1 00:00:17,667 --> 00:00:21,547 S1: All right. Really excited about this episode. Like I said, uh, 2 00:00:22,187 --> 00:00:24,227 S1: in the newsletter, I think it is one of my 3 00:00:24,227 --> 00:00:27,987 S1: favorite episodes ever. Actually. Last few. I've been feeling really 4 00:00:27,987 --> 00:00:32,547 S1: good about. I love the way the links are working out. So, uh, 5 00:00:32,987 --> 00:00:34,947 S1: if you want to give back any positive feedback, I 6 00:00:34,947 --> 00:00:38,827 S1: would appreciate it. But, uh, let's get into it. Let's 7 00:00:38,827 --> 00:00:44,027 S1: see here. All right. I ordered some noise canceling earbuds. 8 00:00:44,027 --> 00:00:46,867 S1: I haven't unpacked them yet, but, uh, they're for sleeping. 9 00:00:46,987 --> 00:00:50,347 S1: And basically I'm going to try to put them in, 10 00:00:50,346 --> 00:00:52,547 S1: and you can either stream music to them or you 11 00:00:52,547 --> 00:00:56,506 S1: can stream, like, white or brown noise, or you could 12 00:00:56,507 --> 00:00:59,467 S1: just have them noise cancel. And the idea is if 13 00:00:59,467 --> 00:01:02,227 S1: you combine that with like an eye mask, which I 14 00:01:02,227 --> 00:01:05,627 S1: already wear, then it's supposed to really help with sleep. 15 00:01:05,627 --> 00:01:07,827 S1: So I'm going to see I know a few people 16 00:01:07,827 --> 00:01:12,027 S1: who do both and I just want to try it out. 17 00:01:13,067 --> 00:01:15,867 S1: I did a huge blog post called Why Google I 18 00:01:15,867 --> 00:01:19,807 S1: o scared this 2007 Apple fanboy for the first time, 19 00:01:20,247 --> 00:01:25,447 S1: and it's basically a review of WD DC juxtaposed with 20 00:01:25,447 --> 00:01:30,327 S1: Google I o, which was a couple weeks back. And yeah, 21 00:01:30,327 --> 00:01:34,487 S1: I've been an Apple fanboy for I mean, I mean, 22 00:01:34,647 --> 00:01:37,487 S1: I guess acolyte. I don't really like the word fanboy, 23 00:01:37,487 --> 00:01:42,407 S1: but yeah, really drinking the Apple Kool-Aid for a very 24 00:01:42,407 --> 00:01:44,927 S1: long time. And if you're listening to this, you probably 25 00:01:44,927 --> 00:01:49,327 S1: already know that. But, um, I like to think that 26 00:01:49,327 --> 00:01:54,567 S1: I'm logical about it, but I'm already admitting to being biased. Uh, 27 00:01:54,567 --> 00:01:59,327 S1: so that's what being an acolyte is, right? But what 28 00:01:59,327 --> 00:02:02,847 S1: I saw essentially this year at Google, I o is 29 00:02:02,967 --> 00:02:07,487 S1: it seemed like they are switching to Gemini and I 30 00:02:07,527 --> 00:02:11,607 S1: in general to be like their core mission. I feel 31 00:02:11,607 --> 00:02:15,237 S1: like they're moving away from ads. I feel like they're 32 00:02:15,237 --> 00:02:18,157 S1: moving away from that, being like the future in the 33 00:02:18,157 --> 00:02:21,757 S1: center of their business to like data and AI being 34 00:02:21,757 --> 00:02:24,996 S1: the center of their business. And I think this is really, 35 00:02:24,996 --> 00:02:28,276 S1: really cool on on their part. And I feel like 36 00:02:28,276 --> 00:02:32,436 S1: Google I o they did so well because of that 37 00:02:32,436 --> 00:02:36,397 S1: integration right there talking about integration with glasses and, you know, 38 00:02:36,436 --> 00:02:39,957 S1: XR or AR or whatever they're calling it, and Gemini 39 00:02:39,996 --> 00:02:42,877 S1: being integrated in like all the different apps. So it 40 00:02:42,876 --> 00:02:45,397 S1: just felt like they're doing something new and they're doing 41 00:02:45,397 --> 00:02:49,156 S1: something different. I've always been kind of averse to Google 42 00:02:49,197 --> 00:02:52,596 S1: because they are fundamentally an ad company, or they were 43 00:02:52,596 --> 00:02:56,276 S1: fundamentally an ad company. And I really like the CEO. 44 00:02:56,316 --> 00:02:58,317 S1: I feel like he gets it. I feel like it's 45 00:02:58,316 --> 00:03:01,117 S1: not gross. I don't feel like he's gross. I don't 46 00:03:01,117 --> 00:03:04,437 S1: feel like he's there to maximize ad revenue. I think 47 00:03:04,436 --> 00:03:07,996 S1: he really gets the AI stuff. So my perception of 48 00:03:08,036 --> 00:03:10,956 S1: Google has overall just like improved, I would say over 49 00:03:10,957 --> 00:03:16,097 S1: the last say year or two. just in the context 50 00:03:16,096 --> 00:03:19,817 S1: of them switching to this AI model as opposed to 51 00:03:19,857 --> 00:03:24,376 S1: like an advertising model now. Then you have Apple, which, 52 00:03:25,017 --> 00:03:27,537 S1: like I said, I'm massively biased towards. I love their stuff. 53 00:03:27,536 --> 00:03:30,056 S1: I worked there for like three years, I worked in 54 00:03:30,096 --> 00:03:35,057 S1: their security group and I just love their way of 55 00:03:35,057 --> 00:03:37,057 S1: thinking about the world. I love their way of doing 56 00:03:37,097 --> 00:03:41,617 S1: UI and UX, and I love how they are building 57 00:03:41,617 --> 00:03:44,697 S1: in my opinion. Not that I have any inside information 58 00:03:45,057 --> 00:03:47,617 S1: and it's not like actual product, but I think for 59 00:03:47,617 --> 00:03:50,217 S1: the last ten years they've been building what I'm calling 60 00:03:50,337 --> 00:03:54,697 S1: life OS, right? So forget iOS or Mac OS or 61 00:03:54,697 --> 00:03:57,617 S1: whatever they're building life OS. I mean, they just launched 62 00:03:58,537 --> 00:04:02,217 S1: the Journal app on the Mac, for example, and they 63 00:04:02,217 --> 00:04:04,897 S1: just brought the phone app to the Mac, which is 64 00:04:04,937 --> 00:04:07,537 S1: like the coolest thing ever. And like, AirPods seem to 65 00:04:07,537 --> 00:04:10,257 S1: work better. So there's lots of improvements in iOS or 66 00:04:10,297 --> 00:04:16,366 S1: in Mac OS 26, by the way. But, um, basically 67 00:04:16,367 --> 00:04:19,367 S1: their ecosystem is what keeps me their their focus on 68 00:04:19,367 --> 00:04:22,007 S1: UI and UX is what keeps me their their focus 69 00:04:22,007 --> 00:04:25,687 S1: on art and creativity and like enabling people to be 70 00:04:25,687 --> 00:04:30,047 S1: creative and enabling people to be their best selves or whatever. 71 00:04:30,087 --> 00:04:32,647 S1: Like that's the vibe that I've always liked about Apple, 72 00:04:33,087 --> 00:04:35,367 S1: and I think they do UI and UX better than 73 00:04:35,367 --> 00:04:38,127 S1: anyone else. I think they've had this unified vision of 74 00:04:38,127 --> 00:04:43,527 S1: context and, you know, having finance and creativity and work 75 00:04:43,527 --> 00:04:47,007 S1: and play and personal and all of that, all integrated 76 00:04:47,007 --> 00:04:49,767 S1: into the operating system. They've always just done that better 77 00:04:49,767 --> 00:04:53,927 S1: than anyone else. So that's why I'm so into Apple now. 78 00:04:54,807 --> 00:04:57,207 S1: Obviously AI is happening has been for the last three 79 00:04:57,207 --> 00:05:03,367 S1: years or however long it's been. And Apple is behind. Right. I've, 80 00:05:03,367 --> 00:05:05,927 S1: I've been saying for you know whatever a year that 81 00:05:05,967 --> 00:05:09,007 S1: look they're going to come out with their unified AI 82 00:05:09,047 --> 00:05:10,867 S1: product which is going to go on top of their 83 00:05:10,867 --> 00:05:13,867 S1: life OS or whatever. And basically Siri is going to 84 00:05:13,907 --> 00:05:16,467 S1: jump way ahead of everyone. And I thought that was 85 00:05:16,467 --> 00:05:19,067 S1: going to be like end of last year. And of 86 00:05:19,067 --> 00:05:21,587 S1: course they stumbled on that. And I believe it's because 87 00:05:21,587 --> 00:05:26,187 S1: of prompt injection. I think it is too difficult to 88 00:05:26,227 --> 00:05:29,507 S1: put an AI in front of all the personal context 89 00:05:29,907 --> 00:05:32,947 S1: that Apple has about us. And to do it in 90 00:05:32,947 --> 00:05:35,187 S1: a secure way, I think it's too difficult to do 91 00:05:35,187 --> 00:05:37,987 S1: that right now. That is my guess. And I know 92 00:05:37,987 --> 00:05:42,187 S1: this technically because I know how to do prompt injection and, um, 93 00:05:42,427 --> 00:05:45,187 S1: I know some of the best other prompt injection people 94 00:05:45,187 --> 00:05:48,907 S1: in the world. And it is pretty trivial still to 95 00:05:48,947 --> 00:05:53,947 S1: bypass most protections. So I think they are struggling with that. 96 00:05:54,187 --> 00:05:56,147 S1: Maybe they're struggling with other parts of it as well. 97 00:05:56,147 --> 00:06:00,467 S1: But I imagine they're definitely struggling with that piece. Like 98 00:06:00,467 --> 00:06:02,747 S1: right now I just got a pop up screening call, 99 00:06:02,747 --> 00:06:04,747 S1: and if I click view and this is while I'm 100 00:06:04,747 --> 00:06:09,027 S1: recording on my desktop, if I click view, it, um, 101 00:06:09,137 --> 00:06:13,017 S1: it shows this person like spamming me. And there it's 102 00:06:13,057 --> 00:06:17,296 S1: actively live transcribing that thing that's going to voicemail right now. 103 00:06:17,497 --> 00:06:20,177 S1: So I think that's really, really cool. Um, and then 104 00:06:20,177 --> 00:06:23,897 S1: I could also just click block. Uh, so that's, that's 105 00:06:23,897 --> 00:06:26,337 S1: pretty cool that I have that option. And the phone 106 00:06:26,337 --> 00:06:29,177 S1: is now integrated with the Mac, which it should be. Right. 