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Speaker 1: Welcome back to a numbers game with Brian Grodowski. Thank

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Speaker 1: you for being here, ladies and gentlemen. So I was

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Speaker 1: looking around and playing around some of the numbers of

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Speaker 1: the listenership of this podcast listenership over the weekend, checking out,

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Speaker 1: you know, where my audience is and who was responding

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Speaker 1: to it. And I really really really want to do

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Speaker 1: a live podcast taping this year, like that would be

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Speaker 1: my goal for this podcast, is that this year we

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Speaker 1: can get an audience together and I could do a

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Speaker 1: live event. I just think of a live ask me

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Speaker 1: any things that going to be so much fun. And

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Speaker 1: I could have a guest come stream in if or

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Speaker 1: can be there, you know, in person, depending on the

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Speaker 1: place and the time. So that's what my goal is.

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Speaker 1: And the show's been growing pretty successfully and the audience

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Speaker 1: has been very loyal and I'm so happy for it.

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Speaker 1: And I was looking at cities. So Houston is the

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Speaker 1: biggest city for this podcast as far as listenership goes.

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Speaker 1: And then it's followed by a city that I never

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Speaker 1: heard of, and I can't believe how big my audience

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Speaker 1: is there, but it's Hutchinson, Kansas. I have never been

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Speaker 1: to Hutchinson Kansas. I've been to Kansas many times, been

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Speaker 1: ever Hutchinson, but I have a strong following in the

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Speaker 1: city of Hodginson, Kansas. After Hutchinson, it's Dallas and New

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Speaker 1: York and Denver and Phoenix and Atlanta and the places

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Speaker 1: you would think, you know, have big listenerships because it's

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Speaker 1: a big population. But if anyone, if there's an organization

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Speaker 1: or a group or anybody that wants me to come

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Speaker 1: to do a live event in your city, email me

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Speaker 1: Ryan at numbers gamepodcast dot com. We charge, you know,

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Speaker 1: a low overhead just to make sure we cover costs.

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Speaker 1: I don't need to make a lot of money off

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Speaker 1: of it. The show's doing great. I have a million

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Speaker 1: other jobs, but I really think a live event would

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Speaker 1: be wonderful. So, uh, if you're interested, email me Ryan

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Speaker 1: at numbers gamepodcast dot com. Same as asked me anything time.

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Speaker 1: Tell me if you know we could pack a house,

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Speaker 1: If we could pack a house, if we can get

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Speaker 1: people there, I'm down to do it. I just think

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Speaker 1: it would be so great. So thank you guys so

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Speaker 1: much for continuing to support this podcast. I really shit

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Speaker 1: you all. Okay, let's talk about some polling from around

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Speaker 1: the country. To a little polling pick me up. In Wisconsin,

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Speaker 1: Republican Tom Tippany is in the margin of error for

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Speaker 1: governor against all leading Democratic candidates. He's down three points

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Speaker 1: against Sarah Rodriguez, two points against Mendela Barnes, who is

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Speaker 1: the lieutenant governor, one point from David Crowley, and he's

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Speaker 1: ahead three points from Francesca Hung. Barnes is leading in

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Speaker 1: the polls, but Francesca actually had a recent poll that

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Speaker 1: had her head. It's more likely Barnes only because a

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Speaker 1: nem Id and support. But Francesca is like the super

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Speaker 1: progressive Candida, very far left candidate. Although Barnes is like,

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Speaker 1: you know, I don't know how you get further off

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Speaker 1: than Barnes, but Francesca managed it. She's just she out

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Speaker 1: did him on you know, hating hating capitalism just a

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Speaker 1: little bit. More Over, in New Hampshire, New Hampshire, a

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Speaker 1: Saint Anselam poll found the Republican governor Kelly Ayott is

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Speaker 1: leading likely Democrat candidate Warmington that's the last name, by

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Speaker 1: six points. It's a healthy lead for a Republican Canada

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Speaker 1: in a state that voted for Kamala Harris on the

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Speaker 1: Senate side. This is the sleeper race, the New Hampshire

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Speaker 1: race no one's paying attention to, but it is the

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Speaker 1: sleeper race of the year. Former Senator John Sunu is

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Speaker 1: down by just three points against Congressman Chris tappis the

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Speaker 1: likely Democrat. You know, Snunu was a senator a while ago.

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Speaker 1: He lost reelection I think in one of the Democratic waves,

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Speaker 1: I think two thousand and eight. But he's from a

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Speaker 1: legacy family. His brother was a super popular governor. His

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Speaker 1: dad was an icon in the state. I think his

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Speaker 1: dad's still alive, but he was George H. W. Bush's

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Speaker 1: chief of staff and he was a governor himself. It's

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Speaker 1: just New Hampshire's this weird place where like four Lebanese

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Speaker 1: families will run the entire state, and the Snow News

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Speaker 1: are one of them, and just incredibly incredibly popular. He's

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Speaker 1: not a Trumper, but he's endorsed by Trump, so there's

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Speaker 1: a community in the Republican Party. And his name ID

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Speaker 1: is not a associate with Trump, which you would need

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Speaker 1: in a year that's not favorable for Democrat or Republicans rather,

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Speaker 1: especially with the Trump brand not being great. I just think,

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Speaker 1: I mean, I'm not saying that he's gonna win. I

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Speaker 1: think it is the sleeper race of the year, especially

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Speaker 1: given that New Hampshire has had such tight federal races

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Speaker 1: in the last couple of cycles that no one really

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Speaker 1: picked up on. Over in Los Angeles, this is not

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Speaker 1: a federal race. This is a local race. Mayor Karen

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Speaker 1: Bass is at twenty five percent against Los Angeles City

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Speaker 1: Council Chairman Nissa Rahman, and I think that's the progressive candidate.

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Speaker 1: She's at seventeen percent and reality show star Spencer Pratt

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Speaker 1: is at fourteen percent. Now, Karen Bass is famous for

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Speaker 1: not being in La during the fires that destroyed an

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Speaker 1: entire neighborhood that now she's trying to get Section eight

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Speaker 1: housing builds in that neighborhood in the Palisades. I've been

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Speaker 1: to the Palisades. It's such a beautiful area. It's horrific

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Speaker 1: what's happened to it and how Karen Bass has been

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Speaker 1: neglectful to say the least. And Spencer press running as

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Speaker 1: like the fact to Republican candidate, which is there a

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Speaker 1: better person? I don't know. He's definitely trying to get

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Speaker 1: his name out there. But Karen Bess, who was away

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Speaker 1: while city burned is still leading in the polls, which

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Speaker 1: is not wonderful. And last, but not least, there was

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Speaker 1: a CBS poll asked do you support a voter ID

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Speaker 1: eighty percent of people supported it, twenty percent opposed. When

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Speaker 1: the poll also asked when do you support requiring proof

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Speaker 1: of citizenship to register to vote? Sixty six percent said yes,

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Speaker 1: thirty three percent opposed. That includes ninety three percent of Republicans,

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Speaker 1: sixty one percent of Independence and forty three percent of Democrats.

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Speaker 1: That also included sixty percent of the black vote. Considering

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Speaker 1: the always say the black vote will suffer from requiring citizenship,

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Speaker 1: sixty percent of black voter support it. Okay, Now I

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Speaker 1: want to talk about a generic ballot because that was

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Speaker 1: you know, a lot of these polls have been. Good

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Speaker 1: news for Republicans is there's tight races. Republicans are not

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Speaker 1: being blown out on some of these races. They're competitive

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Speaker 1: in Wisconsin, competitive in New Hampshire, competitive, you know, in

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Speaker 1: a number of these places. But the generic ballot is

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Speaker 1: moving in the other direction for Republicans, first being the

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Speaker 1: Emerson Pole. The Emerson Pole has Democrats dominating the generic

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Speaker 1: ballot by eight and a half points, winning independent Democrats

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Speaker 1: are winning independence in this poll by eighteen points, and

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Speaker 1: they're winning Latinos by twenty one points. That's actually a

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Speaker 1: pretty good number of Latinos because it shows in a

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Speaker 1: year that looks like twenty eighteen generic ballad wise, the

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Speaker 1: Denver republic Latinos still have not moved back to where

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Speaker 1: they used to be in twenty eighteen numbers where it

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Speaker 1: would be like thirty six or thirty eight or forty

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Speaker 1: point plans lide for Democrats. Twenty one is actually very

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Speaker 1: good considering it shows that the transition of Latino voters

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Speaker 1: is really sticking. Even if they're not happy with Trump

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Speaker 1: right now or the Republican Party, they're not willing to

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Speaker 1: move back to the Democratic Party like they used to.

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Speaker 1: The pole also has Republicans only leading by the white

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Speaker 1: vote by two points. If that I mean, I don't

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Speaker 1: believe that's true. It's never been that small. If that's true,

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Speaker 1: it is the GOP really is screwed. That follows a

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Speaker 1: similar number from from the website. The argument they did

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Speaker 1: their own internal poll. The argument is a left wing outlet,

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Speaker 1: but they're smart left winger is they put out information

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Speaker 1: that makes liberals uncomfortable at times, and I think they're

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Speaker 1: intellectually honest, although they're a bias, but everyone has a bias,

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Speaker 1: so you can't really help. But they're not like what's

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Speaker 1: his name, Gene Morris or something from the he was

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Speaker 1: the economists. He's just like a straight up hack. This

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Speaker 1: is a liberal, I think with a brain, so I

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Speaker 1: read their stuff. Trump's favorability is negative eighteen according to

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Speaker 1: their poll. Seventeen percent of Trump voters disapprove of the

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Speaker 1: job he's doing. And he's underwater with every key demographic

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Speaker 1: and he's just tied among whites without a college degree.

