1 00:00:00,140 --> 00:00:02,779 S1: Are you prepared for whatever shitstorm may hit your desk 2 00:00:02,779 --> 00:00:05,540 S1: during the workday? Auto Max has your back. Check out 3 00:00:05,540 --> 00:00:08,870 S1: the brand new autonomous IT podcast. Listen in as various 4 00:00:08,869 --> 00:00:12,740 S1: IT experts discuss the latest Patch Tuesday releases, mitigation tips, 5 00:00:12,740 --> 00:00:16,310 S1: and custom automations to help with CVE remediations make new 6 00:00:16,310 --> 00:00:20,810 S1: work friends. Listen now to the autonomous IT podcast on Spotify, Apple, 7 00:00:20,810 --> 00:00:25,340 S1: or wherever you tune in to podcasts. Welcome to Unsupervised Learning, 8 00:00:25,340 --> 00:00:28,340 S1: a security, AI, and meaning focused podcast that looks at 9 00:00:28,340 --> 00:00:31,340 S1: how best to thrive as humans in a post AI world. 10 00:00:31,940 --> 00:00:35,930 S1: It combines original ideas, analysis, and mental models to bring 11 00:00:35,930 --> 00:00:38,479 S1: not just the news, but why it matters and how 12 00:00:38,479 --> 00:00:48,810 S1: to respond. All right. Episode 437. What are we doing here? Okay, 13 00:00:48,810 --> 00:00:54,480 S1: so I'm delivering my augmented AI course again live. And 14 00:00:54,480 --> 00:00:58,640 S1: this is on July 26th. It's actually my birthday. And 15 00:00:58,640 --> 00:01:02,930 S1: so that'll be next month. And the theme is moving 16 00:01:02,930 --> 00:01:06,170 S1: towards human 3.0. How to survive and thrive in the 17 00:01:06,170 --> 00:01:11,670 S1: world full of AI. And yeah, I got human 3.0 conversation, 18 00:01:11,670 --> 00:01:15,000 S1: skill sets and mental frames, integrating AI into your life 19 00:01:15,000 --> 00:01:18,990 S1: and work and a whole bunch of resources. Essentially, it's 20 00:01:18,990 --> 00:01:21,090 S1: going to be a lot of hands on, actually showing 21 00:01:21,090 --> 00:01:24,780 S1: you the way that I use AI and adding a 22 00:01:24,780 --> 00:01:29,459 S1: whole new section above and beyond the previous class, which 23 00:01:29,459 --> 00:01:32,460 S1: talks about how to basically get ready, what things do 24 00:01:32,459 --> 00:01:34,710 S1: you actually have to learn. And a big part of 25 00:01:34,709 --> 00:01:36,959 S1: this is so that you could tell the other people 26 00:01:36,959 --> 00:01:39,900 S1: that you care about, especially young people like people in 27 00:01:39,900 --> 00:01:42,660 S1: high school, people in about to go into college, people 28 00:01:42,660 --> 00:01:46,650 S1: in college, or people changing careers who just have massive 29 00:01:46,650 --> 00:01:49,620 S1: anxiety from all this AI stuff. This is basically going 30 00:01:49,620 --> 00:01:53,580 S1: to give my outline of what I think is coming 31 00:01:53,580 --> 00:01:55,890 S1: and how to get ready for it, like how to 32 00:01:55,890 --> 00:01:59,520 S1: not just survive it, but actually be antifragile so that 33 00:01:59,520 --> 00:02:04,260 S1: you actually benefit from it. So that is next month, 34 00:02:04,260 --> 00:02:07,200 S1: the end of July. My friend Monica is launching her 35 00:02:07,200 --> 00:02:12,060 S1: cybersecurity leadership Masterclass. Goes over tons of stuff, which I 36 00:02:12,060 --> 00:02:14,940 S1: talk about here, and it's just a really good course. 37 00:02:14,940 --> 00:02:17,610 S1: I think she really has a good perspective on this, 38 00:02:17,610 --> 00:02:19,020 S1: and I got a link here to sign up for 39 00:02:19,020 --> 00:02:24,240 S1: the class doing a webinar with Elastic Security. And that's 40 00:02:24,240 --> 00:02:28,410 S1: coming up Tuesday, June 25th. And I changed some headers. 41 00:02:28,410 --> 00:02:31,560 S1: Doesn't really matter. All right. My work. So few new 42 00:02:31,560 --> 00:02:34,560 S1: essays this week. So the first one is talking about 43 00:02:34,560 --> 00:02:38,820 S1: ratings and basically how I believe AI is going to 44 00:02:38,820 --> 00:02:43,260 S1: basically do like this. This 123 punch one is teaching 45 00:02:43,260 --> 00:02:47,940 S1: us things. Second is testing us to see how well 46 00:02:47,940 --> 00:02:50,400 S1: we know that material. And the third one is giving 47 00:02:50,400 --> 00:02:53,820 S1: these scores. And, uh, I think it's going to be 48 00:02:53,820 --> 00:02:56,489 S1: really crazy. You should you should check out the whole post. 49 00:02:56,490 --> 00:02:59,250 S1: This is the post here. And I basically talk about 50 00:02:59,250 --> 00:03:02,760 S1: like the different scores. I talk about, like ways and 51 00:03:02,760 --> 00:03:06,090 S1: components of the score that are going to be pretty crazy. 52 00:03:06,090 --> 00:03:10,649 S1: And it starts to get a little bit scary, quite, 53 00:03:10,650 --> 00:03:13,890 S1: quite a bit scary actually. Okay. Imagine you're walking around, 54 00:03:13,889 --> 00:03:17,010 S1: you have like the score above your head because because 55 00:03:17,010 --> 00:03:18,600 S1: it's not just going to be for a test that 56 00:03:18,600 --> 00:03:20,640 S1: you took or whatever, right? It's also going to be 57 00:03:20,639 --> 00:03:24,000 S1: for like, hey, instead of me having to vet every 58 00:03:24,000 --> 00:03:26,850 S1: single person that I want to date or a college 59 00:03:26,850 --> 00:03:29,880 S1: having to vet incoming people, why don't they just send 60 00:03:29,880 --> 00:03:33,960 S1: you to do this Astra test, whatever that is? And 61 00:03:33,960 --> 00:03:36,240 S1: this is some random company, a made up company, right? 