Why the AI apocalypse keeps getting postponed

00:00:00,200 --> 00:00:34,580 [Eric] [upbeat music] Welcome back to the Token Intelligence Show. AI is changing the way that we work, and on the Token Intelligence Show you can get the cutting edge. You can learn to live with wisdom and work with wisdom in the age of AI. And John, today we are talking about a subject that has been, uh, [chuckles] it has been discussed and debated ad nauseam- 00:00:34,580 --> 00:00:34,800 [John] Yeah 00:00:34,800 --> 00:00:44,180 [Eric] ... on social media, in scholarly articles, and it's a subject of abundance versus the permanent underclass. 00:00:44,180 --> 00:00:45,200 [John] Man. 00:00:45,200 --> 00:00:52,080 [Eric] Which is pretty heady stuff. [laughs] So we're kinda g- this, it feels very science fiction, but it has been very, 00:00:53,180 --> 00:00:59,580 [Eric] very prevalent in the conversation around AI. So let's, let's work on some definitions- 00:00:59,580 --> 00:00:59,650 [John] Yeah 00:00:59,650 --> 00:01:01,460 [Eric] ... because this is something we love to do on the show- 00:01:01,460 --> 00:01:01,570 [John] What- 00:01:01,570 --> 00:01:02,520 [Eric] ... and something that's helpful. 00:01:02,520 --> 00:01:09,100 [John] What is the, what is the permanent underclass? Like, what, where did that come from? Is that some kind of reference that I should know? 00:01:09,100 --> 00:01:13,660 [Eric] I, we didn't, uh, we didn't dig into the etymology of that term before the show- 00:01:13,660 --> 00:01:14,010 [John] Right 00:01:14,010 --> 00:01:18,560 [Eric] ... um, which we probably should have, but it's a very dystopian term. 00:01:18,560 --> 00:01:19,220 [John] Yeah. 00:01:19,220 --> 00:01:26,240 [Eric] Uh, why don't you, can you give us, like, a brief understanding of what the permanent underclass concept is- 00:01:26,240 --> 00:01:26,250 [John] Yeah 00:01:26,250 --> 00:01:28,080 [Eric] ... as it relates to AI? 00:01:28,080 --> 00:01:45,940 [John] Yeah. Uh, so, so the permanent underclass is this term that keeps getting thrown out when people are basically saying, "Hey, AI's gonna replace your job, um, and if you don't change and get, you know, get with the new AI version of your job, like, you're gonna be- 00:01:45,940 --> 00:01:45,970 [Eric] Right 00:01:45,970 --> 00:01:47,540 [John] ... in the permanent underclass- 00:01:47,540 --> 00:01:47,740 [Eric] Mm-hmm 00:01:47,740 --> 00:01:52,580 [John] ... never able to, you know, I don't know, make any work again. I don't know. 00:01:52,580 --> 00:01:54,220 [Eric] Right. Which is, 00:01:55,360 --> 00:01:58,640 [Eric] I- I return to dystopian, but you're sort of talking about, you know- 00:01:58,640 --> 00:01:58,760 [John] Right 00:01:58,760 --> 00:02:10,940 [Eric] ... a sort of robot machine class. Uh, a- you know, and some humans on par with that, and then, you know, the underclass. And so what's the opposite? What's the, what's the foil of that view? 00:02:12,200 --> 00:02:17,400 [John] Oh, I thought y- I, I was thinking, like, what's the opposite of the permanent underclass? I don't know if we have a term for that. 00:02:17,400 --> 00:02:19,060 [Eric] Oh, that's interesting. 00:02:19,060 --> 00:02:19,340 [John] Yeah. 00:02:19,340 --> 00:02:21,420 [Eric] Uh, probably universal, 00:02:22,500 --> 00:02:25,500 [Eric] universal income is probably conceptually 00:02:26,980 --> 00:02:27,280 [Eric] the, 00:02:28,460 --> 00:02:29,640 [Eric] the opposite, right? 00:02:29,640 --> 00:02:36,060 [John] Well, I think that was the solve for it, but if you have some group of people that's permanent underclass, like, what is the other group of people? 00:02:36,060 --> 00:02:38,140 [Eric] Oh. Oh, sorry. Yeah, yeah. Okay. 00:02:38,140 --> 00:02:38,320 [John] I don't know. 00:02:38,320 --> 00:02:38,680 [Eric] Yeah, I don't know. 00:02:38,680 --> 00:02:40,519 [John] I don't, haven't heard that named. 00:02:40,520 --> 00:02:41,800 [Eric] Tech overlord- 00:02:41,800 --> 00:02:42,140 [John] [laughs] Yeah 00:02:42,140 --> 00:02:42,820 [Eric] ... I think is- 00:02:42,820 --> 00:02:43,660 [John] I mean, pretty much 00:02:43,660 --> 00:02:44,370 [Eric] ... is true. [laughs] 00:02:44,370 --> 00:02:49,060 [John] But, okay. So, so I think it's helpful to dissect the mindsets a little bit. 00:02:49,060 --> 00:02:49,780 [Eric] Mm-hmm. 00:02:49,780 --> 00:02:59,519 [John] Like, the, the permanent underclass mindset, the winners and losers mindset, which has been, you know... Th- that's a way of thinking regardless of AI. 00:02:59,520 --> 00:03:00,140 [Eric] Mm-hmm. 00:03:00,480 --> 00:03:02,540 [John] And then kind of the abundance mentality- 00:03:02,540 --> 00:03:02,640 [Eric] Yeah 00:03:02,640 --> 00:03:03,080 [John] ... mindset. I think- 00:03:03,080 --> 00:03:05,040 [Eric] Which, let's define the abundance mentality- 00:03:05,040 --> 00:03:05,049 [John] Yeah 00:03:05,049 --> 00:03:20,160 [Eric] ... as it relates to AI, right? So permanent underclass is this concept that they're, you know, AI, AGI robots. Let's sort of, you know, unfairly lump a bunch of that stuff- 00:03:20,160 --> 00:03:20,520 [John] Right 00:03:20,520 --> 00:03:22,670 [Eric] ... in together conceptually. 00:03:22,670 --> 00:03:22,740 [John] Right. 00:03:22,740 --> 00:03:35,220 [Eric] And they take over a bunch of jobs, and so what's left for the people who are not, you know, controlling the robots, um, you know, or who don't have some- 00:03:35,220 --> 00:03:35,820 [John] Right 00:03:35,820 --> 00:03:39,060 [Eric] ... uh, some economic means- 00:03:39,060 --> 00:03:39,400 [John] Right 00:03:39,400 --> 00:03:44,300 [Eric] ... um, are in some sort of permanent underclass, right, that are serving the robots or, or something- 00:03:44,300 --> 00:03:44,310 [John] Right 00:03:44,310 --> 00:03:44,900 [Eric] ... of that nature. 00:03:44,900 --> 00:03:45,210 [John] Right. 00:03:45,210 --> 00:03:46,640 [Eric] Right? V- again, very dystopian- 00:03:46,640 --> 00:03:46,650 [John] Yeah 00:03:46,650 --> 00:03:51,300 [Eric] ... but I mean, not, not a, not an uncommon, [chuckles] you know, topic of discussion- 00:03:51,300 --> 00:03:51,900 [John] Right 00:03:51,900 --> 00:03:55,420 [Eric] ... in the heart of Silicon Valley. Okay. So then there's this abundance 00:03:56,500 --> 00:03:57,180 [Eric] view. 00:03:57,180 --> 00:03:57,240 [John] Right. 