1 00:00:00,000 --> 00:00:12,000 Welcome to Fork Around and Find Out, the podcast about building, running and maintaining software and systems. 2 00:00:19,000 --> 00:00:26,000 Hello and welcome to Fork Around and Find Out. I'm your host Justin Garrison and with me as always is Autumn Nash. How's it going Autumn? 3 00:00:26,000 --> 00:00:29,000 I'm just really excited to see what Joe keeps coming up with. 4 00:00:29,000 --> 00:00:34,000 I can't even think of a joke. I'm just trying to think of like we are co-locating our jokes with our smarts today. 5 00:00:34,000 --> 00:00:35,000 Be funny now. 6 00:00:35,000 --> 00:00:38,000 I know. It's like it's too much pressure. This is why I don't do stand-up. 7 00:00:38,000 --> 00:00:39,000 Exactly. 8 00:00:40,000 --> 00:00:43,000 This is why instead of stand-up I just have kids. 9 00:00:43,000 --> 00:00:44,000 Don't lie. 10 00:00:44,000 --> 00:00:46,000 I'm interrupting me like intro. 11 00:00:46,000 --> 00:00:50,000 You just do like random stand-up. 12 00:00:50,000 --> 00:00:55,000 Yeah. It has to be ad hoc and today on the show we do not have an ad hoc guest. 13 00:00:55,000 --> 00:00:59,000 I guess we have David Aronchik, CEO and founder of Expanso. Welcome to the show David. 14 00:00:59,000 --> 00:01:01,000 Thank you so much. Real pleasure. 15 00:01:01,000 --> 00:01:06,000 David, we met like I feel like it was like a decade ago. It was like one of the first cube concept. 16 00:01:06,000 --> 00:01:07,000 Absolutely. 17 00:01:08,000 --> 00:01:09,000 No, for sure. 18 00:01:09,000 --> 00:01:15,000 And we just kind of been around. It's just infrastructure, Kubernetes, cloud. We've been doing all this. 19 00:01:15,000 --> 00:01:20,000 Tell us about what your journey's been throughout the last decade of doing infrastructure stuff. 20 00:01:20,000 --> 00:01:29,000 I mean, you know, when I was looking back, you know, I've certainly exchanged Twitter and Blue Sky and so on in all these days. 21 00:01:29,000 --> 00:01:35,000 But you're exactly right. You were one of the first Kubernetes adopters. 22 00:01:35,000 --> 00:01:40,000 So my background is I've been doing enterprise and consumer. I'm on my fourth startup. 23 00:01:40,000 --> 00:01:42,000 I'll get to that in a second. 24 00:01:42,000 --> 00:01:47,000 But the most recent arc of my career started in just before you and I met. 25 00:01:47,000 --> 00:02:00,000 I worked at Chef where I was doing director of product management and leading a bunch of their work on, you know, as Docker and all the container stuff was first coming out. 26 00:02:00,000 --> 00:02:08,000 Then I left there to go be the first non-founding PM for Kubernetes, which I did for Google for a bunch of years. 27 00:02:09,000 --> 00:02:14,000 And and help start the cloud native computer foundation and so on and so forth. 28 00:02:14,000 --> 00:02:18,000 And and that's where we met, you know, you, you were at Disney at the time. 29 00:02:18,000 --> 00:02:25,000 You were one of the first big adopters of this from a more traditional enterprise. 30 00:02:25,000 --> 00:02:37,000 And I know Disney is like super forward looking, you know, because of folks like you, but like it was really like, you know, no one really understood like what this Docker thing is what these containers are. 31 00:02:37,000 --> 00:02:40,000 And, you know, how does this going to affect me? 32 00:02:40,000 --> 00:02:46,000 But you were you were absolutely one of the first actually one of your other media proper and not not Disney's media properties. 33 00:02:46,000 --> 00:02:54,000 Although who knows now, though, it's all this consolidation, but another one of your media cohorts was right after you. 34 00:02:54,000 --> 00:03:01,000 I always remember because one of the HBO HBO Max adopted Kubernetes really early as well. 35 00:03:01,000 --> 00:03:08,000 And I remember watching or streaming Game of Thrones on my laptop. 36 00:03:08,000 --> 00:03:12,000 And I was like, oh, my God, this is running on our stuff. 37 00:03:12,000 --> 00:03:14,000 I'm very, very proud of it. 38 00:03:14,000 --> 00:03:17,000 But um, yeah, you know, that's where you and I met. 39 00:03:17,000 --> 00:03:23,000 And so I let I started to GKE the Google Kubernetes engine. 40 00:03:23,000 --> 00:03:27,000 And then I did that for a bunch of years. 41 00:03:27,000 --> 00:03:38,000 I then moved from there into starting a machine learning platform called cube flow, which has been very popular. 42 00:03:38,000 --> 00:03:52,000 And so I did that, then I left Google to go work at Microsoft to lead open source machine learning strategy out of the in for the Azure ML group and out of the office of the CTO. 43 00:03:52,000 --> 00:04:03,000 And I did that for a few years and now I'm on to my startup, which is, you know, crazy, like I honestly never thought I would go back. 44 00:04:03,000 --> 00:04:12,000 Not because I don't like the startup game, but because like I had a perfectly reasonable job at like big toe, like kick your feet up and chill, dude. 45 00:04:12,000 --> 00:04:17,000 Like, but no, I have a vision for the world and I'd like it to to exist. 46 00:04:17,000 --> 00:04:20,000 So I'm off doing the startup thing again. 47 00:04:20,000 --> 00:04:24,000 Is there ever really a kick your feet up and chill moment intact? 48 00:04:24,000 --> 00:04:30,000 You know, this should be like I keep hearing about this like it does. 49 00:04:30,000 --> 00:04:33,000 At what point do you get there? 50 00:04:33,000 --> 00:04:35,000 Some people are wired that way. 51 00:04:35,000 --> 00:04:39,000 I wish I was I truly wish I was. 52 00:04:39,000 --> 00:04:40,000 I didn't I didn't hear the word. 53 00:04:40,000 --> 00:04:42,000 I didn't hear the word wider wired. 54 00:04:42,000 --> 00:04:47,000 I heard whiter like my skin color and I was like, well, yeah, that also probably plays into it too. 55 00:04:47,000 --> 00:04:52,000 Where I've worked with quite a few people that have kicked their feet up and usually they look like me. 56 00:04:52,000 --> 00:04:53,000 Yeah, yeah. 57 00:04:53,000 --> 00:04:56,000 It's autumn takes the biggest drink of her coffee. 58 00:04:56,000 --> 00:05:01,000 She's talking idiot. 59 00:05:01,000 --> 00:05:05,000 Take me back a little bit to the GKE creation. 60 00:05:05,000 --> 00:05:07,000 Was that always the intention of Kubernetes? 61 00:05:07,000 --> 00:05:14,000 Like you open sourced it and it felt like it was meant as just like a pure open source play and then just the popularity was there immediately. 62 00:05:14,000 --> 00:05:20,000 So so it's long enough ago that I think I can say all this stuff without missing too many people off. 63 00:05:20,000 --> 00:05:25,000 But no, the story here goes back to 2003. 64 00:05:25,000 --> 00:05:26,000 Okay. 65 00:05:26,000 --> 00:05:35,000 And the story is that Google came out and released the the Hadoop paper, the MapReduce paper in 2003. 66 00:05:35,000 --> 00:05:43,000 And Yahoo came along and very helpfully read this wonderful paper, this groundbreaking paper and say, wow, this sounds really cool. 67 00:05:43,000 --> 00:05:44,000 Let's go launch something around it. 68 00:05:44,000 --> 00:05:46,000 And they created Hadoop. 69 00:05:46,000 --> 00:05:47,000 Right. 70 00:05:47,000 --> 00:05:49,000 And Google was like, oh, this is good. 71 00:05:49,000 --> 00:05:52,000 You know, we're glad people are out there and Google is a very academic place. 72 00:05:52,000 --> 00:05:57,000 So they like really don't take any like ownership over that until it gets to Google Cloud. 73 00:05:57,000 --> 00:06:07,000 And they like at the time that they launched Google Cloud, they they had to create an HDFS compatibility layer or Hadoop. 74 00:06:07,000 --> 00:06:20,000 And what that meant was you had something that Google invented, re-implemented by someone else, implemented on this like compatibility layer that ultimately went through another layer that ultimately was still running on MapReduce. 75 00:06:20,000 --> 00:06:21,000 Right. 76 00:06:21,000 --> 00:06:23,000 And they're like, why the hell did this happen? 77 00:06:23,000 --> 00:06:25,000 Like we could have just done the thing. 78 00:06:25,000 --> 00:06:26,000 Right. 79 00:06:26,000 --> 00:06:28,000 So that's going to be angle one. 80 00:06:28,000 --> 00:06:32,000 They were like, hey, look, we don't want to, we're going to release something in the world. 81 00:06:32,000 --> 00:06:34,000 Let's actually release something to them. 82 00:06:34,000 --> 00:06:35,000 Okay. 83 00:06:35,000 --> 00:06:36,000 So that's category one. 84 00:06:36,000 --> 00:06:41,000 And category two is they saw AWS and they saw it growing. 85 00:06:41,000 --> 00:06:45,000 And they're like, holy shit, you know, this, this, oh, sorry, I don't know what this is a non-safer word. 86 00:06:45,000 --> 00:06:46,000 No, we're good. 87 00:06:46,000 --> 00:06:47,000 You're good. 88 00:06:47,000 --> 00:06:48,000 We don't, we don't believe in it anymore. 89 00:06:48,000 --> 00:06:49,000 I curse like a sailor. 90 00:06:49,000 --> 00:06:50,000 So you'll have to. 91 00:06:50,000 --> 00:06:54,000 So do I. Justin's very good at not cursing. 92 00:06:54,000 --> 00:06:55,000 I don't have it in me. 93 00:06:55,000 --> 00:06:57,000 I just don't have it in me. 94 00:06:57,000 --> 00:07:05,000 I almost feel like swearing is like, like I look for slightly spicy people because I appreciate their honesty. 95 00:07:05,000 --> 00:07:07,000 Justin's spicy in other ways. 96 00:07:07,000 --> 00:07:08,000 I was going to say that. 97 00:07:08,000 --> 00:07:14,000 Like I'm so excited for you to be here because I love being in between a spicy interview 98 00:07:14,000 --> 00:07:15,000 E and Justin. 99 00:07:15,000 --> 00:07:16,000 Cause it's. 100 00:07:16,000 --> 00:07:19,000 You're in between there. 101 00:07:19,000 --> 00:07:20,000 All right. 102 00:07:20,000 --> 00:07:21,000 Yeah. 103 00:07:21,000 --> 00:07:24,000 This podcast has taken a very interesting turn. 104 00:07:24,000 --> 00:07:25,000 Let me just say that. 105 00:07:25,000 --> 00:07:32,000 Within five minutes of like meeting you, I was like, David's going to be so much better. 106 00:07:32,000 --> 00:07:34,000 Like, I thought this was a spark around to find out. 107 00:07:34,000 --> 00:07:36,000 And this is not a BDSM podcast. 108 00:07:36,000 --> 00:07:37,000 Right. 109 00:07:37,000 --> 00:07:40,000 Oh, thank God. 110 00:07:40,000 --> 00:07:42,000 Tim is in here. 111 00:07:42,000 --> 00:07:43,000 Oh shit. 112 00:07:43,000 --> 00:07:45,000 My things went all blurred here. 113 00:07:45,000 --> 00:07:46,000 Okay. 114 00:07:46,000 --> 00:07:52,000 Um, so, so then, so then, uh, AWS comes along and they're like killing it. 115 00:07:52,000 --> 00:07:53,000 Right. 116 00:07:53,000 --> 00:07:54,000 And we all look at that. 117 00:07:54,000 --> 00:07:57,000 We're like, Hey, but wait, we have a cloud that we're trying to get going here. 118 00:07:57,000 --> 00:08:01,000 Um, like we think that the right thing here, the right. 119 00:08:01,000 --> 00:08:04,000 Or, uh, uh, element is not a via. 120 00:08:04,000 --> 00:08:05,000 Right. 121 00:08:05,000 --> 00:08:08,000 We think the right element is a container and look at Docker. 122 00:08:08,000 --> 00:08:09,000 They're doing great. 123 00:08:09,000 --> 00:08:10,000 Right. 124 00:08:10,000 --> 00:08:11,000 So let's take. 125 00:08:11,000 --> 00:08:12,000 Dockers. 126 00:08:12,000 --> 00:08:18,000 You know, extension and wisdom, which by the way, again, another thing that Google launched. 127 00:08:18,000 --> 00:08:24,000 Again, no one is saying that Docker wasn't an enormous part in arguably the reason that 128 00:08:24,000 --> 00:08:25,000 containers are successful. 129 00:08:25,000 --> 00:08:30,000 Um, but a lot of it was based on, you know, kernel changes that came in, you know, in 130 00:08:30,000 --> 00:08:32,000 2004 or 2005, right? 131 00:08:32,000 --> 00:08:34,000 Like there's an enormous amount of stuff there. 132 00:08:34,000 --> 00:08:36,000 And so they're like, Hey, look, Walker's killing it. 133 00:08:36,000 --> 00:08:39,000 Let's help Docker extend even further. 134 00:08:39,000 --> 00:08:44,000 And let's help people say, you know, can take VMs are not the right thing. 135 00:08:44,000 --> 00:08:45,000 It's just not. 136 00:08:45,000 --> 00:08:49,000 And so, you know, again, I was not here during this time. 137 00:08:49,000 --> 00:08:54,000 Craig McClucky and Joe Beta and Brandon Burns and Tim Hawken and Brian Grant and whatever, 138 00:08:54,000 --> 00:08:58,000 they were like all working on a bunch of stuff internally to Google where they're like, we 139 00:08:58,000 --> 00:09:04,000 think there's a new orchestration paradigm that people should be adopting here. 140 00:09:04,000 --> 00:09:08,000 Um, they were going to build it internally to Google in a project called Omega and you 141 00:09:08,000 --> 00:09:09,000 should go read everyone. 142 00:09:09,000 --> 00:09:13,000 You should go read Brian Grant's blog history of this. 143 00:09:13,000 --> 00:09:16,000 It's so good and it's so real. 144 00:09:16,000 --> 00:09:22,000 It is like transparently going through a nice human, which is like amazing that he's 145 00:09:22,000 --> 00:09:26,000 like a humble, nice human after doing like he's so smart. 146 00:09:26,000 --> 00:09:28,000 He would guess two or three on this show. 147 00:09:28,000 --> 00:09:30,000 So yeah, he's so good. 148 00:09:30,000 --> 00:09:34,000 When you talk to him, you're just like, dude, you're so smart. 149 00:09:34,000 --> 00:09:37,000 Like he is just so intelligent. 150 00:09:37,000 --> 00:09:41,000 So, so, uh, uh, Brandon is a good friend. 151 00:09:41,000 --> 00:09:45,000 And when I first get to Google, he tells me this thing, which is amazing. 152 00:09:45,000 --> 00:09:50,000 He says your goal inside Google is not to be the smartest person on day one or day two 153 00:09:50,000 --> 00:09:52,000 or day 400, right? 154 00:09:52,000 --> 00:09:53,000 Your goal is the following. 155 00:09:53,000 --> 00:09:56,000 Like you should go and you should come up with a smart idea. 156 00:09:56,000 --> 00:09:58,000 We hired you because you have a smart idea. 157 00:09:58,000 --> 00:09:59,000 Okay. 158 00:09:59,000 --> 00:10:02,000 You should go and you should try and figure out where that idea is because I guarantee 159 00:10:02,000 --> 00:10:04,000 somebody internally has already thought about it. 160 00:10:04,000 --> 00:10:05,000 Right. 161 00:10:05,000 --> 00:10:12,000 And there will be a window between you thinking of this idea and the paper and the, that window 162 00:10:12,000 --> 00:10:14,000 will start off at like four years. 163 00:10:14,000 --> 00:10:19,000 Like the idea was four years ago that somebody looked at this and they decided this was a 164 00:10:19,000 --> 00:10:22,000 bad idea or they implemented it or whatever. 165 00:10:22,000 --> 00:10:23,000 And then you should. 166 00:10:23,000 --> 00:10:24,000 Okay. 167 00:10:24,000 --> 00:10:25,000 You get smarter about it. 168 00:10:25,000 --> 00:10:27,000 You read the paper and then you come back and then you do that again. 169 00:10:27,000 --> 00:10:30,000 You're going to come up with another great idea and it'll be two years. 170 00:10:30,000 --> 00:10:31,000 You're like, what? 171 00:10:31,000 --> 00:10:33,000 Two years and then you'll do it again. 172 00:10:33,000 --> 00:10:34,000 It'll be like nine months. 173 00:10:34,000 --> 00:10:35,000 Then you do it again. 174 00:10:35,000 --> 00:10:37,000 It'll be like three months and then you do it again. 175 00:10:37,000 --> 00:10:39,000 You won't find a paper and then you're like that. 176 00:10:39,000 --> 00:10:43,000 That is the thing you should go and implement and, and so on and so forth. 177 00:10:43,000 --> 00:10:45,000 So Brandon says this and I was like, I still take this wisdom way. 178 00:10:45,000 --> 00:10:48,000 I think it's so interesting, especially in the real world where you can go out and you 179 00:10:48,000 --> 00:10:51,000 can research it and you can figure out why things worked and didn't work and so on and 180 00:10:51,000 --> 00:10:52,000 so forth. 181 00:10:52,000 --> 00:10:55,000 Brian is interesting because he's the other half of the coin. 182 00:10:55,000 --> 00:11:00,000 Like he's the one who will like, he just has canonical knowledge of everything. 