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Speaker 1: Pretty awful. The primary driver is the economy. Voters hate

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Speaker 1: this Trump economy and they're blaming him, you know. For

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Speaker 1: I'm down in Ruby Red Louisiana, and voters are just

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Speaker 1: feeling disappointed. They were hoping for the first term all

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Speaker 1: over again, and they're not getting it, and there's anxiety

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Speaker 1: and a lot of people are just barely getting by

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Speaker 1: and they're really struggling. And I think that there's this

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Speaker 1: divorce mentality from how bad it is on the streets

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Speaker 1: from you know, sixteen hundred Pennsylvania Avenue. Sixty five percent

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Speaker 1: of voters feel like the economy isn't keeping up with

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Speaker 1: the cost of living now? Is that true? Not necessarily untrue, right,

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Speaker 1: because we've seen what inflation is and we've seen the

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Speaker 1: growth and job wages. Wages are growing pretty fast and

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Speaker 1: inflation is down significantly from the from the Biden era.

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Speaker 1: Although with gas prices just jacking up because the Iran War,

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Speaker 1: maybe they're feeling it more. I don't know when the

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Speaker 1: pole exactly was taken. I think that gas prices started

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Speaker 1: going up at the same time the pole was being taken.

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Speaker 1: Twenty five percent of voters really our financial situation is improving.

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Speaker 1: Fifty percent of voters think the economy is getting worse

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Speaker 1: once again. Is it getting worse? Not not terribly worse.

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Speaker 1: I mean a lot of this is about feelings. And

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Speaker 1: you know, Ben Shiva always says to facts. You know,

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Speaker 1: facts don't care about your feelings. That is true, but

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Speaker 1: voters don't care about facts. You have to feelings are

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Speaker 1: the only thing that matter sometimes of voters, so you

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Speaker 1: have to kind of feelings matter a lot to people,

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Speaker 1: and you can't tell them they're wrong. You have to

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Speaker 1: justify their feelings and explain how things are getting better

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Speaker 1: to comfort them. So it is what it is. One

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Speaker 1: interesting data point that the pullpount is voters have a

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Speaker 1: preference for lower prices over lower unemployment. I don't find

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Speaker 1: that to be true, only because in this country we

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Speaker 1: have gotten so used to unemployment being under five percent,

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Speaker 1: we don't for most people, we don't remember what ten, twelve,

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Speaker 1: fifty percent unemployment feels like. And that's I think that's

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Speaker 1: why people are arguing on more out prices than actual unemployment.

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Speaker 1: Right now, among all register voters, Democrats leave forty six

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Speaker 1: to forty among this pole, but among those definitely going

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Speaker 1: to vote it is fifty five forty five, because voter

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Speaker 1: enthusiasm is over the moon for Democrats right now. Zacharydnini,

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Speaker 1: who I've had on this podcast, why is such a

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Speaker 1: smart such a smart guy, for he's a data scientist.

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Speaker 1: He actually puts out though when you pull all the

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Speaker 1: polls together, Democrats lead is actually shrunk a little bit

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Speaker 1: in the last few weeks, says Democrats have a overall

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Speaker 1: nationally about four point six despite these polls, although I

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Speaker 1: think they are two good polsters. A D plus eight environment,

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Speaker 1: which is what these two poles say, means that Republicans

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Speaker 1: will lose the Senate a D plus four to six,

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Speaker 1: as Zachary Denini notes, from all the Polsters, that's where they,

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Speaker 1: you know, keep the Senate, lose the House, but not

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Speaker 1: by more than I don't know, fifteen c twelve seats,

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Speaker 1: eighteen seats, ten seats. It's not an overwhelming it's not

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Speaker 1: a forty seat landslide. Now, there are so many factors

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Speaker 1: into why prices are going up. There's you know, there's

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Speaker 1: too much money in the system because our government cannot

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Speaker 1: stop running deficits and adding to the debt. There's the

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Speaker 1: war worthy Ran. There is problems with how companies are

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Speaker 1: restructuring post COVID. There is a lot of instability in

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Speaker 1: the market. That there's the fact that a lot of

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Speaker 1: companies hired too much during COVID and now they're firing people.

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Speaker 1: I will say this is anecdotal, this is not data

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Speaker 1: to provide it. I think one thing going to benefit

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Speaker 1: Republicans is the upcoming tax season with the rebates. I

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Speaker 1: spoke to a relative of mine who is a hairstylist.

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Speaker 1: She lives on tips. Because of Trump's no tax on tips,

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Speaker 1: her tax rebate was seven hundred and fifty percent higher

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Speaker 1: than the year prior it was a life changing amount

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Speaker 1: of money for I want to say life changing, but

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Speaker 1: year changing, a lifemount of money for a person in

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Speaker 1: a blue collar job. And considering that seventeen million Americans

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Speaker 1: work in the tip industry, this could be a lot.

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Speaker 1: This could be bolster working class Americans' incomes substantially going

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Speaker 1: going into the election year. So I am hopeful that

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Speaker 1: come May and soon, as long as people file their

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Speaker 1: taxes on time, which Laura knows I love an extension

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Speaker 1: they are, I think that those tax rebates will help

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Speaker 1: a lot of working class Americans and maybe ease a

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Speaker 1: lot of anxieties around the economy. Another big problem, though,

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Speaker 1: and this is something that won't be feil from a

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Speaker 1: tax on tips, A big problem is too many Americans

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Speaker 1: feel like Trump is focused on the wrong things. I

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Speaker 1: know I'm going to piss people off. He's got to

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Speaker 1: relax the foreign policy stuff. I know it's legacy building,

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Speaker 1: I know it's important. We're in an election cycle. Though

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Speaker 1: Americans don't care about forign policy. They don't they don't

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Speaker 1: care about foreign policy. Venezuela, as I've said on this podcast,

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Speaker 1: was flawlessly done. Flawless ten out of ten. No one cared.

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Speaker 1: It was a brief bump in the polls by a point,

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Speaker 1: and everyone went back to their life. Iran is much

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Speaker 1: more complicated and it's having a lot more repercussions in

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Speaker 1: the economy and locally. Not to mention, there are dead

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Speaker 1: Americans now. Trump at this point needs to start calling

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Speaker 1: all these executives who have promised to open up all

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Speaker 1: these warehouses and factories across the country and hire all

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Speaker 1: these Americans, and start showing up to ribbon cutting ceremonies

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Speaker 1: every single week. Send JD. Vans out, Send your key

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Speaker 1: allies outsend, who are the Commerce secretary whomever, or a

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Speaker 1: Secretary of Treasury whomever. Make sure they are at these

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Speaker 1: ribbon cutting ceremonies where you're going to break brown a

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Speaker 1: new construction, where you're going to hire new workers, take

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Speaker 1: photos with new hired workers. This is of the effort.

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Speaker 1: This is the this is the effort needs to be

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Speaker 1: put in to make sure Americans know that you care

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Speaker 1: about the economy. You know, go invite a bunch of

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Speaker 1: people who got taxes tax relief for there were servers

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Speaker 1: at restaurants, or they were hairdressers or worked in hotels,

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Speaker 1: whatever the cases they got tips to talk about how

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Speaker 1: much of a relief the economy is from the no

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Speaker 1: tax on tips. You really have to emphasize I care

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Speaker 1: about the economy. I'm a businessman. I care with the

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Speaker 1: business of people's everyday lives. Don't worry about what is

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Speaker 1: going on at a war table in Tehran, what is

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Speaker 1: happening at the kitchen table in Dallas, in Detroit, in Cleveland,

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Speaker 1: in all these exerbs and suburbs and rural America. You

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Speaker 1: it has to be concentrated so heavily on the economy

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Speaker 1: right now. And Congress, you know, in this meantime, have

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Speaker 1: to start to bring up all the easy bills. Get

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Speaker 1: Mike Johnson to bring up the easiest bills and make

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Speaker 1: Democrats vote against them, hang it over their head going

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Speaker 1: into election season. Have Foon bring them up to votes.

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Speaker 1: Even if they fail, it doesn't matter. Let Democrats own

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Speaker 1: every bad policy issue. Chris Murphy, who's got an IQ

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Speaker 1: of like well water, he was on I forget which

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Speaker 1: show is MS now or something like that, and he

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Speaker 1: said he was like, the people we care about with

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Speaker 1: the most are undocumented Americans. You have to make them

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Speaker 1: own all these stupid, insane positions. Democrats have so many good,

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Speaker 1: populous positions that they care about on healthcare that I'm

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Speaker 1: not saying they're well thought out, but they're popular with

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Speaker 1: American people on abortion, on healthcare, on tech, more and more.

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Speaker 1: And then they'll be like, here are you know, winning

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Speaker 1: issues left right and center. And by the way, along

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Speaker 1: with these winning issues, you have to let an illegal

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Speaker 1: alien man who identifies as a girl who is thirty

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Speaker 1: seven priors shower next to your daughter. Like they will

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Speaker 1: destroy any type of well wish, any well welcome, any

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Speaker 1: welcome that they have with with voters at all. They

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Speaker 1: just they have to. They can. Their brains are broken

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Speaker 1: on trans issues and illegal immigrants and crime to the

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Speaker 1: point that it actually hurts issues that they're so popular

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Speaker 1: with the American people with. And that's honestly the benefit

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Speaker 1: of the Republicans. But the Republicans have to get on message.

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Speaker 1: Trump needs to get on message. It is of the

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Speaker 1: essence and one of those messages. And you know, I've

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Speaker 1: talked about a lot is ai both parties. Man, they're

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Speaker 1: missing the message. They're missing the point on this right now.

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Speaker 1: And I have a guest on the show coming up next,

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Speaker 1: Andrew Egger and my buddy from the Bullwark. He is

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Speaker 1: here to talk about how both parties are missing out

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Speaker 1: on the AI conversation that everyday Americans are having. That's

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Speaker 1: coming up next. Stay tuned with me on today's episode

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Speaker 1: is my buddy Andrew Egger, who writes the Morning Shop

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Speaker 1: for The Bulwark, the morning newsletter. It's great. I actually

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Speaker 1: do subscribe and read that. Thank you for being here. Andrew, Hey, Ryan,

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Speaker 1: I do. You wrote a really interesting piece called Everybody

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Speaker 1: Hates AI, and you point to the NBC poll. AI

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Speaker 1: is one of the few policy pieces that there's really

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Speaker 1: no consensus of which party has the most trust in,

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Speaker 1: and you wrote this, that's what opportunity looks like. The

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Speaker 1: party that can write the argument over AI is set

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Speaker 1: up for a political windfall and then will be tasked

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Speaker 1: with the shepherding of the country through the massive disruption

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Speaker 1: towards a process rather than a dystopian AI and integrated

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Speaker 1: future if possible. No pressure, But so far it seems

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Speaker 1: like basically everybody is blowing it. Is the question that

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Speaker 1: they're blowing it because it's so new, or are they

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Speaker 1: blowing it because they don't know what side their side

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Speaker 1: should be taking.

