1 00:00:00,320 --> 00:00:05,000 Speaker 1: Speaking of my brother's funeral. He had died of only fifty. 2 00:00:05,040 --> 00:00:11,639 Speaker 1: For it's complete shock to us, the COVID shot killed him, 3 00:00:11,680 --> 00:00:14,040 Speaker 1: I'm sure of it. He was at home, he was 4 00:00:14,080 --> 00:00:16,480 Speaker 1: feeding his beloved dog. His wife was on her way 5 00:00:16,520 --> 00:00:21,880 Speaker 1: home and she would discover him there at the edge 6 00:00:21,920 --> 00:00:26,040 Speaker 1: of the door, coming in from the garage. He loved 7 00:00:26,120 --> 00:00:29,720 Speaker 1: this goofy dog the way we love our dogs. Boy, 8 00:00:29,800 --> 00:00:33,080 Speaker 1: did he love this dog. And he was pouring out 9 00:00:33,240 --> 00:00:38,400 Speaker 1: food for him when he collapsed right there. So I 10 00:00:38,400 --> 00:00:44,360 Speaker 1: didn't get to say goodbye officially. But you know, I 11 00:00:44,440 --> 00:00:46,600 Speaker 1: have lived my life for a number of years where 12 00:00:46,640 --> 00:00:48,960 Speaker 1: I say what I need to say to the people 13 00:00:49,000 --> 00:00:52,400 Speaker 1: I need to say it to. So something happens to 14 00:00:52,440 --> 00:00:55,800 Speaker 1: me on the fly. They don't doubt I've said what 15 00:00:55,880 --> 00:00:59,000 Speaker 1: I needed to say, just long before you know that 16 00:00:59,120 --> 00:01:01,640 Speaker 1: last moment, You know, you wait till last moment, you 17 00:01:01,680 --> 00:01:04,959 Speaker 1: find out that the game gets cut short for a 18 00:01:05,040 --> 00:01:08,920 Speaker 1: lightning strike, and then you don't get to close out 19 00:01:08,959 --> 00:01:13,040 Speaker 1: the way you hope to. At his funeral, I made 20 00:01:13,080 --> 00:01:17,680 Speaker 1: the statement as I opened it was a beautiful day outside, 21 00:01:17,840 --> 00:01:24,520 Speaker 1: and I said, it's a beautiful day. The sky has 22 00:01:24,560 --> 00:01:28,000 Speaker 1: opened up, the sun is shining, the weather is beautiful. 23 00:01:28,040 --> 00:01:31,440 Speaker 1: It was in late January. It is a truly beautiful 24 00:01:31,520 --> 00:01:39,560 Speaker 1: day outside and inside, because we remember a truly beautiful spirit, 25 00:01:40,760 --> 00:01:43,559 Speaker 1: a good person who wore a uniform to help people, 26 00:01:44,480 --> 00:01:47,800 Speaker 1: and he did. He spent his entire life helping other people, 27 00:01:47,880 --> 00:01:53,240 Speaker 1: my parents, me, people, other cops, which is why so 28 00:01:53,320 --> 00:01:56,480 Speaker 1: many loved him and reached out to me and people 29 00:01:56,560 --> 00:02:01,920 Speaker 1: who he went out on calls to help. And I 30 00:02:01,960 --> 00:02:10,280 Speaker 1: feel that way now. I feel that Charlie Kirk, in 31 00:02:10,320 --> 00:02:13,960 Speaker 1: a sense gave his life. I feel that this was 32 00:02:14,000 --> 00:02:16,040 Speaker 1: a wake up call to a lot of people as 33 00:02:16,080 --> 00:02:19,360 Speaker 1: to how brief life is, as to how great the 34 00:02:19,440 --> 00:02:22,880 Speaker 1: threats were, as to how meaningful our work should be. 35 00:02:24,040 --> 00:02:27,600 Speaker 1: If you're raising kids, I'm not suggesting you run out 36 00:02:27,639 --> 00:02:30,440 Speaker 1: and give a speech on a campus in Utah, but 37 00:02:30,480 --> 00:02:34,440 Speaker 1: I'm saying raise those kids up with the mindset that 38 00:02:34,560 --> 00:02:38,240 Speaker 1: Charlie did what he did. And if you're working in 39 00:02:38,280 --> 00:02:41,040 Speaker 1: a plant and people are asking you questions, take a 40 00:02:41,080 --> 00:02:44,560 Speaker 1: moment to answer them about your faith, about this country, 41 00:02:44,600 --> 00:02:47,919 Speaker 1: about what you believe. And if you're wasting a bunch 42 00:02:47,960 --> 00:02:51,000 Speaker 1: of time on Facebook because people going, hey, hope you're happy, 43 00:02:51,040 --> 00:02:54,760 Speaker 1: You're Trump's evil. He's got red hair, and you're spending 44 00:02:54,800 --> 00:02:57,880 Speaker 1: all your time, very in a self indulgent fashion, just 45 00:02:58,040 --> 00:02:59,960 Speaker 1: arguing back at them and coming up with funny men 46 00:03:00,000 --> 00:03:03,360 Speaker 1: tames of how you can share. That's not helping anybody 47 00:03:05,040 --> 00:03:10,560 Speaker 1: use the time you got here productively make hay of it, 48 00:03:11,080 --> 00:03:15,000 Speaker 1: so that if you die today heart attack, stroke, car wreck, 49 00:03:15,200 --> 00:03:20,040 Speaker 1: who knows assassin's bullet, you got a body of work 50 00:03:20,120 --> 00:03:23,280 Speaker 1: that's left behind that you made a difference in this world. 