1 00:00:00,600 --> 00:00:04,120 Speaker 1: Hi, I'm Molly John Fast and this is Fast Politics, 2 00:00:04,360 --> 00:00:07,120 Speaker 1: where we discussed the top political headlines with some of 3 00:00:07,160 --> 00:00:11,320 Speaker 1: today's best minds, and Democrats will continue to control the Senate. 4 00:00:11,480 --> 00:00:14,520 Speaker 1: Tom Vannier is the CEO of Target Smart and he 5 00:00:14,560 --> 00:00:18,240 Speaker 1: will talk to us about how he correctly predicted the midterms. 6 00:00:18,520 --> 00:00:21,760 Speaker 1: Then platformers Casey Newton will talk to us about Elon 7 00:00:21,880 --> 00:00:24,720 Speaker 1: Musk and f t k is bankruptcy. But first we 8 00:00:24,760 --> 00:00:27,960 Speaker 1: have the host of MSNBC is the Lawrence O'Donnell show. 9 00:00:28,400 --> 00:00:33,479 Speaker 1: Help me. Welcome, Laurence O'Donnell. Welcome back to Fast Politics. Lawrence. 10 00:00:33,800 --> 00:00:36,280 Speaker 1: Great to be here. I'm so excited to have you back. 11 00:00:36,320 --> 00:00:41,040 Speaker 1: I wanted to have you back because we talked pretty recently, 12 00:00:41,360 --> 00:00:44,040 Speaker 1: and I want you to talk about like how you 13 00:00:44,159 --> 00:00:47,199 Speaker 1: dealt with these mid terms, because I feel like ultimately 14 00:00:47,240 --> 00:00:49,120 Speaker 1: there were very few people who were right, and you 15 00:00:49,159 --> 00:00:51,800 Speaker 1: were one of them. Well, you know, I was right 16 00:00:52,200 --> 00:00:56,040 Speaker 1: in the in my open declaration, but I didn't know anything. 17 00:00:56,560 --> 00:00:59,680 Speaker 1: And perhaps the other thing I was right about was 18 00:01:00,480 --> 00:01:05,199 Speaker 1: my belief that it is unknowable and that one will 19 00:01:05,280 --> 00:01:10,640 Speaker 1: waste more of a life than anyone ever should by 20 00:01:10,640 --> 00:01:14,399 Speaker 1: trying to figure out what's going to happen in congressional elections. 21 00:01:14,640 --> 00:01:18,440 Speaker 1: I refused, by the way, to use the term mid 22 00:01:18,600 --> 00:01:22,680 Speaker 1: term elections. I think it's one of the great tragedies 23 00:01:22,959 --> 00:01:27,559 Speaker 1: of political coverage in this country and political terminology, because 24 00:01:27,600 --> 00:01:30,480 Speaker 1: it says to people out there, if they ever hear it. 25 00:01:30,640 --> 00:01:32,920 Speaker 1: First of all, what it says to them is, what's that, 26 00:01:33,319 --> 00:01:37,759 Speaker 1: you know, because the kind of non religious voter who's 27 00:01:37,800 --> 00:01:42,440 Speaker 1: not really hit to these election schedules outside of the presidency, 28 00:01:42,720 --> 00:01:47,520 Speaker 1: here's that, and they think it's something weird, unrelated to them. 29 00:01:47,560 --> 00:01:50,120 Speaker 1: It might be city council or school committee or I 30 00:01:50,120 --> 00:01:52,360 Speaker 1: don't know what it is, you know, And so we 31 00:01:52,400 --> 00:01:55,360 Speaker 1: should just call them elections. And if we just keep 32 00:01:55,400 --> 00:01:58,480 Speaker 1: calling them elections, and that we had this country to 33 00:01:58,520 --> 00:02:03,000 Speaker 1: have elections literally every year, you know, not necessarily where 34 00:02:03,040 --> 00:02:07,440 Speaker 1: you live, but you know, we have elections wherever you 35 00:02:07,520 --> 00:02:11,160 Speaker 1: live every two years, every two years. And there are 36 00:02:11,240 --> 00:02:14,560 Speaker 1: some places where they have elections in the odd numbered 37 00:02:14,680 --> 00:02:18,160 Speaker 1: years from mayor and different things like that. And I 38 00:02:18,240 --> 00:02:22,600 Speaker 1: never think about these congressional elections because the chess board 39 00:02:22,680 --> 00:02:26,520 Speaker 1: is way too big and it has become way too complex. 40 00:02:26,720 --> 00:02:29,720 Speaker 1: It did not used to be complex for the you know, 41 00:02:29,840 --> 00:02:33,240 Speaker 1: first forty years of my life, the Democrats won the 42 00:02:33,280 --> 00:02:37,200 Speaker 1: House of Representatives every single time, to the point where 43 00:02:37,240 --> 00:02:39,960 Speaker 1: none of us were aware that it was a contest. 44 00:02:40,240 --> 00:02:44,320 Speaker 1: There wasn't an American who knew that it was optional 45 00:02:44,680 --> 00:02:47,960 Speaker 1: for the Republicans to actually run the House of Representatives. 46 00:02:48,040 --> 00:02:51,280 Speaker 1: We had never seen a Republican speaker. And believe me, 47 00:02:51,320 --> 00:02:55,160 Speaker 1: when they did win in they were shocked. The Republicans 48 00:02:55,280 --> 00:02:57,880 Speaker 1: were shocked. Uh. And none of them knew how to 49 00:02:57,919 --> 00:02:59,960 Speaker 1: handle it. And none of them knew how to be chairman. 50 00:03:00,080 --> 00:03:02,440 Speaker 1: None of them knew how to you know, they obviously 51 00:03:02,440 --> 00:03:04,440 Speaker 1: don't know how to be speaker for you know, and 52 00:03:04,480 --> 00:03:08,000 Speaker 1: so it's just become way too complex to to figure out. 53 00:03:08,200 --> 00:03:11,000 Speaker 1: And so I ignore it and I don't attempt to 54 00:03:11,040 --> 00:03:15,120 Speaker 1: figure it out. And I live with this with a 55 00:03:15,160 --> 00:03:21,680 Speaker 1: condition that apparently, uh, the the American kind of the 56 00:03:21,760 --> 00:03:26,360 Speaker 1: anatomy of the American body politic uh simply cannot bear. 57 00:03:26,760 --> 00:03:30,600 Speaker 1: And that is the condition called suspense. And you know, 58 00:03:30,720 --> 00:03:35,240 Speaker 1: people pay good money to go into dark rooms and 59 00:03:35,360 --> 00:03:39,320 Speaker 1: have the curtain go up and or the screen illuminate, 60 00:03:39,480 --> 00:03:44,480 Speaker 1: and spend two hours in suspense in a theater. They 61 00:03:44,560 --> 00:03:49,280 Speaker 1: pay for that sensation because it is, among other things 62 00:03:49,320 --> 00:03:51,960 Speaker 1: so rare in the rest of our lives. You know, 63 00:03:52,000 --> 00:03:54,960 Speaker 1: we have a neo synthesis, now we have following, we 64 00:03:55,000 --> 00:03:59,040 Speaker 1: have all of these things that have you know, attempted 65 00:03:59,200 --> 00:04:02,320 Speaker 1: to remove suspense as if it's an illness, as if 66 00:04:02,320 --> 00:04:06,160 Speaker 1: it's something we simply physically can't bear. And and so 67 00:04:06,480 --> 00:04:09,680 Speaker 1: my only point of clarity on this, or or being 68 00:04:09,800 --> 00:04:13,280 Speaker 1: in any sense right was in knowing that I couldn't know. 69 00:04:13,680 --> 00:04:17,720 Speaker 1: Therefore I also couldn't be surprised. So all these people 70 00:04:17,720 --> 00:04:20,240 Speaker 1: who are going, I'm really surprised that it's not a 71 00:04:20,279 --> 00:04:24,799 Speaker 1: red wave, Well, they're surprised because they misspent their time 72 00:04:25,040 --> 00:04:28,279 Speaker 1: for the previous year. And the most absurd way you 73 00:04:28,360 --> 00:04:34,200 Speaker 1: can possibly use any ounce of mental capacity is to 74 00:04:34,400 --> 00:04:37,680 Speaker 1: ever look at or refer to what they call the 75 00:04:38,640 --> 00:04:43,040 Speaker 1: generic congressional ballot poll, in which you get this thing 76 00:04:43,200 --> 00:04:47,600 Speaker 1: that says, you know, the voters forty seven percent of 77 00:04:47,680 --> 00:04:51,080 Speaker 1: voters want a Democratic Congress and forty six on a 78 00:04:51,120 --> 00:04:55,080 Speaker 1: Republican Congress or something like that. It's the most idiotic 79 00:04:55,160 --> 00:05:00,800 Speaker 1: thing in the world, especially if you the same time, 80 00:05:00,839 --> 00:05:03,120 Speaker 1: the people, you know, the people who could play with 81 00:05:03,120 --> 00:05:07,680 Speaker 1: this all year, these pundits you know, have in their 82 00:05:07,720 --> 00:05:13,200 Speaker 1: you know ten commandments list. Commandment one issued by House 83 00:05:13,200 --> 00:05:17,360 Speaker 1: Speaker Tip O'Neill, who was from Boston, All politics as local. 84 00:05:17,720 --> 00:05:20,880 Speaker 1: You know, they believe that, right because Tip O'Neal said it, 85 00:05:20,920 --> 00:05:23,640 Speaker 1: and he's he was a political god and a wise 86 00:05:23,680 --> 00:05:26,440 Speaker 1: man and all that, and it absolutely appeared to be 87 00:05:26,480 --> 00:05:29,520 Speaker 1: completely right. And oh, by the way, you know, that 88 00:05:29,720 --> 00:05:33,679 Speaker 1: was the political commandment of life that enabled Tip O'Neal 89 00:05:33,800 --> 00:05:36,880 Speaker 1: as speaker to have a Democratic majority that was so 90 00:05:37,120 --> 00:05:42,119 Speaker 1: big there was never ever ever a risk of any 91 00:05:42,200 --> 00:05:45,240 Speaker 1: bill not passing the House of Representatives because you could 92 00:05:45,279 --> 00:05:48,240 Speaker 1: have dozens of Democrats not vote for it, you know, 93 00:05:48,279 --> 00:05:51,200 Speaker 1: because they're from Texas or Oklahoma and they're uncomfortable with it, 94 00:05:51,400 --> 00:05:53,480 Speaker 1: and you'd still be able to pass it. And Tip 95 00:05:53,560 --> 00:05:56,040 Speaker 1: understood that all politics is local. When the guys from 96 00:05:56,080 --> 00:05:59,080 Speaker 1: Oklahoma can't vote for this, we understand that. And so 97 00:05:59,200 --> 00:06:01,800 Speaker 1: if all Paul tix or local, what the hell are 98 00:06:01,800 --> 00:06:07,919 Speaker 1: you doing with a pole that's supporting seven you know 99 00:06:08,040 --> 00:06:12,440 Speaker 1: what a Republican Congress exactly well. And it's funny because 100 00:06:12,480 --> 00:06:16,599 Speaker 1: with Gretchen Whitmer, who won handily in a swing state 101 00:06:16,760 --> 00:06:19,600 Speaker 1: and who is not being celebrated nearly as much as 102 00:06:20,040 --> 00:06:22,800 Speaker 1: Ron De Santis, who won in a state where there's 103 00:06:22,839 --> 00:06:26,520 Speaker 1: almost no Democratic party structure anymore. Right, But I just 104 00:06:26,560 --> 00:06:29,000 Speaker 1: I'm thinking about that. People said the thing they loved 105 00:06:29,040 --> 00:06:32,040 Speaker 1: about Big Gretch was that she was doing what they 106 00:06:32,120 --> 00:06:36,240 Speaker 1: wanted in their state, you know, fixing roads and doing 107 00:06:36,320 --> 00:06:39,520 Speaker 1: things that they wanted in Michigan. Yeah. And by the way, 108 00:06:39,600 --> 00:06:41,640 Speaker 1: the same thing for De Santis with the voters who 109 00:06:41,680 --> 00:06:44,560 Speaker 1: voted for him. You know, he's you know, putting sending 110 00:06:44,560 --> 00:06:47,400 Speaker 1: airplanes to Texas to fly people in markets Vinyard they 111 00:06:47,400 --> 00:06:50,240 Speaker 1: want that, you know, who lives there, you know, at 112 00:06:50,279 --> 00:06:53,000 Speaker 1: least in that number, right, I mean, there's a good 113 00:06:53,920 --> 00:06:58,279 Speaker 1: of Floridians who were horrified by it, you know. But uh, 114 00:06:58,320 --> 00:07:01,919 Speaker 1: I think you know, with De Santa's proved in the 115 00:07:02,120 --> 00:07:06,719 Speaker 1: current climate and this I suspect it's going to hold 116 00:07:06,760 --> 00:07:10,600 Speaker 1: for a while. There is that Florida is no longer 117 00:07:11,080 --> 00:07:13,520 Speaker 1: a swing state. You can't really think about it that way. 118 00:07:13,600 --> 00:07:18,040 Speaker 1: It's really a Republican