1 00:00:00,080 --> 00:00:02,877 Yeah, it turns out that humans are terrible drivers and if we're going to 2 00:00:02,960 --> 00:00:06,477 allow 16 year olds to drive tanks down the highways at 100 miles an hour, maybe 3 00:00:06,560 --> 00:00:09,437 letting the smart computers do it is a better idea. All right, uh Lon, the next 4 00:00:09,520 --> 00:00:12,157 thing I want to grab is speaking of preference and kind of getting things 5 00:00:12,240 --> 00:00:15,197 right. Um the the two of them discussed 6 00:00:15,280 --> 00:00:19,597 the importance of helping humans do higher value work. Now, when we talked 7 00:00:19,680 --> 00:00:23,357 today about AI agents, increasing model intelligence, we're always thinking 8 00:00:23,440 --> 00:00:26,237 about what's this going to replace in the human labor pool. Is it blue collar 9 00:00:26,320 --> 00:00:28,077 work, white collar work? But often we're 10 00:00:28,160 --> 00:00:31,037 seeing AI today turn up in very high 11 00:00:31,120 --> 00:00:35,837 economic value areas, coding, legal work, that sort of thing. And so when 12 00:00:35,920 --> 00:00:40,077 Alexander Wang discusses where he sees the AI market going, it's really 13 00:00:40,160 --> 00:00:42,797 interesting. Take a listen. there are 14 00:00:42,880 --> 00:00:48,317 there like helping people focus on higher and higher value work uh is is 15 00:00:48,400 --> 00:00:52,477 really I mean that's sort of like the core of human progress in some sense but 16 00:00:52,560 --> 00:00:56,237 um >> but it's uh that that very much so is I 17 00:00:56,320 --> 00:01:00,317 I I strongly believe will be the actual story of AI and machine learning and 18 00:01:00,400 --> 00:01:04,317 it'll have to happen more and more and more for us to be comfortable with it 19 00:01:04,400 --> 00:01:08,397 but um a great example is like truck driving so there's there's a lot all 20 00:01:08,480 --> 00:01:10,477 these automated truck uh truck driving 21 00:01:10,560 --> 00:01:12,157 companies right work with a a lot of 22 00:01:12,240 --> 00:01:16,877 them embark uh Ike etc. Um and uh and 23 00:01:16,960 --> 00:01:21,597 there's sort of the the the naive view is that hey they're just they're going 24 00:01:21,680 --> 00:01:25,357 to automate truck drivers and like if you look at the map of the states like 25 00:01:25,440 --> 00:01:29,197 uh truck driving is a top profession in a lot of states and so seems really bad 26 00:01:29,280 --> 00:01:33,357 but actually if you look if you kind of like think at the system as a whole 27 00:01:33,440 --> 00:01:36,237 there's a shortage there's a national shortage >> of truck drivers 28 00:01:36,320 --> 00:01:39,197 >> in truck drivers in the United States >> and the median age is like 50 or 29 00:01:39,280 --> 00:01:41,277 something crazy. >> Yeah. Exactly. So there's sort of this 30 00:01:41,360 --> 00:01:45,357 like there's this like >> millennials and gen Z's are not becoming truck drivers. 31 00:01:45,440 --> 00:01:48,237 >> So there's this there's this kind of like instability in the market because 32 00:01:48,320 --> 00:01:52,557 of all this stuff, right? And and uh and the the the 33 00:01:52,640 --> 00:01:54,717 >> the automated truck driving systems. 34 00:01:54,800 --> 00:01:56,317 Actually what they would do is automate 35 00:01:56,400 --> 00:01:59,437 the the longhaul middles of these truck 36 00:01:59,520 --> 00:02:02,957 driving which are the boring parts that 37 00:02:03,040 --> 00:02:07,037 displace people from wherever their homes are, etc. and allow the current 38 00:02:07,120 --> 00:02:11,197 truck drivers to focus on these like higher value trips that are sort of like 39 00:02:11,280 --> 00:02:14,157 uh warehouse to a a meeting point or what 40 00:02:14,240 --> 00:02:17,037 >> dreage to like the dreage to the factory 41 00:02:17,120 --> 00:02:21,277 or even the last mile. I mean, who knows? Like maybe these trucks will 42 00:02:21,360 --> 00:02:25,517 change their form factor and be half the size be automated and when the truck 43 00:02:25,600 --> 00:02:28,557 gets off the road, the same truck instead of using 18 wheelers, we might 44 00:02:28,640 --> 00:02:33,437 just use smaller midsize trucks that'll be electric and solar powered so you 45 00:02:33,520 --> 00:02:36,477 have more of them. when they get off they become the delivery truck. Yeah. 46 00:02:36,560 --> 00:02:38,397 >> And they just automatically start delivering. 