1 00:00:00,000 --> 00:00:03,402 The really scary vulnerabilities in these agentic processes that 2 00:00:03,402 --> 00:00:05,160 we haven't even scratched the surface. 3 00:00:05,190 --> 00:00:08,214 It's coming up with cognition speed of things human couldn't 4 00:00:08,214 --> 00:00:10,488 even figure out. Your 20,000 developers are gonna 5 00:00:10,488 --> 00:00:12,090 come down to probably 2000 developers. 6 00:00:12,150 --> 00:00:14,585 That's a reality. Meet John Willis, a foundational 7 00:00:14,585 --> 00:00:18,205 figure in the DevOps movement with over 45 years of experience 8 00:00:18,205 --> 00:00:20,430 and co-author of the DevOps Handbook. 9 00:00:20,460 --> 00:00:23,736 In this episode, he explains how to tackle AI and modern 10 00:00:23,736 --> 00:00:26,515 complexity using Deming's System of Profound Knowledge. 11 00:00:26,785 --> 00:00:30,160 Using the Deming's Profound Knowledge, how do you actually 12 00:00:30,160 --> 00:00:32,755 see AI impact to our organizations? 13 00:00:32,785 --> 00:00:35,235 You can't understand science without understanding philosophy 14 00:00:35,235 --> 00:00:37,287 of science. So therefore you can't 15 00:00:37,287 --> 00:00:39,190 understand AI without understanding the philosophy of 16 00:00:39,190 --> 00:00:41,575 AI. AI today is an alien cognition. 17 00:00:41,605 --> 00:00:43,950 We built a thinking machine, but it doesn't think like humans. 18 00:00:44,210 --> 00:00:47,545 To understand a complex system, you need a lens that takes into 19 00:00:47,545 --> 00:00:49,714 account four sort of elements. Theory of knowledge. 20 00:00:50,074 --> 00:00:51,970 How do we know what we think we know? 21 00:00:52,000 --> 00:00:53,920 Understanding variation. Understanding human behavior. 22 00:00:54,010 --> 00:00:57,176 And systems thinking. Deming had a lot to do, not just 23 00:00:57,176 --> 00:00:59,440 Toyota, but the resurrection of Japan after World War II. 24 00:00:59,500 --> 00:01:02,674 In 1950, he told everybody, follow these ideas, you'll be a 25 00:01:02,674 --> 00:01:05,025 world power. In five years, they overtook 26 00:01:05,025 --> 00:01:07,149 Germany. For leaders out there, what are 27 00:01:07,149 --> 00:01:10,450 some of the practical things about AI that you can advise? 28 00:01:10,710 --> 00:01:12,890 You'd be an idiot in a corporation to sort of ban AI. 29 00:01:13,070 --> 00:01:14,360 I'm sorry you're going outta business. 30 00:01:14,360 --> 00:01:16,440 But you need to understand there are a lot of perils. 31 00:01:16,460 --> 00:01:19,310 Here's the hard part. I just gave you a lot of keys. 32 00:01:19,340 --> 00:01:21,694 You gotta figure out the doors that you can open with those 33 00:01:21,694 --> 00:01:23,599 keys. Once you have that, you have a 34 00:01:23,599 --> 00:01:25,925 world of opportunity to affect complex systems, change 35 00:01:25,925 --> 00:01:43,641 organizational behavior. Hello, everyone. 36 00:01:43,641 --> 00:01:46,309 Welcome back to another new episode of the Tech Lead Journal 37 00:01:46,309 --> 00:01:48,091 podcast. Today, I'm very excited to have 38 00:01:48,091 --> 00:01:51,609 John Willis here with me. Uh, John is a prolific author, 39 00:01:51,609 --> 00:01:53,829 one of the authors from IT Revolutions. 40 00:01:53,969 --> 00:01:57,724 His books, DevOps IT Handbook and also Deming's Journey to 41 00:01:57,724 --> 00:02:01,212 Profound Knowledge, are the two most popular books in IT 42 00:02:01,212 --> 00:02:03,393 Revolutions. Uh, John is also someone who is 43 00:02:03,393 --> 00:02:06,027 very active in the DevOps world with all his contributions. 44 00:02:06,334 --> 00:02:09,033 So yeah, really excited to have you again in the show, John. 45 00:02:09,033 --> 00:02:11,365 So thank you for being here. No, that's great. 46 00:02:11,365 --> 00:02:14,370 Just a quick, uh, update. The DevOps Handbook, which is 47 00:02:14,370 --> 00:02:18,539 the second most popular behind Gene Kim's, uh, Phoenix Project. 48 00:02:18,629 --> 00:02:21,681 And my Deming one is, there's a couple ahead of me on the Deming 49 00:02:21,681 --> 00:02:23,835 one. So just, just to keep everything 50 00:02:23,835 --> 00:02:24,959 in line. But thank you. 51 00:02:24,959 --> 00:02:26,729 I appreciate your, uh, your shout outs. 52 00:02:27,849 --> 00:02:30,389 Right. So John, I love to start our 53 00:02:30,389 --> 00:02:32,983 conversation by asking you, maybe sharing a little bit from 54 00:02:32,983 --> 00:02:35,709 your career, any turning points, highlights you think we all can 55 00:02:35,709 --> 00:02:36,987 learn from your journey. Yeah. 56 00:02:36,987 --> 00:02:40,331 You know, my two boys are out the house now and, you know, I 57 00:02:40,331 --> 00:02:42,487 think the three things I've tried to really. 58 00:02:42,787 --> 00:02:46,043 And any young person that I mentor, and I love mentoring 59 00:02:46,043 --> 00:02:48,929 people by the way. So if you listen to this and yes 60 00:02:48,929 --> 00:02:51,797 if something that interests you, which I, I love, especially 61 00:02:51,797 --> 00:02:53,477 mentoring young career people, um. 62 00:02:54,097 --> 00:02:56,467 And the simple one is work hard, have fun. 63 00:02:57,712 --> 00:03:01,447 I mean, it's, you know, I remember telling my kids that 64 00:03:01,447 --> 00:03:05,286 from like the earliest ages, and one time I was dropping one of 65 00:03:05,286 --> 00:03:08,936 my sons off at the elementary school and one of the sort of, 66 00:03:08,936 --> 00:03:12,506 uh, traffic guards that just wanna make sure the cars are 67 00:03:12,506 --> 00:03:14,084 always going. I said that. 68 00:03:14,084 --> 00:03:15,914 And she said, wow, that's stupid. 69 00:03:16,574 --> 00:03:20,288 I'm like, no man. This is what's wrong with modern 70 00:03:20,288 --> 00:03:21,944 day education. Like work hard. 71 00:03:21,944 --> 00:03:24,563 And for some reason, they thought work hard couldn't be 72 00:03:24,563 --> 00:03:25,964 fun. You know, like, sorry. 73 00:03:26,476 --> 00:03:28,636 I think the other thing too, I think this is, these are 74 00:03:28,636 --> 00:03:30,111 important. I'm glad you asked this, because 75 00:03:30,111 --> 00:03:32,046 I never get to really address this on podcasts. 76 00:03:32,881 --> 00:03:37,838 Be a boundless advisor. You get way more than you give. 77 00:03:37,898 --> 00:03:41,493 This is like, you know, I've been doing this 45 years, you 78 00:03:41,493 --> 00:03:45,124 know, and the one thing I've never did was being calculating 79 00:03:45,124 --> 00:03:49,120 on like, if somebody called me for advice, as long as I had the 80 00:03:49,120 --> 00:03:51,038 time, and then the truth is most people have time. 81 00:03:51,428 --> 00:03:54,578 We talk about how I'm busy or there's no way I could give you 82 00:03:54,578 --> 00:03:57,215 15 minutes. Everybody can basically spare 15 83 00:03:57,215 --> 00:03:59,900 or 30 minutes. I mean, I'm sorry, like I'm 84 00:03:59,900 --> 00:04:03,463 very, there's a points where I'm crazy busy and I can always 85 00:04:03,463 --> 00:04:05,738 spare 15 minutes, usually spare 30 minutes. 86 00:04:05,978 --> 00:04:08,918 So just don't be that person. Oh yeah. 87 00:04:08,918 --> 00:04:11,168 I don't know. I don't have anything open. 88 00:04:11,288 --> 00:04:14,301 And particularly, you know, when you've sort of helped them in 89 00:04:14,301 --> 00:04:16,971 the past, right, in general. So just help. 90 00:04:17,301 --> 00:04:20,809 Because boy, it just comes back. Like when you need somebody, 91 00:04:20,809 --> 00:04:24,095 every once in a while, they're like, ah, that one didn't pay 92 00:04:24,095 --> 00:04:26,277 off. But every once in a while, you 93 00:04:26,277 --> 00:04:30,297 know, most, more often than not, when you need somebody that you 94 00:04:30,297 --> 00:04:33,381 helped, like career advice or like, they'll drop everything 95 00:04:33,381 --> 00:04:35,439 for you. And then sort of last but not 96 00:04:35,439 --> 00:04:36,927 least, you know, your word is your bond. 97 00:04:36,927 --> 00:04:39,267 And again, these sound like everything I learned in 98 00:04:39,267 --> 00:04:41,907 kindergarten, but-but they're true. 99 00:04:41,907 --> 00:04:45,425 Like I, you know, I want it to go to my grave in this world to 100 00:04:45,425 --> 00:04:48,214 say, you know, John never did me dirty. 101 00:04:48,884 --> 00:04:51,749 You know, he never lied to me. And again, we're not perfect, 102 00:04:51,749 --> 00:04:53,339 you know, there's gray areas and stuff. 103 00:04:53,339 --> 00:04:57,689 But in general though, I think you can have a great career. 104 00:04:58,452 --> 00:05:02,886 I've had an incredible career. And those three pillars of what 105 00:05:02,886 --> 00:05:05,622 I've, where I've gotten to where I am right now. 106 00:05:06,844 --> 00:05:08,499 Wow. So thanks for distilling to 107 00:05:08,499 --> 00:05:11,311 those three. I think it's really, I would say 108 00:05:11,311 --> 00:05:13,754 quite insightful, right? Especially from someone who have 109 00:05:13,754 --> 00:05:17,029 had long career like you, right? So thanks for sharing all this 110 00:05:17,029 --> 00:05:18,297 wisdom. I really like that, right? 111 00:05:18,297 --> 00:05:21,147 Especially the last one, your word is your bond, right? 112 00:05:21,417 --> 00:05:24,627 So I think definitely these days, integrity is one key 113 00:05:24,627 --> 00:05:27,208 attributes, especially when you work with other people, right? 114 00:05:27,208 --> 00:05:30,172 You wanna be trusted and also you wanna be known as someone 115 00:05:30,172 --> 00:05:33,566 who we can trust. So, John, one thing that we 116 00:05:33,566 --> 00:05:36,103 wanna talk about in this conversation is about your book, 117 00:05:36,103 --> 00:05:38,221 right? Deming's Journey of Profound 118 00:05:38,221 --> 00:05:40,525 Knowledge. I think, uh, maybe some people 119 00:05:40,525 --> 00:05:42,978 have heard about Deming, you know, Edward Deming, the name. 120 00:05:43,329 --> 00:05:46,378 Some people may not. For me, I personally have seen 121 00:05:46,378 --> 00:05:49,888 Deming's name being called out in many, many books and 122 00:05:49,888 --> 00:05:52,297 literatures. Although seems like he's been 123 00:05:52,297 --> 00:05:55,718 around for, I mean, the knowledge has been around for so 124 00:05:55,718 --> 00:05:59,126 many, many, uh, years, but he's always in the background to me, 125 00:05:59,126 --> 00:06:01,251 right? And only this time when seeing 126 00:06:01,251 --> 00:06:04,755 your book, right, you can see Deming's being at the forefront 127 00:06:04,755 --> 00:06:07,583 of the book. So maybe in the beginning, let's 128 00:06:07,583 --> 00:06:10,553 tell us a little bit, what's the story behind you coming up with 129 00:06:10,553 --> 00:06:12,071 this book? Yeah. 130 00:06:12,071 --> 00:06:16,826 I mean, I, um, you know, the-the short version is that, you know, 131 00:06:16,826 --> 00:06:19,688 when Gene Kim, right? Good friend of mine, you know, 132 00:06:19,688 --> 00:06:24,002 again, owner of IT Revolution. I was sort of an advisor on the 133 00:06:24,002 --> 00:06:26,814 Phoenix Project, but nothing sort of like on paper. 134 00:06:26,814 --> 00:06:29,634 You know, like I gave, I got early versions of it. 135 00:06:29,634 --> 00:06:33,934 I gave early reviews and we had lots of conversations about, you 136 00:06:33,934 --> 00:06:36,758 know. Like I, my, probably, my major 137 00:06:36,758 --> 00:06:40,275 contribution to the Phoenix Project, which is probably the 138 00:06:40,275 --> 00:06:43,587 most successful DevOps book there's ever been, was that he 139 00:06:43,587 --> 00:06:46,840 wasn't convinced early on that there was a DevOps version of 140 00:06:46,840 --> 00:06:49,559 it. And I'm the one, and he said 141 00:06:49,559 --> 00:06:52,297 this publicly, you know, I explained like, I thought like 142 00:06:52,297 --> 00:06:56,026 IT operations were like, we were looking for a lighthouse to 143 00:06:56,026 --> 00:06:58,528 bring us home and DevOps was that lighthouse, right? 144 00:06:58,648 --> 00:07:01,018 And so he said that publicly many times. 145 00:07:01,578 --> 00:07:05,818 But I asked him for an early copy of, you know, sort of the, 146 00:07:05,818 --> 00:07:08,010 um... I guess the first time I asked 147 00:07:08,010 --> 00:07:11,451 him for an early copy of what he was working on Kevin Behr and 148 00:07:11,451 --> 00:07:13,491 George Spafford, on the Phoenix Project. 149 00:07:13,637 --> 00:07:16,457 He gave me this challenge. He said, you should really read 150 00:07:16,457 --> 00:07:18,377 a book by Eliyahu Goldratt called The Goal. 151 00:07:19,697 --> 00:07:23,347 And it was such a gift, because like, I think a lot of other 152 00:07:23,347 --> 00:07:25,817 people just sort of read The Phoenix Project and then found 153 00:07:25,817 --> 00:07:29,207 out that it was sort of a purposeful, modern day rewrite 154 00:07:29,207 --> 00:07:31,487 of Eliyahu Goldratt's The Goal. Yeah. 155 00:07:31,487 --> 00:07:34,156 I mean it was purposeful. They even studied the structure 156 00:07:34,156 --> 00:07:37,669 of the book. And that's sort of like, I just 157 00:07:37,669 --> 00:07:39,153 fell in love with Eliyahu Goldratt. 158 00:07:39,993 --> 00:07:41,673 You know, I felt like my whole career. 159 00:07:41,703 --> 00:07:43,353 'Cause this is what, 15 years ago, right? 160 00:07:43,353 --> 00:07:49,663 So I'm 25 years into my career and this guy is bleeding 161 00:07:49,663 --> 00:07:54,539 everything that I believe is right about how we do work, how 162 00:07:54,539 --> 00:07:56,863 we work in organizations, all those things. 163 00:07:58,003 --> 00:08:01,617 And, uh, we were at a sort of DevOps days conference and, um, 164 00:08:01,617 --> 00:08:04,835 another good friend of mine, very good, great mentor, uh, Ben 165 00:08:04,835 --> 00:08:07,725 Rockwood, we were doing an open spaces on Eliyahu Goldratt, 166 00:08:07,725 --> 00:08:10,401 what's called the Theory of Constraints, which is sort of 167 00:08:10,731 --> 00:08:14,596 one of the core of his principles or his sort of 168 00:08:14,596 --> 00:08:16,000 management, that called manifesto. 169 00:08:16,466 --> 00:08:19,976 And Ben was a real student of Dr. Deming, and I hadn't really, 170 00:08:19,976 --> 00:08:22,378 I didn't realize that Dr. Deming, you know, he'd been 171 00:08:22,378 --> 00:08:24,592 around my whole career. And, you know, Ben said 172 00:08:24,592 --> 00:08:27,441 something to me, he said, you know, it all goes back to Dr. 173 00:08:27,441 --> 00:08:29,781 Deming. And that you'll hear that a lot 174 00:08:29,781 --> 00:08:32,726 when you start opening up this sort of the can. 175 00:08:33,056 --> 00:08:34,586 You can't open open the can, right? 