The three questions that tell you if AI will be disruptive

00:00:00,600 --> 00:00:30,670 [Eric] [upbeat music] Welcome back to Token Intelligence. AI is changing the way that we work, and Token Intelligence shows you the state-of-the-art in AI, helps you cut through the noise, and helps you apply wisdom so you can become an effective leader in the age of AI. And John, you have a question for me today, which I- 00:00:30,670 --> 00:00:30,670 [John] Yes 00:00:30,670 --> 00:00:31,760 [Eric] ... hopefully can break down. 00:00:32,980 --> 00:00:38,740 [John] Yeah. Super excited to dive in. The question of the day: is A- is AI even a big deal? [laughs] 00:00:38,740 --> 00:00:41,300 [Eric] Is AI even a big deal? [laughs] 00:00:41,300 --> 00:00:44,720 [John] Or as big a deal, you know, as we're making of it? 00:00:44,720 --> 00:00:49,780 [Eric] W- why do you think that's an important question, I guess would be my first question. 00:00:49,780 --> 00:00:52,830 [John] So I, I think there's... We've had multiple, 00:00:53,880 --> 00:00:55,310 [John] like, cycles. 00:00:55,310 --> 00:00:55,360 [Eric] Mm-hmm. 00:00:55,360 --> 00:00:56,600 [John] Let's just talk markets- 00:00:56,600 --> 00:00:56,780 [Eric] Mm-hmm 00:00:56,780 --> 00:01:07,180 [John] ... like, first. We've had these, like, like, multiple pretty big cycles, especially in the tech world, of, like, kind of this, these doom cycles and then, and then this, like, hype and then more doom. 00:01:07,180 --> 00:01:07,820 [Eric] Right. 00:01:07,820 --> 00:01:14,060 [John] Um, and I think it's really hard to think clearly if you're just gonna be processing news cycles- 00:01:14,060 --> 00:01:14,070 [Eric] Yep 00:01:14,070 --> 00:01:15,380 [John] ... especially in AI. 00:01:15,380 --> 00:01:15,740 [Eric] Yep. 00:01:15,740 --> 00:01:24,740 [John] And I think the idea here is to think about it in more of a framework, and then almost, like, I'm a m- you know, I'm a data person. So almost like- 00:01:24,740 --> 00:01:24,750 [Eric] [laughs] 00:01:24,750 --> 00:01:27,679 [John] ... how do we, how do we plot AI on, on some graphs? 00:01:27,680 --> 00:01:28,200 [Eric] Yeah, sure. 00:01:28,200 --> 00:01:28,660 [John] Is how I'm thinking about it. 00:01:28,660 --> 00:01:34,390 [Eric] Okay, great. Well, I... You're in luck because I have thought about this way too much. 00:01:34,390 --> 00:01:34,460 [John] Yeah. 00:01:34,460 --> 00:01:40,220 [Eric] And there are... I, I think that you can define 00:01:41,520 --> 00:01:45,380 [Eric] technological impact across three matrices. 00:01:46,600 --> 00:01:48,840 [Eric] Uh, and so I think we should try to do that today. 00:01:48,840 --> 00:01:53,900 [John] Okay. So the first one, so w- let's start with maybe breadth and depth. 00:01:53,900 --> 00:01:54,320 [Eric] Yep. 00:01:54,320 --> 00:01:56,080 [John] So if you wanna maybe define that. 00:01:56,080 --> 00:01:56,480 [Eric] Yep. 00:01:56,480 --> 00:02:03,000 [John] And then, and then help us dig in. So we're imagining, when we talk matrices, think we're imagining four box, you know, four box- 00:02:03,000 --> 00:02:03,420 [Eric] Yep 00:02:03,420 --> 00:02:09,100 [John] ... grids where you've got upper right corner is high high, you know, lower left corner is low low, and then you've got- 00:02:09,100 --> 00:02:09,660 [Eric] Exactly 00:02:09,660 --> 00:02:09,699 [John] ... here. 00:02:09,699 --> 00:02:12,090 [Eric] So, uh, just a, a typical quadrant, right? 00:02:12,090 --> 00:02:12,760 [John] Yeah, exactly. 00:02:12,760 --> 00:02:24,420 [Eric] And yep, so breadth and depth I think is the first one. And the... You can... I think this is, is very familiar to, to everyone, right? So, 00:02:25,440 --> 00:02:38,510 [Eric] um, let's just run through a bunch of technologies where they've had really wide impact. A- and, and a lot of times really high impact technologies have both breadth and depth. They would be- 00:02:38,510 --> 00:02:38,510 [John] Yeah 00:02:38,510 --> 00:02:39,290 [Eric] ... like high high, right? 00:02:39,290 --> 00:02:39,640 [John] Right. Mm-hmm. 00:02:39,640 --> 00:02:39,980 [Eric] Um, 00:02:41,100 --> 00:02:44,140 [Eric] but the internal combustion engine, right? 00:02:44,140 --> 00:02:44,150 [John] Yeah. 00:02:44,150 --> 00:02:45,480 [Eric] I mean breadth and depth. 00:02:45,480 --> 00:02:45,859 [John] Mm-hmm. 00:02:45,860 --> 00:02:54,920 [Eric] Um, you know, so you can think about, you know, of course, like, your yard tools probably, you know, maybe becoming more electric at this point, but- 00:02:54,920 --> 00:02:55,120 [John] Right 00:02:55,120 --> 00:03:00,000 [Eric] ... you know, for many, many decades, you know, yard tools to cruise ships, right? 00:03:00,000 --> 00:03:00,100 [John] Right. 00:03:00,100 --> 00:03:03,040 [Eric] Those are... That's, that's a very wide breadth, right? 00:03:03,040 --> 00:03:04,120 [John] Right. 00:03:04,120 --> 00:03:04,560 [Eric] Um, 00:03:05,900 --> 00:03:12,549 [Eric] but also depth, right? Because it wasn't just moving things around or equipment, right? 00:03:12,549 --> 00:03:12,560 [John] Right. 00:03:12,560 --> 00:03:16,510 [Eric] It changed actually the way that factories ran and other things like that. 00:03:16,510 --> 00:03:16,510 [John] Yeah. 00:03:16,510 --> 00:03:19,600 [Eric] Right? And so it, it also went very, very deep. 00:03:19,600 --> 00:03:21,420 [John] Okay. I've got one for you to plot for me. 00:03:21,420 --> 00:03:21,740 [Eric] Okay. 00:03:21,740 --> 00:03:22,280 [John] Okay? 00:03:22,280 --> 00:03:23,180 [Eric] Yep, I'm ready. 00:03:23,180 --> 00:03:23,820 [John] GPS. 00:03:24,940 --> 00:03:36,640 [Eric] Yes, GPS. So GPS is really interesting because I think this... I, I think GPS speaks to, um, the timescale of breadth and depth. 00:03:37,700 --> 00:03:47,080 [Eric] So GPS, global positioning satellite, um, most of us just know this as, you know, opening Google Maps or Apple Maps- 00:03:47,080 --> 00:03:47,230 [John] Right 00:03:47,230 --> 00:03:48,150 [Eric] ... you know, on our phone. 00:03:49,460 --> 00:04:06,960 [Eric] Uh, but it's navigation, right? So it can... It, it talks to a satellite that can sort of pinpoint the location of the device that you're sending a signal from, and it really revolutionized navigation. But early on it, it revolutionized navigation 00:04:08,080 --> 00:04:10,240 [Eric] in depth, not in breadth. 