1 00:00:02,400 --> 00:00:11,160 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Master's in 2 00:00:11,240 --> 00:00:15,240 Speaker 1: Business with Barry Ridholds on Bloomberg Radio. 3 00:00:16,520 --> 00:00:20,520 Speaker 2: This week on the podcast, another extra special guest. Tony 4 00:00:20,600 --> 00:00:24,200 Speaker 2: Kim is Managing director at Blackrock, where he heads the 5 00:00:24,239 --> 00:00:28,480 Speaker 2: Fundamental Equity Technology Group, helping to oversee all of the 6 00:00:28,560 --> 00:00:33,199 Speaker 2: active technology investments Blackrock makes. In addition to being a 7 00:00:33,240 --> 00:00:36,800 Speaker 2: portfolio manager and running a number of mutual funds and ETFs. 8 00:00:37,320 --> 00:00:42,440 Speaker 2: He is just a world class technology investor who understands 9 00:00:42,520 --> 00:00:46,400 Speaker 2: the sector like few other people do. Not only has 10 00:00:46,440 --> 00:00:50,680 Speaker 2: he put up a very impressive track record, his entire 11 00:00:50,760 --> 00:00:57,320 Speaker 2: approach to the ecosystem of technology, covering everything from robotics 12 00:00:57,360 --> 00:01:03,000 Speaker 2: to AI, to software to semiconduct is really quite fascinating. 13 00:01:03,120 --> 00:01:08,720 Speaker 2: If you're at all interested in technology in AI in 14 00:01:08,760 --> 00:01:13,160 Speaker 2: the process of thinking about tech investing, then you're going 15 00:01:13,240 --> 00:01:16,600 Speaker 2: to find this conversation to be absolutely fascinating. With no 16 00:01:16,680 --> 00:01:20,720 Speaker 2: further ado my discussion with black Rocks, Tony. 17 00:01:20,480 --> 00:01:22,319 Speaker 1: Kim, thank you Berry, pleasure to be. 18 00:01:22,280 --> 00:01:24,360 Speaker 2: Here, pleasure to have you. So let's start out with 19 00:01:24,400 --> 00:01:29,040 Speaker 2: your background bachelor's in Industrial engineering from University of Illinois 20 00:01:29,480 --> 00:01:33,320 Speaker 2: and then an NBA from Columbia. Were there career plans. 21 00:01:33,840 --> 00:01:36,800 Speaker 1: Career plans. Yeah, first of all, thanks for having me 22 00:01:38,080 --> 00:01:41,160 Speaker 1: your show titled Masters in Business. I have no Master 23 00:01:41,480 --> 00:01:42,040 Speaker 1: in Business. 24 00:01:42,120 --> 00:01:45,640 Speaker 2: Well you have an MBA, so you automatically qualify. Yes, 25 00:01:45,640 --> 00:01:48,000 Speaker 2: for sure, that's a master's right. 26 00:01:47,920 --> 00:01:52,120 Speaker 1: Yeah, that's true, that's true. Yeah, the origins of the career. 27 00:01:52,320 --> 00:01:55,480 Speaker 1: You know, I grew up in the Midwest, the first 28 00:01:55,480 --> 00:01:59,280 Speaker 1: phase of my life and growing up in the eighties 29 00:01:59,640 --> 00:02:03,840 Speaker 1: in Illinois, you know, as a I'm from Korea actually, 30 00:02:03,880 --> 00:02:11,240 Speaker 1: but so the natural I was a stem kid, and 31 00:02:11,400 --> 00:02:14,120 Speaker 1: that that kind of propelled me into the engineering side. 32 00:02:14,200 --> 00:02:17,200 Speaker 1: But I always had other interests outside of that. But 33 00:02:17,360 --> 00:02:20,240 Speaker 1: the reason I went to Champagne we were all from 34 00:02:20,560 --> 00:02:23,160 Speaker 1: state of Illinois, and my siblings and I all went 35 00:02:23,200 --> 00:02:26,040 Speaker 1: to school in the state of Illinois, and and I 36 00:02:26,280 --> 00:02:29,560 Speaker 1: gravitated initially to engineering and that's kind of that that 37 00:02:29,560 --> 00:02:33,799 Speaker 1: that got into that, and then eventually I ended up 38 00:02:33,880 --> 00:02:36,600 Speaker 1: in New York and then transitioned into financial. 39 00:02:36,600 --> 00:02:38,800 Speaker 2: Well we're going to talk about that transition in a minute, 40 00:02:38,840 --> 00:02:42,720 Speaker 2: but before we get there, you really begin your career 41 00:02:42,760 --> 00:02:45,720 Speaker 2: as an engineer at Rockwell Automation. Yeah, what did you 42 00:02:45,760 --> 00:02:46,160 Speaker 2: do there? 