51 00:03:24,560 --> 00:03:27,359 Speaker 1: And that's all I'm going to say about Charlie Kirk's assassination. 52 00:03:28,280 --> 00:03:33,160 Speaker 1: I told you on Friday for Friday Evening Show, it 53 00:03:33,240 --> 00:03:34,720 Speaker 1: was all we were going to talk about. And I 54 00:03:34,760 --> 00:03:35,960 Speaker 1: told you it was all we were going to talk 55 00:03:35,960 --> 00:03:38,760 Speaker 1: about yesterday, and I told you that today I had 56 00:03:38,760 --> 00:03:40,720 Speaker 1: some other things planned and that's what we're going to 57 00:03:40,720 --> 00:03:43,160 Speaker 1: talk about. And so that's what we're going to do. 58 00:03:43,880 --> 00:03:46,040 Speaker 1: And you may not like it, and you don't have 59 00:03:46,080 --> 00:03:48,240 Speaker 1: to hang around here. The program directors don't want me 60 00:03:48,280 --> 00:03:51,800 Speaker 1: to say that, but I cannot sit in misery forever, 61 00:03:51,880 --> 00:03:54,839 Speaker 1: and I'm not going to No, you're not going home. 62 00:03:55,240 --> 00:03:58,640 Speaker 1: And I told you this menopause conversation was important, and 63 00:03:58,680 --> 00:04:01,000 Speaker 1: that will start at nine o'clock today, and yes we're 64 00:04:01,040 --> 00:04:03,040 Speaker 1: still going to have it. Yes, life is going to 65 00:04:03,080 --> 00:04:06,920 Speaker 1: go on as it should. He just said that happens 66 00:04:07,240 --> 00:04:13,120 Speaker 1: to Michael Berry showmember sometime to tell. I will give 67 00:04:13,240 --> 00:04:17,480 Speaker 1: you the subject matter, and you tell me how that 68 00:04:18,040 --> 00:04:27,480 Speaker 1: doctor is known. Okay. A doctor of the heart cardiologist 69 00:04:27,720 --> 00:04:35,080 Speaker 1: is correct. A doctor of skin epidemiologist is incorrect. You 70 00:04:35,080 --> 00:04:39,880 Speaker 1: were thinking of the epidermal. Try that again. The I 71 00:04:39,920 --> 00:04:42,360 Speaker 1: don't know if it's Greek or Latin word meaning skin 72 00:04:42,640 --> 00:04:48,719 Speaker 1: is derma. Dermatologists don't ding it. Now I know the 73 00:04:48,760 --> 00:04:54,040 Speaker 1: correct answer. A doctor of let's say, joints and bones. 74 00:04:56,800 --> 00:04:59,680 Speaker 1: That's not doctor. Feel good, I'll give you it is 75 00:04:59,720 --> 00:05:04,599 Speaker 1: the word meaning straight orthopedic. I will give you that. 76 00:05:04,839 --> 00:05:06,200 Speaker 1: I will give you that. I'm being nice to I'm 77 00:05:06,200 --> 00:05:15,400 Speaker 1: being gracious today. A doctor of all things brain craniologist 78 00:05:15,480 --> 00:05:19,839 Speaker 1: is incorrect. Again, go to the root word meaning. The 79 00:05:19,880 --> 00:05:22,000 Speaker 1: good thing about science and medicine is most of these. 80 00:05:22,000 --> 00:05:25,680 Speaker 1: Go to the Greek, Greek or Latin root words. What 81 00:05:25,720 --> 00:05:29,599 Speaker 1: does it mean to relate to the brain or to 82 00:05:29,839 --> 00:05:36,880 Speaker 1: thought processes? The central what system relates to that nervous system? 83 00:05:37,000 --> 00:05:41,479 Speaker 1: So it is a neurologist. No, I am not giving 84 00:05:41,480 --> 00:05:44,760 Speaker 1: you credit for that one. A doctor who deals with 85 00:05:44,839 --> 00:05:53,160 Speaker 1: children is a pediatrician, correct. A doctor of the eyes 86 00:05:54,880 --> 00:06:00,600 Speaker 1: ophthalmologist is good. An optometrist handles your vision and setting 87 00:06:00,600 --> 00:06:04,880 Speaker 1: you for glasses, and ophthalmologists with the medical issues and 88 00:06:05,000 --> 00:06:11,240 Speaker 1: surgeries and the light of the all important eyes. A 89 00:06:11,360 --> 00:06:16,920 Speaker 1: doctor of your mouth and your teeth that is a dentist. 90 00:06:17,000 --> 00:06:21,480 Speaker 1: I will give you that little tougher. Doctor of your kidneys, 91 00:06:23,120 --> 00:06:25,960 Speaker 1: nephrologist is very good. I didn't expect you to get down. 92 00:06:26,800 --> 00:06:33,120 Speaker 1: A doctor of surgery that is a surgeon crush your gut. 93 00:06:33,680 --> 00:06:37,800 Speaker 1: A doctor of cancer you should know this. Living in Houston, 94 00:06:38,120 --> 00:06:43,760 Speaker 1: the cancer capital, the cancer oncologist is correct. A doctor 95 00:06:43,839 --> 00:06:50,000 Speaker 1: of the mind and mental conditions shrink is a no. 96 00:06:50,279 --> 00:06:57,080 Speaker 1: A shrink is a huh. Psychiatrist Yeah. A psychologist cannot 97 00:06:57,120 --> 00:07:01,160 Speaker 1: write you a prescription for the good stuff. Doctor of 98 00:07:01,200 --> 00:07:10,360 Speaker 1: the liver, doctor of the liver h E. P A 99 00:07:10,760 --> 00:07:18,200 Speaker 1: is your root. Hepatologist hepatologist, A doctor of women and 100 00:07:18,360 --> 00:07:23,800 Speaker 1: lady girl parts gynecologist. It is you, weirdo. A doctor 101 00:07:23,920 --> 00:07:30,960 Speaker 1: of the stomach gastroenterologist is correct. I did not think 102 00:07:31,000 --> 00:07:33,240 Speaker 1: you would get that, all right. This one is one 103 00:07:33,280 --> 00:07:36,160 Speaker 1: that Mary Taly Boden calls herself a doctor of your 104 00:07:36,200 --> 00:07:43,160 Speaker 1: ears e NT is technically correct, that's ear nose and throat. 