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 --> 00:01:19,837 S1: o scared this 2007 Apple fanboy for the first time, 19 00:01:20,237 --> 00:01:25,437 S1: and it's basically a review of WD DC juxtaposed with 20 00:01:25,437 --> 00:01:30,357 S1: Google I o, which was a couple weeks back. And yeah, 21 00:01:30,357 --> 00:01:34,517 S1: I've been an Apple fanboy for I mean, I mean, 22 00:01:34,637 --> 00:01:37,477 S1: I guess acolyte. I don't really like the word fanboy, 23 00:01:37,477 --> 00:01:42,397 S1: but yeah, really drinking the Apple Kool-Aid for a very 24 00:01:42,397 --> 00:01:44,917 S1: long time. And if you're listening to this, you probably 25 00:01:44,917 --> 00:01:49,317 S1: already know that. But, um, I like to think that 26 00:01:49,317 --> 00:01:54,557 S1: I'm logical about it, but I'm already admitting to being biased. Uh, 27 00:01:54,557 --> 00:01:59,317 S1: so that's what being an acolyte is, right? But what 28 00:01:59,317 --> 00:02:02,917 S1: I saw essentially this year at Google, I o is 29 00:02:02,957 --> 00:02:07,477 S1: it seemed like they are switching to Gemini and I 30 00:02:07,517 --> 00:02:11,636 S1: in general to be like their core mission. I feel 31 00:02:11,637 --> 00:02:15,246 S1: like they're moving away from ads. I feel like they're 32 00:02:15,246 --> 00:02:18,167 S1: moving away from that, being like the future in the 33 00:02:18,167 --> 00:02:21,727 S1: center of their business to like data and AI being 34 00:02:21,727 --> 00:02:24,966 S1: the center of their business. And I think this is really, 35 00:02:24,966 --> 00:02:28,286 S1: really cool on on their part. And I feel like 36 00:02:28,286 --> 00:02:32,406 S1: Google I o they did so well because of that 37 00:02:32,406 --> 00:02:36,407 S1: integration right there talking about integration with glasses and, you know, 38 00:02:36,447 --> 00:02:39,966 S1: XR or AR or whatever they're calling it, and Gemini 39 00:02:40,006 --> 00:02:42,847 S1: being integrated in like all the different apps. So it 40 00:02:42,846 --> 00:02:45,406 S1: just felt like they're doing something new and they're doing 41 00:02:45,406 --> 00:02:49,167 S1: something different. I've always been kind of averse to Google 42 00:02:49,207 --> 00:02:52,566 S1: because they are fundamentally an ad company, or they were 43 00:02:52,566 --> 00:02:56,287 S1: fundamentally an ad company. And I really like the CEO. 44 00:02:56,327 --> 00:02:58,327 S1: I feel like he gets it. I feel like it's 45 00:02:58,327 --> 00:03:01,087 S1: not gross. I don't feel like he's gross. I don't 46 00:03:01,087 --> 00:03:04,447 S1: feel like he's there to maximize ad revenue. I think 47 00:03:04,447 --> 00:03:08,007 S1: he really gets the AI stuff. So my perception of 48 00:03:08,047 --> 00:03:10,927 S1: Google has overall just like improved, I would say over 49 00:03:10,927 --> 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 --> 00:04:16,356 S1: in Mac OS 26, by the way. But, um, basically 67 00:04:16,357 --> 00:04:19,357 S1: their ecosystem is what keeps me their their focus on 68 00:04:19,357 --> 00:04:21,996 S1: UI and UX is what keeps me their their focus 69 00:04:21,997 --> 00:04:25,677 S1: on art and creativity and like enabling people to be 70 00:04:25,677 --> 00:04:30,077 S1: creative and enabling people to be their best selves or whatever. 71 00:04:30,117 --> 00:04:32,637 S1: Like that's the vibe that I've always liked about Apple, 72 00:04:33,077 --> 00:04:35,357 S1: and I think they do UI and UX better than 73 00:04:35,357 --> 00:04:38,117 S1: anyone else. I think they've had this unified vision of 74 00:04:38,117 --> 00:04:43,517 S1: context and, you know, having finance and creativity and work 75 00:04:43,517 --> 00:04:46,997 S1: and play and personal and all of that, all integrated 76 00:04:46,997 --> 00:04:49,757 S1: into the operating system. They've always just done that better 77 00:04:49,757 --> 00:04:53,916 S1: than anyone else. So that's why I'm so into Apple now. 78 00:04:54,837 --> 00:04:57,197 S1: Obviously AI is happening has been for the last three 79 00:04:57,197 --> 00:05:03,397 S1: years or however long it's been. And Apple is behind. Right. I've, 80 00:05:03,397 --> 00:05:05,957 S1: I've been saying for you know whatever a year that 81 00:05:05,957 --> 00:05:09,037 S1: look they're going to come out with their unified AI 82 00:05:09,077 --> 00:05:10,847 S1: product which is going to go on top of their 83 00:05:10,847 --> 00:05:13,887 S1: life OS or whatever. And basically Siri is going to 84 00:05:13,887 --> 00:05:16,447 S1: jump way ahead of everyone. And I thought that was 85 00:05:16,447 --> 00:05:19,087 S1: going to be like end of last year. And of 86 00:05:19,087 --> 00:05:21,606 S1: course they stumbled on that. And I believe it's because 87 00:05:21,607 --> 00:05:26,167 S1: of prompt injection. I think it is too difficult to 88 00:05:26,207 --> 00:05:29,486 S1: put an AI in front of all the personal context 89 00:05:29,887 --> 00:05:32,927 S1: that Apple has about us. And to do it in 90 00:05:32,927 --> 00:05:35,167 S1: a secure way, I think it's too difficult to do 91 00:05:35,167 --> 00:05:37,967 S1: that right now. That is my guess. And I know 92 00:05:37,967 --> 00:05:42,207 S1: this technically because I know how to do prompt injection and, um, 93 00:05:42,447 --> 00:05:45,167 S1: I know some of the best other prompt injection people 94 00:05:45,167 --> 00:05:48,927 S1: in the world. And it is pretty trivial still to 95 00:05:49,007 --> 00:05:53,967 S1: bypass most protections. So I think they are struggling with that. 96 00:05:54,207 --> 00:05:56,167 S1: Maybe they're struggling with other parts of it as well, 97 00:05:56,167 --> 00:06:00,487 S1: but I imagine they're definitely struggling with that piece. Like 98 00:06:00,487 --> 00:06:02,726 S1: right now I just got a pop up screening call, 99 00:06:02,727 --> 00:06:04,726 S1: and if I click view and this is while I'm 100 00:06:04,727 --> 00:06:09,097 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 --> 00:06:26,377 S1: pretty cool that I have that option. And the phone 106 00:06:26,377 --> 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 --> 00:06:49,856 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 --> 00:06:58,137 S1: and specifically AI. And obviously I wish that Apple had 114 00:06:58,137 --> 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 --> 00:07:06,657 S1: working on UI and UX as well. Like I assume 117 00:07:06,657 --> 00:07:09,707 S1: it's getting better. I've seen some, you know, UI stuff 118 00:07:09,707 --> 00:07:12,347 S1: from them that seems to be halfway decent. So the 119 00:07:12,347 --> 00:07:16,587 S1: real question to me is will Google figure out the UI, 120 00:07:16,667 --> 00:07:22,707 S1: UX life, OS, full integration of ecosystem? Will they figure 121 00:07:22,707 --> 00:07:29,267 S1: that out before Apple, or will Apple figure out data 122 00:07:29,267 --> 00:07:36,347 S1: and AI before Google figures out that UI UX piece? Right. 123 00:07:36,467 --> 00:07:39,947 S1: That's really the question. Which one gets the the one 124 00:07:39,947 --> 00:07:43,387 S1: that they're weak on up to a certain bar first? 