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Speaker 1: All media.

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Speaker 2: For it's the face that I'm winning reward of patience

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Speaker 2: given to her politics with pride. Uh man, I'm in

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Speaker 2: a good mood. I'm glad to start the show here,

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Speaker 2: got a great show for you today. I don't know

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Speaker 2: why I'm starting like that, but glad to talk to you.

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Speaker 2: So let me tell you about today and how things

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Speaker 2: gonna be is going zon It's like what the licks

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Speaker 2: reads you feel me like, this is what we're picking

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Speaker 2: up throwing damn take you about the black black whoop woop.

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Speaker 2: I'm gonna catch you on the fifty third. I don't

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Speaker 2: know why I just became my uncle Edges now rest

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Speaker 2: in peace, but essentially that here's the thing, dude, what

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Speaker 2: we tried to do this year, if you ain't figured out,

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Speaker 2: was to stay more current with our episodes what I

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Speaker 2: did most of last year. That was like kind of

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Speaker 2: my plan to kind of be more professional about my

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Speaker 2: podcast and was like I want to be like at

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Speaker 2: least four episodes ahead every month so that like if

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Speaker 2: I don't know, I gotta go on tour or something

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Speaker 2: like something like that happens, where like I can I'll

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Speaker 2: be good. Like I could like skip recording because there's

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Speaker 2: there's enough in the tank. What kept happening last year

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Speaker 2: was like life just kept moving so fast, Like the

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Speaker 2: news kept moving so fast that it was like, by

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Speaker 2: the time I'm ready to talk about it, it's like

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Speaker 2: old news, you know. And I appreciated y'all like riding

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Speaker 2: with me, but ultimately it was like, man, you kind

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Speaker 2: of gotta you gotta stay on point, bro, Like you

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Speaker 2: gotta stay where where we are. In the episodes that

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Speaker 2: were I'm plugging my headphones in because I realized I

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Speaker 2: can hear myself. But the episodes where we were, okay,

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Speaker 2: see now I can hear myself, the episodes where we

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Speaker 2: were like right on top of like what was happening,

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Speaker 2: ended up being you know, the more higher downloaded you

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Speaker 2: may or may not know. By now, I have yet

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Speaker 2: to reach the numbers of the other Cool Zone series

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Speaker 2: to where I'll be able to like actually make some

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Speaker 2: residuals off this show. So what we're trying to do

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Speaker 2: is get them numbers up, So please like subscribe and share.

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Speaker 2: Dot makes bread out this mug. I feel like I'm

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Speaker 2: doing the Lord's work. But anyway, I did not get

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Speaker 2: sent No. One hundred thousand subscribers to YouTube channel. I

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Speaker 2: ain't salty, feel me, and I'm not salty Like congratulations

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Speaker 2: to the to the Kendrick Lamar of the Cool Zone

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Speaker 2: Media te oh Man, that joke has levels, especially because

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Speaker 2: were gonna talk about Kendrick in a little bit. But yeah,

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Speaker 2: So I've been trying to like make sure that these

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Speaker 2: episodes are much more current and visually stimulating and all

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Speaker 2: that good stuff. But there's such a panoply a good

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Speaker 2: good word right there, of a plethora of things we

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Speaker 2: could cover every week. So much happened, trade wars, tariffs, USAID,

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Speaker 2: the Food and Drug Administration, the Department of Consumer Affairs,

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Speaker 2: like the people that make sure that a car company

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Speaker 2: kids can't sell you a card that don't work, you

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Speaker 2: know what I'm saying, Like they gonn like so much

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Speaker 2: to cover. But in that that made me have to

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Speaker 2: scrap the first episode I made that was supposed to

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Speaker 2: be for the beginning of this year, about the cars

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Speaker 2: driving into the French quarters, about the Tesla catching on fire,

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Speaker 2: which clearly was a metaphor and foreshadowing. I didn't get

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Speaker 2: to talk about South Korea impeaching, like impeaching there there

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Speaker 2: there president or Prime minister about that food, trying to

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Speaker 2: call nat you know, martial law. Like, there's so much

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Speaker 2: I didn't get to talk about because life just happened

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Speaker 2: so fast, you feel me. I even did a whole

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Speaker 2: episode about Nikki Giovanni, but like that'll probably see never

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Speaker 2: see the light of day because like I didn't have

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Speaker 2: my camera set up then and too just so much

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Speaker 2: has happened, but a dope thing fell in my lap

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Speaker 2: today and it's about the RFK of it all. So

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Speaker 2: we got to talk about the likelihood of an entire

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Speaker 2: generation having Measles and Ricketts again. Hood politics, y'all. Okay,

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Speaker 2: you may or may not know our play cousin pod,

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Speaker 2: the House of Pod doctor cave Holda who's been on

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Speaker 2: I've been on his show many times. A lot of

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Speaker 2: us on our team have been on his show, and

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Speaker 2: he's also been on The Bastards Pod many times. We

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Speaker 2: love kave Man. I feel like for me specifically, his

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Speaker 2: show is really much like the medical science version of mine,

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Speaker 2: to where it's like he's taking things that are truly complicated, truly,

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Speaker 2: I mean it's it's medical science, you know what I mean,

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Speaker 2: and explaining it in a way that us who don't

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Speaker 2: have you know, degrees in that stuff could like really understand.

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Speaker 2: And I feel like it's so important because it's like

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Speaker 2: sometimes I feel like I have to have I have

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Speaker 2: to jump a hurdle to convince people that, like, yo,

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Speaker 2: you should you should, you should care about politics, like

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Speaker 2: it really affects you, you know, And I have to

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Speaker 2: try to like walk people through as to why this affection,

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Speaker 2: why it's important for you to know what you're talking about, Okave,

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Speaker 2: is like I mean, I don't have to convince you

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Speaker 2: that you should understand medical medicine in the medical field,

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Speaker 2: like you should understand the medical field because it obviously

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Speaker 2: affects you. I know. My premise is these people aren't

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Speaker 2: smarter than you in the political spectrum, They just use

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Speaker 2: a different language. I can't say that about them doctors.

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Speaker 2: They are exactly they are actually smarter. But he has

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Speaker 2: a way of bringing it, bringing it to us. So

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Speaker 2: I was on an incredible show with an incredible human

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Speaker 2: being who is freakishly smart, and I am not gonna

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Speaker 2: double introduce it because it's gonna get introduced when you

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Speaker 2: guys listen to this episode and I'll put some sort

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Speaker 2: of visual on that you could watch if you on

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Speaker 2: YouTube while you're listening to this episode, it'll probably be

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Speaker 2: some sort of performance video of me doing a show

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Speaker 2: at some point, So you can just watch me perform

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Speaker 2: music at poetry while the sound is not synced up,

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Speaker 2: and just listen to these really smart people talk now.

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Speaker 2: But before we get into that.

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Speaker 3: But look, it's like this. Bulldok is like this.

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Speaker 4: Buldeok is like this.

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Speaker 2: It's been quite a week, y'all. Like I mean, it's

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Speaker 2: not like y'all don't know, it's been quite a week. Tariffs, boy,

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Speaker 2: Trump Trump ignited, no sweet little tariffs. Like, if you're

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Speaker 2: from DC, Maryland, you Trump talk about tariffs, tariffs, tariffs, tariffs.

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Speaker 2: Here a tariff, There're a tariff. You get a tariff. Now,

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Speaker 2: you may or may not know what a tariff is.

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Speaker 2: It's a tax. It's an international tax. Basically, if you

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Speaker 2: want to sell here, you got to pay me more

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Speaker 2: to be able to sell here. If you're gonna sell

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Speaker 2: your stuff here, I'm gonna charge you for access basically,

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Speaker 2: and we have what's called a trade deficit. What does

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Speaker 2: that mean? Well, it means the opposite about China, which

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Speaker 2: I'm gonna have to do a whole show about this.

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Speaker 2: But China got a trade surplus. This is all about tariffs,

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Speaker 2: trust me. And in the what that means is this

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Speaker 2: is that China and US sells more than they buy

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Speaker 2: the products they export. They they're making so much more

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Speaker 2: than they're spending because everybody buy China stuff. And Trump

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Speaker 2: don't like that because we have the opposite. It's called

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Speaker 2: a trade deficit, meaning we buying more from everybody else

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Speaker 2: then we're selling. And Trump's response is rather than or

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Speaker 2: it's two parts. It's like, we need to get our

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Speaker 2: weight up. But the only way to get our weight

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Speaker 2: up is to try to like make it harder for

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Speaker 2: them to sell. And what he's going to do is

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Speaker 2: raise the prices on being on having access to all

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Speaker 2: these buyers, right because clearly America likes buying. Now, he

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Speaker 2: was using that as leverage to the North American countries

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Speaker 2: like Canada and Japan, or Japan, like Canada and Mexico.

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Speaker 2: His reason is, well, y'all got to show up them borders.

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Speaker 2: You got to stop all this fentanyl from coming. Canada,

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Speaker 2: like sir, we represent less than one percent of your

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Speaker 2: fentanyl problem. Mexico, like sir, fentanyl is coming from legal

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Speaker 2: citizens going through legal ports. It's not they're not codes.

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Speaker 2: Why with someone trying to flee a wartorm country looking

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Speaker 2: for asylum bring a drug in? It doesn't make any sense.

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Speaker 2: But whatever, what do you want? And Trump's like, whoever

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Speaker 2: did this last trade? Was a dumb dumb who set

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Speaker 2: up this deal last time? Deal's awful. He set up

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Speaker 2: the deal in twenty eight it's his trade deal. I

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Speaker 2: love it, dude. My man turned in his homework from

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Speaker 2: his junior year as if it was a new essay.

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Speaker 2: I love it. Mexico like, this is what you said,

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Speaker 2: this is what you wanted last time. Well, how about

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Speaker 2: I agree it is? And basically they agreed to what

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Speaker 2: they already agreed to. They said, if not, I'm gonna

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Speaker 2: send ten thousand troops to the border. Mexico like, there's

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Speaker 2: already ten thousand troops. You've already sent them. I don't know,

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Speaker 2: what do you what are you talking about? Anyway, So

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Speaker 2: depends on what channel you watch. It worked because you

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Speaker 2: know Canada and Mexico big old air quotes back down.

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Speaker 2: I don't know, we'll see our groceries are more expensive.

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Speaker 2: And because according to him, you know, it's hard to

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Speaker 2: bring down grocery prices. I oh, man, I don't know.

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Speaker 2: It's just so funny to me. And now you know,

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Speaker 2: the maga folks is like, that's right, you should pay more.

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Speaker 2: It's patriotism. These are patriotisms. These are these are patriot eggs.

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Speaker 2: You know what I'm saying, Like you post them. These

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Speaker 2: are patriot prices. You're support You're willing to pay higher

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Speaker 2: prices for your groceries because you love our country. It's

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Speaker 2: like putting your finger on jello man. Anyway. Yeah, the

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Speaker 2: Consumer Protection Agency shut down. Get him to people that

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Speaker 2: make sure the consumers are protected. Yeah, they gone. According

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Speaker 2: to the Department of Government Deficiency or Efficiency, the douge

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Speaker 2: man got yourself security and I'm gonna tell you that

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Speaker 2: right now. Yeah. And the guy that's been put in

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Speaker 2: place to run it for now, this guy named Russell Boight,

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Speaker 2: who you might know from previous hits such as Project

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Speaker 2: twenty twenty five. You know that thing that Trump said

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Speaker 2: he had nothing to do with that. He has his

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Speaker 2: own plan. Yeah, the guy that wrote that is now

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Speaker 2: in charge of the consumer It's absolutely amazing. We got

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Speaker 2: two additions to the State of the Blackness address that

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Speaker 2: will happen in June, one of which is Ye is

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Speaker 2: officially in the gone ahead Bruh season. Look, you got

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Speaker 2: a Chief. It ain't even no point in us hashing

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Speaker 2: out his tweets the Nazi. You got it, Chief, it's

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Speaker 2: all you And like I've talked y'all before, and this

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Speaker 2: is black speech for you're so dumb and it's not

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Speaker 2: worth our time explaining to you or trying to save

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Speaker 2: you from whatever damage you're doing. Like Kanye West is

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Speaker 2: the Kanye West of hating Kanye West. He's the best

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Speaker 2: at destroying himself. But you know what, you're a genius.

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Speaker 2: You got it.

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Speaker 1: Bro.

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Speaker 2: Trump kind of subtly, not so subtly, absolutely suggested colonial takeover.

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Speaker 2: Oh Gaza man, does it? Like does net y'allhoo understand

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Speaker 2: that this man is not your friend? Then he don't understand.

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Speaker 2: He even got to talk about that costco costco keeping

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Speaker 2: it real. Them fools said, not only is we not

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Speaker 2: ending our DEI we raising our are wages thirty dollars

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Speaker 2: an hour. Listen, go get you a job. Okay, you

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Speaker 2: could eat your dollar fifty slices of pizza for lunch.

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Speaker 2: You can have twenty five pounds of mayonnaise. At a discount,

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Speaker 2: which you won't need because you make thirty dollars an

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Speaker 2: hour gonna get it costco. I don't know about their

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Speaker 2: I know nothing more than that about them, and the

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Speaker 2: fact that my wife goes there, tries to go at

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Speaker 2: least once a month. It's a lot of food. But

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Speaker 2: then we're a family. For with the sports ball that

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Speaker 2: happened during Kendrick Lamar's concert and the Eagles put a whooping,

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Speaker 2: just this is just a bad day for light skins,

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Speaker 2: you know, because yeah, yeah, there's a bad day for

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Speaker 2: the light skinned folk. And I tell you what, the

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Speaker 2: Eagles whooped them boys the way the Democrats got booked,

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Speaker 2: whooped in the election, ran the skull up on them

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Speaker 2: and speaking to run the score up on them. Corn

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Speaker 2: roll Kenji k Lama, pg Lang, you know what I'm saying,

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Speaker 2: Pull it, sir Kenny. Just that was high art. I

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Speaker 2: saw my favorite My favorite post from this was like

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Speaker 2: Kendrick Lamamar's performance was for people who understand tariffs. It's

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Speaker 2: high art. The symbolism, it's just beautiful at Samuel L.

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Speaker 2: Jackson being Uncle Sam and also a stand in for

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Speaker 2: America being a menstrual show. Knowing how to play the game,

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Speaker 2: the game being over, just so many levels. The revolution

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Speaker 2: we will be televised. You pick the right time, but

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Speaker 2: the wrong guy. This is just so I don't know

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Speaker 2: what your faith is. I don't know how you understand

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Speaker 2: higher powers. But listen the way, the way, the way,

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Speaker 2: the way my blackness is set up. There's there's a

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Speaker 2: piece of us that just kind of says, you know what,

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Speaker 2: some people are just God's chosen person, right man for

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Speaker 2: the right time, chosen as the Bible would say, for

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Speaker 2: such a time as this. I look at Kenny, I

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Speaker 2: look at Kendrick, and I'm like, you're just God's choice.

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Speaker 2: You were, you are the right person for this moment.

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Speaker 2: There's no like, no one, there was no These ruby

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Speaker 2: red slippers don't fit nobody but your foot. And as

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Speaker 2: an artist from California that wants to see political change,

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Speaker 2: I'm like, I'm not the right guy for this. Kendrick

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Speaker 2: was the right guy for this. And it's like and

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Speaker 2: it's performances like that that remind you of it. Right man,

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Speaker 2: right time? Boy did he deliver. There's gonna be think

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Speaker 2: pieces forever about all the symbolism, but know that the

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Speaker 2: I know y'all saw the PlayStation remote as like the

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Speaker 2: floor buttons the middle of it was a prison yard,

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Speaker 2: and then him saying, you know, I want to play

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Speaker 2: y'all favorite, but you know these people like to sue.

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Speaker 2: And then it turns it looks at him directly say

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Speaker 2: Drake oh man, you can't Serena krip walking, the guy

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Speaker 2: sneaking out the gaza flag. Just everything was beautiful. And

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Speaker 2: even more beautiful was the response from the Conservative's worst

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Speaker 2: halftime show ever, Lauren Bober talking about not only one

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Speaker 2: that needs subtitles and you just couldn't think of nothing

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Speaker 2: to tweet except for I don't understand him and you

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Speaker 2: know these songs, so stop playing around. Madison Calthorne talking

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Speaker 2: about halftime show should have been Donald Trump just singing

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Speaker 2: to YMCA boy. I want to believe he's kidding. Charlie

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Speaker 2: Kirk talking about this is not my style of music

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Speaker 2: at The Daily Mail, kench Klamar getting slammed for the

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Speaker 2: worst halftime performance ever. Joe Budden's even talking about why

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Speaker 2: you choose these songs? It was a w care what

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Speaker 2: you say? Y'all ready to get into this episode, so

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Speaker 2: allow me to introduce you to our friends. Please go

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Speaker 2: support their pod to the House of Pod. So listen

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Speaker 2: to this science stuff while you watch me do a

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Speaker 2: rap performance.

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Speaker 1: It's like this, and welcome back to the House of Pod.

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Speaker 1: My name, I'm told is doctor kave Hoda. I hope

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Speaker 1: I'm saying that correctly. Thank you for joining our fun

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Speaker 1: little uh humor adjacent medical podcast or medical adjacent podcast.

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Speaker 1: I'm not even sure anymore. We're gonna look at public health,

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Speaker 1: We're gonna look at medicine. We're gonna look where it

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Speaker 1: meets the news and pop culture today. It meets the

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Speaker 1: news hard. It's always right into the news and it's

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Speaker 1: a nasty fender bender and everyone's gawking at it out

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Speaker 1: side of the road because there's blood and guts and

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Speaker 1: it's terrible and it's people are slowing down traffic for it.