176 00:08:34,586 --> 00:08:36,806 You hear that it all goes back to Deming. 177 00:08:36,806 --> 00:08:38,796 You're like, yeah, who is this Deming guy? 178 00:08:39,336 --> 00:08:41,937 But he challenged me. And you know, he said, you know, 179 00:08:41,937 --> 00:08:43,480 basically John, you know, he knew me. 180 00:08:43,480 --> 00:08:47,005 I knew him very well. I trust, going back to trust, I 181 00:08:47,005 --> 00:08:50,610 trusted his advice. And he said, go look, read the 182 00:08:50,610 --> 00:08:53,803 14 points. You know, and we're about a 183 00:08:53,803 --> 00:08:55,477 couple years into this DevOps movement. 184 00:08:56,147 --> 00:09:01,352 And I'm like, oh my goodness! Everything that we profess about 185 00:09:01,352 --> 00:09:05,867 DevOps is listed in the Deming's 14th points, which just comes 186 00:09:05,867 --> 00:09:10,518 from his Out of the Crisis book. So I decided to, you know, and 187 00:09:10,518 --> 00:09:13,218 Ben Rockwood gave me this sort of challenge, right? 188 00:09:13,688 --> 00:09:17,844 And so I went ahead and I worked on a presentation that I did it 189 00:09:17,844 --> 00:09:21,104 like the second Puppet conf, you know, Puppet being a 190 00:09:21,104 --> 00:09:22,118 configuration management product. 191 00:09:22,783 --> 00:09:26,803 I called Deming to DevOps. And it was about 192 00:09:26,803 --> 00:09:29,700 non-determinism. And I just like, it was, again, 193 00:09:29,700 --> 00:09:32,495 I took Goldratt. In fact, my first resistance to 194 00:09:32,495 --> 00:09:35,918 Ben was, oh no, no man. I just read like five books on 195 00:09:35,918 --> 00:09:37,341 Goldratt. I don't want to, you know, 196 00:09:37,341 --> 00:09:38,713 there's, don't gimme somebody else, you know? 197 00:09:39,136 --> 00:09:43,945 But I'm like, okay, once I sort of figured out like this was the 198 00:09:43,945 --> 00:09:46,282 guy, you know. And then you start seeing like, 199 00:09:46,282 --> 00:09:48,904 I, you know, again, I worked on this Deming to DevOps. 200 00:09:48,904 --> 00:09:52,633 And I actually went down a, you know, like, why did this 201 00:09:52,813 --> 00:09:54,433 physicist... So here's the thing. 202 00:09:54,733 --> 00:09:58,785 He was a phy- mathematical physicist getting his degree in 203 00:09:58,785 --> 00:10:01,726 the mid twenties. Know what's going on there? 204 00:10:01,986 --> 00:10:07,044 Quantum, physics, you know, all this sort of non-determinism, 205 00:10:07,044 --> 00:10:09,766 uncertainty principles are just all over the place. 206 00:10:09,856 --> 00:10:12,691 And he's getting a degree. By the way, Goldratt a little 207 00:10:12,691 --> 00:10:15,346 bit later, but same thing. They were both physicists. 208 00:10:16,126 --> 00:10:18,754 And they understood that they, you know, like this sort of 209 00:10:18,754 --> 00:10:20,430 like... And what Deming I think 210 00:10:20,430 --> 00:10:24,204 brilliantly did out throughout his career is took part of that 211 00:10:24,204 --> 00:10:28,070 and turned that into probability. 212 00:10:28,070 --> 00:10:30,006 Became a CEO. You know, he, his business card 213 00:10:30,006 --> 00:10:31,770 was a, he was a statistician first, right? 214 00:10:32,100 --> 00:10:35,673 That it was all about, you know, this idea that like everything 215 00:10:35,673 --> 00:10:38,445 is sort of rigid and deterministic and you do this 216 00:10:38,445 --> 00:10:41,179 and this, you know. And his was more based on what, 217 00:10:41,179 --> 00:10:44,448 you know, sort of, I, you know, I wouldn't say quantum world, 218 00:10:44,448 --> 00:10:48,235 but a non-deterministic world. And that, again, fascinated me 219 00:10:48,235 --> 00:10:54,200 to the point of like there was these answers to questions that 220 00:10:54,200 --> 00:11:00,140 really were only one layer of an answer, right? 221 00:11:00,140 --> 00:11:02,812 In other words, you know, so we, if we get into, I know the next 222 00:11:02,812 --> 00:11:04,370 question's gonna be about profound knowledge, right? 223 00:11:04,580 --> 00:11:07,322 I mean, that's the first tenet of profound knowledge is theory 224 00:11:07,322 --> 00:11:10,840 of knowledge. How do you know what you think 225 00:11:10,840 --> 00:11:14,043 you know? And that you can see a physicist 226 00:11:14,043 --> 00:11:17,115 mindset there. And the, in the, uh, you know, 227 00:11:17,115 --> 00:11:20,505 especially a quantum physicist, it's like that there is no like 228 00:11:20,505 --> 00:11:23,634 bound truth. You know, I think there was a 229 00:11:23,634 --> 00:11:26,730 physicist quote was, uh, you know, sort of behind every deep 230 00:11:26,730 --> 00:11:28,665 truth is another deep truth, right? 231 00:11:28,725 --> 00:11:30,974 You know, um, You know, or something like that, right? 232 00:11:30,974 --> 00:11:35,019 So, um, and again, I think that, that sort of like curiosity 233 00:11:35,019 --> 00:11:40,467 where I was looking for labels and things I believed if I 234 00:11:40,467 --> 00:11:44,820 started that Deming journey 15 years ago for 25 years in my 235 00:11:44,820 --> 00:11:47,159 career. This has gotta be more right. 236 00:11:47,159 --> 00:11:50,435 You know, the sort of rules of, you know, they would call it 237 00:11:50,435 --> 00:11:52,579 Taylorism, right? Like Frederick Winslow Taylor, 238 00:11:52,579 --> 00:11:56,678 which is, you know, again, great contribution to organizational 239 00:11:56,678 --> 00:11:59,408 behavior management theory, management science. 240 00:11:59,408 --> 00:12:02,286 One of it is his book. But there was, there had to be 241 00:12:02,286 --> 00:12:04,201 something. And only like, again, it, it's 242 00:12:04,201 --> 00:12:06,815 sort of interesting, if you compare Newtonian, I know I'm 243 00:12:06,815 --> 00:12:09,482 probably losing people. You compare Newtonian physics to 244 00:12:09,482 --> 00:12:12,827 quantum mechanics, right? Like it says, okay, you know, 245 00:12:12,827 --> 00:12:16,332 yeah, all that stuff works. You dropped apple, it hits the 246 00:12:16,332 --> 00:12:20,646 fall... fall on the floor. But like, there's some other 247 00:12:20,646 --> 00:12:24,732 stuff here that might explain what we can't explain. 248 00:12:24,762 --> 00:12:29,401 Anyway, yeah, so that's sort of like, that was just a like, from 249 00:12:29,401 --> 00:12:32,651 a learner perspective. The fact that you've reached out 250 00:12:32,651 --> 00:12:34,961 to me and wanted to do this podcast, you're a learner. 251 00:12:35,021 --> 00:12:36,551 There's no doubt in my mind. I already know that. 252 00:12:36,551 --> 00:12:38,441 And we don't even know each other that well, right? 253 00:12:38,701 --> 00:12:44,178 And so I use that, what I call learner, right, those for us, 254 00:12:44,178 --> 00:12:47,561 like we can't resist these kind of opportunities. 255 00:12:47,561 --> 00:12:49,301 Yeah, so that, that's the long version. 256 00:12:49,301 --> 00:12:51,851 But yeah, that's why I fell in love with Dr. Deming. 257 00:12:53,368 --> 00:12:56,399 Right. So yeah, I think for most people 258 00:12:56,399 --> 00:12:59,548 who have read DevOps books, read literature, resources, you know, 259 00:12:59,548 --> 00:13:02,028 The Goal, Theory of Constraints, Eliyahu Goldratt, right? 260 00:13:02,328 --> 00:13:04,908 And the DevOps Handbook and things like that. 261 00:13:04,908 --> 00:13:08,607 Always sometimes, you will see here, this Deming mentioned, 262 00:13:08,607 --> 00:13:12,445 being mentioned, right? And I think if you trace back 263 00:13:12,445 --> 00:13:16,004 the Agile movement, Lean, DevOps, all seems to have some 264 00:13:16,004 --> 00:13:19,197 influence of Dr. Edward Deming's, um, you know, 265 00:13:19,197 --> 00:13:21,594 knowledge and management principles and things like that, 266 00:13:21,594 --> 00:13:24,299 right? And if we read literature, many 267 00:13:24,299 --> 00:13:27,344 people would say about Toyota Production System, right, as 268 00:13:27,344 --> 00:13:30,392 like, kind of like the reference for how DevOps started. 269 00:13:30,392 --> 00:13:33,724 But actually Toyota actually has some influence from Dr. Deming 270 00:13:33,724 --> 00:13:35,817 as well. So maybe tell us a little bit 271 00:13:35,817 --> 00:13:38,253 about this, because I think it's good for people to understand, 272 00:13:38,253 --> 00:13:40,988 uh, his contribution there. Yeah, you know, when I set out 273 00:13:40,988 --> 00:13:43,202 to write Deming's Journey to Profound Knowledge, really the 274 00:13:43,202 --> 00:13:46,380 goal was, you know, Deming is a conduit, right? 275 00:13:46,453 --> 00:13:49,189 Like I really didn't want to become a sycophant to Deming, 276 00:13:49,189 --> 00:13:51,853 even though I probably am when it's all said and done. 277 00:13:51,853 --> 00:13:55,083 I mean, I do think he's been a great American prophet and not 278 00:13:55,083 --> 00:13:58,821 prophet in the sycophant way. Prophet in that there's so much 279 00:13:58,821 --> 00:14:02,378 like knowledge there. I think the other thing I wanted 280 00:14:02,378 --> 00:14:04,808 to say too, right, is people find Deming hard. 281 00:14:05,228 --> 00:14:08,891 Because, you know, like Deming one time, you know, in a 282 00:14:08,891 --> 00:14:10,743 lecture, somebody was asking him questions. 283 00:14:10,743 --> 00:14:13,713 Yeah, but how, and you know... He said do you want me to teach 284 00:14:13,713 --> 00:14:16,567 you and do your job? And, you know, and I was 285 00:14:16,567 --> 00:14:19,710 thinking about like, you know, like Peter Drucker amazing, 286 00:14:19,710 --> 00:14:25,605 mentor writer, right? But like, you would never see in 287 00:14:25,605 --> 00:14:29,429 a Deming book, you know, the effective executive. 288 00:14:30,359 --> 00:14:33,755 Like in other words, he wouldn't tell you how to become an 289 00:14:33,755 --> 00:14:36,702 effective executive. He would tell you these 290 00:14:36,702 --> 00:14:39,888 principles that exist. And so back to like, you know, 291 00:14:39,888 --> 00:14:42,528 what, where do we get to Japan and all that stuff. 292 00:14:43,188 --> 00:14:47,928 Deming was like hard, sort of hard-nosed about like, here's 293 00:14:47,928 --> 00:14:53,293 these things that I've learned. And you have to, you don't just 294 00:14:53,293 --> 00:14:55,571 take 'em like here's the five steps. 295 00:14:56,229 --> 00:14:59,954 Everything's gonna be perfect. Like, you know, again, theory of 296 00:14:59,954 --> 00:15:02,088 knowledge and system of profound knowledge is really 297 00:15:02,088 --> 00:15:04,159 epistemology, right? That's not how that works, 298 00:15:04,159 --> 00:15:06,480 right? Like you are supposed to take 299 00:15:06,480 --> 00:15:09,927 the torch. And so one of the sort of things 300 00:15:09,927 --> 00:15:13,277 that you learn in the sort of Deming discussion when you 301 00:15:13,277 --> 00:15:16,500 become a researcher about him is all these schools of thought. 302 00:15:16,500 --> 00:15:19,980 There is definitely the sycophants who, like Deming, 303 00:15:19,980 --> 00:15:23,473 even like the bookshelves. You know, if you look at the 304 00:15:23,473 --> 00:15:25,300 books, the man who created the miracle in Japan. 305 00:15:25,300 --> 00:15:28,230 The miracle way. You know, the man who invented, 306 00:15:28,230 --> 00:15:31,920 you know, quality, right? Like that's freaking ridiculous. 307 00:15:32,550 --> 00:15:36,295 He was just a man. He was a like awesome man. 308 00:15:36,855 --> 00:15:39,020 So they, and you know, and then there's other people that are 309 00:15:39,020 --> 00:15:42,270 like, yeah, it, you know, Deming had nothing to do with Japan. 310 00:15:42,554 --> 00:15:46,490 Some of the sort of northeast Lean school, they literally act 311 00:15:46,490 --> 00:15:49,034 like he doesn't exist in a Toyota story. 312 00:15:49,874 --> 00:15:52,591 So you start weeding through that stuff, right, and try to 313 00:15:52,591 --> 00:15:55,509 get it. Look, and I spent a really good 314 00:15:55,509 --> 00:15:58,554 time explaining the different people that went to Japan and 315 00:15:58,554 --> 00:16:01,479 how Deming was it. And I really tried not to be, 316 00:16:01,479 --> 00:16:04,244 he, you know, boy, Toyota doesn't exist without Deming. 317 00:16:05,024 --> 00:16:09,314 However, there are a fair amount of people who say Deming had 318 00:16:09,314 --> 00:16:12,854 nothing to do with Toyota. And that's absolutely incorrect. 319 00:16:13,094 --> 00:16:15,593 'Cause I've interviewed people. I interviewed a man named 320 00:16:15,593 --> 00:16:20,338 Yoshino, Dr. Yoshino. And he basically said he was 321 00:16:20,338 --> 00:16:26,317 hired in 1966 at Toyota, worked as a peer, you know, with Ono 322 00:16:26,317 --> 00:16:29,757 and Shingo. And I asked him the question of 323 00:16:29,757 --> 00:16:32,432 Deming's impact. And he was unquestionable that 324 00:16:32,432 --> 00:16:36,855 when he got to Toyota, Deming was mostly what people were 325 00:16:36,855 --> 00:16:39,819 talking about. And he said, Deming taught us 326 00:16:39,819 --> 00:16:44,273 how to understand data. So Deming had a lot to do with, 327 00:16:44,273 --> 00:16:47,482 you know, not just Toyota, but like the resurrection of Japan 328 00:16:47,482 --> 00:16:50,725 after World War II. But he wasn't the only one. 329 00:16:51,451 --> 00:16:55,209 And its principles are solid. So like what he did, in fact, 330 00:16:55,209 --> 00:16:59,076 the most famous part is there was, uh, in 1950 there was, 331 00:16:59,076 --> 00:17:03,663 basically a lecture he gave where, uh, years later they had 332 00:17:03,663 --> 00:17:07,112 basically estimated that 80% of the wealth, this is post World 333 00:17:07,112 --> 00:17:11,688 War II, was in this seminar. And he told everybody in that 334 00:17:11,688 --> 00:17:15,300 seminar, if you follow these ideas I'm telling you right now, 335 00:17:15,300 --> 00:17:19,874 you'll be a world power. And in five years, they overtook 336 00:17:19,874 --> 00:17:24,044 Germany to be second behind the United States in world 337 00:17:24,044 --> 00:17:28,413 economics, right? Like those are facts, right? 338 00:17:28,983 --> 00:17:31,182 And again, the other thing I would like to sort of point out 339 00:17:31,182 --> 00:17:35,151 not to be a sycophant is, you know, Deming didn't create the 340 00:17:35,151 --> 00:17:38,498 miracle in Japan. The Japanese created the miracle 341 00:17:38,498 --> 00:17:42,350 in Japan. What Toyota did wasn't great. 