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

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Speaker 2: Independent media just played a truly massive role in this election,

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Speaker 2: and we are so excited about what that means for

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Speaker 2: the future of the show.

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Speaker 3: This is the only place where you can find honest

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Speaker 3: perspectives from the left and the right that simply does

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Speaker 3: not exist anywhere else.

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Speaker 2: So if that is something that's important to you, please

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Speaker 2: go to Breakingpoints dot com. Become a member today and

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Speaker 2: you'll get access to our full shows, unedited, ad free,

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Speaker 2: and all put together for you every morning in your inbox.

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Speaker 3: We need your help to build the future of independent

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Speaker 3: news media and we hope to see you at Breakingpoints

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Speaker 3: dot com. Good morning, everybody, Happy Wednesday. What top of

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Speaker 3: show are we calling this?

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Speaker 1: Emily?

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Speaker 4: What is this?

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Speaker 3: This is breaking democracy, breaking democracy.

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Speaker 5: Sometimes it's status points.

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Speaker 1: Battist points.

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Speaker 3: I would say, get breaking democracy because we've given up

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Speaker 3: on democracy.

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Speaker 1: Right that's oh ah, just joking. No, no, no, it's okay,

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Speaker 1: No more quiet parts out loud. It's Happy Wednesday. Not

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Speaker 1: used to saying that.

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Speaker 3: Thank you for having me. I appreciate it, And to

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Speaker 3: Ryan for switching in. We've got some childcare problems at home,

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Speaker 3: but everything's seem to be working out right. Now, all right,

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Speaker 3: let's see what do we have today on the show?

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Speaker 1: Who usually sets U up? Is it Ryan or you?

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Speaker 1: Who talks?

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Speaker 6: We just we just it's it's how the spirit moves us.

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Speaker 6: Nobody really knows. It's okay, see, are much more regiment totally?

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Speaker 5: Are you totally?

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Speaker 4: Okay?

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Speaker 3: All right, well, why don't I do the first three

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Speaker 3: and then you do the other three? In the spirit

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Speaker 3: of the show, we're going to talk about the economy

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Speaker 3: and the new BLS, the new Bureau of Labor Statistics

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Speaker 3: has a new proposed commissioner who's got some interesting changes,

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Speaker 3: and we're going to check in on inflation. We're also

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Speaker 3: going to talk about a fascinating in some ways a

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Speaker 3: new deal between Donald Trump and Nvidia and AMD, those

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Speaker 3: chip making companies here in the United States, a crazy

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Speaker 3: precedent that's being said there, and also kind of what

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Speaker 3: it tells us about AI. It's a good segue to

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Speaker 3: talking about employment, and basically youth unemployment and or underemployment

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Speaker 3: is going sky high here. There's a lot of AI

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Speaker 3: indications in the overall job market. Obviously, want to check

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Speaker 3: in on that. Youth unemployment not usual a good sign politically,

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Speaker 3: and then we are going to have what is his name,

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Speaker 3: Delano squad Square.

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Speaker 1: He's going to be here in the studio.

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Speaker 3: This is a friend of Emily's, and he's going to

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Speaker 3: talk to us a little bit more. He's more of

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Speaker 3: a conservative guest, but he's going to talk to us.

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Speaker 3: Somebody knows quite a bit about DC crime and he's

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Speaker 3: going to tell us what he thinks about the Trump

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Speaker 3: administration takeover here of MPD.

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Speaker 6: Yeah, of course, and speaking of which, actually Donald Trump

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Speaker 6: is reconsidering or he is considering a reclassification of marijuana.

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Speaker 5: So our co host here, Stephen A. Smith, has lots

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Speaker 5: of thoughts on that.

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Speaker 1: What of Steven's best takes perhaps is only good take?

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Speaker 6: Some people say, we're going to get into what's actually

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Speaker 6: on the table and what it might mean Donald Donald Trump.

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Speaker 5: I actually meant to say.

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Speaker 6: Tucker Carlson had a nun on his program who testified

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Speaker 6: to the conditions that Christian's experience in the Middle East

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Speaker 6: more broadly, but also particularly in the West Bank, and

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Speaker 6: she spoke a bit to Gaza as well. So we're

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Speaker 6: going to do a breakdown of some of the comments

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Speaker 6: that were on Tucker Show and what it could mean

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Speaker 6: if people the administration are watching it, and Soccer may

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Speaker 6: have thoughts on.

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Speaker 5: That for sure.

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Speaker 6: And Andrew Cuomo in the Epstein Files, that's not a

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Speaker 6: surprise to anyone, but zoraon Mom Donnie is now going

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Speaker 6: after him hard for being in the Epstein Files. So

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Speaker 6: we have a fascinating zo run ad to get to

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Speaker 6: and a little bit of news on the Maxwell transcript

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Speaker 6: front to get to as well. So Sager, let's start

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Speaker 6: with that new inflation report that the White House was

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Speaker 6: commenting on.

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Speaker 3: You.

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Speaker 1: Let's get to it.

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Speaker 3: And then it also includes this Bureau of Labor Statistics

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Speaker 3: commission Oh I forgot to say, by the way, Breakingpoints

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Speaker 3: dot com if you are able to help us out

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Speaker 3: monthly yearly memberships, you can go ahead and sign up.

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Speaker 3: And if you can't, no worries, just go ahead and

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Speaker 3: subscribe button on YouTube. If you're listening on a podcast,

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Speaker 3: just go ahead and send this episode or any of

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Speaker 3: your favorite episodes to a friend. So with the BLSID data,

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Speaker 3: we now have a response here from the White House

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Speaker 3: after Trump fired the Bureau of Labor Statistics Commissioner. Basically,

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Speaker 3: remember the Bureau of Labor Satistics, their job is to

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Speaker 3: compile all of the employment data. It is, however, very inaccurate,

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Speaker 3: kind of has been over the last five years. There's

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Speaker 3: a lot of complicated reasons that Chrystal and I got

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Speaker 3: into the last time that we covered it. But with

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Speaker 3: the new commissioner, they're actually proposing actually just doing away

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Speaker 3: with the monthly job support entirely.

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Speaker 1: Here's what the White House had to say, if.

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Speaker 3: The jobs data is not reliable, should Americans trust the

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Speaker 3: inflation data?

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Speaker 7: Well, look, the job's data has had massive revisions. We

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Speaker 7: want to ensure that all of the data, the inflation data,

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Speaker 7: the jobs data, any data point that is coming out

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Speaker 7: of the BLS is trustworthy and is accurate, which is

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Speaker 7: why the President has restored new leadership at the BLS.

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Speaker 3: Trustworthy and accurate. And this is apparently the new justification.

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Speaker 3: Let's go and put this up there on the screen

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Speaker 3: from the Wall Street Journal. This was after the appointment

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Speaker 3: of EJ.

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Speaker 1: And Tony.

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Speaker 3: He's now the chief He was previously the chief economist

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Speaker 3: at the Heritage Foundation. He has been a longtime critic

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Speaker 3: of the Bureau of Labor Statistics. But for our purposes, Emily,

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Speaker 3: what he has proposed is doing away with the monthly

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Speaker 3: job report entirely, and just to give I guess, you know,

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Speaker 3: I don't know if there's both sides of this, but

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Speaker 3: I will at the very least like try to what

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Speaker 3: they are. What they are saying is that because the

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Speaker 3: monthly jobs reports have been so inaccurate that they have

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Speaker 3: proposed doing away with it entirely.

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Speaker 4: Quote.

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Speaker 3: In an interview on Fox News Digital on Monday ahead

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Speaker 3: of his nomination, ej Antoni criticized the monthly employment report

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Speaker 3: as Flaud suggested it be replaced quote with a more accurate,

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Speaker 3: though less timely quarterly data. Now, the thing is is

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Speaker 3: that that monthly report is very closely watched by economists,

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Speaker 3: by the stock market.

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Speaker 1: It's I mean.

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Speaker 3: Obviously even last time. It's been known to move markets previously,

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Speaker 3: and so the doing away with it, and actually the

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Speaker 3: way that they compile it would become a major point

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Speaker 3: of scrutiny for kind of checking in on the economy.

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Speaker 3: You know, the inflation numbers and the jobs numbers are

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Speaker 3: two of the only measures that we really do have

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Speaker 3: as to how things are going in real time. As

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Speaker 3: I said the last time around, Look, you know the

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Speaker 3: idea that you're going to get a critical snapshot of

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Speaker 3: what's actually happened in a month is preposterous. Especially since

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Speaker 3: COVID we've had response rates decline.

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Speaker 1: The stampling, there's so.

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Speaker 3: Many different problems with the way that they do it,

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Speaker 3: but it's much more of actually a technical problem. And

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Speaker 3: by the way, Doge fired the entire team who was

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Speaker 3: trying to update the system in their very first week

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Speaker 3: in office under Howard Lutnik. But so yeah, that's where

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Speaker 3: things are. I mean, the main problem is is that

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Speaker 3: now they're kind of showing quote distrust, you know in

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Speaker 3: the numbers and whatever these no new quarterly numbers are.

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Speaker 3: What they're going to have to do is prove their

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Speaker 3: work and actually do this competently.

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Speaker 1: Do I have full confidence that that's going to happen. No,

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Speaker 1: I do not. But I'm curious what you think. I'm

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Speaker 1: shocked you hear this.

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Speaker 5: So let's put this next element on the screen.

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Speaker 6: This says Joe Wisenthal's reaction, so to get a flavor

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Speaker 6: of how people are reacting in Wall Street world, he wrote, basically,

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Speaker 6: a lot of people would agree that the BLS does

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Speaker 6: need to be shaken up in some way due to

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Speaker 6: the increased cost of collecting data and he says, thanks

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Speaker 6: to inflation as well as lower response rates to surveys

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Speaker 6: and so on a soccer dispension, they're strange in the

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Speaker 6: production of high quality economic data, and having high quality

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Speaker 6: trust with the data is really important. But at this point,

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Speaker 6: hoping that the new BLS chief will credibly modernize economic

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Speaker 6: data collection is a bit like believing that Doge was

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Speaker 6: going to apply the world's most advanced statistical techniques to

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Speaker 6: reducing billing fraud at Medicare and Medicaid. It was a

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Speaker 6: nice idea, didn't happen, and it's hard to imagine it

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Speaker 6: happening now with economic data collection. So Antoni was just

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Speaker 6: until yesterday as Donald Trump plucked him at the Heritage

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Speaker 6: Foundation where he was a chief economist over there, and

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Speaker 6: one reaction, interestingly from someone at the American Enterprise Institute,

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Speaker 6: Dave Hebert, was I've been on several programs with him

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Speaker 6: at this point and have been impressed by two things.

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Speaker 6: His inability to understand basic economics and the speed with

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Speaker 6: which he's gone MAGA. Now, in the conservative world, there

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Speaker 6: were some people who just say, if you go full

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Speaker 6: MAGA and you are an economic populist, then you don't

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Speaker 6: understand basic economics. And the people who criticize him, just

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Speaker 6: from my survey of the comments, appear to be pretty

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Speaker 6: much libertarians whose main quibbles with him are over populism.

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Speaker 6: They see him as overly populist. But if you're Joe Wisenthal,

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Speaker 6: if you're on the street, you're looking at this, Stephen Moore,

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Speaker 6: I don't know if you saw this kind of poured

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Speaker 6: cold water on the idea that they would pull the

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Speaker 6: jobs reports.

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Speaker 5: Okay, so it seems like.

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Speaker 6: They're trying to calm It reminds me a little bit

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Speaker 6: of the terifs they're trying to calm Wall Straight a

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Speaker 6: little bit.

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Speaker 3: Yeah.

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Speaker 6: I mean, because you can understand if you're Joe Wisenthal

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Speaker 6: and you're like, oh, you're taking this maga guy right

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Speaker 6: off of Steve Bannon's war room, and people now have

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Speaker 6: to trade off of the information that he does or

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Speaker 6: does not provide.

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Speaker 1: I think it's entirely fair.

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Speaker 3: And I mean I think this is one again where

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Speaker 3: it's about competence, showing your work, transparency, etc. Not exactly

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Speaker 3: some of the hallmarks so far of dos the Trump

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Speaker 3: administration is basically it's just been a slapshot approach like

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Speaker 3: you may take a problem, you may diagnose it correctly,

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Speaker 3: but then fixing the problem. If you don't fix it correctly,

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Speaker 3: then people are obviously going to still prefer the earlier one.

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Speaker 3: Like to the BLS point, with all of those major

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Speaker 3: revisions under Biden and then significantly under Donald Trump. Just

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Speaker 3: again at a very technical level, the sampling and the

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Speaker 3: way that it was collected was outdated and ridiculous. It

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Speaker 3: is entirely fair to say, Okay, that's going to be done.

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Speaker 3: But one of the points that Wisenthal makes is that

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Speaker 3: the BLS at the end of the day, it's not

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Speaker 3: a political job because it was just compiling data. Like

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Speaker 3: at the end of the day, what your job should be,

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Speaker 3: even if you want to modernize it, if you want

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Speaker 3: to change it, is one that creates and corrects sampling.

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Speaker 3: You're going to have to be very transparent right at

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Speaker 3: the economy level and at the stock market level, to

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Speaker 3: make sure that everybody understands exactly how all of.

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Speaker 1: This is done.

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Speaker 3: Taking away a monthly report was not, in my opinion,

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Speaker 3: the definition of modernization. We're not sure yet right whether

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Speaker 3: this is all going to go through, but the fact

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Speaker 3: that it is, you know, open about it is going

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Speaker 3: to make people very shaky about what the next one is,

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Speaker 3: and they're really going to have to go and to

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Speaker 3: check their work. And the worst possible scenario is like

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Speaker 3: an actual politicization of changing the way that the numbers

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Speaker 3: are calculated and making it so and then you know,

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Speaker 3: basically doing it in the way that would make it

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Speaker 3: so that it's overtly political. And already they have the

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Speaker 3: appearance of that. So it's actually on them to assure

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Speaker 3: any you know, serious market analysts or whatever that that's

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Speaker 3: not the case. They may get away with it, to

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Speaker 3: be honest, I mean, if they actually do have some transparence,

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Speaker 3: if it's.

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Speaker 1: The only number you can rely on, then what the

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Speaker 1: hell are you supposed to do? Right?

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Speaker 3: But their current justification, as I understand it as well,

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Speaker 3: is that if you change the way if the job

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Speaker 3: numbers had been accurate, according to the Secretary Scott Best,

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Speaker 3: and I was just saying yesterday, he's like, then the

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Speaker 3: FED would have cut rate. So part of this is

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Speaker 3: also trying to put pressure on the Federal Reserve. But again,

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Speaker 3: if you juke your numbers, you know, initially to try

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Speaker 3: and engineer some.

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Speaker 1: Sort of other result that's going to look really bad.

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Speaker 3: And I'm not going to sit here in America is

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Speaker 3: the greatest you know, data collector or any of that.

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Speaker 1: In the world.

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Speaker 3: Trust me, We're definitely not if you look at at

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Speaker 3: all the revisions for everything. But the point is more

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Speaker 3: about the trust in this specific person. And then you know,

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Speaker 3: it's not above the Trump administration to try to change

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Speaker 3: things that are happening to try and engineer some sort

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Speaker 3: of stock market.

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Speaker 1: Bomp or you know, a fall or.

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Speaker 3: A FED cut or any of that. And that's where

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Speaker 3: we're started to get into very very dicey territory.

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Speaker 5: It's dicey territory.

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Speaker 6: But what's interesting about everything you just said is that

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Speaker 6: it gets to how fake all of this stuff is

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Speaker 6: to begin with.

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Speaker 5: And that doesn't mean it's That doesn't mean.

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Speaker 6: That in the process of fixing what you see as

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Speaker 6: a bad situation, you sort of make this diagnosis. It

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Speaker 6: doesn't mean that Trump can be trusted not to exploit

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Speaker 6: the opportunity and the very real problem.

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Speaker 5: But it does mean that that doesn't mean that the

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Speaker 5: problem isn't real.

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Speaker 6: And this gets to I think a really fundamental question

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Speaker 6: of policy in the Trump administration because there's direction. Yeah,

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Speaker 6: and there's process, and the distinction direction and process I

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Speaker 6: think gets lost so often in that, like he's sometimes

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Speaker 6: pointed in the right direction and the process is flawed,

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Speaker 6: and the process is sometimes worse than actually trying to

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Speaker 6: approach the problem like.

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Speaker 1: Well, we're gonna talk about this with d C. I

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Speaker 1: think is a perfect example.

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Speaker 3: It's like you could take a problem of US crime

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Speaker 3: in Washington, d c uh and then you could say, Okay,

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Speaker 3: well we're going to deploy the federal troops or whatever,

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Speaker 3: and you're like.

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Speaker 1: Okay, you know, what are you going to do?

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Speaker 3: And it's like and now there are videos coming out

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Speaker 3: of you know, FBI agents and HSI agents in the

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Speaker 3: cleanest and nicest parts of Washington, d C Emily, which

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Speaker 3: you and I have spent time in, and any.

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Speaker 1: Washington d C. Right, it'd be like having it'd be like.

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Speaker 3: We're going to clean up crime in New York and

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Speaker 3: then having the troops like strolling around like the Upper

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Speaker 3: East Side, what like what are we doing here? You know,

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Speaker 3: it's like get out of your broad like there's nothing

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Speaker 3: going on unless it's for you know, journalists and for

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Speaker 3: cameras and all of that, and you know, their theory

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Speaker 3: would be like a grand show of force, and that

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Speaker 3: those of us who have lived here, like, what are

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Speaker 3: you even talking about?

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Speaker 1: It's like, we don't have any issues here.

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Speaker 3: It's exactly it's exactly the same same in la I

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Speaker 3: would say, right, you know, the same type of thing

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Speaker 3: in any partition deportations.

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Speaker 1: You know, we go for the show.

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Speaker 3: Yeah, do same thing, going for the show, not actually

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Speaker 3: being serious. Just by the way, if anybody's interested, our

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Speaker 3: deficit payments significantly went up, outpacing all tariff revenue and

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Speaker 3: everything by over ninety percent. Just in case anybody's interested

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Speaker 3: in any of those numbers, the actual numbers, Let's go

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Speaker 3: ahead and put a four up on the screen.