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Speaker 2: I would say, right now, most of what you are

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Speaker 2: seeing when it comes to AI policy is you have

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Speaker 2: this brand new thing. It's going to be uniquely disruptive economically,

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Speaker 2: it's set up to be uniquely disruptive politic. I think

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Speaker 2: a lot of people are just waking up to this.

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Speaker 2: And that's what that poll kind of demonstrated, is that,

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Speaker 2: you know, voters have their views on which party tends

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Speaker 2: to be more trusted on, for instance, crime or immigration.

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Speaker 2: You know they're going to go for the Republicans or

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Speaker 2: or you know, voters tend to give Democrats the edge

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Speaker 2: on healthcare, you know, civil rights, constitutional you know, protections,

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Speaker 2: those sorts of things. AI is a complete no man's land.

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Speaker 2: And I think that that not only is it a

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Speaker 2: complete noma's land in terms of people knowing which party

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Speaker 2: they they you know, want to want to cling to

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Speaker 2: on it, but even even the idea that that the

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Speaker 2: issue is becoming so much more salient is new. I

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Speaker 2: think a lot of policymakers are waking up and realizing,

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Speaker 2: wait a minute, there's this enormous political latent energy out

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Speaker 2: here where where this issue is only becoming more and

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Speaker 2: more and more and more important, and we need to

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Speaker 2: figure out how we're going to take a stance on it.

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Speaker 2: Some of the part of the problem is, uh that

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Speaker 2: we are really just seeing policymakers kind of fall back

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Speaker 2: on frames of understanding, you know, for the last whole

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Speaker 2: set of technologies in order to kind of do the

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Speaker 2: the opening shots of AI regulation here. So there's a

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Speaker 2: lot of on the Republican side, there's a lot of

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Speaker 2: just let's let's just let it rip, baby, you know, uh,

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Speaker 2: free market type stuff. Get the data centers in there,

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Speaker 2: let's just go. And on the Democratic side, you see

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Speaker 2: a lot of you know, we need to we need

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Speaker 2: to restrain this thing quick and fast. And there are

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Speaker 2: a lot of different kind of uh uh in my view,

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Speaker 2: bad policy levers that they're reaching for to do that.

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Speaker 2: One of which the one I wrote about in the

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Speaker 2: piece uh is is this idea of sort of protecting

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Speaker 2: professional classes, uh, like like doctors or lawyers or therapists

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Speaker 2: or you know, basically anybody that that you need an

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Speaker 2: occupational license from the government to practice in a given

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Speaker 2: state if you're a human being, basically just sort of

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Speaker 2: freezing AI out from from sharing any views on any

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Speaker 2: of these any of these issues.

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Speaker 1: That's like one one way.

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Speaker 2: Another thing is these these sorts of copyright lawsuits, the

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Speaker 2: idea of of you know, basically holding these these companies

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Speaker 2: accountable for uh uh the AIS having had their training

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Speaker 2: data uh you know pulled from perhaps copyrighted works. There

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Speaker 2: are a lot of current lawsuits going on about that.

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Speaker 2: But but you know, there's various sort of policy uh

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Speaker 2: ideas in the pipeline about sort of using those two

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Speaker 2: sort of divert big amounts of the of the profits

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Speaker 2: from these companies back to you know, the people who

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Speaker 2: the data was trained on.

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Speaker 1: Things like that.

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Speaker 2: And then there's just you know, more kind of disruptive

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Speaker 2: even than that, ideas out there, like just sort of

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Speaker 2: banning data centers altogether. And this is actually not just

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Speaker 2: a democratic thing. You've seen people like Ronda Santis in

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Speaker 2: Florida uh uh throw himself. Yeah, yeah, they're there. They're

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Speaker 2: sort of like a populous energy in the Republican Party

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Speaker 2: as well. That that that is basically just no AI,

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Speaker 2: thanks thanks, but it's not for us. You know, we

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Speaker 2: we we've taken a look at it, and we don't

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Speaker 2: really like it. There's an enormous amount of political like

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Speaker 2: energy out there behind this. I've talked to a number

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Speaker 2: of people who sort of do public public opinion research

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Speaker 2: in this field. And I think that the the take

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Speaker 2: you hear from those sorts of people is like, however

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Speaker 2: bad you think it is, it's worse. People just don't

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Speaker 2: like this thing. They they there's that was gonna be

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Speaker 2: a lot of appetite to maybe crush it. And and

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Speaker 2: that's gonna be I think that politicians are going to

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Speaker 2: try to channel.

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Speaker 1: Yeah, And so I want to get the two things

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Speaker 1: just mentioned. One is the difference between state Republicans and

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Speaker 1: federal Republicans are is a world apart. Like you know,

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Speaker 1: it's funny, very funny that the line out of coming

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Speaker 1: out of Washington the Republicans were using were if you

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Speaker 1: don't regulate this, then California will. But the truth is Arkansas, Tennessee, Florida, Texas,

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Speaker 1: they all regulate AI, all these deep red states. It

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Speaker 1: is not just a California thing at all. Now there

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Speaker 1: shouldn't be a federal standard, but the Republican states want

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Speaker 1: to regulate Justice Valley as democratic states too. And I think,

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Speaker 1: and maybe I'm wrong, I think that's because a lot

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Speaker 1: of Republicans in Congress are older. They are not you

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Speaker 1: know it's not the top of the list for them.

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Speaker 1: And two is a lot of them are taking notes

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Speaker 1: and cues from Trump, who is super pro AI. He's

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Speaker 1: the aire, isn't it. What is your opinion on that?

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Speaker 2: Yeah, I think I think that's basically correct. The White House.

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Speaker 2: This White House in part because of sort of campaign

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Speaker 2: like alliances that sort of sprung up during twenty twenty four.

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Speaker 2: I mean, you've seen a much more kind of muscular

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Speaker 2: uh tech coalition, the sort of new tech right that

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Speaker 2: that has the President's ear on a lot of this stuff,

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Speaker 2: a lot of policy things, and and AI is maybe

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Speaker 2: the biggest one. Last year, the White House put out

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Speaker 2: put out an action plan that was basically along the

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Speaker 2: lines of what I was talking about earlier, where where

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Speaker 2: you know, kind of couching it in national security terms,

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Speaker 2: not not not like US versus California, but like US

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Speaker 2: versus China, is kind of more of the more of

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Speaker 2: the frame there where where it's you know, they're developing

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Speaker 2: their models over there, we're developing.

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Speaker 1: Our models over here.

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Speaker 2: We're lucky enough to have all these companies, you know,

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Speaker 2: be American companies, and we basically just need to focus

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Speaker 2: on unleashing them. Obviously, that's been complicated in a couple

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Speaker 2: of different ways recently. I mean, the the Department of

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Speaker 2: Defense is currently in a big fight with Anthropic, one

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Speaker 2: of the leading AI company, over some use policies. We

430
00:23:01,640 --> 00:23:03,359
Speaker 2: can set that aside, but that's been kind of the

431
00:23:03,400 --> 00:23:05,920
Speaker 2: White House's line. And I think that that that sort

432
00:23:05,960 --> 00:23:09,600
Speaker 2: of national security line is another part of why there's

433
00:23:09,720 --> 00:23:11,800
Speaker 2: there's sort of more of an appeal for some like

434
00:23:11,840 --> 00:23:15,520
Speaker 2: you say, older lawmakers who that's one of their primary

435
00:23:15,560 --> 00:23:17,959
Speaker 2: frames for viewing the world. The other part of it

436
00:23:18,000 --> 00:23:20,880
Speaker 2: is just that, I mean, this industry is spending a

437
00:23:20,960 --> 00:23:24,080
Speaker 2: lot of money lobbying on this topic. So it is

438
00:23:24,119 --> 00:23:25,840
Speaker 2: sort of shaping up as well to be one of

439
00:23:25,880 --> 00:23:28,760
Speaker 2: these things where, you know, they realize how much sort

440
00:23:28,800 --> 00:23:32,000
Speaker 2: of like insurgent populist backlash is building up against them,

441
00:23:32,200 --> 00:23:33,840
Speaker 2: They're like, wow, we better get out ahead of this

442
00:23:33,840 --> 00:23:36,080
Speaker 2: thing before we just end up sort of you know,

443
00:23:36,320 --> 00:23:39,320
Speaker 2: legislated out of existence really, And so they are throwing

444
00:23:39,359 --> 00:23:41,560
Speaker 2: a lot of money around us for congressmen who might

445
00:23:41,600 --> 00:23:44,120
Speaker 2: want to take a more pro a EE stance too.

446
00:23:44,320 --> 00:23:45,840
Speaker 2: I say that as it's a bad thing. I'm very

447
00:23:45,840 --> 00:23:49,280
Speaker 2: pro a I myself, I should say but it is

448
00:23:49,280 --> 00:23:52,520
Speaker 2: one of these weird dynamics of sort of populist energy

449
00:23:52,600 --> 00:23:55,080
Speaker 2: versus just a whole lot of money flying around in Washington.

450
00:23:55,160 --> 00:23:58,080
Speaker 1: Yeah, I'm pro AI to a point. I do think

451
00:23:58,080 --> 00:24:01,080
Speaker 1: it needs. It's not the Internet. It's different. It's a

452
00:24:01,119 --> 00:24:05,359
Speaker 1: different beast, and there should be some some regulations around

453
00:24:05,359 --> 00:24:08,000
Speaker 1: it considering that, like you know, every once in a

454
00:24:08,040 --> 00:24:10,440
Speaker 1: while AI developer goes yeah, and there's a forty percent

455
00:24:10,560 --> 00:24:13,959
Speaker 1: chance that ends humanity. I'm like, maybe we should have

456
00:24:14,000 --> 00:24:16,840
Speaker 1: a conversation. That's a bigger number than one or two.