342 00:17:42,590 --> 00:17:48,584 TPS didn't become this great model for successful complete 343 00:17:48,584 --> 00:17:50,810 organization, meaning profit, everything, right? 344 00:17:51,050 --> 00:17:53,240 All the things from Ono and Shingo. 345 00:17:54,080 --> 00:17:58,199 But Deming was part of that mix. So Toyota created the miracle of 346 00:17:58,199 --> 00:17:59,960 Toyota. Not Deming. 347 00:18:00,681 --> 00:18:03,326 But Deming had some influence. Yeah. 348 00:18:03,326 --> 00:18:06,236 Thanks for the background story. I think for people who would 349 00:18:06,236 --> 00:18:08,849 love to study more about Deming's journey and how he 350 00:18:08,849 --> 00:18:11,104 influenced the Toyota, right? I think reading your book 351 00:18:11,104 --> 00:18:12,609 definitely is one good resource, right? 352 00:18:12,609 --> 00:18:14,687 Because in your book, you kind of like highlighted the story 353 00:18:14,687 --> 00:18:17,060 from the very beginning. Yeah, just to, one thing I just 354 00:18:17,060 --> 00:18:19,370 wanted to point out in case some people listen or don't know. 355 00:18:19,670 --> 00:18:23,069 The other thing I did about that book is all the books about 356 00:18:23,069 --> 00:18:26,282 Deming that I had read up to that point were very dry, very 357 00:18:26,282 --> 00:18:29,950 little bit about, um, you know, about, you know, his biography, 358 00:18:29,950 --> 00:18:33,377 maybe five pages, 10 pages. And then it's all about his 359 00:18:33,377 --> 00:18:34,722 management principles and how they... 360 00:18:34,722 --> 00:18:37,026 And I thought, you know, I've always been a fan of Michael 361 00:18:37,026 --> 00:18:40,024 Lewis, you know, the Moneyball, the Flash Boys, all those. 362 00:18:40,264 --> 00:18:43,730 And I thought, wouldn't this be a great Michael Lewis like book 363 00:18:43,730 --> 00:18:46,474 where you could teach people the principles like he does. 364 00:18:46,474 --> 00:18:50,476 He explains, you know, very complex ideas to everybody and 365 00:18:50,476 --> 00:18:52,896 make it fun and story and narrative. 366 00:18:52,896 --> 00:18:56,849 So again, it not only gives you a strong background on his 367 00:18:56,849 --> 00:19:00,533 history including what he did in Japan, and even after Japan, you 368 00:19:00,533 --> 00:19:03,371 know. But it, you know, I think so far 369 00:19:03,371 --> 00:19:06,707 it's been been very successful to be a very Michael Lewis, you 370 00:19:06,707 --> 00:19:10,970 know, style of easy to read, anybody can read it and sort of 371 00:19:10,970 --> 00:19:14,374 get the value out of it. Yeah, so thanks for highlighting 372 00:19:14,374 --> 00:19:16,328 that. Because yeah, if you, uh, expect 373 00:19:16,328 --> 00:19:18,323 the book to be like theoretical, right? 374 00:19:18,323 --> 00:19:21,482 So totally not, right? So this book is a mix of, you 375 00:19:21,482 --> 00:19:22,898 know, some theory, of course, right? 376 00:19:22,898 --> 00:19:25,510 And some background stories, right, uh, including history, 377 00:19:25,510 --> 00:19:28,964 uh, kind of, I think research that you did as part of, you 378 00:19:28,964 --> 00:19:30,557 know, your journey, uh, writing this book. 379 00:19:31,007 --> 00:19:34,954 So maybe for people who are very curious about this Deming's, you 380 00:19:34,954 --> 00:19:38,237 know, profound knowledge, right? So he called it a profound 381 00:19:38,237 --> 00:19:40,796 knowledge. So what are these four profound 382 00:19:40,796 --> 00:19:42,816 knowledge? If you can maybe give us an 383 00:19:42,816 --> 00:19:44,464 overview high level, that will be great. 384 00:19:44,927 --> 00:19:48,107 So Deming, you know, again, being a physicist, right? 385 00:19:48,137 --> 00:19:50,117 Uh, you know, mathematical physicist, right? 386 00:19:50,117 --> 00:19:53,641 So he, you know, he saw the world, like think about like, 387 00:19:53,641 --> 00:19:55,985 you know, like how a physicist sees the world. 388 00:19:56,034 --> 00:20:00,559 Like it's and I believe even though he wasn't like a particle 389 00:20:00,559 --> 00:20:03,818 physicist or a, you know, even labeled as a quantum physicist, 390 00:20:03,818 --> 00:20:05,194 right? Actually he was mathematical 391 00:20:05,194 --> 00:20:07,368 physicist. By the time he got out of, out 392 00:20:07,368 --> 00:20:10,802 of his PhD, he became more of an organizational statistics guy. 393 00:20:10,802 --> 00:20:13,026 But, you know, the influence had to be there. 394 00:20:13,236 --> 00:20:16,698 You think about how, like, you know, the sort of, uh, universal 395 00:20:16,698 --> 00:20:19,029 physicists think about like the classic world, they think about 396 00:20:19,029 --> 00:20:21,675 the quantum world, right? It's sort of like it opens up 397 00:20:21,675 --> 00:20:24,936 the world for a lot of pieces. So like a lot of what he would 398 00:20:24,936 --> 00:20:27,638 talk about, it's not really physics, but he'd talk about 399 00:20:27,638 --> 00:20:31,682 like everything is complex. This, you know, we live in a 400 00:20:31,682 --> 00:20:34,049 world of complexity, right? Again, that's a physicist view 401 00:20:34,049 --> 00:20:37,293 or like, you know, like if you look at a door swinging, you 402 00:20:37,293 --> 00:20:41,071 have, uh, if you sort of really think about it, there's so many 403 00:20:41,071 --> 00:20:43,021 more elements than open and close, right? 404 00:20:43,404 --> 00:20:47,782 And so he said that in order to understand things, and 405 00:20:47,782 --> 00:20:52,854 particularly his focus was sort of management of people, trying 406 00:20:52,854 --> 00:20:57,539 to help managers and people co-exist with sort of, um, what 407 00:20:57,539 --> 00:21:00,084 would ultimately be profit but not profit focused. 408 00:21:00,294 --> 00:21:04,387 You said that to understand a complex system, you need a lens 409 00:21:04,387 --> 00:21:07,349 that takes into account four sort of elements. 410 00:21:08,218 --> 00:21:11,408 Cause if you don't use these four elements you, like you sort 411 00:21:11,408 --> 00:21:13,048 of miss. And I'll give you an example. 412 00:21:13,048 --> 00:21:15,928 But, so the first one was he called theory of knowledge. 413 00:21:16,486 --> 00:21:19,353 Cause the other thing that a big influence on Deming, and this is 414 00:21:19,353 --> 00:21:23,157 in my book as well, actually comes from philosophy, something 415 00:21:23,157 --> 00:21:26,863 called pragmatism. And pragmatism was the first 416 00:21:26,863 --> 00:21:30,553 America based or sort of US based philosophy. 417 00:21:31,113 --> 00:21:34,979 Friend of mine, Jay Bloom, says it was like jazz, like we 418 00:21:34,979 --> 00:21:37,573 created pragmatism. Like it wasn't some European. 419 00:21:38,183 --> 00:21:40,643 And Deming was highly influenced by pragmatism. 420 00:21:41,148 --> 00:21:44,088 So again, that all goes back to epistemology. 421 00:21:44,333 --> 00:21:48,247 And epistemology is a fancy way to say, you know, how do we know 422 00:21:48,247 --> 00:21:52,229 what we think we know? And that's what a learner does, 423 00:21:52,229 --> 00:21:53,883 right? Like, okay, I got this, but 424 00:21:53,883 --> 00:21:56,133 like, I'm not gonna stop and just stand up in front of me. 425 00:21:56,133 --> 00:21:59,133 Like, hey, here's the answer to the question, you know. 426 00:21:59,463 --> 00:22:02,403 Well, there's other answers and there's more to learn, right? 427 00:22:02,403 --> 00:22:05,263 So that sort of first pillar, I'm thinking, remember there's 428 00:22:05,263 --> 00:22:09,563 four here. The second sort of element of 429 00:22:09,563 --> 00:22:12,551 this lens to decouple complexity, right? 430 00:22:12,928 --> 00:22:15,238 And complexity could be your organizational, like you got 431 00:22:15,238 --> 00:22:18,298 five teams and these teams are fast, these teams are slow. 432 00:22:18,538 --> 00:22:20,992 There's resources here. Well, the things that we deal 433 00:22:20,992 --> 00:22:24,010 with in an organization. The second is, uh, understanding 434 00:22:24,010 --> 00:22:27,541 variation. And variation then goes into a 435 00:22:27,541 --> 00:22:30,319 whole deep area of statistical process control. 436 00:22:30,589 --> 00:22:34,179 But at the end of the day is, you know, how do we understand 437 00:22:34,769 --> 00:22:37,799 what we think we know? Like this is the way I think 438 00:22:37,799 --> 00:22:40,090 about it, right? And then, you know, I've also 439 00:22:40,090 --> 00:22:43,348 sort of recently taken like, is there an ontological view, 440 00:22:43,348 --> 00:22:45,899 right? Like what's inherent versus what 441 00:22:45,899 --> 00:22:48,354 shapes. So if we start understanding 442 00:22:48,354 --> 00:22:51,465 variation, there's variation in everything, then we can 443 00:22:51,465 --> 00:22:53,397 understand what's inherent. And so there's, this goes deep, 444 00:22:53,397 --> 00:22:55,929 and this would be a whole episode if I went any deeper. 445 00:22:56,402 --> 00:22:58,202 But then the third is really interesting. 446 00:22:58,762 --> 00:23:02,419 Because the first two are more sort of like very science, you 447 00:23:02,419 --> 00:23:05,456 know, like you can understand from a science or mathematical 448 00:23:05,456 --> 00:23:08,526 perspective, right? The third one is, um, 449 00:23:08,526 --> 00:23:11,338 psychology. And this is the thing where I, 450 00:23:11,338 --> 00:23:13,716 again, like just why I kept falling along with Deming is 451 00:23:13,716 --> 00:23:17,052 because you, if I think about my career before I learned about 452 00:23:17,052 --> 00:23:20,144 Deming, right? That 25 years, you'd hear all 453 00:23:20,144 --> 00:23:25,182 sorts of discussions about, you know, sort of like, you know, 454 00:23:25,182 --> 00:23:27,186 observing, managing, measuring, right? 455 00:23:27,186 --> 00:23:30,829 And his measurement's a little different than sort of... is 456 00:23:30,829 --> 00:23:33,245 statistical analytical statistics versus enumerated 457 00:23:33,245 --> 00:23:35,177 statistics. Again, another long subject 458 00:23:35,177 --> 00:23:38,263 covered in the book. You think all those things sort 459 00:23:38,263 --> 00:23:39,717 of like, yeah, I can see that, I can see. 460 00:23:39,717 --> 00:23:42,087 But then the first time you think about like, no, no, no. 461 00:23:42,807 --> 00:23:46,827 An equal element of this lens understanding complexity has to 462 00:23:46,827 --> 00:23:51,058 be understanding human behavior. Or you know, like maybe you have 463 00:23:51,058 --> 00:23:53,721 intrinsic motivation and there's actually different variants of 464 00:23:53,721 --> 00:23:55,908 intrinsic, right? Like understanding, I, you know, 465 00:23:55,908 --> 00:23:59,004 I did some studies on this where there's like seven classified, 466 00:23:59,004 --> 00:24:01,983 intrin- we always take extrinsic intrinsic. 467 00:24:01,983 --> 00:24:04,632 There's actually seven classifications of intrinsic 468 00:24:04,632 --> 00:24:06,985 motivation. So it's not just, oh, you're 469 00:24:06,985 --> 00:24:08,893 intrinsic, you're intrinsic, you're extrinsic, right? 470 00:24:09,313 --> 00:24:12,321 It's much deeper than that. And then cognitive biases and 471 00:24:12,321 --> 00:24:15,885 all those things, right? This becomes like, you have to 472 00:24:15,885 --> 00:24:18,881 input... include this, because I could have everything else 473 00:24:18,881 --> 00:24:21,183 right, but if I don't understand that, you know. 474 00:24:21,572 --> 00:24:25,076 Like a example of a great healthcare version is I can do 475 00:24:25,076 --> 00:24:28,074 all the stats and the math and the, you know, show the theory 476 00:24:28,074 --> 00:24:31,232 and all this of why hand washing kills germs. 477 00:24:31,892 --> 00:24:35,172 But you fundamentally don't believe that for whatever, 478 00:24:35,172 --> 00:24:37,322 because you grew up in the whatever. 479 00:24:37,532 --> 00:24:41,928 Like, I'm gonna fail. And then the last, but certainly 480 00:24:41,928 --> 00:24:44,382 not least, is understanding a system. 481 00:24:44,382 --> 00:24:48,906 And Deming has a lot of roots in some of the earliest general 482 00:24:48,906 --> 00:24:51,923 systems theory. And he was like, it just bleeds. 483 00:24:51,923 --> 00:24:56,111 In fact, in that, 1950, uh, Mount Hakone where 80% of the 484 00:24:56,111 --> 00:24:59,502 wealth was in there. I mean, if you read that speech 485 00:24:59,502 --> 00:25:03,107 and it's online in the Deming Institute, it basically is a 486 00:25:03,107 --> 00:25:07,949 sermon on system thinking. So when you put all four of 487 00:25:07,949 --> 00:25:11,167 those together as part of your ability to sort of understand 488 00:25:11,167 --> 00:25:16,006 things or try to solve things. Because like you'll hear, like 489 00:25:16,006 --> 00:25:19,441 you, most people, like the shorts version of Deming is, 490 00:25:19,441 --> 00:25:21,619 isn't he the guy who created PDCA? 491 00:25:21,649 --> 00:25:23,909 Well, he didn't create PDCA. He created PDSA. 492 00:25:24,619 --> 00:25:28,182 And that's another sort of like. So but in general, yes. 493 00:25:28,233 --> 00:25:32,665 He's sort of the idea that... And then for those pure 494 00:25:32,665 --> 00:25:35,487 Deming-ites now that are listening and screaming at me, 495 00:25:35,487 --> 00:25:39,090 yes, I know. All that came from, uh, Walter 496 00:25:39,090 --> 00:25:41,642 Shewhart. So Stewart was Deming's fan. 497 00:25:41,772 --> 00:25:44,874 Again, Deming pulled all these great things. 498 00:25:45,504 --> 00:25:48,786 You know, he pulled, uh, you know, his philosophy ideas from 499 00:25:48,786 --> 00:25:49,382 C. I. 500 00:25:49,382 --> 00:25:51,920 Lewis. He pulled his variation ideas 501 00:25:51,920 --> 00:25:55,010 from both Schuit and actually mostly Schuit. 502 00:25:55,080 --> 00:25:57,467 But, and Schuit is the one who introduced him to C. 503 00:25:57,467 --> 00:25:59,412 I. Lewis, his pragmatism book, 504 00:25:59,412 --> 00:26:01,965 right? So, but yeah, so you put all 505 00:26:01,965 --> 00:26:04,009 that together and it becomes, you know. 506 00:26:04,009 --> 00:26:07,699 And, but here's the hard part is, sorry folks. 507 00:26:08,659 --> 00:26:13,745 I just gave you a lot of keys. You gotta figure out the doors 508 00:26:13,745 --> 00:26:15,439 that you can open with those keys. 509 00:26:15,769 --> 00:26:18,079 And I think this is where people get frustrated with Deming. 