47 00:02:38,480 --> 00:02:40,397 >> Yeah. >> Could be a much better model. 48 00:02:40,480 --> 00:02:44,237 >> Yeah. So, so the um these uh these sort 49 00:02:44,320 --> 00:02:48,797 of like the introduction of machine learning to uh to improve the efficiency 50 00:02:48,880 --> 00:02:51,197 of the economy, it'll it'll it'll be 51 00:02:51,280 --> 00:02:53,757 slow because of how the how in general 52 00:02:53,840 --> 00:02:58,557 free market economics work. It'll it'll take effect in areas where there's an 53 00:02:58,640 --> 00:03:02,237 acute problem today, >> right? It'll happen in those places 54 00:03:02,320 --> 00:03:05,517 first. and uh and it'll allow the the 55 00:03:05,600 --> 00:03:07,197 current jobs that exist to become higher 56 00:03:07,280 --> 00:03:10,317 value, more impactful, um etc. 57 00:03:10,400 --> 00:03:12,717 >> Yeah, I feel like his truck driving 58 00:03:12,800 --> 00:03:15,997 example here is really good and and it's 59 00:03:16,080 --> 00:03:21,437 it's a very smart illustration of the larger point that I think makes it easy 60 00:03:21,520 --> 00:03:24,397 to sort of visualize because what he's arguing is, you know, they're not going 61 00:03:24,480 --> 00:03:26,157 to fire all the truck drivers overnight. 62 00:03:26,240 --> 00:03:30,477 It's not like AI's here, AI could drive the trucks. We don't need you anymore. 63 00:03:30,560 --> 00:03:34,237 It's like there are different kinds of truck driving. There's the kind of truck 64 00:03:34,320 --> 00:03:37,917 driving where you're in a straight line down the highway for like three days in 65 00:03:38,000 --> 00:03:40,797 a row. And then there's the more intricate you're going through a 66 00:03:40,880 --> 00:03:44,797 neighborhood, you're going around some sharp turns, you're you're the last mile 67 00:03:44,880 --> 00:03:47,997 of where the truck needs to go to actually make the delivery or what have 68 00:03:48,080 --> 00:03:52,077 you. And so he's saying, you know, like well AI would take part of that, the 69 00:03:52,160 --> 00:03:55,997 like long haul boring part that's sort of uneventful. You don't really need a 70 00:03:56,080 --> 00:04:00,797 human for it. And that's the part that makes a human drive three days away from 71 00:04:00,880 --> 00:04:04,077 their house and then be away from their family for the whole week to get that 72 00:04:04,160 --> 00:04:07,997 haul done. And so he's like, well, you could have the AI do that part and then 73 00:04:08,080 --> 00:04:11,197 local truck drivers take care of the more intricate parts where it helps to 74 00:04:11,280 --> 00:04:14,717 have a human and they get to stay in the same place. So it's like AI makes 75 00:04:14,800 --> 00:04:18,077 everything better and takes care of the stuff that we don't want humans to have 76 00:04:18,160 --> 00:04:21,757 to do. And it's like I don't I don't know if that's actually how all of this 77 00:04:21,840 --> 00:04:25,357 stuff is going to play out, but that strikes me as like the best case 78 00:04:25,440 --> 00:04:27,997 scenario, like the utopian vision for 79 00:04:28,080 --> 00:04:30,637 how AI would integrate into our lives. 80 00:04:30,720 --> 00:04:34,317 Probably the best part of being a founder is watching a project that 81 00:04:34,400 --> 00:04:36,957 started as just an idea blossom into a 82 00:04:37,040 --> 00:04:41,677 full-fledged startup. 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