 00:49:52,080
Speaker 1: That's right.

1040
00:49:52,080 --> 00:49:54,800
Speaker 3: Well, have best of segments and a few others. Special

1041
00:49:54,840 --> 00:49:57,480
Speaker 3: thank you to our crew, to Mac, to Griffin, to

1042
00:49:57,520 --> 00:49:59,279
Speaker 3: everybody back there control room.

1043
00:49:59,400 --> 00:50:00,759
Speaker 1: It makes just all this stuff work.

1044
00:50:01,239 --> 00:50:05,040
Speaker 3: Steve, our audio man genius who baked these cookies, to

1045
00:50:05,320 --> 00:50:07,439
Speaker 3: the graphics team, to all of the other people who

1046
00:50:07,480 --> 00:50:10,080
Speaker 3: work on this show, but perhaps most importantly to our

1047
00:50:10,080 --> 00:50:12,680
Speaker 3: premium subscribers, who literally we could not do any of

1048
00:50:12,680 --> 00:50:15,440
Speaker 3: this stuff without. We love you guys so much. You

1049
00:50:15,440 --> 00:50:17,959
Speaker 3: guys have helped us build this incredible thing. It's gonna

1050
00:50:17,960 --> 00:50:20,200
Speaker 3: be a great year. I'm really excited already. Today we

1051
00:50:20,239 --> 00:50:23,160
Speaker 3: get to the whole show about CRS and policy, I mean,

1052
00:50:23,200 --> 00:50:28,399
Speaker 3: this is this is really what I think makes this

1053
00:50:28,440 --> 00:50:30,719
Speaker 3: show tick and gives it a lot of strength in

1054
00:50:30,760 --> 00:50:32,279
Speaker 3: the next couple of years, which I think will be

1055
00:50:32,320 --> 00:50:32,960
Speaker 3: really interesting.

1056
00:50:33,000 --> 00:50:35,520
Speaker 1: So we will see you all in the new year.

1057
00:50:35,760 --> 00:50:38,319
Speaker 3: Maybe some interim updates or whatever in the future, but

1058
00:50:38,440 --> 00:50:40,960
Speaker 3: excited for this, and please enjoy your time with families

1059
00:50:40,960 --> 00:50:42,919
Speaker 3: and off and others, and we'll see you later.

1060
00:50:43,080 --> 00:50:45,560
Speaker 4: Indeed, happy holidays everybody, see you in the new year.