457
00:24:18,320 --> 00:24:22,000
Speaker 1: And you know more than I do. But the funny

458
00:24:22,000 --> 00:24:24,480
Speaker 1: thing is is that you as you mentioned the populist backlash,

459
00:24:24,520 --> 00:24:26,600
Speaker 1: there was a Yuga pull that came out just like

460
00:24:26,640 --> 00:24:31,120
Speaker 1: an hour ago from this recording, and they asked voters

461
00:24:31,320 --> 00:24:34,679
Speaker 1: what industry has too much power in Washington? And AI

462
00:24:34,960 --> 00:24:44,199
Speaker 1: beat guns, Israel evangelicals. Unions like to do that in

463
00:24:44,280 --> 00:24:48,000
Speaker 1: such a short period of time to break people's generational

464
00:24:48,160 --> 00:24:51,760
Speaker 1: mindset of like, oh, you know, we've heard forever that

465
00:24:51,840 --> 00:24:54,720
Speaker 1: the NRA, which is a functionally bankrupt organization that has

466
00:24:54,800 --> 00:24:58,640
Speaker 1: essentially no power anymore. We've heard from democrats for generations

467
00:24:58,880 --> 00:25:00,720
Speaker 1: NRA has too much power, or even though that is

468
00:25:00,760 --> 00:25:05,840
Speaker 1: not true anymore, to break that mindset of all these

469
00:25:05,960 --> 00:25:08,280
Speaker 1: organizations on the right and the left to be the

470
00:25:08,440 --> 00:25:11,280
Speaker 1: organization has too much control in the midst of the

471
00:25:11,280 --> 00:25:15,399
Speaker 1: Iran war, in the midst of everything shows really that

472
00:25:15,480 --> 00:25:19,199
Speaker 1: there is an anxiety that is so real and palpable,

473
00:25:19,800 --> 00:25:23,040
Speaker 1: and the answer of just let it rip is not

474
00:25:23,320 --> 00:25:27,280
Speaker 1: going to suffice to average voters, especially if the unemployment

475
00:25:27,400 --> 00:25:31,359
Speaker 1: rate does inch up to ten percent or higher. Yeah.

476
00:25:31,440 --> 00:25:33,400
Speaker 2: Yeah, And I would say this is one area where

477
00:25:33,400 --> 00:25:35,280
Speaker 2: the Trump administration, like I said, it's had a pretty

478
00:25:35,320 --> 00:25:37,760
Speaker 2: pro AI stance, and it has actually done some good

479
00:25:37,760 --> 00:25:39,760
Speaker 2: work on the margins of these anxieties.

480
00:25:39,800 --> 00:25:40,480
Speaker 1: Like just just a.

481
00:25:40,400 --> 00:25:42,800
Speaker 2: Couple of weeks ago, they basically rolled out an agreement

482
00:25:42,880 --> 00:25:46,879
Speaker 2: where one of the many sort of populous anxieties about

483
00:25:46,880 --> 00:25:51,119
Speaker 2: AI is this stuff about energy costs and water usage. Right,

484
00:25:51,160 --> 00:25:55,439
Speaker 2: I mean, these data centers they suck up electricity and

485
00:25:55,480 --> 00:25:57,679
Speaker 2: they create a lot more demand on it in a

486
00:25:57,680 --> 00:25:58,200
Speaker 2: given grid.

487
00:25:58,240 --> 00:25:59,000
Speaker 1: They raise prices.

488
00:25:59,000 --> 00:26:01,800
Speaker 2: So the White House rolled out basically a policy that

489
00:26:01,800 --> 00:26:03,560
Speaker 2: that is sort of a handshake agreement with some of

490
00:26:03,560 --> 00:26:07,600
Speaker 2: these companies to shoulder more of those costs of their

491
00:26:07,640 --> 00:26:10,160
Speaker 2: own data centers. For some of these hyper scaling companies

492
00:26:10,720 --> 00:26:13,400
Speaker 2: that in theory addresses that one component. But of course

493
00:26:13,400 --> 00:26:15,719
Speaker 2: that's only one component, right, And as you're kind of mentioning,

494
00:26:15,960 --> 00:26:18,200
Speaker 2: I think that there is there's this sort of belated

495
00:26:18,560 --> 00:26:22,760
Speaker 2: but now very real sort of societal awakening to the

496
00:26:22,920 --> 00:26:25,359
Speaker 2: to the idea that whereas even just a couple of

497
00:26:25,440 --> 00:26:28,159
Speaker 2: years ago, all of this stuff about it really, you know,

498
00:26:29,040 --> 00:26:32,760
Speaker 2: mass deplacement of employee, displacement of employment and things like

499
00:26:32,800 --> 00:26:36,159
Speaker 2: that really did seem like kind of a I don't know,

500
00:26:36,200 --> 00:26:38,639
Speaker 2: like kind of a a a hidden ball trick or

501
00:26:38,680 --> 00:26:40,639
Speaker 2: something like that. It's like, oh, these companies are just

502
00:26:40,760 --> 00:26:44,080
Speaker 2: sort of, you know, making these silly sounding promises to

503
00:26:44,160 --> 00:26:45,200
Speaker 2: hype up their own products.

504
00:26:45,200 --> 00:26:46,680
Speaker 1: Nobody really thinks this is going to happen.

505
00:26:46,720 --> 00:26:48,880
Speaker 2: People are really starting to think it is actually going

506
00:26:48,880 --> 00:26:51,520
Speaker 2: to happen, and it is creating an enormous amount of anxiety.

507
00:26:51,640 --> 00:26:55,439
Speaker 2: I mean, I've been kind of slow waking up to

508
00:26:55,480 --> 00:26:57,680
Speaker 2: all this stuff myself in just the last couple of months.

509
00:26:57,760 --> 00:27:00,159
Speaker 2: I mean, it's we were hardly thinking about AI. At

510
00:27:00,200 --> 00:27:02,760
Speaker 2: least I was hardly thinking about AI this time last year.

511
00:27:02,800 --> 00:27:04,240
Speaker 2: A lot of people were thinking a lot about it.

512
00:27:04,680 --> 00:27:06,800
Speaker 2: But now you know, you turn around and this could

513
00:27:06,840 --> 00:27:10,480
Speaker 2: end up being really the key policy issue of the

514
00:27:10,560 --> 00:27:12,159
Speaker 2: of the twenty twenty eight election. I mean, like we

515
00:27:12,520 --> 00:27:17,200
Speaker 2: may be a couple of years already into some sort

516
00:27:17,240 --> 00:27:21,080
Speaker 2: of AI transition at that point, and it's and nobody.

517
00:27:21,560 --> 00:27:22,919
Speaker 2: The thing that I keep coming back to is that

518
00:27:22,960 --> 00:27:25,520
Speaker 2: nobody really has like a full plan for that. Everybody's

519
00:27:25,560 --> 00:27:28,320
Speaker 2: trying to kind of like, you know, stick their picture

520
00:27:28,400 --> 00:27:30,919
Speaker 2: down into the stream, to like pull a little bit

521
00:27:30,920 --> 00:27:33,720
Speaker 2: of that sentiment out and channel it toward one political

522
00:27:33,800 --> 00:27:35,359
Speaker 2: end or another. But nobody seems to have like a

523
00:27:35,359 --> 00:27:39,639
Speaker 2: real vision for what a society that that interacts with

524
00:27:39,680 --> 00:27:42,840
Speaker 2: this extremely powerful technology in a healthy way actually would

525
00:27:42,840 --> 00:27:43,199
Speaker 2: look like.

526
00:27:43,520 --> 00:27:45,600
Speaker 1: Andrew, I will say, if I have any talent in

527
00:27:45,640 --> 00:27:48,520
Speaker 1: this world, it's picking up political trends like I really,

528
00:27:49,040 --> 00:27:51,439
Speaker 1: I'm actually good at it. And I sat with a

529
00:27:51,760 --> 00:27:54,520
Speaker 1: top member of the Trump administration in twenty twenty three

530
00:27:54,640 --> 00:27:57,399
Speaker 1: before the election, and I said, you have to have

531
00:27:57,480 --> 00:27:59,600
Speaker 1: an answer on this. It is the most important issue

532
00:27:59,600 --> 00:28:02,679
Speaker 1: coming up. And they point blank looked at me and

533
00:28:02,720 --> 00:28:04,080
Speaker 1: said it's not going to be a problem. It's not

534
00:28:04,080 --> 00:28:06,800
Speaker 1: going to display jobs. And I was like, you're wrong, Like,

535
00:28:06,840 --> 00:28:09,040
Speaker 1: I don't know how else to tell you what you're wrong.

536
00:28:09,880 --> 00:28:13,080
Speaker 1: And I had On Rocanna on this show, who's very

537
00:28:13,400 --> 00:28:16,480
Speaker 1: like hawkish on AI. He's actually getting a primary challenge

538
00:28:16,520 --> 00:28:20,040
Speaker 1: from the AI companies about it, and he said on

539
00:28:20,080 --> 00:28:23,880
Speaker 1: the show he was that this was the goal for him,

540
00:28:24,240 --> 00:28:27,520
Speaker 1: was we need to tax AI to do redistribution of

541
00:28:27,560 --> 00:28:29,960
Speaker 1: wealth where everyone gets a check because no one will

542
00:28:30,000 --> 00:28:34,120
Speaker 1: have jobs. And David Shore, it's like Andrew Yang's idea

543
00:28:34,160 --> 00:28:36,119
Speaker 1: and has been for a while. David Shore put out

544
00:28:36,119 --> 00:28:39,560
Speaker 1: a study these the progressive polster is very smart and

545
00:28:39,600 --> 00:28:43,320
Speaker 1: he said that people voters care more about a job

546
00:28:43,400 --> 00:28:45,800
Speaker 1: than a handout, like they don't want to handle they

547
00:28:45,840 --> 00:28:50,960
Speaker 1: want some kind of government backed government back job. And

548
00:28:51,160 --> 00:28:55,560
Speaker 1: even a plurality of Republicans and Trump voters think it

549
00:28:55,600 --> 00:28:59,040
Speaker 1: is more important that AI companies be taxed for the

550
00:28:59,160 --> 00:29:02,200
Speaker 1: jobs that they lose, who the jobs they destroy, rather

551
00:29:02,280 --> 00:29:04,760
Speaker 1: than be able to get profits. I do think we're

552
00:29:04,800 --> 00:29:07,920
Speaker 1: in a different world than we were in ever, because

553
00:29:07,960 --> 00:29:09,720
Speaker 1: I do think this is going to split across party

554
00:29:09,760 --> 00:29:12,160
Speaker 1: lines in a profound way. Yeah.

