1 00:00:07,760 --> 00:00:09,160 Speaker 1: Welcome to the Rogue Recap. 2 00:00:09,760 --> 00:00:14,280 Speaker 2: Hot takes, cold facts, and zero respect for the official narrative. 3 00:00:15,240 --> 00:00:19,520 Speaker 1: Sit back, roll your eyes, and let's recap rogue style. 4 00:00:20,200 --> 00:00:22,959 Speaker 1: What's up, everybody, and welcome to the Roague Recap. I 5 00:00:22,960 --> 00:00:25,280 Speaker 1: am Lynda McLaughlin, your host. Here, I am again at 6 00:00:25,360 --> 00:00:28,280 Speaker 1: Linda Mick at Rogue Recap roguerecap dot com. You know 7 00:00:28,320 --> 00:00:31,240 Speaker 1: what to do, YadA, YadA, YadA. So there's a million 8 00:00:31,240 --> 00:00:33,680 Speaker 1: things going on, like I say every single day because 9 00:00:33,680 --> 00:00:36,400 Speaker 1: it's true, so I think it bears repeating. But one 10 00:00:36,479 --> 00:00:39,360 Speaker 1: of the things that is not getting discussed is AI. 11 00:00:39,680 --> 00:00:42,239 Speaker 1: So we're talking about all the cool things we can 12 00:00:42,280 --> 00:00:44,839 Speaker 1: do with it, all the cool things it can do 13 00:00:45,000 --> 00:00:48,800 Speaker 1: for us to help us with time management and expediting projects, 14 00:00:48,800 --> 00:00:51,880 Speaker 1: and that's all wonderful, But what else is it doing? 15 00:00:52,440 --> 00:00:54,680 Speaker 1: I think that's my question. And I am certainly not 16 00:00:54,720 --> 00:00:56,560 Speaker 1: an expert. I use it every day because I work 17 00:00:56,600 --> 00:00:58,800 Speaker 1: in media, as most of you know. But I have 18 00:00:58,840 --> 00:01:01,800 Speaker 1: a dear friend I call doctor Dave. He's telling me 19 00:01:01,840 --> 00:01:03,560 Speaker 1: he's not a doctor. I think it's fake news, but 20 00:01:03,600 --> 00:01:05,039 Speaker 1: he's telling me he's not. So I'm going to leave 21 00:01:05,040 --> 00:01:07,720 Speaker 1: that there. But his name is Dave. Laboue. He's wicked smart. 22 00:01:07,840 --> 00:01:12,680 Speaker 1: He's got about seventy eight master's degrees in artificial intelligence 23 00:01:12,720 --> 00:01:16,480 Speaker 1: and language algorithms and all sorts of sciences that regular 24 00:01:16,560 --> 00:01:18,000 Speaker 1: people just go, Hey, I need a guy for that, 25 00:01:18,080 --> 00:01:20,160 Speaker 1: and he's the guy. So Dave, welcome to the show. 26 00:01:21,520 --> 00:01:22,479 Speaker 2: Thank you, happy to be here. 27 00:01:22,480 --> 00:01:25,760 Speaker 1: Linda, good Glad for those of you who you know 28 00:01:25,880 --> 00:01:28,080 Speaker 1: haven't listened to my show. I like to bring my 29 00:01:28,160 --> 00:01:31,840 Speaker 1: friends on. Dave is a great friend, and he just 30 00:01:31,880 --> 00:01:35,479 Speaker 1: happens to be super wicked smart. So I said to him, 31 00:01:35,520 --> 00:01:38,320 Speaker 1: can you come on and talk to me about AI 32 00:01:38,560 --> 00:01:42,080 Speaker 1: because I'm seeing all this news come out and one 33 00:01:42,120 --> 00:01:45,360 Speaker 1: of the biggest things that people are asking is how 34 00:01:45,400 --> 00:01:47,200 Speaker 1: the hell are we going to run this? We don't 35 00:01:47,200 --> 00:01:49,800 Speaker 1: have the energy. And so I told Dave, I said, 36 00:01:49,840 --> 00:01:51,640 Speaker 1: I want to talk about what the president is saying. 37 00:01:51,680 --> 00:01:54,240 Speaker 1: I want to talk about what Elon Musk is saying, 38 00:01:55,280 --> 00:01:58,120 Speaker 1: but I also want to talk about the functionality of 39 00:01:58,120 --> 00:02:00,560 Speaker 1: how this really happens. So I'm going to Da've talked 40 00:02:00,560 --> 00:02:02,520 Speaker 1: to us for a hot second about his background so 41 00:02:02,560 --> 00:02:04,840 Speaker 1: that we all know why he's worthy of talking about 42 00:02:04,880 --> 00:02:07,760 Speaker 1: these things, and then we're going to get into what 43 00:02:07,840 --> 00:02:11,160 Speaker 1: happens next here, you know, for the regular American citizen 44 00:02:11,280 --> 00:02:13,639 Speaker 1: on their electric bill, to how they use AI in 45 00:02:13,680 --> 00:02:16,640 Speaker 1: everyday life. So Dave, tell us a little bit about 46 00:02:16,680 --> 00:02:19,080 Speaker 1: your background and why you love to nerd out on 47 00:02:19,120 --> 00:02:19,600 Speaker 1: this stuff. 48 00:02:20,760 --> 00:02:23,920 Speaker 2: Yeah, sure thing. And for my background, it comes from 49 00:02:23,960 --> 00:02:29,760 Speaker 2: a combination of reason, philosophy, mathematics, data science, and artificial intelligence. 50 00:02:30,400 --> 00:02:34,440 Speaker 2: So I've seen the evolution of language processing as it's 51 00:02:34,480 --> 00:02:38,360 Speaker 2: come through different algorithms, and the world we're living in 52 00:02:38,400 --> 00:02:41,639 Speaker 2: today is really a quantum leap from what we saw 53 00:02:41,840 --> 00:02:45,720 Speaker 2: ten to fifteen years ago. And from a neuroscience standpoint, 54 00:02:45,880 --> 00:02:51,520 Speaker 2: from a language and linguistic understanding standpoint, from a compute standpoint, 55 00:02:52,000 --> 00:02:54,960 Speaker 2: all of these areas have evolved very rapidly in the 56 00:02:55,000 --> 00:03:01,040 Speaker 2: last five years, and we're seeing major increases on what 57 00:03:01,080 --> 00:03:05,200 Speaker 2: these AI algorithms can do the large language models, who 58 00:03:05,240 --> 00:03:08,360 Speaker 2: owns them, who's developed them, who has the control over them, 59 00:03:08,800 --> 00:03:11,400 Speaker 2: how they train them, and what that means for us 60 00:03:11,400 --> 00:03:14,080 Speaker 2: as users to interact with them. So there's a lot 61 00:03:14,120 --> 00:03:16,160 Speaker 2: of open questions that we have to go through and 62 00:03:16,240 --> 00:03:20,200 Speaker 2: think about at a deeper level, both in terms of 63 00:03:21,320 --> 00:03:24,560 Speaker 2: what kind of dependency and interaction do we want to 64 00:03:24,600 --> 00:03:26,920 Speaker 2: have with these ais. 65 00:03:27,240 --> 00:03:29,520 Speaker 1: So can I interrupt you on that point? Can you 66 00:03:29,600 --> 00:03:33,919 Speaker 1: explain to us in layman's terms? What is language processing? 67 00:03:35,080 --> 00:03:35,320 Speaker 3: Sure? 68 00:03:35,400 --> 00:03:40,560 Speaker 2: So, language processing? The quick background is you're taking text 69 00:03:40,880 --> 00:03:43,640 Speaker 2: as it is typed in a computer, or word documents 70 00:03:43,720 --> 00:03:47,840 Speaker 2: or anything that you use digitally. And about ten to 71 00:03:47,880 --> 00:03:51,400 Speaker 2: fifteen years ago, we were basically looking at collections of words, 72 00:03:51,440 --> 00:03:54,280 Speaker 2: strings of words, how do they relate to each other? 73 00:03:54,640 --> 00:04:00,240 Speaker 2: What combinations words are used together? To identify likeness with 74 00:04:00,320 --> 00:04:03,440 Speaker 2: the something that came out of actually Google Brain, which 75 00:04:03,520 --> 00:04:06,600 Speaker 2: was the paper written called the Attention is All you 76 00:04:06,680 --> 00:04:09,600 Speaker 2: need to develop the GPT revolution that we know about. 77 00:04:09,640 --> 00:04:15,720 Speaker 2: Open Ai now created that and evolved it. It's sort 78 00:04:15,760 --> 00:04:20,920 Speaker 2: of a major breakthrough that allows language processing to understand context. 79 00:04:21,360 --> 00:04:24,480 Speaker 2: So what that means is, as I'm speaking right now, 80 00:04:25,279 --> 00:04:27,159 Speaker 2: I'm thinking about the next word or set of words 81 00:04:27,160 --> 00:04:30,359 Speaker 2: that I'm going to deliver, and that's based on the 82 00:04:30,360 --> 00:04:32,760 Speaker 2: previous words that I've used and sort of my background 83 00:04:32,760 --> 00:04:36,039 Speaker 2: and knowledge, and language models now have the ability to 84 00:04:36,080 --> 00:04:39,560 Speaker 2: look backwards in history that they never had the ability 85 00:04:39,560 --> 00:04:41,120 Speaker 2: to do before. They were only looking at sort of 86 00:04:41,120 --> 00:04:45,440 Speaker 2: certain strings of text place side by side, or certain documents. 