43 00:02:46,919 --> 00:02:50,000 Speaker 1: This is a first job, right, first job? First real 44 00:02:50,080 --> 00:02:53,799 Speaker 1: job out of school. It really it was the first 45 00:02:54,000 --> 00:02:58,320 Speaker 1: entree into a company, not only a company a this 46 00:02:58,480 --> 00:03:02,840 Speaker 1: was an automation company. It's often known for works with 47 00:03:02,880 --> 00:03:06,080 Speaker 1: many industries, but helping automate. We're help. I was working 48 00:03:06,080 --> 00:03:10,120 Speaker 1: on projects to automate manufacturing. Uh. They had these things 49 00:03:10,160 --> 00:03:16,440 Speaker 1: called plc's, which are basically industrial computers with sensors, with drives, 50 00:03:16,720 --> 00:03:21,080 Speaker 1: drive systems, motor control, robotics and all of these things, 51 00:03:21,160 --> 00:03:23,560 Speaker 1: and then you package them together and you work with 52 00:03:23,600 --> 00:03:27,600 Speaker 1: many different kinds of manufacturing companies in the early days 53 00:03:27,639 --> 00:03:32,280 Speaker 1: of automating manufacturing processes across many industries. So that was 54 00:03:32,320 --> 00:03:37,040 Speaker 1: my first entree in seeing the diversity of of the 55 00:03:37,040 --> 00:03:40,320 Speaker 1: manufacturing base in this country. I was particularly I was 56 00:03:40,320 --> 00:03:44,040 Speaker 1: in I was working on the East Coast, and you know, 57 00:03:44,200 --> 00:03:48,960 Speaker 1: any everything from like pharmaceutical to automotive to to what 58 00:03:49,080 --> 00:03:53,600 Speaker 1: a what a distribution network looked like, what tier one, 59 00:03:53,640 --> 00:03:59,640 Speaker 1: tier two kind of systems integrators were with the technology 60 00:03:59,640 --> 00:04:02,400 Speaker 1: of audio mating manufacturing. And so we're working on different 61 00:04:02,440 --> 00:04:06,800 Speaker 1: projects and see the across a lot of industries. But 62 00:04:06,880 --> 00:04:10,560 Speaker 1: I realized I didn't want to, you know, I had 63 00:04:10,560 --> 00:04:13,320 Speaker 1: other I had other ambitions, and so this is what 64 00:04:14,840 --> 00:04:16,440 Speaker 1: led me to going to graduate school. 65 00:04:16,560 --> 00:04:19,120 Speaker 2: So let's let's talk about some of those other ambitions. 66 00:04:19,160 --> 00:04:22,760 Speaker 2: You end up doing investment banking in New York in 67 00:04:22,800 --> 00:04:27,040 Speaker 2: the mid nineties. What was the transition from being an 68 00:04:27,040 --> 00:04:31,279 Speaker 2: engineer slash operator to an investor? What was that like? 69 00:04:32,720 --> 00:04:35,200 Speaker 1: Well, when I was at when I went to Columbia, 70 00:04:37,640 --> 00:04:40,000 Speaker 1: you know, I did the engineer. I worked at an 71 00:04:40,040 --> 00:04:44,359 Speaker 1: engineering company, and I thought I wanted something a higher level, 72 00:04:44,480 --> 00:04:48,200 Speaker 1: more strategic in nature. I actually thought I wanted to 73 00:04:48,360 --> 00:04:52,520 Speaker 1: I wanted to try to get into consulting. That's a classical, 74 00:04:53,240 --> 00:04:58,800 Speaker 1: classical role for MBA. None of the consultants would wanted 75 00:04:58,800 --> 00:05:02,560 Speaker 1: to hire me. But the somehow the investment banking side 76 00:05:03,839 --> 00:05:07,600 Speaker 1: found me or I found them, and it was an 77 00:05:07,600 --> 00:05:11,400 Speaker 1: engineering here's a guy from engineering with engineering background. And 78 00:05:11,560 --> 00:05:13,279 Speaker 1: you know at the time, those are the early days 79 00:05:13,279 --> 00:05:19,080 Speaker 1: of pre dot com and it was a new emerging industry, 80 00:05:19,720 --> 00:05:23,600 Speaker 1: and so I think they saw that linkage between some 81 00:05:23,680 --> 00:05:28,240 Speaker 1: technical expertise with finance maybe working that with that industry. 