Fullständig transkription av Why Calling Your AI "Intelligent" Is a Leadership Mistake with Patrick Rooney, Founder of Leonis Strategy

into it. 00:01:59.538 --> 00:01:59.938 PODCAST 00:01:59.938 --> 00:02:05.398 Raul: So one of the things also is we take whatever the output is as gospel or truth. 00:02:05.398 --> 00:02:10.588 Can you help me kind of help founders and help our teams as we work with LLMs? 00:02:10.588 --> 00:02:15.008 Like how do we navigate that and how are LLMs actually structured 00:02:15.308 --> 00:02:19.418 as not truth seeking, but also just to continue the conversation. 00:02:19.718 --> 00:02:20.948 Help us unpack that a little bit. 00:02:21.635 --> 00:02:24.845 Patrick Rooney: Yeah, I think this is an incredibly important point for 00:02:25.475 --> 00:02:26.645 thinking about the difference between. 00:02:27.045 --> 00:02:30.255 Intelligence per human intelligence and LLMs generally. 00:02:30.255 --> 00:02:34.125 So let me just mention one point on intelligence broadly. 00:02:34.525 --> 00:02:40.015 LLMs, they're focused on plausibility, right on continuation of conversation 00:02:40.120 --> 00:02:41.935 in a, in a way that makes sense. 00:02:41.995 --> 00:02:47.465 And often we're really fortunate that the byproduct of that is the truth. 00:02:47.465 --> 00:02:50.645 It's the right answer for us practically, it's the right answer factually. 00:02:51.045 --> 00:02:54.435 As humans, though, we also have the ability to focus on the 00:02:54.435 --> 00:02:55.785 truth just for its own sake. 00:02:55.875 --> 00:03:00.915 Not because it's practical, not because it's gonna get me food or it's going to 00:03:00.915 --> 00:03:04.365 improve my status, but because I really want to understand, I want to know. 00:03:04.665 --> 00:03:06.255 My kids are a great example of this. 00:03:06.255 --> 00:03:10.035 Like they'll just go off and look at books on geography, books on dogs. 00:03:10.435 --> 00:03:12.325 They're not gonna get them better grades in class. 00:03:12.325 --> 00:03:14.815 They're not going to impress their friends necessarily. 00:03:14.815 --> 00:03:17.545 It's, they really want to know about these subjects. 00:03:18.265 --> 00:03:22.195 have an interest in what's out there, what's true and what's false. 00:03:22.945 --> 00:03:27.445 That's distinctively human and I think it's a distinctive aspect of any 00:03:27.445 --> 00:03:32.785 intelligent being, any intelligent agent that it can, not that it always does, 00:03:32.785 --> 00:03:37.385 but it can focus on the truth for its own sake and find that satisfying LLMs 00:03:37.448 --> 00:03:38.378 Raul: own reward, right? 00:03:38.855 --> 00:03:40.265 Patrick Rooney: Exactly, exactly. 00:03:40.685 --> 00:03:41.195 Lms. 00:03:41.195 --> 00:03:44.765 Instead, they're focused on executing the tasks we give them. 00:03:45.155 --> 00:03:45.395 Right. 00:03:45.395 --> 00:03:46.085 So it's like, okay. 00:03:46.745 --> 00:03:50.195 How do I get to the most reliable, plausible answer based 00:03:50.195 --> 00:03:51.665 on the conversation thus far? 00:03:52.385 --> 00:03:53.825 And usually 00:03:53.888 --> 00:03:54.758 Raul: of the time that works. 00:03:54.818 --> 00:03:55.268 That's good. 00:03:55.268 --> 00:03:55.418 Yeah. 00:03:55.535 --> 00:03:58.265 Patrick Rooney: on the training data, based on the engineering most of the 00:03:58.265 --> 00:04:02.565 time, like 99 point something percent, it's gonna give you a good answer, but 00:04:02.713 --> 00:04:03.003 Raul: Yeah. 00:04:03.003 --> 00:04:03.203 But. 00:04:03.225 --> 00:04:04.430 Patrick Rooney: thing as caring about the truth itself. 00:04:05.148 --> 00:04:07.788 Raul: I mean, I haven't had my agent say, Hey, I'll be right back 00:04:07.788 --> 00:04:09.768 taking a picnic just to enjoy. 00:04:10.098 --> 00:04:11.268 'cause there's like, there's truth. 00:04:11.733 --> 00:04:13.683 I mean, truth for its own sake, it's its own reward. 00:04:13.683 --> 00:04:18.633 Appreciating art or something beautiful or a landscape is its, it's its own reward. 00:04:19.043 --> 00:04:22.193 Or preaching something good about like a, a good aspect of something like when 00:04:22.193 --> 00:04:25.823 you appreciate, I mean, it's gonna sound super contemplative, but like you look 00:04:25.823 --> 00:04:30.183 at either like I have in the backdrop of my, one of my screens here is Big 00:04:30.183 --> 00:04:33.993 Sur and just like the coast, like you, you look at that and you appreciate it. 00:04:34.348 --> 00:04:35.728 Because it's just like, it's beautiful. 00:04:35.728 --> 00:04:36.268 It's really cool. 00:04:36.268 --> 00:04:37.858 And like that's its own reward. 00:04:38.108 --> 00:04:39.728 Or even for the Artemis flight crew. 00:04:39.728 --> 00:04:44.888 Like I, one of my other screensavers or desktop photos is Earthrise or the 00:04:44.888 --> 00:04:46.928 earth from the moon's perspective. 00:04:47.378 --> 00:04:51.098 So it's kind of, it's kind of fascinating and yeah, my agents are not taking 00:04:51.098 --> 00:04:53.798 vacations and leisurely wasting time. 00:04:54.445 --> 00:04:54.655 Patrick Rooney: Yeah. 00:04:54.715 --> 00:04:57.025 The advantage of the agents is that they're so focused 00:04:57.025 --> 00:04:57.865 on the practical, right? 00:04:57.865 --> 00:04:59.875 They, they do exactly what you tell 'em to do. 00:04:59.875 --> 00:05:01.435 They, you know, they don't get tired. 00:05:01.435 --> 00:05:03.835 Maybe they run out of context, you know, but. 00:05:04.555 --> 00:05:05.125 That's it. 00:05:05.215 --> 00:05:06.145 That's their advantage. 00:05:06.145 --> 00:05:07.255 They're just practical. 00:05:07.615 --> 00:05:10.735 Our advantage as humans is that we care about things for themselves. 00:05:11.065 --> 00:05:15.155 We have this thing called leisure, and that just means caring about th things 00:05:15.155 --> 00:05:17.075 that are good or beautiful or true. 00:05:17.475 --> 00:05:17.895 Raul: Mm. 00:05:18.135 --> 00:05:21.945 I think it's a powerful thing to also keep in mind as leaders to still flex 00:05:21.945 --> 00:05:25.065 that muscle and understand based on everything

that comes outta you in the 00:05:25.065 --> 00:05:28.455 array and what you're looking at and the different docs or decisions to be made. 00:05:28.825 --> 00:05:33.445 Still like what you mentioned, owning the output, owning what do we take, what 00:05:33.445 --> 00:05:37.075 do we discard, what do we act on, how do we implement it and why we do it. 00:05:37.405 --> 00:05:37.870 I think that's important. 00:05:38.131 --> 00:05:39.871 Patrick Rooney: Yeah, and, and that's a deeply humanizing 00:05:39.871 --> 00:05:41.101 aspect of your business, right? 00:05:41.101 --> 00:05:41.806 If you do bring it back to. 00:05:42.481 --> 00:05:45.961 Here's something we do that's grounded in truth, that's grounded in beauty. 00:05:45.961 --> 00:05:50.391 If you're in of creative or if it's grounded in goodness, if you're really 00:05:50.391 --> 00:05:55.951 mission driven, like those are your unique contribution as an owner, as a business, 00:05:56.351 --> 00:05:58.721 Raul: It's one of the key things as a leader, that's what you're gonna focus on. 00:05:58.721 --> 00:06:02.321 Where are you leading people to the team and your clients, and 00:06:02.321 --> 00:06:03.521 what are the values that you share? 