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Speaker 1: Please.

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Speaker 3: This is to the inflation numbers that you were talking

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Speaker 3: about and overall, I mean, this is a number of

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Speaker 3: the Trump administration is very happy about. But there are

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Speaker 3: some serious warning signs inside of it. So they say

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Speaker 3: inflation held steady at two point seven percent.

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Speaker 1: In July.

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Speaker 3: Prices excluding food and energy categories rose some three point

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Speaker 3: one percent over the last twelve months, above forecast, so

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Speaker 3: slightly above target, but two point seven unchanged from June's gain,

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Speaker 3: below the two point eight rise that was expected from

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Speaker 3: the economists surveyed in by the Wall Street Journal. But

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Speaker 3: the core inflation numbers, when you start to actually dig

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Speaker 3: into it, they start to get, you know, a little problematic.

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Speaker 3: And this is I've talked about this before about why

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Speaker 3: trying to put it all in a single number is

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Speaker 3: a problem because it ignores it'd be like GDP numbers.

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Speaker 1: We like, well, GDP went up.

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Speaker 3: I'm like, well, what about home prices in your ability

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Speaker 3: to afford one, you know, which is going to impact

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Speaker 3: you more. It's the same actually when you look at

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Speaker 3: what categories are driving inflation or not.

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Speaker 1: So let's put this up there please.

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Speaker 3: This is a six which has the individual goods where

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Speaker 3: you can see where you've had significant reduction.

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Speaker 1: So eggs, yeah, great.

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Speaker 3: Eggs are actually down by some forty three percent in

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Speaker 3: terms of annualized gain, along with quote non frozen non

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Speaker 3: carbonated juices some five point two per I don't even

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Speaker 3: know what that is. Butter snacks, rice, pasta, cornmeal, lunch meats,

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Speaker 3: baby food, cheese.

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Speaker 1: And related products.

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Speaker 3: All of those have gone down, not significantly except for eggs.

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Speaker 3: But the places where we have seen increase are at

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Speaker 3: the very top. You can see coffee, what's interesting about

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Speaker 3: the coffee number I dug into this is a lot

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Speaker 3: of it is because of these tariffs on Brazil as

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Speaker 3: a result of the Bolsonaro stuff, right, so some fifty

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Speaker 3: percent tariff there. Brazil is a significant exporter of mass

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Speaker 3: market coffee here in the United States, but broadly also

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Speaker 3: just the general trade imbalance for a lot of the

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Speaker 3: other countries that ship coffee to America. The disruption and

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Speaker 3: supply chain has pushed it high as well as has

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Speaker 3: apparently been some bad weather down in Brazil and in

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Speaker 3: Latin America that killed the crop. So those two things

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Speaker 3: together have spiked the cost of coffee to some twenty

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Speaker 3: five percent. That's one of those things you could easily

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Speaker 3: notice because obviously, you know, I think the vast majority

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Speaker 3: of Americans drink coffee every single day. Beef and veal

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Speaker 3: remain some up fifteen percent. Cookies, peanut butter, canned vegetables,

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Speaker 3: can fruit, all of them are over ten percent, ice cream,

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Speaker 3: dried beans, fresh vegetables significantly again up over eight percent.

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Speaker 3: So a decent number of categories there of everyday items

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Speaker 3: which Americans are buying and getting. Some sticker shot coffee

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Speaker 3: being the most noteworthy one, it all comes together, you know,

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Speaker 3: in that three point one percent number, but it remains important,

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Speaker 3: you know, just to look at exactly where things are.

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Speaker 3: Put a five police on the screen as well. This

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Speaker 3: kind of gets to something we're about to talk about

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Speaker 3: in a little bit. But the US has also hit

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Speaker 3: the highest layoffs right now since COVID. So in July

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Speaker 3: there was some sixty two thousand job cuts announced and

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Speaker 3: that's a twenty nine percent jump from June, one hundred

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Speaker 3: and forty percent higher than in July of twenty twenty four. Now,

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Speaker 3: as you can see, a huge part of that is

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Speaker 3: by the government and from DOZE. Now specifically the reason why,

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Speaker 3: and I've said this to some people's shock before. A

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Speaker 3: lot of people don't know, the US government is the

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Speaker 3: largest employer in the United States of America, and so

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Speaker 3: if you trim you know, even a little bit, well,

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Speaker 3: then you're going to end up in that scenario where

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Speaker 3: you have a major number of layoffs, and you're also

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Speaker 3: going to have all of these different layoffs and other

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Speaker 3: categories that continue to hit technologies having a problem retail.

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Speaker 1: That number is very interesting. I'm still I've did some.

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Speaker 3: Research, not fully ready anyone in the industry is not

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Speaker 3: ready to say it's all because of tariffs. There's like

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Speaker 3: other downstream problems inflation, consumer spending, services, and warehousing. But

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Speaker 3: the problem is it's up everywhere and significantly up amongst

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Speaker 3: the government. So overall it's kind of a shaky situation

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Speaker 3: right now. It really is precarious, and the tariffs are

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Speaker 3: one where. Yes, overall the tariff rate remains high, but

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Speaker 3: it's targeted at its most insane levels at only a

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Speaker 3: few countries, which is going to lead to those wonky outcomes.

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Speaker 3: So Brazil is the perfect example. That's our way of

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Speaker 3: the twenty five percent coffee bump. The other most tariff

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Speaker 3: country right now by the United States is India, so

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Speaker 3: there's going to be potential problems in the future on

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Speaker 3: generic pharmaceuticals. It could change the oil business. I'm trying

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Speaker 3: to think of the third most tariff country. I forget, yeah,

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Speaker 3: I did district period, but China our number one trading partner.

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Speaker 3: There's been a suspension and a broad bringdown of terror

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Speaker 3: because they keep getting exemptions and lo and behold. Just yesterday,

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Speaker 3: Trump announced in nine another ninety day pause. So we're

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Speaker 3: going to be coming up on two hundred and seventy

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Speaker 3: days basically of pauses on tariffs with China, which is

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Speaker 3: really the entire ballgame. So that's part of the reason

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Speaker 3: why the Trump administration can say, see, our tariffs are working,

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Speaker 3: when it's like, dude, you don't even have real tariffs

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Speaker 3: on like the original tariffs you announced, you.

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Speaker 1: Took them all away. You can't say that it worked well.

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Speaker 6: Basically the ten percent baseline still that direct, but not basically.

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Speaker 3: But that's not We're not that's not the one hundred

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Speaker 3: and thirty percent which was announced, So that's not the

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Speaker 3: so you know, the major changes or any of that

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Speaker 3: that was there. So yeah, of course things are where

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Speaker 3: they are. And then finally the last thing I've heard

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Speaker 3: from them is well, what about the stock market. I'm like, well,

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Speaker 3: and Nvidia alone is eight percent of the entire S

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Speaker 3: and P five hundred, and a huge part of the

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Speaker 3: S and P five hundred is driven by tech stocks,

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Speaker 3: which are generally not as impacted by tariffs.

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Speaker 1: That's not where I would look.

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Speaker 3: I would look at manufacturing employment and the deficit numbers,

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Speaker 3: as well as US capex like in turn terms of

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Speaker 3: domestic investment. All three of those are down from what

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Speaker 3: I was looking at just yesterday. So look, you know

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Speaker 3: it's not the disaster scenario, but it's not the great

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Speaker 3: success of scenario.

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Speaker 6: I was going say, I was talking to Republicans on

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Speaker 6: Capitol Hill about a week before they left for recess,

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Speaker 6: and I think I mentioned this on the show before,

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Speaker 6: but I was telling them, if you look at this,

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Speaker 6: I think it was a quinnipiek pole from last month

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Speaker 6: that found Trump underwater on the economy, immigration, foreign policy.

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Speaker 6: So three signatured Trump issues and on the economy. I

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Speaker 6: asked them basically, like, what do you think that says

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Speaker 6: if Donald Trump is underwater on the economy, And they said, well,

443
00:19:36,400 --> 00:19:39,160
Speaker 6: this is why we passed the one Big Beautiful Bill

444
00:19:39,280 --> 00:19:43,680
Speaker 6: when we did, because we now expect heading into the midterms,

445
00:19:44,720 --> 00:19:49,600
Speaker 6: that these economic changes are going to trickle towards the consumer.

446
00:19:49,640 --> 00:19:51,880
Speaker 6: I don't think anyone actually used that word. I'm paraphrasing,

447
00:19:51,920 --> 00:19:56,720
Speaker 6: but are going to trickle into different states of communities,

448
00:19:56,720 --> 00:19:58,679
Speaker 6: and people will start feeling the effects of both the

449
00:19:58,720 --> 00:20:01,200
Speaker 6: tariffs and the Big Beautiful Bill in a good way.

450
00:20:01,240 --> 00:20:04,800
Speaker 6: Because they always saw the Big Beautiful Bill as the

451
00:20:05,720 --> 00:20:09,080
Speaker 6: necessary part two of the tariff agenda, basically, like they

452
00:20:09,440 --> 00:20:12,640
Speaker 6: didn't believe that you could just keep doing the tariffs

453
00:20:12,680 --> 00:20:15,639
Speaker 6: if the bill didn't pass, And that's an impossib hypothetical

454
00:20:15,720 --> 00:20:19,200
Speaker 6: to revisit now. But basically, that bill had all of

455
00:20:19,280 --> 00:20:22,640
Speaker 6: what they would not describe but are accurately described as

456
00:20:22,800 --> 00:20:28,720
Speaker 6: a sort of industrial policy, industrial policy decisions that are

457
00:20:28,840 --> 00:20:33,040
Speaker 6: aimed at bringing jobs back, So exemption right offs for

458
00:20:33,520 --> 00:20:37,800
Speaker 6: retroactive to January for things like manufacturing and building and.

459
00:20:37,720 --> 00:20:38,320
Speaker 5: All of that.

460
00:20:38,800 --> 00:20:42,199
Speaker 6: So Soccer right now, they're in the middle of their

461
00:20:42,240 --> 00:20:45,639
Speaker 6: own experiment, and they don't have any control over the

462
00:20:45,680 --> 00:20:49,280
Speaker 6: president who is using tariffs as a foreign policy tool

463
00:20:49,320 --> 00:20:51,879
Speaker 6: in addition to an economic tool. And so that just

464
00:20:52,000 --> 00:20:55,800
Speaker 6: leaves what's been there from the beginning uncertainty. And with

465
00:20:55,880 --> 00:20:58,560
Speaker 6: the uncertainty brings leverage, of course, and that's why you

466
00:20:58,640 --> 00:21:01,120
Speaker 6: end up getting the EU to table at a deal.

467
00:21:01,160 --> 00:21:03,040
Speaker 6: That's how you end up getting care Starmer to the

468
00:21:03,080 --> 00:21:05,879
Speaker 6: table on a deal whatever else. But it's also how

469
00:21:05,920 --> 00:21:07,960
Speaker 6: you end up with ninety day pauses recurring and the

470
00:21:08,040 --> 00:21:09,240
Speaker 6: China can't getay pauses.

471
00:21:09,240 --> 00:21:10,919
Speaker 3: And I would say again, on these deals, and this

472
00:21:10,960 --> 00:21:12,520
Speaker 3: is the other thing that drives me crazy. You know,

473
00:21:12,560 --> 00:21:15,680
Speaker 3: some sort of deal is not necessarily good if they're fake.

474
00:21:15,800 --> 00:21:18,280
Speaker 3: So the six hundred billion dollar pledge from the European

475
00:21:18,400 --> 00:21:21,120
Speaker 3: Union is complete bullshit. I mean, if you look at

476
00:21:21,119 --> 00:21:23,560
Speaker 3: the European Union's own announcement, they were like, yeah, we

477
00:21:23,600 --> 00:21:26,760
Speaker 3: actually have no ability to compel you know, our companies

478
00:21:26,800 --> 00:21:27,240
Speaker 3: to do.

479
00:21:27,119 --> 00:21:29,240
Speaker 1: This, but will pledge it. I guess right.

480
00:21:29,480 --> 00:21:29,639
Speaker 4: You know.

481
00:21:29,720 --> 00:21:30,919
Speaker 1: Same with the Japanese.

482
00:21:31,440 --> 00:21:33,920
Speaker 3: They were shown you know, some of the releases and

483
00:21:33,960 --> 00:21:36,240
Speaker 3: they were like, well, that's not what we promise at all.

484
00:21:36,280 --> 00:21:37,960
Speaker 3: And also at a certain point, you know, some of

485
00:21:38,000 --> 00:21:41,000
Speaker 3: these numbers all just become fake, like four hundred billion,

486
00:21:41,119 --> 00:21:42,000
Speaker 3: five hundred billion.

487
00:21:42,080 --> 00:21:44,560
Speaker 1: It's all about in the execution. There's a reason. Just

488
00:21:44,560 --> 00:21:45,560
Speaker 1: so everybody understands.

489
00:21:46,080 --> 00:21:48,160
Speaker 3: As said this, before my wife worked in trade policy,

490
00:21:48,760 --> 00:21:52,560
Speaker 3: these trade deals used to take years to put together.

491
00:21:52,880 --> 00:21:55,280
Speaker 3: Now that's an indictment of the process, but part of

492
00:21:55,320 --> 00:21:57,399
Speaker 3: the problem. One of the reason why they took years

493
00:21:57,800 --> 00:22:01,480
Speaker 3: was because they would pass congresses and they would also

494
00:22:01,600 --> 00:22:05,400
Speaker 3: pass legislature in wherever their home country was, and it

495
00:22:05,440 --> 00:22:09,840
Speaker 3: became a legally binding agreement between these two countries with

496
00:22:09,920 --> 00:22:13,640
Speaker 3: an arbitration process where if you violate said process, then

497
00:22:13,760 --> 00:22:15,200
Speaker 3: you're going to have to pay a fine or.

498
00:22:15,119 --> 00:22:17,480
Speaker 1: We're going to have some you know, dispute court or whatever.

499
00:22:17,560 --> 00:22:19,920
Speaker 3: I'm not defending those previous ones, because I think a

500
00:22:19,960 --> 00:22:21,600
Speaker 3: lot of those were bad deals. But I'm just saying,

501
00:22:21,640 --> 00:22:24,080
Speaker 3: at a very processed level, you're not supposed to just

502
00:22:24,119 --> 00:22:27,719
Speaker 3: sign some handshake agreement. It has no enforceability, right, So

503
00:22:27,920 --> 00:22:31,760
Speaker 3: whoever comes into office after Trump can do to his

504
00:22:31,920 --> 00:22:35,199
Speaker 3: deals exactly what Trump did to the Iran deal. This

505
00:22:35,280 --> 00:22:36,679
Speaker 3: was part of the problem with the Iran deals. They

506
00:22:36,760 --> 00:22:39,280
Speaker 3: never really passed through Congress, right, so the JCUPO can

507
00:22:39,359 --> 00:22:41,960
Speaker 3: just be ripped up by any executive This. You're not

508
00:22:42,000 --> 00:22:45,119
Speaker 3: supposed to really do stuff this way, especially with the

509
00:22:45,240 --> 00:22:47,840
Speaker 3: US economy, with business, because we now not just have

510
00:22:48,000 --> 00:22:50,080
Speaker 3: uncertainty for the next three and a half years, we

511
00:22:50,119 --> 00:22:53,600
Speaker 3: have uncertainty basically forever. Like whoever becomes a president. Yeah,

512
00:22:53,640 --> 00:22:56,040
Speaker 3: they get to do this whatever they want, last thing

513
00:22:56,080 --> 00:22:59,000
Speaker 3: before we move on. I am told though, that as

514
00:22:59,040 --> 00:23:01,040
Speaker 3: this moves up to the Super Court that they are

515
00:23:01,080 --> 00:23:04,840
Speaker 3: on very dicey territory. A couple conservative court watchers have

516
00:23:04,920 --> 00:23:07,920
Speaker 3: said that the court looks very skeptical, at least now

517
00:23:08,000 --> 00:23:10,760
Speaker 3: so far, on the way that Trump administration will handle

518
00:23:11,000 --> 00:23:13,159
Speaker 3: some of these tariffs that Trump administration is trying to

519
00:23:13,160 --> 00:23:15,840
Speaker 3: buckle it all under the purview of foreign policy, but

520
00:23:15,840 --> 00:23:17,600
Speaker 3: they don't think it's going to pass legal scrutiny.

521
00:23:17,680 --> 00:23:20,320
Speaker 6: Yeah, and yeah, emergency measures and the same thing. We'll

522
00:23:20,320 --> 00:23:22,120
Speaker 6: talk about this in the D block or the DC

523
00:23:22,240 --> 00:23:25,359
Speaker 6: block as well. But yeah, that's and that's another huge

524
00:23:25,520 --> 00:23:29,080
Speaker 6: variable factoring into the uncertainty because now you have to

525
00:23:29,080 --> 00:23:32,480
Speaker 6: take into consideration when you're pricing markets how the Supreme

526
00:23:32,560 --> 00:23:35,560
Speaker 6: Court potentially will react in a decision like that. So

527
00:23:36,960 --> 00:23:40,920
Speaker 6: the more the uncertainty goes on, obviously, the more we're

528
00:23:41,480 --> 00:23:44,840
Speaker 6: likely to start seeing some of these like egg prices

529
00:23:44,880 --> 00:23:47,880
Speaker 6: will go down, but coffee prices will jump. These spotty

530
00:23:47,880 --> 00:23:51,560
Speaker 6: indicators and reports if we get reports, if we start

531
00:23:51,600 --> 00:23:53,479
Speaker 6: getting if we continue to get the data.

532
00:23:56,280 --> 00:23:58,000
Speaker 1: This's been a story I've been watching. This is nerdy,

533
00:23:58,040 --> 00:23:58,960
Speaker 1: but everybody stick with me.

