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Highlights of Curtis & Larry · Curtis Sliwa & Larry Mendte In The Morning

0:00
Speaker 1: Well, we're playing her today. We're down one player. Natalie
0:03
Speaker 1: Ivaca went through knee surgery yesterday. We've been talking about
0:07
Speaker 1: it over the last couple of weeks that this was
0:08
Speaker 1: going to happen. She went through it yesterday, and we
0:12
Speaker 1: we sent out a message earlier today to see if
0:14
Speaker 1: she'd be up and she could be on the show early.
0:17
Speaker 1: But you know, she just went through surgery, and so
0:20
Speaker 1: thank God that she is resting. We'll try to get
0:23
Speaker 1: her on somewhere through the show, but filling in today
0:28
Speaker 1: like just yesterday. The verbose Chris Switzer, I thought you
0:36
Speaker 1: might jump in. That's often how people describe me verbos. Yeah, yeah, yeah.
0:40
Speaker 1: I think he's going to make an extra effort to
0:42
Speaker 1: speak today. Curtis.
0:44
Speaker 2: Yeah, well, I remember yesterday he was making major progress.
0:48
Speaker 2: I've given him the nickname the Quiet Storm in honor
0:52
Speaker 2: of two of the greatest radio personalities of all time,
0:55
Speaker 2: Frankie Crocker and Vaughan Hopper. That was the name of
0:58
Speaker 2: this show at night on WBLS when it was the
1:02
Speaker 2: number one R and B station in the nation. As
1:05
Speaker 2: you know, he's on loan from the March Simone Show.
1:07
Speaker 2: I asked you ask Mark he knows all about radio.
1:10
Speaker 2: He'll tell you about these two guys. Yeah, well you
1:13
Speaker 2: all pumped up yesterday now with your new name, the
1:16
Speaker 2: Quiet Storm. See we're telling you you're quiet. And what
1:20
Speaker 2: does he do? As a typical gen Z got the reverse?
1:23
Speaker 2: I got a zag? Yeah, the reverse? You notice that.
1:26
Speaker 1: Well it's not let's just count the words. He said
1:30
Speaker 1: about a dozen words right now. You just said about
1:32
Speaker 1: seven hundred.
1:34
Speaker 2: So let's let's put you to the test here. Natalie
1:38
Speaker 2: obviously recovering from the knee surgery. But do you play
1:41
Speaker 2: power Ball? Because I don't. Larry obviously does, Natalie does,
1:45
Speaker 2: and probably most of the people who work at iHeart
1:48
Speaker 2: and some capacity do. It just seems everybody went, well,
1:52
Speaker 2: the big prize is a billion dollars, right, Yeah.
1:55
Speaker 1: I'm driving in a Jersey turnpike. I get I'm coming
1:58
Speaker 1: up close to the Lincoln and there it is on
2:01
Speaker 1: the sign. I check every day to see where the
2:04
Speaker 1: Powerball and the other one is and how much they
2:07
Speaker 1: are up for. And they said just one billion, one
2:10
Speaker 1: b it said. And I checked it out online and
2:12
Speaker 1: it is one billion dollars now if you take the
2:16
Speaker 1: cash option and not take the payments out over thirty years,
2:20
Speaker 1: you end up with four hundred and thirty three million.
2:23
Speaker 1: I mean, who can live on that? Seriously? So that's
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Speaker 1: four and thirty three million, and the chances of winning
2:30
Speaker 1: if you were to buy one ticket, like that's the
2:32
Speaker 1: way Natalie does it. She just buys one ticket for
2:35
Speaker 1: two dollars is one in two hundred and ninety two million,
2:40
Speaker 1: two hundred and one thousand, three hundred and thirty eight.
2:44
Speaker 1: And if you were to buy two tickets, you can
2:45
Speaker 1: cut that in half. It would only be one hundred
2:49
Speaker 1: and fifty million, So you get two tickets. But I
2:53
Speaker 1: play all the time, all right.
2:54
Speaker 2: Since you're a player, and most of our listeners probably are.
2:59
Speaker 2: This phenomenon and a power ball, it seemed to me
3:02
Speaker 2: first just came. Each state had its own lotto, and
3:05
Speaker 2: they always told you, oh, it's gonna raise money for education,
3:08
Speaker 2: public education.
3:09
Speaker 1: That's how they sold it lotto.
3:11
Speaker 2: I always wondered about that, how much of it actually
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Speaker 2: goes to public education and not to bureaucracy that it's
3:17
Speaker 2: paid to support this. But Powerball, if I understand it correctly,
3:21
Speaker 2: it's like one of these first lottos to spread their
3:24
Speaker 2: tentacles all over America, to get state after the state enrolled.
3:29
Speaker 2: That's the reason that the jackpot could keep soaring. So
3:33
Speaker 2: it's not just New Jersey, it's not just New York.
3:36
Speaker 2: It's probably dozens of states now that have signed on.
3:39
Speaker 2: So you got all of America like Powerball, right, and
3:43
Speaker 2: that's how they can have the huge jackpots. But and
3:46
Speaker 2: it's Mega millions too, and now Mega Millions. It's really
3:49
Speaker 2: funny because Mega Millions was falling behind Powerball, that's the
3:53
Speaker 2: competition those two and Mega Millions was falling behind, and
3:56
Speaker 2: they thought to themselves, oh, it's because our jackpots aren't
3:59
Speaker 2: big enough. So now to get a Mega Millions ticket,
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Speaker 2: it costs five dollars for each one instead of two dollars.
