1 00:00:00,840 --> 00:00:02,680 Speaker 1: Welcome back to a numbers game with Ryan Griski. Thank 2 00:00:02,680 --> 00:00:05,240 Speaker 1: you guys for being here. We have so much to 3 00:00:05,280 --> 00:00:07,400 Speaker 1: go over today. It's a little crazy, but I want 4 00:00:07,400 --> 00:00:10,640 Speaker 1: to first point out some interesting data that no one 5 00:00:10,680 --> 00:00:14,080 Speaker 1: has covered. The census. Obviously a lot of people cover 6 00:00:14,120 --> 00:00:16,599 Speaker 1: the census data. The data came out that birth rates 7 00:00:16,560 --> 00:00:19,159 Speaker 1: had hit a low point in twenty twenty five. There 8 00:00:19,160 --> 00:00:22,120 Speaker 1: were three point six zero million births compared to three 9 00:00:22,120 --> 00:00:25,000 Speaker 1: point six two million in the year prior. A lot 10 00:00:25,000 --> 00:00:28,200 Speaker 1: of pepll cover that, and it's a big downward slide, 11 00:00:28,280 --> 00:00:29,880 Speaker 1: especially in the last ten years when there was nearly 12 00:00:29,920 --> 00:00:32,519 Speaker 1: four million birth But a question I keep getting in 13 00:00:32,640 --> 00:00:35,839 Speaker 1: my inbox and on social media is, especially when the 14 00:00:36,040 --> 00:00:39,639 Speaker 1: term bi racial right by racial births, people really want 15 00:00:39,720 --> 00:00:42,199 Speaker 1: to know what does that mean? As far as you know, 16 00:00:42,400 --> 00:00:45,960 Speaker 1: is it a white mother, a black father, an Asian father, 17 00:00:46,159 --> 00:00:49,840 Speaker 1: or you know, you know Hispanic mother is what's the combination? 18 00:00:49,960 --> 00:00:52,280 Speaker 1: People are very, very curious. I keep getting these answers, 19 00:00:52,400 --> 00:00:55,000 Speaker 1: and that's it. That's very difficult to break down because 20 00:00:55,000 --> 00:00:57,400 Speaker 1: there's a lot of unknowns. And also race is both 21 00:00:57,440 --> 00:00:59,680 Speaker 1: a science and a social construct, right, Like I have 22 00:00:59,720 --> 00:01:02,880 Speaker 1: friends to a partner's my friend will be white, their 23 00:01:02,960 --> 00:01:06,360 Speaker 1: partner will be half white, half Asian or Latino and 24 00:01:07,600 --> 00:01:10,119 Speaker 1: a white combination, and the child comes out with blonde 25 00:01:10,160 --> 00:01:12,280 Speaker 1: hair and blue eyes, and they class by themselves as white. 26 00:01:12,280 --> 00:01:14,320 Speaker 1: And I have other ones that call themselves biracial or 27 00:01:14,400 --> 00:01:18,000 Speaker 1: Latino or just fully black in that case, like it's 28 00:01:18,560 --> 00:01:20,320 Speaker 1: their own choosing, it's their own way, they want to 29 00:01:20,319 --> 00:01:22,280 Speaker 1: pick it, and you know, I respect every which way. 30 00:01:22,720 --> 00:01:26,319 Speaker 1: So it's I can't really go super heavily into the 31 00:01:26,360 --> 00:01:29,080 Speaker 1: biracial part. But as I was looking and trying to 32 00:01:29,080 --> 00:01:34,679 Speaker 1: do the research right, I started examining couples of the 33 00:01:34,800 --> 00:01:38,319 Speaker 1: same race and birth trends among couples who are both 34 00:01:38,360 --> 00:01:42,120 Speaker 1: partners both the mother and the father were either Black, Latino, Asian, 35 00:01:42,400 --> 00:01:45,720 Speaker 1: or white as non Hispanic white. And I found something 36 00:01:45,920 --> 00:01:48,320 Speaker 1: very interesting that no one has talked about that I 37 00:01:48,360 --> 00:01:51,240 Speaker 1: thought would be a worthy listening to. So, first birth 38 00:01:51,240 --> 00:01:56,320 Speaker 1: among black parents where both partners were black, it's down very, 39 00:01:56,560 --> 00:01:59,559 Speaker 1: very very large, Like it's down three point five percent 40 00:01:59,840 --> 00:02:03,840 Speaker 1: in a single year, and it's down ten percent since 41 00:02:03,920 --> 00:02:09,239 Speaker 1: twenty twenty, I mean a spiraling fall among couples where 42 00:02:09,320 --> 00:02:13,040 Speaker 1: both couples are black. Among black women, it's down everywhere, 43 00:02:13,280 --> 00:02:15,920 Speaker 1: even on interracial couples, but among black couples it's down 44 00:02:16,040 --> 00:02:19,959 Speaker 1: pretty substantially. Asian Americans are also down. They're only down 45 00:02:20,000 --> 00:02:22,960 Speaker 1: slightly from twenty down slightly in the last year. But 46 00:02:23,040 --> 00:02:26,600 Speaker 1: when you consider that since twenty twenty, one point five 47 00:02:26,880 --> 00:02:30,760 Speaker 1: million legal immigrants from Asia have moved to the United States, 48 00:02:31,120 --> 00:02:35,240 Speaker 1: the fact that it's still stagnant in the last five, 49 00:02:35,400 --> 00:02:39,160 Speaker 1: six year or five years is pretty remarkable, and it 50 00:02:39,320 --> 00:02:42,960 Speaker 1: probably begs the question that it's only immigration rates that 51 00:02:43,000 --> 00:02:46,679 Speaker 1: are even keeping it close to the standards. In other words, 52 00:02:46,720 --> 00:02:50,200 Speaker 1: I think that Asian numbers are actually declining per capita 53 00:02:50,240 --> 00:02:52,959 Speaker 1: at a pretty remarkably high rate, and it's only immigration 54 00:02:52,960 --> 00:02:57,080 Speaker 1: it's even keeping remotely afloat. Latinos are down slightly but 55 00:02:57,240 --> 00:02:59,400 Speaker 1: up over ten percent since twenty twenty. This is no 56 00:02:59,440 --> 00:03:01,480 Speaker 1: shock or give that Biden open up the floggates to 57 00:03:01,520 --> 00:03:05,440 Speaker 1: millions of Latinos. But here's the crazy thing. Baby is 58 00:03:05,520 --> 00:03:09,520 Speaker 1: born to two white parents are up for the second 59 00:03:09,639 --> 00:03:12,640 Speaker 1: year in a row. In twenty twenty three, the birth 60 00:03:12,720 --> 00:03:15,440 Speaker 1: rate for two white parents hit an all time low 61 00:03:15,440 --> 00:03:19,720 Speaker 1: of one point four one seven million children born to 62 00:03:19,720 --> 00:03:22,440 Speaker 1: two white parents United States. Then it ticked up by 63 00:03:22,480 --> 00:03:25,520 Speaker 1: a thousand in twenty twenty four, then it ticked up 64 00:03:25,560 --> 00:03:29,240 Speaker 1: by twelve thousand in twenty twenty five. Now, obviously this 65 00:03:29,280 --> 00:03:32,520 Speaker 1: is not because of immigration. This is only because of 66 00:03:32,560 --> 00:03:36,080 Speaker 1: what American white Americans are choosing to do and their 67 00:03:36,200 --> 00:03:40,760 Speaker 1: birth rates. And it's the find very interesting only twenty 68 00:03:40,800 --> 00:03:43,240 Speaker 1: twenty one. You know, the birth rate for two white 69 00:03:43,360 --> 00:03:45,560 Speaker 1: cup parents has been on the decline for some time. 70 00:03:45,880 --> 00:03:48,320 Speaker 1: Only twenty twenty one to we see this big baby 71 00:03:48,320 --> 00:03:50,280 Speaker 1: bum because of the COVID lockdowns. It really had a 72 00:03:50,320 --> 00:03:53,240 Speaker 1: big search. But since then it's been a you know, 73 00:03:53,280 --> 00:03:58,880 Speaker 1: a regular trajectory. So how has this reversal happened, Why 74 00:03:58,920 --> 00:04:01,800 Speaker 1: has it happened, and is it continuing? I don't have 75 00:04:01,880 --> 00:04:04,480 Speaker 1: definitive proof as for any of these reasons, right, I'm 76 00:04:04,600 --> 00:04:09,320 Speaker 1: just going along with this question. Does it mean something 77 00:04:09,360 --> 00:04:12,440 Speaker 1: bigger for the culture? Does it mean something bigger for politics? 78 00:04:12,480 --> 00:04:16,080 Speaker 1: Why is this happening while everything else is in reversal 79 00:04:16,400 --> 00:04:21,280 Speaker 1: except with groups of large immigrant populations. Right, Partially I 80 00:04:21,440 --> 00:04:25,400 Speaker 1: think that it could be, and I'm there's a few ideas. 