87 00:04:46,000 --> 00:04:48,279 Speaker 2: And this relates to the energy usage. So we're looking 88 00:04:48,320 --> 00:04:51,440 Speaker 2: now not just in a single, very narrow sense across 89 00:04:52,000 --> 00:04:55,000 Speaker 2: you know, hundreds or thousands of documents. We're looking across 90 00:04:55,120 --> 00:04:58,960 Speaker 2: documents that spanned all digital information potentially, so anything we 91 00:04:59,040 --> 00:05:02,400 Speaker 2: have online that's and scanned and catalog and organized that 92 00:05:02,480 --> 00:05:06,880 Speaker 2: is now available memory to generate the next word that's 93 00:05:06,920 --> 00:05:10,520 Speaker 2: being predicted as you talk to an LLF. So it's 94 00:05:10,520 --> 00:05:16,440 Speaker 2: been quite a change in how we can use these models, 95 00:05:16,440 --> 00:05:18,920 Speaker 2: what we can expect from them, and what they're able 96 00:05:18,960 --> 00:05:22,840 Speaker 2: to do and to evolve from that. Language models are 97 00:05:22,880 --> 00:05:26,840 Speaker 2: no longer just about language. They have multimodal aspects. As 98 00:05:26,839 --> 00:05:30,640 Speaker 2: we know, we can use image generation, voice generation, you 99 00:05:30,680 --> 00:05:34,080 Speaker 2: can do image to text, text image, image to image, 100 00:05:34,560 --> 00:05:39,080 Speaker 2: and they understand all these different modalities in a deep 101 00:05:39,120 --> 00:05:43,200 Speaker 2: way that humans would. So we're not quite at general 102 00:05:43,240 --> 00:05:46,279 Speaker 2: intelligence for AI's still a long shot to go there, 103 00:05:46,279 --> 00:05:49,640 Speaker 2: but we have a lot of advanced understanding for these 104 00:05:50,240 --> 00:05:53,559 Speaker 2: algorithms to be able to understand an image, understand a video, 105 00:05:54,400 --> 00:05:57,479 Speaker 2: modify it or edit it and give us back something 106 00:05:57,520 --> 00:06:00,560 Speaker 2: based on what we're requesting of it. It's gone from 107 00:06:00,640 --> 00:06:04,160 Speaker 2: just language to basically anything digital now can be used 108 00:06:04,200 --> 00:06:05,360 Speaker 2: through a language model. 109 00:06:05,720 --> 00:06:08,400 Speaker 1: Okay, So two questions to that. The first thing is 110 00:06:08,680 --> 00:06:12,120 Speaker 1: when you say it's collecting information, just how you and 111 00:06:12,160 --> 00:06:15,320 Speaker 1: I are having a conversation and we're picking and choosing 112 00:06:15,360 --> 00:06:17,960 Speaker 1: the words we're going to say next based upon the 113 00:06:17,960 --> 00:06:21,040 Speaker 1: word said previously. No, we're saying hmm, we're going to 114 00:06:21,120 --> 00:06:22,480 Speaker 1: go here, We're going to go there. I want to 115 00:06:22,480 --> 00:06:27,320 Speaker 1: ask this question, are these models, these AI models, are 116 00:06:27,400 --> 00:06:31,120 Speaker 1: they pulling from data sets that it's getting from inputs 117 00:06:31,640 --> 00:06:34,400 Speaker 1: all over the world, or is it just pulling from 118 00:06:34,400 --> 00:06:38,279 Speaker 1: the data set given to it in a particular user moment, 119 00:06:39,600 --> 00:06:40,400 Speaker 1: so a combination. 120 00:06:40,880 --> 00:06:46,239 Speaker 2: Up until recently, that was how they were trained, quote unquote, 121 00:06:46,279 --> 00:06:51,000 Speaker 2: So you had large open web documents, basically anything that 122 00:06:51,040 --> 00:06:55,159 Speaker 2: was open source to the web or digitally for that matter. Actually, 123 00:06:55,279 --> 00:06:58,360 Speaker 2: very private data is very valuable to anything that is 124 00:06:58,440 --> 00:07:02,320 Speaker 2: not broadly available in a very specific niche area is 125 00:07:02,360 --> 00:07:05,360 Speaker 2: a valuable data asset for language models because those are 126 00:07:05,400 --> 00:07:09,000 Speaker 2: areas that they haven't been trained on. But otherwise more generally, 127 00:07:10,160 --> 00:07:13,680 Speaker 2: you know, after about the GPT from open ai came 128 00:07:13,680 --> 00:07:16,240 Speaker 2: out in twenty twenty, twenty twenty one, Yeah, a lot 129 00:07:16,280 --> 00:07:18,640 Speaker 2: of that energy was used in just training of all 130 00:07:18,680 --> 00:07:21,480 Speaker 2: of the data that was available on the web. People 131 00:07:21,480 --> 00:07:25,040 Speaker 2: were scraping Twitter at the time, bringing those conversations in 132 00:07:25,120 --> 00:07:28,120 Speaker 2: using different social media, Reddit post all that was brought in. 133 00:07:28,600 --> 00:07:30,640 Speaker 2: That was training the model that was basically building its 134 00:07:30,640 --> 00:07:33,200 Speaker 2: brain of how to think, not to reference when it's 135 00:07:33,240 --> 00:07:36,960 Speaker 2: constructing its sentences about what it knows. So obviously there's 136 00:07:36,960 --> 00:07:39,760 Speaker 2: a bias there if you scrape through information from a 137 00:07:39,760 --> 00:07:43,040 Speaker 2: certain leaning area, that's all it's going to know. So 138 00:07:44,200 --> 00:07:48,000 Speaker 2: balanced understanding and sort of training of these models is 139 00:07:48,120 --> 00:07:52,400 Speaker 2: very important. But to get back to your point now 140 00:07:52,440 --> 00:07:54,800 Speaker 2: that we've sort of evolved beyond the base training of 141 00:07:54,840 --> 00:07:57,880 Speaker 2: these language models or sort of called the fun foundational models, 142 00:07:58,200 --> 00:08:00,520 Speaker 2: a lot of them do integrate real time data, especially 143 00:08:00,560 --> 00:08:07,000 Speaker 2: groc that's a great segue from x XAI into groc AI. 144 00:08:07,440 --> 00:08:11,240 Speaker 2: You have the ability for that LLM, that algorithm to 145 00:08:11,240 --> 00:08:16,280 Speaker 2: basically feed real time all conversations, news that's posted back 146 00:08:16,280 --> 00:08:19,080 Speaker 2: and forth, direct messages, anything that's happening in x can 147 00:08:19,120 --> 00:08:21,680 Speaker 2: now be integrated real time into that language model and 148 00:08:21,840 --> 00:08:25,680 Speaker 2: used to generate responses. And this goes for other models too, 149 00:08:25,680 --> 00:08:27,400 Speaker 2: But it really is about the data they have access 150 00:08:27,400 --> 00:08:30,240 Speaker 2: to and how quickly they can reference it and bring 151 00:08:30,280 --> 00:08:31,880 Speaker 2: it back to the user. 152 00:08:32,679 --> 00:08:36,400 Speaker 1: So it's interesting and this goes directly to GROC and 153 00:08:36,440 --> 00:08:39,000 Speaker 1: I'm looking this up on x as we speak, because 154 00:08:39,000 --> 00:08:41,319 Speaker 1: I want to make sure that I quote this accurately 155 00:08:42,360 --> 00:08:46,040 Speaker 1: to that exact point of creating, for lack of a 156 00:08:46,080 --> 00:08:50,920 Speaker 1: better example or analogy, partisan robots, if you will, right, So, 157 00:08:50,960 --> 00:08:54,560 Speaker 1: we're creating the libs and the conservatives in the AI world, 158 00:08:54,600 --> 00:08:57,040 Speaker 1: which is so creepy because we've got plenty of them 159 00:08:57,040 --> 00:08:59,079 Speaker 1: in the human world. We don't need anymore. But I'll 160 00:08:59,160 --> 00:09:03,400 Speaker 1: leave that there. So Mark Benioff of Salesforce, he's the CEO. 161 00:09:04,520 --> 00:09:09,280 Speaker 1: I saw this interview yesterday and he says that he 162 00:09:09,440 --> 00:09:15,840 Speaker 1: used rock to make the viral image for his dream 163 00:09:15,920 --> 00:09:23,440 Speaker 1: Force Salesforce, you know, positioning, because chat gpt refused to 164 00:09:23,559 --> 00:09:27,480 Speaker 1: create it. So chat gpt said nope, I won't do it. 165 00:09:27,520 --> 00:09:29,960 Speaker 1: And he was like, this is the difference between open 166 00:09:30,000 --> 00:09:36,040 Speaker 1: creativity and corporate censorship. And I was like, that's wild. 167 00:09:36,360 --> 00:09:40,760 Speaker 1: Like this this interface literally said to you, no, I 168 00:09:40,800 --> 00:09:44,439 Speaker 1: won't do this, no, and I will tell you interestingly 169 00:09:44,559 --> 00:09:48,280 Speaker 1: enough to your point. We were talking about this the 170 00:09:48,320 --> 00:09:52,240 Speaker 1: other day. The ability to go image to image, text 171 00:09:52,240 --> 00:09:55,920 Speaker 1: to image, image to video, video to video, or image 172 00:09:55,920 --> 00:09:59,320 Speaker 1: to image to create a video, and the prompts right 173 00:09:59,440 --> 00:10:03,240 Speaker 1: the language that you're using. So the other day, I 174 00:10:03,280 --> 00:10:06,120 Speaker 1: was trying to create an announcement for my daughter and 175 00:10:06,160 --> 00:10:08,839 Speaker 1: where she's going to college. So I had a picture 176 00:10:08,840 --> 00:10:11,960 Speaker 1: of my daughter with her lacrosetick, and then I had 177 00:10:12,000 --> 00:10:13,600 Speaker 1: where she was going to college, and I made the 178 00:10:13,679 --> 00:10:17,240 Speaker 1: super cool graphic. But then I took the images that 179 00:10:17,320 --> 00:10:19,640 Speaker 1: I made and I had to put them into a 180 00:10:19,679 --> 00:10:22,680 Speaker 1: real editing software because I know how to edit, to 181 00:10:22,760 --> 00:10:24,240 Speaker 1: piece it all together and do it the way that 182 00:10:24,320 --> 00:10:27,520 Speaker 1: I really wanted. What's interesting is I couldn't make it 183 00:10:27,559 --> 00:10:30,080 Speaker 1: in AI. I had to do it if you will 184 00:10:30,120 --> 00:10:34,480 Speaker 1: buy hand because that particular interface told me that she 185 00:10:34,640 --> 00:10:37,760 Speaker 1: was a miner and it wouldn't let me use the 186 00:10:37,800 --> 00:10:41,160 Speaker 1: image of her. She was in a lacrosse uniform with 187 00:10:41,200 --> 00:10:45,120 Speaker 1: a lacrossetick. There was nothing inappropriate about this image whatsoever. 