state, and Michigan remains within the 119 00:07:18,080 --> 00:07:21,880 Speaker 1: definition of a swing state, which makes it more impressive 120 00:07:22,200 --> 00:07:25,600 Speaker 1: to win that way. You know. There are so many 121 00:07:25,640 --> 00:07:30,800 Speaker 1: stories from Tuesday Night of candidates who really like I mean, 122 00:07:31,080 --> 00:07:34,560 Speaker 1: Mandela Barnes like he lost by one point. I mean 123 00:07:34,680 --> 00:07:39,640 Speaker 1: the Republicans were pumping millions of dollars into keeping a 124 00:07:39,680 --> 00:07:42,640 Speaker 1: seat for Ron and on. I mean, like I do 125 00:07:42,800 --> 00:07:45,480 Speaker 1: think there are a lot of stories of like Democrats 126 00:07:45,560 --> 00:07:49,080 Speaker 1: overperforming by large margins. Yeah, And what I have to 127 00:07:49,080 --> 00:07:51,800 Speaker 1: say to Mandela Barnes and this is something I'm actually 128 00:07:51,960 --> 00:07:54,640 Speaker 1: hoping to do on the show and hoping he comes on, 129 00:07:55,120 --> 00:07:59,080 Speaker 1: and hoping that Rourke comes on, and others who came 130 00:07:59,080 --> 00:08:01,080 Speaker 1: in second, what I want to say to them as 131 00:08:01,520 --> 00:08:06,000 Speaker 1: thank you. Imagine a Wisconsin in which a strong Democratic 132 00:08:06,040 --> 00:08:10,640 Speaker 1: candidate side did not emerge to challenge what was you know, 133 00:08:10,720 --> 00:08:14,640 Speaker 1: a strong enough incumbent Republican running for election. That would 134 00:08:14,680 --> 00:08:18,400 Speaker 1: have been just a disservice to democracy to not have 135 00:08:18,520 --> 00:08:21,840 Speaker 1: that contest there. I've seen it, you know when when 136 00:08:21,880 --> 00:08:23,760 Speaker 1: I worked in the Senate. You saw it all the time. 137 00:08:23,880 --> 00:08:27,440 Speaker 1: You saw states where you know, this candidate, this senator 138 00:08:27,520 --> 00:08:30,200 Speaker 1: is so strong that you know that it's going to 139 00:08:30,280 --> 00:08:34,280 Speaker 1: be very hard to find an opponent. And we actually 140 00:08:34,280 --> 00:08:37,439 Speaker 1: had that experience. And I was working for Senator Daniel 141 00:08:37,440 --> 00:08:41,600 Speaker 1: Patrick moynahan, New York who and the suspense with Amoyahn 142 00:08:41,720 --> 00:08:45,000 Speaker 1: Senate campaign was always was he gonna win by sixty 143 00:08:45,080 --> 00:08:48,400 Speaker 1: seven or sixty and the Republicans knew that. And so 144 00:08:48,640 --> 00:08:51,680 Speaker 1: my first year doing this being involved at all was 145 00:08:51,760 --> 00:08:54,960 Speaker 1: his re election campaign of nineteen eighty and the challenge 146 00:08:55,000 --> 00:08:57,760 Speaker 1: was finding an opponent. And there was this big list 147 00:08:57,840 --> 00:09:02,319 Speaker 1: of big Republican stars who lived in New York, Henry Kissinger, 148 00:09:03,160 --> 00:09:07,520 Speaker 1: Jack Kemp, people like this massive speculation in the press, 149 00:09:07,559 --> 00:09:11,079 Speaker 1: someone named Rudolph Giuliani, who was then the U. S. 150 00:09:11,120 --> 00:09:14,400 Speaker 1: Attorney And each one of them, one by one dropped 151 00:09:14,400 --> 00:09:16,640 Speaker 1: out because each one of them stared at it, they 152 00:09:16,679 --> 00:09:18,520 Speaker 1: thought about it, they kind of wanted to do it, 153 00:09:18,600 --> 00:09:21,400 Speaker 1: and they saw, oh no, Wynihan will crush me, and 154 00:09:21,440 --> 00:09:24,360 Speaker 1: so they dropped out, and we finally, you know, at 155 00:09:24,400 --> 00:09:27,920 Speaker 1: the end of you know, twenty names ended up with someone, 156 00:09:28,600 --> 00:09:35,040 Speaker 1: and I remember feeling grateful that that someone stepped forward 157 00:09:35,320 --> 00:09:40,520 Speaker 1: so that there would be this actual democratic process with 158 00:09:40,679 --> 00:09:43,959 Speaker 1: by the way, no suspense. But he was going to 159 00:09:44,080 --> 00:09:47,880 Speaker 1: get out there and argue, you know, the tax argument 160 00:09:48,080 --> 00:09:51,480 Speaker 1: against you know, the liberal Democrat and New Yorkers were 161 00:09:51,480 --> 00:09:54,320 Speaker 1: going to have that uh discussion, and they were going 162 00:09:54,360 --> 00:09:57,559 Speaker 1: to be able to cast those votes and um and 163 00:09:57,559 --> 00:10:02,360 Speaker 1: and so um you know, imagine, imagine a Texas this 164 00:10:02,440 --> 00:10:07,520 Speaker 1: year without Better Rourke. Imagine a year without even hope, 165 00:10:07,920 --> 00:10:11,520 Speaker 1: without even hope, you know. And there are so many 166 00:10:12,880 --> 00:10:16,760 Speaker 1: ways of living in that we go through in the 167 00:10:16,800 --> 00:10:20,320 Speaker 1: course of our lives. The rarest way of living is 168 00:10:20,440 --> 00:10:24,920 Speaker 1: as a winner. The most common way of winning is 169 00:10:25,000 --> 00:10:29,120 Speaker 1: as someone who's not quite getting where you need to be. 170 00:10:29,880 --> 00:10:33,120 Speaker 1: And for Better or Rourke to get up there a 171 00:10:33,440 --> 00:10:39,320 Speaker 1: third time as statewide, knowing that you know, if he 172 00:10:39,360 --> 00:10:42,480 Speaker 1: loses this time, they're gonna say, they're gonna call him 173 00:10:42,520 --> 00:10:46,160 Speaker 1: a loser. The national media is gonna try to humiliate him, 174 00:10:46,160 --> 00:10:50,760 Speaker 1: in effect, because the national media despises losers unless their 175 00:10:50,840 --> 00:10:54,680 Speaker 1: name Trump. I mean, you think about John Kerry, you 176 00:10:54,760 --> 00:10:59,199 Speaker 1: go back to you know, uh yeah, do Caucus. But 177 00:10:59,280 --> 00:11:01,800 Speaker 1: John Carey, you know, you go back to two thousand four, 178 00:11:02,320 --> 00:11:05,840 Speaker 1: if you flip, you know, sixty votes in Ohio, John 179 00:11:05,960 --> 00:11:10,760 Speaker 1: Terry's president. But he was treated by the media as 180 00:11:10,880 --> 00:11:16,040 Speaker 1: if he came in ninety points behind, you know, George W. Bush, 181 00:11:16,080 --> 00:11:19,360 Speaker 1: as if he was this horrible loser to be banished 182 00:11:19,400 --> 00:11:23,360 Speaker 1: from our site. And by the way Democratic voters felt 183 00:11:23,400 --> 00:11:25,720 Speaker 1: that way, people I knew I could feel it like 184 00:11:25,800 --> 00:11:28,760 Speaker 1: they voted for him, and two weeks later they never 185 00:11:28,800 --> 00:11:31,560 Speaker 1: wanted to hear his name again. And so you know, 186 00:11:31,679 --> 00:11:36,640 Speaker 1: the losers to me have a certain kind of nobility 187 00:11:36,720 --> 00:11:42,720 Speaker 1: that I just feel so warmly about and admiringly about. 188 00:11:42,840 --> 00:11:46,400 Speaker 1: And I know I would never do it myself. I 189 00:11:46,440 --> 00:11:50,040 Speaker 1: would never subject myself to that and subject myself to 190 00:11:50,080 --> 00:11:53,600 Speaker 1: that possibility of being the loser. Uh. And these people 191 00:11:53,679 --> 00:11:57,079 Speaker 1: do it, and they do it for us, and they 192 00:11:57,160 --> 00:12:01,520 Speaker 1: do it for at minimum hope, to give people a 193 00:12:01,600 --> 00:12:05,560 Speaker 1: year of feeling hope instead of horror. Well, and also 194 00:12:05,640 --> 00:12:08,960 Speaker 1: there is polling that shows that you do better in 195 00:12:09,000 --> 00:12:11,280 Speaker 1: a state if you run a candidate even if they 196 00:12:11,280 --> 00:12:14,800 Speaker 1: don't win, like it helps down ballot. Yes, and it's 197 00:12:14,880 --> 00:12:18,720 Speaker 1: projectable certainly within our imaginations. You know that there will 198 00:12:18,800 --> 00:12:23,360 Speaker 1: be again, because there were until the nineties, right into 199 00:12:23,400 --> 00:12:28,880 Speaker 1: the nineties, Democratic senators from Texas and Democratic governors of Texas. Again, 200 00:12:29,280 --> 00:12:31,679 Speaker 1: we can see how that can happen as that as 201 00:12:31,720 --> 00:12:37,000 Speaker 1: the demographics are moving there. But these betto campaigns are 202 00:12:37,160 --> 00:12:42,319 Speaker 1: helping lead to whoever the Democratic winner is statewide. Whoever 203 00:12:42,360 --> 00:12:46,240 Speaker 1: that first Democratic winner is statewide in Texas will have 204 00:12:46,480 --> 00:12:50,320 Speaker 1: built that victory on what betto Rourke has been doing 205 00:12:50,400 --> 00:12:53,520 Speaker 1: for years. There. Yeah, that's a really good point and 206 00:12:53,559 --> 00:12:56,840 Speaker 1: I think really important talk to me about where we 207 00:12:56,920 --> 00:12:59,599 Speaker 1: see things go from here. Now, Oh you're asking for 208 00:13:01,840 --> 00:13:05,000 Speaker 1: so alright, alright, Never I dropped out of that business. 209 00:13:05,440 --> 00:13:09,560 Speaker 1: I'll tell you know. Here's another thing about about watching politics. 210 00:13:09,840 --> 00:13:13,120 Speaker 1: There's kind of an optimal level of attention, and it's 211 00:13:13,160 --> 00:13:17,440 Speaker 1: it's lower than most people realize. So I, as I said, 212 00:13:17,440 --> 00:13:21,200 Speaker 1: I first got into this in because there was a 213 00:13:21,240 --> 00:13:24,640 Speaker 1: writer's build strike in Hollywood and my screenwriting career at 214 00:13:24,679 --> 00:13:28,360 Speaker 1: that point was about a year and a half underway. 215 00:13:28,440 --> 00:13:31,640 Speaker 1: And suddenly I was the you know, lowest ranking member 216 00:13:31,720 --> 00:13:36,120 Speaker 1: in a striking Hollywood union and I was pathetically available 217 00:13:36,600 --> 00:13:40,400 Speaker 1: to join a campaign, which was not my idea. I 218 00:13:40,480 --> 00:13:43,280 Speaker 1: knew Senator moynihan and his wife. His wife was a 219 00:13:43,280 --> 00:13:45,960 Speaker 1: campaign manager, and she just kind of asked me to 220 00:13:46,000 --> 00:13:48,920 Speaker 1: come into the campaign and and be the other guy. 221 00:13:49,120 --> 00:13:51,920 Speaker 1: And I did it mostly as a like a tourist, 222 00:13:52,040 --> 00:13:54,040 Speaker 1: like I'll just this would be fun to watch, Like 223 00:13:54,120 --> 00:13:56,920 Speaker 1: I have no idea what this is, right, but here's 224 00:13:56,920 --> 00:14:00,560 Speaker 1: what I knew with an absolute certainty. Every single day 225 00:14:00,559 --> 00:14:04,800 Speaker 1: of nine and nineteen eight. Michael Ducacus is never going 226 00:14:04,880 --> 00:14:07,600 Speaker 1: to be president. There wasn't a single moment when I 227 00:14:07,640 --> 00:14:11,760 Speaker 1: thought that, okay, even when Michael Ducacus had an eighteen 228 00:14:11,800 --> 00:14:15,480 Speaker 1: point lead in the polls. I'm from Boston, he was 229 00:14:15,600 --> 00:14:19,600 Speaker 1: my governor. I had what I thought were I just had. 230 00:14:19,920 --> 00:14:23,520 Speaker 1: I couldn't even explain it to you. I just had 231 00:14:23,560 --> 00:14:27,600 Speaker 1: this feeling, right, And so what I had was the 232 00:14:27,680 --> 00:14:31,200 Speaker 1: advantage of being an amateur. What I had was the 233 00:14:31,280 --> 00:14:35,040 Speaker 1: advantage of never having been down there on the field 234 00:14:35,200 --> 00:14:39,120 Speaker 1: and only watched this thing from way up high in 235 00:14:39,240 --> 00:14:42,120 Speaker 1: the stands, you know, at the fifty yard line, where 236 00:14:42,120 --> 00:14:44,960 Speaker 1: you could just kind of see the whole thing. And 237 00:14:45,120 --> 00:14:49,600 Speaker 1: I I remember saying, after that first year of my life, 238 00:14:49,640 --> 00:14:54,240 Speaker 1: you know in professional politics that you have about six months. 