00:04:10,240 --> 00:04:10,720 [John] Mm-hmm. 00:04:10,720 --> 00:04:42,980 [Eric] Right? Because it was primarily used for industrial applications and military applications. And so of course in those two fields, you know, when you think about things like mining or offshore drilling or other significant, you know, multi-billion dollar international industrial applications or military applications where you're willing to pay a lot of money to be precise about where you are in the world, 00:04:44,020 --> 00:04:45,640 [Eric] it went very deep there- 00:04:45,640 --> 00:04:46,140 [John] Mm-hmm 00:04:46,140 --> 00:05:02,260 [Eric] ... initially, right? And the consumer technology hadn't caught up. And so this is an example of something that initially had very, very... I- it was very high depth, um, initially, but low on breadth. 00:05:03,440 --> 00:05:04,040 [John] Yes. 00:05:04,040 --> 00:05:13,620 [Eric] And then eventually became high on both, you know? And so but time is a very interesting, um, component of, of each of these. 00:05:13,620 --> 00:05:21,940 [John] Yep. Yeah, I think, and I think high breadth typically is, is going to be business and consumer if you, when you cross like- 00:05:21,940 --> 00:05:22,130 [Eric] Yes 00:05:22,130 --> 00:05:24,360 [John] ... both of those lines, I think that's what we're referring to- 00:05:24,360 --> 00:05:24,510 [Eric] Yep 00:05:24,510 --> 00:05:25,180 [John] ... with the high breadth. 00:05:25,180 --> 00:05:26,440 [Eric] Yep. Agreed. Agreed. 00:05:27,600 --> 00:05:30,990 [Eric] So that's the first matrix, I would say. 00:05:30,990 --> 00:05:31,140 [John] Matrix. 00:05:31,140 --> 00:05:33,010 [Eric] So when you're evaluating a technology, 00:05:34,240 --> 00:05:39,120 [Eric] is there breadth and depth, and then which is happening on which timescale? 00:05:39,120 --> 00:05:44,800 [John] Right. Which, yeah, which I think said most simply is does it a- affect my personal life and my life at work? 00:05:44,800 --> 00:05:45,020 [Eric] Yes. 00:05:45,020 --> 00:05:47,880 [John] If the answer is yes to both, then, like, yeah, you've probably got something that's high. 00:05:47,880 --> 00:05:57,120 [Eric] Yep. And, and most often I would say depth happens first commercially because there's, you know, an ability for companies to pay- 00:05:57,120 --> 00:05:57,130 [John] Yes 00:05:57,130 --> 00:05:58,480 [Eric] ... to solve a problem. 00:05:58,480 --> 00:05:58,640 [John] Pay more, yeah. 00:05:58,640 --> 00:06:02,600 [Eric] And then that technology filters out to consumers. 00:06:02,600 --> 00:06:03,340 [John] Yep. 00:06:03,340 --> 00:06:03,920 [Eric] Yep. 00:06:03,920 --> 00:06:09,908 [John] All right. So our next graph is-Rate of improvement and rate of adoption 00:06:09,908 --> 00:06:14,678 [Eric] Yes. This is, this is a very interesting one because 00:06:17,068 --> 00:06:22,148 [Eric] the, the technology can improve very quickly, but 00:06:23,448 --> 00:06:23,988 [Eric] the 00:06:25,188 --> 00:06:32,378 [Eric] amount that it improves is only practical if that improvement can actually be implemented by the end- 00:06:32,378 --> 00:06:32,378 [John] Yes 00:06:32,378 --> 00:06:34,008 [Eric] ... user or the buyer. 00:06:34,008 --> 00:06:35,888 [John] I think I have a good example of this one. 00:06:35,888 --> 00:06:36,587 [Eric] Yep. 00:06:36,588 --> 00:06:39,087 [John] Okay. Fiber, like- 00:06:39,087 --> 00:06:39,208 [Eric] Yes 00:06:39,208 --> 00:06:39,988 [John] ... internet. 00:06:39,988 --> 00:06:40,568 [Eric] Yep. 00:06:40,568 --> 00:06:53,348 [John] Because the technology has been around for years and years and years. Um, but as far as the widespread, like, infrastructure of, like, everything moving to completely fiber, like, we're not there. 00:06:53,348 --> 00:06:54,148 [Eric] Totally. 00:06:54,148 --> 00:06:55,848 [John] And, and it's the capital cost, right? 00:06:55,848 --> 00:06:56,318 [Eric] Yep. 00:06:56,318 --> 00:06:56,328 [John] Yeah. 00:06:56,328 --> 00:06:57,248 [Eric] That's a great example. 00:06:57,248 --> 00:07:02,088 [John] And, and nobody's arguing that, like, it's like, oh, you know, coax is better. 00:07:02,088 --> 00:07:02,588 [Eric] Yep. 00:07:02,588 --> 00:07:03,228 [John] But- 00:07:03,228 --> 00:07:03,468 [Eric] 100% 00:07:03,468 --> 00:07:04,088 [John] ... it's a capital problem. 00:07:04,088 --> 00:07:05,408 [Eric] Yeah, yeah. For sure. Like, 00:07:06,648 --> 00:07:16,508 [Eric] would everything be better, would experiences online be better, apps be better? I mean, there are so many things that could be better if everyone had fiber. 00:07:16,508 --> 00:07:16,568 [John] Mm-hmm. 00:07:16,568 --> 00:07:21,408 [Eric] But it's not easy to adopt. I actually think the iPhone is a very interesting example- 00:07:21,408 --> 00:07:21,438 [John] Hmm 00:07:21,438 --> 00:07:22,888 [Eric] ... across all of these. 00:07:22,888 --> 00:07:23,447 [John] Okay. 00:07:23,448 --> 00:07:32,978 [Eric] Um, but on this one in particular, if you remember when the iPhone launched, it was unbelievably expensive. I mean- 00:07:32,978 --> 00:07:32,978 [John] Yeah 00:07:32,978 --> 00:07:35,618 [Eric] ... it was a statement of affluence to- 00:07:35,618 --> 00:07:36,408 [John] Yeah, sure 00:07:36,408 --> 00:07:37,928 [Eric] ... to, to own an iPhone, right? 00:07:39,028 --> 00:07:42,788 [Eric] And now it's seemingly ubiquitous. 00:07:42,788 --> 00:07:42,938 [John] Right. 00:07:42,938 --> 00:07:43,518 [Eric] Right? Um, 00:07:45,108 --> 00:07:49,188 [Eric] you know, I mean, you, you travel around the world and you see iPhones absolutely everywhere. 00:07:49,188 --> 00:07:54,228 [John] Well, outside of the US, years after it was more affordable here, it was still that. 00:07:54,228 --> 00:07:55,538 [Eric] It was still the same way. 00:07:55,538 --> 00:07:55,568 [John] Stay, that. 00:07:55,568 --> 00:07:56,098 [Eric] Yeah. 00:07:56,098 --> 00:07:56,128 [John] And- 00:07:56,128 --> 00:07:56,408 [Eric] Yeah 00:07:56,408 --> 00:08:04,388 [John] ... and I guess I can't speak for sure globally, but I think you're right. I think now, because there's so many different models and you've got the old model versus the new model, like you can- 00:08:04,388 --> 00:08:04,488 [Eric] Absolutely 00:08:04,488 --> 00:08:06,888 [John] ... pretty much have a, a person at each price point. 