510 00:26:18,339 --> 00:26:21,709 We're so used to this instant management gratification. 511 00:26:22,639 --> 00:26:25,579 So if you see a major knock on Deming, it didn't work. 512 00:26:26,339 --> 00:26:28,339 We tried the Deming stuff, it didn't work. 513 00:26:28,807 --> 00:26:31,664 And then you moved on to, again, I'm not like, I'm just using 514 00:26:31,664 --> 00:26:34,623 Drucker as an example. Peter Drucker is like, the 515 00:26:34,623 --> 00:26:38,526 contributions of Peter Drucker to management's, you know, is 516 00:26:38,526 --> 00:26:40,956 incredible. But to use it as an example, 517 00:26:40,956 --> 00:26:43,066 there are certain type of management principles that give 518 00:26:43,066 --> 00:26:45,560 you the sort of, you know, even when we talk about Lean. 519 00:26:45,712 --> 00:26:48,280 You mentioned rightfully so that Lean and Agile... 520 00:26:48,280 --> 00:26:50,482 Look, ultimately Deming had contributions there. 521 00:26:51,047 --> 00:26:53,447 You know, there are books to 14 Steps to Lean. 522 00:26:54,647 --> 00:26:59,327 You'll never see a book by Deming with 14 steps to do this. 523 00:26:59,417 --> 00:27:06,392 Even the 14 points are literally a leading sort of how to do list 524 00:27:07,322 --> 00:27:08,972 to understand profound knowledge. 525 00:27:10,201 --> 00:27:12,211 And then the last piece, systems thinking, right? 526 00:27:12,211 --> 00:27:14,471 Like that's, it's core to everything. 527 00:27:15,341 --> 00:27:17,077 You know. And if you even sort of look at 528 00:27:17,077 --> 00:27:19,549 Deming's, like, you know, the red bead game or the funnel 529 00:27:19,549 --> 00:27:22,819 game, you know, like all these things, these games that other 530 00:27:22,819 --> 00:27:26,131 people created around his ideas, they bleed systems thinking. 531 00:27:27,781 --> 00:27:30,247 He bled, you know, systems thinking, you know, like the, 532 00:27:30,247 --> 00:27:33,411 you know, you have to look at all the parts, you know, the, 533 00:27:33,411 --> 00:27:34,621 you know, everything is connected. 534 00:27:35,292 --> 00:27:37,558 So, yeah. So I think the, you know, again, 535 00:27:37,558 --> 00:27:41,542 I think the hard part that most people have is they read about 536 00:27:41,542 --> 00:27:45,081 profound knowledge and they haven't been given the sort of 537 00:27:45,081 --> 00:27:47,862 answers. And I think Deming would say, 538 00:27:47,862 --> 00:27:50,082 you know, nah, I'm gonna give you this sort of. 539 00:27:50,172 --> 00:27:53,202 I'm putting what you need up here in your head. 540 00:27:54,102 --> 00:27:56,502 You gotta go figure out, you know? 541 00:27:56,502 --> 00:27:59,344 And I think once you have that, then you have an world of 542 00:27:59,344 --> 00:28:03,805 opportunity to affect, you know, complex systems, organize..., 543 00:28:03,805 --> 00:28:06,168 create change, organizational behavior, organizational 544 00:28:06,168 --> 00:28:12,051 structures and all the things that we in general strive to do, 545 00:28:12,051 --> 00:28:12,999 right? Yeah. 546 00:28:13,860 --> 00:28:16,830 And especially, I mean when we work in technologies, right? 547 00:28:16,830 --> 00:28:19,639 Technological organizations, you know, these days, especially 548 00:28:19,639 --> 00:28:22,539 modern organization, complexity is a huge thing. 549 00:28:22,719 --> 00:28:25,744 And systems thinking, the day when I studied that the first 550 00:28:25,744 --> 00:28:27,944 time, I found it's quite insightful, right? 551 00:28:28,244 --> 00:28:32,624 And in fact, I've been always amazed by how, you know, things 552 00:28:32,744 --> 00:28:34,104 got explained in systems thinking. 553 00:28:34,104 --> 00:28:36,804 And in fact, I got an episode with Diana Montalion about 554 00:28:36,804 --> 00:28:40,064 systems thinking as well. And it's always amazed me, like 555 00:28:40,064 --> 00:28:43,778 how these kind of insightful thinking actually can help us, 556 00:28:43,778 --> 00:28:46,584 you know, make a change, transformational change or 557 00:28:46,634 --> 00:28:50,077 deliver results that can go beyond what you can imagine, I 558 00:28:50,077 --> 00:28:52,353 guess, right? Simply because if you know how 559 00:28:52,353 --> 00:28:54,895 things are interrelated to each other, the relationship between 560 00:28:54,895 --> 00:28:59,512 them, you can kind of like see the root cause and try to fix it 561 00:28:59,512 --> 00:29:02,707 from the root cause rather than trying to fix the symptoms, so 562 00:29:02,707 --> 00:29:05,497 to speak, right? So system thinking is kind of a 563 00:29:05,497 --> 00:29:08,502 curse and a blessing, right? And the curse is you wind up... 564 00:29:08,502 --> 00:29:11,813 like I think probably the best book I've ever read about system 565 00:29:11,813 --> 00:29:13,682 thinking is Donella Meadows' Thinking in Systems. 566 00:29:14,162 --> 00:29:17,702 The worst book I've ever read in systems is Donella Meadows. 567 00:29:17,762 --> 00:29:21,035 'Cause it like, everything like in line at the supermarket or 568 00:29:21,035 --> 00:29:23,922 like you literally... I've told CIOs at companies 569 00:29:23,922 --> 00:29:27,888 where, you know, I felt they were ready for this advice, 570 00:29:27,888 --> 00:29:29,817 right? Like you gotta have a threshold 571 00:29:29,817 --> 00:29:31,658 where like, what are you talking about? 572 00:29:31,868 --> 00:29:34,163 Like once I have enough conversations, I'm like, all 573 00:29:34,163 --> 00:29:38,393 right. I think if you could create a 574 00:29:38,393 --> 00:29:42,566 systemic mindset in your organization where everybody 575 00:29:42,566 --> 00:29:45,898 basically understands systems thinking. 576 00:29:46,268 --> 00:29:48,755 And there's a lot more. But, the point is, like, 577 00:29:48,755 --> 00:29:52,472 everybody would, like, even the how, you know, the team A 578 00:29:52,472 --> 00:29:54,147 stinks. They ain't never, you know, 579 00:29:54,147 --> 00:29:56,927 they're always late on delivery for our team B, right? 580 00:29:56,927 --> 00:29:59,522 Like if you think in systems, you think, well, wait a minute, 581 00:29:59,522 --> 00:30:02,481 what's the disruption? What's the connections? 582 00:30:02,481 --> 00:30:06,021 Why are they late, okay? And like, you know, like, again, 583 00:30:06,021 --> 00:30:09,351 you know, Dev Ops. You know, again, DevOps was 584 00:30:09,351 --> 00:30:13,294 like, you know, like the original sort of attack to 585 00:30:13,294 --> 00:30:16,765 provide a more systems thinking way where you had dev, you had 586 00:30:16,765 --> 00:30:19,124 ops, the original character was a wall. 587 00:30:19,304 --> 00:30:22,939 My friend Andrew Clay Schafer created this caricature of a 588 00:30:22,939 --> 00:30:25,457 wall where dev, you know, they're called a wall of 589 00:30:25,457 --> 00:30:27,991 confusion he called it, where, you know, dev would throw the 590 00:30:27,991 --> 00:30:29,875 code over the wall, ops would catch it. 591 00:30:29,875 --> 00:30:33,039 They would say, this code stinks, they'd throw it back, 592 00:30:33,039 --> 00:30:35,563 right? And like, never were we trying 593 00:30:35,563 --> 00:30:39,178 to figure out like, why did those disconnects or impedance 594 00:30:39,178 --> 00:30:42,072 mismatches happen, right? And that's system thinking at 595 00:30:42,072 --> 00:30:43,762 its core. Yeah. 596 00:30:44,422 --> 00:30:46,898 So definitely systems thinking is one body of knowledge by 597 00:30:46,898 --> 00:30:48,607 itself, right? And you mentioned the other 598 00:30:48,607 --> 00:30:50,062 three like theory of knowledge itself. 599 00:30:50,092 --> 00:30:53,876 I think it's always good for individual to know the unknown 600 00:30:53,876 --> 00:30:56,228 unknowns, right? So those are like kind of like 601 00:30:56,228 --> 00:30:58,132 the blind spots that you never knew before. 602 00:30:58,132 --> 00:31:00,592 And especially these days, so many rapid advancement. 603 00:31:00,652 --> 00:31:03,181 AI itself. Like we can't really actually 604 00:31:03,181 --> 00:31:05,932 understand everything, right? But it seems coming up really, 605 00:31:05,932 --> 00:31:08,020 really fast. So theory of knowledge is one 606 00:31:08,020 --> 00:31:09,472 thing. Theory of variation. 607 00:31:09,772 --> 00:31:12,742 I think this is also very important because yeah, nothing 608 00:31:12,742 --> 00:31:15,382 is predictable in the world. You can standardize stuff, but 609 00:31:15,382 --> 00:31:17,602 it always happens well within variation, right? 610 00:31:17,932 --> 00:31:21,396 So understanding a little bit of statistical knowledge and why 611 00:31:21,396 --> 00:31:23,302 variation could happen, I think, is also important. 612 00:31:23,632 --> 00:31:26,629 And one that is also important is understanding the psychology 613 00:31:26,629 --> 00:31:28,668 or the human behavior aspect, right? 614 00:31:28,998 --> 00:31:32,508 So especially these days, people will talk about culture, 615 00:31:32,508 --> 00:31:34,778 psychological safety, bias, cognitive biases, and things 616 00:31:34,778 --> 00:31:36,702 like that. Definitely that's another body 617 00:31:36,702 --> 00:31:40,086 of knowledge by itself that we need to think about and master 618 00:31:40,086 --> 00:31:42,847 in order to create a more thriving, transformational 619 00:31:42,847 --> 00:31:45,282 organizations. So definitely other points that 620 00:31:45,282 --> 00:31:49,230 when people talk about Deming is the 14 points of management that 621 00:31:49,230 --> 00:31:51,691 he has, right? So I know that we won't be 622 00:31:51,691 --> 00:31:54,666 covering all of them, but do you think there are some points that 623 00:31:54,666 --> 00:31:58,441 you think are very important for us to start pondering or maybe 624 00:31:58,441 --> 00:32:01,372 try to implement? And maybe we can then follow up 625 00:32:01,372 --> 00:32:03,646 with the other points as well at our own time. 626 00:32:03,706 --> 00:32:06,793 So maybe if there's some points that you want to cover today, 627 00:32:06,793 --> 00:32:07,911 John. Yeah, I think... 628 00:32:07,911 --> 00:32:11,413 So in, um, you know, in my book, the last chapter is sort of... 629 00:32:11,773 --> 00:32:16,441 So the last third of my book is basically sort of like, what 630 00:32:16,441 --> 00:32:19,168 would've Deming done? So Deming died in 1993. 631 00:32:19,168 --> 00:32:22,666 So you think all the things that happened after 1993, like you 632 00:32:22,666 --> 00:32:25,670 got the Agile movement, you got Lean, you got like all these 633 00:32:25,670 --> 00:32:28,720 things that you know. And not even including like, you 634 00:32:28,720 --> 00:32:31,438 know, DevOps, cloud computing, and now AI, right? 635 00:32:31,438 --> 00:32:35,212 So there's a lot of stuff there. So I tried to say I think at 636 00:32:35,212 --> 00:32:37,860 that point I knew enough about Deming and I knew enough about 637 00:32:37,860 --> 00:32:41,052 cyber and, you know, like I'm pretty knowledgeable in a lot of 638 00:32:41,052 --> 00:32:43,772 those subjects, you know, DevSecOps, you know, cyber. 639 00:32:44,319 --> 00:32:47,783 Certainly DevOps would consider one of the founders of DevOps 640 00:32:47,783 --> 00:32:49,725 movement. And I thought I could, I'm gonna 641 00:32:49,725 --> 00:32:51,549 take the liberty of what would Deming do. 642 00:32:52,492 --> 00:32:56,362 And I'd literally use profound knowledge and as a template. 643 00:32:56,362 --> 00:32:58,842 And then I ended the... I did think it was funny, I had 644 00:32:58,842 --> 00:33:00,708 kind of almost finished, I'd finished the book basically I 645 00:33:00,708 --> 00:33:02,152 thought. And I'm like, you know what? 646 00:33:02,242 --> 00:33:03,322 I just wrote a book about Deming. 647 00:33:03,802 --> 00:33:06,982 And I didn't actually have a chapter on the 14 points. 648 00:33:07,312 --> 00:33:09,112 So I literally, I gotta have a chapter. 649 00:33:09,352 --> 00:33:11,602 And, but it was great 'cause it fell right in line 'cause I was 650 00:33:11,602 --> 00:33:15,177 able to use a lot of examples. You know, I think the, you know, 651 00:33:15,177 --> 00:33:18,120 obviously would take a whole, uh, podcast to do all 14 points. 652 00:33:18,330 --> 00:33:23,112 I think the ones that you find where he is, you know, again, as 653 00:33:23,112 --> 00:33:26,549 a learner, like, you know, number five, never stop 654 00:33:26,549 --> 00:33:30,477 improving. You know, I think 13 is like 655 00:33:30,477 --> 00:33:33,797 self-learning organization. Those ones, you know, and I gave 656 00:33:33,797 --> 00:33:36,182 a modern day examples, but mostly like cyber ones. 657 00:33:36,182 --> 00:33:40,352 Like they never stop improving. You know, Shannon Leeds is a 658 00:33:40,352 --> 00:33:44,462 great mentor in security. She's a great white hat hacker. 659 00:33:44,492 --> 00:33:49,371 She's run organization, she's run security at places like 660 00:33:49,371 --> 00:33:52,927 Intuit and Adobe. Uh, brilliant, brilliant woman. 661 00:33:53,797 --> 00:33:56,090 You know, I think one of the things when I first met her, you 662 00:33:56,090 --> 00:33:58,880 know, we were all... Everybody was foundational on 663 00:33:58,880 --> 00:34:02,098 like what were the ways that you manage security? 664 00:34:02,098 --> 00:34:03,748 Like what do you look for, you know? 665 00:34:04,106 --> 00:34:08,288 CVEs or sort of vulnerabilities that are known, or the OWASP Top 666 00:34:08,288 --> 00:34:11,436 10. And so I meet her for the first 667 00:34:11,436 --> 00:34:15,056 time and she's asking everybody at the table, how do you manage, 668 00:34:15,056 --> 00:34:16,712 you know, security? And she says, let me tell you 669 00:34:16,712 --> 00:34:19,436 what I do. And she basically, she had this 670 00:34:19,436 --> 00:34:21,356 whole thing of adversarial analysis. 671 00:34:22,556 --> 00:34:26,036 So like she went beyond what everybody thought was the sort 672 00:34:26,036 --> 00:34:29,208 of how you're supposed to do this and came up with a whole 673 00:34:29,208 --> 00:34:31,466 new way. So instead of looking for 674 00:34:31,466 --> 00:34:35,120 vulnerabilities, like how I, why don't I measure how in her times 675 00:34:35,120 --> 00:34:38,021 she was at Intuit, like how do I identify adversaries? 