1 00:00:00,120 --> 00:00:03,320 Speaker 1: Welcome to today's edition of the Clay Travis and buck 2 00:00:03,360 --> 00:00:08,520 Speaker 1: Sexton Show podcast. Welcome in Monday edition Clay Travis buck 3 00:00:08,640 --> 00:00:14,920 Speaker 1: Sexton Show. We just keep stacking wins. Lots to dive into. 4 00:00:16,960 --> 00:00:19,800 Speaker 1: Let's start with the fun. By the way, we've got 5 00:00:19,800 --> 00:00:25,360 Speaker 1: a loaded show. David sachs Ai, czar of the new 6 00:00:25,400 --> 00:00:29,080 Speaker 1: Trump administration, will join us at the bottom of this hour. 7 00:00:29,840 --> 00:00:35,760 Speaker 1: Multi billionaire venture capital investor, brilliant guy. Lots to talk 8 00:00:35,760 --> 00:00:37,960 Speaker 1: about with him. He'll be on with us in this hour. 9 00:00:38,440 --> 00:00:42,520 Speaker 1: Second hour, We're gonna talk with May Mailman. She's inside 10 00:00:42,720 --> 00:00:46,240 Speaker 1: of the Trump White House. She's gonna talk about the 11 00:00:46,400 --> 00:00:49,600 Speaker 1: big blow up that happened in the final hour during 12 00:00:49,600 --> 00:00:52,720 Speaker 1: the final hour of the show on Friday, when the 13 00:00:52,720 --> 00:00:56,240 Speaker 1: governor of Maine said she would be suing Donald Trump 14 00:00:56,360 --> 00:01:00,000 Speaker 1: over men being allowed to continue to play in women's sports, 15 00:01:00,000 --> 00:01:04,240 Speaker 1: but then also lots of spice surrounding it. May was there. 16 00:01:04,720 --> 00:01:06,520 Speaker 1: She's going to come on and tell us all about it. 17 00:01:06,600 --> 00:01:09,880 Speaker 1: Plus there's a viral story that I'm told is not true. 18 00:01:10,280 --> 00:01:12,319 Speaker 1: She'll talk about in more detail. But some of you 19 00:01:12,360 --> 00:01:15,839 Speaker 1: may have seen a report that the Philadelphia Eagles had 20 00:01:15,920 --> 00:01:19,600 Speaker 1: rejected a White House invite. They are the super Bowl champions. 21 00:01:19,640 --> 00:01:23,319 Speaker 1: You'll remember Trump certainly cheered at the Super Bowl. Many 22 00:01:23,720 --> 00:01:27,840 Speaker 1: of those people in attendance were Eagle fans. I'm told 23 00:01:27,880 --> 00:01:31,160 Speaker 1: by the White House that there has not been an 24 00:01:31,160 --> 00:01:35,520 Speaker 1: official offer extended to the Eagles and that there has 25 00:01:35,600 --> 00:01:39,080 Speaker 1: been no rejection that that was fake news. We'll get 26 00:01:39,080 --> 00:01:40,720 Speaker 1: into that maybe a little bit with May and then 27 00:01:40,760 --> 00:01:44,199 Speaker 1: the new head of the EPA Lee Seldon, former congressman 28 00:01:44,280 --> 00:01:47,720 Speaker 1: from New York, nearly beat Kathy Hokel in the twenty 29 00:01:47,760 --> 00:01:52,480 Speaker 1: twenty two governor's race. All of that coming in your 30 00:01:52,720 --> 00:01:57,000 Speaker 1: direction during the course of today's show. But Buck, yesterday, 31 00:01:58,160 --> 00:02:02,440 Speaker 1: I woke up on Sunday morning, rolled over to check 32 00:02:02,480 --> 00:02:05,880 Speaker 1: and see what the news of the day was, and 33 00:02:05,920 --> 00:02:11,560 Speaker 1: there was joy in the Twitter streets. MSNBC far left 34 00:02:11,560 --> 00:02:18,840 Speaker 1: wing moron host Joy Reid fired her show canceled as NBC. 35 00:02:19,120 --> 00:02:23,600 Speaker 1: The MSNBC panics as no one is watching their network 36 00:02:23,639 --> 00:02:27,280 Speaker 1: in the wake of Trump's electoral victory in November. And Buck, 37 00:02:27,360 --> 00:02:30,160 Speaker 1: before we play some of the best moments of Joy Reid. 38 00:02:30,800 --> 00:02:33,239 Speaker 1: To me, the biggest takeaway, and I'm curious what your 39 00:02:33,320 --> 00:02:36,720 Speaker 1: takeaway here would be, was in the wake of twenty 40 00:02:36,800 --> 00:02:41,680 Speaker 1: seventeen when Trump came into office. Being anti Trump and 41 00:02:41,720 --> 00:02:45,000 Speaker 1: a part of the resistance was great for The New 42 00:02:45,080 --> 00:02:51,680 Speaker 1: York Times, The Washington Post, CNN, MSNBC, NBC, CBS, ABC, 43 00:02:51,840 --> 00:02:56,800 Speaker 1: all of their audiences skyrocketed as they bought into the 44 00:02:56,840 --> 00:03:01,880 Speaker 1: hardcore Trump resistance. But so far as we sit over 45 00:03:01,919 --> 00:03:06,200 Speaker 1: a month into the Trump era, there is no rush 46 00:03:06,600 --> 00:03:10,560 Speaker 1: to support. There is no democracy dies in darkness equivalent. 47 00:03:10,960 --> 00:03:14,080 Speaker 1: The resistance, as you and I have talked about, is 48 00:03:14,160 --> 00:03:18,320 Speaker 1: actually sad. We watched the resistance march through the streets 49 00:03:18,400 --> 00:03:22,239 Speaker 1: of DC. The culture appears to have been has shifted, 50 00:03:22,560 --> 00:03:26,680 Speaker 1: and the business imperatives to be able to make money 51 00:03:26,960 --> 00:03:30,840 Speaker 1: are vastly different, and that, to me is the underlying 52 00:03:30,960 --> 00:03:35,880 Speaker 1: foundation of why Joy Reid got fired. Certainly, if there 53 00:03:35,920 --> 00:03:38,680 Speaker 1: were money to be made, I think MSNBC would have 54 00:03:38,680 --> 00:03:42,760 Speaker 1: continued to cash the checks. But she's an embarrassment, she's 55 00:03:42,800 --> 00:03:47,320 Speaker 1: a moron. Her show was not good, and the profitability 56 00:03:47,440 --> 00:03:49,680 Speaker 1: of that show has declined, which I think is the 57 00:03:49,720 --> 00:03:52,520 Speaker 1: signature takeaway of the big picture here. 58 00:03:53,600 --> 00:03:57,240 Speaker 2: You also have the step down of Lester Holt from NBC, 59 00:03:57,880 --> 00:04:00,440 Speaker 2: which was just announced before we came on the air. 60 00:04:01,160 --> 00:04:04,760 Speaker 2: This is part of the ongoing I wouldn't say shake up. 61 00:04:04,840 --> 00:04:08,240 Speaker 2: I would say reckoning that the Democrat aligned media is 62 00:04:08,320 --> 00:04:11,600 Speaker 2: going through right now, Because even if you were somebody 63 00:04:11,600 --> 00:04:13,560 Speaker 2: who liked Joy Reid and we got to get we 64 00:04:13,600 --> 00:04:16,919 Speaker 2: got a montage coming here, Oh we need a montage. 65 00:04:16,960 --> 00:04:19,000 Speaker 2: Even if for somebody who liked Joy Reid or you're 66 00:04:19,000 --> 00:04:23,400 Speaker 2: an MSNBC watcher, you do, I would think live in 67 00:04:23,440 --> 00:04:27,000 Speaker 2: reality enough that you can understand that everything they told 68 00:04:27,040 --> 00:04:29,320 Speaker 2: you that was going to happen did not happen, that 69 00:04:29,400 --> 00:04:32,400 Speaker 2: all the promises that they made were broken correct, and 70 00:04:32,400 --> 00:04:34,760 Speaker 2: that apparently they know nothing about American politics, or the 71 00:04:34,760 --> 00:04:39,120 Speaker 2: American electorate, or the justice system, or the prospects of 72 00:04:39,120 --> 00:04:43,440 Speaker 2: Donald Trump, all of the above. Right if you were 73 00:04:43,640 --> 00:04:47,760 Speaker 2: relying on these people to tell you what the American 74 00:04:47,880 --> 00:04:52,480 Speaker 2: future was going to look like, you are wildly disappointed 75 00:04:52,560 --> 00:04:53,000 Speaker 2: right now. 76 00:04:53,600 --> 00:04:56,080 Speaker 1: And more than that, I think people. 77 00:04:55,760 --> 00:04:59,240 Speaker 2: Are seeing their ideology that hasn't been really tested in 78 00:04:59,279 --> 00:05:02,200 Speaker 2: the public sphere tel in ways and not just test 79 00:05:02,240 --> 00:05:05,200 Speaker 2: it mocked, it's being mocked and Joy read. 80 00:05:05,560 --> 00:05:06,040 Speaker 1: I'll say this. 81 00:05:06,160 --> 00:05:07,880 Speaker 2: You know there's always a little part of me and 82 00:05:07,920 --> 00:05:08,920 Speaker 2: I'm sure you have the same thing. 