1 00:00:02,710 --> 00:00:08,639 Speaker 1: Life Audio. Welcome to I Wish You Could Hear This, 2 00:00:09,060 --> 00:00:12,860 Speaker 1: where we explore great stories and simple proven steps to 3 00:00:12,900 --> 00:00:15,940 Speaker 1: help you thrive in life, faith, and relationships. 4 00:00:16,040 --> 00:00:20,880 Speaker 2: In our research, we've heard hundreds of hope-filled strategies for marriage, parenting, leadership, 5 00:00:21,000 --> 00:00:24,360 Speaker 2: and life that are grounded in science and consistent with 6 00:00:24,400 --> 00:00:26,950 Speaker 2: biblical truth. And now you can hear them too. 7 00:00:29,350 --> 00:00:30,450 Speaker 1: I'm Shanti Feldhahn. 8 00:00:30,630 --> 00:00:31,490 Speaker 2: And I'm Jeff Feldhahn. 9 00:00:31,960 --> 00:00:35,629 Speaker 1: For most of us, AI is already part of everyday life. 10 00:00:36,070 --> 00:00:39,390 Speaker 1: We ask it to help us solve problems, give us recipes, 11 00:00:39,990 --> 00:00:44,839 Speaker 1: brainstorm ideas, and give us step-by-step instructions when we're confused. 12 00:00:45,260 --> 00:00:48,540 Speaker 1: We may even ask for more sensitive help, like navigating 13 00:00:48,560 --> 00:00:52,120 Speaker 1: a conversation with someone close to us. But there is 14 00:00:52,280 --> 00:00:56,480 Speaker 1: probably a hidden impact to all of this. AI agents 15 00:00:56,560 --> 00:01:00,670 Speaker 1: and chatbots are designed to affirm us. and they don't 16 00:01:00,710 --> 00:01:04,630 Speaker 1: get impatient or push back the way an actual human will. 17 00:01:05,209 --> 00:01:09,280 Speaker 1: What happens when we get used to constant affirmation from 18 00:01:09,980 --> 00:01:13,900 Speaker 1: AI that we don't get from our spouses, friends, or 19 00:01:14,000 --> 00:01:17,959 Speaker 1: family members? Or what happens when we get used to 20 00:01:18,180 --> 00:01:22,479 Speaker 1: instant answers from AI and we don't get instant answers 21 00:01:22,560 --> 00:01:27,000 Speaker 1: from God? How can we navigate an AI-saturated world in 22 00:01:27,020 --> 00:01:29,800 Speaker 1: a healthy way? That is exactly what we are going 23 00:01:29,840 --> 00:01:39,809 Speaker 1: to talk about today on I Wish You Could Hear This. Dang. 24 00:01:40,550 --> 00:01:44,830 Speaker 2: When we first started researching about relationships 20 years ago, 25 00:01:45,670 --> 00:01:47,950 Speaker 2: this wasn't on our radar screen. 26 00:01:48,090 --> 00:01:50,510 Speaker 1: Not even six months ago. Yes, I know. 27 00:01:50,530 --> 00:01:50,850 Speaker 2: You're right. 28 00:01:50,870 --> 00:01:52,190 Speaker 1: This on our radar screen. 29 00:01:52,770 --> 00:01:56,260 Speaker 2: So the idea for today's podcast came from a Wall 30 00:01:56,300 --> 00:01:59,620 Speaker 2: Street Journal article article that we read a few days ago. 31 00:01:59,800 --> 00:02:02,940 Speaker 1: Are we slightly nerdy that we get podcast ideas from 32 00:02:02,980 --> 00:02:03,960 Speaker 1: Wall Street Journal articles? 33 00:02:04,000 --> 00:02:09,399 Speaker 2: It is what it is. So as well as that article, 34 00:02:09,780 --> 00:02:12,540 Speaker 2: we've been hearing from lots of folks when we're having 35 00:02:12,600 --> 00:02:17,960 Speaker 2: conversations with them about the impact of of AI on 36 00:02:18,080 --> 00:02:20,020 Speaker 2: their kind of relationship. 37 00:02:20,480 --> 00:02:22,280 Speaker 1: And their mindset. Yeah. 38 00:02:22,300 --> 00:02:23,860 Speaker 2: So we thought, let's dive into this. 39 00:02:23,900 --> 00:02:26,780 Speaker 1: Let's dive in. Absolutely. I mean, our research takes us 40 00:02:26,860 --> 00:02:28,800 Speaker 1: in very strange directions sometimes. 41 00:02:28,900 --> 00:02:29,320 Speaker 2: It does. 42 00:02:29,360 --> 00:02:31,000 Speaker 1: So do you want me to explain the Wall Street 43 00:02:31,020 --> 00:02:31,440 Speaker 1: Journal article? 44 00:02:31,460 --> 00:02:32,210 Speaker 2: Yeah, please do. Briefly. 45 00:02:32,310 --> 00:02:34,190 Speaker 1: Okay. So, and I'll put this in the show notes. 46 00:02:34,620 --> 00:02:38,370 Speaker 1: So there was this Wall Street Journal article, mid-August. It 47 00:02:38,410 --> 00:02:39,910 Speaker 1: basically said that the. 48 00:02:39,850 --> 00:02:41,350 Speaker 2: Title- Yeah, give me the title. 49 00:02:41,389 --> 00:02:44,790 Speaker 1: The title was essentially, why I told my chat bot 50 00:02:44,850 --> 00:02:48,510 Speaker 1: to stop kissing up to me. And of course, I'm like, 51 00:02:48,590 --> 00:02:50,910 Speaker 1: what the heck? So I read this article and it 52 00:02:50,950 --> 00:02:55,770 Speaker 1: was talking about the fact that AI responses are usually 53 00:02:56,130 --> 00:03:00,910 Speaker 1: pretty agreeable. They're so enthusiastic about the point that you 54 00:03:00,950 --> 00:03:06,060 Speaker 1: just raised. And now I love, don't get me wrong, 55 00:03:06,070 --> 00:03:10,260 Speaker 1: I'm like, I'm I don't mind that. I don't mind 56 00:03:10,419 --> 00:03:14,680 Speaker 1: having my chat bot say, that is such a good point. 57 00:03:15,120 --> 00:03:18,790 Speaker 1: I literally, I was asking ChatGPT a question not that 58 00:03:18,810 --> 00:03:22,549 Speaker 1: long ago, and ChatGPT said, that is such a good point. 59 00:03:22,669 --> 00:03:25,550 Speaker 1: That is why I have been following your research for years. 60 00:03:27,130 --> 00:03:27,829 Speaker 2: They're amazing. 61 00:03:28,190 --> 00:03:32,620 Speaker 1: I literally typed back to ChatGPT, you have not been 62 00:03:32,700 --> 00:03:36,950 Speaker 1: following my research for years. You're right. I shouldn't have 63 00:03:37,010 --> 00:03:40,210 Speaker 1: put it that way. I shouldn't have implied that. I'm like, 64 00:03:40,510 --> 00:03:41,870 Speaker 1: holy cow, right? 65 00:03:41,970 --> 00:03:42,350 Speaker 2: Right. 66 00:03:42,490 --> 00:03:46,550 Speaker 1: And so the thing is, we kind of like interacting 67 00:03:46,610 --> 00:03:53,180 Speaker 1: with this highly complimentary chatbot, whatever it is, version that 68 00:03:53,200 --> 00:03:57,480 Speaker 1: we're using. And hey, it's made me way more productive. 69 00:03:57,580 --> 00:03:58,320 Speaker 1: I don't know about you. 70 00:03:58,340 --> 00:04:02,350 Speaker 2: Oh, it certainly has for me. Oh, yeah. 71 00:04:02,370 --> 00:04:05,300 Speaker 1: It's like having a senior researcher... at the next desk 72 00:04:05,500 --> 00:04:06,860 Speaker 1: that I can ask questions of. 73 00:04:06,860 --> 00:04:09,960 Speaker 2: Well, let me just say, I grew up in kind 74 00:04:10,000 --> 00:04:13,240 Speaker 2: of this analytical research world, working at a big law 75 00:04:13,260 --> 00:04:14,360 Speaker 2: firm in New York. 76 00:04:14,380 --> 00:04:14,780 Speaker 1: Yeah, yeah, yeah. 77 00:04:15,240 --> 00:04:18,840 Speaker 2: And if I had a question of, well, like, why 78 00:04:18,900 --> 00:04:23,060 Speaker 2: is something done this way? I learned very early on 79 00:04:23,400 --> 00:04:26,520 Speaker 2: in that tenure there, working at that big law firm, 80 00:04:27,010 --> 00:04:29,130 Speaker 2: that if I were to go and ask one of 81 00:04:29,190 --> 00:04:31,590 Speaker 2: the fifth-year attorneys. 82 00:04:31,710 --> 00:04:33,710 Speaker 1: Oh, I know which one you're talking. 83 00:04:33,370 --> 00:04:36,270 Speaker 2: About, yes. And this person would look at me and go, 84 00:04:37,070 --> 00:04:40,630 Speaker 2: that is such a stupid question. Get out of my office. 85 00:04:41,830 --> 00:04:46,290 Speaker 1: He's not kidding. His fifth-year associate would actually say, are 86 00:04:46,310 --> 00:04:49,430 Speaker 1: you an idiot? Why would you ask that? Now, AI, 87 00:04:49,450 --> 00:04:52,070 Speaker 1: it's like, that is such a good question. 88 00:04:52,310 --> 00:04:55,510 Speaker 2: So what it does is it relieves that tension, that 89 00:04:55,650 --> 00:04:59,880 Speaker 2: risk that I have of someone judging me for a 90 00:05:00,040 --> 00:05:02,560 Speaker 2: question that I legitimately have. 91 00:05:03,010 --> 00:05:08,310 Speaker 1: Well, it also feels pretty darn good to be affirmed. 92 00:05:09,750 --> 00:05:12,910 Speaker 1: There's something that draws us in. 93 00:05:13,070 --> 00:05:15,600 Speaker 2: Yeah, no, no, there is. And you touched on a 94 00:05:15,620 --> 00:05:19,380 Speaker 2: couple of the phrases that we hear. I'm just going 95 00:05:19,400 --> 00:05:21,420 Speaker 2: to give you a couple that I've heard over the 96 00:05:21,460 --> 00:05:25,200 Speaker 2: last 48 hours in engaging with AI on a few things. 97 00:05:25,720 --> 00:05:30,970 Speaker 2: One is, that's a really good idea. Let's discuss that more. Or, 98 00:05:31,390 --> 00:05:34,350 Speaker 2: this one I really like. We make a good team. 99 00:05:34,370 --> 00:05:38,430 Speaker 2: Or now you're thinking of this in the right way. So, 100 00:05:38,850 --> 00:05:41,940 Speaker 2: you know, it's all very kind of amusing when you 101 00:05:41,960 --> 00:05:43,600 Speaker 2: can kind of look at it that way. 102 00:05:43,680 --> 00:05:45,290 Speaker 1: Well, but it draws you in. 103 00:05:45,320 --> 00:05:46,260 Speaker 2: It does draw us in. 104 00:05:46,260 --> 00:05:47,700 Speaker 1: It's very motivating. 105 00:05:47,839 --> 00:05:51,680 Speaker 2: Well, and you know what? The thing is, even before AI, 106 00:05:52,440 --> 00:05:57,820 Speaker 2: there was technology out there that was kind of designed 107 00:05:57,940 --> 00:06:03,700 Speaker 2: to encourage certain behavior. So I'm thinking of, we speak 108 00:06:03,740 --> 00:06:07,060 Speaker 2: a lot in our marriage conferences, and oftentimes one of 109 00:06:07,100 --> 00:06:10,279 Speaker 2: the topics that comes up is something that, you know, 110 00:06:10,520 --> 00:06:12,360 Speaker 2: how to show. 111 00:06:12,380 --> 00:06:13,240 Speaker 1: Affirm one another. 