00:08:06,888 --> 00:08:12,048 [Eric] Oh, I've, I've definitely, like, taken iPhones to different countries, like visiting friends. 00:08:12,048 --> 00:08:12,168 [John] Mm-hmm. 00:08:12,168 --> 00:08:15,948 [Eric] And they would say, "Hey, can you go buy a bunch of iPhones?" [laughs] 00:08:15,948 --> 00:08:17,548 [John] Oh, really? Like in the past? 00:08:17,548 --> 00:08:18,388 [Eric] Oh, yeah. Absolutely. 00:08:18,388 --> 00:08:18,468 [John] Okay. 00:08:18,468 --> 00:08:19,018 [Eric] Absolutely. 00:08:19,018 --> 00:08:19,028 [John] Awesome. 00:08:19,028 --> 00:08:19,668 [Eric] Yeah. 00:08:19,668 --> 00:08:20,088 [John] Yeah. You might- 00:08:20,088 --> 00:08:21,368 [Eric] I mean, not that long ago. 00:08:21,368 --> 00:08:21,398 [John] Yeah. 00:08:21,398 --> 00:08:27,668 [Eric] You know, a year, year and a half ago. Um, 'cause they're just so unbelievably expensive to procure in certain countries. 00:08:27,668 --> 00:08:31,848 [John] What do you, what do you think the markup is in... Or you s- where it was a couple years ago? 00:08:31,848 --> 00:08:33,788 [Eric] Uh, I don't know. I don't know. 00:08:33,788 --> 00:08:33,808 [John] Okay. 00:08:33,808 --> 00:08:34,898 [Eric] 2 or 3X probably. 00:08:34,898 --> 00:08:35,028 [John] Okay. Yep. 00:08:35,028 --> 00:08:36,308 [Eric] It's, it's very expensive. 00:08:36,308 --> 00:08:36,788 [John] Yeah, yeah. 00:08:36,788 --> 00:08:37,188 [Eric] Um, 00:08:38,948 --> 00:08:46,088 [Eric] but it's, it's interesting, right? And so there's, like price was a barrier to, to adoption. 00:08:46,088 --> 00:08:46,208 [John] Yeah. 00:08:46,208 --> 00:08:51,738 [Eric] Right? Even though the technology is, you know, advancing very, very quickly. 00:08:51,738 --> 00:08:51,848 [John] Yeah. 00:08:51,848 --> 00:08:52,028 [Eric] Um, 00:08:54,028 --> 00:08:59,188 [Eric] so that is a very... Th- that to me is one of the most interesting ones, where it's like- 00:08:59,188 --> 00:08:59,198 [John] Mm-hmm 00:08:59,198 --> 00:09:04,847 [Eric] ... how quickly is the technology itself, you know, improving- 00:09:04,848 --> 00:09:04,948 [John] Yep 00:09:06,168 --> 00:09:15,347 [Eric] ... which is a big deal. But practically, people can only sort of consume that as quickly as the rate of adoption allows. 00:09:15,348 --> 00:09:15,588 [John] Right. 00:09:16,748 --> 00:09:18,168 [John] All right, you ready for our third one? 00:09:18,168 --> 00:09:19,407 [Eric] I'm ready. 00:09:19,408 --> 00:09:26,097 [John] All right. Novelty versus prescience. And I love that, I love that we, that... This wa- this was Eric's idea. [laughs] 00:09:26,097 --> 00:09:29,328 [Eric] [laughs] Prescience and precedence- 00:09:29,328 --> 00:09:29,338 [John] Yeah 00:09:29,338 --> 00:09:33,928 [Eric] ... I think is, like, those are two... I think both words are appropriate. 00:09:33,928 --> 00:09:35,118 [John] You like the words. 00:09:35,118 --> 00:09:35,128 [Eric] I- 00:09:35,128 --> 00:09:36,108 [John] You like both of those words. 00:09:36,108 --> 00:09:37,568 [Eric] I do love both of those words. 00:09:37,568 --> 00:09:37,888 [John] Yeah. 00:09:37,888 --> 00:09:38,308 [Eric] Um, 00:09:39,688 --> 00:09:46,248 [Eric] this is really interesting. So I, I'll go back to the... So let's look at two things that we've talked about. Um, 00:09:47,488 --> 00:09:49,548 [Eric] GPS and the iPhone. 00:09:49,548 --> 00:09:49,708 [John] Mm-hmm. 00:09:51,368 --> 00:09:58,548 [Eric] GPS, I think, was truly novel in its first implementation, 00:09:59,568 --> 00:10:00,088 [Eric] in that- 00:10:00,088 --> 00:10:01,208 [John] Mm-hmm. Yeah 00:10:01,208 --> 00:10:06,118 [Eric] ... um, the ability to ping a satellite with your location, 00:10:07,228 --> 00:10:12,548 [Eric] um, and to get a reading of where you are was truly novel. 00:10:12,548 --> 00:10:12,868 [John] Right. 00:10:12,868 --> 00:10:16,688 [Eric] Um, and I mean, people had, you know, 00:10:17,828 --> 00:10:29,438 [Eric] people had, you know, presented theories of this or, you know, maybe even, you know, sort of... I, I'm sure there were a lot of papers written about this. But actually to be able to do this 00:10:30,488 --> 00:10:33,888 [Eric] at a wide scale with a satellite orbiting the Earth- 00:10:33,888 --> 00:10:34,768 [John] Yeah 00:10:34,768 --> 00:10:36,488 [Eric] ... um, was pretty novel. 00:10:36,488 --> 00:10:36,608 [John] Right. 00:10:36,608 --> 00:10:49,508 [Eric] Right? And so it's like, okay, well, wow, this is, this is gonna change a lot of different things. And of course it did, and that goes back to breadth and depth, and so of course in depth it changed, you know, military and commercial applications first. Um, 00:10:51,748 --> 00:11:00,508 [Eric] and so that was very, very novel, right? But if you look at the iPhone, it's, it's easy to look at the iPhone as novel, 00:11:01,808 --> 00:11:03,408 [Eric] but it wasn't. 00:11:03,408 --> 00:11:04,547 [John] Right. 00:11:04,548 --> 00:11:05,108 [Eric] In that 00:11:06,488 --> 00:11:11,208 [Eric] you can access the internet on a mobile device. That's not new. 00:11:11,208 --> 00:11:12,068 [John] Right. 00:11:12,068 --> 00:11:17,028 [Eric] Um, email, not new. Um, text messaging, not new. 00:11:17,028 --> 00:11:17,988 [John] Phone call. 00:11:17,988 --> 00:11:21,568 [Eric] Phone call, not new. I love that I didn't even list that. [laughs] 00:11:22,868 --> 00:11:23,448 [Eric] The, so, 00:11:24,628 --> 00:11:25,668 [Eric] so the iPhone 00:11:27,168 --> 00:11:31,307 [Eric] was a novel form factor, but as a technology- 00:11:31,308 --> 00:11:32,688 [John] Barely, right? 00:11:32,688 --> 00:11:36,547 [Eric] Barely, yes. I mean, you... Even the touch screen, not new, right? 00:11:36,548 --> 00:11:36,808 [John] Yeah. 00:11:36,808 --> 00:11:43,418 [Eric] Um, but packaged and executed in a way that was very, very, very high impact- 00:11:43,418 --> 00:11:43,448 [John] Yeah 00:11:43,448 --> 00:11:43,658 [Eric] ... obviously. 