83 00:05:08,920 --> 00:05:09,320 Speaker 1: Clay. 84 00:05:09,480 --> 00:05:12,000 Speaker 2: Do you celebrate when somebody loses a job, she's made 85 00:05:12,040 --> 00:05:14,440 Speaker 2: millions of dollars. She's not poor. She's going to get 86 00:05:14,480 --> 00:05:15,320 Speaker 2: a job somewhere else. 87 00:05:15,360 --> 00:05:15,680 Speaker 1: Okay. 88 00:05:15,800 --> 00:05:19,559 Speaker 2: This isn't celebrating when like the factory closes and guys 89 00:05:19,600 --> 00:05:21,760 Speaker 2: work in the assembly line don't have a paycheck to 90 00:05:21,800 --> 00:05:23,920 Speaker 2: pay there. Okay, because you know, the libs love it. 91 00:05:24,279 --> 00:05:27,120 Speaker 2: They celebrate whenever anyone on the right loses their job. 92 00:05:27,400 --> 00:05:29,800 Speaker 2: But then when someone like Joy Reid loses her show, 93 00:05:29,800 --> 00:05:32,920 Speaker 2: we're supposed to go, Oh, don't ever celebrate somebody else's lost. 94 00:05:33,160 --> 00:05:35,720 Speaker 2: She's a multimillionaire. She's going to be taken care of. 95 00:05:35,960 --> 00:05:38,960 Speaker 2: She'll get paid lots of money to do nothing somewhere else. Okay, 96 00:05:39,000 --> 00:05:40,160 Speaker 2: it's she's going. 97 00:05:40,120 --> 00:05:40,599 Speaker 1: To be fine. 98 00:05:41,120 --> 00:05:43,240 Speaker 2: That's one thing that I wanted to get out there 99 00:05:43,360 --> 00:05:45,680 Speaker 2: because now we're about to really have some fun and 100 00:05:46,040 --> 00:05:48,720 Speaker 2: dive into joy reads. 101 00:05:49,360 --> 00:05:52,160 Speaker 1: Great. Are you ready for this? Clay Strap it in everybody. 102 00:05:52,200 --> 00:05:53,919 Speaker 1: I can't wait to hear it. I have not heard 103 00:05:53,960 --> 00:05:56,719 Speaker 1: this yet. I was waiting to be able to experience 104 00:05:56,760 --> 00:05:59,320 Speaker 1: it with all of you. This is producer Greg's work, right, 105 00:05:59,440 --> 00:06:00,159 Speaker 1: Let's give him. 106 00:06:01,800 --> 00:06:04,719 Speaker 2: Put on a beret. The artiste has gone to work 107 00:06:04,760 --> 00:06:07,279 Speaker 2: here he pulled it together. He edited over the weekend 108 00:06:07,600 --> 00:06:08,560 Speaker 2: cut for hit It. 109 00:06:08,680 --> 00:06:11,560 Speaker 3: We do have a fascist ground swell in parts of 110 00:06:11,600 --> 00:06:15,360 Speaker 3: this country, mainly among white men. He called himself big 111 00:06:15,720 --> 00:06:18,720 Speaker 3: b a l LS. That's what he calls himself. It 112 00:06:18,760 --> 00:06:21,800 Speaker 3: was a Nazi era on Twitter. That is back, and 113 00:06:21,920 --> 00:06:24,600 Speaker 3: Alon's doing it. Twitter now is useless to me. If 114 00:06:24,600 --> 00:06:27,279 Speaker 3: people like me are leaving, I don't know how Twitter survives. 115 00:06:27,320 --> 00:06:30,120 Speaker 3: Trump has two to eddies, Putin and e La Musk 116 00:06:30,200 --> 00:06:34,000 Speaker 3: a far right dictatorial regime like Hitler's Germany or Franco's 117 00:06:34,080 --> 00:06:38,400 Speaker 3: Spain or Mussolini's Italy. Vice president Kamala Debbie Harris, She's 118 00:06:38,440 --> 00:06:40,960 Speaker 3: about to make history. She's about to become the first 119 00:06:40,960 --> 00:06:45,240 Speaker 3: woman President's an historic, flawlessly run campaign. She had Queen 120 00:06:45,279 --> 00:06:48,400 Speaker 3: Latifa never endorses anyone to keep Hitler at the White House. 121 00:06:48,520 --> 00:06:50,520 Speaker 3: Hitler white House. We keeping them out. 122 00:06:50,640 --> 00:06:51,920 Speaker 1: Restoring the name. 123 00:06:52,000 --> 00:06:54,760 Speaker 3: For Bragg sure looks like an attempt to resegregate the 124 00:06:54,800 --> 00:06:59,680 Speaker 3: military extremist, right wing fascist type government in Florida. It's 125 00:06:59,680 --> 00:07:02,839 Speaker 3: a right wing fantasy land like Disney World, but in hell. 126 00:07:03,120 --> 00:07:05,960 Speaker 3: Come to Florida, the meatest place on earth. The racist 127 00:07:06,120 --> 00:07:10,880 Speaker 3: tomahawk chop, the gesture and chant, promoting stereotypes, caricatures, and 128 00:07:10,880 --> 00:07:14,920 Speaker 3: frankly hatred of Native American people. Sports commentator turned right 129 00:07:14,960 --> 00:07:18,720 Speaker 3: wing political commentator Clay Travis called for Trump supporters in 130 00:07:18,720 --> 00:07:20,440 Speaker 3: New York City to try to get seated on the 131 00:07:20,480 --> 00:07:23,120 Speaker 3: jury and then refuse to convict. That is openly encouraging 132 00:07:23,200 --> 00:07:26,200 Speaker 3: jury tampering. Similarities to what happened in Germany and what's 133 00:07:26,240 --> 00:07:29,960 Speaker 3: happening now in America are just undeniable. History may not 134 00:07:30,080 --> 00:07:31,640 Speaker 3: repeat verbatim, but it sure does. 135 00:07:31,720 --> 00:07:35,600 Speaker 2: Rhyme, oh, Grant, you got a shout out there in 136 00:07:35,640 --> 00:07:40,000 Speaker 2: that montage. What is your favorite of the joy Read isms? 137 00:07:40,480 --> 00:07:42,360 Speaker 2: I still think the greatest. I mean a lot of 138 00:07:42,400 --> 00:07:45,040 Speaker 2: you know, Nazis, and I don't like white Republicans and 139 00:07:45,080 --> 00:07:48,760 Speaker 2: all that, But to me, Kamala ran a flawless campaign 140 00:07:48,760 --> 00:07:50,440 Speaker 2: because I mean, Katifa endorsed her. 141 00:07:50,520 --> 00:07:52,280 Speaker 1: Is the is the greatest thing joy Read has ever 142 00:07:52,320 --> 00:07:56,560 Speaker 1: said on TV after she lost. That's Harmona, Lisa. That's 143 00:07:56,800 --> 00:08:00,840 Speaker 1: that's the one that I think her her entire repertoire 144 00:08:01,120 --> 00:08:03,480 Speaker 1: really kind of comes back to over and over again. 145 00:08:03,760 --> 00:08:06,640 Speaker 1: In fact, here that is if that was part of 146 00:08:06,680 --> 00:08:10,720 Speaker 1: the montage, but it's cut seven, And remember Joy Reid 147 00:08:11,320 --> 00:08:14,960 Speaker 1: during the course of Election Night on MSNBC, and let 148 00:08:15,040 --> 00:08:17,480 Speaker 1: me before we play that, let me also point this out. Buck, 149 00:08:17,560 --> 00:08:19,880 Speaker 1: you said, Hey, she's gonna continue to make money, She's 150 00:08:19,920 --> 00:08:22,560 Speaker 1: gonna go ELSEO. Here is what I would say in general. 151 00:08:22,920 --> 00:08:25,360 Speaker 1: Have you seen anyone upset that her show was canceled? 152 00:08:26,600 --> 00:08:30,640 Speaker 1: Imagine that you have the opportunity, the luxury, the privilege 153 00:08:31,360 --> 00:08:34,760 Speaker 1: to talk to millions of people every night, to come 154 00:08:34,760 --> 00:08:39,720 Speaker 1: into their home, and when you lose your job, no 155 00:08:39,760 --> 00:08:42,720 Speaker 1: one cares because there is no one that is actually 156 00:08:42,800 --> 00:08:45,920 Speaker 1: a fan of you. This is what's happened with Don Lemon, 157 00:08:46,440 --> 00:08:49,480 Speaker 1: this is what has happened with Jim Acosta, this is 158 00:08:49,520 --> 00:08:52,480 Speaker 1: now what is happening with Joy Reid. Whatever you think 159 00:08:52,520 --> 00:08:57,280 Speaker 1: about Tucker, whatever you think about Bill O'Reilly. When their 160 00:08:57,400 --> 00:09:01,800 Speaker 1: shows ended on Fox News, people were really upset that 161 00:09:01,840 --> 00:09:04,000 Speaker 1: they were no longer there. And those guys have been 162 00:09:04,040 --> 00:09:08,120 Speaker 1: able to have careers elsewhere. Piers Morgan, Megan Kelly. There 163 00:09:08,160 --> 00:09:11,280 Speaker 1: are people who talk to you on television that develop 164 00:09:11,320 --> 00:09:14,760 Speaker 1: a connection and a relationship to such an extent that 165 00:09:15,000 --> 00:09:18,840 Speaker 1: you can continue with them and you'll follow them wherever 166 00:09:18,880 --> 00:09:21,120 Speaker 1: they go. I like to think, Buck, if one day 167 00:09:21,160 --> 00:09:23,360 Speaker 1: you or I weren't on this show and we were like, Hey, 168 00:09:23,600 --> 00:09:25,720 Speaker 1: we're going to go do X or Y, then a 169 00:09:25,720 --> 00:09:27,520 Speaker 1: lot of you would be like, Hey, I want to 170 00:09:27,559 --> 00:09:30,000 Speaker 1: go see what you're going to do somewhere else, because 171 00:09:30,240 --> 00:09:34,200 Speaker 1: we have a relationship with all of you. Joy doesn't 172 00:09:34,240 --> 00:09:38,360 