125 00:07:44,187 --> 00:07:47,507 S1: I can't conceivably think of myself switching over to the 126 00:07:47,507 --> 00:07:51,307 S1: Google ecosystem, because there's so many pieces of it that 127 00:07:51,707 --> 00:07:56,107 S1: I know I would miss, but I am. I am 100% 128 00:07:56,107 --> 00:07:59,707 S1: open to it. I honestly, I is a much bigger 129 00:07:59,707 --> 00:08:05,267 S1: movement than any other tech movement. Um, it's bigger than 130 00:08:05,267 --> 00:08:08,637 S1: UI and UX, especially since I shouldn't be typing on 131 00:08:08,637 --> 00:08:13,277 S1: a keyboard anyway. I shouldn't be, um, you know, thumb 132 00:08:13,317 --> 00:08:15,997 S1: typing on a phone, right? This should all be moving 133 00:08:15,997 --> 00:08:21,717 S1: to voice and virtual interfaces and glasses. So as that 134 00:08:21,717 --> 00:08:24,437 S1: starts to happen, like I'm more likely to move towards 135 00:08:24,437 --> 00:08:29,277 S1: the Google side if Apple doesn't have that. Um, keeping 136 00:08:29,277 --> 00:08:31,837 S1: in mind that I really do care about my interface 137 00:08:31,837 --> 00:08:34,277 S1: to tech. Oh, here's the other thing. The other big 138 00:08:34,277 --> 00:08:36,877 S1: reason that I really prefer Apple is because I've been 139 00:08:36,877 --> 00:08:40,997 S1: on the inside, and I know how seriously and crazy, um, 140 00:08:41,277 --> 00:08:44,437 S1: they take the security and privacy stuff like it is 141 00:08:44,437 --> 00:08:47,837 S1: not marketing, it is not made up. It is absolutely 142 00:08:47,837 --> 00:08:51,636 S1: dead serious. So if I'm building my whole life ecosystem 143 00:08:51,636 --> 00:08:54,756 S1: around this, plus agents, I'm building all this stuff on it, 144 00:08:54,756 --> 00:08:57,956 S1: they got to do it perfectly right. Now, I do 145 00:08:57,957 --> 00:09:00,477 S1: think Google is really, really good at security. I just 146 00:09:00,477 --> 00:09:03,477 S1: don't think their incentives have been aligned, you know, in 147 00:09:03,477 --> 00:09:07,047 S1: the past because they were primarily an ad company, the 148 00:09:07,046 --> 00:09:10,806 S1: 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 --> 00:09:35,566 S1: that's the case. Hopefully they're migrating away from that, which 157 00:09:35,567 --> 00:09:39,047 S1: is why I, I just haven't been enthused about using 158 00:09:39,046 --> 00:09:42,487 S1: them as a core OS. All right. I think that's 159 00:09:42,487 --> 00:09:49,526 S1: enough about that. Basically a little bit of disappointment around WD, 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 --> 00:10:21,946 S1: is the type of thing you lose when you go 171 00:10:21,947 --> 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 --> 00:10:45,266 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 --> 00:10:55,066 S1: running these Apple betas early on. But, um, no, it's 181 00:10:55,067 --> 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 --> 00:11:04,516 S1: try out some of these new emoji features or whatever. 184 00:11:05,636 --> 00:11:10,237 S1: All right. Um, got a couple blogs. Yeah. So one 185 00:11:10,237 --> 00:11:16,237 S1: is the WWE one, the other one is, um, uh, 186 00:11:16,477 --> 00:11:19,636 S1: see here. Oh, yeah. It's an argument against. It's just 187 00:11:19,636 --> 00:11:22,197 S1: next token prediction. I thought that was a pretty good 188 00:11:22,197 --> 00:11:25,997 S1: way of encapsulating it. Uh, so definitely check that one out. 189 00:11:25,997 --> 00:11:28,316 S1: I'm about to do, like, a kind of, like a 190 00:11:28,317 --> 00:11:33,676 S1: personal project to profile, like all the CCP members and structure, 191 00:11:33,717 --> 00:11:36,997 S1: just profile them and, like, figure out who are they, 192 00:11:37,036 --> 00:11:41,117 S1: what are they up to? How are they different than she? What, uh, 193 00:11:41,877 --> 00:11:44,077 S1: what are their political opinions? What are they focused on? 194 00:11:44,077 --> 00:11:46,917 S1: What are their areas of expertise? What are their areas 195 00:11:46,916 --> 00:11:51,197 S1: of responsibility like how does legislation get. I guess it's 196 00:11:51,197 --> 00:11:53,997 S1: not legislation because people aren't voting, but how do they 197 00:11:53,997 --> 00:11:56,916 S1: decide what new laws to put out? Is there a process? 198 00:11:56,916 --> 00:11:59,117 S1: Is it on a particular timing? I just kind of 199 00:11:59,156 --> 00:12:03,647 S1: want to understand how the Chinese government works. So I'm 200 00:12:03,646 --> 00:12:07,287 S1: going to start with understanding all the CCP people, um, 201 00:12:07,766 --> 00:12:10,566 S1: because that is the government. And I and I want 202 00:12:10,567 --> 00:12:12,727 S1: to look at the different echelons, like, is there a 203 00:12:13,447 --> 00:12:16,127 S1: like is there like a Politburo? Is that a smaller group? 204 00:12:16,127 --> 00:12:19,926 S1: Is it like a larger thing of like hundreds of people? Um, 205 00:12:19,967 --> 00:12:22,687 S1: what's the difference between those tiers, like stuff like that? 206 00:12:22,687 --> 00:12:25,286 S1: So I'm going to do a study like that. And, uh, 207 00:12:25,286 --> 00:12:27,046 S1: I don't know how I'm going to put that out. 208 00:12:27,166 --> 00:12:29,526 S1: Maybe a blog post, maybe some member content. I have 209 00:12:29,526 --> 00:12:31,926 S1: no idea. Uh, the other thing I'm going to do 210 00:12:31,926 --> 00:12:35,166 S1: is I'm going to do a future trend investment analysis exercise, 211 00:12:35,166 --> 00:12:37,727 S1: which I did, I want to say like five years ago, 212 00:12:38,127 --> 00:12:41,607 S1: and it basically told me to buy Amazon and Nvidia. 213 00:12:42,166 --> 00:12:44,727 S1: I forget what that exercise told us to buy, but 214 00:12:44,727 --> 00:12:48,286 S1: I did it with my good buddy Tai Sabarno, and 215 00:12:48,286 --> 00:12:50,487 S1: I think we ended up making a number of purchases 216 00:12:50,487 --> 00:12:55,167 S1: based on that, and I think they've done well for us. But, um, yeah, 217 00:12:55,166 --> 00:12:57,127 S1: I want to do another one this time. We'll get 218 00:12:57,127 --> 00:13:01,016 S1: to use things like, you know, O3 Pro and I 219 00:13:01,057 --> 00:13:03,456 S1: get to throw in tons of context about myself and 220 00:13:03,457 --> 00:13:06,416 S1: what I'm looking for and the, you know, the types 221 00:13:06,416 --> 00:13:08,497 S1: of investments I'm looking for and the type of risk 222 00:13:08,497 --> 00:13:12,176 S1: I want to be open to. Um, and this isn't 223 00:13:12,176 --> 00:13:16,577 S1: only for investment. That'll be kind of like a side product, um, of, 224 00:13:16,617 --> 00:13:19,817 S1: like the exhaust that comes out of it. The biggest 225 00:13:19,817 --> 00:13:22,136 S1: thing I'm trying to do here is just figure out trends. 