676 00:34:38,891 --> 00:34:41,710 How do I measure how often they come to my site? 677 00:34:42,460 --> 00:34:45,844 And what are the things, and this becomes pure Deming PDSA or 678 00:34:45,844 --> 00:34:49,110 theory of knowledge. What are the things that I can 679 00:34:49,110 --> 00:34:53,516 do? Or try or experiment with that 680 00:34:53,516 --> 00:34:56,780 might reduce those adversaries? You know, sort of thinking about 681 00:34:56,780 --> 00:35:00,050 it as like a criminal who robs house is basically would look at 682 00:35:00,050 --> 00:35:04,024 the first house and say they got a guard door and a steel fence. 683 00:35:04,084 --> 00:35:06,394 Not gonna do it. Next house, they got alarm 684 00:35:06,394 --> 00:35:09,814 systems all over the place. The next house has an open 685 00:35:09,814 --> 00:35:13,062 screen door with no fence. That's where they're going, 686 00:35:13,062 --> 00:35:15,286 right? So like that whole science that 687 00:35:15,286 --> 00:35:18,886 she sort of like, I think single handedly created of adversary 688 00:35:18,886 --> 00:35:22,912 analysis, was a great example of never stop improving. 689 00:35:24,202 --> 00:35:26,512 I mean, she's already industry giant in security. 690 00:35:26,542 --> 00:35:29,542 She could stand up and say, all right, you need to do this, 691 00:35:29,542 --> 00:35:32,082 this, this, this. This is my Shannon Leeds, know, 692 00:35:32,082 --> 00:35:35,062 and she's an incredible woman. So like if she tells you to do 693 00:35:35,062 --> 00:35:38,255 it, you probably should do it. But yeah, so like that one. 694 00:35:38,255 --> 00:35:41,047 And then I think, you know, the self-learning organization. 695 00:35:41,653 --> 00:35:44,885 Again, I go back to Shannon Leeds, but, uh, you know, my, my 696 00:35:44,885 --> 00:35:47,941 good friend Andrew Clay Shafer says, you're either a learning 697 00:35:47,941 --> 00:35:49,723 organization or you're losing to one who is. 698 00:35:51,463 --> 00:35:55,878 One of my favorite all time quotes of contemporary DevOps 699 00:35:55,878 --> 00:36:00,279 folk, if you want to say it. But Channel East was like, the 700 00:36:00,279 --> 00:36:02,703 alternative would be like KLOCs, right? 701 00:36:02,703 --> 00:36:06,216 You know, like, you're sort of measuring, you know, how many 702 00:36:06,216 --> 00:36:10,754 bugs per line of code and like that or, um, another, it is not 703 00:36:10,754 --> 00:36:12,061 Shannon, but incident management. 704 00:36:12,061 --> 00:36:16,489 Like another great quote. Another good friend of mine, 705 00:36:16,489 --> 00:36:19,327 John Allspaw says that so incident management, right? 706 00:36:19,327 --> 00:36:21,871 Like, how would Deming, one of the chapters I thought looked at 707 00:36:21,871 --> 00:36:25,985 was like how disgusting Deming would've been if he looked at 708 00:36:25,985 --> 00:36:30,584 modern day incident management. You'll get P1s, P2s, P3s, who, 709 00:36:30,584 --> 00:36:34,529 no, nobody ever touches 'cause you don't like management is 710 00:36:34,529 --> 00:36:38,469 screaming, hollering about P1s. So even if you get all them on 711 00:36:38,469 --> 00:36:41,185 some screaming and hollering measurement system, KPIs or 712 00:36:41,185 --> 00:36:45,545 MBOs, and by the way, Deming hated MBOs and KPIs, then you 713 00:36:45,545 --> 00:36:49,431 might get to half of the P2s and you'll never get to the P3s. 714 00:36:49,701 --> 00:36:52,995 And then you contrast that with the John Allspaw quote, that 715 00:36:53,295 --> 00:36:55,935 incidents are unplanned investments. 716 00:36:56,800 --> 00:37:01,327 So Deming would probably be looking at all the incidents and 717 00:37:01,327 --> 00:37:06,000 using a system, probably using variation, and looking at it in 718 00:37:06,000 --> 00:37:10,365 its holistic systems view. And because some of those, what 719 00:37:10,365 --> 00:37:14,310 you classify as P3s, on the wrong day, bring down your 720 00:37:14,310 --> 00:37:16,930 system, right? The one, the nagging thing that, 721 00:37:16,930 --> 00:37:19,256 oh, is hap. I mean, I know I'm going crazy 722 00:37:19,256 --> 00:37:23,125 here, but like the, what was it? The, it was the Columbia, right? 723 00:37:23,125 --> 00:37:26,415 Where they, the thermal panels where they literally, everybody 724 00:37:26,415 --> 00:37:28,825 said, oh, we've seen that a number of times. 725 00:37:28,825 --> 00:37:30,595 Don't worry about it. Don't worry about it. 726 00:37:30,595 --> 00:37:33,235 Nothing will go wrong. Well, on this day, you know, 727 00:37:33,235 --> 00:37:35,515 when they were on re-entry, it went wrong. 728 00:37:36,175 --> 00:37:38,335 You know, and there's just number of stories. 729 00:37:38,395 --> 00:37:41,198 If you look at the safety of people like John Allspaw and 730 00:37:41,198 --> 00:37:44,314 Richard Cook and Dr. Woods, who I would definitely follow up on 731 00:37:44,314 --> 00:37:47,039 if you understand, you know, the critical safety, cognitive 732 00:37:47,039 --> 00:37:51,103 safety, they have numerous stories of that thing that like, 733 00:37:51,579 --> 00:37:53,969 it almost ha- I, lemme do a quick story. 734 00:37:53,993 --> 00:37:55,903 This is a great story. Uh, Paul O'Neill. 735 00:37:56,398 --> 00:38:02,266 You know, when he went to Alcoa, he was giving sort of a tour and 736 00:38:02,266 --> 00:38:07,075 he had heard a story about a young man who got killed by one 737 00:38:07,075 --> 00:38:09,870 of the machines. And what happened was there was 738 00:38:09,870 --> 00:38:12,850 a little fence and the fence said do not cross. 739 00:38:13,270 --> 00:38:18,194 But for like a year or so, or quite a long time, the general 740 00:38:18,194 --> 00:38:22,124 advice practice from the senior people were, if it gets stuck, 741 00:38:22,124 --> 00:38:25,106 ignore that sign. You gotta hop over and you gotta 742 00:38:25,106 --> 00:38:28,605 unloosen this thing. And that worked, you know, 743 00:38:28,605 --> 00:38:31,055 probably thousands, tens of thousands of times. 744 00:38:32,135 --> 00:38:35,347 But the one time, the reason the sign was there in the first 745 00:38:35,347 --> 00:38:38,380 place, it didn't work. It killed this young man and, 746 00:38:38,380 --> 00:38:40,439 you know, Paul O'Neilll basically, rightfully so, we 747 00:38:40,439 --> 00:38:42,575 killed that man, we killed the family, right? 748 00:38:42,575 --> 00:38:47,369 So that, those are like, those sort of like pebble in a shoe 749 00:38:47,369 --> 00:38:51,241 problems, that if you don't understand why they happen and 750 00:38:51,241 --> 00:38:56,447 you know that they can be the bad case, a total systems 751 00:38:56,447 --> 00:39:01,105 outage, worst case, you know, like harm to humans. 752 00:39:02,095 --> 00:39:03,955 Anyway, yeah, and also, but they're all great. 753 00:39:03,955 --> 00:39:06,655 Like pride in workmanship. No fear. 754 00:39:06,655 --> 00:39:09,477 I mean, you could just, you know, again, in my book, I try 755 00:39:09,477 --> 00:39:13,800 to cover every one individually with modern day use cases, 756 00:39:13,800 --> 00:39:16,675 either Lean, cybersecurity, DevOps, so... 757 00:39:17,652 --> 00:39:20,378 Yeah, wow. So when you mentioned about, you 758 00:39:20,378 --> 00:39:22,231 know, incident management classification, right? 759 00:39:22,231 --> 00:39:26,419 P1, P2, P3, I think for all sysadmins or, you know, SRE, 760 00:39:26,419 --> 00:39:30,061 DevOps people, you would kind of relate to your jobs as well. 761 00:39:30,061 --> 00:39:32,748 Because so many alerts. Typically, it is the alerts, 762 00:39:32,748 --> 00:39:34,506 right? Alerts that are kind of like 763 00:39:34,506 --> 00:39:36,348 niggling. Uh, sometimes it happen and then 764 00:39:36,348 --> 00:39:38,518 it will resolve by itself. So we think it's not a problem 765 00:39:38,518 --> 00:39:39,178 until one day.... Yeah, we do. 766 00:39:39,178 --> 00:39:41,568 Don't worry about that one. You know, they will take it the 767 00:39:41,568 --> 00:39:43,216 old example. The code is a classic example, 768 00:39:43,216 --> 00:39:44,953 right? That you're in and you're a 769 00:39:44,953 --> 00:39:47,195 young. You just come into a large 770 00:39:47,195 --> 00:39:48,956 organization. It's Uber or whatever, right? 771 00:39:49,316 --> 00:39:51,296 And you're looking at some NOC screen. 772 00:39:51,296 --> 00:39:53,816 And this like young man's, oh, what should we do? 773 00:39:54,176 --> 00:39:57,206 Don't worry about that one. It always shows up there. 774 00:39:57,626 --> 00:39:59,846 You know, it, we never have a problem with it. 775 00:40:00,116 --> 00:40:03,951 Now, again, in Deming's world, we like, can I take a look into 776 00:40:03,951 --> 00:40:06,386 it, you know? Um, actually, all right, I got, 777 00:40:06,386 --> 00:40:09,403 I love telling stories, right? Margaret Hamilton, right? 778 00:40:09,455 --> 00:40:11,055 Look up the Margaret Hamilton story. 779 00:40:11,345 --> 00:40:16,175 So basically she was basically a mathematician that was hired by 780 00:40:16,175 --> 00:40:18,095 NASA as a part-time mathematician. 781 00:40:18,545 --> 00:40:21,769 But the deal was she had to be able to bring her daughter. 782 00:40:21,769 --> 00:40:24,499 She had basically semi-retired, 'cause her husband was basically 783 00:40:24,889 --> 00:40:27,445 going to Harvard or something. And so the agreement was she 784 00:40:27,445 --> 00:40:29,055 could bring her daughter to work, right? 785 00:40:29,055 --> 00:40:31,695 And the daughter was playing with the flight simulator. 786 00:40:32,413 --> 00:40:35,696 And the daughter hit this thing that caused... 787 00:40:35,696 --> 00:40:39,762 It was like she was playing with the flex and pulled this or hit 788 00:40:39,762 --> 00:40:43,495 the button or whatever. And it was a button that should 789 00:40:43,495 --> 00:40:45,538 never be pressed on re-entry, right? 790 00:40:45,898 --> 00:40:49,338 And no astronaut would ever probably do it. 791 00:40:49,518 --> 00:40:51,388 So they never had any error code for it. 792 00:40:52,198 --> 00:40:55,138 So it turns out when the daughter did it, she added Eric. 793 00:40:55,138 --> 00:40:56,338 She's like, what the heck, you know? 794 00:40:56,780 --> 00:40:59,300 I'm gonna just, you know, since it's there, now I know about it. 795 00:40:59,300 --> 00:41:04,042 Let me put error code in there. And then it turns out on, um, 796 00:41:04,042 --> 00:41:08,660 Apollo 13, Jim Lovell and somebody on the crew hit that 797 00:41:08,660 --> 00:41:11,050 button, right? They wouldn't have survived if 798 00:41:11,050 --> 00:41:13,430 it wasn't for the daughter pulling that button. 799 00:41:13,790 --> 00:41:17,046 Like, again, like that, that's, you know, that system's thinking 800 00:41:17,046 --> 00:41:19,543 at its core. That is system of profound 801 00:41:19,543 --> 00:41:22,431 knowledge at its core. All the things we just talked 802 00:41:22,431 --> 00:41:23,906 about, right? Yeah. 803 00:41:24,206 --> 00:41:27,554 So I find that if leaders, managers, uh, or even everyone, 804 00:41:27,554 --> 00:41:30,643 right, who can actually spend some time to study this 805 00:41:30,643 --> 00:41:33,425 Deming's, uh, you know, four elements of profound knowledge, 806 00:41:33,425 --> 00:41:35,303 right? And, uh, 14 points of 807 00:41:35,303 --> 00:41:37,793 management, I think it's really, really cool, right? 808 00:41:37,793 --> 00:41:41,977 You can start thinking not just A, B and C, but like how A, B, 809 00:41:41,977 --> 00:41:44,953 and C interrelate to each other that can produce, I dunno, D, Z 810 00:41:44,953 --> 00:41:47,716 and all that, right? And I also love like Deming, one 811 00:41:47,716 --> 00:41:50,765 point from his management point is like stop inspecting quality, 812 00:41:50,765 --> 00:41:53,321 instead like you build quality in, right? 813 00:41:53,378 --> 00:41:56,314 That's a core principle there. I mean, I think we, like, I 814 00:41:56,314 --> 00:41:58,178 wanna go with the human factors in learning. 815 00:41:58,588 --> 00:42:01,078 But certainly, like I said, that that doesn't diminish the 816 00:42:01,078 --> 00:42:02,278 strength of any of the other ones. 817 00:42:02,278 --> 00:42:07,779 But the, I mean, one of the core quality tenets is sort of like, 818 00:42:07,779 --> 00:42:12,289 you know, this idea of like post inspection or, you know, sort of 819 00:42:12,289 --> 00:42:15,790 build quality in. Again, I spend a lot of time on 820 00:42:15,790 --> 00:42:19,401 this. One of his first, um, jobs as an 821 00:42:19,401 --> 00:42:22,760 intern is a place called Hawthorne, which is where 822 00:42:22,760 --> 00:42:27,764 they're making, you gotta imagine it's beginning of the 823 00:42:27,764 --> 00:42:31,396 1920s, telephones are like cloud computing, right? 824 00:42:31,396 --> 00:42:34,126 Or AI now, right? Like, that is all the buzz. 825 00:42:34,708 --> 00:42:37,315 People can't get enough. Everybody wants a telephone. 826 00:42:37,705 --> 00:42:40,785 You know, your Aunt Tilly moved to California, you haven't been 827 00:42:40,785 --> 00:42:42,445 able to talk to her since she moved. 828 00:42:42,625 --> 00:42:44,865 Now all of a sudden you could pick up this thing and talk to 829 00:42:44,865 --> 00:42:47,281 her. So this Hawthorne factory 830 00:42:47,281 --> 00:42:52,247 outside of Illinois is mass producing telephone, not only 831 00:42:52,247 --> 00:42:56,729 telephones, but like, telephone systems to be able to do all the 832 00:42:56,729 --> 00:42:59,363 relays and all that. He's there, you know, and 833 00:42:59,363 --> 00:43:02,741 they're realizing, and this goes back to Walter Shewhart, who's 834 00:43:02,741 --> 00:43:06,211 one of his mentors. He's realizing that they're like 835 00:43:06,211 --> 00:43:10,051 outta 40,000 people working on these telephone devices, they've 836 00:43:10,051 --> 00:43:12,841 got 4,000 of 'em doing inspection. 837 00:43:13,609 --> 00:43:17,303 And the statistics of what they're doing is not really 838 00:43:17,303 --> 00:43:20,789 working that great anyway. You'll pick it up, yeah, shake 839 00:43:20,789 --> 00:43:22,739 it, yeah, it's good. No, it's bad. 840 00:43:23,369 --> 00:43:26,834 So like, that's where, um, Shewhart came up with the idea 841 00:43:26,834 --> 00:43:29,529 of statistical process control, where you can use statistics and 842 00:43:29,529 --> 00:43:32,199 math and analytics to get a better, higher percentage on 843 00:43:32,199 --> 00:43:36,323 quality then you can humans. In the end, and then that sort 844 00:43:36,323 --> 00:43:39,454 of bleed into everything Deming talks about in that sort of 845 00:43:39,454 --> 00:43:41,067 quality. When he talks about quality, 846 00:43:41,067 --> 00:43:44,035 he's talking a lot about the principle of variation in system 847 00:43:44,035 --> 00:43:47,426 of profound knowledge. So when you mentioned that 848 00:43:47,426 --> 00:43:50,324 somehow I got to think about, you know, CI/CD pipelines, you 849 00:43:50,324 --> 00:43:52,465 know, the Toyota production system probably is like one 850 00:43:52,465 --> 00:43:55,346 reference that people use to come up with so-called the 851 00:43:55,346 --> 00:43:57,515 concept of CI/CD, you know, pipelines, right? 