239 00:14:54,880 --> 00:14:59,040 Speaker 1: It's your first six months in politics when you know anything, 240 00:14:59,160 --> 00:15:02,200 Speaker 1: and after that gonna know nothing. Because in your first 241 00:15:02,320 --> 00:15:07,800 Speaker 1: six months in professional politics, you are them. You are 242 00:15:08,000 --> 00:15:10,840 Speaker 1: one of the people who you're trying to convince you 243 00:15:10,880 --> 00:15:15,280 Speaker 1: are the voter out there. But once you've been professionalized, 244 00:15:15,400 --> 00:15:19,000 Speaker 1: which takes about months, then you have no idea how 245 00:15:19,040 --> 00:15:21,600 Speaker 1: those people think. You spend the rest of your life 246 00:15:21,720 --> 00:15:25,200 Speaker 1: kissing about how those people think, because you're not one 247 00:15:25,280 --> 00:15:29,080 Speaker 1: of them anymore. And so your your optimal moment in 248 00:15:29,120 --> 00:15:33,040 Speaker 1: this is taking it seriously enough to consume the basic 249 00:15:33,120 --> 00:15:36,920 Speaker 1: news about it. But that first six months of really 250 00:15:37,000 --> 00:15:39,920 Speaker 1: kind of watching it is your best. And after that 251 00:15:40,240 --> 00:15:44,400 Speaker 1: you're just kind of, you know, a kind of calloused 252 00:15:44,440 --> 00:15:48,600 Speaker 1: old hack who's just kind of you know, filing one 253 00:15:48,760 --> 00:15:51,760 Speaker 1: hack assumption on top of another hack assumption and some 254 00:15:51,880 --> 00:15:54,520 Speaker 1: other hack. You know, at the bar in the New 255 00:15:54,520 --> 00:15:58,040 Speaker 1: Hampshire primary is telling you something. You buy that, and 256 00:15:58,240 --> 00:16:02,120 Speaker 1: then you go and talk out on TV. Why did 257 00:16:02,160 --> 00:16:04,920 Speaker 1: you think that du Caucus would never win? I just 258 00:16:05,040 --> 00:16:08,440 Speaker 1: knew he wasn't a president. This is great because there's 259 00:16:08,480 --> 00:16:11,440 Speaker 1: no analysis to it. There's nothing that the experts say. 260 00:16:11,560 --> 00:16:15,160 Speaker 1: It's what the voters say, right, and so I would 261 00:16:15,160 --> 00:16:17,520 Speaker 1: have sounded at the time and I would just insist, 262 00:16:17,720 --> 00:16:20,440 Speaker 1: you know, to Senator Moynehan. You know, he was very 263 00:16:20,480 --> 00:16:25,880 Speaker 1: disappointed that he didn't endorse Ducacus early, especially when Ducacus 264 00:16:25,920 --> 00:16:27,760 Speaker 1: was way up in the polls, because it looked like 265 00:16:27,760 --> 00:16:30,240 Speaker 1: there was going to be a Democratic president elected without 266 00:16:30,320 --> 00:16:33,480 Speaker 1: his health. And so he was very jealous that Bill 267 00:16:33,480 --> 00:16:36,440 Speaker 1: Bradley Center from New Jersey had endorsed, you know, like 268 00:16:36,560 --> 00:16:39,000 Speaker 1: Duconcus early so he was going to get to be 269 00:16:39,080 --> 00:16:41,920 Speaker 1: secretary of state or something, and uh, and I was 270 00:16:42,000 --> 00:16:43,880 Speaker 1: sitting there the whole time, go nowhere, he's not gonna 271 00:16:43,880 --> 00:16:48,760 Speaker 1: be president. And it's like they go, why he's not 272 00:16:48,760 --> 00:16:51,360 Speaker 1: gonna be president, you know, because it's like, you know, 273 00:16:51,400 --> 00:16:54,520 Speaker 1: it was all that stuff that a voter sees. The 274 00:16:54,680 --> 00:16:58,320 Speaker 1: cosmetics of him aren't right, you know, the the other 275 00:16:58,360 --> 00:17:03,000 Speaker 1: guy is taller. It's just it's just raw, you know, 276 00:17:03,800 --> 00:17:07,880 Speaker 1: voter imagery. And I liked Ducocus and I liked him 277 00:17:07,880 --> 00:17:10,359 Speaker 1: as a governor. I thought, you know, if you said 278 00:17:10,359 --> 00:17:11,840 Speaker 1: to me he's going to be governor, I go, yeah, 279 00:17:11,840 --> 00:17:14,000 Speaker 1: he's gonna be a governor. You know. Michael lucoccus Is 280 00:17:14,440 --> 00:17:18,360 Speaker 1: stated in writing high school ambition in his Brookline High 281 00:17:18,359 --> 00:17:23,040 Speaker 1: School yearbook was to be governor of Massachusetts. That was achievable. 282 00:17:23,200 --> 00:17:25,879 Speaker 1: But he couldn't get me on that. Now, if you 283 00:17:26,000 --> 00:17:28,560 Speaker 1: run that campaign again, I won't know what to think 284 00:17:28,600 --> 00:17:31,439 Speaker 1: because I'm too close to all the professional analysis. You know. 285 00:17:31,440 --> 00:17:34,119 Speaker 1: It's not like the stock market, where you might maybe 286 00:17:34,359 --> 00:17:37,520 Speaker 1: get rewarded by studying the company is even closer. It's 287 00:17:37,600 --> 00:17:40,720 Speaker 1: much more of a gut thing, you know, the way 288 00:17:41,000 --> 00:17:44,600 Speaker 1: politics works. And you lose your gut after a few 289 00:17:44,600 --> 00:17:48,920 Speaker 1: months of professional studio. So interesting, Lawrence. I'm really glad 290 00:17:48,960 --> 00:17:51,439 Speaker 1: you came back, and I'm really glad like this is 291 00:17:51,480 --> 00:17:54,440 Speaker 1: just sort of a very interesting way to think of 292 00:17:54,480 --> 00:17:57,720 Speaker 1: all of this, and I really appreciate having you. Thank you. 293 00:18:02,200 --> 00:18:06,000 Speaker 1: Tom Bonnier is the CEO of Target Smart. Welcome Too 294 00:18:06,040 --> 00:18:09,280 Speaker 1: Fast Politics, Tom, thanks for having me here. We won 295 00:18:09,359 --> 00:18:12,399 Speaker 1: the mid terms, you and I. We did because we 296 00:18:12,400 --> 00:18:16,320 Speaker 1: were right. And since we're never wrong, certainly never, let's 297 00:18:16,359 --> 00:18:19,399 Speaker 1: talk about that explained to us. How you got it? 298 00:18:19,440 --> 00:18:23,080 Speaker 1: All right? I love this premise, by the way, even 299 00:18:23,119 --> 00:18:27,000 Speaker 1: though I'm slightly uncomfortable with it. But but let's go. 300 00:18:27,480 --> 00:18:29,480 Speaker 1: Let me say I shouldn't say this, I should don't 301 00:18:29,560 --> 00:18:32,439 Speaker 1: lean into it. But I'm not a magician. You know, 302 00:18:32,560 --> 00:18:35,879 Speaker 1: there wasn't any magic here. I'm not a super genius. 303 00:18:36,000 --> 00:18:38,400 Speaker 1: I just followed the data and listened to the data. 304 00:18:38,440 --> 00:18:41,640 Speaker 1: But I also questioned the data. And so yeah, there's 305 00:18:41,680 --> 00:18:45,000 Speaker 1: a lot of information out there. There's the voter registration data, 306 00:18:45,119 --> 00:18:47,440 Speaker 1: there's the polling data, and I think everyone just kind 307 00:18:47,440 --> 00:18:52,840 Speaker 1: of focuses on the polls primarily without a lot of context. 308 00:18:53,080 --> 00:18:55,840 Speaker 1: And yeah, I mean, the short answer is I followed 309 00:18:55,840 --> 00:18:58,520 Speaker 1: the data, I question it, especially when it came to 310 00:18:58,600 --> 00:19:03,040 Speaker 1: the polls, and the data was generally pointing in one direction, 311 00:19:03,320 --> 00:19:06,119 Speaker 1: and it's exactly where we ended up. In fact, the 312 00:19:06,200 --> 00:19:10,480 Speaker 1: only thing that wasn't was this flood of garbage Republican 313 00:19:10,560 --> 00:19:12,760 Speaker 1: polls that we all looked at at the time and 314 00:19:12,800 --> 00:19:16,399 Speaker 1: sort of acknowledged, like, hey, these are garbage Republican polls. 315 00:19:16,720 --> 00:19:19,040 Speaker 1: Then they got used in the averages. Yeah, they got 316 00:19:19,040 --> 00:19:22,439 Speaker 1: thrown into five thirty eight, real clear politics. You know, 317 00:19:22,600 --> 00:19:25,080 Speaker 1: I think a lot of people were frankly nervous because 318 00:19:25,119 --> 00:19:31,800 Speaker 1: they're humans. They lived through man. That was right where 319 00:19:31,800 --> 00:19:35,600 Speaker 1: the needle went from seventy percent everything's gonna be okay 320 00:19:35,640 --> 00:19:38,720 Speaker 1: to your fucked you know, yeah, not great. That was 321 00:19:38,760 --> 00:19:41,439 Speaker 1: not great. And so like we all remember that, or 322 00:19:41,480 --> 00:19:44,200 Speaker 1: maybe we don't remember, maybe we've blacked out from our memory, 323 00:19:44,280 --> 00:19:46,119 Speaker 1: but it's there. It was in there somewhere, and it 324 00:19:46,200 --> 00:19:48,480 Speaker 1: was clear and how people were reacting to these things 325 00:19:48,480 --> 00:19:51,399 Speaker 1: because now everybody's saying the polls were right, the polls 326 00:19:51,440 --> 00:19:53,320 Speaker 1: were right. Well, if the polls were right and the 327 00:19:53,320 --> 00:19:57,679 Speaker 1: outcome is a Democratic victory, why was everybody expecting a 328 00:19:57,720 --> 00:19:59,800 Speaker 1: Republican victory, which is you know, we'd have to bring 329 00:19:59,880 --> 00:20:02,960 Speaker 1: us chologist in for that. But yeah, follow the data. Okay, 330 00:20:03,080 --> 00:20:06,120 Speaker 1: who are the people who did the very partisan GOP 331 00:20:06,280 --> 00:20:10,879 Speaker 1: polling in in late October? Trafalgar and who else? Trafalgar 332 00:20:11,040 --> 00:20:17,520 Speaker 1: was the big one. There's inside er inside advantage. There 333 00:20:17,600 --> 00:20:19,960 Speaker 1: was a whole flood of polls from polsters who I 334 00:20:20,000 --> 00:20:21,919 Speaker 1: had never heard of. I've got to be honest, and 335 00:20:21,960 --> 00:20:27,080 Speaker 1: I maga one to three poles. Yeah, yes, it would 336 00:20:27,080 --> 00:20:31,480 Speaker 1: be like I don't want to pick on kids. There 337 00:20:31,520 --> 00:20:34,000 Speaker 1: were two polling firms that are run by high school 338 00:20:34,119 --> 00:20:36,960 Speaker 1: kids that were all over the averages. One of them, 339 00:20:38,240 --> 00:20:40,800 Speaker 1: I know, I know, I know, I do. I do worry. 340 00:20:41,000 --> 00:20:44,440 Speaker 1: They'll get better next election. They'll be in college exactly 341 00:20:44,480 --> 00:20:47,920 Speaker 1: hopefully yeah or maybe not, maybe not. One of them 342 00:20:47,960 --> 00:20:51,080 Speaker 1: was from my hometown school I didn't go to, but 343 00:20:51,119 --> 00:20:54,040 Speaker 1: Phillips and Over. Oh yeah, I don't feel so bad 344 00:20:54,080 --> 00:20:56,600 Speaker 1: for those kids. Go ahead, no reason to feel bad 345 00:20:56,600 --> 00:20:58,800 Speaker 1: for them. They got a lot going for it. They 346 00:20:58,840 --> 00:21:01,760 Speaker 1: had some of the most Republican in outlier polls. And 347 00:21:01,760 --> 00:21:03,760 Speaker 1: then there was another one from a couple of kids 348 00:21:03,760 --> 00:21:05,520 Speaker 1: who were in high school. It wasn't like a high 349 00:21:05,520 --> 00:21:08,320 Speaker 1: school project. They had some really bad polls. There were 350 00:21:08,359 --> 00:21:11,640 Speaker 1: poles from a Canadian company that were very Republican. There 351 00:21:11,640 --> 00:21:14,960 Speaker 1: were poles from a Brazilian company that were very republican. 352 00:21:15,960 --> 00:21:18,479 Speaker 1: Um Wick was another one that sort of popped up 353 00:21:18,480 --> 00:21:22,000 Speaker 1: in this election and had a lot of outlier results. 