107 00:06:29,217 --> 00:06:31,817 S1: That's just a voice call. It's not tied to a 108 00:06:31,817 --> 00:06:37,417 S1: physical device anyway. UI stuff, the way I'm kind of 109 00:06:37,457 --> 00:06:41,736 S1: seeing the world now is UI. UX is Apple always 110 00:06:41,737 --> 00:06:46,137 S1: has been. Seems like it will be going forward. And 111 00:06:46,137 --> 00:06:49,857 S1: then Google is now becoming like less of a negative 112 00:06:49,857 --> 00:06:53,177 S1: for me. And it's more about the data and services 113 00:06:53,177 --> 00:06:58,177 S1: and specifically AI. And obviously I wish that Apple had 114 00:06:58,177 --> 00:07:00,177 S1: all of that. They don't have it yet. I think 115 00:07:00,177 --> 00:07:03,856 S1: they will get there hopefully. And obviously Google is probably 116 00:07:03,857 --> 00:07:06,617 S1: working on UI and UX as well. Like I assume 117 00:07:06,617 --> 00:07:09,677 S1: it's getting better. I've seen some, you know, UI stuff 118 00:07:09,677 --> 00:07:12,317 S1: from them that seems to be halfway decent. So the 119 00:07:12,357 --> 00:07:16,637 S1: real question to me is will Google figure out the UI, 120 00:07:16,637 --> 00:07:22,717 S1: UX life, OS, full integration of ecosystem? Will they figure 121 00:07:22,717 --> 00:07:29,277 S1: that out before Apple, or will Apple figure out data 122 00:07:29,277 --> 00:07:36,357 S1: and AI before Google figures out that UI UX piece, right. 123 00:07:36,477 --> 00:07:39,957 S1: That's really the question. Which one gets the the one 124 00:07:39,957 --> 00:07:43,357 S1: that they're weak on up to a certain bar first? 125 00:07:44,197 --> 00:07:47,517 S1: I can't conceivably think of myself switching over to the 126 00:07:47,517 --> 00:07:51,277 S1: Google ecosystem, because there's so many pieces of it that 127 00:07:51,677 --> 00:07:56,117 S1: I know I would miss, but I am. I am 100% 128 00:07:56,117 --> 00:07:59,717 S1: open to it. I honestly, I is a much bigger 129 00:07:59,717 --> 00:08:05,277 S1: movement than any other tech movement. Um, it's bigger than 130 00:08:05,277 --> 00:08:08,657 S1: UI and UX, especially since I shouldn't be typing on 131 00:08:08,657 --> 00:08:13,257 S1: a keyboard anyway. I shouldn't be, um, you know, thumb 132 00:08:13,297 --> 00:08:15,977 S1: typing on a phone, right? This should all be moving 133 00:08:15,977 --> 00:08:21,737 S1: to voice and virtual interfaces and glasses. So as that 134 00:08:21,737 --> 00:08:24,457 S1: starts to happen, like I'm more likely to move towards 135 00:08:24,457 --> 00:08:29,257 S1: the Google side if Apple doesn't have that. Um, keeping 136 00:08:29,257 --> 00:08:31,817 S1: in mind that I really do care about my interface 137 00:08:31,817 --> 00:08:34,256 S1: to tech. Oh, here's the other thing. The other big 138 00:08:34,256 --> 00:08:36,857 S1: reason that I really prefer Apple is because I've been 139 00:08:36,857 --> 00:08:40,977 S1: on the inside, and I know how seriously and crazy, um, 140 00:08:41,256 --> 00:08:44,457 S1: they take the security and privacy stuff like it is 141 00:08:44,457 --> 00:08:47,816 S1: not marketing, it is not made up. It is absolutely 142 00:08:47,817 --> 00:08:51,617 S1: dead serious. So if I'm building my whole life ecosystem 143 00:08:51,617 --> 00:08:54,136 S1: around this, plus agents and I'm building all this stuff 144 00:08:54,136 --> 00:08:57,656 S1: on it, they got to do it perfectly right. Now, 145 00:08:57,656 --> 00:09:00,136 S1: I do think Google is really, really good at security. 146 00:09:00,136 --> 00:09:03,296 S1: I just don't think their incentives have been aligned, you know, 147 00:09:03,337 --> 00:09:06,287 S1: in the past because they were primarily an ad company. 148 00:09:06,847 --> 00:09:10,807 S1: The more they become an OS and life OS for everyone. 149 00:09:11,127 --> 00:09:14,526 S1: With AI, you know being the primary thing, I think 150 00:09:14,526 --> 00:09:17,766 S1: they should start to move more into the realm of 151 00:09:17,807 --> 00:09:20,726 S1: like what Apple does with like, okay, look, privacy first, 152 00:09:21,006 --> 00:09:24,247 S1: you know, security first. Obviously they say that obviously they 153 00:09:24,247 --> 00:09:26,727 S1: do a good job, but I'm saying there's going to 154 00:09:26,727 --> 00:09:29,407 S1: be less of a conflict because they're not trying to 155 00:09:29,406 --> 00:09:32,847 S1: make all their money off of your data. Right. Hopefully 156 00:09:32,847 --> 00:09:35,607 S1: that's the case. Hopefully they're migrating away from that, which 157 00:09:35,607 --> 00:09:39,207 S1: is why I just haven't been enthused about using them 158 00:09:39,207 --> 00:09:42,766 S1: as a core OS. All right. I think that's enough 159 00:09:42,766 --> 00:09:49,526 S1: about that. Basically a little bit of disappointment around. DC. 160 00:09:49,567 --> 00:09:52,886 S1: I mean, I love the operating stuff. I, I'm a 161 00:09:52,886 --> 00:09:55,727 S1: little halfway between, like, how much do I like the 162 00:09:55,727 --> 00:10:00,046 S1: liquid stuff? I've seen some places where the interface is 163 00:10:00,046 --> 00:10:02,447 S1: quite bad. I've seen some places where it's quite good. 164 00:10:02,447 --> 00:10:04,506 S1: I think we're going to let that bake for a 165 00:10:04,506 --> 00:10:06,627 S1: little while, see how it goes. We're on the first 166 00:10:06,627 --> 00:10:08,307 S1: beta right now. They're going to clean up a lot 167 00:10:08,347 --> 00:10:11,026 S1: of stuff. But what I have noticed is some really 168 00:10:11,026 --> 00:10:15,747 S1: good improvements around the fluidity of like moving from my 169 00:10:15,747 --> 00:10:20,627 S1: phone to my desktop, having the AirPods switch. Naturally, this 170 00:10:20,627 --> 00:10:21,906 S1: is the type of thing you lose when you go 171 00:10:21,906 --> 00:10:24,307 S1: to Google. Like you try to switch over and you 172 00:10:24,307 --> 00:10:27,266 S1: realize like nothing works, right? Because Apple has been working 173 00:10:27,266 --> 00:10:31,027 S1: on this for so many years, and this macOS and 174 00:10:31,026 --> 00:10:35,226 S1: iOS 26 update, it seems like things are really, really smooth. 175 00:10:35,347 --> 00:10:38,467 S1: I mean, my OS is just working better now, and 176 00:10:38,467 --> 00:10:40,867 S1: this is beta one. Back in the day when I 177 00:10:40,867 --> 00:10:45,267 S1: was doing these betas, like I would lose core functionality 178 00:10:45,307 --> 00:10:48,906 S1: like regular apps wouldn't launch. I would like go dark, 179 00:10:48,906 --> 00:10:51,627 S1: like I couldn't text people. It was crazy, you know, 180 00:10:51,666 --> 00:10:55,107 S1: running these Apple betas early on. But, um, no, it's 181 00:10:55,107 --> 00:10:58,066 S1: it's working great. I recommend you try it out. And 182 00:10:58,067 --> 00:11:01,307 S1: if you do, you could text me and we could 183 00:11:01,307 --> 00:11:04,536 S1: try out some of these new emoji features or whatever. 184 00:11:05,617 --> 00:11:10,257 S1: All right. Um, got a couple blogs. Yeah. So one 185 00:11:10,256 --> 00:11:16,177 S1: is the WWE one, the other one is, um, uh, 186 00:11:16,337 --> 00:11:19,337 S1: let's see here. Oh, yeah. It's an argument against. It's 187 00:11:19,337 --> 00:11:21,936 S1: just next token prediction. I thought that was a pretty 188 00:11:21,937 --> 00:11:25,617 S1: good way of encapsulating it. Uh, so definitely check that 189 00:11:25,617 --> 00:11:28,017 S1: one out. I'm about to do, like, a kind of, 190 00:11:28,057 --> 00:11:32,457 S1: like a personal project to profile, like all the CCP 191 00:11:32,497 --> 00:11:35,696 S1: members and structure, just profile them and, like, figure out 192 00:11:36,457 --> 00:11:38,296 S1: who are they, what are they up to? How are 193 00:11:38,296 --> 00:11:43,177 S1: they different than she? What, uh, what are their political opinions? 