81 00:04:25,440 --> 00:04:30,680 Speaker 1: One ultra religious communities with large families Amish, ultra Orthodox Jews, 82 00:04:30,720 --> 00:04:34,880 Speaker 1: Latin mask Catholics, ultra religious Mormons that their fertility rates 83 00:04:34,960 --> 00:04:38,400 Speaker 1: haven't really come down or changed in the last ten years, 84 00:04:38,800 --> 00:04:42,360 Speaker 1: but because their birth rates are so large, it's starting 85 00:04:42,400 --> 00:04:45,120 Speaker 1: to grow in regular size. So we're just seeing the 86 00:04:45,200 --> 00:04:47,520 Speaker 1: numbers increase because there are more Amish than there were 87 00:04:47,520 --> 00:04:50,200 Speaker 1: ten years ago, there's more aesthetic Jews, there's more ultra 88 00:04:50,400 --> 00:04:53,599 Speaker 1: Orthodox Mormons. There's probably more Latin mask Catholics in their community, 89 00:04:53,640 --> 00:04:57,280 Speaker 1: the SSPX communities. That could be part of the reason. Right. Secondly, 90 00:04:57,320 --> 00:05:00,320 Speaker 1: I'm thinking it also because of the wealthiest America, as 91 00:05:00,360 --> 00:05:03,600 Speaker 1: wealthy Americans have some of the highest birth rates in 92 00:05:03,640 --> 00:05:07,559 Speaker 1: the country, and families that have more than seven hundred 93 00:05:07,600 --> 00:05:10,720 Speaker 1: thousand dollars or a year or more in income have 94 00:05:10,960 --> 00:05:14,320 Speaker 1: a fertility rate above replacement levels or at replacement levels. 95 00:05:14,360 --> 00:05:17,560 Speaker 1: Right it's about two to two point one children per woman, 96 00:05:17,600 --> 00:05:20,040 Speaker 1: which is higher than the norms, higher than the average. 97 00:05:20,240 --> 00:05:23,719 Speaker 1: Billionaires actually have the highest fertility rate in America. The 98 00:05:23,800 --> 00:05:27,880 Speaker 1: number was three percent of all billionaires. They stems from 99 00:05:27,920 --> 00:05:31,760 Speaker 1: Forest magazine. Three percent of all billionaires have more than 100 00:05:31,839 --> 00:05:34,679 Speaker 1: seven children. That you got to go three percent of billions. 101 00:05:34,760 --> 00:05:36,600 Speaker 1: That's not that many people. It's not that many people, 102 00:05:37,120 --> 00:05:41,559 Speaker 1: but in a fixed group that's larger than the norm, 103 00:05:41,640 --> 00:05:44,279 Speaker 1: that's larger than the average of any group. The average 104 00:05:44,279 --> 00:05:46,480 Speaker 1: for families who are billionaires out the way, by the way, 105 00:05:46,520 --> 00:05:48,880 Speaker 1: is three children per woman, which is also higher than 106 00:05:48,880 --> 00:05:51,080 Speaker 1: the norm, higher than the average. So maybe it's because 107 00:05:51,120 --> 00:05:53,279 Speaker 1: the wealthiest people in the country tend to be white, 108 00:05:53,320 --> 00:05:55,520 Speaker 1: and white people with money have had a lot of 109 00:05:55,600 --> 00:05:57,920 Speaker 1: children and that's keeping up, and maybe those numbers are 110 00:05:57,960 --> 00:06:01,920 Speaker 1: expanding as wealth expands. It could also be the push 111 00:06:01,960 --> 00:06:04,559 Speaker 1: by young conservatives like the TPUSA crowd to get married 112 00:06:04,600 --> 00:06:07,200 Speaker 1: to have kids. Maybe that's taking some kind of effect. 113 00:06:07,279 --> 00:06:10,560 Speaker 1: I don't know. I think this is a very interesting question. 114 00:06:10,640 --> 00:06:13,480 Speaker 1: I'm going to keep track of this throughout twenty twenty 115 00:06:13,560 --> 00:06:15,800 Speaker 1: six to see it's a trend that happened for two 116 00:06:15,920 --> 00:06:18,520 Speaker 1: years and then fell off, or whether it's going to continue. 117 00:06:18,560 --> 00:06:21,320 Speaker 1: Because something is happening. I'm going to writing for my 118 00:06:21,360 --> 00:06:23,200 Speaker 1: substack and make it availble to everybody I know. By 119 00:06:23,240 --> 00:06:25,280 Speaker 1: the way, it is my substack writers. I have been 120 00:06:25,480 --> 00:06:28,479 Speaker 1: very bad about publishing last two days. My researcher and 121 00:06:28,560 --> 00:06:31,440 Speaker 1: I have split ways professionally, so I need to find 122 00:06:31,440 --> 00:06:33,680 Speaker 1: a new researcher. It just takes too much time and 123 00:06:33,760 --> 00:06:36,320 Speaker 1: I have twelve million jobs, so I will get back 124 00:06:36,320 --> 00:06:39,080 Speaker 1: to it. Please. If you're so subscribing, thank you, and 125 00:06:39,160 --> 00:06:42,840 Speaker 1: I will start writing more regularly. Okay, Now, let's talk 126 00:06:42,839 --> 00:06:45,799 Speaker 1: about the main issue for this episode, which is money 127 00:06:45,920 --> 00:06:49,400 Speaker 1: in politics. Every campaign in the country had their report 128 00:06:49,440 --> 00:06:52,039 Speaker 1: for the Q four fundraising that's the last three months 129 00:06:52,080 --> 00:06:55,679 Speaker 1: of the year. Obviously that report was doing January first, 130 00:06:55,680 --> 00:06:58,760 Speaker 1: and there's some fascinating numbers to break down. Let's talk 131 00:06:58,800 --> 00:07:03,080 Speaker 1: about individual race first. Democrat candidates running for office are 132 00:07:03,160 --> 00:07:06,600 Speaker 1: raising a lot of money. In twenty twenty five, there 133 00:07:06,600 --> 00:07:09,560 Speaker 1: were five hundred and eighty six million dollars contributed through 134 00:07:09,600 --> 00:07:13,440 Speaker 1: act blow, which is the big website for Democrat candidates. 135 00:07:14,000 --> 00:07:18,040 Speaker 1: This specifically helped Democrats running for state wide office. For 136 00:07:18,080 --> 00:07:21,560 Speaker 1: the US Senate. In Georgia, Democrat John Ausof raised almost 137 00:07:21,640 --> 00:07:24,960 Speaker 1: ten million dollars, while the three Republicans running in their 138 00:07:24,960 --> 00:07:28,360 Speaker 1: primary raised three point five million. In North Carolina, got 139 00:07:28,440 --> 00:07:32,120 Speaker 1: former Governor Roy Cooper raised seven million, while Republican candidate 140 00:07:32,480 --> 00:07:36,320 Speaker 1: basically nominee but candidate Michael Wattley raised three point eight million. 141 00:07:36,600 --> 00:07:39,640 Speaker 1: In Maine, the progressive candidate Grand Platner raised four point 142 00:07:39,680 --> 00:07:42,360 Speaker 1: six Governor Janet Millers were raised two point seven their 143 00:07:42,400 --> 00:07:46,040 Speaker 1: two Democrats, while incumbent Senator Susan Collins only raised two 144 00:07:46,040 --> 00:07:49,280 Speaker 1: point two million. In Ohio, former Senator Cheron Brown raised 145 00:07:49,320 --> 00:07:53,760 Speaker 1: seven point three million dollars, while incumbent Senator John Houston 146 00:07:54,120 --> 00:07:57,680 Speaker 1: raised one point five million. In New Hampshire, Congressman Chris 147 00:07:57,720 --> 00:08:00,920 Speaker 1: Pappus raised two point three million, while former Senator and 148 00:08:01,000 --> 00:08:05,280 Speaker 1: Republican candidate Johnson New raised one point three. In Michigan, 149 00:08:05,360 --> 00:08:07,920 Speaker 1: Haley Stevens, the Democrat, raised the most with two point one. 150 00:08:08,120 --> 00:08:11,520 Speaker 1: All the Democrats together raise five point five. Mike Rodgers, 151 00:08:11,600 --> 00:08:15,520 Speaker 1: the Republican, raised one point nine. In Texas, and Texas 152 00:08:15,560 --> 00:08:19,080 Speaker 1: and Iowa were the exceptions where Republicans outraised Democrats, but 153 00:08:19,120 --> 00:08:23,000 Speaker 1: in Texas it was barely. John Cornyn barely outraised Democrat 154 00:08:23,120 --> 00:08:27,440 Speaker 1: James Tallarco seven million to six point nine. So Senate 155 00:08:27,520 --> 00:08:32,160 Speaker 1: Candad's running Democrats Candid's running statewide are raising lots of money. 156 00:08:32,559 --> 00:08:35,439 Speaker 1: But the good thing for Republicans is that most of 157 00:08:35,480 --> 00:08:38,000 Speaker 1: these Democrats have very competitive primaries. They're going to have 158 00:08:38,040 --> 00:08:39,559 Speaker 1: to spend the money to begin with, right They're to 159 00:08:39,600 --> 00:08:43,200 Speaker 1: spend money early Texas, Michigan, Iowa, Maine, they all have 160 00:08:43,360 --> 00:08:46,640 Speaker 1: very competitive Democratic primaries, so they're going to start fresh 161 00:08:47,120 --> 00:08:49,360 Speaker 1: this year in fundraising. They'll have to exhausted a lot 162 00:08:49,360 --> 00:08:52,240 Speaker 1: of their money to win their primaries. That's why many 163 00:08:52,240 --> 00:08:56,000 Speaker 1: these races. Republicans actually have more cash on hand. In Maine, 164 00:08:56,040 --> 00:08:59,200 Speaker 1: in Texas and Michigan have a big advantage cash on hand. 165 00:08:59,640 --> 00:09:02,360 Speaker 1: But our biggest advantage doesn't come from the candidates. It 166 00:09:02,400 --> 00:09:07,760 Speaker 1: comes from packs and organizations supporting Republicans. Trump's superpack, MAGA Inc. 167 00:09:07,800 --> 00:09:11,800 Speaker 1: Has raised a massive three one hundred and four million 168 00:09:11,920 --> 00:09:14,400 Speaker 1: dollars a three hundred and four million dollars cash on hand. 169 00:09:14,480 --> 00:09:17,120 Speaker 1: It raised one hundred and twelve million in the last 170 00:09:17,160 --> 00:09:21,520 Speaker 1: half of twenty twenty five. These are this is presidential numbers. 