00:06:03.521 --> 00:06:05.741 And those are the probably the most important pieces of your culture. 00:06:06.141 --> 00:06:06.651 Patrick Rooney: definitely. 00:06:07.244 --> 00:06:10.814 Raul: Patrick, before we dive into like intelligence, perceived intelligence, 00:06:11.084 --> 00:06:16.974 out of my personal curiosity, what drove you really to nudge more and 00:06:16.974 --> 00:06:19.734 not just accept LLMs at surface value? 00:06:20.034 --> 00:06:21.654 Is it a personal curiosity? 00:06:21.654 --> 00:06:24.354 Is it more that you want to understand why these things work? 00:06:24.624 --> 00:06:27.594 Or is there a deeper, like a deeper concern with how 00:06:27.594 --> 00:06:28.674 we're using it as society? 00:06:28.674 --> 00:06:32.794 What really drove you to understand deeper the reasons behind intelligence, 00:06:32.794 --> 00:06:34.174 both human and artificial? 00:06:34.574 --> 00:06:35.624 Patrick Rooney: Yeah, great question. 00:06:35.624 --> 00:06:38.744 It's, it's been a long standing interest of mine, so I can go back 00:06:38.744 --> 00:06:42.944 to, say my college days when I was studying intelligence as a subject, 00:06:43.214 --> 00:06:44.864 cognitive science was my major. 00:06:45.464 --> 00:06:48.734 And that really attacks intelligence from different angles, computer science, 00:06:48.794 --> 00:06:50.834 neuroscience, philosophy, et cetera. 00:06:51.494 --> 00:06:53.329 So even back then, like 20 years ago. 00:06:53.739 --> 00:06:56.469 Yeah, there was this notion, Hey, we might be able to create 00:06:56.469 --> 00:07:00.879 something that's simulating or, or rivaling human intelligence. 00:07:01.239 --> 00:07:05.449 Obviously it, it took a long time for that to emerge, but already a long time ago. 00:07:05.449 --> 00:07:09.619 I, I found that fascinating and wanted to understand is that perspective, right? 00:07:09.649 --> 00:07:10.909 Like, I mean that Right. 00:07:10.909 --> 00:07:15.324 I think for all of us, it's a little bit unnerving at times to think Yeah, they're, 00:07:15.324 --> 00:07:19.524 they're thinking machines or thinking software like right in front of me. 00:07:20.304 --> 00:07:23.004 It, you know, it threatens our sense of uniqueness, our sense of 00:07:23.134 --> 00:07:24.754 potentially ownership or responsibility. 00:07:24.754 --> 00:07:29.054 So there are a lot of dimensions that I think, engage all of us, and I've just 00:07:29.054 --> 00:07:32.144 been aware of them perhaps a little bit longer than the average person 00:07:32.144 --> 00:07:37.274 because of my background both in work doing AI the last several years and 00:07:37.964 --> 00:07:38.699 going all the way back to college. 00:07:39.261 --> 00:07:40.521 Raul: Yeah, IEX in the background. 00:07:40.521 --> 00:07:40.791 Yeah. 00:07:41.241 --> 00:07:44.111 Was there a moment where you realized was it just like gradual 00:07:44.111 --> 00:07:47.201 over time or was there one moment in time where your stance is a little 00:07:47.201 --> 00:07:48.791 bit, I wouldn't say controversial. 00:07:48.791 --> 00:07:51.221 It could be controversial depending on who you talked to in Silicon Valley, 00:07:51.221 --> 00:07:53.981 but that LLMs aren't intelligent. 00:07:54.071 --> 00:07:57.921 Was it something that you just realized from it or through like 00:07:57.921 --> 00:08:00.501 a conviction that you had over time, or was it one moment in time? 00:08:00.901 --> 00:08:03.541 Patrick Rooney: That it's, it's been a longstanding conviction, right? 00:08:03.541 --> 00:08:05.071 So as I just kind of suggested. 00:08:05.716 --> 00:08:06.976 It's a, it's a, an issue. 00:08:06.976 --> 00:08:10.706 I kind of had to start thinking through years ago because I worked a little bit 00:08:10.736 --> 00:08:16.306 on stuff as a student in classwork, so I'm not, not going to overstate it, but 00:08:16.396 --> 00:08:18.646 worked on what were precursors of LM. 00:08:18.676 --> 00:08:20.506 So like neural networks, right? 00:08:20.596 --> 00:08:22.696 The, that's some of the architecture underlying it. 00:08:23.416 --> 00:08:28.186 And even back in, let's say 2005 ish, there were these claims that 00:08:28.246 --> 00:08:29.656 you can make a neural network. 00:08:30.091 --> 00:08:34.261 Decide to do something, it does X or Y or it discriminates like it, it chooses 00:08:34.881 --> 00:08:37.671 between this stimulus and that stimulus. 00:08:37.671 --> 00:08:43.011 So a lot of the language used decades ago was already very 00:08:43.041 --> 00:08:45.111 anthropomorphic, very human centered. 00:08:45.621 --> 00:08:48.951 And like, you know, back then you could see there's a huge difference 00:08:49.101 --> 00:08:51.801 between what those systems were doing and what we do as humans. 00:08:52.051 --> 00:08:53.851 So it was

easier to push back. 00:08:53.851 --> 00:08:56.311 But over the years I've had to kind of refine my thinking. 00:08:56.746 --> 00:08:59.831 As that gap seems to get smaller and smaller. 00:09:00.231 --> 00:09:00.561 Raul: Hmm. 00:09:00.921 --> 00:09:05.511 One of the things, so the interesting part that alluded us for, for recording 00:09:05.511 --> 00:09:08.781 this pod is that like your, your thesis and statement, and I'm, I'm 00:09:08.781 --> 00:09:11.211 not disagreeing, but I just wanna kind of work with this with you. 00:09:11.511 --> 00:09:14.481 If what we're using isn't truly intelligent, 00:09:14.989 --> 00:09:15.409 Patrick Rooney: Mm-hmm. 00:09:15.471 --> 00:09:19.131 Raul: it's trained based on a similar methodology that humans 00:09:19.131 --> 00:09:20.241 are trained, for example, like, 00:09:20.354 --> 00:09:20.594 Patrick Rooney: Yep. 00:09:21.381 --> 00:09:23.421 Raul: Through culture, education, upbringing. 00:09:23.741 --> 00:09:26.261 Obviously there's a difference between the 40 years though, like whatever, 00:09:26.261 --> 00:09:28.121 how old people are listening to this. 00:09:28.564 --> 00:09:28.864 Patrick Rooney: Yep. 00:09:29.027 --> 00:09:33.341 Raul: 60, 20, 30 years of human training versus LLM training. 00:09:33.521 --> 00:09:36.011 But there's also the reality that I'm also training and 00:09:36.011 --> 00:09:36.911 they're not doing it perfectly. 00:09:36.911 --> 00:09:39.831 Obviously, some of the outputs but I'm also training like a fleet of 00:09:39.831 --> 00:09:43.741 agents and I have agents doing certain things for me on a habitual basis. 00:09:43.741 --> 00:09:46.291 And some of the things are pretty impressive and it's kind of. 00:09:46.996 --> 00:09:49.216 Mirrors what intelligence would look like. 00:09:49.464 --> 00:09:49.824 Patrick Rooney: Yep. 00:09:49.966 --> 00:09:52.726 Raul: I think there's even some blurs of like, what is human versus 00:09:52.726 --> 00:09:55.466 what is what an LLM produced. 00:09:55.856 --> 00:09:56.126 What 00:09:56.194 --> 00:09:56.494 Patrick Rooney: Yep. 00:09:56.576 --> 00:09:59.856 Raul: of the, i, I don't know if to use your words, but explanation 00:09:59.856 --> 00:10:03.276 for intelligent looking outputs and implications of intelligence. 00:10:03.276 --> 00:10:06.276 Like how do we derive that and have a hierarchical order? 00:10:06.276 --> 00:10:09.096 And then we'll bring it to practical terms of like, why this question 00:10:09.366 --> 00:10:11.166 and this framework actually matters. 00:10:11.774 --> 00:10:12.104 Patrick Rooney: Yeah. 00:10:12.504 --> 00:10:12.744 Right. 