00:11:43,658 --> 00:11:45,838 [John] 'Cause it was essentially one decision, 00:11:46,888 --> 00:11:48,558 [John] and it was the keyboard decision. 00:11:48,558 --> 00:11:48,568 [Eric] Yep. 00:11:48,568 --> 00:11:49,968 [John] Which was very controversial. 00:11:49,968 --> 00:11:51,148 [Eric] Very controversial, yep. 00:11:51,148 --> 00:11:54,608 [John] Was we're gonna get rid of the keyboard. Like, that, that was the decision. [laughs] 00:11:54,608 --> 00:11:55,148 [Eric] Mm-hmm. 00:11:55,148 --> 00:11:56,268 [John] You know? In a nutshell. 00:11:57,488 --> 00:11:59,428 [Eric] And so that's, this to me is, 00:12:00,468 --> 00:12:08,968 [Eric] um, novelty is very difficult to measure. Like, is something truly novel? Is there anything new under the sun? Um, 00:12:10,028 --> 00:12:23,538 [Eric] but there are, you know, again, if you think about GPS, like truly novel. The iPhoneWas, I mean, just, it, it changed so many things. It, it brought about the mobile revolution. 00:12:23,538 --> 00:12:23,548 [John] Mm-hmm. 00:12:23,548 --> 00:12:37,588 [Eric] But interestingly, it wasn't novel. And now, if you go... If you wanna get even more complicated mathematically, that influences, like, rate of adoption, right? If something is... If something has precedent or it is a- 00:12:37,588 --> 00:12:38,038 [John] Mm 00:12:38,038 --> 00:12:40,778 [Eric] ... prescient, like, invention- 00:12:40,778 --> 00:12:40,788 [John] Right 00:12:40,788 --> 00:12:41,618 [Eric] ... then that- 00:12:41,618 --> 00:12:41,628 [John] Right 00:12:41,628 --> 00:12:43,597 [Eric] ... means it's easier to adopt, right? 00:12:43,598 --> 00:12:43,628 [John] Yeah. 00:12:43,628 --> 00:12:44,638 [Eric] 'Cause it means that I'm doing- 00:12:44,638 --> 00:12:44,688 [John] For sure 00:12:44,688 --> 00:12:48,718 [Eric] ... something that I did before. It's just in a new form factor that's, like, way, way better. 00:12:48,718 --> 00:12:48,878 [John] Right. 00:12:48,878 --> 00:12:48,977 [Eric] Right? 00:12:49,978 --> 00:12:57,398 [Eric] And so the reason that I think these three matrices are really interesting is because 00:12:58,718 --> 00:13:02,348 [Eric] I think the limitation of these is, is time, right? Like how- 00:13:02,348 --> 00:13:02,368 [John] Mm-hmm 00:13:02,368 --> 00:13:04,808 [Eric] ... quickly each of these happen independently, and I'm sure- 00:13:04,808 --> 00:13:05,018 [John] Right 00:13:05,018 --> 00:13:08,238 [Eric] ... that someone at some university has done a lot of math to figure out- 00:13:08,238 --> 00:13:08,458 [John] Right 00:13:08,458 --> 00:13:19,498 [Eric] ... [laughs] you know, an actual model for this. But it's pretty hard to think about technology that is in the high, high quadrant- 00:13:19,498 --> 00:13:19,858 [John] Mm-hmm 00:13:19,858 --> 00:13:21,478 [Eric] ... across all three matrices. 00:13:21,478 --> 00:13:22,578 [John] Right. 00:13:22,578 --> 00:13:47,558 [Eric] Um, I, I mean, I've thought about it a good bit, and it's, it's pretty difficult. Um, and w- and let's... I'll, I'll narrow that scope slightly. Like, let's say in the first, you know, decade or maybe even half decade of the technology being in existence, right? So if you look at GPS, if you look at the iPhone, if you look at, you know, and we could pick one and analyze it, um, 00:13:50,458 --> 00:14:02,538 [Eric] you sort of see, like, very quickly whether there is precedent. You can see whether it's... has breadth or depth initially, and then whether it will sort of expand out of breadth or depth. Um, 00:14:03,738 --> 00:14:04,778 [Eric] you know, and 00:14:06,418 --> 00:14:09,398 [Eric] yeah. Anyways, all that to say, the, the... 00:14:11,398 --> 00:14:25,538 [Eric] When a technology is high, high, high, um, on each of them, in the top right quadrant, early in its life, I think that's a pretty clear indicator that it is going to have a significant impact. 00:14:25,538 --> 00:14:30,757 [John] Right. Yeah. We started this out, right, is it a big deal, and, and basically how do we know? 00:14:30,758 --> 00:14:31,158 [Eric] Yep. 00:14:31,158 --> 00:14:37,738 [John] Um, and with the three qu- cro- quadrants, you know, we've got high, high scores [laughs] on all three. 00:14:37,738 --> 00:14:38,618 [Eric] Yes. 00:14:38,618 --> 00:14:39,918 [John] Um, w- 00:14:39,918 --> 00:14:40,098 [Eric] Yeah. 00:14:40,098 --> 00:14:41,078 [John] Seems like... Yeah. 00:14:41,078 --> 00:14:42,398 [Eric] Which is the case with AI, right? 00:14:42,398 --> 00:14:42,638 [John] Mm-hmm. 00:14:42,638 --> 00:14:43,718 [Eric] So breadth and depth. 00:14:44,818 --> 00:14:56,268 [Eric] I mean, I... So let me, let me switch it here. I'm gonna have you analyze AI, 'cause I talked about a bunch of historical stuff. But breadth and depth. So AI breadth and AI depth. 00:14:56,268 --> 00:14:56,298 [John] Yeah. 00:14:56,298 --> 00:14:59,198 [Eric] Like, is it having an impact in both of those areas? 00:14:59,198 --> 00:14:59,978 [John] Yeah. 00:14:59,978 --> 00:15:01,018 [Eric] Or on, on both- 00:15:01,018 --> 00:15:01,618 [John] Yeah 00:15:01,618 --> 00:15:01,667 [Eric] ... factors. 00:15:01,667 --> 00:15:03,378 [John] So as far as AI breadth. 00:15:03,378 --> 00:15:03,598 [Eric] Yep. 00:15:03,598 --> 00:15:11,238 [John] Um, fastest, you know, growing technology ever. Huge, instant, almost instant adoption. 00:15:11,238 --> 00:15:11,658 [Eric] Yep. 00:15:11,658 --> 00:15:14,578 [John] To millions, millions and millions, and then billions of users. 00:15:14,578 --> 00:15:15,678 [Eric] Mm-hmm. 00:15:15,678 --> 00:15:18,098 [John] And then across a lot of domains. 00:15:18,098 --> 00:15:18,278 [Eric] Yep. 00:15:18,278 --> 00:15:21,718 [John] So knowledge work, like software, finance, you know. 00:15:21,718 --> 00:15:22,018 [Eric] Totally. 00:15:22,018 --> 00:15:23,338 [John] All, all of these things. 00:15:23,338 --> 00:15:25,588 [Eric] So breadth and depth. I, I think a good example 00:15:26,658 --> 00:15:31,038 [Eric] is software engineering is depth because it has completely changed- 00:15:31,038 --> 00:15:31,048 [John] Mm-hmm 00:15:31,048 --> 00:15:32,298 [Eric] ... the way that that profession's done. 00:15:33,378 --> 00:15:38,598 [Eric] Um, but almost every knowledge worker is using it in some way, right? 