171 00:09:21,559 --> 00:09:23,320 Speaker 1: These are not numbers for a midterrum. These are numbers 172 00:09:23,360 --> 00:09:26,400 Speaker 1: for a presidential run. MAGA Inc. Has more money than 173 00:09:26,480 --> 00:09:30,240 Speaker 1: any political organization in the country, more than the RNC 174 00:09:30,440 --> 00:09:34,840 Speaker 1: and the DNC combined. Now we don't know how Trump's 175 00:09:34,840 --> 00:09:36,280 Speaker 1: going to spend on it. We don't know if He's 176 00:09:36,320 --> 00:09:39,640 Speaker 1: going to spend It's a big, big mystery. The NRCC, 177 00:09:39,880 --> 00:09:43,719 Speaker 1: that is the organization that deals with house races in 178 00:09:44,360 --> 00:09:47,319 Speaker 1: the country for Republicans they outraised the D Triple C, 179 00:09:47,559 --> 00:09:51,160 Speaker 1: which is the organization for Democrats slightly. They have fifty 180 00:09:51,200 --> 00:09:53,560 Speaker 1: point seven million cash in hand to forty nine million. 181 00:09:53,840 --> 00:09:56,400 Speaker 1: The RNC has ninety five million dollars cash on hand 182 00:09:56,440 --> 00:09:58,960 Speaker 1: with no debts. The DNC has twelve million cash on 183 00:09:59,000 --> 00:10:01,720 Speaker 1: hand with sixteen milli million dollars in debts. The only 184 00:10:01,760 --> 00:10:05,320 Speaker 1: area where Democrats as an organization are actually more money 185 00:10:05,360 --> 00:10:08,000 Speaker 1: cash on hand is the Senate component. The D Double 186 00:10:08,120 --> 00:10:12,160 Speaker 1: C has more money than the NRSC twenty one point 187 00:10:12,160 --> 00:10:15,400 Speaker 1: eight to nineteen. It's not a huge difference, but when 188 00:10:15,440 --> 00:10:19,800 Speaker 1: you look at all the organizations combined, Republicans have all 189 00:10:19,840 --> 00:10:24,120 Speaker 1: the RNC affiliate organizations have three hundred and twenty million 190 00:10:24,240 --> 00:10:28,160 Speaker 1: dollars to Democrats one hundred and sixty seven million. I 191 00:10:28,280 --> 00:10:30,440 Speaker 1: want to flag something for you guys before I keep 192 00:10:30,440 --> 00:10:34,480 Speaker 1: going into this. Mark Kelly, Senator from Arizona, raised and 193 00:10:34,760 --> 00:10:38,559 Speaker 1: eye popping twelve point five million lance quarter, just a quarter, 194 00:10:38,600 --> 00:10:41,560 Speaker 1: not that they have the air quarter, which makes me 195 00:10:41,720 --> 00:10:43,959 Speaker 1: question if he's running for president. I don't know. He's 196 00:10:43,960 --> 00:10:47,679 Speaker 1: not a naming floated around. He's considered a more centrist, 197 00:10:47,840 --> 00:10:49,760 Speaker 1: you know, he's a pretty left wing guy, but a centrist. 198 00:10:49,760 --> 00:10:54,000 Speaker 1: He's considered by the media white guy Swing State. Keep 199 00:10:54,000 --> 00:10:55,800 Speaker 1: an eye out for it. Okay, let's get back to 200 00:10:56,080 --> 00:11:01,240 Speaker 1: the organizations. Beside the official MAGA in pack, there are 201 00:11:01,280 --> 00:11:06,600 Speaker 1: a lot of businesses and industries and affiliated organizations who 202 00:11:06,640 --> 00:11:10,240 Speaker 1: are pouring tens of millions into this race. Let's start 203 00:11:10,240 --> 00:11:13,000 Speaker 1: with AI. They have a super pacical leading the future. 204 00:11:13,000 --> 00:11:17,040 Speaker 1: They raise fifty point three million dollars in the last 205 00:11:17,040 --> 00:11:19,600 Speaker 1: second half of the year, with all that money coming 206 00:11:19,600 --> 00:11:22,760 Speaker 1: from the family of the Open AI founder in dres 207 00:11:22,840 --> 00:11:25,319 Speaker 1: And Horoitz. Their total fundraising for the year is one 208 00:11:25,440 --> 00:11:28,480 Speaker 1: hundred and twenty five million dollars, and they are so 209 00:11:28,559 --> 00:11:32,679 Speaker 1: far spending twenty million on just two house races. The 210 00:11:32,720 --> 00:11:35,760 Speaker 1: first one support guy named Chris Gober. He's running for 211 00:11:36,080 --> 00:11:39,240 Speaker 1: to have the house in Texas's tenth district. He's considered 212 00:11:39,360 --> 00:11:43,559 Speaker 1: AI friendly, opposed to regulations. He's also supported by the 213 00:11:43,559 --> 00:11:45,920 Speaker 1: Club for Growth, President Trump and Governor Gray Abbott, so 214 00:11:45,920 --> 00:11:47,800 Speaker 1: he seems like a bit of a lock. It's a 215 00:11:47,840 --> 00:11:50,720 Speaker 1: district that Trump won by twenty five points, so very 216 00:11:50,840 --> 00:11:53,400 Speaker 1: likely that it's almost certain. It's almost certain he's going 217 00:11:53,440 --> 00:11:56,480 Speaker 1: to be elected to Congress. The second race is very interesting, 218 00:11:56,480 --> 00:11:58,960 Speaker 1: it's New York Aseemi men who is running. His name 219 00:11:59,000 --> 00:12:01,559 Speaker 1: is Alex boris running in the New York twelve district. 220 00:12:01,600 --> 00:12:05,520 Speaker 1: That's the Manhattan district that Jerry Nadler is retiring from. 221 00:12:05,760 --> 00:12:09,440 Speaker 1: Is very left wing, very supportive AI regulations. They're spending 222 00:12:09,520 --> 00:12:15,360 Speaker 1: ten million against him. Their goal, allegedly, according to what 223 00:12:15,400 --> 00:12:18,240 Speaker 1: I'm reading, their goal is to push for new laws 224 00:12:18,280 --> 00:12:22,240 Speaker 1: that prevent states from regulating AI. Now, remember there's this 225 00:12:22,320 --> 00:12:26,400 Speaker 1: big narrative in the media, especially on the right, that 226 00:12:26,640 --> 00:12:29,880 Speaker 1: California and New York are deciding all the AI regulations. 227 00:12:29,920 --> 00:12:31,880 Speaker 1: That is a lie, that is not true. That is 228 00:12:32,040 --> 00:12:36,160 Speaker 1: just a talking point. Texas, Tennessee, Florida are all pushing 229 00:12:36,200 --> 00:12:39,440 Speaker 1: for their own AI regulations. Republican states are pushing for 230 00:12:39,480 --> 00:12:43,480 Speaker 1: AI regulations. It's everywhere. The states are realizing they need 231 00:12:43,520 --> 00:12:46,360 Speaker 1: a regulate AI because the federal government is not doing 232 00:12:46,640 --> 00:12:48,839 Speaker 1: but they want to stop that. There was a bill 233 00:12:48,960 --> 00:12:50,800 Speaker 1: to prevent that was in the Big Beautiful Bill, but 234 00:12:50,880 --> 00:12:53,960 Speaker 1: Josh Holly and Marshall Blackburn killed that amendments of the bill. 235 00:12:54,559 --> 00:12:56,800 Speaker 1: Trump didn't, you know, an executive order, but it doesn't 236 00:12:56,840 --> 00:12:57,360 Speaker 1: mean as much. 237 00:12:57,280 --> 00:12:57,679 Speaker 2: As a law. 238 00:12:58,520 --> 00:13:00,480 Speaker 1: Second group to put a lot of money in this 239 00:13:00,559 --> 00:13:04,960 Speaker 1: election is the Crypto Bros. Crypto is spending even more 240 00:13:05,000 --> 00:13:08,880 Speaker 1: than AI. They verily seventy four million dollars in the 241 00:13:09,000 --> 00:13:12,880 Speaker 1: last six months. They have two one hundred million cash 242 00:13:12,880 --> 00:13:16,360 Speaker 1: on hand. Second only to Donald Trump is the Crypto Bros. 243 00:13:16,679 --> 00:13:20,280 Speaker 1: There's another crypto group called Digital Freedom Fund. They're not 244 00:13:20,280 --> 00:13:22,840 Speaker 1: a affiliate with the major main crypto group. They have 245 00:13:22,880 --> 00:13:26,800 Speaker 1: another twenty million dollars, so tons of money coming from 246 00:13:26,840 --> 00:13:30,880 Speaker 1: crypto people about crypto issues. That is pretty eye popping. 