226 00:13:22,136 --> 00:13:25,057 S1: So here's like the I'll give you like kind of 227 00:13:25,057 --> 00:13:27,497 S1: what I'm going to feed to this thing. Right. I'm 228 00:13:27,497 --> 00:13:29,776 S1: going to set this entire thing up, which is going 229 00:13:29,776 --> 00:13:33,056 S1: to be like 20 pages of me writing up contacts 230 00:13:33,097 --> 00:13:35,857 S1: to my own thoughts and my own predictions, my own like, 231 00:13:36,016 --> 00:13:40,816 S1: concerns or whatever. And I want to know what it 232 00:13:40,817 --> 00:13:42,656 S1: thinks could happen. And I'm sure it's going to be 233 00:13:42,656 --> 00:13:44,776 S1: very careful and be like, look, we can't predict the future, 234 00:13:44,776 --> 00:13:48,857 S1: which obviously everyone knows that. But, um, what I'm going 235 00:13:48,896 --> 00:13:52,497 S1: to be is what I'm going to say is like, look, um, 236 00:13:53,097 --> 00:13:56,857 S1: if this were to happen, what seems obvious to you, 237 00:13:56,857 --> 00:14:01,707 S1: that would be some second order effects. That is the power, right, 238 00:14:01,747 --> 00:14:04,227 S1: that I didn't have before where I is like, well, 239 00:14:04,266 --> 00:14:06,786 S1: obviously this is the type of thing that happens and 240 00:14:06,786 --> 00:14:11,427 S1: it gives you like 150 different things. So when certain 241 00:14:11,426 --> 00:14:14,627 S1: things are scarce, prices of certain other things go up. 242 00:14:14,627 --> 00:14:16,947 S1: Which companies are associated with those? I don't know all 243 00:14:16,987 --> 00:14:19,946 S1: that information. Like I'm not like a commodities expert. I'm 244 00:14:19,987 --> 00:14:23,267 S1: not a trading expert, I'm not a stock expert. So 245 00:14:23,827 --> 00:14:26,107 S1: I think there are some things that don't involve that 246 00:14:26,107 --> 00:14:29,586 S1: much prediction or that much conjecture on the side of 247 00:14:29,587 --> 00:14:33,147 S1: the eye. That's actually just benefits from it. Understanding how 248 00:14:33,147 --> 00:14:38,066 S1: the world works. So I could say, okay, um, like, 249 00:14:38,067 --> 00:14:40,747 S1: what am I? You know, hypotheses is like, okay, private 250 00:14:40,747 --> 00:14:42,946 S1: security is going to go up. Oh, there's probably going 251 00:14:42,947 --> 00:14:46,587 S1: to be a bunch more prisons built, uh, because, you know, 252 00:14:46,867 --> 00:14:49,467 S1: we're stupid. And we took down all the mental health facilities. 253 00:14:49,467 --> 00:14:53,587 S1: So what happens when there's like UBI comes out? But 254 00:14:53,587 --> 00:14:57,997 S1: UBI is limited to only citizens. So they start doing 255 00:14:57,997 --> 00:15:01,477 S1: mass deportations, not because of the current kind of vibe 256 00:15:01,477 --> 00:15:04,717 S1: of like anti-immigrant stuff that's going on, but more along 257 00:15:04,717 --> 00:15:06,717 S1: the lines of like, we need to control how many 258 00:15:06,717 --> 00:15:09,637 S1: people were sending UBI to, we're not going to send 259 00:15:09,637 --> 00:15:12,956 S1: UBI to everyone in the country just because they're physically here. 260 00:15:13,637 --> 00:15:16,117 S1: So now there's a bunch of tech to find who 261 00:15:16,277 --> 00:15:20,677 S1: is and isn't supposed to be receiving UBI, and more 262 00:15:20,677 --> 00:15:23,757 S1: and more people will become homeless because, you know, AI 263 00:15:23,797 --> 00:15:26,836 S1: is taking jobs. Okay, so they're homeless. So then you 264 00:15:26,837 --> 00:15:30,517 S1: have the fact that they don't have a place to live. Uh, 265 00:15:30,517 --> 00:15:33,877 S1: then you have addiction, then you have violence. So who 266 00:15:33,877 --> 00:15:36,997 S1: goes into prison, right? Prison is mostly full of people 267 00:15:36,997 --> 00:15:41,157 S1: who have addiction, who have mental illnesses, and obviously some 268 00:15:41,157 --> 00:15:44,077 S1: people who are violent. Right. And the percentages, you might 269 00:15:44,077 --> 00:15:47,717 S1: be surprised how much it's actually people that just couldn't 270 00:15:47,717 --> 00:15:49,797 S1: find a way to get it going. Right. So they 271 00:15:49,797 --> 00:15:53,237 S1: end up on drugs, they end up mentally ill. Those 272 00:15:53,797 --> 00:15:56,497 S1: ideally in some world, like the world we're supposed to 273 00:15:56,497 --> 00:15:59,617 S1: be building, that would be like drug treatment programs and 274 00:15:59,617 --> 00:16:02,417 S1: it would be mental health facilities. But all of that 275 00:16:02,417 --> 00:16:05,697 S1: is going to collapse down into basically like prisons. Get 276 00:16:05,697 --> 00:16:10,777 S1: them away from society, separate them from society. So what 277 00:16:10,777 --> 00:16:13,137 S1: does that mean? What does that mean for water? What 278 00:16:13,137 --> 00:16:16,177 S1: does that mean for food? What does that mean for, um, 279 00:16:16,297 --> 00:16:19,297 S1: what companies are going to do well in that world? Um, like, 280 00:16:19,337 --> 00:16:22,297 S1: I'll tell you one of mine Costco I love Costco. 281 00:16:22,337 --> 00:16:26,097 S1: I'm like, so like into Costco. Um, and also Google 282 00:16:26,097 --> 00:16:28,497 S1: and also Apple. But those are like my three main 283 00:16:28,497 --> 00:16:32,697 S1: investments right now. So the question is like, how is 284 00:16:32,737 --> 00:16:36,577 S1: how can I find other ways to connect these dots. 285 00:16:36,937 --> 00:16:39,337 S1: And I'm going to tell it, look, don't be doing conjecture. 286 00:16:39,337 --> 00:16:41,217 S1: Don't be trying to predict the future. I'm going to 287 00:16:41,217 --> 00:16:44,057 S1: give you a bunch of options of ways. I think 288 00:16:44,057 --> 00:16:47,137 S1: maybe it could go. But I have no idea. And 289 00:16:47,137 --> 00:16:49,457 S1: you as this I you also have no idea. But 290 00:16:49,457 --> 00:16:54,427 S1: there's there are lanes of probability, Right? Uh, turns out 291 00:16:54,427 --> 00:16:56,347 S1: if people don't have jobs, they have no way to 292 00:16:56,387 --> 00:16:59,267 S1: pay for food, especially for their families. They might get 293 00:16:59,267 --> 00:17:03,427 S1: a little upset. Right. So certain things don't require a 294 00:17:03,467 --> 00:17:07,987 S1: lot of, like, future prediction or futurist type crap. Um, 295 00:17:07,987 --> 00:17:09,547 S1: so I'm going to use the AI to help me 296 00:17:09,587 --> 00:17:13,907 S1: sort of navigate that stuff that seems a little more obvious. And, um, 297 00:17:15,187 --> 00:17:17,227 S1: the main output is going to be like, it looks 298 00:17:17,227 --> 00:17:20,466 S1: like if these things happen, then these things might happen 299 00:17:20,467 --> 00:17:25,347 S1: as well. And then, um, maybe some investment thing of like, okay, well, 300 00:17:25,547 --> 00:17:28,707 S1: for all of those different ones, what companies or what 301 00:17:28,706 --> 00:17:32,947 S1: products or what raw materials or where would I want 302 00:17:32,946 --> 00:17:34,947 S1: to live? Where would I want to move? What is 303 00:17:34,946 --> 00:17:38,107 S1: a good city to be in if something like, you know, 304 00:17:38,147 --> 00:17:40,787 S1: three through seven were to happen, right? That's the type 305 00:17:40,787 --> 00:17:42,786 S1: of analysis I'm going to do. And similar to the 306 00:17:42,787 --> 00:17:44,667 S1: CCP one, I'm not sure how I'm going to put 307 00:17:44,667 --> 00:17:47,227 S1: that out. Maybe I'll make a giant PDF, maybe I'll 308 00:17:47,226 --> 00:17:49,907 S1: do a video. Maybe it'll be like member content, maybe, 309 00:17:49,946 --> 00:17:52,917 S1: you know, charge for it, Make it free. No idea. 310 00:17:54,157 --> 00:17:57,917 S1: Probably some combination of those. All right. What else? Doo 311 00:17:57,917 --> 00:18:02,477 S1: doo doo. All right. Cybersecurity. So Trump overhauled Biden's cybersecurity 312 00:18:02,476 --> 00:18:06,757 S1: policies with a new executive order. Uh, he removed focus 313 00:18:06,757 --> 00:18:09,677 S1: on mandated digital IDs. That must have been, like an 314 00:18:09,716 --> 00:18:15,236 S1: immigration type thing. Accounting, compliance checklists, micromanaging, agency decisions. I 315 00:18:15,236 --> 00:18:17,837 S1: think that might have been like Cisa, maybe like harnessing 316 00:18:18,277 --> 00:18:21,677 S1: or like trying to control Cisa ads focus on defeating 317 00:18:21,677 --> 00:18:26,917 S1: foreign threats, secure software practices, border gateway protection. That's BGP, 318 00:18:27,077 --> 00:18:32,397 S1: I believe. Um, yeah. Like, you know, hijacking BGP routes. 