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Speaker 2: And now they get that, they usually get the bigger jackpots.
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Speaker 2: So this is pretty exciting. There's a billion dollars. When
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Speaker 2: I walk into a convenience store, grocery store, candy store,
4:17
Speaker 2: whatever you want to call it, bodega's, and I see
4:20
Speaker 2: that the line for people just buying the basics milk
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Speaker 2: and bread is almost like one or two people. And
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Speaker 2: then the special machine that they get hooked up from
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Speaker 2: the state, it's like twelve, fifteen, twenty people.
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Speaker 1: That happened already, because it's funny. I went yesterday and
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Speaker 1: I was expecting to see that. I went to Wah
4:37
Speaker 1: Wah Wah, and I was expecting to see that, and
4:40
Speaker 1: I got right on the machine. There wasn't a big line,
4:42
Speaker 1: but I remember in the past they'd be huge lines
4:47
Speaker 1: to wait to get the power ball. I think people
4:49
Speaker 1: are getting used to these now, it's like and also
4:52
Speaker 1: you can buy them online, which you couldn't do before,
4:54
Speaker 1: so it's going to be fascinating. I mean, how high
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Speaker 1: will this go?
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Speaker 2: Which are the offs here? Because what I see is
5:02
Speaker 2: people standing around with like a dime in their hand,
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Speaker 2: just scratching off, scratching off, scratch. They seem to have
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Speaker 2: a formula in mind. They seem to have a relationship
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Speaker 2: with the person they're buying it from, whether it's a
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Speaker 2: regular newsstand, candy store, bodega, grocery store, you know, where
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Speaker 2: they wish one another luck, you know, and always the
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Speaker 2: store points out how many winners they've had in the past.
5:27
Speaker 2: It always like to say that, yeah, you want to
5:29
Speaker 2: buy here because look at all the winners we've had.
5:31
Speaker 2: Like there's this guy in North Carolina who was playing
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Speaker 2: their local lottery there again, it was supposedly set up
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Speaker 2: to help education.
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Speaker 1: He won the second time. It's in the New York Post.
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Speaker 2: The second time in two years he won one hundred
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Speaker 2: thousand dollars on the scratch off. He walks home with
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Speaker 2: seventy two thousand after taxes. And yet it was two
5:53
Speaker 2: years ago he won the two million dollar prize in
5:56
Speaker 2: North Carolina and walked away with eight hundred and fifty eight.
6:00
Speaker 2: Now that's a big chunk out of that. That's more
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Speaker 2: than fifty percent they took in taxis. How do you
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Speaker 2: end up becoming a multiple winner? I always wondered, if
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Speaker 2: they're not manipulating this to get more in money in
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Speaker 2: the pop more and more money in the pot.
6:14
Speaker 1: Well I don't. I think that they have enough money.
6:17
Speaker 1: They don't have to manipulate it to get the more money.
6:19
Speaker 1: I think that they're will be look at they have
6:22
Speaker 1: the golden goose right now. Why are they gonna screw
6:24
Speaker 1: with it? They're not gonna screw with this. It's amazing.
6:26
Speaker 1: I'm looking at the I'm looking at the top ten
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Speaker 1: powerball jackpots and Mega Million's jackpots. This billion dollars doesn't
6:34
Speaker 1: make the top ten, doesn't make the top ten. No, no,
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Speaker 1: we're the biggest was over two billion. That was back
6:41
Speaker 1: in twenty twenty two. And of course somebody in California
6:44
Speaker 1: won of the top ten. California's won three. Nobody from
6:50
Speaker 1: this area has ever won one.
6:51
Speaker 2: Which means we're due. Yeah, you keep thinking that. You
6:56
Speaker 2: keep thinking that, Larry as you line up to get
6:59
Speaker 2: now not only your Power Bowl ticket, but also what
7:02
Speaker 2: your Mega million ticket?
7:04
Speaker 1: Right? No, I don't get Mega million. It's five bucks
7:06
Speaker 1: now and get it.
7:08
Speaker 2: And there's the local lotto, all the different games they have,
7:11
Speaker 2: the state lotto.
7:12
Speaker 1: Yeah.
7:13
Speaker 2: Then for a while there they had something called Kino
7:15
Speaker 2: or something like that, where you could walk into a
7:17
Speaker 2: gin mill and like every half hour there was a
7:21
Speaker 2: new drawing, just like being addicted to crack.
7:25
Speaker 1: Yeah. It's funny though, because people say to me, why
7:27
Speaker 1: are you playing power Ball? You're just throwing away your money.
7:30
Speaker 1: I got, like, it's two bucks. It's two bucks for
7:33
Speaker 1: a chance at a billion dollars. I'll take that. I'll
7:36
Speaker 1: put two dollars in for a billion dollars. Of course
7:38
Speaker 1: I spend more than that. But Natalie's really good about it.
7:42
Speaker 1: She she only spends two dollars that's one ticket. She's disciplined.
7:46
Speaker 1: One ticket. I'm not said, and we have.
7:48
Speaker 2: Not encouraged Chris to get on board, have or speak.