247 00:13:31,240 --> 00:13:34,000 Speaker 1: Lastly is a Pack. They are the organization that is 248 00:13:34,080 --> 00:13:36,800 Speaker 1: pro Israel. They have a super pack. They raise sixty 249 00:13:37,200 --> 00:13:40,959 Speaker 1: one point six million dollars. They have about one hundred 250 00:13:41,040 --> 00:13:44,960 Speaker 1: million dollars cash on hand, tons of money. They're probably 251 00:13:44,960 --> 00:13:48,679 Speaker 1: going to get involved heavily in Democrat primaries because they 252 00:13:48,760 --> 00:13:51,880 Speaker 1: want to stop critics of Israel. For media. Elected to 253 00:13:51,920 --> 00:13:54,120 Speaker 1: the House are people who are unfavorable to Israel. They're 254 00:13:54,120 --> 00:13:56,559 Speaker 1: probably spending against Thomas Massey as well in Kentucky's when 255 00:13:56,559 --> 00:13:59,199 Speaker 1: imaging on the Republican side. They're going to get involved 256 00:13:59,240 --> 00:14:01,520 Speaker 1: in these primaries and invest heavily to sit there and 257 00:14:01,600 --> 00:14:04,200 Speaker 1: change the makeup of Congress to make sure that Congress 258 00:14:04,280 --> 00:14:08,600 Speaker 1: is standing with Israel. Very interesting stuff. This is how 259 00:14:08,720 --> 00:14:12,120 Speaker 1: these organizations, these lobbying groups are going to change the 260 00:14:12,160 --> 00:14:14,400 Speaker 1: country and our law is going into the future. These 261 00:14:14,400 --> 00:14:17,960 Speaker 1: are the three big groups, Crypto AI, Israel, and of 262 00:14:17,960 --> 00:14:20,720 Speaker 1: course President trum looms large, but we don't know where 263 00:14:20,720 --> 00:14:23,480 Speaker 1: he's spending with me today to discuss is Jessica Piper, 264 00:14:23,520 --> 00:14:26,800 Speaker 1: a political very smart data reporter, talk about how money 265 00:14:26,800 --> 00:14:30,480 Speaker 1: in politics is shaping up this midterm election. Stay tuned. 266 00:14:33,960 --> 00:14:37,080 Speaker 1: Jessica Piper is a data journalist from Politico. Very smart, lady, 267 00:14:37,120 --> 00:14:38,320 Speaker 1: Thank you for being here. 268 00:14:38,240 --> 00:14:39,120 Speaker 2: Thank you for having me. 269 00:14:39,520 --> 00:14:41,240 Speaker 1: So I want to start off with an article that 270 00:14:41,400 --> 00:14:44,880 Speaker 1: you wrote about some heavy hitters in the mid terms, 271 00:14:44,960 --> 00:14:47,080 Speaker 1: especially on the Republican side of the isley. So there 272 00:14:47,080 --> 00:14:50,160 Speaker 1: are five top targets in the GUP that breaked in 273 00:14:50,200 --> 00:14:51,920 Speaker 1: more than a million in the last quarter, and seven 274 00:14:51,960 --> 00:14:55,560 Speaker 1: who have three million cash on hand. I know Brian Fitzpatrick. 275 00:14:55,600 --> 00:14:59,520 Speaker 1: He's the Republican moderate Republican from Pennsylvania's first district, leads 276 00:14:59,520 --> 00:15:01,960 Speaker 1: the pack. He's got a lot of money. Brian Fitzpatrick. 277 00:15:02,200 --> 00:15:04,160 Speaker 1: Who else is on the list? Do you remember. 278 00:15:04,080 --> 00:15:07,480 Speaker 3: Marianette Miller Meeks? Yes, both she and her Democratic challenge 279 00:15:07,480 --> 00:15:11,880 Speaker 3: are actually also raised quite a bit of money. Other 280 00:15:12,080 --> 00:15:14,160 Speaker 3: people who were over a million in the fourth quarter 281 00:15:14,320 --> 00:15:17,920 Speaker 3: was Mike Laller in New York seventeen wants system money 282 00:15:18,720 --> 00:15:24,160 Speaker 3: in Arizona, and Tom Barrett. 283 00:15:22,800 --> 00:15:23,560 Speaker 1: Over in Michigan. 284 00:15:23,880 --> 00:15:24,600 Speaker 2: Over in Michigan. 285 00:15:25,000 --> 00:15:27,760 Speaker 1: Well, I mean, it's funny because Miller Meeks is in 286 00:15:28,440 --> 00:15:32,200 Speaker 1: what would typically be a very likely Republican seat, only 287 00:15:32,280 --> 00:15:36,880 Speaker 1: she is a consistent underperformer in elections. Brian Fitzpatrick is 288 00:15:36,920 --> 00:15:40,360 Speaker 1: in a slightly it's a seat that I think Harris 289 00:15:40,400 --> 00:15:45,080 Speaker 1: won by point one, and Mike Lawler's in a Harris 290 00:15:45,080 --> 00:15:47,640 Speaker 1: seat as well, so that makes sense why they have it. 291 00:15:47,720 --> 00:15:49,760 Speaker 1: SISCA money pulling in big numbers like that is also 292 00:15:49,840 --> 00:15:53,480 Speaker 1: kind of crazy. So let's talk about some industries that 293 00:15:53,560 --> 00:15:57,200 Speaker 1: are getting very heavily invested in this election, the first 294 00:15:57,280 --> 00:16:00,400 Speaker 1: being AI. They've had a raised over fifteen million this 295 00:16:00,760 --> 00:16:04,240 Speaker 1: last half year, one hundred million for the year, and 296 00:16:04,280 --> 00:16:07,400 Speaker 1: they seem to only be targeting right now two House seats, 297 00:16:07,600 --> 00:16:10,240 Speaker 1: one in Texas for the Republican side, one in New 298 00:16:10,320 --> 00:16:13,440 Speaker 1: York on the Democratic side, both for and against a 299 00:16:13,440 --> 00:16:16,400 Speaker 1: candidate who's not pro AI or in santi AI or 300 00:16:16,480 --> 00:16:18,760 Speaker 1: pro AI regulation. And to say, what can you tell 301 00:16:18,840 --> 00:16:21,840 Speaker 1: us about the AI industry's investment in this election cycle 302 00:16:21,840 --> 00:16:22,560 Speaker 1: on what they're raison. 303 00:16:24,120 --> 00:16:27,600 Speaker 3: Yeah, well, it's really interesting because these AI groups are 304 00:16:27,640 --> 00:16:29,600 Speaker 3: totally new, right, this is going to be the first 305 00:16:29,640 --> 00:16:34,000 Speaker 3: election cycle where we see major contributions and major spending. 306 00:16:34,040 --> 00:16:36,280 Speaker 3: And I think AI is doing a little bit of 307 00:16:36,320 --> 00:16:39,520 Speaker 3: what like the cryptocurrency industry did over the past two cycles, 308 00:16:39,560 --> 00:16:42,320 Speaker 3: where it sort of comes out of nowhere and there's 309 00:16:42,400 --> 00:16:44,760 Speaker 3: enough money in the industry to fund these super PACs 310 00:16:44,800 --> 00:16:47,560 Speaker 3: that have you know, as you said, tens of millions 311 00:16:47,560 --> 00:16:50,880 Speaker 3: one cause of one hundred million dollars already, which for 312 00:16:50,920 --> 00:16:53,680 Speaker 3: congressional elections is a ton of money. Right, We're seeing 313 00:16:53,680 --> 00:16:55,880 Speaker 3: these super PACs are you know, on the level of 314 00:16:55,960 --> 00:16:58,680 Speaker 3: the Congressional Leadership Fund or the host Majority Pack, Like 315 00:16:59,040 --> 00:17:04,080 Speaker 3: they'reotentially even even stronger than these like long existing party groups. 316 00:17:04,280 --> 00:17:09,240 Speaker 3: And so in any race, particularly for a primary, which 317 00:17:09,240 --> 00:17:11,960 Speaker 3: is where we're seeing the spending so far, they can 318 00:17:12,000 --> 00:17:15,080 Speaker 3: really go in and be the biggest vendor and the race, 319 00:17:15,160 --> 00:17:18,879 Speaker 3: and so particularly you know in open primaries like the 320 00:17:19,280 --> 00:17:21,959 Speaker 3: New York twelve race, they're trying to spend against Alex Morris, 321 00:17:22,080 --> 00:17:25,600 Speaker 3: He's a state legislator. As a state legislator did some 322 00:17:25,800 --> 00:17:28,639 Speaker 3: pass some legislation that the AI industry does not like. 323 00:17:28,760 --> 00:17:31,879 Speaker 3: So their goal is to spend and ensure if he 324 00:17:31,880 --> 00:17:35,240 Speaker 3: doesn't make his way to Congress. And we'll see how 325 00:17:35,240 --> 00:17:38,040 Speaker 3: it plays out. You know, they can do a lot 326 00:17:38,080 --> 00:17:41,040 Speaker 3: to run ads and try to persuade voters, and then 327 00:17:41,600 --> 00:17:44,640 Speaker 3: you know there's also it gives candidate sometimes a case 328 00:17:44,680 --> 00:17:46,680 Speaker 3: to be like, well, these these big money to interests 329 00:17:46,680 --> 00:17:48,320 Speaker 3: are up against me, and you know, they try to 330 00:17:48,320 --> 00:17:51,320 Speaker 3: make the argument to the voters that the super pac 331 00:17:51,359 --> 00:17:53,640 Speaker 3: opposing them is maybe actually a reason to support them. 332 00:17:53,680 --> 00:17:54,840 Speaker 2: And yeah, see. 333 00:17:55,600 --> 00:17:58,199 Speaker 1: Boris is doing right now. Over in New York's twelve 334 00:17:58,480 --> 00:18:00,440 Speaker 1: he was the number one fundraiser. He raised over two 335 00:18:00,440 --> 00:18:03,040 Speaker 1: million in this quarter, which is a lot. And I 336 00:18:03,080 --> 00:18:06,000 Speaker 1: mean Cam Kazinski. I think his name is Cameron Kazinski. 