183 00:11:00,000 --> 00:11:04,000 And so he is whenever I'm trying to come up with a new feature for our platform or, 184 00:11:04,000 --> 00:11:06,000 you know, hey, you know, why didn't people do this? 185 00:11:06,000 --> 00:11:09,000 I go and talk to Brian or another guy, Eric Brewer. 186 00:11:09,000 --> 00:11:11,000 He's, he's also a really wonderful human. 187 00:11:11,000 --> 00:11:14,000 You should have him on if you haven't already. 188 00:11:14,000 --> 00:11:20,000 And the two of them together, you're just kind of like, oh, you know, what's, what about 189 00:11:20,000 --> 00:11:21,000 this idea? 190 00:11:21,000 --> 00:11:22,000 Oh yeah, we didn't look at that. 191 00:11:22,000 --> 00:11:26,000 And this is the problem and distributed to this and consensus that in this year and 192 00:11:26,000 --> 00:11:27,000 you're running into this. 193 00:11:27,000 --> 00:11:30,000 And eventually you'll get to a point where they're like, yeah, that's actually not a 194 00:11:30,000 --> 00:11:31,000 bad idea. 195 00:11:31,000 --> 00:11:32,000 And you're like, ah, I'm going to go with it. 196 00:11:32,000 --> 00:11:36,000 I feel like having Brian as a friend has got to be like some sort of life hack because 197 00:11:36,000 --> 00:11:41,000 to be able to bounce ideas off of someone like that, like God. 198 00:11:41,000 --> 00:11:47,000 I mean, I say that I am, I try and collect smart friends like they're fucking Pokemon. 199 00:11:47,000 --> 00:11:48,000 That is that. 200 00:11:48,000 --> 00:11:49,000 Okay. 201 00:11:49,000 --> 00:11:51,000 Like that is like the top tier. 202 00:11:51,000 --> 00:11:56,000 Like if all of the rest of the world is questionable at the moment, having smart friends and good 203 00:11:56,000 --> 00:11:57,000 friends. 204 00:11:57,000 --> 00:11:58,000 100%. 205 00:11:58,000 --> 00:12:02,000 I mean, just having someone who can be honest with you is like brutally important. 206 00:12:02,000 --> 00:12:07,000 I might tease Justin on the internet, but like having good friends, like top tier. 207 00:12:07,000 --> 00:12:09,000 Like if you want to know how to improve your life. 208 00:12:09,000 --> 00:12:11,000 I don't know if that was including or excluding me. 209 00:12:11,000 --> 00:12:13,000 That's kind of it goes both ways. 210 00:12:13,000 --> 00:12:14,000 Duh. 211 00:12:14,000 --> 00:12:20,000 Like having Justin, but also I have good friends in between your questionable moments of the 212 00:12:20,000 --> 00:12:22,000 fact that you don't drink coffee. 213 00:12:22,000 --> 00:12:25,000 But like, we won't go into that today. 214 00:12:25,000 --> 00:12:28,000 But it's like, I think that drink coffee. 215 00:12:28,000 --> 00:12:30,000 How's that even possible? 216 00:12:30,000 --> 00:12:31,000 Thank you. 217 00:12:31,000 --> 00:12:34,000 Like, like you work in tech and you have children. 218 00:12:34,000 --> 00:12:35,000 What is wrong with you? 219 00:12:35,000 --> 00:12:38,000 He, he does have a Dr. Pepper obsession. 220 00:12:38,000 --> 00:12:39,000 I love Dr. Pepper. 221 00:12:39,000 --> 00:12:43,000 Last night it was at an event and somebody had like, so it's, it's tech week here in 222 00:12:43,000 --> 00:12:44,000 Seattle and it's been phenomenal. 223 00:12:44,000 --> 00:12:47,000 You live in Seattle and I've never met you, David. 224 00:12:47,000 --> 00:12:49,000 What are you doing this afternoon? 225 00:12:49,000 --> 00:12:51,000 There's like three more events. 226 00:12:51,000 --> 00:12:52,000 What? 227 00:12:52,000 --> 00:12:53,000 It's tech week. 228 00:12:53,000 --> 00:12:54,000 Yeah. 229 00:12:54,000 --> 00:12:55,000 It's tech week, man. 230 00:12:55,000 --> 00:12:58,000 But I was going to say I was at an event yesterday afternoon all week. 231 00:12:58,000 --> 00:12:59,000 I've been drinking Diet Coke. 232 00:12:59,000 --> 00:13:00,000 Don't get me wrong. 233 00:13:00,000 --> 00:13:01,000 I love Diet Coke. 234 00:13:01,000 --> 00:13:03,000 But like at the same time, like I was at an event and they had Diet Dr. Pepper. 235 00:13:03,000 --> 00:13:06,000 I'm like, oh, you, how do I get in business with you? 236 00:13:06,000 --> 00:13:08,000 Diet Dr. Pepper is amazing. 237 00:13:08,000 --> 00:13:09,000 I love it. 238 00:13:09,000 --> 00:13:12,000 I love how you said, how do I get in business with you? 239 00:13:12,000 --> 00:13:14,000 I'm making this happen. 240 00:13:14,000 --> 00:13:16,000 I totally understand that. 241 00:13:16,000 --> 00:13:18,000 Do you know how hard it is to find Justin Dr. Pepper? 242 00:13:18,000 --> 00:13:19,000 It's not that hard. 243 00:13:19,000 --> 00:13:23,000 So I continue to ignore him at conference, annoy him at conferences. 244 00:13:23,000 --> 00:13:26,000 I don't understand how everyone is at drinking Diet Dr. Pepper. 245 00:13:26,000 --> 00:13:27,000 It's so much better. 246 00:13:27,000 --> 00:13:31,000 I went to three different stores at scale so I could give, be like, here's a Dr. 247 00:13:31,000 --> 00:13:34,000 Pepper and Rice Krispie so I can keep stealing your charges. 248 00:13:34,000 --> 00:13:37,000 It was the only reason I would go to Texas was to get good Dr. Pepper. 249 00:13:37,000 --> 00:13:39,000 They have the original OG sugar. 250 00:13:39,000 --> 00:13:41,000 Do they have a different Dr. Pepper? 251 00:13:41,000 --> 00:13:45,000 It was invented in Texas and they have real sugar Dr. Pepper. 252 00:13:45,000 --> 00:13:49,000 And so it started to percolate out some other places and one store near me sells it. 253 00:13:49,000 --> 00:13:50,000 And so I go there sometimes. 254 00:13:50,000 --> 00:13:52,000 It's fine. 255 00:13:52,000 --> 00:13:57,000 I said it's fine, but it didn't sound fine at all. 256 00:13:57,000 --> 00:14:00,000 Let me, let me finish up the story so we can get out to other interesting things. 257 00:14:00,000 --> 00:14:04,000 There's too much ADHD here, David. 258 00:14:04,000 --> 00:14:06,000 No shit. 259 00:14:06,000 --> 00:14:09,000 I'm like ADHD, like on ADHD. 260 00:14:10,000 --> 00:14:14,000 So anyhow, so Brian Grant and Brennan and whatever, they come up with these things 261 00:14:14,000 --> 00:14:16,000 and Brennan, you know, literally. 262 00:14:16,000 --> 00:14:19,000 Why are you not in business with Brian? 263 00:14:19,000 --> 00:14:21,000 Because he's got his own thing. 264 00:14:21,000 --> 00:14:22,000 Yeah, he's got his own thing. 265 00:14:22,000 --> 00:14:23,000 Yeah. 266 00:14:23,000 --> 00:14:24,000 I love what he's doing, by the way. 267 00:14:24,000 --> 00:14:25,000 Yeah. 268 00:14:25,000 --> 00:14:29,000 Like the configuration management and easy with another wonderful friend of mine that 269 00:14:29,000 --> 00:14:31,000 I got ideas of all the time, Alexis. 270 00:14:31,000 --> 00:14:32,000 Alexis. 271 00:14:32,000 --> 00:14:33,000 Yeah. 272 00:14:33,000 --> 00:14:34,000 Config hub. 273 00:14:34,000 --> 00:14:35,000 Config hub. 274 00:14:35,000 --> 00:14:36,000 Yeah. 275 00:14:36,000 --> 00:14:37,000 Huge, huge. 276 00:14:37,000 --> 00:14:40,000 I'm going to fly on the wall while you guys are having like technical discussions. 277 00:14:40,000 --> 00:14:42,000 Like, can I just sit in the background? 278 00:14:42,000 --> 00:14:43,000 I mean, we never have it. 279 00:14:43,000 --> 00:14:45,000 We never have a technical discussion. 280 00:14:45,000 --> 00:14:50,000 You get in the room and you're like arguing about like how, you know, whatever, blah, 281 00:14:50,000 --> 00:14:52,000 blah, blah, like bad mouth, blah, blah, blah. 282 00:14:52,000 --> 00:14:55,000 And like, oh, you see what these idiots are doing. 283 00:14:55,000 --> 00:14:59,000 I mean, I feel like your group chat is fire. 284 00:14:59,000 --> 00:15:01,000 More group chats. 285 00:15:01,000 --> 00:15:04,000 They're the best. 286 00:15:04,000 --> 00:15:09,000 I mean, our group chat is hilarious. 287 00:15:09,000 --> 00:15:10,000 I don't know. 288 00:15:10,000 --> 00:15:11,000 I don't know. 289 00:15:11,000 --> 00:15:12,000 It's an interesting question. 290 00:15:12,000 --> 00:15:13,000 Finish your story. 291 00:15:13,000 --> 00:15:21,000 Anyhow, so Brandon gets it running on his laptop in Java, like his total skunkworks. 292 00:15:21,000 --> 00:15:23,000 That was kind of a fork. 293 00:15:23,000 --> 00:15:28,000 It wasn't a fork, but it was kind of like a conceptual fork of the thing they were doing 294 00:15:28,000 --> 00:15:29,000 internally to Google. 295 00:15:29,000 --> 00:15:35,000 And then it starts to catch fire and somehow it breaks through, like, because Google was 296 00:15:35,000 --> 00:15:38,000 really internally opposed to Kubernetes. 297 00:15:38,000 --> 00:15:44,000 Not that they were, there was just a lot of motion around like what the hell is going 298 00:15:44,000 --> 00:15:47,000 on and, you know, what kind of team do we spin up? 299 00:15:47,000 --> 00:15:53,000 And then, you know, Craig McClucky and like I said, Brandon and Brian and all these people 300 00:15:53,000 --> 00:15:57,000 ended up forcing it through, get like, I think releasing it to the world like forcibly 301 00:15:57,000 --> 00:16:00,000 and then, you know, just kind of cascaded forward from there. 302 00:16:00,000 --> 00:16:04,000 And so I joined in January of 2015. 303 00:16:04,000 --> 00:16:09,000 And Craig was like, Hey, look, I need someone to take over Kubernetes management for me 304 00:16:09,000 --> 00:16:12,000 because I'm going to go off and work on three other things. 305 00:16:12,000 --> 00:16:14,000 I mean, there's another genius for you. 306 00:16:14,000 --> 00:16:19,000 And so he, he proceeds to go and do that. 307 00:16:19,000 --> 00:16:22,000 And I like launch GK. 308 00:16:22,000 --> 00:16:26,000 And so they're like, Well, all right, we're going to have this open source thing. 309 00:16:26,000 --> 00:16:30,000 We've got to, you know, get this project going. 310 00:16:30,000 --> 00:16:32,000 It was already like put it already been written in. 311 00:16:32,000 --> 00:16:34,000 There were some early versions and so on and so forth. 312 00:16:34,000 --> 00:16:42,000 But, you know, I started leading it and, and, you know, it was the three, three core pillars of 313 00:16:42,000 --> 00:16:54,000 of GKE compute under Navneet, Paul Nash, who was off running compute and 314 00:16:55,000 --> 00:16:56,000 totally blanking on his name. 315 00:16:56,000 --> 00:16:59,000 I feel terrible, but like it was the lead for App Engine. 316 00:16:59,000 --> 00:17:00,000 This was, this was 10 years ago. 317 00:17:00,000 --> 00:17:01,000 We're not old. 318 00:17:01,000 --> 00:17:03,000 I know, but I can't remember his name. 319 00:17:03,000 --> 00:17:05,000 I feel really terrible because he was great. 320 00:17:05,000 --> 00:17:08,000 Crazy, like in 10 years, how much things have changed? 321 00:17:08,000 --> 00:17:09,000 Oh, absolutely. 322 00:17:09,000 --> 00:17:10,000 Absolutely. 323 00:17:10,000 --> 00:17:11,000 So anyhow, so that was it. 324 00:17:11,000 --> 00:17:14,000 And it was just like, let's help people adopt containers. 325 00:17:14,000 --> 00:17:18,000 And, and for better or worse, it's not that we're opposed to AWS. 326 00:17:18,000 --> 00:17:20,000 It's just, we don't want people building on VMs. 327 00:17:20,000 --> 00:17:21,000 That was it. 328 00:17:21,000 --> 00:17:25,000 And we think the world is better if, if everyone isn't completely married to a 329 00:17:25,000 --> 00:17:30,000 VM, because a VM is so heavy weight, even the lightest weight VM to have an 330 00:17:30,000 --> 00:17:36,000 entire kernel to care about your serial port and your Ethernet driver. 331 00:17:36,000 --> 00:17:38,000 And I mean, it's just like, it's insane. 332 00:17:38,000 --> 00:17:42,000 Like let's, let's give people what they want an isolated environment that allows 333 00:17:42,000 --> 00:17:43,000 you to execute against things. 334 00:17:43,000 --> 00:17:46,000 And, and that was the, the whole idea of on the container. 335 00:17:46,000 --> 00:17:49,000 And then obviously letting people do a whole bunch of those at the same time was 336 00:17:49,000 --> 00:17:50,000 really powerful. 337 00:17:50,000 --> 00:17:54,000 And even just to like paint the scene of people that weren't in technology or 338 00:17:54,000 --> 00:17:57,000 weren't doing infrastructure around this time, right? 339 00:17:57,000 --> 00:18:04,000 Like Docker was kind of launched in, in 2014, the first Docker con was 2014. 340 00:18:04,000 --> 00:18:06,000 So this is still super early. 341 00:18:06,000 --> 00:18:11,000 ECS came out from AWS, which was like basically just like a big Docker engine 342 00:18:11,000 --> 00:18:13,000 in 2014. 343 00:18:13,000 --> 00:18:16,000 So this is within six months of all these other things. 344 00:18:16,000 --> 00:18:21,000 Google already had the app engine, which was already kind of this like, 345 00:18:21,000 --> 00:18:25,000 has sort of, you know, you didn't have to care that it was a container sort of 346 00:18:25,000 --> 00:18:27,000 environment where it's like, Hey, you just bring us your application that looks 347 00:18:27,000 --> 00:18:29,000 like this, we'll run it for you. 348 00:18:29,000 --> 00:18:32,000 No VM, no S management, all of that stuff is going to work. 349 00:18:32,000 --> 00:18:37,000 And then launching this new, very configurable kind of complex looking 350 00:18:37,000 --> 00:18:41,000 container engine into the world had to have contention because I know like all 351 00:18:41,000 --> 00:18:45,000 the internal Google stuff around Borg is like, well, you can't just ship Borg to 352 00:18:45,000 --> 00:18:46,000 other people. 353 00:18:46,000 --> 00:18:50,000 How do you wrap that to make it easier just like Hadoop? 354 00:18:50,000 --> 00:18:52,000 It must, it must have been like political struggle. 355 00:18:52,000 --> 00:18:56,000 I think even more than a technical struggle to be able to push that through. 356 00:18:56,000 --> 00:19:00,000 No, I mean, look, you know, we, again, we all forget, right? 357 00:19:00,000 --> 00:19:04,000 But, but Google was not successful and open source at that point, right? 358 00:19:04,000 --> 00:19:06,000 They were very successful publishing papers. 359 00:19:06,000 --> 00:19:13,000 But to that point, they had Android, which they bought and they had Angular. 360 00:19:13,000 --> 00:19:16,000 But other than that, they had Go. 361 00:19:16,000 --> 00:19:17,000 That's true. 362 00:19:17,000 --> 00:19:18,000 They didn't go. 363 00:19:18,000 --> 00:19:19,000 I take that back. 364 00:19:19,000 --> 00:19:20,000 That's the only other thing. 365 00:19:20,000 --> 00:19:23,000 But Go wasn't, Go wasn't as popular as it is now. 366 00:19:23,000 --> 00:19:25,000 It was like, you know what I mean? 367 00:19:25,000 --> 00:19:26,000 Yeah. 368 00:19:26,000 --> 00:19:27,000 Yeah. 369 00:19:27,000 --> 00:19:28,000 Yeah. 370 00:19:28,000 --> 00:19:29,000 Go. 371 00:19:29,000 --> 00:19:31,000 I don't think people know how old Go is because Go got so popular in the last 372 00:19:31,000 --> 00:19:32,000 few years. 373 00:19:32,000 --> 00:19:35,000 And then when they, like, and because so much of Kubernetes and certain 374 00:19:35,000 --> 00:19:37,000 infrastructure is built on it. 375 00:19:37,000 --> 00:19:41,000 Now it's like, I won't say it's like Java, but it's like, you can't avoid 376 00:19:41,000 --> 00:19:42,000 Go in a lot of ways. 377 00:19:42,000 --> 00:19:45,000 So much of the infrastructure tooling was Ruby before that. 378 00:19:45,000 --> 00:19:46,000 Right. 379 00:19:46,000 --> 00:19:47,000 Because Ruby on Rails exploded. 380 00:19:47,000 --> 00:19:48,000 That makes me so mad. 381 00:19:48,000 --> 00:19:49,000 And then there was. 382 00:19:49,000 --> 00:19:52,000 I have like flashbacks. 383 00:19:52,000 --> 00:19:55,000 Chef and Puppet were like, that was like, if you were doing infrastructure, 384 00:19:55,000 --> 00:19:58,000 you were doing config management and you had to know Ruby to be able to write 385 00:19:58,000 --> 00:19:59,000 Chef and Puppet. 386 00:19:59,000 --> 00:20:01,000 Oh, and now so many things at AWS make sense. 387 00:20:01,000 --> 00:20:02,000 Yeah. 388 00:20:02,000 --> 00:20:03,000 Yeah. 389 00:20:03,000 --> 00:20:04,000 Absolutely. 390 00:20:04,000 --> 00:20:06,000 I was like, why would you do this? 391 00:20:06,000 --> 00:20:07,000 Yeah. 392 00:20:07,000 --> 00:20:08,000 Absolutely. 393 00:20:08,000 --> 00:20:10,000 Well, now it's all TypeScript at AWS. 394 00:20:10,000 --> 00:20:11,000 Yeah. 395 00:20:11,000 --> 00:20:14,000 So I mean, like, again, it's, it was like, and so. 396 00:20:14,000 --> 00:20:15,000 I have a question. 