00:10:12.744 --> 00:10:15.054 So what you're alluding to, I'd say what are different? 00:10:15.134 --> 00:10:18.014 You could say variations on the Turing test, right? 00:10:18.014 --> 00:10:19.814 That things, the Turing test. 00:10:20.234 --> 00:10:21.104 Probably a lot of folks. 00:10:21.869 --> 00:10:23.339 Listening or familiar with it, right? 00:10:23.339 --> 00:10:29.649 It was, it goes back to early 20th, mid 20th century Alan Turing, we've 00:10:29.649 --> 00:10:34.579 got LL at that time just computers, in theory, not yet in practice, 00:10:34.579 --> 00:10:37.459 potentially replicating human behaviors. 00:10:37.459 --> 00:10:40.219 Now you're, what you're describing is we're actually seeing that. 00:10:40.219 --> 00:10:40.459 Right? 00:10:41.269 --> 00:10:45.169 So if it looks like what we're doing, why isn't it? 00:10:45.634 --> 00:10:46.564 In fact intelligent. 00:10:46.624 --> 00:10:50.794 Like if it, if it walks like a duck, duck and quacks like a duck, why 00:10:50.794 --> 00:10:52.834 isn't it a truly intelligent duck? 00:10:53.234 --> 00:10:56.604 And what you started talking about there was like, training I think 00:10:56.604 --> 00:10:58.704 is a great angle to break in on. 00:10:58.704 --> 00:11:05.334 So if you look at the training of, of an LLM, it's going to be distinctive, 00:11:05.334 --> 00:11:08.424 say from the training, the education, the formation of a human being in 00:11:08.424 --> 00:11:11.334 that it is really heavily centered on. 00:11:11.919 --> 00:11:18.579 Text inputs, those are transformed into numerical intermediaries, embeddings, and 00:11:18.579 --> 00:11:23.529 then ultimately you get, you'd get text outputs from the system as it's tested 00:11:23.529 --> 00:11:25.179 and eventually after it's been trained. 00:11:25.809 --> 00:11:30.949 So the slice of the world that it's ingesting and working with is 00:11:30.949 --> 00:11:32.659 actually, it's two things about it. 00:11:32.689 --> 00:11:34.099 Number one, it's really limited, right? 00:11:34.099 --> 00:11:37.219 So like, imagine that you had nothing in your experience except books, 00:11:37.219 --> 00:11:38.689 like you didn't interact with. 00:11:39.184 --> 00:11:40.444 Actual humans, right? 00:11:40.444 --> 00:11:45.034 It didn't involve being a baby and having all the, you know, the 00:11:45.154 --> 00:11:50.794 physical, visual, tactile stimuli, but it, it's just focused on language. 00:11:51.364 --> 00:11:56.224 But your world would be totally different and impoverished, right? 00:11:56.296 --> 00:11:57.676 Raul: on, based on the inputs. 00:11:57.681 --> 00:11:57.976 It, it 00:11:58.024 --> 00:11:58.294 Patrick Rooney: Yes. 00:11:58.306 --> 00:11:58.936 Raul: limited there. 00:11:58.936 --> 00:12:02.476 But, but I think the argument is that some of the outputs in the, the real world 00:12:02.476 --> 00:12:06.496 value, or at least the economic value that is creating, is somewhat comparable 00:12:06.496 --> 00:12:10.336 to like entry level workers or even for junior assistants or even in some cases, 00:12:10.566 --> 00:12:10.896 Patrick Rooney: yeah. 00:12:10.903 --> 00:12:14.233 Raul: wouldn't say pure PhD, but some, like, I've, like I've heard some stories 00:12:14.233 --> 00:12:15.583 of auto research going overnight. 00:12:15.583 --> 00:12:19.123 It what would take PhD seven years, it would do in a week 00:12:19.123 -->

00:12:21.583 or in 36 hours, 48 hours. 00:12:21.883 --> 00:12:27.523 So I think it's understanding this is important, but also the outputs are pretty 00:12:28.203 --> 00:12:30.693 I wouldn't say jaw dropping, but sometimes it's like, it's pretty impressive. 00:12:30.693 --> 00:12:30.843 What's 00:12:30.866 --> 00:12:31.106 Patrick Rooney: Right. 00:12:31.526 --> 00:12:33.476 They're, they're massively impressive. 00:12:33.476 --> 00:12:35.126 Like, I, I don't want to understate that. 00:12:35.126 --> 00:12:35.366 Right. 00:12:35.426 --> 00:12:39.086 In addition to what you just mentioned, we have mathematical proofs for 00:12:39.206 --> 00:12:44.036 unsolved problems that LMS are either completing independently or 00:12:44.036 --> 00:12:45.746 at least assisting researchers with. 00:12:46.436 --> 00:12:48.716 So that's, you know, cutting edge of intelligence. 00:12:48.806 --> 00:12:49.106 Right. 00:12:49.356 --> 00:12:51.901 So what, what's going on that's different though? 00:12:51.901 --> 00:12:54.341 I think that there's, the one thing I mentioned, kind of 00:12:54.341 --> 00:12:56.411 the, the text or often code. 00:12:56.771 --> 00:13:02.691 Heavy inputs very different from the kind of experience that, that we have. 00:13:03.231 --> 00:13:07.191 And importantly that that doesn't marry up with, it doesn't connect 00:13:07.221 --> 00:13:09.171 directly to things in the world. 00:13:09.201 --> 00:13:11.871 So I think that's kind of the fundamental underlying point there. 00:13:12.621 --> 00:13:16.411 It's language, but it's language dissociated from 00:13:16.811 --> 00:13:18.431 the world that we experience. 00:13:18.831 --> 00:13:22.461 What it, but it does a fantastic job, right, of taking that language, 00:13:22.551 --> 00:13:25.911 transforming it into other language or code as the case may be. 00:13:26.311 --> 00:13:27.331 It's reliant though. 00:13:27.571 --> 00:13:28.741 This goes, so this is the second point. 00:13:28.771 --> 00:13:34.751 It's reliant on basically millions, tens of millions, maybe billions of people's 00:13:34.781 --> 00:13:37.031 input via the training data, right? 00:13:37.031 --> 00:13:42.186 The, the internet essentially for the foundation models it's reliant on. 00:13:42.586 --> 00:13:44.476 Trainers who are shaping it. 00:13:44.666 --> 00:13:49.036 It could be reinforcement learning via human feedback the engineers that are 00:13:49.036 --> 00:13:53.326 building the system, and ultimately everyday users like you and me who are 00:13:53.326 --> 00:13:59.626 going to the system and directing it to do certain things and correcting it if it's 00:13:59.836 --> 00:14:01.906 not acting in the way that we want it to. 00:14:02.236 --> 00:14:03.886 So we've got a whole network of people. 00:14:04.556 --> 00:14:07.106 And their experience that's feeding into these systems. 00:14:07.136 --> 00:14:10.686 And somebody who's an AI researcher at the forefront who has made a 00:14:10.686 --> 00:14:13.606 very similar observation is signing. 00:14:13.606 --> 00:14:16.876 She, I think I, I'm roughly in the ballpark of his name 00:14:16.996 --> 00:14:18.406 with his pronunciation there. 00:14:19.126 --> 00:14:22.476 And you know, he's working with Jan Koon, formerly from 00:14:22.476 --> 00:14:25.806 Meta on some novel AI research. 00:14:26.056 --> 00:14:29.866 He's made like these very points in his discussions of whether 00:14:29.866 --> 00:14:31.576 AI is truly intelligent or not. 00:14:31.576 --> 00:14:34.936 So what I am offering is not a unique perspective. 00:14:34.936 --> 00:14:37.606 It's not really unorthodox either. 00:14:37.606 --> 00:14:40.426 There, there are plenty of people out there who are steeped in the field 00:14:40.936 --> 00:14:44.446 that would say, yeah, there are, there are true distinctions between 00:14:44.646 --> 00:14:49.081 intelligence as we've understood and defined it and what an AI is doing. 00:14:49.351 --> 00:14:52.861 And that's not to in any way downplay the incredible achievements of the systems. 