00:03:57,240 --> 00:03:59,740 [Eric] So what's the abund- define the abundance view. 00:04:00,320 --> 00:04:01,520 [John] Yeah. I mean, I think, 00:04:02,620 --> 00:04:10,360 [John] I think there's a number of people that have this view long term, and then, and then a few people that even have it, like, mid and... I don't know if I wanna say short term, but at least mid term. 00:04:10,360 --> 00:04:11,120 [Eric] Mm-hmm. 00:04:11,120 --> 00:04:11,300 [John] That 00:04:12,440 --> 00:04:13,060 [John] basically, 00:04:14,160 --> 00:04:24,180 [John] sure, AI is going to change things, but believes deeply in humans' resilience and creativity and abilities to adapt- 00:04:24,180 --> 00:04:24,380 [Eric] Mm-hmm 00:04:24,380 --> 00:04:32,150 [John] ... and realizes that while things might not look the same way they do now, things are gonna change and there's gonna be new jobs. Like- 00:04:32,150 --> 00:04:32,150 [Eric] Mm 00:04:32,150 --> 00:04:35,070 [John] ... some existing jobs might change or go away, and then there's- 00:04:35,070 --> 00:04:35,070 [Eric] Yep 00:04:35,070 --> 00:04:36,320 [John] ... gonna be new jobs. 00:04:36,320 --> 00:04:36,880 [Eric] Yep. 00:04:36,880 --> 00:04:52,820 [John] And that the abundance is the belief in that, in, and partially a belief that b- c- 'cause a lot of the pushback is like, "Well, it's gonna happen so fast, people can't change," and it's like partially, part of the belief in abundance is, like, is actually the belief in people that they are gonna be able to change quickly. 00:04:52,820 --> 00:04:53,440 [Eric] Hmm. 00:04:53,440 --> 00:04:53,570 [John] Um, 00:04:55,100 --> 00:05:02,780 [John] or, or at least a large enough, you know, quantity of people where, where it's not some kind of massive event. 00:05:02,780 --> 00:05:13,260 [Eric] Yep. So let's talk about a couple of things here. So the first question that I wanna tackle is why are people scared? Because- 00:05:13,260 --> 00:05:13,480 [John] Right 00:05:13,480 --> 00:05:17,500 [Eric] ... there is a lot of ink being spill- being spilled about 00:05:18,680 --> 00:05:33,020 [Eric] why we should be concerned about AI. There's, you know, there are very doomsday-esque predictions in the job market. You know, these industries are gonna be decimated, um, 00:05:34,080 --> 00:05:37,810 [Eric] you know, from a wide variety of people. You know? 00:05:37,810 --> 00:05:37,820 [John] Right. 00:05:37,820 --> 00:05:53,640 [Eric] We... Independent journalists, people working within AI, you know, technology founders who are in Silicon Valley. And so why are people... Like, why are people scared, right? And I think, and I, and again, let me put a sharp point on the two groups of people. 00:05:54,880 --> 00:06:10,360 [Eric] I see fear from people who we would say are inside the machine, right?... multiple articles from founders in Silicon Valley who are saying, "Hey, everyone, wake up. Like, you don't understand how powerful this is." 00:06:10,360 --> 00:06:10,460 [John] Right. 00:06:10,460 --> 00:06:17,480 [Eric] "It's going to take over so many jobs. We are not prepared. We need to think about universal, you know, basic income," et cetera, right? 00:06:17,480 --> 00:06:18,360 [John] Right. 00:06:18,360 --> 00:06:36,270 [Eric] And then you have average, let's say the average consumer who's sort of reading the, that type of thing, reading the headlines, and who is, you know, they are also scared because maybe they don't know how to interpret it, right? Am I reading that correctly? What's your read on how pe- 00:06:36,270 --> 00:06:36,270 [John] Yeah 00:06:36,270 --> 00:06:37,210 [Eric] ... like why people are scared? 00:06:38,940 --> 00:06:48,880 [John] So I th- I think people are scared about AI, um, for a couple of different reasons. I'll address, like, the, let's call it the Main Street crowd first. 00:06:48,880 --> 00:06:49,760 [Eric] Yep. 00:06:49,760 --> 00:06:54,100 [John] And I think a lot of that is driven by fear of the unknown, 00:06:55,120 --> 00:07:10,400 [John] um, and just not understanding. Like, what, like, like, I think there's a lot of fear, like I should be adopting AI. I should be incorporating AI into my company. I don't know how. I think we use it s- some, but I don't know if we're using it right. 00:07:10,400 --> 00:07:10,880 [Eric] Mm-hmm. 00:07:10,880 --> 00:07:11,000 [John] Like, 00:07:12,040 --> 00:07:14,440 [John] fear of the unknown and then, like, FOMO. 00:07:14,440 --> 00:07:14,540 [Eric] Mm. 00:07:14,540 --> 00:07:25,800 [John] Like some combination of that. Um, so what are we missing, and then, like, w- what do we not know? And then on the other side, people that, you know, at the heart of the innovation, um, 00:07:27,600 --> 00:07:31,680 [John] number one, I mean, that's what they, [chuckles] that's what they spend all day doing. 00:07:31,680 --> 00:07:32,320 [Eric] Right. 00:07:32,320 --> 00:07:32,640 [John] And 00:07:33,660 --> 00:07:35,030 [John] it's funny, like, all, all of the, 00:07:36,040 --> 00:07:44,000 [John] all of the labs, that's what we call them, um, they're labs. Like labs are not reality. 00:07:44,000 --> 00:07:44,620 [Eric] Hmm. 00:07:44,620 --> 00:07:48,480 [John] So part of the reason for their feelings is they work in labs. 00:07:50,280 --> 00:07:50,640 [Eric] Yeah. 00:07:50,640 --> 00:08:07,300 [John] Like, not, not that, like, there's not validity to some of what they're saying, but, um, I mean, just recently, like we've talked about this before, there's, there's multiple, um, consulting, like implementation companies coming out of OpenAI and Anthropic. 00:08:07,300 --> 00:08:07,890 [Eric] Right. 00:08:07,890 --> 00:08:18,900 [John] Um, so I... And, and we've talked about this before too, but there's a bubble. There, there's a lab bubble of, like, people interacting with people in the bubble. 00:08:18,900 --> 00:08:18,949 [Eric] Yeah. 00:08:18,949 --> 00:08:29,940 [John] You know? And, and I think some of what they say is true, and some of what I can't verify is true or not, 'cause, you know, they would have access to information that we don't have access to. 00:08:29,940 --> 00:08:30,780 [Eric] Mm-hmm. 00:08:30,780 --> 00:08:38,360 [John] Um, but some of it has to be influenced by the bubble. I don't think you can say that has zero impact on their thinking. 00:08:38,360 --> 00:08:38,600 [Eric] Right. 00:08:39,840 --> 00:08:41,080 [Eric] It's a tricky... 00:08:42,460 --> 00:08:48,420 [Eric] It really is tricky because as I think about building out my team, 00:08:50,280 --> 00:08:59,920 [Eric] I am looking specifically for people who know how to wield AI at a very, very high level. 00:08:59,920 --> 00:09:00,920 [John] Right. 