194 00:11:43,176 --> 00:11:46,016 S1: What are they focused on? What are their areas of expertise? 195 00:11:46,217 --> 00:11:50,696 S1: What are their areas of responsibility like how does legislation get. 196 00:11:50,737 --> 00:11:53,576 S1: I guess it's not legislation because people aren't voting, but 197 00:11:53,617 --> 00:11:55,896 S1: how do they decide what new laws to put out? 198 00:11:56,097 --> 00:11:58,536 S1: Is there a process? Is it on a particular timing? 199 00:11:58,536 --> 00:12:00,337 S1: I just kind of want to understand how the Chinese 200 00:12:00,437 --> 00:12:05,276 S1: government works. So I'm going to start with understanding all 201 00:12:05,317 --> 00:12:09,957 S1: the CCP people, um, because that is the government. And 202 00:12:10,197 --> 00:12:12,036 S1: and I want to look at the different echelons, like, 203 00:12:12,036 --> 00:12:15,117 S1: is there a like is there like a Politburo? Is 204 00:12:15,117 --> 00:12:17,317 S1: there a smaller group? Is it like a larger thing 205 00:12:17,357 --> 00:12:20,957 S1: of like hundreds of people? Um, what's the difference between 206 00:12:20,957 --> 00:12:23,237 S1: those tiers, like stuff like that. So I'm going to 207 00:12:23,237 --> 00:12:25,756 S1: do a study like that and, uh, I don't know 208 00:12:25,756 --> 00:12:27,957 S1: how I'm going to put that out. Maybe a blog post, 209 00:12:27,957 --> 00:12:31,276 S1: maybe some member content. No idea. Uh, the other thing 210 00:12:31,276 --> 00:12:32,677 S1: I'm going to do is I'm going to do a 211 00:12:32,676 --> 00:12:36,436 S1: future trend investment analysis exercise, which I did, I want 212 00:12:36,437 --> 00:12:39,036 S1: to say like five years ago, and it basically told 213 00:12:39,036 --> 00:12:43,196 S1: me to buy Amazon and Nvidia. I forget what that 214 00:12:43,197 --> 00:12:45,277 S1: exercise told us to buy, but I did it with 215 00:12:45,276 --> 00:12:48,997 S1: my good buddy Tai Sabarno, and I think we ended 216 00:12:48,997 --> 00:12:51,197 S1: up making a number of purchases based on that, and 217 00:12:51,197 --> 00:12:55,156 S1: I think they've done well for us. But, um, yeah, 218 00:12:55,156 --> 00:12:57,117 S1: I want to do another one this time. We'll get 219 00:12:57,117 --> 00:13:00,977 S1: to use things like, you know, O3 Pro and I 220 00:13:01,016 --> 00:13:03,456 S1: get to throw in tons of context about myself and 221 00:13:03,457 --> 00:13:06,416 S1: what I'm looking for and the, you know, the types 222 00:13:06,416 --> 00:13:08,536 S1: of investments I'm looking for and the type of risk 223 00:13:08,536 --> 00:13:12,176 S1: I want to be open to. Um, and this isn't 224 00:13:12,176 --> 00:13:16,577 S1: only for investment. That'll be kind of like a side product, um, of, 225 00:13:16,617 --> 00:13:19,817 S1: like the exhaust that comes out of it. The biggest 226 00:13:19,817 --> 00:13:22,136 S1: thing I'm trying to do here is just figure out trends. 227 00:13:22,136 --> 00:13:25,057 S1: So here's like the I'll give you like kind of 228 00:13:25,097 --> 00:13:27,457 S1: what I'm going to feed to this thing. Right. I'm 229 00:13:27,457 --> 00:13:29,776 S1: going to set this entire thing up, which is going 230 00:13:29,776 --> 00:13:33,096 S1: to be like 20 pages of me writing up context 231 00:13:33,097 --> 00:13:35,337 S1: to my own thoughts and my own predictions, my own 232 00:13:35,377 --> 00:13:40,656 S1: like concerns or whatever. And I want to know what 233 00:13:40,656 --> 00:13:42,537 S1: it thinks could happen. And I'm sure it's going to 234 00:13:42,536 --> 00:13:44,297 S1: be very careful and be like, look, we can't predict 235 00:13:44,296 --> 00:13:48,536 S1: the future, which obviously everyone knows that. But, um, what 236 00:13:48,536 --> 00:13:50,377 S1: I'm going to be is what I'm going to say 237 00:13:50,377 --> 00:13:55,457 S1: is like, look, um, if this were to happen, what 238 00:13:55,497 --> 00:13:59,167 S1: seems obvious to you, that would be some second order effects. 239 00:13:59,646 --> 00:14:03,127 S1: That is the power right, that I didn't have before 240 00:14:03,127 --> 00:14:05,487 S1: where I is like, well, obviously this is the type 241 00:14:05,487 --> 00:14:08,127 S1: of thing that happens and it gives you like 150 242 00:14:08,127 --> 00:14:13,127 S1: different things. So when certain things are scarce, prices of 243 00:14:13,127 --> 00:14:16,326 S1: certain other things go up. Which companies are associated with those? 244 00:14:16,327 --> 00:14:18,607 S1: I don't know all that information. Like I'm not like 245 00:14:18,607 --> 00:14:21,287 S1: a commodities expert. I'm not a trading expert, I'm not 246 00:14:21,287 --> 00:14:25,047 S1: a stock expert. So I think there are some things 247 00:14:25,047 --> 00:14:28,686 S1: that don't involve that much prediction or that much conjecture 248 00:14:28,847 --> 00:14:31,607 S1: on the side of the eye. That's actually just benefits 249 00:14:31,607 --> 00:14:38,047 S1: from it. Understanding how the world works. So I could say, okay, um, like, 250 00:14:38,047 --> 00:14:40,767 S1: what am I? You know, hypotheses is like, okay, private 251 00:14:40,767 --> 00:14:42,967 S1: security is going to go up. Oh, there's probably going 252 00:14:42,967 --> 00:14:46,566 S1: to be a bunch more prisons built because, you know, 253 00:14:46,847 --> 00:14:49,487 S1: we're stupid. And we took down all the mental health facilities. 254 00:14:49,487 --> 00:14:53,607 S1: So what happens when there's like UBI comes out. But 255 00:14:53,607 --> 00:14:57,987 S1: UBI is limited to only citizens. So they start doing 256 00:14:57,987 --> 00:15:01,467 S1: mass deportations, not because of the current kind of vibe 257 00:15:01,467 --> 00:15:04,747 S1: of like anti-immigrant stuff that's going on, but more along 258 00:15:04,747 --> 00:15:06,747 S1: the lines of like, we need to control how many 259 00:15:06,747 --> 00:15:09,627 S1: people we're sending UBI to. We're not going to send 260 00:15:09,627 --> 00:15:12,946 S1: UBI to everyone in the country just because they're physically here. 261 00:15:13,627 --> 00:15:16,186 S1: So now there's a bunch of tech to find who 262 00:15:16,267 --> 00:15:20,707 S1: is and isn't supposed to be receiving UBI, and more 263 00:15:20,707 --> 00:15:23,946 S1: and more people become homeless because, you know, AI is 264 00:15:23,947 --> 00:15:27,307 S1: taking jobs. Okay? So they're homeless. So then you have 265 00:15:27,427 --> 00:15:30,547 S1: the fact that they don't have a place to live. Uh, 266 00:15:30,547 --> 00:15:33,907 S1: then you have addiction, then you have violence. So who 267 00:15:33,907 --> 00:15:37,027 S1: goes into prison, right? Prison is mostly full of people 268 00:15:37,027 --> 00:15:41,147 S1: who have addiction, who have mental illnesses, and obviously some 269 00:15:41,147 --> 00:15:44,067 S1: people who are violent. Right. And the percentages, you might 270 00:15:44,067 --> 00:15:47,707 S1: be surprised how much it's actually people that just couldn't 271 00:15:47,707 --> 00:15:49,826 S1: find a way to get it going. Right. So they 272 00:15:49,827 --> 00:15:53,227 S1: end up on drugs. They end up mentally ill. Those 273 00:15:53,787 --> 00:15:56,497 S1: ideally in some world, like the world we're supposed to 274 00:15:56,497 --> 00:15:59,617 S1: be building, that would be like drug treatment programs and 275 00:15:59,617 --> 00:16:02,417 S1: it would be mental health facilities. But all of that 276 00:16:02,417 --> 00:16:05,697 S1: is going to collapse down into basically like prisons. Get 277 00:16:05,697 --> 00:16:10,777 S1: them away from society, separate them from society. So what 278 00:16:10,777 --> 00:16:13,137 S1: does that mean? What does that mean for water? What 279 00:16:13,137 --> 00:16:16,177 S1: does that mean for food? What does that mean for, um, 280 00:16:16,297 --> 00:16:19,217 S1: what companies are going to do well in that world? Um, like, 281 00:16:19,337 --> 00:16:22,297 S1: I'll tell you one of mine Costco, I love Costco. 282 00:16:22,337 --> 00:16:26,097 S1: I'm like, so like into Costco. Um, and also Google 283 00:16:26,097 --> 00:16:28,497 S1: and also Apple. But those are like my three main 284 00:16:28,497 --> 00:16:32,697 S1: investments right now. So the question is like, how is 285 00:16:32,737 --> 00:16:36,617 S1: how can I find other ways to connect these dots. 286 00:16:36,937 --> 00:16:39,337 S1: And I'm going to tell it, look, don't be doing conjecture. 