105 00:07:43,600 --> 00:07:46,239 Speaker 1: But this is an ologist of some sort. What would 106 00:07:46,240 --> 00:07:51,440 Speaker 1: that be? Remember, she refers to herself as an oto laryngologist, 107 00:07:52,960 --> 00:08:00,560 Speaker 1: and a doctor of animals, a veterinarian, and finally a 108 00:08:00,680 --> 00:08:08,760 Speaker 1: doctor who deals with the lungs. Pull monologist is correct. 109 00:08:10,000 --> 00:08:14,040 Speaker 1: My dad is going to the same pullmologists who treated 110 00:08:14,080 --> 00:08:17,520 Speaker 1: my mom here in Houston's name is Tim Conley, great doctor, 111 00:08:18,680 --> 00:08:23,200 Speaker 1: great bedside manner. He came highly recommended from the doctors 112 00:08:23,200 --> 00:08:27,120 Speaker 1: I trust. But it's interesting to me because my dad 113 00:08:27,120 --> 00:08:30,840 Speaker 1: had never seen him. And my dad, having worked at 114 00:08:31,200 --> 00:08:36,240 Speaker 1: a chemical plant with asbestos, had advanced asbestosis, although we've 115 00:08:36,240 --> 00:08:40,000 Speaker 1: been able to treat that pretty well through steroids and 116 00:08:40,320 --> 00:08:46,079 Speaker 1: breathing treatments over the years, but he had never seen 117 00:08:46,360 --> 00:08:49,720 Speaker 1: this doctor. And when we'd bring my mom over to 118 00:08:49,800 --> 00:08:54,280 Speaker 1: see the promonologist doctor Conley, by the time he saw 119 00:08:54,320 --> 00:08:56,760 Speaker 1: her she was pretty far advanced. So my cardiologist Stan 120 00:08:56,840 --> 00:09:02,640 Speaker 1: Duckman and my pullmonologist Conley, we're seeing her, but we 121 00:09:02,640 --> 00:09:04,760 Speaker 1: were on the backside at that point because her condition 122 00:09:05,040 --> 00:09:10,600 Speaker 1: had advanced rather rapidly or overall health had deteriorated. She 123 00:09:10,640 --> 00:09:15,600 Speaker 1: had a condition of ALS, and basically we were trying 124 00:09:15,640 --> 00:09:17,920 Speaker 1: to treat it as some sort of we don't know 125 00:09:17,920 --> 00:09:19,560 Speaker 1: if it was a back problem. We don't know if 126 00:09:19,600 --> 00:09:23,319 Speaker 1: it was a lung problem, but it was basically, as 127 00:09:23,360 --> 00:09:27,600 Speaker 1: you know, ALS is a decline in the musculature and 128 00:09:27,640 --> 00:09:30,920 Speaker 1: the function of your systems. It's a horrible, horrible, horrible 129 00:09:31,240 --> 00:09:34,839 Speaker 1: way to go. And so nothing would perform the way 130 00:09:34,840 --> 00:09:36,760 Speaker 1: it was supposed to and they couldn't figure out why. 131 00:09:36,800 --> 00:09:40,040 Speaker 1: So there was testing on. But last week we took 132 00:09:40,080 --> 00:09:42,760 Speaker 1: my dad to doctor Conley now that I've moved him 133 00:09:42,760 --> 00:09:47,199 Speaker 1: to Houston, and we needed to get a baseline on 134 00:09:47,400 --> 00:09:51,640 Speaker 1: his lung condition. And when I called doctor Conley to 135 00:09:51,679 --> 00:09:54,520 Speaker 1: go see him, he said what's wrong with him? And 136 00:09:54,559 --> 00:09:57,680 Speaker 1: I said, right now, nothing? And he said, okay, what 137 00:09:57,720 --> 00:09:59,320 Speaker 1: am I seeing for? I said, I'd like to get 138 00:09:59,320 --> 00:10:02,040 Speaker 1: a baseline because he had a doctor in Beaumont. I'd 139 00:10:02,040 --> 00:10:04,120 Speaker 1: like to see where he is so that if we 140 00:10:04,160 --> 00:10:06,600 Speaker 1: since we're in a decline, we got something to work against. 