354 00:21:22,040 --> 00:21:24,520 Speaker 1: Never heard of them before. But if you go through 355 00:21:24,600 --> 00:21:27,200 Speaker 1: going five thirty eight and like look at the worst states, 356 00:21:27,240 --> 00:21:29,879 Speaker 1: the biggest misses, which are like, you know, the Washington 357 00:21:30,240 --> 00:21:33,840 Speaker 1: Senate race, New Hampshire Senate race, New York governor, New 358 00:21:33,920 --> 00:21:37,760 Speaker 1: York AG, New York AG. She won by like seventy 359 00:21:37,880 --> 00:21:42,439 Speaker 1: three percent. I mean she percent of the vote, didn't 360 00:21:42,440 --> 00:21:46,399 Speaker 1: you Yeah, And and I think it was Travalgart was 361 00:21:46,440 --> 00:21:48,280 Speaker 1: one of those who had a poll that showed that 362 00:21:48,359 --> 00:21:52,200 Speaker 1: being like a margin of vera race. So those polls 363 00:21:52,320 --> 00:21:55,800 Speaker 1: were not great, I think is the kindest way to 364 00:21:55,840 --> 00:21:58,280 Speaker 1: put it. What I think is interesting is like clearly 365 00:21:58,359 --> 00:22:01,280 Speaker 1: we're in an inflection point with ulsters. And the thing 366 00:22:01,320 --> 00:22:03,920 Speaker 1: that I had thought about was like I had wondered 367 00:22:03,920 --> 00:22:05,960 Speaker 1: when we were coming up into this election. I just 368 00:22:06,000 --> 00:22:08,239 Speaker 1: want to actually point something out that's important about these 369 00:22:08,240 --> 00:22:11,679 Speaker 1: polls in this race that is still being counted, but 370 00:22:12,160 --> 00:22:15,919 Speaker 1: probably Lauren beau Bert is going to win. There was 371 00:22:16,119 --> 00:22:19,719 Speaker 1: a really good Democratic candidate who we actually had on 372 00:22:19,760 --> 00:22:23,639 Speaker 1: this podcast, and he had a lot of trouble raising 373 00:22:23,680 --> 00:22:27,520 Speaker 1: money because he was shown to be a like a huge, 374 00:22:28,480 --> 00:22:30,600 Speaker 1: you know, a kind of a candidate who couldn't win, 375 00:22:31,240 --> 00:22:34,560 Speaker 1: and in fact, he really could have won. And you know, 376 00:22:34,680 --> 00:22:37,480 Speaker 1: he's maybe bau Bert will win by a couple of 377 00:22:37,480 --> 00:22:41,200 Speaker 1: thousand votes, maybe last maybe she won't even win, maybe 378 00:22:41,200 --> 00:22:44,119 Speaker 1: because they're still not done counting. But I'm using this 379 00:22:44,240 --> 00:22:47,840 Speaker 1: to illustrate that these polls actually have real world implications 380 00:22:48,160 --> 00:22:50,520 Speaker 1: and I want to talk about that. Yeah, they do, 381 00:22:50,720 --> 00:22:52,760 Speaker 1: and I've seen that. Look, you know, outside of Twitter, 382 00:22:52,840 --> 00:22:55,840 Speaker 1: my day job is actually working with campaigns. We have 383 00:22:55,880 --> 00:22:59,040 Speaker 1: a polling theme ourselves and analytics team, and we provide 384 00:22:59,119 --> 00:23:01,919 Speaker 1: data to Democratic candidates progressive organs around the country, and 385 00:23:01,920 --> 00:23:05,160 Speaker 1: so we see that impact. It's maybe why I feel 386 00:23:05,200 --> 00:23:07,840 Speaker 1: these things even more deeply as we saw that happening 387 00:23:07,920 --> 00:23:11,159 Speaker 1: in races where there were bad poles out there, and 388 00:23:11,200 --> 00:23:13,120 Speaker 1: we knew they were bad poles because you can look 389 00:23:13,160 --> 00:23:15,879 Speaker 1: at the sample and you see, like, well, gosh, this 390 00:23:16,280 --> 00:23:19,480 Speaker 1: likely voter sample in this state is based on a 391 00:23:19,600 --> 00:23:22,480 Speaker 1: notion that young people are just going to flee from 392 00:23:22,480 --> 00:23:25,120 Speaker 1: the state. They won't vote because there won't be any 393 00:23:25,200 --> 00:23:27,480 Speaker 1: Like literally, there's a poll in Pennsylvania that came out 394 00:23:27,560 --> 00:23:29,920 Speaker 1: a few days after the debate that was one night, 395 00:23:30,080 --> 00:23:33,080 Speaker 1: two hour window poll that had Doctors up by a 396 00:23:33,080 --> 00:23:34,600 Speaker 1: couple of points, right, that was the kind of thing 397 00:23:34,640 --> 00:23:36,199 Speaker 1: we're dealing with, And we should have been able to 398 00:23:36,200 --> 00:23:39,320 Speaker 1: look at those and say this is nonsense. Forget about it, 399 00:23:39,320 --> 00:23:41,720 Speaker 1: doesn't get into averages. We're not going to talk about it. 400 00:23:42,000 --> 00:23:44,360 Speaker 1: We're not as reporters going to go out and pretend 401 00:23:44,440 --> 00:23:47,640 Speaker 1: like suddenly this race changed and start writing stories about 402 00:23:47,920 --> 00:23:50,600 Speaker 1: why John Fetterman is losing. We won't do it, but 403 00:23:50,680 --> 00:23:53,879 Speaker 1: we did do it. And the Bobert race is a 404 00:23:53,920 --> 00:23:56,480 Speaker 1: great example of one that wasn't on the radar at all, 405 00:23:56,960 --> 00:24:00,240 Speaker 1: though he was on this podcast. Yeah, and kudos to you, 406 00:24:00,280 --> 00:24:02,320 Speaker 1: because that's incredible because when you look at like that 407 00:24:02,400 --> 00:24:04,600 Speaker 1: was a district that wasn't even in like the Cook 408 00:24:04,720 --> 00:24:08,560 Speaker 1: ratings for like even like the likely Republican whatever the 409 00:24:08,560 --> 00:24:11,359 Speaker 1: thing is right behind Safe, it just wasn't there. But 410 00:24:11,440 --> 00:24:13,720 Speaker 1: I think like some of these higher profile races too, 411 00:24:13,920 --> 00:24:17,560 Speaker 1: like Mandela Barnes, the fact is that's a one point race. 412 00:24:17,720 --> 00:24:20,240 Speaker 1: Right now. It's we're talking about what like a swing 413 00:24:20,280 --> 00:24:24,480 Speaker 1: of thirteen thousand votes, and and he'd be winning that race. 414 00:24:25,160 --> 00:24:27,440 Speaker 1: And you look at the polls, and we saw how 415 00:24:27,480 --> 00:24:30,400 Speaker 1: the polls changed the narrative in that race so dramatically, 416 00:24:30,440 --> 00:24:32,840 Speaker 1: where he was winning right after the primary, and then 417 00:24:32,920 --> 00:24:35,640 Speaker 1: a few of these garbage poles come in and suddenly 418 00:24:36,119 --> 00:24:39,000 Speaker 1: all the narratives are about, well, why is Mandela Barnes losing? 419 00:24:39,000 --> 00:24:41,720 Speaker 1: Its crime, it's obviously the concern of crime and inflation. 420 00:24:41,840 --> 00:24:44,119 Speaker 1: Let's write stories about crime and inflation. And so if 421 00:24:44,119 --> 00:24:46,359 Speaker 1: you're a voter in Wisconsin, you're waking up every morning 422 00:24:46,400 --> 00:24:50,120 Speaker 1: the stories about crime and inflation to fit the narrative 423 00:24:50,480 --> 00:24:53,600 Speaker 1: to explain why Mandela bar Barns is losing. When you 424 00:24:53,600 --> 00:24:56,560 Speaker 1: look at the pole averages there, it wasn't a huge miss, 425 00:24:56,640 --> 00:24:58,200 Speaker 1: but it was a big enough mess where I think 426 00:24:58,200 --> 00:25:01,800 Speaker 1: they had him losing by you know, four points on average, 427 00:25:01,840 --> 00:25:05,680 Speaker 1: the difference between someone losing or perceiving that they're losing 428 00:25:05,720 --> 00:25:08,240 Speaker 1: by four or five points. If you're down four or 429 00:25:08,280 --> 00:25:10,639 Speaker 1: five points, you're probably not going to win if you 430 00:25:10,720 --> 00:25:14,000 Speaker 1: believe the polls. But in reality, those polls were wrong. 431 00:25:14,480 --> 00:25:16,240 Speaker 1: He was down one and I think there would have 432 00:25:16,280 --> 00:25:18,880 Speaker 1: been a lot of more resources directed to that campaign. 433 00:25:19,320 --> 00:25:22,480 Speaker 1: The news stories would have been much less negative. Uh, 434 00:25:22,560 --> 00:25:25,000 Speaker 1: they would have been looking at why is Ron Johnson 435 00:25:25,160 --> 00:25:29,000 Speaker 1: the incumbent barely hanging right? And and well they're spending 436 00:25:29,040 --> 00:25:32,840 Speaker 1: I mean remember Ron and On got a humongous cash 437 00:25:32,960 --> 00:25:38,760 Speaker 1: infusion because of his sketchy taxation of pass fruit through companies. 438 00:25:38,840 --> 00:25:41,120 Speaker 1: And you know, there were a couple of donors who 439 00:25:41,200 --> 00:25:44,359 Speaker 1: just literally put the money they saved in taxes into 440 00:25:44,400 --> 00:25:47,639 Speaker 1: his campaign. And and by the way, Ron and his 441 00:25:47,800 --> 00:25:50,560 Speaker 1: kids trust fund saved a lot of money on taxes too. 442 00:25:50,720 --> 00:25:54,320 Speaker 1: I mean just completely as sketchy. And I say this 443 00:25:54,400 --> 00:25:58,399 Speaker 1: as someone who you know myself has been a beneficiary 444 00:25:58,560 --> 00:26:02,480 Speaker 1: of like familiar all large asse as we say, and 445 00:26:02,720 --> 00:26:06,640 Speaker 1: I am just like furious. I mean it is so corrupt. 446 00:26:06,720 --> 00:26:08,840 Speaker 1: I mean, you can't just change the taxes so they 447 00:26:08,880 --> 00:26:13,960 Speaker 1: benefit you know, but he but he could, he could, 448 00:26:14,119 --> 00:26:15,960 Speaker 1: and he did, and now he has six more years 449 00:26:16,000 --> 00:26:18,320 Speaker 1: to try. Yeah, well the lesson we learned there. If 450 00:26:18,359 --> 00:26:21,200 Speaker 1: you can create the sort of perception of inevitability driven 451 00:26:21,240 --> 00:26:24,359 Speaker 1: by bad polls, um then you're going to be in 452 00:26:24,400 --> 00:26:26,480 Speaker 1: better shape. And I think that's really unfortunate. And I 453 00:26:26,480 --> 00:26:29,480 Speaker 1: think the people are saying that the polls are right. Generally, 454 00:26:29,480 --> 00:26:31,600 Speaker 1: what they're doing is like, well, yeah, if you look 455 00:26:31,640 --> 00:26:34,879 Speaker 1: at it from about a hundred miles away or forty 456 00:26:35,320 --> 00:26:38,800 Speaker 1: feet in the air, yeah, big picture. They say, well, 457 00:26:38,840 --> 00:26:41,520 Speaker 1: the poll said, yeah, the Senate was going to be close. Though, 458 00:26:41,680 --> 00:26:45,560 Speaker 1: to be fair, the models did not predict the seat, 459 00:26:45,960 --> 00:26:48,840 Speaker 1: you know, a Democratic pick up, an actual gain, and 460 00:26:48,880 --> 00:26:52,960 Speaker 1: that's maybe where we'll end up. That's a possible outcome. So, 461 00:26:53,200 --> 00:26:56,639 Speaker 1: but the reality is campaigns don't engage at the forty 462 00:26:57,040 --> 00:27:00,239 Speaker 1: foot level, and donors don't either. I think that's the 463 00:27:00,280 --> 00:27:03,560 Speaker 1: most important point. And can we have confidence in the 464 00:27:03,640 --> 00:27:06,840 Speaker 1: polls at the micro level, at the state by state level? Now, no, 465 00:27:07,040 --> 00:27:09,960 Speaker 1: we can't. Look at the Senate races, and a third 466 00:27:10,080 --> 00:27:13,560 Speaker 1: of the competitive Senate races had polls that where the 467 00:27:13,680 --> 00:27:17,119 Speaker 1: error was greater than four and a half points. And 468 00:27:17,200 --> 00:27:22,040 Speaker 1: so the problem is that error is not predictable. So sure, 469 00:27:22,480 --> 00:27:24,920 Speaker 1: the average error was four points, and people say that's 470 00:27:24,920 --> 00:27:29,840 Speaker 1: not bad, but you don't know which. Yes, sorry, you 471 00:27:29,880 --> 00:27:34,119 Speaker 1: don't know which. I've get I often called it all 472 00:27:34,200 --> 00:27:36,600 Speaker 1: runs together at this point. But the problem is you 473 00:27:36,680 --> 00:27:39,320 Speaker 1: don't know which are the third of states that are 474 00:27:39,359 --> 00:27:42,240 Speaker 1: going to be these massive outliers, and so if you 475 00:27:42,359 --> 00:27:45,719 Speaker 1: don't know which, how can you have confidence? Should Democrats 476 00:27:45,720 --> 00:27:48,040 Speaker 1: have followed the polls and put tons of money into 477 00:27:48,040 --> 00:27:51,040 Speaker 1: Washington State for Patty Murray. Thank god that didn't happen, 478 00:27:51,160 --> 00:27:53,800 Speaker 1: but some amount of money went in there because of 479 00:27:53,840 --> 00:27:56,359 Speaker 1: bad polls, because they're only bad polls. Again, New Hampshire 480 00:27:56,400 --> 00:27:58,560 Speaker 1: is a great example of that too. Yeah, New Hampshire 481 00:27:58,600 --> 00:28:01,040 Speaker 1: is a great example. I mean that is you know, 482 00:28:01,240 --> 00:28:04,280 Speaker 1: I was watching on Tuesday morning when I woke up 483 00:28:04,320 --> 00:28:08,000 Speaker 1: ready for a blood bath, you know, filled with dread. 