534
00:23:59,000 --> 00:24:02,360
Speaker 3: It's about and so the company I just mentioned earlier,

535
00:24:02,680 --> 00:24:07,000
Speaker 3: they manufacture the most advanced AI chips. Obviously that's what's

536
00:24:07,040 --> 00:24:09,480
Speaker 3: made them a multi trillion dollar company. I believe n

537
00:24:09,600 --> 00:24:12,440
Speaker 3: Video alone is more worth more than the entire UK

538
00:24:12,520 --> 00:24:13,639
Speaker 3: stock market.

539
00:24:13,720 --> 00:24:16,560
Speaker 1: Just for reference, continues to boom.

540
00:24:17,119 --> 00:24:20,240
Speaker 3: So there has been a lot of question over Nvidia

541
00:24:20,400 --> 00:24:22,920
Speaker 3: and being able to sell to China. Now there's been

542
00:24:23,160 --> 00:24:26,320
Speaker 3: a China kind of Hawk contingent, including Steve Bannon and

543
00:24:26,400 --> 00:24:28,720
Speaker 3: others part of the MAGA movement, and actually a lot

544
00:24:28,720 --> 00:24:31,600
Speaker 3: of former Trump administration officials from the first term who

545
00:24:31,600 --> 00:24:35,000
Speaker 3: have heavily criticized and Video. Their CEO, Jensen Wog and

546
00:24:35,080 --> 00:24:38,240
Speaker 3: kind of cozying up to the CCP and Video sees

547
00:24:38,280 --> 00:24:41,439
Speaker 3: it not as existential but as important to their future

548
00:24:41,440 --> 00:24:44,919
Speaker 3: profit revenues to be able to sell their H twenty chips,

549
00:24:44,960 --> 00:24:46,680
Speaker 3: one of the kind of their bread and broader chip

550
00:24:46,920 --> 00:24:49,760
Speaker 3: for a lot of AI processing, a lot of the

551
00:24:49,840 --> 00:24:53,160
Speaker 3: more basic open source models run on this AGE twenty trip,

552
00:24:53,160 --> 00:24:55,720
Speaker 3: as well as the ability to preserve the future sales

553
00:24:56,000 --> 00:24:59,720
Speaker 3: into China as core to their business and to growing right.

554
00:25:00,119 --> 00:25:03,520
Speaker 3: What Jensen has done is that he has heavily lobbied

555
00:25:03,680 --> 00:25:07,359
Speaker 3: the Trump administration, including paying a million dollars to attend

556
00:25:07,440 --> 00:25:10,240
Speaker 3: Amar a Lago dinner, and now struck one of the

557
00:25:10,240 --> 00:25:15,040
Speaker 3: most extraordinary business deals in American history, where under the

558
00:25:15,040 --> 00:25:18,640
Speaker 3: Trump administration, he will be allowed to sell these eight

559
00:25:18,760 --> 00:25:22,960
Speaker 3: twenty chips to China. In exchange, he must cut fifteen

560
00:25:23,000 --> 00:25:27,040
Speaker 3: percent of those sales to the US government, an individual

561
00:25:27,080 --> 00:25:30,760
Speaker 3: company must pay a portion to the US government. Here's

562
00:25:30,800 --> 00:25:32,960
Speaker 3: Trump confirming that deal at the White House. Let's take

563
00:25:32,960 --> 00:25:34,360
Speaker 3: a listen on China.

564
00:25:34,520 --> 00:25:38,800
Speaker 6: Your administration agreed to send the most advanced or advanced

565
00:25:39,040 --> 00:25:40,920
Speaker 6: NVIDIA and AMD.

566
00:25:40,720 --> 00:25:44,280
Speaker 8: Chip no absolute no obsolition and then fifteen percent of

567
00:25:44,320 --> 00:25:46,880
Speaker 8: prom the twenties no absolute no.

568
00:25:46,800 --> 00:25:49,320
Speaker 5: Obsoligon well and then fifteen percent of the prom means

569
00:25:49,320 --> 00:25:49,840
Speaker 5: the twenties.

570
00:25:50,040 --> 00:25:51,800
Speaker 4: No, no, that's this is.

571
00:25:51,760 --> 00:25:58,120
Speaker 8: An all chip that China already has. And I deal

572
00:25:58,160 --> 00:26:02,760
Speaker 8: with Jensen, who's a great guy, and na Vidia. The

573
00:26:02,920 --> 00:26:06,920
Speaker 8: chip that we're talking about, the H twenty, it's it's

574
00:26:06,960 --> 00:26:10,800
Speaker 8: an old chip. China already has it in a different form,

575
00:26:10,920 --> 00:26:15,080
Speaker 8: different name, but they have it, or they have a

576
00:26:15,119 --> 00:26:17,840
Speaker 8: combination of two will make up for it. And even then,

577
00:26:17,880 --> 00:26:21,400
Speaker 8: so now Jensen also has Jensen's a very brilliant guy.

578
00:26:22,119 --> 00:26:23,639
Speaker 8: And Jensen also has a new.

579
00:26:23,560 --> 00:26:24,600
Speaker 4: Chip, the Blackwell.

580
00:26:24,640 --> 00:26:25,800
Speaker 1: Do you know what the Blackwell is?

581
00:26:25,840 --> 00:26:26,760
Speaker 4: The Blackwell is.

582
00:26:27,160 --> 00:26:30,679
Speaker 8: Super duper advanced. I wouldn't make a deal with that,

583
00:26:30,880 --> 00:26:39,600
Speaker 8: although it's possible, I'd make a deal a somewhat enhanced

584
00:26:39,920 --> 00:26:43,280
Speaker 8: in a negative way. Blackwell, in other words, take thirty

585
00:26:43,280 --> 00:26:46,399
Speaker 8: percent to fifty percent off of it. But that's the

586
00:26:46,800 --> 00:26:48,960
Speaker 8: latest of the greatest in the world. Nobody has it.

587
00:26:49,000 --> 00:26:51,280
Speaker 8: They won't have it for five years. But the age

588
00:26:51,359 --> 00:26:53,520
Speaker 8: twenty is obsolete, you know. It's one of those things,

589
00:26:53,560 --> 00:26:56,160
Speaker 8: but it still has a market. So I said, listen,

590
00:26:56,240 --> 00:26:58,720
Speaker 8: I want twenty percent if I'm going to prove this

591
00:26:58,800 --> 00:26:59,400
Speaker 8: for you, for the.

592
00:26:59,320 --> 00:27:00,920
Speaker 4: Country, country, for the US.

593
00:27:00,960 --> 00:27:01,960
Speaker 8: I don't want it myself.

594
00:27:02,680 --> 00:27:02,840
Speaker 3: You know.

595
00:27:02,920 --> 00:27:07,399
Speaker 8: Every time I say, like like seven forty seven, I want,

596
00:27:07,480 --> 00:27:11,080
Speaker 8: I want yeah, for the air force. So I just

597
00:27:11,119 --> 00:27:13,719
Speaker 8: want to So when I say I want twenty, I

598
00:27:13,800 --> 00:27:14,679
Speaker 8: want for the country.

599
00:27:14,680 --> 00:27:15,879
Speaker 4: I only care about the country.

600
00:27:15,920 --> 00:27:16,399
Speaker 6: I don't like.

601
00:27:16,880 --> 00:27:19,240
Speaker 8: I don't know, if you know, we will sometimes sell

602
00:27:19,359 --> 00:27:22,760
Speaker 8: fighter jets to a country and we'll give them twenty

603
00:27:22,760 --> 00:27:24,800
Speaker 8: percent less than we.

604
00:27:24,800 --> 00:27:27,639
Speaker 1: Have, all right, So just put this Wall Street Journal

605
00:27:27,640 --> 00:27:28,280
Speaker 1: story up here.

606
00:27:28,520 --> 00:27:31,960
Speaker 3: The details of this are so crazy, so cool, with

607
00:27:32,080 --> 00:27:35,359
Speaker 3: billions at risk and video CEO buys his way out

608
00:27:35,400 --> 00:27:38,560
Speaker 3: of the trade battle. So, like I said, effectively, what

609
00:27:38,680 --> 00:27:43,560
Speaker 3: Jensen has done is he is allowing the FEDS. And

610
00:27:43,600 --> 00:27:46,320
Speaker 3: by the way, it's not just in Video, it's also AMD,

611
00:27:46,880 --> 00:27:48,640
Speaker 3: by the way, which is run by his cousin, which

612
00:27:48,640 --> 00:27:51,400
Speaker 3: is crazy that these two cousins of this family run

613
00:27:51,400 --> 00:27:53,760
Speaker 3: the two of the largest chip manufacturers in the world.

614
00:27:53,840 --> 00:27:57,879
Speaker 3: But so Jensen and AMD, these two companies are going

615
00:27:57,960 --> 00:28:01,159
Speaker 3: to have to cut some fifteen percent, maybe even twenty

616
00:28:01,240 --> 00:28:04,200
Speaker 3: The full details are not yet available to the government,

617
00:28:04,480 --> 00:28:07,879
Speaker 3: just of its chip sales to China in exchange for

618
00:28:08,040 --> 00:28:11,840
Speaker 3: issuing export licenses. So basically it's a pay to play

619
00:28:11,960 --> 00:28:15,960
Speaker 3: model in China. Now, I mean, theoretically, I guess that's

620
00:28:16,119 --> 00:28:19,840
Speaker 3: fine if it's applied across the board. But the process

621
00:28:19,960 --> 00:28:22,560
Speaker 3: in which this all came about, as I said, effectively

622
00:28:22,600 --> 00:28:25,720
Speaker 3: came down from Jensen desperately trying to get the Trump

623
00:28:25,720 --> 00:28:29,120
Speaker 3: administration to lift export licenses of his sales to China.

624
00:28:29,200 --> 00:28:32,000
Speaker 3: He has recently spent a lot of time in Beijing

625
00:28:32,280 --> 00:28:34,560
Speaker 3: CosIng up to the CCP. He's like, we're going to

626
00:28:34,640 --> 00:28:36,520
Speaker 3: do everything we can to preserve our business here in

627
00:28:36,600 --> 00:28:39,240
Speaker 3: China because the Chinese companies, a lot of these Deep

628
00:28:39,240 --> 00:28:42,040
Speaker 3: Seek models and others, are desperate for some of these

629
00:28:42,080 --> 00:28:44,680
Speaker 3: Age twenty trips. But the point really beyond all of

630
00:28:44,680 --> 00:28:47,480
Speaker 3: this is that all of it came about in the

631
00:28:47,520 --> 00:28:50,840
Speaker 3: same way that Tim Cook brought that solid bar of

632
00:28:50,920 --> 00:28:54,040
Speaker 3: gold to Donald Trump and the Oval Office, Like basically,

633
00:28:54,240 --> 00:28:59,000
Speaker 3: you know, providing offering to the Great ConA literally paying

634
00:28:59,000 --> 00:29:01,760
Speaker 3: tribute to the Great That's the way this is working.

635
00:29:01,840 --> 00:29:04,240
Speaker 3: And they're like, well, in exchange for you being allowed

636
00:29:04,240 --> 00:29:06,760
Speaker 3: to do business, you must cut a portion of revenue

637
00:29:07,240 --> 00:29:07,880
Speaker 3: to the state.

638
00:29:08,200 --> 00:29:10,640
Speaker 1: And look again, it's fine if.

639
00:29:10,520 --> 00:29:13,080
Speaker 3: It was applied across the board, but the point being

640
00:29:13,400 --> 00:29:17,480
Speaker 3: that there were significant geopolitical implications around this chip. Part

641
00:29:17,520 --> 00:29:21,320
Speaker 3: of the reason why Americans, especially the more Chinahawks, wanted

642
00:29:21,360 --> 00:29:24,320
Speaker 3: to stop the export of the H twenty trip is

643
00:29:24,440 --> 00:29:27,840
Speaker 3: it would not allow the continued development or of these

644
00:29:27,960 --> 00:29:31,040
Speaker 3: open source models, which became a major threat and according

645
00:29:31,080 --> 00:29:33,480
Speaker 3: to the AI companies ours, a lot of the technology

646
00:29:33,480 --> 00:29:35,920
Speaker 3: and others was stolen from them. Well, put a nine

647
00:29:36,080 --> 00:29:39,960
Speaker 3: up there on the screen. Because what's important beyond all

648
00:29:40,000 --> 00:29:44,720
Speaker 3: of this is that Chinese authorities have been doing talking

649
00:29:44,800 --> 00:29:47,160
Speaker 3: out of two sides of their mouth. One is they're like,

650
00:29:47,200 --> 00:29:50,680
Speaker 3: we want this age twenty trip, it's critical for our industry. Second, though,

651
00:29:50,760 --> 00:29:53,680
Speaker 3: just yesterday they came out and they were like, hey, everybody,

652
00:29:53,760 --> 00:29:57,880
Speaker 3: we need to stop using the Nvidia H twenty trip quote,

653
00:29:57,880 --> 00:30:02,480
Speaker 3: particularly for government related perp, because they don't even want

654
00:30:02,680 --> 00:30:04,560
Speaker 3: the pretense of a vulnerability.

655
00:30:04,600 --> 00:30:06,360
Speaker 1: So effectively, what they're doing flip it is.

656
00:30:06,400 --> 00:30:09,479
Speaker 3: They're playing us where they're like, yeah, oh yeah, we'll

657
00:30:09,520 --> 00:30:12,040
Speaker 3: keep buying it for now. This is what they've done

658
00:30:12,440 --> 00:30:14,320
Speaker 3: all the time. They're like, yeah, yeah, we'll keep buying

659
00:30:14,320 --> 00:30:15,600
Speaker 3: it for now. We'll keep buying it for now, and

660
00:30:15,640 --> 00:30:17,760
Speaker 3: at the same time using the full hammer of the

661
00:30:17,840 --> 00:30:20,720
Speaker 3: Chinese government to say that we're going to develop something new.

662
00:30:20,880 --> 00:30:22,680
Speaker 3: So just to present the other side of the argument

663
00:30:22,760 --> 00:30:25,280
Speaker 3: from a lot of people who defend selling age twenty,

664
00:30:25,440 --> 00:30:27,640
Speaker 3: they're like, well, we should keep selling it to them,

665
00:30:27,880 --> 00:30:30,440
Speaker 3: because if we don't, then they're going to just use

666
00:30:30,480 --> 00:30:33,959
Speaker 3: something else and develop their own native AI chips and company.

667
00:30:34,280 --> 00:30:37,520
Speaker 1: They're already doing it, which I just showed you the evidence.

668
00:30:37,560 --> 00:30:41,320
Speaker 3: They're already doing it no matter what, because they're not stupid,

669
00:30:41,440 --> 00:30:45,160
Speaker 3: unlike us. If they see even a single vulnerability, a

670
00:30:45,200 --> 00:30:47,840
Speaker 3: critical one that could come down the road, like with

671
00:30:47,880 --> 00:30:50,760
Speaker 3: all this trade uncertainty, they just solve it for themselves.

672
00:30:50,840 --> 00:30:52,880
Speaker 3: They've done this with cars, even those with batteries, with

673
00:30:52,960 --> 00:30:56,720
Speaker 3: minerals refining, I mean airplanes. Now with the chips, which

674
00:30:56,760 --> 00:31:00,200
Speaker 3: is really the only area technologically where they continue to

675
00:31:00,280 --> 00:31:03,719
Speaker 3: kind of ride behind us. But they're pouring oceans of

676
00:31:03,760 --> 00:31:08,560
Speaker 3: government backed money into developing their alternatives and to try

677
00:31:08,600 --> 00:31:11,920
Speaker 3: and be chip independent from America in the next four

678
00:31:12,120 --> 00:31:12,920
Speaker 3: to five years.

679
00:31:12,960 --> 00:31:14,800
Speaker 1: And so, I mean the point then comes down.

680
00:31:14,680 --> 00:31:16,120
Speaker 3: To is like, well, if they're going to be independent,

681
00:31:16,200 --> 00:31:18,560
Speaker 3: maybe make it a little complicated and difficult for them,

682
00:31:18,800 --> 00:31:21,400
Speaker 3: or at least negotiate some leverage here, you know, in

683
00:31:21,440 --> 00:31:25,080
Speaker 3: the interim but extent, we got this weird corrupt bargain

684
00:31:25,360 --> 00:31:29,520
Speaker 3: between Gensen AMD and the government. And you know, again,

685
00:31:29,640 --> 00:31:31,640
Speaker 3: you know, for a lot of libertarians and people out there,

686
00:31:31,680 --> 00:31:34,680
Speaker 3: they've always complained about crony capitalism. I mean, there's no

687
00:31:34,800 --> 00:31:38,160
Speaker 3: better example than this. It's pure just like pay to

688
00:31:38,160 --> 00:31:40,560
Speaker 3: play and fluffing the ego. That's a problem I have

689
00:31:40,600 --> 00:31:44,160
Speaker 3: around it doesn't make any geopolitical or strategic sense either way.

690
00:31:44,480 --> 00:31:45,520
Speaker 1: It should just make sense.

691
00:31:45,680 --> 00:31:47,320
Speaker 3: You know, fine, if you want to let them sell

692
00:31:47,320 --> 00:31:50,240
Speaker 3: it for the reasons I just said, yeah, well, we

693
00:31:50,280 --> 00:31:52,959
Speaker 3: don't want them to develop their own domestic manufacturer. They

694
00:31:53,040 --> 00:31:55,920
Speaker 3: want them to remain reliant on us. Okay, you know,

695
00:31:56,040 --> 00:31:58,520
Speaker 3: well we should still have some much more of a process.

696
00:31:58,520 --> 00:32:00,880
Speaker 1: But that's not how this deal ended up coming about.

697
00:32:01,000 --> 00:32:01,760
Speaker 4: No, it's the issue.

698
00:32:01,840 --> 00:32:03,720
Speaker 5: No, it was completely paid a play.