Speaker 1: have any audience. I didn't see anybody complaining about her 173 00:09:38,440 --> 00:09:42,680 Speaker 1: being fired. There was a mass exultation, and it's because 174 00:09:42,720 --> 00:09:46,120 Speaker 1: of moronic takes like this, Kamala Harris, whatever you want 175 00:09:46,120 --> 00:09:48,280 Speaker 1: to say. According to Joy Reid, she really did run 176 00:09:48,320 --> 00:09:52,720 Speaker 1: a flawless campaign. She got endorsed by Queen Latifa and 177 00:09:52,800 --> 00:09:56,240 Speaker 1: Beyonce and Taylor Swift. That was the entirety of her 178 00:09:56,440 --> 00:10:01,640 Speaker 1: argument of how flawless the campaign was. Listen, this really was. 179 00:10:01,600 --> 00:10:06,240 Speaker 3: An historic, flawlessly run campaign. She had Queen Latina never 180 00:10:06,360 --> 00:10:11,640 Speaker 3: endorses anyone she you know. I mean, she had every 181 00:10:11,679 --> 00:10:16,040 Speaker 3: prominent celebrity voice. She had, she had the Taylor Swifties 182 00:10:16,120 --> 00:10:18,960 Speaker 3: to the Swifties, she had the beehive, Like, you could 183 00:10:19,080 --> 00:10:21,040 Speaker 3: not have run a better campaign in that short period 184 00:10:21,040 --> 00:10:22,400 Speaker 3: of time. And I think that's still true. 185 00:10:24,240 --> 00:10:26,760 Speaker 1: I mean, I don't even know that there's anyone on 186 00:10:26,840 --> 00:10:30,240 Speaker 1: the Kamala campaign buck that would say Kamala ran a 187 00:10:30,280 --> 00:10:31,199 Speaker 1: flawless campaign. 188 00:10:31,480 --> 00:10:36,760 Speaker 2: Joy reviews it as solidarity with a black female presidential candidate. 189 00:10:37,040 --> 00:10:39,920 Speaker 2: And she's just going to say that Kamala was awesome 190 00:10:39,960 --> 00:10:42,920 Speaker 2: no matter what Kamala did, and that that's what her 191 00:10:43,000 --> 00:10:47,439 Speaker 2: job was, essentially on MSNBC, to say whatever the left 192 00:10:47,480 --> 00:10:49,880 Speaker 2: wing wanted her to say at any point in time. 193 00:10:50,440 --> 00:10:55,760 Speaker 2: There's also often a real nastiness in her commentary. You know, yeah, 194 00:10:55,800 --> 00:10:59,360 Speaker 2: there was a there was a glee and anybody in 195 00:10:59,400 --> 00:11:02,160 Speaker 2: Trump world, by the way, j six people getting locked 196 00:11:02,200 --> 00:11:04,600 Speaker 2: up having their lives ruined, not a nice person. I mean, 197 00:11:04,720 --> 00:11:07,240 Speaker 2: I have I don't think she has any talent. I 198 00:11:07,280 --> 00:11:09,400 Speaker 2: think she's a DEI higher I think, and I think 199 00:11:09,400 --> 00:11:13,480 Speaker 2: everybody at MSNBC knew that. And I think that everything 200 00:11:13,520 --> 00:11:17,880 Speaker 2: about the media that we saw engaging in this kind 201 00:11:17,880 --> 00:11:23,120 Speaker 2: of commentary before Trump's win proves that they have no honesty, 202 00:11:23,160 --> 00:11:26,680 Speaker 2: they've no intellectual curiosity, they don't really know anything. And 203 00:11:27,320 --> 00:11:29,600 Speaker 2: it's a good thing that there has finally been a 204 00:11:29,679 --> 00:11:32,320 Speaker 2: walk away from this. Now there will be other left 205 00:11:32,320 --> 00:11:35,840 Speaker 2: wing voices that grow in some ways right the oldest 206 00:11:36,240 --> 00:11:38,960 Speaker 2: what is the oldest aphorism? A crisis is an opportunity 207 00:11:39,800 --> 00:11:41,960 Speaker 2: for people on the left right now we talk about 208 00:11:41,960 --> 00:11:45,120 Speaker 2: the politicians, for commentators as well. For someone to come 209 00:11:45,160 --> 00:11:47,719 Speaker 2: forward and be able to appeal to the left who 210 00:11:47,760 --> 00:11:52,280 Speaker 2: has not been essentially mauled, you know, hasn't had their 211 00:11:52,320 --> 00:11:55,840 Speaker 2: credibility fed into the woodshipper by the Trump machine just 212 00:11:55,880 --> 00:11:59,160 Speaker 2: completely running them over. That's I think an opportunity. But 213 00:11:59,240 --> 00:12:01,720 Speaker 2: we'll see who that is is, because right now there's 214 00:12:01,760 --> 00:12:03,840 Speaker 2: not even a we talk about this. It'd there's some 215 00:12:03,960 --> 00:12:06,160 Speaker 2: things I know you've been doing. Piers Morgan, which looks 216 00:12:06,160 --> 00:12:08,079 Speaker 2: like fun. That's a left right show, though right in 217 00:12:08,160 --> 00:12:11,160 Speaker 2: peers refereeing, there's not a left wing show. I mean, 218 00:12:11,200 --> 00:12:12,560 Speaker 2: I don't think, because I don't even think Bill mar 219 00:12:12,679 --> 00:12:14,520 Speaker 2: is a left wing show. And I agree on that 220 00:12:15,080 --> 00:12:16,440 Speaker 2: there's not a left wing show that I would go 221 00:12:16,440 --> 00:12:18,120 Speaker 2: on right now, because there's nobody that I care to 222 00:12:18,120 --> 00:12:20,920 Speaker 2: speak to from the left who's worth the worth the effort, 223 00:12:21,000 --> 00:12:23,520 Speaker 2: worth the problem that's or you know, the the irritation. 224 00:12:23,559 --> 00:12:25,839 Speaker 1: I would like to go on the View. I would 225 00:12:25,880 --> 00:12:27,880 Speaker 1: like to go on the View because of the carnage 226 00:12:27,920 --> 00:12:31,520 Speaker 1: that would ensue, not because I respect their intelligence or 227 00:12:31,559 --> 00:12:34,079 Speaker 1: the quality of their show, which is on some level 228 00:12:34,120 --> 00:12:37,560 Speaker 1: what you're saying. But just because the Jerry Springer like 229 00:12:37,720 --> 00:12:40,480 Speaker 1: nature of the appearance would go so viral, you think 230 00:12:40,520 --> 00:12:42,640 Speaker 1: that do you think they would you do you think 231 00:12:42,679 --> 00:12:45,040 Speaker 1: they would come at you right away? Or would they 232 00:12:45,080 --> 00:12:47,440 Speaker 1: try to pretend like they're going to be respectful of 233 00:12:47,480 --> 00:12:48,000 Speaker 1: the guests? 234 00:12:48,080 --> 00:12:48,240 Speaker 4: You know? 235 00:12:48,600 --> 00:12:52,320 Speaker 1: I mean, I bet every single person listening to us 236 00:12:52,400 --> 00:12:56,040 Speaker 1: right now would watch me going on the view, and 237 00:12:56,160 --> 00:12:59,000 Speaker 1: I wouldn't hold back. I wouldn't tiptoe up to anything. 238 00:12:59,120 --> 00:13:00,800 Speaker 1: I mean, I would argue with them. 239 00:13:00,720 --> 00:13:04,760 Speaker 2: But I'm I, if you know, I tend to err 240 00:13:04,760 --> 00:13:06,600 Speaker 2: on the side of polite, at least in the beginning. 241 00:13:07,080 --> 00:13:10,160 Speaker 2: Clay would just start throwing bombs right away, Clay is 242 00:13:10,200 --> 00:13:12,520 Speaker 2: throwing bombs, I'd be like, pardon me, ladies, we're having 243 00:13:12,559 --> 00:13:14,760 Speaker 2: an exchange of ideas here. Clay would just be like, 244 00:13:16,000 --> 00:13:17,000 Speaker 2: shut your faces. 245 00:13:17,280 --> 00:13:20,840 Speaker 1: You know, we get it. I react to the vibe 246 00:13:20,920 --> 00:13:23,840 Speaker 1: under which I am talked to, but also I have 247 00:13:23,920 --> 00:13:26,600 Speaker 1: no filter, and that can be good and bad, but 248 00:13:26,679 --> 00:13:29,240 Speaker 1: it means that, you know, like the University of Chicago 249 00:13:29,320 --> 00:13:32,760 Speaker 1: thing that I did that went megaviral, I wasn't planning 250 00:13:32,840 --> 00:13:36,199 Speaker 1: to say the exact phraseology that I was. But when 251 00:13:36,240 --> 00:13:38,280 Speaker 1: she cut me off, for instance, and said, oh, but 252 00:13:38,320 --> 00:13:42,040 Speaker 1: Trump's a grandpa. It's like, well, I'm going to react 253 00:13:42,040 --> 00:13:44,480 Speaker 1: to that. Or when they said if you watch that clip, oh, 254 00:13:44,480 --> 00:13:47,679 Speaker 1: Trump's an awful father, and I'm like, actually, his five 255 00:13:47,800 --> 00:13:50,760 Speaker 1: kids have a great deal of affection for him. I mean, 256 00:13:50,800 --> 00:13:52,720 Speaker 1: there are lots of things you can attack people for, 257 00:13:53,080 --> 00:13:56,320 Speaker 1: but when I see them as being completely dishonest, I 258 00:13:56,360 --> 00:13:59,400 Speaker 1: can't hold back. I just naturally react. I think it 259 00:13:59,440 --> 00:14:03,400 Speaker 1: would be one of the most viral moments in the 260 00:14:03,480 --> 00:14:05,360 Speaker 1: view history if they had me on I don't think 261 00:14:05,360 --> 00:14:07,079 Speaker 1: they would have me on Buck because I actually think 262 00:14:07,080 --> 00:14:10,000 Speaker 1: it would go well for me and poorly for them. 263 00:14:10,000 --> 00:14:12,320 Speaker 1: But actually they should, right, I'll tell you. 264 00:14:12,360 --> 00:14:14,760 Speaker 2: And having done this Zoo, I don't know if you've 265 00:14:14,800 --> 00:14:17,480 Speaker 2: ever been three or four on one before. I've done 266 00:14:17,480 --> 00:14:20,560 Speaker 2: it many times back in the day CNN and other places. 