00:09:00,920 --> 00:09:01,420 [Eric] Um, 00:09:03,460 --> 00:09:09,740 [Eric] and very practically, what that means is that I'm looking for 00:09:11,180 --> 00:09:14,160 [Eric] what is today a pretty small subset of people, 00:09:15,940 --> 00:09:20,240 [Eric] and outside of that subset, like I'm not really considering people- 00:09:20,240 --> 00:09:20,620 [John] Right 00:09:20,620 --> 00:09:23,960 [Eric] ... who a year ago, or let's say a year and a half ago, 00:09:25,380 --> 00:09:28,440 [Eric] would have been traditionally a great fit for the job. 00:09:28,440 --> 00:09:28,780 [John] Interesting. 00:09:28,780 --> 00:09:29,300 [Eric] Right? And so 00:09:30,380 --> 00:09:46,200 [Eric] I think when you extrapolate that out from the inside of Silicon Valley, you sort of... It, it's not hard to get to a place where you say, "Oh, wow," like the job pool at a theoretical 100% has been 00:09:47,560 --> 00:09:49,640 [Eric] dramatically decreased, right? 00:09:49,640 --> 00:09:49,990 [John] Right. 00:09:49,990 --> 00:09:54,980 [Eric] To 20% or 15%, right? And those are the only people that I'm- 00:09:54,980 --> 00:09:57,039 [John] Oh, you're, you're saying candidates as far as selection. 00:09:57,040 --> 00:09:58,680 [Eric] Candidates, right. 00:09:58,680 --> 00:09:59,180 [John] Interesting. 00:09:59,180 --> 00:10:07,069 [Eric] And so that's, that's like, that's pretty... That's a... It's interesting, right? So that's, that's... I don't know. That's just a very interesting, 00:10:08,120 --> 00:10:08,640 [Eric] um... 00:10:09,690 --> 00:10:10,140 [Eric] I, I don't, 00:10:11,360 --> 00:10:13,410 [Eric] I don't necessarily agree with that 00:10:14,560 --> 00:10:23,090 [Eric] mindset in general as a way to view the economy or to view jobs, but I definitely understand it because- 00:10:23,090 --> 00:10:23,090 [John] Right 00:10:23,090 --> 00:10:32,400 [Eric] ... I look at things like that and it's like, wow, I have worked with a lot of extremely talented people who I would not hire based on the current requirements that I have. 00:10:32,400 --> 00:10:33,200 [John] Interesting. 00:10:33,200 --> 00:10:33,380 [Eric] Right? 00:10:34,740 --> 00:10:37,490 [John] Here's an interesting thought, a little bit of a hot take. Um, 00:10:39,820 --> 00:10:41,580 [John] I think I would say that 00:10:42,720 --> 00:10:49,220 [John] some, some of the, that for sure that some people are more adept at picking up AI than others. 00:10:49,220 --> 00:10:49,390 [Eric] Yep. 00:10:49,390 --> 00:10:50,040 [John] That's fair? 00:10:50,040 --> 00:10:50,840 [Eric] Yep. 00:10:50,840 --> 00:10:51,080 [John] Um, 00:10:52,260 --> 00:10:58,880 [John] and I think that's influenced on what they have spent years, um, refining. 00:10:58,880 --> 00:10:59,880 [Eric] Hmm. 00:11:00,060 --> 00:11:02,340 [John] You know, like what d- like, for example... 00:11:03,860 --> 00:11:15,050 [John] And I've, like, worked with employees and customers and, and lots of people, different people across different, like, paradigms or, and industries, and even personally, like showing family members, like, you know, talking about AI, 00:11:16,160 --> 00:11:22,560 [John] and there's just some people that, like, they just, they just get it faster and pick it up faster- 00:11:22,560 --> 00:11:22,570 [Eric] Yep 00:11:22,570 --> 00:11:23,660 [John] ... and can use it faster. 00:11:23,660 --> 00:11:23,880 [Eric] Mm-hmm. 00:11:25,020 --> 00:11:29,780 [John] And it's not the... And it's not, like, clear. It's not always clear why. 00:11:29,780 --> 00:11:30,880 [Eric] Hmm. 00:11:30,880 --> 00:11:31,220 [John] Um- 00:11:31,220 --> 00:11:31,290 [Eric] Yep 00:11:32,580 --> 00:11:36,510 [John] ... and my hypothesis on it is 00:11:38,060 --> 00:11:49,100 [John] there's, like, a certain group of thinking skills and, like, understanding and cognition and ability to learn and, and things like that, that are really important 00:11:50,220 --> 00:11:51,680 [John] for, like, this next era. 00:11:51,680 --> 00:11:52,700 [Eric] Yes. 00:11:52,700 --> 00:11:53,020 [John] And 00:11:54,460 --> 00:11:58,500 [John] I think there's a certain group before that maybe were not as strong there- 00:11:58,500 --> 00:11:58,740 [Eric] Yep 00:11:58,740 --> 00:12:02,510 [John] ... that had spent years refining the doing part. 00:12:02,510 --> 00:12:02,520 [Eric] Mm-hmm. 00:12:02,520 --> 00:12:07,720 [John] Like getting good at coding or good at writing or good at editing or had, like, lots of strong templates they could start from- 00:12:07,720 --> 00:12:07,960 [Eric] Yes 00:12:07,960 --> 00:12:16,564 [John] ... or whatever.... that is showing up now that, that there's maybe a little bit of a gap in like, well, you don't actually deeply understand that or you don't ac- 00:12:16,564 --> 00:12:16,704 [Eric] Yep 00:12:16,704 --> 00:12:18,124 [John] You know what I mean? So- 00:12:19,264 --> 00:12:19,584 [Eric] I- 00:12:19,584 --> 00:12:20,864 [John] Are you seeing that as well? 00:12:20,864 --> 00:12:25,714 [Eric] Uh, I am, and I think this actually goes to our s- to the second point that I wanted to ask you about, 00:12:27,024 --> 00:12:35,883 [Eric] uh, which this is, this goes back to Mark Twain, which this is not a perfect... I actually learned this 'cause I'm reading a biography about Mark Twain, but- 00:12:35,884 --> 00:12:36,624 [John] Ooh, fun 00:12:36,624 --> 00:12:37,104 [Eric] ... the, 00:12:38,144 --> 00:12:43,724 [Eric] um, there's a really famous quote that's great, that, "Reports of my death are greatly exaggerated." [chuckles] 00:12:43,724 --> 00:12:45,624 [John] Yeah, sure. 00:12:45,624 --> 00:12:50,924 [Eric] And, uh, it was-- He didn't say exactly that, but it was really close, and it was really funny- 00:12:50,924 --> 00:12:51,134 [John] Right 00:12:51,134 --> 00:12:54,134 [Eric] ... you know, 'cause he was a pretty funny guy. Actually, a very cynical guy. But, um, 00:12:56,413 --> 00:13:06,194 [Eric] th- so this is what's interesting for me. So I just said that, you know, I maybe reduced my job pool to, you know, tw- you know, 15 to 20% of what it was before. 00:13:06,194 --> 00:13:06,204 [John] Okay. 00:13:06,204 --> 00:13:07,884 [Eric] And that's a real thing, right? 00:13:07,884 --> 00:13:08,014 [John] Sure. 00:13:08,014 --> 00:13:12,954 [Eric] And, and actually, I think that for the type of work that we do, that's the new bar, right? 