62 00:03:36,240 --> 00:03:38,520 S1: But they're going to get your blood work and they're 63 00:03:38,520 --> 00:03:42,540 S1: going to do all of this, your personal life history there. 64 00:03:42,540 --> 00:03:45,660 S1: And keep in mind this is an AI avatar asking 65 00:03:45,660 --> 00:03:49,170 S1: you these things. There's a camera on you watching your 66 00:03:49,170 --> 00:03:52,380 S1: facial expressions and your body language. And keep in mind, 67 00:03:52,380 --> 00:03:55,290 S1: this is like GPT six or GPT seven or whatever. 68 00:03:55,290 --> 00:03:58,350 S1: So it's like extremely advanced AI. So it could read 69 00:03:58,350 --> 00:04:00,510 S1: your body language. It knows when you're lying, it knows 70 00:04:00,510 --> 00:04:03,450 S1: when you're uncomfortable about a thing, but it's going to 71 00:04:03,450 --> 00:04:05,790 S1: ask you about your traumas. It's going to ask you 72 00:04:05,790 --> 00:04:09,420 S1: about your like, your beliefs about different things. It's going 73 00:04:09,420 --> 00:04:11,940 S1: to ask you about morality. It's also going to test 74 00:04:11,940 --> 00:04:15,240 S1: your technical skills, ask you to actually build things. It's 75 00:04:15,240 --> 00:04:18,660 S1: going to deep dive into your opinions on like tons 76 00:04:18,660 --> 00:04:20,910 S1: of different things. It's going to look at your base 77 00:04:20,910 --> 00:04:25,740 S1: core knowledge, math, physics, biology, history, economics, all these other disciplines. 78 00:04:25,740 --> 00:04:29,220 S1: It's basically going to understand like how smart you are, 79 00:04:29,250 --> 00:04:33,420 S1: all the different stuff that you know, your maturity across 80 00:04:33,420 --> 00:04:37,770 S1: lots of different areas like emotional maturity, your wisdom, things 81 00:04:37,770 --> 00:04:41,520 S1: like that. And then it's going to ask about your finances. 82 00:04:41,520 --> 00:04:43,469 S1: It's going to ask about like how much money do 83 00:04:43,470 --> 00:04:45,900 S1: you make? What is your earning potential, why do you 84 00:04:45,900 --> 00:04:47,970 S1: think you can make that much money, blah, blah, blah. 85 00:04:48,000 --> 00:04:50,880 S1: All of that combined think of like a three day 86 00:04:50,880 --> 00:04:53,760 S1: test where eight hours a day, you're talking to this 87 00:04:53,760 --> 00:04:56,789 S1: AI avatar, and the whole time you're on camera and 88 00:04:56,790 --> 00:04:58,890 S1: a lot of people are thinking, why would anyone sign 89 00:04:58,890 --> 00:05:01,890 S1: up for that? The answer is because they want to 90 00:05:01,890 --> 00:05:05,370 S1: know that about themselves. They also have some measure of 91 00:05:05,370 --> 00:05:07,800 S1: trust that the videos aren't going to go out, but 92 00:05:07,800 --> 00:05:11,010 S1: that which isn't going to happen. Those companies are absolute 93 00:05:11,160 --> 00:05:14,070 S1: going to get hacked. Right? So that's that's a mess. 94 00:05:14,070 --> 00:05:16,350 S1: But it doesn't it doesn't matter. Everyone's going to get 95 00:05:16,350 --> 00:05:18,690 S1: hacked in this way. So it kind of just like 96 00:05:18,690 --> 00:05:21,990 S1: that doesn't matter. So you have the videos, the AI 97 00:05:21,990 --> 00:05:25,230 S1: does the analysis. And keep in mind, the AI knows 98 00:05:25,230 --> 00:05:29,700 S1: everything about human psychology. It knows all the subject matter 99 00:05:29,700 --> 00:05:34,320 S1: that you're talking about history, physics, you know, security, like 100 00:05:34,320 --> 00:05:37,050 S1: all the different things that you're good at. It knows 101 00:05:37,050 --> 00:05:39,330 S1: how good you are at it based on the fact 102 00:05:39,330 --> 00:05:42,030 S1: that it did the the interview with you. So it's 103 00:05:42,029 --> 00:05:44,700 S1: like taking you into deep water to see how deep 104 00:05:44,700 --> 00:05:48,900 S1: you can go. Right? Well, now, now think of companies 105 00:05:48,900 --> 00:05:51,570 S1: who want to hire somebody. They don't have to do 106 00:05:51,570 --> 00:05:55,740 S1: all this deep vetting, deep vetting, a three day set 107 00:05:55,740 --> 00:05:58,529 S1: of interviews, or a two week set of interviews with 108 00:05:58,529 --> 00:06:01,739 S1: like five different groups of people. And they're asking like 109 00:06:01,740 --> 00:06:05,550 S1: dumbass questions. That does not work. You see how bad 110 00:06:05,550 --> 00:06:09,310 S1: people can be even after they go through the interview process. Okay. 111 00:06:09,310 --> 00:06:12,760 S1: In current hiring methods, there's like a gauntlet of all 112 00:06:12,760 --> 00:06:17,230 S1: these interviews. That gauntlet of interviews does not yield great 113 00:06:17,230 --> 00:06:21,039 S1: people a high percentage of the time. It does sometimes, 114 00:06:21,040 --> 00:06:25,180 S1: but not nearly as much as it actually should, given 115 00:06:25,180 --> 00:06:28,659 S1: like how extensive that is. Now compare that with somebody 116 00:06:28,660 --> 00:06:32,409 S1: who's like, hey, look, if you have an astro score, 117 00:06:32,440 --> 00:06:34,270 S1: I would love for you to share it. You don't 118 00:06:34,270 --> 00:06:36,729 S1: have to. We're not going to ask you to do it. 119 00:06:36,730 --> 00:06:43,029 S1: But of course, hiring managers, companies assuming it's legal, that's 120 00:06:43,029 --> 00:06:44,560 S1: going to be a problem. There's going to be some 121 00:06:44,560 --> 00:06:47,140 S1: regulation against stuff like this, because it's going to be 122 00:06:47,140 --> 00:06:51,190 S1: so good that it's going to instantly put great people way, 123 00:06:51,220 --> 00:06:54,940 S1: way above. And because it's so good and it's because 124 00:06:54,940 --> 00:06:58,960 S1: it's so merit meritocratic, there's going to be massive pushback. 