267 00:14:21,160 --> 00:14:23,520 Speaker 2: It's very hard because people talk over you and you 268 00:14:23,560 --> 00:14:25,480 Speaker 2: have so little time to talk, and then what they 269 00:14:25,520 --> 00:14:30,000 Speaker 2: do is they accuse you of interrupting them while they're 270 00:14:30,360 --> 00:14:35,280 Speaker 2: interrupting you, and you know this, it's so Yes, it 271 00:14:35,280 --> 00:14:37,800 Speaker 2: would be interesting, it would be good television, but I'm 272 00:14:37,840 --> 00:14:40,080 Speaker 2: telling you it would turn into it would turn into 273 00:14:40,080 --> 00:14:42,440 Speaker 2: a melee very fast point being though there's nobody on 274 00:14:42,440 --> 00:14:43,800 Speaker 2: the left you'd want to sit down with and say, 275 00:14:43,880 --> 00:14:45,440 Speaker 2: let's have a real exchange of let's have a real 276 00:14:45,480 --> 00:14:48,240 Speaker 2: exchange of ideas here, like let's really get into the 277 00:14:48,360 --> 00:14:52,320 Speaker 2: meat the substance of the debates in this country right now. 278 00:14:52,320 --> 00:14:54,240 Speaker 2: We'll come into this, come back into this in a second. 279 00:14:54,240 --> 00:14:57,280 Speaker 2: We have even mentioned yet, Clay, some of the phenomenal 280 00:14:57,320 --> 00:14:59,640 Speaker 2: dose stuff that is underway, would no definitely want to 281 00:14:59,640 --> 00:15:02,560 Speaker 2: get into, which I'm excited about. You know, more eyes 282 00:15:02,600 --> 00:15:05,760 Speaker 2: are on the economy these days, especially after Friday's report 283 00:15:06,040 --> 00:15:08,760 Speaker 2: on consumer confidence there was nearly a two percent drop 284 00:15:08,800 --> 00:15:12,200 Speaker 2: on the Dow Jones. In times like these, you want 285 00:15:12,200 --> 00:15:15,880 Speaker 2: to prepare now before things get rocky out there. And 286 00:15:15,920 --> 00:15:18,480 Speaker 2: that's where there's real value in owning gold that's part 287 00:15:18,520 --> 00:15:22,040 Speaker 2: of your portfolio. It's a stable, solid investment. And whether 288 00:15:22,080 --> 00:15:24,080 Speaker 2: you choose to place your gold investment in your savings 289 00:15:24,120 --> 00:15:26,800 Speaker 2: account or four to one k birch gold can help 290 00:15:26,840 --> 00:15:29,480 Speaker 2: you do that. Today the value of gold has increased 291 00:15:29,480 --> 00:15:31,360 Speaker 2: substantially in recent years. You can take a look for 292 00:15:31,360 --> 00:15:33,840 Speaker 2: yourself and just check the price of gold. Then you'll see, 293 00:15:34,200 --> 00:15:37,400 Speaker 2: especially over the past year, or so gold prevails in 294 00:15:37,480 --> 00:15:40,240 Speaker 2: large part because it's a limited commodity, and with all 295 00:15:40,240 --> 00:15:43,680 Speaker 2: that inflation out there, it is a store of value 296 00:15:43,720 --> 00:15:47,720 Speaker 2: that's a hedge against that continue inflation chipping away at 297 00:15:47,760 --> 00:15:50,360 Speaker 2: your savings. I've been an investor in gold for years, 298 00:15:50,400 --> 00:15:53,800 Speaker 2: and I just recently purchased more myself from my own 299 00:15:53,840 --> 00:15:55,400 Speaker 2: private holdings from the. 300 00:15:55,360 --> 00:15:56,440 Speaker 1: Birch Gold Group. 301 00:15:56,680 --> 00:15:58,880 Speaker 2: Birch Gold Group also just released a new study on 302 00:15:58,920 --> 00:16:00,600 Speaker 2: how gold will fare in the tr Trump Era, with 303 00:16:00,640 --> 00:16:03,120 Speaker 2: a forward by Donald Trump Junior. To you get your 304 00:16:03,200 --> 00:16:06,560 Speaker 2: free copy along with Birch Gold's free information kit, text 305 00:16:06,560 --> 00:16:08,480 Speaker 2: my name Buck to the number ninety eight ninety eight 306 00:16:08,600 --> 00:16:11,640 Speaker 2: ninety eight, or go online to Birch Gold dot com 307 00:16:11,680 --> 00:16:14,560 Speaker 2: slash buck Again, text my name Buck do the number 308 00:16:14,600 --> 00:16:16,480 Speaker 2: ninety eight ninety eight ninety eight, or go online to 309 00:16:16,560 --> 00:16:19,560 Speaker 2: Birch Gold dot com slash buck for your free copy 310 00:16:19,600 --> 00:16:22,040 Speaker 2: of the Ultimate Guide for Gold in the Trump Era. 311 00:16:30,240 --> 00:16:33,520 Speaker 2: So we just had to do a eulogy for the 312 00:16:33,600 --> 00:16:39,280 Speaker 2: readout the Joy Reid Show that is no longer on MSNBC. 313 00:16:39,560 --> 00:16:43,440 Speaker 2: You know who does not love MSNBC. Donald Trump. He 314 00:16:43,600 --> 00:16:46,840 Speaker 2: was at c Pack and here is what he said 315 00:16:46,920 --> 00:16:49,400 Speaker 2: about what I think is his least favorite TV network, 316 00:16:49,400 --> 00:16:51,560 Speaker 2: although maybe a CNN fake news play two. 317 00:16:51,800 --> 00:16:54,800 Speaker 4: I watched them that really screwed up. I watched this MSNBC, 318 00:16:54,960 --> 00:17:00,240 Speaker 4: which is a threat to democracy. Actually there's Stonecomb mean, 319 00:17:00,320 --> 00:17:05,040 Speaker 4: but they're stuttering. They're all screwed up. They're all mentally 320 00:17:05,080 --> 00:17:07,160 Speaker 4: screwed up. They don't know what their ratings have gone 321 00:17:07,200 --> 00:17:10,200 Speaker 4: down the tubes. I don't even talk about CNN. CNN 322 00:17:10,280 --> 00:17:13,760 Speaker 4: sort of like, I don't know that they're pathetic. 323 00:17:13,880 --> 00:17:16,560 Speaker 1: Actually, but MSNBC was mean. 324 00:17:16,640 --> 00:17:19,880 Speaker 4: Their ratings are absolutely down. This Rachel Badow, what does 325 00:17:20,000 --> 00:17:25,520 Speaker 4: she have. She's got nothing nothing. She took us abbatical 326 00:17:26,080 --> 00:17:28,040 Speaker 4: where she worked one day that week. They paid her 327 00:17:28,080 --> 00:17:30,919 Speaker 4: a lot of money. She gets no ratings. I should 328 00:17:30,920 --> 00:17:32,840 Speaker 4: go against her in the ratings. 329 00:17:33,520 --> 00:17:35,879 Speaker 2: Clay, I just you know they they were rooting for 330 00:17:35,920 --> 00:17:38,920 Speaker 2: Trump to go to prison and for so much bad 331 00:17:38,960 --> 00:17:41,800 Speaker 2: stuff to happen. I love Trump taking a victory lap 332 00:17:42,160 --> 00:17:48,159 Speaker 2: in the graveyard of reputations at CNN and MSNBC and. 333 00:17:48,200 --> 00:17:51,240 Speaker 1: The graveyard of their business, which is going to be 334 00:17:51,320 --> 00:17:53,760 Speaker 1: I think one of the great legacies of the Trump 335 00:17:53,760 --> 00:17:56,560 Speaker 1: era in general is he was the rock upon which 336 00:17:56,600 --> 00:18:00,320 Speaker 1: they batched themselves and destroyed their own business. Most college 337 00:18:00,400 --> 00:18:03,520 Speaker 1: universities in our country received millions of dollars in financial 338 00:18:03,560 --> 00:18:07,200 Speaker 1: assistance from the federal government, let alone the state government, 339 00:18:07,320 --> 00:18:09,879 Speaker 1: often in the tens of millions of dollars. But Hillsdale 340 00:18:10,040 --> 00:18:14,040 Speaker 1: does not accept any federal existence, not assistance, not even 341 00:18:14,080 --> 00:18:17,600 Speaker 1: a dime. They're self sufficient and they want their students 342 00:18:17,640 --> 00:18:19,960 