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Speaker 1: It's been about two weeks since President trump second term

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Speaker 1: has begun, and there's been what seems like a large

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Speaker 1: scale attack on the public health infrastructure here in the

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Speaker 1: United States. It's been focused mainly with online information that doctors, scientists,

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Speaker 1: healthcare professionals use to take care of our patients and

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Speaker 1: our communities, but also feels like more than that, we're

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Speaker 1: going to talk about it. We're going to talk about

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Speaker 1: what's been happening, get the latest updates, talk about what

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Speaker 1: that means, maybe if there's any good news to mine

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Speaker 1: out of what's happening these days as well. To do that.

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Speaker 1: I have two good friends and I'm excited to have

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Speaker 1: them both on because they're both friends from different parts

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Speaker 1: of my life and I like them meeting and introducing.

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Speaker 1: I like doing this. This is exciting for me. I

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Speaker 1: hope they get along. I'm really worried that they're not

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Speaker 1: and they're going to fight, and which would be great air,

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Speaker 1: would be great audio, it'd be a great podcast, but

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Speaker 1: it would be weird for me because I love them both.

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Speaker 1: Let's start with the guests with the most appearances. We

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Speaker 1: have Jason Emmanuel Petty, known as Propaganda aka prop He

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Speaker 1: is an MC, a teacher, a host of the very

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Speaker 1: popular Hood Politics, which is one of my favorite podcasts,

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Speaker 1: and all around swell fella, prop Welcome back to the

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Speaker 1: show Man.

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Speaker 2: Thank you, And I'm really mad that I didn't think

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Speaker 2: of saying the House of pod is in effect, y'all

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Speaker 2: in every body steps up is getting wrecked. I can't

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Speaker 2: believe I've never thought about that the House of Pain's

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Speaker 2: classic album starts with the House of Pod is in effect, y'all.

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Speaker 2: I'm like, why have I never thought of it? I

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Speaker 2: don't know why I've never said House of Pain puns

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Speaker 2: every time I saw you.

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Speaker 1: I don't know, because looking at me, I feel like

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Speaker 1: I would be a shoeing for one of the House

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Speaker 1: of Pain.

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Speaker 2: Could have joined you, could have jumped around with him.

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Speaker 2: You know what I'm saying.

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Speaker 1: I do jump around, jump up and get down. You

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Speaker 1: feel like less of these days jumping these days exact

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Speaker 1: more like rocking back and forth rhythmically. You know, you

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Speaker 1: are very attuned to politics, and you follow things very carefully,

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Speaker 1: and your show, as I mentioned, covers a lot of

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Speaker 1: these issues. I love having you on to discuss this stuff.

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Speaker 1: I also brought on someone else that can help us

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Speaker 1: dive in to the public health aspect and what's happening

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Speaker 1: someone who's been following this very closely. My friend, doctor

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Speaker 1: Jeremy Faust. He's an er doctor. He does a lot

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Speaker 1: of health policy work. He's got a very good sub

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Speaker 1: stack called Inside Medicine. Jeremy, welcome back to the show. Buddy.

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Speaker 4: Good to be here.

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Speaker 1: Is is it good to be here. How do you feel.

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Speaker 3: I feel terrible, but it's better to be three less company.

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Speaker 3: Yeah right, it's less terrible when you're with me, right exactly.

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Speaker 1: Yeah.

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Speaker 3: Also, your your whole social media avatar is always you

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Speaker 3: high in the air, jumping up. Literally you like jump Yeah, no,

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Speaker 3: it really does follow that you should be jump up

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Speaker 3: and jump up, jump up and get down.

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Speaker 1: Yeah. I had decent ups when we played basketball. I

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Speaker 1: had decent I mean, it's got you a whole Yeah,

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Speaker 1: I mean I was a below the rim guy. Let's

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Speaker 1: be fair. But if I had to get up, I could.

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Speaker 1: I just became so much harder, so much harder. All right, right, breakaway.

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Speaker 1: We'll get back to that later. Let's talk a little

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Speaker 1: bit about what's happening, Jeremy. Let's start with sort of

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Speaker 1: an overview of the organizations before we go into what

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Speaker 1: the Trump administration is doing to these important health policy organizations.

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Speaker 1: Maybe we could get like a one liner on what

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Speaker 1: these organizations do, or at least historically have done, like,

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Speaker 1: for example, the NIH. Everyone's talking about the NIH these days.

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Speaker 1: For our listeners who may not be totally familiar with that,

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Speaker 1: what is the NAIH? What do they do?

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Speaker 4: So the NIH is the United States federal governments.

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Speaker 3: It's the National Institutes of Health, So it is a

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Speaker 3: bunch of institutes.

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Speaker 4: Within a bigger.

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Speaker 3: Organization, and they study science, they study medicine, they do

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Speaker 3: research internally, so we have scientists professionals who work there

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Speaker 3: to advance.

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Speaker 4: Knowledge for us all.

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Speaker 3: And for example, it was a scientist at the NIH

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Speaker 3: who took the sequence of the coronavirus, the novel coronavirus

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Speaker 3: that was published online in January of twenty twenty, and

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Speaker 3: she put that into a experiment essentially to create the

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Speaker 3: first vaccine that was going to be tested. So someone

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Speaker 3: had to take that information and get that vaccine up

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Speaker 3: and running so Maderena Andfizer could do that. It was

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Speaker 3: an NIH scientist who was entrusted with that work. We

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Speaker 3: owe her Kisi Korbett, you know, a detO gratitude. So

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Speaker 3: they do everything from science that benefits us and saves

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Speaker 3: lives today to research on chronic diseases cancer HIV that's internal.

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Speaker 3: And then the NIH also is a grant giving body,

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Speaker 3: so scientists in academics Stanford University, UCSF, Harvard, where I'm at,

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Speaker 3: any place anywhere, apply for grants and I say, hey,

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Speaker 3: we want to study X, Y Z, and here's how

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Speaker 3: we're going to do it. And the NIH is essentially

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Speaker 3: where where those where decisions on those grants are made,

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Speaker 3: and how federal funds are distributed. So there are NIH

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Speaker 3: funded researchers at literally one hundreds, if not thousands, of

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Speaker 3: institutions around the country. So the NIH does its own research,

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Speaker 3: and it funds external research extraordinarily powerful.

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Speaker 1: Would it be safe to say that, like, it's where

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Speaker 1: a lot of the new good ideas revolving around science

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Speaker 1: come out of, Like when we're looking for new cures,

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Speaker 1: when we're looking for something serious, when when a family

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Speaker 1: member says, isn't there anything that can be done? Is

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Speaker 1: it safe to say that's really where the NIH comes in.

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Speaker 3: Yeah, absolutely, Because the NIH funds projects that might seem

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Speaker 3: a little far afield, like oh, why are we putting

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Speaker 3: money into that? And the answer is, you just never

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Speaker 3: know what's going to materialize in an unexpected way. For example, again,

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Speaker 3: I'll use them, I'll use the COVID experience. There were

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Speaker 3: NAH funded studies back to the nineties and two thousands

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Speaker 3: that someone figured out, oh you know this little mRNA strand,

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Speaker 3: that's a strand of genetic material that our bodies used

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Speaker 3: to make anything, your eyebrows, your finger nails, whatever it is.

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Speaker 4: The mRNA is.

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Speaker 3: Like the little messenger that tells our body how to

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Speaker 3: make stuff. But that little molecule is really fragile. And

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Speaker 3: so some project I think it was nah funded, was, oh,

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Speaker 3: you know what, we figured out a way to stabilize

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Speaker 3: that molecule to make it's less fragile. We don't know

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Speaker 3: really how that's going to help in the future, but

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Speaker 3: isn't that cool We can actually take a thing and

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Speaker 3: make it less fragile and make it more robust.

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Speaker 4: Well, fast forward twenty years.

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Speaker 3: You need that technology to make the vaccines that we

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Speaker 3: used in COVID. So no one knew in the nineties

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Speaker 3: or two thousands when they funded that project where it

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Speaker 3: was going to lead. And a lot of things are

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Speaker 3: dead ends. But that's kind of how it works, and

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Speaker 3: it's sort of a venture capital approach. And cave to

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Speaker 3: your point, like, there are a lot of diseases that

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Speaker 3: affect very few people, so there's not going to be

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Speaker 3: an industry model for that. If a thousand people have

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Speaker 3: a deadly disease, it's very tragic. But I'm sorry Fizer

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Speaker 3: just not going to care because they need they need

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Speaker 3: a patient. But the NIH government research can say, you

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Speaker 3: know what, we have the resources to look after these

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Speaker 3: forgotten things.

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Speaker 4: So yeah, and it's hugely important.

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Speaker 2: I think I think you you you nailed something that

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Speaker 2: I feel like if you if any person is like

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Speaker 2: works in a field to where they've gotten to they've

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Speaker 2: done their ten thousand hours, it's like I've read the

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Speaker 2: level of nerdery that I know I can't really talk

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Speaker 2: about at cocktail hour, Like nobody's going to get this,

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Speaker 2: you know what I'm saying. Like, So when you hit

429
00:25:41,440 --> 00:25:44,680
Speaker 2: that level in your field, I feel like when you're

430
00:25:44,720 --> 00:25:47,520
Speaker 2: reading someone else or listening to someone talk about their

431
00:25:47,560 --> 00:25:50,480
Speaker 2: field that you you don't know shit about, you can

432
00:25:50,520 --> 00:25:54,840
Speaker 2: tell if like you're like, okay, nah, I'm just I

433
00:25:55,359 --> 00:25:56,880
Speaker 2: know that you know what you're talking about.

434
00:25:56,920 --> 00:26:00,159
Speaker 1: Yeah, you know when you see it, you know what

435
00:26:00,160 --> 00:26:00,560
Speaker 1: I'm saying.

436
00:26:00,600 --> 00:26:02,880
Speaker 2: So Like, and one of the things you just brought

437
00:26:02,960 --> 00:26:04,679
Speaker 2: up right now, which is something that I've tried to

438
00:26:04,680 --> 00:26:08,360
Speaker 2: say on my podcast, I'm like with when it comes

439
00:26:08,440 --> 00:26:11,840
Speaker 2: to medicine, especially during the pandemic, it's like you you

440
00:26:11,880 --> 00:26:14,360
Speaker 2: want me to tell you you want these you want

441
00:26:14,359 --> 00:26:17,399
Speaker 2: these scientists to tell you, oh, it's this, and I'm like,

442
00:26:17,760 --> 00:26:20,159
Speaker 2: any scientist that knows what they talking about is going

443
00:26:20,200 --> 00:26:22,560
Speaker 2: to be like, I don't know. I think it's that.

444
00:26:22,880 --> 00:26:25,760
Speaker 2: You know, we're still looking at this. We guessed in

445
00:26:25,800 --> 00:26:29,160
Speaker 2: the nineties, we thought it was this. Turns out there's nothing.

446
00:26:29,200 --> 00:26:31,080
Speaker 2: Like you said, it's like some stuff. You research it,

447
00:26:31,119 --> 00:26:33,560
Speaker 2: You're just like, oh, yeah, it was, it was nothing.

448
00:26:33,600 --> 00:26:35,480
Speaker 2: And I'm like the only reason I know it was

449
00:26:35,520 --> 00:26:38,040
Speaker 2: nothing is because we spent twenty years on it, you

450
00:26:38,040 --> 00:26:41,760
Speaker 2: know what I'm saying, So like, so to me, to

451
00:26:41,880 --> 00:26:44,320
Speaker 2: the it's almost like to the degree for which you

452
00:26:44,359 --> 00:26:47,359
Speaker 2: can't give me a yes or no answer, especially in

453
00:26:47,400 --> 00:26:50,520
Speaker 2: that field, is makes me go, this will knows any

454
00:26:50,760 --> 00:26:52,800
Speaker 2: persons they're talking about.

455
00:26:52,840 --> 00:26:56,000
Speaker 3: That is so important because a lot of the people

456
00:26:56,119 --> 00:26:58,520
Speaker 3: on what I would call the anti science side, who

457
00:26:58,560 --> 00:27:01,560
Speaker 3: put forward things that are not scientific. They love to

458
00:27:01,680 --> 00:27:04,440
Speaker 3: lead with so much certainty, and then we say, well,

459
00:27:04,440 --> 00:27:06,800
Speaker 3: here's what we think we know, and we couch it

460
00:27:06,840 --> 00:27:09,840
Speaker 3: around all this language like you're saying, and we're trying

461
00:27:09,880 --> 00:27:12,520
Speaker 3: to be humble and we're trying to adhere to that

462
00:27:12,560 --> 00:27:15,960
Speaker 3: scientific principle which you just you just you just described

463
00:27:16,440 --> 00:27:18,840
Speaker 3: but some people will say, oh, they're not even sure

464
00:27:18,840 --> 00:27:21,720
Speaker 3: it's a theory. It's not it's not a fact, and

465
00:27:21,760 --> 00:27:24,840
Speaker 3: we don't even know, like the theory of gravity is

466
00:27:24,840 --> 00:27:28,280
Speaker 3: a theory, you know, that's I'm pretty sure exactly so

467
00:27:28,640 --> 00:27:31,320
Speaker 3: and so. Yeah, but there is this whole thing about

468
00:27:31,760 --> 00:27:36,400
Speaker 3: about how to communicate uncertainty, and we struggle. We struggle

469
00:27:36,440 --> 00:27:38,720
Speaker 3: with it because we don't want to say something. We

470
00:27:38,760 --> 00:27:42,200
Speaker 3: don't want to downplay what we how strong we feel,

471
00:27:42,320 --> 00:27:45,240
Speaker 3: how strongly we feel. But we also don't want to

472
00:27:45,440 --> 00:27:49,120
Speaker 3: be you know, eating our eating.

473
00:27:48,800 --> 00:27:50,400
Speaker 4: It when if something is wrong.

474
00:27:50,440 --> 00:27:51,639
Speaker 3: And that's, by the way, one of the things I

475
00:27:51,640 --> 00:27:55,400
Speaker 3: said during the pandemic was the difference between an expert

476
00:27:55,920 --> 00:27:58,480
Speaker 3: and a non expert is an expert will change their mind.

477
00:27:58,720 --> 00:28:01,119
Speaker 3: An expert will say, we just learned something.

478
00:28:01,800 --> 00:28:04,560
Speaker 1: Yes, right, No, that's a very good point. I don't

479
00:28:04,600 --> 00:28:07,959
Speaker 1: totally agree with the gravity thing, because, as we discuss,

480
00:28:08,080 --> 00:28:12,520
Speaker 1: my ups defy it. But other than that, I absolutely agree.

481
00:28:12,640 --> 00:28:15,720
Speaker 1: Whenever there's like a gray zone, it's a vacuum and

482
00:28:15,760 --> 00:28:18,879
Speaker 1: it's filled by all these bad actors, and no matter

483
00:28:18,880 --> 00:28:22,880
Speaker 1: how hard we try to educate, it's always more appealing

484
00:28:23,280 --> 00:28:26,680
Speaker 1: to the mass. If someone says, here's the answer. It's

485
00:28:26,720 --> 00:28:31,120
Speaker 1: this versus here's what we think so far, like that's

486
00:28:31,160 --> 00:28:34,240
Speaker 1: always more appealing. It fits our brains better. It's that

487
00:28:34,320 --> 00:28:37,160
Speaker 1: little rush that people want. All right, let's talk about

488
00:28:37,160 --> 00:28:39,560
Speaker 1: some of the other organizations that are big in the news.

489
00:28:39,760 --> 00:28:41,520
Speaker 1: The CDC. What does the CDC do.

490
00:28:42,600 --> 00:28:46,440
Speaker 3: The Centaers for Disas Control and Prevention is in Atlanta,

491
00:28:46,760 --> 00:28:49,719
Speaker 3: and they are It's a massive organization. Does a lot

492
00:28:49,760 --> 00:28:56,680
Speaker 3: of important work, ranging from just pure epidemiology, meaning they

493
00:28:56,880 --> 00:28:59,760
Speaker 3: keep statistics like how many people die, what did they

494
00:28:59,760 --> 00:29:05,440
Speaker 3: die from? That's something that they do. The CDC studies disease.

495
00:29:05,640 --> 00:29:10,760
Speaker 3: It helps scientists and physicians like me and you know

496
00:29:11,400 --> 00:29:14,280
Speaker 3: what the latest recommendation is. So they have a vaccine

497
00:29:14,360 --> 00:29:17,280
Speaker 3: committee that says, okay, what should the scheduling for pediatric

498
00:29:17,360 --> 00:29:22,480
Speaker 3: vaccinations be? And we know that that has saved so

499
00:29:22,600 --> 00:29:24,280
Speaker 3: many lives the past century.

500
00:29:24,680 --> 00:29:25,760
Speaker 1: So they do that.

501
00:29:25,920 --> 00:29:31,200
Speaker 3: They and they also partner with other organizations, both in

502
00:29:31,240 --> 00:29:34,080
Speaker 3: the United States, so it could be nonprofits, it could

503
00:29:34,120 --> 00:29:39,680
Speaker 3: be state public health officials, but also around the world

504
00:29:40,240 --> 00:29:43,920
Speaker 3: in detecting and combating outbreaks. So I'll give you an

505
00:29:43,920 --> 00:29:47,080
Speaker 3: example of that. Right now, there is an outbreak of

506
00:29:47,440 --> 00:29:53,280
Speaker 3: ebola virus in Uganda, and I wouldn't recommend getting ebola,

507
00:29:53,360 --> 00:29:59,040
Speaker 3: but CDC scientists in general would hear about this, and

508
00:29:59,080 --> 00:30:01,160
Speaker 3: they would and there, their experts would be on a

509
00:30:01,160 --> 00:30:05,280
Speaker 3: plane the next day to go to Uganda and say,

510
00:30:05,280 --> 00:30:07,440
Speaker 3: how can we help, What do you have, what do

511
00:30:07,520 --> 00:30:11,120
Speaker 3: you need? How can we augment your resources, your expertise.