141 00:10:06,640 --> 00:10:10,480 Speaker 1: And he said, Michael, nobody does that. I wish everybody 142 00:10:10,520 --> 00:10:13,679 Speaker 1: would do that. And so there is my plea to you. 143 00:10:13,800 --> 00:10:16,240 Speaker 1: I'm not lecturing you on health matters. Lord knows I 144 00:10:16,240 --> 00:10:17,920 Speaker 1: could take better care of myself, but I've gotten a 145 00:10:17,960 --> 00:10:20,680 Speaker 1: lot better. I do read a lot, and I will 146 00:10:20,720 --> 00:10:25,199 Speaker 1: tell you this. There are certain health conditions that are 147 00:10:25,520 --> 00:10:28,480 Speaker 1: likely to be a problem if you live long enough, 148 00:10:29,240 --> 00:10:32,280 Speaker 1: and the earlier you can get a baseline in your 149 00:10:32,320 --> 00:10:39,840 Speaker 1: body on your blood pressure, your A one C, your lungs, 150 00:10:40,640 --> 00:10:46,360 Speaker 1: your heartbeat, These sorts of things are very important. Your testosterone, 151 00:10:46,559 --> 00:10:49,160 Speaker 1: These sorts of things are very important to understand where 152 00:10:49,200 --> 00:10:53,560 Speaker 1: you are at thirty, so that if at forty six 153 00:10:53,600 --> 00:10:55,760 Speaker 1: you notice, you know what it feels like, I'm having 154 00:10:55,800 --> 00:10:59,200 Speaker 1: this problem. If they test you and they go, well, 155 00:10:59,200 --> 00:11:01,800 Speaker 1: this is lower than it should be, you don't know 156 00:11:02,360 --> 00:11:05,400 Speaker 1: if it's always been low, or you don't know if 157 00:11:05,400 --> 00:11:09,800 Speaker 1: it used to be at perfectly normal function and now 158 00:11:09,880 --> 00:11:13,200 Speaker 1: it has degraded to this point. Because the trend lines 159 00:11:13,240 --> 00:11:16,840 Speaker 1: are everything. The earlier you can get in and get 160 00:11:16,880 --> 00:11:19,320 Speaker 1: your report, hold on to it because you might move, 161 00:11:19,400 --> 00:11:21,880 Speaker 1: you might change doctors, your doctor might die, your doctor 162 00:11:21,960 --> 00:11:24,720 Speaker 1: might retire. Go in and get all of those Go 163 00:11:24,800 --> 00:11:27,520 Speaker 1: into to my memorial hearing, folks, if you want a 164 00:11:27,600 --> 00:11:29,720 Speaker 1: doctor for all of those things, Whether I have a 165 00:11:29,760 --> 00:11:33,800 Speaker 1: doctor Tim Conley, the lung doctor that I recommend, is 166 00:11:33,800 --> 00:11:37,720 Speaker 1: not a show sponsor. Stan Duckman, my cardiologist, not a 167 00:11:37,760 --> 00:11:42,240 Speaker 1: show sponsor, but Jim Munts my general mo with Kara. 168 00:11:42,240 --> 00:11:45,560 Speaker 1: I'm a eurologists. I am believers in these guys. But 169 00:11:45,679 --> 00:11:48,200 Speaker 1: go in and get your numbers checked and hold on 170 00:11:48,280 --> 00:11:51,640 Speaker 1: to that so that later you got something to compare 171 00:11:51,679 --> 00:11:55,000 Speaker 1: against to see if you have declined or not. You 172 00:11:55,040 --> 00:11:57,000 Speaker 1: can't get that time back. It's like before and after 173 00:11:57,040 --> 00:12:03,079 Speaker 1: picture Southern Pride Fried to Michael Berry show following me 174 00:12:04,080 --> 00:12:11,600 Speaker 1: AI situation with increasing interest. One of the things that 175 00:12:11,679 --> 00:12:14,640 Speaker 1: comes up out of all of it is how much 176 00:12:14,800 --> 00:12:21,320 Speaker 1: energy it demands. AI. I have a friend named Brian 177 00:12:21,400 --> 00:12:25,480 Speaker 1: mcmcken who is in the warehouse business. Well he's in 178 00:12:25,480 --> 00:12:29,720 Speaker 1: the real estate business, and one of his big clients 179 00:12:30,840 --> 00:12:40,080 Speaker 1: is data centers. And the reason is because the new 180 00:12:40,720 --> 00:12:47,680 Speaker 1: big thing is data. So I think of it sort 181 00:12:47,679 --> 00:12:51,120 Speaker 1: of like there was a time where people rode horses, 182 00:12:51,880 --> 00:12:54,760 Speaker 1: and if you were a person of means, you would 183 00:12:54,800 --> 00:12:58,360 Speaker 1: have a buggy and you'd have your buggy behind the 184 00:12:58,400 --> 00:13:01,719 Speaker 1: horse because is more comfortable to sit on cushion. See, 185 00:13:01,840 --> 00:13:06,240 Speaker 1: And that's the way things were until the moment there 186 00:13:06,320 --> 00:13:09,680 Speaker 1: was the automobile, and there were actually some early versions 187 00:13:09,679 --> 00:13:12,400 Speaker 1: of electric automobiles, but they were not very efficient or 188 00:13:12,400 --> 00:13:16,240 Speaker 1: effective for that matter, because again battery technology has always 189 00:13:16,240 --> 00:13:20,120 Speaker 1: been the challenge. But then there was the gas powered automobile, 190 00:13:20,600 --> 00:13:24,800 Speaker 1: and like that it took off. So now all of 191 00:13:24,840 --> 00:13:28,160 Speaker 1: a sudden you had to have stations where you could 192 00:13:28,200 --> 00:13:33,320 Speaker 1: get the energy to fuel this means of transportation. So 193 00:13:33,480 --> 00:13:37,680 Speaker 1: all of a sudden, the gas station industry explodes overnight 194 00:13:38,600 --> 00:13:42,800 Speaker 1: and everything that went with servicing the personal means of 195 00:13:42,840 --> 00:13:48,040 