319 00:18:32,436 --> 00:18:38,677 S1: Post-quantum cryptography, modern encryption protocols, AI for vulnerabilities, IoT security 320 00:18:38,677 --> 00:18:44,956 S1: standards and limiting sanctions scope. So that was, uh, a 321 00:18:44,956 --> 00:18:48,797 S1: combination of mine and an AI analysis of the talking points, 322 00:18:48,797 --> 00:18:51,887 S1: or it's called a fact sheet. Bellingcat tests whether or 323 00:18:51,887 --> 00:18:54,927 S1: not I can actually geolocate photos, and it found that 324 00:18:54,927 --> 00:18:59,966 S1: O3 actually did the best and beat out Google Lens, actually. 325 00:19:00,527 --> 00:19:04,767 S1: Sentinel one reveals details on Chinese supply chain attack attempt. 326 00:19:05,047 --> 00:19:10,446 S1: Protonvpn sees 1,000% sign up surge after Pornhub blocks France. 327 00:19:11,007 --> 00:19:16,567 S1: So France evidently loves Protonvpn. And, uh, yeah, they went 328 00:19:16,567 --> 00:19:20,287 S1: over there so they could still get access. Microsoft Teams 329 00:19:20,287 --> 00:19:23,767 S1: with Indian police to shut down fake tech support scammers 330 00:19:24,847 --> 00:19:28,887 S1: and Bishop Fox 2025 red team tools list. All right. 331 00:19:28,927 --> 00:19:32,206 S1: National security OpenAI published their annual report on how bad 332 00:19:32,206 --> 00:19:35,847 S1: actors are using AI maliciously, and I love the fact 333 00:19:35,847 --> 00:19:37,567 S1: that they put these out. So four out of ten 334 00:19:37,567 --> 00:19:40,927 S1: major abuse cases look like they came from China, from 335 00:19:40,927 --> 00:19:45,247 S1: social engineering to cyber threats. They're seeing deceptive employment schemes. 336 00:19:45,247 --> 00:19:50,736 S1: So task scams from like Cambodia comment spamming from Philippines 337 00:19:51,377 --> 00:19:54,897 S1: and a covert influence operations potentially leaked to Russia and 338 00:19:54,897 --> 00:20:00,137 S1: Iran using AI as force multipliers. Um, I'm actually going 339 00:20:00,137 --> 00:20:02,256 S1: to mention something right now. I can't figure out what 340 00:20:02,257 --> 00:20:03,857 S1: I'm going to do with this. I might do another 341 00:20:03,857 --> 00:20:06,297 S1: report like the one I just the two I just 342 00:20:06,297 --> 00:20:12,017 S1: talked about. So my Twitter feed is full, full of 343 00:20:12,017 --> 00:20:17,217 S1: what I am absolutely certain is like widespread propaganda. So 344 00:20:17,377 --> 00:20:20,577 S1: the content that I see is like, don't you hate 345 00:20:20,577 --> 00:20:24,137 S1: black people because they do this? Um, don't you wish 346 00:20:24,137 --> 00:20:26,057 S1: the world looked like this? And then if you click 347 00:20:26,097 --> 00:20:28,097 S1: the video, it's like a whole bunch of white people 348 00:20:28,097 --> 00:20:30,857 S1: walking around and there's like, no crime and there's like 349 00:20:30,857 --> 00:20:34,977 S1: a fountain with, like, a nice water sound, and there's, like, 350 00:20:34,976 --> 00:20:38,097 S1: somebody buying something at, like, an Abercrombie or something, and 351 00:20:38,097 --> 00:20:42,216 S1: it's like. It's like massive propaganda to like, uh, don't 352 00:20:42,216 --> 00:20:45,657 S1: you wish the world was the way it was before? Before, like, 353 00:20:45,696 --> 00:20:49,227 S1: brown people messed it all up, right? And then there'll 354 00:20:49,267 --> 00:20:51,706 S1: be another one. It'll be like about fighting or something 355 00:20:51,706 --> 00:20:55,067 S1: like that. Or it'll be like masculine movies. Don't you 356 00:20:55,067 --> 00:20:58,307 S1: wish movies were like this? And it's like someone holding 357 00:20:58,307 --> 00:21:02,067 S1: a Budweiser. Budweiser. And, like, shooting a crossbow or something. 358 00:21:02,587 --> 00:21:04,987 S1: And I'm just like, what is going on? So every 359 00:21:05,027 --> 00:21:06,787 S1: time I go back to Twitter, which I probably check 360 00:21:06,787 --> 00:21:09,186 S1: it like, I don't know, like 15, 20 times a 361 00:21:09,186 --> 00:21:11,466 S1: day or something, and then I'll just be browsing to 362 00:21:11,466 --> 00:21:12,786 S1: see what people are saying. But if I go to 363 00:21:12,827 --> 00:21:15,427 S1: the home tab, which is like impossible to clean up 364 00:21:15,427 --> 00:21:17,466 S1: because I block all these things when I see them, 365 00:21:17,747 --> 00:21:20,266 S1: although lately I've started bookmarking them because I'm going to 366 00:21:20,267 --> 00:21:24,306 S1: do this research project. But normally I just block, block, block. 367 00:21:24,307 --> 00:21:28,107 S1: But it's just an endless supply. Like these people have 368 00:21:28,147 --> 00:21:31,387 S1: like multiple accounts. So what I started doing is clicking 369 00:21:31,387 --> 00:21:35,427 S1: on the account and scrolling through their feed, and it's nonstop. 370 00:21:35,427 --> 00:21:39,587 S1: It's nonstop narratives about how messed up the country is, 371 00:21:40,547 --> 00:21:44,547 S1: reasons for why the country got messed up, which is scapegoating, basically. 372 00:21:45,067 --> 00:21:48,647 S1: And then, um. Don't you wish it was like, uh, 373 00:21:48,647 --> 00:21:50,807 S1: the new thing, right? Don't you wish it was like 374 00:21:50,807 --> 00:21:52,647 S1: it used to be? Don't you know? Don't you think 375 00:21:52,647 --> 00:21:56,246 S1: we could do better? And I'm just, like, seeing this everywhere. 376 00:21:56,247 --> 00:21:59,327 S1: Just like hundreds of accounts doing this. And I sent 377 00:21:59,327 --> 00:22:02,327 S1: this a few of these accounts to, uh, some friends 378 00:22:02,327 --> 00:22:06,326 S1: of mine who go and research this stuff. And, yeah, 379 00:22:06,327 --> 00:22:12,726 S1: I'm just really surprised that, like, I guess I'm not surprised, given, uh, 380 00:22:12,847 --> 00:22:15,326 S1: given Elon's situation and the fact that he lost so 381 00:22:15,327 --> 00:22:18,407 S1: many advertisers and the fact that I guess he's like 382 00:22:18,607 --> 00:22:23,647 S1: conspiracy friendly, I would say, um, but ultimately people click 383 00:22:23,647 --> 00:22:26,527 S1: on this stuff. People watch this stuff because it is, 384 00:22:26,647 --> 00:22:30,127 S1: you know, specifically designed to be that way, to be 385 00:22:30,167 --> 00:22:35,007 S1: very watchy and clicky. Um, so I guess that's why 386 00:22:35,007 --> 00:22:37,486 S1: it's there. But but it's really gross. Like, I really 387 00:22:37,486 --> 00:22:39,687 S1: wish there were a platform or I wish there were 388 00:22:39,686 --> 00:22:43,287 S1: options in this platform where it's like, look, I'm paying money. Like, 389 00:22:43,936 --> 00:22:46,256 S1: Can you not show me this garbage? I want to 390 00:22:46,257 --> 00:22:50,057 S1: see stuff from people I follow and stuff that's very 391 00:22:50,057 --> 00:22:52,417 S1: tightly correlated with people I follow. Which he says he's 392 00:22:52,456 --> 00:22:55,337 S1: working on that actually, he says he's working on similar 393 00:22:55,337 --> 00:22:57,617 S1: content matching, which is similar to the app I have 394 00:22:57,657 --> 00:23:02,057 S1: called threshold. But, um, yeah, this whole thread of like, 395 00:23:02,097 --> 00:23:04,577 S1: who is sending all this stuff to us, who is 396 00:23:04,577 --> 00:23:08,377 S1: basically trying to change the narrative or try to put 397 00:23:08,657 --> 00:23:12,617 S1: inject into our minds, like this feeling of the US 398 00:23:12,657 --> 00:23:16,297 S1: is messed up. It was messed up by them pointing 399 00:23:16,297 --> 00:23:20,937 S1: at trans people or brown people or black people or whatever. 