512
00:30:11,320 --> 00:30:12,840
Speaker 3: They're not there to tell them how to do it.

513
00:30:13,040 --> 00:30:16,600
Speaker 3: They are there to see where our resources can plug

514
00:30:16,640 --> 00:30:18,800
Speaker 3: holes and be of assistance. And that is the work

515
00:30:18,840 --> 00:30:22,640
Speaker 3: that we do with the w WHO, the World Health Organization,

516
00:30:23,520 --> 00:30:25,920
Speaker 3: and our in our in our own foreign aid. Trump

517
00:30:25,960 --> 00:30:29,240
Speaker 3: put a pause on that last week, so we do

518
00:30:29,360 --> 00:30:31,760
Speaker 3: not have those people on the ground helping that outbreak.

519
00:30:32,080 --> 00:30:34,320
Speaker 3: So that's an example of things that the CBCD is.

520
00:30:34,360 --> 00:30:37,360
Speaker 3: They track down outbreaks. They'll send out literal they call

521
00:30:37,400 --> 00:30:40,480
Speaker 3: them disease hunters, just like the nickname. Someone says, oh,

522
00:30:40,520 --> 00:30:44,600
Speaker 3: there's something happening in rural Washington, these kids are getting sick.

523
00:30:44,760 --> 00:30:47,680
Speaker 3: Someone goes out there and looks into it and figures out, oh,

524
00:30:47,800 --> 00:30:50,320
Speaker 3: oh gosh, there's like there's a poisoning in Salmonila. You know,

525
00:30:50,320 --> 00:30:52,240
Speaker 3: we got to make sure that people know about it.

526
00:30:52,320 --> 00:30:54,720
Speaker 4: So they do everything from keeping.

527
00:30:54,440 --> 00:30:58,920
Speaker 3: Track of big, big data sets, tracking debts, COVID hospitalization,

528
00:30:59,040 --> 00:31:05,440
Speaker 3: tracking disparities and outcomes, and how to treat diseases to

529
00:31:05,640 --> 00:31:07,320
Speaker 3: responding to crisis.

530
00:31:08,800 --> 00:31:12,320
Speaker 1: And just remind the world again for those who may

531
00:31:12,320 --> 00:31:15,120
Speaker 1: have forgotten, why is it important for us, the United

532
00:31:15,120 --> 00:31:19,640
Speaker 1: States to have involvement in infectious disease elsewhere in the world?

533
00:31:19,680 --> 00:31:22,560
Speaker 1: Why is that important for us?

534
00:31:23,200 --> 00:31:26,120
Speaker 3: So even if you yeah, you could just say why

535
00:31:26,120 --> 00:31:28,640
Speaker 3: do I want to help anybody? You could, you could

536
00:31:28,640 --> 00:31:32,680
Speaker 3: say why should we be saving lives? And you know,

537
00:31:33,120 --> 00:31:37,320
Speaker 3: I don't freaking know Slovenia pop new game.

538
00:31:38,400 --> 00:31:40,840
Speaker 1: They give us basketball players who get traded and make

539
00:31:40,880 --> 00:31:43,160
Speaker 1: a big headline. We're not going to talk about that

540
00:31:43,160 --> 00:31:45,120
Speaker 1: because props a Lakers fan, I don't want to deal with. Yes,

541
00:31:45,160 --> 00:31:47,360
Speaker 1: we are sorry, Yes, it's a different podcast.

542
00:31:47,600 --> 00:31:50,280
Speaker 2: Listen, listen. Don't you lose your train of thought. Please,

543
00:31:50,320 --> 00:31:51,280
Speaker 2: don't lose your train of thought.

544
00:31:51,800 --> 00:31:53,520
Speaker 3: Talk about it. I'm an, I have to talk. I

545
00:31:53,800 --> 00:31:55,520
Speaker 3: can get I can get interrupted and go right back.

546
00:31:55,640 --> 00:32:01,080
Speaker 2: Yes, listen, have you ever listened? My way I think

547
00:32:01,080 --> 00:32:03,920
Speaker 2: about this is like if you've if you've ever been

548
00:32:03,960 --> 00:32:08,400
Speaker 2: hit on by somebody incredibly out of your league, super hot,

549
00:32:08,480 --> 00:32:11,120
Speaker 2: and you're like, what am I missing? Why are I?

550
00:32:11,200 --> 00:32:11,280
Speaker 3: Like?

551
00:32:11,440 --> 00:32:15,280
Speaker 2: I feel like I'm decent, like I'm like a good catch,

552
00:32:15,360 --> 00:32:17,640
Speaker 2: but I'm like what what am my business of it?

553
00:32:17,760 --> 00:32:20,400
Speaker 2: Why are you? Why are you talking to me? That's

554
00:32:20,440 --> 00:32:24,120
Speaker 2: the way I feel about us getting Luca, because I'm like, what.

555
00:32:24,160 --> 00:32:27,840
Speaker 1: Am I why? There's less of a stretch. It's less

556
00:32:27,840 --> 00:32:32,520
Speaker 1: of a stretch for you. You're a handsome guy, you're artist,

557
00:32:32,560 --> 00:32:33,440
Speaker 1: the Lakers.

558
00:32:33,640 --> 00:32:35,600
Speaker 2: I get it, you know, And like you said, like

559
00:32:35,640 --> 00:32:37,280
Speaker 2: you know, I'm a pretty talented brother. You know what

560
00:32:37,320 --> 00:32:39,320
Speaker 2: I'm saying. But still like you.

561
00:32:39,280 --> 00:32:42,720
Speaker 1: Don't, you don't get them. No, I'm a I'm a

562
00:32:42,800 --> 00:32:44,520
Speaker 1: Warriors guy, so a personality guy.

563
00:32:47,200 --> 00:32:49,040
Speaker 3: That's how I hear. This is how I felt when

564
00:32:49,040 --> 00:32:50,960
Speaker 3: Cave started, you know, damning me a little bit. I

565
00:32:51,040 --> 00:32:52,160
Speaker 3: was like, oh, coved holder.

566
00:32:52,720 --> 00:32:55,920
Speaker 1: Yeah, you're saying I slipped into your DMS? Is that

567
00:32:55,920 --> 00:32:56,800
Speaker 1: what you're trying to say?

568
00:32:57,000 --> 00:32:59,520
Speaker 3: I'm not sure which way I went. It was a romance.

569
00:32:59,320 --> 00:33:03,920
Speaker 1: Anyway, Yeah, Yeah, go on, sorry, let's le'll get it

570
00:33:03,960 --> 00:33:04,360
Speaker 1: back to time.

571
00:33:04,560 --> 00:33:06,320
Speaker 3: So why do we want to help people? That's the

572
00:33:06,400 --> 00:33:09,040
Speaker 3: question you wanted to ask? Why would we do that?

573
00:33:10,240 --> 00:33:11,440
Speaker 1: So you could you could take.

574
00:33:11,520 --> 00:33:13,400
Speaker 3: The humanitarianis side and say why do we care about

575
00:33:13,400 --> 00:33:16,960
Speaker 3: people in Papa, New Guinea wherever? But there's actually a

576
00:33:17,000 --> 00:33:19,960
Speaker 3: geopolitical reason that we help people. One is our own

577
00:33:19,960 --> 00:33:24,960
Speaker 3: security and one is soft power. So our own security

578
00:33:25,080 --> 00:33:28,880
Speaker 3: is if we can go help a novel outbreak get controlled,

579
00:33:28,960 --> 00:33:31,480
Speaker 3: it won't reach our shores and we don't have that problem,

580
00:33:31,680 --> 00:33:34,520
Speaker 3: that would be lovely. Another one is for HIV for example,

581
00:33:34,520 --> 00:33:37,680
Speaker 3: we provide up until recently, although the question is whether

582
00:33:37,720 --> 00:33:40,200
Speaker 3: it's back online or not, it's very unclear, but up

583
00:33:40,240 --> 00:33:44,160
Speaker 3: until recently, we since the Bush administration, which always shocks people,

584
00:33:44,160 --> 00:33:44,640
Speaker 3: George W.

585
00:33:44,720 --> 00:33:45,920
Speaker 4: Bush started this, we.

586
00:33:45,880 --> 00:33:48,760
Speaker 3: Have been We had this program called PEP Farm, which

587
00:33:48,800 --> 00:33:52,840
Speaker 3: has provided HIV medications to poor nations and has saved

588
00:33:52,880 --> 00:33:56,920
Speaker 3: twenty five million lives. Twenty five million lives, which is

589
00:33:57,640 --> 00:33:58,680
Speaker 3: pretty substantial.

590
00:33:58,720 --> 00:34:00,720
Speaker 4: It's it's a government project that worked.

591
00:34:00,760 --> 00:34:05,600
Speaker 3: Actually, it's like amazing, and so yes, you could make

592
00:34:05,640 --> 00:34:08,280
Speaker 3: the argument like why do we care? But when people

593
00:34:08,520 --> 00:34:12,600
Speaker 3: actually have their HIV controlled by medications, there's less likely

594
00:34:12,680 --> 00:34:16,440
Speaker 3: for resistant bad strains to emerge again, which can come

595
00:34:16,480 --> 00:34:18,439
Speaker 3: here and make our lives more miserable. So it could

596
00:34:18,440 --> 00:34:21,160
Speaker 3: be selfish. Could you can make the altruistic argument, but

597
00:34:21,200 --> 00:34:24,600
Speaker 3: there are selfish reasons to save lives overseas. And then

598
00:34:24,600 --> 00:34:26,279
Speaker 3: the last one is I would say that that I

599
00:34:26,320 --> 00:34:29,239
Speaker 3: could think of is this soft power thing we why

600
00:34:29,280 --> 00:34:32,759
Speaker 3: as America? Why does America have a positive reputation or

601
00:34:32,840 --> 00:34:34,960
Speaker 3: why did it? And a lot of it has to

602
00:34:34,960 --> 00:34:37,799
Speaker 3: do with things you don't always think about. It could

603
00:34:37,840 --> 00:34:40,440
Speaker 3: be literally things like we make the best movies, we

604
00:34:40,520 --> 00:34:44,520
Speaker 3: have the best music, we we have great writers. Culture, right,

605
00:34:44,600 --> 00:34:47,680
Speaker 3: culture is a way to express that a society is

606
00:34:47,719 --> 00:34:48,240
Speaker 3: doing well.

607
00:34:48,640 --> 00:34:49,880
Speaker 4: But another way.

608
00:34:49,680 --> 00:34:52,719
Speaker 3: To do it is to to do the kind of

609
00:34:52,719 --> 00:34:55,400
Speaker 3: work in public health that we've done. And so people

610
00:34:55,440 --> 00:34:57,479
Speaker 3: actually say, oh, the cash the Americans, they really screwed

611
00:34:57,480 --> 00:34:59,760
Speaker 3: it up on this thing in this war, this foreign policy.

612
00:35:00,040 --> 00:35:02,040
Speaker 3: But on the other hand, they did save twenty five

613
00:35:02,120 --> 00:35:05,040
Speaker 3: million lives, so maybe we shouldn't be complete jerks to them.

614
00:35:05,080 --> 00:35:07,160
Speaker 3: So it's it's actually, there's a lot of reasons why

615
00:35:07,160 --> 00:35:07,880
Speaker 3: we do this work.

616
00:35:08,360 --> 00:35:10,440
Speaker 2: This is this again. Now we're crossing into my world

617
00:35:10,560 --> 00:35:13,120
Speaker 2: because of the soft power, cultural power and how to

618
00:35:13,120 --> 00:35:15,040
Speaker 2: be just how to be the man, you know what

619
00:35:15,080 --> 00:35:17,440
Speaker 2: I'm saying, Like, this is how this is how you

620
00:35:17,520 --> 00:35:19,120
Speaker 2: run this is how you run a city. You be

621
00:35:19,160 --> 00:35:21,279
Speaker 2: the guy they go to, no matter how much of

622
00:35:21,320 --> 00:35:22,960
Speaker 2: a jerk that you are. If I'm the dude you

623
00:35:23,000 --> 00:35:27,839
Speaker 2: got to go to, then then if I'm to put

624
00:35:27,920 --> 00:35:29,480
Speaker 2: up with a lot, you put up with a lot.

625
00:35:29,560 --> 00:35:31,000
Speaker 2: Number one, you put up with a lot because you

626
00:35:31,040 --> 00:35:34,359
Speaker 2: have to come to me. And then number two, if

627
00:35:34,600 --> 00:35:36,080
Speaker 2: if you're the guy that you got to go to

628
00:35:36,280 --> 00:35:40,560
Speaker 2: and you're a good dude, then like when you're in need,

629
00:35:40,840 --> 00:35:43,319
Speaker 2: you're gonna be the first person they think of, right

630
00:35:43,360 --> 00:35:46,680
Speaker 2: when when it's when it's my turn, or if there's

631
00:35:46,800 --> 00:35:49,479
Speaker 2: like if we're lobbying for something like okay, I could

632
00:35:49,480 --> 00:35:53,160
Speaker 2: sell you know, we could give this ten million gallons

633
00:35:53,200 --> 00:35:55,359
Speaker 2: of crude oil to this country, or we could give

634
00:35:55,400 --> 00:35:57,959
Speaker 2: it to them. Then again, they did save twenty million lives,

635
00:35:57,960 --> 00:35:59,920
Speaker 2: and maybe we should sell it to them first and

636
00:36:00,080 --> 00:36:02,839
Speaker 2: maybe at a discounted rate. You know. So yeah, there's that,

637
00:36:02,920 --> 00:36:05,560
Speaker 2: there's that. But I think the best part to me

638
00:36:05,680 --> 00:36:07,839
Speaker 2: is what you're saying as far as the selfs side

639
00:36:07,840 --> 00:36:09,880
Speaker 2: of Like you act like there's a force field at

640
00:36:09,920 --> 00:36:14,800
Speaker 2: the forty seventh parallel that just stops the air from flowing.

641
00:36:14,920 --> 00:36:17,200
Speaker 2: Like what is you talking about? Like is one planet?

642
00:36:17,640 --> 00:36:20,759
Speaker 2: Like why would it not get here like like, there's

643
00:36:20,800 --> 00:36:23,839
Speaker 2: no we don't really have a wall, guys. And even

644
00:36:23,880 --> 00:36:26,920
Speaker 2: if we did, like if COVID taught.

645
00:36:26,760 --> 00:36:29,920
Speaker 1: Us nothing else, it should be that what's happening in

646
00:36:30,000 --> 00:36:33,040
Speaker 1: other countries will impact us. There is no way we're

647
00:36:33,040 --> 00:36:34,960
Speaker 1: going to be able to plan with that. Oh it's

648
00:36:35,200 --> 00:36:39,040
Speaker 1: it's mental. But you know what else is unavoidable and inevitable.

649
00:36:39,600 --> 00:36:42,680
Speaker 1: It's the commercials that are coming right into your ears.

650
00:36:42,760 --> 00:36:44,520
Speaker 1: Stay tuned, we'll be right back. We're going to talk

651
00:36:44,520 --> 00:36:47,279
Speaker 1: about what's happening to these organizations. We'll be right back,

652
00:36:48,960 --> 00:36:51,839
Speaker 1: and we're back. That break was also brought to you

653
00:36:51,880 --> 00:36:55,840
Speaker 1: by my favorite hot sauce in the whole widest world.

654
00:36:56,200 --> 00:37:00,000
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655
00:37:00,239 --> 00:37:03,920
Speaker 1: And you know what, My guests are gonna go home

656
00:37:04,360 --> 00:37:07,760
Speaker 1: with a four pack of my favorite sauces from Lucky

657
00:37:07,800 --> 00:37:11,080
Speaker 1: Dog because I love them, I love Lucky Dog, and

658
00:37:11,200 --> 00:37:14,200
Speaker 1: I love the combination of them having Lucky Dog in them.

659
00:37:14,480 --> 00:37:16,759
Speaker 1: So that's what's gonna happen. If you're out there in

660
00:37:16,760 --> 00:37:18,680
Speaker 1: the world and you want to check out Lucky Dog,

661
00:37:18,880 --> 00:37:21,399
Speaker 1: please do that. Lucky Dog Hot Sauce dot Com. Anyways, Okay,

662
00:37:21,440 --> 00:37:24,520
Speaker 1: we are back and we're going to talk a little

663
00:37:24,560 --> 00:37:29,960
Speaker 1: bit more about what is happening to these organizations. So

664
00:37:30,040 --> 00:37:34,759
Speaker 1: the CDC, let's start with that. Jeremy, what is happening online?

665
00:37:34,880 --> 00:37:39,920
Speaker 1: It's what do we make of a federal Health and

666
00:37:39,960 --> 00:37:46,840
Speaker 1: Disease agency deleting pages of their website and censoring words?

667
00:37:47,120 --> 00:37:49,160
Speaker 1: What the This was my question.

668
00:37:49,400 --> 00:37:51,359
Speaker 2: I was like when he said, I was like, can

669
00:37:51,400 --> 00:37:56,320
Speaker 2: somebody tell me why? Like this?

670
00:37:56,320 --> 00:37:56,440
Speaker 1: This?

671
00:37:56,880 --> 00:37:57,960
Speaker 2: Are we in a movie?

672
00:37:58,040 --> 00:37:58,160
Speaker 3: Like?

673
00:37:58,239 --> 00:38:00,439
Speaker 2: This is a movie. That's what you're doing a movie?

674
00:38:00,920 --> 00:38:04,520
Speaker 2: Mortal scientists did not say to science shit, Jason?

675
00:38:04,520 --> 00:38:06,719
Speaker 1: Has this ever happened before? I mean, I feel like

676
00:38:06,719 --> 00:38:09,960
Speaker 1: you study history. Has there ever been a time in

677
00:38:10,320 --> 00:38:13,839
Speaker 1: our world where they've limited what the scientists can say?