Speaker 1: transportation because a horse was pretty inefficient compared to the automobile, 196 00:13:48,760 --> 00:13:51,000 Speaker 1: and by that time you had forms of mass transit 197 00:13:52,200 --> 00:13:57,720 Speaker 1: train being the most efficient. But when you got the automobile, 198 00:13:58,760 --> 00:14:04,040 Speaker 1: it changed everything. The value of oil all of a 199 00:14:04,120 --> 00:14:07,760 Speaker 1: sudden for so much more than just heating your home 200 00:14:08,280 --> 00:14:13,440 Speaker 1: for traveling about. Throughout history, the ability to travel about 201 00:14:14,000 --> 00:14:17,360 Speaker 1: has been very, very important. So back to the issue 202 00:14:17,360 --> 00:14:23,320 Speaker 1: of AI. You know, I remember around two thousand, the 203 00:14:23,360 --> 00:14:26,320 Speaker 1: turn of the millennium, we had this big tech bubble, 204 00:14:27,240 --> 00:14:31,480 Speaker 1: and things aren't called a bubble until they burst, and 205 00:14:31,520 --> 00:14:33,160 Speaker 1: then you go back and go, oh, that was a 206 00:14:33,200 --> 00:14:39,520 Speaker 1: tech bubble. People thought that having a website was going 207 00:14:39,560 --> 00:14:43,280 Speaker 1: to be everything. And so you had these these little 208 00:14:43,320 --> 00:14:51,000 Speaker 1: millennial millionaires who were getting insane amounts of money because 209 00:14:51,440 --> 00:14:56,160 Speaker 1: they created a website or later an app. And so 210 00:14:56,440 --> 00:14:59,600 Speaker 1: had you had kids in coffee shops, sitting in coffee 211 00:14:59,600 --> 00:15:03,160 Speaker 1: shops banging away on their keyboard that they put in 212 00:15:03,160 --> 00:15:05,200 Speaker 1: a backpack and walk down there and drinking coffee all 213 00:15:05,280 --> 00:15:09,840 Speaker 1: day and then beer all night. And they were flipping 214 00:15:09,840 --> 00:15:13,560 Speaker 1: these companies to private equity. And some of them made it, 215 00:15:13,600 --> 00:15:16,360 Speaker 1: and most of them didn't. That's all right. That still 216 00:15:16,360 --> 00:15:22,920 Speaker 1: goes on today in different ways. But the rise of AI, 217 00:15:24,920 --> 00:15:28,280 Speaker 1: this is on a scale we never imagined. You want 218 00:15:28,280 --> 00:15:31,360 Speaker 1: to know how big this is going to be, Let 219 00:15:31,360 --> 00:15:39,480 Speaker 1: me give you an example. So Mark Zuckerberg at Meta, 220 00:15:40,000 --> 00:15:44,000 Speaker 1: which may he made his fortune off of Facebook, he 221 00:15:44,080 --> 00:15:47,920 Speaker 1: has dedicated I think it's seventy billion dollars. I'll find 222 00:15:47,960 --> 00:15:51,280 Speaker 1: it's in the middle of this story. He has dedicated 223 00:15:51,360 --> 00:15:59,160 Speaker 1: seventy billion with a B dollars to AI. Well, everybody 224 00:16:00,160 --> 00:16:04,280 Speaker 1: is in that in that at that level is investing 225 00:16:04,280 --> 00:16:13,360 Speaker 1: in AI. Meta lost a series of folks that went 226 00:16:13,440 --> 00:16:17,800 Speaker 1: to work for Elon Musk in order to get them 227 00:16:17,880 --> 00:16:20,960 Speaker 1: to stay or in some cases he was trying to 228 00:16:20,960 --> 00:16:26,360 Speaker 1: recruit them. Compared to Musk, he offered them, you ready 229 00:16:26,360 --> 00:16:32,560 Speaker 1: for this two hundred and fifty million dollars each with 230 00:16:32,680 --> 00:16:38,320 Speaker 1: a one hundred million dollar signing bonus, direct access to Zuckerberg. 231 00:16:39,080 --> 00:16:42,400 Speaker 1: That's very important for people that do what they do. 232 00:16:42,680 --> 00:16:45,200 Speaker 1: They don't want to be stuck in some back office, 233 00:16:45,680 --> 00:16:49,280 Speaker 1: some back cubicle when they have a brilliant idea. They 234 00:16:49,320 --> 00:16:50,240 Speaker 1: want to be able to come in and go, hey, 235 00:16:50,280 --> 00:16:51,560 Speaker 1: I need this to be able to make this happen, 236 00:16:52,240 --> 00:17:00,680 Speaker 1: and supposedly unlimited resources, and yet they left anyway. They 237 00:17:00,800 --> 00:17:05,639 Speaker 1: turned down one hundred million dollars and in a signing 238 00:17:05,680 --> 00:17:09,359 Speaker 1: bonus and two hundred and fifty million dollars for each 239 00:17:09,359 --> 00:17:13,920 Speaker 1: of the workers, for each for a person. They didn't 240 00:17:13,960 --> 00:17:18,320 Speaker 1: go to Google, they didn't go to open aie. They 241 00:17:18,400 --> 00:17:23,040 Speaker 1: joined a startup with twelve hundred employees, a startup that 242 00:17:23,119 --> 00:17:27,000 Speaker 1: at this point has been unwilling to match the offers 243 00:17:27,520 --> 