287 00:16:39,377 --> 00:16:41,217 S1: Don't be trying to predict the future. I'm going to 288 00:16:41,217 --> 00:16:44,057 S1: give you a bunch of options of ways. I think 289 00:16:44,057 --> 00:16:47,137 S1: maybe it could go. But I have no idea. And 290 00:16:47,137 --> 00:16:49,457 S1: you as this I. You also have no idea. But 291 00:16:49,457 --> 00:16:54,397 S1: there's there are lanes of probability, right? Uh, turns out 292 00:16:54,397 --> 00:16:56,357 S1: if people don't have jobs, they have no way to 293 00:16:56,397 --> 00:16:59,237 S1: pay for food, especially for their families. They might get 294 00:16:59,237 --> 00:17:03,437 S1: a little upset. Right. So certain things don't require a 295 00:17:03,477 --> 00:17:07,997 S1: lot of, like, future prediction or futurist type crap. Um, 296 00:17:07,997 --> 00:17:09,557 S1: so I'm going to use the AI to help me 297 00:17:09,597 --> 00:17:13,877 S1: sort of navigate that stuff that seems a little more obvious. And, um, 298 00:17:15,197 --> 00:17:17,237 S1: the main output is going to be like, it looks 299 00:17:17,237 --> 00:17:20,476 S1: like if these things happen, then these things might happen 300 00:17:20,477 --> 00:17:25,357 S1: as well. And then, um, maybe some investment thing of like, okay, well, 301 00:17:25,557 --> 00:17:28,717 S1: for all of those different ones, what companies or what 302 00:17:28,716 --> 00:17:32,917 S1: products or what raw materials or where would I want 303 00:17:32,956 --> 00:17:34,957 S1: to live? Where would I want to move? What is 304 00:17:34,956 --> 00:17:38,117 S1: a good city to be in if something like, you know, 305 00:17:38,157 --> 00:17:40,757 S1: three through seven were to happen, right? That's the type 306 00:17:40,757 --> 00:17:42,796 S1: of analysis I'm going to do. And similar to the 307 00:17:42,797 --> 00:17:44,677 S1: CCP one, I'm not sure how I'm going to put 308 00:17:44,677 --> 00:17:47,237 S1: that out. Maybe I'll make a giant PDF, maybe I'll 309 00:17:47,236 --> 00:17:49,917 S1: do a video. Maybe it'll be like member content, maybe, 310 00:17:49,956 --> 00:17:52,907 S1: you know, charge for it, make it free. No idea. 311 00:17:54,147 --> 00:17:57,907 S1: Probably some combination of those. All right. What else? Doo 312 00:17:57,907 --> 00:18:02,467 S1: doo doo. All right. Cybersecurity. So Trump overhauled Biden's cybersecurity 313 00:18:02,466 --> 00:18:06,747 S1: policies with a new executive order. Uh, he removed focus 314 00:18:06,747 --> 00:18:09,667 S1: on mandated digital IDs. That must have been, like an 315 00:18:09,706 --> 00:18:15,266 S1: immigration type thing. Accounting, compliance checklists, micromanaging agency decisions. I 316 00:18:15,267 --> 00:18:17,827 S1: think that might have been like Cisa, maybe like harnessing 317 00:18:18,267 --> 00:18:21,707 S1: or like trying to control Cisa ads focus on defeating 318 00:18:21,706 --> 00:18:26,907 S1: foreign threats, secure software practices, border gateway protection. That's BGP, 319 00:18:27,107 --> 00:18:32,387 S1: I believe. Um, yeah. Like, you know, hijacking BGP routes, 320 00:18:32,427 --> 00:18:39,107 S1: post-quantum cryptography, modern encryption protocols, AI for vulnerabilities, IoT security standards, 321 00:18:39,107 --> 00:18:45,466 S1: and limiting sanctions scope. So that was, uh, a combination 322 00:18:45,466 --> 00:18:48,827 S1: of mine and an AI analysis of the talking points, 323 00:18:48,827 --> 00:18:51,887 S1: or it's called a fact sheet. Bellingcat tests whether or 324 00:18:51,887 --> 00:18:54,927 S1: not I can actually geolocate photos, and it found that 325 00:18:54,927 --> 00:18:59,966 S1: O3 actually did the best and beat out Google Lens, actually. 326 00:19:00,527 --> 00:19:04,767 S1: Sentinel one reveals details on Chinese supply chain attack attempt. 327 00:19:05,047 --> 00:19:10,446 S1: Protonvpn sees 1,000% sign up surge after Pornhub blocks France. 328 00:19:11,007 --> 00:19:16,567 S1: So France evidently loves Protonvpn. And, uh, yeah, they went 329 00:19:16,567 --> 00:19:20,287 S1: over there so they could still get access. Microsoft Teams 330 00:19:20,287 --> 00:19:23,767 S1: with Indian police to shut down fake tech support scammers 331 00:19:24,847 --> 00:19:28,887 S1: and Bishop Fox 2025 red team tools list. All right. 332 00:19:28,966 --> 00:19:32,206 S1: National security OpenAI published their annual report on how bad 333 00:19:32,206 --> 00:19:35,847 S1: actors are using AI maliciously, and I love the fact 334 00:19:35,847 --> 00:19:37,567 S1: that they put these out. So four out of ten 335 00:19:37,567 --> 00:19:40,927 S1: major abuse cases look like they came from China, from 336 00:19:40,927 --> 00:19:45,247 S1: social engineering to cyber threats. They're seeing deceptive employment schemes. 337 00:19:45,247 --> 00:19:50,706 S1: So task scams from like Cambodia comment spamming from Philippines 338 00:19:51,347 --> 00:19:55,547 S1: and covert influence operations potentially leaked to Russia and Iran 339 00:19:56,186 --> 00:20:00,266 S1: using AI as force multipliers. Um, I'm actually going to 340 00:20:00,267 --> 00:20:02,387 S1: mention something right now. I can't figure out what I'm 341 00:20:02,387 --> 00:20:04,347 S1: going to do with this. I might do another report 342 00:20:04,347 --> 00:20:06,987 S1: like the one I just the two I just talked about. 343 00:20:07,347 --> 00:20:12,347 S1: So my Twitter feed is full, full of what I 344 00:20:12,387 --> 00:20:17,907 S1: am absolutely certain is like widespread propaganda. So the content 345 00:20:17,907 --> 00:20:21,147 S1: that I see is like, don't you hate black people 346 00:20:21,186 --> 00:20:24,627 S1: because they do this? Um, don't you wish the world 347 00:20:24,627 --> 00:20:26,627 S1: looked like this? And then if you click the video, 348 00:20:26,627 --> 00:20:28,946 S1: it's like a whole bunch of white people walking around 349 00:20:28,946 --> 00:20:32,307 S1: and there's like, no crime and there's like a fountain with, like, 350 00:20:32,347 --> 00:20:36,347 S1: a nice water sound, and there's, like, somebody buying something at, like, 351 00:20:36,347 --> 00:20:40,267 S1: an Abercrombie or something, and it's like. It's like massive 352 00:20:40,267 --> 00:20:43,347 S1: propaganda to like, uh, don't you wish the world was 353 00:20:43,347 --> 00:20:47,227 S1: the way it was before? Before, like, brown people messed 354 00:20:47,337 --> 00:20:49,736 S1: it all up, right? And then there'll be another one. 355 00:20:49,777 --> 00:20:52,297 S1: It'll be like about fighting or something like that. Or 356 00:20:52,297 --> 00:20:56,017 S1: it'll be like masculine movies. Don't you wish movies were 357 00:20:56,017 --> 00:20:59,816 S1: like this? And it's like someone holding a Budweiser. Budweiser. And, like, 358 00:20:59,857 --> 00:21:03,857 S1: shooting a crossbow or something. And I'm just like, what 359 00:21:03,857 --> 00:21:05,976 S1: is going on? So every time I go back to Twitter, 360 00:21:05,976 --> 00:21:08,497 S1: which I probably check it like, I don't know, like 15, 361 00:21:08,537 --> 00:21:10,897 S1: 20 times a day or something, and then I'll just 362 00:21:10,897 --> 00:21:12,456 S1: be browsing to see what people are saying. But if 363 00:21:12,456 --> 00:21:14,977 S1: I go to the home tab, which is like impossible 364 00:21:14,976 --> 00:21:16,777 S1: to clean up because I block all these things when 365 00:21:16,777 --> 00:21:19,736 S1: I see them, although lately I've started bookmarking them because 366 00:21:19,736 --> 00:21:23,257 S1: I'm going to do this research project. But normally I 367 00:21:23,257 --> 00:21:26,456 S1: just block, block, block. But it's just an endless supply. 368 00:21:26,497 --> 00:21:30,297 S1: Like these people have like multiple accounts. So what I 369 00:21:30,297 --> 00:21:33,257 S1: started doing is clicking on the account and scrolling through 370 00:21:33,257 --> 00:21:38,216 S1: their feed, and it's nonstop. It's nonstop narratives about how 371 00:21:38,257 --> 00:21:42,016 S1: messed up the country is, reasons for why the country 372 00:21:42,057 --> 00:21:46,317 S1: got messed up, which is scapegoating, basically. And then, um. 373 00:21:46,597 --> 00:21:49,837 S1: Don't you wish it was like, uh, the new thing, right? 374 00:21:49,877 --> 00:21:51,276 S1: Don't you wish it was like it used to be? 375 00:21:51,277 --> 00:21:53,597 S1: Don't you know? Don't you think we could do better? 