555
00:29:12,240 --> 00:29:14,160
Speaker 2: Yeah, And part of it is just that it all

556
00:29:14,200 --> 00:29:17,960
Speaker 2: remains so opaque, right, so vague, nobody even really knows

557
00:29:18,280 --> 00:29:19,880
Speaker 2: what the impacts are going to be, where they're going

558
00:29:19,920 --> 00:29:22,680
Speaker 2: to be felt first, how quickly they will arrive, because

559
00:29:22,720 --> 00:29:24,760
Speaker 2: all because we're all still just sort of like groping

560
00:29:24,800 --> 00:29:27,440
Speaker 2: our way toward even what the technologies are going to

561
00:29:27,440 --> 00:29:29,400
Speaker 2: be capable of a year from now or three years

562
00:29:29,400 --> 00:29:31,560
Speaker 2: from now. I mean, like even among the people who

563
00:29:31,600 --> 00:29:36,080
Speaker 2: are building these things, views diverge wildly about how much

564
00:29:36,280 --> 00:29:39,880
Speaker 2: more quickly this can continue to scale, right, and how

565
00:29:39,960 --> 00:29:43,360
Speaker 2: how where the bottlenecks are going to be, where where

566
00:29:43,360 --> 00:29:46,600
Speaker 2: the actual like like uh uh, an economy can sort

567
00:29:46,600 --> 00:29:50,520
Speaker 2: of trundle along inefficiently without like perfect allocation of capital

568
00:29:50,720 --> 00:29:53,480
Speaker 2: for a long, long long time because of various you know,

569
00:29:53,720 --> 00:29:55,880
Speaker 2: actual human interests that are involved. And this seems like

570
00:29:55,920 --> 00:29:57,600
Speaker 2: a situation where there's going to be a ton of that.

571
00:29:57,880 --> 00:30:01,080
Speaker 2: And so the question is, can we can we manage

572
00:30:01,080 --> 00:30:03,560
Speaker 2: that sort of situation? Can we can we allow these

573
00:30:03,600 --> 00:30:07,360
Speaker 2: technologies to continue to be deployed while also finding different

574
00:30:07,360 --> 00:30:10,320
Speaker 2: ways to to sort of like blunt the harms, allow

575
00:30:10,400 --> 00:30:12,880
Speaker 2: the unleashing of the technology and the and the productivity

576
00:30:12,880 --> 00:30:14,400
Speaker 2: and all of those sorts of things, but blunt the

577
00:30:14,440 --> 00:30:17,760
Speaker 2: harms or is it actually a situation where where these

578
00:30:17,880 --> 00:30:20,680
Speaker 2: these you're better off without it where where you're where

579
00:30:20,680 --> 00:30:24,200
Speaker 2: where it's actually fine for these different different politicians to

580
00:30:24,280 --> 00:30:26,920
Speaker 2: basically subject this technology to a death of a thousand

581
00:30:27,000 --> 00:30:30,720
Speaker 2: regulatory cuts and and uh and and nobody really knows,

582
00:30:30,720 --> 00:30:33,240
Speaker 2: but they do know one thing, which is that the

583
00:30:33,280 --> 00:30:36,160
Speaker 2: hatred for it out there, the anxiety about about it

584
00:30:36,160 --> 00:30:38,560
Speaker 2: out there that is one hundred percent real, Like that's

585
00:30:38,600 --> 00:30:41,240
Speaker 2: not just some like possible future development.

586
00:30:41,320 --> 00:30:42,120
Speaker 1: That's there now.

587
00:30:42,320 --> 00:30:43,840
Speaker 2: And I think that's part of why we're starting to

588
00:30:43,840 --> 00:30:47,240
Speaker 2: see this wave of of, like I said, different sort

589
00:30:47,280 --> 00:30:50,400
Speaker 2: of pretty hostile AI policy legislation.

590
00:30:50,040 --> 00:30:53,320
Speaker 1: Out there, and it's going to be bigger. The AI packs,

591
00:30:53,400 --> 00:30:57,800
Speaker 1: the I alleged affiliated packs, are spending millions on several

592
00:30:57,920 --> 00:31:00,200
Speaker 1: races because they want to put the fear of and

593
00:31:00,240 --> 00:31:02,800
Speaker 1: his politicians. There's New Year twelve. If you haven't covered

594
00:31:02,840 --> 00:31:04,960
Speaker 1: the New York five primary, you should. It is the

595
00:31:05,040 --> 00:31:07,840
Speaker 1: race to replace Jerry Nadler's old seat. There's a guy

596
00:31:07,960 --> 00:31:11,200
Speaker 1: named Alex Boris. He's an assemblyman. He worked for Pallenteer

597
00:31:11,240 --> 00:31:13,840
Speaker 1: and he wrote the AI regulation for New York State.

598
00:31:14,280 --> 00:31:17,880
Speaker 1: They are spending like I think, north of a fifteen

599
00:31:17,920 --> 00:31:21,440
Speaker 1: million dollars to defeat him. He's running against a John

600
00:31:21,520 --> 00:31:27,240
Speaker 1: Kennedy's grandson and George Conway. I think, you know, like great,

601
00:31:27,280 --> 00:31:30,160
Speaker 1: great parade of people. But he's actually a serious legislator,

602
00:31:30,200 --> 00:31:31,960
Speaker 1: even when even though I disagree with him on like

603
00:31:32,160 --> 00:31:35,040
Speaker 1: seventy five percent of things, he's serious And like I

604
00:31:35,080 --> 00:31:38,000
Speaker 1: keep telling the number, he has to win only so

605
00:31:38,320 --> 00:31:42,440
Speaker 1: the conversation doesn't end, Like, if he wins, the fear

606
00:31:42,600 --> 00:31:46,400
Speaker 1: of all this money dials down a lot, and then

607
00:31:46,400 --> 00:31:49,320
Speaker 1: we could actually have a smart conversation. If he loses,

608
00:31:49,880 --> 00:31:51,600
Speaker 1: a lot of people are going to shut up out

609
00:31:51,600 --> 00:31:55,280
Speaker 1: of fear. But that's really what this election cycle. More

610
00:31:55,280 --> 00:31:57,960
Speaker 1: than I think who wins the House, who wins the Senate,

611
00:31:58,000 --> 00:32:00,720
Speaker 1: The real story is these these and I've been talking

612
00:32:00,720 --> 00:32:02,680
Speaker 1: a lot about money and politics for this podcast list

613
00:32:02,720 --> 00:32:07,840
Speaker 1: episodes Apac, Crypto and AI specifically AI spending money is

614
00:32:07,880 --> 00:32:12,080
Speaker 1: to whoever wins the House really control what is allowed

615
00:32:12,120 --> 00:32:14,040
Speaker 1: to be talked about and regulated or not. And that's

616
00:32:14,080 --> 00:32:17,640
Speaker 1: why like Bores has to win so we could actually

617
00:32:17,640 --> 00:32:21,400
Speaker 1: have an adult conversation. Does that make any sense? Yeah, no,

618
00:32:21,880 --> 00:32:22,840
Speaker 1: I see what you're talking about.

619
00:32:23,040 --> 00:32:26,080
Speaker 2: I'm also very very interested in the way that this

620
00:32:26,120 --> 00:32:27,800
Speaker 2: plays out on the Republican side. I mean, I was

621
00:32:27,840 --> 00:32:30,760
Speaker 2: mentioning earlier the sort of split we talked about, the

622
00:32:31,040 --> 00:32:34,000
Speaker 2: kind of populist versus institutionalist or maybe institutionalist is not

623
00:32:34,040 --> 00:32:36,480
Speaker 2: the right word, but the anti versus pro AI side

624
00:32:36,560 --> 00:32:39,360
Speaker 2: on the Republicans, And like a lot of it's concentrated

625
00:32:39,400 --> 00:32:42,360
Speaker 2: at the state level, but that's not guaranteed to remain.

626
00:32:42,480 --> 00:32:43,640
Speaker 2: I mean, this has been one of the kind of

627
00:32:43,680 --> 00:32:47,160
Speaker 2: fault lines in the MAGA coalition for quite a while.

628
00:32:47,320 --> 00:32:50,640
Speaker 2: Is just this sort of like tech futurist guys, the

629
00:32:50,640 --> 00:32:53,360
Speaker 2: Peter Thiels of the world, on the one hand, and

630
00:32:53,400 --> 00:32:57,200
Speaker 2: the more kind of like return to tradition social cons

631
00:32:57,240 --> 00:32:59,680
Speaker 2: on the other side of that particular split. And it

632
00:32:59,680 --> 00:33:02,240
Speaker 2: really feel like a side show for a lot of

633
00:33:02,280 --> 00:33:04,200
Speaker 2: the I mean, certainly for the first Trump term, for

634
00:33:04,280 --> 00:33:05,720
Speaker 2: a lot of what we've seen so far in the

635
00:33:05,720 --> 00:33:07,920
Speaker 2: second Trump term. In terms of most of the policies

636
00:33:07,920 --> 00:33:10,560
Speaker 2: that have been front and center, they tend to agree,

637
00:33:10,560 --> 00:33:12,520
Speaker 2: they tend to align. There have been little flare ups,

638
00:33:12,560 --> 00:33:14,479
Speaker 2: like you know, the fight over H one B visas

639
00:33:14,560 --> 00:33:17,880
Speaker 2: or something like that, but we're really looking at a

640
00:33:17,920 --> 00:33:20,880
Speaker 2: world where if this does in fact become kind of

641
00:33:20,920 --> 00:33:23,960
Speaker 2: like one of the defining policy issues of the back

642
00:33:24,000 --> 00:33:26,600
Speaker 2: half of Trump's second term. I don't know how that

643
00:33:26,640 --> 00:33:29,160
Speaker 2: coalition hangs together on this issue because it just seems

644
00:33:29,160 --> 00:33:34,280
Speaker 2: so like they're so diametrically on opposite sides of the

645
00:33:34,480 --> 00:33:37,920
Speaker 2: very very basic fundamentals of the issue.