00:13:12,954 --> 00:13:12,984 [John] Mm-hmm. 00:13:12,984 --> 00:13:15,424 [Eric] Which again, you can sort of be like, "Whoa, that's-" 00:13:15,424 --> 00:13:15,524 [John] Right 00:13:15,524 --> 00:13:17,104 [Eric] ... "that's a pretty non-trivial thing," right? 00:13:17,104 --> 00:13:17,194 [John] Right. 00:13:18,484 --> 00:13:40,684 [Eric] But reports of that impact across the economy are literally greatly exaggerated relative to the articles that came out, you know, let's say like three to six months ago, that were saying like, you know, we're gonna have a, you know, sort of an apocalyptic effect on the economy with this, and that's not true, actually. Interestingly enough, Lenny, um, 00:13:41,824 --> 00:13:43,264 [Eric] uh, Lenny's podcast, you know- 00:13:43,264 --> 00:13:43,294 [John] Mm-hmm 00:13:43,294 --> 00:13:48,174 [Eric] ... which is a really great show, newsletter, um, product-focused, right? 00:13:48,174 --> 00:13:48,184 [John] Yep. 00:13:48,184 --> 00:13:55,474 [Eric] So anyone related to product engineering, anything AI, definitely a must listen. And he does a jobs report because he has a- 00:13:55,474 --> 00:13:55,474 [John] Mm 00:13:55,474 --> 00:13:56,304 [Eric] ... you know, a huge following- 00:13:56,304 --> 00:13:56,334 [John] Yep 00:13:56,334 --> 00:13:58,714 [Eric] ... and huge access, um, to these things. And 00:13:59,984 --> 00:14:06,003 [Eric] software engineering was one of the areas that... I mean, it is fundamentally changed. And then I, I- 00:14:06,004 --> 00:14:06,304 [John] Yep 00:14:06,304 --> 00:14:07,704 [Eric] ... I do wanna point that out, like, 00:14:08,844 --> 00:14:14,724 [Eric] a lot of the people who are concerned about this work in software engineering, and it has fundamentally... AI has- 00:14:14,724 --> 00:14:14,844 [John] Right 00:14:14,844 --> 00:14:24,424 [Eric] ... absolutely fundamentally changed the way that the job is done, right? And so they're legitimately stepping back and saying, "Whoa, this is, this is a big deal," right? 00:14:24,424 --> 00:14:29,263 [John] Right. Well, and, and it's affecting, um, college majors already. Like- 00:14:29,264 --> 00:14:29,394 [Eric] Yeah 00:14:29,394 --> 00:14:31,704 [John] ... computer science enrollment is down- 00:14:31,704 --> 00:14:31,774 [Eric] Yes 00:14:31,774 --> 00:14:35,524 [John] ... and, um, like robotics programs are way up, for example. 00:14:35,524 --> 00:14:44,654 [Eric] Interestingly enough, though, like Lenny's recent reports are that there have n- like we're way up on openings for software engineers- 00:14:44,654 --> 00:14:44,684 [John] Yep 00:14:44,684 --> 00:14:46,304 [Eric] ... and for product people. 00:14:46,304 --> 00:14:46,924 [John] Yep. 00:14:46,924 --> 00:14:59,043 [Eric] Right? And so there's this really interesting contrast of like, are reports of this economic, you know, catastrophe greatly exaggerated, right? And- 00:14:59,044 --> 00:14:59,864 [John] Right 00:14:59,864 --> 00:15:01,844 [Eric] ... practically, yes, right? 00:15:01,844 --> 00:15:01,904 [John] Right. 00:15:01,904 --> 00:15:11,233 [Eric] Because I think that the jobs reports, you know, show... And, and when I say job reports, I'm not making a blanket economic statement about the entire, you know, US GDP, but- 00:15:11,233 --> 00:15:11,233 [John] Right 00:15:11,233 --> 00:15:11,894 [Eric] ... we're talking about- 00:15:11,894 --> 00:15:11,894 [John] Yeah 00:15:11,894 --> 00:15:13,064 [Eric] ... within our realm, right? 00:15:13,064 --> 00:15:13,884 [John] Right. 00:15:13,884 --> 00:15:21,004 [Eric] Um, more engineering job openings, more product job openings, right? Which is a little bit different than- 00:15:21,004 --> 00:15:21,354 [John] Right 00:15:21,354 --> 00:15:23,664 [Eric] ... or a lot a bit different than what a lot of people thought. 00:15:23,664 --> 00:15:44,484 [John] Yep. I think another reason they're exaggerated is, is really practical. Um, and I think there's actually two aspects as to why the, um, what's it called? The AI, this AI adoption. This, AI adoption is one, one aspect of it, but like I'm thinking through the next year, year and a half. 00:15:45,584 --> 00:15:50,624 [John] Like in the next year or year and a half, I think we're gonna see OpenAI and Anthropic go public. 00:15:50,624 --> 00:15:51,604 [Eric] Mm-hmm. 00:15:51,604 --> 00:15:59,614 [John] Um, and there's gonna be a process for that, and then, and then IPO, probably two of the biggest IPOs in history. But 00:16:00,724 --> 00:16:02,804 [John] publicly traded companies are highly scrutinized. 00:16:02,804 --> 00:16:03,464 [Eric] Mm-hmm. 00:16:03,464 --> 00:16:07,004 [John] And there's going to be drag from that. 00:16:07,004 --> 00:16:07,164 [Eric] Yep. 00:16:07,164 --> 00:16:14,364 [John] And that's probably in the next six months or eight months. So we're gonna have a drag from that, like forever, right? As long as they're publicly traded companies. 00:16:14,364 --> 00:16:14,564 [Eric] Yep. 00:16:15,584 --> 00:16:18,304 [John] So n- so there, there, there's that. And then number two, 00:16:19,384 --> 00:16:26,844 [John] we ha- this is... I've thought a lot about this. You have to have layers of stability for innovation on top of other innovation. 00:16:26,844 --> 00:16:28,324 [Eric] Hmm. 00:16:28,324 --> 00:16:33,183 [John] And there's gonna be pull, so the, the publicly traded company is like some internal drag. 00:16:33,184 --> 00:16:33,443 [Eric] Mm-hmm. 00:16:33,444 --> 00:16:43,944 [John] But the external drag is way bigger, and that is, okay, if you wanted this innovation to happen on top of the OpenAI and Anthropic APIs, for example- 00:16:43,944 --> 00:16:45,544 [Eric] Mm-hmm 00:16:45,544 --> 00:16:50,364 [John] ... you can't just arbitrarily change the shape of them every couple months. 00:16:50,364 --> 00:16:50,704 [Eric] Mm-hmm. 00:16:50,704 --> 00:16:52,684 [John] Like you have to have high stability there. 00:16:52,684 --> 00:16:52,974 [Eric] Right. 00:16:52,974 --> 00:17:04,084 [John] And then you have to have layers on top of that, that like some level of stability. And, and then even think about pricing. You can't drastically be changing pricing all the time if you want layers of innovation on top of the labs. 00:17:04,084 --> 00:17:04,224 [Eric] Right. 00:17:04,224 --> 00:17:05,524 [John] So I think because of that, 00:17:06,564 --> 00:17:07,384 [John] those two things, 00:17:08,504 --> 00:17:11,064 [John] it's like you're gonna have... It's gonna be slower. 