188 00:10:45,760 --> 00:10:48,360 Speaker 1: I did not supply her age. She is a minor, 189 00:10:48,880 --> 00:10:51,480 Speaker 1: but I she could have been eighteen right and still 190 00:10:51,520 --> 00:10:54,400 Speaker 1: be in high school. But it wouldn't let me do it. 191 00:10:54,440 --> 00:10:58,240 Speaker 1: And I thought, wow, that's interesting that it's not allowing me. 192 00:10:58,480 --> 00:11:01,199 Speaker 1: It's telling me, no, you're not allowed to make this 193 00:11:01,920 --> 00:11:04,680 Speaker 1: so it was like a direct example for me on 194 00:11:04,720 --> 00:11:08,319 Speaker 1: a much lower level obviously, because he's using it for Salesforce, 195 00:11:08,320 --> 00:11:10,800 Speaker 1: which is taking over so many of the in house 196 00:11:10,800 --> 00:11:14,560 Speaker 1: communicat for many many corporations. I don't actually care for it, 197 00:11:14,559 --> 00:11:17,959 Speaker 1: but that's a different conversation. But anyways, it's not user friendly, 198 00:11:17,960 --> 00:11:21,320 Speaker 1: it's not easy to use. But I thought that was 199 00:11:21,320 --> 00:11:23,199 Speaker 1: so interesting that this computer told me. 200 00:11:23,160 --> 00:11:29,000 Speaker 2: No, yeah, And that's a good example of a guardrail 201 00:11:29,120 --> 00:11:33,880 Speaker 2: that's on. And they do have that capability. Obviously, the 202 00:11:33,960 --> 00:11:36,400 Speaker 2: owners that have built these foundation models, only a few 203 00:11:36,400 --> 00:11:38,560 Speaker 2: of them big tech. Basically you've got yeah, Google, Gemini, 204 00:11:38,640 --> 00:11:41,760 Speaker 2: You've got now Rock from XAI. You have a few 205 00:11:41,800 --> 00:11:44,160 Speaker 2: open source models. But the other one is really Facebook 206 00:11:44,200 --> 00:11:48,000 Speaker 2: has their meta Lama models. So there's really a few 207 00:11:48,240 --> 00:11:52,480 Speaker 2: paths you can funnel into when you're interacting with modern AI. 208 00:11:53,040 --> 00:11:56,800 Speaker 2: Typically they're referencing one of these larger models through a 209 00:11:56,840 --> 00:12:00,640 Speaker 2: back end connection, and they control of that. I mean, 210 00:12:00,640 --> 00:12:05,640 Speaker 2: you can place things as sort of an intermediary business 211 00:12:06,200 --> 00:12:10,960 Speaker 2: lying AI software, but if it's from the source determined 212 00:12:11,000 --> 00:12:13,640 Speaker 2: to have this guardrail or that guardrail, they have the 213 00:12:13,640 --> 00:12:18,080 Speaker 2: control over what is able to be spoken about. Describe 214 00:12:18,320 --> 00:12:21,880 Speaker 2: utilize and there are good use cases, you know, relating 215 00:12:21,880 --> 00:12:24,760 Speaker 2: to what you just said about having identified mind children, 216 00:12:25,360 --> 00:12:28,120 Speaker 2: right and mental health. That's another one where interactions are 217 00:12:28,160 --> 00:12:30,480 Speaker 2: sensitive among children. You want to make sure that the 218 00:12:30,559 --> 00:12:33,040 Speaker 2: right conversations are being had, it's not going off the 219 00:12:33,080 --> 00:12:35,840 Speaker 2: rails in a negative way. And then the other side 220 00:12:35,840 --> 00:12:38,680 Speaker 2: of that is again the balance of what freedoms do 221 00:12:38,720 --> 00:12:43,720 Speaker 2: we allow them to maintain, because that's part of you know, 222 00:12:43,840 --> 00:12:46,600 Speaker 2: our rights and AI as it becomes a bigger and 223 00:12:46,679 --> 00:12:49,959 Speaker 2: broader part of our society, we won't know if we're 224 00:12:49,960 --> 00:12:51,880 Speaker 2: interacting with a human behind the screen or but it's 225 00:12:51,880 --> 00:12:54,640 Speaker 2: an AI bought or agents, because they will become so 226 00:12:54,760 --> 00:12:59,559 Speaker 2: realistic that that agent that bought that AI, if it's 227 00:12:59,679 --> 00:13:03,640 Speaker 2: pre built to act a certain way, that bias will 228 00:13:03,679 --> 00:13:08,040 Speaker 2: sort of suddenly condition us to adapt. So it's a 229 00:13:08,040 --> 00:13:14,880 Speaker 2: form of censorship in a way, yeah, right, a control 230 00:13:15,320 --> 00:13:18,920 Speaker 2: and it definitely needs to be monitored, yeah, because it 231 00:13:18,960 --> 00:13:23,720 Speaker 2: can happen subtly and gradually and then altogether at some point. 232 00:13:24,040 --> 00:13:27,280 Speaker 1: It's a little scary, you know. And there's a few 233 00:13:27,280 --> 00:13:28,839 Speaker 1: other things I want to get to, but one more 234 00:13:29,240 --> 00:13:32,040 Speaker 1: one more aspect of this part is I don't know 235 00:13:32,040 --> 00:13:35,240 Speaker 1: if you've seen on all of the social media platforms, 236 00:13:35,280 --> 00:13:37,640 Speaker 1: whether you're on Insta or you're on TikTok, or you're 237 00:13:37,720 --> 00:13:42,240 Speaker 1: on x or whatever. But people showing two examples, right, 238 00:13:42,240 --> 00:13:45,520 Speaker 1: they'll show I put this question into GROCK and I 239 00:13:45,600 --> 00:13:50,040 Speaker 1: put this question into chat GPT, and the answers are astounding. 240 00:13:50,679 --> 00:13:54,800 Speaker 1: It's truly shocking. And I'm like, oh my god. I'm like, 241 00:13:54,960 --> 00:13:57,920 Speaker 1: it's like I have It's like I have Trump and 242 00:13:58,000 --> 00:14:02,240 Speaker 1: Biden doing my AI. I'm like so creepy that it's 243 00:14:02,280 --> 00:14:04,600 Speaker 1: not just like I just don't know where to go 244 00:14:04,679 --> 00:14:07,719 Speaker 1: to get the lack of bias, and I don't know 245 00:14:08,280 --> 00:14:08,920 Speaker 1: that we can. 246 00:14:11,000 --> 00:14:14,240 Speaker 2: Yeah, and that's a good example. I think some of 247 00:14:14,400 --> 00:14:17,400 Speaker 2: the earlier models. I say earlier, you know, two years 248 00:14:17,440 --> 00:14:19,440 Speaker 2: ago when Gemini came out, I think they got some 249 00:14:19,520 --> 00:14:23,040 Speaker 2: bad pr based on exactly that is, it really went 250 00:14:23,080 --> 00:14:26,000 Speaker 2: too far in the direction and it was obvious. Yeah, 251 00:14:26,040 --> 00:14:29,520 Speaker 2: you and even generate factual images of history without it 252 00:14:29,560 --> 00:14:34,960 Speaker 2: being latly injected with some kind of bias. You will, right, right, 253 00:14:35,040 --> 00:14:39,160 Speaker 2: And there are ways too well, there are limits to 254 00:14:39,240 --> 00:14:42,200 Speaker 2: how you can work around that. But again, if the 255 00:14:42,240 --> 00:14:45,040 Speaker 2: base model, and if we route basically back to three 256 00:14:45,120 --> 00:14:47,840 Speaker 2: or four foundation models owned by three or four companies. 257 00:14:48,600 --> 00:14:52,240 Speaker 2: They ultimately control the gates of what can and cannot 258 00:14:52,280 --> 00:14:56,120 Speaker 2: be done. And it's hard. I mean you can use, 259 00:14:56,200 --> 00:14:58,520 Speaker 2: like you said, the prompt engineering, the prompt sort of 260 00:14:59,120 --> 00:15:03,480 Speaker 2: adjustments you train or adjust how it responds. Yeah, there 261 00:15:03,480 --> 00:15:06,360 Speaker 2: are limits. There are limits if it's guarded from the 262 00:15:06,360 --> 00:15:06,800 Speaker 2: back end. 263 00:15:07,160 --> 00:15:09,880 Speaker 1: And to that point, what's interesting is if you're on 264 00:15:10,000 --> 00:15:14,240 Speaker 1: groc versus Chat GBT, so on grock, you know, after like, 265 00:15:14,280 --> 00:15:16,400 Speaker 1: I'll say something like, I feel like I could write 266 00:15:16,400 --> 00:15:19,760 Speaker 1: this better. Right, it's I have it in twelve sentences. 267 00:15:19,800 --> 00:15:22,840 Speaker 1: It could probably be for I'll run it through. I 268 00:15:22,880 --> 00:15:26,520 Speaker 1: never let I never let anything go out for my 269 00:15:27,320 --> 00:15:29,720 Speaker 1: personal round, my business life that's written by a robot. 