646
00:33:37,920 --> 00:33:39,720
Speaker 1: Here. Well, I'll tell you a secret. So this is

647
00:33:39,720 --> 00:33:42,040
Speaker 1: the secret in the Republican primary right now, because I've

648
00:33:42,080 --> 00:33:47,440
Speaker 1: been seeing this NonStop, is the AI gives buckets. The

649
00:33:47,480 --> 00:33:49,840
Speaker 1: Club for Growth, The Club for Growth has a super

650
00:33:49,840 --> 00:33:52,680
Speaker 1: close relationship with Trump. Trump endorses the same primary can

651
00:33:52,760 --> 00:33:55,720
Speaker 1: the Cloud for Growth does, and everybody wins. So we're

652
00:33:55,760 --> 00:33:59,040
Speaker 1: not We're going to have a serious conversation. We have

653
00:33:59,200 --> 00:34:03,680
Speaker 1: not yet had that because Club AI and Trump are

654
00:34:03,720 --> 00:34:07,400
Speaker 1: all in codes too. They all like everyone's singing Kumbayad together.

655
00:34:07,960 --> 00:34:10,959
Speaker 1: Once that ends, whether through it's Trump's presidency or through

656
00:34:11,080 --> 00:34:13,439
Speaker 1: I don't know what, or a backlash is too big

657
00:34:13,480 --> 00:34:17,600
Speaker 1: to withhold. But once that ends, then we'll have the conversation.

658
00:34:17,800 --> 00:34:21,359
Speaker 1: But so often when a party's has the presidency, even

659
00:34:21,400 --> 00:34:25,080
Speaker 1: they start this to Obama, when Mohama's president, when Bush's president,

660
00:34:25,160 --> 00:34:28,120
Speaker 1: you can't have dynamic conversations outside of what the White

661
00:34:28,160 --> 00:34:30,520
Speaker 1: House is saying because everyone takes us from the White House.

662
00:34:31,280 --> 00:34:34,160
Speaker 1: So yeah, I'm fus shared on the Republican side deeply

663
00:34:34,360 --> 00:34:38,480
Speaker 1: as somebody who does politics for a living. The Democratic side,

664
00:34:38,520 --> 00:34:40,799
Speaker 1: I think has a lot of energy and they have

665
00:34:40,880 --> 00:34:43,000
Speaker 1: a lot of they're open to a lot of conversations.

666
00:34:43,040 --> 00:34:44,800
Speaker 1: A lot of bad ideas are coming out of Democratic

667
00:34:44,880 --> 00:34:47,719
Speaker 1: side too, but they're having a conversation, which is what

668
00:34:48,360 --> 00:34:52,760
Speaker 1: American people are. And Sure's data show that like economic

669
00:34:52,800 --> 00:34:56,440
Speaker 1: populism tied to AI means a four point swing in

670
00:34:56,480 --> 00:34:59,279
Speaker 1: Democrats favor nationally, you go from a Trump twenty twenty

671
00:34:59,280 --> 00:35:01,360
Speaker 1: four election to a i'm a twenty twenty twelve election

672
00:35:01,719 --> 00:35:04,480
Speaker 1: specifically on AI. So long as you don't make the

673
00:35:04,480 --> 00:35:07,239
Speaker 1: other policy positions like a grown man should be able

674
00:35:07,239 --> 00:35:08,880
Speaker 1: to shower with a twelve year old daughter, like you

675
00:35:08,880 --> 00:35:10,600
Speaker 1: know what I mean, Like it's like co identifies as one, Like,

676
00:35:10,640 --> 00:35:12,480
Speaker 1: as long as you don't do that, Like, actually, there's

677
00:35:12,520 --> 00:35:16,080
Speaker 1: a lot going for Democrats. Okay, Where where could people

678
00:35:16,160 --> 00:35:18,200
Speaker 1: go to read your stuff to hear more about you?

679
00:35:18,200 --> 00:35:20,080
Speaker 1: Because I think you put out some really really interesting

680
00:35:20,080 --> 00:35:21,640
Speaker 1: stuff your morning newsletter.

681
00:35:22,239 --> 00:35:24,720
Speaker 2: Yeah thanks, So I'm the White Houst correspondent for the Bullwork.

682
00:35:24,840 --> 00:35:27,520
Speaker 2: I write our morning newsletter with Bill Crystal every day

683
00:35:27,960 --> 00:35:30,279
Speaker 2: Monday through Friday. It's a free newsletter, goes out at

684
00:35:30,280 --> 00:35:33,400
Speaker 2: the Bullwark dot com. And I'm also I'm also all

685
00:35:33,440 --> 00:35:36,160
Speaker 2: over our YouTube, all over our subject video stuff. You know,

686
00:35:36,280 --> 00:35:38,080
Speaker 2: follow the Bullwork on YouTube if you feel like it,

687
00:35:38,320 --> 00:35:40,080
Speaker 2: or a head over to Substack for our video stuff

688
00:35:40,080 --> 00:35:40,440
Speaker 2: there as well.

689
00:35:40,480 --> 00:35:42,920
Speaker 1: And I'm on Twitter eger DC. Great guy. Do not

690
00:35:43,040 --> 00:35:45,480
Speaker 1: let the Bulwark scare you. He's somebody worth listening to.

691
00:35:45,760 --> 00:35:48,719
Speaker 1: Absolutely all right, Andrewe thanks for this podcast. I appreciate it.

692
00:35:48,960 --> 00:35:54,560
Speaker 1: Thanks Ryan. Now it's time for the Ask Me Anything segment.

693
00:35:54,600 --> 00:35:56,560
Speaker 1: If your money part of the Ask Me Anything segment

694
00:35:56,640 --> 00:35:59,600
Speaker 1: on me Ryan at Numbers Gamepodcast dot com. That's Ryan

695
00:35:59,640 --> 00:36:02,239
Speaker 1: at umbers proll Numbers Gamepodcast dot com. I got a

696
00:36:02,239 --> 00:36:04,799
Speaker 1: lot of them. I love getting these. I think they

697
00:36:04,800 --> 00:36:07,719
Speaker 1: make the show so wonderful and it makes me know

698
00:36:07,760 --> 00:36:10,520
Speaker 1: that someone's listening. Okay, this question comes from Eric, he

699
00:36:10,560 --> 00:36:13,279
Speaker 1: writes Ryan. The House Representatives has been four hundred and

700
00:36:13,320 --> 00:36:16,480
Speaker 1: thirty five representatives since the early part of the nineteen

701
00:36:16,560 --> 00:36:20,680
Speaker 1: hundreds nineteen twenty nine. Since then, our population has increased

702
00:36:20,680 --> 00:36:22,439
Speaker 1: to over three hundred and thirty million. I'm a strong

703
00:36:22,480 --> 00:36:25,560
Speaker 1: advocate of expanding the number of representatives. Earlier this year,

704
00:36:25,680 --> 00:36:28,440
Speaker 1: many states did redistricting to increase ours or D's. My

705
00:36:28,480 --> 00:36:31,120
Speaker 1: opinion is, why not simply increase the number of Republic

706
00:36:31,200 --> 00:36:34,280
Speaker 1: of representatives. I think the problem is the current reps

707
00:36:34,440 --> 00:36:37,360
Speaker 1: do not want to water down their power. In your opinion,

708
00:36:37,400 --> 00:36:39,839
Speaker 1: what is the correct number of representatives the US House

709
00:36:39,880 --> 00:36:42,000
Speaker 1: should have and how do we get the House to

710
00:36:42,080 --> 00:36:44,520
Speaker 1: Expand you know, I talked to Matt Gates about this

711
00:36:44,600 --> 00:36:46,480
Speaker 1: when he was in Congress and he just was always like,

712
00:36:47,680 --> 00:36:51,319
Speaker 1: his opinion always was will the House operate better with

713
00:36:51,560 --> 00:36:54,880
Speaker 1: more representatives? And that was his big opposition to it.

714
00:36:55,800 --> 00:37:02,480
Speaker 1: We have one of the smaller like parliaments or House

715
00:37:02,520 --> 00:37:07,160
Speaker 1: representatives in the lower representatives lower body rather a parliament

716
00:37:07,280 --> 00:37:10,600
Speaker 1: of our Parliament, of our Congress. In the world, we

717
00:37:10,640 --> 00:37:14,239
Speaker 1: are number twenty four as far as the size of

718
00:37:14,280 --> 00:37:19,319
Speaker 1: our Lower House representatives. Other countries, like you know, other

719
00:37:19,440 --> 00:37:22,280
Speaker 1: mid size and low sized countries have much larger parliaments

720
00:37:22,320 --> 00:37:28,440
Speaker 1: than we do. France, Mexico, Japan, in England, Ethiopia, Turkey, Germany,

721
00:37:28,880 --> 00:37:32,600
Speaker 1: they all have larger bodies than US legislative bodies. So

722
00:37:33,520 --> 00:37:37,760
Speaker 1: it's it's not like we would be out of step

723
00:37:37,760 --> 00:37:41,440
Speaker 1: with the world if we increased it. But why we

724
00:37:41,520 --> 00:37:44,000
Speaker 1: don't increase it, I think partially is I don't think

725
00:37:44,040 --> 00:37:47,320
Speaker 1: congressmen feel a need to do it, and I don't

726
00:37:47,520 --> 00:37:52,239
Speaker 1: think that they think Congress will work any better if

727
00:37:52,320 --> 00:37:55,279
Speaker 1: you increase it. What do I think is the appropriate

728
00:37:55,320 --> 00:37:59,160
Speaker 1: size population? So I started looking at what other countries

729
00:37:59,280 --> 00:38:02,520
Speaker 1: have as far as population. We have about seven hundred

730
00:38:02,560 --> 00:38:06,239
Speaker 1: and sixty thousand people in every congressional district on average.