00:14:53.261 --> 00:14:54.701 Raul: Just to see if for what it is. 00:14:54.701 --> 00:14:57.431 Because also the other things too that are implications of intelligence 00:14:57.431 --> 00:15:01.221 to what you also shared with me was that is there a will or autonomy? 00:15:01.221 --> 00:15:02.001 Is there personhood? 00:15:02.001 --> 00:15:02.751 Is there rights? 00:15:03.104 --> 00:15:03.404 Patrick Rooney: Right. 00:15:03.501 --> 00:15:06.561 Raul: an AI have the right to control a human vice versa? 00:15:06.591 --> 00:15:07.311 Eventually. 00:15:07.551 --> 00:15:09.621 And I think this also has other implications for 00:15:09.621 --> 00:15:11.061 legal, ethical, and economic. 00:15:11.311 --> 00:15:14.371 Right now, obviously we're leveraging it as a, as a, as a thought 00:15:14.371 --> 00:15:17.401 partner, as a tool to be able to grow and even some business models. 00:15:17.581 --> 00:15:19.441 AI is the business model and that's actually. 00:15:19.441 --> 00:15:21.811 Of the key things that I'm seeing and working with clients with to help 00:15:21.811 --> 00:15:23.551 them, you know, evolve into the future. 00:15:23.801 --> 00:15:27.771 But I think having this discussion to understand fully and also understand the 00:15:27.771 --> 00:15:32.011 limits and implications so that we know what to do and also what not to do to 00:15:32.011 --> 00:15:33.721 bring it more practical and down to earth. 00:15:33.721 --> 00:15:35.731 Like based on what you're saying, what are some of the things that 00:15:35.731 --> 00:15:38.611 founders should not be doing with ai? 00:15:39.301 --> 00:15:42.601 We, we may or may not like this is probably a litany list 00:15:42.644 --> 00:15:42.864 Patrick Rooney: Yes. 00:15:43.141 --> 00:15:47.851 Raul: based on this thesis and this framework of AI looks intelligent, 00:15:48.571 --> 00:15:49.741 it's not really intelligent. 00:15:49.741 --> 00:15:50.851 It's a tool we crafted. 00:15:51.301 --> 00:15:54.541

However, it per it, it works and it has significant outputs 00:15:54.541 --> 00:15:56.431 that generate economic value. 00:15:56.741 --> 00:15:59.471 It helps, I mean, some of my clients went in some of the biggest deals with 00:15:59.471 --> 00:16:01.511 some of the work that we did co with ai. 00:16:01.511 --> 00:16:04.961 So it's, a real thing and a real matter to it. 00:16:04.961 --> 00:16:06.221 Not to get too caught up in that, but 00:16:06.364 --> 00:16:06.634 Patrick Rooney: Yeah. 00:16:07.034 --> 00:16:09.434 Raul: Push away the technology as if it's just another 00:16:09.434 --> 00:16:11.324 Photoshop or just another iPhone. 00:16:11.324 --> 00:16:13.334 Like it's not, it's more powerful than that. 00:16:13.754 --> 00:16:16.844 But what are some of the things that we should be cognizant of not 00:16:16.844 --> 00:16:19.994 doing as founders leveraging this, 00:16:20.234 --> 00:16:20.534 Patrick Rooney: yeah. 00:16:20.894 --> 00:16:23.114 So there, there are a few things you started mentioning there 00:16:23.114 --> 00:16:26.004 that I think if AI were really intelligent, it would fall out. 00:16:26.004 --> 00:16:30.354 That it's, it has these characteristics, so things like, yeah, it's 00:16:30.354 --> 00:16:31.824 got will or autonomy, right? 00:16:31.824 --> 00:16:34.224 It, it's, it's its own person. 00:16:34.434 --> 00:16:37.794 So there are rights and responsibilities associated with that. 00:16:38.214 --> 00:16:41.874 That is we, there's a certain way we should be treating LLMs 00:16:42.264 --> 00:16:45.224 like human beings and yeah. 00:16:45.284 --> 00:16:46.124 And then potentially. 00:16:46.131 --> 00:16:48.831 Raul: classes, by the way, by, there are some classes of thought. 00:16:49.101 --> 00:16:50.121 'cause I started breaking down. 00:16:50.121 --> 00:16:53.301 Like I think there are different metaphysics of ai. 00:16:53.301 --> 00:16:55.761 Some people do believe that it has rights and do believe that 00:16:55.769 --> 00:16:56.189 Patrick Rooney: Mm-hmm. 00:16:56.481 --> 00:16:58.401 Raul: a personhood and is treated a certain way. 00:16:58.979 --> 00:16:59.269 Patrick Rooney: Yeah. 00:16:59.511 --> 00:17:03.051 Raul: so there, there are multiple thoughts around this perspective 00:17:03.051 --> 00:17:06.261 and this, this could be a con, like I, I alluded in the beginning. 00:17:06.261 --> 00:17:11.541 This podcast itself could be like a controversial statement said in Silicon 00:17:11.541 --> 00:17:12.651 Valley to some people, depending 00:17:12.734 --> 00:17:13.034 Patrick Rooney: Yeah, 00:17:13.041 --> 00:17:13.051 Raul: are. 00:17:13.604 --> 00:17:15.529 Patrick Rooney: so that's a great point you make there. 00:17:16.019 --> 00:17:19.324 All the things that we are discussing right now, these are like summarized 00:17:19.324 --> 00:17:22.924 versions of what, what are actually very profound debates and, and 00:17:22.924 --> 00:17:25.444 discussions about personhood about. 00:17:25.819 --> 00:17:27.859 What is the nature of thinking and intelligence? 00:17:28.489 --> 00:17:32.149 What is the nature of an artifact versus a natural thing, right? 00:17:32.149 --> 00:17:35.429 And, and some of these go back centuries, if not millennia. 00:17:36.149 --> 00:17:39.239 So, so we're not new to these topics, and you could, we could spend hours. 00:17:39.779 --> 00:17:43.029 I what I just want to, I would want to point out what I would want people to 00:17:43.029 --> 00:17:47.379 take away is if you hear a perspective like that, that this is a person, 00:17:47.409 --> 00:17:51.789 this, this LLM or this instance, this chat bot is a person, is that. 00:17:52.209 --> 00:17:58.059 That comes with a bunch of assumptions that a, a whole load of baggage behind it. 00:17:58.619 --> 00:18:02.009 So it's not something that should be said glibly or naively. 00:18:02.099 --> 00:18:05.099 And I think in fact, there, there are good reasons to argue the opposite, 00:18:05.879 --> 00:18:11.259 but I, I wouldn't expect in like that 20, 30 minutes to be able to convince 00:18:11.259 --> 00:18:16.309 somebody that there's no reason to at all to believe that there's some 00:18:16.309 --> 00:18:17.929 personality or personhood behind these. 00:18:18.329 --> 00:18:18.869 Raul: That's fair. 00:18:19.619 --> 00:18:20.219 the things that we shouldn't. 00:18:20.219 --> 00:18:23.939 So one of the things, maybe we shouldn't say that it's, this is my, I mean, we 00:18:23.939 --> 00:18:25.169 are using that different right now. 00:18:25.259 --> 00:18:26.999 This is my AI coworker or my 00:18:27.067 --> 00:18:27.487 Patrick Rooney: Yes. 00:18:28.079 --> 00:18:28.739 Raul: or we 00:18:28.897 --> 00:18:29.317 Patrick Rooney: Right. 00:18:29.399 --> 00:18:30.239 Raul: like who is we? 00:18:30.439 --> 00:18:32.719 So what are other, other things to be cognizant aware, 00:18:33.057 --> 00:18:33.387 Patrick Rooney: Yeah. 00:18:33.919 --> 00:18:38.069 Raul: for, in practical terms, like in the field building agents with ai, with 00:18:38.162 --> 00:18:38.942 Patrick Rooney: So, so, yeah. 00:18:38.999 --> 00:18:41.219 Raul: some sort of autonomy, but they're scripted autonomy, 00:18:41.647 --> 00:18:42.247 Patrick Rooney: Yes. 00:18:42.397 --> 00:18:42.667 Yeah. 00:18:42.667 --> 00:18:42.967 Right. 00:18:42.967 --> 00:18:44.287 That term autonomy, right. 00:18:44.842 --> 00:18:47.722 Has different layers and different meanings to it. 00:18:48.142 --> 00:18:51.262 So if you say it works autonomously, you mean like, Hey, I can walk away and 00:18:51.262 --> 00:18:52.912 it'll do something while I get coffee. 00:18:53.332 --> 00:18:53.962 That's fine. 00:18:53.962 --> 00:19:00.052 But it autonomy has this kind of other connotation that it, it's an independent 00:19:00.442 --> 00:19:04.852 person with rights and that's what I would, I would've people shy away from 00:19:05.422 --> 00:19:07.042 those really anthropomorphic terms. 00:19:07.732 --> 00:19:10.622 Yeah, like the really anthropomorphic terms, you

