1 00:00:00,400 --> 00:00:03,240 Speaker 1: Hey, it's Mark Simone. Welcome to the bonus segment just 2 00:00:03,279 --> 00:00:05,800 Speaker 1: for you podcast listeners out there. You know, I forgot 3 00:00:05,840 --> 00:00:08,720 Speaker 1: to mention this week. Middle of this week it was 4 00:00:08,760 --> 00:00:11,760 Speaker 1: the fifth anniversary of the death of Rush Limbaugh, of course, 5 00:00:11,800 --> 00:00:15,480 Speaker 1: who we all miss very very much. Even President Trump 6 00:00:15,480 --> 00:00:18,760 Speaker 1: putting out a great statement about Rush. We miss him. 7 00:00:18,760 --> 00:00:22,599 Speaker 1: There will never be anybody like him. And now I 8 00:00:22,600 --> 00:00:25,720 Speaker 1: did know this. He said, I'd never met Rush until 9 00:00:25,720 --> 00:00:28,680 Speaker 1: I announced I was running before he ran for president. 10 00:00:28,800 --> 00:00:32,199 Speaker 1: Never met Rush, which is hard to do because there 11 00:00:32,240 --> 00:00:34,120 Speaker 1: was a time where Rush was all over in New 12 00:00:34,240 --> 00:00:38,120 Speaker 1: York City before leaving for Florida. But he said the 13 00:00:38,159 --> 00:00:42,040 Speaker 1: first time he ran, he said, I'll never forget twenty fifteen. 14 00:00:42,920 --> 00:00:45,360 Speaker 1: I got a call and he said, people told me, 15 00:00:45,400 --> 00:00:48,720 Speaker 1: we're all excited. Rush just endorsed you. He said, I'd 16 00:00:48,760 --> 00:00:51,400 Speaker 1: never met him. He liked my opening speech, he endorsed me, 17 00:00:52,159 --> 00:00:54,920 Speaker 1: and it really made a difference in the campaign, made 18 00:00:54,960 --> 00:00:59,040 Speaker 1: a big, big, big difference. Hey, elon Omar, you know, 19 00:00:59,080 --> 00:01:01,040 Speaker 1: she's one of those people in Congress that doesn't have 20 00:01:01,080 --> 00:01:05,520 Speaker 1: a dime, totally broke as like a dollar fifty goes 21 00:01:05,560 --> 00:01:08,000 Speaker 1: to Congress and within a year or two is worth 22 00:01:08,280 --> 00:01:11,920 Speaker 1: millions and millions and millions and millions of dollars, And 23 00:01:11,959 --> 00:01:16,920 Speaker 1: what did Harry Truman say, Any politician that gets rich 24 00:01:17,000 --> 00:01:20,560 Speaker 1: in politics is definitely a crook something like that. So 25 00:01:20,959 --> 00:01:26,959 Speaker 1: it was actually she claims her husband started a hedge fund. Well, 26 00:01:26,959 --> 00:01:29,520 Speaker 1: if you're not a hedge fund person or a hedge 27 00:01:29,520 --> 00:01:31,120 Speaker 1: fund guy, and you don't have any money and you 28 00:01:31,160 --> 00:01:33,520 Speaker 1: start a hedge fund and suddenly it made a fifty 29 00:01:33,520 --> 00:01:36,720 Speaker 1: million bus, something is wrong somewhere. And then according to 30 00:01:36,760 --> 00:01:41,360 Speaker 1: the paperwork she filed, everything was misleading. And now a 31 00:01:41,400 --> 00:01:44,119 Speaker 1: lot of people in Congress calling for a full investigation 32 00:01:44,920 --> 00:01:50,120 Speaker 1: because the disclosure forms she filled out were totally totally misleading. 33 00:01:50,480 --> 00:01:54,320 Speaker 1: The information in her financial disclosures, her public statements are 34 00:01:54,360 --> 00:01:58,240 Speaker 1: totally implausible. There needs to be an investigation. And the 35 00:01:58,440 --> 00:02:02,240 Speaker 1: husband who supposedly is the one who made all this 36 00:02:02,360 --> 00:02:05,200 Speaker 1: money in the hedge fund is a really suspicious character. 