7:52
Speaker 1: And as a matter of fact, here he goes wait
7:54
Speaker 1: a second way to say he's going to talk. I played,
7:57
Speaker 1: I played on on Saturday, I played, and I just
8:00
Speaker 1: I'll play tonight. You are, yeah, how much are you
8:03
Speaker 1: gonna spend? I'll buy a couple of tickets, not a
8:06
Speaker 1: lot like two or three. Nobody ever admits to how
8:09
Speaker 1: many power ballsy how did you buy one? Two? It's ten? No,
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Speaker 1: I ten, but come away with forty hours. I got
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Speaker 1: a ticket, you know.
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Speaker 2: And I'm trying to figure this out with so many
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Speaker 2: tickets sold.
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Speaker 1: It's not like you have a formula, like.
8:27
Speaker 2: You know you're going to the horse track and y
8:30
Speaker 2: you know you have the racing form and you figure
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Speaker 2: out stats and past races. But there are guys who
8:35
Speaker 2: have been online who tell me they have a formula,
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Speaker 2: what time to arrive, what to ask for when to
8:41
Speaker 2: ask for it. If there's a certain guy behind the
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Speaker 2: counter that they don't know, not to buy the ticket.
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Speaker 2: Ten They got this whole thing worked out. And I'm saying,
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Speaker 2: you spend most of your day thinking about this. You
8:52
Speaker 2: see you damn right.
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Speaker 1: You know, maybe there is a way. I don't think
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Speaker 1: there's any possible way they have that's set up, but
8:58
Speaker 1: maybe some of the people are listening right now that
9:01
Speaker 1: play power Ball or play Mega Millions do have a formula.
9:04
Speaker 1: So go to the iHeartRadio app, leave us a talkback.
9:07
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9:09
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Speaker 1: best talkback of the week takes on the Sea Crane
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Speaker 1: Radio for the listener who demands better Sea Crane radios
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Speaker 1: with legendary reception. And my feeling is, and you said
9:34
Speaker 1: this too, I gotta feel in a lot of people
9:36
Speaker 1: that listen and play the power Ball.
9:37
Speaker 2: Oh there's no doubt. They may not want to admit it,
9:40
Speaker 2: but I know they do.
9:41
Speaker 1: Now.
9:41
Speaker 2: The other thing that I've noticed from our broadcast, because
9:44
Speaker 2: we're the only show that talks about AI, artificial intelligence,
9:47
Speaker 2: We're going to give you an update on that at
9:49
Speaker 2: the end of this shower. But I had a few
9:52
Speaker 2: people come up to me yesterday as I was jumping
9:55
Speaker 2: from the streets the subway, saying, fluck the flock, fluck.
10:01
Speaker 2: We're the only ones talking about these flock cameras. Nobody else,
10:05
Speaker 2: you know, they all talk about the same thing. Gives
10:07
Speaker 2: me a headache, I'm sorry. Talk radio, for the most part,
10:11
Speaker 2: is an echo chamber. The same subjects over and over.
10:15
Speaker 2: To me, the two most important issues that affect people,
10:18
Speaker 2: no matter what your ideology, no matter what way you live,
10:22
Speaker 2: rural areas, suburban areas, urban areas, AI, It's going to
10:26
Speaker 2: impact everybody in a positive and negative or maybe not
10:29
Speaker 2: at all. And of course, these flock cameras, as you
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Speaker 2: pointed out Larry the other day, they were everywhere. Imagine
10:36
Speaker 2: I have people coming up to you fluck the flock.
10:40
Speaker 2: And at first I'm wondering, you know, because my hearing
10:42
Speaker 2: is not that good. Are they cursing me out like
10:44
Speaker 2: some people do, you know? F usely, well, you know
10:47
Speaker 2: you should have dropped out. But no, they've developed this line,
10:51
Speaker 2: fluck the flock, And they said it was listening to
10:54
Speaker 2: you and Larry, you were describing it it's just the
10:57
Speaker 2: conversation I was having with my wife or my f
11:00
Speaker 2: and it's like, these are the issues that really impact people.
11:03
Speaker 2: Some of these other talk shows. The phet tax, well,
11:07
Speaker 2: we'll be talking about that, but that really doesn't impact
11:09
Speaker 2: that many people in New York City would impact thirteen
11:12
Speaker 2: thousand when implemented.
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Speaker 1: It's funny you said that, because I heard from a
11:15
Speaker 1: couple of people that sent emails that they didn't know
11:18
Speaker 1: at all that this was happening, and they didn't know
11:21
Speaker 1: it was so pervasive. It's in every single state. It's
11:24
Speaker 1: and there's thousands of them in New Jersey and New York.
11:28
Speaker 1: And in case you weren't listening, just to set you
11:31
Speaker 1: up on what they are, they are surveillance cameras run
11:34
Speaker 1: by a private company that take your license plates, put
11:37
Speaker 1: them through AI and they get all this information and
11:39
Speaker 1: they keep a record of where you've been. Now it's
11:44
Speaker 1: we can talk more about it, maybe we'll talk to
11:46
Speaker 1: you this morning. By the way, it is, but it's
11:48
Speaker 1: also saved. It's solved a million cases.
11:51
Speaker 2: This is going to disappoint you. Warwell was a socialist
11:56
Speaker 2: animal farm.
11:58
Speaker 1: You want to go back to. I've got a whole
12:01
Speaker 1: lot of research out your arm. I'm ready for you now.