699
00:32:03,720 --> 00:32:06,360
Speaker 6: It was Jensen Wynn coosing up to Trump and to

700
00:32:06,520 --> 00:32:10,160
Speaker 6: others in the administration and getting basically what he wanted

701
00:32:10,160 --> 00:32:13,320
Speaker 6: out of it. So Don Bacon, who I think did

702
00:32:13,320 --> 00:32:15,880
Speaker 6: he he's formally announced he's not running for reelection, I

703
00:32:15,880 --> 00:32:19,680
Speaker 6: think so. Yeah, he's among the many China Hawks that

704
00:32:19,960 --> 00:32:22,280
Speaker 6: flipped out. So he said, We've got to realize we're

705
00:32:22,320 --> 00:32:24,840
Speaker 6: in an intellectual war, technology war with China, and we're

706
00:32:24,840 --> 00:32:28,560
Speaker 6: in an AI competition. Having Nvidia providing this tech to

707
00:32:28,840 --> 00:32:32,200
Speaker 6: China is a mistake. He was on a news nation

708
00:32:32,520 --> 00:32:36,480
Speaker 6: last night and said that so it's Trump was like

709
00:32:36,520 --> 00:32:37,840
Speaker 6: the great champion.

710
00:32:37,600 --> 00:32:38,760
Speaker 5: Of a lot of the China Hawks.

711
00:32:38,800 --> 00:32:42,600
Speaker 6: Yes, that's true, which is also hilarious for many reasons.

712
00:32:43,360 --> 00:32:46,600
Speaker 5: You know, we can talk about installing Elon Musk at

713
00:32:46,600 --> 00:32:48,560
Speaker 5: the top of like the government.

714
00:32:48,200 --> 00:32:52,280
Speaker 6: Efficiency effort and all of Musk's various ties to China.

715
00:32:52,920 --> 00:32:56,080
Speaker 6: We could talk about Trump's relationship was Hi Jin Pang.

716
00:32:56,400 --> 00:32:59,320
Speaker 6: But the China Hawks are in for such a wake

717
00:32:59,400 --> 00:33:02,320
Speaker 6: up call to what Donald Trump actually thinks. And there's

718
00:33:02,360 --> 00:33:04,400
Speaker 6: a non zero chance, by the way, that the way

719
00:33:04,480 --> 00:33:08,920
Speaker 6: Trump approaches China would be better than a president Mike Pompeio,

720
00:33:10,000 --> 00:33:14,280
Speaker 6: because you know, it's not belligerent and warmongering.

721
00:33:14,960 --> 00:33:18,160
Speaker 3: My issue yet, my issue around this is the lack

722
00:33:18,200 --> 00:33:21,440
Speaker 3: of thought and it's just all egotistical' that's my you know,

723
00:33:21,600 --> 00:33:23,200
Speaker 3: like I said, if you want to ship it to him, fine,

724
00:33:23,240 --> 00:33:26,200
Speaker 3: do it for strategic sense, make a case. But this

725
00:33:26,240 --> 00:33:28,719
Speaker 3: is really just about it's because the thing is it

726
00:33:28,720 --> 00:33:31,240
Speaker 3: fits within the broader context of the way all the

727
00:33:31,280 --> 00:33:34,400
Speaker 3: text CEOs are approaching this. This is why Apple is

728
00:33:34,440 --> 00:33:37,320
Speaker 3: making their fake announcement over five hundred billion dollars and

729
00:33:37,400 --> 00:33:40,920
Speaker 3: presenting a solid bar of gold to Donald Trump. Right,

730
00:33:41,080 --> 00:33:43,560
Speaker 3: this is who do you think Jensen learned all this from.

731
00:33:43,640 --> 00:33:46,680
Speaker 3: He looked at Bezos and at Zuckerberg and at Sundhar

732
00:33:46,720 --> 00:33:49,000
Speaker 3: Pichai and all these other guys who have been doing

733
00:33:49,040 --> 00:33:50,880
Speaker 3: business with Trump, and they're like, okay, well this is

734
00:33:50,920 --> 00:33:53,120
Speaker 3: how I make sure that I get my exemption. You

735
00:33:53,240 --> 00:33:55,840
Speaker 3: think Apple is freaking out? You know, yes, they're gonna

736
00:33:55,840 --> 00:33:58,080
Speaker 3: have to pay I think a billion dollars in tear

737
00:33:58,080 --> 00:34:00,840
Speaker 3: of charges. But they could have been new ked overnight.

738
00:34:01,120 --> 00:34:03,760
Speaker 3: You know, with between China and India, they've gotten major

739
00:34:03,840 --> 00:34:07,240
Speaker 3: exemptions from all of their tariffs. They basically have from

740
00:34:07,360 --> 00:34:09,279
Speaker 3: day one. And then you'll recall you know, in the

741
00:34:09,320 --> 00:34:12,239
Speaker 3: early days they were telling us, Howard Lutnick and others,

742
00:34:12,280 --> 00:34:14,879
Speaker 3: how we're going to build iPhones here, and that's evaporated.

743
00:34:14,920 --> 00:34:16,600
Speaker 3: You know, It's like now it's like a single piece

744
00:34:16,880 --> 00:34:19,760
Speaker 3: of the glass, like right here. You know, it maybe

745
00:34:19,920 --> 00:34:22,799
Speaker 3: come from Kentucky and depending on the phone, you know

746
00:34:22,920 --> 00:34:25,799
Speaker 3: that you get, and it pales in comparison to all

747
00:34:25,840 --> 00:34:28,120
Speaker 3: of the investments that they remain making a broad as

748
00:34:28,160 --> 00:34:29,640
Speaker 3: long as though as they give him. I mean, I

749
00:34:29,680 --> 00:34:33,400
Speaker 3: can think of Sam Altman, that whole Stargate thing, five

750
00:34:33,520 --> 00:34:36,000
Speaker 3: hundred billion dollars. It's like, yeah, where's the money is

751
00:34:36,040 --> 00:34:38,200
Speaker 3: it happening? As Elon said, they don't even have the money.

752
00:34:38,239 --> 00:34:40,920
Speaker 3: It's true, it's absolutely ridiculous the idea that they're going

753
00:34:40,960 --> 00:34:43,120
Speaker 3: to be spending all of this. But as long as

754
00:34:43,160 --> 00:34:45,799
Speaker 3: they get a press conference and they're sucking up to him,

755
00:34:45,880 --> 00:34:48,239
Speaker 3: they basically just continue business as usual. Remember the same

756
00:34:48,280 --> 00:34:50,000
Speaker 3: biggest do you remember the fox Kind deal and just

757
00:34:50,239 --> 00:34:52,840
Speaker 3: what going on. It's a great example. We were yeah,

758
00:34:53,800 --> 00:34:54,200
Speaker 3: ends up.

759
00:34:54,480 --> 00:34:59,280
Speaker 6: Yeah, it's splashy public relations announcements and then doesn't necessarily

760
00:34:59,360 --> 00:35:01,600
Speaker 6: pan out. Well that be the case with every single example. No,

761
00:35:01,760 --> 00:35:04,120
Speaker 6: I think there are some anecdotes where we are seeing

762
00:35:04,800 --> 00:35:08,360
Speaker 6: certain communities get good plants that are coming back, but

763
00:35:08,440 --> 00:35:11,440
Speaker 6: it's too early to say if that's happening at scale,

764
00:35:11,760 --> 00:35:15,879
Speaker 6: and the indications are not universally positive to say the very.

765
00:35:15,880 --> 00:35:20,000
Speaker 3: Lest yes, that's right, we're going to turn down to

766
00:35:20,200 --> 00:35:22,839
Speaker 3: youth unemployment underemployment. Let's go and put this up there

767
00:35:22,880 --> 00:35:25,520
Speaker 3: on the screen, very closely watched numbers, relying on the

768
00:35:25,560 --> 00:35:26,800
Speaker 3: Bureau of Labor Statistics.

769
00:35:26,880 --> 00:35:28,040
Speaker 1: So let's hope that they're right.

770
00:35:28,400 --> 00:35:32,160
Speaker 3: But even according to the revisions you can see here

771
00:35:32,440 --> 00:35:35,759
Speaker 3: this chart quote, is a generational jobs crisis brewing. The

772
00:35:35,920 --> 00:35:39,560
Speaker 3: underemployment rate for young adults in America is up sharply.

773
00:35:39,640 --> 00:35:43,680
Speaker 3: So if you look after twenty ten, after the Great Recession,

774
00:35:43,760 --> 00:35:46,399
Speaker 3: this is the story of like that lost generation which

775
00:35:46,480 --> 00:35:50,480
Speaker 3: really got screwed. Underemployment was up some thirty percent at

776
00:35:50,480 --> 00:35:53,600
Speaker 3: one point in twenty ten. Basically all stayed high all

777
00:35:53,640 --> 00:35:56,600
Speaker 3: the way to twenty and fifteen, when the jobs market

778
00:35:56,640 --> 00:35:59,360
Speaker 3: started becoming a little bit better. It started to trend

779
00:35:59,360 --> 00:36:02,000
Speaker 3: down very nice sleep back to two thousand levels until

780
00:36:02,200 --> 00:36:06,080
Speaker 3: the COVID pandemic of twenty twenty. Massive spike after that

781
00:36:06,160 --> 00:36:10,320
Speaker 3: actually even surpassing twenty ten. It came back down during

782
00:36:10,360 --> 00:36:13,680
Speaker 3: the whole like tight labor market era of twenty twenty one.

783
00:36:13,960 --> 00:36:16,279
Speaker 3: But you're watching how very quickly just in the last

784
00:36:16,280 --> 00:36:19,719
Speaker 3: couple of years it's ticked up and now it's actually

785
00:36:19,760 --> 00:36:22,920
Speaker 3: back to levels around two thousand and nine before that

786
00:36:23,080 --> 00:36:26,799
Speaker 3: massive explosion in twenty ten, and something very closely to watch.

787
00:36:26,840 --> 00:36:29,880
Speaker 3: You got to ask, like why, what exactly is happening?

788
00:36:29,920 --> 00:36:33,000
Speaker 3: And I think there's a lot of reasons for it.

789
00:36:33,160 --> 00:36:36,160
Speaker 3: Number one is ai this is at the high levels

790
00:36:36,200 --> 00:36:37,880
Speaker 3: of the job market. We're going to talk about that

791
00:36:38,200 --> 00:36:40,440
Speaker 3: in a little bit. Number two is at the low

792
00:36:40,520 --> 00:36:42,520
Speaker 3: levels of entry. Well, yeah, it's funny, it's spoted to

793
00:36:42,520 --> 00:36:45,080
Speaker 3: the low and high more. What I meant is it's

794
00:36:45,200 --> 00:36:49,919
Speaker 3: impacting college graduates for entry level work. So but those

795
00:36:50,000 --> 00:36:52,400
Speaker 3: are already people they spent a lot of money on

796
00:36:52,400 --> 00:36:54,960
Speaker 3: their education. They were probably going to be higher earners.

797
00:36:55,160 --> 00:36:57,760
Speaker 3: But there's a big question too at the lower levels

798
00:36:57,840 --> 00:37:00,000
Speaker 3: as to why that's not happening for the non college

799
00:37:00,080 --> 00:37:02,680
Speaker 3: graduate people who are in trades or in others. Remember

800
00:37:02,680 --> 00:37:05,440
Speaker 3: in twenty twenty one they had a major explosion. But

801
00:37:05,640 --> 00:37:08,560
Speaker 3: problem right we've seen right now is with the unemployment

802
00:37:08,560 --> 00:37:12,280
Speaker 3: figures and others. We're seeing like a softening of underemployment

803
00:37:12,360 --> 00:37:14,120
Speaker 3: for a lot of those people as well, And so

804
00:37:14,160 --> 00:37:17,239
Speaker 3: when you combine it together, you kind of have potentially

805
00:37:17,360 --> 00:37:21,719
Speaker 3: another lost generation post COVID, where just like in twenty ten,

806
00:37:21,920 --> 00:37:25,040
Speaker 3: the elder millennials, in particular those who graduated in ownA,

807
00:37:25,120 --> 00:37:28,640
Speaker 3: I mean, they were just destroyed. There's no even talk

808
00:37:28,760 --> 00:37:31,000
Speaker 3: like it's difficult to describe what it was like for

809
00:37:31,120 --> 00:37:33,440
Speaker 3: a lot of those people. We're not there yet, but

810
00:37:33,480 --> 00:37:35,680
Speaker 3: there are very troubling signs, and I think it goes

811
00:37:35,719 --> 00:37:38,920
Speaker 3: to the shakiness of the overall labor market, especially for

812
00:37:38,960 --> 00:37:39,399
Speaker 3: young people.

813
00:37:39,640 --> 00:37:39,839
Speaker 5: Yeah.

814
00:37:39,840 --> 00:37:41,160
Speaker 6: No, And I think that's a good point, and it

815
00:37:41,160 --> 00:37:43,760
Speaker 6: also speaks to about how much longer. So to some extent,

816
00:37:44,040 --> 00:37:46,239
Speaker 6: if we look at the uncertainty that Trump says is

817
00:37:46,239 --> 00:37:48,880
Speaker 6: strategically meant to show the United States has leverage at

818
00:37:48,920 --> 00:37:52,480
Speaker 6: a certain point, the uncertainty that the weight of the

819
00:37:52,560 --> 00:37:56,279
Speaker 6: uncertainty and the economy starts to become even by his

820
00:37:56,440 --> 00:37:59,440
Speaker 6: own explanation. So if we're taking him at face value

821
00:37:59,440 --> 00:38:00,800
Speaker 6: and saying I agree with it, I do think the

822
00:38:00,880 --> 00:38:04,719
Speaker 6: uncertainty is creates some leverage at to a point, no,

823
00:38:04,920 --> 00:38:07,400
Speaker 6: for sure, but then the weight on the economy becomes

824
00:38:07,520 --> 00:38:13,279
Speaker 6: so significant that it has effects like on an unemployment

825
00:38:13,480 --> 00:38:16,840
Speaker 6: for people who are coming out of college, that you

826
00:38:17,160 --> 00:38:18,960
Speaker 6: have to look at it and say, even by what

827
00:38:19,040 --> 00:38:22,160
Speaker 6: your argument is here that you're bringing jobs back at

828
00:38:22,200 --> 00:38:25,359
Speaker 6: a certain point, when does the bleeding stop at a

829
00:38:25,360 --> 00:38:29,520
Speaker 6: certain point? What is you letting the blood just seep

830
00:38:29,560 --> 00:38:32,400
Speaker 6: into the economy? Not worth what you're talking about anymore,

831
00:38:32,480 --> 00:38:34,840
Speaker 6: But at what point is a net cost by your argument?

832
00:38:34,920 --> 00:38:37,279
Speaker 3: Totally, and this was actually really driven home at the

833
00:38:37,320 --> 00:38:41,799
Speaker 3: technological level and especially with the general like breaking of

834
00:38:41,840 --> 00:38:43,440
Speaker 3: the American dream by this story.

835
00:38:43,520 --> 00:38:45,239
Speaker 1: It's an amazing story. It's been gone viral for a

836
00:38:45,280 --> 00:38:45,920
Speaker 1: number of reasons.

837
00:38:46,040 --> 00:38:48,120
Speaker 3: Put B two up here on the screen, and the

838
00:38:48,120 --> 00:38:50,960
Speaker 3: headline here is, quote, goodbye one hundred and sixty five

839
00:38:51,000 --> 00:38:55,080
Speaker 3: thousand dollars tech jobs. Student coders seek work at Chipotle.

840
00:38:55,600 --> 00:38:55,920
Speaker 1: Quote.

841
00:38:55,960 --> 00:38:59,360
Speaker 3: As companies like Amazon and Microsoft lay off workers and

842
00:38:59,400 --> 00:39:02,920
Speaker 3: embrace a coding tools, computer science graduates say they are

843
00:39:02,960 --> 00:39:05,520
Speaker 3: struggling to land tech jobs. I would add h one

844
00:39:05,560 --> 00:39:07,960
Speaker 3: B into this, by the way, because we talked previously.

845
00:39:08,000 --> 00:39:09,839
Speaker 3: We did a segment here on the show about how

846
00:39:09,880 --> 00:39:12,120
Speaker 3: Microsoft fired a bunch of workers and then applied for

847
00:39:12,160 --> 00:39:14,600
Speaker 3: a bunch of h one B visas. But the point

848
00:39:14,680 --> 00:39:18,040
Speaker 3: is basically the pursuit of cheap labor AI and then

849
00:39:18,160 --> 00:39:21,800
Speaker 3: also just a general softening of the overall market means

850
00:39:21,880 --> 00:39:24,120
Speaker 3: that people like this woman who they profile here in

851
00:39:24,160 --> 00:39:26,880
Speaker 3: the story are really getting screwed. So you know, they're

852
00:39:27,000 --> 00:39:31,640
Speaker 3: lead off by basically talking about how this woman monsign Mishra,

853
00:39:32,120 --> 00:39:35,440
Speaker 3: She is twenty one years old. She grew up in California,

854
00:39:35,640 --> 00:39:39,720
Speaker 3: graduated from Purdue with a degree in computer science. Basically

855
00:39:39,760 --> 00:39:42,959
Speaker 3: did everything right, you know, but quote, after a year

856
00:39:43,000 --> 00:39:46,319
Speaker 3: of job hunting for tech jobs and internships, she graduated

857
00:39:46,320 --> 00:39:49,560
Speaker 3: in May without any job offer. The only company that

858
00:39:49,600 --> 00:39:52,480
Speaker 3: even called me for an interview, she said, was Chipotle.

859
00:39:52,880 --> 00:39:55,200
Speaker 3: Since the early twenty tens, you know, they've talked about

860
00:39:55,360 --> 00:39:57,520
Speaker 3: kind of the booms of how it was so awesome

861
00:39:57,560 --> 00:40:00,480
Speaker 3: in twenty fourteen twenty fifteen to go work Google and

862
00:40:00,600 --> 00:40:03,960
Speaker 3: Facebook with all this free stuff and healthcares, etc. But

863
00:40:04,080 --> 00:40:07,359
Speaker 3: a lot of these AI programming tools basically have eliminated

864
00:40:07,719 --> 00:40:10,719
Speaker 3: all of the original scott work that people like this

865
00:40:10,960 --> 00:40:13,640
Speaker 3: were put into the pipeline for. And I'm not denigrating

866
00:40:13,680 --> 00:40:15,520
Speaker 3: it because scott work. I did a lot of Scott

867
00:40:15,520 --> 00:40:18,319
Speaker 3: work after I graduated. That's what you do, because you

868
00:40:18,400 --> 00:40:21,360
Speaker 3: do it so that you're around you do. You do

869
00:40:21,440 --> 00:40:25,440
Speaker 3: these basic menial tasks while you learn bigger skills and

870
00:40:25,480 --> 00:40:27,480
Speaker 3: you get promoted up into the company. But at the

871
00:40:27,560 --> 00:40:29,520
Speaker 3: beginning they're like, well, we have everybody who has all

872
00:40:29,560 --> 00:40:31,640
Speaker 3: the higher level skills already. We don't need to put

873
00:40:31,640 --> 00:40:34,080
Speaker 3: any more people into the pipeline or pay them. We

874
00:40:34,120 --> 00:40:36,879
Speaker 3: can just use our AI tools to basically eliminate ten

875
00:40:36,880 --> 00:40:40,640
Speaker 3: to fifteen percent of our payroll. And that's basically what's happening.