37 00:02:05,920 --> 00:02:08,239 Speaker 1: Nobody could figure out exactly this hedgefront. Didn't seem to 38 00:02:08,240 --> 00:02:11,799 Speaker 1: have an office, nobody knew where it was, and then 39 00:02:12,280 --> 00:02:14,920 Speaker 1: as soon as all this controversy started, he closed it 40 00:02:14,960 --> 00:02:18,560 Speaker 1: down immediately. But apparently when she took office with her husband, 41 00:02:18,560 --> 00:02:21,720 Speaker 1: they had fifty one thousand dollars one year. This is 42 00:02:21,720 --> 00:02:24,600 Speaker 1: according to a paperwork she filed with Congress. First year 43 00:02:24,639 --> 00:02:28,560 Speaker 1: they had fifty one thousand, the next year they had 44 00:02:28,680 --> 00:02:32,120 Speaker 1: thirty million. They went from fifty one thousand to thirty million. 45 00:02:32,160 --> 00:02:35,880 Speaker 1: I think that definitely needs to be investigated. It makes 46 00:02:35,919 --> 00:02:39,280 Speaker 1: no sense. Hey, you had this terrible mass shooting this 47 00:02:39,320 --> 00:02:41,240 Speaker 1: week in Rhode Island and it was one of those 48 00:02:41,280 --> 00:02:45,800 Speaker 1: trans women. And this is another There's been a number 49 00:02:45,840 --> 00:02:49,960 Speaker 1: of these mass shootings where it's a trans shooter. Now, 50 00:02:50,639 --> 00:02:52,480 Speaker 1: there can't be a coincidence. There has to be something 51 00:02:52,520 --> 00:02:55,560 Speaker 1: going on here with the whole trans process, the crazy 52 00:02:55,600 --> 00:02:58,840 Speaker 1: medication that they're on. But this was a trans shooter 53 00:02:58,919 --> 00:03:02,840 Speaker 1: and a complete nut. The trans woman or whatever I 54 00:03:02,880 --> 00:03:06,240 Speaker 1: guess it was a woman. The trans woman had gigantic 55 00:03:06,400 --> 00:03:12,160 Speaker 1: gnat Nazi tattoos, Nazi tattoos, gigantic ones all over the body, 56 00:03:12,800 --> 00:03:16,880 Speaker 1: long history of racist, anti Semitic views. There was a 57 00:03:17,080 --> 00:03:21,600 Speaker 1: hero in this crowd. As the mass shooting started, this 58 00:03:21,639 --> 00:03:25,480 Speaker 1: guy tackled the shooter. Unbelievable. It was at a high 59 00:03:25,520 --> 00:03:31,000 Speaker 1: school hockey game. Now, this crazy trans mass shooter killed 60 00:03:31,000 --> 00:03:35,960 Speaker 1: his wife, his ex wife, adult son, and three others. Now, 61 00:03:35,960 --> 00:03:38,120 Speaker 1: you probably didn't hear a lot about this is just 62 00:03:38,240 --> 00:03:42,160 Speaker 1: the point of all this. It's getting no coverage at all. Now. 63 00:03:42,160 --> 00:03:45,160 Speaker 1: You know, if it was a guy with a maga hat, 64 00:03:45,640 --> 00:03:47,400 Speaker 1: it'd be front page news day and night. It'd be 65 00:03:47,480 --> 00:03:50,240 Speaker 1: the lead story all day and night. But because it's 66 00:03:50,280 --> 00:03:55,040 Speaker 1: another trans shooter, no coverage of this. It's an amazing story. 