337 00:18:06,040 --> 00:18:10,520 Speaker 1: He was a survivor of the shooting in Florida. I 338 00:18:10,520 --> 00:18:13,600 Speaker 1: forget which I'm by a stone stone but I'm sorry, 339 00:18:13,600 --> 00:18:16,280 Speaker 1: I forgot the name of whatever. Yeah, it's a famous 340 00:18:16,320 --> 00:18:20,200 Speaker 1: gun ConTroll. He dropped out of the race. President John 341 00:18:20,240 --> 00:18:23,600 Speaker 1: Kenny's grandson is still in the race. Me raised some 342 00:18:23,680 --> 00:18:26,480 Speaker 1: pretty okay money for a first time candidate. It seems 343 00:18:26,480 --> 00:18:29,159 Speaker 1: that the goal of the AI industry from everything I 344 00:18:29,320 --> 00:18:32,720 Speaker 1: have been reading, is they want to support candidates who 345 00:18:32,760 --> 00:18:36,960 Speaker 1: will create laws to stop states from regulating the industry 346 00:18:36,960 --> 00:18:39,720 Speaker 1: because that's really what their biggest obstacle is and it 347 00:18:39,800 --> 00:18:43,080 Speaker 1: was in the big beautiful bill, but obviously Pres. Senator 348 00:18:43,240 --> 00:18:46,200 Speaker 1: Marshall Blackburn really killed it. What is that accurate? Is 349 00:18:46,200 --> 00:18:48,680 Speaker 1: there anything else there? You know they're trying to push for. 350 00:18:50,040 --> 00:18:51,879 Speaker 3: Well, I think they want They want a lot of things, 351 00:18:51,880 --> 00:18:53,080 Speaker 3: but that's certainly one of them. 352 00:18:53,160 --> 00:18:53,320 Speaker 2: Right. 353 00:18:53,359 --> 00:18:57,160 Speaker 3: They certainly want to ensure that states cannot independently regulate 354 00:18:57,240 --> 00:18:59,080 Speaker 3: AI because they don't want to be dealing with like 355 00:18:59,080 --> 00:19:02,800 Speaker 3: fifty different and you know, regulatory environments in every state. 356 00:19:03,680 --> 00:19:06,159 Speaker 3: I think more generally, you know, they also want to 357 00:19:07,240 --> 00:19:10,840 Speaker 3: you know, establish or kind of recruit and help candidates 358 00:19:10,840 --> 00:19:13,480 Speaker 3: get across the finish line who were kind of open to. 359 00:19:15,040 --> 00:19:15,200 Speaker 2: Wait. 360 00:19:15,240 --> 00:19:16,800 Speaker 3: You know, they see it like the benefits of AI, 361 00:19:17,040 --> 00:19:20,080 Speaker 3: so that can be like data centers and ensuring that 362 00:19:20,119 --> 00:19:22,600 Speaker 3: you know, there's the right regulatory framework to build new 363 00:19:22,640 --> 00:19:25,239 Speaker 3: data centers that these companies rely on, and it can 364 00:19:25,320 --> 00:19:28,320 Speaker 3: be like there's potential you know, eventually for for federal 365 00:19:28,400 --> 00:19:31,199 Speaker 3: legislation that could regulate AI too, so making sure that 366 00:19:31,240 --> 00:19:34,359 Speaker 3: the people who are in positioners to craft that legislation 367 00:19:34,800 --> 00:19:37,480 Speaker 3: or the industry wants them to be friendlier to it. 368 00:19:38,359 --> 00:19:40,320 Speaker 2: So I think definitely in the short. 369 00:19:40,200 --> 00:19:42,960 Speaker 3: Term, you know, state legislation is a big part. But 370 00:19:43,000 --> 00:19:44,560 Speaker 3: I think they're thinking long term. 371 00:19:44,560 --> 00:19:44,800 Speaker 2: Two. 372 00:19:45,400 --> 00:19:47,960 Speaker 1: Yeah, So let's go to someone who raised who's spending 373 00:19:48,000 --> 00:19:52,440 Speaker 1: even more money right now, and that's crypto. Crypto is spending. 374 00:19:52,960 --> 00:19:55,320 Speaker 1: I mean, I've worked on campaigns for a very long time, 375 00:19:55,359 --> 00:19:58,520 Speaker 1: and two years ago or four years ago, twenty twenty two, 376 00:19:58,560 --> 00:20:01,160 Speaker 1: so twenty to four years ago, the crypto was really 377 00:20:01,200 --> 00:20:03,000 Speaker 1: just starting to get in the race, and they were 378 00:20:03,040 --> 00:20:05,920 Speaker 1: throwing crazy numbers at people, and I was like, well, 379 00:20:06,240 --> 00:20:08,840 Speaker 1: how long is this going to last? They're blowing past 380 00:20:08,880 --> 00:20:13,840 Speaker 1: those numbers, you know, by large, large estimates. And there 381 00:20:13,880 --> 00:20:17,879 Speaker 1: are also multiple crypto packs. Now there's the Digital Freedom Fund, 382 00:20:18,080 --> 00:20:20,840 Speaker 1: which is different from the main one, which is raised 383 00:20:20,880 --> 00:20:23,399 Speaker 1: seventy four million. They have two hundred million cash in 384 00:20:23,840 --> 00:20:28,040 Speaker 1: only behind President Trump, and it has an industry ever 385 00:20:28,119 --> 00:20:30,000 Speaker 1: had this much money. 386 00:20:31,040 --> 00:20:33,440 Speaker 3: I mean, certainly not, you know, I mean you could 387 00:20:33,440 --> 00:20:36,080 Speaker 3: try to adjust for inflation and go way way back 388 00:20:36,400 --> 00:20:38,600 Speaker 3: to someone, but we haven't seen any kind of anything 389 00:20:38,640 --> 00:20:41,200 Speaker 3: like this kind of money in politics. Fair Shake, which 390 00:20:41,200 --> 00:20:44,200 Speaker 3: is the end of the was the biggest pack last cycle. 391 00:20:44,240 --> 00:20:48,840 Speaker 3: It still is of the cryptocurrency superpacks, is posishing itself 392 00:20:48,880 --> 00:20:52,679 Speaker 3: to just really swoop into mostly primaries, it looks like, 393 00:20:52,720 --> 00:20:55,680 Speaker 3: but both on the Republican and Democratic side and kind 394 00:20:55,680 --> 00:20:59,800 Speaker 3: of boost candidates who are more aligned with you know, 395 00:21:00,119 --> 00:21:03,400 Speaker 3: it's looking for out of cryptocurrency regulation, and that's sort 396 00:21:03,400 --> 00:21:06,240 Speaker 3: of the new dynamic this cycle compared to last cycle, 397 00:21:06,359 --> 00:21:10,600 Speaker 3: is that there's also more Republican aligned cryptocurrency groups. Fair 398 00:21:10,640 --> 00:21:13,119 Speaker 3: Shake makes a big deal out of you know, it's bipartisan. 399 00:21:13,520 --> 00:21:15,720 Speaker 3: There are some races where it backs Democrats and some 400 00:21:15,840 --> 00:21:18,159 Speaker 3: races where it backs Republicans, you know, depending on the 401 00:21:18,160 --> 00:21:23,440 Speaker 3: specific race. But some more kind of conservative like leaning 402 00:21:24,760 --> 00:21:27,760 Speaker 3: money cryptocurrency people like the Winklebost twins being the most 403 00:21:27,760 --> 00:21:30,560 Speaker 3: prominent ones, are kind of setting up their own group 404 00:21:30,680 --> 00:21:34,520 Speaker 3: to look for cryptocurrency friendly candidates, but specifically Republicans. And 405 00:21:34,960 --> 00:21:36,520 Speaker 3: so that's going to be a new dynamic I think 406 00:21:36,520 --> 00:21:36,880 Speaker 3: this side. 407 00:21:37,000 --> 00:21:39,679 Speaker 1: Yeah, And I mean so I met a guy earlier 408 00:21:39,680 --> 00:21:42,399 Speaker 1: this year who was big for Andrew Cuomo and his like, 409 00:21:42,480 --> 00:21:45,160 Speaker 1: I think his ask was that crypto be taught in schools. 410 00:21:45,160 --> 00:21:48,040 Speaker 1: It was something that was nothing, you know, over the top, right, 411 00:21:48,160 --> 00:21:50,600 Speaker 1: But he was like a twenty five year old or 412 00:21:50,600 --> 00:21:52,679 Speaker 1: twenty six ye old, and he was retired already, and 413 00:21:52,720 --> 00:21:55,600 Speaker 1: he was close to being a billionaire from crypto, and 414 00:21:55,640 --> 00:21:58,920 Speaker 1: he was just basically using this as gambling money. When 415 00:21:59,040 --> 00:22:00,639 Speaker 1: I mean, I guess with ends on how like the 416 00:22:00,680 --> 00:22:04,560 Speaker 1: bitcoin market ups and ups and flows, but this seems 417 00:22:04,640 --> 00:22:09,679 Speaker 1: to be almost an endless money like money. Well for 418 00:22:09,880 --> 00:22:12,800 Speaker 1: these candidates who are on board. Has there been any 419 00:22:12,840 --> 00:22:17,080 Speaker 1: reporting done about candidates who have changed positions out of 420 00:22:17,240 --> 00:22:19,600 Speaker 1: fear of getting on the wrong side of either AI 421 00:22:19,720 --> 00:22:20,200 Speaker 1: or crypto. 