484 00:28:08,600 --> 00:28:12,320 Speaker 1: I thought to myself, if Democrats can hold New Hampshire, 485 00:28:12,880 --> 00:28:15,199 Speaker 1: it won't be a blood bath. And you know, not 486 00:28:15,320 --> 00:28:18,639 Speaker 1: only did Democrats hold New Hampshire, but she was not 487 00:28:18,680 --> 00:28:21,520 Speaker 1: going to lose. And it just is so shocking to 488 00:28:21,560 --> 00:28:24,520 Speaker 1: me how awful out of these polls were. Yeah, but again, 489 00:28:24,560 --> 00:28:27,840 Speaker 1: if you had actually followed the data, and by the data, 490 00:28:27,880 --> 00:28:30,240 Speaker 1: I mean not just the polls, and in fact very 491 00:28:30,280 --> 00:28:33,119 Speaker 1: little the polls, but but if you look at what 492 00:28:33,240 --> 00:28:36,600 Speaker 1: happened in Kansas, and then you looked at the gender 493 00:28:36,600 --> 00:28:41,160 Speaker 1: gaps and voter registration around the country after Kansas, after Dobbs, 494 00:28:41,280 --> 00:28:43,720 Speaker 1: as we were seeing, and we were reporting on and 495 00:28:43,760 --> 00:28:46,840 Speaker 1: you were lifting this up consistently over that time period. 496 00:28:47,160 --> 00:28:49,520 Speaker 1: And then you look at the early vote. You look 497 00:28:49,560 --> 00:28:52,880 Speaker 1: at New York nineteen, you look at Alaska. Every indicator 498 00:28:52,960 --> 00:28:56,160 Speaker 1: was pointing in this exact same direction. And if you 499 00:28:56,200 --> 00:28:58,480 Speaker 1: were someone who I think like the narrative generally, maybe 500 00:28:58,520 --> 00:29:02,200 Speaker 1: around late August was fairly consistent. If you went to 501 00:29:02,200 --> 00:29:04,360 Speaker 1: sleep in late August and woke up today, you look 502 00:29:04,360 --> 00:29:07,160 Speaker 1: at the result and be like, okay, but not surprising. 503 00:29:07,280 --> 00:29:11,160 Speaker 1: But we all suddenly jumped in bought the narrative of 504 00:29:11,200 --> 00:29:15,200 Speaker 1: these garbage polls. Row happened too early, right as a 505 00:29:15,280 --> 00:29:19,640 Speaker 1: feminist and as a person for whom Row was just 506 00:29:19,720 --> 00:29:21,880 Speaker 1: so even though I saw it coming with s B 507 00:29:22,040 --> 00:29:25,400 Speaker 1: eight in Texas a year before. When it happened, it 508 00:29:25,520 --> 00:29:29,080 Speaker 1: was such a profound moment in my life because I 509 00:29:29,120 --> 00:29:31,080 Speaker 1: thought this could never be taken away from us, and 510 00:29:31,120 --> 00:29:34,280 Speaker 1: all of a sudden it was I you know, immediately 511 00:29:34,680 --> 00:29:37,360 Speaker 1: the next day they were like ten essays, this won't 512 00:29:37,720 --> 00:29:39,880 Speaker 1: this is not you know, we had all the sort 513 00:29:39,880 --> 00:29:42,920 Speaker 1: of bad faith Republicans out there being like, this is 514 00:29:42,920 --> 00:29:45,200 Speaker 1: not going to move the needle, voters don't care. And 515 00:29:45,240 --> 00:29:48,440 Speaker 1: then that after that narrative, we saw this polling, We 516 00:29:48,480 --> 00:29:53,720 Speaker 1: saw these specials where Democrats overperformed, and then in September 517 00:29:53,800 --> 00:29:56,840 Speaker 1: we started with these stories again row happened too early, 518 00:29:57,040 --> 00:30:00,360 Speaker 1: voters don't remember. I want to ask you about this idea. 519 00:30:00,440 --> 00:30:06,720 Speaker 1: It does seem like punden think voters are morons. Yes, 520 00:30:07,160 --> 00:30:09,000 Speaker 1: I mean, I'll tell you to your point. I was 521 00:30:09,040 --> 00:30:11,920 Speaker 1: on CNN at some point in September and I was 522 00:30:11,960 --> 00:30:15,400 Speaker 1: talking about what we were seeing in the voter registration data, 523 00:30:15,800 --> 00:30:20,000 Speaker 1: and I was asked by a male host, well, but yeah, 524 00:30:20,040 --> 00:30:22,360 Speaker 1: that was a long time ago, Dobbs, was a long 525 00:30:22,440 --> 00:30:26,480 Speaker 1: time ago. Don't you think that's faded by now? Isn't 526 00:30:26,520 --> 00:30:29,360 Speaker 1: that what we're seeing? And I genuinely didn't know how 527 00:30:29,440 --> 00:30:32,600 Speaker 1: to answer that question. It's such a bizarre notion, but 528 00:30:32,920 --> 00:30:36,000 Speaker 1: that became the commonly held perception, a lot of it 529 00:30:36,040 --> 00:30:38,920 Speaker 1: again driven by polls. But like we're talking about the 530 00:30:38,920 --> 00:30:43,200 Speaker 1: polls as if there's some sort of like abstract you know, android, 531 00:30:43,320 --> 00:30:46,240 Speaker 1: that it's not actual humans making decisions in these polls, 532 00:30:46,280 --> 00:30:48,920 Speaker 1: that there aren't actual humans pushing this narrative, And then 533 00:30:48,960 --> 00:30:52,120 Speaker 1: as if there aren't actual humans, the pundits, the media 534 00:30:52,640 --> 00:30:55,520 Speaker 1: and frankly, the political professionals whose job it is to 535 00:30:55,520 --> 00:30:57,560 Speaker 1: take these things and learn from them but also filter 536 00:30:57,720 --> 00:31:00,040 Speaker 1: through them, as if they shouldn't have been able to 537 00:31:00,080 --> 00:31:02,360 Speaker 1: look at that and see, like, hey, this whole false 538 00:31:02,400 --> 00:31:08,800 Speaker 1: dichotomy about the economy or inflation versus abortion is a problem, 539 00:31:08,840 --> 00:31:10,680 Speaker 1: and no one was saying that. Well not no one 540 00:31:10,720 --> 00:31:12,479 Speaker 1: was saying that. You were saying that, Many others were 541 00:31:12,560 --> 00:31:15,760 Speaker 1: saying that, but many more people were saying that it 542 00:31:15,760 --> 00:31:19,200 Speaker 1: looks like voters have shifted back. They're just concerned about 543 00:31:19,200 --> 00:31:22,200 Speaker 1: the economy. Now, well, yeah, so many reasons that that 544 00:31:22,240 --> 00:31:25,600 Speaker 1: framework is problematic, but that became the dominant framework, and 545 00:31:25,600 --> 00:31:29,160 Speaker 1: I think contributed more than anything else to this notion 546 00:31:29,200 --> 00:31:32,120 Speaker 1: that the red wave was back on. Yeah, I also think, 547 00:31:32,280 --> 00:31:34,920 Speaker 1: you know, I was on this Twitter spaces last night 548 00:31:34,920 --> 00:31:38,240 Speaker 1: with John Stewart, who I don't know at all, and 549 00:31:38,280 --> 00:31:41,320 Speaker 1: he was saying this thing, which I actually thought was 550 00:31:41,640 --> 00:31:45,160 Speaker 1: quite smart. I don't want to beat up the mainstream 551 00:31:45,200 --> 00:31:47,600 Speaker 1: media a because I'm lucky and that I'm on the 552 00:31:47,600 --> 00:31:50,239 Speaker 1: opinion side, so I don't have to pretend not to 553 00:31:50,240 --> 00:31:54,520 Speaker 1: believe in things. But also I do think that it's 554 00:31:54,520 --> 00:31:56,480 Speaker 1: been a bad time for the media and people have 555 00:31:56,560 --> 00:31:59,960 Speaker 1: just been so horrendous. But I do think that they 556 00:32:00,000 --> 00:32:04,000 Speaker 1: there is you know, the horse race narrative is where 557 00:32:04,040 --> 00:32:07,520 Speaker 1: a lot of this election coverage is stock. I share 558 00:32:07,560 --> 00:32:10,200 Speaker 1: your hesitation in terms of being critical of the media, 559 00:32:10,560 --> 00:32:15,080 Speaker 1: largely because it is their responsibility to explain what is 560 00:32:15,120 --> 00:32:19,600 Speaker 1: happening and maybe add some context as to why and when. 561 00:32:19,800 --> 00:32:22,320 Speaker 1: All they are being fed from these polls and frankly 562 00:32:22,360 --> 00:32:25,560 Speaker 1: from political If they were only hearing it from Republican 563 00:32:26,120 --> 00:32:28,320 Speaker 1: campaign operatives, then that's one thing. They have to be 564 00:32:28,360 --> 00:32:31,040 Speaker 1: more skeptical. But they were hearing this from democratic campaign 565 00:32:31,080 --> 00:32:33,720 Speaker 1: operatives to let's be real, not all of them, but 566 00:32:33,960 --> 00:32:37,320 Speaker 1: enough of them where there is credibility, and you look 567 00:32:37,360 --> 00:32:40,240 Speaker 1: at these bad polls that are showing them something. So 568 00:32:40,480 --> 00:32:42,600 Speaker 1: to them, the narrative now is the red wave is 569 00:32:42,640 --> 00:32:46,960 Speaker 1: back on. Voters have forgotten about jobs, or at least 570 00:32:47,120 --> 00:32:50,160 Speaker 1: it's not as important as maybe we thought it was 571 00:32:50,280 --> 00:32:53,960 Speaker 1: to them and their decision making, and therefore I've got 572 00:32:53,960 --> 00:32:56,479 Speaker 1: to go out and write stories about why that is 573 00:32:56,640 --> 00:32:59,760 Speaker 1: and what's happening. And that's generally what happened, and so like. 574 00:33:00,120 --> 00:33:02,200 Speaker 1: Would have been nice if they were digging in a 575 00:33:02,240 --> 00:33:04,760 Speaker 1: little deeper on the pulse. But to expect every political 576 00:33:04,840 --> 00:33:08,400 Speaker 1: reporter to also be a polling expert is not exactly fair, 577 00:33:09,720 --> 00:33:12,200 Speaker 1: and it is I mean, I just want to point 578 00:33:12,240 --> 00:33:14,800 Speaker 1: out there were a lot of people on the right 579 00:33:16,040 --> 00:33:19,440 Speaker 1: pushing the narrative that like, because I remember I did 580 00:33:19,560 --> 00:33:22,080 Speaker 1: a TV hit I don't know, a couple of weeks 581 00:33:22,080 --> 00:33:24,880 Speaker 1: ago with Maddie and we were talking about how, like 582 00:33:25,240 --> 00:33:28,680 Speaker 1: I actually thought voters would vote for democracy. I thought 583 00:33:28,720 --> 00:33:32,160 Speaker 1: that that would really that they want calm, and uh, 584 00:33:32,280 --> 00:33:34,400 Speaker 1: you know, I was being made fun of by people 585 00:33:34,400 --> 00:33:37,200 Speaker 1: on the right, Like no, people if they keep you know, 586 00:33:37,320 --> 00:33:40,320 Speaker 1: all they care about or gas prices and you know, look, 587 00:33:40,520 --> 00:33:43,560 Speaker 1: gas prices are a serious thing, especially if you drive 588 00:33:43,640 --> 00:33:45,680 Speaker 1: for a living or you have you know, if you 589 00:33:45,800 --> 00:33:48,720 Speaker 1: live very far from where you