67 00:03:55,440 --> 00:04:00,640 Speaker 1: Just the hero who tackled the shooter, unbelievable heroes just grabbed, 68 00:04:00,640 --> 00:04:02,440 Speaker 1: I went for the gun. I got my hand caught 69 00:04:02,480 --> 00:04:07,080 Speaker 1: and we struggled. As we struggled, additional loaded magazines fell 70 00:04:07,080 --> 00:04:10,560 Speaker 1: out of the fell from the suspect, and then the 71 00:04:10,640 --> 00:04:12,680 Speaker 1: suspect had a second gun. In the middle of the 72 00:04:12,680 --> 00:04:17,000 Speaker 1: struggle because he had grabbed the gun, the suspect pulled 73 00:04:17,000 --> 00:04:21,599 Speaker 1: out a second gun and killed himself with it, fired 74 00:04:21,600 --> 00:04:25,600 Speaker 1: it out himself. Media, it's an amazing story. Media totally 75 00:04:25,760 --> 00:04:29,840 Speaker 1: completely downplaying the coverage of it. You know. The other 76 00:04:29,880 --> 00:04:33,400 Speaker 1: thing is AOC goes to that Munich conference last weekend, 77 00:04:33,920 --> 00:04:36,919 Speaker 1: makes an absolute fool out of herself. She's asked the 78 00:04:37,240 --> 00:04:42,760 Speaker 1: simplest foreign policy question, would we defend Taiwan if China invades? 79 00:04:42,839 --> 00:04:47,520 Speaker 1: Now there's two answers to that. One is you know. Actually, 80 00:04:47,520 --> 00:04:50,080 Speaker 1: the official position of the United States on that is 81 00:04:50,120 --> 00:04:53,240 Speaker 1: what they call strategic ambiguity. In other words, we don't 82 00:04:53,279 --> 00:04:55,840 Speaker 1: say one way or another. You kind of say, take 83 00:04:55,880 --> 00:04:57,960 Speaker 1: both sides because you don't want China to know what 84 00:04:58,000 --> 00:05:01,800 Speaker 1: you'll do. But the other answer is if you don't know, 85 00:05:01,880 --> 00:05:07,360 Speaker 1: if you're a complete idiot and you don't know, you say, well, 86 00:05:07,640 --> 00:05:11,040 Speaker 1: it's a very complex issue. I've been looking at this 87 00:05:11,080 --> 00:05:14,120 Speaker 1: and I need to study it further. And right now 88 00:05:14,160 --> 00:05:16,640 Speaker 1: I can't say what you say something like that. But 89 00:05:16,800 --> 00:05:20,880 Speaker 1: instead she starts babbling some crazy word salad, which means 90 00:05:20,920 --> 00:05:26,159 Speaker 1: she's beyond stupid. She's just incredibly dumb. So President Trump, 91 00:05:26,200 --> 00:05:28,440 Speaker 1: but going after her this week, saying was a career 92 00:05:28,640 --> 00:05:32,000 Speaker 1: ending performance. In other words, she can't run in twenty 93 00:05:32,000 --> 00:05:36,560 Speaker 1: twenty eight based on this, It's absolutely impossible. Her answer 94 00:05:36,640 --> 00:05:40,799 Speaker 1: was so dumb, so rambling, so stupid. There's no way 95 00:05:40,880 --> 00:05:44,560 Speaker 1: on earth, no way on earth, that she can run 96 00:05:44,600 --> 00:05:46,880 Speaker 1: for president. So she could try to run for Senate, 97 00:05:46,920 --> 00:05:49,400 Speaker 1: because I don't care how stupid you are, how left 98 00:05:49,440 --> 00:05:52,280 Speaker 1: wing you are. You can run for things in New 99 00:05:52,360 --> 00:05:54,720 Speaker 1: York City. Look at Mom, Donnie, look at whoever. You 100 00:05:54,760 --> 00:05:56,840 Speaker 1: can do it in New York. Hey, you know all 101 00:05:56,880 --> 00:06:00,960 Speaker 1: these people moving to Florida. It's all you hear. Everybody's leaving, 102 00:06:01,000 --> 00:06:05,640 Speaker 1: everybody's moving to Florida. Well, actually not anymore. Migration to 103 00:06:05,680 --> 00:06:10,640 Speaker 1: Florida is down ninety three percent in the last three years. 