876
00:40:40,760 --> 00:40:43,959
Speaker 3: They even point actually to a survey. More recently, state

877
00:40:44,000 --> 00:40:47,600
Speaker 3: schools Maryland, Texas, Washington. These are decent colleges as well

878
00:40:47,640 --> 00:40:52,600
Speaker 3: as prefit private universities Cornell and Stanford. Many tech graduates

879
00:40:52,640 --> 00:40:55,799
Speaker 3: computer science graduates said that they had applied to hundreds

880
00:40:56,000 --> 00:41:00,319
Speaker 3: and in several cases thousands of tech jobs at companies, nonprofits,

881
00:41:00,320 --> 00:41:04,080
Speaker 3: and a government agencies. All of them were given coding assignments,

882
00:41:04,320 --> 00:41:06,600
Speaker 3: many of these other things. Even with these month long

883
00:41:06,680 --> 00:41:08,879
Speaker 3: jobs quest the vast majority of them they either ended

884
00:41:08,920 --> 00:41:11,600
Speaker 3: up ghosted, they didn't end up being hired. I actually

885
00:41:11,640 --> 00:41:13,840
Speaker 3: did warn about this a few months ago, after Liberation

886
00:41:13,960 --> 00:41:17,359
Speaker 3: Day in April, because I had heard for a lot

887
00:41:17,360 --> 00:41:19,480
Speaker 3: of those people who are graduating in May that people

888
00:41:19,520 --> 00:41:20,600
Speaker 3: were pulling their offers.

889
00:41:20,600 --> 00:41:21,759
Speaker 1: This is like, sorry, we can't do it.

890
00:41:21,880 --> 00:41:23,960
Speaker 3: The stock market is down, which is we're not where

891
00:41:24,000 --> 00:41:26,560
Speaker 3: we can't make any major commitments. Now, the summer is

892
00:41:26,600 --> 00:41:28,279
Speaker 3: coming to an end. People are about to start going

893
00:41:28,320 --> 00:41:30,799
Speaker 3: back to school in the next month or two. And

894
00:41:30,880 --> 00:41:33,399
Speaker 3: if you think like that's devastating, you know for people

895
00:41:33,400 --> 00:41:37,279
Speaker 3: who graduated. They even say here that one individual who

896
00:41:37,280 --> 00:41:40,839
Speaker 3: graduated in twenty twenty three had applied for five seven

897
00:41:40,880 --> 00:41:44,240
Speaker 3: hundred and sixty two tech jobs. Diligence resulted in thirteen

898
00:41:44,320 --> 00:41:47,000
Speaker 3: job interviews and no full time offers.

899
00:41:47,120 --> 00:41:48,840
Speaker 1: I mean that is devastating.

900
00:41:48,960 --> 00:41:51,879
Speaker 3: Later on quote he applied for a job at McDonald's

901
00:41:51,920 --> 00:41:55,400
Speaker 3: to help cover expenses, was rejected for his lack of experience,

902
00:41:55,640 --> 00:41:58,440
Speaker 3: has moved back to Shorewood, Oregon, and is now receiving

903
00:41:58,680 --> 00:42:02,000
Speaker 3: unemployment benefits from previous I mean, that's that's.

904
00:42:01,560 --> 00:42:02,480
Speaker 1: That's terrible, right.

905
00:42:02,840 --> 00:42:06,640
Speaker 3: This is exactly the story from nine I remember hearing

906
00:42:06,640 --> 00:42:09,560
Speaker 3: it reading about it of people who graduate again did

907
00:42:09,600 --> 00:42:12,000
Speaker 3: everything right. There's even a lot of stories about the

908
00:42:12,080 --> 00:42:13,640
Speaker 3: law school class of two thousands.

909
00:42:14,560 --> 00:42:16,040
Speaker 1: Yeah, they had it.

910
00:42:17,400 --> 00:42:20,839
Speaker 3: They went in expecting to make one hundred and seventy

911
00:42:20,880 --> 00:42:23,440
Speaker 3: five thousand dollars or whatever in two thousand and eight money.

912
00:42:23,440 --> 00:42:24,480
Speaker 1: They were going to be bawling.

913
00:42:24,640 --> 00:42:27,960
Speaker 3: Like when they graduated, they had offers at the big

914
00:42:28,000 --> 00:42:30,480
Speaker 3: banks or the major corporate law firms. By the time

915
00:42:30,520 --> 00:42:32,560
Speaker 3: they were out, they were like, you're done, you have nothing,

916
00:42:32,640 --> 00:42:34,600
Speaker 3: I have a lot of people had to move home.

917
00:42:34,719 --> 00:42:36,840
Speaker 3: Some of them it ended up working out okay, but

918
00:42:37,160 --> 00:42:39,839
Speaker 3: it just created like mass chaos and of course debt,

919
00:42:40,040 --> 00:42:42,040
Speaker 3: you know, for a lot of these people. So look,

920
00:42:42,160 --> 00:42:44,200
Speaker 3: the point I think in all of this is that

921
00:42:44,600 --> 00:42:47,480
Speaker 3: it's at both levels of the economy. But if it

922
00:42:47,560 --> 00:42:50,360
Speaker 3: starts getting so dicey at the higher level of labor,

923
00:42:50,840 --> 00:42:54,120
Speaker 3: that is, again these are people saddled problem statistically with

924
00:42:54,400 --> 00:42:57,120
Speaker 3: hundreds of thousands of dollars in debt, no promise to

925
00:42:57,160 --> 00:42:59,719
Speaker 3: pay it off, and then you wonder why zoron has

926
00:42:59,719 --> 00:43:03,040
Speaker 3: happened or any of these other you know, political effects

927
00:43:03,040 --> 00:43:05,520
Speaker 3: of what youth unemployment looked like in our economy. I

928
00:43:05,560 --> 00:43:07,360
Speaker 3: just thought it was a really sad story when you

929
00:43:07,360 --> 00:43:08,040
Speaker 3: look at it broadly.

930
00:43:08,160 --> 00:43:10,040
Speaker 6: Ye, it is, and I think those parallels are really

931
00:43:10,040 --> 00:43:12,280
Speaker 6: important to point out. So I have this other headline

932
00:43:12,280 --> 00:43:14,120
Speaker 6: and I should have added it to the rundown, but

933
00:43:14,160 --> 00:43:15,480
Speaker 6: this is from last month.

934
00:43:15,600 --> 00:43:16,320
Speaker 5: This is unfortune.

935
00:43:16,320 --> 00:43:18,720
Speaker 6: The headline here is gen z men with college degrees

936
00:43:18,800 --> 00:43:22,759
Speaker 6: now have the same unemployment rate as non grads. A

937
00:43:22,840 --> 00:43:25,080
Speaker 6: sign that the higher education payoff is dead. Well, we've

938
00:43:25,080 --> 00:43:27,480
Speaker 6: had plenty of those signs over the years, but this

939
00:43:27,640 --> 00:43:30,719
Speaker 6: is I mean, that's not just a sign. That's your

940
00:43:31,000 --> 00:43:33,960
Speaker 6: example that the higher education payoff is debt. It's not

941
00:43:34,000 --> 00:43:35,960
Speaker 6: just a sign that it's going to happen. It's it's

942
00:43:36,080 --> 00:43:39,400
Speaker 6: clearly the indication that it has happened. And so you

943
00:43:39,520 --> 00:43:42,839
Speaker 6: then have this massive debt hangover. We saw it with

944
00:43:42,880 --> 00:43:46,360
Speaker 6: millennials after the Great Recession that lasts into your thirties

945
00:43:46,360 --> 00:43:48,600
Speaker 6: and forties student loan debt. You're not able to pay

946
00:43:48,600 --> 00:43:51,600
Speaker 6: it off because you were not making the money that

947
00:43:51,640 --> 00:43:53,560
Speaker 6: you thought you would make when you took out the loans,

948
00:43:54,120 --> 00:43:57,640
Speaker 6: and those jobs just don't exist. And so millennials, it

949
00:43:57,680 --> 00:44:01,920
Speaker 6: looked like there's a trend of gen z getting married,

950
00:44:02,040 --> 00:44:04,799
Speaker 6: potentially buying houses earlier after the pandemic.

951
00:44:05,040 --> 00:44:07,440
Speaker 5: And actually what we could.

952
00:44:07,280 --> 00:44:09,240
Speaker 6: End up seeing from this is something similar to millennials

953
00:44:09,239 --> 00:44:11,120
Speaker 6: where they were getting married later than they said they

954
00:44:11,120 --> 00:44:13,480
Speaker 6: wanted to, having children later than they said they wanted to.

955
00:44:13,880 --> 00:44:16,200
Speaker 6: And all of this is just to say we aren't

956
00:44:16,600 --> 00:44:20,560
Speaker 6: only looking at costs on the economy. We're also looking

957
00:44:20,600 --> 00:44:26,160
Speaker 6: at people's lives and happiness. And it's not just numbers

958
00:44:26,280 --> 00:44:28,759
Speaker 6: on a page or on the screen. There are all

959
00:44:28,840 --> 00:44:31,520
Speaker 6: kinds of cultural effects that come on with this that

960
00:44:31,560 --> 00:44:36,279
Speaker 6: people remember vividly from the recession. And so you know,

961
00:44:36,640 --> 00:44:39,360
Speaker 6: if that's what we're looking at, and there's some really

962
00:44:39,440 --> 00:44:42,880
Speaker 6: troubling signs, we kind of have already run the play.

963
00:44:43,560 --> 00:44:46,480
Speaker 3: I think it's really devastating, the AI part of it.

964
00:44:46,480 --> 00:44:49,319
Speaker 3: It has been overstated about mass job loss, but the

965
00:44:49,840 --> 00:44:51,960
Speaker 3: layoffs are here and we knew part of that was

966
00:44:52,000 --> 00:44:54,200
Speaker 3: going to happen. Let's put B four for example on

967
00:44:54,239 --> 00:44:58,360
Speaker 3: the screen. So this is about AI driven AI driven

968
00:44:58,440 --> 00:45:03,040
Speaker 3: layoffs shrinking the job mark, specifically for recent graduates. So

969
00:45:03,080 --> 00:45:05,759
Speaker 3: what they say, as I have said here, these entry

970
00:45:05,840 --> 00:45:09,840
Speaker 3: level roles are being hardest hit. Companies are automating tasks

971
00:45:09,840 --> 00:45:12,560
Speaker 3: traditionally handled by the most junior staff, and at the

972
00:45:12,600 --> 00:45:16,000
Speaker 3: same time, the broader job market is slowing. Rising employment

973
00:45:16,239 --> 00:45:19,319
Speaker 3: with recent graduates and with young tech workers. And you know,

974
00:45:19,400 --> 00:45:22,359
Speaker 3: even if you look at some of the Federal Reserve data,

975
00:45:22,400 --> 00:45:24,480
Speaker 3: so there was some more recently data I think it

976
00:45:24,520 --> 00:45:28,040
Speaker 3: was from the Federal Reserve Bank of Atlanta, and what

977
00:45:28,080 --> 00:45:31,040
Speaker 3: they showed in the Federal Reserve Bank of Atlanta was

978
00:45:31,080 --> 00:45:35,399
Speaker 3: a similar kind of softening of the overall job market.

979
00:45:35,440 --> 00:45:38,560
Speaker 3: I'm trying to look specifically at the unemployment rate, but

980
00:45:39,080 --> 00:45:42,400
Speaker 3: they what they showed was that this software engineering and

981
00:45:42,480 --> 00:45:47,319
Speaker 3: computer science degree in particular saw major changes just over

982
00:45:47,360 --> 00:45:49,640
Speaker 3: the last couple of years. And it just makes it

983
00:45:49,680 --> 00:45:53,480
Speaker 3: again where I've seen, you know, for examples I'm trying

984
00:45:53,520 --> 00:45:56,560
Speaker 3: to think about. I talked about one somebody who I

985
00:45:56,600 --> 00:45:58,440
Speaker 3: know who works in consulting, and they were like, well,

986
00:45:58,480 --> 00:46:00,960
Speaker 3: when I came into consulting, he's like ten years into

987
00:46:00,960 --> 00:46:02,960
Speaker 3: his career now, but he's like, my initial job was

988
00:46:03,000 --> 00:46:06,000
Speaker 3: just note taking and arranging minutes and all these other things.

989
00:46:06,040 --> 00:46:08,080
Speaker 3: It was. It sucks, but it's like that's how you

990
00:46:08,080 --> 00:46:09,880
Speaker 3: you know, start your climb up the ladder.

991
00:46:09,920 --> 00:46:12,040
Speaker 1: He's like, we don't even need somebody to do that anymore.

992
00:46:12,120 --> 00:46:14,480
Speaker 3: We just have Microsoft Teams, which has the you know,

993
00:46:14,600 --> 00:46:18,200
Speaker 3: open ai chat, gpt ai function or whatever built into it.

994
00:46:18,200 --> 00:46:21,120
Speaker 3: It automates everything, it does summaries, and so we're just

995
00:46:21,160 --> 00:46:24,240
Speaker 3: not giving offers, you know, to nearly the same number

996
00:46:24,440 --> 00:46:26,440
Speaker 3: of people. Well, that sucks, you know, for a lot

997
00:46:26,480 --> 00:46:30,160
Speaker 3: of people similarly, especially when they quote did everything right

998
00:46:30,480 --> 00:46:32,920
Speaker 3: and then they just get smacked, you know, initially by

999
00:46:32,960 --> 00:46:35,400
Speaker 3: this mass social change. And I can definitely think at

1000
00:46:35,400 --> 00:46:37,480
Speaker 3: the white collar levels where we're going to see the

1001
00:46:37,520 --> 00:46:41,120
Speaker 3: most impact in the law, in consulting, in technology, But

1002
00:46:41,440 --> 00:46:43,640
Speaker 3: don't make a mistake to say that it's not coming

1003
00:46:43,680 --> 00:46:44,799
Speaker 3: for blue.

1004
00:46:44,480 --> 00:46:45,640
Speaker 1: Collar workers as well.

1005
00:46:45,760 --> 00:46:47,879
Speaker 3: Like if you're a tradesman with a house skill, that's

1006
00:46:47,880 --> 00:46:49,640
Speaker 3: going to be one thing. But everyone talks about the

1007
00:46:49,719 --> 00:46:52,759
Speaker 3: McDonald's kiosks, more automation, all of those things that just

1008
00:46:52,800 --> 00:46:55,080
Speaker 3: reduces the overall head count that a lot of people need,

1009
00:46:55,239 --> 00:46:56,680
Speaker 3: or it just means that the type of job you're

1010
00:46:56,719 --> 00:46:58,640
Speaker 3: going to get is not necessarily the one that is

1011
00:46:58,680 --> 00:47:00,880
Speaker 3: going to pay very much future will give you any

1012
00:47:00,960 --> 00:47:02,360
Speaker 3: upward mobility or advancement.

1013
00:47:02,480 --> 00:47:06,839
Speaker 6: Well, and trade shops also support admin roles that are

1014
00:47:06,880 --> 00:47:09,239
Speaker 6: going to be hit by AI as well. So I

1015
00:47:09,280 --> 00:47:14,120
Speaker 6: mean it's yeah, that's significant and underscores to me the

1016
00:47:14,440 --> 00:47:19,160
Speaker 6: incredible urgency of having our pipeline from high school to

1017
00:47:19,200 --> 00:47:21,799
Speaker 6: college or to the job market is probably a better

1018
00:47:21,800 --> 00:47:22,400
Speaker 6: way to put it.

1019
00:47:22,440 --> 00:47:23,719
Speaker 5: Adapt And I.

1020
00:47:23,719 --> 00:47:27,200
Speaker 6: Don't know, sorry, I mean, it's like we're still it

1021
00:47:27,200 --> 00:47:30,040
Speaker 6: seems like we're still an automnpilot and still doing the

1022
00:47:30,040 --> 00:47:33,239
Speaker 6: same thing over and over again. When you know, when

1023
00:47:33,280 --> 00:47:35,000
Speaker 6: you look at the consulting example, do I think it's

1024
00:47:35,000 --> 00:47:36,680
Speaker 6: probably better that a lot of people aren't going to

1025
00:47:36,840 --> 00:47:37,600
Speaker 6: be at McKinsey.

1026
00:47:37,640 --> 00:47:39,920
Speaker 1: Yeah, that's the funny thing. It looks. But it's real

1027
00:47:39,960 --> 00:47:41,440
Speaker 1: easy for me to say sitting here.

1028
00:47:41,640 --> 00:47:43,560
Speaker 3: Of course, it's not so easy to say to somebody

1029
00:47:44,000 --> 00:47:46,440
Speaker 3: graduating with an expectation of actually getting a job, and.

1030
00:47:46,360 --> 00:47:47,120
Speaker 4: Where else do they go?

1031
00:47:47,200 --> 00:47:51,239
Speaker 6: Yeah, exactly, because what just happened is the bottom rung

1032
00:47:51,560 --> 00:47:54,320
Speaker 6: and this is slightly different than the recession. The bottom

1033
00:47:54,400 --> 00:47:56,319
Speaker 6: rung of the latter was kicked out, and you have

1034
00:47:56,360 --> 00:47:59,000
Speaker 6: no way to get to the middle rung, let alone

1035
00:47:59,040 --> 00:47:59,759
Speaker 6: to the top rung.