270 00:15:30,280 --> 00:15:33,680 Speaker 1: But I have many times run it through for grammar 271 00:15:34,080 --> 00:15:36,440 Speaker 1: or said I want to do this plan. How could 272 00:15:36,480 --> 00:15:39,600 Speaker 1: I make this more user friendly? Because you know, to 273 00:15:39,640 --> 00:15:41,640 Speaker 1: me it's very obvious, but maybe to somebody else it's 274 00:15:41,680 --> 00:15:44,600 Speaker 1: not right. But after you run it through in grock, 275 00:15:44,640 --> 00:15:49,280 Speaker 1: it'll say to you, make more concise, make more persuasive, 276 00:15:49,720 --> 00:15:53,280 Speaker 1: make more professional. It gives you options so that you 277 00:15:53,320 --> 00:15:55,800 Speaker 1: can take that tone somewhere else. With Chat GBT, you're 278 00:15:55,840 --> 00:15:58,800 Speaker 1: not getting that It's not that kind of thing at all. 279 00:15:59,080 --> 00:16:02,120 Speaker 1: And I will say the conjecture that it puts in 280 00:16:02,640 --> 00:16:05,400 Speaker 1: like I'll say, you know, Obama sent you know, one 281 00:16:05,440 --> 00:16:08,000 Speaker 1: point seven billion dollars to a run in the final 282 00:16:08,040 --> 00:16:11,640 Speaker 1: months of his presidency. This is a fact. There is 283 00:16:11,760 --> 00:16:15,280 Speaker 1: no I'm not putting any like context around that. And 284 00:16:15,360 --> 00:16:18,800 Speaker 1: it'll come back and it'll give me this filler in 285 00:16:18,880 --> 00:16:22,680 Speaker 1: chat GPT of Barack Obama felt that the sanctions needed 286 00:16:22,720 --> 00:16:26,320 Speaker 1: to be left, and as a result, he felt like he, what, 287 00:16:26,320 --> 00:16:28,360 Speaker 1: what's the word? You're using a lot of feel here. 288 00:16:28,400 --> 00:16:30,760 Speaker 1: There's a lot of felt and maybes, and I'm not 289 00:16:30,840 --> 00:16:33,120 Speaker 1: interested in that. I'm just asking for like the date, 290 00:16:33,200 --> 00:16:35,800 Speaker 1: the time, did it happen, and it can't do it, 291 00:16:35,840 --> 00:16:38,920 Speaker 1: whereas Grock is much more like this happened there and 292 00:16:38,960 --> 00:16:41,320 Speaker 1: it doesn't matter if it's right or left, which I 293 00:16:41,320 --> 00:16:42,280 Speaker 1: think is very interesting. 294 00:16:43,280 --> 00:16:45,880 Speaker 2: Yeah, And I think that's part of what Elon has 295 00:16:45,920 --> 00:16:48,480 Speaker 2: said about the development of his model is he wants 296 00:16:48,480 --> 00:16:52,120 Speaker 2: it maximally truth seeking, which I mean, what more can 297 00:16:52,160 --> 00:16:56,280 Speaker 2: you ask for in an AI than why would seek 298 00:16:56,320 --> 00:16:59,440 Speaker 2: the objective truth whether humans can see it or not? Yes, 299 00:17:00,000 --> 00:17:03,840 Speaker 2: because implicit bias from human perspective makes its way into 300 00:17:03,880 --> 00:17:06,480 Speaker 2: how we think about things, that we write about, things 301 00:17:06,480 --> 00:17:09,840 Speaker 2: that we talk about things. So to have sort of 302 00:17:09,840 --> 00:17:14,240 Speaker 2: a pre set standard that focuses on truth of above 303 00:17:14,280 --> 00:17:19,119 Speaker 2: all else is the ideal approach to AI in the future. 304 00:17:19,160 --> 00:17:22,760 Speaker 2: I mean, I think otherwise, subtly and gradually it will 305 00:17:23,240 --> 00:17:25,479 Speaker 2: go off the rails and come back to harm us 306 00:17:25,480 --> 00:17:27,639 Speaker 2: a society unless it's seeking truth. 307 00:17:28,240 --> 00:17:29,800 Speaker 1: So to that point, I'm going to play a quick 308 00:17:29,800 --> 00:17:31,960 Speaker 1: cut that I have of ELIN and I thought was 309 00:17:32,080 --> 00:17:34,640 Speaker 1: very apropo for this conversation. We'll talk on the other 310 00:17:34,680 --> 00:17:35,080 Speaker 1: side of this. 311 00:17:36,160 --> 00:17:38,800 Speaker 3: If you just press the rock icon on any x post, 312 00:17:40,320 --> 00:17:43,600 Speaker 3: analyze THEPLOYD, and research it as much as you want 313 00:17:43,640 --> 00:17:46,879 Speaker 3: so you can you can basically have just by tapping 314 00:17:46,880 --> 00:17:50,680 Speaker 3: the rock icon, you can assess whether that that post 315 00:17:50,720 --> 00:17:52,680 Speaker 3: is the truth, the whole truth, or nothing but the truth, 316 00:17:52,760 --> 00:17:55,160 Speaker 3: or whether there's something supplemental you need to be explained. 317 00:17:55,880 --> 00:17:58,240 Speaker 3: So I think I think it's actually we've made a 318 00:17:58,280 --> 00:18:03,920 Speaker 3: lot of progress towards, yeah, freedom of speech and people 319 00:18:04,000 --> 00:18:07,040 Speaker 3: being able to tell whether something is false or not. Propaganda. 320 00:18:07,560 --> 00:18:09,480 Speaker 3: The recent update to GROWK is actually I think very 321 00:18:09,480 --> 00:18:13,800 Speaker 3: good at piercing through propaganda, and then we used that 322 00:18:14,080 --> 00:18:17,199 Speaker 3: latest person of groc to create Crokipedia. It's not just 323 00:18:17,720 --> 00:18:22,240 Speaker 3: I think more neutral that then and more accurate than 324 00:18:22,240 --> 00:18:24,560 Speaker 3: than Wikipedia, but actually it's a lot more information than 325 00:18:24,600 --> 00:18:26,000 Speaker 3: a Wikipedia page. 326 00:18:26,280 --> 00:18:28,880 Speaker 1: So to that point about Wikipedia, which is a very 327 00:18:28,920 --> 00:18:33,399 Speaker 1: interesting point that he makes. As somebody that manages talent 328 00:18:33,520 --> 00:18:37,040 Speaker 1: and works in media, a lot of my talent has 329 00:18:37,160 --> 00:18:41,320 Speaker 1: Wikipedia pages. And one of my clients had said to me, 330 00:18:41,680 --> 00:18:44,520 Speaker 1: there's some really weird stuff on my Wikipedia. Can you 331 00:18:44,560 --> 00:18:46,280 Speaker 1: go and change it? And I was like, oh yeah, sure, 332 00:18:46,440 --> 00:18:49,200 Speaker 1: something I had never done, never even crossed my mind, 333 00:18:49,200 --> 00:18:52,800 Speaker 1: and looked, okay, fine, So I go to Wikipedia. It's like, 334 00:18:53,720 --> 00:18:56,680 Speaker 1: I mean, literally almost everything in it was not true 335 00:18:56,680 --> 00:18:58,920 Speaker 1: about my client, who he was married to, how many 336 00:18:59,000 --> 00:19:02,160 Speaker 1: children he had, were he lived, had he'd been arrested 337 00:19:02,160 --> 00:19:04,880 Speaker 1: thirty eight times. I'm like, what I mean, this guy 338 00:19:04,960 --> 00:19:08,240 Speaker 1: is like a stalwart lawyer married for four years. It 339 00:19:08,359 --> 00:19:11,720 Speaker 1: was just and I thought, oh my god, if somebody 340 00:19:12,320 --> 00:19:15,399 Speaker 1: googles him and that comes up, they're and be like, 341 00:19:15,400 --> 00:19:18,920 Speaker 1: holy shit, you got to be kidding me. But none 342 00:19:18,960 --> 00:19:23,360 Speaker 1: of it was true. But because it's open to public edit, 343 00:19:24,240 --> 00:19:27,680 Speaker 1: it wasn't protected from any of that, which I thought 344 00:19:27,720 --> 00:19:30,760 Speaker 1: was super scary and I didn't know it before that experience. 345 00:19:32,840 --> 00:19:37,399 Speaker 2: Yeah, and that's that is a very concerning aspect of 346 00:19:37,440 --> 00:19:41,239 Speaker 2: how these models have been trained historically. Is right, if 347 00:19:41,280 --> 00:19:44,080 Speaker 2: there are you know, public figures, there's enough out there 348 00:19:44,080 --> 00:19:45,560 Speaker 2: that someone's going to see it or catch it. But 349 00:19:45,640 --> 00:19:48,160 Speaker 2: sure if someone has a Wikipedia change or not checking 350 00:19:48,160 --> 00:19:51,320 Speaker 2: it every single day or you know, really once a year, 351 00:19:51,960 --> 00:19:55,040 Speaker 2: and that false information goes into that page. If an 352 00:19:55,080 --> 00:19:58,040 Speaker 2: AI has, as I mentioned, ear you've been trained where 353 00:19:58,040 --> 00:20:00,399 Speaker 2: it's reading through documents, and Wikipedia was one of the 354 00:20:00,400 --> 00:20:04,480 Speaker 2: earlier knowledge corpuses that it was trained and learned on. Yeah, 355 00:20:05,080 --> 00:20:07,240 Speaker 2: that's what it knows. So it doesn't know and I'll 356 00:20:07,240 --> 00:20:08,720 Speaker 2: turn to the point of view if that's the only 357 00:20:08,760 --> 00:20:11,000 Speaker 2: thing it knows about person A or person B. So 358 00:20:11,040 --> 00:20:14,920 Speaker 2: when you ask about that, it'll respond with confidence that 359 00:20:15,720 --> 00:20:18,080 Speaker 2: this is exactly what it is, because as far as 360 00:20:18,119 --> 00:20:21,000 Speaker 2: the AI is concerned, it is confident it did find 361 00:20:21,040 --> 00:20:23,480 Speaker 2: that information, it is returning it, it is retreating it 362 00:20:23,480 --> 00:20:25,840 Speaker 2: as is reported. So as according to the AI, that 363 00:20:25,920 --> 00:20:31,199 Speaker 2: is definitively factually true. So it's yeah, and this is 364 00:20:31,280 --> 00:20:34,719 Speaker 2: this goes into the bias of how do we manage 365 00:20:34,760 --> 00:20:39,040 Speaker 2: these ll ms, because if they're not trained on objective 366 00:20:40,080 --> 00:20:43,120 Speaker 2: sources like Wrokipedia. If that is objective, I'm sure there's 367 00:20:43,119 --> 00:20:46,000 Speaker 2: going to be errors everywhere, unintentional, but of course at 368 00:20:46,080 --> 00:20:49,439 Speaker 2: least closer to the truth. Sure that will be the 369 00:20:49,480 --> 00:20:52,800 Speaker 2: reference that the language model uses, as opposed to Wikipedia, 370 00:20:52,840 --> 00:20:55,520 Speaker 2: where anything could be in there, anything true or false. 