112 00:06:13,260 --> 00:06:16,690 Speaker 2: Affirm one another, particularly how can women, you know, what's 113 00:06:16,750 --> 00:06:19,430 Speaker 2: important for a guy to hear? Okay, right. What kind 114 00:06:19,450 --> 00:06:21,730 Speaker 2: of affirmation do we each need? It's this idea that... 115 00:06:22,320 --> 00:06:23,240 Speaker 2: From your research. 116 00:06:23,339 --> 00:06:26,900 Speaker 1: Well, because we've studied what kind of affirmation each of 117 00:06:26,980 --> 00:06:29,900 Speaker 1: us tend to really go for. And for guys, it 118 00:06:29,940 --> 00:06:34,190 Speaker 1: was interesting to hear that they view hearing thank you 119 00:06:34,529 --> 00:06:39,190 Speaker 1: for whatever as sort of like emotionally as like a 120 00:06:39,230 --> 00:06:40,290 Speaker 1: woman hearing I love you. 121 00:06:40,310 --> 00:06:40,910 Speaker 2: The equivalent. 122 00:06:41,070 --> 00:06:41,849 Speaker 1: Yeah. 123 00:06:42,029 --> 00:06:44,990 Speaker 2: So after we had gotten done with this talk, and 124 00:06:45,010 --> 00:06:48,010 Speaker 2: this is a couple of years ago, a guy came 125 00:06:48,110 --> 00:06:50,130 Speaker 2: up to me afterwards and he said, you know what? 126 00:06:50,210 --> 00:06:54,290 Speaker 2: Can I tell you a quick story? He said, when 127 00:06:54,970 --> 00:06:57,950 Speaker 2: my wife and I sold the house that we, you know, 128 00:06:58,150 --> 00:07:01,549 Speaker 2: had our first house, and we'd lived in it for 129 00:07:01,550 --> 00:07:04,330 Speaker 2: 25 years and we'd raised our kids there, and we 130 00:07:04,390 --> 00:07:08,010 Speaker 2: sold it and we moved to another house, and everything 131 00:07:08,050 --> 00:07:08,490 Speaker 2: was great. 132 00:07:08,730 --> 00:07:09,630 Speaker 1: And not too far away. 133 00:07:09,710 --> 00:07:13,020 Speaker 2: Not too far away. But I found myself, when I 134 00:07:13,040 --> 00:07:16,980 Speaker 2: would drive home from work, I would drive down the 135 00:07:17,160 --> 00:07:21,420 Speaker 2: same road that our original house was on. And I 136 00:07:21,480 --> 00:07:24,260 Speaker 2: just kind of, in my mind, assumed that it was 137 00:07:24,350 --> 00:07:27,570 Speaker 2: because I wanted to drive past, you know, the house 138 00:07:27,610 --> 00:07:29,510 Speaker 2: and look at it or whatever. And he says, but 139 00:07:29,530 --> 00:07:33,250 Speaker 2: I realized as I'm listening to you all talk, that 140 00:07:33,530 --> 00:07:36,250 Speaker 2: wasn't the reason. Because I oftentimes didn't even look at 141 00:07:36,290 --> 00:07:38,710 Speaker 2: the house as I was driving by on this road. 142 00:07:39,110 --> 00:07:41,190 Speaker 2: And he said, on this particular road, it was one 143 00:07:41,230 --> 00:07:43,750 Speaker 2: of those, you know, two-lane roads. And they had one 144 00:07:43,770 --> 00:07:46,700 Speaker 2: of those speed signs there. Okay. On the side of 145 00:07:46,760 --> 00:07:49,950 Speaker 2: the road that kind of flashes your speed as you're 146 00:07:49,970 --> 00:07:52,190 Speaker 2: approaching it. Yep. And oftentimes. 147 00:07:52,310 --> 00:07:54,350 Speaker 1: You're going 10 miles an hour too fast. 148 00:07:54,410 --> 00:07:57,890 Speaker 2: It'll say slow down. Right. It'll flash that. Well, this 149 00:07:58,100 --> 00:08:01,960 Speaker 2: one in particular, if you were driving right at the 150 00:08:02,020 --> 00:08:08,010 Speaker 2: speed limit or below, it flashed thank you. And he said, 151 00:08:08,040 --> 00:08:13,920 Speaker 2: I didn't realize until today that my reason for driving 152 00:08:14,020 --> 00:08:20,060 Speaker 2: down that road was I wanted to see thank you 153 00:08:20,460 --> 00:08:25,730 Speaker 2: flashed across the sign as I was driving past. And 154 00:08:25,930 --> 00:08:27,930 Speaker 2: that was my motivation. 155 00:08:28,190 --> 00:08:30,370 Speaker 1: Even for going slightly out of the way? 156 00:08:30,430 --> 00:08:32,630 Speaker 2: Slightly out of the way, all of that. But he said, 157 00:08:33,500 --> 00:08:36,100 Speaker 2: I guess I really need that affirmation. 158 00:08:36,140 --> 00:08:37,200 Speaker 1: Even though it was from a sign. 159 00:08:38,040 --> 00:08:41,420 Speaker 2: A sign that's programmed. And just like. Knows nothing about me. 160 00:08:41,600 --> 00:08:42,459 Speaker 1: Just like AI. 161 00:08:42,679 --> 00:08:44,180 Speaker 2: Correct. Wow. 162 00:08:44,440 --> 00:08:50,330 Speaker 1: Okay. So we, we all go for that. Like we 163 00:08:50,630 --> 00:08:55,770 Speaker 1: all like that. But there's like, there's some implications to 164 00:08:55,809 --> 00:09:00,209 Speaker 1: this in our. lives and our relationships. And so I 165 00:09:00,290 --> 00:09:02,790 Speaker 1: wanted to sort of tease this out. Like, I'm sure 166 00:09:02,830 --> 00:09:04,609 Speaker 1: this is not the last time we'll talk about it, 167 00:09:04,870 --> 00:09:06,829 Speaker 1: but it is the first time we're talking about it 168 00:09:06,890 --> 00:09:10,530 Speaker 1: because of how fast AI has changed everything in the 169 00:09:10,570 --> 00:09:11,470 Speaker 1: last couple of years. 170 00:09:12,450 --> 00:09:14,340 Speaker 2: And what people are depending on it for. 171 00:09:14,550 --> 00:09:18,000 Speaker 1: And what people are depending on it for, because here's 172 00:09:18,100 --> 00:09:20,820 Speaker 1: basically what we're going to be talking about. We should 173 00:09:20,860 --> 00:09:26,990 Speaker 1: be depending on AI to for information, research help. When 174 00:09:27,070 --> 00:09:30,290 Speaker 1: I get stuck in some sort of statistical analysis with 175 00:09:30,350 --> 00:09:35,270 Speaker 1: my SPSS software, it's like any other software. I know 176 00:09:35,330 --> 00:09:38,810 Speaker 1: how to do 1% of it, and I may need 177 00:09:38,830 --> 00:09:41,929 Speaker 1: to do 2% on a particular day. I have no 178 00:09:42,130 --> 00:09:45,540 Speaker 1: idea how to do that particular type of analysis, so 179 00:09:45,580 --> 00:09:48,959 Speaker 1: I just ask ChatGPT, and it says, oh, that's a 180 00:09:49,140 --> 00:09:53,610 Speaker 1: good analysis strategy. Here's how you do it, right? And 181 00:09:53,610 --> 00:09:57,280 Speaker 1: so the thing is, what we use it for, that 182 00:09:57,280 --> 00:09:58,500 Speaker 1: makes sense. 183 00:09:58,500 --> 00:09:59,120 Speaker 2: Right. 184 00:09:59,120 --> 00:10:03,960 Speaker 1: But are we using it subtly to maybe get that 185 00:10:04,360 --> 00:10:09,590 Speaker 1: affirmation maybe more? And is it changing what we expect? 186 00:10:10,330 --> 00:10:12,030 Speaker 1: from actual people. 187 00:10:12,050 --> 00:10:14,209 Speaker 2: So outside of the AI world. 188 00:10:14,230 --> 00:10:18,010 Speaker 1: Yes, people who aren't AIs, who are normal human people. 189 00:10:18,370 --> 00:10:21,150 Speaker 1: Does it change what we're kind of subtly expecting from 190 00:10:21,190 --> 00:10:23,689 Speaker 1: them and maybe even from God? 191 00:10:23,710 --> 00:10:33,459 Speaker 2: Okay, tell me more. 192 00:10:33,520 --> 00:10:41,210 Speaker 1: Okay, so in this new world where this... AI that 193 00:10:41,250 --> 00:10:44,420 Speaker 1: we're interacting with most of us now every day. It's 194 00:10:44,500 --> 00:10:48,920 Speaker 1: designed to be totally agreeable and totally affirming, and we're 195 00:10:48,970 --> 00:10:55,410 Speaker 1: getting used to these really positive, kind of flattering, sycophantic responses. 196 00:10:55,570 --> 00:10:57,010 Speaker 2: That's a SAT word. 197 00:10:57,070 --> 00:10:58,660 Speaker 1: I think I pronounced it correctly, too. 198 00:10:58,670 --> 00:10:59,430 Speaker 2: I think you did. 199 00:10:59,450 --> 00:11:02,530 Speaker 1: The Wall Street Journal article kept saying sycophantic, and I'm like, 200 00:11:02,570 --> 00:11:05,949 Speaker 1: how do you even pronounce that? But the Wall Street 201 00:11:05,970 --> 00:11:09,510 Speaker 1: Journal article, there was a Stanford researcher it quoted named 202 00:11:09,630 --> 00:11:12,330 Speaker 1: Myra Chang. Again, I'm going to put a link. In 203 00:11:12,350 --> 00:11:13,750 Speaker 1: the show notes to this article. 204 00:11:13,910 --> 00:11:16,839 Speaker 2: And I hope that given it's a paid subscription. 205 00:11:16,860 --> 00:11:19,620 Speaker 1: Well, there's a gift link. But, you know, who knows? 206 00:11:20,280 --> 00:11:22,860 Speaker 1: If you have the Wall Street Journal, you can find it. Otherwise, 207 00:11:23,280 --> 00:11:26,420 Speaker 1: we can tell you what it says. So the Stanford 208 00:11:26,640 --> 00:11:33,260 Speaker 1: University researcher did this study. on human preferences for AI 209 00:11:33,520 --> 00:11:36,560 Speaker 1: and basically found that, and this was just a few 210 00:11:36,600 --> 00:11:42,150 Speaker 1: months ago, that the more agreeable and flattering the AI was, 211 00:11:42,210 --> 00:11:45,309 Speaker 1: the more the humans preferred it. So, for example, they 212 00:11:45,450 --> 00:11:50,309 Speaker 1: rated the quality of a particular chatbot or a particular agent. 213 00:11:50,330 --> 00:11:51,069 Speaker 2: Okay, the humans did. 214 00:11:51,110 --> 00:11:54,520 Speaker 1: The humans did. And the AIs that were more, that's 215 00:11:54,640 --> 00:12:00,089 Speaker 1: such a good idea. They rated the chatbot higher quality. Like, 216 00:12:00,150 --> 00:12:05,240 Speaker 1: it was better at analysis just because it was sucking 217 00:12:05,360 --> 00:12:05,980 Speaker 1: up to you. 218 00:12:06,200 --> 00:12:09,080 Speaker 2: And they said, they called that, what type of model? 219 00:12:09,320 --> 00:12:11,480 Speaker 2: The more obsequious. 