125 00:06:58,960 --> 00:07:02,620 S1: But I think that it's going to be so useful 126 00:07:02,620 --> 00:07:06,040 S1: and so powerful for startups that they're going to do 127 00:07:06,040 --> 00:07:11,050 S1: it anyway, especially this especially matters in a world of AI, 128 00:07:11,080 --> 00:07:14,770 S1: because people are going to have much smaller teams. It's 129 00:07:14,770 --> 00:07:17,320 S1: going to be like me, the founder, and I need 130 00:07:17,320 --> 00:07:21,130 S1: a total, total badass to be like my chief of 131 00:07:21,130 --> 00:07:24,910 S1: staff because they're going to run my 13 different companies, 132 00:07:24,910 --> 00:07:27,880 S1: which are mostly AI powered. But I need a total 133 00:07:27,880 --> 00:07:30,520 S1: killer to be that person, which means I need a 134 00:07:30,520 --> 00:07:35,740 S1: high astro score specifically in like fluid intelligence, raw IQ, 135 00:07:35,740 --> 00:07:40,390 S1: communication skills, like all these particular things. And what's crazy 136 00:07:40,390 --> 00:07:44,200 S1: is I can describe in an interview to AI exactly 137 00:07:44,200 --> 00:07:46,690 S1: what I need, exactly what my product does. In fact, 138 00:07:46,690 --> 00:07:48,970 S1: the AI will just go get all that stuff. It 139 00:07:48,970 --> 00:07:51,400 S1: will then understand that perfectly. Then it will look at 140 00:07:51,400 --> 00:07:56,440 S1: someone's astro score and just basically find matches and be like, look, 141 00:07:56,440 --> 00:07:59,860 S1: this person is pre-vetted. Their astro score perfectly matches I 142 00:07:59,860 --> 00:08:04,030 S1: validated the Astro score is real, which Astro will also do. 143 00:08:04,030 --> 00:08:05,890 S1: And by the way, Astro, this is a made up company. 144 00:08:05,890 --> 00:08:10,030 S1: It's not real, but there will be many Astros. So 145 00:08:10,030 --> 00:08:13,240 S1: now it's like hey, do you want to talk to them? Yeah, sure. 146 00:08:13,240 --> 00:08:15,880 S1: So you get on a call with them and you're like, hey. Yeah. 147 00:08:15,880 --> 00:08:19,480 S1: So I mean, the AI says that you're really good. 148 00:08:19,480 --> 00:08:24,070 S1: My my da, my da. My personal digital assistant says, 149 00:08:24,070 --> 00:08:27,460 S1: you seem to be really good on paper, which means 150 00:08:27,460 --> 00:08:30,280 S1: through all this AI vetting. So you have a conversation 151 00:08:30,280 --> 00:08:33,460 S1: and what do you know? You start talking about books. 152 00:08:33,460 --> 00:08:36,760 S1: You start talking about what? The way you see the future, 153 00:08:36,760 --> 00:08:39,460 S1: you start talking about ambition. And they're like, oh yeah, 154 00:08:39,460 --> 00:08:43,569 S1: I'm reading those same books. I'm listening to those same podcasts. Yeah, 155 00:08:43,570 --> 00:08:45,760 S1: I just worked out this morning and you're like, Holy crap, 156 00:08:45,760 --> 00:08:50,080 S1: this person's just like me. Total badass. Yeah. Perfect match. 157 00:08:50,080 --> 00:08:53,290 S1: What do you know? What are the odds? Quite good, actually. 158 00:08:53,290 --> 00:08:56,500 S1: Quite good because of the Astro score. So now think 159 00:08:56,500 --> 00:09:02,199 S1: of companies can just rely on the score. What about dating? Dating? Well, 160 00:09:02,200 --> 00:09:04,900 S1: look at all these questionnaires people are filling out and 161 00:09:04,900 --> 00:09:07,600 S1: they're not happy with the dates. And Astro score is 162 00:09:07,600 --> 00:09:10,000 S1: going to be a way better measure of a person 163 00:09:10,000 --> 00:09:13,750 S1: than this. And of course, there's going to be nasty 164 00:09:13,750 --> 00:09:16,959 S1: gross astro scores. Keep in mind it's going to be like, oh, 165 00:09:16,960 --> 00:09:21,339 S1: how pretty is this, this person, how rich is this person? 166 00:09:21,340 --> 00:09:24,579 S1: And they're going to have like whatever, an Omni score 167 00:09:24,580 --> 00:09:29,320 S1: and it's going to be like Douchebaggery score essentially. So 168 00:09:29,320 --> 00:09:32,770 S1: you shouldn't think that just because it's AI generated that 169 00:09:32,770 --> 00:09:36,250 S1: it's a good measure of a person. That's not true. 170 00:09:36,250 --> 00:09:39,370 S1: What I'm talking about is something that's really deep across 171 00:09:39,370 --> 00:09:42,850 S1: multiple facets of a person, where it actually is kind 172 00:09:42,850 --> 00:09:47,230 S1: of capturing how good they are and how valuable they are, 173 00:09:47,230 --> 00:09:51,670 S1: and importantly, how useful they are for a particular application, 174 00:09:51,670 --> 00:09:55,450 S1: such as working inside this company, working on your particular problems, 175 00:09:55,450 --> 00:09:57,910 S1: or if you're trying to build a family and