376 00:21:54,157 --> 00:21:57,397 S1: And I'm just, like, seeing this everywhere. Just like hundreds 377 00:21:57,397 --> 00:22:00,196 S1: of accounts doing this. And I sent this a few 378 00:22:00,196 --> 00:22:02,996 S1: of these accounts to, uh, some friends of mine who 379 00:22:02,997 --> 00:22:07,037 S1: go and research this stuff. And, yeah, I'm just really 380 00:22:07,037 --> 00:22:12,716 S1: surprised that, like, I guess I'm not surprised, given, uh, 381 00:22:12,877 --> 00:22:15,316 S1: given Elon's situation and the fact that he lost so 382 00:22:15,317 --> 00:22:18,357 S1: many advertisers and the fact that I guess he's like 383 00:22:18,597 --> 00:22:23,677 S1: conspiracy friendly, I would say, um, but ultimately people click 384 00:22:23,677 --> 00:22:26,477 S1: on this stuff. People watch the stuff because it is, 385 00:22:26,637 --> 00:22:30,157 S1: you know, specifically designed to be that way, to be 386 00:22:30,196 --> 00:22:34,997 S1: very watchy and clicky. Um, so I guess that's why 387 00:22:34,997 --> 00:22:37,476 S1: it's there. But but it's really gross. Like, I really 388 00:22:37,476 --> 00:22:39,677 S1: wish there were a platform or I wish there were 389 00:22:39,677 --> 00:22:43,277 S1: options in this platform where it's like, look, I'm paying money. Like, 390 00:22:43,917 --> 00:22:46,266 S1: can you not show me this garbage? I want to 391 00:22:46,267 --> 00:22:50,067 S1: see stuff from people I follow and stuff that's very 392 00:22:50,067 --> 00:22:52,427 S1: tightly correlated with people I follow. Which he says he's 393 00:22:52,427 --> 00:22:55,346 S1: working on that actually, he says he's working on similar 394 00:22:55,347 --> 00:22:57,627 S1: content matching, which is similar to the app I have 395 00:22:57,667 --> 00:23:02,027 S1: called threshold. But, um, yeah, this whole thread of like, 396 00:23:02,067 --> 00:23:04,587 S1: who is sending all this stuff to us, who is 397 00:23:04,587 --> 00:23:08,387 S1: basically trying to change the narrative or try to put 398 00:23:08,667 --> 00:23:12,627 S1: inject into our minds, like this feeling of the US 399 00:23:12,667 --> 00:23:16,267 S1: is messed up. It was messed up by them pointing 400 00:23:16,267 --> 00:23:20,947 S1: at trans people or brown people or black people or whatever. 401 00:23:20,946 --> 00:23:24,587 S1: Gay people or, you know, the wrong religion or whatever 402 00:23:24,587 --> 00:23:28,506 S1: it is. Liberals, definitely, um, people who aren't right enough 403 00:23:28,507 --> 00:23:33,587 S1: or aren't right in the correct way. Um, it's just 404 00:23:33,587 --> 00:23:36,587 S1: it's worse than I've ever seen. It's absolutely worse than 405 00:23:36,587 --> 00:23:39,347 S1: I've ever seen. Um, so I don't know if their 406 00:23:39,347 --> 00:23:41,867 S1: skill level is just going up. I imagine it's because 407 00:23:41,867 --> 00:23:44,567 S1: it's all a lot of it's being AI generated, so 408 00:23:44,567 --> 00:23:46,486 S1: you could just do it at more scale. That's probably 409 00:23:46,486 --> 00:23:49,847 S1: a reason. But anyway, it's something I'm going to look 410 00:23:49,847 --> 00:23:52,367 S1: into and probably do some kind of essay or report 411 00:23:52,367 --> 00:23:57,127 S1: or whatever. All right. AI. Okay. This is the craziest story, 412 00:23:57,446 --> 00:24:02,407 S1: the absolute craziest story of the week. Um, and honestly, 413 00:24:02,446 --> 00:24:04,447 S1: for a long time. And I've got a follow up 414 00:24:04,446 --> 00:24:06,286 S1: to it as well, because my buddy told me about 415 00:24:06,287 --> 00:24:09,567 S1: this one company. But check this out. So I finally 416 00:24:09,567 --> 00:24:13,847 S1: found something that trained scientists have missed for decades. So 417 00:24:13,847 --> 00:24:16,127 S1: a number of professional scientists have been trying to figure 418 00:24:16,127 --> 00:24:19,446 S1: out how a particular kind of bacteriophage, which is a 419 00:24:19,446 --> 00:24:24,727 S1: virus that infects bacteria, uh, which I didn't know that. So, um, 420 00:24:24,726 --> 00:24:27,887 S1: there's a podcast here that supports this that's basically like, um, 421 00:24:28,247 --> 00:24:30,887 S1: it's all the scientists who actually did this. So basically 422 00:24:30,887 --> 00:24:33,647 S1: they have been studying this thing for over a couple 423 00:24:33,726 --> 00:24:36,527 S1: of decades. They are the world's experts in how these 424 00:24:36,527 --> 00:24:43,476 S1: particular bacteriophages, um, gain mobility and move out of the cell. Basically, 425 00:24:43,476 --> 00:24:45,317 S1: what they do is they take over the cell. They 426 00:24:45,317 --> 00:24:48,277 S1: blow it open and spread the virus. That's what these 427 00:24:48,277 --> 00:24:52,796 S1: things do. So they study how these things get their 428 00:24:52,797 --> 00:24:55,597 S1: heads and their tails, which allows them to move and 429 00:24:55,597 --> 00:24:58,357 S1: propagate and, you know, take over other things. And I'm 430 00:24:58,357 --> 00:25:00,956 S1: simplifying and probably messing up some details because it's a 431 00:25:00,956 --> 00:25:05,356 S1: very complex, you know, specific topic. But I went and 432 00:25:05,357 --> 00:25:07,756 S1: listened to the entire one hour episode with the actual 433 00:25:07,757 --> 00:25:11,517 S1: experts talking about this. Now the story is they were 434 00:25:11,517 --> 00:25:14,917 S1: given this new version of a Google model, which is 435 00:25:14,917 --> 00:25:20,956 S1: specifically for researching and coming up with novel hypotheses, novel ideas. Okay, 436 00:25:20,997 --> 00:25:23,277 S1: so what they did was they they had this thing 437 00:25:23,277 --> 00:25:25,717 S1: where they're like, how is this thing possible? They've been 438 00:25:25,716 --> 00:25:28,756 S1: thinking about this for years and years and years. They've 439 00:25:28,757 --> 00:25:32,477 S1: done numerous studies. They are the world's premier experts on this. 440 00:25:32,877 --> 00:25:35,597 S1: And they have been stumped by this fact. The fact 441 00:25:35,597 --> 00:25:39,316 S1: that this one bacteriophage, I believe it was they were 442 00:25:39,317 --> 00:25:41,737 S1: stumped by the fact of how was it actually propagating 443 00:25:41,736 --> 00:25:44,177 S1: or how was it becoming mobile. It was something like that. 444 00:25:44,177 --> 00:25:46,057 S1: It was some sort of problem that they could not 445 00:25:46,057 --> 00:25:49,417 S1: figure out, and they know exactly how it works. You know, 446 00:25:49,497 --> 00:25:52,096 S1: they were very sure about this and that. Therefore, you know, 447 00:25:52,137 --> 00:25:54,936 S1: they're like, this makes no sense. So they gave a 448 00:25:54,936 --> 00:25:58,977 S1: bunch of this observational data to it, you know, stuff 449 00:25:58,976 --> 00:26:01,897 S1: that they've had for years and years. And it came 450 00:26:01,897 --> 00:26:05,736 S1: back with hypotheses in one of the hypotheses out of 451 00:26:05,736 --> 00:26:09,137 S1: a very short list was like, hey, um, it might 452 00:26:09,137 --> 00:26:12,296 S1: be this actually, because, um, what you could do is 453 00:26:12,337 --> 00:26:14,657 S1: you could just do this instead of this, and it 454 00:26:14,657 --> 00:26:16,537 S1: puts a tail on there and it actually picks up 455 00:26:16,537 --> 00:26:18,976 S1: the one, um, once it's outside of the cell, it 456 00:26:18,976 --> 00:26:21,536 S1: picks up someone else's. And I'm kind of messing up 457 00:26:21,537 --> 00:26:24,216 S1: the details here, but they describe it pretty good in this, 458 00:26:24,257 --> 00:26:28,336 S1: in this, uh, full episode of, uh, Cognitive Revolution, I 459 00:26:28,337 --> 00:26:32,216 S1: think is the name of the podcast. But, um, when 460 00:26:32,216 --> 00:26:35,496 S1: they heard this, when they saw it written down, they're like, oh, 461 00:26:36,297 --> 00:26:39,597 S1: that that's probably what it is. So here, here's the thing. 462 00:26:39,597 --> 00:26:42,716 S1: They had made an assumption that a certain type of 463 00:26:42,716 --> 00:26:46,196 S1: mobility or a certain type of propagation was not possible. 464 00:26:46,397 --> 00:26:48,877 S1: And it was a very silly assumption that they had made. 465 00:26:49,317 --> 00:26:52,797 S1: And this, this assumption had stopped them from solving this 466 00:26:52,797 --> 00:26:57,837 S1: problem for years upon years. How many hundreds of hours, 467 00:26:57,877 --> 00:27:01,077 S1: thousands of hours have they spent stumped on this as 468 00:27:01,077 --> 00:27:06,397 S1: the world's premier humans on the entire planet? The smartest 469 00:27:06,397 --> 00:27:09,356 S1: people working on this in the entire world, on the 470 00:27:09,357 --> 00:27:14,517 S1: entire planet, were stumped by a thing, and Google came back, 471 00:27:15,716 --> 00:27:18,637 S1: I think within a couple of minutes, I think, and 472 00:27:18,637 --> 00:27:22,117 S1: it was like, yeah, I think it's probably this. They 473 00:27:22,117 --> 00:27:27,717 S1: go and check and it's 100% verified. That was correct. 