422 00:22:21,640 --> 00:22:24,640 Speaker 3: I mean, I think one example, and his people might 423 00:22:24,680 --> 00:22:27,720 Speaker 3: disagree that he's changed sides on this. But Senator, former 424 00:22:27,760 --> 00:22:30,399 Speaker 3: Senator Shared Brown in Alfio was the target of a 425 00:22:30,440 --> 00:22:33,520 Speaker 3: ton of cryptocurrency money last cycle. You know, if he 426 00:22:33,560 --> 00:22:35,680 Speaker 3: was the chair of the Senate Banking Committee, he had 427 00:22:35,880 --> 00:22:39,760 Speaker 3: gone about, you know, some some framework for regulations and 428 00:22:39,840 --> 00:22:42,200 Speaker 3: in ways that the cryptocurrency interest did not like. They 429 00:22:42,240 --> 00:22:45,480 Speaker 3: spent forty million dollars against him. He lost to Bernie Murray. 430 00:22:45,560 --> 00:22:47,480 Speaker 3: Now you know, he's running for senator again he's trying 431 00:22:47,480 --> 00:22:50,800 Speaker 3: to knock off Senator John Hosted in Ohio this year, 432 00:22:51,000 --> 00:22:54,000 Speaker 3: and he's made a few comments that are a little 433 00:22:54,000 --> 00:22:59,360 Speaker 3: bit more friendly towards cryptocurrency, not like big iron side commitments, 434 00:22:59,400 --> 00:23:04,399 Speaker 3: but just saying, you know, the industry, you know, is 435 00:23:04,440 --> 00:23:07,040 Speaker 3: an important part of the economy, and you know he's 436 00:23:07,080 --> 00:23:09,480 Speaker 3: interested in that or whatever. So I think he can 437 00:23:09,520 --> 00:23:11,800 Speaker 3: point to that as an example where I don't think 438 00:23:11,800 --> 00:23:14,879 Speaker 3: that cryptocurrency interest are ever going to get behind Shared Brown. 439 00:23:14,960 --> 00:23:17,600 Speaker 3: But you could see some of the comments he's made 440 00:23:18,000 --> 00:23:21,400 Speaker 3: as you know, maybe he's hoping that they won't spend 441 00:23:21,440 --> 00:23:24,240 Speaker 3: another forty million dollars against him the psyche. 442 00:23:24,600 --> 00:23:28,200 Speaker 1: So cryptos seems to be pretty friendly towards Republicans from 443 00:23:28,200 --> 00:23:30,440 Speaker 1: my experiences more I'm not looking any ded I'm just 444 00:23:30,440 --> 00:23:33,880 Speaker 1: saying from my experiences, cryptos seems very friendly towards Republicans. 445 00:23:33,880 --> 00:23:37,119 Speaker 1: And they, I mean, you have some senators. The outgoing 446 00:23:37,160 --> 00:23:41,439 Speaker 1: Senator to Lomis, I mean, was the biggest champion of 447 00:23:41,480 --> 00:23:44,320 Speaker 1: crypto I could possibly imagine. That's like a Wyoming senator. 448 00:23:44,320 --> 00:23:46,480 Speaker 1: That's not an issue. I think that's on the you know, 449 00:23:46,600 --> 00:23:50,760 Speaker 1: the baited breath of every Wyoming resident. So it seems 450 00:23:50,800 --> 00:23:53,160 Speaker 1: very interesting. Is AI the same way? Are they very 451 00:23:53,280 --> 00:23:55,560 Speaker 1: split between Democrats and Republicans? You think? 452 00:23:56,200 --> 00:23:59,080 Speaker 3: I think AI is a really interesting issue that hasn't 453 00:23:59,080 --> 00:24:03,000 Speaker 3: really fallen cleaning along partisan lines yet, you know, which 454 00:24:03,000 --> 00:24:06,520 Speaker 3: you were talking earlier about state AI regulations and that's 455 00:24:06,560 --> 00:24:09,800 Speaker 3: something where someone like Ron DeSantis down in Florida has 456 00:24:09,840 --> 00:24:14,479 Speaker 3: been very interested in exploring potential legislation around is that 457 00:24:14,480 --> 00:24:17,840 Speaker 3: that Florida can regulate some of the negative aspects of AI. 458 00:24:18,480 --> 00:24:20,879 Speaker 3: But you've also seen, you know, democratic governors doing the 459 00:24:20,880 --> 00:24:24,320 Speaker 3: same thing in other states. We'll see, you know, where 460 00:24:24,359 --> 00:24:26,440 Speaker 3: the money ends up flowing the cycle, but it's it's 461 00:24:26,480 --> 00:24:29,200 Speaker 3: not something that's falling in the along partisan lines yet. 462 00:24:29,280 --> 00:24:33,960 Speaker 1: Okay, let's talk about Trump three hundred million dollars in 463 00:24:34,160 --> 00:24:37,199 Speaker 1: MAGA ink. Now they've made a commitment against Thomas Massey 464 00:24:37,280 --> 00:24:38,919 Speaker 1: that I saw. Obviously, it's not going to be three 465 00:24:38,960 --> 00:24:41,439 Speaker 1: hundred million dollars, but maybe it will, but I doubt it, 466 00:24:42,400 --> 00:24:46,760 Speaker 1: but it will be several million there. How, how what 467 00:24:47,320 --> 00:24:50,280 Speaker 1: has MAGA inc Given any indicators of where they're putting 468 00:24:50,440 --> 00:24:53,600 Speaker 1: this in this almost it's a presidential war chess is 469 00:24:53,640 --> 00:24:56,840 Speaker 1: what it is. I mean, it is substantial. 470 00:24:57,520 --> 00:24:59,760 Speaker 3: Oh, absolutely, it's it's huge. I mean, we talk about 471 00:24:59,800 --> 00:25:03,679 Speaker 3: these cryptocurrency groups having tons and tons of money, and 472 00:25:03,720 --> 00:25:05,920 Speaker 3: Trump has even more than they do. Right, we don't 473 00:25:05,920 --> 00:25:07,880 Speaker 3: know yet where they're going to spend it so far. 474 00:25:08,160 --> 00:25:11,320 Speaker 3: You know, there is a super packed in Kentucky opposing 475 00:25:11,400 --> 00:25:15,760 Speaker 3: Nassi called Mega ky Mega Kentucky, but it actually hasn't 476 00:25:15,800 --> 00:25:18,600 Speaker 3: gotten funding for maga Ink yet. It's been funded by 477 00:25:18,640 --> 00:25:21,960 Speaker 3: other donors. So Trump could either give money through that group, 478 00:25:22,000 --> 00:25:25,120 Speaker 3: which is run by his allies, or maga In could 479 00:25:25,160 --> 00:25:28,720 Speaker 3: just spend directly against Massy. I think there's a real 480 00:25:28,800 --> 00:25:32,720 Speaker 3: question kind of strategically for Trump. Obviously, there's now several 481 00:25:32,800 --> 00:25:37,520 Speaker 3: races where he's you know, endorsed against incumbents, someone like Nassy, 482 00:25:37,600 --> 00:25:40,960 Speaker 3: someone like the Senate race in Louisiana where he's endorsed 483 00:25:41,320 --> 00:25:44,800 Speaker 3: Julia Letlow rebuild Cassidy. I think the question for Trump 484 00:25:44,880 --> 00:25:47,679 Speaker 3: might be on these races where he's endorsed, does he 485 00:25:47,720 --> 00:25:50,399 Speaker 3: also need to throw his money behind it or is 486 00:25:50,440 --> 00:25:54,000 Speaker 3: the endorsement potentially enough with Republican voters and then he 487 00:25:54,040 --> 00:25:56,760 Speaker 3: can hang on to the super pac money for the 488 00:25:56,840 --> 00:25:57,560 Speaker 3: general event? 489 00:25:57,720 --> 00:26:00,000 Speaker 1: Well, how much is for growth really trying to fill 490 00:26:00,040 --> 00:26:02,439 Speaker 1: on those things because Club for Growth really was pushing 491 00:26:02,560 --> 00:26:03,680 Speaker 1: very heavily for Lutlow. 492 00:26:04,720 --> 00:26:06,280 Speaker 2: Yes, and that's that's another thing. 493 00:26:06,320 --> 00:26:10,240 Speaker 3: In some of these races, there's all these other various interests, 494 00:26:10,280 --> 00:26:14,240 Speaker 3: whether it's the cryptocurrency or the AI groups or Club 495 00:26:14,280 --> 00:26:17,080 Speaker 3: for Growth that might jump in and spend their own 496 00:26:17,119 --> 00:26:20,160 Speaker 3: money to try to get their preferred candidate, and sometimes 497 00:26:20,160 --> 00:26:23,320 Speaker 3: that might align with with Trump's referred candidate. There might 498 00:26:23,320 --> 00:26:25,280 Speaker 3: be races where it doesn't. We'll see, you know, some 499 00:26:25,400 --> 00:26:27,560 Speaker 3: of this stuff is still playing out, So you know, 500 00:26:27,640 --> 00:26:30,920 Speaker 3: are there primaries where Maggeting could even spend against one 501 00:26:30,920 --> 00:26:31,800 Speaker 3: of these other groups? 