104 00:06:10,920 --> 00:06:14,120 Speaker 1: Miami still pulls in a lot of people, especially high 105 00:06:14,120 --> 00:06:19,800 Speaker 1: income financial types, but people moving to Florida has dropped dramatically. 106 00:06:20,200 --> 00:06:23,040 Speaker 1: Last year, how many people you think moved to Florida 107 00:06:23,160 --> 00:06:26,279 Speaker 1: from whatever state? How many people moved to Florida last year, 108 00:06:27,480 --> 00:06:32,600 Speaker 1: twenty two thousand, twenty two thousand. Well, the year before 109 00:06:32,640 --> 00:06:36,040 Speaker 1: it was fifty eight thousand, and the year before that 110 00:06:36,040 --> 00:06:39,440 Speaker 1: it was one hundred and eighty three thousand, and before 111 00:06:39,480 --> 00:06:41,680 Speaker 1: that it was three hundred and ten thousand a year. 112 00:06:41,760 --> 00:06:44,360 Speaker 1: So the amount of people moving to Florida is down 113 00:06:45,200 --> 00:06:50,560 Speaker 1: ninety three percent. Now. Other news Apple News. You know, 114 00:06:50,600 --> 00:06:53,680 Speaker 1: if you have your iPhone there there's an Apple News 115 00:06:53,760 --> 00:06:56,200 Speaker 1: app that brings you all the news. It is the 116 00:06:56,240 --> 00:07:03,520 Speaker 1: most biased, slanted, left wing, totally biased, totally corrupt news site. 117 00:07:04,080 --> 00:07:07,359 Speaker 1: And a lot of politicians and you know, the Federal 118 00:07:07,440 --> 00:07:09,480 Speaker 1: Trade Commission and all that want to start going after 119 00:07:09,560 --> 00:07:13,280 Speaker 1: it because it's totally corrupt. It's supposedly done by AI. 120 00:07:13,400 --> 00:07:15,800 Speaker 1: But the AI is trained only to give you the 121 00:07:15,840 --> 00:07:20,040 Speaker 1: news from the crazy left wing sites. Mainstream mid you know, 122 00:07:20,840 --> 00:07:25,720 Speaker 1: moderate sites. Conservative sites are not included. It's only left 123 00:07:25,720 --> 00:07:30,840 Speaker 1: wing partisan sites. So be very careful of Apple News. 124 00:07:30,880 --> 00:07:33,360 Speaker 1: Don't even look at it as the most slanted news 125 00:07:33,400 --> 00:07:35,560 Speaker 1: app there is. Hey, you know the other thing, And 126 00:07:35,600 --> 00:07:38,000 Speaker 1: I still I don't know why this is. If you 127 00:07:38,040 --> 00:07:41,160 Speaker 1: have an iPhone, there's a weather app on it. It's 128 00:07:41,160 --> 00:07:45,240 Speaker 1: the Apple Weather app. It is the most unreliable, inaccurate 129 00:07:45,280 --> 00:07:48,560 Speaker 1: weather app I have ever seen. It is always wrong, 130 00:07:49,840 --> 00:07:52,680 Speaker 1: it's never right, and now I've done a little research 131 00:07:52,720 --> 00:07:56,240 Speaker 1: on it to see why. Apparently there's no weather people 132 00:07:56,280 --> 00:07:59,920 Speaker 1: involved in it. It's all AI, and the AI scrapes 133 00:08:00,200 --> 00:08:02,760 Speaker 1: raw data from somewhere and they say, one of the 134 00:08:02,800 --> 00:08:05,640 Speaker 1: reasons it's always wrong, it's often reading the wrong location, 135 00:08:06,360 --> 00:08:08,840 Speaker 1: or it's incorrectly reading the data. If you want to 136 00:08:08,880 --> 00:08:11,840 Speaker 1: get the weather on your