00:17:11,064 --> 00:17:11,524 [Eric] Hmm. 00:17:11,524 --> 00:17:14,824 [John] Even if technically in the lab they didn't slow down. 00:17:14,824 --> 00:17:15,944 [Eric] Yep. 00:17:15,944 --> 00:17:22,624 [John] But if you want adoption, and you wanna keep raising money, and you wanna keep earning money, like you gotta slow down. 00:17:22,624 --> 00:17:32,084 [Eric] Yeah. So, okay, let's cover the two points which I think that we've covered already that lead into the third point. So one is why are people scared? 00:17:32,084 --> 00:17:32,184 [John] Right. 00:17:32,184 --> 00:17:38,584 [Eric] And I think it's because there are a lot of people who have seen the immense power of this technology. 00:17:38,584 --> 00:17:39,284 [John] Yes. 00:17:39,284 --> 00:17:46,784 [Eric] And I think that their understanding of it and experience of it is founded. 00:17:46,784 --> 00:17:47,064 [John] Yep. 00:17:47,064 --> 00:17:50,944 [Eric] Right? I don't think that's unfounded, right? But to our second point, 00:17:52,104 --> 00:17:56,064 [Eric] the results of that have not hit the wider economy yet- 00:17:56,064 --> 00:17:56,664 [John] Right 00:17:56,664 --> 00:17:58,404 [Eric] ... in a macro way, right? 00:17:58,404 --> 00:17:59,144 [John] Right. 00:17:59,144 --> 00:18:04,144 [Eric] And when we say macro way, what we mean is mass job loss due to AI. 00:18:04,144 --> 00:18:04,294 [John] Right. 00:18:04,294 --> 00:18:09,464 [Eric] Right? Of course, like energy bills and, you know, all of the GPU usage and everything- 00:18:09,464 --> 00:18:09,494 [John] Right 00:18:09,494 --> 00:18:13,924 [Eric] ... of course. It is, it is a non-zero impact on the economy, obviously, but- 00:18:13,924 --> 00:18:14,024 [John] Right 00:18:14,024 --> 00:18:18,174 [Eric] ... from our perspective, we're talking about, you know, sort of mass unemployment, right? 00:18:18,174 --> 00:18:18,204 [John] Right. 00:18:18,204 --> 00:18:21,444 [Eric] Which would be the more, you know, permanent underclass view. 00:18:21,444 --> 00:18:22,264 [John] Right. 00:18:22,264 --> 00:18:22,524 [Eric] So- 00:18:22,524 --> 00:18:33,772 [John] Yeah, and the, and my comments on... on speed is that I think most people state that as the main problem. The, the speed of change is the main problem. If it was gonna be over 20 years, it would be fine, is, like, kind of the general view. 00:18:33,772 --> 00:18:34,752 [Eric] Yes. 00:18:34,752 --> 00:18:35,332 [John] Um- 00:18:35,332 --> 00:18:39,991 [Eric] But, but okay, that gets to our third point, though. What's theory and what's reality? 00:18:39,992 --> 00:18:40,462 [John] Yep. Right. 00:18:40,462 --> 00:18:46,692 [Eric] This is the, this is the question, right? Because there are people who... I remember I was in 00:18:48,472 --> 00:18:49,612 [Eric] San Francisco 00:18:50,672 --> 00:18:52,632 [Eric] six months ago, um, 00:18:54,032 --> 00:19:04,212 [Eric] and, you know, for anyone in, you know, who's been in and around Silicon Valley, there's a, um... which they're in other cities now, but Blue Bottle Coffee- 00:19:04,212 --> 00:19:04,432 [John] Yeah 00:19:04,432 --> 00:19:19,642 [Eric] ... um, you know, in Mint Plaza, uh, which is, you know, sort of heart of, heart of downtown San Francisco. And so I was going there v- I was... It was super early in the morning because I had to be to, be at the office for a video recording. 00:19:20,872 --> 00:19:22,232 [Eric] And I remember reading this post, 00:19:23,312 --> 00:19:27,622 [Eric] uh, on X, and it was this person who said... They basically were... 00:19:28,692 --> 00:19:31,552 [Eric] It was more of the permanent underclass view. 00:19:31,552 --> 00:19:31,672 [John] Mm-hmm. 00:19:31,672 --> 00:19:33,452 [Eric] And they said, um, 00:19:34,892 --> 00:19:44,502 [Eric] you know, "To all my friends and family," I'm paraphrasing here, "but, like, I need you to wake up because AI is, like, completely changing 00:19:45,652 --> 00:19:55,762 [Eric] absolutely everything." And it, it went to the extent of making, you know, sort of broad financial recommendations. Like, I would think about- 00:19:55,762 --> 00:19:55,762 [John] Yeah 00:19:55,762 --> 00:20:02,032 [Eric] ... you know, your assets. I would think about... You know, which was pretty, pretty wild, right? 00:20:02,032 --> 00:20:02,571 [John] Right. 00:20:02,572 --> 00:20:02,982 [Eric] And 00:20:04,152 --> 00:20:06,312 [Eric] experientially, like, 00:20:07,492 --> 00:20:08,952 [Eric] I'm not saying that his, 00:20:10,012 --> 00:20:16,112 [Eric] I'm not saying that his, he underestimated AI's power- 00:20:16,112 --> 00:20:16,572 [John] Right 00:20:16,572 --> 00:20:31,202 [Eric] ... but it hasn't had the net economic impact, right? And so you have this sort of, like, theory versus experience, right? And what's challenging for me, and so I'm gonna throw you this great conundrum, is he's not wrong about the power- 00:20:32,392 --> 00:20:32,621 [John] Right 00:20:32,621 --> 00:20:32,621 [Eric] ... 00:20:34,052 --> 00:20:37,992 [Eric] but his prediction about the near-term impact in six months has not played out. 00:20:37,992 --> 00:20:38,932 [John] Right. 00:20:38,932 --> 00:20:41,992 [Eric] So what is theory versus reality? 00:20:41,992 --> 00:20:44,552 [John] So I'm gonna take this back to crypto. 00:20:44,552 --> 00:20:45,312 [Eric] Hmm. 00:20:46,452 --> 00:20:50,312 [John] 'Cause I think it's a perfect example of the theory and reality thing. 00:20:50,312 --> 00:20:50,492 [Eric] Yep. 00:20:51,712 --> 00:20:52,472 [John] So, you know, circa 00:20:53,592 --> 00:21:04,092 [John] 2018, 2019, especially pandemic 20- 2020 to '21, we had this, like, massive takeoff, you know, of, of cryptocurrency. 00:21:04,092 --> 00:21:04,532 [Eric] Yep. 00:21:04,532 --> 00:21:06,972 [John] And there were a lot of reasons for it. Um, 00:21:08,052 --> 00:21:13,512 [John] and then, you know, AI comes along and, and overshadows it a bit, as in, in the tech world of, like- 00:21:13,512 --> 00:21:13,772 [Eric] Mm-hmm 00:21:13,772 --> 00:21:21,022 [John] ... which, which is more transformational. And I think, you know, AI wins that conversation. And there's still gonna be lots of, like, things that come out of, 00:21:22,092 --> 00:21:23,712 [John] um, crypto, I think- 00:21:23,712 --> 00:21:24,352 [Eric] Mm-hmm 00:21:24,352 --> 00:21:35,912 [John] ... um, long term. But, but to say that crypto today is what people thought it would be five years ago or six years ago, like, no way. No way. 00:21:37,312 --> 00:21:37,532 [Eric] I... 00:21:39,032 --> 00:21:45,392 [Eric] It's a good example, but it's a, I think, an unfair comparison because 00:21:47,472 --> 00:21:48,372 [Eric] crypto 00:21:49,892 --> 00:21:50,552 [Eric] was 00:21:52,432 --> 00:21:54,532 [Eric] deep in certain- 00:21:54,532 --> 00:21:54,632 [John] Right 00:21:54,632 --> 00:21:59,172 [Eric] ... areas and not broad, if we go back to our previous episode about- 00:21:59,172 --> 00:21:59,182 [John] Right 00:21:59,182 --> 00:22:04,691 [Eric] ... matrices, right? Like, so crypto impacted multiple industries on a very, very deep level. 