678
00:38:13,920 --> 00:38:17,279
Speaker 2: They've sid I just want to It's on the tip

679
00:38:17,320 --> 00:38:18,920
Speaker 2: of my tongue.

680
00:38:20,480 --> 00:38:21,799
Speaker 1: Oh, Jeremy, what's going on?

681
00:38:22,200 --> 00:38:23,640
Speaker 2: Yeah?

682
00:38:23,719 --> 00:38:24,439
Speaker 1: I read this book.

683
00:38:24,520 --> 00:38:26,160
Speaker 3: I read this book a bunch of years ago called

684
00:38:26,200 --> 00:38:29,640
Speaker 3: How Democracies Die, and they were like, check check check check,

685
00:38:29,680 --> 00:38:31,440
Speaker 3: check check check, you know, and this is just right

686
00:38:31,520 --> 00:38:36,640
Speaker 3: on that playlist and our checklist, and yeah, it's what's

687
00:38:36,680 --> 00:38:39,560
Speaker 3: funny to me? It's not funny, None of it's funny,

688
00:38:39,680 --> 00:38:42,080
Speaker 3: but what's funny to me is if you look at

689
00:38:42,080 --> 00:38:46,439
Speaker 3: what they're doing, is how how sensitive they are. Oh no, oh,

690
00:38:46,480 --> 00:38:49,319
Speaker 3: oh gosh if someone were to say this word, what

691
00:38:49,360 --> 00:38:52,360
Speaker 3: would your virgin, precious eyes do with that information?

692
00:38:53,239 --> 00:38:57,200
Speaker 4: Really, I can't say gender, like come on, grow up?

693
00:38:57,440 --> 00:38:59,560
Speaker 3: Like what what is wrong with these people? So I

694
00:38:59,560 --> 00:39:01,960
Speaker 3: get that they have a particular agenda that I don't

695
00:39:02,000 --> 00:39:05,480
Speaker 3: agree with, But what I think is just worth noting

696
00:39:06,000 --> 00:39:09,239
Speaker 3: is what a bunch of snowflakes, you know, Like, come on,

697
00:39:10,080 --> 00:39:13,920
Speaker 3: if we talk about these issues, is the world going

698
00:39:13,960 --> 00:39:18,399
Speaker 3: to stop turning? And so what they've done is there's

699
00:39:18,400 --> 00:39:20,440
Speaker 3: been a couple levels of censorship with the CDC. It's

700
00:39:20,440 --> 00:39:22,520
Speaker 3: been escalating and then a little bit of de escalation

701
00:39:22,600 --> 00:39:26,480
Speaker 3: and then escalation again. So at first Trump said there

702
00:39:26,480 --> 00:39:29,640
Speaker 3: was a complete gag order on all communications from any

703
00:39:30,000 --> 00:39:33,800
Speaker 3: federal agency but including public health agencies, so they couldn't

704
00:39:33,800 --> 00:39:34,439
Speaker 3: get anything out.

705
00:39:34,520 --> 00:39:36,600
Speaker 4: So the CDC's usual.

706
00:39:37,000 --> 00:39:42,960
Speaker 3: Scientific journal gone, the CDC's weekly emails about whatever they follow,

707
00:39:43,000 --> 00:39:45,960
Speaker 3: whether it's COVID or whether it's how many people died

708
00:39:46,000 --> 00:39:50,520
Speaker 3: of opioids this week gone, There was do comms freeze.

709
00:39:50,680 --> 00:39:55,040
Speaker 3: And then on Friday of just three four days ago,

710
00:39:55,200 --> 00:39:57,360
Speaker 3: and I say that because it feels like about sixteen

711
00:39:57,440 --> 00:40:00,760
Speaker 3: years ago, three or four days ago, they actually wiped

712
00:40:00,800 --> 00:40:03,920
Speaker 3: their websites of like tons of stuff, and some of

713
00:40:03,960 --> 00:40:06,680
Speaker 3: it would fall under the I roll my eyes, but

714
00:40:06,800 --> 00:40:09,360
Speaker 3: I understand what they're doing. They don't think trans is

715
00:40:09,400 --> 00:40:12,759
Speaker 3: a thing, so they're gonna scrub the CDC data website

716
00:40:12,760 --> 00:40:17,160
Speaker 3: of websites that I think are at at minimum they're

717
00:40:17,160 --> 00:40:20,160
Speaker 3: harmless and at best they're helpful, So like how to

718
00:40:20,200 --> 00:40:23,600
Speaker 3: help people who are who identify as transgender get their

719
00:40:23,600 --> 00:40:26,400
Speaker 3: healthcare or get their the medications they may need to

720
00:40:26,400 --> 00:40:28,920
Speaker 3: prevent HIV, the prep that's called right, So they got

721
00:40:29,000 --> 00:40:30,440
Speaker 3: rid of all those and I kind of I would

722
00:40:30,480 --> 00:40:33,239
Speaker 3: I roll my eyes and just like, you know, have

723
00:40:33,320 --> 00:40:36,040
Speaker 3: a seizure when they do that, because I think it's

724
00:40:36,040 --> 00:40:38,680
Speaker 3: just so silly and cruel that they would do that.

725
00:40:39,400 --> 00:40:41,520
Speaker 3: But it kind of matches what their worldview is, and

726
00:40:41,560 --> 00:40:43,920
Speaker 3: they think that that's what the election gave them sort

727
00:40:43,960 --> 00:40:45,960
Speaker 3: of a mandate to do. But they also took down

728
00:40:46,160 --> 00:40:49,680
Speaker 3: vaccine recommendations. They took down data dot CDC dot gov,

729
00:40:49,719 --> 00:40:53,680
Speaker 3: which just has just statistics on stuff like heart disease

730
00:40:53,960 --> 00:40:57,799
Speaker 3: and car accidents and just things that aren't political, and

731
00:40:58,280 --> 00:41:01,000
Speaker 3: also they could scrape it of this langue and that

732
00:41:01,160 --> 00:41:03,839
Speaker 3: one political appointee has to approve before it goes back.

733
00:41:04,440 --> 00:41:06,560
Speaker 1: That is the there's so many crazy parts about this,

734
00:41:06,640 --> 00:41:09,120
Speaker 1: and I will get back to how they're planning to

735
00:41:09,160 --> 00:41:12,200
Speaker 1: do this, But why, I mean, what what is even

736
00:41:12,239 --> 00:41:16,440
Speaker 1: their reasoning for like scrapping things like information on maternal

737
00:41:16,480 --> 00:41:20,239
Speaker 1: morbidity or opioid use. What is the what is their

738
00:41:20,360 --> 00:41:22,480
Speaker 1: long term gain from that? Are they just trying They

739
00:41:22,480 --> 00:41:24,239
Speaker 1: don't want people to know about it, They don't want

740
00:41:24,280 --> 00:41:27,200
Speaker 1: people to know what the racial disparities in like maternal

741
00:41:27,200 --> 00:41:30,279
Speaker 1: health outcomes that like African American women are doing worse.

742
00:41:30,400 --> 00:41:32,520
Speaker 1: What is what is their reasoning for it?

743
00:41:34,880 --> 00:41:37,839
Speaker 3: Well, their stated reasoning is to get rid of wok ideologies,

744
00:41:37,840 --> 00:41:38,720
Speaker 3: whatever that means.

745
00:41:39,000 --> 00:41:41,560
Speaker 2: So what the yes, that's what I'm still like, what

746
00:41:41,600 --> 00:41:43,680
Speaker 2: does that mean? What do you mean? What do you

747
00:41:43,719 --> 00:41:44,200
Speaker 2: mean by that?

748
00:41:44,320 --> 00:41:47,400
Speaker 3: But anyway, finish, I mean, it's it's a really important

749
00:41:47,440 --> 00:41:49,520
Speaker 3: question for the lawyer is to tear apart like what

750
00:41:49,600 --> 00:41:52,760
Speaker 3: the hell does that mean? So that's their stated reason

751
00:41:53,480 --> 00:41:56,640
Speaker 3: and but then it's so wide that people don't know

752
00:41:56,680 --> 00:42:01,839
Speaker 3: how to apply it. So why on earth would they

753
00:42:01,880 --> 00:42:06,560
Speaker 3: take down. Oh, here's one who should what kind of

754
00:42:06,640 --> 00:42:09,880
Speaker 3: contraception women should should have after pregnancy?

755
00:42:10,280 --> 00:42:11,880
Speaker 4: And you think, oh that, why is that complicated?

756
00:42:11,920 --> 00:42:14,600
Speaker 3: Well, actually, it turns out that if you were just pregnant,

757
00:42:14,640 --> 00:42:17,440
Speaker 3: you just gave birth, and now you're going to get

758
00:42:17,440 --> 00:42:22,279
Speaker 3: back on contraception, it actually matters that you don't use

759
00:42:22,320 --> 00:42:25,200
Speaker 3: certain things. Maybe you developed a complication during pregnancy and

760
00:42:25,200 --> 00:42:28,200
Speaker 3: now you're on a medication and the contraception that you

761
00:42:28,280 --> 00:42:31,680
Speaker 3: used to use could actually really harm you. Okay, that

762
00:42:31,800 --> 00:42:33,840
Speaker 3: is something that is not easy to memorize. So the

763
00:42:33,840 --> 00:42:36,880
Speaker 3: CDC has a chart for that. And so why did

764
00:42:36,880 --> 00:42:39,040
Speaker 3: they get rid of that chart because it had the

765
00:42:39,040 --> 00:42:44,440
Speaker 3: word gender on it? Oh my god, gender, you know gender.

766
00:42:44,800 --> 00:42:49,880
Speaker 3: And again I'm like, god, y'all, it's not a bad word.

767
00:42:50,120 --> 00:42:51,920
Speaker 3: I mean, it's just a you know gone.

768
00:42:51,800 --> 00:42:56,360
Speaker 2: And it's also in his executive order you said there's

769
00:42:56,440 --> 00:43:02,480
Speaker 2: two genders. Mm hm, So you can't say gender just

770
00:43:02,640 --> 00:43:05,799
Speaker 2: so you mean we're so you mean we're genderless.

771
00:43:06,360 --> 00:43:08,319
Speaker 3: They're trying to make this sex gender. They're trying to

772
00:43:08,320 --> 00:43:10,440
Speaker 3: make this whole sex gender thing like tex is what

773
00:43:10,480 --> 00:43:13,680
Speaker 3: you're born, you know, what your chromosomes are, ok, And

774
00:43:13,760 --> 00:43:15,680
Speaker 3: gender is how you present, and that's what they're trying

775
00:43:15,760 --> 00:43:17,640
Speaker 3: to do. But the but the crazy thing is that

776
00:43:17,680 --> 00:43:20,640
Speaker 3: even CDC epidemiologists who I've worked with, I've published papers

777
00:43:20,640 --> 00:43:22,719
Speaker 3: with them once in a while, like they'll just put

778
00:43:22,760 --> 00:43:25,399
Speaker 3: like sexual gender interchangeably. They don't actually like don't think

779
00:43:25,400 --> 00:43:28,400
Speaker 3: about it too much. And so they're like, Oh, we

780
00:43:28,440 --> 00:43:30,160
Speaker 3: don't have to change anything. We just have to go

781
00:43:30,360 --> 00:43:32,440
Speaker 3: We literally have to take this website down until we

782
00:43:32,560 --> 00:43:35,560
Speaker 3: change the word sex gender to sex. And there's thousands

783
00:43:35,600 --> 00:43:37,279
Speaker 3: of pages and it's and there's no way to do

784
00:43:37,320 --> 00:43:38,000
Speaker 3: it with AI.

785
00:43:38,080 --> 00:43:40,840
Speaker 2: There's no FQ, right, yeah.

786
00:43:40,600 --> 00:43:43,239
Speaker 3: And so it's it's it's that kind of thing. Another

787
00:43:43,280 --> 00:43:46,840
Speaker 3: one is they got really up up in arms about

788
00:43:46,920 --> 00:43:50,279
Speaker 3: pregnant people versus pregnant women. And I'll just say, as

789
00:43:50,280 --> 00:43:53,240
Speaker 3: someone who was like born a long time ago, pregnant

790
00:43:53,239 --> 00:43:54,960
Speaker 3: people is one of the terms that like I have

791
00:43:55,000 --> 00:43:58,120
Speaker 3: to remember to say because I think of pregnancy as

792
00:43:58,120 --> 00:44:00,279
Speaker 3: something that happens to women. But then I'm like, oh,

793
00:44:00,280 --> 00:44:02,120
Speaker 3: but there are these educations and you can tell me like, oh,

794
00:44:02,120 --> 00:44:03,520
Speaker 3: this person was born this way and they have a

795
00:44:03,600 --> 00:44:05,520
Speaker 3: uterist but they're a man in terms of their gender,

796
00:44:05,640 --> 00:44:07,480
Speaker 3: So I get it. I'm woke okay, but it's still

797
00:44:07,520 --> 00:44:09,120
Speaker 3: hard for me to always get the words out the

798
00:44:09,120 --> 00:44:12,719
Speaker 3: first time, you know, just because that's how I'm from here.

799
00:44:12,800 --> 00:44:15,120
Speaker 1: Yeah, yeah, I mean, but it makes me want to

800
00:44:15,120 --> 00:44:17,640
Speaker 1: say it more than ever. Totally, This makes me want

801
00:44:17,680 --> 00:44:20,440
Speaker 1: to be I'm not I don't even consider myself like

802
00:44:20,680 --> 00:44:23,040
Speaker 1: necessarily woke. I don't, but like I don't even know

803
00:44:23,040 --> 00:44:25,400
Speaker 1: what that means anymore. But like, I don't even consider

804
00:44:25,480 --> 00:44:28,360
Speaker 1: myself that like progressive, But this makes me want to

805
00:44:28,480 --> 00:44:31,200
Speaker 1: use pronouns. This makes me want to do that stuff.

806
00:44:31,239 --> 00:44:35,560
Speaker 1: It's ridiculous. They don't understand, like adults are doing this.

807
00:44:36,239 --> 00:44:39,000
Speaker 2: Yeah, I first of all, am like, I refuse to

808
00:44:39,040 --> 00:44:42,040
Speaker 2: succeed the definition of woke to these people. Like, that's

809
00:44:42,080 --> 00:44:45,080
Speaker 2: not what it means, right, My granddaddy said woke. What

810
00:44:45,120 --> 00:44:48,120
Speaker 2: he's what he means is are you aware for which

811
00:44:48,160 --> 00:44:50,680
Speaker 2: are you? Are you awake to the ways for which

812
00:44:50,680 --> 00:44:54,640
Speaker 2: you're being subjucated? Are you aware of your politics? You

813
00:44:54,760 --> 00:44:58,440
Speaker 2: have to stay awake, so stay woke. And I'm like,

814
00:44:59,040 --> 00:45:01,040
Speaker 2: if anything, I'm like you just as wek as we are,

815
00:45:01,719 --> 00:45:05,480
Speaker 2: because what you're saying is you being subjugated and your

816
00:45:05,520 --> 00:45:07,440
Speaker 2: way of life is being taken from you, and you

817
00:45:07,480 --> 00:45:09,640
Speaker 2: are staying aware and fighting back. I'm like you just

818
00:45:09,640 --> 00:45:11,440
Speaker 2: as well as I am, so I don't understand what

819
00:45:11,440 --> 00:45:13,960
Speaker 2: you're talking about, right number one? And number two, I

820
00:45:14,040 --> 00:45:16,120
Speaker 2: think to your point about just I've always said that,

821
00:45:16,239 --> 00:45:19,560
Speaker 2: like privilege makes you brittle man, And I'm like, I

822
00:45:19,719 --> 00:45:22,799
Speaker 2: just and then and then the weird mixture of that

823
00:45:22,880 --> 00:45:25,840
Speaker 2: and just this whole like masculinity man of sphere of stuff,

824
00:45:25,840 --> 00:45:29,440
Speaker 2: and I'm like, I don't. I don't, like you said,

825
00:45:29,480 --> 00:45:31,080
Speaker 2: I don't carry a lot of your a lot of

826
00:45:31,120 --> 00:45:33,719
Speaker 2: their views of like what it means to be a

827
00:45:33,760 --> 00:45:36,080
Speaker 2: masculine man. But there's one thing that I do have

828
00:45:36,400 --> 00:45:39,440
Speaker 2: is most of the time, I really don't care what

829
00:45:39,440 --> 00:45:42,759
Speaker 2: that man's doing over there. That's not my concern. You

830
00:45:42,760 --> 00:45:44,839
Speaker 2: want to you want to say gender, what do I care?

831
00:45:45,400 --> 00:45:48,400
Speaker 2: What do I care? If you want to say pregnant people?

832
00:45:48,480 --> 00:45:49,520
Speaker 2: I don't like.

833
00:45:49,520 --> 00:45:50,920
Speaker 1: I got shit to do.

834
00:45:50,960 --> 00:45:52,839
Speaker 2: You know what I'm saying, Like, I don't like, why

835
00:45:52,960 --> 00:45:54,240
Speaker 2: is you so worried about?

836
00:45:54,920 --> 00:45:55,200
Speaker 1: To me?

837
00:45:55,600 --> 00:45:59,520
Speaker 3: That sounds pretty and to be you know what I'm saying.

838
00:46:00,040 --> 00:46:01,840
Speaker 3: And they claim that they're doing this to defend women.

839
00:46:02,480 --> 00:46:05,719
Speaker 3: And my problem with that is that they don't do

840
00:46:06,239 --> 00:46:08,520
Speaker 3: things that actually defend.