397 00:20:15,000 --> 00:20:16,000 Oh, sorry, please. 398 00:20:16,000 --> 00:20:21,000 What do you think is harder politics trying to get things done internally and 399 00:20:21,000 --> 00:20:24,000 then like a mega corporation or open source? 400 00:20:24,000 --> 00:20:28,000 Because I feel like they're very two different, like. 401 00:20:28,000 --> 00:20:29,000 They're. 402 00:20:29,000 --> 00:20:31,000 You break a really interesting question. 403 00:20:31,000 --> 00:20:34,000 I, I, you know, they just are very different. 404 00:20:34,000 --> 00:20:39,000 The nice part about internal politics is there are at least defined motivations. 405 00:20:39,000 --> 00:20:41,000 It's very rare that someone's just absolute chaos. 406 00:20:41,000 --> 00:20:42,000 Right. 407 00:20:42,000 --> 00:20:45,000 Every now and then I'm like, you play D&D, huh? 408 00:20:45,000 --> 00:20:48,000 Cause you are just a chaos goblin for no reason. 409 00:20:48,000 --> 00:20:52,000 Like you just walk in and you're just like, for no reason. 410 00:20:52,000 --> 00:20:53,000 But yeah. 411 00:20:53,000 --> 00:20:54,000 So that's very rare. 412 00:20:54,000 --> 00:20:56,000 And I'm like, I don't know. 413 00:20:56,000 --> 00:20:57,000 I don't know. 414 00:20:57,000 --> 00:20:58,000 I don't know. 415 00:20:58,000 --> 00:20:59,000 I don't know. 416 00:20:59,000 --> 00:21:00,000 I don't know. 417 00:21:00,000 --> 00:21:01,000 I don't know. 418 00:21:01,000 --> 00:21:02,000 I don't know. 419 00:21:02,000 --> 00:21:03,000 Yeah. 420 00:21:03,000 --> 00:21:04,000 So that's very rare internally. 421 00:21:04,000 --> 00:21:08,000 At least you can say, okay, well that person has this job and this VP asked them to do 422 00:21:08,000 --> 00:21:09,000 this. 423 00:21:09,000 --> 00:21:10,000 So like that's a thing. 424 00:21:10,000 --> 00:21:14,000 I don't agree with that thing, but at least you can like unpack what, what they're doing. 425 00:21:14,000 --> 00:21:16,000 I think knowing your audience is important. 426 00:21:16,000 --> 00:21:17,000 100%. 427 00:21:17,000 --> 00:21:18,000 No. 428 00:21:18,000 --> 00:21:19,000 100%. 429 00:21:19,000 --> 00:21:23,000 And, and those politics almost always come down from cheese. 430 00:21:23,000 --> 00:21:27,000 I'm responsible for a thing and you are risking that thing. 431 00:21:27,000 --> 00:21:30,000 So I'm going to like be a dick to you. 432 00:21:30,000 --> 00:21:31,000 Right. 433 00:21:31,000 --> 00:21:37,000 And so you got to figure out because I love the way that you like, you're like, can we 434 00:21:37,000 --> 00:21:38,000 be friends? 435 00:21:38,000 --> 00:21:40,000 You're like, so this person has motivation. 436 00:21:40,000 --> 00:21:41,000 So they're going to be a dick to you. 437 00:21:41,000 --> 00:21:44,000 Like I felt that in my soul. 438 00:21:44,000 --> 00:21:50,000 So, but, but in open source, the, all you have is ego, right? 439 00:21:50,000 --> 00:21:53,000 And so ego can be way more irrational. 440 00:21:53,000 --> 00:21:59,000 It's, but sometimes it's like really like, you got to love the purist, right? 441 00:21:59,000 --> 00:22:04,000 Like, you know, like we, I feel like we all like really care about open source, but every 442 00:22:04,000 --> 00:22:08,000 now you get someone and you're like, do you go outside? 443 00:22:08,000 --> 00:22:09,000 Like, 444 00:22:09,000 --> 00:22:13,000 I don't, you know, it's funny. 445 00:22:13,000 --> 00:22:15,000 So let me give you an example. 446 00:22:15,000 --> 00:22:22,000 When I was leading Kubernetes, they're one of the first. 447 00:22:22,000 --> 00:22:24,000 Hardy debates we had in public. 448 00:22:24,000 --> 00:22:26,000 I was excited about what you're going to say. 449 00:22:26,000 --> 00:22:28,000 He said a party in the way you perked up. 450 00:22:28,000 --> 00:22:30,000 I was like, this is going to be good. 451 00:22:30,000 --> 00:22:37,000 There was a, I can't remember it because we just introduced job sets. 452 00:22:37,000 --> 00:22:39,000 So I can't remember what was the name. 453 00:22:39,000 --> 00:22:41,000 Oh, maybe it's a stateful set. 454 00:22:41,000 --> 00:22:44,000 But like, I think that's what we were calling it. 455 00:22:44,000 --> 00:22:45,000 We're still calling it that. 456 00:22:45,000 --> 00:22:49,000 But like at the time, 2015, there was a thing called pet set. 457 00:22:49,000 --> 00:22:50,000 Right. 458 00:22:51,000 --> 00:22:52,000 And 459 00:22:52,000 --> 00:22:53,000 What name do these things? 460 00:22:53,000 --> 00:22:55,000 What is a pets? 461 00:22:55,000 --> 00:22:57,000 So this is the old name for states. 462 00:22:57,000 --> 00:22:58,000 You bring up the right point. 463 00:22:58,000 --> 00:22:59,000 Right. 464 00:22:59,000 --> 00:23:01,000 So there was a thing called pet set. 465 00:23:01,000 --> 00:23:03,000 And that's because there was a whole idea. 466 00:23:03,000 --> 00:23:05,000 Oh, or things pet cattle or pets. 467 00:23:05,000 --> 00:23:06,000 Okay. 468 00:23:06,000 --> 00:23:10,000 Like, because you, you, you're going to put a bullet in a cattle, but you're not going 469 00:23:10,000 --> 00:23:12,000 to put a bullet in a, in a, right. 470 00:23:12,000 --> 00:23:15,000 And so the whole idea was like to keep it around. 471 00:23:15,000 --> 00:23:19,000 And this person submitted a bug to say, Hey, you should change the name of pets. 472 00:23:20,000 --> 00:23:25,000 And they, they get going to this big, long explanation, like, look, you know, animal 473 00:23:25,000 --> 00:23:28,000 welfare and this, that and the other and so on and so forth. 474 00:23:29,000 --> 00:23:31,000 And I'm not dismissing. 475 00:23:31,000 --> 00:23:36,000 Like what their feelings were, but like, that's, that's your deal, dude. 476 00:23:37,000 --> 00:23:38,000 That's not our deal. 477 00:23:38,000 --> 00:23:41,000 Like I, I, I get you want that, but we don't. 478 00:23:41,000 --> 00:23:43,000 It like, that's not going to help the project. 479 00:23:43,000 --> 00:23:44,000 The fact that this is the motivation. 480 00:23:44,000 --> 00:23:48,000 Now that said, the name is terrible. 481 00:23:48,000 --> 00:23:50,000 Exactly what you said on it. 482 00:23:50,000 --> 00:23:51,000 What the fuck is that? 483 00:23:51,000 --> 00:23:52,000 What is that? 484 00:23:52,000 --> 00:23:53,000 What does that set even mean? 485 00:23:53,000 --> 00:23:54,000 That you're refusing. 486 00:23:54,000 --> 00:23:56,000 Why are you saying this? 487 00:23:56,000 --> 00:24:00,000 And so this is why having a lot of really smart friends that have been in the 488 00:24:00,000 --> 00:24:02,000 field for a long time is good. 489 00:24:02,000 --> 00:24:06,000 But every now and then a name comes out and I'm like, this is how we know you 490 00:24:06,000 --> 00:24:09,000 don't talk to anybody else besides white guys that have been in tech for forever. 491 00:24:09,000 --> 00:24:12,000 Who else knows what this means? 492 00:24:12,000 --> 00:24:14,000 And, and here you go. 493 00:24:14,000 --> 00:24:16,000 You can go search for it. 494 00:24:16,000 --> 00:24:18,000 It was, I can tell by Justin's face. 495 00:24:18,000 --> 00:24:20,000 He's already searching for it. 496 00:24:20,000 --> 00:24:21,000 Please. 497 00:24:21,000 --> 00:24:22,000 Please. 498 00:24:22,000 --> 00:24:24,000 It's, it's a 27, 4, 30. 499 00:24:24,000 --> 00:24:28,000 And this was, and for the time, small community. 500 00:24:28,000 --> 00:24:35,000 This was a, I don't know, 50 comment thread here. 501 00:24:35,000 --> 00:24:39,000 Like it was pretty, like I was a lot of annoying stuff that I love that there 502 00:24:39,000 --> 00:24:41,000 was a 50 comment thread here. 503 00:24:41,000 --> 00:24:44,000 There was a lot of annoying stuff that I love that there was 50. 504 00:24:44,000 --> 00:24:46,000 This is just, this is the epitome of open. 505 00:24:46,000 --> 00:24:47,000 Okay. 506 00:24:47,000 --> 00:24:49,000 So when you get to the conversion, right? 507 00:24:49,000 --> 00:24:52,000 The like conversion zone of like open source. 508 00:24:52,000 --> 00:24:53,000 Oh no, sorry. 509 00:24:53,000 --> 00:24:56,000 120, 150, 150 total comments on this. 510 00:24:56,000 --> 00:24:57,000 Shut up. 511 00:24:57,000 --> 00:24:59,000 I love this. 512 00:24:59,000 --> 00:25:01,000 Like insane, insane. 513 00:25:01,000 --> 00:25:05,000 This is like those people who are so into open source that they refuse to use any 514 00:25:05,000 --> 00:25:06,000 proprietary software. 515 00:25:06,000 --> 00:25:08,000 So they refuse to use Google maps or anything. 516 00:25:08,000 --> 00:25:11,000 And you're just like, bro. 517 00:25:11,000 --> 00:25:13,000 But like, okay. 518 00:25:13,000 --> 00:25:15,000 How do you, okay. 519 00:25:15,000 --> 00:25:19,000 I think that Google was, I won't say one of the first, but you guys, your time at 520 00:25:19,000 --> 00:25:24,000 Google, I imagine that you really learned that conversion zone of proprietary 521 00:25:24,000 --> 00:25:27,000 software and corporate and open source. 522 00:25:27,000 --> 00:25:31,000 And I feel like we're in this kind of, I don't know. 523 00:25:31,000 --> 00:25:34,000 I don't know if I'd say a transition, but weird time. 524 00:25:34,000 --> 00:25:39,000 So like, what do you think about the politics of trying to both balance 525 00:25:39,000 --> 00:25:47,000 corporation politics, but also like interacting with like open source, you 526 00:25:47,000 --> 00:25:52,000 know, because it's very, it's, it makes it even more complicated when you're both 527 00:25:52,000 --> 00:25:56,000 arguing for something internally, but then you have to go to the politics of 528 00:25:56,000 --> 00:25:57,000 open source, right? 529 00:25:57,000 --> 00:26:02,000 Which I think a lot of open source is corporations right now, but when you're 530 00:26:02,000 --> 00:26:07,000 actually the person that's inside doing the arguing with that company, you have 531 00:26:07,000 --> 00:26:12,000 to really know what that company's goals and business and leadership principles 532 00:26:12,000 --> 00:26:16,000 and then fighting it in open source is a whole different battle. 533 00:26:16,000 --> 00:26:21,000 And I feel like it's almost like sometimes if people only work in proprietary 534 00:26:21,000 --> 00:26:25,000 software and they only work in open source, don't realize what it's like to 535 00:26:25,000 --> 00:26:30,000 kind of, so can you speak on the whole, like how you do that and like your 536 00:26:30,000 --> 00:26:34,000 experience and kind of making those worlds happen? 537 00:26:34,000 --> 00:26:38,000 I think you, you touched on it earlier, right? 538 00:26:38,000 --> 00:26:41,000 It's like, how to understand their motivations. 539 00:26:41,000 --> 00:26:46,000 You know, corporate, people who are like in that open source or in the 540 00:26:46,000 --> 00:26:53,000 corporation are both humans and corporate employees, right? 541 00:26:53,000 --> 00:26:59,000 And so you've got to figure out how to balance all of that mess and the 542 00:26:59,000 --> 00:27:05,000 extent to which you can help them achieve their corporate goals, but still 543 00:27:05,000 --> 00:27:07,000 enable them to be humans. 544 00:27:07,000 --> 00:27:11,000 I mean, that's, that's the sweet spot you're like looking to do. 545 00:27:11,000 --> 00:27:15,000 I think trying to teach open source to people that are used to corporate 546 00:27:15,000 --> 00:27:20,000 America, you know, and corporate internals and then trying to like show 547 00:27:20,000 --> 00:27:22,000 the business value of open source. 548 00:27:22,000 --> 00:27:26,000 It's like both hard, but like my favorite thing because there's so much 549 00:27:26,000 --> 00:27:32,000 value in open source and trying to get people to invest, but also understand 550 00:27:32,000 --> 00:27:36,000 the mindset of people that work in open source and what open source customers 551 00:27:36,000 --> 00:27:42,000 want because it's very different than proprietary like software, right? 552 00:27:42,000 --> 00:27:46,000 So trying to teach companies that are into proprietary software. 553 00:27:46,000 --> 00:27:49,000 Like I think Google is really interesting because they, they do, they're 554 00:27:49,000 --> 00:27:52,000 very academic in the papers and kind of where that flows. 555 00:27:52,000 --> 00:27:57,000 But if you look at it like Kubernetes is one of the like most thought out, like 556 00:27:57,000 --> 00:28:01,000 well built open source projects, which I think is going to like really 557 00:28:01,000 --> 00:28:03,000 changed how that's done. 558 00:28:03,000 --> 00:28:07,000 Like if you look at how like the Linux foundation in Kubernetes works, like 559 00:28:07,000 --> 00:28:10,000 you can tell that the rest foundation is really taking that into account when 560 00:28:10,000 --> 00:28:11,000 building it. 561 00:28:11,000 --> 00:28:16,000 So I feel like it's going to like change the future of how those like, so 562 00:28:16,000 --> 00:28:20,000 what's it like kind of seeing that from like the start and how corporate and 563 00:28:20,000 --> 00:28:23,000 open source kind of start at this marriage and how we're still trying to 564 00:28:23,000 --> 00:28:24,000 navigate that. 565 00:28:24,000 --> 00:28:31,000 I mean, I think the thing is like it's the extent to which you become part of 566 00:28:31,000 --> 00:28:38,000 the critical chain of a corporate supply chain is where you start to do it. 567 00:28:38,000 --> 00:28:39,000 Right. 568 00:28:39,000 --> 00:28:44,000 So like, you know, to use a terrible example, you know, analogy here, right? 569 00:28:44,000 --> 00:28:45,000 It's like energy. 570 00:28:45,000 --> 00:28:46,000 Okay. 571 00:28:46,000 --> 00:28:48,000 You know, corporations don't care about energy. 572 00:28:48,000 --> 00:28:49,000 They don't care about electricity. 573 00:28:49,000 --> 00:28:50,000 Most of them don't, right? 574 00:28:50,000 --> 00:28:53,000 They're just like, all right, I plug in my laptop and it works and that allows me 575 00:28:53,000 --> 00:28:55,000 to, you know, produce some Excel files. 576 00:28:55,000 --> 00:29:00,000 If you ask them to like give a shit about, you know, what, what the 577 00:29:00,000 --> 00:29:06,000 transformer up the street is or what the, you know, greenness of your, you know, 578 00:29:06,000 --> 00:29:10,000 you know, power coming in is they're just like, Hey, that sounds great, but like 579 00:29:10,000 --> 00:29:14,000 unless we're very forward looking, we're just not going to care. 580 00:29:14,000 --> 00:29:20,000 Now, if you can align it to whatever their business goals are, then you're 581 00:29:20,000 --> 00:29:22,000 going to be off to the races. 582 00:29:22,000 --> 00:29:24,000 And so if you're like, Hey, you know what? 583 00:29:24,000 --> 00:29:30,000 It turns out that putting a 10 kilowatt battery on every retail outlet means 584 00:29:30,000 --> 00:29:35,000 that, you know, they get less brownouts or something like that. 585 00:29:35,000 --> 00:29:39,000 And that improves the ability to sell and great, they're going to do that. 586 00:29:39,000 --> 00:29:45,000 And now it happens to forward your other goals of being green and resilient and, 587 00:29:45,000 --> 00:29:49,000 and, you know, getting rid of fossil fuels and whatever, but like, that's 588 00:29:49,000 --> 00:29:51,000 not what you don't sell it that way. 589 00:29:51,000 --> 00:29:55,000 You sell it as, as part of this other thing that they care about. 590 00:29:55,000 --> 00:29:57,000 Open source is the same way, right? 591 00:29:57,000 --> 00:30:02,000 Like, you know, you're not, it is very unlikely that you're going to be able to 592 00:30:02,000 --> 00:30:05,000 walk into someone and say, Hey, you know what, you should do this because this 593 00:30:05,000 --> 00:30:08,000 is a social good and you need to support the Linux kernel. 594 00:30:08,000 --> 00:30:12,000 You need to support, you know, engine X or whatever because of X. 595 00:30:12,000 --> 00:30:18,000 What you need to say is like, Hey, do you know that like 84% of our stack runs on 596 00:30:18,000 --> 00:30:22,000 this open source project and we have zero upstream developers on it? 597 00:30:22,000 --> 00:30:24,000 Like that seems like a supply chain risk. 598 00:30:24,000 --> 00:30:28,000 We are not going to go and build this better than they do. 599 00:30:28,000 --> 00:30:35,000 Uh, so let's put someone on maintaining it or, or allocate some dollars or whatever 600 00:30:35,000 --> 00:30:37,000 it is, because that's a supply chain component. 