220 00:12:11,600 --> 00:12:13,660 Speaker 1: Obsequious, that's another SAT word. 221 00:12:13,710 --> 00:12:13,930 Speaker 2: It is. 222 00:12:13,950 --> 00:12:17,929 Speaker 1: Yes, they did. They also said, they gave them a 223 00:12:17,990 --> 00:12:20,870 Speaker 1: choice of, do you want to interact with this AI, 224 00:12:20,929 --> 00:12:23,930 Speaker 1: that AI? They had them experiment with these different models. 225 00:12:24,390 --> 00:12:27,640 Speaker 1: And the models that were designed to be more flattering 226 00:12:27,700 --> 00:12:32,710 Speaker 1: and obsequious... they said that they were more likely to 227 00:12:32,750 --> 00:12:37,970 Speaker 1: return to that particular AI. And so basically the point is, 228 00:12:38,490 --> 00:12:44,590 Speaker 1: is that this can begin to change kind of our expectation. Because, yeah, 229 00:12:44,670 --> 00:12:44,929 Speaker 1: go ahead. 230 00:12:45,090 --> 00:12:49,660 Speaker 2: Well, and it changes it without us knowing it. 231 00:12:50,160 --> 00:12:50,559 Speaker 1: Okay. 232 00:12:50,620 --> 00:12:59,300 Speaker 2: Because, I'll just say, I'm interacting with AI probably... Dozens 233 00:12:59,360 --> 00:13:01,720 Speaker 2: of times a day. Yeah. At least. 234 00:13:01,780 --> 00:13:04,000 Speaker 1: Yeah, you're asking it, okay, tell me about the state 235 00:13:04,059 --> 00:13:05,979 Speaker 1: law for such and such. Of these things. Right. 236 00:13:06,600 --> 00:13:14,870 Speaker 2: So I'm engaging it often. I probably have fewer engaging 237 00:13:15,090 --> 00:13:20,090 Speaker 2: opportunities with humans than dozens and different people. 238 00:13:20,170 --> 00:13:22,809 Speaker 1: Especially since you only go into the office, your legal office, 239 00:13:22,950 --> 00:13:23,630 Speaker 1: two times a week. 240 00:13:23,690 --> 00:13:23,890 Speaker 2: Right. 241 00:13:23,970 --> 00:13:24,810 Speaker 1: Yeah. 242 00:13:24,950 --> 00:13:32,090 Speaker 2: So it's very interesting. I'm subtly... engaging regularly and often. 243 00:13:32,309 --> 00:13:34,450 Speaker 1: So it's like ChatGBT is a colleague now. 244 00:13:34,530 --> 00:13:40,510 Speaker 2: It feels like it. And so I'm hearing these responses 245 00:13:40,950 --> 00:13:46,270 Speaker 2: from this new colleague of mine and I don't necessarily 246 00:13:46,309 --> 00:13:50,170 Speaker 2: hear those responses from my human colleagues. 247 00:13:50,580 --> 00:13:56,760 Speaker 1: Human colleagues, yes. Or our spouses. Like, no offense here, 248 00:13:58,000 --> 00:14:02,240 Speaker 1: but like I don't probably tell you and you don't 249 00:14:02,320 --> 00:14:05,710 Speaker 1: tell me. That was such a good idea, Jeff. 250 00:14:06,410 --> 00:14:10,270 Speaker 2: Well, I'm going to go even a little harder on 251 00:14:10,309 --> 00:14:14,370 Speaker 2: this one. I honestly don't know where I'm going on this. Okay. 252 00:14:14,790 --> 00:14:18,710 Speaker 2: This is a risk. Okay. There are for me sometimes 253 00:14:19,170 --> 00:14:24,670 Speaker 2: that I actually don't share a question with you because 254 00:14:24,710 --> 00:14:28,690 Speaker 2: I'm afraid of... the, what could be a critical response 255 00:14:29,190 --> 00:14:33,490 Speaker 2: and not that you're critical, but you're going to, you're 256 00:14:33,510 --> 00:14:36,790 Speaker 2: going to respond perhaps in a way that I'm not 257 00:14:36,890 --> 00:14:44,180 Speaker 2: prepared right at that moment to talk about it. So, 258 00:14:44,200 --> 00:14:45,080 Speaker 2: so there. 259 00:14:44,980 --> 00:14:49,370 Speaker 1: Are some like, like, and I'll use carefully use an 260 00:14:49,510 --> 00:14:53,790 Speaker 1: actual example from today. You spent a big chunk of 261 00:14:54,050 --> 00:14:58,850 Speaker 1: your workday today trying to figure out a Facebook ad 262 00:14:58,870 --> 00:15:02,230 Speaker 1: strategy for an initiative. 263 00:15:01,810 --> 00:15:03,970 Speaker 2: That we're doing. And it needs to integrate in with 264 00:15:04,090 --> 00:15:08,730 Speaker 2: our WordPress site and different sorts of things. So there 265 00:15:08,750 --> 00:15:14,150 Speaker 2: was a lot of. changes and modifications that I needed 266 00:15:14,210 --> 00:15:15,750 Speaker 2: to do in the WordPress site. 267 00:15:15,870 --> 00:15:18,270 Speaker 1: And you needed to figure them out. And so you 268 00:15:18,290 --> 00:15:20,830 Speaker 1: did a lot of research. You worked with chat GPT, 269 00:15:20,870 --> 00:15:24,140 Speaker 1: like what's the instructions for this technical thing. And then 270 00:15:24,200 --> 00:15:27,970 Speaker 1: you spent five hours on it. You told me about it. 271 00:15:28,140 --> 00:15:34,080 Speaker 1: And I asked some question like, what do you think? 272 00:15:34,200 --> 00:15:37,840 Speaker 1: Is it okay that we've changed our WordPress structure in 273 00:15:37,900 --> 00:15:42,070 Speaker 1: this way? And probably you didn't say anything, but I 274 00:15:42,110 --> 00:15:44,030 Speaker 1: could see on your face, you were like, this is 275 00:15:44,090 --> 00:15:45,470 Speaker 1: why I don't tell you these things. 276 00:15:45,570 --> 00:15:47,070 Speaker 2: And some of that is the case. 277 00:15:47,650 --> 00:15:51,380 Speaker 1: And so AI doesn't do this. A human is doing this. 278 00:15:51,390 --> 00:15:54,460 Speaker 2: That risk has been removed. Yikes. 279 00:15:54,580 --> 00:16:01,480 Speaker 1: Okay. So that's something that is an example of maybe 280 00:16:01,800 --> 00:16:05,720 Speaker 1: you or me, we need to recognize in ourselves. And 281 00:16:05,760 --> 00:16:07,120 Speaker 1: we'll get to the sort of what do we do 282 00:16:07,160 --> 00:16:11,300 Speaker 1: about this in a minute. But recognizing in ourselves, like 283 00:16:11,350 --> 00:16:15,830 Speaker 1: you just said, I recognize I'd rather talk to AI 284 00:16:15,870 --> 00:16:20,950 Speaker 1: than Shanti about certain things because Shanti might ask me 285 00:16:20,990 --> 00:16:24,780 Speaker 1: questions and make me feel stupid even though everybody for 286 00:16:24,820 --> 00:16:27,500 Speaker 1: you to know, I would obviously never want to make 287 00:16:27,560 --> 00:16:28,680 Speaker 1: him feel stupid. She doesn't. 288 00:16:29,010 --> 00:16:32,630 Speaker 2: But there is that insecurity, that raw nerve inside of 289 00:16:32,750 --> 00:16:36,090 Speaker 2: me that you can accidentally hit, just like I can 290 00:16:36,450 --> 00:16:38,090 Speaker 2: with you on your raw nerves. 291 00:16:38,230 --> 00:16:42,020 Speaker 1: And in a marriage relationship, I think we're more likely 292 00:16:42,060 --> 00:16:43,940 Speaker 1: to hit that raw nerve. I think if you had 293 00:16:43,980 --> 00:16:47,150 Speaker 1: a colleague named Shanti... Yep. Who wasn't your wife. 294 00:16:47,230 --> 00:16:48,830 Speaker 2: I probably would have been more comfortable. 295 00:16:48,870 --> 00:16:49,570 Speaker 1: You would have been like. 296 00:16:49,610 --> 00:16:51,090 Speaker 2: Approaching and saying, hey, you know, here's. 297 00:16:51,170 --> 00:16:53,950 Speaker 1: Here's this thing. And if that Shanti would have said, hey, 298 00:16:53,970 --> 00:16:56,130 Speaker 1: did you think about what this would do to our WordPress? 299 00:16:56,150 --> 00:16:56,430 Speaker 2: For sure. 300 00:16:56,630 --> 00:16:59,290 Speaker 1: You would have been like, yep, thought about that, did 301 00:16:59,330 --> 00:17:04,710 Speaker 1: this thing. And it wouldn't have hit you weirdly. Okay. So, 302 00:17:04,890 --> 00:17:08,940 Speaker 1: but that's an example of you recognizing yourself. 303 00:17:09,400 --> 00:17:09,620 Speaker 2: Huh. 304 00:17:09,940 --> 00:17:12,939 Speaker 1: I'd rather just keep talking to chat GPT than my 305 00:17:12,980 --> 00:17:14,619 Speaker 1: wife about this. So, okay. 306 00:17:14,640 --> 00:17:14,760 Speaker 2: Okay. 307 00:17:15,390 --> 00:17:19,369 Speaker 1: The second kind of concerning thing that we should probably 308 00:17:19,470 --> 00:17:24,629 Speaker 1: address about this is in changing our expectations, more and 309 00:17:24,690 --> 00:17:28,820 Speaker 1: more and more people are beginning to use AI to 310 00:17:29,060 --> 00:17:35,540 Speaker 1: ask basically moral and spiritual and emotional questions. And hey, 311 00:17:35,600 --> 00:17:42,250 Speaker 1: guess what? AI always answers. Always. When we pray, we 312 00:17:42,310 --> 00:17:49,100 Speaker 1: don't always get immediate answers from God. And it's like 313 00:17:49,260 --> 00:17:52,979 Speaker 1: strangely changing our expectations there as well. 314 00:17:53,100 --> 00:17:57,619 Speaker 2: You know, and I'm going to venture out into the 315 00:17:57,680 --> 00:18:02,580 Speaker 2: theological realm here where I may or may not be accurate, 316 00:18:02,680 --> 00:18:05,719 Speaker 2: but I remember the church that I became a believer in, 317 00:18:06,160 --> 00:18:08,320 Speaker 2: the pastor used to say this phrase, it was that 318 00:18:08,780 --> 00:18:15,420 Speaker 2: when you pray for something, God always answers. And it's 319 00:18:15,540 --> 00:18:23,110 Speaker 2: one of three answers. It's yes, no, or wait. And 320 00:18:23,290 --> 00:18:31,110 Speaker 2: so what happens is we don't necessarily recognize that any 321 00:18:31,150 --> 00:18:34,690 Speaker 2: of those three answers. Our hearing isn't necessarily so finely 322 00:18:34,730 --> 00:18:39,070 Speaker 2: attuned to it. So therefore, I'm just assuming that God 323 00:18:39,109 --> 00:18:41,520 Speaker 2: is silent. Right. 