00:17:33,080 Speaker 1: that Zuckerberg was making. But as the as the case 244 00:17:33,080 --> 00:17:41,760 Speaker 1: study shows, this startup elon Musk's empire, which he calls XAI, 245 00:17:43,119 --> 00:17:47,600 Speaker 1: offers them the opportunity to do so much more, and 246 00:17:47,640 --> 00:17:50,160 Speaker 1: he has a better track record. Well, here's the data 247 00:17:50,200 --> 00:17:55,000 Speaker 1: point meta or Facebook is spending seventy billion dollars on 248 00:17:55,119 --> 00:17:59,960 Speaker 1: AI this year alone. They had acquired one startup for 249 00:18:00,119 --> 00:18:03,879 Speaker 1: fourteen point three billion dollars from a guy named Alexander Wang, 250 00:18:04,960 --> 00:18:11,320 Speaker 1: So they are throwing funny money at things. Despite all this, 251 00:18:11,920 --> 00:18:18,760 Speaker 1: Elon Musk stole eighteen of Zuckerberg's best engineers. Zuckerberg lost 252 00:18:18,800 --> 00:18:20,879 Speaker 1: his mind offer him two hundred fifty million dollars. One 253 00:18:20,960 --> 00:18:26,480 Speaker 1: hundred million dollars right now. We've never seen anything like 254 00:18:26,560 --> 00:18:32,720 Speaker 1: this in any industry ever before. I mean, I guess 255 00:18:32,720 --> 00:18:38,320 Speaker 1: you could say the NFL for top players, AI programmers, 256 00:18:38,720 --> 00:18:44,080 Speaker 1: AI innovators. And by the way, we're not talking about 257 00:18:44,280 --> 00:18:47,480 Speaker 1: we're talking about young people. We're talking about the kind 258 00:18:47,520 --> 00:18:52,800 Speaker 1: of people that Trump I mean that Elon brought into Dosee. 259 00:18:52,920 --> 00:18:56,720 Speaker 1: I had people say I'd lost my mind when I 260 00:18:56,760 --> 00:19:01,399 Speaker 1: was talking about the people Elon brought in to people 261 00:19:01,440 --> 00:19:04,399 Speaker 1: like big balls, people like some of those. Let me 262 00:19:04,440 --> 00:19:06,800 Speaker 1: tell you something, I don't know where to find those people. 263 00:19:07,800 --> 00:19:11,160 Speaker 1: Trump doesn't know where to find those people. You don't 264 00:19:11,200 --> 00:19:14,040 Speaker 1: know where to find those people. The government doesn't know 265 00:19:14,119 --> 00:19:18,720 Speaker 1: where to find those people. Those people have snot hanging 266 00:19:18,760 --> 00:19:22,479 Speaker 1: out of their nose. They live in places you don't know. 267 00:19:22,600 --> 00:19:29,000 Speaker 1: They live online, they're gamers, they're weird, but they got 268 00:19:29,000 --> 00:19:33,360 Speaker 1: this little quirky skill set. And you don't take out 269 00:19:33,400 --> 00:19:37,840 Speaker 1: an ad On indeed or monster and find them. They 270 00:19:38,040 --> 00:19:43,200 Speaker 1: gravitate to their god, King Elon Musk, and he brought 271 00:19:43,240 --> 00:19:46,200 Speaker 1: them into the government. He brought them into the government 272 00:19:47,680 --> 00:19:51,200 Speaker 1: so they could run their little spiders through the system 273 00:19:51,280 --> 00:19:54,040 Speaker 1: using terms that only they could figure out. All right, 274 00:19:55,240 --> 00:20:01,080 Speaker 1: what if you're getting welfare payments but you're an illegal 275 00:20:01,119 --> 00:20:04,800 Speaker 1: alien you have no identification. How do we do that? 276 00:20:05,920 --> 00:20:08,840 Speaker 1: And it runs through all the systems, harvests all the 277 00:20:08,920 --> 00:20:13,000 Speaker 1: data and says, here's the addresses of the payments. Would 278 00:20:13,000 --> 00:20:16,639 Speaker 1: you like me to shut those payments off? Yes, you 279 00:20:16,760 --> 00:20:22,720 Speaker 1: don't have the man power in government employees lazily showing 280 00:20:22,800 --> 00:20:25,240 Speaker 1: up late, leaving early and taking a long lunch to 281 00:20:25,320 --> 00:20:28,240 Speaker 1: go through and dig out that data. Hey, I could 282 00:20:28,320 --> 00:20:30,800 Speaker 1: do it in an hour. I don't know that in 283 00:20:30,840 --> 00:20:33,880 Speaker 1: a very short period of time. It's like lawyers. It's 284 00:20:33,920 --> 00:20:40,119 Speaker 1: not all bad. And this isn't an Elon Musk worship session. Damn. 285 00:20:40,160 --> 00:20:40,960 Speaker 1: I'm glad he's honest. 286 00:20:43,000 --> 00:20:45,120 Speaker 2: And you listen to the Michael Berry Show. 287 00:20:45,359 --> 00:20:52,440 Speaker 1: Good not I I saw a stat bade me think, 288 00:20:53,119 --> 00:20:56,800 Speaker 1: I've never seen Raiders of Lost Art? Have you Lost Art? Sorry? 289 00:20:59,520 --> 00:21:02,679 Speaker 1: What was the theme song to that movie? You remember? 