400 00:23:20,936 --> 00:23:24,577 S1: Gay people or you know, the wrong religion or whatever 401 00:23:24,577 --> 00:23:28,496 S1: it is. Liberals, definitely, um, people who aren't right enough 402 00:23:28,497 --> 00:23:33,577 S1: or aren't right in the correct way. Um, it's just 403 00:23:33,577 --> 00:23:36,577 S1: it's worse than I've ever seen. It's absolutely worse than 404 00:23:36,577 --> 00:23:39,337 S1: I've ever seen. Um, so I don't know if their 405 00:23:39,337 --> 00:23:41,857 S1: skill level is just going up. I imagine it's because 406 00:23:41,857 --> 00:23:44,587 S1: it's all a lot of it's being AI generated. So 407 00:23:44,587 --> 00:23:46,506 S1: you could just do it at more scale. That's probably 408 00:23:46,507 --> 00:23:49,827 S1: a reason. But anyway, it's something I'm going to look 409 00:23:49,827 --> 00:23:52,387 S1: into and probably do some kind of essay or report 410 00:23:52,387 --> 00:23:57,147 S1: or whatever. All right. AI. Okay. This is the craziest story, 411 00:23:57,427 --> 00:24:02,467 S1: the absolute craziest story of the week. Um, and honestly, 412 00:24:02,466 --> 00:24:04,427 S1: for a long time. And I've got a follow up 413 00:24:04,427 --> 00:24:06,266 S1: to it as well, because my buddy told me about 414 00:24:06,267 --> 00:24:09,587 S1: this one company. But check this out. So I finally 415 00:24:09,587 --> 00:24:13,867 S1: found something that trains scientists have missed for decades. So 416 00:24:13,867 --> 00:24:16,147 S1: a number of professional scientists have been trying to figure 417 00:24:16,147 --> 00:24:19,466 S1: out how a particular kind of bacteriophage, which is a 418 00:24:19,466 --> 00:24:24,707 S1: virus that infects bacteria, uh, which I didn't know that. So, um, 419 00:24:24,706 --> 00:24:27,907 S1: there's a podcast here that supports this that's basically like, um, 420 00:24:28,226 --> 00:24:30,907 S1: it's all the scientists who actually did this. So basically 421 00:24:30,907 --> 00:24:33,667 S1: they have been studying this thing for over a couple 422 00:24:33,706 --> 00:24:36,507 S1: of decades. They are the world's experts in how these 423 00:24:36,507 --> 00:24:43,476 S1: particular bacteriophages, um, gain mobility and move out of the cell. Basically, 424 00:24:43,476 --> 00:24:45,317 S1: what they do is they take over the cell. They 425 00:24:45,317 --> 00:24:48,277 S1: blow it open and spread the virus. That's what these 426 00:24:48,277 --> 00:24:52,796 S1: things do. So they study how these things get their 427 00:24:52,797 --> 00:24:55,556 S1: heads and their tails, which allows them to move and 428 00:24:55,557 --> 00:24:58,357 S1: propagate and, you know, take over other things. And I'm 429 00:24:58,357 --> 00:25:00,996 S1: simplifying and probably messing up some details because it's a 430 00:25:00,997 --> 00:25:05,397 S1: very complex, you know, specific topic. But I went and 431 00:25:05,397 --> 00:25:07,756 S1: listened to the entire one hour episode with the actual 432 00:25:07,757 --> 00:25:11,517 S1: experts talking about this. Now the story is they were 433 00:25:11,517 --> 00:25:14,917 S1: given this new version of a Google model, which is 434 00:25:14,917 --> 00:25:20,877 S1: specifically for researching and coming up with novel hypotheses, novel ideas. Okay, 435 00:25:20,997 --> 00:25:23,277 S1: so what they did was they they had this thing 436 00:25:23,277 --> 00:25:25,717 S1: where they're like, how is this thing possible? They've been 437 00:25:25,716 --> 00:25:28,716 S1: thinking about this for years and years and years. They've 438 00:25:28,716 --> 00:25:32,476 S1: done numerous studies. They are the world's premier experts on this. 439 00:25:32,877 --> 00:25:35,597 S1: And they have been stumped by this fact. The fact 440 00:25:35,597 --> 00:25:39,316 S1: that this one bacteriophage, I believe it was there, were 441 00:25:39,317 --> 00:25:41,726 S1: stumped by the fact of how was it actually propagating 442 00:25:41,767 --> 00:25:44,086 S1: or how was it becoming mobile. It was something like that. 443 00:25:44,087 --> 00:25:46,047 S1: It was some sort of problem that they could not 444 00:25:46,047 --> 00:25:49,407 S1: figure out, and they know exactly how it works. You know, 445 00:25:49,486 --> 00:25:52,087 S1: they were very sure about this and that. Therefore, you know, 446 00:25:52,127 --> 00:25:54,927 S1: they're like, this makes no sense. So they gave a 447 00:25:54,927 --> 00:25:58,967 S1: bunch of this observational data to it, you know, stuff 448 00:25:58,966 --> 00:26:01,887 S1: that they've had for years and years. And it came 449 00:26:01,887 --> 00:26:05,726 S1: back with hypotheses. And one of the hypotheses out of 450 00:26:05,726 --> 00:26:09,167 S1: a very short list was like, hey, um, it might 451 00:26:09,167 --> 00:26:12,286 S1: be this actually, because, um, what you could do is 452 00:26:12,327 --> 00:26:14,647 S1: you could just do this instead of this, and it 453 00:26:14,647 --> 00:26:16,527 S1: puts a tail on there and it actually picks up 454 00:26:16,527 --> 00:26:18,966 S1: the one, um, once it's outside of the cell, it 455 00:26:18,966 --> 00:26:21,526 S1: picks up someone else's. And I'm kind of messing up 456 00:26:21,527 --> 00:26:24,206 S1: the details here, but they describe it pretty good in this, 457 00:26:24,247 --> 00:26:28,327 S1: in this, uh, full episode of, uh, Cognitive Revolution, I 458 00:26:28,327 --> 00:26:32,247 S1: think is the name of the podcast. But, um, when 459 00:26:32,247 --> 00:26:35,526 S1: they heard this, when they saw it written down, they're like, oh, 460 00:26:36,287 --> 00:26:39,576 S1: that that's probably what it is. So here. Here's the thing. 461 00:26:39,577 --> 00:26:42,697 S1: They had made an assumption that a certain type of 462 00:26:42,696 --> 00:26:46,177 S1: mobility or a certain type of propagation was not possible, 463 00:26:46,417 --> 00:26:48,857 S1: and it was a very silly assumption that they had made. 464 00:26:49,337 --> 00:26:52,777 S1: And this, this assumption had stopped them from solving this 465 00:26:52,777 --> 00:26:57,817 S1: problem for years upon years. How many hundreds of hours, 466 00:26:57,857 --> 00:27:01,097 S1: thousands of hours have they spent stumped on this as 467 00:27:01,097 --> 00:27:06,417 S1: the world's premier humans on the entire planet? The smartest 468 00:27:06,417 --> 00:27:09,336 S1: people working on this in the entire world, on the 469 00:27:09,337 --> 00:27:14,537 S1: entire planet, were stumped by a thing. And Google came back, 470 00:27:15,736 --> 00:27:18,617 S1: I think within a couple of minutes, I think, and 471 00:27:18,617 --> 00:27:22,137 S1: it was like, yeah, I think it's probably this. They 472 00:27:22,137 --> 00:27:27,696 S1: go and check and it's 100% verified that was correct. 