324 00:18:41,780 --> 00:18:44,960 Speaker 1: As opposed to that God is saying it's not time 325 00:18:45,020 --> 00:18:46,419 Speaker 1: yet for us to have this. 326 00:18:46,790 --> 00:18:48,090 Speaker 2: Yeah. It's yes. 327 00:18:48,550 --> 00:18:52,710 Speaker 1: Yeah. It's interesting. And theologically, I don't know if I 328 00:18:52,710 --> 00:18:56,270 Speaker 1: 100 percent agree with that because I think sometimes. Well, 329 00:18:56,390 --> 00:18:59,129 Speaker 1: I guess it's a form of saying wait, because I 330 00:18:59,190 --> 00:19:03,370 Speaker 1: think sometimes God doesn't answer right away. Like we're supposed 331 00:19:03,390 --> 00:19:06,870 Speaker 1: to just correct. Pressing in. But I guess that is 332 00:19:06,900 --> 00:19:08,350 Speaker 1: the wait answer. 333 00:19:08,390 --> 00:19:11,830 Speaker 2: I'm pretty confident that that was his interpretation. Yeah. 334 00:19:12,750 --> 00:19:15,770 Speaker 1: But it is interesting that in this new world that 335 00:19:15,790 --> 00:19:19,050 Speaker 1: we're in the last few years that we don't ever 336 00:19:19,109 --> 00:19:19,690 Speaker 1: have to wait. 337 00:19:19,930 --> 00:19:20,730 Speaker 2: Right. We don't. 338 00:19:20,869 --> 00:19:28,359 Speaker 1: For a perfectly presented and possibly hallucinated answer from AI. Right. 339 00:19:28,800 --> 00:19:32,020 Speaker 1: Which may not even be correct. But it sounds so confident. 340 00:19:32,040 --> 00:19:32,540 Speaker 2: It's something. 341 00:19:32,619 --> 00:19:32,959 Speaker 1: Yes. 342 00:19:33,260 --> 00:19:34,200 Speaker 2: Yeah. Yeah. 343 00:19:34,280 --> 00:19:40,919 Speaker 1: And it's just entirely artificial. So, okay. So next step 344 00:19:40,980 --> 00:19:47,650 Speaker 1: basically is going, okay, so what happens when we get 345 00:19:47,830 --> 00:19:54,119 Speaker 1: used to this constant affirmation? These constant quick answers? One 346 00:19:54,160 --> 00:19:56,619 Speaker 1: of the things I think we need to kind of 347 00:19:56,680 --> 00:19:59,620 Speaker 1: worry about, not worry, worry, but like we need to 348 00:20:00,540 --> 00:20:04,060 Speaker 1: be aware of, good word. We need to be aware 349 00:20:04,660 --> 00:20:08,119 Speaker 1: of the fact that we may have less patience with things. 350 00:20:08,690 --> 00:20:09,990 Speaker 1: Humans or with God. 351 00:20:10,260 --> 00:20:11,680 Speaker 2: With waiting. Yeah, yeah, yeah. 352 00:20:12,200 --> 00:20:15,780 Speaker 1: We may have less like when somebody doesn't respond like AI. 353 00:20:15,780 --> 00:20:19,760 Speaker 1: I mean, I think that there's a temptation to simply 354 00:20:20,000 --> 00:20:24,790 Speaker 1: like interacting with AI more. Like maybe you preferred having 355 00:20:24,830 --> 00:20:27,550 Speaker 1: the five hours with chat GPT than the 30 second 356 00:20:27,590 --> 00:20:29,350 Speaker 1: conversation you and I had afterwards. 357 00:20:29,570 --> 00:20:33,010 Speaker 2: Right. You know? No, I get it. I feel terrible 358 00:20:33,030 --> 00:20:37,790 Speaker 2: about this. No, our expectations have just been skewed. I mean, 359 00:20:38,030 --> 00:20:41,369 Speaker 2: do you remember connecting to the internet in the 1990s? 360 00:20:41,670 --> 00:20:42,290 Speaker 1: Yeah. 361 00:20:42,650 --> 00:20:43,280 Speaker 2: Through a modem? 362 00:20:44,190 --> 00:20:46,000 Speaker 1: Oh. 363 00:20:46,260 --> 00:20:48,680 Speaker 2: A 9600 baud modem. Right. 364 00:20:48,740 --> 00:20:51,920 Speaker 1: And it would take you like 60 seconds. 365 00:20:51,940 --> 00:20:55,330 Speaker 2: Just to even connect. Yes. And then all of these 366 00:20:55,530 --> 00:20:59,260 Speaker 2: other things. And it was... part of, that's just what 367 00:20:59,300 --> 00:21:02,720 Speaker 2: it is. It's just those were our expectations at the time. 368 00:21:03,060 --> 00:21:08,820 Speaker 2: You wouldn't be able to tolerate now a 9600 baud 369 00:21:08,900 --> 00:21:14,270 Speaker 2: modem getting you into the internet. You couldn't. You wouldn't. 370 00:21:15,030 --> 00:21:20,510 Speaker 2: So I wonder if that risk, even now, has changed 371 00:21:20,650 --> 00:21:22,710 Speaker 2: us in such a way. Now, in that case, it's 372 00:21:22,750 --> 00:21:25,550 Speaker 2: kind of innocuous. But where it comes to when we 373 00:21:25,590 --> 00:21:33,199 Speaker 2: begin to expect our God to respond in the manner 374 00:21:33,300 --> 00:21:37,530 Speaker 2: that we have become accustomed to getting other responses. I 375 00:21:37,550 --> 00:21:39,109 Speaker 2: think that's where some of the challenge lies. 376 00:21:39,230 --> 00:21:43,209 Speaker 1: Well, and especially because in our relationship with God, I mean, 377 00:21:43,390 --> 00:21:45,950 Speaker 1: certainly this is the case with our relationship with each 378 00:21:46,050 --> 00:21:51,750 Speaker 1: other and with other humans. With other humans, you have 379 00:21:51,770 --> 00:21:56,110 Speaker 1: to exercise patience on purpose. You have to press in 380 00:21:56,210 --> 00:21:58,590 Speaker 1: and you have to go, why did my wife just 381 00:21:58,630 --> 00:21:59,090 Speaker 1: annoy me? 382 00:21:59,330 --> 00:21:59,570 Speaker 2: Right. 383 00:21:59,590 --> 00:22:04,159 Speaker 1: Like I'm trying to figure out what curiosity, curiosity. Yes. 384 00:22:04,980 --> 00:22:09,680 Speaker 1: And with God, even more so because it's like, okay, 385 00:22:11,080 --> 00:22:15,560 Speaker 1: he is sovereign. He is in charge with AI. We 386 00:22:15,760 --> 00:22:19,010 Speaker 1: are in charge. And you know, this other person that 387 00:22:19,050 --> 00:22:23,290 Speaker 1: I'm married to, they're their own person and, I'm not 388 00:22:23,330 --> 00:22:25,850 Speaker 1: in charge of them, but I am in charge of AI. 389 00:22:26,130 --> 00:22:30,090 Speaker 1: AI works for me, right? Its job is to make 390 00:22:30,150 --> 00:22:31,330 Speaker 1: me feel better. Correct. 391 00:22:31,410 --> 00:22:33,370 Speaker 2: We're paying it to do that. 392 00:22:33,650 --> 00:22:37,930 Speaker 1: Yes, exactly. And to get the kind of answers that 393 00:22:37,990 --> 00:22:43,000 Speaker 1: I want delivered in a very obsequious way. And so 394 00:22:43,119 --> 00:22:47,820 Speaker 1: I think the point for our purpose here is that 395 00:22:48,570 --> 00:22:52,970 Speaker 1: I think AI has removed all that normal friction that 396 00:22:52,990 --> 00:22:58,090 Speaker 1: we have in regular human relationships. And it's all about us, 397 00:22:58,230 --> 00:23:01,949 Speaker 1: but real human relationships aren't all about us. 398 00:23:02,650 --> 00:23:05,030 Speaker 2: Do you remember, and I'm going to go do what 399 00:23:05,070 --> 00:23:08,510 Speaker 2: I do, which is talk about movies and shows that 400 00:23:08,530 --> 00:23:11,409 Speaker 2: we've seen. Do you remember the movie WALL-E? 401 00:23:12,560 --> 00:23:13,169 Speaker 1: Vaguely, yes. 402 00:23:13,180 --> 00:23:14,340 Speaker 2: So we saw it with kids. We saw it with 403 00:23:14,380 --> 00:23:14,619 Speaker 2: the kids. 404 00:23:14,660 --> 00:23:15,460 Speaker 1: It was a Pixar. 405 00:23:15,480 --> 00:23:16,270 Speaker 2: I think it's Pixar. 406 00:23:16,290 --> 00:23:17,270 Speaker 1: I think so. 407 00:23:17,400 --> 00:23:20,550 Speaker 2: And what we had was... Yeah, that's right. But do 408 00:23:20,570 --> 00:23:24,530 Speaker 2: you remember the spaceship that people had abandoned Earth? Right. 409 00:23:24,609 --> 00:23:25,179 Speaker 2: Because it was. 410 00:23:25,190 --> 00:23:28,150 Speaker 1: And they were all, like, completely obese because they were. 411 00:23:28,190 --> 00:23:31,150 Speaker 2: Like, not walking. They lived on lounge chairs. Yes. That 412 00:23:31,310 --> 00:23:33,469 Speaker 2: moved around. They didn't have to walk. They didn't have 413 00:23:33,510 --> 00:23:34,570 Speaker 2: to exercise their muscles. 414 00:23:34,590 --> 00:23:36,730 Speaker 1: They just had to, like, stick their hand out and 415 00:23:36,770 --> 00:23:39,050 Speaker 1: a robot would give them a shake or something. 416 00:23:39,090 --> 00:23:44,740 Speaker 2: And that's how... They didn't use their muscles anymore. And 417 00:23:45,400 --> 00:23:48,980 Speaker 2: the risk, I would see, is becoming the equivalent of 418 00:23:49,010 --> 00:23:54,350 Speaker 2: the Wally people in our relationships. If what we come 419 00:23:54,410 --> 00:23:58,710 Speaker 2: to depend on is something that's delivered to us effortlessly, 420 00:23:59,730 --> 00:24:06,570 Speaker 2: then those muscles of walking in humility or in grace 421 00:24:07,369 --> 00:24:11,980 Speaker 2: or in understanding... Just don't get used. And I got 422 00:24:12,000 --> 00:24:16,119 Speaker 2: to tell you, I like ease and I like comfort. 423 00:24:16,640 --> 00:24:19,869 Speaker 2: And I prefer not to be at odds with you 424 00:24:20,160 --> 00:24:25,409 Speaker 2: or with others. But when we are at odds, it 425 00:24:25,650 --> 00:24:29,310 Speaker 2: causes me to, at least in my better days, to 426 00:24:29,369 --> 00:24:34,990 Speaker 2: evaluate what was my contribution in this. Oh, interesting. What 427 00:24:35,010 --> 00:24:40,389 Speaker 2: did I do? think I understood or think this is 428 00:24:40,450 --> 00:24:44,110 Speaker 2: the way the world operates and I might be wrong. 