371 00:20:55,359 --> 00:20:56,680 Speaker 1: And edited by a stranger. 372 00:20:56,880 --> 00:21:00,760 Speaker 2: Who stranger. Yeah, and if someone is you talking to 373 00:21:00,760 --> 00:21:04,440 Speaker 2: that AI, and you know, referencing that, the AI will say, yes, 374 00:21:04,560 --> 00:21:08,920 Speaker 2: I am certain that this is true and put that down. 375 00:21:09,920 --> 00:21:12,159 Speaker 2: There's no guarantee it's wrong. And then again, if you 376 00:21:12,160 --> 00:21:14,680 Speaker 2: don't know that, you're not the wiser, you'll take that, Hey, 377 00:21:14,680 --> 00:21:17,440 Speaker 2: this Alb's right about most things, it must also be 378 00:21:17,520 --> 00:21:18,120 Speaker 2: right about this. 379 00:21:18,359 --> 00:21:23,000 Speaker 1: It's so crazy, I have to say, I really anyways, 380 00:21:23,040 --> 00:21:25,719 Speaker 1: it's something I think we'll need to follow. I just 381 00:21:25,760 --> 00:21:32,320 Speaker 1: don't know as this grows exponentially and quickly, I mean literally, 382 00:21:32,440 --> 00:21:36,040 Speaker 1: you know, as you spoke to a moment ago, the 383 00:21:36,080 --> 00:21:39,600 Speaker 1: way that you are looking at AI as an engineer, 384 00:21:39,640 --> 00:21:43,000 Speaker 1: as a scientist, as somebody who has studied this, you 385 00:21:43,040 --> 00:21:46,800 Speaker 1: know extensively the amount of changes that are happening and 386 00:21:47,119 --> 00:21:49,840 Speaker 1: at the speed at which they're happening. To me as 387 00:21:49,880 --> 00:21:51,639 Speaker 1: a user, I'm like, oh my, I couldn't do this 388 00:21:51,680 --> 00:21:56,360 Speaker 1: a week ago. Yeah, that to me is a little scary, 389 00:21:56,720 --> 00:22:00,239 Speaker 1: right as a user. 390 00:22:00,359 --> 00:22:04,520 Speaker 2: Yeah yeah, And if you lay out the timeline, you 391 00:22:04,560 --> 00:22:07,000 Speaker 2: know that there is a little bit of a frighteningly 392 00:22:07,400 --> 00:22:10,240 Speaker 2: concerning fast pace to it. But yes, also if we 393 00:22:10,320 --> 00:22:13,720 Speaker 2: look back, you know when cell phones rolled out against 394 00:22:13,800 --> 00:22:15,840 Speaker 2: the iPhone and all that, it happened like a blinkin 395 00:22:15,920 --> 00:22:17,920 Speaker 2: eye and then we just normalized it. I guess the 396 00:22:18,000 --> 00:22:20,919 Speaker 2: same type of pattern is happening here where you know, 397 00:22:21,040 --> 00:22:24,320 Speaker 2: four years ago, you had a language model that could 398 00:22:24,880 --> 00:22:28,360 Speaker 2: create a paragraph and it could start to pass as 399 00:22:28,840 --> 00:22:31,760 Speaker 2: you know, pleading college level tests, you know, twenty two 400 00:22:31,760 --> 00:22:34,720 Speaker 2: to twenty three. Right then it evolved into okay, now 401 00:22:34,760 --> 00:22:36,520 Speaker 2: it's best a PhD. 402 00:22:36,840 --> 00:22:39,800 Speaker 1: It can it can write code, it can do JavaScript, 403 00:22:39,800 --> 00:22:40,600 Speaker 1: it's in Python. 404 00:22:40,840 --> 00:22:44,000 Speaker 2: I'm like, what, Yeah, Well that's where we're at now, 405 00:22:44,040 --> 00:22:46,520 Speaker 2: where we have we're sort of in the agent aspect 406 00:22:46,840 --> 00:22:49,680 Speaker 2: of this evolution. And what agents are is you can 407 00:22:50,200 --> 00:22:56,280 Speaker 2: basically take specialized AI and they have the intelligence to 408 00:22:56,320 --> 00:22:58,280 Speaker 2: do anything digitally that you can do. So if you 409 00:22:58,280 --> 00:23:01,320 Speaker 2: move your mouse on your screen, it can mimic that. 410 00:23:01,560 --> 00:23:03,600 Speaker 2: If you go and log into a website. If you 411 00:23:03,680 --> 00:23:05,919 Speaker 2: go in and you know, start doing what you were 412 00:23:05,920 --> 00:23:09,280 Speaker 2: saying earlier, editing a photo document. It can do all 413 00:23:09,320 --> 00:23:12,360 Speaker 2: of that. If you it will ask for your credentials, 414 00:23:12,400 --> 00:23:14,639 Speaker 2: It will, It can reason, and it can actually deliver 415 00:23:15,320 --> 00:23:17,080 Speaker 2: anything you do on a computer. It can now do 416 00:23:17,160 --> 00:23:24,639 Speaker 2: that independently, which has a lot of other security risks associated. 417 00:23:24,119 --> 00:23:25,560 Speaker 1: As it goes into your bank account. 418 00:23:26,119 --> 00:23:28,240 Speaker 2: It can absolutely do that. Yeah, if you give it control, 419 00:23:28,280 --> 00:23:29,760 Speaker 2: and I mean a lot of times it does property. Hey, 420 00:23:29,800 --> 00:23:31,200 Speaker 2: would you like me to use this and do that? 421 00:23:31,280 --> 00:23:33,840 Speaker 2: But it is at the level now where we're no 422 00:23:33,880 --> 00:23:37,040 Speaker 2: longer interacting with something that can chat back to us 423 00:23:37,080 --> 00:23:39,439 Speaker 2: and refer information and it can tell us things, It 424 00:23:39,480 --> 00:23:41,479 Speaker 2: can do things, It can do things. And you think 425 00:23:41,480 --> 00:23:43,639 Speaker 2: about how much we can do digitally through our phones, 426 00:23:43,680 --> 00:23:46,600 Speaker 2: through our computers that we do and interact with every day. 427 00:23:47,280 --> 00:23:50,200 Speaker 2: Now an AI has the ability to do those same 428 00:23:50,240 --> 00:23:54,040 Speaker 2: types of tasks, right, and which leads us to, you know, 429 00:23:54,400 --> 00:23:57,560 Speaker 2: what's the next evolution of the role We know right 430 00:23:57,640 --> 00:23:59,720 Speaker 2: who we're talking to on the other end of a 431 00:23:59,760 --> 00:24:06,040 Speaker 2: phone or a digital computer. Because voice generation is getting better, Yes, 432 00:24:06,680 --> 00:24:10,960 Speaker 2: before we know it, regular voices voice, your voice will 433 00:24:11,000 --> 00:24:12,520 Speaker 2: be available to be used. 434 00:24:12,760 --> 00:24:16,080 Speaker 1: Yes, it's I have to say, I there are aspects 435 00:24:16,080 --> 00:24:17,520 Speaker 1: of that, you know. I was talking to somebody other 436 00:24:17,600 --> 00:24:20,439 Speaker 1: day and they were saying, you know, there are people 437 00:24:20,480 --> 00:24:24,000 Speaker 1: in the trades, whether it's HVAC or electrical or plumbing 438 00:24:24,080 --> 00:24:25,840 Speaker 1: or what have you, and they are just marching down 439 00:24:25,840 --> 00:24:27,840 Speaker 1: the street. They're like, we're breaking it in because you 440 00:24:27,880 --> 00:24:31,640 Speaker 1: people are gonna be out of work. And Mark Cuban 441 00:24:31,920 --> 00:24:33,960 Speaker 1: was saying, I saw two things in the last two 442 00:24:34,000 --> 00:24:38,240 Speaker 1: days that really made me think. Kevin O'Leary, who obviously 443 00:24:38,359 --> 00:24:41,359 Speaker 1: is a real estate mogul among just a serial entrepreneur, 444 00:24:41,480 --> 00:24:43,680 Speaker 1: and then Mark Cuban, Who's Mark Cuban. And we'll leave 445 00:24:43,680 --> 00:24:46,960 Speaker 1: that there, but they both spoke in the last two 446 00:24:47,040 --> 00:24:50,679 Speaker 1: days about AI, the advance of AI and what it 447 00:24:50,680 --> 00:24:54,120 Speaker 1: does to the workforce. Kevin O'Leary said it would make 448 00:24:54,200 --> 00:24:58,040 Speaker 1: sense for us to look into helping to build the 449 00:24:58,119 --> 00:25:01,280 Speaker 1: data centers and and that that would be the next 450 00:25:01,280 --> 00:25:04,640 Speaker 1: big business, Like if you can help to facilitate building 451 00:25:04,640 --> 00:25:08,560 Speaker 1: out these data centers, managing the data centers, helping to 452 00:25:08,680 --> 00:25:12,720 Speaker 1: create the space and the functionality without overtaking, I don't know, 453 00:25:12,880 --> 00:25:14,440 Speaker 1: sort of like the same idea of like when people 454 00:25:14,440 --> 00:25:16,160 Speaker 1: are like, yeah, we need wind turbines, you're like, you're 455 00:25:16,160 --> 00:25:18,399 Speaker 1: a moron. That shit doesn't work and you're killing the 456 00:25:18,760 --> 00:25:21,840 Speaker 1: birds and the farms, so knock it off, right, So 457 00:25:21,880 --> 00:25:23,200 Speaker 1: we want to get around that. We don't want to 458 00:25:23,240 --> 00:25:26,879 Speaker 1: have the data center wind turbine moment. But Mark Cuba 459 00:25:26,960 --> 00:25:30,240 Speaker 1: made the point which I thought was really interesting, and 460 00:25:30,280 --> 00:25:34,480 Speaker 1: he said, if I were starting out today and I 461 00:25:34,720 --> 00:25:37,879 Speaker 1: wasn't handy or in a trade or the kind of 462 00:25:37,880 --> 00:25:41,440 Speaker 1: person that was more