1 00:00:00,560 --> 00:00:04,080 Speaker 1: What do you think, Jerry, Amazon planning to cut thirty 2 00:00:04,200 --> 00:00:10,640 Speaker 1: thousand corporate jobs today and this might be just the 3 00:00:10,680 --> 00:00:14,720 Speaker 1: first round. They have one and a half million total 4 00:00:14,760 --> 00:00:19,159 Speaker 1: employees worldwide they or had at the end of the 5 00:00:19,200 --> 00:00:19,920 Speaker 1: second quarter. 6 00:00:20,840 --> 00:00:21,440 Speaker 2: What is this? 7 00:00:21,480 --> 00:00:24,520 Speaker 1: What are we looking at here? And I just want 8 00:00:24,560 --> 00:00:27,000 Speaker 1: to add this caveat. You got to go back and 9 00:00:27,040 --> 00:00:31,240 Speaker 1: listen to Tucker Carlson's conversation with the Teamsters union guide, 10 00:00:31,280 --> 00:00:34,080 Speaker 1: the head of the Teamsters that are going after Amazon 11 00:00:34,200 --> 00:00:37,080 Speaker 1: for some of their practices, which is really good and 12 00:00:37,120 --> 00:00:42,800 Speaker 1: the guy lives up to every stereotype, stereotypical movie teamsters 13 00:00:42,840 --> 00:00:45,360 Speaker 1: guy you've ever seen in a movie in your life. 14 00:00:45,680 --> 00:00:48,640 Speaker 1: This Tucker Carlson podcast. But that's just a side note. 15 00:00:49,840 --> 00:00:53,280 Speaker 1: Is this so thirty thousand corporate jobs? White collar? 16 00:00:53,440 --> 00:00:56,520 Speaker 2: Yeah? I mean, if we're being honest, I think this 17 00:00:56,640 --> 00:00:59,400 Speaker 2: is ai? Is Ai? This is AI. 18 00:01:00,880 --> 00:01:03,760 Speaker 3: There are a lot of companies around here that deal 19 00:01:03,800 --> 00:01:06,200 Speaker 3: in tech that are doing a lot of layoffs, and 20 00:01:06,240 --> 00:01:07,960 Speaker 3: a lot of them are caused because of AI. 21 00:01:09,319 --> 00:01:12,440 Speaker 1: What is a Okay, let me ask you this. You 22 00:01:12,480 --> 00:01:15,080 Speaker 1: might not know the total answer. What is AI doing 23 00:01:15,120 --> 00:01:16,479 Speaker 1: differently or more efficiently? 24 00:01:16,680 --> 00:01:18,040 Speaker 2: So I'll give you this example. 25 00:01:18,080 --> 00:01:19,959 Speaker 3: Well, let's give let's do something as basic as a 26 00:01:19,959 --> 00:01:23,160 Speaker 3: password reset instead of calling T Mobile and speaking to 27 00:01:23,200 --> 00:01:26,240 Speaker 3: a representative and doing your password reset. Now you can 28 00:01:26,240 --> 00:01:28,320 Speaker 3: do it yourself. Now, if we can get it to 29 00:01:28,400 --> 00:01:30,520 Speaker 3: the point where everybody can do it them selves, then 30 00:01:30,560 --> 00:01:32,640 Speaker 3: do we need somebody here to set passwords? 31 00:01:32,880 --> 00:01:32,959 Speaker 2: No? 32 00:01:33,640 --> 00:01:36,640 Speaker 3: What other things can we automate besides passwords? And that's 33 00:01:36,680 --> 00:01:37,840 Speaker 3: these jobs that we are losing. 34 00:01:37,880 --> 00:01:40,720 Speaker 1: It seems like that's an infinite number of. 35 00:01:41,200 --> 00:01:43,760 Speaker 3: Yeah, it couldn't go anywhere, right, So another thing like 36 00:01:43,800 --> 00:01:45,600 Speaker 3: I talked to people and I think I don't remember 37 00:01:45,640 --> 00:01:47,319 Speaker 3: who was that I talked about it? With it here 38 00:01:47,640 --> 00:01:49,360 Speaker 3: when you look at factories, we had just had a 39 00:01:49,360 --> 00:01:52,559 Speaker 3: factory close not too long ago, and those. 40 00:01:52,480 --> 00:01:53,760 Speaker 2: People got to go to work somewhere. 41 00:01:54,280 --> 00:01:56,000 Speaker 3: So at what point in time do we have to 42 00:01:56,080 --> 00:01:58,880 Speaker 3: offer universal basic income because AI is going to take 43 00:01:59,320 --> 00:01:59,880 Speaker 3: these jobs? 44 00:02:00,200 --> 00:02:03,520 Speaker 2: WHOA I'm saying the jobs are gone? 45 00:02:03,520 --> 00:02:05,240 Speaker 1: Hey, look moved to New York City. 46 00:02:05,560 --> 00:02:10,240 Speaker 2: Well we'll all be broke there. But like, it's something 47 00:02:10,240 --> 00:02:11,320 Speaker 2: we have to think about. 