473 00:27:27,696 --> 00:27:32,536 S1: And they here's here's the thing. They were kind of, uh, 474 00:27:32,537 --> 00:27:35,296 S1: I believe what they were saying is they were about 475 00:27:35,297 --> 00:27:38,477 S1: to figure this out. Maybe. Maybe this year. Maybe next year. 476 00:27:38,476 --> 00:27:41,277 S1: They were doing some experiments that might have got them 477 00:27:41,277 --> 00:27:43,597 S1: to this. But the point is, Google found it instantly. 478 00:27:43,597 --> 00:27:47,717 S1: Which means if they had this Google research assistant earlier, 479 00:27:47,956 --> 00:27:51,196 S1: maybe last year, maybe years ago, maybe it would have 480 00:27:51,196 --> 00:27:53,917 S1: found this right. And they not would not have had 481 00:27:53,917 --> 00:27:56,677 S1: this problem for this entire time. Now, what excites me 482 00:27:56,677 --> 00:27:59,957 S1: about this is not this specific use case. It's the 483 00:27:59,997 --> 00:28:03,476 S1: how they reacted to it. They were like, this is insane. 484 00:28:04,037 --> 00:28:07,356 S1: Because the thing I'm so excited about, and it's the 485 00:28:07,357 --> 00:28:10,836 S1: same thing they were talking about, is how many other 486 00:28:10,837 --> 00:28:14,637 S1: things are like this. There is data everywhere. There is 487 00:28:14,637 --> 00:28:19,436 S1: evidence everywhere. There are dots. Okay, think of connect the dots. 488 00:28:19,476 --> 00:28:22,517 S1: Think of how many dots are lying around in papers, 489 00:28:22,757 --> 00:28:28,517 S1: lying around in data sets. Raw data sets. Unfiltered. Unreviewed. Why? 490 00:28:28,557 --> 00:28:33,717 S1: Because there aren't enough academics. There aren't enough researchers. Forget academics. 491 00:28:33,716 --> 00:28:38,687 S1: There aren't enough humans with the training required, write either 492 00:28:38,687 --> 00:28:42,287 S1: the formal current training or like even rudimentary training. There 493 00:28:42,287 --> 00:28:46,607 S1: aren't enough human eyes on all this raw data. There 494 00:28:46,607 --> 00:28:48,807 S1: are no human eyes to look at the stuff and 495 00:28:48,807 --> 00:28:54,247 S1: find the dots and connect them. How many cures for illnesses? 496 00:28:54,927 --> 00:28:58,367 S1: How many novel ways are we sitting on the ability 497 00:28:58,567 --> 00:29:01,927 S1: to improve our IQs by 20%? Are we sitting on 498 00:29:01,927 --> 00:29:07,007 S1: the ability to solve aging? I would guess we absolutely are. 499 00:29:07,287 --> 00:29:10,967 S1: We absolutely are. And that the problem is not enough. 500 00:29:10,967 --> 00:29:14,967 S1: People are studying it using not enough data. So what 501 00:29:15,007 --> 00:29:18,847 S1: I now allows us to do with tools like this, 502 00:29:18,847 --> 00:29:21,207 S1: which are about to be drastically better. I mean, this 503 00:29:21,207 --> 00:29:25,087 S1: thing that found this is about to be 100% stupid 504 00:29:25,087 --> 00:29:27,927 S1: compared to whatever comes out next year or next week. 505 00:29:27,967 --> 00:29:31,847 S1: Right now, just imagine giving all that raw data and 506 00:29:31,847 --> 00:29:35,737 S1: even going to collect more, right? Getting sensory data from 507 00:29:35,737 --> 00:29:38,977 S1: cells or whatever from, you know, different layers of the 508 00:29:38,977 --> 00:29:42,137 S1: world and basically feeding it into this thing all the 509 00:29:42,137 --> 00:29:45,417 S1: time and saying your job is to connect dots. That's 510 00:29:45,457 --> 00:29:48,057 S1: that's the promise of AI. Your job is to connect dots. 511 00:29:48,097 --> 00:29:52,097 S1: So what we've basically done at that point is simulate 512 00:29:53,417 --> 00:29:58,177 S1: billions more people on the planet with training for connecting dots, 513 00:29:59,137 --> 00:30:00,657 S1: and then they could go, look, I came up with 514 00:30:00,657 --> 00:30:03,937 S1: all these hypotheses. And if you go test this, it's 515 00:30:03,937 --> 00:30:05,697 S1: probably going to work. Oh, and by the way, that 516 00:30:05,697 --> 00:30:09,577 S1: is actually a cure for, you know, 86% of cancers 517 00:30:09,577 --> 00:30:13,337 S1: or that will extend your life by 42 years, um, 518 00:30:13,337 --> 00:30:17,097 S1: or whatever. This will allow you to transfer your brain 519 00:30:17,097 --> 00:30:22,777 S1: into a, you know, digital, uh, synthetic form or whatever. Um, 520 00:30:23,217 --> 00:30:25,377 S1: I'm just blown away by this. Now, I sent this 521 00:30:25,377 --> 00:30:28,337 S1: article and my write up of this to a buddy, 522 00:30:28,337 --> 00:30:31,297 S1: and he's like, yeah, I'm actually, um, you know, invested 523 00:30:31,297 --> 00:30:36,067 S1: in and, you know, uh, an advisor for this company 524 00:30:36,107 --> 00:30:40,067 S1: who does that. Their specific thing is they are already 525 00:30:40,067 --> 00:30:44,867 S1: going and crawling all this other data. They are going 526 00:30:44,867 --> 00:30:48,987 S1: to find they have actively collected and are collecting all 527 00:30:48,987 --> 00:30:51,667 S1: this raw data and all these studies that basically didn't 528 00:30:51,667 --> 00:30:55,067 S1: go anywhere because they lost funding or whatever. That company, 529 00:30:55,347 --> 00:30:58,547 S1: this company he's talking about, it is actively doing that. 530 00:30:58,547 --> 00:31:02,787 S1: And guess what they're doing? They build the labs and 531 00:31:02,787 --> 00:31:06,787 S1: they're automating the labs. So when they do a hypothesis 532 00:31:06,947 --> 00:31:09,306 S1: and this is crazy, they already have use cases of 533 00:31:09,307 --> 00:31:12,027 S1: this working. So it's kind of even further than the 534 00:31:12,067 --> 00:31:15,587 S1: main story here. They have a use case of an 535 00:31:15,587 --> 00:31:20,307 S1: example of it actually, um, came up with a hypothesis. 536 00:31:21,187 --> 00:31:25,787 S1: It built a full, uh, methodology and schematic for how 537 00:31:25,787 --> 00:31:29,667 S1: to build the lab. And humans basically took that and 538 00:31:29,667 --> 00:31:31,877 S1: went into the lab. They built a little thing. They 539 00:31:31,877 --> 00:31:34,437 S1: basically set up the experiment exactly the way that the 540 00:31:34,437 --> 00:31:39,837 S1: I said to. And it 100% was true. They confirmed 541 00:31:40,397 --> 00:31:44,117 S1: experimentally that this hypothesis that it could come up with 542 00:31:44,117 --> 00:31:46,717 S1: was true. Now, if it had had the ability to 543 00:31:46,757 --> 00:31:50,517 S1: actually automate those tests, um, if it had the raw 544 00:31:50,517 --> 00:31:53,036 S1: materials in the lab and they had robotics or whatever, 545 00:31:53,637 --> 00:31:55,717 S1: that would have been even more insane, right? What if 546 00:31:55,717 --> 00:31:58,317 S1: it had the raw materials to to combine these molecules 547 00:31:58,317 --> 00:32:00,077 S1: or whatever? And there you got to be a little 548 00:32:00,077 --> 00:32:02,357 S1: bit careful because you don't want to just prompt inject 549 00:32:02,357 --> 00:32:05,477 S1: and it builds a, you know, a meth lab or a, 550 00:32:05,917 --> 00:32:10,117 S1: you know, sarin gas lab or whatever, a Covid lab, right. 