work. But and they 590 00:33:48,760 --> 00:33:53,120 Speaker 1: really are. But but ultimately, like the American voter can 591 00:33:53,240 --> 00:33:57,040 Speaker 1: walk and chew gon yes, yeah, they always do. They 592 00:33:57,040 --> 00:34:02,640 Speaker 1: always do, and and they they pay uh less attention 593 00:34:02,680 --> 00:34:04,240 Speaker 1: to a lot of these issues than we think. But 594 00:34:04,280 --> 00:34:06,280 Speaker 1: in other ways they pay much more attention, or they 595 00:34:06,320 --> 00:34:09,960 Speaker 1: pay attention in different ways because it's what's real to them, 596 00:34:10,040 --> 00:34:13,840 Speaker 1: it's what's actually breaking through. These polls distill it down 597 00:34:13,880 --> 00:34:17,120 Speaker 1: into such a simplistic framework, and the fact the matter is, 598 00:34:17,719 --> 00:34:22,040 Speaker 1: when you ask someone to predict their own behavior or psychology, 599 00:34:22,280 --> 00:34:24,000 Speaker 1: there are a lot of problems with that. They're going 600 00:34:24,040 --> 00:34:26,120 Speaker 1: to tell you what they think is the right answer, 601 00:34:26,200 --> 00:34:28,160 Speaker 1: or what they should say, or what they would like 602 00:34:28,280 --> 00:34:30,759 Speaker 1: to think. You know, the person who they would like 603 00:34:30,920 --> 00:34:33,440 Speaker 1: to be versus the person who they are. In the end, 604 00:34:33,480 --> 00:34:37,440 Speaker 1: we're poor predictors of our own psychology and behavior. But 605 00:34:37,520 --> 00:34:39,920 Speaker 1: if you ask someone if money is important to you, 606 00:34:40,520 --> 00:34:43,480 Speaker 1: or if it's disturbing to you that things cost more 607 00:34:43,560 --> 00:34:46,160 Speaker 1: than they used to, which is basically the framework of saying, 608 00:34:46,239 --> 00:34:50,200 Speaker 1: is the economy important? Are you concerned about inflation? Everyone's 609 00:34:50,200 --> 00:34:52,799 Speaker 1: going to say yes. And that's the other issue is 610 00:34:53,000 --> 00:34:56,239 Speaker 1: that the the issues that elevate to the top are 611 00:34:56,320 --> 00:35:00,399 Speaker 1: those that have sort of bipartisan symmetry and response, meaning 612 00:35:00,440 --> 00:35:04,960 Speaker 1: Democrats and Republicans both say the economy is important to them, 613 00:35:05,080 --> 00:35:07,600 Speaker 1: so it elevates to the top of the issue list. 614 00:35:07,840 --> 00:35:11,719 Speaker 1: Do democrats and Republicans think the same thing about the 615 00:35:11,760 --> 00:35:15,680 Speaker 1: economy interms? Not at all, not one bit. And so 616 00:35:16,000 --> 00:35:18,320 Speaker 1: you're seeing choice. What I was seeing in the data 617 00:35:18,440 --> 00:35:22,800 Speaker 1: when you go under the hood with these polls was well, sure, overall, 618 00:35:22,920 --> 00:35:26,040 Speaker 1: choice wasn't as big of an issue and self report 619 00:35:26,200 --> 00:35:30,239 Speaker 1: as other issues because almost no Republicans were elevating it 620 00:35:30,320 --> 00:35:32,840 Speaker 1: because that was the quote right answer for them. But 621 00:35:32,840 --> 00:35:35,279 Speaker 1: when you looked at the voters who Democrats needed to 622 00:35:35,360 --> 00:35:39,799 Speaker 1: exceed expectations and to win in this election, meaning mobilization 623 00:35:39,840 --> 00:35:43,440 Speaker 1: targets Democrats who maybe wouldn't vote unless we persuaded them to, 624 00:35:43,840 --> 00:35:47,520 Speaker 1: or persuasion targets voters who could vote Democrat or Republican, 625 00:35:47,840 --> 00:35:53,160 Speaker 1: choice was a very salient issue. Yeah, so interesting. Thank you, 626 00:35:53,239 --> 00:35:55,799 Speaker 1: thank you, thank you, Tom. Yeah, thank you so much 627 00:35:55,920 --> 00:36:00,279 Speaker 1: to thrill to be here. Casey Newton is a tech 628 00:36:00,320 --> 00:36:03,359 Speaker 1: reporter or a platform er and the host of the 629 00:36:03,440 --> 00:36:08,799 Speaker 1: podcast hard Fork Welcome Too Fast Politics. Casey, Hey, thanks 630 00:36:08,840 --> 00:36:12,880 Speaker 1: for having me, Molly. Today's Friday. This will air Monday. 631 00:36:13,000 --> 00:36:16,440 Speaker 1: We will have it. Is two weeks, fourteen days, right 632 00:36:16,600 --> 00:36:19,919 Speaker 1: or maybe fifteen days fourteen days really of Elon Musk 633 00:36:19,960 --> 00:36:24,960 Speaker 1: owning Twitter? Whoa go? Yeah, I think that's exactly like 634 00:36:25,000 --> 00:36:28,200 Speaker 1: the only sort of a rational response to the two 635 00:36:28,239 --> 00:36:30,560 Speaker 1: weeks of Elon owning Twitter is just a series of 636 00:36:30,560 --> 00:36:35,000 Speaker 1: confused noises, Right, exactly did this go the way you 637 00:36:35,040 --> 00:36:38,880 Speaker 1: thought it would? And if so, Wow, Well, I think 638 00:36:39,000 --> 00:36:42,719 Speaker 1: everything is moving much faster. You know, I think it's 639 00:36:42,760 --> 00:36:47,200 Speaker 1: important to remember that while Twitter had its challenges, it 640 00:36:47,280 --> 00:36:50,959 Speaker 1: was still an okay business when Elon Musk took it over. 641 00:36:51,000 --> 00:36:53,000 Speaker 1: You know, it makes billions of dollars a year, it 642 00:36:53,040 --> 00:36:56,920 Speaker 1: has hundreds of millions of users. And yet somehow, within 643 00:36:57,160 --> 00:37:00,000 Speaker 1: two weeks you have Elon Musk appearing before the company 644 00:37:00,120 --> 00:37:02,440 Speaker 1: saying that bankruptcy isn't out of the question. So that 645 00:37:02,440 --> 00:37:06,200 Speaker 1: feels very fast to me. Let's talk about what happened here. 646 00:37:06,280 --> 00:37:08,640 Speaker 1: So he he didn't really want to buy the company, 647 00:37:08,719 --> 00:37:10,360 Speaker 1: and I think he didn't think he was going to 648 00:37:10,520 --> 00:37:14,560 Speaker 1: have to until he did. One of the strangest turns 649 00:37:14,640 --> 00:37:17,600 Speaker 1: in this story is that after he bought it, he 650 00:37:17,680 --> 00:37:20,480 Speaker 1: seemed to immediately change his mind. He spent most of 651 00:37:20,480 --> 00:37:22,319 Speaker 1: the year trying to get out of it, and then 652 00:37:22,480 --> 00:37:25,120 Speaker 1: some sort of rulings started to go against him in 653 00:37:25,160 --> 00:37:27,320 Speaker 1: the Delaware Court of Chancery, and he changes to and 654 00:37:27,480 --> 00:37:29,040 Speaker 1: he said, Okay, I want it, and I'm going to 655 00:37:29,040 --> 00:37:30,960 Speaker 1: close the deal now. And I still don't really think 656 00:37:31,000 --> 00:37:34,000 Speaker 1: we've got an adequate explanation for how or why that happened. 657 00:37:34,160 --> 00:37:35,920 Speaker 1: But yeah, then he showed up two weeks ago and 658 00:37:36,000 --> 00:37:39,120 Speaker 1: started making changes, and he really overpaid for it, right, 659 00:37:39,160 --> 00:37:42,520 Speaker 1: Like he has thirteen billion dollars of personal exposure on 660 00:37:42,600 --> 00:37:45,400 Speaker 1: this too. Oh yeah, I mean there's not one person 661 00:37:45,440 --> 00:37:47,879 Speaker 1: who thought Twitter was worth forty four billion dollars when 662 00:37:47,880 --> 00:37:50,120 Speaker 1: he actually acquired it, right because the tech stalks had 663 00:37:50,120 --> 00:37:53,120 Speaker 1: all declined so much since then. So Elon Musk really 664 00:37:53,160 --> 00:37:55,279 Speaker 1: bought it at the peak. Um in a way that 665 00:37:55,400 --> 00:37:58,279 Speaker 1: is amusing to something I would say. So, now we 666 00:37:58,360 --> 00:38:03,120 Speaker 1: have this situation where he fired half the staff. Basically 667 00:38:03,400 --> 00:38:07,000 Speaker 1: this entire time, people have been telling you what's happening 668 00:38:07,040 --> 00:38:11,040 Speaker 1: as it's going, tell us what's happened. It really has 669 00:38:11,160 --> 00:38:15,239 Speaker 1: been chaos, you know. So a week ago today, from 670 00:38:15,239 --> 00:38:18,319 Speaker 1: the day that we're recording this, Elon Musk laid off 671 00:38:18,400 --> 00:38:23,239 Speaker 1: half of the staff, over thirty people, and then less 672 00:38:23,239 --> 00:38:26,040 Speaker 1: than twenty four hours later started to reach out to 673 00:38:26,120 --> 00:38:28,120 Speaker 1: some of those same people that had just been laid 674 00:38:28,120 --> 00:38:30,960 Speaker 1: off and said, actually, um, we might need you back 675 00:38:31,600 --> 00:38:34,480 Speaker 1: in just a sort of ready fire aim approach to 676 00:38:35,040 --> 00:38:37,240 Speaker 1: right sizing the company. And of course you can imagine 677 00:38:37,280 --> 00:38:39,480 Speaker 1: all the turmoil that that cause for you know, employees. 678 00:38:39,760 --> 00:38:42,640 Speaker 1: Did some people come back? I have not spoken to 679 00:38:42,680 --> 00:38:45,880 Speaker 1: anyone who is in that position, and in fact, a 680 00:38:45,880 --> 00:38:47,799 Speaker 1: lot of the folks I know, after they got that 681 00:38:47,840 --> 00:38:52,320 Speaker 1: initial notice, we're looking forward to ninety days of essentially 682 00:38:52,360 --> 00:38:56,480 Speaker 1: paid vacation. There's this sixty day warm Act notice that 683 00:38:56,520 --> 00:38:59,120 Speaker 1: they get paid out for, and then there is thirty 684 00:38:59,160 --> 00:39:01,239 Speaker 1: days of severance that are being offered. So most people 685 00:39:01,280 --> 00:39:03,800 Speaker 1: would rather take that than work at the new Twitter, 686 00:39:03,960 --> 00:39:06,400 Speaker 1: and so people are hoping that they're not forced to 687 00:39:06,440 --> 00:39:08,640 Speaker 1: go take their old jobs back. Ellen is also going 688 00:39:08,719 --> 00:39:11,440 Speaker 1: to have to pay millions of dollars in fees, right, 689 00:39:11,600 --> 00:39:14,200 Speaker 1: fees for what for laying off half the company, and 690 00:39:14,239 --> 00:39:18,359 Speaker 1: because California has laws that are labor laws right well, 691 00:39:18,400 --> 00:39:21,480 Speaker 1: so my understanding of that is usually usually the penalty 692 00:39:21,560 --> 00:39:24,520 Speaker 1: for violating the warnack is paying back pay. So that's 693 00:39:24,560 --> 00:39:27,280 Speaker 1: why all these workers are being given their sixty day notice. 694 00:39:27,480 --> 00:39:30,719 Speaker 1: Now there are a couple of class action lawsuits. The 695 00:39:30,800 --> 00:39:32,920 Speaker 1: lawyers we've talked to. You You don't seem to think they 696 00:39:32,920 --> 00:39:35,040 Speaker 1: have all that great of a chance of making any 697 00:39:35,080 --> 00:39:37,400 Speaker 1: real money for anyone but the lawyers. But yeah, the 698 00:39:37,640 --> 00:39:39,800 Speaker 1: real payment, at least at first, is just going to 699 00:39:39,880 --> 00:39:43,000 Speaker 1: be sixty days of this warnack notice. Pay let's talk 700 00:39:43,040 --> 00:39:46,279 Speaker 1: about what Twitter looks like right now. Last night there 701 00:39:46,400 --> 00:39:49,799 Speaker 1: was more Twitter carnage. Well, there was certainly a lot 702 00:39:49,880 --> 00:39:53,240 Speaker 1: of confusion around the ongoing disaster of the verification badge. 