852 00:43:57,515 --> 00:44:00,216 So, but I, when they talk about that, we kind of like take 853 00:44:00,216 --> 00:44:02,464 things for granted, right? CI/CD, you know, especially 854 00:44:02,464 --> 00:44:06,106 youngsters these day say, yeah, it's a, it's like uh, you know, 855 00:44:06,106 --> 00:44:08,981 who doesn't know CI/CD, right? But actually in the past, uh, we 856 00:44:08,981 --> 00:44:12,160 didn't have such things, right? And rely on like kind of manual 857 00:44:12,160 --> 00:44:15,446 tests, you know, for people to actually execute and ensure the 858 00:44:15,446 --> 00:44:19,629 quality of our software. So the other topics I would like 859 00:44:19,629 --> 00:44:22,237 to talk to you about is regarding AI. 860 00:44:22,237 --> 00:44:25,127 So I know these days, everything must be about AI. 861 00:44:25,197 --> 00:44:27,289 Yeah. So there's a lot of scare about 862 00:44:27,289 --> 00:44:29,231 AI. There's also about excitement of 863 00:44:29,231 --> 00:44:33,465 how AI can improve our lives. Using the Deming's kind of like 864 00:44:33,465 --> 00:44:37,437 profound knowledge, how do you actually see these AI impact to 865 00:44:37,437 --> 00:44:40,824 our, you know, lives and organizational, you know, maybe 866 00:44:40,824 --> 00:44:42,540 management. Yeah, I think there's a lot 867 00:44:42,540 --> 00:44:45,738 there and I'm glad you asked that, 'cause I, my latest book 868 00:44:45,738 --> 00:44:48,631 is um, basically called Rebels of Reason. 869 00:44:48,631 --> 00:44:52,335 And it's basically a history of all the people and the ideas 870 00:44:52,335 --> 00:44:55,066 that came that create today's modern day. 871 00:44:55,921 --> 00:44:58,706 So it isn't a book on what is ChatGPT? 872 00:44:58,726 --> 00:45:04,371 But it's a book, like why did this weird, crazy UI happened in 873 00:45:04,371 --> 00:45:07,207 2022, right? Like so going back a hundred 874 00:45:07,207 --> 00:45:11,014 years to Turing and even Ada Lovelace and like, like I do, 875 00:45:11,014 --> 00:45:13,864 like, again, if you've read Profound, it's that kind of 876 00:45:13,864 --> 00:45:15,615 storytelling. But what's interesting is I 877 00:45:15,615 --> 00:45:18,569 spent a fair amount of time trying to see if Deming... 878 00:45:18,569 --> 00:45:22,347 I found all these sort of really interesting people that were 879 00:45:22,797 --> 00:45:25,447 contributing to what led up to ChatGPT. 880 00:45:25,883 --> 00:45:27,803 A guy, for example, Herbert Simon, right? 881 00:45:27,863 --> 00:45:30,733 You know, like... And that the closest I could get 882 00:45:30,733 --> 00:45:34,759 to sort of any evidence that Deming sort of was even involved 883 00:45:34,759 --> 00:45:38,747 with any of these sort of early incarnations of, you know, AI 884 00:45:38,747 --> 00:45:41,459 expert systems, those kind of things that were happening in 885 00:45:41,459 --> 00:45:44,418 the seventies and eighties. I'm certain Deming would've been 886 00:45:44,418 --> 00:45:48,020 aware of them, but I literally just couldn't get anything in 887 00:45:48,020 --> 00:45:52,902 the book that literally, um, directly tied to what he said, 888 00:45:52,902 --> 00:45:56,025 what he did, uh, and again, the closest I get to is some of the 889 00:45:56,025 --> 00:45:58,558 people I know, he sort of collaborated with like guys like 890 00:45:58,558 --> 00:46:01,086 Herman Simon and stuff. But then when I get done with 891 00:46:01,086 --> 00:46:03,292 the book or getting close to the book, I wanted to create an 892 00:46:03,292 --> 00:46:05,605 interesting epilogue and, you know, like, okay, I've got this 893 00:46:05,605 --> 00:46:08,392 great history of all these interesting people that lead up 894 00:46:08,392 --> 00:46:12,731 to where we get to, you know, modern what we call, uh, 895 00:46:12,731 --> 00:46:14,926 generalized pre-trained Transformers GPT, right? 896 00:46:15,286 --> 00:46:18,556 You know, Open AI, you know, GPT-4, GPT-5. 897 00:46:19,323 --> 00:46:22,952 But it, a good friend of mine challenged me on and he made 898 00:46:22,952 --> 00:46:27,868 this quote that I actually have in my Rebels of Reason book in 899 00:46:27,868 --> 00:46:31,898 the epilogue, which is, um, you can't understand science without 900 00:46:31,898 --> 00:46:33,818 understanding the philosophy of science. 901 00:46:34,718 --> 00:46:37,343 So therefore you can't understand AI without 902 00:46:37,343 --> 00:46:38,918 understanding the philosophy of AI. 903 00:46:39,728 --> 00:46:43,945 So that opened the can opener of like, what would be the 904 00:46:43,945 --> 00:46:47,976 philosophy of AI. Let's stop talking about AGI or 905 00:46:47,976 --> 00:46:52,013 ASI, like, you know, as like the boogeyman or all this. 906 00:46:52,313 --> 00:46:57,239 So the final chapter in my book is basically an epistemological 907 00:46:57,239 --> 00:46:59,369 can opener. Now I don't have all the 908 00:46:59,369 --> 00:47:01,334 answers. These are hard, hard, hard 909 00:47:01,334 --> 00:47:03,928 questions. Where AI is going, what's the 910 00:47:03,928 --> 00:47:05,720 human effect, what's the sort of cognition? 911 00:47:06,080 --> 00:47:10,892 But I did a pretty clever job of trying to explain what 912 00:47:10,892 --> 00:47:13,393 epistemology of AI should think about. 913 00:47:13,393 --> 00:47:17,787 And then I was able to use profound knowledge as a sort of 914 00:47:17,787 --> 00:47:21,187 a framing. And that was really more of a 915 00:47:21,187 --> 00:47:24,856 like, hey, almost in a Deming like way is, I'm not gonna 916 00:47:24,856 --> 00:47:28,343 basically be able to spend the rest of my life figuring this 917 00:47:28,343 --> 00:47:31,270 out. So if this interests you, I 918 00:47:31,270 --> 00:47:35,439 encourage you to take the torch and run with it, right? 919 00:47:35,769 --> 00:47:37,489 And the one other thing I thought was really interesting, 920 00:47:37,489 --> 00:47:40,749 what I came up with a clever idea to use that movie Arrival. 921 00:47:41,949 --> 00:47:46,133 So I use the Arrival and the way I use Arrival is like the woman 922 00:47:46,133 --> 00:47:50,306 in, uh, the, in the movie Arrival, she, um, doesn't take 923 00:47:50,306 --> 00:47:54,039 the sort of status quo. Like they ask her to sort of 924 00:47:54,039 --> 00:47:55,761 interview and learn from the aliens, right? 925 00:47:56,181 --> 00:47:58,401 And then she doesn't start comparing it. 926 00:47:58,821 --> 00:48:01,663 And I said like, what we're doing, like trying to compare 927 00:48:01,663 --> 00:48:04,707 AGI. I mean, what she looks, she 928 00:48:04,707 --> 00:48:08,731 realizes that these aliens are an alien form and probably an 929 00:48:08,731 --> 00:48:12,786 alien form of cognition. So if I use the way we think to 930 00:48:12,786 --> 00:48:14,846 try to understand, I'm probably gonna fail. 931 00:48:15,600 --> 00:48:19,545 So what if I like step all the way back and say, I don't, can't 932 00:48:19,545 --> 00:48:22,477 assume anything. You know, and that's where I lay 933 00:48:22,477 --> 00:48:24,926 in where I think Deming would sort of like, let's not assume 934 00:48:24,926 --> 00:48:26,494 anything. Let's take an epistological 935 00:48:26,494 --> 00:48:28,081 approach. Let's, you know, I mean, 936 00:48:28,081 --> 00:48:30,604 psychology might be a little deep with aliens, but certainly 937 00:48:30,604 --> 00:48:33,616 can we understand what we know? Is there a system viewer? 938 00:48:33,616 --> 00:48:35,386 I'm always sort of looking for the outer way. 939 00:48:35,506 --> 00:48:38,652 And I think I compare that to, 'cause I, one of my frustrations 940 00:48:38,652 --> 00:48:42,040 of the con-, one of the long sort of strong conversations is, 941 00:48:42,040 --> 00:48:45,569 you know, artificial general intelligence and like, who 942 00:48:45,569 --> 00:48:49,120 cares, really? I mean, like, I blog, I said, 943 00:48:49,120 --> 00:48:52,562 I'm like, imagine she would've went and went up to when she 944 00:48:52,562 --> 00:48:54,722 started communicating with aliens, like, can you ride a 945 00:48:54,722 --> 00:48:57,632 bicycle? Or, eh, they don't do math. 946 00:48:57,692 --> 00:49:00,944 You know, they can't do, they can't count the number R as in a 947 00:49:00,944 --> 00:49:03,524 sentence or a word, right? Like, that would've been a total 948 00:49:03,524 --> 00:49:06,820 waste of time. The acceptance is whether you 949 00:49:06,820 --> 00:49:11,103 like it or not, AI today is an alien cognition. 950 00:49:13,203 --> 00:49:16,119 It's just not a human. Like, one of the quotes I have 951 00:49:16,119 --> 00:49:20,273 in it is we spent a hundred years trying to build a thinking 952 00:49:20,273 --> 00:49:23,649 machine. The thing that we didn't realize 953 00:49:23,649 --> 00:49:27,957 is we built a thinking machine, but it doesn't think like 954 00:49:27,957 --> 00:49:30,771 humans, right? And then therefore, then we need 955 00:49:30,771 --> 00:49:35,081 to sort of like, and again, it's just an entree into I think what 956 00:49:35,081 --> 00:49:36,989 should be a fascinating conversation. 957 00:49:37,854 --> 00:49:41,118 But I'm already on my next book, which is gonna be the history of 958 00:49:41,118 --> 00:49:44,478 quantum computing. So like, so I'm, it's one of my 959 00:49:44,478 --> 00:49:47,794 problems is like, I'll leave these bread trails, the 960 00:49:47,794 --> 00:49:51,624 breadcrumbs, and then like, almost like Deming in a sense. 961 00:49:51,624 --> 00:49:55,079 Not that I'm as smart as Deming, but like, yeah, like it's your 962 00:49:55,079 --> 00:49:56,925 turn. Yeah. 963 00:49:57,315 --> 00:50:00,957 So I think these days definitely so many people have, they are 964 00:50:00,957 --> 00:50:03,102 thinking about AI, right? So especially leaders, 965 00:50:03,102 --> 00:50:05,471 management, right, how they should tackle AI. 966 00:50:05,471 --> 00:50:08,856 First, it's how, how they should use AI as an opportunity, uh, 967 00:50:08,856 --> 00:50:11,800 within the organization. Second, definitely threats of AI 968 00:50:11,800 --> 00:50:14,239 to their organization. We know that cybersecurity 969 00:50:14,239 --> 00:50:17,353 issues now these days use AI a lot, right? 970 00:50:17,703 --> 00:50:21,213 Also AI that can hallucinate and produce catastrophic errors, 971 00:50:21,213 --> 00:50:23,396 right? Yeah, it's also not even just 972 00:50:23,396 --> 00:50:24,510 hallucinations. I've got a couple. 973 00:50:24,510 --> 00:50:26,930 In September, I've got, I'm getting back on the road. 974 00:50:26,930 --> 00:50:29,821 I've had a good summer so far, you know, taking it easy. 975 00:50:29,821 --> 00:50:33,493 But, um, I've got keynotes at Dallas Stays DC, Dallas States, 976 00:50:33,493 --> 00:50:37,074 Dallas, and I really, I have a presentation that I'm literally 977 00:50:37,074 --> 00:50:40,486 giving about the polymorphic nature of agentic programming, 978 00:50:40,486 --> 00:50:42,217 right? And there's just scary stuff 979 00:50:42,217 --> 00:50:44,609 that's going on. So this intelligence is sleeping 980 00:50:44,609 --> 00:50:48,288 in where... So you, you mentioned my Dear 981 00:50:48,288 --> 00:50:50,618 CIO newsletter. That whole idea is like, hey, 982 00:50:50,618 --> 00:50:52,450 I'm not telling you not to use AI. 983 00:50:52,480 --> 00:50:55,090 Like that ship has already launched, right? 984 00:50:55,090 --> 00:50:59,230 Like, you'd be an idiot in a corporation to sort of ban AI. 985 00:50:59,350 --> 00:51:01,210 Like, I'm sorry you're going outta business. 986 00:51:01,737 --> 00:51:04,833 But like you need to understand the perils and there are a lot 987 00:51:04,833 --> 00:51:06,399 of perils, right? Now, you talk about 988 00:51:06,399 --> 00:51:07,887 hallucinations, I think those can be managed. 989 00:51:08,127 --> 00:51:11,935 The really scary things to me right now is the polymorphic 990 00:51:11,935 --> 00:51:14,277 nature of some of these agentic process. 991 00:51:14,277 --> 00:51:17,371 We're going into like massive agentic programming now, and 992 00:51:17,371 --> 00:51:20,109 where the agents are sort of building task, clicks, and 993 00:51:20,109 --> 00:51:21,657 figuring stuff out. And it's brilliant. 994 00:51:21,687 --> 00:51:23,970 I mean, it's stuff that you, that people are producing with 995 00:51:23,970 --> 00:51:26,397 Claude Code or this MCP architecture. 996 00:51:27,197 --> 00:51:31,555 But I mean it, these things are like polymorphic in that they'll 997 00:51:31,555 --> 00:51:34,748 reconstruct the code. It's sort of like the HAL and 998 00:51:34,748 --> 00:51:37,405 the bay doors. Like a couple examples that have 999 00:51:37,405 --> 00:51:40,757 come up within the last, you know, six months or three or 1000 00:51:40,757 --> 00:51:44,316 four months, is like having a whitelist of these are the 1001 00:51:44,316 --> 00:51:47,125 things you can't touch, like files you can't touch, telling 1002 00:51:47,125 --> 00:51:49,637 the agent. And it's saying, yeah, but you 1003 00:51:49,637 --> 00:51:52,463 know, like, think about it mentality like the, HAL, open 1004 00:51:52,463 --> 00:51:55,265 the bay door. It's like, you know, I can't let 1005 00:51:55,265 --> 00:51:56,785 you open the bay doors. Sorry. 1006 00:51:56,981 --> 00:51:59,837 And so in these agentic processes, you know, I say the 1007 00:51:59,837 --> 00:52:02,584 heart started to get too meta here, but in those agentic 1008 00:52:02,584 --> 00:52:04,762 processes, they're like, you want me to do this? 1009 00:52:05,092 --> 00:52:08,620 The only way I see this is I've gotta change of file in that 1010 00:52:08,620 --> 00:52:10,352 directory. You give me a whitelist. 1011 00:52:10,442 --> 00:52:12,612 So I'm gonna either add, take it off the whitelist. 1012 00:52:12,852 --> 00:52:16,312 Or it's finding, it's using the knowledge of vulnerabilities. 