601 00:30:37,000 --> 00:30:39,000 And again, that's just one example. 602 00:30:39,000 --> 00:30:40,000 There are many other reasons. 603 00:30:40,000 --> 00:30:45,000 Like I'm so passionate about like trying to explain that to corporate America 604 00:30:45,000 --> 00:30:49,000 because I feel like it's not only way that we're going to move forward with this 605 00:30:49,000 --> 00:30:54,000 kind of like, this new like change in open source, then trying to license 606 00:30:54,000 --> 00:30:57,000 everything and trying to make it, people pay for it in a different way. 607 00:30:57,000 --> 00:31:00,000 I don't think we're going to get that forward movement that they think they 608 00:31:00,000 --> 00:31:01,000 want. 609 00:31:01,000 --> 00:31:06,000 But I think really showing like, uh, corporate, like companies, like 610 00:31:06,000 --> 00:31:10,000 70% of infrastructure I think is built on open source. 611 00:31:10,000 --> 00:31:14,000 And I think really showing people the value of like, Hey, contribute these 612 00:31:14,000 --> 00:31:19,000 things upstream, learn to get into the political, the politics and contributing 613 00:31:19,000 --> 00:31:21,000 and being a part of the community. 614 00:31:21,000 --> 00:31:25,000 And like it for one, it's like everybody is doing more with less right now. 615 00:31:25,000 --> 00:31:26,000 Right. 616 00:31:26,000 --> 00:31:30,000 So the more that you're contributing upstream, everybody's on the same page. 617 00:31:30,000 --> 00:31:32,000 It's easier to maintain software together. 618 00:31:32,000 --> 00:31:34,000 There's so much actual business value. 619 00:31:34,000 --> 00:31:35,000 Absolutely. 620 00:31:35,000 --> 00:31:41,000 And I just feel so passionately about trying to get people on that page 621 00:31:41,000 --> 00:31:45,000 because I think that we can, for one, we can get people paid to be 622 00:31:45,000 --> 00:31:46,000 maintainers, right? 623 00:31:46,000 --> 00:31:47,000 Absolutely. 624 00:31:47,000 --> 00:31:50,000 But we can all, like people are going to be doing this development anyways. 625 00:31:50,000 --> 00:31:53,000 And instead of taking from open source and internally developing it, 626 00:31:53,000 --> 00:31:59,000 contributing it back to open sources, not only going to make you a better 627 00:31:59,000 --> 00:32:04,000 steward of that community, but also why maintain it solo and like 628 00:32:04,000 --> 00:32:06,000 siloed when you can maintain it. 629 00:32:06,000 --> 00:32:09,000 Like look at how log for J was like, so, you know, 630 00:32:09,000 --> 00:32:14,000 I mean, I would say also maintaining things solo is easier than trying to 631 00:32:14,000 --> 00:32:18,000 trying to get the, like you slow down sometimes in what you're doing, 632 00:32:18,000 --> 00:32:22,000 but like trying to get it depends on how big the project is. 633 00:32:22,000 --> 00:32:23,000 Yeah, absolutely. 634 00:32:23,000 --> 00:32:26,000 There's a tipping point because like at some point, like if you're going to 635 00:32:26,000 --> 00:32:29,000 like a legacy software like Java Linux, all these places. 636 00:32:29,000 --> 00:32:31,000 Thank you for saying Java's legacy. 637 00:32:31,000 --> 00:32:34,000 Why are you always trying to hurt my soul? 638 00:32:34,000 --> 00:32:35,000 You said it. 639 00:32:35,000 --> 00:32:37,000 I just said how good you were friends. 640 00:32:37,000 --> 00:32:39,000 Oh yeah, she is. 641 00:32:39,000 --> 00:32:40,000 Terrible. 642 00:32:40,000 --> 00:32:41,000 Terrible. 643 00:32:41,000 --> 00:32:42,000 Come on. 644 00:32:42,000 --> 00:32:43,000 Join the 21st century. 645 00:32:43,000 --> 00:32:44,000 I'm still IT. 646 00:32:44,000 --> 00:32:47,000 My defense, I haven't got to write Java and God knows how long. 647 00:32:47,000 --> 00:32:52,000 So like apparently I'm a Python and everything else head and decrepit C 648 00:32:52,000 --> 00:32:54,000 and every open source. 649 00:32:54,000 --> 00:32:55,000 Yeah. 650 00:32:55,000 --> 00:32:56,000 But like, you know what I mean? 651 00:32:56,000 --> 00:32:58,000 Like if you think about it, they're the amount. 652 00:32:58,000 --> 00:32:59,000 Okay. 653 00:32:59,000 --> 00:33:06,000 In this world as engineers in 2025, everybody is so under like headcount. 654 00:33:06,000 --> 00:33:09,000 We haven't had headcount for years, right? 655 00:33:09,000 --> 00:33:13,000 We are doing more with less to get the extreme knowledge that you need for 656 00:33:13,000 --> 00:33:15,000 some of these open source. 657 00:33:15,000 --> 00:33:17,000 Think about how big Kubernetes is. 658 00:33:17,000 --> 00:33:22,000 Think about how big Linux is, how big Java is to get that type of 659 00:33:22,000 --> 00:33:26,000 of specialized knowledge. 660 00:33:26,000 --> 00:33:28,000 You're not going to just, unless you're buying a 661 00:33:28,000 --> 00:33:33,000 maintainer for a million dollars, like, and you'd have to have multiple of them, 662 00:33:33,000 --> 00:33:34,000 right? 663 00:33:34,000 --> 00:33:39,000 So if you can get, if you are putting that money towards getting developers to 664 00:33:39,000 --> 00:33:45,000 learn how to like become parts of those ecosystems, you now are like a force 665 00:33:45,000 --> 00:33:50,000 multiplying your developers because you're maintaining it and you're 666 00:33:50,000 --> 00:33:51,000 contributing to this ecosystem. 667 00:33:51,000 --> 00:33:54,000 So if you have four corporations that are huge and they have the smartest 668 00:33:54,000 --> 00:33:59,000 minds and they're all now adding to this open source project, really, that's a 669 00:33:59,000 --> 00:34:03,000 force multiplier because when you have a horrible bug like log4j or something, 670 00:34:03,000 --> 00:34:08,000 if you have four smart, huge, like not huge, but smart teams, right, that are 671 00:34:08,000 --> 00:34:12,000 working at the same problem, that's more now secure than it would have been if 672 00:34:12,000 --> 00:34:13,000 you siloed that. 673 00:34:13,000 --> 00:34:14,000 Yeah. 674 00:34:14,000 --> 00:34:15,000 You know what I mean? 675 00:34:15,000 --> 00:34:18,000 That's like the whole reason for the CNCF as a foundation, right? 676 00:34:18,000 --> 00:34:21,000 So that these big corporations can work together in a neutral place because... 677 00:34:21,000 --> 00:34:25,000 But we have to teach people that because a lot of corporate America just thinks 678 00:34:25,000 --> 00:34:29,000 of like, I'm going to go pull this repo down, pretend like I didn't pull it down. 679 00:34:29,000 --> 00:34:34,000 And then, you know, and it's like, bro, it's better for your business. 680 00:34:34,000 --> 00:34:39,000 It is more business value for you to contribute these things upstream. 681 00:34:39,000 --> 00:34:42,000 I know you're going to have a little bit of politics, but hire someone who knows 682 00:34:42,000 --> 00:34:43,000 how to do that. 683 00:34:43,000 --> 00:34:46,000 They are in the market right now and they could use a job, you know what I mean? 684 00:34:46,000 --> 00:34:50,000 And do the work because it really is a force multiplier when you look at it. 685 00:34:50,000 --> 00:34:51,000 You know what I mean? 686 00:34:51,000 --> 00:34:55,000 So David, we're going to skip over everything you did at Microsoft because it doesn't work. 687 00:34:55,000 --> 00:34:57,000 And we're just going to jump right into it. 688 00:34:57,000 --> 00:34:58,000 I want to know all the things. 689 00:34:58,000 --> 00:34:59,000 Tell us all the cool things. 690 00:34:59,000 --> 00:35:01,000 I will talk for as long as you'd like. 691 00:35:01,000 --> 00:35:03,000 I'll come back for part two or whatever. 692 00:35:03,000 --> 00:35:07,000 I'm not that interesting, but I love hearing my own voice because I'm narcissistic or 693 00:35:07,000 --> 00:35:08,000 I don't know. 694 00:35:08,000 --> 00:35:14,000 I'm not going to give you any key that you'd like at any of these places that I haven't 695 00:35:14,000 --> 00:35:15,000 gotten into the politics. 696 00:35:15,000 --> 00:35:16,000 All right. 697 00:35:16,000 --> 00:35:17,000 Cube float. 698 00:35:17,000 --> 00:35:18,000 There's a lot of drama there. 699 00:35:18,000 --> 00:35:24,000 Oh, you are like my favorite type of human because not only do you have all the intellect, 700 00:35:24,000 --> 00:35:27,000 but you actually have a personality, right? 701 00:35:27,000 --> 00:35:32,000 Because like, bro, you all know that sometimes you get the engineers and you're just like, 702 00:35:32,000 --> 00:35:33,000 oh, good Lord. 703 00:35:33,000 --> 00:35:39,000 Pulling any kind of personality and socialness out of them is just like, it's so hard. 704 00:35:39,000 --> 00:35:41,000 That is incredibly kind. 705 00:35:41,000 --> 00:35:42,000 I don't know. 706 00:35:42,000 --> 00:35:45,000 I don't know if I deserve that, but thank you. 707 00:35:45,000 --> 00:35:47,000 So I will skip over all that stuff. 708 00:35:47,000 --> 00:35:48,000 What else would you like to? 709 00:35:48,000 --> 00:35:50,000 So I'm kind of curious. 710 00:35:50,000 --> 00:35:57,000 What point did you decide that data co-located with compute was a problem to solve? 711 00:35:57,000 --> 00:35:59,000 And what are you doing that's new? 712 00:35:59,000 --> 00:36:04,000 Out of all the interesting questions you could ask David, like all the tea, look at his eyes. 713 00:36:04,000 --> 00:36:10,000 Like they're just, it's hiding behind the eyes and it wants to come out. 714 00:36:10,000 --> 00:36:15,000 And the funny part is like, it's funny that we started with Kubernetes because that was it. 715 00:36:15,000 --> 00:36:16,000 Right? 716 00:36:16,000 --> 00:36:20,000 It's one of these things where you're like, when you hear something for like 10 years 717 00:36:20,000 --> 00:36:25,000 and you're like, oh yeah, I actually heard this problem a million years ago. 718 00:36:25,000 --> 00:36:27,000 So a little bit more history for you. 719 00:36:27,000 --> 00:36:32,000 When we were, when I was at Google, like one of the first features that I launched, I wanted 720 00:36:32,000 --> 00:36:36,000 to launch the first PRDs I wrote when I was at Google or on the Kubernetes team was a 721 00:36:36,000 --> 00:36:38,000 product called Uber Netties. 722 00:36:38,000 --> 00:36:43,000 And it was the idea of how do you federate Kubernetes clusters together? 723 00:36:43,000 --> 00:36:44,000 Right? 724 00:36:44,000 --> 00:36:45,000 How do you have an API server? 725 00:36:45,000 --> 00:36:47,000 I'm sad this didn't work out because the name is cool. 726 00:36:47,000 --> 00:36:49,000 I mean, it was like, it was genius. 727 00:36:49,000 --> 00:36:51,000 It was a genius name, Uber Netties, right? 728 00:36:51,000 --> 00:36:57,000 But the problem was is that these things don't work together, right? 729 00:36:57,000 --> 00:36:58,000 Kubernetes is incredible. 730 00:36:58,000 --> 00:37:04,000 It's not going anywhere, but it is built for a world in which nodes have continual connectivity 731 00:37:04,000 --> 00:37:06,000 between itself and the API server. 732 00:37:06,000 --> 00:37:12,000 And if it goes away for even a small amount of time, Kubernetes is really unhappy. 733 00:37:12,000 --> 00:37:20,000 And, and so then you had someone come along like Brian from Chick-fil-A who has that amazing 734 00:37:20,000 --> 00:37:25,000 2017 talk about how they have a Kubernetes cluster in every single Chick-fil-A. 735 00:37:25,000 --> 00:37:27,000 And they still have that today, right? 736 00:37:27,000 --> 00:37:28,000 It's incredible. 737 00:37:28,000 --> 00:37:29,000 Doesn't Walmart have that too? 738 00:37:29,000 --> 00:37:30,000 Yeah. 739 00:37:30,000 --> 00:37:32,000 Almost everybody starts it in a bunch of other people. 740 00:37:32,000 --> 00:37:35,000 And it's all kind of insane, right? 741 00:37:35,000 --> 00:37:38,000 Like you're kind of like, hey, you know, there's something weird about this, right? 742 00:37:38,000 --> 00:37:41,000 Why isn't there a platform that sits over the top? 743 00:37:41,000 --> 00:37:46,000 And, you know, when I was thinking about it, it really was around, you know, data, right? 744 00:37:46,000 --> 00:37:50,000 And it's the, it's the idea that like data is the challenge here. 745 00:37:50,000 --> 00:37:54,000 I like to say like there, there are three things that, that will never change, right? 746 00:37:54,000 --> 00:37:58,000 One, data growing, not really in dispute, data will continue to grow. 747 00:37:58,000 --> 00:38:01,000 But, but the key is that it will grow everywhere, right? 748 00:38:01,000 --> 00:38:06,000 Not in a central data center in Iowa or Oregon or Tokyo, but, you know, 749 00:38:06,000 --> 00:38:10,000 cross zone, cross region, cross cloud on Prem, Edge, IoT, blah, blah, blah, blah, blah. 750 00:38:10,000 --> 00:38:15,000 Like data is coming from all those places and Kubernetes is not in those places, 751 00:38:15,000 --> 00:38:17,000 nor are any of these other giant data warehouses. 752 00:38:17,000 --> 00:38:21,000 They're all sitting inside of your massive data center as they should. 753 00:38:21,000 --> 00:38:24,000 But somehow that data is there and you got to figure out how to get it in. 754 00:38:24,000 --> 00:38:26,000 And it can't just be a log shipper. 755 00:38:26,000 --> 00:38:27,000 Cause guess what? 756 00:38:27,000 --> 00:38:29,000 Look at what happened with log 4j. 757 00:38:29,000 --> 00:38:30,000 Exactly what you're saying earlier. 758 00:38:30,000 --> 00:38:33,000 You ship raw stuff from the edge. 759 00:38:33,000 --> 00:38:35,000 Go your central data warehouse. 760 00:38:35,000 --> 00:38:36,000 Bam. 761 00:38:36,000 --> 00:38:37,000 Security bullet really. 762 00:38:37,000 --> 00:38:38,000 Guaranteed. 763 00:38:38,000 --> 00:38:40,000 And we haven't even gotten into the other things, right? 764 00:38:40,000 --> 00:38:44,000 So that number one, that's a beautiful number two, speed of light, not getting any faster. 765 00:38:44,000 --> 00:38:45,000 Right? 766 00:38:45,000 --> 00:38:46,000 Just it is what it is. 767 00:38:46,000 --> 00:38:52,000 In 10,000 years, it will still be 49 millisecond ping time between Boston and LA. 768 00:38:52,000 --> 00:38:53,000 It just will. 769 00:38:53,000 --> 00:38:58,000 And so if you want to do anything that takes is faster than that, you're going to need a system 770 00:38:58,000 --> 00:39:01,000 that like can take the action remotely. 771 00:39:01,000 --> 00:39:04,000 But on top of that, like networking is just not keeping up. 772 00:39:04,000 --> 00:39:07,000 And it's not because they aren't out there busting their ass. 773 00:39:07,000 --> 00:39:09,000 It's cause data is growing even faster. 774 00:39:09,000 --> 00:39:11,000 And then the captain gets you every time. 775 00:39:11,000 --> 00:39:12,000 Sorry, go ahead. 776 00:39:12,000 --> 00:39:14,000 The captain gets you every time. 777 00:39:14,000 --> 00:39:15,000 Captain gets you every time. 778 00:39:15,000 --> 00:39:16,000 Thank you very much. 779 00:39:16,000 --> 00:39:20,000 So then the third is around security and regulations and those are growing, right? 780 00:39:20,000 --> 00:39:22,000 So GPR and HIPAA and things like that. 781 00:39:22,000 --> 00:39:27,000 You, you tend to put yourself at risk the moment you move a bit off wherever you generated it. 782 00:39:27,000 --> 00:39:28,000 Right? 783 00:39:28,000 --> 00:39:32,000 Now you, you're a wonderful segue because that's exactly it. 784 00:39:32,000 --> 00:39:37,000 Every major platform today is built around the C and the A of cap theorem, right? 785 00:39:37,000 --> 00:39:42,000 Consistency and consensus, whatever you want to say and availability. 786 00:39:42,000 --> 00:39:43,000 Right. 787 00:39:43,000 --> 00:39:44,000 That's amazing. 788 00:39:44,000 --> 00:39:46,000 Something should be built around the other half of it. 789 00:39:46,000 --> 00:39:49,000 Availability and support for network partitioning. 790 00:39:49,000 --> 00:39:52,000 And that's because of all those things I just said. 791 00:39:52,000 --> 00:39:56,000 And, and, you know, when you go and look at the Chick-fil-A example or the Home Depot or 792 00:39:56,000 --> 00:40:00,000 the millions of other folks out there who have these multiple deployments, retail outlets, 793 00:40:00,000 --> 00:40:04,000 manufacturing, security, et cetera, et cetera, this is the problem. 