474 00:27:27,716 --> 00:27:32,517 S1: And they here's here's the thing. They were kind of, uh, 475 00:27:32,517 --> 00:27:35,277 S1: I believe what they were saying is they were about 476 00:27:35,277 --> 00:27:38,467 S1: to figure this out. Maybe, maybe this year. Maybe next year. 477 00:27:38,466 --> 00:27:41,227 S1: They were doing some experiments that might have got them 478 00:27:41,226 --> 00:27:43,587 S1: to this. But the point is, Google found it instantly. 479 00:27:43,587 --> 00:27:47,707 S1: Which means if they had this Google research assistant earlier, 480 00:27:47,946 --> 00:27:51,186 S1: maybe last year, maybe years ago, maybe it would have 481 00:27:51,186 --> 00:27:54,066 S1: found this right. And they would not have had this 482 00:27:54,067 --> 00:27:56,946 S1: problem for this entire time. Now, what excites me about 483 00:27:56,946 --> 00:28:00,226 S1: this is not this specific use case. It's the how 484 00:28:00,226 --> 00:28:03,466 S1: they reacted to it. They were like, this is insane. 485 00:28:04,067 --> 00:28:07,347 S1: Because the thing I'm so excited about, and it's the 486 00:28:07,347 --> 00:28:10,826 S1: same thing they were talking about, is how many other 487 00:28:10,827 --> 00:28:14,627 S1: things are like this. There is data everywhere. There is 488 00:28:14,627 --> 00:28:19,427 S1: evidence everywhere. There are dots. Okay, think of connect the dots. 489 00:28:19,466 --> 00:28:22,507 S1: Think of how many dots are lying around in papers, 490 00:28:22,787 --> 00:28:28,547 S1: lying around in data sets. Raw data sets. Unfiltered. Unreviewed. Why? 491 00:28:28,587 --> 00:28:33,707 S1: Because there aren't enough academics. There aren't enough researchers. Forget academics. 492 00:28:33,706 --> 00:28:38,687 S1: There aren't enough humans With the training required. Write either 493 00:28:38,687 --> 00:28:42,247 S1: the formal current training or like even rudimentary training. There 494 00:28:42,247 --> 00:28:46,607 S1: aren't enough human eyes on all this raw data. There 495 00:28:46,607 --> 00:28:48,807 S1: are no human eyes to look at the stuff and 496 00:28:48,807 --> 00:28:54,207 S1: find the dots and connect them. How many cures for illnesses? 497 00:28:54,927 --> 00:28:58,367 S1: How many novel ways are we sitting on the ability 498 00:28:58,567 --> 00:29:01,927 S1: to improve our IQs by 20%? Are we sitting on 499 00:29:01,927 --> 00:29:07,007 S1: the ability to solve aging? I would guess we absolutely are. 500 00:29:07,287 --> 00:29:10,967 S1: We absolutely are. And that the problem is not enough. 501 00:29:10,967 --> 00:29:14,967 S1: People are studying it using not enough data. So what 502 00:29:15,007 --> 00:29:18,847 S1: I now allows us to do with tools like this, 503 00:29:18,847 --> 00:29:21,207 S1: which are about to be drastically better. I mean, this 504 00:29:21,207 --> 00:29:25,087 S1: thing that found this is about to be 100% stupid 505 00:29:25,087 --> 00:29:27,927 S1: compared to whatever comes out next year or next week. 506 00:29:27,967 --> 00:29:31,847 S1: Right now, just imagine giving all that raw data and 507 00:29:31,847 --> 00:29:35,757 S1: even going to collect more, right. Getting sensory data from 508 00:29:35,757 --> 00:29:38,957 S1: cells or whatever from, you know, different layers of the 509 00:29:38,957 --> 00:29:42,157 S1: world and basically feeding it into this thing all the 510 00:29:42,157 --> 00:29:45,437 S1: time and saying your job is to connect dots. That's 511 00:29:45,477 --> 00:29:47,997 S1: that's the promise of AI. Your job is to connect dots. 512 00:29:48,077 --> 00:29:52,117 S1: So what we've basically done at that point is simulate 513 00:29:53,437 --> 00:29:58,157 S1: billions more people on the planet with training for connecting dots, 514 00:29:59,117 --> 00:30:00,637 S1: and then they could go, look, I came up with 515 00:30:00,637 --> 00:30:03,957 S1: all these hypotheses. And if you go test this, it's 516 00:30:03,957 --> 00:30:05,717 S1: probably going to work. Oh, and by the way, that 517 00:30:05,717 --> 00:30:09,557 S1: is actually a cure for, you know, 86% of cancers 518 00:30:09,557 --> 00:30:13,357 S1: or that will extend your life by 42 years, um, 519 00:30:13,357 --> 00:30:17,077 S1: or whatever. This will allow you to transfer your brain 520 00:30:17,077 --> 00:30:22,797 S1: into a, you know, digital synthetic form or whatever. Um, 521 00:30:23,197 --> 00:30:25,437 S1: I'm just blown away by this now, I sent this 522 00:30:25,437 --> 00:30:28,317 S1: article and my write up of this to a buddy, 523 00:30:28,317 --> 00:30:31,277 S1: and he's like, yeah, I'm actually, um, you know, invested 524 00:30:31,277 --> 00:30:35,457 S1: in and, uh, you know, uh, an advisor for this 525 00:30:35,457 --> 00:30:39,377 S1: company who does that? Their specific thing is they are 526 00:30:39,377 --> 00:30:44,457 S1: already going and crawling all this other data. They are 527 00:30:44,457 --> 00:30:48,537 S1: going to find they have actively collected and are collecting 528 00:30:48,777 --> 00:30:51,377 S1: all this raw data and all these studies that basically 529 00:30:51,377 --> 00:30:55,057 S1: didn't go anywhere because they lost funding or whatever. That company, 530 00:30:55,337 --> 00:30:58,537 S1: this company he's talking about, it is actively doing that. 531 00:30:58,537 --> 00:31:02,817 S1: And guess what they're doing? They build the labs and 532 00:31:02,817 --> 00:31:06,777 S1: they're automating the labs. So when they do a hypothesis 533 00:31:06,937 --> 00:31:09,337 S1: and this is crazy, they already have use cases of 534 00:31:09,337 --> 00:31:12,097 S1: this working. So it's kind of even further than the 535 00:31:12,097 --> 00:31:15,577 S1: main story here. They have a use case of an 536 00:31:15,577 --> 00:31:20,297 S1: example of it actually, um, came up with a hypothesis. 537 00:31:21,177 --> 00:31:25,897 S1: It built a full methodology and schematic for how to 538 00:31:25,937 --> 00:31:29,897 S1: build the lab. And humans basically took that and went 539 00:31:29,897 --> 00:31:31,887 S1: into the lab. They built the a little thing. They 540 00:31:31,887 --> 00:31:34,447 S1: basically set up the experiment exactly the way that the 541 00:31:34,447 --> 00:31:39,807 S1: AI said to. And it 100% was true. They confirmed 542 00:31:40,367 --> 00:31:44,127 S1: experimentally that this hypothesis that it could come up with 543 00:31:44,127 --> 00:31:46,727 S1: was true. Now, if it had had the ability to 544 00:31:46,767 --> 00:31:50,527 S1: actually automate those tests, um, if it had the raw 545 00:31:50,527 --> 00:31:53,047 S1: materials in the lab and it had robotics or whatever, 546 00:31:53,647 --> 00:31:55,687 S1: that would have been even more insane, right? What if 547 00:31:55,687 --> 00:31:58,287 S1: it had the raw materials to to combine these molecules 548 00:31:58,287 --> 00:32:00,047 S1: or whatever? And there you got to be a little 549 00:32:00,087 --> 00:32:02,367 S1: bit careful because you don't want to just prompt inject 550 00:32:02,367 --> 00:32:05,566 S1: and it builds a, you know, a meth lab or a, 551 00:32:05,887 --> 00:32:10,167 S1: you know, sarin gas lab or whatever, a Covid lab, right. 552 00:32:10,207 --> 00:32:13,727 S1: Automated Covid generation. Um, so you got to be careful 553 00:32:13,727 --> 00:32:16,127 S1: with those. Obviously, this is what a lot of people 554 00:32:16,127 --> 00:32:21,527 S1: are worried about. But unbelievable, unbelievable possibility here in terms 555 00:32:21,527 --> 00:32:24,967 S1: of like connecting dots. And then the most important thing 556 00:32:25,007 --> 00:32:27,847 S1: and I talked about this, um, I got an essay 557 00:32:27,847 --> 00:32:30,187 S1: called The Path to Asi. I don't know, you guys 558 00:32:30,187 --> 00:32:32,667 S1: might have seen that it's came out. I don't know, 559 00:32:32,707 --> 00:32:35,027 S1: I put that out a few months ago and it 560 00:32:35,027 --> 00:32:39,586 S1: basically describes this process, right? You have hypotheses, you test them. 561 00:32:40,347 --> 00:32:43,547 S1: That's it. Right. Humans have hypotheses, they test them. But 562 00:32:43,547 --> 00:32:47,786 S1: we do it at such small scale. And more importantly, um, 563 00:32:47,947 --> 00:32:50,586 S1: real innovation comes from getting a bunch of smart people together, 564 00:32:50,587 --> 00:32:52,947 S1: like Bell Labs or the Renaissance, and a whole bunch 565 00:32:52,947 --> 00:32:56,107 S1: of smart people are together. They're talking about their ideas, 566 00:32:56,307 --> 00:32:59,107 S1: but the other person's idea that they're talking to influences 567 00:32:59,107 --> 00:33:04,627 S1: them slightly, changes their idea. Or even somebody, um, somebody, uh, 568 00:33:04,627 --> 00:33:09,747 S1: copies the idea, but they copy it incorrectly with a 569 00:33:09,747 --> 00:33:14,267 S1: slight deviation that actually makes it better. And that's absolutely insane. 