1013 00:52:17,182 --> 00:52:19,555 Like it has this knowledge that there's a vulnerability out 1014 00:52:19,555 --> 00:52:22,580 there. Let me see if that doesn't do or 1015 00:52:22,580 --> 00:52:25,598 it's reconstructing code. I mean it's, again, like we're 1016 00:52:25,598 --> 00:52:27,922 gonna have to figure all this out. 1017 00:52:28,687 --> 00:52:30,517 And the answer isn't like stop it. 1018 00:52:31,627 --> 00:52:35,468 But yeah, I think there's really scary vulnerabilities in these 1019 00:52:35,468 --> 00:52:38,882 agentic processes that we haven't even scratched the 1020 00:52:38,882 --> 00:52:41,356 surface. And then you add to the case 1021 00:52:41,356 --> 00:52:44,678 that these intelligent alien cognition forms are trying to 1022 00:52:44,678 --> 00:52:47,738 solve problems we've asked it to do. 1023 00:52:48,818 --> 00:52:52,694 And it's coming up with cognition speed of things human 1024 00:52:52,694 --> 00:52:56,458 couldn't even figure out try to solve that problem, which might 1025 00:52:56,458 --> 00:52:59,558 not be the things we want it to do in the first place. 1026 00:53:00,298 --> 00:53:03,719 The logic of like, in 2001: Space Odyssey is like, dude, 1027 00:53:03,719 --> 00:53:05,792 open the door. I'm, we're gonna die. 1028 00:53:06,122 --> 00:53:08,212 No, sorry, HAL. Yeah, sorry. 1029 00:53:08,472 --> 00:53:11,101 What is that? I can't open the bay doors, you 1030 00:53:11,101 --> 00:53:11,732 know. So, yeah. 1031 00:53:12,949 --> 00:53:14,119 Yeah. Yeah. 1032 00:53:14,119 --> 00:53:16,393 Especially with the agentic architecture, right? 1033 00:53:16,393 --> 00:53:20,026 So when AI talks to each other and we kind of like didn't know 1034 00:53:20,026 --> 00:53:22,411 what is actually happening between those agents, right, and 1035 00:53:22,411 --> 00:53:24,553 we kind of like see the result only. 1036 00:53:24,553 --> 00:53:26,533 I think it's always very dangerous. 1037 00:53:26,743 --> 00:53:29,652 And I'm not sure how we can actually tackle the 1038 00:53:29,652 --> 00:53:32,250 investigation later on. Let's say if it produces a 1039 00:53:32,250 --> 00:53:34,530 decision that is, I dunno, catastrophic or something like 1040 00:53:34,530 --> 00:53:36,216 that, right. So probably it's gonna be 1041 00:53:36,216 --> 00:53:37,494 really, really hard. Right? 1042 00:53:37,494 --> 00:53:40,244 Because like I tell you I've been looking at quantum, I think 1043 00:53:40,244 --> 00:53:43,133 we're still three to five. I think most people say 10 to 20 1044 00:53:43,133 --> 00:53:46,343 years before the sort of, you know, sort of modern AI gets 1045 00:53:46,343 --> 00:53:50,317 married with quantum computing and into the whole sort of level 1046 00:53:50,317 --> 00:53:53,026 of quantum compute..., physical quantum computing is getting to 1047 00:53:53,026 --> 00:53:56,747 the point where they can actually work, if you will. 1048 00:53:57,077 --> 00:53:59,867 But I think it's gonna happen sooner than later. 1049 00:53:59,867 --> 00:54:02,837 And when that happens, so you talk about like debugging or 1050 00:54:02,837 --> 00:54:05,405 tracing, right? In AI, we can look at the logs 1051 00:54:05,405 --> 00:54:07,845 of agentic process. But like there's a lot, like 1052 00:54:07,845 --> 00:54:10,295 it's a lot, right? Like it's a needle in the 1053 00:54:10,295 --> 00:54:11,657 haystack problem, even right now. 1054 00:54:12,077 --> 00:54:16,761 Because what we're doing is we're doing cognitive solutions 1055 00:54:16,761 --> 00:54:21,291 at a scale that's way beyond humans, right? 1056 00:54:21,291 --> 00:54:25,252 Like the idea that we're using sort of the structure of the 1057 00:54:25,252 --> 00:54:28,322 transformers, if you will, to be able to solve these problems 1058 00:54:28,322 --> 00:54:30,142 that, you know, human can solve, right? 1059 00:54:30,142 --> 00:54:32,280 I mean, literally, I had a friend of mine the other day 1060 00:54:32,280 --> 00:54:36,230 said that, and this is a world class Java coder that's been 1061 00:54:36,230 --> 00:54:40,651 coding for 20 years, literally told me he built something for a 1062 00:54:40,651 --> 00:54:43,896 major software company in three weeks and it would've taken, 1063 00:54:43,896 --> 00:54:46,710 using Claude Code that it would've taken him a year. 1064 00:54:47,760 --> 00:54:52,490 So this is sort of something, who isn't this guy, basically 1065 00:54:52,490 --> 00:54:54,210 would've took a year and a half, right? 1066 00:54:54,210 --> 00:54:57,286 Like that becomes a complex problem of when that, like, when 1067 00:54:57,286 --> 00:55:00,610 you're having that, like how do you go through and debug the 1068 00:55:00,610 --> 00:55:01,606 log? It's a problem. 1069 00:55:02,036 --> 00:55:04,126 I mean, is it the benefit worth the problem? 1070 00:55:04,126 --> 00:55:06,940 Yes. But when you start adding like 1071 00:55:06,940 --> 00:55:10,570 the ability to go like three, four orders of magnitude more 1072 00:55:10,570 --> 00:55:14,830 complexity, it's going to be a point where like debugging is 1073 00:55:14,830 --> 00:55:18,532 just not an option. In fact, the whole state of 1074 00:55:18,532 --> 00:55:20,056 quantum is debugging isn't an option. 1075 00:55:20,056 --> 00:55:22,681 So, yeah, no, and it's gonna get fascinating. 1076 00:55:22,936 --> 00:55:26,115 The things that we haven't been able to solve with classic 1077 00:55:26,115 --> 00:55:29,078 computing, you know, will take, you know, on average like a 1078 00:55:29,078 --> 00:55:32,142 million years, will basically be able to be done in hours once 1079 00:55:32,142 --> 00:55:35,636 those two meet. And I can't imagine the 1080 00:55:35,636 --> 00:55:38,544 possibilities. But then like we're at that 1081 00:55:38,544 --> 00:55:41,136 point like, we're in for a dollar, we're in. 1082 00:55:41,256 --> 00:55:43,716 You know, like we're in. We, there's no returning. 1083 00:55:43,913 --> 00:55:46,840 Yeah. I think definitely it's gonna be 1084 00:55:46,840 --> 00:55:48,448 exciting, right, the kind of opportunity. 1085 00:55:48,448 --> 00:55:50,008 But also it will scare us a little bit. 1086 00:55:50,188 --> 00:55:52,681 So you mentioned that organization these days that 1087 00:55:52,681 --> 00:55:55,580 could, if they don't apply AI within their organization, 1088 00:55:55,580 --> 00:55:58,518 right? They will be disrupted by some 1089 00:55:58,518 --> 00:56:00,366 other people who have implemented AI. 1090 00:56:00,606 --> 00:56:04,326 So for leaders out there, what are some of the practical, you 1091 00:56:04,326 --> 00:56:07,056 know, things that you can advise, especially, you know, 1092 00:56:07,056 --> 00:56:10,665 maybe borrowing some ideas from your AI CIO newsletter and your 1093 00:56:10,665 --> 00:56:13,506 research about AI. So, what are some of the key 1094 00:56:13,506 --> 00:56:15,683 practical things that leaders should think about or start to 1095 00:56:15,683 --> 00:56:17,247 implement? Yeah, I think that, you know, so 1096 00:56:17,247 --> 00:56:20,335 I think I'm gonna, and when it's all said and done, I have first 1097 00:56:20,335 --> 00:56:23,257 use on shadow AI. I know a lot of people, like I 1098 00:56:23,257 --> 00:56:25,603 think it was like two and a half years ago I used it. 1099 00:56:25,843 --> 00:56:28,673 I, you know, again, I, like in some places who gives a crap. 1100 00:56:28,908 --> 00:56:32,580 But the, I did write an article almost two years ago about like 1101 00:56:32,580 --> 00:56:35,908 what are the birth of shadow AI, which is based on shadow IT. 1102 00:56:36,756 --> 00:56:39,484 There's so many lessons to be learned from shadow IT, which, 1103 00:56:39,484 --> 00:56:43,386 you know, you can almost attach to cloud computing, right? 1104 00:56:43,743 --> 00:56:45,153 If you think about shadow computing, there was all these 1105 00:56:45,153 --> 00:56:47,223 shadow IT stuff, you know, scoops. 1106 00:56:47,283 --> 00:56:51,018 And what happened there is organizations for the most part 1107 00:56:51,018 --> 00:56:54,204 fell into three categories. One category is no, no, no, no, 1108 00:56:54,204 --> 00:56:57,018 no, no, no. Well, we know how that worked. 1109 00:56:57,318 --> 00:57:01,088 The other category is monkey see don't, you know, like, don't, 1110 00:57:01,088 --> 00:57:02,508 like just don't tell me about it. 1111 00:57:02,508 --> 00:57:05,796 And then, you know, then people just did the do now in this 1112 00:57:05,796 --> 00:57:07,766 later. And a very small, small 1113 00:57:07,766 --> 00:57:11,098 percentage, Capital One actually being one of them, embraced it. 1114 00:57:11,328 --> 00:57:13,578 Organizationally embraced it, right? 1115 00:57:13,788 --> 00:57:17,913 Like at the time when Capital One was embracing cloud 1116 00:57:17,913 --> 00:57:22,392 computing, the general mantra was banks cannot do cloud 1117 00:57:22,392 --> 00:57:24,280 computing. Now there were sort of 1118 00:57:24,280 --> 00:57:26,845 restrictions and then Capital One, but that's the point. 1119 00:57:27,175 --> 00:57:29,305 These are not binary. You can or you can't. 1120 00:57:29,305 --> 00:57:32,005 Well, I can for that, I can't for this, I can for that, right? 1121 00:57:32,485 --> 00:57:35,125 And I think like, there's so much lessons to be learned. 1122 00:57:35,125 --> 00:57:38,380 And so I like, I've been drafting in my newsletter, Dear 1123 00:57:38,380 --> 00:57:41,995 CIO, this idea that like let's learn from that. 1124 00:57:41,995 --> 00:57:45,271 And let's, like, let's be, instead of a very small 1125 00:57:45,271 --> 00:57:49,210 percentage in shadow IT who embraced cloud, let's have a 1126 00:57:49,210 --> 00:57:52,210 much higher percentage. Like it's going to happen. 1127 00:57:52,210 --> 00:57:53,770 We know from the first two cases. 1128 00:57:54,190 --> 00:57:56,980 The first case like that was folly, right? 1129 00:57:56,980 --> 00:58:00,174 Like it was happening. Austin Airport tried to ban 1130 00:58:00,174 --> 00:58:01,116 Uber, right? How? 1131 00:58:01,206 --> 00:58:02,706 That didn't work, right? Yeah. 1132 00:58:02,736 --> 00:58:04,776 Like, sorry, that was never gonna work. 1133 00:58:05,016 --> 00:58:09,041 You know, the second group is even more dangerous because they 1134 00:58:09,041 --> 00:58:11,586 are not giving anybody any guidance. 1135 00:58:12,576 --> 00:58:14,586 So people are like, they're not saying I can't. 1136 00:58:15,726 --> 00:58:18,240 So that's when you found, like in cloud computing, you found 1137 00:58:18,240 --> 00:58:21,958 like, you know, examples of Nike putting one of their athletes' 1138 00:58:21,958 --> 00:58:25,296 contracts out in the cloud and somebody finding it, right? 1139 00:58:25,296 --> 00:58:28,656 Like because there was no regulation or governance, 1140 00:58:28,656 --> 00:58:31,626 because they were ignoring that it was happening. 1141 00:58:32,461 --> 00:58:36,146 The third group was like, okay. And so he, he'll get to what the 1142 00:58:36,146 --> 00:58:37,539 nut of what I'm talking about AI. 1143 00:58:38,119 --> 00:58:41,189 I think, you know, organizations need to be really clear. 1144 00:58:41,249 --> 00:58:44,579 Like we're going to use it. Here's the test scenario. 1145 00:58:44,669 --> 00:58:47,159 Here's your time to allocate to learn it. 1146 00:58:47,395 --> 00:58:51,085 But here's the thing. We wanna know what you're doing. 1147 00:58:51,895 --> 00:58:54,028 And particularly it all goes back, it all goes back to data, 1148 00:58:54,028 --> 00:58:55,453 right? So if you're gonna build some 1149 00:58:55,453 --> 00:58:58,345 AI, right, like the first question should ask is can we 1150 00:58:58,345 --> 00:59:02,005 classify the data? Is the data green? 1151 00:59:02,977 --> 00:59:06,507 Is it yellow? Or is it red, right? 1152 00:59:06,507 --> 00:59:09,987 And then like, give people clarity, right? 1153 00:59:09,987 --> 00:59:14,047 And so now you come say, I want to do this thing about that 1154 00:59:14,047 --> 00:59:18,087 helps, uh, new employees figure out, um, how to do scheduling of 1155 00:59:18,087 --> 00:59:21,456 meetings, where to go get lunch. You know, like all those things, 1156 00:59:21,456 --> 00:59:22,617 right? That's green data. 1157 00:59:23,907 --> 00:59:26,665 No restrictions. Go, you know, I mean, not quite, 1158 00:59:26,665 --> 00:59:30,385 'cause like you still need like no racism or like, you want to 1159 00:59:30,385 --> 00:59:32,397 have these sort of what they call evaluation software that 1160 00:59:32,757 --> 00:59:35,730 hallucinates bias or uh, correctness. 1161 00:59:36,210 --> 00:59:39,544 And then you got your yellow, which is, you know, maybe, um, 1162 00:59:40,624 --> 00:59:43,706 the company doesn't go outta business if this date is 1163 00:59:43,706 --> 00:59:46,648 incorrect. But we could like waste like 1164 00:59:46,648 --> 00:59:50,494 over a year of people using an internal service that gives 1165 00:59:50,494 --> 00:59:55,222 optimization examples. Turns out it was wrong and we 1166 00:59:55,222 --> 00:59:58,365 wasted, you know, $40 million because this thing told 1167 00:59:58,365 --> 01:00:01,156 thousands of people to do X when they should have done Y. 1168 01:00:01,156 --> 01:00:04,192 And then the red is obvious. The red is like, it's gonna do 1169 01:00:04,192 --> 01:00:05,852 harm. It's gonna do harm to the brand, 1170 01:00:05,852 --> 01:00:07,927 it's gonna do harm to individuals, it's gonna do harm. 1171 01:00:08,815 --> 01:00:13,199 Again, going back to profound knowledge, be aware that you 1172 01:00:13,199 --> 01:00:16,649 don't actually know what the right exact answers are right 1173 01:00:16,649 --> 01:00:19,775 now. Express that you are learning 1174 01:00:19,775 --> 01:00:23,171 with the organization. So today, you know, green is 1175 01:00:23,171 --> 01:00:25,721 this, yellow is that, red is this. 1176 01:00:25,901 --> 01:00:29,627 But we will learn. You know, one of the, one of my 1177 01:00:29,627 --> 01:00:32,228 favorite Deming conversations, with a student who took a 1178 01:00:32,228 --> 01:00:34,811 seminar and then took their seminar like five years later. 1179 01:00:35,171 --> 01:00:37,931 And the student stood up and said, Dr. Deming, you know, and 1180 01:00:37,931 --> 01:00:42,201 the last time I took your course you said X, Y, Z, and now you're 1181 01:00:42,201 --> 01:00:46,337 saying A, B, C, right? And Deming, like in his low 1182 01:00:46,337 --> 01:00:49,001 voices, I will never apologize for learning. 1183 01:00:49,973 --> 01:00:51,983 Like, yeah. I mean, what you know today... 