phone, the best one is 137 00:08:11,920 --> 00:08:15,720 Speaker 1: ACU Weather. Acu Weather's an app you can download. Accu 138 00:08:15,840 --> 00:08:18,960 Speaker 1: Weather's like the NFL uses it. NASA uses very very 139 00:08:19,080 --> 00:08:22,960 Speaker 1: very good ACU weather. Download that also the Weather Channel 140 00:08:23,040 --> 00:08:25,920 Speaker 1: that's pretty good. The Weather Channel app you can download that. 141 00:08:26,680 --> 00:08:28,880 Speaker 1: But that Apple Weather app its the one that comes 142 00:08:28,880 --> 00:08:30,760 Speaker 1: on your phone, and I know a lot of people think, well, 143 00:08:31,040 --> 00:08:33,840 Speaker 1: Apple Weather app, it's got to be it's totally unreliable, 144 00:08:34,480 --> 00:08:38,400 Speaker 1: stay away from it. Now. The problem is both these sites. 145 00:08:38,520 --> 00:08:42,240 Speaker 1: It's AI that's the problem. And AI has been programmed 146 00:08:42,240 --> 00:08:45,760 Speaker 1: by a bunch of left wing Silicon Valley types and 147 00:08:45,800 --> 00:08:50,800 Speaker 1: it's programmed very very very very left wing, and a 148 00:08:51,480 --> 00:08:53,880 Speaker 1: lot of politicians now talking about something has to be 149 00:08:53,920 --> 00:08:56,160 Speaker 1: done about this. It's got to be stopped in the 150 00:08:56,200 --> 00:08:59,360 Speaker 1: early stages of AI. This has got to be cured. 151 00:08:59,679 --> 00:09:03,800 Speaker 1: You know, is certain FCC and Federal Trade Commission standards. 152 00:09:04,360 --> 00:09:07,959 Speaker 1: You got to have some balance, some fairness. But AI 153 00:09:08,040 --> 00:09:10,280 Speaker 1: is not covered by that. But a lot of politicians 154 00:09:10,280 --> 00:09:12,680 Speaker 1: looking at how to fix that, make sure that AI 155 00:09:12,920 --> 00:09:15,800 Speaker 1: isn't just crazy. You know, we've got media right now, 156 00:09:15,840 --> 00:09:18,080 Speaker 1: mainstream media, the networks at New York Times all that 157 00:09:18,080 --> 00:09:21,120 Speaker 1: that's totally slanted left. You don't want AI to be 158 00:09:21,160 --> 00:09:23,599 Speaker 1: slanted even further to the left. That's not going to 159 00:09:23,640 --> 00:09:29,800 Speaker 1: be good. Oh remember hooters, of course you do. Hey, 160 00:09:29,800 --> 00:09:31,760 Speaker 1: there was one in Manhattan right over there, like a 161 00:09:31,800 --> 00:09:36,480 Speaker 1: block up that way. That's long gone, but long Island 162 00:09:36,520 --> 00:09:40,360 Speaker 1: just lost the last Hooters they had Farmingdale, Long Island, 163 00:09:40,400 --> 00:09:45,440 Speaker 1: the very last Hooters location closed. It's kind of a 164 00:09:45,480 --> 00:09:47,719 Speaker 1: sad thing, I mean it. I mean the sad part 165 00:09:47,760 --> 00:09:49,760 Speaker 1: is she went from years ago the Playboy Club that 166 00:09:49,800 --> 00:09:55,320 Speaker 1: was a very elegant, chic, unbelievable, you know, sophisticated atmosphere, 167 00:09:55,480 --> 00:09:58,320 Speaker 1: great food, big name entertainment. That was a Playboy club 168 00:09:58,600 --> 00:10:02,520 Speaker 1: out of women wore bunnycote. But that was the sixties. 