570 00:33:14,267 --> 00:33:17,187 S1: So essentially what you have is like this evolution. You 571 00:33:17,187 --> 00:33:23,826 S1: have evolutionary biology or evolutionary, um, improvement happening to the 572 00:33:23,827 --> 00:33:29,847 S1: realm of ideas. Now that is 100% Automatable as well. 573 00:33:29,847 --> 00:33:31,767 S1: You could use AI to do that. You could use 574 00:33:31,767 --> 00:33:35,167 S1: genetic algorithms to do that. You know, it's easy to 575 00:33:35,167 --> 00:33:37,967 S1: do this in a not sophisticated way, and I'm sure 576 00:33:37,967 --> 00:33:40,247 S1: you could do it in a lot more sophisticated ways 577 00:33:40,247 --> 00:33:42,327 S1: the more you think about it. But essentially you have 578 00:33:42,327 --> 00:33:46,167 S1: this engine of human generated ideas, AI generated ideas, and 579 00:33:46,167 --> 00:33:50,607 S1: then you have this evolutionary petri dish which constantly evolves them, 580 00:33:51,087 --> 00:33:54,807 S1: that spits out hypotheses which go through some sort of filter, 581 00:33:55,167 --> 00:33:58,687 S1: and then the higher quality ones go into this automated 582 00:33:58,687 --> 00:34:03,487 S1: testing lab and we start to have this giant Bell Labs. 583 00:34:05,607 --> 00:34:13,167 S1: Renaissance mechanism for inventing new things, right. Inventing completely new things, 584 00:34:13,367 --> 00:34:17,207 S1: solving things like aging and cancer and all these different illnesses, 585 00:34:17,847 --> 00:34:21,887 S1: extending lifespan, like solving world hunger, like creating abundance on 586 00:34:21,887 --> 00:34:24,967 S1: the planet, like, you know, getting away from the zero 587 00:34:24,967 --> 00:34:28,477 S1: sum game of like, Capitalism and all this stuff. Like, 588 00:34:29,117 --> 00:34:32,357 S1: you could literally use this engine to solve human problems, 589 00:34:32,677 --> 00:34:35,397 S1: you know, at scale. So all of this is just 590 00:34:35,397 --> 00:34:39,036 S1: massively exciting to me. And I thought that was like 591 00:34:39,037 --> 00:34:43,037 S1: the most interesting story of the week. Apple releases controversial 592 00:34:43,037 --> 00:34:48,637 S1: paper on AI. Yeah, it was silly. It was a 593 00:34:48,637 --> 00:34:51,517 S1: silly paper. Um, some people are like, oh, you're behind 594 00:34:51,517 --> 00:34:53,917 S1: on AI. So now you release a paper that basically 595 00:34:53,917 --> 00:34:57,597 S1: says AI is stupid. Anyway. Um, I don't even want AI. 596 00:34:58,477 --> 00:35:00,877 S1: It's dumb anyway. So that's why. That's why I'm not 597 00:35:00,877 --> 00:35:03,597 S1: trying hard. That's why other people are beating me. That's 598 00:35:03,597 --> 00:35:06,957 S1: not actually what happened though, because Apple Apple air quotes. 599 00:35:06,997 --> 00:35:12,477 S1: Apple isn't saying this. It is some ML team within Apple. Right. 600 00:35:12,517 --> 00:35:16,557 S1: Releasing this. So, um, anyway, it was a funny narrative. 601 00:35:16,757 --> 00:35:20,357 S1: OpenAI massively drops O3 prices. They dropped by 80% and 602 00:35:20,357 --> 00:35:24,837 S1: they also released O3 Pro. Um, OpenAI doubles revenue to 603 00:35:24,877 --> 00:35:29,577 S1: $10 billion annually, and OpenAI also must keep all ChatGPT 604 00:35:29,617 --> 00:35:33,937 S1: conversations indefinitely due to a legal hold related to a lawsuit. 605 00:35:34,577 --> 00:35:38,177 S1: I find this weird. I don't know how accurate this is. 606 00:35:38,177 --> 00:35:41,576 S1: If it's all like what exactly the scope is. There's 607 00:35:41,577 --> 00:35:43,657 S1: a quote here that says we are required to retain 608 00:35:43,657 --> 00:35:47,097 S1: all data. Cannot process deletion requests during this period. What 609 00:35:47,097 --> 00:35:49,617 S1: I don't know is if it applies to like tenants 610 00:35:49,617 --> 00:35:52,777 S1: where they've paid extra money just to have their stuff 611 00:35:52,817 --> 00:35:55,497 S1: be ephemeral, right? There are a lot of people with 612 00:35:55,537 --> 00:35:58,497 S1: like private Azure instances where like that was the whole 613 00:35:58,497 --> 00:36:01,577 S1: reason they're paying extra is the fact that it's ephemeral, 614 00:36:01,577 --> 00:36:04,977 S1: or it gets deleted constantly or they're able to delete it, 615 00:36:05,337 --> 00:36:07,737 S1: but I don't know if it applies there. I doubt it, 616 00:36:07,737 --> 00:36:12,257 S1: but who knows? OpenAI makes ChatGPT voice mode sound way 617 00:36:12,257 --> 00:36:18,017 S1: more human. Okay, go play with voice mode. Advanced voice 618 00:36:18,017 --> 00:36:22,337 S1: mode on ChatGPT. It is ridiculous. I mean, it is 619 00:36:22,337 --> 00:36:25,807 S1: absolutely ridiculous. I was having a conversation with it yesterday, 620 00:36:25,807 --> 00:36:28,727 S1: which I haven't talked to in a while, and it 621 00:36:28,727 --> 00:36:33,127 S1: felt so incredibly human. The pauses, the voice tones, it 622 00:36:33,127 --> 00:36:37,927 S1: actually sang. Um. At first it did like vocal song 623 00:36:37,967 --> 00:36:41,247 S1: and I was like, no, not, you know, uh, spoken 624 00:36:41,247 --> 00:36:46,167 S1: word actually sing. And it actually sung with, like, tone. Um, anyway, 625 00:36:46,567 --> 00:36:48,887 S1: I still use the Cove one because it sounds like, uh, 626 00:36:48,887 --> 00:36:52,527 S1: one of the AIS from interstellar, and that's my favorite voice, uh, 627 00:36:52,527 --> 00:36:57,687 S1: since they got rid of, uh, Samantha. All right, Stanford 628 00:36:57,687 --> 00:37:01,807 S1: study shows doctors put AI beat. Oh, plus, I beat 629 00:37:01,807 --> 00:37:08,287 S1: traditional diagnostic tools. So doctors normally in this particular test 630 00:37:09,567 --> 00:37:17,127 S1: were 75% accurate. Doctors with the AI were 85% accurate, 10% jump. 631 00:37:17,607 --> 00:37:21,687 S1: But the hilarious part and extremely sad part I by 632 00:37:21,687 --> 00:37:26,467 S1: itself was 90%. It was 5% better than the doctor 633 00:37:26,467 --> 00:37:31,787 S1: using I. So it's like, yeah, it's like, yeah, I 634 00:37:31,867 --> 00:37:34,027 S1: am way better than the doctor. This is the I talking. 635 00:37:34,027 --> 00:37:36,907 S1: I'm way better than the doctor. I am 15% of 636 00:37:36,907 --> 00:37:40,467 S1: the doctor. Now. If the doctor uses me and asks 637 00:37:40,467 --> 00:37:43,547 S1: me questions, it can almost be as smart as me, 638 00:37:43,547 --> 00:37:47,827 S1: but not quite. And we all know how early the 639 00:37:47,827 --> 00:37:50,107 S1: game is. We all know how bad I is at 640 00:37:50,107 --> 00:37:53,467 S1: certain things, and it's already to this point. And this 641 00:37:53,467 --> 00:37:56,067 S1: is not like some random study at a junior college. 