841
00:46:32,800 --> 00:46:36,040
Speaker 1: I mean. Also, no one's ever gotten mad at me

842
00:46:36,320 --> 00:46:40,040
Speaker 1: for not saying pregnant people like, you know, I should

843
00:46:40,080 --> 00:46:41,879
Speaker 1: use the term. I agree I should use it. I'm

844
00:46:41,880 --> 00:46:44,400
Speaker 1: gonna be much more mindful about using it now because

845
00:46:44,400 --> 00:46:46,600
Speaker 1: of this. But no one ever got mad at me

846
00:46:46,640 --> 00:46:49,239
Speaker 1: in the past. Ever. You know, maybe what someone would

847
00:46:49,280 --> 00:46:52,160
Speaker 1: correct you. But like they make it sound like there's

848
00:46:52,200 --> 00:46:56,120
Speaker 1: this army of quote unquote woke people online, scientists and

849
00:46:56,200 --> 00:46:59,960
Speaker 1: doctors like, no, man, no one's gotten mad at me that.

850
00:47:00,680 --> 00:47:04,360
Speaker 2: I've missed a few days, then a couple of times. Yeah,

851
00:47:04,400 --> 00:47:08,120
Speaker 2: And like you said, no one blinks because we've shown

852
00:47:09,280 --> 00:47:13,480
Speaker 2: we actually care that other. I care about how you feel. Like,

853
00:47:13,920 --> 00:47:15,960
Speaker 2: if that's important to you, I'm gonna try to do

854
00:47:16,040 --> 00:47:18,200
Speaker 2: something that's important to you. You understand that. That's the

855
00:47:18,239 --> 00:47:21,399
Speaker 2: way I feel. It's no skin off my Like, that's

856
00:47:21,440 --> 00:47:23,120
Speaker 2: not a hill I need to die on. What are

857
00:47:23,120 --> 00:47:25,400
Speaker 2: you talking about? They wanted to call you this, I

858
00:47:25,480 --> 00:47:25,960
Speaker 2: just forgot.

859
00:47:26,080 --> 00:47:27,080
Speaker 1: Yeah.

860
00:47:27,160 --> 00:47:30,319
Speaker 3: And there's also this whole thing about the extreme, the

861
00:47:30,360 --> 00:47:35,000
Speaker 3: extremes of this. So the Trump administration argument against all

862
00:47:35,120 --> 00:47:39,200
Speaker 3: this stuff, or against DEI is that on the liberal

863
00:47:39,200 --> 00:47:42,440
Speaker 3: progressive side, these things are requirements to get grants or

864
00:47:42,480 --> 00:47:45,960
Speaker 3: to get hired or whatever, and that's just not true.

865
00:47:46,280 --> 00:47:49,680
Speaker 3: What is true, but is true, is that on that

866
00:47:49,760 --> 00:47:52,839
Speaker 3: side of the spectrum, there's interest in those things so

867
00:47:52,880 --> 00:47:55,680
Speaker 3: that we have representations, so that we serve the communities

868
00:47:55,920 --> 00:47:58,719
Speaker 3: that we are trying to serve. And there's so there's

869
00:47:58,800 --> 00:48:00,719
Speaker 3: interest in that. And I think I'll know that that's

870
00:48:00,840 --> 00:48:03,480
Speaker 3: not only true, but it's important that there's interest in that.

871
00:48:03,760 --> 00:48:07,320
Speaker 3: But there's no there's They have this fantasy that people

872
00:48:07,360 --> 00:48:10,320
Speaker 3: get hired or things happen one hundred percent only because

873
00:48:10,400 --> 00:48:12,920
Speaker 3: they check those boxes and when in fact it's a

874
00:48:12,960 --> 00:48:16,640
Speaker 3: piece of the pie. On the flip side, they're banning

875
00:48:16,640 --> 00:48:19,560
Speaker 3: it one hundred percent because of that. Yeah, they so

876
00:48:19,680 --> 00:48:22,040
Speaker 3: they are actually taking something that is a part of

877
00:48:22,080 --> 00:48:24,600
Speaker 3: the pie and saying thou shalt not do that, and

878
00:48:24,640 --> 00:48:26,200
Speaker 3: they're making it the whole pie.

879
00:48:27,200 --> 00:48:28,960
Speaker 2: I think I get. I think to your point, I

880
00:48:29,000 --> 00:48:33,120
Speaker 2: get the The real the sinister part about that is

881
00:48:33,239 --> 00:48:41,040
Speaker 2: like that the not so subtext is you are either

882
00:48:42,040 --> 00:48:47,759
Speaker 2: qualified or diverse, right as if to say that if

883
00:48:47,800 --> 00:48:50,560
Speaker 2: I am of a diverse group, I am not qualified.

884
00:48:50,960 --> 00:48:54,160
Speaker 2: And then and out of that same subtext is the

885
00:48:54,239 --> 00:48:58,080
Speaker 2: idea that like, well, you've just you've defined what you

886
00:48:58,200 --> 00:48:59,040
Speaker 2: mean by diverse.

887
00:48:59,600 --> 00:49:00,200
Speaker 3: You know what I'm saying.

888
00:49:00,200 --> 00:49:05,560
Speaker 2: So you're saying, that's everybody except right. If you not

889
00:49:05,600 --> 00:49:07,640
Speaker 2: a white dude, you part of diversity, you know what

890
00:49:07,680 --> 00:49:09,680
Speaker 2: I'm saying. And like a straight white dude, then you

891
00:49:09,760 --> 00:49:12,440
Speaker 2: part of diversity. So you're saying, no one is qualified.

892
00:49:12,640 --> 00:49:17,560
Speaker 1: Yeah, it's a fundamental, fundamental misunderstanding of how it works. Yeah,

893
00:49:17,600 --> 00:49:19,719
Speaker 1: fundamental understanding how it works.

894
00:49:19,840 --> 00:49:22,040
Speaker 2: Yes, And then I'm saying, but like I'm saying, if

895
00:49:22,080 --> 00:49:25,760
Speaker 2: you were just a pure evil capitalist, just the worst

896
00:49:25,840 --> 00:49:27,960
Speaker 2: version of a late stave capitalist, you would be like,

897
00:49:28,160 --> 00:49:31,720
Speaker 2: I'm I am going to fish in the largest ocean

898
00:49:31,800 --> 00:49:35,640
Speaker 2: possible to find the best person. But instead you saying

899
00:49:35,760 --> 00:49:38,360
Speaker 2: I Am not gonna find the best person possible. Is

900
00:49:38,360 --> 00:49:40,759
Speaker 2: what you just said. That's what you're actually saying is

901
00:49:41,800 --> 00:49:45,320
Speaker 2: you're removing this, You're eliminating an entire pool of people

902
00:49:45,640 --> 00:49:48,080
Speaker 2: that are probably better than you because you don't want

903
00:49:48,080 --> 00:49:51,480
Speaker 2: to diverse higher. That's what you're saying. Just like I'm like,

904
00:49:51,680 --> 00:49:53,400
Speaker 2: I thought you liked money.

905
00:49:53,520 --> 00:49:55,000
Speaker 1: Just the fact that our science is going to be

906
00:49:55,080 --> 00:49:59,640
Speaker 1: limited in any way is not good science. Our science

907
00:49:59,640 --> 00:50:02,719
Speaker 1: should be open, and as Jeremy said earlier, sometimes you

908
00:50:02,760 --> 00:50:05,520
Speaker 1: may not see the importance of one small study. Maybe

909
00:50:05,520 --> 00:50:08,399
Speaker 1: they did a study on squid neurons twenty years ago,

910
00:50:08,440 --> 00:50:11,200
Speaker 1: and you're like, why would you study squid neurons? Fast

911
00:50:11,200 --> 00:50:13,720
Speaker 1: forward to thirty years down the road, people are using

912
00:50:13,719 --> 00:50:18,200
Speaker 1: it for paraplegics in studies there that you did an

913
00:50:18,280 --> 00:50:21,400
Speaker 1: expensive study on coffee stains. Someone could joke about that

914
00:50:21,480 --> 00:50:24,239
Speaker 1: and laugh about that, but looking at that helps us

915
00:50:24,239 --> 00:50:26,440
Speaker 1: with signs somewhere down the road that you can't even imagine.

916
00:50:26,480 --> 00:50:30,000
Speaker 1: It's yeah anyways, and it's not like it's any random

917
00:50:30,040 --> 00:50:34,080
Speaker 1: schmuck can just get a study funded by the NIH

918
00:50:34,440 --> 00:50:37,440
Speaker 1: or do work with these things. It's a difficult process.

919
00:50:37,480 --> 00:50:40,640
Speaker 1: You have to go through steps, you have to prove

920
00:50:40,760 --> 00:50:43,120
Speaker 1: things to get there. It's absurd to think that they

921
00:50:43,280 --> 00:50:45,600
Speaker 1: just get it because they're using these terms that are

922
00:50:45,680 --> 00:50:46,480
Speaker 1: quote unquote woke.

923
00:50:47,360 --> 00:50:49,759
Speaker 2: Jeremy, I have a question actually based on that. Like,

924
00:50:49,960 --> 00:50:54,680
Speaker 2: so obviously, the what we're discussing right now is the

925
00:50:54,760 --> 00:50:57,279
Speaker 2: reasons that things are being scrubbed from the website. And

926
00:50:57,320 --> 00:50:59,839
Speaker 2: it's it's not because it's junk science, is because as

927
00:50:59,840 --> 00:51:04,120
Speaker 2: you words, you don't like, where are there any instances

928
00:51:04,280 --> 00:51:07,560
Speaker 2: that you can think of in your time as a

929
00:51:07,600 --> 00:51:09,759
Speaker 2: professional where you were like, yeah, we should probably pull

930
00:51:09,840 --> 00:51:12,880
Speaker 2: that down because that's actually bad science.

931
00:51:12,960 --> 00:51:15,360
Speaker 3: You know what I'm saying, good question, the great question.

932
00:51:15,960 --> 00:51:21,919
Speaker 3: I think that an example of science changing and websites

933
00:51:22,360 --> 00:51:27,240
Speaker 3: responding to that change is the degree to which COVID

934
00:51:27,280 --> 00:51:30,960
Speaker 3: spreads through the air word that is like and by

935
00:51:30,960 --> 00:51:33,240
Speaker 3: the way, this example is one of my favorites because

936
00:51:34,880 --> 00:51:40,640
Speaker 3: it does actually show why content moderation is really really tricky.

937
00:51:41,000 --> 00:51:44,359
Speaker 3: Because I'll give this example because it's what's great about

938
00:51:44,360 --> 00:51:46,400
Speaker 3: this example is it kind of it goes in the

939
00:51:46,480 --> 00:51:48,840
Speaker 3: pendulum in all these different directions. So if in twenty

940
00:51:48,880 --> 00:51:52,280
Speaker 3: twenty I went on Twitter and I said COVID is airborne,

941
00:51:52,760 --> 00:51:55,440
Speaker 3: somebody would be really a lot of really smart scientists

942
00:51:55,480 --> 00:51:59,480
Speaker 3: would say that is false information, that is misinformation, Get

943
00:51:59,480 --> 00:52:01,400
Speaker 3: that out of here. And so the concept moderation that

944
00:52:01,400 --> 00:52:04,880
Speaker 3: people actually think they want would have actually suppressed that information.

945
00:52:05,160 --> 00:52:06,680
Speaker 4: And at the time I really didn't know what to

946
00:52:06,680 --> 00:52:08,239
Speaker 4: do with it, because you know.

947
00:52:08,200 --> 00:52:10,520
Speaker 3: I thought we knew what we knew, and I was

948
00:52:10,520 --> 00:52:12,239
Speaker 3: wearing a mask because I'm like, look, I don't know

949
00:52:12,239 --> 00:52:14,759
Speaker 3: how airborne this thing is or isn't, but it's airborne enough,

950
00:52:14,880 --> 00:52:16,600
Speaker 3: you know, it's like it's gonna get in my mouth

951
00:52:16,680 --> 00:52:17,160
Speaker 3: or something.

952
00:52:17,360 --> 00:52:18,319
Speaker 4: So I wasn't sure.

953
00:52:18,560 --> 00:52:22,279
Speaker 3: And then the argument in the data came forward and

954
00:52:22,320 --> 00:52:26,560
Speaker 3: then we realized, yeah, it's much more spread through droplet

955
00:52:26,560 --> 00:52:29,640
Speaker 3: and air than anyone ever realized. And so then we

956
00:52:29,680 --> 00:52:35,200
Speaker 3: had scientists and public health organizations had to say, oh,

957
00:52:35,360 --> 00:52:38,560
Speaker 3: we gotta we have to change this. Actually yeah, yeah,

958
00:52:38,640 --> 00:52:41,239
Speaker 3: so you go with it. So I think that's an

959
00:52:41,280 --> 00:52:44,479
Speaker 3: example of both. Like everyone got really freaked out about

960
00:52:44,480 --> 00:52:46,480
Speaker 3: CONCEP moderation a few weeks ago because Meta and all

961
00:52:46,520 --> 00:52:48,520
Speaker 3: of them took away there a fact checking. But I

962
00:52:48,520 --> 00:52:51,399
Speaker 3: always use that example. It's not so easy because they

963
00:52:51,480 --> 00:52:55,280
Speaker 3: would have absolutely censored covid is airborne back in February

964
00:52:55,320 --> 00:52:57,960
Speaker 3: twenty twenty. And just like they and they could have,

965
00:52:58,000 --> 00:53:00,319
Speaker 3: by the way, if they if they said that, I know,

966
00:53:00,600 --> 00:53:03,560
Speaker 3: norovirus is airborne, that's wrong. You know, it's still that's

967
00:53:03,560 --> 00:53:06,680
Speaker 3: still wrong. So it's really it's it's hard, but yeah,

968
00:53:07,239 --> 00:53:10,000
Speaker 3: the answer is we do it. We do change our minds.

969
00:53:10,440 --> 00:53:12,880
Speaker 3: And I think that's a really important thing. And I

970
00:53:13,239 --> 00:53:17,040
Speaker 3: it drives me crazy when people won't admit it. You know,

971
00:53:17,160 --> 00:53:20,920
Speaker 3: when they'll stick. They'll stick to what they know, regardless

972
00:53:20,920 --> 00:53:23,280
Speaker 3: of the of the of the data.

973
00:53:24,520 --> 00:53:27,399
Speaker 1: Well, it came back to like this concept of all

974
00:53:27,400 --> 00:53:31,120
Speaker 1: this information in the CDC being censored. How are they

975
00:53:31,239 --> 00:53:33,279
Speaker 1: going to do that? Are they just running some sort

976
00:53:33,280 --> 00:53:37,080
Speaker 1: of program that's gonna AI look through every paper or

977
00:53:37,160 --> 00:53:39,720
Speaker 1: is it gonna be one person? Because to my knowledge,

978
00:53:39,760 --> 00:53:43,160
Speaker 1: there's only been one political point thus far to the CDC.

979
00:53:43,760 --> 00:53:47,359
Speaker 1: Is this one person the new acting director? Is that

980
00:53:47,440 --> 00:53:51,440
Speaker 1: the person now responsible for looking at every study that

981
00:53:51,560 --> 00:53:57,040
Speaker 1: comes their way? Yes, that is all mental answer.

982
00:53:59,200 --> 00:54:00,000
Speaker 2: Do you think that's good?

983
00:54:00,160 --> 00:54:05,280
Speaker 3: Be well it's slow, yeah, yeah, yeah, or clunky?

984
00:54:05,640 --> 00:54:08,280
Speaker 1: How many papers would you estimate? Just a rough estimate.

985
00:54:08,560 --> 00:54:11,160
Speaker 3: I really can't tell you that. I don't know, but

986
00:54:11,280 --> 00:54:14,200
Speaker 3: it's a lot. And it's also these data sets, right,

987
00:54:14,200 --> 00:54:16,480
Speaker 3: A lot of these data sets went down because if

988
00:54:16,480 --> 00:54:18,799
Speaker 3: you had a data set that was saying, how many

989
00:54:18,840 --> 00:54:21,080
Speaker 3: people here's the one this is a real example, how

990
00:54:21,160 --> 00:54:25,040
Speaker 3: many people have gotten their vaccine against RSV, a virus

991
00:54:25,040 --> 00:54:28,920
Speaker 3: that is a seasonal respiratory virus, and the CDC tracks

992
00:54:28,960 --> 00:54:31,239
Speaker 3: that and they report it pretty much as close to

993
00:54:31,280 --> 00:54:33,920
Speaker 3: real time as they can. That went away because I

994
00:54:33,960 --> 00:54:38,760
Speaker 3: think they had women as one of the metrics eighteen

995
00:54:38,760 --> 00:54:41,600
Speaker 3: to forty nine women, fifty to sixty four women, sixty

996
00:54:41,600 --> 00:54:44,759
Speaker 3: five plus women men. Oh no, that had to come down,

997
00:54:44,840 --> 00:54:46,719
Speaker 3: and now it's back on. It's back live because now

998
00:54:46,760 --> 00:54:49,600
Speaker 3: I think it's as females because that's sex versus gender.

999
00:54:49,880 --> 00:54:53,200
Speaker 3: That is the level of creepy obsession they have. They're

1000
00:54:53,239 --> 00:54:56,040
Speaker 3: obsessed with something that nobody cares about. And so that

1001
00:54:56,160 --> 00:54:58,560
Speaker 3: so and literally one person at the top has to

1002
00:54:58,640 --> 00:55:03,239
Speaker 3: have to reprove that there might be some degree of devolving,

1003
00:55:03,320 --> 00:55:06,759
Speaker 3: meaning the top person saying, okay for issues that are

1004
00:55:06,960 --> 00:55:08,000
Speaker 3: gender sex.

1005
00:55:08,040 --> 00:55:09,000
Speaker 4: That one I just gave you.

1006
00:55:09,120 --> 00:55:10,839
Speaker 3: That can be done by people below me, and I'm

1007
00:55:10,840 --> 00:55:12,719
Speaker 3: gonna sign off on that. But I have not heard

1008
00:55:12,719 --> 00:55:16,720
Speaker 3: that happening. So that's literally what is what it takes.