794 00:40:04,000 --> 00:40:05,000 Right? 795 00:40:05,000 --> 00:40:06,000 Because that data is over there. 796 00:40:06,000 --> 00:40:09,000 I want to do things declaratively. 797 00:40:09,000 --> 00:40:14,000 I want to take action over my data before I move it, but I still want it to move. 798 00:40:14,000 --> 00:40:21,000 So how do I do that when the network could go away for a minute, an hour, a day, because 799 00:40:21,000 --> 00:40:25,000 I'm going to put a backhoe through something who knows what, I want those systems and those 800 00:40:25,000 --> 00:40:26,000 systems to keep working. 801 00:40:26,000 --> 00:40:30,000 But when they reconnect, I want someone to like eventually bring this to consistency. 802 00:40:30,000 --> 00:40:31,000 And that's what we provided. 803 00:40:31,000 --> 00:40:38,000 I also think that's even more value because for one, the more you spread your data, the 804 00:40:38,000 --> 00:40:40,000 more you're spreading your attack surface. 805 00:40:40,000 --> 00:40:41,000 You know what I mean? 806 00:40:41,000 --> 00:40:42,000 Absolutely. 807 00:40:42,000 --> 00:40:46,000 And then I think that networking and security are both things that developers aren't always 808 00:40:46,000 --> 00:40:50,000 educated on and they're very like in depth areas, right? 809 00:40:50,000 --> 00:40:56,000 So the more that you can do in those areas to set them up for success, the better because 810 00:40:56,000 --> 00:41:01,000 and now that we're going to have more agents just, you know. 811 00:41:01,000 --> 00:41:02,000 Doing whatever agents do? 812 00:41:02,000 --> 00:41:03,000 Yeah. 813 00:41:03,000 --> 00:41:08,000 Well, the thing is, is that we didn't, we are struggling to teach developer security, 814 00:41:08,000 --> 00:41:09,000 right? 815 00:41:09,000 --> 00:41:13,000 So now if they don't understand all of the security and then they are giving permissions 816 00:41:13,000 --> 00:41:19,000 to agents that they already don't understand, that it's just a recipe for like the more that 817 00:41:19,000 --> 00:41:23,000 you're scaling this, yeah, you're scaling it, but you're scaling disaster in some ways. 818 00:41:23,000 --> 00:41:24,000 You know what I mean? 819 00:41:24,000 --> 00:41:30,000 So it's like, I think that this is going to, that has like so much value just on so many. 820 00:41:30,000 --> 00:41:32,000 I mean, I'm sorry, please. 821 00:41:32,000 --> 00:41:36,000 David, what you just described is basically just the, all the benefits of edge computing, 822 00:41:36,000 --> 00:41:37,000 right? 823 00:41:37,000 --> 00:41:38,000 Yeah. 824 00:41:38,000 --> 00:41:41,000 Like we can, we can, how do we get more compute at the edge? 825 00:41:41,000 --> 00:41:42,000 How do we make it more powerful? 826 00:41:42,000 --> 00:41:45,000 How do we make it easier to manage from a central place? 827 00:41:45,000 --> 00:41:48,000 So what is it that you're doing that's different than like, I don't know what to say, traditional 828 00:41:48,000 --> 00:41:53,000 edge computing, but like the, the idea is behind edge computing is just like, put compute 829 00:41:53,000 --> 00:41:58,000 closer milliseconds, have it better, have better storage, whatever, to where the data's 830 00:41:58,000 --> 00:41:59,000 being created. 831 00:41:59,000 --> 00:42:00,000 Yeah. 832 00:42:00,000 --> 00:42:02,000 So, you know, that's, we don't provide the edge compute. 833 00:42:02,000 --> 00:42:07,000 That is my, what do you call it here, by the way, my visual aid, my Raspberry Pi, right? 834 00:42:07,000 --> 00:42:08,000 That we run great on. 835 00:42:08,000 --> 00:42:11,000 Well, I don't know why you're coming apart here. 836 00:42:11,000 --> 00:42:12,000 That's weird. 837 00:42:12,000 --> 00:42:14,000 It's a Raspberry Pi. 838 00:42:14,000 --> 00:42:15,000 Yeah, exactly. 839 00:42:15,000 --> 00:42:17,000 But no, you're exactly right. 840 00:42:17,000 --> 00:42:21,000 And what we provide is the distributed consensus layer. 841 00:42:21,000 --> 00:42:28,000 And what that means is that like, turns out that that thing that you put on the edge is 842 00:42:28,000 --> 00:42:31,000 wonderful, but how do I know what is there? 843 00:42:31,000 --> 00:42:33,000 How do I know it stays running? 844 00:42:33,000 --> 00:42:35,000 How do I change the configuration? 845 00:42:35,000 --> 00:42:39,000 How do I do all this in a network and disconnected friendly way? 846 00:42:39,000 --> 00:42:41,000 That is the challenge of distributed computing. 847 00:42:41,000 --> 00:42:44,000 That is 40 years of academic research. 848 00:42:44,000 --> 00:42:51,000 And what we give you is a Kubernetes or container or orchestrated like experience, but one that 849 00:42:51,000 --> 00:42:55,000 is resilient to eventual consistency. 850 00:42:55,000 --> 00:42:59,000 And so if something happened on our side while you were disconnected, if something happened 851 00:42:59,000 --> 00:43:03,000 on that side while you were disconnected, you know, we, we give you, you know, the one 852 00:43:03,000 --> 00:43:08,000 of the big things that we keep seeing is we call it intelligent data pipelines, right? 853 00:43:08,000 --> 00:43:15,000 Where you can much more intelligently do some, not all some of your process before you move 854 00:43:15,000 --> 00:43:16,000 it. 855 00:43:16,000 --> 00:43:21,000 So a trivial example I talk about all the time is a lot of times factory owners, for example, 856 00:43:21,000 --> 00:43:24,000 will have all these sensors all over the place and they're great. 857 00:43:24,000 --> 00:43:30,000 But the sensors usually come straight from a warehouse in Shenzhen and get installed immediately, 858 00:43:30,000 --> 00:43:31,000 right? 859 00:43:31,000 --> 00:43:36,000 And they have no information, you know, no GPS, no, no schema. 860 00:43:36,000 --> 00:43:41,000 A lot of times they'll just output like a raw text string and they'll, you, you jam that 861 00:43:41,000 --> 00:43:43,000 into a backend, right? 862 00:43:43,000 --> 00:43:48,000 Well, geez, the moment you do that, you now have to reverse engineer all of the information 863 00:43:48,000 --> 00:43:50,000 that you lost along the way, right? 864 00:43:50,000 --> 00:43:52,000 What kind of machine was it running on? 865 00:43:52,000 --> 00:43:53,000 What was the firmware? 866 00:43:53,000 --> 00:43:54,000 What was the disk? 867 00:43:54,000 --> 00:43:57,000 What was, you know, what, where in the room was it? 868 00:43:57,000 --> 00:43:59,000 You know, so on and so forth. 869 00:43:59,000 --> 00:44:02,000 And so if you took something, right, you took one of these Raspberry Pis, you stuck it inside 870 00:44:02,000 --> 00:44:08,000 that factory and you said, Hey, you know what, before you send this raw into the back end, 871 00:44:08,000 --> 00:44:14,000 send it to this local machine or like local being like within the region and attach some 872 00:44:14,000 --> 00:44:19,000 metadata to it and do some initial data model enforcement and do some schematization. 873 00:44:19,000 --> 00:44:23,000 So change it from a flat text string into JSON or, you know, structured logging or whatever 874 00:44:23,000 --> 00:44:26,000 and take your pick, but still go to the backend. 875 00:44:26,000 --> 00:44:31,000 All you're doing is you're moving, like I say, you know, you may have a 17 step ETL pipeline 876 00:44:31,000 --> 00:44:34,000 and all your enterprise customers are like, Yeah, right. 877 00:44:34,000 --> 00:44:35,000 Add a zero buddy, right? 878 00:44:35,000 --> 00:44:42,000 But like, you know, take, you take those first, I don't know, four or five steps of your pipeline, 879 00:44:42,000 --> 00:44:48,000 data and model enforcement, schematization, adding metadata, adding providence, adding location, 880 00:44:48,000 --> 00:44:53,000 filtering, aggregation, just do some of those things before you move it. 881 00:44:53,000 --> 00:44:59,000 And magically, all those things downstream become better, faster, smarter. 882 00:44:59,000 --> 00:45:01,000 You can multi home stuff. 883 00:45:01,000 --> 00:45:05,000 So a lot of times, for example, you know, you might have these all these sensors pushing 884 00:45:05,000 --> 00:45:11,000 into the back end, no matter how fast your pipeline is, it might take, you know, five, 885 00:45:11,000 --> 00:45:15,000 10 minutes to ultimately go all the way through the pipeline, very, very common, not because 886 00:45:15,000 --> 00:45:19,000 the pipeline isn't like busting its ass, but because it needs to aggregate from all these 887 00:45:19,000 --> 00:45:21,000 sources before it does anything. 888 00:45:21,000 --> 00:45:27,000 Imagine that you have a factory floor where four different sensors are simultaneously saying, 889 00:45:27,000 --> 00:45:31,000 Hey, you know what, we're above our temperature threshold, right? 890 00:45:31,000 --> 00:45:35,000 Um, do you want to wait 10 minutes to know that? 891 00:45:35,000 --> 00:45:39,000 Wouldn't it be nice if you could trigger an event from that location? 892 00:45:39,000 --> 00:45:40,000 We can do that for you. 893 00:45:40,000 --> 00:45:44,000 And again, it's just by taking some of this and moving that out there and saying, Hey, 894 00:45:44,000 --> 00:45:48,000 you know what, we're still going to send all the raw data back, but simultaneously, we're 895 00:45:48,000 --> 00:45:52,000 also going to track her, you know, trigger pager duty or whatever, take your pick by 896 00:45:52,000 --> 00:45:56,000 your hydrant, you know, any kind of other endpoint sqs, we're going to trigger that from this 897 00:45:56,000 --> 00:45:57,000 location. 898 00:45:57,000 --> 00:45:59,000 We can help you do that too. 899 00:45:59,000 --> 00:46:02,000 Again, we're not like stopping the rest of it. 900 00:46:02,000 --> 00:46:05,000 We're not even, you know, we can save you money if you want, we can reduce data if you 901 00:46:05,000 --> 00:46:10,000 want, we can do other things, but even just putting us there helps you be intelligent 902 00:46:10,000 --> 00:46:12,000 in a more distributed way. 903 00:46:12,000 --> 00:46:18,000 And to your point earlier about what edge compute, uh, doesn't do here, it's not that 904 00:46:18,000 --> 00:46:20,000 edge compute isn't critical to this. 905 00:46:20,000 --> 00:46:23,000 We can't operate without some form of edge compute. 906 00:46:23,000 --> 00:46:27,000 It's really about the orchestration of that job. 907 00:46:27,000 --> 00:46:32,000 So again, let's say you're like, Hey, you know what, I want to change this from being 908 00:46:32,000 --> 00:46:37,000 for, you know, uh, sensors going bad to five sensors going bad. 909 00:46:37,000 --> 00:46:39,000 Imagine what that's involved today. 910 00:46:39,000 --> 00:46:40,000 Right. 911 00:46:40,000 --> 00:46:41,000 How do you actually push that down? 912 00:46:41,000 --> 00:46:44,000 How do you know what version of this job is running? 913 00:46:44,000 --> 00:46:48,000 How do you know what the last time you saw this error is, whatever it might be. 914 00:46:48,000 --> 00:46:51,000 Um, all of that is hard to get down to these places. 915 00:46:51,000 --> 00:46:56,000 And we give you a clean API that is resilient to these networks that gives you, you know, 916 00:46:56,000 --> 00:47:01,000 full and rich intelligent pipelines at the edge and help you push that stuff through. 917 00:47:01,000 --> 00:47:06,000 Uh, and by the way, when people talk about the next trillion devices that are out there, 918 00:47:06,000 --> 00:47:09,000 all of which doing inference, either way, we do that too. 919 00:47:09,000 --> 00:47:10,000 Right. 920 00:47:10,000 --> 00:47:16,000 Like, because at the end of the day, you know, inference is just remote data being processed 921 00:47:16,000 --> 00:47:19,000 and, and we hope you, you, you do that as well. 922 00:47:19,000 --> 00:47:25,000 I think this is going to be really cool too, because now with AI and all the different, 923 00:47:25,000 --> 00:47:27,000 we want to get data from everything. 924 00:47:27,000 --> 00:47:32,000 It's the promise of the future, but to be able to analyze all that people don't understand 925 00:47:32,000 --> 00:47:34,000 that distributed systems. 926 00:47:34,000 --> 00:47:39,000 Like you either, like, especially when it comes to either like, so their IOT is one aspect, 927 00:47:39,000 --> 00:47:42,000 but then you have the retail aspect where you don't want to charge people's cards more 928 00:47:42,000 --> 00:47:43,000 than once. 929 00:47:43,000 --> 00:47:47,000 So you have to have that eventually consistent and really worry about how you're, so like 930 00:47:47,000 --> 00:47:51,000 I went and made an order from happy lemon the other day and I was on my way to a tattoo 931 00:47:51,000 --> 00:47:55,000 appointment and I was trying to speed this up because I'm always late just in those, 932 00:47:55,000 --> 00:48:00,000 but, but I ended up getting there and I'm like, why isn't my order like ready? 933 00:48:00,000 --> 00:48:05,000 So I go and buy it and then all of a sudden my order comes through and I think it was 934 00:48:05,000 --> 00:48:08,000 because they had a like connection issue and I'm just sitting there like something's going 935 00:48:08,000 --> 00:48:14,000 on in your back end that you have an eventually consistent like, like database, but I'm sitting 936 00:48:14,000 --> 00:48:18,000 there and happy lemon, like trying to figure out what's wrong with their back end. 937 00:48:18,000 --> 00:48:22,000 This is, this is why we're friends because I often will like something. 938 00:48:22,000 --> 00:48:24,000 I was diagnosing their problem. 939 00:48:24,000 --> 00:48:26,000 Is this a backup job? 940 00:48:26,000 --> 00:48:28,000 Is this, do you need more workers? 941 00:48:28,000 --> 00:48:30,000 Is this of Kafka something? 942 00:48:30,000 --> 00:48:35,000 Yes, but see the thing is, is that like what you're, okay, so people don't realize that 943 00:48:35,000 --> 00:48:40,000 when you're streaming that amount of data, like David's talking about, you get now this 944 00:48:40,000 --> 00:48:45,000 bottleneck when all of it starts to come back in that like it's so bad. 945 00:48:45,000 --> 00:48:50,000 So the fact that you're doing, like this is another, it's almost like the concept of breaking 946 00:48:50,000 --> 00:48:53,000 different, just kind of compartmentalizing it. 947 00:48:53,000 --> 00:48:55,000 What's the word I'm looking for? 948 00:48:55,000 --> 00:48:59,000 Like, wouldn't you break up things when people want to do like service oriented or kind of 949 00:48:59,000 --> 00:49:04,000 like, so you're not monolith, but you're kind of in different services. 950 00:49:04,000 --> 00:49:05,000 Yes. 951 00:49:05,000 --> 00:49:07,000 Not, but what's the other way to do it? 952 00:49:07,000 --> 00:49:08,000 Not microservices. 953 00:49:08,000 --> 00:49:10,000 So a service oriented architecture. 954 00:49:10,000 --> 00:49:15,000 Sort of, but like basically you're keeping it so that way when one area breaks, it doesn't 955 00:49:15,000 --> 00:49:17,000 completely break everything else. 956 00:49:17,000 --> 00:49:21,000 But like if you, so if you're processing this, like for one people are bad at processing 957 00:49:21,000 --> 00:49:25,000 and schemas and all of that stuff anyways, but if you're doing some of the work in these 958 00:49:25,000 --> 00:49:30,000 individual places that when you get backed up and then you send it all to the same pipeline, 959 00:49:30,000 --> 00:49:34,000 you're now not creating the same stress and bottleneck on your pipelines. 960 00:49:34,000 --> 00:49:38,000 And because we're going to get more and more and more data and people are going to want 961 00:49:38,000 --> 00:49:43,000 to like do all these crazy things with it, like that's great, but it's going to cause 962 00:49:43,000 --> 00:49:44,000 more and more stress. 963 00:49:44,000 --> 00:49:49,000 Like we keep making, like at this point, like Jenkins is not made for the amount of crazy 964 00:49:49,000 --> 00:49:51,000 stuff that like, cause think about it. 965 00:49:51,000 --> 00:49:53,000 It wasn't made for anything. 