00:15:38,598 --> 00:15:45,938 [John] Which, I mean, even think about the term knowledge worker. Like, did you talk about knowledge workers all the time until AI? 00:15:45,938 --> 00:15:46,878 [Eric] [laughs] I didn't, actually. 00:15:46,878 --> 00:15:47,618 [John] I didn't either. 00:15:47,618 --> 00:15:48,138 [Eric] But I've written- 00:15:48,138 --> 00:15:48,168 [John] I- 00:15:48,168 --> 00:15:49,368 [Eric] ... a lot about it since then. 00:15:49,368 --> 00:15:52,478 [John] Yeah. But I, I remember, I remember the term from, 00:15:53,578 --> 00:15:54,938 [John] like, business books, essentially. 00:15:54,938 --> 00:15:55,138 [Eric] Yeah, totally. 00:15:55,138 --> 00:15:56,588 [John] But, like, it wasn't something that- 00:15:56,588 --> 00:15:56,588 [Eric] Yeah 00:15:56,588 --> 00:15:57,498 [John] ... people talked about. 00:15:57,558 --> 00:15:58,248 [Eric] Right. Right. 00:15:58,248 --> 00:16:01,668 [John] So we needed this new big category called a knowledge worker- 00:16:01,668 --> 00:16:01,668 [Eric] Right 00:16:01,668 --> 00:16:03,208 [John] ... to talk about this technology- 00:16:03,208 --> 00:16:03,208 [Eric] Yep 00:16:03,208 --> 00:16:06,078 [John] ... when we never would've categorized all those people together- 00:16:06,078 --> 00:16:06,268 [Eric] Yep 00:16:06,268 --> 00:16:06,818 [John] ... before. 00:16:06,818 --> 00:16:07,038 [Eric] Yep. 00:16:08,268 --> 00:16:11,278 [Eric] And then rate of improvement and rate of adoption. 00:16:12,298 --> 00:16:15,398 [Eric] This one, I think, is where it's, it's very interesting- 00:16:15,398 --> 00:16:15,598 [John] Right 00:16:15,598 --> 00:16:17,778 [Eric] ... compared to historical technologies. 00:16:17,778 --> 00:16:21,548 [John] The rate of, the rate of m- improvement question is confusing 00:16:22,918 --> 00:16:27,138 [John] because there, like, there's a million different ways to benchmark it, right? 00:16:27,138 --> 00:16:28,058 [Eric] Yep. 00:16:28,058 --> 00:16:34,958 [John] And adoption is also confusing 'cause there's a million [laughs] different ways to measure it. And- 00:16:34,958 --> 00:16:38,938 [Eric] Yeah, but let's... Okay, let's break this down to... Uh, the... I, 00:16:40,438 --> 00:16:44,478 [Eric] I agree, if you wanna be scientific about it, like, what are we measuring? 00:16:44,478 --> 00:16:45,078 [John] Right. 00:16:45,078 --> 00:16:47,878 [Eric] But if we think about rate of improvement, um, 00:16:49,938 --> 00:16:53,558 [Eric] if you just look at image generation- 00:16:53,558 --> 00:16:53,938 [John] Yeah 00:16:53,938 --> 00:16:56,488 [Eric] ... over the last 24 months, 00:16:57,758 --> 00:17:00,198 [Eric] the, the rate of improvement is mind-boggling. 00:17:00,198 --> 00:17:00,878 [John] Yeah. 00:17:00,878 --> 00:17:04,098 [Eric] Right? It's just, it's gotten so much better. 00:17:04,098 --> 00:17:04,408 [John] Right. 00:17:04,408 --> 00:17:04,818 [Eric] Right? 00:17:04,818 --> 00:17:07,458 [John] And we, and we've talked about writing, 'cause you do a lot of writing- 00:17:07,458 --> 00:17:07,708 [Eric] Mm-hmm 00:17:07,708 --> 00:17:07,888 [John] ... for your job. 00:17:07,888 --> 00:17:08,338 [Eric] Mm-hmm. 00:17:08,338 --> 00:17:16,798 [John] I've been doing some, some writing recently too, and that one's frustrating because it feels like it hasn't improved along the same axis. 00:17:16,798 --> 00:17:16,838 [Eric] Yes. 00:17:16,838 --> 00:17:18,958 [John] But I think we had the discussion of 00:17:20,178 --> 00:17:25,298 [John] that may... That probably was just core to the, you know, 2020, 2021, 20- 00:17:25,298 --> 00:17:25,418 [Eric] Right 00:17:25,418 --> 00:17:26,218 [John] ... like, that was cur- core- 00:17:26,218 --> 00:17:28,168 [Eric] Because they're large language models. 00:17:28,168 --> 00:17:28,638 [John] Early. Yeah. 00:17:28,638 --> 00:17:28,998 [Eric] Yeah, yeah. 00:17:28,998 --> 00:17:31,058 [John] So, so there's a point where we, we as 00:17:32,118 --> 00:17:35,958 [John] the general public didn't get to participate in the up- 00:17:35,958 --> 00:17:36,448 [Eric] Right. Yeah, yeah. 00:17:36,448 --> 00:17:37,618 [John] ... like, you know what I mean? 00:17:37,618 --> 00:17:38,378 [Eric] Yeah, yeah. Totally. 00:17:38,378 --> 00:17:39,808 [John] Yeah, yeah. So... 00:17:39,808 --> 00:17:40,008 [Eric] But 00:17:42,098 --> 00:17:45,708 [Eric] in aggregate, the models have improved dramatically. 00:17:45,708 --> 00:17:45,758 [John] Right. 00:17:45,758 --> 00:17:49,658 [Eric] I would say, like, at, at a minimum, year over year, and probably- 00:17:49,658 --> 00:17:49,738 [John] Right 00:17:49,738 --> 00:17:50,798 [Eric] ... at a faster rate than that. 00:17:50,798 --> 00:17:51,118 [John] Right. 00:17:51,118 --> 00:17:51,837 [Eric] And- 00:17:51,838 --> 00:17:54,358 [John] And there's, there's a bunch of axes of improvement too. 00:17:54,358 --> 00:17:54,618 [Eric] Sure. 00:17:54,618 --> 00:17:58,748 [John] Like speed, you know, quality across, like- 00:17:58,748 --> 00:17:59,718 [Eric] Yep, context window. 00:17:59,718 --> 00:17:59,918 [John] Yeah, con- 00:17:59,918 --> 00:18:00,438 [Eric] I mean- 00:18:00,438 --> 00:18:05,098 [John] All, all of the things, and, and we've definitely seen it in image. We've seen it in video too. 00:18:05,098 --> 00:18:05,418 [Eric] Yeah. 00:18:05,418 --> 00:18:07,758 [John] We've seen it in ability to code, for sure, over the last year. 00:18:07,758 --> 00:18:08,578 [Eric] 100%. 00:18:08,578 --> 00:18:10,717 [John] Writing's, uh, probably the most debatable, but- 00:18:10,718 --> 00:18:10,858 [Eric] Sure 00:18:10,858 --> 00:18:12,298 [John] ... also the most subjective. 00:18:12,298 --> 00:18:12,558 [Eric] Sure. 00:18:12,558 --> 00:18:12,657 [John] Um- 00:18:12,658 --> 00:18:13,098 [Eric] Totally. 00:18:13,098 --> 00:18:13,598 [John] So. 00:18:13,598 --> 00:18:13,758 [Eric] So 00:18:15,738 --> 00:18:19,298 [Eric] I think objectively, major improvements on a very short timescale- 00:18:19,298 --> 00:18:19,938 [John] Mm-hmm 00:18:19,938 --> 00:18:24,008 [Eric] ... and can be adopted immediately. I mean, they're just available- 00:18:24,008 --> 00:18:24,438 [John] Every improvement is immediately 00:18:24,438 --> 00:18:25,308 [Eric] ... immediately. 00:18:25,308 --> 00:18:25,668 [John] Yeah. Right. 