1036
00:48:00,120 --> 00:48:02,360
Speaker 5: So what do you do, I mean, seriously, what do

1037
00:48:02,400 --> 00:48:03,960
Speaker 5: you do? You know.

1038
00:48:05,400 --> 00:48:08,759
Speaker 6: We're right now it seems like just continuing on the

1039
00:48:08,800 --> 00:48:12,759
Speaker 6: autopilot default of sending people through this normal college to

1040
00:48:12,800 --> 00:48:17,440
Speaker 6: white collar job pipeline, that it's changed so much that

1041
00:48:17,680 --> 00:48:22,680
Speaker 6: it's the route reflex is outdated.

1042
00:48:22,880 --> 00:48:24,960
Speaker 3: Yeah, I totally agree. Okay, why don't we move on

1043
00:48:25,000 --> 00:48:26,400
Speaker 3: to DC. Let's see what's going on.

1044
00:48:26,520 --> 00:48:33,240
Speaker 5: Let's do it. This morning. Videos are rolling in of law.

1045
00:48:33,120 --> 00:48:36,080
Speaker 6: Enforcement taking to the streets of Sager get this Georgetown.

1046
00:48:36,280 --> 00:48:38,560
Speaker 6: So if you've been here to DC or know Georgetown

1047
00:48:38,760 --> 00:48:41,160
Speaker 6: as one of the more upscale neighborhoods, although it certainly

1048
00:48:41,160 --> 00:48:43,120
Speaker 6: had its problems smash and grabs and all of that

1049
00:48:43,239 --> 00:48:44,440
Speaker 6: during the pandemic.

1050
00:48:44,840 --> 00:48:47,000
Speaker 5: So joining us to discuss.

1051
00:48:46,600 --> 00:48:48,960
Speaker 6: All of this is Delano Squire's He has a research

1052
00:48:49,000 --> 00:48:52,000
Speaker 6: fellow over at the Heritage Foundation, but also somebody who

1053
00:48:52,120 --> 00:48:55,359
Speaker 6: has spent unlike many conservatives who are now frothing at

1054
00:48:55,360 --> 00:48:57,759
Speaker 6: the mouth to talk about all of this, and.

1055
00:48:57,680 --> 00:48:59,319
Speaker 5: People on the left as well. We're going to get

1056
00:48:59,320 --> 00:48:59,640
Speaker 5: into that.

1057
00:49:00,000 --> 00:49:02,840
Speaker 6: Allanta has put in a lot of time actually working

1058
00:49:03,200 --> 00:49:05,920
Speaker 6: in DC. So Delana was the rehearstfal Thank you for coming,

1059
00:49:06,000 --> 00:49:09,360
Speaker 6: and secondly, just tell people give them a little background

1060
00:49:09,560 --> 00:49:12,640
Speaker 6: on your career working for DC before you ended up

1061
00:49:12,640 --> 00:49:13,200
Speaker 6: at Heritage.

1062
00:49:13,280 --> 00:49:13,480
Speaker 4: Yeah.

1063
00:49:13,520 --> 00:49:16,400
Speaker 9: So I worked in DC DC government for about fifteen years,

1064
00:49:17,520 --> 00:49:19,520
Speaker 9: starting in two thousand and seven, always sort of in

1065
00:49:19,520 --> 00:49:23,920
Speaker 9: a public facing arena for most of that, I managed

1066
00:49:24,000 --> 00:49:27,600
Speaker 9: a technology program called Connect DC where we help low

1067
00:49:27,640 --> 00:49:30,400
Speaker 9: income residents get access to technology, So that means I

1068
00:49:30,440 --> 00:49:32,759
Speaker 9: spent a lot of time in public housing. I've been

1069
00:49:32,800 --> 00:49:35,759
Speaker 9: to DC jail at least four times, talking to guys

1070
00:49:35,760 --> 00:49:38,160
Speaker 9: who as they were ready to transition back into the community,

1071
00:49:39,480 --> 00:49:41,920
Speaker 9: you know, doing a lot of stuff with senior citizens

1072
00:49:42,000 --> 00:49:44,680
Speaker 9: with K through twelve students. And then the last year

1073
00:49:45,600 --> 00:49:48,239
Speaker 9: I was in the Office of Gun Violence Prevention, which

1074
00:49:48,440 --> 00:49:51,560
Speaker 9: was a newly created office in the city.

1075
00:49:52,360 --> 00:49:52,920
Speaker 4: I think this was.

1076
00:49:52,840 --> 00:49:57,279
Speaker 9: Around twenty twenty twenty twenty one. Obviously, you know, with

1077
00:49:57,360 --> 00:50:01,239
Speaker 9: everything around George Floyd and BLM riots, the city wanted

1078
00:50:01,239 --> 00:50:04,520
Speaker 9: to approach gun violence from sort of a public health perspective,

1079
00:50:04,800 --> 00:50:07,880
Speaker 9: and they pulled together people from all across district agencies.

1080
00:50:07,960 --> 00:50:11,680
Speaker 9: So I was there again twenty twenty one into twenty

1081
00:50:11,719 --> 00:50:15,760
Speaker 9: twenty two, interfacing with residents as they would call complain

1082
00:50:15,840 --> 00:50:17,759
Speaker 9: about shootings and violence in their neighborhoods.

1083
00:50:17,880 --> 00:50:19,080
Speaker 5: That was during the spike in DC.

1084
00:50:19,320 --> 00:50:25,600
Speaker 9: Yes, yes, one gentleman sent ten videos from his security

1085
00:50:25,600 --> 00:50:29,400
Speaker 9: camera to the city capturing shootings in his neighborhood. I

1086
00:50:29,440 --> 00:50:31,160
Speaker 9: figured he lived in a house that costs at least

1087
00:50:31,160 --> 00:50:34,160
Speaker 9: seven hundred and fifty thousand dollars. So there were frustrations

1088
00:50:34,200 --> 00:50:37,000
Speaker 9: there from residents all across the city, you know, about

1089
00:50:37,040 --> 00:50:41,360
Speaker 9: shootings and gun violence. So it really showed me the

1090
00:50:41,440 --> 00:50:45,360
Speaker 9: difference between what the activist class says, you know, remember

1091
00:50:45,400 --> 00:50:47,520
Speaker 9: when they were on to defund the police train, and

1092
00:50:47,560 --> 00:50:50,960
Speaker 9: what residents, sometimes in the poorest parts of the city

1093
00:50:51,000 --> 00:50:52,839
Speaker 9: were saying, which is, we want more police on the street.

1094
00:50:52,880 --> 00:50:55,400
Speaker 3: Yeah, and I want to spoke focus specifically on that actually,

1095
00:50:55,480 --> 00:50:59,120
Speaker 3: because there's quite a bit of armchair quarterbacking going on,

1096
00:50:59,200 --> 00:51:01,920
Speaker 3: and even frank even probably from us right, I've lived

1097
00:51:01,960 --> 00:51:03,440
Speaker 3: in DC for fifteen years.

1098
00:51:03,880 --> 00:51:05,160
Speaker 1: I lived in you know, I went to.

1099
00:51:05,120 --> 00:51:08,000
Speaker 3: College in Foggy Bottom, which is you know, and where

1100
00:51:08,000 --> 00:51:11,720
Speaker 3: the State Department is and Northwest Washington. Never lived really

1101
00:51:11,960 --> 00:51:14,520
Speaker 3: outside of that, which traditionally has always been kind of

1102
00:51:14,520 --> 00:51:17,080
Speaker 3: the home of lesser crime, although it did, you know,

1103
00:51:17,320 --> 00:51:20,080
Speaker 3: get quite a bit worse over like a ten year period.

1104
00:51:20,120 --> 00:51:22,239
Speaker 3: So I'm curious because at the end of the day,

1105
00:51:22,320 --> 00:51:24,880
Speaker 3: that doesn't matter. It really matters for the permanent residence

1106
00:51:24,880 --> 00:51:28,040
Speaker 3: and particularly people in the poor areas. So I'm curious

1107
00:51:28,040 --> 00:51:30,040
Speaker 3: before we even get to some of the media reaction

1108
00:51:30,440 --> 00:51:34,759
Speaker 3: for your reaction to the way, first the Trump administration's deployment,

1109
00:51:35,200 --> 00:51:38,439
Speaker 3: the conception behind it, and then how you have seen

1110
00:51:38,480 --> 00:51:41,080
Speaker 3: it played out now. So far we've only had one day,

1111
00:51:41,200 --> 00:51:42,719
Speaker 3: but there's been a lot of show of force, right,

1112
00:51:42,960 --> 00:51:43,520
Speaker 3: So what do you think?

1113
00:51:43,760 --> 00:51:45,360
Speaker 9: Definitely not And I'll say this, not only did I

1114
00:51:45,400 --> 00:51:47,640
Speaker 9: live in DC for fifteen years, but ye work in

1115
00:51:47,680 --> 00:51:49,480
Speaker 9: DC for fifteen years. I lived in DC for five

1116
00:51:49,560 --> 00:51:52,840
Speaker 9: years in northeast of DC in ward seven my wife

1117
00:51:52,840 --> 00:51:53,600
Speaker 9: and our kids.

1118
00:51:53,960 --> 00:51:56,800
Speaker 4: So I'll let me put my cars on the table.

1119
00:51:56,920 --> 00:52:00,319
Speaker 9: I was in favor of Operation Legend in the first

1120
00:52:00,320 --> 00:52:07,040
Speaker 9: Trump term that married federal resources so atf dj DEA

1121
00:52:07,840 --> 00:52:11,000
Speaker 9: and took it took those resources into a handful of cities.

1122
00:52:11,040 --> 00:52:14,360
Speaker 9: I think it was eight cities across the country to

1123
00:52:14,560 --> 00:52:16,040
Speaker 9: address violent street crime.

1124
00:52:16,400 --> 00:52:17,200
Speaker 4: I supported that.

1125
00:52:17,520 --> 00:52:20,120
Speaker 9: In fact, I said if I was advising the president,

1126
00:52:20,120 --> 00:52:22,480
Speaker 9: I would talk about that five times as much as

1127
00:52:22,520 --> 00:52:25,680
Speaker 9: the first step back. So I'm not opposed to sort

1128
00:52:25,719 --> 00:52:30,319
Speaker 9: of federal involvement in addressing local crime. Generally speaking, I

1129
00:52:30,360 --> 00:52:34,000
Speaker 9: think the takeover, the fact that it was sort of

1130
00:52:34,320 --> 00:52:39,239
Speaker 9: precipitated by obviously the incident involving the dult staffer, to me,

1131
00:52:39,560 --> 00:52:44,640
Speaker 9: feels somewhat disjointed because it's not that. Oh, there have

1132
00:52:44,760 --> 00:52:49,160
Speaker 9: been forty five miners, you know, under the age of

1133
00:52:49,200 --> 00:52:51,320
Speaker 9: eighteen who've been shot and killed in DC over the

1134
00:52:51,400 --> 00:52:55,000
Speaker 9: last two years. We're marshaling federal resources to address that.

1135
00:52:55,640 --> 00:52:58,440
Speaker 9: It's been I think a lot of the complaints, oftentimes

1136
00:52:58,440 --> 00:53:00,799
Speaker 9: for people who don't actually live in the city that

1137
00:53:00,840 --> 00:53:02,359
Speaker 9: have been have been driving the.

1138
00:53:02,360 --> 00:53:04,480
Speaker 4: Narrative that's what the res are the wrong way.

1139
00:53:04,480 --> 00:53:06,560
Speaker 9: But I mean, if it leads to better results for

1140
00:53:06,600 --> 00:53:09,680
Speaker 9: the residents and for people who work in the city

1141
00:53:09,800 --> 00:53:12,600
Speaker 9: and for people who visit the city, then I'm all

1142
00:53:12,640 --> 00:53:12,960
Speaker 9: for it.

1143
00:53:13,880 --> 00:53:14,640
Speaker 4: The show of force.

1144
00:53:14,680 --> 00:53:18,480
Speaker 9: And again I've seen a few videos, you know, atf

1145
00:53:18,520 --> 00:53:20,440
Speaker 9: agents walking around Georgetown.

1146
00:53:20,280 --> 00:53:22,000
Speaker 1: Okay, I mean or the National Mall.

1147
00:53:22,239 --> 00:53:24,520
Speaker 3: This is hard to explain because for most people on

1148
00:53:24,560 --> 00:53:27,040
Speaker 3: television or what your like House of Cards, they're like, oh,

1149
00:53:27,040 --> 00:53:30,080
Speaker 3: it's the National Mall, right, Like nobody who live It's

1150
00:53:30,080 --> 00:53:32,120
Speaker 3: like Times Square, nobody lives.

1151
00:53:32,160 --> 00:53:34,920
Speaker 1: I've been there in five years, Okay, reason to go?

1152
00:53:35,520 --> 00:53:37,160
Speaker 1: What are you I written down there? Okay?

1153
00:53:37,160 --> 00:53:39,480
Speaker 3: All right? Other than other than exercise, Like, there's no

1154
00:53:39,600 --> 00:53:41,640
Speaker 3: reason to go down there. It's a place for tourists

1155
00:53:41,680 --> 00:53:43,400
Speaker 3: and for business that's fine. Hey, they need to be

1156
00:53:43,440 --> 00:53:45,680
Speaker 3: safe as well. But there is no crime there either, okay,

1157
00:53:45,680 --> 00:53:46,919
Speaker 3: and there hasn't been for many years.

1158
00:53:47,200 --> 00:53:48,280
Speaker 4: Yeah, generally speaking.

1159
00:53:48,280 --> 00:53:49,879
Speaker 9: And I think that's what people a lot of people

1160
00:53:49,920 --> 00:53:52,120
Speaker 9: don't don't think about when they think about DC.

1161
00:53:52,200 --> 00:53:54,360
Speaker 4: They think about the mall and the Capitol, and the

1162
00:53:54,400 --> 00:53:55,359
Speaker 4: White House and the Mint.

1163
00:53:55,880 --> 00:53:57,960
Speaker 9: But this is a city of seven hundred thousand people

1164
00:53:58,040 --> 00:54:00,160
Speaker 9: in the eight wards, you know, spread out across for

1165
00:54:00,320 --> 00:54:03,680
Speaker 9: different quadrants, and the violent crime tends to be clustered

1166
00:54:03,719 --> 00:54:07,879
Speaker 9: in a handful of neighborhoods, particularly the homicides of the shootings, yes,

1167
00:54:08,239 --> 00:54:10,880
Speaker 9: sixty percent happened you know, east of the Anacostia River,

1168
00:54:10,920 --> 00:54:13,440
Speaker 9: in Ward seven and Ward eight, poorest parts of the city,

1169
00:54:13,680 --> 00:54:18,000
Speaker 9: the most disproportionately black parts of the city, and other

1170
00:54:18,040 --> 00:54:22,240
Speaker 9: parts of the city. Again, you may have certainly assaults, robbery,

1171
00:54:22,320 --> 00:54:24,520
Speaker 9: carjacking that sort of spread out a little bit more,

1172
00:54:25,280 --> 00:54:27,920
Speaker 9: but it's not the same as.

1173
00:54:27,880 --> 00:54:29,560
Speaker 4: What you get sort of east of the river.

1174
00:54:29,680 --> 00:54:34,200
Speaker 9: So if the federal resources were being used to address

1175
00:54:34,440 --> 00:54:38,839
Speaker 9: crime in the highest crime parts of DC, I think

1176
00:54:38,840 --> 00:54:40,920
Speaker 9: that would be something different. And in fact, two years ago,

1177
00:54:41,719 --> 00:54:44,640
Speaker 9: council Member Treyon White, who represents Ward eight once again,

1178
00:54:45,960 --> 00:54:50,920
Speaker 9: once again call for the National Guard in August. I

1179
00:54:50,920 --> 00:54:54,440
Speaker 9: think that request was ultimately, you know, declined. But it's

1180
00:54:54,480 --> 00:54:56,680
Speaker 9: not to say that there aren't city leaders who have

1181
00:54:57,280 --> 00:54:59,680
Speaker 9: brought this issue up. But obviously what you hear from

1182
00:54:59,680 --> 00:55:02,400
Speaker 9: the left does that. Well, crime, violent crime is on

1183
00:55:02,480 --> 00:55:05,960
Speaker 9: a steep decline. And while that may be the case,

1184
00:55:06,040 --> 00:55:09,200
Speaker 9: even including homicides and shootings, the question is, Okay, where

1185
00:55:09,200 --> 00:55:10,680
Speaker 9: are we starting from. Yeah, Like I grew up in

1186
00:55:10,719 --> 00:55:13,600
Speaker 9: New York in nineteen call it ninety two, there were

1187
00:55:13,640 --> 00:55:16,880
Speaker 9: twenty five hundred murders in New York. Now they're about

1188
00:55:17,280 --> 00:55:19,920
Speaker 9: in a given year three seventy five to four to twenty,

1189
00:55:20,120 --> 00:55:23,160
Speaker 9: which obviously is a steep decline from the nineteen nineties,

1190
00:55:23,600 --> 00:55:28,920
Speaker 9: but it's still you're talking about concentrated areas where that

1191
00:55:29,120 --> 00:55:32,480
Speaker 9: is part of the local rhythm, Like the everyday rhythm

1192
00:55:32,520 --> 00:55:37,600
Speaker 9: of life is hearing gunshots and knowing somebody and including

1193
00:55:37,680 --> 00:55:40,280
Speaker 9: children who get shot and killed in the streets.

1194
00:55:40,320 --> 00:55:41,799
Speaker 5: Well, that's exactly what I want to focus on.