00:22:04,692 --> 00:22:05,772 [John] Right. 00:22:05,772 --> 00:22:05,982 [Eric] Um- 00:22:07,292 --> 00:22:12,082 [John] So I'm limiting the illustration to if we just talked about crypto and banking, no other impact outside of that. 00:22:12,082 --> 00:22:13,432 [Eric] Okay. Okay. Yeah, yeah, yeah. 00:22:13,432 --> 00:22:15,452 [John] Absolutely did not revolutionize- 00:22:15,452 --> 00:22:16,472 [Eric] Oh, yes. Yeah, yeah, yeah 00:22:16,472 --> 00:22:19,552 [John] ... banking in the way that, like, a lot of people thought it would. 00:22:19,552 --> 00:22:21,161 [Eric] Yes. Totally. Totally. For sure. 00:22:21,161 --> 00:22:23,612 [John] So AI is way bigger, but- 00:22:23,612 --> 00:22:24,252 [Eric] Hmm. 00:22:24,252 --> 00:22:24,772 [John] And- 00:22:24,772 --> 00:22:25,592 [Eric] Yes. Okay, I see 00:22:25,592 --> 00:22:30,512 [John] ... but those same pressures that were there that, like, prevented, you know, crypto from- 00:22:30,512 --> 00:22:30,622 [Eric] Yeah 00:22:30,622 --> 00:22:31,272 [John] ... revolutionizing. 00:22:31,272 --> 00:22:31,612 [Eric] Yeah, yeah, yeah. 00:22:31,612 --> 00:22:33,572 [John] I think, I think you have the same pressures there for AI. 00:22:33,572 --> 00:22:34,792 [Eric] Yeah, yeah. For sure. 00:22:34,792 --> 00:22:34,912 [John] Um- 00:22:34,912 --> 00:22:36,012 [Eric] For sure 00:22:36,012 --> 00:22:36,532 [John] ... and I think 00:22:39,532 --> 00:22:44,592 [John] a lot of the, back to the underclass narrative stuff, a lot of the narrative around the underclass 00:22:45,832 --> 00:22:56,932 [John] is a very, like, helpless narrative of, like, humans currently are in full control of the capital. Humans are in full control of the, you know- 00:22:56,932 --> 00:22:57,192 [Eric] Yeah 00:22:57,192 --> 00:22:58,872 [John] ... employment. Like, all these things. 00:22:58,872 --> 00:22:59,672 [Eric] Yeah. 00:22:59,672 --> 00:23:05,152 [John] And it's kinda bizarre. Like, the, the humans are still in charge, um- 00:23:05,152 --> 00:23:05,332 [Eric] Yeah 00:23:05,332 --> 00:23:10,051 [John] ... at this point, and can control adoption through, like- 00:23:10,052 --> 00:23:10,122 [Eric] Yeah 00:23:10,122 --> 00:23:11,572 [John] ... a million different levers. 00:23:11,572 --> 00:23:11,732 [Eric] Mm-hmm. Mm-hmm. 00:23:11,732 --> 00:23:13,972 [John] Like, it's kinda, it's kinda weird. 00:23:13,972 --> 00:23:15,032 [Eric] It is kinda weird, yeah. 00:23:15,032 --> 00:23:15,472 [John] Um- 00:23:15,472 --> 00:23:30,662 [Eric] Yeah, that's a great call on crypto because I think that's one of the... You know, you have, I'm sure it's the same for you and probably for a lot of our listeners, where, you know, maybe you have relatives that are not quite as plugged into the day-to-day tech news, right? And it's sort of like, "What happened to crypto," right? 00:23:30,662 --> 00:23:30,672 [John] Right. 00:23:30,672 --> 00:23:31,172 [Eric] And it's like, well, 00:23:32,212 --> 00:23:35,962 [Eric] actually, it's an ama- Like, blockchain is an amazing- 00:23:35,962 --> 00:23:35,992 [John] Right 00:23:35,992 --> 00:23:37,622 [Eric] ... technology. 00:23:37,622 --> 00:23:37,672 [John] Right. 00:23:37,672 --> 00:23:41,272 [Eric] And it absolutely could revolutionize- 00:23:41,272 --> 00:23:41,652 [John] Right 00:23:41,652 --> 00:23:45,482 [Eric] ... you know, banking in third world countries, and it's not like there hasn't been progress there. 00:23:45,482 --> 00:23:45,512 [John] Right. 00:23:45,512 --> 00:23:49,682 [Eric] But largely it's used for, you know, illicit, [laughs] you know? 00:23:49,682 --> 00:23:50,782 [John] [laughs] Yeah. And there's- 00:23:50,782 --> 00:23:52,342 [Eric] There's mining and, you know- 00:23:52,342 --> 00:23:52,422 [John] Yeah 00:23:52,422 --> 00:23:54,722 [Eric] ... illicit trading and all this sort of stuff, right? 00:23:54,722 --> 00:23:58,111 [John] And there's cool things, there's cool things happen- happening with, like, stable coins. 00:23:58,112 --> 00:23:58,732 [Eric] Mm-hmm. 00:23:58,732 --> 00:24:05,152 [John] And, like, and there's a crossover. I think there's a crossover with crypto and AI that, that's probably in the future. 00:24:05,152 --> 00:24:05,611 [Eric] Yep. 00:24:05,612 --> 00:24:15,572 [John] Um, but my only point in bringing up crypto was the drastic, um, underestimation of the power of what exists- 00:24:15,572 --> 00:24:15,832 [Eric] Yes 00:24:15,832 --> 00:24:18,352 [John] ... like resisting what could be. 00:24:18,352 --> 00:24:19,102 [Eric] Yeah, yeah, totally. 00:24:19,102 --> 00:24:32,460 [John] Um, so back to our question of, like, theory versus reality. I think with, with AI, um, and we already talked about this a little bit, but you've got theYou've got the innovation problem of, like, we need layers of stability to be able to innovate. 00:24:32,460 --> 00:24:33,420 [Eric] Mm-hmm. 00:24:33,420 --> 00:24:40,000 [John] Um, so that's, that's one component that, that's a problem bringing the theory to the reality. 00:24:40,000 --> 00:24:40,280 [Eric] Yep. 00:24:40,280 --> 00:24:43,700 [John] Because it, because the labs literally will have to slow down- 00:24:43,700 --> 00:24:44,000 [Eric] Exactly 00:24:44,000 --> 00:24:45,440 [John] ... to make theory reality. 00:24:45,440 --> 00:24:46,720 [Eric] Yep. The rate of adoption, yeah. 00:24:46,720 --> 00:24:46,940 [John] Right. 00:24:46,940 --> 00:24:48,140 [Eric] People can't consume it as quickly. 00:24:48,140 --> 00:24:53,200 [John] Right. And then the second piece is, like, okay, say all that's, like, solved. 00:24:53,200 --> 00:24:54,220 [Eric] Mm-hmm. 00:24:54,220 --> 00:25:02,120 [John] And, and honestly, the easiest sol- if you wanted to maximize speed of adoption, it's essentially the labs doing more and more, just internally. 00:25:02,120 --> 00:25:02,540 [Eric] Yes. 00:25:02,540 --> 00:25:04,940 [John] So instead of just doing chat or just doing- 00:25:04,940 --> 00:25:05,220 [Eric] Mm-hmm 00:25:05,220 --> 00:25:10,160 [John] ... coding agents, they, like, all of knowledge work is done via one of the labs. 00:25:10,160 --> 00:25:10,620 [Eric] Yep. 00:25:10,620 --> 00:25:12,460 [John] I mean, that would be the biggest, broadest thing. 