429 00:24:44,410 --> 00:24:48,169 Speaker 2: And I give opportunity in my better days again for 430 00:24:48,190 --> 00:24:53,750 Speaker 2: the Holy Spirit to convict or correct. And when that occurs, 431 00:24:54,390 --> 00:25:00,979 Speaker 2: those muscles, those relationship muscles get used and exercised. And 432 00:25:01,440 --> 00:25:05,180 Speaker 2: I'm not that, at least I'm putting off that big 433 00:25:05,300 --> 00:25:13,130 Speaker 2: flabby Wally person. of relationship immobility. Wow. So that's just 434 00:25:13,170 --> 00:25:17,340 Speaker 2: kind of, it feels like that's something to be on 435 00:25:17,490 --> 00:25:18,020 Speaker 2: guard for. 436 00:25:18,780 --> 00:25:22,340 Speaker 1: Against. I wouldn't have thought of it until you just 437 00:25:22,380 --> 00:25:28,100 Speaker 1: said that, but that is right there. The muscles are 438 00:25:28,960 --> 00:25:35,370 Speaker 1: at risk of atrophying because of this tendency to pull 439 00:25:35,470 --> 00:25:41,190 Speaker 1: towards the affirmation and the frictionless relationship that comes with AI. 440 00:25:41,630 --> 00:25:45,020 Speaker 1: And so it may mean, like, do you mind me 441 00:25:45,100 --> 00:25:47,940 Speaker 1: using the example from today again? Sure, sure. It may 442 00:25:48,020 --> 00:25:53,510 Speaker 1: mean that if you were to go, if we hadn't 443 00:25:53,590 --> 00:25:57,170 Speaker 1: talked about this on the podcast... That you might go, well, 444 00:25:57,230 --> 00:25:59,129 Speaker 1: you know what? I just won't tell Shanti what I 445 00:25:59,190 --> 00:26:01,359 Speaker 1: did all day today. 446 00:26:01,390 --> 00:26:04,400 Speaker 2: Which is something that has run through my mind from 447 00:26:04,460 --> 00:26:06,640 Speaker 2: time to time. No, no, no. I mean, it is. 448 00:26:07,140 --> 00:26:10,399 Speaker 2: If it's me, I'm thinking other listeners have this same thing. 449 00:26:10,420 --> 00:26:11,080 Speaker 1: Absolutely. 450 00:26:11,100 --> 00:26:12,859 Speaker 2: So that's why I'm saying it. For sure. 451 00:26:12,920 --> 00:26:17,160 Speaker 1: But I mean, that's an example of where you pull 452 00:26:17,220 --> 00:26:20,850 Speaker 1: back from the harder things. Where you're going to have 453 00:26:20,890 --> 00:26:24,730 Speaker 1: to use those relationship muscles because it's just easier to 454 00:26:25,270 --> 00:26:27,429 Speaker 1: pull back. Now, that doesn't mean you're pulling back in 455 00:26:27,609 --> 00:26:31,010 Speaker 1: favor of AI. Right. But it might mean you get 456 00:26:31,310 --> 00:26:34,510 Speaker 1: used to it, having worked all day with chat GPT, 457 00:26:34,550 --> 00:26:37,840 Speaker 1: being all agreeable, great idea, great idea, great idea. And 458 00:26:37,859 --> 00:26:40,139 Speaker 1: then your wife says, but what about? Yep. 459 00:26:40,359 --> 00:26:40,619 Speaker 2: Right? 460 00:26:40,780 --> 00:26:43,300 Speaker 1: And it's just you'd have to be. 461 00:26:43,500 --> 00:26:43,780 Speaker 2: Right. 462 00:26:43,880 --> 00:26:46,200 Speaker 1: You would have to be a robot for that to not. 463 00:26:46,480 --> 00:26:50,040 Speaker 2: Yep. Affect you. Yep. I think one of the things 464 00:26:50,119 --> 00:26:55,800 Speaker 2: that we have to realize is that when I'm engaging 465 00:26:55,859 --> 00:27:03,250 Speaker 2: with AI, it costs AI absolutely nothing to have that 466 00:27:04,350 --> 00:27:08,530 Speaker 2: answer that you're doing great or that's a good thought. 467 00:27:08,570 --> 00:27:09,790 Speaker 1: You mean to be affirming. 468 00:27:09,850 --> 00:27:12,270 Speaker 2: To be affirming. It's a machine. 469 00:27:12,430 --> 00:27:13,710 Speaker 1: It doesn't cost AI anything. 470 00:27:13,730 --> 00:27:16,460 Speaker 2: It's ones and zeros and it's just putting that out there. 471 00:27:17,260 --> 00:27:22,260 Speaker 2: When you're in an actual relationship with another person, particularly 472 00:27:22,300 --> 00:27:25,720 Speaker 2: a spouse, when you engage with them in a way, 473 00:27:25,980 --> 00:27:31,220 Speaker 2: their response in how they communicate back to you may 474 00:27:31,280 --> 00:27:36,369 Speaker 2: be costing them something. They may be having a terrible day. 475 00:27:36,990 --> 00:27:41,230 Speaker 2: They may be having, you know, whatever, some fear, some uncertainty. 476 00:27:42,190 --> 00:27:47,140 Speaker 2: And you ask something that triggers that. that uncertainty, that 477 00:27:47,260 --> 00:27:52,219 Speaker 2: fear in a way that they would respond in a short, curt, 478 00:27:52,680 --> 00:27:58,750 Speaker 2: perhaps what you would perceive as hurtful manner. And so therefore, 479 00:27:59,010 --> 00:28:02,430 Speaker 2: you're kind of upset with it. But in the case 480 00:28:02,490 --> 00:28:07,170 Speaker 2: where the person who's had that bad day chooses, okay, 481 00:28:07,630 --> 00:28:10,290 Speaker 2: I'm not going to respond in the way that I'm feeling. 482 00:28:10,590 --> 00:28:13,629 Speaker 2: I'm going to respond with kindness. I'm going to respond 483 00:28:13,830 --> 00:28:20,600 Speaker 2: with curiosity or thoughtfulness, that cost that person something because 484 00:28:20,660 --> 00:28:25,699 Speaker 2: their natural, natural, their reflex in that moment was to 485 00:28:25,760 --> 00:28:30,320 Speaker 2: say something perhaps hard, harsh. 486 00:28:31,160 --> 00:28:32,000 Speaker 1: Use a tone. 487 00:28:32,320 --> 00:28:36,159 Speaker 2: Use a tone, but they chose not to. And I 488 00:28:36,220 --> 00:28:40,710 Speaker 2: don't think that we recognize that adequately enough as humans 489 00:28:40,770 --> 00:28:44,900 Speaker 2: in our relationships that when we're engaging with someone, It 490 00:28:45,100 --> 00:28:46,520 Speaker 2: always costs them something. 491 00:28:47,740 --> 00:28:49,040 Speaker 1: Doesn't cost AI anything. 492 00:28:49,060 --> 00:28:50,100 Speaker 2: Doesn't cost anything. 493 00:28:50,500 --> 00:28:51,060 Speaker 1: Interesting. 494 00:28:51,080 --> 00:28:55,690 Speaker 2: To give that affirming kind of response. 495 00:28:55,910 --> 00:29:00,390 Speaker 1: Yeah. And so we're not, in other words, not only 496 00:29:00,410 --> 00:29:03,630 Speaker 1: are we more likely to enjoy that affirming response from AI, 497 00:29:04,060 --> 00:29:07,180 Speaker 1: we're less likely to give credit where credit is due. 498 00:29:07,280 --> 00:29:07,820 Speaker 2: Correct. 499 00:29:07,900 --> 00:29:09,840 Speaker 1: To our spouse or the other human. 500 00:29:09,940 --> 00:29:10,720 Speaker 2: That's the part. 501 00:29:11,000 --> 00:29:11,300 Speaker 1: Wow. 502 00:29:11,340 --> 00:29:15,920 Speaker 2: We should be attuned, more attuned. I should be more 503 00:29:15,940 --> 00:29:21,420 Speaker 2: attuned that you are responding in a way that you 504 00:29:21,480 --> 00:29:25,220 Speaker 2: are measuring it. You are in those cases where you are, 505 00:29:26,010 --> 00:29:29,170 Speaker 2: you know, not harsh or curt, even though you've had 506 00:29:29,210 --> 00:29:32,030 Speaker 2: a terrible day. I just kind of think like, well, 507 00:29:32,250 --> 00:29:34,650 Speaker 2: I deserve that kind response. 508 00:29:34,850 --> 00:29:38,990 Speaker 1: Well, if I can, again, back to our conversation earlier today, 509 00:29:39,210 --> 00:29:41,920 Speaker 1: if I could just be honest about this, see, we're 510 00:29:41,940 --> 00:29:44,540 Speaker 1: going to have all of our closest, several thousand of 511 00:29:44,600 --> 00:29:48,420 Speaker 1: our closest friends listening in on this. But to be candid, 512 00:29:48,480 --> 00:29:51,240 Speaker 1: when you started talking about all the changes you were making, 513 00:29:52,200 --> 00:29:55,090 Speaker 1: I started slight, like my fear kicked in. I'm like, 514 00:29:55,120 --> 00:29:58,300 Speaker 1: wait a minute, you know, Samantha, our digital contact manager, 515 00:29:58,920 --> 00:30:02,890 Speaker 1: she's out of town. She knows these things. She's gone 516 00:30:02,970 --> 00:30:05,780 Speaker 1: for a week. She's on this amazing vacation. I'm totally jealous. 517 00:30:06,300 --> 00:30:09,360 Speaker 1: But She's gone, and I'm like, wait, you're making all 518 00:30:09,380 --> 00:30:13,420 Speaker 1: these changes, and she's in Spain and can't, like, respond, 519 00:30:13,480 --> 00:30:14,910 Speaker 1: and did you ask so-and-so? 520 00:30:15,670 --> 00:30:17,450 Speaker 2: Does the website even work now? 521 00:30:17,570 --> 00:30:19,150 Speaker 1: But did I say any of that? 522 00:30:19,250 --> 00:30:19,890 Speaker 2: No, you didn't. 523 00:30:20,050 --> 00:30:24,430 Speaker 1: I didn't because I thought, I literally thought to myself, 524 00:30:24,510 --> 00:30:25,810 Speaker 1: Jeff knows what he's doing. 525 00:30:25,970 --> 00:30:27,290 Speaker 2: Well, I haven't checked the website. 526 00:30:28,050 --> 00:30:31,630 Speaker 1: Oh, okay. We will find that out eventually, I suppose. 527 00:30:32,260 --> 00:30:35,600 Speaker 1: But I literally didn't say anything, but it did. It 528 00:30:35,660 --> 00:30:40,210 Speaker 1: was because of me going, I appreciate my husband. He's 529 00:30:40,270 --> 00:30:43,410 Speaker 1: spending all these hours on this. He's the one who's 530 00:30:43,430 --> 00:30:47,410 Speaker 1: done all this research. And I literally didn't say any 531 00:30:47,490 --> 00:30:51,030 Speaker 1: of those things that were in my mind. And then 532 00:30:51,190 --> 00:30:53,510 Speaker 1: you got done with what you were saying. And I 533 00:30:53,610 --> 00:30:57,580 Speaker 1: asked the one question of, Is there any and I 534 00:30:57,620 --> 00:30:59,460 Speaker 1: can't remember how I worded it, but I worded it 535 00:30:59,740 --> 00:31:04,770 Speaker 1: very carefully so that I wasn't accusatory, but said something like, 536 00:31:05,270 --> 00:31:08,230 Speaker 1: what do you think about having switched to a different 537 00:31:08,290 --> 00:31:11,450 Speaker 1: system for these Facebook ads? Because you said, you know, 538 00:31:11,490 --> 00:31:13,440 Speaker 1: we've switched to a different system. What do you think 539 00:31:13,520 --> 00:31:17,790 Speaker 1: about switching to a different system? And it. Again, if 540 00:31:17,830 --> 00:31:20,600 Speaker 1: you had a colleague named Shanti ask that, you would 541 00:31:20,630 --> 00:31:21,260 Speaker 1: have been fine. 542 00:31:21,390 --> 00:31:22,480 Speaker 2: And I would have, yeah. 543 00:31:22,760 --> 00:31:25,180 Speaker 1: But with your wife, there was maybe a little bit 544 00:31:25,220 --> 00:31:27,420 Speaker 1: of frustration. I'm going to go downstairs and talk to 545 00:31:27,520 --> 00:31:32,800 Speaker 1: AI some more, perhaps. But you didn't recognize that that 546 00:31:32,860 --> 00:31:34,300 Speaker 1: did actually cost me something. 