502 00:26:32,280 --> 00:26:33,520 Speaker 1: Is there any potentially don't? 503 00:26:33,960 --> 00:26:37,040 Speaker 2: I mean, they don't have to, you can't. 504 00:26:37,080 --> 00:26:40,320 Speaker 3: There's no like expiration date on political funds. You know, 505 00:26:40,480 --> 00:26:43,040 Speaker 3: they can sit with that money in their bank account 506 00:26:43,040 --> 00:26:46,240 Speaker 3: as long as they want. It's obviously it's kind of 507 00:26:46,240 --> 00:26:49,760 Speaker 3: a weird situation, like there's never been a sitting US 508 00:26:49,880 --> 00:26:54,000 Speaker 3: president with a you know, affiliated super pac that has 509 00:26:54,080 --> 00:26:58,600 Speaker 3: this kind of cash and Trump, you know, in twenty 510 00:26:58,640 --> 00:27:01,040 Speaker 3: twenty four, magget inkst a lot of money to help 511 00:27:01,160 --> 00:27:04,600 Speaker 3: Trump's on campaign, but he can't run for re election 512 00:27:04,720 --> 00:27:07,359 Speaker 3: in twenty twenty eight. So I mean, if he wanted to, 513 00:27:07,440 --> 00:27:10,800 Speaker 3: you know, hang on to that money to help Republicans 514 00:27:11,200 --> 00:27:15,879 Speaker 3: keep the White House or also to potentially help sway 515 00:27:15,960 --> 00:27:20,600 Speaker 3: that twenty twenty primary. Those are all options, and yeah, 516 00:27:20,640 --> 00:27:21,520 Speaker 3: we don't. 517 00:27:21,359 --> 00:27:22,600 Speaker 2: Know what he'll do. 518 00:27:23,040 --> 00:27:25,920 Speaker 1: Yeah, it's just interesting because I mean, if they have 519 00:27:26,000 --> 00:27:28,560 Speaker 1: if jd. Vance and he's the likely nominee, and I 520 00:27:28,600 --> 00:27:30,240 Speaker 1: worked for him, so I know him a little bit, 521 00:27:30,440 --> 00:27:34,119 Speaker 1: but Advance is the nominee, I'm sure it would be 522 00:27:34,280 --> 00:27:37,760 Speaker 1: very helpful for President Trump in his sunset years or 523 00:27:38,280 --> 00:27:40,280 Speaker 1: however old he'll be at the time to have a 524 00:27:40,320 --> 00:27:44,000 Speaker 1: friendly Attorney General's office and you know that they don't 525 00:27:44,280 --> 00:27:46,639 Speaker 1: try to reprosecute him all over again and chase him 526 00:27:46,680 --> 00:27:48,120 Speaker 1: for the rest of his life. I don't know. I'm 527 00:27:48,119 --> 00:27:51,280 Speaker 1: trying to think of where there's this is insane level 528 00:27:51,359 --> 00:27:54,400 Speaker 1: of money between these organizations. Its close to a billion dollars. Okay, 529 00:27:54,480 --> 00:27:57,280 Speaker 1: last organization I want to ask APAC. They are the 530 00:27:57,320 --> 00:28:02,199 Speaker 1: pro Israel group. They have more than It's very funny. 531 00:28:02,240 --> 00:28:07,120 Speaker 1: AI is more affects Americans more than any of these 532 00:28:07,119 --> 00:28:10,760 Speaker 1: other industries. They catch less heat than APAC does. APAC 533 00:28:10,880 --> 00:28:14,520 Speaker 1: has a tremendous amount of the targets coming at them, 534 00:28:15,840 --> 00:28:19,320 Speaker 1: and they really want to punish some people for becoming 535 00:28:19,520 --> 00:28:22,520 Speaker 1: critical of Israel rightly or wrongly, whatever, but they want 536 00:28:22,560 --> 00:28:24,600 Speaker 1: to be punished with Who are some of their top 537 00:28:24,640 --> 00:28:26,960 Speaker 1: targets that they're looking at in this election cycle, because 538 00:28:26,960 --> 00:28:29,680 Speaker 1: they raised tens of millions as well. 539 00:28:29,880 --> 00:28:33,040 Speaker 3: Yeah, so APAC has as an affiliated super pac called 540 00:28:33,040 --> 00:28:36,760 Speaker 3: the United Democracy Project, and that group raised sixty one 541 00:28:36,760 --> 00:28:39,280 Speaker 3: million in the second half of the year, which, again, 542 00:28:39,360 --> 00:28:42,320 Speaker 3: like this is not quite cryptom numbers, but it's a 543 00:28:42,360 --> 00:28:43,000 Speaker 3: ton of cash. 544 00:28:43,080 --> 00:28:43,320 Speaker 2: Right. 545 00:28:43,480 --> 00:28:46,240 Speaker 3: The one race with that super pac is currently spending 546 00:28:46,320 --> 00:28:49,600 Speaker 3: is there's an uptime special election in New Jersey's eleventh 547 00:28:49,640 --> 00:28:55,000 Speaker 3: district the king Governor. There's a pretty big Democratic primary. 548 00:28:55,560 --> 00:28:57,160 Speaker 3: I'm not an expert, I'm not even sure who the 549 00:28:57,200 --> 00:29:00,720 Speaker 3: favorite is, but NEWDP has been spending again against former 550 00:29:00,760 --> 00:29:04,360 Speaker 3: Representative Tom l Andelski and the candidates in that race. 551 00:29:05,080 --> 00:29:07,440 Speaker 3: So that's been their kind of first investment of the cycle. 552 00:29:07,560 --> 00:29:10,680 Speaker 3: And there's other races that there certainly could jump into. 553 00:29:11,160 --> 00:29:15,520 Speaker 3: In Missouri, former Representative Corey Bush, who a pact played 554 00:29:15,720 --> 00:29:18,280 Speaker 3: a role in ousting last cycle, is trying to make 555 00:29:18,280 --> 00:29:21,840 Speaker 3: a comeback against Representative Wesley Bell. So if he needs 556 00:29:21,880 --> 00:29:24,200 Speaker 3: any assistance you know that might be a race they're 557 00:29:24,240 --> 00:29:25,160 Speaker 3: looking at. 558 00:29:25,680 --> 00:29:27,840 Speaker 1: Yeah, they don't always look for the conservative Democrat. They 559 00:29:27,920 --> 00:29:31,680 Speaker 1: just look for like the non anti Israel Democrat. 560 00:29:32,600 --> 00:29:35,600 Speaker 3: Right, it's not Yeah, it's not necessarily about like a 561 00:29:35,640 --> 00:29:39,440 Speaker 3: candidate's overall politics and whether they're progressive or not. It's 562 00:29:39,480 --> 00:29:44,240 Speaker 3: it's pretty narrowly focused on anti Semitism. 563 00:29:44,760 --> 00:29:47,880 Speaker 1: Yeah, Richie Ritchie towards one of their favorites, and he's 564 00:29:47,960 --> 00:29:52,360 Speaker 1: very left wing. Yes, so that's very very interesting. It's 565 00:29:52,400 --> 00:29:56,080 Speaker 1: going to be interesting to see what people think. I mean, 566 00:29:56,120 --> 00:29:58,240 Speaker 1: money does play a big part in politics, but money 567 00:29:58,320 --> 00:30:03,120 Speaker 1: has a negated effect after a while. Like if money 568 00:30:03,120 --> 00:30:07,120 Speaker 1: decided every election, Michael Bloomberg would have been present for life. Uh, 569 00:30:07,280 --> 00:30:10,320 Speaker 1: it doesn't, and only he won was American small. It 570 00:30:10,360 --> 00:30:15,480 Speaker 1: doesn't decide everything. So it will be interesting. As tens 571 00:30:15,480 --> 00:30:17,720 Speaker 1: of millions turns into hundreds of millions in some of 572 00:30:17,760 --> 00:30:21,760 Speaker 1: these races, at what point is it just consultants and 573 00:30:21,800 --> 00:30:24,480 Speaker 1: ad buyers getting a lot of money and making themselves rich. 574 00:30:25,880 --> 00:30:28,120 Speaker 3: Yeah, I think that's a it's a real question. I 575 00:30:28,120 --> 00:30:33,080 Speaker 3: mean I think we saw obviously and the groups like 576 00:30:33,200 --> 00:30:36,120 Speaker 3: to talk about all the races that they win, you know, 577 00:30:36,240 --> 00:30:39,440 Speaker 3: fair shake the cryptocurrency super PACs done in dozens of 578 00:30:39,440 --> 00:30:42,440 Speaker 3: congressional races, and they won I think a majority of them, 579 00:30:42,480 --> 00:30:44,360 Speaker 3: but there were there were quite a few that they lost. 580 00:30:45,320 --> 00:30:48,960 Speaker 3: Colorado's eighth district they backed Representative Yadira Carraveo, who was 581 00:30:49,000 --> 00:30:49,600 Speaker 3: a Democrat. 582 00:30:50,080 --> 00:30:50,680 Speaker 2: She lost. 583 00:30:52,080 --> 00:30:55,920 Speaker 3: In Alaska, they had backed Mary Platola, she lost Nick Bagach. 584 00:30:56,320 --> 00:30:59,400 Speaker 3: So they don't have a perfect track record by any means, 585 00:30:59,480 --> 00:31:01,640 Speaker 3: and so, and I think part of that is that 586 00:31:02,280 --> 00:31:06,040 Speaker 3: there's a lot of things outside of the things that 587 00:31:06,400 --> 00:31:08,720 Speaker 3: groups spend money on, Like groups spend money on ads 588 00:31:08,720 --> 00:31:10,920 Speaker 3: for the most part, but ads aren't the only thing 589 00:31:10,960 --> 00:31:12,280 Speaker 3: that influences voters. 