966 00:49:53,000 --> 00:49:56,000 It wasn't, but it was originally made for Java a million years ago. 967 00:49:56,000 --> 00:50:01,000 And now people are trying to use it in this new modern way or just pipeline services that 968 00:50:01,000 --> 00:50:05,000 were not made up for this amount of heavy data streaming that we're doing, you know, so 969 00:50:05,000 --> 00:50:06,000 we're 970 00:50:06,000 --> 00:50:08,000 David, what do you think the next bottleneck is? 971 00:50:08,000 --> 00:50:09,000 Right? 972 00:50:09,000 --> 00:50:13,000 Cause I do think that data is the obvious one in connectivity to, especially if you're looking 973 00:50:13,000 --> 00:50:14,000 at edge, right? 974 00:50:14,000 --> 00:50:18,000 You're like, Oh, there's some capacity limitation in an edge environment, whether that's compute, 975 00:50:18,000 --> 00:50:19,000 whether that's data. 976 00:50:19,000 --> 00:50:23,000 That's kind of like what we were talking about with LA too, though, because about like how 977 00:50:23,000 --> 00:50:24,000 hardware is moving faster. 978 00:50:24,000 --> 00:50:30,000 Like I don't know if the different parts of computing like are in sync with how some 979 00:50:30,000 --> 00:50:33,000 are moving so fast, you know, it's interesting to see. 980 00:50:33,000 --> 00:50:34,000 Yeah. 981 00:50:34,000 --> 00:50:35,000 Which one do you think is going to outpace the other thing? 982 00:50:35,000 --> 00:50:38,000 Cause like you said, never, uh, let's speed of light is never going to get faster, but 983 00:50:38,000 --> 00:50:40,000 the pipes are getting a little bigger. 984 00:50:40,000 --> 00:50:44,000 But is that a, do we just need better compute to compress that? 985 00:50:44,000 --> 00:50:50,000 No, I, I, I, again, my personal opinion is, uh, first, you know, it's, it's not about 986 00:50:50,000 --> 00:50:51,000 the pipes. 987 00:50:51,000 --> 00:50:52,000 It's about the latency. 988 00:50:52,000 --> 00:50:53,000 Right. 989 00:50:53,000 --> 00:50:54,000 And that will never change. 990 00:50:54,000 --> 00:50:58,000 No amount of compute will, will ever improve latency cause speed of light can't get. 991 00:50:58,000 --> 00:51:01,000 They'll lie to you and say, well, though, like they're just like, and then if you do 992 00:51:01,000 --> 00:51:05,000 this, and I'm just like, no, that's how that works. 993 00:51:05,000 --> 00:51:11,000 But, but, but that said, you know, the, the, the fact is, is that the compute, one of the 994 00:51:11,000 --> 00:51:16,000 things that I talked to a lot of people about is like, the compute is also unused. 995 00:51:16,000 --> 00:51:22,000 Like it's just, you know, a lot of this stuff, again, this Raspberry Pi can, can do a ridiculous 996 00:51:22,000 --> 00:51:27,500 amount of throughput, like ridiculous, um, uh, far more than people would think. 997 00:51:27,500 --> 00:51:30,500 And you're like, well, shit, you know, I'm already have it out there. 998 00:51:30,500 --> 00:51:31,500 It's already doing this other stuff. 999 00:51:31,500 --> 00:51:33,500 I might as well. 1000 00:51:33,500 --> 00:51:36,500 Um, and what I would say is I will contest your point. 1001 00:51:36,500 --> 00:51:40,500 Like, yeah, pipes are getting better, but they, they're getting bigger even faster. 1002 00:51:40,500 --> 00:51:41,500 That's all I'm saying. 1003 00:51:41,500 --> 00:51:45,500 Like, and so it really is like the amount of, and we're going to be making just useless 1004 00:51:45,500 --> 00:51:50,500 data because people like, I think that they just really like data is the most valuable, 1005 00:51:50,500 --> 00:51:56,500 you know, commodity, but also because we have all of these sensors and we have all of this 1006 00:51:56,500 --> 00:51:58,500 AI trying to make all of this data. 1007 00:51:58,500 --> 00:52:03,500 I think we're going to end up just, and we have, we're going to have so much compute 1008 00:52:03,500 --> 00:52:08,500 power with all these data centers that like, so people are just almost, I don't think they 1009 00:52:08,500 --> 00:52:11,500 realize how much infrastructure and data are growing. 1010 00:52:11,500 --> 00:52:12,500 Yeah. 1011 00:52:12,500 --> 00:52:13,500 Totally agree. 1012 00:52:13,500 --> 00:52:14,500 Totally agree. 1013 00:52:14,500 --> 00:52:20,500 So all, all I want to say though is, is, uh, Justin is, uh, um, you know, you are, it 1014 00:52:20,500 --> 00:52:26,500 will be a challenge in a year or two years, five years time to have anything, even this 1015 00:52:26,500 --> 00:52:30,500 Raspberry Pi, not have acceleration on it, right? 1016 00:52:30,500 --> 00:52:33,500 Like just at current power, right? 1017 00:52:33,500 --> 00:52:37,500 It will still have an acceleration and maybe that's because of the system on a chip or whatever 1018 00:52:37,500 --> 00:52:38,500 it might be. 1019 00:52:38,500 --> 00:52:44,500 But then you're like, it's not that I don't want to use my Blackwell plus plus, you know, 1020 00:52:44,500 --> 00:52:48,500 whatever as a central thing, but why don't I have it work on the more interesting problems 1021 00:52:48,500 --> 00:52:53,500 and have whatever a GPU that's sitting on this thing do some of the work? 1022 00:52:53,500 --> 00:53:01,500 Like I, I gave a demo, um, uh, earlier this week that showed, uh, onboarding from 20 nodes, 1023 00:53:01,500 --> 00:53:08,500 uh, on three different clouds using Expanso, um, uh, to all of the, to, to, uh, BigQuery, 1024 00:53:08,500 --> 00:53:09,500 for example, right? 1025 00:53:09,500 --> 00:53:10,500 As a backend. 1026 00:53:10,500 --> 00:53:16,500 And, uh, I was able to push, uh, 27 million rows in under 40 seconds, right? 1027 00:53:16,500 --> 00:53:17,500 From all of these things. 1028 00:53:17,500 --> 00:53:21,500 And that's not because, you know, there was something magical happening here. 1029 00:53:21,500 --> 00:53:27,500 It was because I was adding together the aggregate of all that bandwidth at the same time. 1030 00:53:27,500 --> 00:53:32,500 And there's just, there's no way to like make that any faster. 1031 00:53:32,500 --> 00:53:38,500 Like no amount of network will ever achieve what you can do with the same network multiplied 1032 00:53:38,500 --> 00:53:41,500 times, you know, the number of nodes I have, right? 1033 00:53:41,500 --> 00:53:42,500 It's just the way that works. 1034 00:53:42,500 --> 00:53:44,500 So like I would argue, yeah. 1035 00:53:44,500 --> 00:53:49,500 I was just like, that's exactly the age old problem we've had in infrastructure where 1036 00:53:49,500 --> 00:53:54,500 you can vertically scale one machine and say, I need, I need this big Oracle database on 1037 00:53:54,500 --> 00:53:59,500 that one machine and it needs a terabyte of memory, or you can go with, give me five different 1038 00:53:59,500 --> 00:54:02,500 racks and I'm going to spread that amount of memory across them. 1039 00:54:02,500 --> 00:54:07,500 And there is overhead for the coordination, but we've found those just better performance 1040 00:54:07,500 --> 00:54:11,500 in general for resiliency and for all these other things. 1041 00:54:11,500 --> 00:54:17,500 So being able to spread out that and aggregate it is obviously like we can't make single 1042 00:54:17,500 --> 00:54:18,500 machines big enough. 1043 00:54:18,500 --> 00:54:22,500 We can't make single pipes big enough that are, that are economic, right? 1044 00:54:22,500 --> 00:54:24,500 Cause we can do like Japan just broke a record. 1045 00:54:24,500 --> 00:54:27,500 They're doing like a, like you could download all of Netflix in under a minute. 1046 00:54:27,500 --> 00:54:28,500 Right. 1047 00:54:28,500 --> 00:54:31,500 It's like, yeah, it's like petabyte of throughput, but I can't buy that and no one's going to, 1048 00:54:31,500 --> 00:54:32,500 no one's going to buy that one. 1049 00:54:32,500 --> 00:54:35,500 They're like, actually, I'm just going to go spend it on 10 gig networks everywhere 1050 00:54:35,500 --> 00:54:37,500 rather than one giant pipe. 1051 00:54:37,500 --> 00:54:38,500 I think this is interesting. 1052 00:54:38,500 --> 00:54:42,500 I think it's interesting if you think about like this conversation we're having with 1053 00:54:42,500 --> 00:54:47,500 LA about how like much like hardware has advanced, right? 1054 00:54:47,500 --> 00:54:50,500 And now everybody really wants to run their own stuff. 1055 00:54:50,500 --> 00:54:55,500 But I think that we were almost like optimizing like these, the different hardware because 1056 00:54:55,500 --> 00:54:57,500 for a long time everyone was using cloud. 1057 00:54:57,500 --> 00:55:02,500 So they were building it for these cloud companies and then for AI making this really advanced 1058 00:55:02,500 --> 00:55:06,500 hardware that people weren't playing with and experimenting as much because they were using 1059 00:55:06,500 --> 00:55:07,500 it in the cloud. 1060 00:55:07,500 --> 00:55:11,500 And now that I think people are getting almost like reacquaint it with hardware and what 1061 00:55:11,500 --> 00:55:13,500 hardware can do that. 1062 00:55:13,500 --> 00:55:17,500 It's really interesting to see what people are going to push the limits with this hardware 1063 00:55:17,500 --> 00:55:22,500 because it's so optimized for AI and cloud and all these different places that it was 1064 00:55:22,500 --> 00:55:23,500 being used in. 1065 00:55:23,500 --> 00:55:29,500 And now developers and startups are getting that actual hardware back in their hands. 1066 00:55:29,500 --> 00:55:30,500 Yeah. 1067 00:55:30,500 --> 00:55:31,500 And I think it's going to be interesting. 1068 00:55:31,500 --> 00:55:35,500 Like you said, like what Raspberry Pi can do, you know what I mean? 1069 00:55:35,500 --> 00:55:36,500 Yeah. 1070 00:55:36,500 --> 00:55:37,500 Absolutely. 1071 00:55:37,500 --> 00:55:38,500 Absolutely. 1072 00:55:38,500 --> 00:55:41,500 And there's a threshold of when that hardware advancement becomes just universally available. 1073 00:55:41,500 --> 00:55:43,500 That's what I'm saying because there's so much of it. 1074 00:55:43,500 --> 00:55:48,500 This is so cheap that it's economical for me just to like redevelop something so that 1075 00:55:48,500 --> 00:55:49,500 it uses that. 1076 00:55:49,500 --> 00:55:53,500 I remember at Disney animation at one point we were switching out hardware because we switched 1077 00:55:53,500 --> 00:55:57,500 out racks and racks of servers because the new chip had this. 1078 00:55:57,500 --> 00:56:01,500 I forget what the math was, but it was some function that we do a lot in rendering on 1079 00:56:01,500 --> 00:56:02,500 the CPU. 1080 00:56:02,500 --> 00:56:08,500 And we're like, actually, we will just render this movie like 10% faster by swapping out 1081 00:56:08,500 --> 00:56:09,500 all these CPUs. 1082 00:56:09,500 --> 00:56:13,000 And it's going to cost us millions of dollars to swap out all these CPUs, but we're going 1083 00:56:13,000 --> 00:56:15,500 to get the movie done in time versus delaying it. 1084 00:56:15,500 --> 00:56:17,000 And that's absolutely worth it or whatever. 1085 00:56:17,000 --> 00:56:20,500 It's just like, yeah, no, we're going to do that part in hardware and no longer do it 1086 00:56:20,500 --> 00:56:21,500 in software. 1087 00:56:21,500 --> 00:56:22,500 I think we're going to see a lot of that. 1088 00:56:22,500 --> 00:56:28,000 Like if you look at how Apple is processing AI inside of the iPhones just because that 1089 00:56:28,000 --> 00:56:31,500 way it's technically safer and it's more secure and they could promise more. 1090 00:56:31,500 --> 00:56:32,500 But Siri's still the dumbest one. 1091 00:56:32,500 --> 00:56:33,500 I don't know. 1092 00:56:33,500 --> 00:56:39,500 But still that's, but that like, if somebody told you, if someone told you 10 years ago 1093 00:56:39,500 --> 00:56:44,000 that you are not only going to be able to run an AI model and have it processed in a chip 1094 00:56:44,000 --> 00:56:45,500 that's in your pocket, that's still amazing. 1095 00:56:45,500 --> 00:56:46,500 I don't care what you say. 1096 00:56:46,500 --> 00:56:48,500 I mean, there's, oh, sorry, please. 1097 00:56:48,500 --> 00:56:50,500 But just, you know what I mean? 1098 00:56:50,500 --> 00:56:52,500 Like hardware is changing so much. 1099 00:56:52,500 --> 00:56:56,500 And like there's whole developers that went half a whole career or like young developers, 1100 00:56:56,500 --> 00:57:00,500 like maybe that are mid developers now that have never got to play with that kind of hardware. 1101 00:57:00,500 --> 00:57:07,500 And we're making it at such a fast speed and they're making these chips for AI that so much 1102 00:57:07,500 --> 00:57:12,500 hardware is going to now just have like either it's going to be overproduced at some point 1103 00:57:12,500 --> 00:57:16,500 or they're going to get rid of all the old hardware and now it's going to get so cheap. 1104 00:57:16,500 --> 00:57:21,500 I keep saying, I'm so excited for three to four years from now with all of these GPUs 1105 00:57:21,500 --> 00:57:22,500 hit secondhand market. 1106 00:57:22,500 --> 00:57:23,500 Yeah. 1107 00:57:23,500 --> 00:57:28,500 Everyone has super powerful broad today and video chips like actually I can run so much 1108 00:57:28,500 --> 00:57:29,500 stuff. 1109 00:57:29,500 --> 00:57:34,500 Like the, just the way that the tech market's been really weird and all of that. 1110 00:57:34,500 --> 00:57:40,500 Like I just wonder what cool advancements are going to come out of that, you know, moment. 1111 00:57:40,500 --> 00:57:41,500 Yeah. 1112 00:57:41,500 --> 00:57:42,500 You know, it's, it's, it's fascinating. 1113 00:57:42,500 --> 00:57:44,500 So there's an amazing story. 1114 00:57:44,500 --> 00:57:49,500 I remember way back when the plenty of fish guy. 1115 00:57:49,500 --> 00:57:55,500 Did anyone remember plenty of fish fish was a dating site and it was like a competition 1116 00:57:55,500 --> 00:57:59,500 to okay, Cupid and all these various things out there. 1117 00:57:59,500 --> 00:58:03,500 And it was really, really popular and very, very funny. 1118 00:58:03,500 --> 00:58:07,500 Like at the time this is way before containers and things like that. 1119 00:58:07,500 --> 00:58:13,500 He had built in the entire thing to be a vertical massive machine. 1120 00:58:13,500 --> 00:58:17,500 And it was the like he, I think what I was at Microsoft the first time and I think he 1121 00:58:17,500 --> 00:58:24,500 had the largest like single instance thing that we knew about it was like seriously was 1122 00:58:24,500 --> 00:58:26,500 like 128 CPUs or something like that. 1123 00:58:26,500 --> 00:58:28,500 It was like just an absurd thing. 1124 00:58:28,500 --> 00:58:31,500 And he's, and everyone's like, why do you do like build this out? 1125 00:58:31,500 --> 00:58:33,500 And he's like, cause I don't need to and it's easier. 1126 00:58:33,500 --> 00:58:42,500 And like you go to like Monzo's thing that a graph of microservices that got like then 1127 00:58:42,500 --> 00:58:44,500 everyone got up in arms about a few years ago. 1128 00:58:44,500 --> 00:58:46,500 Oh my God, why are you doing all these microservices? 1129 00:58:46,500 --> 00:58:49,500 Like, and, and they're not wrong. 1130 00:58:49,500 --> 00:58:55,500 You know, like you don't get complexity for like that's a cost. 1131 00:58:55,500 --> 00:58:57,500 But the cost may be worth the benefit. 1132 00:58:57,500 --> 00:58:58,500 Right. 1133 00:58:58,500 --> 00:59:01,500 And so like exactly like you were just saying on them, like we're going to have all this 1134 00:59:01,500 --> 00:59:06,500 compute out there and we're going to have this, but we need to have lower like it needs 1135 00:59:06,500 --> 00:59:11,500 to be sub linear cost in scaling that every additional machine should not cost the same. 1136 00:59:11,500 --> 00:59:14,500 It should be very, very small increment and benefit. 1137 00:59:14,500 --> 00:59:18,500 And, and you know, I'm trying to participate in that by like offering this platform that 1138 00:59:18,500 --> 00:59:21,500 helps with that, but like Kubernetes certainly did that. 1139 00:59:21,500 --> 00:59:25,500 And, and, you know, Docker with its portability certainly did that and all these kind of things. 1140 00:59:25,500 --> 00:59:26,500 Right. 1141 00:59:26,500 --> 00:59:29,500 There's just a bunch of different ways to go out and tackle this thing. 1142 00:59:29,500 --> 00:59:35,500 And so what I would say is that, you know, you're exactly right. 