1195
00:55:41,800 --> 00:55:44,839
Speaker 6: Because the Washington Post before this was hyper polarized because

1196
00:55:44,880 --> 00:55:47,560
Speaker 6: Trump took it on, did a couple months ago a

1197
00:55:47,640 --> 00:55:52,440
Speaker 6: sweeping investigation into the spike intruancy, and it's significant. I

1198
00:55:52,440 --> 00:55:55,680
Speaker 6: mean it's still not back down below pandemic levels. Was

1199
00:55:55,719 --> 00:55:59,480
Speaker 6: already high before the pandemic. Huge problem. We've seen the

1200
00:55:59,800 --> 00:56:03,239
Speaker 6: like quoteen takeovers of Navy Yard three four times just

1201
00:56:03,280 --> 00:56:05,240
Speaker 6: in the last couple of months, where if people haven't,

1202
00:56:05,600 --> 00:56:07,360
Speaker 6: if you're not a local, you probably haven't seen the

1203
00:56:07,400 --> 00:56:10,080
Speaker 6: footage or the images from it. But it's literally hundreds

1204
00:56:10,080 --> 00:56:13,760
Speaker 6: of teenagers who go into the lobbies of apartment buildings

1205
00:56:13,920 --> 00:56:16,520
Speaker 6: or shooting Roman candles at the windows during the Fourth

1206
00:56:16,560 --> 00:56:19,840
Speaker 6: of July, but actually like creating a situation where the

1207
00:56:19,880 --> 00:56:22,920
Speaker 6: cops could not control the seat the streets because they

1208
00:56:22,960 --> 00:56:29,400
Speaker 6: were totally overrun. Basically, so that to me, if I

1209
00:56:29,440 --> 00:56:32,640
Speaker 6: look at the truancy, I look at what Donald Trump

1210
00:56:32,680 --> 00:56:36,840
Speaker 6: is doing with the show of force, Delana, is what

1211
00:56:36,840 --> 00:56:40,200
Speaker 6: you were saying, what is going to address the kind

1212
00:56:40,239 --> 00:56:43,799
Speaker 6: of root cause? So basically my question for you is,

1213
00:56:43,920 --> 00:56:46,640
Speaker 6: I actually think the show of force can probably do something.

1214
00:56:46,719 --> 00:56:50,040
Speaker 6: I think pushing the Metropolitan Police Department here to do

1215
00:56:50,120 --> 00:56:54,520
Speaker 6: something probably will make them better and will force them

1216
00:56:54,520 --> 00:56:57,440
Speaker 6: to finally confront some of their own failures. The statistics

1217
00:56:57,440 --> 00:56:59,400
Speaker 6: are being debated right now. There's a police commander who

1218
00:56:59,440 --> 00:56:59,920
Speaker 6: was suspended.

1219
00:57:00,080 --> 00:57:00,719
Speaker 1: This is C four.

1220
00:57:00,800 --> 00:57:03,120
Speaker 6: We can put it on the screen for cooking the books.

1221
00:57:03,600 --> 00:57:05,680
Speaker 6: The police union says the books are being cooked. It's

1222
00:57:05,680 --> 00:57:09,759
Speaker 6: hard to know exactly what the numbers are, but all

1223
00:57:09,840 --> 00:57:12,880
Speaker 6: that to say is there. I mean, it seems like

1224
00:57:12,920 --> 00:57:15,919
Speaker 6: there's just a lot more that the Trump administration could

1225
00:57:15,920 --> 00:57:19,240
Speaker 6: be doing that's not sending people in net gators and

1226
00:57:19,280 --> 00:57:20,920
Speaker 6: bullet professed on the National Mall.

1227
00:57:21,200 --> 00:57:23,640
Speaker 9: And let me say this because because you know, I

1228
00:57:23,680 --> 00:57:27,919
Speaker 9: want to be fair to the Bowser administration. Right, Mayor

1229
00:57:27,920 --> 00:57:29,360
Speaker 9: bows to her credit and I think she's on her

1230
00:57:29,360 --> 00:57:32,440
Speaker 9: third term now, to her credit, is not build de Blasio.

1231
00:57:32,960 --> 00:57:35,280
Speaker 9: I don't think she uttered the words defund the police

1232
00:57:35,360 --> 00:57:38,120
Speaker 9: one time in twenty twenty. Now, I won't be critical

1233
00:57:38,120 --> 00:57:40,120
Speaker 9: in this sense. I think she cave to the BLM

1234
00:57:40,160 --> 00:57:43,320
Speaker 9: activists by making you know, Black Lives Matter Plaza, which

1235
00:57:43,360 --> 00:57:46,720
Speaker 9: I saw as somewhat of a troll for President towards

1236
00:57:46,760 --> 00:57:49,480
Speaker 9: President Trump in twenty twenty. And they hate her anyway,

1237
00:57:49,680 --> 00:57:52,200
Speaker 9: right partly because she is she is pro police.

1238
00:57:53,000 --> 00:57:55,720
Speaker 4: So I think the city has tried to do a lot.

1239
00:57:55,520 --> 00:57:59,920
Speaker 9: To address you know, gun violence, and again spent millions

1240
00:58:00,120 --> 00:58:03,640
Speaker 9: dollars resources to try to, you know, bring these numbers down.

1241
00:58:03,640 --> 00:58:04,560
Speaker 4: Then they have come down.

1242
00:58:06,480 --> 00:58:09,520
Speaker 9: But from my perspective and just given the things that

1243
00:58:09,560 --> 00:58:12,920
Speaker 9: I research in terms of you know, family structure, the

1244
00:58:12,920 --> 00:58:16,160
Speaker 9: stuff about truancy and chronic absentee is a while an

1245
00:58:16,200 --> 00:58:19,880
Speaker 9: issue nationwide. When you have schools, high schools in DC

1246
00:58:20,240 --> 00:58:23,760
Speaker 9: where the chronic absentee res the truancy rate is like

1247
00:58:23,880 --> 00:58:27,240
Speaker 9: ninety percent, it's insane. That is first and foremost a

1248
00:58:27,320 --> 00:58:31,200
Speaker 9: problem with the family and part of the issue. And

1249
00:58:31,240 --> 00:58:33,400
Speaker 9: I addressed this, and I would bring this up periodically

1250
00:58:33,440 --> 00:58:35,400
Speaker 9: even when I was in the city. Is like, Okay,

1251
00:58:35,440 --> 00:58:39,960
Speaker 9: we have twelve, thirteen, fourteen, fifteen year olds carjacking people,

1252
00:58:40,000 --> 00:58:43,080
Speaker 9: shooting people, but the first adults that come up for

1253
00:58:43,080 --> 00:58:46,360
Speaker 9: an accountability check are the mayor, the police chief, and

1254
00:58:46,960 --> 00:58:48,200
Speaker 9: you know, the school superintendent.

1255
00:58:48,800 --> 00:58:51,600
Speaker 4: That is completely out of order. Right. So what I

1256
00:58:51,640 --> 00:58:52,480
Speaker 4: would love to hear.

1257
00:58:52,640 --> 00:58:54,840
Speaker 9: An elected officials say is say is something to the

1258
00:58:54,880 --> 00:58:58,200
Speaker 9: effect that, look, we're doing everything we can to address

1259
00:58:58,240 --> 00:59:01,919
Speaker 9: the crime, but it is not our responsibility to parent

1260
00:59:02,080 --> 00:59:05,880
Speaker 9: your children. That's your job, because if you can't control them,

1261
00:59:05,920 --> 00:59:07,320
Speaker 9: how would you expect me to control them?

1262
00:59:07,400 --> 00:59:10,880
Speaker 4: They live in your house, they're under your rules. I

1263
00:59:10,920 --> 00:59:11,800
Speaker 4: would hope.

1264
00:59:11,680 --> 00:59:15,320
Speaker 9: At some point, some mayor somewhere, some police chief somewhere

1265
00:59:15,720 --> 00:59:19,600
Speaker 9: would say something to that effect and galvanize the community

1266
00:59:19,920 --> 00:59:25,240
Speaker 9: to take responsibility for their children, our children in ways

1267
00:59:25,240 --> 00:59:27,680
Speaker 9: that help address some of these issues, because it can't

1268
00:59:27,720 --> 00:59:32,120
Speaker 9: just be root causes being an euphemism for more spending

1269
00:59:32,160 --> 00:59:35,120
Speaker 9: on the library, twenty four hour rec center, you know,

1270
00:59:35,280 --> 00:59:39,080
Speaker 9: just keeping kids busy twenty four to seven, which again.

1271
00:59:40,120 --> 00:59:40,800
Speaker 4: I know what it's like.

1272
00:59:41,040 --> 00:59:44,480
Speaker 1: Yeah, not a horrible thing, not going to solve the problem, but.

1273
00:59:44,400 --> 00:59:47,080
Speaker 9: That is we let me say it this way, we

1274
00:59:47,120 --> 00:59:50,240
Speaker 9: do not apply that same framework when it's other types

1275
00:59:50,280 --> 00:59:54,000
Speaker 9: of crimes. I've never heard a Democrat or progressive say, man,

1276
00:59:54,120 --> 00:59:58,080
Speaker 9: you know, if only those you know, clan members had

1277
00:59:58,160 --> 01:00:01,160
Speaker 9: more jobs and economic opt tunity in the nineteen forties,

1278
01:00:01,320 --> 01:00:03,200
Speaker 9: maybe they wouldn't have terrorized black neighborhoods.

1279
01:00:03,200 --> 01:00:04,920
Speaker 4: No, nobody says that they.

1280
01:00:04,800 --> 01:00:07,600
Speaker 9: Only do it when it comes to urban crime and

1281
01:00:07,600 --> 01:00:10,720
Speaker 9: when it comes to low income black populations. And I

1282
01:00:10,760 --> 01:00:13,480
Speaker 9: believe people have more agency in that than that, but

1283
01:00:13,560 --> 01:00:17,200
Speaker 9: they have to be called up and activated to do

1284
01:00:17,240 --> 01:00:18,320
Speaker 9: what needs to be done.

1285
01:00:18,160 --> 01:00:20,280
Speaker 3: Well at the same time, and I see, I appreciate

1286
01:00:20,320 --> 01:00:22,120
Speaker 3: this is actually more of a nuanced conversation. We're not

1287
01:00:22,160 --> 01:00:24,400
Speaker 3: sitting here fluffing than Trump administration. We're like, yeah, there's

1288
01:00:24,400 --> 01:00:26,320
Speaker 3: some major issues happening, but you know, we're not going

1289
01:00:26,400 --> 01:00:28,560
Speaker 3: to sit here in gaslight everybody and say there's no problem.

1290
01:00:28,600 --> 01:00:31,000
Speaker 3: But that's unfortunately what a lot of the media has

1291
01:00:31,040 --> 01:00:33,640
Speaker 3: done on this. So here we have MSNBC Simone Sanders,

1292
01:00:33,760 --> 01:00:34,439
Speaker 3: let's take a listen.

1293
01:00:34,440 --> 01:00:39,400
Speaker 10: We'll get reaction that it is perceived violence amplified by

1294
01:00:39,480 --> 01:00:43,480
Speaker 10: some actual real acts of violence. But the way I've

1295
01:00:43,560 --> 01:00:46,720
Speaker 10: heard DC being described this morning is like it's a

1296
01:00:46,760 --> 01:00:49,720
Speaker 10: city under siege, like it's a dangerous place, clutching your pearls,

1297
01:00:49,760 --> 01:00:51,840
Speaker 10: you got to keep your bag under your dress when

1298
01:00:51,840 --> 01:00:54,400
Speaker 10: you leave the house, And that's that's just not true.

1299
01:00:54,880 --> 01:00:58,440
Speaker 10: What we are talking about, though, is these instances of

1300
01:00:58,560 --> 01:01:02,200
Speaker 10: juvenile crime. My concern is that the president is using

1301
01:01:02,760 --> 01:01:06,400
Speaker 10: instances of juvenile crime in the city of Washington, d C.

1302
01:01:06,840 --> 01:01:09,000
Speaker 10: As a pretext for what I would describe as his

1303
01:01:09,360 --> 01:01:10,600
Speaker 10: authoritarian overreach.

1304
01:01:10,720 --> 01:01:11,920
Speaker 5: When you walk down the streets of.

1305
01:01:11,840 --> 01:01:14,320
Speaker 10: Georgetown, you don't see a police officer on every corner,

1306
01:01:14,320 --> 01:01:16,680
Speaker 10: but you don't feel unsafe. So what is it about

1307
01:01:16,760 --> 01:01:20,120
Speaker 10: talking about places like southeast DC right ward eight. If

1308
01:01:20,120 --> 01:01:23,080
Speaker 10: you will that people say, well, we need more officers

1309
01:01:23,120 --> 01:01:25,440
Speaker 10: to make us safe. I think we have to rethink

1310
01:01:25,840 --> 01:01:27,600
Speaker 10: what safety means in America.

1311
01:01:28,160 --> 01:01:31,120
Speaker 1: All right, So this is why the pretext of this matters.

1312
01:01:31,200 --> 01:01:33,120
Speaker 3: Right. So she said, when you walk down the streets

1313
01:01:33,120 --> 01:01:36,880
Speaker 3: to Georgian again, the richest neighborhood, probably statistically the richest

1314
01:01:36,880 --> 01:01:39,640
Speaker 3: neighborhood in all of Washington, DC, you don't see a

1315
01:01:39,680 --> 01:01:40,560
Speaker 3: cop on every corner.

1316
01:01:40,720 --> 01:01:43,560
Speaker 1: Now apparently do because the Trumpet streets. They don't need

1317
01:01:43,600 --> 01:01:44,880
Speaker 1: to be there, there's nothing going on.

1318
01:01:45,600 --> 01:01:49,640
Speaker 3: But that is a level of not just like paternalism,

1319
01:01:49,760 --> 01:01:54,240
Speaker 3: but also of denial, which just completely absconds the entire problem. Right.

1320
01:01:54,840 --> 01:01:58,600
Speaker 9: And my frustration when I see that is these are

1321
01:01:58,640 --> 01:02:01,680
Speaker 9: the same people that claim that they speak for the

1322
01:02:01,680 --> 01:02:05,320
Speaker 9: black community. Right now, again, most of the homicides, I mean,

1323
01:02:05,760 --> 01:02:07,400
Speaker 9: when I work in officer of gun violence Prevention, every

1324
01:02:07,400 --> 01:02:09,000
Speaker 9: morning we would start with a call from the assistant

1325
01:02:09,000 --> 01:02:12,400
Speaker 9: police chief going through the contact shootings the last last

1326
01:02:12,400 --> 01:02:14,720
Speaker 9: twenty four or seventy two hours if you're coming off

1327
01:02:14,720 --> 01:02:17,880
Speaker 9: a weekend. Every I mean ninety nine percent of the time,

1328
01:02:17,960 --> 01:02:20,680
Speaker 9: young black male, young black male, young black male. But

1329
01:02:20,800 --> 01:02:24,920
Speaker 9: when samone Sanders and Roland Martin and other people in

1330
01:02:25,120 --> 01:02:28,960
Speaker 9: sort of the aristocracy, right, the sort of black progressive elites.

1331
01:02:29,360 --> 01:02:33,840
Speaker 9: When they talk, they are only interested in mobilizing federal resources.

1332
01:02:34,400 --> 01:02:36,680
Speaker 9: If you're investigating, you know, what you think is a

1333
01:02:36,720 --> 01:02:39,400
Speaker 9: noose in a NASCAR garage with Bubba Wallace, If you're

1334
01:02:39,440 --> 01:02:44,280
Speaker 9: investigating Jesse Smolett, if Joe Biden is saying that the

1335
01:02:44,560 --> 01:02:48,720
Speaker 9: greatest threat to America is white domestic terrorism, then they'll say,

1336
01:02:48,760 --> 01:02:51,760
Speaker 9: we need the federal government to marshal office resources to

1337
01:02:51,760 --> 01:02:56,080
Speaker 9: address these problems. So they are more concerned with fake

1338
01:02:56,160 --> 01:02:59,320
Speaker 9: hate crimes than real street crimes. And the reason is

1339
01:02:59,760 --> 01:03:03,880
Speaker 9: because for the left and the right, the primary role

1340
01:03:03,920 --> 01:03:06,320
Speaker 9: that the black community plays within sort of the criminal

1341
01:03:06,360 --> 01:03:11,200
Speaker 9: justice ecosystem is often victims of a racist system and

1342
01:03:11,280 --> 01:03:12,800
Speaker 9: not victims of violent crime.

1343
01:03:13,160 --> 01:03:14,560
Speaker 4: And that's why people like Simon.

1344
01:03:14,360 --> 01:03:16,880
Speaker 9: Sanders can say with a straight face, Oh, you know,

1345
01:03:17,040 --> 01:03:20,360
Speaker 9: it's not that bad, And why are there more police

1346
01:03:20,560 --> 01:03:22,720
Speaker 9: in Southeast than there aren't Georgetown. Well that's where the

1347
01:03:22,720 --> 01:03:25,840
Speaker 9: shootings occur, right, that is where the shootings occur. So

1348
01:03:26,160 --> 01:03:28,840
Speaker 9: when I see that type of thing, it frustrates me deeply.

1349
01:03:28,960 --> 01:03:31,400
Speaker 9: I'll give you a quick story when I was still

1350
01:03:31,440 --> 01:03:33,600
Speaker 9: in DC, there was an activist he went by the

1351
01:03:33,680 --> 01:03:37,400
Speaker 9: name Seaweb who had been tracking homicides just in his

1352
01:03:37,400 --> 01:03:42,120
Speaker 9: neighborhood over by the Convention Center right since the nineteen nineties.

1353
01:03:42,480 --> 01:03:46,040
Speaker 4: He had a brick wall full of names.

1354
01:03:45,600 --> 01:03:48,160
Speaker 9: And faces of guys who have been shot and killed

1355
01:03:48,240 --> 01:03:50,680
Speaker 9: just in that sort of two block radius. And he

1356
01:03:50,760 --> 01:03:52,680
Speaker 9: told me part of the reason he does it is

1357
01:03:52,680 --> 01:03:56,400
Speaker 9: because he mentors some of these guys children, and these

1358
01:03:56,440 --> 01:03:58,480
Speaker 9: are some of the only photos and videos that they've

1359
01:03:58,480 --> 01:04:00,920
Speaker 9: ever seen of their father. So he had been doing

1360
01:04:00,960 --> 01:04:04,680
Speaker 9: it twenty plus years. So when I hear again these

1361
01:04:04,720 --> 01:04:10,040
Speaker 9: sort of you know, pundits trying to sweep these issues

1362
01:04:10,120 --> 01:04:12,520
Speaker 9: under the rug, it frustrates me deeply.