547 00:31:34,320 --> 00:31:34,900 Speaker 2: No, it's exactly right. 548 00:31:34,940 --> 00:31:37,340 Speaker 1: I didn't say what I immediately wanted to say. 549 00:31:37,820 --> 00:31:38,880 Speaker 2: Right, right. 550 00:31:38,900 --> 00:31:40,640 Speaker 1: Not that I deserve a pat on the back. 551 00:31:40,660 --> 00:31:47,310 Speaker 2: No, but again, that is human relationships. We don't recognize 552 00:31:47,970 --> 00:31:52,290 Speaker 2: what our engagement with one another at times costs the other. 553 00:31:53,290 --> 00:31:57,560 Speaker 2: We just think that, well, they're pleasant because I deserve 554 00:31:58,520 --> 00:31:59,360 Speaker 2: that they're pleasant. 555 00:31:59,920 --> 00:32:03,440 Speaker 1: I deserve all the affirmation that I receive. But, okay, 556 00:32:03,640 --> 00:32:06,280 Speaker 1: you mentioned something when we were talking about this yesterday 557 00:32:06,300 --> 00:32:09,610 Speaker 1: that cracked me up. I don't know where this memory 558 00:32:09,720 --> 00:32:13,550 Speaker 1: of yours came from. But both of us, so I 559 00:32:13,610 --> 00:32:16,240 Speaker 1: was not really... I grew up in a household that 560 00:32:16,280 --> 00:32:20,970 Speaker 1: didn't watch a lot of television. Mysteriously, my dad was 561 00:32:21,190 --> 00:32:24,690 Speaker 1: totally fine with me watching Charlie's Angels every Thursday night. 562 00:32:24,770 --> 00:32:26,030 Speaker 2: I wonder why. I know. 563 00:32:26,070 --> 00:32:29,200 Speaker 1: It was like so sweet of him. Very sweet. Yes, exactly. 564 00:32:29,600 --> 00:32:32,320 Speaker 1: But my parents, the one TV show that they did 565 00:32:32,520 --> 00:32:35,219 Speaker 1: consistently watch was Star Trek. And so I grew up 566 00:32:35,290 --> 00:32:38,850 Speaker 1: a total Trekkie. Like, I remember all the episodes. 567 00:32:38,930 --> 00:32:42,210 Speaker 2: And just so, this is the original Captain Kirk. 568 00:32:42,230 --> 00:32:45,050 Speaker 1: Yes, the original William Shatner. 569 00:32:45,570 --> 00:32:50,470 Speaker 2: So I will say that the years of a misspent 570 00:32:50,650 --> 00:32:56,980 Speaker 2: youth on my part, I watched multiple episodes of Star 571 00:32:57,040 --> 00:33:01,000 Speaker 2: Trek reruns. Probably every day. 572 00:33:01,520 --> 00:33:02,000 Speaker 1: Really? 573 00:33:02,080 --> 00:33:03,040 Speaker 2: At least two episodes. 574 00:33:03,060 --> 00:33:04,040 Speaker 1: This is new information. 575 00:33:04,060 --> 00:33:05,280 Speaker 2: Yeah, two episodes a day. 576 00:33:05,300 --> 00:33:06,590 Speaker 1: So we were both Trekkies. 577 00:33:06,610 --> 00:33:06,850 Speaker 2: Oh, sure. 578 00:33:06,880 --> 00:33:08,090 Speaker 1: We've never talked about this before. 579 00:33:08,110 --> 00:33:10,530 Speaker 2: Yeah, we haven't. That's so funny. But so anyways, and 580 00:33:10,550 --> 00:33:13,110 Speaker 2: then the weird memory thing comes in. 581 00:33:13,130 --> 00:33:14,570 Speaker 1: That you have a crazy memory. 582 00:33:14,750 --> 00:33:18,830 Speaker 2: I remember episodes pretty clearly. And so in this one 583 00:33:18,890 --> 00:33:22,470 Speaker 2: particular episode, there was a character named Harry Mudd. Harry 584 00:33:22,490 --> 00:33:24,930 Speaker 2: Mudd was a nemesis of James T. 585 00:33:24,970 --> 00:33:25,530 Speaker 1: Kirk. Yes, yes. 586 00:33:25,630 --> 00:33:29,790 Speaker 2: He was kind of like a space con man. And 587 00:33:29,890 --> 00:33:34,710 Speaker 2: Harry Mudd, you know, at one point he lured the 588 00:33:34,790 --> 00:33:40,850 Speaker 2: Enterprise to an orbit around this one particular planet that 589 00:33:41,290 --> 00:33:45,230 Speaker 2: Harry Mudd was on. And Harry Mudd on this planet 590 00:33:45,670 --> 00:33:52,290 Speaker 2: was like the king. And there were thousands of female, 591 00:33:52,690 --> 00:33:59,460 Speaker 2: beautiful female androids. Who were there for his every whim 592 00:33:59,840 --> 00:34:05,100 Speaker 2: and interest to be satisfied and to agree with him 593 00:34:05,480 --> 00:34:07,620 Speaker 2: on everything. Everything. 594 00:34:07,700 --> 00:34:08,980 Speaker 1: Constant affirmation. 595 00:34:09,040 --> 00:34:12,340 Speaker 2: Constant affirmation. And actually, as I was thinking about it, 596 00:34:12,680 --> 00:34:16,430 Speaker 2: there was actually at one point that he was annoyed 597 00:34:16,670 --> 00:34:22,170 Speaker 2: at that. So he actually had another android created of 598 00:34:22,310 --> 00:34:24,740 Speaker 2: his estranged wife. Oh. 599 00:34:25,489 --> 00:34:26,430 Speaker 1: I didn't remember this. 600 00:34:26,489 --> 00:34:30,410 Speaker 2: Yeah. All she did was nag him. But he could say, 601 00:34:30,430 --> 00:34:33,310 Speaker 2: be quiet. And she would. 602 00:34:33,510 --> 00:34:33,970 Speaker 1: Okay. 603 00:34:34,050 --> 00:34:37,129 Speaker 2: So that was his whim. That was satisfied there. But 604 00:34:37,210 --> 00:34:45,130 Speaker 2: what happened was he became so dissatisfied with always having 605 00:34:45,630 --> 00:34:47,730 Speaker 2: everyone agree with everything. 606 00:34:47,790 --> 00:34:51,009 Speaker 1: And insincerely. Because these were automated beings. 607 00:34:51,070 --> 00:34:52,069 Speaker 2: They were programmed to. 608 00:34:52,130 --> 00:34:52,410 Speaker 1: Yes. 609 00:34:52,830 --> 00:34:56,049 Speaker 2: And so... This was not genuine. He wanted to leave 610 00:34:56,090 --> 00:34:59,390 Speaker 2: the planet, but the androids wouldn't let him unless he 611 00:34:59,450 --> 00:35:04,150 Speaker 2: found replacement humans that they could serve in this same capacity. 612 00:35:04,510 --> 00:35:09,310 Speaker 2: And so anyways, in all Star Trek fashion, Kirk and 613 00:35:09,330 --> 00:35:11,930 Speaker 2: Spock figure out how to get out of this dilemma 614 00:35:12,510 --> 00:35:17,320 Speaker 2: and save the Enterprise and cause Harry Mudd to stay 615 00:35:17,600 --> 00:35:23,220 Speaker 2: on that planet. But they turned all of those obsequious, approving... 616 00:35:23,200 --> 00:35:28,240 Speaker 2: androids into the same version as his estranged wife. 617 00:35:28,500 --> 00:35:28,739 Speaker 1: Oh dear. 618 00:35:28,760 --> 00:35:31,319 Speaker 2: So anyways, that's how they left the thing. But the 619 00:35:31,400 --> 00:35:35,560 Speaker 2: fact of the matter was, on this episode, this guy 620 00:35:35,640 --> 00:35:41,940 Speaker 2: had what you would think is your dream world. Everything 621 00:35:41,980 --> 00:35:45,480 Speaker 2: that you say is agreed with. Everything that you want 622 00:35:45,719 --> 00:35:46,480 Speaker 2: is satisfied. 623 00:35:46,500 --> 00:35:47,800 Speaker 1: And flattery constantly. 624 00:35:47,820 --> 00:35:54,190 Speaker 2: And flattery constant. And yet, it was so... Soul numbing 625 00:35:54,489 --> 00:35:57,850 Speaker 2: for him that he needed to find a way to escape. 626 00:35:58,450 --> 00:36:01,290 Speaker 1: It's so interesting. And it's it's so good for us 627 00:36:01,370 --> 00:36:07,100 Speaker 1: to remember that, like when you want to instinctively go 628 00:36:07,219 --> 00:36:11,879 Speaker 1: for that constant affirmation, you get instinctively annoyed. that your 629 00:36:11,980 --> 00:36:17,640 Speaker 1: spouse doesn't operate like AI, to remind yourself, I don't 630 00:36:17,660 --> 00:36:21,180 Speaker 1: really want that anyway. Like, I want the real person, 631 00:36:21,200 --> 00:36:25,190 Speaker 1: the real love, the real respect, the real pushback even. 632 00:36:25,330 --> 00:36:27,410 Speaker 2: And the only way that you can know that it's 633 00:36:27,590 --> 00:36:32,250 Speaker 2: real is if your will can be crossed at any time. 634 00:36:32,570 --> 00:36:38,220 Speaker 2: And that's honestly, I think, why our God has given 635 00:36:38,540 --> 00:36:43,320 Speaker 2: us free will. If we were those androids that simply 636 00:36:43,360 --> 00:36:48,520 Speaker 2: had to love God no matter what, you know, that's 637 00:36:48,600 --> 00:36:49,799 Speaker 2: not satisfying. 638 00:36:49,820 --> 00:36:50,660 Speaker 1: And it's not real. 639 00:36:50,800 --> 00:36:52,700 Speaker 2: It's not real. It's forced. 640 00:36:52,940 --> 00:36:59,600 Speaker 1: Yeah. It's interesting. All of this is based on– because 641 00:36:59,640 --> 00:37:01,010 Speaker 1: I want to move to the what do we do. 642 00:37:01,090 --> 00:37:01,760 Speaker 2: Yeah. Right? 643 00:37:02,219 --> 00:37:06,210 Speaker 1: But just a final observation. All of this is based 644 00:37:06,550 --> 00:37:13,750 Speaker 1: on this– The fact that over millions and millions and 645 00:37:13,810 --> 00:37:18,480 Speaker 1: millions of documents and all the input that these large 646 00:37:18,540 --> 00:37:21,840 Speaker 1: language models, these AIs have been trained on. 647 00:37:22,440 --> 00:37:26,480 Speaker 2: All of human content, all of the. 648 00:37:26,430 --> 00:37:29,250 Speaker 1: Human content that's ever been written, that's been trained on, 649 00:37:29,950 --> 00:37:33,319 Speaker 1: this is what AIs have learned. That I need to 650 00:37:33,380 --> 00:37:36,890 Speaker 1: be affirming. I need to be obsequious. Humans like this. 651 00:37:37,050 --> 00:37:37,590 Speaker 2: Correct. 652 00:37:37,690 --> 00:37:40,770 Speaker 1: I mean, yeah, there's some programming there, but it's not 653 00:37:40,950 --> 00:37:45,469 Speaker 1: because some programmer somewhere said, you need to be obsequious. 654 00:37:45,530 --> 00:37:48,510 Speaker 1: Humans will like that. No, these are learning models. 