703 00:39:53,320 --> 00:39:56,800 Speaker 1: Explained to those of us who are not like extremely online, 704 00:39:57,360 --> 00:40:00,879 Speaker 1: what the verification badges, what Ellen wanted to do with it, 705 00:40:00,960 --> 00:40:04,120 Speaker 1: and the chaos that ensued. Yeah, so the verification badge 706 00:40:04,160 --> 00:40:06,600 Speaker 1: was created in two thousand and nine and it has 707 00:40:06,680 --> 00:40:09,040 Speaker 1: only ever had one point, which is to say that 708 00:40:09,120 --> 00:40:12,920 Speaker 1: whoever is tweeting, whether it's a person a government agency, 709 00:40:13,360 --> 00:40:15,600 Speaker 1: is who they say they are, Right, so when I tweet, 710 00:40:15,640 --> 00:40:18,160 Speaker 1: you know that it's me Casey Newton who's tweeting. Ellen 711 00:40:18,200 --> 00:40:20,520 Speaker 1: thought that that was a little too elitist because not 712 00:40:20,600 --> 00:40:23,600 Speaker 1: everyone could have access to verification, and he wanted to 713 00:40:23,640 --> 00:40:25,480 Speaker 1: expand that. And by the way, I think that's a 714 00:40:25,480 --> 00:40:27,439 Speaker 1: good idea. I think Twitter could be better if people 715 00:40:27,480 --> 00:40:31,160 Speaker 1: could optionally verify their identity. Right. But he goes about 716 00:40:31,200 --> 00:40:34,279 Speaker 1: it in the strangest way imaginable, which is that he 717 00:40:34,320 --> 00:40:37,279 Speaker 1: takes that same blue checkmark that has been synonymous with 718 00:40:37,480 --> 00:40:39,239 Speaker 1: you are who you say you are for over a 719 00:40:39,280 --> 00:40:42,560 Speaker 1: decade and he starts giving it away to anyone who 720 00:40:42,600 --> 00:40:46,799 Speaker 1: will pay him eight dollars, and he finds himself surprised 721 00:40:46,840 --> 00:40:49,040 Speaker 1: when people do exactly what you and I would have 722 00:40:49,120 --> 00:40:51,759 Speaker 1: guessed that they would have done if they were given 723 00:40:51,800 --> 00:40:54,319 Speaker 1: this feature. So you have people creating uh, you know, 724 00:40:54,400 --> 00:40:57,919 Speaker 1: fake Tesla accounts, getting verified and then tweeting jokes about 725 00:40:58,000 --> 00:41:01,920 Speaker 1: running over pedestrians. You have people creating an Eli Lily account, 726 00:41:02,040 --> 00:41:05,600 Speaker 1: getting it verified and said tweeting that insulin is free. Right, So, 727 00:41:05,960 --> 00:41:07,800 Speaker 1: and all of this stuff is like kind of funny, 728 00:41:07,800 --> 00:41:09,319 Speaker 1: and I think most of the things that we've seen 729 00:41:09,360 --> 00:41:11,920 Speaker 1: so far are kind of in that zone of like hijinks. 730 00:41:11,960 --> 00:41:15,000 Speaker 1: But you can imagine much darker and scarier uses of 731 00:41:15,040 --> 00:41:20,200 Speaker 1: this kind of mischief. Yes, absolutely, completely and utterly true. Now, 732 00:41:20,480 --> 00:41:23,399 Speaker 1: were there trust in safety people who left last night 733 00:41:23,480 --> 00:41:25,960 Speaker 1: or was that not true? That is true. So the 734 00:41:26,040 --> 00:41:28,319 Speaker 1: head of trust and safety on the site, you know, 735 00:41:28,440 --> 00:41:31,160 Speaker 1: l Roth, resigned from the company. A number of other 736 00:41:31,239 --> 00:41:33,320 Speaker 1: people who I think I'm as sort of the watchdogs 737 00:41:33,360 --> 00:41:36,640 Speaker 1: of Twitter left too. So the chief information security officer, 738 00:41:36,840 --> 00:41:41,000 Speaker 1: the chief privacy officer, members of various like security and 739 00:41:41,120 --> 00:41:43,759 Speaker 1: risk teams have all been resigning as well. So some 740 00:41:43,880 --> 00:41:47,719 Speaker 1: really important rules that the company are now empty. I 741 00:41:47,760 --> 00:41:50,680 Speaker 1: feel like that is just like two on the nose, right, 742 00:41:50,800 --> 00:41:53,680 Speaker 1: like if you're writing that book, it's weekend of burnings. 743 00:41:54,560 --> 00:41:56,440 Speaker 1: I'm a little bit. I mean, you know, Ellen is 744 00:41:56,600 --> 00:41:59,880 Speaker 1: very actively involved. You know, he is in the office. 745 00:42:00,160 --> 00:42:03,600 Speaker 1: He does have workers who are there working almost around 746 00:42:03,600 --> 00:42:06,239 Speaker 1: the clock to sort of respond to his whims and 747 00:42:06,280 --> 00:42:09,080 Speaker 1: get features into development. So you know, there are still 748 00:42:09,120 --> 00:42:11,400 Speaker 1: more than three thousand people who work at this company 749 00:42:11,400 --> 00:42:14,120 Speaker 1: and and they're doing things right. The whole thing is 750 00:42:14,200 --> 00:42:18,080 Speaker 1: just so strange what happens now. Ellen is like very 751 00:42:18,080 --> 00:42:22,000 Speaker 1: worried about losing money. He's got these eight dollars. The 752 00:42:22,080 --> 00:42:25,879 Speaker 1: verification is gone, but you can't do Twitter Blue now, 753 00:42:25,920 --> 00:42:29,120 Speaker 1: and there's a badge that's as Official. Yeah. So on 754 00:42:29,440 --> 00:42:33,200 Speaker 1: Thursday night, in response to all of the criticisms that 755 00:42:33,280 --> 00:42:36,600 Speaker 1: exactly what people said was gonna happened happened. They paused 756 00:42:36,680 --> 00:42:39,479 Speaker 1: new sign ups for Blue, so you cannot go get 757 00:42:39,520 --> 00:42:41,800 Speaker 1: another one of these check marks, and they're going to 758 00:42:41,880 --> 00:42:44,160 Speaker 1: try to figure it out. They also have started to 759 00:42:44,160 --> 00:42:48,120 Speaker 1: give out this secondary badge called official, which they're giving 760 00:42:48,160 --> 00:42:50,799 Speaker 1: two accounts that had been verified under the old you 761 00:42:50,880 --> 00:42:53,040 Speaker 1: are who you say you are policy. Then, of course 762 00:42:53,040 --> 00:42:55,839 Speaker 1: the really funny thing about that is that Musk is 763 00:42:55,960 --> 00:43:00,480 Speaker 1: recreating the exact sort of elitist like system that he 764 00:43:00,719 --> 00:43:02,719 Speaker 1: used to have there. They just have a different color 765 00:43:02,880 --> 00:43:04,840 Speaker 1: check mark now. So the other thing I want to 766 00:43:04,880 --> 00:43:07,880 Speaker 1: ask you about is Musk is all this debt he 767 00:43:07,920 --> 00:43:10,880 Speaker 1: needs to service, right, he has his personal exposure, and 768 00:43:10,920 --> 00:43:13,160 Speaker 1: then he has all this debt. He has banks, he 769 00:43:13,200 --> 00:43:17,200 Speaker 1: has Tesla stock, The Tesla stock is going down. Explain 770 00:43:17,320 --> 00:43:21,360 Speaker 1: to me what is his sort of idea here about 771 00:43:21,400 --> 00:43:23,920 Speaker 1: how he's going to make this all work. So what 772 00:43:24,040 --> 00:43:27,280 Speaker 1: he's telling employees is that he wants to get fifty 773 00:43:27,360 --> 00:43:31,960 Speaker 1: percent of revenues coming from subscriptions, so something like that 774 00:43:32,000 --> 00:43:35,120 Speaker 1: Twitter blue eight dollars a month thing. You know. I 775 00:43:35,160 --> 00:43:38,040 Speaker 1: reported earlier this week that he has also talked to 776 00:43:38,160 --> 00:43:41,200 Speaker 1: his advisors about putting the entire site behind a paywall. 777 00:43:41,280 --> 00:43:43,880 Speaker 1: So like, maybe you browse Twitter for an hour a month, 778 00:43:43,920 --> 00:43:45,960 Speaker 1: but if you go over that hour, he asked you 779 00:43:46,000 --> 00:43:48,759 Speaker 1: to pay him. That's what they want to do. But 780 00:43:48,880 --> 00:43:51,080 Speaker 1: that's a challenge, as you know, for reasons that we've 781 00:43:51,120 --> 00:43:52,600 Speaker 1: just seen. It's it's it's hard to build that and 782 00:43:52,640 --> 00:43:55,160 Speaker 1: it's hard to get people to pay eight dollars a month, right, 783 00:43:55,400 --> 00:43:58,000 Speaker 1: So that's kind of challenge number one. He wants us 784 00:43:58,040 --> 00:44:00,799 Speaker 1: to pay for the free content we're making. Exactly if 785 00:44:00,800 --> 00:44:02,279 Speaker 1: you want to be a part of Twitter, he wants 786 00:44:02,320 --> 00:44:04,840 Speaker 1: you to essentially subscribe to see it. So it's you know, 787 00:44:04,920 --> 00:44:07,640 Speaker 1: like Netflix for for texts or something like that. But 788 00:44:07,680 --> 00:44:10,359 Speaker 1: you know, he has this more immediate problem, Molly, which 789 00:44:10,480 --> 00:44:13,160 Speaker 1: is that he owns a company that makes eight nine 790 00:44:13,200 --> 00:44:17,080 Speaker 1: percent of its revenue from advertising, and several big advertisers 791 00:44:17,080 --> 00:44:19,600 Speaker 1: have paused all spending on the platform. You know, they've 792 00:44:19,640 --> 00:44:22,400 Speaker 1: been looking to him for reassurance, and it is not 793 00:44:22,480 --> 00:44:25,160 Speaker 1: reassuring when the head of trust and safety quits, when 794 00:44:25,160 --> 00:44:27,759 Speaker 1: a bunch of your privacy and security people quit, right. 795 00:44:28,200 --> 00:44:30,520 Speaker 1: The two top ad people in New York quit too, 796 00:44:30,600 --> 00:44:32,840 Speaker 1: write that is true, although one of them was lured 797 00:44:32,880 --> 00:44:36,600 Speaker 1: back after she quit yesterday, So it'll be interesting to see, 798 00:44:36,719 --> 00:44:39,480 Speaker 1: you know, sort of what effect that has. But you know, Ellen, 799 00:44:39,560 --> 00:44:42,560 Speaker 1: instead of really courting those advertisers, he's just been tweeting 800 00:44:42,600 --> 00:44:45,680 Speaker 1: about how you know they're they're pulling out because you know, 801 00:44:45,760 --> 00:44:48,480 Speaker 1: like the woke leftist mob you know, wants to kill 802 00:44:48,520 --> 00:44:51,000 Speaker 1: free speech in America or something. And that's also not 803 00:44:51,120 --> 00:44:53,880 Speaker 1: something that generally makes advertisers want to open up their wallets. 804 00:44:53,920 --> 00:44:56,239 Speaker 1: So you know, what happens to ads on Twitter is 805 00:44:56,280 --> 00:44:58,560 Speaker 1: a big open question here in the next few weeks. 806 00:44:58,880 --> 00:45:02,880 Speaker 1: So he's basically doing everything to mess himself up, like 807 00:45:02,920 --> 00:45:05,080 Speaker 1: he's self destructing. I would say, there's a lot of 808 00:45:05,200 --> 00:45:09,120 Speaker 1: unforced errors. And you know, something that the Musk team 809 00:45:09,200 --> 00:45:11,960 Speaker 1: believed when they came into Twitter, according to the people 810 00:45:11,960 --> 00:45:15,120 Speaker 1: that I've spoken to, there was that most of these 811 00:45:15,120 --> 00:45:18,239 Speaker 1: Twitter employees were not that smart right there. There was 812 00:45:18,239 --> 00:45:21,040 Speaker 1: this view of them that they were these sort of 813 00:45:21,239 --> 00:45:24,880 Speaker 1: you know, coddled leftists who had just been you know, 814 00:45:25,040 --> 00:45:28,480 Speaker 1: paralyzed for years and had never shipped a useful feature 815 00:45:28,520 --> 00:45:30,560 Speaker 1: and had no idea what they were doing. And Ellen 816 00:45:30,600 --> 00:45:32,760 Speaker 1: was going to come in and sort of jump start 817 00:45:32,800 --> 00:45:35,840 Speaker 1: this company. And I think the darkly funny thing about 818 00:45:35,840 --> 00:45:38,560 Speaker 1: all of this is that Twitter, and the same Twitter 819 00:45:38,600 --> 00:45:41,160 Speaker 1: employees that he's so disdained and you know and and 820 00:45:41,200 --> 00:45:43,960 Speaker 1: gutted the staff, it was those employees who told him 821 00:45:44,000 --> 00:45:46,080 Speaker 1: every single thing that was about to happen to him, 822 00:45:46,120 --> 00:45:48,520 Speaker 1: and he ignored them and went ahead anyway, and guess 823 00:45:48,520 --> 00:45:52,240 Speaker 1: what it all happened. It's amazing. I actually love Twitter. 