48 00:02:12,720 --> 00:02:15,080 Speaker 1: Universal basic in I takes jobs. 49 00:02:15,160 --> 00:02:18,160 Speaker 3: If AI takes jobs, people got to earn money somehow. 50 00:02:18,919 --> 00:02:21,600 Speaker 2: What do we do learn the code? Well, hey, guy's 51 00:02:21,639 --> 00:02:22,280 Speaker 2: gonna do that for you. 52 00:02:23,520 --> 00:02:28,000 Speaker 1: I walked straight into that one, didn't I what do 53 00:02:28,040 --> 00:02:30,800 Speaker 1: you think about the the unions going after? 54 00:02:31,080 --> 00:02:33,720 Speaker 3: I think it was so funny because Bezos is team 55 00:02:33,840 --> 00:02:36,760 Speaker 3: Lefty and you're not. You won't even let your employees unionize, 56 00:02:36,840 --> 00:02:38,400 Speaker 3: yet that's a staple of the left. 57 00:02:38,520 --> 00:02:39,799 Speaker 2: Starbucks did the same thing. 58 00:02:40,040 --> 00:02:43,320 Speaker 3: You can't unionize, aren't you guys on the unionized side. 59 00:02:43,639 --> 00:02:46,880 Speaker 1: It's I heard this clip again, this conversation with the 60 00:02:46,919 --> 00:02:50,080 Speaker 1: Teamsters union guy and Tucker Carlson. And one of the 61 00:02:50,120 --> 00:02:52,240 Speaker 1: things that Amazon does. I don't think it's any secrets 62 00:02:52,360 --> 00:02:57,280 Speaker 1: that they outsourced their deliveries to the the United States, 63 00:02:57,320 --> 00:03:03,440 Speaker 1: the USPS, Postal Service and UPS and they pay them 64 00:03:03,560 --> 00:03:05,400 Speaker 1: more than they're paying their own employees. 65 00:03:05,480 --> 00:03:06,200 Speaker 2: I didn't know that. 66 00:03:07,360 --> 00:03:10,480 Speaker 1: If I understood this correctly, if I understood the clip 67 00:03:10,520 --> 00:03:18,359 Speaker 1: clip correctly, they yeah, I might want to. I might 68 00:03:18,400 --> 00:03:20,720 Speaker 1: have to go double check on that. But like a 69 00:03:20,720 --> 00:03:23,679 Speaker 1: buddy of mine, the official mailman of the Hammer and 70 00:03:23,800 --> 00:03:26,880 Speaker 1: Nigel Show, my buddy works in Brownsburg, says, I mean 71 00:03:26,919 --> 00:03:31,960 Speaker 1: they just get I mean just slammed with Amazon deliveries. 72 00:03:32,680 --> 00:03:33,240 Speaker 2: If they didn't have. 73 00:03:35,200 --> 00:03:39,680 Speaker 1: So so so it's like their third party contractors get 74 00:03:39,680 --> 00:03:42,080 Speaker 1: treated better than the actual employees. That's one of the 75 00:03:42,080 --> 00:03:45,080 Speaker 1: things they're going for. I don't know how I feel 76 00:03:45,120 --> 00:03:49,160 Speaker 1: about the unions and in going after Amazon, but it 77 00:03:49,240 --> 00:03:54,920 Speaker 1: seems like I hear story after news report after story 78 00:03:55,000 --> 00:03:58,880 Speaker 1: about the working conditions in some of these warehouses and 79 00:03:58,920 --> 00:04:02,240 Speaker 1: how like you lose points for going to take a leak. 80 00:04:03,320 --> 00:04:05,360 Speaker 2: But then here's the kicker, how do you fix that 81 00:04:05,840 --> 00:04:07,080 Speaker 2: with robots in the warehouse? 