82 00:05:28,279 --> 00:05:33,000 Speaker 1: So that was But the the finance is what would 83 00:05:33,000 --> 00:05:34,880 Speaker 1: pull me in on the investment banking more so than 84 00:05:34,920 --> 00:05:36,919 Speaker 1: the consulting because of that angle. 85 00:05:36,640 --> 00:05:39,960 Speaker 2: I think, and your timing was perfect in the every nineties, 86 00:05:40,160 --> 00:05:43,720 Speaker 2: great time to be doing I banking and technology. Tell 87 00:05:43,800 --> 00:05:47,000 Speaker 2: us about some of the transactions you saw late nineties 88 00:05:47,040 --> 00:05:50,800 Speaker 2: early two thousands. What sort of deals were you working on? 89 00:05:51,279 --> 00:05:54,520 Speaker 1: Yeah, just that transition, you know. I was originally hired 90 00:05:54,520 --> 00:05:58,240 Speaker 1: by sgu Orber, which was a British investment bank. It 91 00:05:58,320 --> 00:06:00,040 Speaker 1: got acquired and then after. 92 00:05:59,839 --> 00:06:02,360 Speaker 2: The that became Warburg Pinkus that. 93 00:06:02,279 --> 00:06:07,120 Speaker 1: Became SBC Warburg and then UBS bot in the UBS 94 00:06:07,240 --> 00:06:10,719 Speaker 1: Warburg and then the Warburg name went away. But I 95 00:06:10,839 --> 00:06:14,400 Speaker 1: was there right at the time when Warburg was acquired, 96 00:06:14,400 --> 00:06:21,000 Speaker 1: and then that transition, I joined Merrill Lynch and then 97 00:06:21,120 --> 00:06:24,880 Speaker 1: Merrill Lynch that go West young Man, right, Okay, So. 98 00:06:24,839 --> 00:06:28,360 Speaker 2: I remember Merrill Lynch during the nineteen nineties was absolutely 99 00:06:28,400 --> 00:06:33,200 Speaker 2: a powerhouse, or at least became a powerhouse towards the 100 00:06:33,240 --> 00:06:34,480 Speaker 2: back half of that decade. 101 00:06:34,600 --> 00:06:39,760 Speaker 1: Yeah, yeah, So it was very much a new thing 102 00:06:39,800 --> 00:06:42,120 Speaker 1: for them in the West coast, and so I went 103 00:06:42,839 --> 00:06:46,320 Speaker 1: and I still recalled to this day. There were several 104 00:06:46,360 --> 00:06:50,320 Speaker 1: of us that were the origins of the m and 105 00:06:50,400 --> 00:06:55,920 Speaker 1: a group on the West Coast from Merrill Lynch. In fact, 106 00:06:56,120 --> 00:07:00,760 Speaker 1: three of those people twenty some years later we're back 107 00:07:00,760 --> 00:07:02,520 Speaker 1: at joined at Blackrock, and I can tell you the 108 00:07:02,520 --> 00:07:03,040 Speaker 1: story of that. 109 00:07:03,160 --> 00:07:04,039 Speaker 2: Sure, let's hear that. 110 00:07:04,120 --> 00:07:07,240 Speaker 1: Oh okay, yeah, there were there were There are three 111 00:07:07,279 --> 00:07:10,360 Speaker 1: of us that were vps and directors at the M 112 00:07:10,360 --> 00:07:10,560 Speaker 1: and a. 113 00:07:10,600 --> 00:07:12,640 Speaker 2: Group was feel free to drop names. 114 00:07:12,840 --> 00:07:17,000 Speaker 1: A guy named Draga Raschkeovic who is now vice chairman 115 00:07:17,000 --> 00:07:19,760 Speaker 1: of JP Morgan runs the tech M and a this 116 00:07:19,840 --> 00:07:23,440 Speaker 1: guy Michael Leitner, and then myself and then we worked 117 00:07:23,440 --> 00:07:25,720 Speaker 1: for this guy named Rob Stewart, and then Mark Schaeffer 118 00:07:25,720 --> 00:07:30,560 Speaker 1: above him led the group, but Mike Michael at Tenebau. 119 00:07:30,840 --> 00:07:33,760 Speaker 1: Blackrock later acquired them and he was one of the 120 00:07:33,800 --> 00:07:37,280 Speaker 1: partners at Tennemo and then recently Blackrock bought g I P. 121 00:07:38,640 --> 00:07:40,880 Speaker 1: And then Rob is one of the partners at g 122 00:07:41,000 --> 00:07:45,760 Speaker 1: I P. So three of the four of us, Rob, myself, 123 00:07:46,840 --> 00:07:50,040 Speaker 1: Michael all ended up at Blackrock and some fans. 124 00:07:49,840 --> 00:07:51,120 Speaker 2: Let's get the band back together. 