824 00:45:52,480 --> 00:45:55,440 Speaker 1: Oh me too. Yeah, So as much as like, I 825 00:45:55,960 --> 00:45:59,320 Speaker 1: don't mind seeing this happen to Ellen and it seems 826 00:45:59,360 --> 00:46:02,200 Speaker 1: like he's you know, he's sort of like Chorus, right, 827 00:46:02,680 --> 00:46:05,319 Speaker 1: But I just wonder, like, is there a world in 828 00:46:05,480 --> 00:46:09,560 Speaker 1: which things work out here? We are still in the 829 00:46:09,680 --> 00:46:13,600 Speaker 1: early days of whatever this is. It's possible that Twitter 830 00:46:13,719 --> 00:46:17,160 Speaker 1: will have a next act, right, maybe Musk will sort 831 00:46:17,160 --> 00:46:20,880 Speaker 1: of gradually be willing to seed more control to people 832 00:46:20,920 --> 00:46:23,920 Speaker 1: who actually know how to run a social network and 833 00:46:24,160 --> 00:46:26,319 Speaker 1: he'll kind of go from there. But you know, if 834 00:46:26,360 --> 00:46:28,520 Speaker 1: you talk to people who have worked with him at 835 00:46:28,560 --> 00:46:31,480 Speaker 1: Tesla as I have, that's not how he runs that company. 836 00:46:31,600 --> 00:46:35,120 Speaker 1: You know. He has a sort of very intense, you know, micromanage, 837 00:46:35,520 --> 00:46:39,480 Speaker 1: whim based style of management there as well. So it's 838 00:46:39,520 --> 00:46:41,200 Speaker 1: hard for me to be optimistic, but you know, I 839 00:46:41,200 --> 00:46:43,200 Speaker 1: would love to be proven wrong because, like you, I 840 00:46:43,200 --> 00:46:45,719 Speaker 1: love Twitter. Yeah. I know, I'm not supposed to ask 841 00:46:45,760 --> 00:46:48,040 Speaker 1: people to predict the future, but predict the future, I 842 00:46:48,040 --> 00:46:49,879 Speaker 1: mean a few things that I can predict. A lot 843 00:46:49,920 --> 00:46:52,200 Speaker 1: of the engineers I've talked to you some are still there, 844 00:46:52,360 --> 00:46:54,480 Speaker 1: some have left. They think that Twitter is going to 845 00:46:54,560 --> 00:46:56,920 Speaker 1: have a significant outage, Like they think they've gotten rid 846 00:46:56,960 --> 00:46:59,760 Speaker 1: of so many technical people who are necessary to operate 847 00:46:59,800 --> 00:47:02,080 Speaker 1: this right, that something is going to go down and 848 00:47:02,080 --> 00:47:03,960 Speaker 1: it's just gonna take a long time to fix. So 849 00:47:04,360 --> 00:47:07,239 Speaker 1: that's something that I'm sort of looking at. I think 850 00:47:07,280 --> 00:47:10,280 Speaker 1: Ellen is going to continue to tweet about new products 851 00:47:10,280 --> 00:47:12,279 Speaker 1: and services that are coming. You know, he's we're gonna 852 00:47:12,320 --> 00:47:13,759 Speaker 1: do this, and we're going to do that, right the 853 00:47:13,840 --> 00:47:15,279 Speaker 1: sort of, you know, much of the same way that 854 00:47:15,320 --> 00:47:17,799 Speaker 1: Donald Trump used to tweet about new policies, and then 855 00:47:17,880 --> 00:47:19,879 Speaker 1: just as what Trump, we're gonna have to see, well, 856 00:47:20,040 --> 00:47:21,840 Speaker 1: like does it actually happen? Is this something that he 857 00:47:21,880 --> 00:47:24,080 Speaker 1: forgets about in two hours or is this something that 858 00:47:24,080 --> 00:47:26,799 Speaker 1: actually gets built? That's gonna have a lot of consequences 859 00:47:26,800 --> 00:47:29,080 Speaker 1: for employees who are going to be learning on Twitter 860 00:47:29,239 --> 00:47:30,920 Speaker 1: what their jobs are supposed to be, right, which was 861 00:47:30,960 --> 00:47:32,800 Speaker 1: another thing that you know, you'll you'll remember from the 862 00:47:32,800 --> 00:47:35,880 Speaker 1: Trump administration. So lots of chaos and lots more to 863 00:47:35,880 --> 00:47:38,160 Speaker 1: write about and podcast about Twitter in the the coming 864 00:47:38,160 --> 00:47:40,520 Speaker 1: weeks and months. I think, do you think there's like 865 00:47:40,560 --> 00:47:42,759 Speaker 1: another Twitter that's going to come along? There's never been 866 00:47:42,760 --> 00:47:45,000 Speaker 1: a better time to start when you know there's this 867 00:47:45,280 --> 00:47:48,839 Speaker 1: service mastered on that's existed since that. Some people are 868 00:47:48,920 --> 00:47:52,080 Speaker 1: kind of I have them mastered on. I actually looked 869 00:47:52,080 --> 00:47:54,759 Speaker 1: you up on masted on. Yeah I am there, but 870 00:47:54,880 --> 00:47:58,799 Speaker 1: you haven't tweeted since two thousands. You haven't massed don Yeah, 871 00:47:58,880 --> 00:48:00,359 Speaker 1: it's been well. I I logged on the other day 872 00:48:00,360 --> 00:48:01,840 Speaker 1: and was just sort of like, hey, what's up, But 873 00:48:01,880 --> 00:48:04,839 Speaker 1: like I haven't been there. You know, since some people 874 00:48:04,840 --> 00:48:06,879 Speaker 1: are getting into it, we'll have to see. I sort 875 00:48:06,880 --> 00:48:09,000 Speaker 1: of think it will have a relatively short shelf life. 876 00:48:09,000 --> 00:48:10,400 Speaker 1: I mean, I like, to me, this is just right, 877 00:48:10,440 --> 00:48:12,040 Speaker 1: Like this is what silicon value is supposed to be 878 00:48:12,040 --> 00:48:14,640 Speaker 1: good at, is just seeing the market opportunity, seating some 879 00:48:14,840 --> 00:48:17,080 Speaker 1: you know, smart team of people, and just letting them 880 00:48:17,080 --> 00:48:18,719 Speaker 1: go do their things. So I think we need a 881 00:48:18,719 --> 00:48:22,279 Speaker 1: new text based social alternative, and you know, the time's 882 00:48:22,400 --> 00:48:25,080 Speaker 1: right to build it. I think, Yeah, does it end 883 00:48:25,160 --> 00:48:27,560 Speaker 1: up that the bank owns Twitter in a year? Let's see, 884 00:48:27,640 --> 00:48:29,600 Speaker 1: a year is a long time, really hard to say. 885 00:48:29,880 --> 00:48:33,040 Speaker 1: I will say that some employees do think that that 886 00:48:33,200 --> 00:48:35,080 Speaker 1: the company is on a path to bankruptcy if for 887 00:48:35,160 --> 00:48:37,840 Speaker 1: know the reason that it will let Elon restructure his 888 00:48:37,920 --> 00:48:40,680 Speaker 1: debt obligations in a way that's outdvantageous to him, So, 889 00:48:40,840 --> 00:48:43,440 Speaker 1: you know, I do think that there's a significant likelihood 890 00:48:43,440 --> 00:48:45,319 Speaker 1: that that's something like that might be a foot. Can 891 00:48:45,360 --> 00:48:47,880 Speaker 1: you talk to us about f t X, explain it 892 00:48:47,880 --> 00:48:50,600 Speaker 1: to us like we're stupid, And by we I mean me, 893 00:48:50,800 --> 00:48:53,040 Speaker 1: sure well, and so you know, we're we're we're sort 894 00:48:53,080 --> 00:48:55,600 Speaker 1: of at the very edge of my knowledge here. This 895 00:48:55,640 --> 00:48:58,239 Speaker 1: isn't my main beat, but we did talk about it 896 00:48:58,440 --> 00:49:00,920 Speaker 1: this week on my new podcast, hard Work, which I 897 00:49:00,960 --> 00:49:02,640 Speaker 1: do with The New York Times, and my co host 898 00:49:02,840 --> 00:49:05,080 Speaker 1: Kevin Ruce really sort of helped me understand this. And 899 00:49:05,080 --> 00:49:08,239 Speaker 1: basically f t X is a place where you could 900 00:49:08,320 --> 00:49:11,560 Speaker 1: go and buy cryptocurrencies. So maybe you want some bitcoin, 901 00:49:11,600 --> 00:49:13,560 Speaker 1: that's a place where you could go. And if I 902 00:49:13,600 --> 00:49:15,719 Speaker 1: say that to you, you know, you like me, probably think, well, 903 00:49:15,719 --> 00:49:17,360 Speaker 1: that doesn't seem like that risk. You have a business, 904 00:49:17,400 --> 00:49:20,359 Speaker 1: you know. But what seems to have happened is that 905 00:49:20,440 --> 00:49:23,759 Speaker 1: f t X, which is owned or created by this guy, 906 00:49:23,800 --> 00:49:26,360 Speaker 1: Sam Bateman Freed, is become you know, this major democratic 907 00:49:26,520 --> 00:49:30,080 Speaker 1: and progressive donor. He also had this other business, Allometer Research, 908 00:49:30,120 --> 00:49:33,040 Speaker 1: which was a hedge fund, and somehow it seems like 909 00:49:33,200 --> 00:49:37,160 Speaker 1: customer funds got commingled, so funds that people had put 910 00:49:37,239 --> 00:49:40,680 Speaker 1: into f t X. You never want that. It's the 911 00:49:40,719 --> 00:49:44,320 Speaker 1: classic mistake. Molly is the daughter of a divorced lawyer. 912 00:49:44,560 --> 00:49:47,439 Speaker 1: You never wanted to touch this stuff, you know that's 913 00:49:47,480 --> 00:49:51,240 Speaker 1: in the Escaroa camp. Yeah. So that's basically what seems 914 00:49:51,280 --> 00:49:53,960 Speaker 1: like happened. You know, it's truly astonishing because f t 915 00:49:54,200 --> 00:49:57,080 Speaker 1: X was seen as this sort of rock solid crypto business. 916 00:49:57,080 --> 00:49:59,600 Speaker 1: In fact, it's spent most of this year buying up 917 00:49:59,640 --> 00:50:03,160 Speaker 1: just rest crypto companies that had gotten into trouble themselves, 918 00:50:03,160 --> 00:50:05,960 Speaker 1: and so everyone saw them as sort of the savior 919 00:50:06,080 --> 00:50:09,120 Speaker 1: of the crypto world. And then overnight the entire thing 920 00:50:09,160 --> 00:50:15,480 Speaker 1: imploded and Sam bankman Fried lost of his wealth. Wow, unbelievable. 921 00:50:16,080 --> 00:50:18,919 Speaker 1: So interesting. I hope you will come back. I would 922 00:50:18,960 --> 00:50:28,600 Speaker 1: love to come back. Thank you. Jesse Cannon, Molly John Fasts. 923 00:50:28,719 --> 00:50:31,520 Speaker 1: So we're tapping this on Sunday night, and it seems 924 00:50:31,560 --> 00:50:34,080 Speaker 1: like one side is going to have a very very 925 00:50:34,239 --> 00:50:36,800 Speaker 1: narrow lead over the other in Congress. It's going to 926 00:50:36,880 --> 00:50:39,560 Speaker 1: be very very tight. Our moment of fuccory is that, 927 00:50:39,719 --> 00:50:42,520 Speaker 1: believe it or not, this house is going to be 928 00:50:42,560 --> 00:50:45,800 Speaker 1: so tight. The idea here is that in this hundred 929 00:50:45,880 --> 00:50:50,560 Speaker 1: and seventeen Congress. Last the last congress Um, there were 930 00:50:50,840 --> 00:50:56,560 Speaker 1: fifteen special elections for congressional seats, and so if Republicans 931 00:50:56,560 --> 00:51:00,719 Speaker 1: have a one seat majority or even a two seat jority, 932 00:51:00,719 --> 00:51:07,400 Speaker 1: every congressional special election is going to be like Georgia 933 00:51:07,440 --> 00:51:13,520 Speaker 1: was two years ago. And if Democrats win special congressional elections, 934 00:51:13,640 --> 00:51:18,280 Speaker 1: then the power in Congress could switch back and forth, 935 00:51:18,560 --> 00:51:22,719 Speaker 1: and so that will be a lot of funckory. And 936 00:51:22,840 --> 00:51:26,600 Speaker 1: so it is our moment of gery. That's it for 937 00:51:26,640 --> 00:51:30,440 Speaker 1: this episode of Fast Politics. Tune in every Monday, Wednesday, 938 00:51:30,480 --> 00:51:33,439 Speaker 1: and Friday to her the best minds in politics, makes 939 00:51:33,440 --> 00:51:36,760 Speaker 1: sense of all this chaos. If you enjoyed what you've heard, 940 00:51:37,120 --> 00:51:40,040 Speaker 1: please send it to a friend and keep the conversation going. 941 00:51:40,440 --> 00:51:42,120 Speaker 1: And again, thanks for listening.