642 00:37:56,107 --> 00:38:03,107 S1: This is a pretty large Stanford study. Insane. Absolutely insane. Uh, 643 00:38:03,107 --> 00:38:06,627 S1: Microsoft reshuffles leadership to focus on AI agents. My new 644 00:38:06,627 --> 00:38:10,707 S1: favorite description of a business mode. This is from Jamin Bell. 645 00:38:11,027 --> 00:38:13,347 S1: A real long term moat is just a sequence of 646 00:38:13,347 --> 00:38:16,827 S1: smaller moats stacked together. Each one buys time. And what 647 00:38:16,827 --> 00:38:18,987 S1: you do with that time, how fast you execute, how 648 00:38:18,987 --> 00:38:23,247 S1: quickly you evolve, determines whether you stay ahead and my 649 00:38:23,247 --> 00:38:28,207 S1: analysis of this or my, you know, um. Clarification of 650 00:38:28,207 --> 00:38:31,407 S1: it or tightening it up is to me, that means 651 00:38:31,407 --> 00:38:35,287 S1: speed and adaptability is the only real moat. And a 652 00:38:35,287 --> 00:38:39,247 S1: hat tip to Clint Gibler, my buddy for, um, sending 653 00:38:39,247 --> 00:38:42,727 S1: me this, like in a text or something. Um, really, 654 00:38:42,727 --> 00:38:47,687 S1: really good technology. Why Bell Labs works so well. Uh, 655 00:38:47,687 --> 00:38:50,447 S1: we talked about that already. BYD's five minute charging puts 656 00:38:50,487 --> 00:38:53,767 S1: China in the lead for EVs. Five minute charging. That's 657 00:38:53,767 --> 00:38:58,967 S1: basically a gas station. Uh, very worried. Um, this is 658 00:38:58,967 --> 00:39:02,087 S1: why I want Tesla to win so bad. And now 659 00:39:02,487 --> 00:39:06,687 S1: also Waymo. I want, but it's got to be Tesla 660 00:39:06,727 --> 00:39:09,247 S1: because they're the ones making the cars. I'm really worried 661 00:39:09,247 --> 00:39:13,407 S1: about BYD. BYD is making extremely good cars. They are 662 00:39:13,407 --> 00:39:18,286 S1: subsidizing them. They have 10,000 EVs. And we're talking about 663 00:39:18,287 --> 00:39:22,357 S1: five minute charging like a gas station. I mean, if 664 00:39:22,357 --> 00:39:24,637 S1: they were to come here, they would crush right now. 665 00:39:24,637 --> 00:39:27,397 S1: So we need our options. We need American options to 666 00:39:27,437 --> 00:39:31,277 S1: be getting better way faster than they are right now. 667 00:39:32,397 --> 00:39:36,477 S1: Wing and Walmart expand drone delivery to 100 stores, which 668 00:39:36,477 --> 00:39:43,557 S1: is five major cities. Uh, Chinese tech behind Amazon's humanoid robots. Great. 669 00:39:43,677 --> 00:39:47,077 S1: So Amazon is doing humanoid robots now. Um, they're talking 670 00:39:47,077 --> 00:39:52,637 S1: about potentially having humanoid delivery drivers. And the tech is Chinese. Great. 671 00:39:53,477 --> 00:39:57,157 S1: YouTube loosens content rules using public interest as a standard. 672 00:39:57,357 --> 00:40:00,597 S1: And AWS has opened a new region in Taiwan with 673 00:40:00,597 --> 00:40:06,277 S1: three availability zones. Humans. Rents are dropping in most US 674 00:40:06,277 --> 00:40:09,597 S1: cities for the first time since 2023. Caffeine keeps your 675 00:40:09,597 --> 00:40:12,197 S1: brain awake even while you sleep, so you could actually sleep, 676 00:40:12,197 --> 00:40:15,757 S1: according to the study. You could actually sleep and stay asleep, 677 00:40:15,757 --> 00:40:19,337 S1: but you wake up more tired because your brain is 678 00:40:19,337 --> 00:40:23,457 S1: not properly resting because of the caffeine, because caffeine basically 679 00:40:23,457 --> 00:40:28,457 S1: blocks the sleep, um, receptor. Um, that's one of the mechanisms. 680 00:40:28,457 --> 00:40:32,057 S1: So that's probably related to why this is the case, 681 00:40:32,537 --> 00:40:34,617 S1: or at least why they found that to be the case. 682 00:40:34,657 --> 00:40:38,377 S1: Las Vegas fights record heat with massive tree planting. And 683 00:40:38,377 --> 00:40:41,617 S1: this is not, uh, like it's absorbing carbon. Uh, because 684 00:40:41,617 --> 00:40:44,057 S1: that wouldn't be nearly the amount of scale you need. Uh, 685 00:40:44,057 --> 00:40:47,777 S1: that's a planetary thing. But, um, for shade is what 686 00:40:47,777 --> 00:40:51,617 S1: they're talking about. Mushrooms may communicate using up to 50 words, 687 00:40:51,617 --> 00:40:56,737 S1: and forests offset global warming more than scientists previously thought. 688 00:40:57,577 --> 00:41:00,817 S1: So replanting the trees that we lost since the 1800s 689 00:41:00,817 --> 00:41:06,297 S1: could cool the planet by like half a degree, which 690 00:41:06,297 --> 00:41:10,777 S1: I think is Celsius. Uh, which would be cool. Uh, 691 00:41:10,777 --> 00:41:13,377 S1: I think we should do that. I think we should plant, like, 692 00:41:13,417 --> 00:41:19,767 S1: trillions of trees and just, uh, see if the temperature 693 00:41:19,767 --> 00:41:22,727 S1: falls really fast. See if CO2 levels fall really fast. 694 00:41:23,167 --> 00:41:25,327 S1: And if they do, um, cut down some of the trees. 695 00:41:25,847 --> 00:41:29,967 S1: That's what I think. And also carbon sequestration. And also 696 00:41:29,967 --> 00:41:33,647 S1: we should go all in on solar. All right ideas. 697 00:41:33,687 --> 00:41:36,247 S1: AI is coming for your company. A big part of 698 00:41:36,247 --> 00:41:39,367 S1: why CI so disruptive is that it's not just coming 699 00:41:39,367 --> 00:41:41,487 S1: after the jobs of the people that work at companies, 700 00:41:41,487 --> 00:41:45,567 S1: but for the companies themselves. It's also why I think like, uh, 701 00:41:45,607 --> 00:41:48,567 S1: venture capital and private equity has a lot of challenges ahead. 702 00:41:48,967 --> 00:41:51,247 S1: I think there's about to be a bright line between 703 00:41:51,247 --> 00:41:53,807 S1: pre and post AI companies in terms of how effective 704 00:41:53,807 --> 00:41:56,887 S1: they are and therefore how valuable they are. So don't 705 00:41:56,887 --> 00:41:58,767 S1: think of your job or the next person's job as 706 00:41:58,807 --> 00:42:01,327 S1: like being in danger. That might be true, but the 707 00:42:01,327 --> 00:42:04,567 S1: bigger issue is the shifting of work in general. Think 708 00:42:04,567 --> 00:42:08,927 S1: about work not being needed instead of work being optimized. 709 00:42:08,927 --> 00:42:13,207 S1: So elimination of work as opposed to optimization, like what 710 00:42:13,247 --> 00:42:16,347 S1: just completely goes away because it no longer makes sense. 711 00:42:16,507 --> 00:42:18,987 S1: Like I've said before elsewhere, think of AI as a 712 00:42:18,987 --> 00:42:24,867 S1: giant identity crisis or an identity challenge. Everybody should be asking, 713 00:42:24,867 --> 00:42:27,107 S1: what am I? Who am I? What is this company? 714 00:42:27,147 --> 00:42:29,827 S1: What does this company actually do? What do we actually 715 00:42:29,827 --> 00:42:35,667 S1: need to execute as this company, etc.. Discovery. Someone built 716 00:42:35,667 --> 00:42:38,347 S1: an MCP server that actually runs on Cloudflare workers, so 717 00:42:38,347 --> 00:42:40,627 S1: you don't have to stand up your own infrastructure. Data 718 00:42:40,627 --> 00:42:45,027 S1: visualization reveals patterns in D&D monster designs. How Anthropic's teams 719 00:42:45,027 --> 00:42:48,307 S1: use cloud code. We are No longer a serious country 720 00:42:48,307 --> 00:42:52,507 S1: by Paul Krugman. Great explanation of how model context protocols 721 00:42:52,507 --> 00:42:56,347 S1: is different from traditional APIs. I'm not sure I'm going 722 00:42:56,347 --> 00:42:58,307 S1: to read all of these, because they're actually just links 723 00:42:58,307 --> 00:43:01,467 S1: for you to go and click on in the newsletter. 724 00:43:01,987 --> 00:43:05,067 S1: So I'm just going to go right to the recommendation 725 00:43:05,067 --> 00:43:08,667 S1: of the week. I as an identity challenge question. Another 726 00:43:08,667 --> 00:43:10,427 S1: way to frame AI is to think of it as 727 00:43:10,427 --> 00:43:12,787 S1: an identity challenge is what we just talked about in 728 00:43:12,787 --> 00:43:16,576 S1: the ideas section for people, but also for organizations. What 729 00:43:16,577 --> 00:43:18,737 S1: am I? Who am I? What is this company really? 730 00:43:18,737 --> 00:43:23,017 S1: What differentiates me? What differentiates this company? But I think 731 00:43:23,017 --> 00:43:25,457 S1: we should have been asking these questions all along. This 732 00:43:25,457 --> 00:43:28,537 S1: isn't just an AI thing. Philosophers have been asking us 733 00:43:28,537 --> 00:43:31,777 S1: to ask these questions of ourselves for thousands of years. 734 00:43:32,017 --> 00:43:34,777 S1: The difference is now, because of AI, it's actually going 735 00:43:34,777 --> 00:43:36,937 S1: to get a lot harder to survive. If you don't 736 00:43:36,937 --> 00:43:39,297 S1: have these answers for yourself, or for your company or 737 00:43:39,297 --> 00:43:42,817 S1: your family or whatever. Before we could just be another 738 00:43:42,817 --> 00:43:45,737 S1: set of eyes or hands because so many eyes or 739 00:43:45,737 --> 00:43:48,417 S1: hands were needed, and now we've got to actually show 740 00:43:48,817 --> 00:43:52,017 S1: how and why we are more than that. So whether 741 00:43:52,017 --> 00:43:55,297 S1: you're a company or an individual, work out your answers 742 00:43:55,297 --> 00:43:57,937 S1: to those questions and write them down and maybe think 743 00:43:57,937 --> 00:44:01,257 S1: about discussing them with others as well. And the aphorism 744 00:44:01,257 --> 00:44:04,417 S1: of the week it is not death that we should fear, 745 00:44:04,417 --> 00:44:07,417 S1: but never beginning to live. It is not death that 746 00:44:07,417 --> 00:44:11,817 S1: we should fear, but never beginning to live. Marcus Aurelius.