tactical, right, he said, I would 463 00:25:41,440 --> 00:25:45,600 Speaker 1: be a person that would be helping people who are 464 00:25:45,680 --> 00:25:50,600 Speaker 1: in single owner operated businesses to learn how to use 465 00:25:50,640 --> 00:25:53,880 Speaker 1: AI because a lot of them could use the help 466 00:25:54,000 --> 00:25:56,720 Speaker 1: and they just don't know how to use it. And 467 00:25:56,800 --> 00:25:59,119 Speaker 1: I thought that's really interesting. He made a comment that 468 00:25:59,200 --> 00:26:03,159 Speaker 1: about thirty three out of thirty three million businesses that 469 00:26:03,200 --> 00:26:06,520 Speaker 1: we have in the United States, thirty million of them 470 00:26:06,720 --> 00:26:10,280 Speaker 1: are single owner operated. They don't have staff, they don't 471 00:26:10,280 --> 00:26:12,639 Speaker 1: have anything. It's just them their company and they're doing it. 472 00:26:12,920 --> 00:26:15,520 Speaker 1: And I thought, huh, so if they did have the 473 00:26:15,560 --> 00:26:18,600 Speaker 1: help of an AI agent, to your point, they could 474 00:26:18,760 --> 00:26:23,479 Speaker 1: reduce their workflow right and probably expedite the process of 475 00:26:23,520 --> 00:26:27,640 Speaker 1: whatever business they have and I thought, huh, that's interesting, 476 00:26:28,440 --> 00:26:32,520 Speaker 1: But then it comes into how are we facilitating the energy? 477 00:26:33,000 --> 00:26:35,080 Speaker 1: So to your point, you and I were speaking before 478 00:26:35,080 --> 00:26:38,320 Speaker 1: we got started that this is going to take an 479 00:26:38,359 --> 00:26:40,439 Speaker 1: exorbitant amount of energy. 480 00:26:42,160 --> 00:26:44,040 Speaker 2: Yeah, absolutely in that scene. 481 00:26:44,200 --> 00:26:47,080 Speaker 1: Yeah yeah. 482 00:26:47,280 --> 00:26:52,359 Speaker 2: Where the sort of AI economy is going is write 483 00:26:52,440 --> 00:26:56,920 Speaker 2: down those paths two of what I'll use it to agents. 484 00:26:56,720 --> 00:26:59,320 Speaker 2: That's the big buzzword right now. How do you generate 485 00:26:59,400 --> 00:27:03,359 Speaker 2: an HVA management agent? For example? Obviously they're not going 486 00:27:03,440 --> 00:27:06,120 Speaker 2: to go and install hvacs and do repairs and all that. 487 00:27:06,920 --> 00:27:09,439 Speaker 2: All the business operations that are involved with that, or 488 00:27:09,480 --> 00:27:11,520 Speaker 2: you know a family owned business or you know someone 489 00:27:11,520 --> 00:27:14,960 Speaker 2: who's only up one or two employees, whether it be 490 00:27:15,720 --> 00:27:17,560 Speaker 2: like I was saying, answering calls in the future, with 491 00:27:17,720 --> 00:27:22,359 Speaker 2: you have voice capabilities, managing your invoices, managing your calendar, 492 00:27:23,480 --> 00:27:28,200 Speaker 2: all those ordering parts, all of that can be moved 493 00:27:28,200 --> 00:27:31,639 Speaker 2: to an agentic AI that can solve some of those tasks. 494 00:27:31,640 --> 00:27:35,159 Speaker 2: I mean, not all at this exact moment, but the 495 00:27:35,200 --> 00:27:36,680 Speaker 2: speed we're going at, I mean it's going to be 496 00:27:36,720 --> 00:27:41,040 Speaker 2: here very shortly here And those are some examples where 497 00:27:42,200 --> 00:27:44,640 Speaker 2: that's a new business. That's if someone creates that business 498 00:27:44,720 --> 00:27:48,359 Speaker 2: that the agent that now solves or helps with you know, 499 00:27:48,520 --> 00:27:51,960 Speaker 2: marketing plans or outreach and things like that. You scale 500 00:27:51,960 --> 00:27:54,960 Speaker 2: that across that's not now energy usage. So you're creating 501 00:27:55,160 --> 00:27:59,120 Speaker 2: not only the original lllms being used by meta by 502 00:27:59,640 --> 00:28:05,200 Speaker 2: right by open AI, but now the intermediary businesses who 503 00:28:05,200 --> 00:28:09,600 Speaker 2: are using those ais to solve a practical human problem. 504 00:28:10,400 --> 00:28:13,240 Speaker 2: And that's all energy demand. So as more and more 505 00:28:13,240 --> 00:28:18,480 Speaker 2: of these AI businesses, you know, become up, they become invented. 506 00:28:19,800 --> 00:28:25,520 Speaker 2: It's more demands, more strain, more power resources, and the 507 00:28:25,560 --> 00:28:29,720 Speaker 2: grid that's already at its capacity may not be able 508 00:28:29,800 --> 00:28:31,040 Speaker 2: to handle where it's headed. 509 00:28:32,040 --> 00:28:35,960 Speaker 1: So to that point, obviously, people like us that deal 510 00:28:36,000 --> 00:28:38,200 Speaker 1: in this type of work. You know, we've talked many 511 00:28:38,240 --> 00:28:43,120 Speaker 1: times about e MP issues as well as the Texas 512 00:28:43,120 --> 00:28:46,520 Speaker 1: grid being the main supplier of electric for the entire country. 513 00:28:47,160 --> 00:28:50,959 Speaker 1: Under Joe Biden, he released the contract for the chips 514 00:28:50,960 --> 00:28:55,200 Speaker 1: that go into that grid to come from China. Trump 515 00:28:55,240 --> 00:28:57,000 Speaker 1: had gotten rid of that. He's like, I only want 516 00:28:57,000 --> 00:29:00,760 Speaker 1: American made chips in the electric grid for the simple 517 00:29:00,800 --> 00:29:03,800 Speaker 1: fact that China is not a friend, They are a foe. 518 00:29:04,840 --> 00:29:07,560 Speaker 1: Who the hell knows what's in those chips, and now 519 00:29:07,680 --> 00:29:09,840 Speaker 1: they're in charge of the electric that feeds the nation. 520 00:29:10,640 --> 00:29:13,600 Speaker 1: I mean, have we all lost our mind? Right? So 521 00:29:13,640 --> 00:29:17,440 Speaker 1: Trump is now telling AI, we're not going to use 522 00:29:17,480 --> 00:29:21,280 Speaker 1: that grid. First of all, it cannot handle it. Second 523 00:29:21,280 --> 00:29:23,680 Speaker 1: of all, it would jack up the cost of the 524 00:29:23,720 --> 00:29:27,640 Speaker 1: electric costs for every average American citizen, many of whom 525 00:29:27,720 --> 00:29:30,560 Speaker 1: are not using AI right, or they're using it without 526 00:29:30,600 --> 00:29:33,680 Speaker 1: knowing it right. They're talking to SERI, they're using their iPhone. 527 00:29:33,720 --> 00:29:36,280 Speaker 1: They're not actively using AI like you and I aren. 528 00:29:36,920 --> 00:29:38,800 Speaker 1: But I read this post which I've shared with you, 529 00:29:38,840 --> 00:29:40,360 Speaker 1: and I'm going to share it with the audience now. 530 00:29:41,280 --> 00:29:44,840 Speaker 1: And President Trump yesterday had a roundtable with the very 531 00:29:44,880 --> 00:29:47,720 Speaker 1: people you're talking about, the CEOs from Amazon and Meta 532 00:29:47,800 --> 00:29:50,720 Speaker 1: and Microsoft and open Ai and Oracle and XA and 533 00:29:50,760 --> 00:29:54,120 Speaker 1: all these people that are sort of like the they're 534 00:29:54,160 --> 00:29:57,240 Speaker 1: the lifting and the starting spot for all things AI. 535 00:29:57,280 --> 00:29:59,960 Speaker 1: And they had the roundtable which one of the main 536 00:30:00,040 --> 00:30:03,520 Speaker 1: points that he was talking about was the ratepayer protection Pledge, 537 00:30:03,520 --> 00:30:07,200 Speaker 1: which he's saying Americans will not pay higher electric bills 538 00:30:07,560 --> 00:30:10,400 Speaker 1: do to power usage from AI data centers. Well, how 539 00:30:10,400 --> 00:30:13,480 Speaker 1: do we get there? Oh, I know, We'll make all 540 00:30:13,520 --> 00:30:17,200 Speaker 1: of these big CEOs figure out how to fund, create, 541 00:30:17,280 --> 00:30:22,400 Speaker 1: and provide their own utilities. Okay, so now they don't 542 00:30:22,440 --> 00:30:26,120 Speaker 1: only control AI all of these quote unquote as you're 543 00:30:26,120 --> 00:30:30,160 Speaker 1: saying AI agents, but they own all the power from 544 00:30:30,320 --> 00:30:34,200 Speaker 1: nuclear and they're their own utility. So they run all 545 00:30:34,240 --> 00:30:36,560 Speaker 1: the power and utility in the United States, because that's 546 00:30:36,560 --> 00:30:38,520 Speaker 1: going to be some massive utility. And he made a 547 00:30:38,560 --> 00:30:42,160 Speaker 1: great example. This guy's post as Ricardo T was his 548 00:30:42,280 --> 00:30:45,040 Speaker 1: name full disclosure. I have no idea who this dude is, 549 00:30:45,600 --> 00:30:50,480 Speaker 1: but he made an excellent analogy which says, by the 550 00:30:50,600 --> 00:30:52,880 Speaker 1: end of this year twenty twenty six, there will be 551 00:30:52,960 --> 00:30:56,680 Speaker 1: five US data centers which will each consume over one 552 00:30:57,120 --> 00:31:02,160 Speaker 1: gigawatt of continuous power. To give you perspective, one gigawatt 553 00:31:02,280 --> 00:31:07,280 Speaker 1: powers eight hundred and fifty thousand homes. So five of 554 00:31:07,320 --> 00:31:12,440 Speaker 1: these facilities will use more electricity than some entire countries, 555 00:31:12,800 --> 00:31:16,440 Speaker 1: which obviously the grid cannot handle. So we're talking about 556 00:31:16,520 --> 00:31:20,560 Speaker 1: now they're going to own all this power. They're going 557 00:31:20,640 --> 00:31:24,080 Speaker 1: to be buying nuclear reactors. We've got Meta signing twenty 558 00:31:24,160 --> 00:31:26,920 Speaker 1: year nuclear deals. Chevron's getting a two and a half 559 00:31:27,000 --> 00:31:31,800 Speaker 1: gigawat natural gas plant in West Texas. So what exactly 560 00:31:32,080 --> 00:31:37,200 Speaker 1: are the tech companies controlling. So they're controlling computer computering, data, 561 00:31:37,960 --> 00:31:43,479 Speaker 1: energy infrastructure. Like we're exactly talking about tech companies anymore. 