125 00:07:52,120 --> 00:07:54,880 Speaker 1: Drago did not Dragon still is that Jimmy Morgan right now? So? So, 126 00:07:55,360 --> 00:07:57,200 Speaker 1: but those were the original days, and then you know 127 00:07:57,240 --> 00:08:03,040 Speaker 1: the transactions. You know, this was pre dot com and 128 00:08:03,160 --> 00:08:05,400 Speaker 1: you know the Internet was just getting going. 129 00:08:05,920 --> 00:08:08,360 Speaker 2: Are you talking early nineties. 130 00:08:08,080 --> 00:08:11,440 Speaker 1: Uh, mid midnighties, mid late mid to late nineties. 131 00:08:11,480 --> 00:08:14,520 Speaker 2: Like I remember being on a training desk in ninety 132 00:08:14,560 --> 00:08:17,040 Speaker 2: six when the Netscape and I was not allowed to 133 00:08:17,080 --> 00:08:20,720 Speaker 2: trade it. When the Netscape IPO happened, that was really 134 00:08:20,760 --> 00:08:25,640 Speaker 2: what kicked off a giant explosion. Were you there around 135 00:08:25,640 --> 00:08:26,120 Speaker 2: that time? 136 00:08:27,200 --> 00:08:29,680 Speaker 1: Yes, in that time, and these were the deals when 137 00:08:29,680 --> 00:08:33,400 Speaker 1: when Cisco was going crazy and there were you know, 138 00:08:34,880 --> 00:08:38,800 Speaker 1: there's so many transactions and networking, there was the optical 139 00:08:38,840 --> 00:08:42,440 Speaker 1: communications boom, some of the original software and Internet assets, 140 00:08:42,960 --> 00:08:46,120 Speaker 1: and so I did transactions in this especially a lot 141 00:08:46,160 --> 00:08:49,880 Speaker 1: in the networking telecom. I remember working on one or 142 00:08:49,920 --> 00:08:53,480 Speaker 1: two software deals and I did that for a while, 143 00:08:53,920 --> 00:08:58,720 Speaker 1: and then then I really I decided to leave investment baking, 144 00:08:58,720 --> 00:09:03,480 Speaker 1: which I learned a tremendous amount, especially the you know, putting, 145 00:09:03,559 --> 00:09:07,200 Speaker 1: you know, the strategic nature of looking at industries and companies, 146 00:09:07,280 --> 00:09:11,959 Speaker 1: and of course all the financial acumen, the rigor of 147 00:09:12,840 --> 00:09:17,680 Speaker 1: of doing very intensive financial analysis. But you're always working 148 00:09:17,679 --> 00:09:20,440 Speaker 1: into the behest of a client right working on it 149 00:09:20,520 --> 00:09:24,960 Speaker 1: was transactional related and and this is when I decided 150 00:09:25,000 --> 00:09:28,920 Speaker 1: to go and take a take a career path change 151 00:09:29,000 --> 00:09:30,360 Speaker 1: to the investment site. 152 00:09:30,600 --> 00:09:33,040 Speaker 2: So tell us what that transition was like, what is 153 00:09:33,080 --> 00:09:36,840 Speaker 2: it like going from transactional M and A on the 154 00:09:36,840 --> 00:09:40,439 Speaker 2: West coast to no, I just want to find companies 155 00:09:40,559 --> 00:09:44,000 Speaker 2: public and private and invest capital in them. 156 00:09:44,480 --> 00:09:49,600 Speaker 1: Yeah, I think that's that was the transition. The the 157 00:09:49,760 --> 00:09:56,520 Speaker 1: financial financial analysis is the same effectively, maybe it's even 158 00:09:56,559 --> 00:09:59,000 Speaker 1: more intensive on the on the on the M and 159 00:09:59,000 --> 00:10:01,680 Speaker 1: A side, because you're doing much more detailed work. The 160 00:10:01,920 --> 00:10:05,200 Speaker 1: way you look at industries and companies are relatively similar. 