Speaker 1: to do the same. And one way they do it's 343 00:18:19,960 --> 00:18:23,240 Speaker 1: by creating all sorts of opportunities for you guys to 344 00:18:23,359 --> 00:18:26,520 Speaker 1: experience what Hillsdale is all about. And right now there 345 00:18:26,560 --> 00:18:29,800 Speaker 1: are forty courses online that all of you can watch, 346 00:18:29,840 --> 00:18:33,719 Speaker 1: whether it's about Mark Twain, the Roman Empire, World War One, 347 00:18:33,760 --> 00:18:37,520 Speaker 1: World War two, learning about the Constitution. All you have 348 00:18:37,600 --> 00:18:39,680 Speaker 1: to do is go online to Clay and Buck for 349 00:18:39,840 --> 00:18:44,080 Speaker 1: Hillsdale dot com. No cost, easy to get started. Clay 350 00:18:44,080 --> 00:18:48,040 Speaker 1: and Buck fo r Hillsdale dot com to register one 351 00:18:48,080 --> 00:19:00,920 Speaker 1: more time. Clayanbuck for Hillsdale dot com joined now by 352 00:19:01,080 --> 00:19:05,600 Speaker 1: AI and Cryptos are David Sachs, super successful investor. I 353 00:19:05,600 --> 00:19:07,760 Speaker 1: think the last time we talked to you, David was 354 00:19:07,960 --> 00:19:11,199 Speaker 1: up in Milwaukee. You came by our set and we 355 00:19:11,200 --> 00:19:12,440 Speaker 1: appreciate you coming by again. 356 00:19:12,520 --> 00:19:14,800 Speaker 2: Now, host of the host of the All In Podcast, 357 00:19:14,880 --> 00:19:16,760 Speaker 2: co host of it, which I have to mention because 358 00:19:16,760 --> 00:19:18,840 Speaker 2: my brother loves it and I have to always be like, well, 359 00:19:18,880 --> 00:19:20,720 Speaker 2: you listen to my show first, and he kind of 360 00:19:20,760 --> 00:19:21,520 Speaker 2: pleads the fifth. 361 00:19:21,600 --> 00:19:27,000 Speaker 1: So anyway, David, big news Apple announcing they're going to 362 00:19:27,080 --> 00:19:31,479 Speaker 1: invest five hundred billion dollars twenty thousand jobs. You are 363 00:19:31,520 --> 00:19:35,560 Speaker 1: plugged in with the overall universe of tech better than 364 00:19:35,560 --> 00:19:39,080 Speaker 1: almost anybody. How would you assess the first month of 365 00:19:39,160 --> 00:19:42,560 Speaker 1: the Trump administration? And what do you see and hear 366 00:19:42,960 --> 00:19:46,359 Speaker 1: from people in your universe as they are coming to 367 00:19:46,400 --> 00:19:48,600 Speaker 1: grips with the new Trump term. How excited are they? 368 00:19:48,600 --> 00:19:50,119 Speaker 1: What do they expect, what do they want, what do 369 00:19:50,119 --> 00:19:50,480 Speaker 1: they need? 370 00:19:51,800 --> 00:19:55,080 Speaker 5: Well, I think the first thirty days has just been incredible. 371 00:19:55,119 --> 00:19:58,119 Speaker 5: I mean really across the board. I mean, the president 372 00:19:58,280 --> 00:20:00,760 Speaker 5: has just begun the term with so much much energy, 373 00:20:00,920 --> 00:20:03,800 Speaker 5: so much action, so many executive orders. It's been a 374 00:20:03,800 --> 00:20:08,080 Speaker 5: total sea change. I mean just outside of economics and 375 00:20:08,280 --> 00:20:11,280 Speaker 5: AI and tech for a second, I mean, the President's 376 00:20:11,280 --> 00:20:15,720 Speaker 5: basically solved the border crisis within days. Biden, for the 377 00:20:15,800 --> 00:20:17,879 Speaker 5: last year of his term, pretended that he needed a 378 00:20:17,880 --> 00:20:21,919 Speaker 5: congressional action to do that, and this was somehow a 379 00:20:21,960 --> 00:20:25,600 Speaker 5: problem that was just unsolvable. And President Trump comes in 380 00:20:25,640 --> 00:20:28,440 Speaker 5: there and within days he's basically solved the border crisis. 381 00:20:28,800 --> 00:20:31,200 Speaker 5: It's just incredible, and so many other things are like that. 382 00:20:31,840 --> 00:20:35,040 Speaker 5: And so I would say there's an overall feeling in 383 00:20:35,080 --> 00:20:39,080 Speaker 5: the country right now that we can actually change things, 384 00:20:39,119 --> 00:20:41,560 Speaker 5: we can do things. I mean, the government seemed to 385 00:20:41,600 --> 00:20:47,080 Speaker 5: be this unfixable problem, just basically this bureaucracy that you 386 00:20:47,080 --> 00:20:48,919 Speaker 5: can never get anything done, and we just have to 387 00:20:49,000 --> 00:20:52,320 Speaker 5: live with a border crisis. We just had to live 388 00:20:52,960 --> 00:20:55,159 Speaker 5: with a two trillion dollars death set. And now you 389 00:20:55,200 --> 00:20:58,040 Speaker 5: see that Doge just going line by line through the 390 00:20:58,080 --> 00:21:02,280 Speaker 5: budget identifying the waste and FRAU to eliminate that. So 391 00:21:02,840 --> 00:21:06,000 Speaker 5: I think there's just in general this can do attitude, 392 00:21:07,400 --> 00:21:09,760 Speaker 5: and I think that with respect to tech, it applied 393 00:21:09,840 --> 00:21:14,520 Speaker 5: to that as well. I mean, the President has promised 394 00:21:14,560 --> 00:21:19,320 Speaker 5: that we're going to be rescinding regulations unnecessary regulations. For 395 00:21:19,359 --> 00:21:22,240 Speaker 5: every new regulation, I think the an Agency has to 396 00:21:22,320 --> 00:21:26,640 Speaker 5: proposed I mean, like tend more to eliminate WHI shouldn't 397 00:21:26,640 --> 00:21:30,080 Speaker 5: be hard is given the sheer number of regulations that 398 00:21:30,119 --> 00:21:33,879 Speaker 5: we have hated. So I think in general, there's just 399 00:21:33,960 --> 00:21:37,879 Speaker 5: a very optimistic attitude and I think even in liberal 400 00:21:37,920 --> 00:21:42,600 Speaker 5: Silicon Valley there is I would say almost like a 401 00:21:42,600 --> 00:21:46,760 Speaker 5: a grudging appreciation for what President Trump is doing. And 402 00:21:46,840 --> 00:21:49,399 Speaker 5: you saw it the inauguration. The leaders of all the 403 00:21:49,440 --> 00:21:52,760 Speaker 5: major tech companies were there. It's a very different attitude 404 00:21:52,840 --> 00:21:56,760 Speaker 5: than I think in twenty sixteen, where you know, I 405 00:21:56,800 --> 00:21:59,280 Speaker 5: think a lot of parts of Silicon Valley solved themselves 406 00:21:59,280 --> 00:22:02,359 Speaker 5: as part of the resils distance. Now I think they're 407 00:22:02,640 --> 00:22:06,240 Speaker 5: I think they're fairly positive about the Trump administration. They 408 00:22:06,280 --> 00:22:09,360 Speaker 5: may not all be saying that publicly, but I think 409 00:22:09,359 --> 00:22:11,159 Speaker 5: they know that on the whole it's going to be 410 00:22:11,240 --> 00:22:13,360 Speaker 5: much better for business than the Biden administration. 411 00:22:14,080 --> 00:22:16,400 Speaker 2: David, it's buck appreciate you making the time for us 412 00:22:16,880 --> 00:22:18,520 Speaker 2: on the issue of AI. 413 00:22:19,520 --> 00:22:19,960 Speaker 1: Got a lot of. 414 00:22:19,920 --> 00:22:23,640 Speaker 2: Attention recently because of that moment where everyone freaked out 415 00:22:23,680 --> 00:22:26,560 Speaker 2: about deep seek and is China going to get ahead 416 00:22:26,560 --> 00:22:29,720 Speaker 2: of us? And without getting into the weeds on that, 417 00:22:29,880 --> 00:22:32,200 Speaker 2: I just wanted to know, what do you think this 418 00:22:32,280 --> 00:22:36,679 Speaker 2: strategic vision is for the Trump administration to stay ahead 419 00:22:36,720 --> 00:22:39,440 Speaker 2: of China when it comes to AI, which obviously has 420 00:22:39,560 --> 00:22:43,199 Speaker 2: enormous implications for the economy, but even for things like 421 00:22:43,320 --> 00:22:46,879 Speaker 2: defense and beyond. So, how do we make sure that 422 00:22:46,960 --> 00:22:49,639 Speaker 2: we stay ahead of China. And what's that strategy look like. 423 00:22:50,840 --> 00:22:53,840 Speaker 5: Well, the President has been really clear that we just 424 00:22:53,920 --> 00:22:57,720 Speaker 5: have to win the AI race. America has to maintain 425 00:22:57,760 --> 00:23:00,760 Speaker 5: this global leadership and even dominant is a word that 426 00:23:00,840 --> 00:23:03,080 Speaker 5: the President has used, and in his first week he 427 00:23:03,200 --> 00:23:08,040 Speaker 5: signed an executive order stating that the gold administration was 428 00:23:08,240 --> 00:23:12,040 Speaker 5: to win the AI race and maintain our global dominance 429 00:23:12,080 --> 00:23:16,320 Speaker 5: in AI. And you're right about why. I mean AI 430 00:23:16,480 --> 00:23:20,480 Speaker 5: I think is the most profound and significant new technology. 