731
00:38:06,920 --> 00:38:10,600
Speaker 1: Germany has one hundred and sixteen thousand, England has one

732
00:38:10,719 --> 00:38:14,080
Speaker 1: hundred thousand, Japan has two hundred and seventy two thousand.

733
00:38:14,600 --> 00:38:19,680
Speaker 1: Most pure nations are between two to three hundred thousand.

734
00:38:20,000 --> 00:38:25,640
Speaker 1: So I started looking at historical precedences, the Qube rule,

735
00:38:26,040 --> 00:38:30,719
Speaker 1: what ideal representation would look like, and most most very

736
00:38:30,760 --> 00:38:34,000
Speaker 1: smart out analysis said between four hundred and eighty thousand

737
00:38:34,520 --> 00:38:40,560
Speaker 1: to six hundred thousand people per district. Now this is

738
00:38:40,960 --> 00:38:43,160
Speaker 1: purely an intellectual game. I don't know how you would

739
00:38:43,160 --> 00:38:46,799
Speaker 1: get to an actual number that would matter, but I

740
00:38:46,840 --> 00:38:49,239
Speaker 1: started looking also. The European Union with their body is

741
00:38:49,239 --> 00:38:50,960
Speaker 1: they have one hundred million more people than us, But

742
00:38:51,760 --> 00:38:56,200
Speaker 1: we're going to get there with immigration. So I had said,

743
00:38:56,280 --> 00:39:00,080
Speaker 1: let's I say a good number that would restore there

744
00:39:00,120 --> 00:39:04,839
Speaker 1: were more balance in Congress to representation representatives. Representatives per

745
00:39:04,880 --> 00:39:08,719
Speaker 1: people would probably be about seven hundred and twenty one, right,

746
00:39:08,800 --> 00:39:11,840
Speaker 1: it's about the European Union size with one extracy because

747
00:39:11,880 --> 00:39:16,400
Speaker 1: we can't have ties. And that would decrease the number

748
00:39:16,440 --> 00:39:19,439
Speaker 1: of people per district to the nineteen seventies level, about

749
00:39:19,480 --> 00:39:22,200
Speaker 1: four hundred and sixty thousand people per district. It would

750
00:39:22,239 --> 00:39:26,200
Speaker 1: just be much be much easier, I think for people

751
00:39:26,239 --> 00:39:29,600
Speaker 1: to interact with their representatives. It would cut the population

752
00:39:29,719 --> 00:39:33,320
Speaker 1: in half, and I think it would give representation better

753
00:39:33,400 --> 00:39:39,120
Speaker 1: representation to states that have you know, that are that

754
00:39:39,200 --> 00:39:43,120
Speaker 1: are diverse. But what happens when the population gap is

755
00:39:43,160 --> 00:39:46,040
Speaker 1: so high is that one city represents the entire states.

756
00:39:46,120 --> 00:39:48,760
Speaker 1: Like a state like Delaware, they have one congress person

757
00:39:49,920 --> 00:39:52,359
Speaker 1: who gets all their votes out of the northern part

758
00:39:52,360 --> 00:39:55,040
Speaker 1: of the state. It's all Democrat. If there was a

759
00:39:55,320 --> 00:39:57,440
Speaker 1: fair or if it was if they had if they

760
00:39:57,440 --> 00:39:59,480
Speaker 1: reduced number to four hundred and sixty thousand, there was

761
00:39:59,480 --> 00:40:02,120
Speaker 1: a fair map, they'd actually have a Republican seat and

762
00:40:02,120 --> 00:40:05,480
Speaker 1: a Democratic seat because southern Delaware is very Republican, but

763
00:40:05,520 --> 00:40:09,960
Speaker 1: they don't get any representation. Likewise, New Hampshire have Republican seats,

764
00:40:09,920 --> 00:40:12,720
Speaker 1: massachuse to have Republican seats. Texts would have more democratic

765
00:40:12,800 --> 00:40:16,040
Speaker 1: seats if everything was done, you know fairly, which I'm saying,

766
00:40:16,040 --> 00:40:17,920
Speaker 1: that's a big if. And I was looking at some

767
00:40:17,960 --> 00:40:20,959
Speaker 1: states like Delaware would go from one to two seats,

768
00:40:21,000 --> 00:40:23,880
Speaker 1: Connecticut would go from five to eight, Idaho will go

769
00:40:23,960 --> 00:40:26,799
Speaker 1: from two to four, Louisiana go from six to ten.

770
00:40:26,880 --> 00:40:30,120
Speaker 1: Texas would go from thirty to sixty three. It would

771
00:40:30,120 --> 00:40:32,000
Speaker 1: be a bigger size. It's a hard thing to wrap

772
00:40:32,040 --> 00:40:34,480
Speaker 1: your mind around, but I would say about seven hundred

773
00:40:34,520 --> 00:40:38,279
Speaker 1: and twenty one would be a good number. Anyway, it's

774
00:40:38,280 --> 00:40:41,640
Speaker 1: an interesting question. Okay, last question for the show. This

775
00:40:41,800 --> 00:40:45,160
Speaker 1: question comes from Ryan. He writes, I am a man.

776
00:40:45,719 --> 00:40:47,920
Speaker 1: I am a man. Oh okay, because I asked you.

777
00:40:48,040 --> 00:40:49,279
Speaker 1: He wrote to me last time. I said, I know

778
00:40:49,320 --> 00:40:50,719
Speaker 1: Brian is a man or a woman because there are

779
00:40:50,760 --> 00:40:53,120
Speaker 1: female Ryans, not many, but there are here. I am

780
00:40:53,160 --> 00:40:55,600
Speaker 1: a man. I know you read it on your pod

781
00:40:55,680 --> 00:40:59,160
Speaker 1: and you aren't sure my name is My name is Ryan. Anyway,

782
00:40:59,200 --> 00:41:02,640
Speaker 1: you brought up your your grandmother's meatball recipes. On the

783
00:41:02,719 --> 00:41:04,960
Speaker 1: last pot. I love making meat balls. What can you

784
00:41:04,960 --> 00:41:10,920
Speaker 1: share from your grandma's recipe? Okay, this is probably the

785
00:41:10,960 --> 00:41:13,800
Speaker 1: most important question that was ever asked. I am Italian,

786
00:41:13,840 --> 00:41:18,920
Speaker 1: I'm fifty percent Sicilian. Making sauce making meatballs is like

787
00:41:19,280 --> 00:41:22,200
Speaker 1: it's it's our I mean, the smell of garlic and

788
00:41:22,200 --> 00:41:26,880
Speaker 1: olive oil is like my ancestral legacy. So and I, oh,

789
00:41:26,960 --> 00:41:30,440
Speaker 1: you always have to make sauce with your meat balls.

790
00:41:30,480 --> 00:41:32,440
Speaker 1: I don't believe in just making meeples. I really, honestly,

791
00:41:32,440 --> 00:41:34,239
Speaker 1: I don't know how to do it. It's this weird thing

792
00:41:34,320 --> 00:41:38,480
Speaker 1: Italians have, where like I can cook very easily for

793
00:41:38,560 --> 00:41:41,200
Speaker 1: eighty five people, I do not know how to make

794
00:41:41,239 --> 00:41:43,200
Speaker 1: a meal for three people. Though it is, I am

795
00:41:43,280 --> 00:41:46,160
Speaker 1: dumbfounded at the concept of what porches actually look like.

796
00:41:46,400 --> 00:41:50,400
Speaker 1: It's everything is in bulk, like literally, Costco is just

797
00:41:50,520 --> 00:41:55,000
Speaker 1: Italian people's everyday life. So this is how you make

798
00:41:55,080 --> 00:41:59,080
Speaker 1: my grandmother's meatball and sauce recipe. It's not like we

799
00:41:59,120 --> 00:42:01,239
Speaker 1: don't know, we're not marking. It is what it is.

800
00:42:01,280 --> 00:42:03,319
Speaker 1: Every time. It's basically the same thing with a little

801
00:42:03,360 --> 00:42:06,880
Speaker 1: thing different. You're there. It's a very basic thing. But

802
00:42:06,960 --> 00:42:09,160
Speaker 1: I'll explain it to you since you asked and this

803
00:42:09,239 --> 00:42:11,440
Speaker 1: has asked me anything. So the first thing you want

804
00:42:11,480 --> 00:42:13,600
Speaker 1: to do. Get a big pot, and you want to

805
00:42:13,840 --> 00:42:16,879
Speaker 1: coat the bottom of the pot with a good olive oil.

806
00:42:17,200 --> 00:42:18,920
Speaker 1: Shouldn't be dripping. It shouldn't be like an inch of

807
00:42:18,960 --> 00:42:20,880
Speaker 1: olive oil. You should just coat the bottom of the

808
00:42:20,880 --> 00:42:24,520
Speaker 1: pan a good amount. I don't measure a lot of things.

809
00:42:24,520 --> 00:42:26,960
Speaker 1: So that's that. And then you need to put chopped

810
00:42:27,040 --> 00:42:30,560
Speaker 1: garlic on the bottom of this. Listen to me very carefully.