590 00:31:12,320 --> 00:31:12,480 Speaker 2: You know. 591 00:31:12,560 --> 00:31:14,680 Speaker 3: There can be things that happen in the news. There 592 00:31:14,680 --> 00:31:17,560 Speaker 3: can be things specific the candidates. There can be big 593 00:31:17,880 --> 00:31:21,080 Speaker 3: overall nationwide things that can be more important trends. 594 00:31:21,200 --> 00:31:23,320 Speaker 1: Yeah, yeah, Look, I mean, the Club for Growth spent 595 00:31:23,560 --> 00:31:27,480 Speaker 1: so much against both jad Events and Donald Trump, right and. 596 00:31:28,280 --> 00:31:29,040 Speaker 2: Where they are now? 597 00:31:29,120 --> 00:31:33,040 Speaker 1: Right, Well, thank you so much for coming on this podcast. 598 00:31:33,080 --> 00:31:35,720 Speaker 1: Where do people go to read your your articles so 599 00:31:35,800 --> 00:31:37,560 Speaker 1: that I think you're a really really good journalist. 600 00:31:38,360 --> 00:31:42,080 Speaker 3: Oh well, thanks for having me. Politico dot Com subscribe 601 00:31:42,120 --> 00:31:44,320 Speaker 3: to our newsletters. So he usually be linked in there. 602 00:31:44,560 --> 00:31:50,120 Speaker 1: Thank you. Now it's time for the Ask Me Anything segment. 603 00:31:50,160 --> 00:31:52,080 Speaker 1: If you want a part of the Ask Me Anything segment, 604 00:31:52,080 --> 00:31:55,160 Speaker 1: email me Ryan at Numbers Gamepodcast dot com. That's Ryan 605 00:31:55,240 --> 00:31:58,840 Speaker 1: at Plural Numbers Gamepodcast dot com. First question comes from 606 00:31:58,880 --> 00:32:01,240 Speaker 1: Tristan in both ends Emerson and end of your Time's polling. 607 00:32:01,280 --> 00:32:04,800 Speaker 1: So a giant increases towards Trump's approval with Hispanics Postmaduro 608 00:32:05,120 --> 00:32:08,200 Speaker 1: fifteen percent for Emerson thirteen year times. How well do 609 00:32:08,280 --> 00:32:10,480 Speaker 1: you think this will hold and will translate to general 610 00:32:10,560 --> 00:32:15,680 Speaker 1: generic Republicans. Okay, Hispanics in almost every pole very favorable 611 00:32:15,720 --> 00:32:20,080 Speaker 1: towards the Maduro arrest. However, I think it one depends 612 00:32:20,120 --> 00:32:22,640 Speaker 1: on what kind of Hispanic Are you right? Mexicans are 613 00:32:22,680 --> 00:32:25,560 Speaker 1: different than Venezuelans. Mexicans actually, when they broke this down 614 00:32:25,600 --> 00:32:29,440 Speaker 1: by ethnicity, we're against the Maduro arrests, and they're very 615 00:32:29,520 --> 00:32:32,000 Speaker 1: left wings sometimes in their politics. Not all of them, 616 00:32:32,040 --> 00:32:34,800 Speaker 1: but I mean a number, especially in America. So I 617 00:32:34,840 --> 00:32:36,840 Speaker 1: think it depends on what type of Hispanics. I think 618 00:32:36,880 --> 00:32:40,360 Speaker 1: for Venezuelans it probably will mean a lot. Secondly, you 619 00:32:40,400 --> 00:32:42,840 Speaker 1: should be a little careful about these cross tabs. I 620 00:32:42,840 --> 00:32:45,840 Speaker 1: always believe the cross tabs are sexy. I love cross tabs. However, 621 00:32:46,880 --> 00:32:49,880 Speaker 1: look at these sample size. How large is it to 622 00:32:49,960 --> 00:32:52,960 Speaker 1: affect the overall outcome. I looked at the Emerson Pole, 623 00:32:53,640 --> 00:32:56,600 Speaker 1: Very very very few Latinos were asked in that poll, 624 00:32:56,640 --> 00:32:59,960 Speaker 1: which could be causing such swings, and there's large more 625 00:33:00,000 --> 00:33:02,520 Speaker 1: which is the error. If you look at the Emerson 626 00:33:02,600 --> 00:33:05,880 Speaker 1: Pole which was D plus three, the Fox poll which 627 00:33:05,920 --> 00:33:08,080 Speaker 1: was DP plus fourteen, and the New York Times poll 628 00:33:08,080 --> 00:33:11,880 Speaker 1: which was DEEP plus sixteen among Latinos, they're saying a 629 00:33:11,920 --> 00:33:15,120 Speaker 1: story which is this Latinos are going to shift left 630 00:33:15,160 --> 00:33:18,120 Speaker 1: in this election. They do not believe it is as 631 00:33:18,280 --> 00:33:21,800 Speaker 1: left as it was in twenty eighteen, in twenty sixteen 632 00:33:22,080 --> 00:33:27,200 Speaker 1: before the realignment political realignment happened. So basically, there's the 633 00:33:27,720 --> 00:33:29,680 Speaker 1: four dropped out, but it doesn't go as low as 634 00:33:29,680 --> 00:33:32,200 Speaker 1: it used to go for Republicans, and that's what we're 635 00:33:32,200 --> 00:33:34,360 Speaker 1: gonna have to wait and see. Also, a big part 636 00:33:34,400 --> 00:33:37,160 Speaker 1: of that question will be voter intensity. A lot of 637 00:33:37,200 --> 00:33:40,000 Speaker 1: Latinos that vote for Trump in twenty twenty four very 638 00:33:40,240 --> 00:33:42,720 Speaker 1: very not strong voters in terms of turnout and regular 639 00:33:42,760 --> 00:33:45,080 Speaker 1: turnout goes. So we'll have to wait and see but 640 00:33:45,120 --> 00:33:47,000 Speaker 1: as of right now, it shows the Latino vote is 641 00:33:47,040 --> 00:33:49,280 Speaker 1: moving left, not as left as it used to go 642 00:33:49,360 --> 00:33:53,000 Speaker 1: back in the Hillary Clinton days. Next question comes from Meg. 643 00:33:53,160 --> 00:33:57,400 Speaker 1: She writes, I met Meg before, very nice person in megro. 644 00:33:57,560 --> 00:34:00,400 Speaker 1: Please do not be bullied into changing the way you speak. 645 00:34:00,440 --> 00:34:03,040 Speaker 1: One of the many casualties of globalization is the loss 646 00:34:03,080 --> 00:34:06,800 Speaker 1: of regionalism and regional loyalties, especially with dialects. You should 647 00:34:06,840 --> 00:34:09,759 Speaker 1: change me in the podcast to sit there with Ryane Gerdowsky. 648 00:34:10,239 --> 00:34:12,840 Speaker 1: Thank you so much for that. I appreciate that. I 649 00:34:12,880 --> 00:34:16,480 Speaker 1: don't feel bullied. I listen. I'm very thick skin, I'm okay. 650 00:34:16,600 --> 00:34:18,680 Speaker 1: I like that my audience is there and thinks it's 651 00:34:18,680 --> 00:34:21,680 Speaker 1: funny to trade barbs. It's totally okay, and I always 652 00:34:21,719 --> 00:34:24,040 Speaker 1: I am of the belief I do not make fun 653 00:34:24,080 --> 00:34:26,680 Speaker 1: of somebody unless I like them. If I don't like you, 654 00:34:26,719 --> 00:34:28,600 Speaker 1: I won't make fun of you because I'll just ignore you. 655 00:34:28,640 --> 00:34:31,080 Speaker 1: But if I like you, making fun of you is 656 00:34:31,120 --> 00:34:35,080 Speaker 1: a term of demon to me. Maybe that is the incorrect, 657 00:34:35,239 --> 00:34:38,000 Speaker 1: the incorrect lesson to be learning right now, but that's 658 00:34:38,040 --> 00:34:40,120 Speaker 1: the one that I have, So I don't take any 659 00:34:40,160 --> 00:34:41,880 Speaker 1: hostility to it, and I can't change the way I 660 00:34:41,920 --> 00:34:43,360 Speaker 1: speak if I wanted to or not. I mean, this 661 00:34:43,520 --> 00:34:45,640 Speaker 1: is this is who I am. If I ever do 662 00:34:45,680 --> 00:34:48,799 Speaker 1: an episode where I have my friends from New York 663 00:34:48,840 --> 00:34:50,839 Speaker 1: City on, I promise you no one will be able 664 00:34:50,840 --> 00:34:52,919 Speaker 1: to understand a word where any of us are saying 665 00:34:53,000 --> 00:34:55,920 Speaker 1: because our dialects are so strong. So I appreciate that, Meke, 666 00:34:56,000 --> 00:34:57,759 Speaker 1: I really really do, and I'm not offended. I've been 667 00:34:57,760 --> 00:34:59,680 Speaker 1: called to fat you most of my entire life, so 668 00:34:59,719 --> 00:35:01,319 Speaker 1: I mean, like it is what it is. I'm not 669 00:35:01,320 --> 00:35:04,400 Speaker 1: even Jewish. You just have to roll with the punches 670 00:35:04,480 --> 00:35:06,239 Speaker 1: as how you make your way through the world and 671 00:35:06,360 --> 00:35:08,800 Speaker 1: laugh it off. So thank you. Appreciate that, Meg, appreciate 672 00:35:08,880 --> 00:35:10,800 Speaker 1: you all for listening. If you like this podcast, please 673 00:35:10,920 --> 00:35:13,440 Speaker 1: like and subscribe on the iHeartRadio app, Apple podcasts, or 674 00:35:13,440 --> 00:35:15,759 Speaker 1: get your podcasts and on YouTube. I will see you 675 00:35:15,800 --> 00:35:16,720 Speaker 1: guys on Friday.