1143 00:59:35,500 --> 00:59:42,500 We need to enable this, but we need to enable it smartly because it is really, really, really 1144 00:59:42,500 --> 00:59:46,500 easy to go and get a massive machine, two terabyte machine and just stick everything 1145 00:59:46,500 --> 00:59:47,500 on there. 1146 00:59:47,500 --> 00:59:49,500 This piece of cake single machine. 1147 00:59:49,500 --> 00:59:50,500 I know where to log in. 1148 00:59:50,500 --> 00:59:53,500 I can manage the entire thing with SSH and call it a day. 1149 00:59:53,500 --> 00:59:58,500 That's not particularly efficient, but it's also not terrible. 1150 00:59:58,500 --> 01:00:03,500 But also just a lot of tax service and exactly is just looking at this. 1151 01:00:03,500 --> 01:00:04,500 Exactly. 1152 01:00:04,500 --> 01:00:06,500 They're going to have so much fun with you. 1153 01:00:06,500 --> 01:00:10,500 But what I like to say is the reason I built this company is like they're these immutable 1154 01:00:10,500 --> 01:00:11,500 truths. 1155 01:00:11,500 --> 01:00:16,500 Like the fact is, is no matter how great that machine is, my entire business does not exist. 1156 01:00:17,500 --> 01:00:24,500 In co-located with that machine, like I have real physical things in the world, whatever 1157 01:00:24,500 --> 01:00:29,500 they may be users that are accessing my website from all over the world or retail outlets 1158 01:00:29,500 --> 01:00:35,500 or hospitals or factories or cars, like that stuff is happening too. 1159 01:00:35,500 --> 01:00:38,500 And, and so then people are like, well, don't worry. 1160 01:00:38,500 --> 01:00:42,500 I'll just take all that stuff and I'll build a digital twin and I'll just mimic all that 1161 01:00:42,500 --> 01:00:43,500 stuff. 1162 01:00:43,500 --> 01:00:45,500 And I'm like, oh, that's not see that. 1163 01:00:45,500 --> 01:00:51,500 But like, you want to be redundant, but that's not all that is not just making an exact copy 1164 01:00:51,500 --> 01:00:52,500 is not always it. 1165 01:00:52,500 --> 01:00:53,500 100%. 1166 01:00:53,500 --> 01:00:57,500 I think that we're, I think we're going to be into a weird space though, because we're 1167 01:00:57,500 --> 01:01:02,500 removing so much abstraction, like cloud was an abstraction from hardware, but AI is 1168 01:01:02,500 --> 01:01:04,500 like a super extraction. 1169 01:01:04,500 --> 01:01:11,500 And we're not only forcing engineers to use it, but we're going to grow a whole like 1170 01:01:11,500 --> 01:01:13,500 generation of developers on AI. 1171 01:01:13,500 --> 01:01:18,500 So you've got people that are either experimenting with hardware and they're in the like thick 1172 01:01:18,500 --> 01:01:23,500 of it, or they are even more abstracted than the whole generation of developers that we 1173 01:01:23,500 --> 01:01:25,500 just had that came into the cloud. 1174 01:01:25,500 --> 01:01:30,500 So how will we kind of like educate people on how to use those things, because like you 1175 01:01:30,500 --> 01:01:34,500 said, it's really easy, especially with the money that people are throwing at certain 1176 01:01:34,500 --> 01:01:39,500 like things where they're just going to buy this huge hardware and put everything, you 1177 01:01:39,500 --> 01:01:40,500 know what I mean? 1178 01:01:40,500 --> 01:01:44,500 Because it's going to be simple and it's going to be less permissions to give AI. 1179 01:01:44,500 --> 01:01:47,500 And like, how do we even educate people to do that? 1180 01:01:47,500 --> 01:01:48,500 Absolutely. 1181 01:01:48,500 --> 01:01:49,500 Absolutely right. 1182 01:01:49,500 --> 01:01:50,500 Yeah. 1183 01:01:50,500 --> 01:01:53,500 There's just a, I mean, you just put your finger, you're what do you call it? 1184 01:01:53,500 --> 01:01:56,500 And the key is, again, you touched on it. 1185 01:01:56,500 --> 01:02:02,500 The education will be giving people a full picture of the world, right? 1186 01:02:02,500 --> 01:02:06,500 Like, you know, when we were first trying to get people to adopt the cloud, there were 1187 01:02:06,500 --> 01:02:08,500 so many times people like, Oh, I don't know. 1188 01:02:08,500 --> 01:02:10,500 You know, I got these machines and so on and so forth. 1189 01:02:10,500 --> 01:02:14,500 And we're like, you know, like they would be like, why would I go out and pay for something 1190 01:02:14,500 --> 01:02:17,500 when I already have the assets in house? 1191 01:02:17,500 --> 01:02:21,500 And, and the conversation was like, well, are you really capturing what you have and 1192 01:02:21,500 --> 01:02:22,500 what you're doing? 1193 01:02:22,500 --> 01:02:26,500 Like, do you want to let go and reboot the machine at three o'clock in the morning? 1194 01:02:26,500 --> 01:02:31,500 Do you want to migrate, you know, the OS when you have to the, you know, the hypervisor, 1195 01:02:31,500 --> 01:02:32,500 et cetera, et cetera? 1196 01:02:32,500 --> 01:02:35,500 Again, no one's saying that's not an answer. 1197 01:02:35,500 --> 01:02:42,500 But when you're doing this, you need to think about the entire scope of the problem and 1198 01:02:42,500 --> 01:02:44,500 capture all the costs. 1199 01:02:44,500 --> 01:02:47,500 Because if it's just this, that's not going to be enough. 1200 01:02:47,500 --> 01:02:49,500 And so that's very much what it is. 1201 01:02:49,500 --> 01:02:53,500 I think that's like the thing we get these trends and everybody wants to do it. 1202 01:02:53,500 --> 01:02:57,500 And then they never like, yeah, you can be in the cloud, but then you have to think about 1203 01:02:57,500 --> 01:03:00,500 how expensive the cloud is and the fact that you're like abstracted. 1204 01:03:00,500 --> 01:03:04,500 But then you get on-prem and then you have to figure out, do you have a DBA to run all 1205 01:03:04,500 --> 01:03:05,500 this stuff? 1206 01:03:05,500 --> 01:03:10,500 And like, you have to be able to be good at kind of figuring out your future. 1207 01:03:10,500 --> 01:03:11,500 But okay. 1208 01:03:11,500 --> 01:03:15,500 So when you're talking about Kubernetes and Dockers, it made me think of Corey Quinn's 1209 01:03:15,500 --> 01:03:19,500 scale talk about how he compared Docker to like that. 1210 01:03:19,500 --> 01:03:21,500 Was it like a F-14 or something? 1211 01:03:21,500 --> 01:03:23,500 I didn't see it. 1212 01:03:23,500 --> 01:03:29,500 But he basically like was comparing Docker and Kubernetes and basically said it was like 1213 01:03:29,500 --> 01:03:30,500 the worst, but it was so funny. 1214 01:03:30,500 --> 01:03:34,500 It was basically he was just really explaining Kubernetes and Docker and the differences. 1215 01:03:34,500 --> 01:03:41,500 But what do you think the next, like, what is the next big, because everything in tech 1216 01:03:41,500 --> 01:03:44,500 is like databases are databases. 1217 01:03:44,500 --> 01:03:45,500 Compute is compute. 1218 01:03:45,500 --> 01:03:53,500 What's the big next, I guess, like revolution and compute and Kubernetes and Docker. 1219 01:03:53,500 --> 01:03:58,500 Obviously, I personally have this general opinion, right? 1220 01:03:58,500 --> 01:04:03,500 Obviously, I think edge and distributed things is going to be enormous. 1221 01:04:03,500 --> 01:04:06,500 And I very much hope to be a part of that. 1222 01:04:06,500 --> 01:04:11,500 Because again, I love building on things that can never change, right? 1223 01:04:11,500 --> 01:04:13,500 All those things I said earlier will never change. 1224 01:04:13,500 --> 01:04:17,500 Data will grow, speed of light will get faster, regulations will be out there, all that kind 1225 01:04:17,500 --> 01:04:18,500 of stuff. 1226 01:04:19,500 --> 01:04:22,500 I mean, we're old that we like the old constant things. 1227 01:04:22,500 --> 01:04:24,500 Does that mean that we're old people now? 1228 01:04:24,500 --> 01:04:26,500 No, it's because like you just recognize it now. 1229 01:04:26,500 --> 01:04:27,500 Yeah. 1230 01:04:27,500 --> 01:04:30,500 Well, I mean, it's one of these things where it's like, you know what, the secret sauce 1231 01:04:30,500 --> 01:04:35,500 behind Manhattan is not because, you know, like why it gets so tall buildings is because 1232 01:04:35,500 --> 01:04:37,500 they know where the bedrock is, right? 1233 01:04:37,500 --> 01:04:42,500 And so they're able to drill down to things that will never change for better or worse. 1234 01:04:42,500 --> 01:04:46,500 And so like, I think that's critically important to understand the things that will never change 1235 01:04:46,500 --> 01:04:50,500 and then figure out what will happen inside that is what will be next. 1236 01:04:50,500 --> 01:04:55,500 So my take on it is, and again, I say this with a highly biased thing is like, it will 1237 01:04:55,500 --> 01:05:03,500 be how do I act like a cloud, but in a thing that respects those immutable truths and matches 1238 01:05:03,500 --> 01:05:07,500 where the data and the compute and the things actually are growing. 1239 01:05:07,500 --> 01:05:12,500 And so when you see Jensen stand on stage and talk about the next, you know, trillion devices 1240 01:05:12,500 --> 01:05:17,500 where you talk about, you know, me being able to have instant response and instant memory 1241 01:05:17,500 --> 01:05:24,500 on my phone or whatever it might be, that's not everything going back to a central API. 1242 01:05:24,500 --> 01:05:31,500 That's that's those things out there having smarts at a level that is incredibly, you know, 1243 01:05:31,500 --> 01:05:32,500 that feels integrated. 1244 01:05:32,500 --> 01:05:38,500 And again, it's where it gets to that sublinear scaling because like I'm telling you the Gemini 1245 01:05:38,500 --> 01:05:43,500 AI and the anthropic people, they're like, they don't want to be out there like, you know, 1246 01:05:43,500 --> 01:05:48,500 managing why, you know, something isn't working at some factory and whatever, you know, the 1247 01:05:48,500 --> 01:05:53,500 Philippines, they want to have like a very easy way for someone out there to deploy a 1248 01:05:53,500 --> 01:05:59,500 model and run it and have it be reliable and debug it and, you know, have metadata around 1249 01:05:59,500 --> 01:06:05,500 it, which is the other thing that I think is super lost and something that we support 1250 01:06:05,500 --> 01:06:06,500 actively. 1251 01:06:06,500 --> 01:06:12,500 But it's, it's, I think that everything is a graph, right? 1252 01:06:12,500 --> 01:06:20,500 And that, you know, all these things workflows and transformations and so on are super under 1253 01:06:20,500 --> 01:06:23,500 invested in by us as a community. 1254 01:06:23,500 --> 01:06:30,500 And it really is it distills down to the simplest possible thing, which is here's this artifact, 1255 01:06:30,500 --> 01:06:33,500 this binary thing that I want you to run. 1256 01:06:33,500 --> 01:06:40,500 And as you do that, I want to record in a way that is programmatic for the, the computer 1257 01:06:40,500 --> 01:06:46,500 to understand what went into that thing, what the thing did, and then what the output was, 1258 01:06:46,500 --> 01:06:47,500 right? 1259 01:06:47,500 --> 01:06:54,500 And simply by having a structured way to approach that will change so much, it will change CICD, 1260 01:06:54,500 --> 01:06:57,500 it will change execution, it will change all these various things. 1261 01:06:57,500 --> 01:07:02,500 And there are many like larger efforts around stuff like this open telemetry is a perfect 1262 01:07:02,500 --> 01:07:03,500 example, right? 1263 01:07:03,500 --> 01:07:06,500 Where you start to think about things as traces and so on. 1264 01:07:06,500 --> 01:07:13,500 But I do think that when you hear the word reproducibility crisis, or you, you see someone 1265 01:07:13,500 --> 01:07:16,500 at two o'clock in the morning trying to figure out what the fuck is going on and why things 1266 01:07:16,500 --> 01:07:18,500 are debug hard to debug. 1267 01:07:18,500 --> 01:07:21,500 It's almost always that problem. 1268 01:07:21,500 --> 01:07:23,500 I don't know what went into this thing. 1269 01:07:23,500 --> 01:07:25,500 I don't know how it ran. 1270 01:07:25,500 --> 01:07:29,500 And I don't know how it came out in a deterministic way. 1271 01:07:29,500 --> 01:07:36,500 And if you don't have that, we will continue to like try and build these like incredibly 1272 01:07:36,500 --> 01:07:41,500 hacky scripts to parse stack traces to figure out what the fuck was going on. 1273 01:07:41,500 --> 01:07:44,500 Do you think AI is going to contribute to that problem? 1274 01:07:44,500 --> 01:07:49,500 No, I think it'll be much worse because, because in its core AI is not deterministic. 1275 01:07:49,500 --> 01:07:53,500 And so I mean, like contribute to making more of it. 1276 01:07:53,500 --> 01:07:55,500 Like, so, okay, technically think about it. 1277 01:07:55,500 --> 01:07:58,500 If AI starts vibe coding a bunch of these things. 1278 01:07:58,500 --> 01:08:03,500 Oh, and we're going to be like, you know, we're already increasing the amount like of agents 1279 01:08:03,500 --> 01:08:05,500 and different things that are coming back to us. 1280 01:08:05,500 --> 01:08:08,500 When you don't know what the expected output should be. 1281 01:08:08,500 --> 01:08:09,500 Absolutely. 1282 01:08:09,500 --> 01:08:11,500 It's really hard to diagnose a problem. 1283 01:08:11,500 --> 01:08:16,500 So I think that not only are you onto things as just development in general, but like that 1284 01:08:16,500 --> 01:08:21,500 is going to almost be like multiplied by the new way of development. 1285 01:08:21,500 --> 01:08:23,500 Yeah, I totally misunderstood what you're saying. 1286 01:08:23,500 --> 01:08:24,500 Yes, exactly. 1287 01:08:24,500 --> 01:08:28,500 It's the, it's the fact that those models are not deterministic that are so brutal. 1288 01:08:28,500 --> 01:08:34,500 And, and whoever breaks through the determinism around AI. 1289 01:08:34,500 --> 01:08:37,500 I mean, you can do it. 1290 01:08:37,500 --> 01:08:42,500 You can get close with things like rappers and things like that, but it's not there. 1291 01:08:42,500 --> 01:08:46,500 And I think the thing is, is it's hard to be deterministic as a developer. 1292 01:08:46,500 --> 01:08:49,500 That's a human because there's so many ways to build things, right? 1293 01:08:49,500 --> 01:08:54,500 Like, and there's so many ways to like, argue like what in half the way, half the time you're like, 1294 01:08:54,500 --> 01:08:56,500 is it because you did this before? 1295 01:08:56,500 --> 01:08:58,500 Is it because you like this method? 1296 01:08:58,500 --> 01:08:59,500 You know what I mean? 1297 01:08:59,500 --> 01:09:01,500 So then the fact that humans can do it. 1298 01:09:01,500 --> 01:09:05,500 Yeah, there's another incredibly smart friend of mine who's, who's right up here. 1299 01:09:05,500 --> 01:09:07,500 Who's saying exactly what you're saying. 1300 01:09:07,500 --> 01:09:12,500 And, and, you know, the new hotness around ML is, so his name's homel. 1301 01:09:12,500 --> 01:09:14,500 He has a courses on this and things like that. 1302 01:09:14,500 --> 01:09:16,500 It's all about evals, right? 1303 01:09:16,500 --> 01:09:24,500 That is such a brutally important and totally must think by a lot of the ML people adopting ML and AI right out, 1304 01:09:24,500 --> 01:09:32,500 which is like, how do I programmatically verify that this model does what I said it should do, right? 1305 01:09:32,500 --> 01:09:37,500 Like, unless you have that, like, do not even begin to go down the ML path. 1306 01:09:37,500 --> 01:09:42,500 Because my God, you know, like, unless you put a human in the loop, which is fine, 1307 01:09:42,500 --> 01:09:47,500 you're never going to be able to like train or build your model in an insensible way. 1308 01:09:47,500 --> 01:09:48,500 I think I'm trying to figure that out. 1309 01:09:48,500 --> 01:09:54,500 Like, how do you use AI to be faster at things, but also the fact that you have to then go verify. 1310 01:09:54,500 --> 01:09:55,500 Is it faster? 1311 01:09:55,500 --> 01:10:00,500 You know, like, I'm trying to figure out how do you use it to learn and to get better at things 1312 01:10:00,500 --> 01:10:06,500 without just losing the abstraction and the knowledge that you need to gain? 1313 01:10:06,500 --> 01:10:12,500 I mean, you know, the, the, that, that piece that came out, I think they got it pretty wrong about the like, 1314 01:10:12,500 --> 01:10:16,500 oh, you know, coders are slower when they use the ML and so on. 1315 01:10:16,500 --> 01:10:17,500 I think that missed it. 1316 01:10:17,500 --> 01:10:21,500 Like, because it didn't really represent the way that people do this, right? 