00:25:12,460 --> 00:25:12,620 [Eric] Sure. 00:25:12,620 --> 00:25:16,870 [John] They literally replaced everything and, and you either pick, like, OpenAI or- 00:25:16,870 --> 00:25:17,060 [Eric] Right 00:25:17,060 --> 00:25:17,400 [John] ... whatever. 00:25:17,400 --> 00:25:18,190 [Eric] Which is, you know- 00:25:18,190 --> 00:25:18,190 [John] Um 00:25:18,190 --> 00:25:20,179 [Eric] ... I mean, which is actually not- 00:25:20,180 --> 00:25:20,340 [John] Yeah 00:25:20,340 --> 00:25:22,520 [Eric] ... wrong on the, on the general trajectory. 00:25:22,520 --> 00:25:31,380 [John] It's, it, yeah. It's within the realm of possibility, but unlikely that there will basically only be four or five software companies in the future. 00:25:31,380 --> 00:25:31,900 [Eric] Right. 00:25:31,900 --> 00:25:33,960 [John] Unlikely. Um- 00:25:33,960 --> 00:25:34,660 [Eric] So- 00:25:34,660 --> 00:25:37,140 [John] But, but if that were the case, like, 00:25:38,540 --> 00:25:46,440 [John] you still have the problem of the specific application. Because if you, if that happened, let's say there's five software companies and none others exist in- 00:25:46,440 --> 00:25:46,720 [Eric] Mm-hmm 00:25:46,720 --> 00:25:47,019 [John] ... in- 00:25:47,020 --> 00:25:47,200 [Eric] Mm-hmm 00:25:47,200 --> 00:25:47,950 [John] ... several years, 00:25:49,000 --> 00:25:54,900 [John] you, they would have to build so broadly that there would be, there's gonna be an immense amount of work 00:25:56,120 --> 00:25:59,180 [John] applying their tool to all these different situations, 00:26:00,560 --> 00:26:02,840 [John] um, as far as businesses and how they actually operate. 00:26:02,840 --> 00:26:03,700 [Eric] Yep. 00:26:03,700 --> 00:26:12,480 [John] Training people, uh, train, like, feeding data back into these things to make them, th- like, let's call them AI agents, to make them useful. 00:26:12,480 --> 00:26:13,300 [Eric] Sure. 00:26:13,300 --> 00:26:21,500 [John] Um, so even if they already own, like, there's only five of them, everybody has one of the five, and there's no other software, like, there's still a lot of work to be done. 00:26:21,500 --> 00:26:21,920 [Eric] Totally. Totally. 00:26:23,200 --> 00:26:24,050 [Eric] Yeah, I think that's 00:26:25,600 --> 00:26:27,150 [Eric] unlikely because of the- 00:26:27,150 --> 00:26:27,150 [John] Right 00:26:27,150 --> 00:26:31,260 [Eric] ... generative nature and, like, creative unlock that AI is. 00:26:31,260 --> 00:26:31,900 [John] Right. 00:26:31,900 --> 00:26:38,640 [Eric] But to land the plane on our original question of abundance versus- 00:26:38,640 --> 00:26:38,690 [John] Right 00:26:38,690 --> 00:26:39,620 [Eric] ... permanent underclass, 00:26:41,680 --> 00:26:46,480 [Eric] this may sound like a simple question, but should you be afraid? Are you afraid, John? 00:26:48,300 --> 00:26:51,640 [John] I'm really not afraid of the permanent underclass. Um, 00:26:52,900 --> 00:26:55,900 [John] I think way more highly of people than that. 00:26:55,900 --> 00:26:56,730 [Eric] Hmm. 00:26:56,730 --> 00:27:05,120 [John] In that, let's say worst case scenario, there's this continued massive takeoff. We figure out a lot of the problems that I've already stated in some way. 00:27:05,120 --> 00:27:06,000 [Eric] Mm-hmm. 00:27:06,000 --> 00:27:06,299 [John] Um, 00:27:07,540 --> 00:27:10,480 [John] I think people are inherently creative- 00:27:10,480 --> 00:27:11,420 [Eric] Mm-hmm 00:27:11,420 --> 00:27:14,660 [John] ... and inherently designed to work and want to work. 00:27:14,660 --> 00:27:15,260 [Eric] Mm-hmm. 00:27:15,260 --> 00:27:18,980 [John] And, um, we'll f- we'll figure out things to do. 00:27:18,980 --> 00:27:19,080 [Eric] Yep. 00:27:19,080 --> 00:27:25,840 [John] Um, th- and, and even, you know, companies, I know, I know there's a lot of, like, you know, 00:27:27,140 --> 00:27:31,780 [John] companies are evil, like, mentality and, you know, there's [chuckles] some evidence of that for sure. 00:27:31,780 --> 00:27:32,200 [Eric] [laughs] Sure. 00:27:32,200 --> 00:27:42,900 [John] But I, but I think there's a piece there too where the people make up the companies and people, people are going to, um, maybe even change some companies from within that you might be surprised. 00:27:42,900 --> 00:27:42,960 [Eric] Mm-hmm. 00:27:42,960 --> 00:27:48,520 [John] Like, if a lot of this does come true, that, that the companies will change too because the peoples will force the companies to change- 00:27:48,520 --> 00:27:48,600 [Eric] Yeah 00:27:48,600 --> 00:27:49,580 [John] ... and how they operate. 00:27:49,580 --> 00:27:50,020 [Eric] Yeah. 00:27:50,020 --> 00:27:55,680 [John] And we, and we might, you know, have org charts that look different and companies that operate in different ways. 00:27:55,680 --> 00:27:56,000 [Eric] Yep. 00:27:56,000 --> 00:27:57,440 [John] But, um, no, I think 00:27:58,800 --> 00:28:00,760 [John] I'm gonna side with the people- 00:28:00,760 --> 00:28:01,090 [Eric] Yeah, yeah [laughs] 00:28:01,090 --> 00:28:03,340 [John] ... on this one. The humanity versus the, 00:28:04,420 --> 00:28:06,020 [John] versus the AI. 00:28:06,020 --> 00:28:06,740 [Eric] Yeah. 00:28:06,740 --> 00:28:07,800 [John] What do you think? 00:28:07,800 --> 00:28:12,220 [Eric] I think about movies or books a lot. Um, 00:28:14,340 --> 00:28:14,900 [Eric] and 00:28:16,520 --> 00:28:19,560 [Eric] not to get too philosophical on a show about, you know, sort of 00:28:20,800 --> 00:28:28,360 [Eric] AI and, and business leadership, but what do, what have people celebrated historically, right? The, the- 00:28:28,360 --> 00:28:28,430 [John] Hmm 00:28:28,430 --> 00:28:33,510 [Eric] ... this idea of science fiction and computers taking over and automation is not a new idea. 00:28:33,510 --> 00:28:33,520 [John] No. 00:28:33,520 --> 00:28:35,200 [Eric] It's a very, very old idea- 00:28:35,200 --> 00:28:35,460 [John] Right 00:28:35,460 --> 00:28:47,040 [Eric] ... in, in literature. Very old. Actually, my wife just started, she just picked up, um, my parents are renovating their house, and so they, you know, g- you know, emptying the bookshelves and- 00:28:47,040 --> 00:28:47,820 [John] Yeah 00:28:47,820 --> 00:28:56,050 [Eric] ... have the kids come over and figure out if they want any of the books. And so there is a science fiction trilogy. She is not a science fiction person- 00:28:56,050 --> 00:28:56,059 [John] Yeah 00:28:56,059 --> 00:28:56,170 [Eric] ... at all. 00:28:56,170 --> 00:28:57,240 [John] I wouldn't have pictured her. 00:28:57,240 --> 00:28:57,360 [Eric] No. 00:28:57,360 --> 00:28:57,620 [John] Yeah. 00:28:57,620 --> 00:28:57,950 [Eric] She's not. 00:28:59,020 --> 00:29:09,840 [Eric] For, for the listeners who are, who don't know my wife, she's a florist, and so she's sort of a creative at heart and, um, you know, science fiction and technology is just not, she just doesn't love it- 00:29:09,840 --> 00:29:10,000 [John] Right 00:29:10,000 --> 00:29:10,150 [Eric] ... right? 