you 176 00:09:57,910 --> 00:09:59,980 S1: want to know, like how good of a dad is 177 00:09:59,980 --> 00:10:01,689 S1: this person going to be? How good of a mom 178 00:10:01,690 --> 00:10:04,960 S1: is this person going to be? Well, now it's focusing 179 00:10:04,960 --> 00:10:08,230 S1: on those areas which were covered in the interview. The 180 00:10:08,230 --> 00:10:14,290 S1: point is, many, many things in society require and desire 181 00:10:14,650 --> 00:10:18,699 S1: to vet. This is universal vetting. That's what this is. 182 00:10:18,700 --> 00:10:22,300 S1: This is universal vetting. You need to vet people to 183 00:10:22,300 --> 00:10:24,670 S1: work on a company with you, to do a project 184 00:10:24,670 --> 00:10:27,670 S1: with you, to be a babysitter for you, to be 185 00:10:27,670 --> 00:10:31,030 S1: a dog sitter, for you to be a life partner. Right. 186 00:10:31,030 --> 00:10:34,840 S1: So these types of scores, the better they are. Let's 187 00:10:34,840 --> 00:10:37,120 S1: say this Astro one is the best one. It's like 188 00:10:37,120 --> 00:10:41,979 S1: a seven day on site, in person talk with all 189 00:10:41,980 --> 00:10:45,250 S1: these different eyes, and they're hitting you from different angles. 190 00:10:45,370 --> 00:10:48,490 S1: They're watching you do jumping jacks. I mean, it's going 191 00:10:48,490 --> 00:10:50,319 S1: to be crazy. Oh, by the way, you submitted a 192 00:10:50,320 --> 00:10:53,920 S1: blood sample. Right now it's looking at your genome. Yeah, yeah. 193 00:10:53,920 --> 00:10:56,770 S1: And that's why when I came up with the name, 194 00:10:56,770 --> 00:11:01,719 S1: I used AI for that Astra. I forget what it means, but. Yeah. Uh, 195 00:11:01,720 --> 00:11:05,920 S1: what's that movie with Ethan Hawke? What's the one where, like, 196 00:11:05,920 --> 00:11:09,630 S1: the guy wasn't chosen to go on the. Flight anyway. 197 00:11:09,630 --> 00:11:14,580 S1: It's dystopian. Okay, 100%. All this stuff is dystopian. All 198 00:11:14,580 --> 00:11:19,020 S1: this awesome stuff is simultaneously dystopian. The question is, can 199 00:11:19,020 --> 00:11:22,260 S1: we take it in good directions? Right. That's the only question. 200 00:11:22,260 --> 00:11:24,810 S1: We know it's going to go in bad directions. That's 201 00:11:24,809 --> 00:11:27,570 S1: a guarantee. I mean, have you seen humanity? Of course 202 00:11:27,570 --> 00:11:30,060 S1: it's going to go in bad directions. The question is, 203 00:11:30,059 --> 00:11:32,819 S1: can we limit some of those? Can we control some 204 00:11:32,820 --> 00:11:36,719 S1: of those? Can we discourage some of those and steer 205 00:11:36,720 --> 00:11:41,130 S1: them in positive like human 3.0 directions. Like ideally you 206 00:11:41,130 --> 00:11:44,160 S1: would have something like an astro score that's super deep. 207 00:11:44,160 --> 00:11:47,850 S1: It's super knowledgeable, but it's not like man, not as 208 00:11:47,850 --> 00:11:51,780 S1: attractive man. Oh, there could be a flaw here. We 209 00:11:51,780 --> 00:11:55,440 S1: don't talk to flawed people. A real astro score is 210 00:11:55,440 --> 00:11:57,959 S1: going to be like, hey, this person is nuanced and 211 00:11:57,960 --> 00:12:02,040 S1: this person has, you know, warts. And this person is like, 212 00:12:02,040 --> 00:12:06,240 S1: flawed and and complex and multiple layers and blah, blah, blah. 213 00:12:06,240 --> 00:12:09,300 S1: But I think at the core level, you guys could 214 00:12:09,300 --> 00:12:12,990 S1: be soulmates. And here's the list of questions to like 215 00:12:12,990 --> 00:12:17,250 S1: initiate that initial discovery period or whatever. It's not about perfection. 216 00:12:17,400 --> 00:12:19,559 S1: Not in the human based world, not in the human 217 00:12:19,559 --> 00:12:22,260 S1: 3.0 based world. It's not based on perfection. It's based 218 00:12:22,260 --> 00:12:28,960 S1: on deep matches. On the things that matter. And spending 219 00:12:28,960 --> 00:12:35,260 S1: less time or getting confused by surface level things. He's 220 00:12:35,260 --> 00:12:38,590 S1: very attractive. He's very rich. But you find out three 221 00:12:38,590 --> 00:12:42,550 S1: years later, you know, he's in your mind. He's garbage, right? 222 00:12:42,550 --> 00:12:47,350 S1: Because he doesn't treat you well like he treats people poorly. Right. 223 00:12:47,350 --> 00:12:50,080 S1: And the whole thing that he postured with you was 224 00:12:50,080 --> 00:12:52,000 S1: all an act, and you just wasted three years of 225 00:12:52,000 --> 00:12:54,040 S1: your life, and you were supposed to be having kids 226 00:12:54,040 --> 00:12:56,800 S1: by now. So, like, there's a positive way to go 227 00:12:56,800 --> 00:13:00,819 S1: with this. And I think we need to realize, one, 228 00:13:00,820 --> 00:13:05,630 S1: that there are positive opportunities to. Bad things are going 229 00:13:05,630 --> 00:13:08,750 S1: to happen from these types of scores and not be 230 00:13:08,750 --> 00:13:12,290 S1: confused in thinking that because bad things happen, mean the 231 00:13:12,290 --> 00:13:14,750 S1: whole thing is a wash. First of all, doesn't matter 232 00:13:14,750 --> 00:13:16,790 S1: if it's a wash because it's going to happen anyway. 233 00:13:16,970 --> 00:13:20,449 S1: So either way you should be prepared. So lots of 234 00:13:20,450 --> 00:13:23,569 S1: different ways to get value from this type of analysis. 