161 00:10:05,760 --> 00:10:11,280 Speaker 1: It's that on the transactional side, you work on projects 162 00:10:11,320 --> 00:10:13,960 Speaker 1: for a short duration of time and then you move 163 00:10:14,040 --> 00:10:16,480 Speaker 1: on and move on and move on, and hopefully over 164 00:10:16,520 --> 00:10:19,719 Speaker 1: time you persist, you have persistence, and you learn more 165 00:10:19,760 --> 00:10:22,800 Speaker 1: about about that industry and the domain. When you go 166 00:10:22,920 --> 00:10:26,560 Speaker 1: to the investment side. I started as an analyst, I 167 00:10:26,679 --> 00:10:31,000 Speaker 1: wasn't you know, And here you are looking at wider 168 00:10:31,080 --> 00:10:37,160 Speaker 1: array of companies. You're doing financial analysis, but not as 169 00:10:37,440 --> 00:10:40,400 Speaker 1: detailed as you were working on one deal, one transaction 170 00:10:40,480 --> 00:10:44,800 Speaker 1: for months at a time and then but you yet 171 00:10:44,800 --> 00:10:48,880 Speaker 1: you have persistence because you're able to look at sectors 172 00:10:48,880 --> 00:10:52,719 Speaker 1: and industries and companies for a longer period of time consistently, 173 00:10:53,640 --> 00:10:58,439 Speaker 1: and so you build deeper domain knowledge. And and so 174 00:10:58,559 --> 00:11:00,559 Speaker 1: that was one. The second is that you're no longer 175 00:11:00,600 --> 00:11:04,320 Speaker 1: working for a client. You you you are working to 176 00:11:04,520 --> 00:11:08,439 Speaker 1: find the best you know, investments and put your own 177 00:11:08,440 --> 00:11:12,600 Speaker 1: capital at risk. Right, And so that was a change 178 00:11:13,720 --> 00:11:18,440 Speaker 1: of the mindset of how to assess because you're you're 179 00:11:18,440 --> 00:11:23,480 Speaker 1: not working really you're not just servicing a client here, 180 00:11:23,480 --> 00:11:26,480 Speaker 1: You're putting your own capital at risk. And and you know, 181 00:11:26,559 --> 00:11:31,040 Speaker 1: that was the That was the first big change of 182 00:11:31,080 --> 00:11:34,440 Speaker 1: just assessing how that works, and then and then going 183 00:11:34,480 --> 00:11:37,760 Speaker 1: from and then and then learning many many, many domains. 184 00:11:39,480 --> 00:11:43,200 Speaker 1: And then that was the working with many different kinds 185 00:11:43,200 --> 00:11:46,360 Speaker 1: of investors, different kinds of investment philosophies. I must have 186 00:11:46,400 --> 00:11:50,640 Speaker 1: worked for thirty forty partpillar managers across four four or 187 00:11:50,640 --> 00:11:55,079 Speaker 1: five investment firms, and that's that was, Like, I guess 188 00:11:55,080 --> 00:12:00,200 Speaker 1: my second era here was to learn the skills of investing. 189 00:12:01,000 --> 00:12:04,720 Speaker 2: Huh, we're going to spend more time on what you've 190 00:12:04,800 --> 00:12:08,240 Speaker 2: learned in a little bit. Yeah, you said something I 191 00:12:08,480 --> 00:12:13,600 Speaker 2: have to explore a little bit. Sure, it was more 192 00:12:13,600 --> 00:12:17,559 Speaker 2: in depth, more intensive on the M and A side 193 00:12:17,600 --> 00:12:21,040 Speaker 2: than the investing side. I'm curious as to why the 194 00:12:21,160 --> 00:12:24,760 Speaker 2: two ideas that immediately pop into mind. You're covering a 195 00:12:24,760 --> 00:12:29,520 Speaker 2: whole lot more companies on the investment side, but one 196 00:12:29,600 --> 00:12:33,000 Speaker 2: can help but imagine on the M and A side. Hey, 197 00:12:33,080 --> 00:12:35,800 Speaker 2: it's all in. You're taking the whole thing as an investor. 