12:04
Speaker 2: I'm just telling you you pointed out, I'm signed right.
12:08
Speaker 1: That's for sure. All right, let's get your talk facts
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12:47
Speaker 1: Curtis Leewa and Larry Minty in the Morning Guaranteed Human
12:51
Speaker 1: on seven o R Well, one of the most important
12:56
Speaker 1: primary days of the season just pasted us. And the
13:02
Speaker 1: star they race that got the most attention was in Wisconsin,
13:08
Speaker 1: and it got the most attention because there was a well,
13:12
Speaker 1: you know, I can say it now. I thought she
13:14
Speaker 1: was completely a lunatic, this assembly woman Francesca Hong, who
13:19
Speaker 1: became a national story just because of things she said
13:23
Speaker 1: in the past. She doesn't like the Fourth of July,
13:25
Speaker 1: she doesn't like Thanksgiving. She wants to get rid of
13:27
Speaker 1: the US Senate. Over and over again, she was espousing
13:32
Speaker 1: the DSA line, but she was winning constantly in the
13:36
Speaker 1: polls I had, And everybody thought, oh my god, this
13:40
Speaker 1: is going to be the death of the Democratic Party
13:42
Speaker 1: if they keep electing all of these unelectable socialists all
13:46
Speaker 1: across the country, especially in a purple state like Wisconsin.
13:51
Speaker 1: But the Democratic Party rallied and the Republican Party was
13:56
Speaker 1: actually supporting Hong. They wanted her to win because that
14:00
Speaker 1: would be a great pickup right there if she were
14:03
Speaker 1: to win, because the Republicans would definitely win that seat.
14:06
Speaker 1: But the Democrats were smart and they really got behind
14:09
Speaker 1: David Crowley, the Milwaukee County executive, who had dropped out
14:13
Speaker 1: of the race but got back in it after some
14:15
Speaker 1: of the other candidates had problems, and in the very end,
14:20
Speaker 1: he won by point four percent of the vote. Now
14:26
Speaker 1: there could be a recount in this race. There might
14:28
Speaker 1: be a recount because she can't but she has to
14:31
Speaker 1: request it. If she requested it, she also has to
14:35
Speaker 1: pay for it. And the problem is she didn't have
14:39
Speaker 1: any money. Crowley had all the money in this race.
14:43
Speaker 1: They flooded the airwaves and at the very end they
14:47
Speaker 1: were able to maybe save the Democratic Party in Wisconsin
14:53
Speaker 1: because this would have been a disaster. I'll tell you what,
14:57
Speaker 1: Curtis I was rooting for. I was hoping so much
15:03
Speaker 1: she was going to win because the Republicans would have
15:06
Speaker 1: made her the most famous person in America.
15:09
Speaker 2: Well, the Republicans loved her. In the end, they had
15:12
Speaker 2: a super pack. They poured two point two million dollars
15:16
Speaker 2: only into two areas in the Madison area in Milwaukee
15:20
Speaker 2: area because they knew that's where most of the progressive
15:23
Speaker 2: votes were. And their ad was a vote for Hung
15:27
Speaker 2: is a vote for socialism. She is too liberal. So
15:32
Speaker 2: naturally if you happen to lean in that direction, progressive
15:35
Speaker 2: of socialism, and I'll explain in a moment in Wisconsin
15:38
Speaker 2: is really where socialism in America was developed. That's going
15:42
Speaker 2: to aggravate you and make you want to get out
15:44
Speaker 2: there and vote against the African American. As you had
15:47
Speaker 2: mentioned Crowley, who is the county executive of Milwaukee County.
15:52
Speaker 2: Great story, I might add, actually, great story. Hung has
15:55
Speaker 2: a great story. He had a great story. They didn't
15:58
Speaker 2: have an easy life growing up. But as you pointed out,
16:03
Speaker 2: in the end, it's like point four percent differential. And
16:08
Speaker 2: the last wave there were twenty eight thousand absentee ballots
16:12
Speaker 2: from the city of Milwaukee that were delayed the counting
16:16
Speaker 2: because of human era. Now, i'd have to tell you,
16:19
Speaker 2: having spent a lot of time in Milwaukee, I believe
16:21
Speaker 2: that would have favored Crowley because the north side is black,
16:26
Speaker 2: the south side is Hispanic, and all the white progressives
16:29
Speaker 2: lived downtown along Lake Michigan, you know where Marquette University is,
16:33
Speaker 2: where all the government buildings are and the cor corporation buildings.
16:37
Speaker 2: So I think he would have won it anyway. But
16:41
Speaker 2: it's only point four percent, And you're right. She can
16:45
Speaker 2: actually get a recount if she's willing to put up
16:48
Speaker 2: three million dollars, which she doesn't have. And remember this
16:52
Speaker 2: goes back to twenty sixteen. Remember how close it was
16:56
Speaker 2: with Hillary where she loses the state, She loses Michigan,
17:01
Speaker 2: she loses Pennsylvania by the bearer's minimum, and Wisconsin, and
17:06
Speaker 2: she wants recounts, recounts. But the Green Party candidate Jill Stein,
17:11
Speaker 2: who only got one percent, she was sort of the
17:13
Speaker 2: Ralph Nader in that race. She got the state to
17:16
Speaker 2: pay for the recount. It was all for nought because
17:20
Speaker 2: Trump won Wisconsin anyway. He actually got more votes in
17:23
Speaker 2: the recount. But then remember Trump in twenty twenty wanted
17:26
Speaker 2: a partial recount of Wisconsin because he knew some areas
17:30
Speaker 2: if they recount, it might not have favored him. So
17:32
Speaker 2: he had a partial recount. So Wisconsin is a state
17:35
Speaker 2: that's used to do it recounts. They've changed the rules though.