235 00:13:23,840 --> 00:13:27,620 S1: And here we are. We're we're like 15 20 minutes 236 00:13:27,620 --> 00:13:32,030 S1: in or whatever ten 15 minutes in. And uh that 237 00:13:32,030 --> 00:13:36,559 S1: was one essay okay. Next one the fast slow problem. 238 00:13:36,860 --> 00:13:38,540 S1: You know what? This one's short enough. I'm going to 239 00:13:38,540 --> 00:13:43,130 S1: go ahead and perform this one because that was called performing. Okay. 240 00:13:43,130 --> 00:13:46,370 S1: So I've been obsessed lately with the concept of slow 241 00:13:46,370 --> 00:13:50,750 S1: versus fast. I'm calling it the fast. Slow problem refers 242 00:13:50,750 --> 00:13:53,660 S1: to the speed and amount of dopamine that you get 243 00:13:53,660 --> 00:13:56,330 S1: from a thing. So in the distant past, an apple 244 00:13:56,330 --> 00:13:59,000 S1: would have been a treat, and especially an apple pie 245 00:13:59,000 --> 00:14:02,180 S1: because it took so long to prepare and enhance those 246 00:14:02,210 --> 00:14:05,630 S1: apples which were by themselves not easy to come by. 247 00:14:05,630 --> 00:14:09,830 S1: That's slow from the difficulty of finding the thing, and 248 00:14:09,830 --> 00:14:13,160 S1: from the long process of creating an apple pie. A 249 00:14:13,160 --> 00:14:17,240 S1: box of donuts, on the other hand, is fast. First 250 00:14:17,240 --> 00:14:19,490 S1: of all, you could just walk in and say, give 251 00:14:19,490 --> 00:14:22,820 S1: me a dozen donuts. And second of all, a donut 252 00:14:22,820 --> 00:14:27,230 S1: has been engineered to taste better than a hundred crates 253 00:14:27,230 --> 00:14:29,990 S1: of apples. So if you eat like six donuts, one 254 00:14:29,990 --> 00:14:32,840 S1: after another and then you bite into an apple, it 255 00:14:32,840 --> 00:14:37,190 S1: tastes like a 3D piece of like printed styrofoam. Like 256 00:14:37,190 --> 00:14:39,680 S1: it just does not compare to a donut. And there's 257 00:14:39,680 --> 00:14:42,200 S1: lots of other examples of this, like porn being the 258 00:14:42,200 --> 00:14:46,370 S1: fast extracted version of sex, TikTok being the fast extracted 259 00:14:46,370 --> 00:14:49,910 S1: version of reading a book or watching a movie. And 260 00:14:49,910 --> 00:14:54,200 S1: I worry that this is what's happening with human relationships, 261 00:14:54,200 --> 00:14:57,109 S1: like dating. They say that young people don't want to 262 00:14:57,110 --> 00:15:00,170 S1: get into relationships or have sex with each other anymore, 263 00:15:00,170 --> 00:15:03,170 S1: and evidently it's because they would rather do other things. 264 00:15:03,170 --> 00:15:07,040 S1: And this feels very much like a fast, slow problem. 265 00:15:07,040 --> 00:15:11,450 S1: What if this is as simple as teenagers and young 266 00:15:11,450 --> 00:15:14,660 S1: adults having more fast options on their phones and TVs, 267 00:15:14,660 --> 00:15:17,450 S1: and they are so good that the slow options of 268 00:15:17,450 --> 00:15:22,610 S1: companionship and relationships simply cannot compete. It's one thing when 269 00:15:22,610 --> 00:15:25,940 S1: a donut competes with an apple, but in this case, 270 00:15:25,940 --> 00:15:29,300 S1: the fast versus slow problem might actually be affecting the 271 00:15:29,300 --> 00:15:32,270 S1: future of the species. I'm not sure what the complete 272 00:15:32,270 --> 00:15:34,910 S1: answer is here, because I'm sure there are places where 273 00:15:34,940 --> 00:15:39,080 S1: fast versions are simply better, and not just faster. But 274 00:15:39,080 --> 00:15:40,880 S1: what I do know is that I think we should 275 00:15:40,880 --> 00:15:45,230 S1: start being very cautious about replacing slow versions of things 276 00:15:45,260 --> 00:15:50,360 S1: with the fast versions of things. There is meaning in 277 00:15:50,360 --> 00:15:55,280 S1: doing things slowly. Slow walks while holding hands. Reading physical 278 00:15:55,280 --> 00:15:59,960 S1: books in natural light and making apple pies from scratch. 279 00:15:59,960 --> 00:16:02,420 S1: I think we should do our best to figure out 280 00:16:02,420 --> 00:16:06,050 S1: what those slow, meaningful things are and ensure that we 281 00:16:06,050 --> 00:16:09,410 S1: keep as many of them in our lives as possible. 282 00:16:09,410 --> 00:16:13,280 S1: That was that one. All right list of hard won 283 00:16:13,280 --> 00:16:15,890 S1: life lessons. I think I'm going to do a separate 284 00:16:15,890 --> 00:16:20,720 S1: one for this, but essentially it's a list of pretty 285 00:16:20,720 --> 00:16:25,130 S1: decent wisdom here. Yeah, I'll give you one. In practice, 286 00:16:25,130 --> 00:16:29,900 S1: weak people behave very similarly to evil people. Don't tie 287 00:16:29,900 --> 00:16:33,050 S1: yourself to either. So that's the vibe there. It's a 288 00:16:33,050 --> 00:16:35,450 S1: whole bunch of those. So I do want to move 289 00:16:35,450 --> 00:16:37,490 S1: to a model where I support a lot more of 290 00:16:37,490 --> 00:16:42,050 S1: my work with memberships, courses and revenue from apps that 291 00:16:42,050 --> 00:16:45,860 S1: I'm building. So I would love for you all to 292 00:16:45,860 --> 00:16:49,790 S1: actually subscribe and become members of UL. So you just 293 00:16:49,790 --> 00:16:54,800 S1: go to Daniel meister.com/upgrade and you can become a member. 