00:18:26,434 --> 00:18:29,254 [Eric] And so that high high is really interesting. 00:18:29,254 --> 00:18:29,814 [John] Mm-hmm. 00:18:29,814 --> 00:18:32,044 [Eric] Um, there's no... I mean, 00:18:34,554 --> 00:18:40,274 [Eric] if we wanna get into nitty-gritty details, like, there are some price barriers, but not like the iPhone. 00:18:40,274 --> 00:18:40,413 [John] Right. 00:18:40,414 --> 00:18:41,434 [Eric] Right? Um- 00:18:41,434 --> 00:18:42,253 [John] Yep 00:18:42,253 --> 00:18:44,014 [Eric] ... it's very different than the iPhone, where it's like a- 00:18:44,014 --> 00:18:47,474 [John] You, you already have the $200 version, right? 'Cause there's- 00:18:47,474 --> 00:18:48,274 [Eric] Exactly. Yeah, yeah 00:18:48,274 --> 00:18:51,094 [John] ... there's open source Ch- or Chinese models that are already at that $200 version- 00:18:51,094 --> 00:18:51,644 [Eric] Totally. Yep 00:18:51,644 --> 00:18:52,264 [John] ... if you want that. 00:18:52,264 --> 00:18:52,574 [Eric] Yep. Exactly. 00:18:52,574 --> 00:18:53,634 [John] All the way up to... Yeah. 00:18:53,634 --> 00:18:59,643 [Eric] Yep. And so there, there really isn't, like, a major price barrier to the improvements, right? 00:18:59,643 --> 00:18:59,654 [John] Yeah. 00:18:59,654 --> 00:19:03,234 [Eric] And so you can just sort of immediately access them. 00:19:03,234 --> 00:19:03,334 [John] Right. 00:19:03,334 --> 00:19:04,954 [Eric] Which is really interesting. 00:19:04,954 --> 00:19:05,264 [John] Right. 00:19:05,264 --> 00:19:07,074 [Eric] I... That, that to me is one of the most interesting- 00:19:07,074 --> 00:19:07,794 [John] Right 00:19:07,794 --> 00:19:08,694 [Eric] ... matrices. Um- 00:19:08,694 --> 00:19:18,714 [John] You know what another interesting thing is with AI? That it's filled in a lot of, like, gaps that we just never got to. So for example, mobile apps. 00:19:18,714 --> 00:19:19,254 [Eric] Mm. 00:19:19,254 --> 00:19:25,614 [John] Like bus- we never had full, like, parity between apps we use every day for business- 00:19:25,614 --> 00:19:25,714 [Eric] Mm-hmm 00:19:25,714 --> 00:19:26,494 [John] ... between mobile- 00:19:26,494 --> 00:19:26,554 [Eric] Mm-hmm 00:19:26,554 --> 00:19:27,234 [John] ... and desktop. 00:19:27,234 --> 00:19:27,674 [Eric] Mm-hmm. 00:19:27,674 --> 00:19:28,734 [John] We never got there. 00:19:28,734 --> 00:19:28,794 [Eric] Yeah. 00:19:28,794 --> 00:19:29,874 [John] Like, in general. 00:19:29,874 --> 00:19:30,094 [Eric] Right. 00:19:30,094 --> 00:19:30,834 [John] Isn't that funny? 00:19:30,834 --> 00:19:31,293 [Eric] Yeah. 00:19:31,293 --> 00:19:31,813 [John] And like- 00:19:31,814 --> 00:19:34,354 [Eric] And the AI... And AI is like... Yeah, it is really interesting. 00:19:34,354 --> 00:19:41,254 [John] And AI is, like, for the most part mobile native and desktop and, and there's, like, pretty high parity between the two solutions. 00:19:41,254 --> 00:19:41,974 [Eric] Yeah. 00:19:41,974 --> 00:19:42,644 [John] Um- 00:19:42,644 --> 00:19:43,614 [Eric] Well, I actually- 00:19:43,614 --> 00:19:44,164 [John] It's just an interesting... Yeah 00:19:44,164 --> 00:19:50,204 [Eric] ... I think that g- kinda goes into the third matrix, which is novelty and precedent. 00:19:50,204 --> 00:19:50,294 [John] Mm-hmm. 00:19:50,294 --> 00:19:52,034 [Eric] Right? So 00:19:53,154 --> 00:19:57,294 [Eric] AI is very novel, right? Like- 00:19:57,294 --> 00:19:58,254 [John] Yeah 00:19:58,254 --> 00:20:04,894 [Eric] ... so and, and what's interesting is it's high high, and this is a very... This one is very, very interesting. 00:20:05,954 --> 00:20:10,414 [Eric] It is, it, it is truly novel because it's generative, 00:20:12,114 --> 00:20:12,304 [Eric] but 00:20:13,694 --> 00:20:17,334 [Eric] we are used to, like, chat, right? 00:20:17,334 --> 00:20:17,554 [John] Mm-hmm. 00:20:17,554 --> 00:20:27,134 [Eric] And we are used to, like, recommendations being made and summaries, right? And so there is a ton of precedent for this, but it is actually truly novel. 00:20:27,134 --> 00:20:27,774 [John] Right. 00:20:27,774 --> 00:20:29,654 [Eric] And that's a weird one. 00:20:29,654 --> 00:20:30,034 [John] Yep. 00:20:30,034 --> 00:20:37,523 [Eric] Being high high on novelty and precedent is pretty wild. Like, there are very few things that I can think of- 00:20:37,523 --> 00:20:37,523 [John] Yeah 00:20:37,523 --> 00:20:39,534 [Eric] ... that are high on that, on, on both. 00:20:39,534 --> 00:20:49,094 [John] So really fun example of AI being novel, um, was listening to an interview with one of the co-founders of, of OpenAI, um, Andrej Karpathy- 00:20:49,094 --> 00:20:49,474 [Eric] Mm-hmm 00:20:49,474 --> 00:20:52,164 [John] ... who ironically is now going to work for [chuckles] Anthropic- 00:20:52,164 --> 00:20:52,164 [Eric] Yes 00:20:52,164 --> 00:21:07,054 [John] ... which we talked about last episode. And he just made the awesome comment about how when this stuff is built out originally, we made a decision toward, um, com- compute being, like, RAM and CPU. 00:21:07,054 --> 00:21:07,273 [Eric] Mm-hmm. 00:21:07,274 --> 00:21:31,974 [John] Like, those are, like, two of the [chuckles] fundamental, like, pieces of compute. And then, um, he also made the comment, like, we're moving toward this new world where the, the neural net, which, like, so far is more of this TPU, GPU, which is kind of a different hardware set, is becoming, like, the baseline. And then the, you know, CPU, like, memory is, is more, like, auxiliary. 00:21:31,974 --> 00:21:32,614 [Eric] Right. 00:21:32,614 --> 00:21:41,243 [John] Um, so that's, like, a super technical explanation, but, but the point is, like, the novelty will go all the way down to the hardware and the data centers- 00:21:41,243 --> 00:21:41,243 [Eric] Mm 00:21:41,243 --> 00:21:44,034 [John] ... and, like, how the servers are gonna be formed- 00:21:44,034 --> 00:21:44,314 [Eric] Yep 00:21:44,314 --> 00:21:46,394 [John] ... and built. It is not just a, 00:21:47,434 --> 00:21:49,554 [John] like, software novelty. 00:21:49,554 --> 00:21:49,894 [Eric] Yes. 00:21:49,894 --> 00:21:53,334 [John] Like, it's all the way down to how we're gonna build data centers. I thought that was a really good point. 