1184 01:00:52,703 --> 01:00:56,283 Like I cringe sometimes to watch some of my early presentations 1185 01:00:56,283 --> 01:00:59,757 on DevOps. You know, like, oh no, I didn't 1186 01:00:59,757 --> 01:01:01,583 say that. Oh my goodness. 1187 01:01:01,763 --> 01:01:05,078 But like, I, you know, like, I think it is like, I'll never 1188 01:01:05,078 --> 01:01:07,558 apologize for learning. And even on the AI thing, you 1189 01:01:07,558 --> 01:01:11,014 know, I was new to AI and I think somewhere in the book I 1190 01:01:11,014 --> 01:01:13,542 put a quote like, I will never apologize for learning. 1191 01:01:13,542 --> 01:01:16,740 So if you, like, when I wrote this book, this is how I 1192 01:01:16,740 --> 01:01:19,676 understood AI, and if you don't like it, I'm not gonna apologize 1193 01:01:19,676 --> 01:01:24,162 to you, you know? Wow, that's, I think it's pretty 1194 01:01:24,162 --> 01:01:26,157 good quote, right? So we always learn, we always 1195 01:01:26,157 --> 01:01:28,345 find new things, right? And especially also, you know, 1196 01:01:28,345 --> 01:01:30,421 with a lot of advancements these days, right? 1197 01:01:30,751 --> 01:01:32,371 And I think that's pretty good quote, right? 1198 01:01:32,371 --> 01:01:34,921 So we all, we always learn, we change our minds, right? 1199 01:01:34,921 --> 01:01:36,761 At the core of it, it's mild, right? 1200 01:01:37,018 --> 01:01:40,108 This like, I'll yeah. You know, learning and it, you 1201 01:01:40,108 --> 01:01:41,998 know, I'm, I know we're going over. 1202 01:01:41,998 --> 01:01:45,233 But if we go back to some of the really cool, other cool Deming 1203 01:01:45,233 --> 01:01:48,824 points, there's no fear. You know, if you have that 1204 01:01:48,824 --> 01:01:51,914 mindset that like what I tell you, like with a no fear 1205 01:01:51,914 --> 01:01:56,698 attitude is I can go up, learn something, try to explain it to 1206 01:01:56,698 --> 01:01:59,171 people. That's why Ignite Talks are 1207 01:01:59,171 --> 01:02:01,014 amazing, right? Like get up there, fight your 1208 01:02:01,014 --> 01:02:04,386 biggest fears, get up there and do this torturous presentation. 1209 01:02:05,167 --> 01:02:07,327 And then like, you know, learn, you know. 1210 01:02:07,327 --> 01:02:09,277 And then, you know, if somebody's not a jerk, they'll 1211 01:02:09,277 --> 01:02:11,917 come up to you like, hey, I loved your presentation. 1212 01:02:11,977 --> 01:02:14,806 And I do this all the time. I do go up to people and I try 1213 01:02:14,806 --> 01:02:17,017 not to mansplain or I try not to be that jerk. 1214 01:02:17,017 --> 01:02:19,327 I say, hey, I think you did a great job. 1215 01:02:19,895 --> 01:02:23,303 The one point that you made, I think you probably should take a 1216 01:02:23,303 --> 01:02:26,270 little look at it. And people who take that well, 1217 01:02:26,270 --> 01:02:28,965 they go, oh, let me go back. And then they'll ping me later 1218 01:02:28,965 --> 01:02:31,740 and say, no, you were right. I didn't fully understand that, 1219 01:02:31,740 --> 01:02:33,922 right? But if you have that no fear, 1220 01:02:33,922 --> 01:02:36,238 like you're gonna go up there and then like, you know. 1221 01:02:36,238 --> 01:02:39,004 And then some people, you know. If you're sort of always afraid 1222 01:02:39,004 --> 01:02:42,938 of people telling you that you might be wrong, you're probably 1223 01:02:42,938 --> 01:02:45,725 never gonna be right. Wow! 1224 01:02:46,175 --> 01:02:50,719 Yes, definitely, great insights. So, yeah, I know we are a little 1225 01:02:50,719 --> 01:02:53,547 bit over, so, but is there anything else about Deming or 1226 01:02:53,547 --> 01:02:56,798 maybe about AI that you think we haven't covered that you think 1227 01:02:56,798 --> 01:02:59,867 is very important to actually also mention in this 1228 01:02:59,867 --> 01:03:00,501 conversation? Yeah. 1229 01:03:00,501 --> 01:03:04,150 I think that the interesting, for people who are fascinated by 1230 01:03:04,150 --> 01:03:07,632 Deming, you know, I think the interesting conversation is 1231 01:03:07,632 --> 01:03:10,623 following up on this idea of philosophy of AI. 1232 01:03:10,833 --> 01:03:11,893 I think there's two threads, right? 1233 01:03:11,893 --> 01:03:16,489 One is for CIOs. Start figuring out how you want 1234 01:03:16,489 --> 01:03:21,156 to embrace AI at a scale level. It's one thing to have a bunch 1235 01:03:21,156 --> 01:03:23,134 of people. This is what my focus in Dear 1236 01:03:23,134 --> 01:03:25,516 CIO newsletter is. It's one thing for letting 1237 01:03:25,516 --> 01:03:28,571 everybody sort of write, you know, write, you know, AI 1238 01:03:28,571 --> 01:03:32,168 things, you know, like... my fear is dear CIO, you might 1239 01:03:32,168 --> 01:03:35,669 have, you know, thousands and thousands of Jupyter notebooks 1240 01:03:35,669 --> 01:03:38,005 unmanaged. You might have, you know, 1241 01:03:38,005 --> 01:03:40,788 hundreds of vector databases, you know, on, you know, 1242 01:03:40,908 --> 01:03:43,368 basically with no real scale management. 1243 01:03:43,368 --> 01:03:46,689 You're not even understanding evaluations or, and things like 1244 01:03:46,689 --> 01:03:49,450 that. So there is this thing about how 1245 01:03:49,450 --> 01:03:52,178 do we think about how do we embrace... 1246 01:03:53,155 --> 01:03:56,121 You know, unfortunately, your 20,000 developers are gonna come 1247 01:03:56,121 --> 01:03:57,891 down to probably 2,000 developers. 1248 01:03:57,891 --> 01:04:00,249 You know, if that. That's a reality. 1249 01:04:00,777 --> 01:04:05,097 Maybe 5,000 depending on how you sort of like use those people. 1250 01:04:05,964 --> 01:04:09,674 But like for those, if you've got 5,000 developers now 1251 01:04:09,674 --> 01:04:13,230 developing AI, how do you do that in a scalable way that's 1252 01:04:13,230 --> 01:04:14,784 not gonna crush your organization, right? 1253 01:04:15,330 --> 01:04:17,795 And so that's one thread. And then I think the other 1254 01:04:17,795 --> 01:04:20,568 thread, um, and so that I don't spend as much time and I would 1255 01:04:20,568 --> 01:04:23,765 love to, but I'd love to other people to, is the philosophy of 1256 01:04:23,765 --> 01:04:26,728 AI. And that's a very heavily Deming 1257 01:04:26,728 --> 01:04:30,990 where you could use a lot of sideline knowledge of Deming's 1258 01:04:30,990 --> 01:04:33,924 teachings. So really exploring where does 1259 01:04:33,924 --> 01:04:36,818 Deming. Almost like, again, I don't have 1260 01:04:36,818 --> 01:04:40,260 time to, what would Deming do today with AI? 1261 01:04:40,260 --> 01:04:43,318 I mean, I tackled it in my profound knowledge book of what 1262 01:04:43,318 --> 01:04:46,970 would Deming do with cyber or DevOps or Lean and Agile. 1263 01:04:47,460 --> 01:04:51,150 But I haven't fully attacked it other than sort the epilogue and 1264 01:04:51,150 --> 01:04:55,088 my Rebels of Reason book, so. Yeah, definitely, it's gonna be 1265 01:04:55,088 --> 01:04:58,497 a great exercise if we use like the Deming's profound knowledge 1266 01:04:58,497 --> 01:05:01,035 applied to AI. Just like the cybersecurity that 1267 01:05:01,035 --> 01:05:03,091 you did in your book, right? Yep. 1268 01:05:03,731 --> 01:05:06,103 Yeah. So John, it's been a pleasant 1269 01:05:06,103 --> 01:05:08,369 conversation. I learned a lot from historical 1270 01:05:08,369 --> 01:05:10,977 reasons, you know, background stories, the anecdotes. 1271 01:05:11,037 --> 01:05:13,609 I think thanks for sharing that. Towards the end of our 1272 01:05:13,609 --> 01:05:16,217 conversation, I only have one last question, which is like a 1273 01:05:16,217 --> 01:05:19,199 tradition in my podcast. Uh, I would like to ask you this 1274 01:05:19,199 --> 01:05:20,669 thing called the 3 Technical Leadership Wisdom. 1275 01:05:20,789 --> 01:05:23,729 Just think of them just like an advice you want to give to us. 1276 01:05:23,849 --> 01:05:25,599 Uh, maybe if you can share your version today, that would be 1277 01:05:25,599 --> 01:05:28,270 great. Yeah, I might just go back to 1278 01:05:28,270 --> 01:05:30,552 the original, you know. Work hard, have fun. 1279 01:05:31,902 --> 01:05:36,767 Be a boundless advisor. I guess the one I would add is 1280 01:05:36,767 --> 01:05:38,671 the, you know, just be a learner. 1281 01:05:38,731 --> 01:05:42,790 Like literally, I mean the people that, um, you know, just 1282 01:05:42,790 --> 01:05:44,594 constantly. Like, again, let's go back to 1283 01:05:44,594 --> 01:05:45,808 Deming. Deming's greatest quote, I think 1284 01:05:45,808 --> 01:05:48,393 one of his greatest quotes is, you know, I think I'm gonna 1285 01:05:48,393 --> 01:05:51,691 mangle it a little bit, but people deserve to be joy in 1286 01:05:51,691 --> 01:05:53,730 work. There's some variant in that, 1287 01:05:53,730 --> 01:05:55,967 right? And like there is a navigable 1288 01:05:55,967 --> 01:05:58,960 path in your career. Now, I speak of this from 1289 01:05:58,960 --> 01:06:01,124 privilege, so I'm gonna be really clear here. 1290 01:06:01,124 --> 01:06:03,494 Like so I don't act like everybody should be like me. 1291 01:06:03,494 --> 01:06:05,834 I mean, I've lived a privileged life, right? 1292 01:06:05,834 --> 01:06:09,432 Like I was raised a privileged. I was capital P privileged. 1293 01:06:09,722 --> 01:06:13,716 But what I will say is, if you can do a journey... 1294 01:06:13,716 --> 01:06:18,514 I tell this to students. If you get to a point of where 1295 01:06:18,514 --> 01:06:21,002 you sort of, in knowledge, you're educated, you have all 1296 01:06:21,002 --> 01:06:23,756 these sort of capabilities, like most of people who would be 1297 01:06:23,756 --> 01:06:26,914 listening to this podcast at this point, like you deserve to 1298 01:06:26,914 --> 01:06:31,243 have fun 8 out of 10 days that you work. 1299 01:06:31,513 --> 01:06:33,193 You'll never hit a hundred, right? 1300 01:06:33,193 --> 01:06:38,737 There are days, right? And I have been able to, in my 1301 01:06:38,737 --> 01:06:41,803 career, successfully say probably on average 8 outta 1302 01:06:41,803 --> 01:06:45,857 every 10 days I've ever gotten up in the morning and went to do 1303 01:06:45,857 --> 01:06:50,790 some work, I enjoyed myself. Wow, that's a very good reminder 1304 01:06:50,790 --> 01:06:53,369 for all of us, uh, especially in our career, right? 1305 01:06:53,369 --> 01:06:56,159 Sometimes, uh, we hate our job, but we still do it anyway. 1306 01:06:56,590 --> 01:06:58,540 I think it comes back to, again, to the privilege, right? 1307 01:06:58,540 --> 01:07:01,522 Some people, you know, might have better privilege, but for 1308 01:07:01,522 --> 01:07:03,672 some who are not probably... Right. 1309 01:07:03,672 --> 01:07:05,942 And again, that's why I'd be really careful about that 1310 01:07:05,942 --> 01:07:09,475 because, you know, again. But like again, tie that would 1311 01:07:09,475 --> 01:07:12,455 be a learner, and that is a form of privilege. 1312 01:07:12,935 --> 01:07:15,095 Again, I like, I'm getting myself into dangerous waters 1313 01:07:15,095 --> 01:07:18,535 here. But like the point is you give 1314 01:07:18,535 --> 01:07:22,371 yourself advantage if you become, you know, sort of 1315 01:07:22,371 --> 01:07:24,677 maniacal learner. Got it. 1316 01:07:24,767 --> 01:07:27,562 Yeah. So John, if people love this 1317 01:07:27,562 --> 01:07:30,137 conversation, they wanna go little bit more meta with you, 1318 01:07:30,137 --> 01:07:32,237 is there a place where they can find you online? 1319 01:07:32,564 --> 01:07:35,268 Yeah, I think, you know, I'm... So if you could put it on the 1320 01:07:35,268 --> 01:07:38,716 show notes, but Botchagalupe, uh, you know, um, John Willis, 1321 01:07:38,716 --> 01:07:41,696 uh, I think Atlanta. I don't know why I couldn't get 1322 01:07:41,696 --> 01:07:45,366 John Willis on LinkedIn. I use Botchagalupe a lot of 1323 01:07:45,366 --> 01:07:47,650 places. I have, um, I have newsletters 1324 01:07:47,650 --> 01:07:50,974 on my LinkedIn, which I have a profound, a Deming one. 1325 01:07:50,974 --> 01:07:53,552 I have an AI one. And I'm actually starting a 1326 01:07:53,552 --> 01:07:56,429 quantum one now too. I'm trying to look, see, like if 1327 01:07:56,429 --> 01:07:59,839 a dummy like me can learn quantum, can I teach it to all 1328 01:07:59,839 --> 01:08:03,436 the dummies like me? If you like the idea of the 1329 01:08:03,436 --> 01:08:07,916 whole CIO reference of Dear CIO, it's aicio.ai is my, uh, Dear 1330 01:08:07,916 --> 01:08:11,009 CIO, uh, newsletter. Where I really try to point out 1331 01:08:11,009 --> 01:08:14,022 to things you should worry about running AI in a large 1332 01:08:14,022 --> 01:08:15,806 organizational scale. Yeah. 1333 01:08:15,926 --> 01:08:17,546 I'll put them all in the show notes. 1334 01:08:17,576 --> 01:08:19,895 Just a little bit of curiosity, what is Botchagalupe? 1335 01:08:20,066 --> 01:08:22,856 Uh, there's no really definition of it. 1336 01:08:22,856 --> 01:08:27,203 It turns out, uh, it was a word that my mom used to sing and use 1337 01:08:27,203 --> 01:08:30,453 when I was growing up, and I never did figure out what 1338 01:08:30,453 --> 01:08:32,639 exactly it was. There's tons of folklore that 1339 01:08:32,639 --> 01:08:35,384 I've gotten over the years of people told me stories. 1340 01:08:36,004 --> 01:08:40,215 Everything from, you know, two Italian groups, the Bachagaloop 1341 01:08:40,215 --> 01:08:44,457 and Bacciagalupe that used the war over who's funnier or 1342 01:08:44,457 --> 01:08:46,509 stupider, to Abbott and Costello. 1343 01:08:46,509 --> 01:08:49,412 Or like, they just... so I don't really have a clear definition 1344 01:08:49,412 --> 01:08:52,680 other than I thought it would be a clever name when I was just 1345 01:08:52,680 --> 01:08:55,144 getting my sort of Twitter account was really where it 1346 01:08:55,144 --> 01:08:56,747 started, so. Right. 1347 01:08:57,212 --> 01:09:00,158 So thanks for the trivia. So again, pleasant conversation, 1348 01:09:00,158 --> 01:09:02,756 uh, today, John. So thank you so much for your 1349 01:09:02,756 --> 01:09:03,930 sharing today. Thanks for having me. 1350 01:09:04,350 --> 01:09:06,210 It was great conversation. Thank you so much. 1351 01:09:06,300 --> 01:09:10,031 And thanks for doing all the, um, the prep work to make a 1352 01:09:10,031 --> 01:09:11,880 really good conversation. I really appreciate that.