294 00:16:54,800 --> 00:16:57,830 S1: And there's just so many benefits. The biggest one is 295 00:16:57,830 --> 00:17:01,130 S1: the actual community, which it's just the most amazing people 296 00:17:01,130 --> 00:17:03,830 S1: that I've ever met. It's uh it's great. We got 297 00:17:03,830 --> 00:17:08,480 S1: a book club, monthly meetups, significant discounts. So I that 298 00:17:08,480 --> 00:17:11,450 S1: augmented course that's going to be $200 off if you 299 00:17:11,450 --> 00:17:13,909 S1: become a member. And guess what? Becoming a member is 300 00:17:13,910 --> 00:17:16,940 S1: only 100 bucks a year. So you kind of like 301 00:17:16,940 --> 00:17:19,550 S1: make up for it instantly. And there's just a whole 302 00:17:19,550 --> 00:17:22,159 S1: bunch of other stuff. We actually have physical meetups. It's 303 00:17:22,160 --> 00:17:26,210 S1: pretty cool. All right. Stories House just passed a bill 304 00:17:26,210 --> 00:17:32,270 S1: that bans DJI drones from using FCC frequencies. Disgruntled ex-employee 305 00:17:32,270 --> 00:17:37,669 S1: costs over $600,000 by deleting 180 test servers. Using a 306 00:17:37,670 --> 00:17:40,520 S1: script that he found on Google, Wells Fargo fired over 307 00:17:40,520 --> 00:17:45,859 S1: a dozen employees for faking keyboard activity to look busy. Yeah, 308 00:17:46,460 --> 00:17:50,510 S1: AWS has added Fido Passkeys for MFA, and root users 309 00:17:50,510 --> 00:17:54,409 S1: must enable this by July of 2024. I love this, 310 00:17:54,410 --> 00:17:59,179 S1: I don't think there's anything better recently for security than passkeys. 311 00:17:59,180 --> 00:18:02,390 S1: Like maybe the like the last ten years. Like it's 312 00:18:02,390 --> 00:18:05,270 S1: just really good. New York Times source code was stolen 313 00:18:05,270 --> 00:18:09,199 S1: using an exposed GitHub token like this is an older story. 314 00:18:09,200 --> 00:18:12,500 S1: Maybe it happened again. iOS 18 will let you automatically 315 00:18:12,500 --> 00:18:16,040 S1: record and transcribe phone calls through the phone app. I 316 00:18:16,040 --> 00:18:19,070 S1: have the beta. I need to get that going. Thanks 317 00:18:19,070 --> 00:18:23,810 S1: to a project discovery for sponsoring, Canada is proposing potentially 318 00:18:23,810 --> 00:18:28,370 S1: life in prison for hate speech, and maybe even preemptively 319 00:18:28,369 --> 00:18:32,150 S1: restrict their freedom based on anticipated crimes. Based on what 320 00:18:32,150 --> 00:18:35,060 S1: I saw happen to Jordan Peterson, who I'm not a 321 00:18:35,060 --> 00:18:39,290 S1: complete fan of, by the way, especially lately. But what 322 00:18:39,290 --> 00:18:42,590 S1: I saw that Canada did to him, not a huge 323 00:18:42,590 --> 00:18:47,600 S1: fan linked out. LinkedIn is rolling out AI career coaches. Yeah, 324 00:18:47,600 --> 00:18:50,000 S1: see my essay above on the Astro scores. That's what 325 00:18:50,000 --> 00:18:54,500 S1: we just talked about. AI and LMS are transforming cyber insurance. Yeah, 326 00:18:54,500 --> 00:18:56,450 S1: I really needed to do a whole piece on this. 327 00:18:56,450 --> 00:19:00,950 S1: But real time risk adjustments and pricing adjustments based on 328 00:19:00,950 --> 00:19:05,720 S1: that risk I plus insurance. Yeah that's spicy. It's going 329 00:19:05,720 --> 00:19:07,610 S1: to be good. And when I say good I mean 330 00:19:07,609 --> 00:19:10,850 S1: good for insurance companies. Scale came out with a controversial 331 00:19:10,850 --> 00:19:14,990 S1: stance that they will hire on merit and merit alone. 332 00:19:14,990 --> 00:19:19,310 S1: This thing is worth reading. Okay, skip that one. Tesla 333 00:19:19,310 --> 00:19:24,740 S1: shareholders have backed Elon Musk's $56 billion pay package. Got 334 00:19:24,740 --> 00:19:27,980 S1: two things here where I left the subtitle in there 335 00:19:27,980 --> 00:19:31,980 S1: do not like. Okay. Yeah. So they basically approved $56 336 00:19:31,980 --> 00:19:36,080 S1: billion pay package. This feels very Atlas Shrugged to me. 337 00:19:36,080 --> 00:19:39,920 S1: Basically give the actual builders anything they want because there's 338 00:19:39,920 --> 00:19:42,740 S1: so few of them left and they're so special. I 339 00:19:42,740 --> 00:19:46,939 S1: really like how it's like pro entrepreneur, but I feel 340 00:19:46,940 --> 00:19:50,180 S1: like it comes with a lot of toxicity. Like give 341 00:19:50,210 --> 00:19:53,990 S1: Buck Reinhold a harem because Buck deserves it and there's 342 00:19:53,990 --> 00:19:57,050 S1: cheering in the background. I think we need like a 343 00:19:57,050 --> 00:20:03,889 S1: healthy hybrid, right where we have this massive respect for founders, builders, creators, entrepreneurs. 344 00:20:03,890 --> 00:20:08,359 S1: But we build in the assumption that, look, it's not 345 00:20:08,359 --> 00:20:12,469 S1: just the 1%, they're not special people. Why isn't everyone 346 00:20:12,470 --> 00:20:15,830 S1: doing that? And that's what that's what human 3.0 is, 347 00:20:15,859 --> 00:20:19,730 S1: is getting everyone into this category. So guess what? There's 348 00:20:19,730 --> 00:20:22,969 S1: no more God complexes because everyone's supposed to be that way. Yeah, 349 00:20:23,000 --> 00:20:27,020 S1: great timing for this one. Evidently, Musk has had sexual 350 00:20:27,020 --> 00:20:32,119 S1: relationships with multiple SpaceX employees, including a former intern, which 351 00:20:32,119 --> 00:20:34,790 S1: he hired on to his executive team. And this is 352 00:20:34,790 --> 00:20:38,840 S1: according to Wall Street Journal. Now, yeah, I got complex 353 00:20:38,840 --> 00:20:43,280 S1: thoughts on this. I think radical honesty and like voicing 354 00:20:43,280 --> 00:20:46,310 S1: your desire to be with somebody, all of that is like, 355 00:20:46,310 --> 00:20:49,450 S1: I think Musk is actually doing that better than. Most people. 