551 00:32:10,237 --> 00:32:13,757 S1: Automated Covid generation. Um, so you got to be careful 552 00:32:13,757 --> 00:32:16,117 S1: with those. Obviously, this is what a lot of people 553 00:32:16,117 --> 00:32:21,557 S1: are worried about. But unbelievable, unbelievable possibility here in terms 554 00:32:21,557 --> 00:32:24,957 S1: of like connecting dots. And then the most important thing 555 00:32:24,997 --> 00:32:27,837 S1: and I talked about this, um, I got an essay 556 00:32:27,837 --> 00:32:30,167 S1: called The Path to Asi. I don't know. You guys 557 00:32:30,167 --> 00:32:32,647 S1: might have seen that it's came out. I don't know. 558 00:32:32,687 --> 00:32:35,007 S1: I put that out a few months ago and it 559 00:32:35,007 --> 00:32:39,567 S1: basically describes this process, right? You have hypotheses, you test them. 560 00:32:40,327 --> 00:32:43,567 S1: That's it. Right. Humans have hypotheses. They test them. But 561 00:32:43,567 --> 00:32:47,807 S1: we do it at such small scale. And more importantly, um, 562 00:32:47,967 --> 00:32:50,567 S1: real innovation comes from getting a bunch of smart people together, 563 00:32:50,567 --> 00:32:52,927 S1: like Bell Labs or the Renaissance, and a whole bunch 564 00:32:52,927 --> 00:32:56,047 S1: of smart people are together there talking about their ideas. 565 00:32:56,287 --> 00:32:59,127 S1: But the other person's idea that they're talking to influences 566 00:32:59,127 --> 00:33:04,647 S1: them slightly, changes their idea. Or even somebody, um, somebody, uh, 567 00:33:04,647 --> 00:33:09,727 S1: copies the idea, but they copy it incorrectly with a 568 00:33:09,727 --> 00:33:14,127 S1: slight deviation that actually makes it better. And that's absolutely insane. 569 00:33:14,247 --> 00:33:17,207 S1: So essentially what you have is like this evolution. You 570 00:33:17,207 --> 00:33:23,807 S1: have evolutionary biology or evolutionary, um, improvement happening to the 571 00:33:23,807 --> 00:33:29,827 S1: realm of ideas. Now, that is 100% Sense automatable as well. 572 00:33:29,827 --> 00:33:31,747 S1: You could use AI to do that. You could use 573 00:33:31,747 --> 00:33:35,187 S1: genetic algorithms to do that. You know, it's easy to 574 00:33:35,187 --> 00:33:37,947 S1: do this in a not sophisticated way, and I'm sure 575 00:33:37,947 --> 00:33:40,307 S1: you could do it in a lot more sophisticated ways 576 00:33:40,307 --> 00:33:42,306 S1: the more you think about it. But essentially you have 577 00:33:42,307 --> 00:33:46,187 S1: this engine of human generated ideas, AI generated ideas, and 578 00:33:46,187 --> 00:33:50,627 S1: then you have this evolutionary petri dish which constantly evolves them, 579 00:33:51,067 --> 00:33:54,827 S1: that spits out hypotheses which go through some sort of filter, 580 00:33:55,187 --> 00:33:58,667 S1: and then the higher quality ones go into this automated 581 00:33:58,707 --> 00:34:03,467 S1: testing lab and we start to have this giant Bell Labs. 582 00:34:05,627 --> 00:34:13,147 S1: Renaissance mechanism for inventing new things. Right. Inventing completely new things, 583 00:34:13,387 --> 00:34:17,227 S1: solving things like aging and cancer and all these different illnesses, 584 00:34:17,867 --> 00:34:21,907 S1: extending lifespan, like solving world hunger, like creating abundance on 585 00:34:21,907 --> 00:34:24,947 S1: the planet, like, you know, getting away from the zero 586 00:34:24,947 --> 00:34:28,477 S1: sum game of like capitalism and all this stuff. Like 587 00:34:29,117 --> 00:34:32,357 S1: you could literally use this engine to solve human problems, 588 00:34:32,677 --> 00:34:35,957 S1: you know, at scale. So all this is just massively 589 00:34:35,957 --> 00:34:39,117 S1: exciting to me. And I thought that was like the 590 00:34:39,117 --> 00:34:43,357 S1: most interesting story of the week. Apple releases controversial paper 591 00:34:43,357 --> 00:34:48,677 S1: on AI. Yeah, yeah. It was silly. It was a 592 00:34:48,677 --> 00:34:51,517 S1: silly paper. Um, some people are like, oh, you're behind 593 00:34:51,517 --> 00:34:53,917 S1: on AI. So now you release a paper that basically 594 00:34:53,917 --> 00:34:58,477 S1: says AI is stupid. Anyway. Um, I didn't even want AI. Uh, 595 00:34:58,477 --> 00:35:00,837 S1: it's dumb anyway. So that's why. That's why I'm not 596 00:35:00,837 --> 00:35:03,556 S1: trying hard. That's why other people are beating me. That's 597 00:35:03,557 --> 00:35:06,957 S1: not actually what happened though, because Apple Apple air quotes. 598 00:35:06,997 --> 00:35:12,477 S1: Apple isn't saying this. It is some ML team within Apple. Right. 599 00:35:12,517 --> 00:35:16,557 S1: Releasing this. So, um, anyway, it was a funny narrative. 600 00:35:16,717 --> 00:35:20,357 S1: OpenAI massively drops O3 prices. They dropped by 80% and 601 00:35:20,357 --> 00:35:24,877 S1: they also released O3 Pro. Um, OpenAI doubles revenue to 602 00:35:25,007 --> 00:35:29,567 S1: $10 billion annually. And OpenAI also must keep all ChatGPT 603 00:35:29,607 --> 00:35:33,927 S1: conversations indefinitely due to a legal hold related to a lawsuit. 604 00:35:34,567 --> 00:35:38,207 S1: I find this weird. I don't know how accurate this is. 605 00:35:38,207 --> 00:35:41,607 S1: If it's all like what exactly the scope is. There's 606 00:35:41,607 --> 00:35:43,647 S1: a quote here that says we are required to retain 607 00:35:43,647 --> 00:35:47,087 S1: all data. Cannot process deletion requests during this period. What 608 00:35:47,087 --> 00:35:49,607 S1: I don't know is if it applies to like tenants 609 00:35:49,607 --> 00:35:52,767 S1: where they've paid extra money just to have their stuff 610 00:35:52,807 --> 00:35:55,487 S1: be ephemeral, right? There are a lot of people with 611 00:35:55,527 --> 00:35:58,487 S1: like private Azure instances where like that was the whole 612 00:35:58,487 --> 00:36:01,607 S1: reason they're paying extra is the fact that it's ephemeral, 613 00:36:01,607 --> 00:36:04,967 S1: or it gets deleted constantly or they're able to delete it, 614 00:36:05,327 --> 00:36:07,767 S1: but I don't know if it applies there. I doubt it, 615 00:36:07,767 --> 00:36:12,286 S1: but who knows? OpenAI makes ChatGPT voice mode sound way 616 00:36:12,287 --> 00:36:18,007 S1: more human. Okay, go play with voice mode. Advanced voice 617 00:36:18,007 --> 00:36:22,367 S1: mode on ChatGPT. It is ridiculous. I mean, it is 618 00:36:22,367 --> 00:36:25,777 S1: absolutely ridiculous. I was having a conversation with it yesterday, 619 00:36:25,777 --> 00:36:28,737 S1: which I haven't talked to in a while, and it 620 00:36:28,737 --> 00:36:33,137 S1: felt so incredibly human. The pauses, the voice tones, it 621 00:36:33,137 --> 00:36:37,937 S1: actually sang. Um. At first it did like vocal song 622 00:36:37,977 --> 00:36:41,217 S1: and I was like, no, not, you know, uh, spoken 623 00:36:41,217 --> 00:36:46,177 S1: word actually sing. And it actually sung with, like, tone. Um, anyway, 624 00:36:46,577 --> 00:36:48,897 S1: I still use the Cove one because it sounds like, uh, 625 00:36:48,897 --> 00:36:52,497 S1: one of the AIS from interstellar, and that's my favorite voice, uh, 626 00:36:52,497 --> 00:36:57,697 S1: since they got rid of, uh, Samantha. All right, Stanford 627 00:36:57,697 --> 00:37:01,777 S1: study shows doctors put AI beat. Oh, plus, I beat 628 00:37:01,777 --> 00:37:08,297 S1: traditional diagnostic tools. So doctors normally in this particular test 629 00:37:09,537 --> 00:37:17,097 S1: were 75% accurate. Doctors with the AI were 85% accurate, 10% jump. 630 00:37:17,577 --> 00:37:21,657 S1: But the hilarious part and extremely sad part I by 631 00:37:21,787 --> 00:37:26,467 S1: itself was 90%. It was 5% better than the doctor 632 00:37:26,467 --> 00:37:31,787 S1: using I. So it's like, yeah, it's like, yeah, I 633 00:37:31,827 --> 00:37:34,027 S1: am way better than the doctor. This is the I talking. 