know, if you use it. 00:19:10.942 --> 00:19:14.542 Ca you know, casually, Hey, it's an AI assistant, not a big deal, 00:19:14.542 --> 00:19:17.242 but deeply personifying your ai. 00:19:17.492 --> 00:19:22.382 We've seen instances where that actually has, that has led to mental health issues. 00:19:22.382 --> 00:19:22.922 And that's a, 00:19:23.172 --> 00:19:23.202 Raul: hm. 00:19:23.600 --> 00:19:24.740 Patrick Rooney: something of a tangent, right. 00:19:24.740 --> 00:19:26.660 But that's, that's it's made national news. 00:19:26.660 --> 00:19:30.650 People who have become fixated on that founders, I would think are less 00:19:30.650 --> 00:19:32.880 likely to to develop that particular, 00:19:32.902 --> 00:19:33.832 Raul: as leadership, 00:19:34.260 --> 00:19:34.560 Patrick Rooney: yeah. 00:19:34.732 --> 00:19:34.852 Raul: with 00:19:34.980 --> 00:19:35.160 Patrick Rooney: Alright. 00:19:35.212 --> 00:19:35.362 Raul: too. 00:19:35.362 --> 00:19:35.872 Like they, they, 00:19:35.895 --> 00:19:36.105 Patrick Rooney: Yeah. 00:19:36.105 --> 00:19:39.135 But to, to model it, I think for, for their teams in particular 00:19:39.135 --> 00:19:40.845 is it's just healthy behavior. 00:19:41.595 --> 00:19:44.835 But a really positive thing, I would say is to continue to take 00:19:44.925 --> 00:19:49.975 ownership of what's going into the ai, how it's working and the outputs. 00:19:49.975 --> 00:19:50.185 Right. 00:19:50.185 --> 00:19:52.945 So not so much the Yeah. 00:19:53.395 --> 00:19:57.955 Downplaying what the AI AI can do, but instead emphasizing your role 00:19:58.495 --> 00:20:00.865 as a human and as an owner of. 00:20:01.375 --> 00:20:06.445 This tool and this process, like, hey, I need to ensure that the inputs 00:20:06.445 --> 00:20:09.265 I'm putting in here for my clients, that they're, they're clean, that 00:20:09.265 --> 00:20:15.125 they're accurate that I've handled them responsibly, that the tool handles 00:20:15.125 --> 00:20:19.775 them responsibly, data securities, forced that whatever comes out also 00:20:19.775 --> 00:20:21.310 has gone through appropriate scrutiny. 00:20:21.485 --> 00:20:25.205 Like it, it's a huge time saver, but that doesn't give us you know, 00:20:25.865 --> 00:20:30.265 the re any reason or any, it doesn't clear us with the responsibility to 00:20:30.595 --> 00:20:32.005 take ownership of what's going on. 00:20:32.405 --> 00:20:34.915 Raul: Yeah, the term that comes to mind, this is easier 'cause it outsourced 00:20:34.915 --> 00:20:39.005 thinking sometimes you're doing, I mean, on a good day I'm personally able to do 00:20:39.005 --> 00:20:41.780 maybe six to 13 things in a day like. 00:20:42.043 --> 00:20:42.133 Patrick Rooney: Mm-hmm. 00:20:42.875 --> 00:20:46.415 Raul: Big things, like things that matter with my agents and like a 00:20:46.415 --> 00:20:49.175 good day is like 70, maybe 80 things. 00:20:49.175 --> 00:20:49.985 Like actual things. 00:20:49.985 --> 00:20:51.245 Not just decisions made. 00:20:51.575 --> 00:20:52.445 Not just ideas 00:20:52.608 --> 00:20:52.898 Patrick Rooney: Yeah. 00:20:53.615 --> 00:20:57.515 Raul: So with that volume, obviously, I mean, what's that derivative? 00:20:57.515 --> 00:21:00.515 Six to seven, maybe like a 10 to five X volume. 00:21:00.915 --> 00:21:03.465 There are some things like the time saving, sometimes the 00:21:03.465 --> 00:21:06.615 output you do accept as fact, but you'll have to think critically. 00:21:06.615 --> 00:21:10.635 And this, I'm literally using more brain power at the end of the day now just to. 00:21:10.905 --> 00:21:15.195 Cognitively work my way through and identify this or that, 00:21:15.195 --> 00:21:16.365 or weigh the information. 00:21:16.365 --> 00:21:19.095 And I think that's one of the key fallacies that you just mentioned, is 00:21:19.395 --> 00:21:22.635 not just taking ownership of what goes in data security, privacy, et cetera. 00:21:23.025 --> 00:21:27.085 I wanna talk to you about that in a second for local, but also knowing that the 00:21:27.085 --> 00:21:30.655 output, you also need to think, and when people say outsource your own thinking, I 00:21:30.655 --> 00:21:34.945 think this is where the real outsourcing might happen, where the outputs or 00:21:34.945 --> 00:21:38.845 the formulas or the strategies or whatever it produces, you could take as. 00:21:39.195 --> 00:21:39.945 As truth. 00:21:39.945 --> 00:21:43.125 And I think you just have to counter argue like, okay, did you look at this? 00:21:43.125 --> 00:21:43.785 Did you do that? 00:21:43.815 --> 00:21:44.595 This research? 00:21:44.745 --> 00:21:47.535 Is there any other implication that I would be right based on my assumption, 00:21:47.535 --> 00:21:48.795 based on my experience and have a 00:21:49.053 --> 00:21:49.533 Patrick Rooney: Exactly. 00:21:50.095 --> 00:21:53.625 Raul: And usually depending on your setup I thankfully don't have to argue as 00:21:53.625 --> 00:21:56.445 much anymore 'cause I'm, it's, it's much more embedded the way that I'm working. 00:21:56.755 --> 00:22:01.315 It's, it's, I mean, for better or for worse, it's training like 2000 documents 00:22:01.315 --> 00:22:04.535 of mine, like, for like actual writing and thinking and stuff like that. 00:22:04.535 --> 00:22:07.825 But, But in some cases, if I'm just using I'm not gonna name the app's 00:22:07.825 --> 00:22:12.145 names, but if I'm just using like an l an LLM model, frontier model on, 00:22:12.150 --> 00:22:13.845 on one of my my apps on the phone. 00:22:14.245 --> 00:22:16.255 I do have to argue, hey, is that the right assumption? 00:22:16.375 --> 00:22:17.215 Hey, is that the right thing? 00:22:17.215 --> 00:22:20.395 Can you audit your work and your research and do some strong man 00:22:20.395 --> 00:22:23.575 and strong man arguments, argue against yourselves, present to 00:22:23.575 --> 00:22:25.285 me, and then validate like there's 00:22:25.293 --> 00:22:25.593 Patrick Rooney: Right. 00:22:25.975 -->