431 00:23:21,720 --> 00:23:23,720 Speaker 5: I think it will be, you know, of this decade. 432 00:23:24,400 --> 00:23:26,720 Speaker 5: I think it's a little bit like the Internet was 433 00:23:26,760 --> 00:23:30,280 Speaker 5: in the late nineties, but maybe even more important than that. 434 00:23:30,960 --> 00:23:33,960 Speaker 5: It's going to have huge economic gratifications, and it's also 435 00:23:34,000 --> 00:23:37,240 Speaker 5: a dual use technology, so it has military applications as well. 436 00:23:37,760 --> 00:23:41,000 Speaker 5: If we lose the AI race to China, it's just 437 00:23:41,280 --> 00:23:44,879 Speaker 5: hard to imagine America maintaining its leadership in the world 438 00:23:45,000 --> 00:23:47,960 Speaker 5: as the most powerful country. So if you care about 439 00:23:48,000 --> 00:23:50,439 Speaker 5: the balance of power and you want America to be 440 00:23:50,520 --> 00:23:54,240 Speaker 5: number one, it's just really intolerable to think that we 441 00:23:54,280 --> 00:23:56,200 Speaker 5: could lose this AI race. So I think the President 442 00:23:56,280 --> 00:24:00,600 Speaker 5: understands that. One of the things he did very first 443 00:24:00,600 --> 00:24:04,679 Speaker 5: week was recind this one hundred page Biden EO that 444 00:24:04,840 --> 00:24:09,719 Speaker 5: was a monstrosity of of unnecessarily burden some regulations and 445 00:24:09,880 --> 00:24:12,040 Speaker 5: with a fair amount of regulatory capture in there by 446 00:24:12,080 --> 00:24:15,880 Speaker 5: certain companies. UH. And so he, you know, with the President, 447 00:24:15,960 --> 00:24:19,160 Speaker 5: I think has a vision of allowing the tech ecosystem 448 00:24:19,240 --> 00:24:23,640 Speaker 5: to compete and use that use that competition to spur 449 00:24:23,760 --> 00:24:27,919 Speaker 5: us forward. At the same time, he's also identified certain 450 00:24:28,000 --> 00:24:31,160 Speaker 5: critical enablers or building blocks and the technology that he's 451 00:24:31,200 --> 00:24:34,720 Speaker 5: going to help unleash. And number one is energy because 452 00:24:35,320 --> 00:24:39,240 Speaker 5: these new AI data centers consume vast amounts of energy 453 00:24:39,320 --> 00:24:41,840 Speaker 5: much more. You know that they're they're powered by GPUs 454 00:24:42,200 --> 00:24:46,160 Speaker 5: as opposed to CPUs, and GPUs use a lot more electricity. 455 00:24:46,320 --> 00:24:49,639 Speaker 5: So we so it's it's it's interesting the AI rates 456 00:24:49,720 --> 00:24:55,040 Speaker 5: is very connected to to the to the energy program 457 00:24:55,040 --> 00:24:58,399 Speaker 5: as the President has uh, and so one will fuel 458 00:24:58,480 --> 00:25:01,000 Speaker 5: the other. Uh. Then that's that's one of the major 459 00:25:01,040 --> 00:25:03,480 Speaker 5: things that the President is doing now to make sure 460 00:25:03,480 --> 00:25:04,359 Speaker 5: we win this race. 461 00:25:04,880 --> 00:25:08,520 Speaker 1: We're talking to David Tack's AI cryptosar part of the 462 00:25:08,560 --> 00:25:12,560 Speaker 1: Trump administration. You've known Elon Musk for a long time. 463 00:25:13,200 --> 00:25:17,320 Speaker 1: Elon voted for Joe Biden in twenty twenty. I believe 464 00:25:17,359 --> 00:25:21,080 Speaker 1: he voted for Hillary in twenty sixteen. He didn't endorse 465 00:25:21,119 --> 00:25:24,879 Speaker 1: Trump officially until July in the immediate aftermath of the 466 00:25:24,920 --> 00:25:29,800 Speaker 1: assassination attempt in Butler. When you see Elon being attacked 467 00:25:29,960 --> 00:25:33,240 Speaker 1: as some sort of far right wing zealot for trying 468 00:25:33,280 --> 00:25:37,800 Speaker 1: to find fraud and waste in the federal government, what 469 00:25:37,960 --> 00:25:41,160 Speaker 1: is your reaction As somebody that's had a long personal 470 00:25:41,240 --> 00:25:44,879 Speaker 1: relationship with Elon, would you have ever believed that he 471 00:25:44,920 --> 00:25:47,080 Speaker 1: would be being attacked as a right wing zelot? 472 00:25:48,359 --> 00:25:50,439 Speaker 5: No? And I you know, I think you're right. This 473 00:25:50,480 --> 00:25:53,080 Speaker 5: goes back a long time. Elon was politically neutral for 474 00:25:53,119 --> 00:25:57,280 Speaker 5: the longest time, but he leaned left or leaned Democrat. 475 00:25:57,359 --> 00:25:59,000 Speaker 5: I think he voted for Obama, and I think at 476 00:25:59,040 --> 00:26:01,480 Speaker 5: one point he voted thank you, voted for Biden or 477 00:26:01,520 --> 00:26:05,159 Speaker 5: said he did, And I think that's public knowledge. But 478 00:26:05,280 --> 00:26:08,360 Speaker 5: you know, the Biden administration did everything possible to alienate him. 479 00:26:08,359 --> 00:26:10,560 Speaker 5: And it all started where I think at the beginning 480 00:26:10,600 --> 00:26:13,679 Speaker 5: of the Biden administration, they held an ev summit at 481 00:26:13,680 --> 00:26:18,880 Speaker 5: the White House, and you know, Biden didn't invite Elon, 482 00:26:18,920 --> 00:26:21,920 Speaker 5: who created Tesla, which is by far the most successful 483 00:26:21,920 --> 00:26:25,120 Speaker 5: electric vehicle company in the world and is the sole 484 00:26:25,280 --> 00:26:29,520 Speaker 5: reason that we have an EV revolution and why the 485 00:26:29,600 --> 00:26:32,119 Speaker 5: United States is a player in EV. He's as opposed 486 00:26:32,160 --> 00:26:35,040 Speaker 5: to it all being China or some other country. And 487 00:26:35,080 --> 00:26:37,480 Speaker 5: not only was he not invited, Biden then goes up 488 00:26:37,520 --> 00:26:40,320 Speaker 5: there and gives credit to Mary Barraff for creating the 489 00:26:40,359 --> 00:26:43,520 Speaker 5: EV revolution, when you know what she did at GM 490 00:26:43,640 --> 00:26:46,880 Speaker 5: was a total failure. And that was just pure cronyism 491 00:26:47,080 --> 00:26:49,199 Speaker 5: for Biden. I mean, that was the whole pattern of 492 00:26:49,200 --> 00:26:53,199 Speaker 5: the Biden administration, is making life harder for entrepreneurs and 493 00:26:53,240 --> 00:26:57,680 Speaker 5: then rewarding their supporters and cronies. And you know, that 494 00:26:58,080 --> 00:26:59,879 Speaker 5: was just the beginning. I got much worse after that. 495 00:27:00,800 --> 00:27:03,680 Speaker 5: When Elon bought Twitter and restore it to a free 496 00:27:03,680 --> 00:27:07,400 Speaker 5: speech platform. That's when the left really declared war on him. 497 00:27:07,440 --> 00:27:09,480 Speaker 5: Let's just be clear about that. I mean, Elon was 498 00:27:09,520 --> 00:27:13,840 Speaker 5: a darling of the left because again he was responsible 499 00:27:14,040 --> 00:27:17,920 Speaker 5: for bringing about the EV revolution, which in their view, 500 00:27:18,040 --> 00:27:20,480 Speaker 5: would help solve climate change. Whether you agree with that 501 00:27:20,600 --> 00:27:24,359 Speaker 5: or not, that was their view. And it all changed 502 00:27:24,400 --> 00:27:27,600 Speaker 5: once he bought Twitter because he wanted to restore free speech. 