1009
00:55:16,840 --> 00:55:18,719
Speaker 3: And it's not, by the way, this is not conjecture,

1010
00:55:19,000 --> 00:55:22,239
Speaker 3: this is design. The Trump administration said anything that goes

1011
00:55:22,280 --> 00:55:24,799
Speaker 3: out of these agencies have to be approved by a

1012
00:55:24,840 --> 00:55:27,840
Speaker 3: political appointee, and that's a Trump administration person.

1013
00:55:27,880 --> 00:55:30,759
Speaker 4: That's one Person's just.

1014
00:55:30,840 --> 00:55:34,080
Speaker 1: Gonna be such a terrible time, such a terrible time

1015
00:55:34,160 --> 00:55:37,719
Speaker 1: the next coming years in terms of getting new information

1016
00:55:37,840 --> 00:55:42,560
Speaker 1: out and learning about anything new, anything new, it's gonna be.

1017
00:55:43,040 --> 00:55:45,279
Speaker 1: It's really I don't know if it's getting enough. I

1018
00:55:45,280 --> 00:55:49,120
Speaker 1: don't think it's getting enough attention. I you know, last

1019
00:55:49,160 --> 00:55:51,960
Speaker 1: week there was a lot of confusion with all our

1020
00:55:52,000 --> 00:55:57,320
Speaker 1: friends in academic medicine or anything resembling academic medicine about

1021
00:55:57,680 --> 00:56:02,680
Speaker 1: the freeze spending freeze. Can you explain a little bit

1022
00:56:02,680 --> 00:56:04,680
Speaker 1: about why there was so much confusion and has that

1023
00:56:04,760 --> 00:56:05,600
Speaker 1: cleared up at all?

1024
00:56:07,239 --> 00:56:11,600
Speaker 3: Yes, the administration an ount through a memo that all

1025
00:56:11,640 --> 00:56:14,040
Speaker 3: federal grants had to stop not.

1026
00:56:16,560 --> 00:56:18,600
Speaker 4: Judging new ones. That had to stop.

1027
00:56:18,640 --> 00:56:23,040
Speaker 3: We knew that, which is stupid enough, but existing ones

1028
00:56:23,120 --> 00:56:25,799
Speaker 3: had to be frozen. So spending couldn't happen until they

1029
00:56:25,800 --> 00:56:28,680
Speaker 3: made sure that the terms that they don't like were

1030
00:56:28,760 --> 00:56:34,439
Speaker 3: not involved D whatever, transgender, And the issue there is

1031
00:56:34,480 --> 00:56:39,040
Speaker 3: that the scientists at these institutions could be whatever. The

1032
00:56:39,120 --> 00:56:45,439
Speaker 3: University of Massachusetts are funded by the NIH and they

1033
00:56:45,440 --> 00:56:49,960
Speaker 3: don't want When they get NAH funding, it's so audited.

1034
00:56:50,200 --> 00:56:52,800
Speaker 3: Everything is checked. You have to justify every dollar you spent,

1035
00:56:53,280 --> 00:56:55,200
Speaker 3: and that's how they make sure that there's no corruption

1036
00:56:55,360 --> 00:56:59,560
Speaker 3: and whatever. So if they spent money during that last week,

1037
00:56:59,600 --> 00:57:02,600
Speaker 3: they were people were worried that they would be accused

1038
00:57:02,640 --> 00:57:06,080
Speaker 3: of having violated a federal action in an executive order

1039
00:57:06,239 --> 00:57:08,879
Speaker 3: and that their grant, their million dollar grant for five

1040
00:57:08,960 --> 00:57:12,080
Speaker 3: years to support ten researchers, whatever it is, would be

1041
00:57:12,280 --> 00:57:17,080
Speaker 3: canceled tomorrow. So that meant that, for example, someone who

1042
00:57:17,400 --> 00:57:19,800
Speaker 3: just pays for like some software that they used for

1043
00:57:19,840 --> 00:57:21,880
Speaker 3: statistics and it is five ninety nine a month and

1044
00:57:21,920 --> 00:57:24,640
Speaker 3: it's just on an auto pay, if that wasn't canceled,

1045
00:57:24,880 --> 00:57:29,120
Speaker 3: they violated the agreement. Some people. Yeah, so some people

1046
00:57:29,120 --> 00:57:31,880
Speaker 3: felt like felt like panicked to that level, and they

1047
00:57:31,880 --> 00:57:33,760
Speaker 3: were literally trying to figure out how do I make

1048
00:57:33,800 --> 00:57:36,960
Speaker 3: sure I don't lose my grant on a technicality. Others

1049
00:57:36,960 --> 00:57:39,000
Speaker 3: were like, no, they're not going to be that crazy,

1050
00:57:39,240 --> 00:57:41,400
Speaker 3: But I don't know how they knew because there was

1051
00:57:41,400 --> 00:57:44,600
Speaker 3: no guidance. So then fortunately the American Public Health Association

1052
00:57:44,680 --> 00:57:46,440
Speaker 3: and a few other nonprofits.

1053
00:57:45,880 --> 00:57:47,400
Speaker 4: Sued it got to stay.

1054
00:57:47,680 --> 00:57:50,400
Speaker 3: That bought pause. Then then the Chump administration said, okay,

1055
00:57:50,400 --> 00:57:53,680
Speaker 3: just kidding that we're pulling that memo. Turned out that

1056
00:57:53,720 --> 00:57:56,720
Speaker 3: maybe they claimed it hadn't actually been their intention, which

1057
00:57:56,760 --> 00:58:00,400
Speaker 3: I'm like, okay, that's just in competence, and so.

1058
00:58:00,320 --> 00:58:03,800
Speaker 1: They're fine if that really wasn't competent. Yeah, yeah, massive

1059
00:58:03,880 --> 00:58:04,280
Speaker 1: level that.

1060
00:58:06,040 --> 00:58:07,680
Speaker 2: You can't just be like oh wait I was just

1061
00:58:07,720 --> 00:58:10,360
Speaker 2: playing or wait not. I mean what I was trying

1062
00:58:10,400 --> 00:58:14,400
Speaker 2: to say was like, you don't like this talk about

1063
00:58:14,840 --> 00:58:18,439
Speaker 2: I'm like, this is a CBS receipt, like the list

1064
00:58:18,520 --> 00:58:21,120
Speaker 2: we're talking about of things that you shut down on

1065
00:58:21,120 --> 00:58:22,520
Speaker 2: some I mean to do that.

1066
00:58:23,440 --> 00:58:24,520
Speaker 1: Yeah, it's crazy.

1067
00:58:24,520 --> 00:58:27,520
Speaker 3: It was Actually it was actually really even more bizarre

1068
00:58:27,560 --> 00:58:31,160
Speaker 3: than that, because they pulled the memo and they said, oh, sorry,

1069
00:58:31,240 --> 00:58:32,760
Speaker 3: that memo was not cleared.

1070
00:58:33,240 --> 00:58:35,280
Speaker 4: Uh, it came from the Office of Management and Budget.

1071
00:58:35,280 --> 00:58:38,760
Speaker 3: We didn't do that. But yeah, yeah, I was like,

1072
00:58:38,800 --> 00:58:41,520
Speaker 3: somebody press, you know, send And and then at the

1073
00:58:41,560 --> 00:58:44,000
Speaker 3: press conference where they announced that, they're like, oh so

1074
00:58:44,120 --> 00:58:47,800
Speaker 3: that means that the freeze is over right, and the

1075
00:58:47,840 --> 00:58:50,400
Speaker 3: press secretary said, oh no, the freeze still but wait,

1076
00:58:50,440 --> 00:58:53,240
Speaker 3: can you just tell us? How can you please just

1077
00:58:53,280 --> 00:58:55,440
Speaker 3: tell us what you want to be happening, Like we

1078
00:58:55,480 --> 00:58:57,640
Speaker 3: don't have to agree with it, like we may hate it,

1079
00:58:57,720 --> 00:58:59,440
Speaker 3: but can you at least just do us the service

1080
00:58:59,480 --> 00:59:01,800
Speaker 3: of letting us know the oppression that you're trying to do.

1081
00:59:02,560 --> 00:59:05,160
Speaker 1: Yes, do effective at your fascism? Please?

1082
00:59:05,320 --> 00:59:08,560
Speaker 2: Am I gonna get paid on the fifteenth and that Yeah, right,

1083
00:59:09,280 --> 00:59:10,360
Speaker 2: Like what are you saying?

1084
00:59:10,520 --> 00:59:13,160
Speaker 3: Yeah? Can I just say that what you just said

1085
00:59:13,200 --> 00:59:15,720
Speaker 3: is actually the most important thing, because what's happening here,

1086
00:59:15,760 --> 00:59:19,560
Speaker 3: whether it's the NAH grant recipient at whatever Rutgers University,

1087
00:59:19,920 --> 00:59:22,840
Speaker 3: or whether it's the CDC scientist who has to pull

1088
00:59:22,880 --> 00:59:27,240
Speaker 3: their paper because it says the word woman not female,

1089
00:59:27,280 --> 00:59:34,440
Speaker 3: everyone is complying because can I pay my rent? Yeah,

1090
00:59:34,480 --> 00:59:36,840
Speaker 3: because they may they may be right, and some lawyer

1091
00:59:36,840 --> 00:59:40,280
Speaker 3: may sue for them, and they may retroactively be told, oh,

1092
00:59:40,480 --> 00:59:42,440
Speaker 3: you can't be fired. You get your job back. But

1093
00:59:42,480 --> 00:59:45,320
Speaker 3: in the meantime, the payroll stops and you're going to

1094
00:59:45,480 --> 00:59:47,440
Speaker 3: mis rent and you're going to be evicted. And so

1095
00:59:47,520 --> 00:59:51,120
Speaker 3: everyone is being forced to pick their battles so that

1096
00:59:51,120 --> 00:59:54,120
Speaker 3: they don't become homeless. And that and that is that

1097
00:59:54,240 --> 00:59:58,720
Speaker 3: is so to me just just disgusting. Yeah, yeah, it will.

1098
00:59:59,000 --> 01:00:02,080
Speaker 1: Other than rescinding this memo, has there been any other

1099
01:00:02,160 --> 01:00:05,320
Speaker 1: bits of good news you referred maybe to something with

1100
01:00:05,520 --> 01:00:07,600
Speaker 1: that pep FAR if you could also explain what pep

1101
01:00:07,640 --> 01:00:08,040
Speaker 1: FAR is.

1102
01:00:09,040 --> 01:00:13,280
Speaker 3: Yeah. Pep FAR is the President's Emergency Relief Plan for

1103
01:00:13,520 --> 01:00:17,720
Speaker 3: HIV AIDS that was announced by President George W. Bush

1104
01:00:17,760 --> 01:00:21,320
Speaker 3: when he was president. It was such an amazing amount

1105
01:00:21,360 --> 01:00:24,560
Speaker 3: of funding for the United States to commit to fighting

1106
01:00:24,680 --> 01:00:30,320
Speaker 3: HIV AIDS globally by giving less wealthy nations inexpensive HIV drugs.

1107
01:00:30,520 --> 01:00:34,800
Speaker 3: It was so massive that I remember this. All these

1108
01:00:34,800 --> 01:00:37,840
Speaker 3: liberal progressive activists were like, what George, but now this

1109
01:00:37,880 --> 01:00:38,400
Speaker 3: can't be read.

1110
01:00:38,560 --> 01:00:39,040
Speaker 2: Yeah.

1111
01:00:39,120 --> 01:00:41,520
Speaker 3: They were literally like, we're being We didn't have the

1112
01:00:41,560 --> 01:00:45,280
Speaker 3: turn back then gas lit and it turned out no.

1113
01:00:45,480 --> 01:00:48,200
Speaker 3: He just believed that this was something that was important

1114
01:00:48,240 --> 01:00:51,120
Speaker 3: to do and it saved twenty five million laps so far,

1115
01:00:51,440 --> 01:00:54,520
Speaker 3: and so that was halted last week. That was the

1116
01:00:54,520 --> 01:00:57,120
Speaker 3: one that really just like turned the knife in a

1117
01:00:57,160 --> 01:01:00,280
Speaker 3: lot of our hearts, because it's one thing to do

1118
01:01:00,400 --> 01:01:02,920
Speaker 3: something that's going to affect science down the road. This

1119
01:01:03,080 --> 01:01:07,880
Speaker 3: is like someone in you know, South Africa going to

1120
01:01:07,960 --> 01:01:11,440
Speaker 3: their clinic on Monday to get their HIV med and

1121
01:01:11,640 --> 01:01:15,160
Speaker 3: maybe there's someone whose immune system really got to the

1122
01:01:15,160 --> 01:01:17,120
Speaker 3: brink where they almost died a month ago, but now

1123
01:01:17,120 --> 01:01:19,560
Speaker 3: they got on meds, they're just reconstituting their immune system.

1124
01:01:19,720 --> 01:01:21,760
Speaker 3: These meds are a bridge to their life. If they

1125
01:01:21,760 --> 01:01:23,640
Speaker 3: don't get their meds this week, they're going the other direction.

1126
01:01:23,680 --> 01:01:26,120
Speaker 3: They're dying and so for that to stop, I mean,

1127
01:01:26,160 --> 01:01:29,080
Speaker 3: we just people lost in the United States health care

1128
01:01:29,560 --> 01:01:32,400
Speaker 3: help with health we lost our minds. So that apparently

1129
01:01:32,480 --> 01:01:35,640
Speaker 3: was reversed, and so supposedly the aid's coming out, But

1130
01:01:35,680 --> 01:01:37,840
Speaker 3: then we don't even know because a lot of the

1131
01:01:37,960 --> 01:01:41,720
Speaker 3: organizations that that work on this rely on our funding

1132
01:01:41,760 --> 01:01:44,080
Speaker 3: and that funding is frozen from other ways, so we're

1133
01:01:44,120 --> 01:01:46,120
Speaker 3: a little unclear on what's actually happening.

1134
01:01:47,320 --> 01:01:51,640
Speaker 1: It's amazing, It's it is really amazing. I you know,

1135
01:01:51,800 --> 01:01:55,600
Speaker 1: in regards to you know, sometimes these presidents from the

1136
01:01:55,640 --> 01:02:00,920
Speaker 1: past that we had lower you know, lower esteem for

1137
01:02:00,920 --> 01:02:02,840
Speaker 1: these I still do. I'm not gonna say that George W.

1138
01:02:02,920 --> 01:02:04,880
Speaker 1: Bush is by any means a good dude or a

1139
01:02:04,880 --> 01:02:06,760
Speaker 1: good president. We're not gonna that's not gonna happen on

1140
01:02:06,800 --> 01:02:10,600
Speaker 1: the show, on any of our shows. But it is

1141
01:02:10,640 --> 01:02:13,760
Speaker 1: such an amazing contrast to see what we're seeing now, yea,

1142
01:02:13,880 --> 01:02:17,800
Speaker 1: and to see the the direct impact this sort of

1143
01:02:17,840 --> 01:02:21,000
Speaker 1: thing we'll have on patients. And it's funny because you

1144
01:02:21,000 --> 01:02:26,480
Speaker 1: you can imagine someone a Republican arguing, well, why waste

1145
01:02:26,520 --> 01:02:29,760
Speaker 1: that money and not take care of the person here

1146
01:02:29,800 --> 01:02:33,360
Speaker 1: who needs medicine, And the answer is obviously yes, you're

1147
01:02:33,360 --> 01:02:36,560
Speaker 1: almost there. Let's do that too correct.

1148
01:02:37,160 --> 01:02:39,920
Speaker 3: Well, no, there's also this idea of it's kind of

1149
01:02:39,920 --> 01:02:43,560
Speaker 3: at this point a term of dis bastardized but effect

1150
01:02:43,600 --> 01:02:46,200
Speaker 3: of altruism, which is like doing the most with what

1151
01:02:46,240 --> 01:02:48,120
Speaker 3: you have. And so if you have one hundred dollars

1152
01:02:48,120 --> 01:02:49,560
Speaker 3: to spend to save a life, like what is the

1153
01:02:49,560 --> 01:02:51,480
Speaker 3: best way to spend that? Is it on yourself or

1154
01:02:51,520 --> 01:02:54,080
Speaker 3: is it on someone else? And it might be something

1155
01:02:54,120 --> 01:02:57,000
Speaker 3: like a mosquito net for malaria will save twenty lives,

1156
01:02:57,040 --> 01:03:00,440
Speaker 3: you know, and so but also for our own thing again,

1157
01:03:00,560 --> 01:03:03,880
Speaker 3: like giving HIV meds to a country that wouldn't have them,

1158
01:03:04,040 --> 01:03:05,960
Speaker 3: even though it doesn't seem like it directly helps us,

1159
01:03:06,080 --> 01:03:09,920
Speaker 3: probably a thirty day supply of those HIV meds going

1160
01:03:09,960 --> 01:03:12,600
Speaker 3: to that place actually saves more American lives than directly

1161
01:03:12,600 --> 01:03:13,760
Speaker 3: giving it to the American.

1162
01:03:13,440 --> 01:03:14,680
Speaker 4: Person who didn't need it anyway.

1163
01:03:14,920 --> 01:03:17,000
Speaker 3: So in a way it actually you have to do

1164
01:03:17,120 --> 01:03:18,480
Speaker 3: like the second order effect.

1165
01:03:19,440 --> 01:03:22,680
Speaker 2: Yeah, that's a funny story about Pep far Well maybe

1166
01:03:22,680 --> 01:03:24,680
Speaker 2: it's not funny, but just a little bit of color there.