634 00:37:34,027 --> 00:37:36,907 S1: I'm way better than the doctor. I am 15% of 635 00:37:36,907 --> 00:37:40,427 S1: the doctor. Now, if the doctor uses me and asks 636 00:37:40,427 --> 00:37:43,547 S1: me questions, it can almost be as smart as me, 637 00:37:43,547 --> 00:37:47,827 S1: but not quite. And we all know how early the 638 00:37:47,827 --> 00:37:50,107 S1: game is. We all know how bad I is at 639 00:37:50,107 --> 00:37:53,467 S1: certain things, and it's already to this point. And this 640 00:37:53,467 --> 00:37:56,107 S1: is not like some random study at a junior college. 641 00:37:56,147 --> 00:38:03,067 S1: This is a pretty large Stanford study. Insane. Absolutely insane. Uh, 642 00:38:03,067 --> 00:38:06,627 S1: Microsoft reshuffles leadership to focus on AI agents. My new 643 00:38:06,627 --> 00:38:10,747 S1: favorite description of a business mode. This is from Jamin Bell. 644 00:38:10,987 --> 00:38:13,347 S1: A real long term moat is just a sequence of 645 00:38:13,347 --> 00:38:16,827 S1: smaller moats stacked together. Each one buys time. And what 646 00:38:16,827 --> 00:38:18,987 S1: you do with that time, how fast you execute, how 647 00:38:18,987 --> 00:38:23,277 S1: quickly you evolve, determines whether you stay ahead. And my 648 00:38:23,277 --> 00:38:28,197 S1: analysis of this or my. You know, um. Clarification of 649 00:38:28,197 --> 00:38:31,357 S1: it or tightening it up is to me, that means 650 00:38:31,397 --> 00:38:35,237 S1: speed and adaptability is the only real moat. And a 651 00:38:35,237 --> 00:38:38,917 S1: hat tip to, uh, Clint Gibler, uh, my buddy for, uh, 652 00:38:38,917 --> 00:38:42,717 S1: sending me this, like, in a text or something. Um, really, 653 00:38:42,717 --> 00:38:47,637 S1: really good technology. Why Bell labs works so well. Uh, 654 00:38:47,637 --> 00:38:50,437 S1: we talked about that already. BYD's five minute charging puts 655 00:38:50,477 --> 00:38:53,717 S1: China in the lead for EVs. Five minute charging. That's 656 00:38:53,717 --> 00:38:58,957 S1: basically a gas station. Uh, very worried. Um, this is 657 00:38:58,957 --> 00:39:02,157 S1: why I want Tesla to win so bad. And now 658 00:39:02,157 --> 00:39:06,397 S1: also Waymo. Uh, I want, but it's got to be 659 00:39:06,397 --> 00:39:08,997 S1: Tesla because they're the ones making the cars. I'm really 660 00:39:08,997 --> 00:39:13,237 S1: worried about BYD. BYD is making extremely good cars. They 661 00:39:13,277 --> 00:39:18,077 S1: are subsidizing them. They have 10,000 EVs. And we're talking 662 00:39:18,077 --> 00:39:20,977 S1: about five minute charging like a gas station. I mean, 663 00:39:22,057 --> 00:39:24,617 S1: if they were to come here, they would crush right now. 664 00:39:24,617 --> 00:39:27,417 S1: So we need our options. We need American options to 665 00:39:27,457 --> 00:39:31,297 S1: be getting better way faster than they are right now. 666 00:39:32,417 --> 00:39:36,497 S1: Wing and Walmart expand drone delivery to 100 stores, which 667 00:39:36,497 --> 00:39:43,657 S1: is five major cities. Uh, Chinese tech behind Amazon's humanoid robots. Great. 668 00:39:43,657 --> 00:39:47,097 S1: So Amazon is doing humanoid robots now. Um, they're talking 669 00:39:47,097 --> 00:39:52,657 S1: about potentially having humanoid delivery drivers. And the tech is Chinese. Great. 670 00:39:53,497 --> 00:39:57,137 S1: YouTube loosens content rules using public interest as a standard. 671 00:39:57,377 --> 00:40:00,497 S1: And AWS has opened a new region in Taiwan with 672 00:40:00,617 --> 00:40:06,257 S1: three availability zones. Humans. Rents are dropping in most US 673 00:40:06,257 --> 00:40:09,617 S1: cities for the first time since 2023. Caffeine keeps your 674 00:40:09,617 --> 00:40:12,177 S1: brain awake even while you sleep, so you could actually sleep. 675 00:40:12,177 --> 00:40:15,777 S1: According to the study. You could actually sleep and stay asleep, 676 00:40:15,777 --> 00:40:19,347 S1: but you wake up more tired because your brain is 677 00:40:19,347 --> 00:40:24,067 S1: not properly resting. Because the caffeine. Because caffeine basically blocks 678 00:40:24,547 --> 00:40:28,427 S1: the sleep, um, receptor. Um, that's one of the mechanisms. 679 00:40:28,427 --> 00:40:32,067 S1: So that's probably related to why this is the case, 680 00:40:32,507 --> 00:40:34,627 S1: or at least why they found that to be the case. 681 00:40:34,667 --> 00:40:38,387 S1: Las Vegas fights record heat with massive tree planting. And 682 00:40:38,387 --> 00:40:41,587 S1: this is not, uh, like it's absorbing carbon. Uh, because 683 00:40:41,627 --> 00:40:44,067 S1: that wouldn't be nearly the amount of skill you need. Uh, 684 00:40:44,067 --> 00:40:47,787 S1: that's a planetary thing. But, um, for shade is what 685 00:40:47,787 --> 00:40:51,587 S1: they're talking about. Mushrooms may communicate using up to 50 words. 686 00:40:51,587 --> 00:40:56,787 S1: And forests offset global warming more than scientists previously thought. 687 00:40:57,547 --> 00:41:00,827 S1: So replanting the trees that we lost since the 1800s 688 00:41:00,827 --> 00:41:06,267 S1: could cool the planet by like half a degree, which 689 00:41:06,267 --> 00:41:10,747 S1: I think is Celsius. Uh, which would be cool. Uh, 690 00:41:10,747 --> 00:41:13,387 S1: I think we should do that. I think we should plant, like, 691 00:41:13,427 --> 00:41:19,797 S1: trillions of trees and just, uh, see if the temperature 692 00:41:19,797 --> 00:41:22,757 S1: falls really fast. See if CO2 levels fall really fast. 693 00:41:23,157 --> 00:41:25,317 S1: And if they do, um, cut down some of the trees. 694 00:41:25,877 --> 00:41:29,997 S1: That's what I think. And also carbon sequestration. And also 695 00:41:29,997 --> 00:41:34,317 S1: we should go all in on solar discovery. Someone built 696 00:41:34,317 --> 00:41:36,997 S1: an MCP server that actually runs on Cloudflare workers. So 697 00:41:36,997 --> 00:41:39,317 S1: you don't have to stand up your own infrastructure. Data 698 00:41:39,317 --> 00:41:43,717 S1: visualization reveals patterns in D&D monster designs. How anthropic teams 699 00:41:43,717 --> 00:41:46,957 S1: use cloud code. We are No longer a serious country 700 00:41:46,957 --> 00:41:51,197 S1: by Paul Krugman. Great explanation of how model context protocols 701 00:41:51,197 --> 00:41:54,997 S1: is different from traditional APIs. I'm not sure I'm going 702 00:41:55,037 --> 00:41:57,117 S1: to read all these, because they're actually just links for 703 00:41:57,117 --> 00:42:00,637 S1: you to go and click on in the newsletter. Um, 704 00:42:00,637 --> 00:42:03,677 S1: so I'm just going to go right to aphorism of 705 00:42:03,677 --> 00:42:06,797 S1: the week. It is not death that we should fear, 706 00:42:06,797 --> 00:42:09,797 S1: but never beginning to live. It is not death that 707 00:42:09,797 --> 00:42:14,157 S1: we should fear, but never beginning to live. Marcus Aurelius.