00:21:53,334 --> 00:21:53,474 [Eric] Yeah. 00:21:54,664 --> 00:21:55,424 [Eric] Uh, it, it is, 00:21:56,734 --> 00:22:03,594 [Eric] it is so interesting to talk to people who, across the spectrum, who interact with AI. 00:22:03,594 --> 00:22:04,474 [John] Mm-hmm. 00:22:04,474 --> 00:22:06,743 [Eric] Because you have people who 00:22:08,254 --> 00:22:09,814 [Eric] the precedent is 00:22:11,414 --> 00:22:13,864 [Eric] this is so much better than Google Search, right? 00:22:13,864 --> 00:22:13,924 [John] Mm-hmm. 00:22:13,924 --> 00:22:26,634 [Eric] And it's like, yes, that is... Google Search is a great precedent, and yes, it is better than Google for this, right? But it's so much more novel than a better Google Search, right? It- 00:22:26,634 --> 00:22:26,714 [John] Right 00:22:26,714 --> 00:22:28,053 [Eric] ... it can do so much more. 00:22:28,054 --> 00:22:28,214 [John] Right. 00:22:28,214 --> 00:22:42,114 [Eric] And so I think with any technology, but we're discussing AI specifically, if you're high high on e- on all three of those quadrants- 00:22:42,114 --> 00:22:42,574 [John] Right 00:22:42,574 --> 00:22:45,773 [Eric] ... I think it's pretty safe to say that it's a big deal. 00:22:45,773 --> 00:22:46,054 [John] Right. 00:22:46,054 --> 00:22:51,734 [Eric] Um, so that's, that's where I'm at. AI is actually a big deal. 00:22:51,734 --> 00:22:51,994 [John] Wow. 00:22:53,114 --> 00:23:06,404 [John] So what, what would you say, like, wrapping this up, other, other than these quadrants, is there, is there... What's, what is the less measurable piece that makes you say it's a big deal? 00:23:06,404 --> 00:23:06,434 [Eric] Mm. 00:23:06,434 --> 00:23:13,604 [John] So we, we just spent, you know, 20 minutes on, like, as scientifically as we, you know, some boxes to measure it. 00:23:13,604 --> 00:23:13,764 [Eric] Right, right, right. 00:23:13,764 --> 00:23:19,974 [John] But what's the personal, every day I work with this piece as far as, like, AI is a big deal? 00:23:23,834 --> 00:23:26,234 [Eric] You can do so much more 00:23:28,034 --> 00:23:30,294 [Eric] working with AI. Um, 00:23:31,514 --> 00:23:38,154 [Eric] it's an amplifier. So I'll, I'll s- maybe take two angles on this. 00:23:40,034 --> 00:23:43,454 [Eric] If you are already really good at something- 00:23:43,454 --> 00:23:44,574 [John] Mm-hmm 00:23:44,574 --> 00:23:55,074 [Eric] ... it's an unbelievable amplifier of those skills, and I actually think that's what probably people notice the most- 00:23:55,074 --> 00:23:55,304 [John] Right 00:23:55,304 --> 00:23:56,554 [Eric] ... who are very, 00:23:58,174 --> 00:23:58,514 [Eric] uh, 00:23:59,594 --> 00:24:01,994 [Eric] you know, who, who talk about AI a lot- 00:24:01,994 --> 00:24:02,714 [John] Right 00:24:02,714 --> 00:24:04,634 [Eric] ... um, is that 00:24:05,834 --> 00:24:08,354 [Eric] I was already good at this, and I feel like I can do it 00:24:10,014 --> 00:24:15,353 [Eric] 10 times faster or 10 times better or 10 times more of it. 00:24:15,354 --> 00:24:15,554 [John] Right. 00:24:15,554 --> 00:24:15,734 [Eric] Right? 00:24:16,754 --> 00:24:17,004 [Eric] Um, 00:24:18,734 --> 00:24:24,134 [Eric] and so it's a huge, huge amplifier, which is also a, you know, that can be a time saver, but it can also- 00:24:24,134 --> 00:24:24,314 [John] Right 00:24:24,314 --> 00:24:25,913 [Eric] ... mean that you just, you know, sort of- 00:24:25,914 --> 00:24:26,714 [John] Just do more work 00:24:26,714 --> 00:24:36,150 [Eric] ... you know, do more work. Um-I think probably the piece that is not talked about nearly as much is that 00:24:37,290 --> 00:24:42,530 [Eric] for the curious person, it's such a tool for learning and education. 00:24:42,530 --> 00:24:43,310 [John] Right. 00:24:43,310 --> 00:24:49,800 [Eric] Um, it is so much better to try to get your arms around a concept or a technology. I mean, 00:24:50,990 --> 00:24:53,190 [Eric] I mean, so often I will 00:24:54,330 --> 00:24:57,610 [Eric] have AI teach me about something that I don't know. 00:24:58,870 --> 00:24:59,050 [John] Right. 00:24:59,050 --> 00:25:01,150 [Eric] Um, and it's so great at that. 00:25:01,150 --> 00:25:01,290 [John] Right. 00:25:01,290 --> 00:25:04,390 [Eric] It can build a mini course for you. It can build a quiz for you- 00:25:04,390 --> 00:25:04,399 [John] Right 00:25:04,399 --> 00:25:11,550 [Eric] ... to test your [laughs] knowledge on something, right? And so it's not just about skills that you already have, but I think, um, 00:25:12,750 --> 00:25:21,930 [Eric] it's really incredible for the curious person, amplification of skills and then r- like, much more rapid acquisition of new skills and new knowledge- 00:25:21,930 --> 00:25:22,460 [John] Right 00:25:22,460 --> 00:25:24,350 [Eric] ... is incredible. Um, 00:25:25,510 --> 00:25:26,540 [Eric] and I mean, I would say, 00:25:28,230 --> 00:25:28,550 [Eric] uh, 00:25:29,710 --> 00:25:36,530 [Eric] I have talked to multiple people who have described it almost like a drug. 00:25:36,530 --> 00:25:36,970 [John] Mm-hmm. 00:25:36,970 --> 00:25:43,150 [Eric] And I don't, I don't think that that's inaccurate necessarily. Um, 00:25:44,790 --> 00:25:46,290 [Eric] I wanna be careful with that because- 00:25:46,290 --> 00:25:46,300 [John] Right 00:25:46,300 --> 00:25:50,389 [Eric] ... I don't, I don't, you know, there- that's a very loaded analogy. 00:25:50,390 --> 00:25:50,510 [John] Right. 00:25:50,510 --> 00:25:50,950 [Eric] Um, 00:25:52,190 --> 00:25:56,030 [Eric] but if you have... If your mind is super hungry 00:25:57,170 --> 00:26:01,690 [Eric] and you're a discerning person, AI is a very, very powerful tool. 00:26:01,690 --> 00:26:04,570 [John] Right. Do you remember the movie Limitless? 00:26:04,570 --> 00:26:06,210 [Eric] I do. [laughs] Yes, I do. 00:26:06,210 --> 00:26:08,270 [John] That's what I'm picturing when you're saying drug. 00:26:08,270 --> 00:26:09,130 [Eric] Yeah. Yeah. 00:26:09,130 --> 00:26:11,050 [John] Is that kinda what, what you're picturing? 