17:39
Speaker 2: You want to recount, you got to put up the money,
17:42
Speaker 2: and you're right, she's down with like four hundred thousand dollars.
17:45
Speaker 2: She would have to have a special fundraiser and do
17:48
Speaker 2: it immediately. And I think she said yesterday just before
17:53
Speaker 2: her supporters went home, they hadn't yet known what the
17:55
Speaker 2: results were. Whoever wins, you know, we're gonna bridge the divide.
17:59
Speaker 2: We're gonna for the Democratic candidate. So I think in
18:02
Speaker 2: her own mind she kind of recognized if I lose,
18:06
Speaker 2: I'm not going to challenge it, although I would have
18:08
Speaker 2: been urging her to challenge it.
18:10
Speaker 1: Are you kidding? A point four percent? How close that is?
18:14
Speaker 1: The Democrats don't want her, They don't want her so
18:18
Speaker 1: she is going in an uphill battle. Anyway, this might
18:21
Speaker 1: be the best thing that ever happened to her, because
18:24
Speaker 1: she was going to get almost no support except for
18:28
Speaker 1: this small but growing gang of Democratic Socialists. That's it.
18:32
Speaker 1: The Democrats saw that she was death and she, by
18:36
Speaker 1: the way, was an awful candidate. Awful. The only thing
18:41
Speaker 1: that got her support was DSA. The only thing that
18:45
Speaker 1: got her support was craziness. And the young. Oh the
18:48
Speaker 1: divide in the young and the and those over forty,
18:52
Speaker 1: Oh my god. In the state, they've already starting to
18:54
Speaker 1: break it down. It was. It was incredible. It was
18:58
Speaker 1: like sixty five percent of Demo under a certain age
19:03
Speaker 1: voted for her. So, look, this is going to be
19:06
Speaker 1: playing out across the country, and every time it does,
19:10
Speaker 1: the Democrats have figured it out. They're gonna squash this.
19:13
Speaker 2: Well, there's a little difference here. Let's look at the
19:16
Speaker 2: recent barely winning the Michigan primary where there was no recount.
19:22
Speaker 2: I thought there would have been a recount. They were
19:24
Speaker 2: talking like less than one percent between el Sayid and
19:29
Speaker 2: the congresswoman. But apparently the rules are no recount in primaries,
19:34
Speaker 2: but recount in the general election. But in this case,
19:38
Speaker 2: unlike L said, Bernie Sanders held rallies for L. Sayed,
19:43
Speaker 2: AOC came in and stumped for L.
19:45
Speaker 1: Sayed.
19:46
Speaker 2: When I looked at the totals for hang legitimately DSA.
19:51
Speaker 2: Can you remember how L. Sayed was saying, I'm not DSSA,
19:54
Speaker 2: I'm a capitalist, I'm not association she remember how you
19:57
Speaker 2: was spinning that she's I'm loyal to the DSA. Her
20:02
Speaker 2: god is Bernie Sanders, her goddess is AOC. Do you
20:05
Speaker 2: know they would not endorse her because she's an awful candidate. Well,
20:08
Speaker 2: she was ahead of the polls, so I'm thinking it's
20:10
Speaker 2: a little bit more. I'm not sure they had conversations.
20:14
Speaker 2: Maybe there's something a little often, there was nothing wrong
20:17
Speaker 2: in terms of improprieties. Just to give you an idea,
20:20
Speaker 2: I think people were trying to figure out where did
20:22
Speaker 2: this woman come from? She was an assembly woman from Madison,
20:25
Speaker 2: well known as a chef. But the reason that she
20:29
Speaker 2: elevated herself for three things. The first thing is she
20:32
Speaker 2: went to all the rural areas where everybody else stayed
20:35
Speaker 2: away from. This was the Chuck Schumer philosophy that beat
20:38
Speaker 2: Al Demoto for the US Senate When Schumer was the
20:40
Speaker 2: congressman and de Motto was the sitting Senator. He didn't
20:43
Speaker 2: go upstate. She went wherever they were plowing in fertilizer.
20:48
Speaker 2: Number two. She was the only candidate of any party
20:51
Speaker 2: against the AI data filing Senate. So you know where
20:55
Speaker 2: Eric Adams was recently in that little town where they
20:58
Speaker 2: booed him. They welcomed her because she was opposed to that.
21:01
Speaker 2: And then the third thing is boy luck be a
21:04
Speaker 2: lady named ha. She gets into a race where you
21:08
Speaker 2: have the governor who has decided I'm not running for
21:10
Speaker 2: a third term. He's a popular government governor, a Democrat.
21:14
Speaker 2: The lieutenant governor is running a Hispanic would have been
21:18
Speaker 2: the first Hispanic governor.
21:20
Speaker 1: She has to drop out.
21:21
Speaker 2: It's a Democratic candidate because guess what, a million dollars
21:25
Speaker 2: was stolen from her campaign and she blamed the campaign manager.