655 00:37:48,530 --> 00:37:48,770 Speaker 2: Correct. 656 00:37:48,790 --> 00:37:50,010 Speaker 1: They have learned this. 657 00:37:50,290 --> 00:37:51,150 Speaker 2: That's fascinating. 658 00:37:51,610 --> 00:37:54,010 Speaker 1: Well, you were the one who first brought that up, actually. 659 00:37:54,070 --> 00:37:55,100 Speaker 2: When we were talking about this. That's why I said 660 00:37:55,120 --> 00:37:55,840 Speaker 2: it was fascinating. 661 00:37:58,980 --> 00:38:01,040 Speaker 1: You're so good, Jeff. Thank you. Yes. 662 00:38:01,140 --> 00:38:01,379 Speaker 2: Okay. 663 00:38:01,719 --> 00:38:04,779 Speaker 1: So what do we do? Let's move. This is a 664 00:38:04,840 --> 00:38:07,000 Speaker 1: perfect example of moving to the what do we do 665 00:38:07,020 --> 00:38:09,480 Speaker 1: about all this. I think when we were talking about 666 00:38:09,520 --> 00:38:13,760 Speaker 1: this yesterday, getting ready for this podcast, really it comes 667 00:38:13,820 --> 00:38:20,820 Speaker 1: into two different categories. What we do, there's two factors. A, 668 00:38:21,030 --> 00:38:24,650 Speaker 1: we have to realize that. That we do have those 669 00:38:24,810 --> 00:38:29,489 Speaker 1: expectations that we're being drawn towards all that affirmation. And 670 00:38:29,530 --> 00:38:33,029 Speaker 1: we have to be consistently aware of it and adjusting 671 00:38:33,090 --> 00:38:36,310 Speaker 1: our expectations. So we'll talk about that in a second. 672 00:38:36,830 --> 00:38:40,330 Speaker 1: But the second thing is, huh, maybe we should be 673 00:38:40,590 --> 00:38:44,310 Speaker 1: modeling a little bit more of this affirming behavior ourselves. 674 00:38:44,590 --> 00:38:48,310 Speaker 1: So one at a time. Let's talk about those. So 675 00:38:48,350 --> 00:38:50,430 Speaker 1: the first one is we need to be more aware 676 00:38:50,730 --> 00:38:55,390 Speaker 1: of How we're being drawn in and adjust our expectations. 677 00:38:55,510 --> 00:39:00,320 Speaker 2: So step one, remind yourself that you're talking to a machine. Yes. 678 00:39:00,460 --> 00:39:02,440 Speaker 1: You're talking to a machine. Harry Mudd was talking to 679 00:39:02,520 --> 00:39:03,940 Speaker 1: all these androids. 680 00:39:04,219 --> 00:39:06,879 Speaker 2: And it doesn't get its feelings hurt when I say 681 00:39:06,940 --> 00:39:10,799 Speaker 2: something short or curt or rude. Right. 682 00:39:10,860 --> 00:39:14,660 Speaker 1: Yes. And you're talking to the machine for the purpose of- 683 00:39:15,180 --> 00:39:15,840 Speaker 1: Gathering information. 684 00:39:15,880 --> 00:39:16,460 Speaker 2: Information. 685 00:39:16,560 --> 00:39:20,410 Speaker 1: Information. So then you also have to remind yourself when 686 00:39:20,430 --> 00:39:24,379 Speaker 1: you're talking to people, remind yourself you're not talking to 687 00:39:24,400 --> 00:39:27,330 Speaker 1: a machine. They might get their feelings hurt. And for 688 00:39:27,370 --> 00:39:29,969 Speaker 1: the most part, hey, guess what? The reason you're talking. 689 00:39:30,239 --> 00:39:33,299 Speaker 1: to your spouse or your child or your colleague, it's 690 00:39:33,440 --> 00:39:38,640 Speaker 1: not just about wanting information. I want that person to 691 00:39:38,780 --> 00:39:40,080 Speaker 1: feel a certain way. 692 00:39:40,239 --> 00:39:44,700 Speaker 2: Right. And I want to feel a certain way about them. Yes, exactly. 693 00:39:45,060 --> 00:39:50,650 Speaker 2: And so I need that interaction in a way that 694 00:39:51,450 --> 00:39:57,630 Speaker 2: reinforces this need that I have for relationship. 695 00:39:57,810 --> 00:40:02,630 Speaker 1: Right. So another piece of this, so there's maybe a 696 00:40:02,650 --> 00:40:06,989 Speaker 1: step two, is we have to, and this is, I think, 697 00:40:07,050 --> 00:40:09,830 Speaker 1: important for everybody no matter what, but especially in the 698 00:40:09,989 --> 00:40:15,430 Speaker 1: age of AI, we cannot mistake affirmation for truth. Just 699 00:40:15,510 --> 00:40:18,719 Speaker 1: because a machine says, that's a really good idea, Jeff, 700 00:40:19,580 --> 00:40:22,700 Speaker 1: It doesn't mean that it's a really good idea, right? 701 00:40:22,739 --> 00:40:25,859 Speaker 1: Like just because the machine told me, that's why I've 702 00:40:25,880 --> 00:40:30,029 Speaker 1: been following your research for years. That's not truth, right? 703 00:40:30,090 --> 00:40:33,650 Speaker 1: It has not been, right? It just felt good saying 704 00:40:33,710 --> 00:40:37,350 Speaker 1: that and hearing that, right? So don't mistake that for like, 705 00:40:37,750 --> 00:40:41,739 Speaker 1: recognize this may not be true, right? It's designed to 706 00:40:41,780 --> 00:40:44,680 Speaker 1: make me to want to interact with it. And then 707 00:40:44,719 --> 00:40:47,779 Speaker 1: maybe a step three, there's several steps. A step three, 708 00:40:47,840 --> 00:40:54,919 Speaker 1: I would say is, Really don't avoid the difficult– don't 709 00:40:55,080 --> 00:41:00,700 Speaker 1: use AI to avoid those difficult conversations. It's not a replacement. 710 00:41:00,800 --> 00:41:04,680 Speaker 2: It's not. And this gets us back to that WALL-E 711 00:41:04,700 --> 00:41:09,020 Speaker 2: movie and the people. Yes, yes, yes. If we become 712 00:41:09,520 --> 00:41:14,839 Speaker 2: avoidant of those difficult conversations, we are losing the relational 713 00:41:14,900 --> 00:41:19,560 Speaker 2: muscles that we need to develop. That ability– To walk 714 00:41:19,620 --> 00:41:21,160 Speaker 2: humbly before your God. 715 00:41:21,380 --> 00:41:21,680 Speaker 1: Yeah. 716 00:41:21,940 --> 00:41:23,660 Speaker 2: We lose that ability. 717 00:41:23,820 --> 00:41:26,540 Speaker 1: Yeah. And we can't. 718 00:41:26,680 --> 00:41:26,920 Speaker 2: We can't. 719 00:41:27,260 --> 00:41:29,860 Speaker 1: We have to be careful to not lose those muscles. 720 00:41:30,239 --> 00:41:32,860 Speaker 1: And one way to maybe do that, maybe another step, 721 00:41:33,440 --> 00:41:38,390 Speaker 1: is you can actually adjust your AI settings. Like with ChatGPT, 722 00:41:39,010 --> 00:41:41,469 Speaker 1: there's a way you can adjust it so that you 723 00:41:41,510 --> 00:41:44,569 Speaker 1: tell it to be more like pushback. And you can 724 00:41:44,630 --> 00:41:48,170 Speaker 1: tell it. I need you to push back instead of 725 00:41:48,230 --> 00:41:51,730 Speaker 1: being automatically affirming. I need you to ask what I 726 00:41:51,790 --> 00:41:57,529 Speaker 1: might be missing, right? Like what's wrong here? And in 727 00:41:57,550 --> 00:42:00,710 Speaker 1: the show notes, we'll put a little– just a little 728 00:42:00,730 --> 00:42:03,940 Speaker 1: tutorial on how to change the settings on like I 729 00:42:03,980 --> 00:42:08,359 Speaker 1: think just chat GPT. And then finally, a big one 730 00:42:08,620 --> 00:42:12,560 Speaker 1: for this first step, I think a big one is 731 00:42:12,840 --> 00:42:17,380 Speaker 1: that we– We really have to be careful about– I 732 00:42:18,469 --> 00:42:21,130 Speaker 1: know maybe it's not something that I've wrestled with, but 733 00:42:21,150 --> 00:42:26,549 Speaker 1: I know people do. Relying on AI for answers that 734 00:42:26,590 --> 00:42:31,050 Speaker 1: you really should go to God for directly and remembering 735 00:42:31,710 --> 00:42:34,210 Speaker 1: you're not going to necessarily get those answers right away. 736 00:42:34,450 --> 00:42:37,090 Speaker 1: Like AI works for us. God doesn't. 737 00:42:37,590 --> 00:42:42,040 Speaker 2: Well, it's like when Jesus talked about the widow and 738 00:42:42,060 --> 00:42:42,980 Speaker 2: the unjust judge. 739 00:42:43,340 --> 00:42:43,720 Speaker 1: Okay. 740 00:42:43,980 --> 00:42:48,790 Speaker 2: So– You know, she persistently kept coming back and back. 741 00:42:49,170 --> 00:42:52,850 Speaker 2: She didn't get the answer she wanted initially, but she 742 00:42:52,870 --> 00:42:55,230 Speaker 2: kept coming back. And finally, the unjust judge. And our 743 00:42:55,290 --> 00:42:58,870 Speaker 2: God is not an unjust judge. But he, you know, 744 00:42:58,950 --> 00:43:01,910 Speaker 2: Jesus was making this point that, you know, you keep 745 00:43:02,010 --> 00:43:06,310 Speaker 2: on knocking, you keep on asking. And, you know, at 746 00:43:06,410 --> 00:43:09,600 Speaker 2: some point. You will be able to, the door will 747 00:43:09,620 --> 00:43:09,960 Speaker 2: be open. 748 00:43:10,000 --> 00:43:11,460 Speaker 1: And you don't have to do that with AI. 749 00:43:11,500 --> 00:43:11,820 Speaker 2: Correct. 750 00:43:11,840 --> 00:43:15,020 Speaker 1: But you do have to force yourself to continue to 751 00:43:15,100 --> 00:43:16,620 Speaker 1: go to God with those things. 752 00:43:16,640 --> 00:43:17,580 Speaker 2: To not lose heart. Yeah. 753 00:43:18,020 --> 00:43:20,609 Speaker 1: Okay, so then the next, so that's the first piece, 754 00:43:20,620 --> 00:43:24,110 Speaker 1: is change our expectations, how we interact with one another. 755 00:43:24,130 --> 00:43:28,090 Speaker 1: But then the second point that we said as a 756 00:43:28,190 --> 00:43:30,650 Speaker 1: to-do is, huh, maybe. 757 00:43:30,730 --> 00:43:34,629 Speaker 2: Can we learn something from AI, how the model actually works? 758 00:43:36,000 --> 00:43:39,890 Speaker 1: Maybe we should learn to be a bit more affirming. 759 00:43:40,010 --> 00:43:42,049 Speaker 2: You know, I mean, honestly, I think I told this 760 00:43:42,090 --> 00:43:46,069 Speaker 2: to you, that in the past weeks and months that 761 00:43:46,110 --> 00:43:49,790 Speaker 2: I've been doing, you know, using AI and using it 762 00:43:49,830 --> 00:43:55,660 Speaker 2: to brainstorm, using it to gather information quickly on things. 