00:26:11,050 --> 00:26:17,770 [Eric] Yeah, for sure. I mean, I guess, you know, I, I wrote about this on my blog, I don't know, weeks or months ago, but, um... 00:26:19,410 --> 00:26:22,670 [John] And, and I'm trying to capture a feeling. Like, the... I don't even remember what all the- 00:26:22,670 --> 00:26:22,680 [Eric] Yeah. [laughs] 00:26:22,680 --> 00:26:27,570 [John] ... what all he gets out of the drug in Limitless, but just kind of a feeling for a lot of people. 00:26:27,570 --> 00:26:32,270 [Eric] I put off a project that I needed to do. Um, 00:26:34,810 --> 00:26:37,669 [Eric] it was a, it was actually a presentation for our, like, 00:26:39,190 --> 00:26:41,370 [Eric] annual company gathering, and I was giving- 00:26:41,370 --> 00:26:41,380 [John] Okay 00:26:41,380 --> 00:26:42,330 [Eric] ... a presentation on- 00:26:42,330 --> 00:26:42,670 [John] Okay 00:26:42,670 --> 00:26:44,990 [Eric] ... AI engine optimization. [laughs] 00:26:46,150 --> 00:26:49,210 [Eric] And they do such a good job at Vercel of 00:26:50,390 --> 00:26:52,700 [Eric] having someone review your talk with you, and- 00:26:52,700 --> 00:26:52,700 [John] Sure 00:26:52,700 --> 00:26:54,100 [Eric] ... you know, they wanna make sure it's super high quality. 00:26:54,100 --> 00:26:54,330 [John] Right. It's good, yeah. 00:26:54,330 --> 00:26:55,720 [Eric] They vet the submissions and- 00:26:55,720 --> 00:26:55,720 [John] Right 00:26:55,720 --> 00:27:07,110 [Eric] ... everything, and, um... And just compared to, like, the amount of... I would've started on this, like, weeks and weeks before. 00:27:07,110 --> 00:27:07,489 [John] Mm-hmm. 00:27:07,490 --> 00:27:07,890 [Eric] Um, 00:27:09,030 --> 00:27:18,540 [Eric] but it was just a very different sensation, if you wanna talk about the feeling. It wasn't that I was talking about something that I didn't know about, 'cause it's something that I sort of, like, work in and problems I solve- 00:27:18,540 --> 00:27:18,540 [John] Right 00:27:18,540 --> 00:27:21,450 [Eric] ... like, every day, but I hadn't codified it into 00:27:22,650 --> 00:27:24,010 [Eric] an actual presentation. 00:27:24,010 --> 00:27:24,950 [John] Right. 00:27:24,950 --> 00:27:30,470 [Eric] Um, and I waited way longer than I would've ever done in my career previously. 00:27:30,470 --> 00:27:31,430 [John] Yeah. Right. 00:27:31,430 --> 00:27:36,310 [Eric] But it's because I knew that I had a really good handle on, like, the basics of what I- 00:27:36,310 --> 00:27:36,320 [John] Right 00:27:36,320 --> 00:27:37,010 [Eric] ... wanted to say, 00:27:38,030 --> 00:27:41,399 [Eric] and my guess was that with AI, I could, 00:27:42,590 --> 00:27:45,090 [Eric] I could get the structure done in two hours. 00:27:45,090 --> 00:27:45,810 [John] Right. 00:27:45,810 --> 00:27:46,610 [Eric] And I did. 00:27:46,610 --> 00:27:46,950 [John] Yeah. 00:27:46,950 --> 00:27:48,670 [Eric] And it was great. Um, 00:27:49,830 --> 00:27:57,030 [Eric] and I'm not saying that as, like, I'm gonna procrastinate. It just is, it's a very potent tool- 00:27:57,030 --> 00:27:57,230 [John] Right 00:27:57,230 --> 00:27:57,670 [Eric] ... um, 00:27:58,970 --> 00:28:02,290 [Eric] you know, if you sort of know how to wield it. 00:28:02,290 --> 00:28:04,850 [John] Yep. All right, I have a personal example. 00:28:04,850 --> 00:28:05,470 [Eric] All right. 00:28:05,470 --> 00:28:10,410 [John] So gave a, I gave a best man speech at a wedding a few months ago. 00:28:10,410 --> 00:28:11,270 [Eric] Mm-hmm. 00:28:11,270 --> 00:28:14,470 [John] Um, and I was determined, I'm not gonna use AI to write this. 00:28:14,470 --> 00:28:14,630 [Eric] Mm-hmm. 00:28:14,630 --> 00:28:17,330 [John] Like, like, I want this to be real. I want this to be authentic. 00:28:17,330 --> 00:28:17,340 [Eric] Mm. 00:28:17,340 --> 00:28:24,570 [John] I want it to be me. But AI was so helpful. So I did, I did multiple iterations of, like, brain dumps, like transcripts. 00:28:24,570 --> 00:28:25,550 [Eric] Mm-hmm. 00:28:25,550 --> 00:28:29,320 [John] Like, clean those up, like, use AI, like, kind of as a light editor. 00:28:29,320 --> 00:28:29,770 [Eric] Mm-hmm. 00:28:29,770 --> 00:28:32,450 [John] But the most interesting thing was the voice AI 00:28:33,550 --> 00:28:39,950 [John] and, like, reading it and, like, getting feedback per sections, talking about delivery of, like- 00:28:39,950 --> 00:28:40,530 [Eric] Yep 00:28:40,530 --> 00:28:44,070 [John] ... what should I do here, what should I do here, as far as, you know- 00:28:44,070 --> 00:28:44,410 [Eric] Yeah 00:28:44,410 --> 00:28:45,649 [John] ... you know, like a coach, right? 00:28:45,649 --> 00:28:45,930 [Eric] Totally. 00:28:45,930 --> 00:28:46,750 [John] It was awesome. 00:28:46,750 --> 00:28:47,430 [Eric] Totally. 00:28:47,430 --> 00:29:00,250 [John] And, and I think those are the things that, like, people are gonna find are super useful, where, like, most people... Like, say you've graduated. Like, you don't just use it as Google, but then you've graduated to using it to do actual work. 00:29:00,250 --> 00:29:00,730 [Eric] Right. 00:29:00,730 --> 00:29:05,310 [John] I think there's this, like, third thing where you'll, you'll do things you wouldn't have done before. 00:29:05,310 --> 00:29:05,490 [Eric] Yes. 00:29:05,490 --> 00:29:15,580 [John] Like, I never would've, like, worked on delivery of that. I would've just, like, written it down, like, spent a bunch of time on it, like, last minute, like, read through it a few times, and, like, you know. 00:29:15,580 --> 00:29:15,590 [Eric] Yeah. 00:29:15,590 --> 00:29:16,370 [John] It is what it is. 00:29:16,370 --> 00:29:17,550 [Eric] Yeah, yeah. Exactly. 00:29:17,550 --> 00:29:19,090 [John] Um, so... 00:29:19,090 --> 00:29:25,550 [Eric] I think we're going to see an unbelievable unleashing of human creativity because- 00:29:25,550 --> 00:29:25,560 [John] Yeah 00:29:25,560 --> 00:29:28,310 [Eric] ... it's, because AI is high, high on every quadrant. 00:29:28,310 --> 00:29:28,630 [John] Yep. 00:29:28,630 --> 00:29:30,330 [Eric] Like, there are gonna be so many new things. 00:29:30,330 --> 00:29:32,590 [John] Yep. I think that's the perfect way to end this. 00:29:32,590 --> 00:29:32,990 [Eric] All right. 00:29:34,370 --> 00:29:38,210 [Eric] Thank you for joining Token Intelligence, and we will catch you on the next one. 00:29:42,229 --> 00:29:46,690 [Eric] [outro music]