290 00:21:04,520 --> 00:21:07,480 Speaker 1: Listen to this one you find it. Raiders of the 291 00:21:07,520 --> 00:21:13,840 Speaker 1: Lost Art came out in nineteen guess what year? Nineteen 292 00:21:13,840 --> 00:21:16,960 Speaker 1: eighty one is good, but it was set in what year? 293 00:21:19,880 --> 00:21:24,800 Speaker 1: Forty is very close? It was set in thirty six, right, 294 00:21:25,480 --> 00:21:31,400 Speaker 1: so it was set forty five years earlier. If they 295 00:21:31,640 --> 00:21:35,040 Speaker 1: remade it, not in nineteen eighty one as it was before, 296 00:21:35,359 --> 00:21:43,560 Speaker 1: but in twenty twenty five, with the same gap as 297 00:21:43,600 --> 00:21:50,520 Speaker 1: it had forty five years, it would be set in 298 00:21:50,600 --> 00:21:53,000 Speaker 1: nineteen eighty one. Well, that'll be true in a couple 299 00:21:52,960 --> 00:21:57,919 Speaker 1: of months. Isn't that crazy? In nineteen eighty one, we 300 00:21:58,000 --> 00:22:04,000 Speaker 1: thought nineteen thirty six was centuries ago, and now think 301 00:22:04,040 --> 00:22:10,439 Speaker 1: about this, Think about how long ago forty five years 302 00:22:11,080 --> 00:22:16,960 Speaker 1: was in the Grands game, It's crazy, you know. I 303 00:22:16,960 --> 00:22:21,320 Speaker 1: read a quote from Thomas Jefferson in seventeen eighty seven. 304 00:22:23,160 --> 00:22:25,919 Speaker 1: He's a big fan of raisers. I did not know that. Oh, okay, 305 00:22:26,760 --> 00:22:30,000 Speaker 1: he wasn't in it, right. He said, I think our 306 00:22:30,080 --> 00:22:38,040 Speaker 1: governments will remain virtuous for many centuries as long as 307 00:22:38,040 --> 00:22:43,160 Speaker 1: they are chiefly agricultural, and this will be as long 308 00:22:43,200 --> 00:22:47,040 Speaker 1: as there shall be vacant lands in any part of America. 309 00:22:48,840 --> 00:22:52,800 Speaker 1: When they get piled upon one another in large cities, 310 00:22:53,560 --> 00:22:59,080 Speaker 1: as in Europe, they will become corrupt, as in Europe. 311 00:23:00,600 --> 00:23:04,240 Speaker 1: And I got to thinking about that. When I think 312 00:23:04,280 --> 00:23:13,000 Speaker 1: about trustworthy, honorable people, they are, by and large country folk. 313 00:23:15,160 --> 00:23:21,160 Speaker 1: City folk hate country folk, and they talk about them 314 00:23:21,640 --> 00:23:25,080 Speaker 1: as if they're stupid. But you know what's interesting about that? 315 00:23:28,119 --> 00:23:36,680 Speaker 1: You ask somebody from the city who you ask them, Hey, 316 00:23:37,359 --> 00:23:40,640 Speaker 1: a farmer's not going to bring you your food. You've 317 00:23:40,640 --> 00:23:43,760 Speaker 1: got to do that on your own. They wouldn't know 318 00:23:43,760 --> 00:23:47,320 Speaker 1: where to begin. How do you get milk from a cow? 319 00:23:49,440 --> 00:23:54,560 Speaker 1: How do you make cheese out of it? Where do 320 00:23:54,640 --> 00:24:00,960 Speaker 1: you get the eggs for your morning fancy omelet? Where 321 00:24:01,000 --> 00:24:06,000 Speaker 1: do you get the coffee that you pay so much for? 322 00:24:07,840 --> 00:24:12,200 Speaker 1: Where do you get all these fish that you pay 323 00:24:12,640 --> 00:24:15,919 Speaker 1: so dearly for? You know, I think Mike Rose dirty jobs. 324 00:24:18,040 --> 00:24:23,280 Speaker 1: I think that is a ground breaking moment in modern 325 00:24:23,320 --> 00:24:29,320 Speaker 1: American life because he paid tribute to the guy that 326 00:24:29,520 --> 00:24:35,600 Speaker 1: never got any credit. If you were to say, we're 327 00:24:35,600 --> 00:24:40,520 Speaker 1: going to decide there is x amount of as the 328 00:24:40,600 --> 00:24:43,560 Speaker 1: kids say, flowers to give out. We're going to give 329 00:24:43,600 --> 00:24:47,479 Speaker 1: people their flowers. We're going to give praise to people 330 00:24:48,800 --> 00:24:56,320 Speaker 1: based on behaviors undertaken that we want as a society 331 00:24:57,200 --> 00:25:02,920 Speaker 1: to reward. Guess where we would give the flowers. Mothers 332 00:25:03,000 --> 00:25:11,280 Speaker 1: for giving birth, that's hard work man, mothers for taking 333 00:25:11,320 --> 00:25:17,480 Speaker 1: care of children, teaching children, disciplining children, Fathers who are 334 00:25:17,600 --> 00:25:24,280 Speaker 1: actively involved in the lives of their children. People who 335 00:25:24,320 --> 00:25:28,000 Speaker 1: take care of the little old lady next door, people 336 00:25:28,080 --> 00:25:34,320 Speaker 1: who volunteered at the school, in the league, in the neighborhood. 