356 00:20:49,450 --> 00:20:53,409 S1: The problem is he works with these people. Right. So 357 00:20:54,160 --> 00:20:57,280 S1: I've got a fake example here. Julie, great work on 358 00:20:57,280 --> 00:21:01,240 S1: the Montague project I admire you. You should have my babies. 359 00:21:03,310 --> 00:21:05,020 S1: And keep in mind, this is not a real quote, 360 00:21:05,020 --> 00:21:08,770 S1: but this is kind of the vibe, right? This would 361 00:21:08,770 --> 00:21:12,699 S1: be wrong if nothing happened from it. Because even if 362 00:21:12,700 --> 00:21:15,250 S1: that worked out, if that conversation worked out, her career 363 00:21:15,250 --> 00:21:18,760 S1: was great. His career was great. Like no problems. It 364 00:21:18,760 --> 00:21:21,460 S1: would still be wrong because it has so many chances 365 00:21:21,460 --> 00:21:25,180 S1: of going wrong. And this is why this is not allowed, 366 00:21:25,420 --> 00:21:28,540 S1: because the the allegation here, which nobody knows if this 367 00:21:28,540 --> 00:21:31,030 S1: is true, but the allegation is that, uh, some of 368 00:21:31,030 --> 00:21:35,350 S1: the women supposedly said that they were given this offer, 369 00:21:35,350 --> 00:21:38,530 S1: they declined, and then they got screwed because of it. Right? 370 00:21:38,530 --> 00:21:41,500 S1: They didn't get the promotion, they got fired. Whatever. Whatever 371 00:21:41,500 --> 00:21:44,530 S1: the case was that's being alleged. All right. California has 372 00:21:44,530 --> 00:21:47,590 S1: the biggest wage gap in the US. This seems to 373 00:21:47,590 --> 00:21:50,650 S1: make sense to me. It's like it's the most extreme 374 00:21:50,650 --> 00:21:52,300 S1: version of a big city. So you're going to have 375 00:21:52,300 --> 00:21:55,209 S1: higher highs and lower lows. So that makes sense. We 376 00:21:55,210 --> 00:21:59,200 S1: just broke ground on America's first next gen nuclear facility. 377 00:21:59,200 --> 00:22:01,930 S1: And this is a piece by Bill gates. Uh, really 378 00:22:01,930 --> 00:22:04,660 S1: cool to see him working on energy the way he is. 379 00:22:04,660 --> 00:22:09,910 S1: Russia replaced the dollar and the euro with the Chinese currency, 380 00:22:09,910 --> 00:22:14,200 S1: the yuan yuan, yuan, something like that. There's a fungus 381 00:22:14,200 --> 00:22:18,429 S1: that can eat plastic. I'm worried. What else it can eat? 382 00:22:18,430 --> 00:22:20,290 S1: That's just me. Apple left out a lot of the 383 00:22:20,290 --> 00:22:26,230 S1: small updates in, uh, WWDC keynote, so. Oh, I already 384 00:22:26,230 --> 00:22:30,669 S1: used this. So I speak Spanish pretty well, and I 385 00:22:30,700 --> 00:22:34,540 S1: type in Spanish a lot, and it's not auto correcting me. 386 00:22:34,540 --> 00:22:38,409 S1: It's letting me type in Spanish mixed with English. Absolutely 387 00:22:38,410 --> 00:22:41,800 S1: love it. So that must be already turned on. Flashlight 388 00:22:41,800 --> 00:22:47,000 S1: app has new animations. Widgets are easier to resize. The 389 00:22:47,000 --> 00:22:51,260 S1: Vision Pro shows your keyboard in VR, and Mac OS 390 00:22:51,470 --> 00:22:56,330 S1: has some new nostalgic wallpapers. Apple Vision Pro lets you 391 00:22:56,330 --> 00:22:59,270 S1: watch House of the Dragon, which is the new Game 392 00:22:59,270 --> 00:23:04,940 S1: of Thrones thing, in an updated, immersive Iron Throne room environment. 393 00:23:05,270 --> 00:23:09,740 S1: Spatial personas envision two I just updated mine and recorded 394 00:23:09,740 --> 00:23:13,010 S1: a new persona. Someone wants a FaceTime? Let me know. 395 00:23:13,010 --> 00:23:15,649 S1: And got an argument here that Jupyter notebooks are the 396 00:23:15,650 --> 00:23:19,430 S1: fast food of coding. Convenient but often unhealthy for long 397 00:23:19,430 --> 00:23:24,440 S1: term projects. And Jensen Huang told Caltech grads to pursue 398 00:23:24,470 --> 00:23:27,919 S1: $0 billion markets, which are markets with no current value 399 00:23:27,920 --> 00:23:30,859 S1: but huge future potential. And I just figured out what 400 00:23:30,859 --> 00:23:33,650 S1: I love so much about Jensen. He's like a permanently 401 00:23:33,650 --> 00:23:37,129 S1: nice version of Elon and we've all seen Elon be nice. 402 00:23:37,130 --> 00:23:40,940 S1: I actually love Elon. I think he's amazing when I 403 00:23:40,940 --> 00:23:44,869 S1: see him talk about space or the future or saving humanity. 