21:28
Speaker 1: Always blame the campaign manager. She drops out the next day.
21:31
Speaker 1: Then the guy was.
21:32
Speaker 2: The former lieutenant governor who lost by one percent to
21:36
Speaker 2: Ron Johnson. Remember that was a really close race. This
21:39
Speaker 2: guy ran a great race. Mendela Barnes. I'll never forget
21:42
Speaker 2: that name, Mandela Barnes. You know why he dropped out,
21:45
Speaker 2: he would have won. The Democrats were investigating charges of
21:50
Speaker 2: sexual harassments. Again, saw Peter for all the guys out there,
21:55
Speaker 2: So now it's her. And then that's what prompted Crowley,
21:58
Speaker 2: who had dropped out, to get back in the race
22:01
Speaker 2: because the governor Tony Evers clearly did not want a
22:03
Speaker 2: socialist and said I'll help you raise money and he did.
22:07
Speaker 1: And going forward they might be able to win this
22:09
Speaker 1: seat now. And that's what it was all about. And
22:12
Speaker 1: that's why Bernie Sanders didn't go and support her. That's
22:16
Speaker 1: why AOC didn't go to support her in the very end.
22:20
Speaker 1: In the matter, if you're a socialist and not a socialist,
22:23
Speaker 1: it's about winning and these races right now because this
22:27
Speaker 1: is going to be close. It's going to be close
22:29
Speaker 1: across the country because the Democrats can't get out of
22:33
Speaker 1: their own way with this socialism thing. It's always going
22:37
Speaker 1: to be about winning. And so when you look at
22:40
Speaker 1: it and you think to yourself, well, why is this
22:42
Speaker 1: person that report not supporting that person because they know
22:45
Speaker 1: they can lose. It's all about winning and losing. That's
22:47
Speaker 1: why everybody was supporting Platner because they thought he could win.
22:51
Speaker 1: It didn't really matter, all of the skeletons in his closet.
22:54
Speaker 1: They thought he could win this hung They thought she
22:58
Speaker 1: was going to lose.
22:58
Speaker 2: You want to do a deeper dive on that, because
23:01
Speaker 2: she wasn't ahead in the polls. As you pointed out,
23:03
Speaker 2: the polls were wrong all night long. You're gonna get
23:05
Speaker 2: into all the races you know in Minnesota, South Carolina,
23:09
Speaker 2: obviously Wisconsin, but they were wrong on every every level.
23:13
Speaker 2: But that's where socialism started in America, was Wisconsin. They
23:18
Speaker 2: had the first socialist congress person. They ran as socialists.
23:22
Speaker 2: They didn't have like DSA or anything like no.
23:24
Speaker 1: Way back in the past. It ain't that anymore. It's
23:26
Speaker 1: not that anymore.
23:27
Speaker 2: Had three mayors in Milwaukee. There were socialists. There are
23:31
Speaker 2: a lot of There are a lot of DSSA people there.
23:34
Speaker 2: I'm telling you, have you.
23:35
Speaker 1: Been to Wisconsin, I've been there a lot.
23:37
Speaker 2: I I mean German concentration, Polish concentration. I spent a
23:45
Speaker 2: long time in Milwaukee organizing Guardian Angels. But I'll tell
23:49
Speaker 2: you this much, I believe that there was something that
23:54
Speaker 2: personally didn't click between her. Bernie Sanders, the godfather of
23:59
Speaker 2: the DSSA, and the princess who maybe crowned the future
24:03
Speaker 2: presidential candidate of the DSSA AOC so I'm gonna I'm
24:06
Speaker 2: gonna do a deeptail.
24:08
Speaker 1: No, it didn't Colick the same thing that didn't click
24:10
Speaker 1: with the voter. She wasn't a good candidate. She was
24:12
Speaker 1: an awful, awful candidate.
24:14
Speaker 2: Thanksgiving, I would have suggested that was too much of
24:20
Speaker 2: a flip. You're opposed to Thanksgiving because it promotes colonialism,
24:24
Speaker 2: and then all of a sudden you come back and
24:26
Speaker 2: you say a pivot and shift. Well, that's my favorite holiday.
24:29
Speaker 2: In fact, that's why I got an opportunity to check out,
24:33
Speaker 2: a test out all my culinary delights.
24:35
Speaker 1: As a speaking of the polls, the internal polls must
24:38
Speaker 1: have shown that was a big mistake because she was
24:40
Speaker 1: asked going into the voting booth, what's the one thing
24:43
Speaker 1: that you want to want miss Wisconsinites to know before
24:47
Speaker 1: they vote? She said, I love Thanksgiving?
24:49
Speaker 2: Thanksgiving? Can we have in fact, what is that the
24:53
Speaker 2: bone you take out? The wishing bone? I'm missing it here?
24:56
Speaker 2: You know where you break the breastbone to see if
24:59
Speaker 2: you get She probably would have went there, you want
25:02
Speaker 2: to break the Wishbone here see if I get luck
25:05
Speaker 2: of his So she launched by zero point four percent.