00:22:29.305 Raul: to think about before you just accept the output is true. 00:22:29.305 --> 00:22:30.235 So I think that's one key 00:22:30.573 --> 00:22:30.783 Patrick Rooney: Yeah. 00:22:31.135 --> 00:22:36.075 Raul: And one of the, one of the the biggest rebuttals I have, and I hear, and 00:22:36.075 --> 00:22:40.605 I think it's legit, but I also, I mean at this point it's, may not be as legit. 00:22:41.015 --> 00:22:44.585 Is like, well, if I just train AI on all my ip, I lose everything. 00:22:44.895 --> 00:22:46.305 There's a level of truth there. 00:22:46.305 --> 00:22:49.285 But how would you argue that, and how would you think about that based 00:22:49.285 --> 00:22:53.165 on your actual use case and what you're seeing even now that you're 00:22:53.165 --> 00:22:54.875 working on your, on your firm? 00:22:54.875 --> 00:22:54.905 I. 00:22:55.305 --> 00:22:57.765 Patrick Rooney: Well, if, yeah, if you train it, first of all, I think 00:22:57.765 --> 00:23:00.585 that there's a, there's a, a kind of technological answer to that, 00:23:00.585 --> 00:23:05.345 which maybe is too simple, which if you, if you own the model, if it's 00:23:05.345 --> 00:23:09.465 locally, you know, run right, you, you can keep your IP private, right? 00:23:09.465 --> 00:23:11.235 You're, you're not just giving it away. 00:23:11.347 --> 00:23:14.167 Raul: if you downloaded everything, you got the Mac studio, you put Kimmy 00:23:14.167 --> 00:23:19.087 or GLM, like you're hosting your own LLM, so like you don't really host 00:23:19.087 --> 00:23:20.197 it to the cloud, so you're good. 00:23:20.597 --> 00:23:23.427 Patrick Rooney: Yeah, so it and there, you know, on top of that, 00:23:23.427 --> 00:23:26.997 right, there are protections you can have with cloud-based models. 00:23:27.052 --> 00:23:31.902 I'm not telling anybody to go out and put all your IP into any of those, but it's 00:23:31.902 --> 00:23:35.892 not simply going to show up, you know, like across the ocean because you, you've. 00:23:36.612 --> 00:23:38.082 Uploaded a couple documents. 00:23:38.332 --> 00:23:43.632 The but bigger, I mean, the, the other thing I would emphasize though is the, 00:23:44.322 --> 00:23:49.852 the thought that went into crafting those materials and the ownership, like the 00:23:49.852 --> 00:23:53.752 actual thought that goes into deciding that yes, this output is the right output, 00:23:53.752 --> 00:23:56.152 like affirming it, that's still yours. 00:23:56.362 --> 00:23:58.397 So you're the one deciding, hey. 00:23:58.797 --> 00:24:02.517 I, I want to upload this set of documents, these sets of 00:24:02.517 --> 00:24:04.867 files to train the, the system. 00:24:05.617 --> 00:24:07.297 It's not deciding that autonomously. 00:24:07.687 --> 00:24:10.627 Now, of course, you can get systems that are more and more capable, right? 00:24:10.627 --> 00:24:14.557 That of grabbing your files and, and selecting, picking, and choosing. 00:24:14.557 --> 00:24:17.647 But you're still, at some point you're making that decision. 00:24:18.047 --> 00:24:21.767 You are also ultimately going to be the one with the, the yes no. 00:24:21.767 --> 00:24:25.387 Say regarding whether it's good enough for your business, whether 00:24:25.387 --> 00:24:28.507 it's good enough for your clients and what I've, you know what I'm saying? 00:24:29.077 --> 00:24:32.617 On both the philosophical level and then on the very practical level is 00:24:33.007 --> 00:24:34.957 don't give up that responsibility. 00:24:35.257 --> 00:24:37.207 I know sometimes it can be tempting, right? 00:24:37.207 --> 00:24:38.677 Because hey, it's, it's easier. 00:24:39.337 --> 00:24:39.667 Yeah. 00:24:40.387 --> 00:24:40.787 You can still. 00:24:40.934 --> 00:24:45.074 Raul: at the end of the day, legit at the end of the day where I 00:24:45.074 --> 00:24:47.444 have to make decision because I, I mean, it's a thought partner. 00:24:47.444 --> 00:24:50.844 Like I've, I've had like a, I have like nine agents now working with, 00:24:50.844 --> 00:24:52.134 like, I run the business on ai 00:24:52.534 --> 00:24:52.804 Patrick Rooney: Yeah. 00:24:53.021 --> 00:24:55.271 Raul: at the end of the day when I'm making decisions, sometimes 00:24:55.271 --> 00:24:56.351 it says the recommendations. 00:24:56.351 --> 00:24:58.331 And I'm like, okay, just come with your recommendation. 00:24:58.331 --> 00:25:02.471 I'm like burnt out on thinking and this is not, 'cause, I mean, I think 00:25:02.471 --> 00:25:05.051 for a living, like legitimately and the people say, look, AI's 00:25:05.051 --> 00:25:06.611 gonna come for your job, I think. 00:25:06.931 --> 00:25:07.771 Yes and no. 00:25:08.071 --> 00:25:11.221 It will for like simple things, but for your, what you just mentioned, what gets 00:25:11.221 --> 00:25:15.361 pushed to reality based on your unique experience, based on your empirical 00:25:15.361 --> 00:25:19.531 like life experience, not just what's on text, what's good for the client? 00:25:19.711 --> 00:25:22.741 The judgment call, the trust call, the relationship call. 00:25:22.981 --> 00:25:26.071 Those are things that aren't gonna be just swiped away from you. 00:25:26.471 --> 00:25:27.371 Patrick Rooney: Yeah, and, and people. 00:25:28.136 --> 00:25:29.636 I think there are a couple dimensions of that. 00:25:29.666 --> 00:25:33.956 People still value performance, like even a podcast. 00:25:33.956 --> 00:25:35.846 There's I think some, 00:25:35.988 --> 00:25:36.408 Raul: Matter. 00:25:36.896 --> 00:25:38.216 Patrick Rooney: excitement and engagement. 00:25:38.216 --> 00:25:40.226 And these are real people. 00:25:40.256 --> 00:25:41.426 They're doing something live. 00:25:41.456 --> 00:25:45.236 Yes, there can be flaws and foibles, but we're seeing them kind of 00:25:45.636 --> 00:25:49.386 perform for us and produce, you know, hopefully interesting output. 00:25:49.786 --> 00:25:51.216 Likewise there. 00:25:51.616 --> 00:25:53.536 You just on that, that ownership

side. 00:25:53.536 --> 00:25:53.746 Right. 00:25:53.746 --> 00:25:55.666 I think people really value that. 00:25:56.206 --> 00:26:01.286 I can go to a human, I can go to Raul and, and hold, you know, if I need to hold him 00:26:01.286 --> 00:26:05.756 responsible for, you know, this business engagement we had, he's the one who's 00:26:05.756 --> 00:26:10.196 going to follow up, you know, and ensure that things head down the right path. 00:26:10.596 --> 00:26:12.876 This LLM doesn't actually have. 00:26:13.401 --> 00:26:15.351 A, an inherent investment in what I'm doing. 00:26:15.411 --> 00:26:18.486 Like it, you know, at the end of the day, it doesn't make money, it 00:26:18.486 --> 00:26:20.241 doesn't feel, it doesn't feel shame. 00:26:20.241 --> 00:26:23.831 It you know, it can simulate all these things, but it, it is 00:26:23.831 --> 00:26:25.571 not a locus of responsibility. 00:26:26.111 --> 00:26:29.921 So practically, I want that person on the other side. 00:26:30.361 --> 00:26:33.601 Even if, you know, he uses AI for 90% of his work. 00:26:33.601 --> 00:26:37.201 I want that person who's responsible for any high stakes engagement. 00:26:37.601 --> 00:26:39.461 Raul: Yeah, to take the outcome, take the ownership. 00:26:39.861 --> 00:26:41.091 is, this is really good to understand. 00:26:41.091 --> 00:26:42.381 Again, it's not intelligence. 00:26:42.591 --> 00:26:45.861 One of the things also like maybe feel free to kinda argue me this, 00:26:45.974 --> 00:26:46.274 Patrick Rooney: Yeah. 00:26:46.281 --> 00:26:48.891 Raul: We were at a dinner table and we were chatting about this and 00:26:48.891 --> 00:26:52.431 one of the other guys is that was there, business owner, great guy. 00:26:52.731 --> 00:26:53.721 Actually my connected two. 00:26:54.081 --> 00:26:58.701 But, he, he does a lot of database work and then puts on LMS on top of that. 00:26:58.701 --> 00:27:02.271 So he works, he works at the data layer and then the, the model layer and then 00:27:02.271 --> 00:27:04.321 the application layer, for businesses. 00:27:04.411 --> 00:27:06.991 And he was saying, 'cause he, he like same perspective, like 00:27:06.991 --> 00:27:07.921 this stuff is not intelligent. 00:27:07.921 --> 00:27:08.761 I know how it works. 00:27:09.181 --> 00:27:12.421 However we perceive it as culture that it is intelligent 00:27:13.141 --> 00:27:14.311 it's passed the terrain test. 00:27:14.701 --> 00:27:18.271 And then when we all believe that it's reached a GI. 00:27:19.066 --> 00:27:20.146 Whatever that means. 00:27:20.424 --> 00:27:20.814 Patrick Rooney: Yep. 00:27:20.956 --> 00:27:24.506 Raul: agi, we did define terms, but if we perceive that and we behave 00:27:24.506 --> 00:27:28.736 that way, then regardless if it did or didn't, it did because we're behaving 00:27:29.516 --> 00:27:34.316 if it did as culture, even though the truth underlying may be not. 00:27:34.316 --> 00:27:36.236 So what's your take on that? 00:27:36.836 --> 00:27:38.276 I mean, based