503 00:27:27,760 --> 00:27:29,720 Speaker 5: And that's when they really came after him, and that's 504 00:27:29,720 --> 00:27:32,040 Speaker 5: when you saw Biden say, we got to look at 505 00:27:32,040 --> 00:27:33,840 Speaker 5: the sky, you know, from the White House podium, we 506 00:27:33,880 --> 00:27:36,400 Speaker 5: need to investigate him. And then they brought a whole 507 00:27:36,480 --> 00:27:43,280 Speaker 5: raft of ridiculous investigations in lawsuits. Tesla was investigated for 508 00:27:43,359 --> 00:27:48,000 Speaker 5: building a glass house or something absurd like that. Space X, 509 00:27:48,440 --> 00:27:52,960 Speaker 5: which has national security applications, was investigated for not hiring 510 00:27:53,200 --> 00:27:56,920 Speaker 5: enough asylum seekers, which is to say, illegal aliens, which 511 00:27:56,920 --> 00:27:59,560 Speaker 5: they're not even allowed to do under you know it 512 00:27:59,720 --> 00:28:03,080 Speaker 5: tar to the National Security law. So you know, the 513 00:28:03,119 --> 00:28:07,240 Speaker 5: Biden administration did everything they could to alienate Elon and 514 00:28:07,280 --> 00:28:11,000 Speaker 5: then simultaneously just you know, higher level. This is a 515 00:28:11,040 --> 00:28:13,159 Speaker 5: feeling that was felt by people by many people in 516 00:28:13,200 --> 00:28:16,439 Speaker 5: Silicon Valley, like Marc Andreessen, where they felt like the 517 00:28:16,440 --> 00:28:21,720 Speaker 5: Biden administration was demonizing entrepreneurs and entrepreneurship. And you know, 518 00:28:21,760 --> 00:28:24,239 Speaker 5: these are all people, I mean in Silicon Valley who 519 00:28:24,359 --> 00:28:29,280 Speaker 5: started from a more liberal mindset and they gradually got 520 00:28:29,320 --> 00:28:33,080 Speaker 5: red pilled and moved for Republican because frankly, the Democrats 521 00:28:33,880 --> 00:28:37,520 Speaker 5: became socialists, I mean, they became communists, or at least 522 00:28:37,720 --> 00:28:42,920 Speaker 5: the faction within the party that represents that hard left 523 00:28:43,040 --> 00:28:46,479 Speaker 5: was in control of domestic policies. So I think that 524 00:28:46,520 --> 00:28:49,680 Speaker 5: it's not just Elon, it's lots of people got red pilled. 525 00:28:50,600 --> 00:28:53,440 Speaker 5: But you know, they didn't set out to be you know, 526 00:28:53,480 --> 00:28:56,440 Speaker 5: they're not They're not like right wingers. They just believe 527 00:28:57,440 --> 00:29:02,240 Speaker 5: in allowing America to compete and allow entrepreneurs to create 528 00:29:02,280 --> 00:29:07,960 Speaker 5: new businesses. Socially, they tend to be pretty moderate or liberal. 529 00:29:09,120 --> 00:29:12,880 Speaker 5: But the Democratic Party just became so hard left that 530 00:29:13,360 --> 00:29:15,960 Speaker 5: they became toxic to anyone in the center. 531 00:29:18,040 --> 00:29:21,520 Speaker 1: David, I think that's such an interesting analysis. Buck and 532 00:29:21,560 --> 00:29:23,840 Speaker 1: I were out to dinner with a couple of prominent 533 00:29:23,880 --> 00:29:28,120 Speaker 1: business executives recently and they said that, and I know 534 00:29:28,200 --> 00:29:31,320 Speaker 1: you were a huge part of this. Elon firing seventy 535 00:29:31,360 --> 00:29:35,160 Speaker 1: five or eighty percent of Twitter's employees and actually ending 536 00:29:35,240 --> 00:29:38,600 Speaker 1: up with the far more profitable business was something that 537 00:29:38,760 --> 00:29:42,440 Speaker 1: tons of managers, tons of owners have thought about doing before. 538 00:29:42,800 --> 00:29:45,480 Speaker 1: It seems to be the blueprint now for what he's 539 00:29:45,520 --> 00:29:48,640 Speaker 1: trying to do for fraud and waste. How much fraud 540 00:29:48,680 --> 00:29:50,960 Speaker 1: and waste do you think, based on what you've seen 541 00:29:51,000 --> 00:29:54,680 Speaker 1: there is in our government expenditures, and how important was 542 00:29:54,720 --> 00:29:57,760 Speaker 1: the experience of going through those cuts at Twitter to 543 00:29:57,840 --> 00:29:59,920 Speaker 1: giving you an idea what to do here with the government. 544 00:30:01,400 --> 00:30:03,960 Speaker 5: Yeah, you know, I was there at Twitter for the 545 00:30:04,000 --> 00:30:08,160 Speaker 5: Twitter transition, and I mean it was really remarkable. So 546 00:30:09,120 --> 00:30:12,400 Speaker 5: Elon did some really simple things in hindsight that you 547 00:30:12,440 --> 00:30:15,280 Speaker 5: wonder why they'd never done before. So, uh, you know, 548 00:30:15,320 --> 00:30:19,200 Speaker 5: in a tech company, engineers are the lifeblood of the company. 549 00:30:19,280 --> 00:30:22,720 Speaker 5: So Elon did something really simple. He just asked or 550 00:30:22,800 --> 00:30:26,959 Speaker 5: he looked at the code repository and just search how 551 00:30:27,000 --> 00:30:31,840 Speaker 5: many engineers have actually written code checking code in the 552 00:30:31,920 --> 00:30:35,000 Speaker 5: last several months, and something like only fifty percent of 553 00:30:35,080 --> 00:30:40,239 Speaker 5: the engineers at Twitter we're even writing code. And you 554 00:30:40,240 --> 00:30:41,640 Speaker 5: know that's their sole output. 555 00:30:43,280 --> 00:30:45,560 Speaker 2: So if you're scooping ice cream for a living and 556 00:30:45,600 --> 00:30:47,680 Speaker 2: you don't scoop any ice cream, that's a that's a 557 00:30:47,720 --> 00:30:48,280 Speaker 2: bad sign. 558 00:30:49,040 --> 00:30:51,400 Speaker 5: It's very easy to measure this, Yeah, I mean it's 559 00:30:51,480 --> 00:30:54,920 Speaker 5: very The outpoint is very easily measured. And you know, 560 00:30:54,960 --> 00:30:57,280 Speaker 5: I saw somebody, you know, I saw a former Twitter 561 00:30:57,360 --> 00:31:01,320 Speaker 5: employee on MSNBC complaining of about this how DRACONI and 562 00:31:01,360 --> 00:31:05,800 Speaker 5: Elon was because she was required to, you know, do 563 00:31:06,080 --> 00:31:09,360 Speaker 5: a test to prove that she could code. Well. Sorry, 564 00:31:09,440 --> 00:31:11,760 Speaker 5: the reason why that happened is because half the Twitter 565 00:31:11,800 --> 00:31:15,600 Speaker 5: employees weren't checking in code. So then instead of just 566 00:31:15,640 --> 00:31:18,640 Speaker 5: firing them, Elon gave them a chance to complete a 567 00:31:18,680 --> 00:31:22,200 Speaker 5: code exercise to prove they actually could code. And if 568 00:31:22,320 --> 00:31:24,480 Speaker 5: you know, and if they weren't willing to prove that 569 00:31:24,520 --> 00:31:27,040 Speaker 5: they could code and they hadn't actually checked in code, 570 00:31:27,160 --> 00:31:29,520 Speaker 5: they got fired. I mean, it's just that they weren't 571 00:31:29,520 --> 00:31:31,640 Speaker 5: doing the job and it wasn't clear that they were 572 00:31:31,640 --> 00:31:32,640 Speaker 5: capable of doing the job. 573 00:31:33,080 --> 00:31:34,560 Speaker 2: David, we're going to be coming into a heart break 574 00:31:34,560 --> 00:31:36,000 Speaker 2: here in a second. So I just wanted to I 575 00:31:36,000 --> 00:31:38,160 Speaker 2: wanted to ask you one question that's crypto. Over later 576 00:31:38,200 --> 00:31:40,320 Speaker 2: we're speaking to David Sachs. The crypto and ais are 577 00:31:40,400 --> 00:31:43,560 Speaker 2: for the White House and a venture capitalists, for a 578 00:31:43,560 --> 00:31:46,800 Speaker 2: lot of people who don't owin own bitcoin and aren't 579 00:31:46,840 --> 00:31:49,320 Speaker 2: necessarily involved in crypto. I think the question is, and 580 00:31:49,560 --> 00:31:52,040 Speaker 2: I'll just give you, sorry a minute to explain this, 581 00:31:52,160 --> 00:31:55,400 Speaker 2: or so, why does crypto matter for everyone, for the country, 582 00:31:55,400 --> 00:31:57,120 Speaker 2: for the economy, for the Trump administration. 583 00:31:57,400 --> 00:31:59,480 Speaker 1: What what is it that matters about this? 584 00:32:00,440 --> 00:32:03,600 Speaker 5: Well, I think crypto represents a new type of digital platform. 