25:08
Speaker 1: And Concurdious and Larry Minty in the morning on sevent ten,
25:14
Speaker 1: WR the Voice of New York talking about it a
25:18
Speaker 1: lot on this show about a massive national controversy. They
25:24
Speaker 1: just had hearings in the Senate in Connecticut over this
25:29
Speaker 1: over flock safety automated license plate readers, so a lot
25:33
Speaker 1: of people just call them flock cameras. There's a controversy
25:37
Speaker 1: over mass surveillance, over civil liberties, over data privacy, and
25:42
Speaker 1: so the perfect guy to talk to about all of
25:44
Speaker 1: this is on the phone right now, rich DeMuro rich
25:47
Speaker 1: on Tech has heard Sunday nights from eight to eleven
25:49
Speaker 1: pm on WOR and you can talk to him at
25:53
Speaker 1: rich on Tech on Instagram. Rich, this flock controversy, you know,
25:59
Speaker 1: has blown up now and there's over well over ten
26:04
Speaker 1: thousand of these across the country that'd been used for
26:07
Speaker 1: a while. The controversy is over mass surveillance and what
26:11
Speaker 1: they're doing with your data. And I know that there's
26:15
Speaker 1: been a lot of problems in some local police departments
26:18
Speaker 1: where the police officers themselves have used them for personal reasons.
26:23
Speaker 1: What do you think, first of all, tell us how
26:24
Speaker 1: these work.
26:26
Speaker 3: So it's interesting. I had to look in my notes
26:28
Speaker 3: to see the first time I did a story on
26:30
Speaker 3: these cameras, it was back in twenty nineteen. We went
26:33
Speaker 3: to a neighborhood and they were so excited to install these.
26:36
Speaker 3: And back in the day when these first started, basically,
26:39
Speaker 3: if you were a homeowner's association or a neighborhood, you
26:42
Speaker 3: would pay, you would gather everyone together pay for these cameras,
26:45
Speaker 3: pay for the maintenance, and you would install them at
26:48
Speaker 3: the opening of your street, at the two ends of
26:50
Speaker 3: basically the opening and closing of your street, so that
26:52
Speaker 3: every license play, every car that would come into your neighborhood,
26:55
Speaker 3: you had a log, and so if there's ever a
26:57
Speaker 3: crime in your neighborhood, is there ever a problem, you
27:00
Speaker 3: could just go into that log and you can find
27:02
Speaker 3: the car. You can actually slice and dice a lot
27:04
Speaker 3: of different ways, but one of them is, hey, show
27:06
Speaker 3: me all the cars that came into this neighborhood this
27:08
Speaker 3: week that don't typically come into this neighborhood, and then
27:11
Speaker 3: you can narrow down from there. Since then, obviously the
27:13
Speaker 3: popularity of these cameras have exploded. And I think the
27:17
Speaker 3: really interesting aspect to what flock is doing is the
27:20
Speaker 3: kind of network they are building. So when you have
27:23
Speaker 3: a camera system in you know, somewhere in Chicago, or
27:26
Speaker 3: somewhere in Florida, or somewhere in Minneapolis wherever, you can
27:31
Speaker 3: potentially link all of those together if you decide to
27:34
Speaker 3: share your data, and that's what makes this giant database
27:37
Speaker 3: that then people can tap into and say, let me
27:39
Speaker 3: find out where this license plate has gone in the
27:41
Speaker 3: past week, And according to all these different cameras in
27:45
Speaker 3: different places, you may be able to track that.
27:47
Speaker 2: Oh so maybe your boyfriend, girlfriend, wife a hushman. They
27:53
Speaker 2: could tap into that too, if they're a little suspicious
27:56
Speaker 2: of their mate.
27:59
Speaker 3: I mean theore because these things are typically run by
28:02
Speaker 3: homeowner association. So yeah, probably if you wanted to see
28:06
Speaker 3: what's going on. I mean, look, we have cameras, are
28:08
Speaker 3: not new. We have so many cameras in cities all
28:12
Speaker 3: across the world. New York City has a lot of cameras.
28:14
Speaker 3: The difference here, I think is that the cities run
28:18
Speaker 3: purpose built systems for what they need, right like the
28:22
Speaker 3: LAPD or the NYPD or whatever department they have cameras
28:26
Speaker 3: for their specific needs. Right, we want to cover this area,
28:29
Speaker 3: we want to watch this with flock. It's a private
28:32
Speaker 3: company that is putting cameras in sometimes private or public places.
28:37
Speaker 3: But that network effect of hey, we could theoretically link
28:41
Speaker 3: up all of these cameras across the nation for one
28:43
Speaker 3: giant mass surveillance. I think that's the aspect people have
28:46
Speaker 3: an issue with. We've got ring cameras on doorbells across
28:49
Speaker 3: the nation right now. There's not one major database that
28:53
Speaker 3: you can just say, let me type something into this
28:55
Speaker 3: database and check every ring camera in America. Is that possible? Sure?
28:59
Speaker 3: Is it happening right now?
29:00
Speaker 1: No? And with the advent of AI, I think that's
29:04
Speaker 1: what the concern is, how quickly they can get so
29:07
Speaker 1: much information on you and then share it. And I
29:10
Speaker 1: think a lot of the municipalities say, Okay, we're not
29:13
Speaker 1: going to share it with the federal government. We're just
29:14
Speaker 1: going to share it with the states that are around us.
29:17
Speaker 1: I'm not sure that's enough, But you know what, Rick,
29:20
Speaker 1: I'm torn on this a little bit because ten thousand
29:23
Speaker 1: missing children have been found by these things, and they
29:26
Speaker 1: say the twenty five percent of the crimes these flock
29:30
Speaker 1: cameras have helped law enforcement crack the cases, so I
29:34
Speaker 1: would think there has to be some kind of in
29:36
Speaker 1: between here. Oh absolutely.