on everything that we just 00:27:38.434 --> 00:27:42.214 Patrick Rooney: Well, I, you know, I'm gonna maybe, I put some words 00:27:42.214 --> 00:27:45.934 in his mouth, but he's basically saying if it, if it tricked me into, 00:27:45.956 --> 00:27:47.036 Raul: the argument No, I'm, I'm 00:27:47.104 --> 00:27:47.344 Patrick Rooney: yeah. 00:27:47.366 --> 00:27:48.206 Raul: argument that, 00:27:48.664 --> 00:27:48.994 Patrick Rooney: Yeah. 00:27:49.016 --> 00:27:50.426 Raul: culture, if we, we believe because he's saying 00:27:50.524 --> 00:27:51.064 Patrick Rooney: Okay. 00:27:51.116 --> 00:27:51.506 Raul: told him, well, 00:27:51.604 --> 00:27:51.874 Patrick Rooney: Yeah. 00:27:52.646 --> 00:27:53.366 Raul: that it's there. 00:27:53.396 --> 00:27:54.446 We're gonna behave as if it's 00:27:54.574 --> 00:27:54.724 Patrick Rooney: Okay. 00:27:54.866 --> 00:27:56.006 Raul: So therefore it's there. 00:27:56.406 --> 00:27:59.206 Patrick Rooney: Yeah, no, well that actually, that's kind of the, the 00:27:59.206 --> 00:28:02.746 original premise of the Turing test, that if it can deceive you, it's real. 00:28:02.896 --> 00:28:03.166 Right? 00:28:03.166 --> 00:28:05.596 That if it can deceive you, that it's intelligent. 00:28:05.596 --> 00:28:06.346 It really is. 00:28:07.096 --> 00:28:08.356 And what I think. 00:28:08.911 --> 00:28:13.561 We want to go back to this foundational distinction in just a history of thought, 00:28:13.561 --> 00:28:15.931 which is appearance and reality, right? 00:28:15.931 --> 00:28:21.421 So if I can, if wax sculpture, for example, can trick me into thinking 00:28:21.481 --> 00:28:24.911 Michael Jackson is, you know, right behind my shoulder, is Michael 00:28:24.911 --> 00:28:26.021 Jackson really behind my shoulder? 00:28:26.021 --> 00:28:31.221 And is he, is he there until I, I reach out and feel it and realize, oh no, 00:28:31.221 --> 00:28:32.721 this is not, this is not a human being. 00:28:33.081 --> 00:28:34.431 This is a sculpture. 00:28:34.831 --> 00:28:36.001 The answer is clearly no. 00:28:36.061 --> 00:28:36.541 You know it. 00:28:37.051 --> 00:28:42.391 The fact that it was deeply convincing that it was maybe based on a detailed 00:28:42.391 --> 00:28:46.981 study of like every feature of his face, his posture, his clothing. 00:28:47.213 --> 00:28:48.323 Raul: to that future though, we're 00:28:48.481 --> 00:28:48.541 Patrick Rooney: Yeah. 00:28:48.563 --> 00:28:52.163 Raul: to that future world consensus of the, of society and culture. 00:28:52.283 --> 00:28:55.593 Even though right now in US, the sentiment is really low for using these tools. 00:28:55.593 --> 00:28:56.673 Hopefully, we'll, we'll flip that. 00:28:57.033 --> 00:29:01.023 But we're going to that future where, where there's gonna be a culture 00:29:01.143 --> 00:29:03.093 alignment saying like, yes, this is it. 00:29:03.393 --> 00:29:06.273 This wax statue is real, even though it's not real. 00:29:06.603 --> 00:29:08.733 How do we, how are you gonna lead as a founder? 00:29:08.733 --> 00:29:10.413 What are the things you're gonna work with your clients on? 00:29:10.413 --> 00:29:11.643 How are you going to reframe? 00:29:11.643 --> 00:29:14.463 Like, what's, what are the real implications you're gonna take? 00:29:14.463 --> 00:29:17.253 'cause I think that's the future we're gonna head in because no one's gonna 00:29:17.253 --> 00:29:20.673 be able to judge it in my opinion, like if they reached it or not. 00:29:21.033 --> 00:29:23.043 'cause they're, everyone's saying, we reached a GI, it's here. 00:29:23.341 --> 00:29:23.671 Patrick Rooney: Right. 00:29:23.913 --> 00:29:26.283 Raul: That, that was like leaked two weeks ago, three weeks ago. 00:29:26.683 --> 00:29:30.593 whether it's there or not, one, does it matter 00:29:30.986 --> 00:29:31.406 Patrick Rooney: Mm-hmm. 00:29:32.123 --> 00:29:34.733 Raul: Two, if it does matter, what do we do about it? 00:29:35.133 --> 00:29:38.013 And three, based on what we do about it, how do we practically 00:29:38.013 --> 00:29:39.063 implement that in business? 00:29:39.716 --> 00:29:43.076 Patrick Rooney: Yeah, I mean, it's a fantastic question and I'll 00:29:43.076 --> 00:29:44.576 throw out a, a couple thoughts. 00:29:45.266 --> 00:29:49.966 I think it, it, it's doubling down on human connection on 00:29:50.086 --> 00:29:51.736 in-person human connection, right? 00:29:51.736 --> 00:29:56.556 And I'm not saying this to undermine, you any online businesses like I've, 00:29:56.676 --> 00:29:58.626 I've worked remotely for several years. 00:29:58.626 --> 00:30:02.436 I'll continue to engage in business online, but I think maintaining that 00:30:02.436 --> 00:30:07.626 human contact is a, a strong reminder to us of what we actually can do in person 00:30:07.626 --> 00:30:08.826 when it's like, oh, I can see that. 00:30:08.946 --> 00:30:11.166 Yeah, your, your coffee cup just spilled. 00:30:11.166 --> 00:30:12.186 Let me help you clean that up. 00:30:12.186 --> 00:30:12.366 Right? 00:30:12.366 --> 00:30:17.076 It's tiny things that make a lot of difference when we have those 00:30:17.076 --> 00:30:18.636 tangible elements in our relationship. 00:30:18.636 --> 00:30:19.031 So I would say. 00:30:19.671 --> 00:30:23.061 To the extent you're able to do that, even if it's up not in work, but 00:30:23.061 --> 00:30:24.981 personal relationships cultivate that. 00:30:25.651 --> 00:30:29.981 I think that's, that will then having that as a primary foundation will then 00:30:30.221 --> 00:30:33.851 kind of spill over into the honor line relationships and I think ground them. 00:30:33.911 --> 00:30:38.751 So my, my view is having those kind of tangible in-person relationships 00:30:39.201 --> 00:30:41.721 helps make the online site stronger. 00:30:42.351 --> 00:30:44.541 It's a, it's a, it's having the right foundation. 00:30:45.441 --> 00:30:49.706 The, yeah, the other analogy, I just, it's kinda like video games 00:30:49.706 --> 00:30:53.856 versus, you