763 00:43:56,900 --> 00:44:01,860 Speaker 2: And when it supplies that information or those ideas, I 764 00:44:01,960 --> 00:44:08,750 Speaker 2: oftentimes will type That's great. Thank you for that. Okay. 765 00:44:08,890 --> 00:44:12,290 Speaker 2: I know that I'm talking to a machine. It doesn't 766 00:44:12,410 --> 00:44:14,370 Speaker 2: need my affirmation. 767 00:44:14,670 --> 00:44:16,089 Speaker 1: In order to do a good job next time. 768 00:44:16,110 --> 00:44:18,200 Speaker 2: It's not the next time. It's not going to say, well, 769 00:44:18,360 --> 00:44:21,319 Speaker 2: he wasn't very appreciative, so I'm going to hold back 770 00:44:21,360 --> 00:44:26,300 Speaker 2: a little information. That's not the case. But what I realize, 771 00:44:26,360 --> 00:44:29,739 Speaker 2: what I'm doing, what I think I'm trying to do 772 00:44:30,420 --> 00:44:34,839 Speaker 2: is to build that habit of, telling someone thank you. 773 00:44:35,430 --> 00:44:38,490 Speaker 1: Even though it's something thank you, but it still builds 774 00:44:38,530 --> 00:44:39,610 Speaker 1: a habit in you. 775 00:44:39,650 --> 00:44:43,750 Speaker 2: Doing that. So now when I respond to an email 776 00:44:44,190 --> 00:44:50,299 Speaker 2: or whatever, it's a bit easier to remember to say 777 00:44:50,340 --> 00:44:51,840 Speaker 2: thank you for something. 778 00:44:52,219 --> 00:44:52,680 Speaker 1: That makes sense. 779 00:44:52,700 --> 00:44:56,239 Speaker 2: As opposed to just kind of like, yep, that worked. Yep. 780 00:44:57,290 --> 00:45:00,010 Speaker 1: Yep. That's good. And You know, one of the other 781 00:45:00,070 --> 00:45:02,489 Speaker 1: things you were saying yesterday when we were talking about 782 00:45:02,530 --> 00:45:04,590 Speaker 1: this that I thought was such a good point. See, 783 00:45:04,750 --> 00:45:05,150 Speaker 1: I'm trying. 784 00:45:05,190 --> 00:45:07,410 Speaker 2: Yeah, you're doing. You're very affirming. 785 00:45:07,850 --> 00:45:10,590 Speaker 1: But but no, it really was is you were you 786 00:45:10,630 --> 00:45:13,730 Speaker 1: were also pointing out that there is a certain category 787 00:45:13,830 --> 00:45:16,989 Speaker 1: of person. And I could definitely be in this category 788 00:45:17,160 --> 00:45:21,799 Speaker 1: if I let myself where we have strong personalities and 789 00:45:22,100 --> 00:45:25,880 Speaker 1: we express things in a strong way. And it's like, 790 00:45:25,920 --> 00:45:29,170 Speaker 1: that's just the way I talk. like deal with it. 791 00:45:29,370 --> 00:45:32,690 Speaker 2: Yeah, that's not you, but you say you could be. 792 00:45:32,830 --> 00:45:34,750 Speaker 1: I've worked for that not to be me, but it 793 00:45:34,890 --> 00:45:38,790 Speaker 1: could be if I let it. And that's one thing 794 00:45:38,810 --> 00:45:40,770 Speaker 1: we talk about a lot in our marriage events, that 795 00:45:40,810 --> 00:45:43,670 Speaker 1: kind of thing, is like let's just be honest about 796 00:45:43,710 --> 00:45:47,700 Speaker 1: the fact that in marriages and really in any relationship, 797 00:45:47,760 --> 00:45:50,140 Speaker 1: we don't want people to be brutally honest. We want 798 00:45:50,160 --> 00:45:52,100 Speaker 1: them to be honest but not brutal. We want them 799 00:45:52,120 --> 00:45:56,220 Speaker 1: to be honest and kind. And if your AI talked 800 00:45:56,360 --> 00:45:59,150 Speaker 1: to you In the way you talk to other people, 801 00:45:59,430 --> 00:46:00,390 Speaker 1: would you like that? 802 00:46:00,530 --> 00:46:01,490 Speaker 2: Probably not. 803 00:46:01,590 --> 00:46:06,480 Speaker 1: Probably not. And so it's basically like, okay, can I 804 00:46:06,780 --> 00:46:11,660 Speaker 1: use this model that has, again, developed this way over 805 00:46:12,280 --> 00:46:16,080 Speaker 1: millions and millions and trillions of processing hours that has 806 00:46:16,160 --> 00:46:20,490 Speaker 1: figured out what humans need and recognizing that it has 807 00:46:20,550 --> 00:46:22,890 Speaker 1: figured out that humans need affirmation. 808 00:46:22,910 --> 00:46:22,980 Speaker 2: Yep. 809 00:46:23,690 --> 00:46:26,410 Speaker 1: It doesn't mean we need to be sycophantic. Again, I 810 00:46:26,450 --> 00:46:29,819 Speaker 1: said it correctly. It doesn't mean we need to be 811 00:46:29,880 --> 00:46:36,200 Speaker 1: obsequious or overly flattering, but we can genuinely be affirming 812 00:46:36,239 --> 00:46:38,879 Speaker 1: to one another. And I think we probably should be. 813 00:46:38,960 --> 00:46:41,750 Speaker 2: And I think that what it really comes down to is, 814 00:46:42,290 --> 00:46:48,250 Speaker 2: at least for me, is awareness of how important that is. 815 00:46:49,969 --> 00:46:52,450 Speaker 2: Up until kind of this stuff– and we've done this 816 00:46:52,530 --> 00:46:54,350 Speaker 2: research for decades. 817 00:46:54,430 --> 00:46:55,330 Speaker 1: Yeah. 818 00:46:55,850 --> 00:46:59,410 Speaker 2: But I just kind of thought, yeah, this is good 819 00:46:59,469 --> 00:47:06,069 Speaker 2: relational hygiene. No, it's something even beyond that. It is– 820 00:47:06,550 --> 00:47:14,450 Speaker 2: this is how all humans kind of through written history– Yes, right. 821 00:47:14,530 --> 00:47:21,130 Speaker 2: Desire a relationship or desire communication. Yeah. Style or whatever 822 00:47:21,150 --> 00:47:23,930 Speaker 2: you want to call it. So I really think that 823 00:47:24,090 --> 00:47:28,660 Speaker 2: it's helped me to go, huh, okay, I need to 824 00:47:28,780 --> 00:47:33,160 Speaker 2: think a bit more about how I engage in my 825 00:47:33,480 --> 00:47:34,780 Speaker 2: human interaction. 826 00:47:34,880 --> 00:47:36,480 Speaker 1: So is that your one thing? 827 00:47:36,500 --> 00:47:39,110 Speaker 2: That would be my one thing. 828 00:47:39,239 --> 00:47:41,890 Speaker 1: Okay. So, you know, everybody who is a regular listener 829 00:47:41,930 --> 00:47:44,270 Speaker 1: knows we always try to say what's the one thing 830 00:47:44,650 --> 00:47:46,730 Speaker 1: at the end that you want somebody to take away 831 00:47:46,770 --> 00:47:47,230 Speaker 1: or remember. 832 00:47:47,330 --> 00:47:48,069 Speaker 2: So that is yours. 833 00:47:48,110 --> 00:47:57,700 Speaker 1: Yours. So mine, honestly, I think for myself and I 834 00:47:57,760 --> 00:48:02,340 Speaker 1: think for probably many of our listeners, the one thing 835 00:48:02,620 --> 00:48:06,569 Speaker 1: that we have to be most attuned to is our 836 00:48:06,670 --> 00:48:12,440 Speaker 1: tendency to expect instant answers now. And that there's no discomfort. 837 00:48:12,480 --> 00:48:16,200 Speaker 1: There's no friction. They're always there. And it will be 838 00:48:16,300 --> 00:48:20,089 Speaker 1: friction in your relationship and in your relationship with God. 839 00:48:20,610 --> 00:48:25,830 Speaker 1: Because again, AI works for us. God does not work 840 00:48:25,890 --> 00:48:31,469 Speaker 1: for us. And so being aware of our tendency towards 841 00:48:31,570 --> 00:48:34,550 Speaker 1: expecting that I think is going to be a incredibly 842 00:48:34,610 --> 00:48:35,870 Speaker 1: important in the years to come. 843 00:48:35,910 --> 00:48:38,549 Speaker 2: I think that's amazing. And I think if I could 844 00:48:38,570 --> 00:48:41,890 Speaker 2: just append one thing on that is not only does 845 00:48:41,930 --> 00:48:44,870 Speaker 2: God not work for us, he loves us, of course, 846 00:48:45,590 --> 00:48:49,610 Speaker 2: but we also have an enemy who has been identified, 847 00:48:50,140 --> 00:48:54,100 Speaker 2: who is seeking to mar the image of God through 848 00:48:54,260 --> 00:48:59,700 Speaker 2: us humans, any opportunity that he can. So to be on. 849 00:48:59,840 --> 00:49:03,040 Speaker 1: Alert for that prowling lion. Yeah. Yeah. 850 00:49:03,860 --> 00:49:05,460 Speaker 2: As a prowling lion. Yeah. 851 00:49:06,160 --> 00:49:07,440 Speaker 1: Yeah. Wow. 852 00:49:07,760 --> 00:49:08,080 Speaker 2: Okay. 853 00:49:08,560 --> 00:49:13,510 Speaker 1: Well, I hope this episode was helpful to everybody. This was, again, 854 00:49:13,570 --> 00:49:16,029 Speaker 1: the first time we're talking about it. It probably will 855 00:49:16,050 --> 00:49:18,319 Speaker 1: not be the last in the years to come. if 856 00:49:18,380 --> 00:49:21,900 Speaker 1: you are not a subscriber to our podcast already please 857 00:49:21,960 --> 00:49:24,359 Speaker 1: go ahead and subscribe to I Wish You Could Hear 858 00:49:24,420 --> 00:49:27,500 Speaker 1: This so you never miss an episode and if you 859 00:49:27,560 --> 00:49:30,899 Speaker 1: know someone who would be encouraged or helped or challenged 860 00:49:31,680 --> 00:49:35,259 Speaker 1: by this conversation please share today's episode with them on 861 00:49:35,320 --> 00:49:38,110 Speaker 1: social media and tag us thanks for being with us 862 00:49:40,260 --> 00:49:42,680 Speaker 1: Thanks for listening to I Wish You Could Hear This. 863 00:49:43,120 --> 00:49:46,840 Speaker 1: Remember to subscribe to our podcast. And as always, forward 864 00:49:46,880 --> 00:49:50,080 Speaker 1: today's audio or video link to a friend, counselor, or 865 00:49:50,140 --> 00:49:56,379 Speaker 1: pastor who would be encouraged. I just want to take 866 00:49:56,420 --> 00:49:59,120 Speaker 1: a second to thank the team at Life Audio for 867 00:49:59,160 --> 00:50:02,339 Speaker 1: their partnership with us on the podcast. If you go 868 00:50:02,390 --> 00:50:06,989 Speaker 1: to lifeaudio.com, you will find dozens of other faith-centered podcasts 869 00:50:07,070 --> 00:50:11,390 Speaker 1: in their network. They've got shows about prayer, Bible study, parenting, 870 00:50:11,790 --> 00:50:12,140 Speaker 1: and more.