00:29:10,150 --> 00:29:10,440 [John] Right. 00:29:10,440 --> 00:29:11,370 [Eric] Which I do, you know. 00:29:11,370 --> 00:29:11,379 [John] Right. 00:29:11,380 --> 00:29:13,640 [Eric] That's sort of my thing. Um- 00:29:13,640 --> 00:29:15,060 [John] I think her job's pretty safe. 00:29:15,060 --> 00:29:16,809 [Eric] It, I think her job is very safe. 00:29:16,809 --> 00:29:16,860 [John] [laughs] 00:29:16,860 --> 00:29:17,820 [Eric] Right? Um, 00:29:19,720 --> 00:29:24,700 [Eric] but she picked up this, she picked up Out of the Silent Planet by C.S. Lewis, which is, you know, part of- 00:29:24,760 --> 00:29:25,800 [John] Yeah. I know that one, yeah 00:29:25,800 --> 00:29:31,330 [Eric] ... you know. And it was really interesting. I hadn't s- it was a book that's, it was printed in the '70s, and so it's, like- 00:29:31,330 --> 00:29:31,330 [John] Cool 00:29:31,330 --> 00:29:32,800 [Eric] ... really old with a cool cover. 00:29:32,800 --> 00:29:33,760 [John] Yeah. 00:29:33,760 --> 00:29:34,140 [Eric] And- 00:29:34,140 --> 00:29:34,980 [John] I have that one, I think 00:29:34,980 --> 00:29:36,820 [Eric] ... um, yeah, it's super cool. 00:29:36,820 --> 00:29:37,170 [John] Mm-hmm. 00:29:37,170 --> 00:29:44,900 [Eric] But he, in the, in the, you know, you open the cover and there's an author's note, and C.S. Lewis talks about H.G. Wells, you know? 00:29:44,900 --> 00:29:45,050 [John] Mm-hmm. 00:29:45,050 --> 00:29:57,380 [Eric] And he's like, you know, essentially says, like, you know, "This is, I, I can't, like, uh, you can't read this book without knowing that I'm standing on the shoulders of, like- 00:29:57,380 --> 00:29:57,430 [John] Mm-hmm 00:29:57,430 --> 00:29:58,280 [Eric] ... H.G. Wells," right? 00:29:58,280 --> 00:29:58,700 [John] Mm-hmm. 00:29:58,700 --> 00:30:01,590 [Eric] Um, who wrote books, like, a long time ago, right? 00:30:01,590 --> 00:30:01,600 [John] Right. 00:30:01,600 --> 00:30:03,960 [Eric] And so we're not talking about new things here. 00:30:03,960 --> 00:30:04,680 [John] Right. 00:30:04,680 --> 00:30:05,160 [Eric] Um, 00:30:06,380 --> 00:30:07,400 [Eric] and 00:30:08,500 --> 00:30:09,040 [Eric] no one 00:30:10,920 --> 00:30:11,999 [Eric] celebrates, 00:30:13,220 --> 00:30:13,660 [Eric] um, 00:30:15,220 --> 00:30:22,290 [Eric] inventing technology that is ultimately, like, you know, sort of 00:30:23,420 --> 00:30:26,380 [Eric] limits the things that we love most about humans. 00:30:26,380 --> 00:30:26,980 [John] Yeah. 00:30:26,980 --> 00:30:34,710 [Eric] I think, you know, there are worldviews that have that as sort of their inevitable end, right? 00:30:34,710 --> 00:30:34,740 [John] Right. 00:30:34,740 --> 00:30:51,646 [Eric] Or if you're sort of a... technologist who is, you know, over-rotating on, on the inevitable of AGI, then you can get there. But if you look at... Actually, let me give you a really specific example. Um, 00:30:53,616 --> 00:30:56,676 [Eric] did you watch the movie The Iron Giant? It's an animated movie. 00:30:56,676 --> 00:30:57,956 [John] Yeah. Seen that one. 00:30:57,956 --> 00:30:58,686 [Eric] Right? And so- 00:30:58,686 --> 00:30:58,686 [John] It's been a while 00:30:58,686 --> 00:31:04,106 [Eric] ... okay, so th- is this, is that AI, right? It's an autonomous- 00:31:04,106 --> 00:31:04,106 [John] Hmm 00:31:04,106 --> 00:31:04,995 [Eric] ... robot. 00:31:04,996 --> 00:31:05,616 [John] Mm-hmm. 00:31:05,616 --> 00:31:18,316 [Eric] Um, it is a self-healing robot. It is the combination of intelligence, the ability to learn, with a physical machine. You have all these things, right? 00:31:18,316 --> 00:31:18,616 [John] Right. 00:31:18,616 --> 00:31:27,436 [Eric] And what do we celebrate, right? Like, we celebrate the robot's ability to understand human empathy. 00:31:27,436 --> 00:31:28,176 [John] Right. Right. 00:31:28,176 --> 00:31:29,166 [Eric] You know, like, that's the- 00:31:29,166 --> 00:31:29,166 [John] Yeah, yeah. Right 00:31:29,166 --> 00:31:30,586 [Eric] ... that's the end game, you know? 00:31:30,586 --> 00:31:30,596 [John] Yeah. 00:31:30,596 --> 00:31:32,566 [Eric] And so, [laughs] like, if we look at- 00:31:32,566 --> 00:31:33,956 [John] It's, it's not some measurement of its- 00:31:33,956 --> 00:31:33,966 [Eric] Yeah 00:31:33,966 --> 00:31:34,876 [John] ... like, physical strength or- 00:31:34,876 --> 00:31:35,716 [Eric] Right 00:31:35,716 --> 00:31:35,856 [John] ... whatever. 00:31:35,856 --> 00:31:36,736 [Eric] Or intelligence, right? 00:31:36,736 --> 00:31:37,396 [John] Or intelligence, yeah. 00:31:37,396 --> 00:31:42,176 [Eric] Like, we look at all these stories, and it's like, so if you look at this, um, 00:31:43,976 --> 00:31:50,976 [Eric] throughout history, really, like, I think it should make us question our view of humanity- 00:31:50,976 --> 00:31:51,396 [John] Right 00:31:51,396 --> 00:32:02,676 [Eric] ... right, and human creativity. And the permanent underclass people, I think, dramatically, I think they have a wrong view of humans- 00:32:02,676 --> 00:32:03,236 [John] Right 00:32:03,236 --> 00:32:05,136 [Eric] ... uh, and the way that we're designed- 00:32:05,136 --> 00:32:05,416 [John] Right 00:32:05,416 --> 00:32:10,676 [Eric] ... uh, I think, at the end of the day. So I'm not afraid either. Sorry, that was a very long-winding answer- 00:32:10,676 --> 00:32:11,216 [John] I like it 00:32:11,216 --> 00:32:14,306 [Eric] ... that included the Iron Giant [laughs] as an example. 00:32:14,306 --> 00:32:16,096 [John] [laughs] You just wanted to weave that in. I could tell. 00:32:16,096 --> 00:32:20,246 [Eric] I didn't even think about it before the show. [laughs] All right. Well, thanks for- 00:32:20,246 --> 00:32:20,246 [John] Awesome 00:32:20,246 --> 00:32:22,396 [Eric] ... joining Token Intelligence, and we'll catch you on the next one. 00:32:27,176 --> 00:32:31,156 [Eric] [outro music]