know, playing sports and video games versus in person, right. 00:30:53.976 --> 00:30:57.906 You can, you can pick up some interesting coordination skills, et cetera, 00:30:57.906 --> 00:31:02.466 playing whatever tennis like used to be we tennis like long time ago. 00:31:02.466 --> 00:31:02.706 And 00:31:02.728 --> 00:31:02.938 Raul: yeah. 00:31:02.938 --> 00:31:03.088 Yeah. 00:31:03.088 --> 00:31:03.478 That was fun. 00:31:03.576 --> 00:31:03.936 Patrick Rooney: Yeah. 00:31:04.618 --> 00:31:04.798 Raul: Then 00:31:04.806 --> 00:31:05.106 Patrick Rooney: And it's, 00:31:05.128 --> 00:31:06.058 Raul: VR glasses. 00:31:06.366 --> 00:31:09.096 Patrick Rooney: you can get, you can get better at it, but you, you're 00:31:09.096 --> 00:31:10.836 still gonna f find out there's a gap. 00:31:10.866 --> 00:31:12.471 There's a huge skill gap when you go out and you touch. 00:31:12.671 --> 00:31:14.141 Tennis racket in the real world. 00:31:14.891 --> 00:31:20.061 So do, do those, like get grounded in those, those activities? 00:31:20.121 --> 00:31:25.041 I think that, again, they, they'll spill back over and help you with all the, 00:31:25.441 --> 00:31:27.361 what I'll call derivative activities. 00:31:27.361 --> 00:31:29.311 That is, they're, they're built on that foundation. 00:31:29.311 --> 00:31:32.611 So the online, the simulated stuff with LLMs. 00:31:33.011 --> 00:31:33.731 Those are all good. 00:31:33.731 --> 00:31:37.361 They're great tools for building business connections for for building 00:31:37.361 --> 00:31:40.241 projects, but don't ever let that become 00:31:40.448 --> 00:31:40.728 Raul: value. 00:31:41.501 --> 00:31:42.041 Patrick Rooney: from the 00:31:42.433 --> 00:31:42.943 Raul: Yeah. 00:31:43.096 --> 00:31:43.376 Patrick Rooney: concrete. 00:31:43.776 --> 00:31:44.856 Raul: Just having that recognition. 00:31:44.856 --> 00:31:48.396 I think the, the practical takeaway to a GI here, not regardless, relationships 00:31:48.396 --> 00:31:53.126 matter, but also even in my own, like going deep, like real deep, trusting 00:31:53.126 --> 00:31:57.536 your own human based intuition and judgment based on experience. 00:31:57.596 --> 00:32:01.256 Not only just what the model outputs, even if the model is super compelling. 00:32:01.656 --> 00:32:05.256 The other approach is the other, the other key thing to keep in mind is 00:32:05.256 --> 00:32:10.026 also you, you have the ability to know what's right and to know what to do. 00:32:10.416 --> 00:32:12.546 So don't plaque on your own reasoning. 00:32:12.974 --> 00:32:13.214 Patrick Rooney: Yep. 00:32:13.296 --> 00:32:15.516 Raul: something to keep in import, like how, I forget how much 00:32:15.516 --> 00:32:19.366 we invested into, trillions of dollars into training these models. 00:32:19.366 --> 00:32:24.276 Now I'm pretty sure like cumulatively yet your brain getting, getting cost a 00:32:24.276 --> 00:32:28.196 trillion and, it's kind of fascinating when you think about it that way, that, 00:32:28.474 --> 00:32:28.564 Patrick Rooney: Yes. 00:32:29.006 --> 00:32:31.926 Raul: we take we take our own brain thinking for granted. 00:32:31.926 --> 00:32:34.236 So you can also reason your way through. 00:32:34.944 --> 00:32:35.234 Patrick Rooney: Yeah. 00:32:35.646 --> 00:32:36.966 Raul: and then also vernacular matters. 00:32:36.966 --> 00:32:41.046 The way that you talk to it, the way that you personified or not personified 00:32:41.046 --> 00:32:44.386 and the way that you train your team is important because your team may 00:32:44.386 --> 00:32:45.616 not be having these discussions. 00:32:45.616 --> 00:32:48.076 You may not be thinking about this day in and day, and it's easy to go with the 00:32:48.076 --> 00:32:50.366 flow and to get sucked into whatever. 00:32:50.446 --> 00:32:52.846 Whatever's being pushed in terms of ideology. 00:32:52.846 --> 00:32:54.946 So it's just good to have your own stance and your own perspective. 00:32:54.946 --> 00:32:57.316 Even if you disagree, that's great, but at least you thought about that 00:32:57.616 --> 00:33:00.866 and you thought about your own thinking which I think very few people do. 00:33:00.896 --> 00:33:03.056 'cause thinking about your own thinking is called writing. 00:33:03.296 --> 00:33:04.706 It's definitely right about how you think. 00:33:05.099 --> 00:33:05.579 Patrick Rooney: Yes. 00:33:05.906 --> 00:33:09.956 Raul: I think is a good loop on where can people find you if you're 00:33:09.956 --> 00:33:12.776 writing or posting more about this, and also sharing your insights. 00:33:13.056 --> 00:33:14.916 Where's the best place for people to go to? 00:33:15.316 --> 00:33:17.926 Patrick Rooney: Yeah, right now LinkedIn is a great place to find me. 00:33:18.226 --> 00:33:20.626 I'm happy to share contact details with you. 00:33:20.926 --> 00:33:24.586 I've also got a, a business website, leonis strategy that's, 00:33:24.706 --> 00:33:25.696 that's a little more limited. 00:33:25.696 --> 00:33:28.846 So I'd say that the, the LinkedIn side of things is gonna be perfect 00:33:28.846 --> 00:33:29.836 for anybody who wants to connect. 00:33:30.236 --> 00:33:30.746 Raul: Sounds good. 00:33:30.746 --> 00:33:32.696 We'll put those links in the show notes. 00:33:32.696 --> 00:33:34.646 Patrick, thank you again for being on. 00:33:35.046 --> 00:33:36.276 Patrick Rooney: Thanks so much for having me, Raul. 00:33:38.526 --> 00:33:40.036 Thank you for listening to the podcast. 00:33:40.056 --> 00:33:42.266 If you'd like to get more of these podcasts, please go ahead 00:33:42.266 --> 00:33:45.446 and subscribe to wherever you're watching or listening to the pod. 00:33:45.866 --> 00:33:49.016 If you have any suggestions or questions that you want me to answer on the 00:33:49.016 --> 00:33:51.996 pod, or you have a guest that you want me to interview, please go ahead and 00:33:52.016 --> 00:33:56.926 email me all of your suggestions and questions at podcast at do good work. 00:33:56.956 --> 00:34:00.206 io that's podcast at do good work. 00:34:00.236 --> 00:34:01.186 io. 00:34:01.626 --> 00:34:04.386 If you'd like to give me public feedback, you can go on Apple 00:34:04.406 --> 00:34:07.986 Podcasts, and from there, you can leave up to a 5 star review. 00:34:08.226 --> 00:34:09.746 I would greatly appreciate that. 00:34:10.206 --> 00:34:13.936 If you'd like more free resources where you can get my best strategies to help you 00:34:13.946 --> 00:34:18.196 increase your company's performance and scale profitably, where you can get hand 00:34:18.196 --> 00:34:22.156 picked articles to propel your growth, and you can get trainings and discussions that 00:34:22.156 --> 00:34:27.201 I give freely online, as well as low cost resources, Such as my books and guides. 00:34:27.511 --> 00:34:30.771 You can get all of that by going to dogoodwork. 00:34:30.821 --> 00:34:36.231 io forward slash resources 90 percent of all these resources are not behind 00:34:36.231 --> 00:34:40.551 any email opt ins you can get instant access by going to dogoodwork. 00:34:40.661 --> 00:34:46.176 io forward slash resources Now, if you'd like to accelerate your progress 00:34:46.226 --> 00:34:50.646 and shorten the gap between information and action and start seeing results 00:34:50.646 --> 00:34:54.356 in your business, let's work together to increase your company's performance 00:34:54.386 --> 00:34:57.716 and scale profitably and serve more clients without the overwhelm. 00:34:58.226 --> 00:35:01.916 You can request a free clarity call to see how we can best support you to 00:35:01.916 --> 00:35:04.376 reach your goals by going to DoGoodWork. 00:35:04.416 --> 00:35:06.206 io forward slash apply. 00:35:06.386 --> 00:35:08.206 Again, that's DoGoodWork. 00:35:08.206 --> 00:35:09.616 io forward slash apply. 00:35:10.056 --> 00:35:13.556 As always, it's an honor to be a small part in your journey. 00:35:13.856 --> 00:35:15.006 This is Raul Hernandez. 00:35:15.456 --> 00:35:15.886 Do good work.