1167
01:03:24,960 --> 01:03:29,480
Speaker 2: I got a chance to like do some lobbying through

1168
01:03:29,520 --> 01:03:32,200
Speaker 2: the one campaign that gets a lot of his like

1169
01:03:33,080 --> 01:03:36,840
Speaker 2: one campaign kind of started around this time that Bush

1170
01:03:36,960 --> 01:03:40,960
Speaker 2: was doing this thing. And the story goes, Bush was

1171
01:03:41,000 --> 01:03:44,320
Speaker 2: such a Bono fan, Bono gets hip to all this stuff,

1172
01:03:44,360 --> 01:03:48,600
Speaker 2: and then Bono invites just like Christian rock artists. So

1173
01:03:48,640 --> 01:03:51,600
Speaker 2: this dude named Michael W. Smith in this other group

1174
01:03:51,720 --> 01:03:54,640
Speaker 2: called the Newsboys. So they come and they sit down

1175
01:03:54,640 --> 01:03:57,880
Speaker 2: and they meet like just Christian rock. They just come

1176
01:03:57,920 --> 01:04:00,560
Speaker 2: and sit down and talk to it, and they convince

1177
01:04:00,640 --> 01:04:03,360
Speaker 2: him that this is the godly thing to do. And

1178
01:04:05,160 --> 01:04:08,760
Speaker 2: fast forward, fifty percent of AIDS in Africa's has been eradicated.

1179
01:04:08,800 --> 01:04:12,920
Speaker 2: You know what I'm saying off of just bringing just

1180
01:04:13,800 --> 01:04:14,880
Speaker 2: a youth group band.

1181
01:04:16,040 --> 01:04:16,840
Speaker 1: That's wild.

1182
01:04:17,040 --> 01:04:19,160
Speaker 2: It's so bonkers, right, so wild.

1183
01:04:19,400 --> 01:04:21,360
Speaker 1: But there's nothing like that for Trump, Like what does

1184
01:04:21,400 --> 01:04:23,960
Speaker 1: he care? Obviously he doesn't care about religion. He is not,

1185
01:04:24,080 --> 01:04:27,320
Speaker 1: Like I mean, George W. Bush might have actually believed

1186
01:04:27,400 --> 01:04:29,760
Speaker 1: in the stuff, but Trump clearly doesn't.

1187
01:04:30,160 --> 01:04:33,680
Speaker 2: That's crazy, right, Yeah, that story.

1188
01:04:33,480 --> 01:04:35,680
Speaker 3: Is a good example of how we change minds and

1189
01:04:35,720 --> 01:04:38,000
Speaker 3: we and I'm really mindful of this in my work,

1190
01:04:38,040 --> 01:04:40,560
Speaker 3: but I don't always stick to it. I try, which

1191
01:04:40,640 --> 01:04:43,160
Speaker 3: is that if you want to change minds, dunking is

1192
01:04:43,200 --> 01:04:46,400
Speaker 3: not the way. The way is to reach across and

1193
01:04:46,520 --> 01:04:52,200
Speaker 3: try to make a connection. But there is clear, clear

1194
01:04:52,600 --> 01:04:56,880
Speaker 3: data and experience showing that people trust people on their

1195
01:04:56,960 --> 01:05:00,120
Speaker 3: own side. And so what you want to do is

1196
01:05:00,120 --> 01:05:03,480
Speaker 3: is work talk to someone like that. You know, so

1197
01:05:03,600 --> 01:05:06,320
Speaker 3: Bono found the Christian Rockers so they could talk about books.

1198
01:05:06,480 --> 01:05:10,040
Speaker 4: Find someone you know who voted for Trump and say hey,

1199
01:05:10,040 --> 01:05:12,320
Speaker 4: this is why this one matters, and try to make

1200
01:05:12,320 --> 01:05:13,800
Speaker 4: that difference because they're not going to hear it from you,

1201
01:05:13,840 --> 01:05:16,520
Speaker 4: but they might hear from your friend who is on

1202
01:05:16,560 --> 01:05:18,960
Speaker 4: their side or who was or who has connections.

1203
01:05:19,280 --> 01:05:19,760
Speaker 1: That's actually I.

1204
01:05:19,760 --> 01:05:21,800
Speaker 3: Think what's happening with pep far now is like the

1205
01:05:22,400 --> 01:05:24,320
Speaker 3: roller coaster we're on with pep far has a lot

1206
01:05:24,360 --> 01:05:26,960
Speaker 3: to do with people who are sort of peripheral Trump

1207
01:05:26,960 --> 01:05:29,800
Speaker 3: World individuals who are trying to say, hey, look to

1208
01:05:29,840 --> 01:05:32,720
Speaker 3: the extent that you haven't completely gone off the rails,

1209
01:05:33,560 --> 01:05:37,320
Speaker 3: this one really matters. And even Marco Rubio, who's the

1210
01:05:37,360 --> 01:05:41,240
Speaker 3: Secretary of State, he co authored the bill to renew

1211
01:05:41,320 --> 01:05:43,800
Speaker 3: pep FAR. In twenty eighteen, he wrote a Fox News

1212
01:05:43,800 --> 01:05:46,720
Speaker 3: op ed saying pepfar is one of our greatest achievements

1213
01:05:46,760 --> 01:05:48,920
Speaker 3: as a nation. So for him to sort of be

1214
01:05:48,960 --> 01:05:51,360
Speaker 3: in there it tells you how small the circle is

1215
01:05:51,400 --> 01:05:54,800
Speaker 3: that they did this, But I still think that that

1216
01:05:55,320 --> 01:05:59,080
Speaker 3: their hearts and minds are in play a little and

1217
01:05:59,160 --> 01:06:01,200
Speaker 3: we have to just try. Well.

1218
01:06:01,200 --> 01:06:03,680
Speaker 1: That brings me to my last question for you, which

1219
01:06:03,720 --> 01:06:08,560
Speaker 1: is that you and I have both bumped heads online

1220
01:06:08,880 --> 01:06:11,400
Speaker 1: with some people who are going to be getting some

1221
01:06:11,760 --> 01:06:15,920
Speaker 1: pretty important positions within the NIH and HHS, and you

1222
01:06:16,000 --> 01:06:20,160
Speaker 1: more so than me, And you're very diplomatic and you

1223
01:06:20,200 --> 01:06:23,200
Speaker 1: know fair and balanced way I feel much much much

1224
01:06:23,240 --> 01:06:26,760
Speaker 1: more so than me. But these are now people who

1225
01:06:26,840 --> 01:06:29,880
Speaker 1: don't necessarily like all of us. They don't like me

1226
01:06:30,040 --> 01:06:33,520
Speaker 1: that much. Some of them they don't love you because

1227
01:06:33,560 --> 01:06:37,120
Speaker 1: you've been outspoken and you've been critical of their some

1228
01:06:37,160 --> 01:06:41,360
Speaker 1: of their stances on vaccines and masking, et cetera. How

1229
01:06:41,400 --> 01:06:44,800
Speaker 1: are you going to work with these people? How do

1230
01:06:44,920 --> 01:06:47,440
Speaker 1: you feel you're going to approach this coming going forward?

1231
01:06:47,480 --> 01:06:49,960
Speaker 1: Because you do have a very vocal voice. People look

1232
01:06:50,000 --> 01:06:53,000
Speaker 1: to you people they don't know already. They can watch

1233
01:06:53,040 --> 01:06:55,400
Speaker 1: you on like the news all the time or interviewing

1234
01:06:55,520 --> 01:06:58,760
Speaker 1: you know, important people on your your sub stack and

1235
01:06:58,880 --> 01:07:01,520
Speaker 1: your your channels. So how are you going to approach

1236
01:07:01,560 --> 01:07:02,280
Speaker 1: this going forward?

1237
01:07:03,600 --> 01:07:05,760
Speaker 3: I have to be honest this is something that's in flux.

1238
01:07:06,480 --> 01:07:09,120
Speaker 3: I had a plan, and in the words of the

1239
01:07:09,160 --> 01:07:11,440
Speaker 3: Great Mike Tyson, everyone's got a plan until someone punches

1240
01:07:11,480 --> 01:07:17,000
Speaker 3: you in the face. My plan had been, Okay, I

1241
01:07:17,200 --> 01:07:22,040
Speaker 3: have a few connections in Trump world who respect me.

1242
01:07:22,320 --> 01:07:24,960
Speaker 3: We have a sort of an understanding that we don't agree,

1243
01:07:25,120 --> 01:07:28,400
Speaker 3: but we can work on things, and that if the

1244
01:07:28,640 --> 01:07:31,280
Speaker 3: Harris had won, I would have had a red phone

1245
01:07:31,320 --> 01:07:32,600
Speaker 3: that they would call me and say, hey, just we

1246
01:07:32,600 --> 01:07:34,640
Speaker 3: want to flag this is one thing, and would you

1247
01:07:34,680 --> 01:07:36,800
Speaker 3: help us with this, and sort of in exchange if

1248
01:07:36,800 --> 01:07:39,280
Speaker 3: their side one, I got a red phone and say hey, look,

1249
01:07:39,520 --> 01:07:41,640
Speaker 3: so there's a certain amount of clout. And my plan

1250
01:07:41,680 --> 01:07:43,680
Speaker 3: had been, all right, I'm gonna go write a book

1251
01:07:43,680 --> 01:07:45,560
Speaker 3: about something else. I'm gonna do my usual work. I'm

1252
01:07:45,600 --> 01:07:47,560
Speaker 3: gonna keep my head down, but I'm going to work

1253
01:07:47,600 --> 01:07:50,160
Speaker 3: behind the scenes with people that I think are in

1254
01:07:50,240 --> 01:07:54,320
Speaker 3: play and choose wisely. When I tried to make the

1255
01:07:54,360 --> 01:07:57,120
Speaker 3: call and say hey, maybe on this one could we

1256
01:07:57,160 --> 01:08:00,360
Speaker 3: do something that was plan at And then the last

1257
01:08:00,400 --> 01:08:02,400
Speaker 3: two weeks happened and I like had to speak out.

1258
01:08:02,520 --> 01:08:04,520
Speaker 3: So now I'm worried that those calls aren't going to

1259
01:08:04,560 --> 01:08:07,120
Speaker 3: be easy to make But I'm trying even now, even now,

1260
01:08:07,120 --> 01:08:09,640
Speaker 3: I'm trying to do it in a way that doesn't

1261
01:08:09,800 --> 01:08:12,760
Speaker 3: doesn't like bastard, it doesn't like dunk on someone's intent,

1262
01:08:12,960 --> 01:08:14,960
Speaker 3: or it doesn't say anything about about individuals. And I'll

1263
01:08:14,960 --> 01:08:17,360
Speaker 3: give you just like a very you know, cave is

1264
01:08:17,439 --> 01:08:19,880
Speaker 3: kind of dancing around. There's a very specific example is

1265
01:08:20,439 --> 01:08:24,400
Speaker 3: the guy who might run the NIH. Not too many

1266
01:08:24,400 --> 01:08:28,559
Speaker 3: people can say that the nominee to run NIH has

1267
01:08:28,600 --> 01:08:30,840
Speaker 3: written a hit piece on them in the Wall Street Journal,

1268
01:08:30,960 --> 01:08:32,640
Speaker 3: But I can say that I could say that he

1269
01:08:32,680 --> 01:08:34,880
Speaker 3: did that. He did that to me and uh and

1270
01:08:34,960 --> 01:08:37,680
Speaker 3: I responded by email to say, you know, you might

1271
01:08:37,720 --> 01:08:40,360
Speaker 3: want to correct this just literally wrong. It's a fact check.

1272
01:08:41,320 --> 01:08:43,880
Speaker 3: I watch to tell your editor and he's like, okay, thanks.

1273
01:08:44,280 --> 01:08:46,400
Speaker 3: You know, was like a number he got wrong.

1274
01:08:46,520 --> 01:08:51,040
Speaker 2: I was like, actually it's twenty exactly like it was.

1275
01:08:51,080 --> 01:08:52,880
Speaker 4: Really you got the number wrong, just in case you care.

1276
01:08:54,320 --> 01:08:55,120
Speaker 4: He's like, got it.

1277
01:08:56,479 --> 01:08:58,599
Speaker 1: But you know, a power that's a power move.

1278
01:08:58,640 --> 01:09:02,040
Speaker 2: That's hard. I love that, Like that's that's look, that's

1279
01:09:02,439 --> 01:09:04,920
Speaker 2: that's that's a g move. I actually really like that.

1280
01:09:05,200 --> 01:09:05,519
Speaker 1: Yeah.

1281
01:09:05,560 --> 01:09:08,680
Speaker 3: So and and you know, I responded the journal, let

1282
01:09:08,680 --> 01:09:10,640
Speaker 3: me write a little thing saying like why he was

1283
01:09:10,640 --> 01:09:15,679
Speaker 3: wrong and and I've debated him on a podcast one time,

1284
01:09:15,920 --> 01:09:17,840
Speaker 3: and you know, we we you know, he butt heads

1285
01:09:17,920 --> 01:09:19,639
Speaker 3: and we I don't think there's a lot of love,

1286
01:09:21,800 --> 01:09:25,719
Speaker 3: but you know, uh and I but I I twice

1287
01:09:25,760 --> 01:09:28,080
Speaker 3: this past two weeks I mentioned him in my in

1288
01:09:28,160 --> 01:09:32,880
Speaker 3: my newsletter, and I did it in ways that I again,

1289
01:09:32,960 --> 01:09:36,200
Speaker 3: I they're meant. It's meant to highlight a disagreement, but

1290
01:09:36,280 --> 01:09:39,200
Speaker 3: not to say that this guy is evil or that

1291
01:09:39,280 --> 01:09:41,599
Speaker 3: he can't get nothing redeeming about him.

1292
01:09:41,840 --> 01:09:42,880
Speaker 4: And I'm not sure.

1293
01:09:43,040 --> 01:09:44,439
Speaker 3: You know, he's got a pretty you know, I've heard,

1294
01:09:44,439 --> 01:09:45,920
Speaker 3: I've heard rumors he's got a little bit of a

1295
01:09:45,960 --> 01:09:47,720
Speaker 3: thin skin. But yeah, maybe it's not true. Maybe he

1296
01:09:47,760 --> 01:09:51,200
Speaker 3: wants to work. Who knows. And so I'm trying my best,

1297
01:09:51,280 --> 01:09:53,479
Speaker 3: even while I'm calling this out more than I thought

1298
01:09:53,479 --> 01:09:55,880
Speaker 3: I would, being much more of a public voice in

1299
01:09:55,880 --> 01:09:58,040
Speaker 3: the past couple of weeks than I intended to, and

1300
01:09:58,080 --> 01:09:59,320
Speaker 3: put in my neck a little higher.

1301
01:10:00,560 --> 01:10:03,000
Speaker 4: I'm still hoping that even with doing that, there.

1302
01:10:02,840 --> 01:10:04,960
Speaker 3: Will be ways to do Plan A, which is to

1303
01:10:05,000 --> 01:10:06,759
Speaker 3: work behind the scenes to make some differences.

1304
01:10:06,800 --> 01:10:07,519
Speaker 4: But I don't know, We'll see.

1305
01:10:08,280 --> 01:10:12,320
Speaker 2: I have a question about maybe him specifically, Like, so,

1306
01:10:12,479 --> 01:10:16,960
Speaker 2: but let me, I'm gonna back into this question. Uh okay,

1307
01:10:17,160 --> 01:10:20,200
Speaker 2: I'm born and raised in California. We just saw the Grammys.

1308
01:10:20,400 --> 01:10:23,120
Speaker 2: The fact is Kendrick is the superior rapper. It is

1309
01:10:23,160 --> 01:10:27,040
Speaker 2: what it is. But Drake is a cornball to me.

1310
01:10:27,320 --> 01:10:30,080
Speaker 2: But it's not like he can't rap, like he's a

1311
01:10:30,080 --> 01:10:30,639
Speaker 2: great rapper.

1312
01:10:30,640 --> 01:10:31,080
Speaker 1: He's just a.

1313
01:10:31,000 --> 01:10:35,800
Speaker 2: Cornball and he's just not better than Kendrick. So my

1314
01:10:35,920 --> 01:10:39,880
Speaker 2: professional opinion again is I mean, Drake's a great rapper,

1315
01:10:40,000 --> 01:10:42,559
Speaker 2: you know. So my question is if you can't see

1316
01:10:42,560 --> 01:10:43,920
Speaker 2: where I'm going here is.

1317
01:10:44,600 --> 01:10:48,120
Speaker 4: And I do, and I'm terrified because I don't have.

1318
01:10:48,280 --> 01:10:51,920
Speaker 2: No because you are you are clearly the expert in

1319
01:10:51,960 --> 01:10:54,639
Speaker 2: this in this scenario, in the scenario, you're the Kendrick

1320
01:10:54,760 --> 01:10:58,760
Speaker 2: like you clearly know what you're talking about. Yes, So

1321
01:10:59,040 --> 01:11:01,160
Speaker 2: my question is when you look across the table with

1322
01:11:01,280 --> 01:11:05,200
Speaker 2: somebody like him, are you like, bro, what the hell

1323
01:11:05,240 --> 01:11:08,280
Speaker 2: are you talking about? Or is it like, No, he's smart.

1324
01:11:08,320 --> 01:11:10,519
Speaker 2: I mean, I just added I don't. I think I

1325
01:11:10,560 --> 01:11:12,840
Speaker 2: think he's misinformed on certain things. But no, he's I mean,

1326
01:11:12,880 --> 01:11:17,040
Speaker 2: he's you know, he knows he's you know, Yeah, it's fine.

1327
01:11:17,120 --> 01:11:19,760
Speaker 3: You know what I mean, it's so complicated. It's a

1328
01:11:19,760 --> 01:11:22,599
Speaker 3: great question because I do see some people who over

1329
01:11:22,680 --> 01:11:27,559
Speaker 3: the past number of years, they've gone increasingly from where

1330
01:11:27,600 --> 01:11:31,320
Speaker 3: I was at to much more in the in the

1331
01:11:31,400 --> 01:11:35,240
Speaker 3: extreme category, and it's and watching that's very depressing and sad.