811
00:42:31,239 --> 00:42:35,080
Speaker 1: Do not burn the garlic. You brown the garlic. If

812
00:42:35,080 --> 00:42:37,239
Speaker 1: you burn the garlic, throw the whole thing away and

813
00:42:37,239 --> 00:42:42,080
Speaker 1: start over again. You cannot burn the garlic. Once the

814
00:42:42,080 --> 00:42:45,040
Speaker 1: garlic is brown. You add tomato paste. It's a small

815
00:42:45,040 --> 00:42:47,799
Speaker 1: little can you get from from the grocery store. It's

816
00:42:47,920 --> 00:42:50,600
Speaker 1: very thick. Put that in. Fill the can up with water,

817
00:42:50,719 --> 00:42:53,440
Speaker 1: throw that in. Mix it, get it nice and smooth,

818
00:42:54,880 --> 00:42:58,120
Speaker 1: and make sure that you know everything's even. Then after

819
00:42:58,200 --> 00:43:01,800
Speaker 1: tomato paste, you get tomato crush, tomatoes and tomato pury.

820
00:43:02,600 --> 00:43:05,960
Speaker 1: Those are much bigger cans. Take the tomato puy, put

821
00:43:06,000 --> 00:43:08,520
Speaker 1: water in that. Put that also in there, and you

822
00:43:08,560 --> 00:43:11,600
Speaker 1: want to put it on a low temperature I'm talking

823
00:43:11,840 --> 00:43:14,759
Speaker 1: like if you have electric stove low or warm. If

824
00:43:14,760 --> 00:43:17,840
Speaker 1: you have a gas stove, like the lowest flame possible,

825
00:43:18,320 --> 00:43:22,880
Speaker 1: you add some basal pinch of sugar, fur, the acidity, salt,

826
00:43:22,920 --> 00:43:25,560
Speaker 1: and pepper and heat that thing and let it go

827
00:43:25,680 --> 00:43:30,200
Speaker 1: stir occasionally. Then you go to your meat poles. I

828
00:43:30,360 --> 00:43:32,560
Speaker 1: just think a little differently than what my grandmother did.

829
00:43:32,600 --> 00:43:34,920
Speaker 1: Everyone has their own kind of specialty. What I have

830
00:43:35,120 --> 00:43:38,520
Speaker 1: found in making meatballs is this, you use a chop

831
00:43:38,640 --> 00:43:41,680
Speaker 1: meat that is very lean. So I do like a

832
00:43:41,760 --> 00:43:44,399
Speaker 1: ninety three seven or eighty twenty chop meat to chop

833
00:43:44,480 --> 00:43:47,400
Speaker 1: meat that is very very lean, very little fat. You

834
00:43:47,400 --> 00:43:48,640
Speaker 1: can go to a butcher, you can go to whole

835
00:43:48,680 --> 00:43:51,239
Speaker 1: foods find that, and then I take that and I

836
00:43:51,400 --> 00:43:54,799
Speaker 1: mix it with pork, usually from a sausage Like I'll

837
00:43:54,800 --> 00:43:57,040
Speaker 1: cut the sausage open, I'll take the skin of it off,

838
00:43:57,080 --> 00:43:59,480
Speaker 1: and I'll put the sausage in or like a ground

839
00:43:59,560 --> 00:44:02,520
Speaker 1: pork with like a healthy amount of fat. And people

840
00:44:02,520 --> 00:44:04,560
Speaker 1: say fifty to fifty, I do it seventy five beef

841
00:44:04,600 --> 00:44:06,520
Speaker 1: twenty five work. Whatever the case may be, it's we

842
00:44:06,640 --> 00:44:09,120
Speaker 1: usually do seventy beef twenty five percent pork for like

843
00:44:09,160 --> 00:44:12,879
Speaker 1: a pound pound and a half. The leanness of the

844
00:44:13,320 --> 00:44:16,360
Speaker 1: of the ground beef and the fat on the pork

845
00:44:16,520 --> 00:44:19,360
Speaker 1: are beautiful together like they are gorgeous. It's like a

846
00:44:19,400 --> 00:44:23,600
Speaker 1: big bowl. You put an egg, half a cup of regatza.

847
00:44:24,120 --> 00:44:28,040
Speaker 1: It is not pronounced rakatti is regatta. Put regatta in

848
00:44:28,160 --> 00:44:33,160
Speaker 1: the bowl. And then you put an insane amount of

849
00:44:33,560 --> 00:44:37,359
Speaker 1: peccorina romano cheese. Like you put an insane amount. They say,

850
00:44:37,480 --> 00:44:39,319
Speaker 1: put a put like a half a cup. No, put

851
00:44:39,320 --> 00:44:41,880
Speaker 1: a full cup. Put as much as you want, heavy

852
00:44:42,080 --> 00:44:46,359
Speaker 1: on the cheese. Then you do two tablespoons of salt,

853
00:44:46,360 --> 00:44:50,239
Speaker 1: tables of pepper, a chopped garlic. Again, half a cup

854
00:44:50,239 --> 00:44:52,399
Speaker 1: of Italian bread crumps. Some Italians make their own bread cumps.

855
00:44:52,400 --> 00:44:53,840
Speaker 1: I don't got time for that. Just put it, you know,

856
00:44:53,920 --> 00:44:57,360
Speaker 1: it's fine. Put regular bot bread crumbs, Italian bread crumbs

857
00:44:57,480 --> 00:45:00,840
Speaker 1: in the in the meat, and then parsley, like a

858
00:45:00,880 --> 00:45:02,879
Speaker 1: lot of pars like half a cup of parsley. Then

859
00:45:02,920 --> 00:45:05,920
Speaker 1: you throw your meat in. You mix it with your hands.

860
00:45:06,560 --> 00:45:08,839
Speaker 1: Wash your hands obviously beforehand. Mix it with your hands.

861
00:45:08,880 --> 00:45:11,560
Speaker 1: Your hands will be freezing from this whole process. You

862
00:45:11,560 --> 00:45:14,000
Speaker 1: will type to go out and like warm your hands

863
00:45:14,080 --> 00:45:16,840
Speaker 1: up from this entire thing because it is cold. Uh,

864
00:45:16,920 --> 00:45:20,200
Speaker 1: you mix that, you roll it. Here is where like

865
00:45:20,280 --> 00:45:22,760
Speaker 1: the magic is made. And this is a controversial opinion

866
00:45:22,760 --> 00:45:28,040
Speaker 1: depending on the person. You put the meat raw directly

867
00:45:28,080 --> 00:45:31,239
Speaker 1: in the sauce to cook it. The flavor of the

868
00:45:31,280 --> 00:45:34,840
Speaker 1: meat is absorbed in the sauce. You cook the sauce

869
00:45:34,880 --> 00:45:37,440
Speaker 1: for three hours so the meat will cook. It's not

870
00:45:37,520 --> 00:45:40,319
Speaker 1: gonna be raw after the entire thing is over. But

871
00:45:40,600 --> 00:45:44,239
Speaker 1: that is how you really marinate the meat and the

872
00:45:44,280 --> 00:45:47,400
Speaker 1: sauce together over that time. And it should literally take

873
00:45:47,440 --> 00:45:49,320
Speaker 1: about two and a half to three hours, no minimum

874
00:45:49,320 --> 00:45:50,680
Speaker 1: than that, because then it will be just it'd be

875
00:45:50,719 --> 00:45:52,600
Speaker 1: two tomato and it won't be good. Like. You got

876
00:45:52,600 --> 00:45:54,360
Speaker 1: to be thick like the like the sauce has to

877
00:45:54,400 --> 00:45:57,840
Speaker 1: be thick to a bite where it sticks onto the

878
00:45:57,840 --> 00:46:01,080
Speaker 1: past it sticks onto the meat. And you occasionally put

879
00:46:01,120 --> 00:46:02,719
Speaker 1: in some salt pepper if you need to, but you

880
00:46:02,719 --> 00:46:05,440
Speaker 1: don't have to have for that stir occasionally keep it going.

881
00:46:06,600 --> 00:46:10,520
Speaker 1: And that's how you make my Grandmo's meatball and saucers speak.

882
00:46:10,520 --> 00:46:12,879
Speaker 1: If you want to bake it beforehand, by the way,

883
00:46:13,040 --> 00:46:14,799
Speaker 1: put in for ten minutes the meat balls and then

884
00:46:14,800 --> 00:46:16,840
Speaker 1: put them in the sauce just to make a harder

885
00:46:16,880 --> 00:46:19,319
Speaker 1: outside so they don't completely fall apart. But if you

886
00:46:19,440 --> 00:46:21,160
Speaker 1: pack it right, it's not going to fall apart. It's

887
00:46:21,160 --> 00:46:23,000
Speaker 1: going to be delicious. As I said, two and a

888
00:46:23,040 --> 00:46:24,920
Speaker 1: half to three hours. It's how you cook it on

889
00:46:24,960 --> 00:46:28,000
Speaker 1: a low heat. If it's bubbling everywhere and exploding, the

890
00:46:28,040 --> 00:46:30,440
Speaker 1: heat's too hot, lower the heat and you will have

891
00:46:30,600 --> 00:46:34,320
Speaker 1: Sunday dinner. I make Italian Sunday dinner every single Sunday.

892
00:46:35,080 --> 00:46:38,239
Speaker 1: Catholicism and pasta on Sunday is the two things I

893
00:46:38,280 --> 00:46:41,839
Speaker 1: still have in the old country. So after all these generations,

894
00:46:41,920 --> 00:46:45,720
Speaker 1: I barely register as an Italian anymore. It's just sauce

895
00:46:45,760 --> 00:46:48,800
Speaker 1: and church, so and it comes on the same day. Anyway,

896
00:46:48,840 --> 00:46:50,640
Speaker 1: Thank you for listening this podcast. I hope you like this.

897
00:46:50,640 --> 00:46:52,440
Speaker 1: I hope you like the recipe. If you like this podcast,

898
00:46:52,480 --> 00:46:54,799
Speaker 1: please like and subscribe on the iHeartRadio app. Have a

899
00:46:54,840 --> 00:46:57,359
Speaker 1: podcast like on YouTube. Give me a five star review

900
00:46:57,360 --> 00:46:59,040
Speaker 1: if you're feeling generous, and I will talk to you

901
00:46:59,040 --> 00:47:00,359
Speaker 1: guys on Friday. Thanks