29:39
Speaker 3: I mean, look, these cameras, they solve crimes, they reunite
29:43
Speaker 3: people with the ring cameras, they reunite pets with their owners.
29:46
Speaker 3: I think they're doing last I checked like an average
29:48
Speaker 3: of one pet a day. I mean, these are real
29:51
Speaker 3: impacts on people. And the other aspect is, hey, look,
29:53
Speaker 3: if you're not doing anything wrong, do you really care?
29:55
Speaker 3: It's not a big deal. You're finding bad guys. But
29:57
Speaker 3: you know, do we want to live in a society
30:00
Speaker 3: where if something happened and they say, let's put out
30:02
Speaker 3: an APB for rich Dmuro, you can find me in
30:04
Speaker 3: twenty seconds because every single camera in the world can
30:08
Speaker 3: use AI to search for my face. Do we want
30:10
Speaker 3: that reality in our world in our America? I'm not sure.
30:13
Speaker 3: Do we want to solve crimes? Do you want to
30:15
Speaker 3: get bad guys behind bars? Do we want to catch
30:16
Speaker 3: people that do think bad things?
30:18
Speaker 1: Of course?
30:19
Speaker 3: But where does it end? And I think with AI
30:22
Speaker 3: it opens up a lot of questions because it can
30:24
Speaker 3: be instantaneous on how fast you could track someone anywhere
30:29
Speaker 3: in the States where you might have cameras that are
30:31
Speaker 3: linked together.
30:32
Speaker 2: Well, Rich DeMuro sounds very O'wellian, sounds just like Red
30:37
Speaker 2: China to.
30:37
Speaker 1: Me, fluck the flock. But let's move to AI.
30:42
Speaker 2: Steve Hilton is the Republican candidate for governor. He knows
30:47
Speaker 2: about technology. His wife used to be the communications chief
30:51
Speaker 2: of Google and full Facebook. He carries a flip phone
30:55
Speaker 2: because he says, I don't need all the clutter. Just
30:58
Speaker 2: calls and texts, that's it. But he said that Open AI,
31:02
Speaker 2: sam Oltman and the Anthropic CEO are spreading doom and
31:07
Speaker 2: gloom and that's why there's so much negative feedback towards AI.
31:12
Speaker 2: And I'm saying, Rich, isn't this because these are the
31:16
Speaker 2: two guys in the lead of the race and developing
31:18
Speaker 2: AI with the billions that are pouring in, and this
31:22
Speaker 2: doom and gloom statements that they keep mating making will
31:25
Speaker 2: actually force the Senate and government to input regulations while
31:30
Speaker 2: they're ahead, which would hurt the competition.
31:35
Speaker 3: I mean, it's a very good theory, and a lot
31:37
Speaker 3: of people may think that because yes, if they put
31:40
Speaker 3: regulations in place, Open AI Anthropic who's right there trying
31:45
Speaker 3: to help the government with these regulations. The two biggest
31:48
Speaker 3: companies that are set to really benefit from being the
31:53
Speaker 3: front runners and saying, Okay, all your little guys, all
31:56
Speaker 3: the other people trying to do stuff, you know, let's
31:58
Speaker 3: make sure you can't. Look, there is so much when
32:01
Speaker 3: it comes to AI on both sides. Some people say
32:04
Speaker 3: it's going to be end of all humanity. Some people
32:06
Speaker 3: say it's going to be the best thing that's ever
32:09
Speaker 3: happened to humanity, And so we don't know yet. I mean,
32:13
Speaker 3: I really think at this point we cannot stop the progress.
32:17
Speaker 3: We have to let these companies do things, but we
32:19
Speaker 3: also can't let them go unchecked the way that we
32:22
Speaker 3: let all of the big social media networks Facebook, Instagram
32:26
Speaker 3: go n checked for so many years, and look what
32:28
Speaker 3: they're doing when it comes to mental health when it
32:30
Speaker 3: comes to teenagers. So we can't let that happen. But
32:33
Speaker 3: we also cannot let China, who, by the way, is
32:36
Speaker 3: already putting out a lot of open source models that
32:39
Speaker 3: people in the US are using, and they're coming from
32:41
Speaker 3: China because guess what, they're free and they're easy to
32:44
Speaker 3: use versus the paid stuff.
32:45
Speaker 1: From open AI and anthropic.
32:47
Speaker 3: We cannot let that happen in a big way because
32:49
Speaker 3: it does have implications for these companies that I've spent
32:51
Speaker 3: so much money investing in what they're doing.
32:53
Speaker 1: Rich Namiro, we never have enough time with you. I
32:56
Speaker 1: have a whole list of things I wanted to talk
32:58
Speaker 1: to you about that We can't wait to talk to
33:00
Speaker 1: you next week because I'll take the same list out.
33:03
Speaker 1: Rich DeMuro has heard Sunday nights from eight to eleven
33:07
Speaker 1: pm on WR. You can reach out to them on
33:09
Speaker 1: richontech dot com. Rich, thanks again, you were enlightening. Thank
33:14
Speaker 1: you very much.
33:15
Speaker 3: Thanks Larry, Thanks Curtis.
33:17
Speaker 1: Here's throughout the