585 00:32:03,760 --> 00:32:07,520 Speaker 5: But for finance or for money, there's a few different 586 00:32:07,520 --> 00:32:11,120 Speaker 5: components to it. You got bitcoin, which is a store 587 00:32:11,120 --> 00:32:14,480 Speaker 5: of value. You've got stable coins, which are potentially a 588 00:32:14,480 --> 00:32:17,800 Speaker 5: new kind of payment system, and then you've got just 589 00:32:17,880 --> 00:32:20,600 Speaker 5: blockchains in general on which you can build new kinds 590 00:32:20,640 --> 00:32:23,480 Speaker 5: of applications. And I think it's still very early to 591 00:32:23,560 --> 00:32:26,080 Speaker 5: say what the innovation is going to be that comes 592 00:32:26,080 --> 00:32:29,120 Speaker 5: from this, but it's a burgeoning area. It's a new 593 00:32:29,240 --> 00:32:32,320 Speaker 5: kind of you call it decentralized computing platform, and new 594 00:32:32,400 --> 00:32:36,080 Speaker 5: kinds of financial applications can be written on it. And 595 00:32:36,160 --> 00:32:38,760 Speaker 5: so it's just it's because it is, I think, a 596 00:32:38,840 --> 00:32:42,480 Speaker 5: really interesting area of software. You just want that innovation 597 00:32:42,600 --> 00:32:45,160 Speaker 5: happening in the United States. And I think that what 598 00:32:45,200 --> 00:32:48,560 Speaker 5: the Biden administration was doing, they just literally declared war 599 00:32:48,640 --> 00:32:50,800 Speaker 5: on crypto, and they were doing everything in their power 600 00:32:51,280 --> 00:32:53,720 Speaker 5: to kill the industry, and they were driving all the 601 00:32:53,840 --> 00:32:57,440 Speaker 5: entrepreneurship offshore. And you know, I think what we want 602 00:32:57,440 --> 00:32:59,640 Speaker 5: to do is allow the innovation to happen here in 603 00:32:59,680 --> 00:33:03,280 Speaker 5: America so that it's the United States that ultimately benefits 604 00:33:03,320 --> 00:33:06,320 Speaker 5: from these startups and the innovation that they will bring. 605 00:33:07,120 --> 00:33:09,200 Speaker 5: And there's just no reason to kill this thing in 606 00:33:09,200 --> 00:33:13,440 Speaker 5: the cradle. The consumer protection can be achieved by having 607 00:33:13,520 --> 00:33:16,960 Speaker 5: sensible rules around what people are able to do. But 608 00:33:17,200 --> 00:33:19,840 Speaker 5: keep in mind that the biggest fraud in the history 609 00:33:19,880 --> 00:33:23,960 Speaker 5: of crypto, which was FTX, was offshore in the Bahamas. 610 00:33:24,040 --> 00:33:27,200 Speaker 5: And that's what happens when you drive these companies offshore 611 00:33:27,360 --> 00:33:29,640 Speaker 5: is that it's hard to know the good ones from 612 00:33:29,680 --> 00:33:31,520 Speaker 5: the bad ones. And so so part of the reason 613 00:33:31,520 --> 00:33:34,520 Speaker 5: why I think we want to allow crypto to happen 614 00:33:34,560 --> 00:33:38,240 Speaker 5: in the US and to sort of to allow the 615 00:33:38,440 --> 00:33:41,440 Speaker 5: entrepreneurs to do things onshore is that it makes it 616 00:33:41,480 --> 00:33:44,200 Speaker 5: easier for them to be supervised by regulators so that 617 00:33:44,240 --> 00:33:46,680 Speaker 5: we can help help the good ones from the bad ones. 618 00:33:47,440 --> 00:33:50,040 Speaker 2: David Sachs, Crypto and ARS are for the Trump White House, 619 00:33:50,040 --> 00:33:51,400 Speaker 2: So we're going to need you to come back and 620 00:33:51,440 --> 00:33:54,440 Speaker 2: talk more about the future and amazing things if you 621 00:33:54,560 --> 00:33:55,600 Speaker 2: would make the time for us. 622 00:33:55,840 --> 00:33:57,640 Speaker 1: So thank you so much for coming on today. 623 00:33:58,360 --> 00:33:58,880 Speaker 5: Anytime. 624 00:34:00,160 --> 00:34:10,160 Speaker 1: Thank you. Team will be right back, friends. 625 00:34:10,200 --> 00:34:12,800 Speaker 2: We're going to dive into the latest from a doge 626 00:34:12,840 --> 00:34:14,799 Speaker 2: and the cleaning up of the government mess here in 627 00:34:14,840 --> 00:34:17,080 Speaker 2: just a couple of minutes and also next hour. It's 628 00:34:17,120 --> 00:34:20,960 Speaker 2: going to be joined by a spokeswoman from the White 629 00:34:21,040 --> 00:34:23,759 Speaker 2: House talking to her about some major policy stuff. In 630 00:34:23,800 --> 00:34:27,520 Speaker 2: third hour, EPA administrator Lee Zelden is going to be 631 00:34:27,600 --> 00:34:32,120 Speaker 2: with us talk about the EPA made famous by Walter 632 00:34:32,239 --> 00:34:35,200 Speaker 2: Peck in the original Ghostbusters movie A long time ago. 633 00:34:35,480 --> 00:34:37,439 Speaker 2: That was the first That was actually the first time 634 00:34:37,480 --> 00:34:39,040 Speaker 2: I had ever heard of the EPA when I saw 635 00:34:39,040 --> 00:34:39,439 Speaker 2: that movie. 636 00:34:39,440 --> 00:34:40,080 Speaker 1: So there you go. 637 00:34:40,760 --> 00:34:43,000 Speaker 2: But you know, just real quick, everybody, I want to 638 00:34:43,000 --> 00:34:47,040 Speaker 2: take a moment here. It's important that we stay focused 639 00:34:47,160 --> 00:34:49,440 Speaker 2: on how we can all take action the pro life 640 00:34:49,480 --> 00:34:51,520 Speaker 2: community and save lives day. 641 00:34:51,320 --> 00:34:52,480 Speaker 1: In and day out. 642 00:34:52,520 --> 00:34:54,799 Speaker 2: And there are still so many abortions that are taking 643 00:34:54,800 --> 00:34:58,040 Speaker 2: place despite Roe v. Wade being overturned. What can you 644 00:34:58,120 --> 00:35:00,719 Speaker 2: do now, Well, you can help protec the lives of 645 00:35:00,800 --> 00:35:04,400 Speaker 2: unborn children by working by donating rather to the staff 646 00:35:04,440 --> 00:35:07,440 Speaker 2: working at the Preborn Network of Clinics. They're on the 647 00:35:07,440 --> 00:35:12,000 Speaker 2: front lines nationwide, all standby for women deciding between life 648 00:35:12,080 --> 00:35:15,120 Speaker 2: or abortion for their babies. Preborn seeks these women out 649 00:35:15,120 --> 00:35:17,320 Speaker 2: to help them choose life, not just for their babies, 650 00:35:17,320 --> 00:35:20,080 Speaker 2: but for themselves. By introducing these women to the tiny 651 00:35:20,120 --> 00:35:23,360 Speaker 2: life growing inside them with an ultrasound, they are twice 652 00:35:23,480 --> 00:35:26,560 Speaker 2: as likely to choose life. Based on the statistics. You 653 00:35:26,600 --> 00:35:29,640 Speaker 2: see hearing that heartbeat changes everything. Would you be a 654 00:35:29,719 --> 00:35:33,080 Speaker 2: voice for Preborn and become a monthly donor. Twenty eight 655 00:35:33,120 --> 00:35:35,560 Speaker 2: dollars a month could be the difference between life and 656 00:35:35,640 --> 00:35:39,360 Speaker 2: death for so many tiny babies right now. This is 657 00:35:39,400 --> 00:35:44,040 Speaker 2: a totally tax deductible donation to donate securely. Become a 658 00:35:44,200 --> 00:35:48,080 Speaker 2: monthly donor if you can dial pound two five zero 659 00:35:48,200 --> 00:35:51,719 Speaker 2: and say the keyword baby, that's pound two fifty say 660 00:35:51,719 --> 00:35:55,000 Speaker 2: the word baby, or visit preborn dot com slash buck 661 00:35:55,320 --> 00:35:59,040 Speaker 2: that's preborn dot com slash b u c K sponsored 662 00:35:59,120 --> 00:36:00,959 Speaker 2: by pre Clai. 663 00:36:01,120 --> 00:36:01,880 Speaker 1: I want to dive. 664 00:36:01,760 --> 00:36:04,239 Speaker 2: Into some of the latest Elon stuff because it's incredible 665 00:36:04,480 --> 00:36:08,000 Speaker 2: what's going on here and the I can tell everyone 666 00:36:08,080 --> 00:36:11,480 Speaker 2: that from folks I've talked to you inside the federal government, 667 00:36:12,440 --> 00:36:15,880 Speaker 2: there has been a dramatic shift from they're not really 668 00:36:15,920 --> 00:36:18,360 Speaker 2: going to do anything to us to oh my gosh, 669 00:36:18,360 --> 00:36:19,600 Speaker 2: they're going to do a lot of things. 670 00:36:20,320 --> 00:36:23,799 Speaker 1: Well, you, we had this idea because you worked in 671 00:36:23,800 --> 00:36:27,120 Speaker 1: the federal government that most people just say, hey, presidents 672 00:36:27,160 --> 00:36:28,960 Speaker 1: come and go, but we get to stay here. No 673 00:36:28,960 --> 00:36:32,479 Speaker 1: matter what. That has certainly changed. We need to dive 674 00:36:32,520 --> 00:36:34,600 Speaker 1: into I saw your tweet about that and I was 675 00:36:34,680 --> 00:36:37,560 Speaker 1: laughing a little bit, reading it as suddenly federal employees 676 00:36:37,600 --> 00:36:40,359 Speaker 1: are worried about their jobs. We'll break all that down 677 00:36:40,400 --> 00:36:42,200 Speaker 1: for you, and we'll go to the White House to 678 00:36:42,239 --> 00:36:44,640 Speaker 1: get the latest from there as well.