169 00:10:02,559 --> 00:10:05,559 Speaker 1: That was the Don Draper years. Then that evolved somehow 170 00:10:05,640 --> 00:10:09,600 Speaker 1: into Hooters, which looks like a truck stop somewhere, and 171 00:10:09,800 --> 00:10:13,280 Speaker 1: they called it Hooters because well, you do the math. 172 00:10:13,400 --> 00:10:17,640 Speaker 1: But it's a little outdated now. And the problem is, 173 00:10:18,160 --> 00:10:20,840 Speaker 1: I think they once said the Internet kind of killed 174 00:10:20,840 --> 00:10:23,240 Speaker 1: their business because people used to go to Hooters to 175 00:10:23,280 --> 00:10:28,319 Speaker 1: see some hooters. But apparently with the Internet, you can 176 00:10:28,320 --> 00:10:30,280 Speaker 1: go see all you want all day and you don't 177 00:10:30,280 --> 00:10:32,160 Speaker 1: need to go into any Hooters for that. Hey, you 178 00:10:32,200 --> 00:10:33,960 Speaker 1: know what the big thing in restaurants now is, especially 179 00:10:34,000 --> 00:10:40,480 Speaker 1: in New York City, Georgian restaurants. Georgian restaurants is expected 180 00:10:40,520 --> 00:10:44,200 Speaker 1: to be the next big trend, Not Atlanta, Georgia, not Georgia, 181 00:10:44,240 --> 00:10:47,920 Speaker 1: the state Georgia, which used to be part of Russia, 182 00:10:48,679 --> 00:10:50,640 Speaker 1: and it kind of got swallowed up into Russia for 183 00:10:50,679 --> 00:10:53,960 Speaker 1: so many years. But now that Georgia is independent and 184 00:10:54,040 --> 00:10:58,720 Speaker 1: apparently they've had this great cuisine for years. Georgian cuisine. 185 00:10:58,760 --> 00:11:01,839 Speaker 1: It's built around all sorts of spices and flavor. It's 186 00:11:01,840 --> 00:11:05,240 Speaker 1: a cross between Mediterranean and Middle Eastern for a lot 187 00:11:05,240 --> 00:11:07,600 Speaker 1: of people love it. But a lot of these Georgians 188 00:11:07,640 --> 00:11:09,400 Speaker 1: are now in New York City, a lot of them 189 00:11:09,440 --> 00:11:12,840 Speaker 1: in Brooklyn, and that became like a whole stretch of 190 00:11:12,880 --> 00:11:17,160 Speaker 1: Georgian restaurants in Brooklyn. Now they're opening in Manhattan. Now 191 00:11:17,160 --> 00:11:21,280 Speaker 1: they're becoming very popular. Are now forty Georgian restaurants around 192 00:11:21,360 --> 00:11:23,200 Speaker 1: New York City. So keep an eye on this. This 193 00:11:23,280 --> 00:11:27,720 Speaker 1: is expected to be the next big restaurant trend. He's 194 00:11:27,720 --> 00:11:30,920 Speaker 1: speaking of eating and when you eat at home. Now, 195 00:11:30,960 --> 00:11:32,720 Speaker 1: maybe when you're a kid, you all sat around the 196 00:11:32,760 --> 00:11:36,240 Speaker 1: dining room table, Well that's kind of over with, you know. 197 00:11:37,040 --> 00:11:39,800 Speaker 1: You remember your father would yell, no television while we're watching, 198 00:11:39,880 --> 00:11:44,319 Speaker 1: while we're eating, no television. Well, now eating is built 199 00:11:44,320 --> 00:11:48,080 Speaker 1: around first the television and now the phone and more 200 00:11:48,120 --> 00:11:50,520 Speaker 1: and more people the percentage of people that don't eat 201 00:11:50,559 --> 00:11:53,840 Speaker 1: in a dining room anymore is through the roof. Most 202 00:11:53,920 --> 00:11:57,480 Speaker 1: people now eat on a couch and they eat while 203 00:11:57,480 --> 00:11:59,560 Speaker 1: they're on their phone, They eat while they're watching TV. 204 00:12:00,080 --> 00:12:04,520 Speaker 1: Eatingly on the couch has become totally normal as a result. 