337 00:25:35,160 --> 00:25:40,080 Speaker 1: We would we would we would reward those people. Police 338 00:25:40,119 --> 00:25:42,840 Speaker 1: officers who go above and beyond, firefighters who go above 339 00:25:42,880 --> 00:25:47,800 Speaker 1: and beyond, teachers who go above and beyond, service members 340 00:25:47,920 --> 00:25:52,400 Speaker 1: who make a sacrifice. All of these people, we would 341 00:25:52,440 --> 00:25:57,919 Speaker 1: brag on them. Right, Coaches who make a difference in 342 00:25:57,960 --> 00:26:03,879 Speaker 1: the lives of young people, teachers, mentors, deacons at the church. 343 00:26:05,920 --> 00:26:11,080 Speaker 1: These would be celebrated and esteemed positions in society, and 344 00:26:11,119 --> 00:26:16,760 Speaker 1: we would openly compliment them and praise them. Now, is 345 00:26:16,800 --> 00:26:22,080 Speaker 1: that what we do? No, because you get more of 346 00:26:22,119 --> 00:26:28,000 Speaker 1: what you reward. You see, that thing which you extol 347 00:26:28,160 --> 00:26:33,840 Speaker 1: the virtues of you will get more of. And things 348 00:26:33,920 --> 00:26:38,200 Speaker 1: that you neglect and ignore, will be neglected and ignored 349 00:26:39,359 --> 00:26:46,320 Speaker 1: by young people. So when half your television shows are 350 00:26:46,400 --> 00:26:50,920 Speaker 1: women who've had every inch of their body cut up, 351 00:26:51,560 --> 00:26:54,679 Speaker 1: plastered on put here in an attempt to look like 352 00:26:54,720 --> 00:27:00,560 Speaker 1: Kim Kardashian, and that is who your biggest influencers are 353 00:27:00,600 --> 00:27:03,840 Speaker 1: going to be, how does that come to be the case? 354 00:27:04,600 --> 00:27:08,080 Speaker 1: Who's being influenced by these people that ought to tell 355 00:27:08,119 --> 00:27:10,959 Speaker 1: you everything you need to know? We had that audio 356 00:27:11,040 --> 00:27:14,720 Speaker 1: of Jasmine Crockett a little while back on do you 357 00:27:14,720 --> 00:27:17,200 Speaker 1: have that handy? See if you can find that. She 358 00:27:18,280 --> 00:27:23,159 Speaker 1: tried out her newest accent and she's talking about Republicans, 359 00:27:23,640 --> 00:27:28,960 Speaker 1: and everyone focuses on Jasmine Crockett, But just pause for 360 00:27:28,960 --> 00:27:34,919 Speaker 1: a second. She didn't do that. She didn't go to 361 00:27:35,040 --> 00:27:38,879 Speaker 1: the Kennedy Center and roll that out. She didn't go 362 00:27:38,920 --> 00:27:42,280 Speaker 1: to your Sunday church service and roll that out. She 363 00:27:42,320 --> 00:27:45,720 Speaker 1: didn't go the book club and roll that out. She 364 00:27:46,000 --> 00:27:50,040 Speaker 1: tried that on an audience. She assumed to be stupid 365 00:27:50,160 --> 00:27:57,480 Speaker 1: enough to feel comforted by this street gutter language she 366 00:27:57,520 --> 00:28:03,000 Speaker 1: was speaking, and a parent they bought into it. That 367 00:28:03,119 --> 00:28:07,520 Speaker 1: says more about them than her. She's just ambitious and 368 00:28:07,600 --> 00:28:08,639 Speaker 1: I'm no fan of hers. 369 00:28:10,240 --> 00:28:12,879 Speaker 2: Maybe because these people they are crazy because they always 370 00:28:12,920 --> 00:28:15,159 Speaker 2: talking about how Christian they is. Yeah, I don't know 371 00:28:15,200 --> 00:28:18,240 Speaker 2: how many of them on that side are getting divorced 372 00:28:18,240 --> 00:28:21,200 Speaker 2: because they getting caught up sleeping with their coworker staffers 373 00:28:21,560 --> 00:28:24,720 Speaker 2: and turns all the things. Yeah, you ain't gotta believe me. 374 00:28:24,840 --> 00:28:27,520 Speaker 2: Just go google. You'll find some of it. I'm telling you. 375 00:28:27,560 --> 00:28:30,240 Speaker 2: And the wives is being messy and petty. They putting 376 00:28:30,240 --> 00:28:32,880 Speaker 2: it into divorce. I'm like, WHOA, that's gotta be true, 377 00:28:32,920 --> 00:28:35,439 Speaker 2: because your lawyer would know that they gonna lose it. 378 00:28:38,480 --> 00:28:44,040 Speaker 1: Nobody called her out there? What was she doing? What 379 00:28:44,200 --> 00:28:47,320 Speaker 1: exactly what you ever heard? You ever heard her talk 380 00:28:47,680 --> 00:28:52,560 Speaker 1: going back to twenty nineteen. That's not her natural cadence, 381 00:28:52,600 --> 00:28:57,320 Speaker 1: that's not her natural patchois her natural her natural means 382 00:28:57,360 --> 00:29:03,640 Speaker 1: of communication. She learned this, she assumed this. That tells 383 00:29:03,680 --> 00:29:07,240 Speaker 1: you everything you need to know about this woman. But 384 00:29:07,240 --> 00:29:09,680 Speaker 1: more importantly, it tells you everything you need to know 385 00:29:09,760 --> 00:29:13,400 Speaker 1: about the kind of people who vote