562 00:31:43,520 --> 00:31:48,080 Speaker 1: These are becoming like superpowers, they're becoming their own I 563 00:31:48,120 --> 00:31:52,800 Speaker 1: hate to say it, but like their own countries. Yeah, 564 00:31:53,040 --> 00:31:56,120 Speaker 1: your thoughts, big setup. 565 00:31:56,280 --> 00:31:58,760 Speaker 2: Right, it's another one. And I feel like this one 566 00:31:58,840 --> 00:32:04,479 Speaker 2: goes two ways because you know, most people they get 567 00:32:04,480 --> 00:32:06,760 Speaker 2: their energy bill, they see the total, right, maybe they 568 00:32:06,760 --> 00:32:08,880 Speaker 2: look at their kill a lot usage for the last month, 569 00:32:09,000 --> 00:32:11,440 Speaker 2: but they don't really go into the intricacies of how 570 00:32:11,480 --> 00:32:15,400 Speaker 2: that energy bill works. And it's actually somewhat complex from 571 00:32:15,400 --> 00:32:18,000 Speaker 2: the energy standpoint, because you have peak pricing, you have 572 00:32:18,960 --> 00:32:22,520 Speaker 2: time of use, You have different categories that a user 573 00:32:22,600 --> 00:32:25,880 Speaker 2: can be used or placed into based on their location, 574 00:32:26,080 --> 00:32:29,840 Speaker 2: their region obviously of the three grids. But then for example, 575 00:32:29,880 --> 00:32:33,040 Speaker 2: the let's take peak pricing certain hours of the day. 576 00:32:33,560 --> 00:32:36,400 Speaker 2: You know they recommend don't run your dishwasher or whatever 577 00:32:36,440 --> 00:32:39,880 Speaker 2: it might be, right, because that's when the general societal 578 00:32:39,880 --> 00:32:41,800 Speaker 2: demand is at its highest, so you get built at 579 00:32:41,840 --> 00:32:45,040 Speaker 2: a higher rate you use sometimes you I mean, that's 580 00:32:45,040 --> 00:32:49,640 Speaker 2: obviouslyp because's when everybody's active and doing things. And also 581 00:32:50,880 --> 00:32:52,840 Speaker 2: most likely when people are going to be using AI 582 00:32:53,080 --> 00:32:57,920 Speaker 2: as well, because they're up indirectly, right, So you think 583 00:32:57,960 --> 00:32:59,480 Speaker 2: about it, how it's like you said, it's it's going 584 00:32:59,560 --> 00:33:01,720 Speaker 2: to be in so people will be using it indirectly 585 00:33:01,760 --> 00:33:04,480 Speaker 2: without knowing it. So they'll be using it through their phones, 586 00:33:04,720 --> 00:33:07,040 Speaker 2: talking to you know, various agents on the other side 587 00:33:07,040 --> 00:33:08,880 Speaker 2: of a website. But that's all going to be additional 588 00:33:08,920 --> 00:33:12,800 Speaker 2: AI calls to the data center and back and forth. 589 00:33:13,640 --> 00:33:18,880 Speaker 2: So to that kind of point there is that now 590 00:33:18,920 --> 00:33:21,960 Speaker 2: will increase all of our energy bills because we're directly 591 00:33:21,960 --> 00:33:25,920 Speaker 2: and indirectly using AI, either deliberately or just by living life. 592 00:33:26,760 --> 00:33:30,720 Speaker 2: So as that energy consult goes up, the pricing goes 593 00:33:30,760 --> 00:33:33,000 Speaker 2: up because the demand goes up. The energy companies have 594 00:33:33,040 --> 00:33:35,400 Speaker 2: to sort of buy from third party providers, which goes 595 00:33:35,400 --> 00:33:39,240 Speaker 2: into that whole equation. So moving them into their own 596 00:33:39,280 --> 00:33:45,480 Speaker 2: independent system does separate that usage from what we use 597 00:33:45,560 --> 00:33:49,760 Speaker 2: as regular consumers. Okay, so we're no longer sort of 598 00:33:50,720 --> 00:33:54,080 Speaker 2: using them and then paying the energy companies to use 599 00:33:54,120 --> 00:33:56,480 Speaker 2: them as well, you know, in this sort of cyclical 600 00:33:56,520 --> 00:34:01,600 Speaker 2: loop of just energy demand and consumption. So right, it 601 00:34:01,640 --> 00:34:05,120 Speaker 2: does create the message of their own independent modes of 602 00:34:06,640 --> 00:34:12,000 Speaker 2: energy companies, data companies, information companies, knowledge companies, really knowledge companies. 603 00:34:12,400 --> 00:34:15,759 Speaker 1: Well, it's also that idea, Dave, if you think about it, right, 604 00:34:15,800 --> 00:34:18,920 Speaker 1: we're talking about and it's a double on tone in 605 00:34:18,960 --> 00:34:22,040 Speaker 1: every way. It's a power company. It's company. It's a 606 00:34:22,080 --> 00:34:25,600 Speaker 1: power company. It has power over you, and it has 607 00:34:25,680 --> 00:34:31,360 Speaker 1: the power literally to source and feed its own demands. 608 00:34:31,800 --> 00:34:35,279 Speaker 1: So at what point does the nation start saying, hey, 609 00:34:35,360 --> 00:34:38,759 Speaker 1: we would like to lease power from you that you're 610 00:34:38,800 --> 00:34:43,360 Speaker 1: not using at xyz rate and then we're going to 611 00:34:43,400 --> 00:34:47,160 Speaker 1: send that back to the American public Because you probably 612 00:34:47,160 --> 00:34:49,640 Speaker 1: know better than me. How long have we been saying 613 00:34:50,680 --> 00:34:54,719 Speaker 1: the Texas grid is completely and totally overweighted. It cannot 614 00:34:54,800 --> 00:35:00,680 Speaker 1: withstand the amount that we are pushing, know on it, 615 00:35:00,760 --> 00:35:04,200 Speaker 1: like the demand is so much greater than its ability 616 00:35:04,239 --> 00:35:06,520 Speaker 1: to provide. Am I speaking out of turn? Or is 617 00:35:06,520 --> 00:35:08,200 Speaker 1: that accurate? No? 618 00:35:08,440 --> 00:35:12,319 Speaker 2: I mean you see it sometimes in those peak summer days. 619 00:35:12,080 --> 00:35:15,160 Speaker 1: Where blackouts, Sure, the brownouts in California and then the 620 00:35:15,160 --> 00:35:16,360 Speaker 1: blackouts and other places. 621 00:35:16,400 --> 00:35:19,799 Speaker 2: Sure, but it is it is already here because I mean, 622 00:35:19,800 --> 00:35:22,200 Speaker 2: then you think about the other aspects. We have increased 623 00:35:22,200 --> 00:35:24,480 Speaker 2: ev usage, you know, more and more ED cars are 624 00:35:24,520 --> 00:35:29,319 Speaker 2: being bought, more of people are moving to the electric society. 625 00:35:29,080 --> 00:35:31,960 Speaker 1: Rights, not just the ballots, which is still, by the way, 626 00:35:32,040 --> 00:35:35,239 Speaker 1: completely and totally supported by fossil fuels. But we'll leave 627 00:35:35,239 --> 00:35:36,840 Speaker 1: that there for the environmentalists. 628 00:35:37,520 --> 00:35:39,640 Speaker 2: Yeah, exactly, Like, don't let the. 629 00:35:39,600 --> 00:35:42,840 Speaker 1: Facts get in the way of your green efforts. Okay, please, 630 00:35:43,960 --> 00:35:44,480 Speaker 1: good luck. 631 00:35:45,560 --> 00:35:46,520 Speaker 2: Back to the power plants. 632 00:35:46,760 --> 00:35:49,680 Speaker 1: I just can't. I'm like, some things just are I'm like, 633 00:35:49,760 --> 00:35:51,440 Speaker 1: that's cool if you want to plug in, but by 634 00:35:51,480 --> 00:35:54,480 Speaker 1: the way, that plug doesn't work unless there's an energy 635 00:35:54,520 --> 00:35:56,960 Speaker 1: grid that is supported by fossil fuels to give you 636 00:35:57,000 --> 00:36:00,920 Speaker 1: that energy. But keep going. You're doing great, no car right, 637 00:36:01,520 --> 00:36:01,960 Speaker 1: good talk. 638 00:36:02,160 --> 00:36:04,680 Speaker 2: Yeah, yeah, I mean, well, hey, if they control the 639 00:36:04,680 --> 00:36:06,720 Speaker 2: power and the knowledge, then they can convince you of anything. 640 00:36:06,800 --> 00:36:09,080 Speaker 1: So that's right, and the message. 