82 00:04:07,160 --> 00:04:08,520 Speaker 3: Now? What do we do with all the employees that 83 00:04:08,520 --> 00:04:11,560 Speaker 3: we're working in the warehouse? So we're back to where 84 00:04:11,560 --> 00:04:11,840 Speaker 3: we were. 85 00:04:13,240 --> 00:04:19,840 Speaker 1: Federal agents and law enforcement partners rated Chicus Bonitas. I 86 00:04:20,400 --> 00:04:26,560 Speaker 1: was waiting for you, okays, someone who goes by Indie 87 00:04:26,760 --> 00:04:33,760 Speaker 1: Spanglish on social media. Chica's Benitas cabaret in Dallas this week. 88 00:04:34,480 --> 00:04:40,279 Speaker 1: This was targeting suspected human trafficking. This is a strip club. 89 00:04:40,560 --> 00:04:44,720 Speaker 1: Forty one illegal immigrants arrested for I don't know there 90 00:04:44,760 --> 00:04:50,120 Speaker 1: was some like administrative immigration violations, but twenty nine of 91 00:04:50,160 --> 00:04:55,279 Speaker 1: the forty one were suspected of illegally working at the club. 92 00:04:55,440 --> 00:05:00,839 Speaker 1: But the polls on the stage. They seized authorities like 93 00:05:00,920 --> 00:05:05,560 Speaker 1: thirty thousand dollars in cash and business records. Ongoing criminal 94 00:05:05,600 --> 00:05:07,120 Speaker 1: investigation blah blah blah blah. 95 00:05:06,920 --> 00:05:09,560 Speaker 2: Blah, now being rebranded as no maschkas. 96 00:05:11,200 --> 00:05:17,400 Speaker 1: Chicas bonitas no More, because this is a result again 97 00:05:17,440 --> 00:05:20,479 Speaker 1: in the past four years of like I see my 98 00:05:20,560 --> 00:05:25,480 Speaker 1: buddies post things about ice and removing illegal immigrants, and 99 00:05:25,520 --> 00:05:28,560 Speaker 1: do these guys get out of unmarked cars and masks 100 00:05:28,560 --> 00:05:32,719 Speaker 1: and grab people off the street. It's just not happening 101 00:05:33,279 --> 00:05:37,360 Speaker 1: the way they're describing it. And it's all because of 102 00:05:37,880 --> 00:05:41,320 Speaker 1: the policies of the Biden administration the past four years. 103 00:05:41,720 --> 00:05:45,800 Speaker 1: The easy Way, just the app. They were able to 104 00:05:45,839 --> 00:05:50,560 Speaker 1: get in to come through the app, and the cartels 105 00:05:50,600 --> 00:05:54,840 Speaker 1: took advantage of that. And I believe the Biden administration 106 00:05:55,400 --> 00:05:58,960 Speaker 1: has blood on their hands in terms of human trafficking, 107 00:05:59,360 --> 00:06:01,480 Speaker 1: in terms of fentonyl coming through the. 108 00:06:01,440 --> 00:06:05,600 Speaker 4: Border, kids, no clue where the kids are at, no 109 00:06:05,640 --> 00:06:07,560 Speaker 4: clue where the kids just sent them to an address 110 00:06:07,600 --> 00:06:10,480 Speaker 4: that was pinned on their back, and literal kids are 111 00:06:10,520 --> 00:06:15,240 Speaker 4: turned are trafficked yep as well. And I mean, if 112 00:06:15,279 --> 00:06:18,440 Speaker 4: people the story, this is one story of a thousand 113 00:06:18,600 --> 00:06:22,680 Speaker 4: different stories in the United States of I'm glad they 114 00:06:22,720 --> 00:06:23,719 Speaker 4: got these guys. 115 00:06:23,440 --> 00:06:25,800 Speaker 3: But people that are against illegal immigration, it's listen. I 116 00:06:26,720 --> 00:06:30,240 Speaker 3: am a child of immigrants. Nobody can be treated worse 117 00:06:30,560 --> 00:06:32,680 Speaker 3: than somebody who can't go to the police. 118 00:06:33,240 --> 00:06:35,440 Speaker 2: Think about that as US citizensive. Something goes wrong, you 119 00:06:35,480 --> 00:06:36,240 Speaker 2: can call the police. 120 00:06:36,360 --> 00:06:39,520 Speaker 3: If I'm an illegal immigrant, you can basically do whatever, 121 00:06:39,920 --> 00:06:42,240 Speaker 3: and I can't call anybody because I'm scared I'm going. 122 00:06:42,240 --> 00:06:43,640 Speaker 2: To be kicked out of the country if I do. 123 00:06:44,040 --> 00:06:44,160 Speaker 1: So. 124 00:06:44,200 --> 00:06:47,000 Speaker 2: That's how they end up in these horrible situations. 125 00:06:46,480 --> 00:06:49,000 Speaker 1: And that's how they end up, and that's how the 126 00:06:49,000 --> 00:06:52,200 Speaker 1: cartels end up making billions of dollars. Yeah, pay me off. 127 00:06:52,240 --> 00:06:54,719 Speaker 1: I'll protect you, but you are going to the police. 128 00:06:55,000 --> 00:06:56,599 Speaker 1: You can't, or you get going back