205 00:12:05,600 --> 00:12:07,920 Speaker 1: As a result, you know what's coming back. Remember those 206 00:12:07,920 --> 00:12:09,920 Speaker 1: TV trays. It was like a little tray you'd set 207 00:12:10,000 --> 00:12:12,240 Speaker 1: up in front of your chair and eat your TV 208 00:12:12,280 --> 00:12:14,520 Speaker 1: dinner on the TV. Well, TV trays are coming back 209 00:12:15,200 --> 00:12:19,360 Speaker 1: better than ever. Designers are working on them like crazy, Target, 210 00:12:19,440 --> 00:12:21,640 Speaker 1: West Elm, all these stories. It's a big thing now, 211 00:12:21,760 --> 00:12:25,320 Speaker 1: these versatile TV trays, as more and more and more 212 00:12:25,360 --> 00:12:29,600 Speaker 1: people eat on the couch. I mean, you know, be honestly, 213 00:12:29,920 --> 00:12:33,400 Speaker 1: I have a dining room table. You know what it's for. 214 00:12:33,960 --> 00:12:36,160 Speaker 1: You throw your mail on there when you come home. 215 00:12:36,200 --> 00:12:37,679 Speaker 1: Whatever you got, you throw it on the day. It's 216 00:12:37,720 --> 00:12:39,720 Speaker 1: a pile of stuff on the dining room table. Nobody's 217 00:12:39,760 --> 00:12:44,120 Speaker 1: ever sat at it. Nobody ever will. Hey, Amazon, now 218 00:12:44,160 --> 00:12:47,600 Speaker 1: you would think who's doing better than Amazon. They're laying 219 00:12:47,640 --> 00:12:49,720 Speaker 1: off a lot of people. It just laid off one 220 00:12:49,800 --> 00:12:53,240 Speaker 1: hundred and thirty five employees in one building here right 221 00:12:53,320 --> 00:12:56,520 Speaker 1: up the street in Manhattan, one hundred and sixty nine 222 00:12:56,559 --> 00:12:58,960 Speaker 1: workers have just been laid off in New York. All together, 223 00:12:58,960 --> 00:13:02,720 Speaker 1: they're laying off six sixteen thousand workers. Now, it's not 224 00:13:02,760 --> 00:13:05,880 Speaker 1: that Amazon's not doing well, of course, obviously they're but 225 00:13:05,920 --> 00:13:09,120 Speaker 1: they're streamlining everything so they need less people. And then 226 00:13:09,240 --> 00:13:11,640 Speaker 1: AI is taken over. You know, when it comes to 227 00:13:11,679 --> 00:13:16,000 Speaker 1: the logistics of delivering and packages and warehouse and AI 228 00:13:16,080 --> 00:13:18,400 Speaker 1: can do a lot of that. So it's going to 229 00:13:18,440 --> 00:13:22,240 Speaker 1: be a problem in the next decade or so AI layoffs. 230 00:13:22,240 --> 00:13:24,199 Speaker 1: A lot of jobs are gonna be lost through AI. 231 00:13:24,800 --> 00:13:28,559 Speaker 1: But that happened with the Industrial revolution when the computers 232 00:13:28,600 --> 00:13:31,680 Speaker 1: came along with digital revolution. It always happens. Takes years 233 00:13:31,720 --> 00:13:34,079 Speaker 1: to get everybody in their new jobs, but it always 234 00:13:34,120 --> 00:13:36,559 Speaker 1: corrects itself. Hey, we're out of time. This has been 235 00:13:36,600 --> 00:13:40,040 Speaker 1: the bonus segment. Thanks for listening. You can normally hear 236 00:13:40,080 --> 00:13:43,800 Speaker 1: me Live ten to noon every weekday seven to ten 237 00:13:44,000 --> 00:13:48,040 Speaker 1: War or the podcast wherever you get podcasts. Thanks for listening. 238 00:13:48,200 --> 00:13:49,120 Speaker 1: Have a great weekend.