1332
01:11:36,160 --> 01:11:40,120
Speaker 3: But then there are people who you just disagree on

1333
01:11:40,160 --> 01:11:43,640
Speaker 3: certain issues. I'll give you an example for him, like,

1334
01:11:43,800 --> 01:11:46,960
Speaker 3: I know he is not I know he has he

1335
01:11:47,080 --> 01:11:49,000
Speaker 3: has intelligent neurons in his brain.

1336
01:11:49,080 --> 01:11:50,839
Speaker 4: I know that in fact.

1337
01:11:50,800 --> 01:11:55,040
Speaker 3: They He wrote a paper in twenty twelve that was

1338
01:11:55,040 --> 01:11:57,559
Speaker 3: published in the Journal of the American Medical Association, which

1339
01:11:57,560 --> 01:12:01,000
Speaker 3: is like one of our top journals, that measured the

1340
01:12:01,040 --> 01:12:06,479
Speaker 3: mortality effect of pepfar Wow. And it was. It was.

1341
01:12:06,520 --> 01:12:08,960
Speaker 3: And there's two things that blew me away about this.

1342
01:12:09,080 --> 01:12:12,519
Speaker 3: Number One, of course, it showed the incredible early effects

1343
01:12:12,560 --> 01:12:14,840
Speaker 3: that pep bar was already having in twenty twelve. So

1344
01:12:15,040 --> 01:12:17,479
Speaker 3: now it was a very very well done study. It

1345
01:12:17,560 --> 01:12:21,240
Speaker 3: was very rigorously done. And the second thing that was

1346
01:12:21,280 --> 01:12:22,960
Speaker 3: so interesting to me about that was it used a

1347
01:12:22,960 --> 01:12:25,880
Speaker 3: lot of the theme statistical techniques that a lot of

1348
01:12:25,880 --> 01:12:29,040
Speaker 3: COVID researchers did to show that mitigation worked. But when

1349
01:12:29,040 --> 01:12:31,400
Speaker 3: it came to that he didn't agree, So I thought

1350
01:12:31,400 --> 01:12:33,559
Speaker 3: it was interesting, like, oh, it's interesting. He used difference

1351
01:12:33,600 --> 01:12:36,920
Speaker 3: in different techniques, which is a statistical technique to look

1352
01:12:36,920 --> 01:12:39,080
Speaker 3: at pep far and slam dunk, just the way people

1353
01:12:39,160 --> 01:12:41,000
Speaker 3: use that technique to look at some COVID outcomes.

1354
01:12:41,040 --> 01:12:42,840
Speaker 4: But when it came to that, he wasn't on board.

1355
01:12:42,560 --> 01:12:45,880
Speaker 3: Because he felt that we overdid it with our COVID response. Yeah,

1356
01:12:45,920 --> 01:12:48,040
Speaker 3: and so I look, I look at that, and I'm

1357
01:12:48,080 --> 01:12:50,920
Speaker 3: just I'm just really kind of just puzzled by it

1358
01:12:51,240 --> 01:12:53,519
Speaker 3: because I know that he's a smart guy who can

1359
01:12:53,600 --> 01:12:56,720
Speaker 3: do great work. But I also know that, like everything

1360
01:12:56,720 --> 01:12:59,720
Speaker 3: in the world, your priors, your bias you bring into

1361
01:12:59,720 --> 01:13:02,519
Speaker 3: something just changes how you how you process knowledge. So

1362
01:13:02,600 --> 01:13:04,800
Speaker 3: I said across if I remember across the table from him,

1363
01:13:04,880 --> 01:13:07,840
Speaker 3: I'm gonna take it on an issue by issue basis. Literally,

1364
01:13:07,920 --> 01:13:12,559
Speaker 3: I'm just gonna be Okay, how do we are the

1365
01:13:12,560 --> 01:13:14,920
Speaker 3: neurons doing their thing or not? Are they firing or

1366
01:13:15,200 --> 01:13:16,040
Speaker 3: are they misfiring?

1367
01:13:16,560 --> 01:13:18,720
Speaker 2: Yeah, you totally answer the question. That's exactly what I

1368
01:13:18,760 --> 01:13:20,080
Speaker 2: was asking. Thank you.

1369
01:13:20,080 --> 01:13:24,600
Speaker 1: You're looking for internal consistency. And speaking of consistency, you

1370
01:13:24,640 --> 01:13:28,360
Speaker 1: know what's consistently great your substack let's plug it right

1371
01:13:28,400 --> 01:13:30,120
Speaker 1: now so people can find you there.

1372
01:13:31,240 --> 01:13:32,000
Speaker 4: Thank you so much.

1373
01:13:32,080 --> 01:13:36,200
Speaker 3: My substack is called inside Medicine Inside Medicine dot substock

1374
01:13:36,280 --> 01:13:38,960
Speaker 3: dot com. It used to come out twice a week

1375
01:13:39,120 --> 01:13:41,439
Speaker 3: for free during the week and then a paywall about

1376
01:13:41,479 --> 01:13:43,240
Speaker 3: my life as an er doctor on the weekends.

1377
01:13:43,880 --> 01:13:45,719
Speaker 4: Currently is coming out more than once.

1378
01:13:45,560 --> 01:13:47,839
Speaker 1: A day because I can't stop.

1379
01:13:48,800 --> 01:13:53,320
Speaker 3: That's that's not sustainable, but I'll do it until I collapse.

1380
01:13:54,479 --> 01:13:58,200
Speaker 3: And you know other places blue Sky and Threads and

1381
01:13:58,240 --> 01:14:00,680
Speaker 3: Instagram actually Instagram my full name.

1382
01:14:00,760 --> 01:14:03,000
Speaker 4: My mom's very happy. Jeremy Samuel Faust.

1383
01:14:03,439 --> 01:14:09,160
Speaker 1: Yeah, it's a sweet boy, that Jeremy Samuel Faust. It

1384
01:14:09,280 --> 01:14:11,519
Speaker 1: is a great substack. I do read it. It is

1385
01:14:11,600 --> 01:14:13,559
Speaker 1: where I get a lot of my up to date

1386
01:14:13,640 --> 01:14:17,760
Speaker 1: information keeps me going on this show and it helps me.

1387
01:14:17,800 --> 01:14:19,559
Speaker 1: It's a real source of information for me. So it's

1388
01:14:19,640 --> 01:14:22,320
Speaker 1: really it's really well written. It's not so long that

1389
01:14:22,439 --> 01:14:24,479
Speaker 1: it's not manageable. You can read it in like one

1390
01:14:24,520 --> 01:14:26,600
Speaker 1: easy sitting. You get a lot of information. It's a

1391
01:14:26,640 --> 01:14:29,439
Speaker 1: very high impact and it's well ritten. So I highly

1392
01:14:29,479 --> 01:14:31,439
Speaker 1: recommend it. Jeremy, thank you so much for coming on.

1393
01:14:31,479 --> 01:14:36,320
Speaker 1: I appreciate it, you know, prop Yeah, there's so much

1394
01:14:36,720 --> 01:14:39,920
Speaker 1: that you do, and you do very well at a

1395
01:14:39,960 --> 01:14:43,600
Speaker 1: very high level. I'm not sure what to plug, but

1396
01:14:44,360 --> 01:14:47,920
Speaker 1: let's start. Let's start with the show a Hood Politics

1397
01:14:47,920 --> 01:14:50,840
Speaker 1: for those who are listening to Hood Politics right now

1398
01:14:51,160 --> 01:14:53,640
Speaker 1: and are listening to the House of Pod where can

1399
01:14:53,640 --> 01:14:55,800
Speaker 1: people find Hood Politics? And can you tell us again

1400
01:14:55,840 --> 01:14:58,360
Speaker 1: a little bit about that show, which again I love?

1401
01:14:58,880 --> 01:15:01,640
Speaker 2: Yeah, yeah, so the politics for PROP is on all

1402
01:15:01,680 --> 01:15:04,759
Speaker 2: the pods, It's on the video versions on my YouTube,

1403
01:15:04,840 --> 01:15:10,120
Speaker 2: which is just prop hip hop. And yeah, the ideas that,

1404
01:15:10,320 --> 01:15:14,599
Speaker 2: like the cornerstone premise is like if you've grown up

1405
01:15:14,640 --> 01:15:18,439
Speaker 2: in an urban area, you understand politics more than you

1406
01:15:18,479 --> 01:15:22,160
Speaker 2: think you do, and that it's just more of a

1407
01:15:23,360 --> 01:15:26,240
Speaker 2: just a language thing. Like so, like, so what I'm

1408
01:15:26,280 --> 01:15:27,720
Speaker 2: trying to do on the show is use what you

1409
01:15:27,840 --> 01:15:31,240
Speaker 2: already know to help show you that the stuff you

1410
01:15:31,280 --> 01:15:33,320
Speaker 2: think you don't know, you actually know better than you

1411
01:15:33,360 --> 01:15:39,439
Speaker 2: think you do. So we covered, you know, from filibustering

1412
01:15:39,640 --> 01:15:42,040
Speaker 2: all the way to what's happening with the CDC. You

1413
01:15:42,040 --> 01:15:44,920
Speaker 2: know what I'm saying, and just try to like explain

1414
01:15:44,960 --> 01:15:49,240
Speaker 2: it in a way that's like not so much analysis

1415
01:15:49,240 --> 01:15:53,320
Speaker 2: and commentary but more like translation, Like this what that means.

1416
01:15:53,320 --> 01:15:56,559
Speaker 2: They're saying this about this, and you would probably feel

1417
01:15:56,560 --> 01:15:58,439
Speaker 2: the way I feel about that because you grew up

1418
01:15:58,439 --> 01:16:00,880
Speaker 2: like I grew up, you know so, uh so that's

1419
01:16:00,960 --> 01:16:01,599
Speaker 2: kind of the idea.

1420
01:16:01,880 --> 01:16:02,000
Speaker 1: Uh.

1421
01:16:02,280 --> 01:16:05,040
Speaker 2: I also do music and poetry. All that's on prop

1422
01:16:05,120 --> 01:16:09,840
Speaker 2: hip hop dot com and probably shutting down the coffee

1423
01:16:09,840 --> 01:16:11,880
Speaker 2: thing because I'm out of money. I love coffee, But

1424
01:16:12,320 --> 01:16:15,439
Speaker 2: just running a business turns out that's I don't know

1425
01:16:15,479 --> 01:16:16,639
Speaker 2: if you know that's hard.

1426
01:16:16,840 --> 01:16:19,120
Speaker 1: I've heard, yeah, I've heard things.

1427
01:16:19,360 --> 01:16:22,920
Speaker 2: Starting a business is hard and clearly not my skill set.

1428
01:16:23,680 --> 01:16:27,080
Speaker 1: Well, let me listen. You can't be amazing at everything.

1429
01:16:27,200 --> 01:16:30,040
Speaker 1: It's just not fair. Okay, it's all right to take

1430
01:16:30,080 --> 01:16:32,320
Speaker 1: a take a hit on this one. Take a one.

1431
01:16:32,360 --> 01:16:37,200
Speaker 1: It's okay, okay, because honestly, uh you are an amazing

1432
01:16:37,200 --> 01:16:42,280
Speaker 1: I use teacher in your introduction specifically, and if you

1433
01:16:42,320 --> 01:16:44,360
Speaker 1: look at your page up on Wikipedia, doesn't have that,

1434
01:16:44,400 --> 01:16:46,479
Speaker 1: I don't think, but like, to me, that is such

1435
01:16:46,520 --> 01:16:49,040
Speaker 1: an important part of what you do, how you make

1436
01:16:49,120 --> 01:16:53,479
Speaker 1: things relatable. Jeremy's never been related to Kendrick Lamar before.

1437
01:16:53,560 --> 01:16:55,080
Speaker 1: But actually that's not true.

1438
01:16:55,280 --> 01:16:56,840
Speaker 4: I say that is not true.

1439
01:16:57,280 --> 01:16:58,360
Speaker 1: Let's go ship.

1440
01:16:58,479 --> 01:16:59,320
Speaker 4: That is not true.

1441
01:17:00,200 --> 01:17:03,479
Speaker 3: So I actually wrote a Slate article when he won

1442
01:17:03,520 --> 01:17:07,759
Speaker 3: the Pulitzer Prize about about the politic because the Polser

1443
01:17:07,760 --> 01:17:10,120
Speaker 3: Prize is a very classical music kind of thing usually,

1444
01:17:10,439 --> 01:17:13,160
Speaker 3: and I'm in that world and actually had been involved

1445
01:17:13,200 --> 01:17:15,800
Speaker 3: with a Politzer Prize winning piece from a year or

1446
01:17:15,840 --> 01:17:18,600
Speaker 3: two before that, and so, and I actually had the

1447
01:17:18,840 --> 01:17:21,960
Speaker 3: privilege of informing somebody that they won the Politzer Prize

1448
01:17:21,960 --> 01:17:24,280
Speaker 3: for music, and so when it was such a great

1449
01:17:24,320 --> 01:17:27,920
Speaker 3: moment and when and so when Kendrick won it, I

1450
01:17:27,960 --> 01:17:30,400
Speaker 3: went and listened to that album a handful of times,

1451
01:17:30,640 --> 01:17:32,920
Speaker 3: and I was like, I get why this one. Let

1452
01:17:32,960 --> 01:17:36,639
Speaker 3: me write about from a classical perspective, why.

1453
01:17:36,479 --> 01:17:38,679
Speaker 4: I think the Poltic committee kind of did its job here.

1454
01:17:39,000 --> 01:17:43,360
Speaker 1: Well, it proves my point even more. He nailed that

1455
01:17:42,120 --> 01:17:45,599
Speaker 1: in like just not even knowing that, just meeting you

1456
01:17:45,640 --> 01:17:46,639
Speaker 1: for this short period of time.

1457
01:17:46,800 --> 01:17:48,360
Speaker 3: Anyway, when he got to the whole Drake thing, I

1458
01:17:48,400 --> 01:17:51,120
Speaker 3: was like, I know there's beef, I know there's tracks,

1459
01:17:51,120 --> 01:17:52,559
Speaker 3: but I'm out of my element.

1460
01:17:52,640 --> 01:17:56,120
Speaker 1: So well, there's other podcasts for that. But again, hood

1461
01:17:56,160 --> 01:18:00,439
Speaker 1: politics amazing. The music also very good, underrated really my opinion,

1462
01:18:00,439 --> 01:18:02,920
Speaker 1: it's fantastic stuff. And I really enjoyed it. If you

1463
01:18:03,200 --> 01:18:06,360
Speaker 1: haven't already rate and review this show the House of

1464
01:18:06,439 --> 01:18:09,360
Speaker 1: Pod you can find us anywhere you find your podcast.

1465
01:18:09,960 --> 01:18:11,920
Speaker 1: Thanks again to Lucky Dog Hot Sauce, and thank you

1466
01:18:11,920 --> 01:18:14,880
Speaker 1: both so much. This was super helpful and I learned

1467
01:18:14,880 --> 01:18:19,599
Speaker 1: a time. All right, bye bye bye bye.

1468
01:18:19,640 --> 01:18:34,320
Speaker 2: All right, all right, now don't you hit stop on

1469
01:18:34,360 --> 01:18:36,960
Speaker 2: this pod. You better listen to these credits. I need

1470
01:18:37,000 --> 01:18:39,400
Speaker 2: you to finish this thing so I can get the

1471
01:18:39,560 --> 01:18:44,320
Speaker 2: download numbers. Okay, so don't stop it yet, but listen.

1472
01:18:44,640 --> 01:18:48,360
Speaker 2: This was recorded in East Lost Boyle Heights by your

1473
01:18:48,360 --> 01:18:51,719
Speaker 2: boy Propaganda. Tap in with me at prop hip hop

1474
01:18:51,760 --> 01:18:54,840
Speaker 2: dot com. If you're in the cold Brew coffee we

1475
01:18:54,920 --> 01:18:58,599
Speaker 2: got Terraform Coldbrew. You can go there dot com and

1476
01:18:59,000 --> 01:19:01,439
Speaker 2: use promo code hood get twenty percent off get yourself

1477
01:19:01,479 --> 01:19:05,680
Speaker 2: some coffee. This was mixed, edited and mastered by your

1478
01:19:05,720 --> 01:19:08,640
Speaker 2: boy Matt Alsowski killing the beat softly. Check out his

1479
01:19:08,720 --> 01:19:12,519
Speaker 2: website Matdowsowski dot com. I'm a speller for you because

1480
01:19:12,840 --> 01:19:17,720
Speaker 2: I know m A T. T O S O W

1481
01:19:18,240 --> 01:19:22,519
Speaker 2: s Ki dot com Matdowsowski dot com. He got more

1482
01:19:22,600 --> 01:19:25,200
Speaker 2: music and stuff like that on there, So gonna check

1483
01:19:25,240 --> 01:19:28,479
Speaker 2: out The Heat Politics is a member of Cool Zone Media,

1484
01:19:29,080 --> 01:19:34,200
Speaker 2: Executive produced by Sophie Lichterman, part of the iHeartMedia podcast network.

1485
01:19:34,880 --> 01:19:37,360
Speaker 2: Your theme music and scoring is also by the one

1486
01:19:37,400 --> 01:19:41,400
Speaker 2: and Overly Mattowsowski. Still killing the beat softly, so listen.

1487
01:19:41,920 --> 01:19:44,679
Speaker 2: Don't let nobody lie to you. If you understand urban living,

1488
01:19:44,720 --> 01:19:47,880
Speaker 2: you understand politics. These people is not smarter than you.

1489
01:19:48,200 --> 01:19:56,280
Speaker 2: We'll see y'all next week.

1490
01:20:00,520 --> 01:20:01,000
Speaker 1: But the