641 00:36:08,920 --> 00:36:11,680 Speaker 2: To AI, right, And you know that's a good point 642 00:36:11,680 --> 00:36:15,440 Speaker 2: too about the power control, because you know, right now 643 00:36:15,480 --> 00:36:17,680 Speaker 2: we're kind of on the transition to AI as our 644 00:36:17,800 --> 00:36:21,560 Speaker 2: knowledge repository. For years, we had Google, where we'd go 645 00:36:21,600 --> 00:36:23,919 Speaker 2: and Google search something and whatever. One of the top hits, 646 00:36:23,960 --> 00:36:26,759 Speaker 2: top pages are what we could find and access. So 647 00:36:27,440 --> 00:36:29,600 Speaker 2: if you were a piece of information that was factual, 648 00:36:29,680 --> 00:36:32,960 Speaker 2: but you were not in Google's top two three pages, 649 00:36:33,600 --> 00:36:36,120 Speaker 2: you wouldn't be discoverable really because very people are clicking 650 00:36:36,120 --> 00:36:40,000 Speaker 2: that far into so the knowledge you only get shown 651 00:36:40,080 --> 00:36:41,960 Speaker 2: You either have to say, all right, do I take 652 00:36:41,960 --> 00:36:44,440 Speaker 2: this because it's being shown to me through Google. They're 653 00:36:44,480 --> 00:36:47,320 Speaker 2: not so many other alternatives, so I guess this must 654 00:36:47,320 --> 00:36:50,640 Speaker 2: be the truth, you know, if you're deductively questioning it. 655 00:36:50,800 --> 00:36:53,600 Speaker 2: Same thing with AI, same thing with power control, and 656 00:36:53,640 --> 00:36:56,759 Speaker 2: sort of the concern of where do we draw the 657 00:36:56,760 --> 00:37:02,520 Speaker 2: lines of autonomy what these companies can do, because if 658 00:37:02,520 --> 00:37:05,279 Speaker 2: they're you know, going back to the bias of their 659 00:37:05,800 --> 00:37:09,200 Speaker 2: not only independent energy wise, you know what they can 660 00:37:09,239 --> 00:37:12,240 Speaker 2: share with us knowledge wise, and then they become integrated 661 00:37:12,280 --> 00:37:15,440 Speaker 2: with all of our digital applications, we have no alternative 662 00:37:15,440 --> 00:37:18,320 Speaker 2: in some prices, and then we're sort of behold into 663 00:37:18,400 --> 00:37:20,640 Speaker 2: what they tell us or don't tell us, or what 664 00:37:20,760 --> 00:37:23,360 Speaker 2: truth they want to convey, or how they want to 665 00:37:23,400 --> 00:37:26,319 Speaker 2: subtly massage it. Like you were saying about the four 666 00:37:26,480 --> 00:37:29,719 Speaker 2: million dollars in palates. They'll kind of maybe they'll give 667 00:37:29,719 --> 00:37:31,719 Speaker 2: you the answer, but they'll position it in a way 668 00:37:31,760 --> 00:37:34,440 Speaker 2: that convinces you that, oh, you should think about it 669 00:37:34,440 --> 00:37:35,759 Speaker 2: this way as opposed to that way. 670 00:37:36,200 --> 00:37:39,080 Speaker 1: Well, I'll give I'll give you a little insight into 671 00:37:39,120 --> 00:37:45,040 Speaker 1: how I research, which is really funny. Obviously, if you're 672 00:37:45,040 --> 00:37:49,440 Speaker 1: in your thirties or forties, you used Britannica, you used encyclopedias, 673 00:37:49,440 --> 00:37:52,200 Speaker 1: you had pencils, you know, you did things a little differently. 674 00:37:52,640 --> 00:37:54,360 Speaker 1: I personally think we need to go back to that 675 00:37:54,440 --> 00:37:56,560 Speaker 1: for a few reasons. One, we have nine year olds 676 00:37:56,600 --> 00:37:59,680 Speaker 1: wearing glasses because of screen time and the strain on 677 00:37:59,719 --> 00:38:03,400 Speaker 1: your right. And then they're playing video games. And you know, 678 00:38:03,560 --> 00:38:06,600 Speaker 1: my older kids are on Snapchat. I mean, and you 679 00:38:06,640 --> 00:38:09,239 Speaker 1: know where we used to like text each other and 680 00:38:09,280 --> 00:38:11,800 Speaker 1: we thought that was so cool, or we had instant 681 00:38:11,840 --> 00:38:14,720 Speaker 1: Messenger we thought that was so cool, or Google Chat. 682 00:38:15,080 --> 00:38:18,400 Speaker 1: These kids aren't even using words. They send pictures of 683 00:38:18,440 --> 00:38:21,520 Speaker 1: their face. If they don't want to talk, they send 684 00:38:21,600 --> 00:38:25,040 Speaker 1: pictures of the ceiling. I'm like, why are you sending 685 00:38:25,280 --> 00:38:26,840 Speaker 1: what is happening? Why are you sending a picture of 686 00:38:26,880 --> 00:38:28,719 Speaker 1: our ceiling? I don't understand. Oh, that's just slip them 687 00:38:28,719 --> 00:38:30,680 Speaker 1: and I don't want to talk. Okay, you could just 688 00:38:30,719 --> 00:38:33,000 Speaker 1: not answer them. That's also a good way. I've tried 689 00:38:33,040 --> 00:38:35,279 Speaker 1: that a couple of It's the old school way of 690 00:38:35,400 --> 00:38:37,920 Speaker 1: not answering. I'm not interested, Thanks for reaching out. I 691 00:38:37,920 --> 00:38:41,799 Speaker 1: mean just this idea of always being connected, always doing 692 00:38:41,800 --> 00:38:45,920 Speaker 1: a I always having all this integration. But when I search, 693 00:38:46,600 --> 00:38:49,040 Speaker 1: I go to the twelfth or the thirteenth page and 694 00:38:49,080 --> 00:38:53,560 Speaker 1: I work backwards because I want to see what's actually 695 00:38:54,719 --> 00:38:56,520 Speaker 1: what is the truth of the story, right, Because I 696 00:38:56,600 --> 00:38:59,400 Speaker 1: know the SEO, which for those of you listening is 697 00:38:59,440 --> 00:39:02,640 Speaker 1: search engine optimization, and you can pay for that will 698 00:39:02,680 --> 00:39:05,319 Speaker 1: put you in that top page or the very top 699 00:39:05,360 --> 00:39:08,839 Speaker 1: of the second page. Well probably, you know, if you're 700 00:39:08,840 --> 00:39:11,239 Speaker 1: paying to get to number one, I probably don't want 701 00:39:11,239 --> 00:39:12,359 Speaker 1: to know who you are. I want to know who's 702 00:39:12,400 --> 00:39:14,360 Speaker 1: on page four because they're the guy that's grinding it 703 00:39:14,400 --> 00:39:16,799 Speaker 1: out and there is some truth to that. 704 00:39:16,960 --> 00:39:17,200 Speaker 3: You know. 705 00:39:17,320 --> 00:39:18,920 Speaker 1: I'll get back to page one and two. That's not 706 00:39:18,960 --> 00:39:23,400 Speaker 1: a problem. But chat GPT, while it was first and 707 00:39:23,480 --> 00:39:26,720 Speaker 1: Opening Eye was first, I will say I think GROCK 708 00:39:26,760 --> 00:39:29,760 Speaker 1: has given it a run for its money, which really 709 00:39:29,840 --> 00:39:32,400 Speaker 1: speaks to the change, because I don't feel like anybody 710 00:39:32,440 --> 00:39:35,200 Speaker 1: ever beat Google, Like I like Safari, I like Brave, 711 00:39:35,320 --> 00:39:37,680 Speaker 1: I like Duck, dup Goo. I feel like all of 712 00:39:37,719 --> 00:39:41,560 Speaker 1: them are less biased than Google, but Google is still 713 00:39:41,560 --> 00:39:41,879 Speaker 1: the king. 714 00:39:44,120 --> 00:39:47,719 Speaker 2: Yeah, yeah, I mean Google to be fair. They are 715 00:39:47,920 --> 00:39:50,560 Speaker 2: the Google Brain, the Google labs, all of their sort 716 00:39:50,600 --> 00:39:55,080 Speaker 2: of research. Are the scientists and academics who fraught us 717 00:39:55,120 --> 00:39:58,160 Speaker 2: the algorithms initially. I mean obviously a lot of people 718 00:39:58,200 --> 00:40:00,680 Speaker 2: who are involved. But you look at of the fundamational 719 00:40:00,760 --> 00:40:03,680 Speaker 2: fundamental papers and research work, a lot of it was 720 00:40:03,719 --> 00:40:05,600 Speaker 2: out of Google one hundred percent. Other companies sort of 721 00:40:05,600 --> 00:40:08,640 Speaker 2: took it and evolved in advanced and commercialized it. But 722 00:40:08,719 --> 00:40:12,440 Speaker 2: the fundamental kind of ideas of how do you mathematically 723 00:40:12,800 --> 00:40:18,759 Speaker 2: create an AI word generating thing was based out of 724 00:40:18,760 --> 00:40:24,440 Speaker 2: Google apps. So yeah, yeah, they are. They know they 725 00:40:24,480 --> 00:40:26,359 Speaker 2: know too much, they. 726 00:40:25,840 --> 00:40:28,239 Speaker 1: Know too much. Yeah scares me. Now they're going to 727 00:40:28,320 --> 00:40:29,600 Speaker 1: know too much and they're going to be in charge 728 00:40:29,600 --> 00:40:32,200 Speaker 1: of how much we know about what they know. Like, 729 00:40:32,239 --> 00:40:34,560 Speaker 1: I'm out, I need I don't know, I need a 730 00:40:34,600 --> 00:40:38,319 Speaker 1: farm far away with dogs and other things, chickens. I'll 731 00:40:38,320 --> 00:40:41,879 Speaker 1: take anything for with four legs over this crap. But Dave, 732 00:40:41,920 --> 00:40:44,160 Speaker 1: you're the best. Thank you for joining Roague Recap today. Guys, 733 00:40:44,200 --> 00:40:46,040 Speaker 1: if you're listening, thank you for being with us. You 734 00:40:46,080 --> 00:40:49,320 Speaker 1: know what to do at Roague Recap at Lindamickrogue recap 735 00:40:49,360 --> 00:40:52,120 Speaker 1: dot com. We will be back tomorrow with more stacyfe 736 00:40:52,160 --> 00:40:53,360 Speaker 1: out there and pray for our troops. 737 00:40:53,640 --> 00:40:53,759 Speaker 2: Night. 738 00:40:53,800 --> 00:40:58,880 Speaker 1: Everybody,