1317 01:10:21,500 --> 01:10:25,500 Like, what they'll do is they'll like stand, you know, vibe code something, 1318 01:10:25,500 --> 01:10:29,500 and then they'll like try and compile it, or then they'll lint it, and then they'll actually run it, 1319 01:10:29,500 --> 01:10:34,500 and then they'll Google like something, you know, and see whether or not this was a good approach. 1320 01:10:34,500 --> 01:10:36,500 And then they'll go back to vibe code some more. 1321 01:10:36,500 --> 01:10:37,500 Right. 1322 01:10:37,500 --> 01:10:41,500 So it's like, it's not this like vibe code only or hand code only. 1323 01:10:41,500 --> 01:10:42,500 It really is a mix and match. 1324 01:10:42,500 --> 01:10:50,500 And, and right now the only way to solve what you're describing is with that human in the loop where they look at the thing. 1325 01:10:50,500 --> 01:10:52,500 They become the evaluator. 1326 01:10:52,500 --> 01:10:54,500 Do you think we're going to break Stack Overflow though? 1327 01:10:54,500 --> 01:10:57,500 Because this is what I was conspiring and thinking about last night. 1328 01:10:57,500 --> 01:11:00,500 Okay, like, I mean Stack Overflow, I love it to death. 1329 01:11:00,500 --> 01:11:01,500 I listened to it. 1330 01:11:01,500 --> 01:11:04,500 I listened to that podcast from day one. 1331 01:11:04,500 --> 01:11:10,500 But like the website, like we so many developers kind of depend on the fact that we're all hitting these issues, right? 1332 01:11:10,500 --> 01:11:15,500 And someone hit the problem before you, and then we wrote it down somewhere and it's like the, 1333 01:11:15,500 --> 01:11:19,500 it's the notebook that we all go and look in and we're like, hey, did you have this error? 1334 01:11:19,500 --> 01:11:22,500 I'm old enough that that notebook has moved a couple of times on the internet. 1335 01:11:22,500 --> 01:11:25,500 Like if you're, if you're of a certain vintage, you remember. 1336 01:11:25,500 --> 01:11:27,500 But it's the fact that no one's going to write it down. 1337 01:11:27,500 --> 01:11:28,500 David, do you remember Experts Exchange? 1338 01:11:28,500 --> 01:11:29,500 Of course. 1339 01:11:29,500 --> 01:11:30,500 Yeah. 1340 01:11:30,500 --> 01:11:32,500 Experts Exchange was the expert sex change. 1341 01:11:32,500 --> 01:11:33,500 Yeah, exactly. 1342 01:11:33,500 --> 01:11:36,500 But the models are getting it though, because we're like, you know what I mean? 1343 01:11:36,500 --> 01:11:42,500 Like, like if nobody asks questions to like other humans anymore and we're only asking it to AI, 1344 01:11:42,500 --> 01:11:45,500 do we then break the loop of knowledge? 1345 01:11:45,500 --> 01:11:46,500 You know what I mean? 1346 01:11:46,500 --> 01:11:50,500 I mean, what's going to happen is I already know what's going to happen, right? 1347 01:11:50,500 --> 01:11:54,500 The one of these models right now, it's probably going to be Claude, right? 1348 01:11:54,500 --> 01:12:01,500 Or we'll, we'll get the majority of the questions and it will be able to do a little mini loop and say like, 1349 01:12:01,500 --> 01:12:06,500 oh, you know, this person did this in Python and then they ask it again and then it did it again. 1350 01:12:06,500 --> 01:12:10,500 And they'll be able to tie those together and say like, okay, this is what actually was happening. 1351 01:12:10,500 --> 01:12:12,500 And then their model will magically just become smarter. 1352 01:12:12,500 --> 01:12:18,500 Now, if they were like a social good, they would release those datasets to the world and make it easy, 1353 01:12:18,500 --> 01:12:20,500 but that's not going to be it either. 1354 01:12:20,500 --> 01:12:22,500 So it's funny. 1355 01:12:22,500 --> 01:12:25,500 I gave a talk and ML talk in the winter. 1356 01:12:25,500 --> 01:12:32,500 And it was an experiment because I took the exact same talk I gave when I was launching Kubeflow in 2018, 1357 01:12:32,500 --> 01:12:38,500 I want to say, and I gave it again with no changes, not a single change to the slides. 1358 01:12:38,500 --> 01:12:41,500 Everything was the exact same, which is hilarious, right? 1359 01:12:42,500 --> 01:12:49,500 It was all about security vulnerabilities through ML and like what you're going to face here and like how you defend your model 1360 01:12:49,500 --> 01:12:51,500 and how you do this and how you do that. 1361 01:12:51,500 --> 01:12:54,500 And one of them was around distillation of models, right? 1362 01:12:54,500 --> 01:13:03,500 And so what I said is true, Claude or whatever, you know, frontier model will walk away with like the best coding model today. 1363 01:13:04,500 --> 01:13:14,500 And someone else, the number two or number three on the list will use number one as a verifier as it's going through its testing. 1364 01:13:14,500 --> 01:13:15,500 Okay. 1365 01:13:15,500 --> 01:13:16,500 And it doesn't need to be a lot. 1366 01:13:16,500 --> 01:13:21,500 It's like a thousand queries or 2000 queries and they will get so much smarter. 1367 01:13:21,500 --> 01:13:22,500 They will. 1368 01:13:22,500 --> 01:13:24,500 It will not be defendable. 1369 01:13:24,500 --> 01:13:29,500 It will not whether not ethically or unethically that will leak out. 1370 01:13:29,500 --> 01:13:32,500 And then the second model will be good, right? 1371 01:13:32,500 --> 01:13:38,500 And then the third model and now you will have this consensus around these models and that will lift all the boats. 1372 01:13:38,500 --> 01:13:47,500 And so, you know, I would love if, if the, you know, whoever becomes number one model, like just releases data sets so that we can all grow together. 1373 01:13:47,500 --> 01:13:51,500 But, you know, what will not happen is that will never stay secret. 1374 01:13:51,500 --> 01:13:52,500 David, hold on. 1375 01:13:55,500 --> 01:13:56,500 David. 1376 01:13:56,500 --> 01:13:57,500 Yes. 1377 01:13:58,500 --> 01:13:59,500 David, we lost you. 1378 01:13:59,500 --> 01:14:00,500 Oh. 1379 01:14:00,500 --> 01:14:02,500 You just, you just broke up for the last like two minutes. 1380 01:14:02,500 --> 01:14:03,500 I didn't hear it. 1381 01:14:03,500 --> 01:14:04,500 No. 1382 01:14:04,500 --> 01:14:05,500 Did you hear it on him? 1383 01:14:05,500 --> 01:14:06,500 Okay. 1384 01:14:06,500 --> 01:14:07,500 What's the last thing? 1385 01:14:07,500 --> 01:14:08,500 I dropped it too. 1386 01:14:08,500 --> 01:14:09,500 Yeah. 1387 01:14:09,500 --> 01:14:10,500 I just got a notice saying it's there. 1388 01:14:10,500 --> 01:14:11,500 Can you hear me? 1389 01:14:12,500 --> 01:14:13,500 Still not. 1390 01:14:13,500 --> 01:14:14,500 Yeah. 1391 01:14:14,500 --> 01:14:15,500 You're back. 1392 01:14:15,500 --> 01:14:16,500 Okay. 1393 01:14:16,500 --> 01:14:17,500 Where do you want me to go? 1394 01:14:19,500 --> 01:14:24,500 Go, go back to, to what will happen once the second model starts training on the first model. 1395 01:14:24,500 --> 01:14:25,500 Yeah. 1396 01:14:25,500 --> 01:14:26,500 Okay. 1397 01:14:26,500 --> 01:14:33,500 The, the second model will come along and they will begin to train using some of the wisdom from the first model. 1398 01:14:33,500 --> 01:14:36,500 They'll use it as a tool just as a verifier. 1399 01:14:36,500 --> 01:14:42,500 And as for as much as the first model wants to block it, it will not be possible to block because it doesn't require very much. 1400 01:14:42,500 --> 01:14:52,500 Like you're talking about literally thousands of total queries over a few days and you can get a very accurate representation of the underlying model. 1401 01:14:52,500 --> 01:14:57,500 And then the second model will be good and the third model will be good and the open source one will be good. 1402 01:14:57,500 --> 01:15:02,500 And now everyone's boats are lifted and then you're going to do this again and again and again and again. 1403 01:15:02,500 --> 01:15:12,500 And so what does that mean for developers and for the tribal knowledge that we all share on the internet so we can all, you know, excellent question. 1404 01:15:12,500 --> 01:15:18,500 I think that, you know, I love Stack Overflow, but I think it's going to go away or whatever. 1405 01:15:18,500 --> 01:15:19,500 I don't know. 1406 01:15:19,500 --> 01:15:22,500 It will migrate to a new community, right? 1407 01:15:22,500 --> 01:15:29,500 Because I think people will ask their first few questions of this and then want to talk to a human. 1408 01:15:29,500 --> 01:15:39,500 But over time, the chat, whatever the chat interface with your code will become so good, you're like, maybe I don't need to talk to a human. 1409 01:15:39,500 --> 01:15:40,500 I don't know. 1410 01:15:40,500 --> 01:15:41,500 I don't know. 1411 01:15:41,500 --> 01:15:47,500 But does that mean that the humans no longer hold that knowledge and they don't need us? 1412 01:15:47,500 --> 01:15:49,500 I don't. 1413 01:15:49,500 --> 01:15:53,500 I think we'll have the same depth of coding. 1414 01:15:53,500 --> 01:16:03,500 No, I mean, you know, to some degree, like, I haven't, you know, I took compilers years ago as a class and hit the compiler. 1415 01:16:03,500 --> 01:16:05,500 I haven't done anything with a compiler. 1416 01:16:05,500 --> 01:16:08,500 I'd like to know how compilers work. 1417 01:16:08,500 --> 01:16:14,500 And I think it helps me when I am coding, you know, which is rare, like be a better coder. 1418 01:16:14,500 --> 01:16:22,500 But it becomes an abstraction layer that I just, I don't think about, you know, 99% of my day. 1419 01:16:22,500 --> 01:16:26,500 Maybe a lot of these concepts reach that point. 1420 01:16:26,500 --> 01:16:28,500 I don't know. 1421 01:16:28,500 --> 01:16:29,500 I don't know. 1422 01:16:29,500 --> 01:16:33,500 I mean, like half a dozen standard questions you ask when you're interviewing at Big Tech. 1423 01:16:33,500 --> 01:16:40,500 How do you do, you know, what do you want to pick a stack or a heap or whatever, like, you know, people are going to be like, why are you even asking that question? 1424 01:16:40,500 --> 01:16:43,500 I don't know why they ask it half the time. 1425 01:16:43,500 --> 01:17:00,500 It's, you know, you know, Joel, Joel Spolsky from from Stack Overflow and Joel on software talked about this, whatever, 20 years ago, he said that he asked, he would ask questions about, you know, basic HTML concepts, right, as part of his thing. 1426 01:17:00,500 --> 01:17:05,500 And he said that, like, it was just, it was just a filter. 1427 01:17:05,500 --> 01:17:06,500 That really is it. 1428 01:17:06,500 --> 01:17:16,500 It's not to say whether or not you know that, but like the amount that people would like exaggerate, I'll put it politely on their resume and not even be able to answer the most basic thing. 1429 01:17:16,500 --> 01:17:21,500 That's, that's really all you're like looking for from this question. 1430 01:17:21,500 --> 01:17:24,500 But, you know, I'm with you. 1431 01:17:24,500 --> 01:17:32,500 Like people are like, oh, you know, I have a, I want you to develop a queue that can handle throttling and so on and so forth. 1432 01:17:32,500 --> 01:17:33,500 And you're like, all right, really? 1433 01:17:33,500 --> 01:17:34,500 Like, I get it. 1434 01:17:34,500 --> 01:17:36,500 Nobody's going to actually build that. 1435 01:17:36,500 --> 01:17:38,500 I mean, you will, you will. 1436 01:17:38,500 --> 01:17:41,500 But the first thing you're going to go do is Google how to do it. 1437 01:17:41,500 --> 01:17:43,500 That's what I'm saying. 1438 01:17:43,500 --> 01:17:44,500 Yeah. 1439 01:17:44,500 --> 01:17:47,500 Like no one does that off of like knowledge. 1440 01:17:47,500 --> 01:17:52,500 You're going to go look it up and then you're going to compare three different things and optimize it and. 1441 01:17:52,500 --> 01:17:54,500 David, this has been fun. 1442 01:17:54,500 --> 01:17:56,500 Where should people find you on the internet? 1443 01:17:56,500 --> 01:17:59,500 So I'm a big blue sky guy. 1444 01:17:59,500 --> 01:18:02,500 I certainly continue to spew there. 1445 01:18:02,500 --> 01:18:04,500 Please come try. 1446 01:18:04,500 --> 01:18:06,500 Try our platform. 1447 01:18:06,500 --> 01:18:07,500 I would love to hear from you. 1448 01:18:07,500 --> 01:18:10,500 If you're a data, if you're touch data, right? 1449 01:18:10,500 --> 01:18:16,500 If you spend any meaningful amount on data, I want to hear how you, we can make our distributed data pipelines better. 1450 01:18:16,500 --> 01:18:18,500 Expanso.io. 1451 01:18:18,500 --> 01:18:23,500 I post all my talks at my website, David Aron check.com. 1452 01:18:23,500 --> 01:18:25,500 And I just love talking to people. 1453 01:18:25,500 --> 01:18:34,500 I, you know, I am the first person to say I like to be the dumbest person in the room, which is very easy because, you know, when you're this dumb, it's your, everyone's smart. 1454 01:18:34,500 --> 01:18:41,500 But like, I, you know, I just want to be smarter about like you and your business and your, I don't know what your opinions are. 1455 01:18:41,500 --> 01:18:48,500 I got in an argument last night with a guy who like rewrote basically all of Excel and raw JavaScript, like from scratch. 1456 01:18:48,500 --> 01:18:50,500 And I was like, what the hell are you doing, man? 1457 01:18:50,500 --> 01:18:53,500 He was like, well, this isn't this because he wanted it. 1458 01:18:53,500 --> 01:18:54,500 He's just a JavaScript guy. 1459 01:18:54,500 --> 01:18:56,500 And I was like, my God, how do you even do that? 1460 01:18:56,500 --> 01:19:00,500 Not TypeScript, not coffee script, raw JavaScript. 1461 01:19:00,500 --> 01:19:02,500 That's a quote. 1462 01:19:02,500 --> 01:19:03,500 I know. 1463 01:19:03,500 --> 01:19:05,500 That is, that is, but anyhow, I loved it. 1464 01:19:05,500 --> 01:19:08,500 It was amazing conversation. 1465 01:19:08,500 --> 01:19:12,500 We will, we have you in the blue sky starter pack for our guests. 1466 01:19:12,500 --> 01:19:14,500 I need to convert that to a list at some point. 1467 01:19:14,500 --> 01:19:15,500 So people can find you there. 1468 01:19:15,500 --> 01:19:17,500 I think you're iron yuppie on blue sky. 1469 01:19:17,500 --> 01:19:18,500 I am iron yuppie. 1470 01:19:18,500 --> 01:19:19,500 That is me. 1471 01:19:19,500 --> 01:19:20,500 So yeah. 1472 01:19:20,500 --> 01:19:26,500 And we'll, we'll definitely, we'll figure out a time we can have you on for a second round of history, some of your antics at Kubeflow and Microsoft. 1473 01:19:26,500 --> 01:19:27,500 I have so many questions. 1474 01:19:27,500 --> 01:19:28,500 I just want to know the tea. 1475 01:19:28,500 --> 01:19:31,500 Like the next whole episode has to be the tea. 1476 01:19:31,500 --> 01:19:34,500 I will talk about this until I'm blue in the face. 1477 01:19:34,500 --> 01:19:38,500 I like, I say this as someone like, I just don't believe in like speaking ill of people. 1478 01:19:38,500 --> 01:19:42,500 So like, don't, don't tune in if you think I'm going to like badmouth someone. 1479 01:19:42,500 --> 01:19:46,500 Like it's just the navigations of these things happening is just so fascinating. 1480 01:19:46,500 --> 01:19:50,500 I feel so lucky that I couldn't be there. 1481 01:19:50,500 --> 01:19:51,500 All right. 1482 01:19:51,500 --> 01:19:52,500 Thank you so much. 1483 01:19:52,500 --> 01:19:53,500 And thank you everyone for listening. 1484 01:19:53,500 --> 01:19:55,500 We will talk to you again soon. 1485 01:20:02,500 --> 01:20:06,500 Thank you for listening to this episode of fork around and find out. 1486 01:20:06,500 --> 01:20:11,500 If you like this show, please consider sharing it with a friend, a coworker, a family member, or even an enemy. 1487 01:20:11,500 --> 01:20:16,500 However, we get the word out about this show helps it to become sustainable for the long term. 1488 01:20:16,500 --> 01:20:26,500 If you want to sponsor this show, please go to fafo.fm slash sponsor and reach out to us there about what you're interested in sponsoring and how we can help. 1489 01:20:26,500 --> 01:20:30,500 We hope your system stay available and your pagers stay quiet. 1490 01:20:30,500 --> 01:20:32,500 We'll see you again next time.