1363
01:04:12,600 --> 01:04:14,880
Speaker 3: Well, then let's talk about this thirty year So this

1364
01:04:14,920 --> 01:04:17,880
Speaker 3: is a new talking point. DC violent crime is at

1365
01:04:17,880 --> 01:04:20,160
Speaker 3: a thirty year low. Now, first of all, as you

1366
01:04:20,240 --> 01:04:22,480
Speaker 3: just said, the police commanders are investigation for duke and

1367
01:04:22,480 --> 01:04:25,360
Speaker 3: the stats, So who knows whether that's even true, But

1368
01:04:25,720 --> 01:04:29,440
Speaker 3: you know, at the context that you've laid out, we

1369
01:04:29,520 --> 01:04:31,720
Speaker 3: still have a lot of murders compared to New York.

1370
01:04:31,760 --> 01:04:34,000
Speaker 3: I believe it's the highest in the entire United's or

1371
01:04:34,040 --> 01:04:36,600
Speaker 3: one of the highest among US cities at a per

1372
01:04:36,680 --> 01:04:41,640
Speaker 3: capita basis. And even then, just focusing on murders does

1373
01:04:41,680 --> 01:04:43,560
Speaker 3: not address quality.

1374
01:04:43,200 --> 01:04:43,760
Speaker 1: Of life crime.

1375
01:04:43,920 --> 01:04:46,160
Speaker 3: I've always talked about this with San Francisco. San Francisco

1376
01:04:46,160 --> 01:04:47,240
Speaker 3: doesn't have a ton of murders.

1377
01:04:47,400 --> 01:04:50,640
Speaker 1: Go visit. It's not nice. Why is it not nice?

1378
01:04:50,920 --> 01:04:56,720
Speaker 3: Car crime, property, theft, heroin, drug addiction, everywhere. So just contextualize,

1379
01:04:57,080 --> 01:04:59,600
Speaker 3: I think what it means for the DC resident and

1380
01:04:59,680 --> 01:05:03,280
Speaker 3: for for what the problem is at a crime level,

1381
01:05:03,280 --> 01:05:05,080
Speaker 3: at a quality of life level, and then what does

1382
01:05:05,120 --> 01:05:08,200
Speaker 3: the solution actually look like? Because look the show of

1383
01:05:08,280 --> 01:05:10,040
Speaker 3: force and the activists and all that.

1384
01:05:10,040 --> 01:05:11,120
Speaker 1: That's going to happen no matter what.

1385
01:05:11,240 --> 01:05:13,560
Speaker 3: But people here, if you actually want to know what

1386
01:05:13,600 --> 01:05:14,959
Speaker 3: you could do about it, what do you think?

1387
01:05:15,160 --> 01:05:16,840
Speaker 4: Yeah, I mean, I think you raise a good point. Right.

1388
01:05:16,840 --> 01:05:19,920
Speaker 9: The quality of life stuff is separate and related, but

1389
01:05:20,040 --> 01:05:23,000
Speaker 9: separate from the violent crime, you know, as you said,

1390
01:05:23,160 --> 01:05:24,480
Speaker 9: I mean, they're they're porch pirates.

1391
01:05:24,520 --> 01:05:26,680
Speaker 4: So every you're gonna ring in the.

1392
01:05:26,600 --> 01:05:30,880
Speaker 9: Community, it's like, Okay, another package is stolen. It's the carjackings, right,

1393
01:05:31,000 --> 01:05:34,240
Speaker 9: It's it's the assaults. Sometimes it's just it's just the intimidation.

1394
01:05:34,720 --> 01:05:38,800
Speaker 9: It's it's the homelessness, it's the vagrancy. These are things

1395
01:05:38,840 --> 01:05:41,200
Speaker 9: that frustrate people, and these are things that drive people

1396
01:05:41,360 --> 01:05:43,760
Speaker 9: you know away and make them move to to you know,

1397
01:05:43,880 --> 01:05:48,760
Speaker 9: Virginia or Maryland. I think addressing each of the issues

1398
01:05:49,240 --> 01:05:53,760
Speaker 9: you know comprehensively makes sense, but each of them has

1399
01:05:53,840 --> 01:05:55,120
Speaker 9: their own set of root causes.

1400
01:05:55,160 --> 01:05:58,720
Speaker 4: Again, with the youth crime, parents have to be held

1401
01:05:58,720 --> 01:05:59,200
Speaker 4: to account.

1402
01:05:59,200 --> 01:06:01,240
Speaker 9: I'm not saying that they have to be arrested, but

1403
01:06:01,720 --> 01:06:03,680
Speaker 9: I mean a mayor or a police chief can at

1404
01:06:03,760 --> 01:06:06,760
Speaker 9: least start by talking about the importance of family as

1405
01:06:06,760 --> 01:06:09,960
Speaker 9: it relates to addressing this particular issue. Some of the

1406
01:06:09,960 --> 01:06:14,760
Speaker 9: other stuff, I might be more radical than most, Yeah,

1407
01:06:15,880 --> 01:06:18,000
Speaker 9: I have and some certain certain ideas.

1408
01:06:18,040 --> 01:06:21,520
Speaker 4: Only talk about my wife because I might look.

1409
01:06:22,280 --> 01:06:25,960
Speaker 9: I would be open to at least putting the idea

1410
01:06:25,960 --> 01:06:30,560
Speaker 9: on the table of rounding up known gang members and saying, look, guys,

1411
01:06:31,160 --> 01:06:32,880
Speaker 9: if you as a hell bent on hurting each other,

1412
01:06:33,440 --> 01:06:34,720
Speaker 9: we'll go somewhere else where.

1413
01:06:34,720 --> 01:06:35,840
Speaker 4: You can do that. You can sign the.

1414
01:06:35,760 --> 01:06:40,200
Speaker 9: Waivers, you can you can have the whatever uh level

1415
01:06:40,200 --> 01:06:43,000
Speaker 9: of force you all agree to. We can have it out,

1416
01:06:43,120 --> 01:06:46,120
Speaker 9: but we're not going to allow you to terrorize this neighborhood.

1417
01:06:46,400 --> 01:06:50,480
Speaker 9: We're three year olds and toddlers and senior citizens are

1418
01:06:50,520 --> 01:06:53,640
Speaker 9: the ones who get caught and crossfire because you guys

1419
01:06:53,800 --> 01:06:55,440
Speaker 9: one have no respect for yourself.

1420
01:06:55,440 --> 01:06:57,200
Speaker 1: We're talking about the Wire. This is season three of

1421
01:06:57,200 --> 01:06:58,040
Speaker 1: the Wire, you know, yeah.

1422
01:06:57,960 --> 01:07:01,560
Speaker 5: Answer season now in Anacostia, right, So I.

1423
01:07:01,520 --> 01:07:03,240
Speaker 9: Would be open to that right. It might not pass

1424
01:07:03,360 --> 01:07:07,960
Speaker 9: legal mustard, but open to the idea. And I think again,

1425
01:07:08,480 --> 01:07:12,240
Speaker 9: I'm fine with the zero tiles policy for vagrancy and

1426
01:07:12,400 --> 01:07:13,959
Speaker 9: some of these other quality of life crimes.

1427
01:07:14,000 --> 01:07:15,360
Speaker 4: I know a lot of people don't like that.

1428
01:07:15,840 --> 01:07:19,720
Speaker 9: But I think law abiding, tax paying citizens have a

1429
01:07:19,800 --> 01:07:22,600
Speaker 9: right to live in neighborhoods that are clean and orderly,

1430
01:07:23,080 --> 01:07:26,280
Speaker 9: and getting there is not always going to feel comfortable.

1431
01:07:26,280 --> 01:07:28,720
Speaker 9: Like I saw one of the videos that was certainly yesterday.

1432
01:07:29,160 --> 01:07:30,640
Speaker 9: You know, you have some federal agents look like they

1433
01:07:30,640 --> 01:07:33,280
Speaker 9: were walking through an alley. They come up on, you know,

1434
01:07:33,640 --> 01:07:36,280
Speaker 9: a group of might have been two or three young

1435
01:07:36,320 --> 01:07:38,520
Speaker 9: black men and you know, hey, guys, you know.

1436
01:07:38,520 --> 01:07:39,120
Speaker 4: You have your ID.

1437
01:07:39,720 --> 01:07:42,920
Speaker 9: Now, most people don't carry ID when they're you know,

1438
01:07:43,000 --> 01:07:43,600
Speaker 9: on the porch.

1439
01:07:44,480 --> 01:07:46,280
Speaker 4: It was a very respectful interaction.

1440
01:07:46,600 --> 01:07:49,120
Speaker 9: You know, the federal officer was basically explaining what this

1441
01:07:49,160 --> 01:07:53,040
Speaker 9: initiative was, but I've seen many that aren't aren't nearly

1442
01:07:53,080 --> 01:07:54,000
Speaker 9: as respectful.

1443
01:07:54,680 --> 01:07:58,240
Speaker 4: And this is where this is where the nuance comes in.

1444
01:07:58,640 --> 01:08:01,160
Speaker 9: And part of my concern, Earn, is that you have

1445
01:08:01,280 --> 01:08:05,040
Speaker 9: conservatives talking about life and blue cities the way liberals

1446
01:08:05,080 --> 01:08:06,120
Speaker 9: talk about life.

1447
01:08:05,880 --> 01:08:06,560
Speaker 4: In the Deep South.

1448
01:08:06,600 --> 01:08:08,800
Speaker 1: I think that's very smart, right, and it's and.

1449
01:08:08,720 --> 01:08:10,800
Speaker 9: What it does is it conditions, not just a sense

1450
01:08:10,840 --> 01:08:13,560
Speaker 9: of fear, which I felt probably when I was driving

1451
01:08:13,600 --> 01:08:16,760
Speaker 9: my family from DC to Texas through Mississippi and saying,

1452
01:08:16,760 --> 01:08:18,759
Speaker 9: oh my gosh, what if I get stopped in downtown

1453
01:08:19,720 --> 01:08:21,120
Speaker 9: that had been conditioned to me. I grew up in

1454
01:08:21,120 --> 01:08:21,960
Speaker 9: New York, I don't I don't know.

1455
01:08:21,920 --> 01:08:22,960
Speaker 4: Anything about Mississippi.

1456
01:08:23,160 --> 01:08:25,920
Speaker 9: Yeah, but I don't want families who are coming here

1457
01:08:25,920 --> 01:08:28,479
Speaker 9: from Iowa Wisconsin to think as soon as I step

1458
01:08:28,520 --> 01:08:31,720
Speaker 9: off the Greyhound or the Amtrak, then I'm gonna get mugged.

1459
01:08:31,479 --> 01:08:33,000
Speaker 1: And murdered Greyhound.

1460
01:08:33,040 --> 01:08:37,360
Speaker 4: That might appen, well, but it's not.

1461
01:08:37,400 --> 01:08:40,360
Speaker 9: It's not just the fear that the conditions, but it

1462
01:08:40,400 --> 01:08:43,600
Speaker 9: makes it easier to say, these are not real Americans

1463
01:08:43,680 --> 01:08:46,840
Speaker 9: like us, and we're willing to do whatever it takes

1464
01:08:47,960 --> 01:08:50,200
Speaker 9: to to sort of make this problem go away.

1465
01:08:50,439 --> 01:08:52,000
Speaker 6: And so this is my last question because one of

1466
01:08:52,040 --> 01:08:54,320
Speaker 6: the things that Samon Sanders gets at is the age

1467
01:08:54,320 --> 01:08:58,520
Speaker 6: old debate between the sort of presence of law enforcement

1468
01:08:59,000 --> 01:09:02,000
Speaker 6: and whether or not is gonna go down because you

1469
01:09:02,040 --> 01:09:04,160
Speaker 6: have more cops in the street with stop and frisk

1470
01:09:04,280 --> 01:09:08,120
Speaker 6: or whatever it is, broken windows, all of these. I mean,

1471
01:09:08,200 --> 01:09:12,160
Speaker 6: the progressive left basically tried their experiment as backlash to

1472
01:09:12,240 --> 01:09:15,400
Speaker 6: those policies in San Francisco and Oakland, and the experiment

1473
01:09:15,439 --> 01:09:17,160
Speaker 6: has been a failure to the point where even its

1474
01:09:17,160 --> 01:09:20,000
Speaker 6: own proponents have walked back. Maryel Bowser here in DC

1475
01:09:20,160 --> 01:09:23,840
Speaker 6: gave into a lot of progressing policing policy ideas and

1476
01:09:23,880 --> 01:09:24,680
Speaker 6: walked some of them back.

1477
01:09:24,720 --> 01:09:25,799
Speaker 5: She's even walking back.

1478
01:09:25,680 --> 01:09:28,960
Speaker 6: The sanctuary city status now as well, to the extent

1479
01:09:29,000 --> 01:09:31,880
Speaker 6: that she kind of can. So, is there though a

1480
01:09:32,040 --> 01:09:34,439
Speaker 6: risk I mean, just steal manning the way that we

1481
01:09:34,520 --> 01:09:36,320
Speaker 6: and probably all three of us think about this, Is

1482
01:09:36,320 --> 01:09:39,519
Speaker 6: there a risk that a militarized police force, which a

1483
01:09:39,560 --> 01:09:42,559
Speaker 6: lot of like libertarian leaning conservatives are already don't like.

1484
01:09:43,280 --> 01:09:46,080
Speaker 5: Is there a risk that that makes the problem worse

1485
01:09:46,280 --> 01:09:49,040
Speaker 5: in any way? Or is it only going.

1486
01:09:48,880 --> 01:09:52,639
Speaker 9: To from your perspective, help, I don't see it making

1487
01:09:52,680 --> 01:09:53,799
Speaker 9: the problem worse.

1488
01:09:55,840 --> 01:09:57,439
Speaker 4: That doesn't mean that it will necessarily help.

1489
01:09:57,560 --> 01:09:59,960
Speaker 9: Again, one of the things I liked about Operation Legend

1490
01:10:00,000 --> 01:10:02,519
Speaker 9: and again back in the first Trump term, is that

1491
01:10:02,680 --> 01:10:04,439
Speaker 9: they were using it wasn't just the show of force

1492
01:10:04,479 --> 01:10:07,720
Speaker 9: on the streets. It was the intelligence. It was the

1493
01:10:07,720 --> 01:10:11,759
Speaker 9: prosecution that was getting you know, not just bad guys.

1494
01:10:11,520 --> 01:10:13,280
Speaker 4: But murderers off the street.

1495
01:10:13,640 --> 01:10:17,519
Speaker 9: So if federal resources were used sort of to help

1496
01:10:17,760 --> 01:10:21,960
Speaker 9: in investigations closing cases, I think that's a lot different

1497
01:10:22,000 --> 01:10:24,479
Speaker 9: than just the show of force and having agents on

1498
01:10:24,520 --> 01:10:26,400
Speaker 9: the street, particularly they're just walking around the hands in

1499
01:10:26,439 --> 01:10:29,760
Speaker 9: their pockets, you know, in Georgetown or Navy Yard. So

1500
01:10:30,120 --> 01:10:32,920
Speaker 9: I don't think it will necessarily make it worse. That

1501
01:10:32,920 --> 01:10:35,160
Speaker 9: doesn't mean it'll make it better. One of the things

1502
01:10:35,160 --> 01:10:38,240
Speaker 9: that I've often said is like, look, people like Simone

1503
01:10:38,240 --> 01:10:41,240
Speaker 9: Sanders and you know other folks on the left are

1504
01:10:41,360 --> 01:10:45,520
Speaker 9: very much pro police. They either live in gated communities,

1505
01:10:45,600 --> 01:10:49,519
Speaker 9: right or they have security guards or they pay for

1506
01:10:49,560 --> 01:10:50,440
Speaker 9: private security.

1507
01:10:50,880 --> 01:10:53,200
Speaker 4: The only people who I legitimately.

1508
01:10:52,600 --> 01:10:56,040
Speaker 9: Believe are open to the police going away are guys

1509
01:10:56,080 --> 01:10:59,320
Speaker 9: who are in the streets. So whether you're street gang,

1510
01:10:59,439 --> 01:11:02,640
Speaker 9: organized crime, or you're related to someone or friends with

1511
01:11:02,680 --> 01:11:06,320
Speaker 9: someone who handles their own business in the streets. Everybody

1512
01:11:06,320 --> 01:11:10,599
Speaker 9: else from noise complaints. If a homeless person is sitting

1513
01:11:10,680 --> 01:11:14,200
Speaker 9: on your front porch, nine one one can you. So

1514
01:11:14,320 --> 01:11:16,640
Speaker 9: I don't believe this notion that they don't think that

1515
01:11:16,720 --> 01:11:19,920
Speaker 9: police matter. It takes more than just police on the streets,

1516
01:11:19,920 --> 01:11:23,320
Speaker 9: because I mean, criminals are not smart in the sense

1517
01:11:23,360 --> 01:11:26,080
Speaker 9: that they're engaging in dangerous activities, but it's smart enough

1518
01:11:26,120 --> 01:11:29,400
Speaker 9: to know, Okay, we know the police surge between three

1519
01:11:29,439 --> 01:11:31,920
Speaker 9: pm and seven pm. When the sun goes down, they

1520
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Speaker 9: go home, we come out and we terrorize the streets.

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Speaker 9: So I think a lot of it is about the coordination.

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Speaker 9: And when I was in DC, they would say this

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Speaker 9: frequently that DC is resource rich, but coordination poor.

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Speaker 4: When you add the federal layer.

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Speaker 9: On top of that, that could make the problem worse,

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Speaker 9: and that no one knows exactly what it is that

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Speaker 9: they're supposed to be doing. But I think there's a

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Speaker 9: potential to make it better if again those federal resources,

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Speaker 9: the advanced technology, more prosecutors can help close cases and

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Speaker 9: bring some level of closure to victims and their families.

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Speaker 1: Well, I really appreciate joining US Man.

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Speaker 3: I really learned a lot and I hope the audience

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Speaker 3: found it helpful, so thank you for joining US.

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Speaker 1: I appreciate it.

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Speaker 4: Thank you,