404 00:23:44,869 --> 00:23:47,960 S1: I'm like this guy is literally my hero. Then I 405 00:23:47,960 --> 00:23:51,260 S1: see him posting memes on Twitter about politics and I'm like. 406 00:23:51,880 --> 00:23:56,440 S1: Do not be this person. Do not let people make 407 00:23:56,440 --> 00:23:59,560 S1: you into this person. And I get very angry at him. 408 00:23:59,560 --> 00:24:02,290 S1: So it's great to see that you can have like 409 00:24:02,290 --> 00:24:07,970 S1: an Elan type, like an Atlas shrug type. Who's also nice. 410 00:24:08,000 --> 00:24:12,290 S1: It's amazing. Got someone predicting that generative AI will actually 411 00:24:12,290 --> 00:24:15,320 S1: increase the demand for software engineers over the next 20 years, 412 00:24:15,320 --> 00:24:18,890 S1: not decrease it. I think there's some validity to that. 413 00:24:18,890 --> 00:24:21,560 S1: I think we'll also decrease it, but also increase. The 414 00:24:21,560 --> 00:24:25,220 S1: question is which one wins out? I think probably increase 415 00:24:25,220 --> 00:24:28,129 S1: wins out in the beginning, widely held view that sperm 416 00:24:28,130 --> 00:24:30,170 S1: counts and men are dropping around the world may be 417 00:24:30,170 --> 00:24:34,730 S1: wrong in according to a new study and University of Manchester. 418 00:24:34,730 --> 00:24:37,430 S1: So I tend to go by the quality of these 419 00:24:37,430 --> 00:24:41,840 S1: things to determine if I include it or not. And 420 00:24:41,840 --> 00:24:43,610 S1: if I really want to look, I run it through 421 00:24:43,609 --> 00:24:47,750 S1: fabric and see like how it scores the paper. Curiosity 422 00:24:47,750 --> 00:24:52,700 S1: Yeah argues that perfectionism is about optimizing at the wrong scale. 423 00:24:52,850 --> 00:24:55,939 S1: I love that, I love that statement. I love the sentence. 424 00:24:55,940 --> 00:25:00,260 S1: Perfection is about optimizing at the wrong scale. This reminds 425 00:25:00,260 --> 00:25:03,229 S1: me of another thing that I really love. The worst 426 00:25:03,230 --> 00:25:06,170 S1: way to lose is to win at the wrong game. 427 00:25:06,500 --> 00:25:11,100 S1: And that's what this reminds me of. Perfectionism. Perfectionism is 428 00:25:11,100 --> 00:25:14,670 S1: optimizing at the wrong scale. Love it. 38% of web 429 00:25:14,670 --> 00:25:20,379 S1: pages that existed in 2013 are no longer accessible. Derek 430 00:25:20,380 --> 00:25:23,950 S1: Sivers explains why you should create a now page. I've 431 00:25:23,950 --> 00:25:26,409 S1: been doing this for a long time. I don't currently 432 00:25:26,410 --> 00:25:28,810 S1: have one up. That's because I'm going to have it 433 00:25:28,810 --> 00:25:32,080 S1: in my personal API soon, which will be all AI powered. 434 00:25:32,080 --> 00:25:35,770 S1: Basically a Da going to be cool stuff that's going 435 00:25:35,770 --> 00:25:37,810 S1: to take a little bit longer. I'm not actively working 436 00:25:37,810 --> 00:25:40,600 S1: on it yet. Waiting for the tech to evolve a 437 00:25:40,600 --> 00:25:44,470 S1: few more months. Okay, ideas. Increased production of goods and 438 00:25:44,470 --> 00:25:48,609 S1: services after AI, and how does that make sense if 90% 439 00:25:48,609 --> 00:25:50,950 S1: of people don't have money to buy the stuff? So 440 00:25:50,950 --> 00:25:54,040 S1: I'm talking about the economics there. Got someone arguing that 441 00:25:54,040 --> 00:25:56,290 S1: in a world dominated by AI and robots, the key 442 00:25:56,290 --> 00:26:00,250 S1: to human survival isn't being smarter, but being more mediocre. 443 00:26:00,280 --> 00:26:06,610 S1: I included it just because I like alternative views. Pop cultures, oligopoly, 444 00:26:06,609 --> 00:26:10,119 S1: dream machine, AI model. Yeah, lumen labs, this thing's blowing up. 445 00:26:10,119 --> 00:26:15,369 S1: Nvidia Warp Infinite content ideas generator can now design and 446 00:26:15,369 --> 00:26:20,740 S1: manufacture your own chips. LM Mojo Olama new version enhanced 447 00:26:20,770 --> 00:26:26,440 S1: GPU discovery SQLite is likely the most used database engine. 448 00:26:26,440 --> 00:26:29,920 S1: Pretty cool argument there. There's a new Starlink coming out, 449 00:26:29,920 --> 00:26:32,560 S1: a tiny one that's really good for like camping and 450 00:26:32,560 --> 00:26:36,520 S1: RVs and stuff. And, uh, Max lighter has a simple 451 00:26:36,520 --> 00:26:39,609 S1: but powerful tip ship something every day. Doesn't have to 452 00:26:39,609 --> 00:26:42,250 S1: be big, just something you can point to. Recommendation of 453 00:26:42,250 --> 00:26:44,950 S1: the week. I recommend you check out my list of 454 00:26:44,950 --> 00:26:47,590 S1: hard won life lessons I was talking about earlier. The 455 00:26:47,590 --> 00:26:50,260 S1: full list is pretty good, and the aphorism of the week. 456 00:26:50,260 --> 00:26:53,020 S1: The sad truth is that most evil is done by 457 00:26:53,020 --> 00:26:55,570 S1: people who never make up their minds to be good 458 00:26:55,570 --> 00:26:59,020 S1: or evil. The sad truth is that most evil is 459 00:26:59,020 --> 00:27:02,020 S1: done by people who never make up their minds to 460 00:27:02,020 --> 00:27:07,129 S1: be good or evil. Hannah Arendt. Unsupervised Learning is produced 461 00:27:07,130 --> 00:27:10,160 S1: and edited by Daniel Meisler on a Neumann U87 AI 462 00:27:10,160 --> 00:27:14,450 S1: microphone using Hindenburg. Intro and outro music is by zombie 463 00:27:14,450 --> 00:27:17,420 S1: with the why and to get the text and links 464 00:27:17,420 --> 00:27:19,639 S1: from this episode, sign up for the newsletter version of 465 00:27:19,640 --> 00:27:25,200 S1: the show at Daniel meisler.com/newsletter. We'll see you next time.