198 00:12:35,800 --> 00:12:38,800 Speaker 2: If you buy something and you have second thoughts, well 199 00:12:38,960 --> 00:12:41,480 Speaker 2: you sell a few million shares and you're done. You 200 00:12:41,520 --> 00:12:44,080 Speaker 2: could walk away with maybe a little worse for the 201 00:12:44,400 --> 00:12:47,600 Speaker 2: wear and tear. But when you buy an entire company, Hey, 202 00:12:47,720 --> 00:12:49,600 Speaker 2: that's really hard to unwine, that, isn't it. 203 00:12:50,440 --> 00:12:55,959 Speaker 1: Yeah, that's right, you know, and you're buying the whole thing, 204 00:12:56,280 --> 00:12:58,199 Speaker 1: or you're representing, or you're selling the whole thing, or 205 00:12:58,240 --> 00:13:01,760 Speaker 1: you're selling pieces of it, and you're working on one 206 00:13:01,800 --> 00:13:04,280 Speaker 1: company and another company, bee two companies at a time, 207 00:13:05,080 --> 00:13:11,760 Speaker 1: and you want to get every number right, every comma, 208 00:13:11,880 --> 00:13:16,040 Speaker 1: every nuts and bolts to the as many as much 209 00:13:16,080 --> 00:13:19,960 Speaker 1: detail as you can. So the precision and the accuracy 210 00:13:20,840 --> 00:13:24,200 Speaker 1: and the information fidelity is much higher because that's what 211 00:13:24,240 --> 00:13:27,319 Speaker 1: you're just working on that one company, that one transaction, 212 00:13:27,559 --> 00:13:32,200 Speaker 1: versus like you said, you're looking at hundreds of companies 213 00:13:33,080 --> 00:13:37,760 Speaker 1: and you can make a decision with the push of 214 00:13:37,800 --> 00:13:43,400 Speaker 1: a button and sell or buy, And so the time 215 00:13:43,559 --> 00:13:48,760 Speaker 1: spent on that analysis will invariably be less than the 216 00:13:48,840 --> 00:13:51,760 Speaker 1: time spent on this one definitive transaction. 217 00:13:52,120 --> 00:13:56,760 Speaker 2: Huh really really interesting. So you've been in black Rock 218 00:13:56,800 --> 00:14:02,600 Speaker 2: since twenty thirteen. Obviously passive has been a huge success 219 00:14:02,679 --> 00:14:06,200 Speaker 2: for Blackrock. You're on the active side. Is there any 220 00:14:06,240 --> 00:14:10,600 Speaker 2: crossover do you get pulled into any discussions from you know, 221 00:14:10,640 --> 00:14:15,760 Speaker 2: any of the big black rock ETF sector funds passive indexes? 222 00:14:17,160 --> 00:14:20,920 Speaker 1: So the passive industry, passive part of Blackrock is separate 223 00:14:21,080 --> 00:14:26,400 Speaker 1: to the active part. I guess what would be one 224 00:14:26,480 --> 00:14:31,080 Speaker 1: trend is that we are also launching many active ETFs, 225 00:14:31,720 --> 00:14:35,240 Speaker 1: which is the container in which most of passive funds 226 00:14:35,280 --> 00:14:39,600 Speaker 1: are traded at. And then there's like passive decisions, you know, 227 00:14:40,040 --> 00:14:42,360 Speaker 1: you know, a lot of the passive index thing is 228 00:14:42,400 --> 00:14:44,840 Speaker 1: now an active decision. I guess you could say, if 229 00:14:44,840 --> 00:14:48,840 Speaker 1: that's a day it always has been, it always has been, right, Yes. 230 00:14:49,160 --> 00:14:51,680 Speaker 2: It's it's hey, we're gonna make it market cap index. 231 00:14:51,800 --> 00:14:55,360 Speaker 2: That's an active decision. We're gonna we're gonna cap Apple 232 00:14:55,440 --> 00:14:59,960 Speaker 2: and Vidia Microsoft at right, there's lots of active decisions. 233 00:15:00,080 --> 00:15:02,520 Speaker 2: People don't realize there's quite a bit of active in 234 00:15:02,560 --> 00:15:03,160 Speaker 2: their passes. 235 00:15:03,240 --> 00:15:08,120 Speaker 1: Yeah, so now we're joining that that party as well. 236 00:15:08,200 --> 00:15:12,280 Speaker 1: We have now active ets. We launched two recently, one 237 00:15:12,320 --> 00:15:15,800 Speaker 1: on the AI site. So where we fail that dynamism, 238 00:15:16,280 --> 00:15:19,680 Speaker 1: especially an industry that is in a rapid change like 239 00:15:19,720 --> 00:15:23,760 Speaker 1: an AI, I think you need a lot of adaptation 240 00:15:24,040 --> 00:15:26,960 Speaker 1: flexibility because things are changing so rapidly. 241 00:15:27,160 --> 00:15:29,280 Speaker 2: So I want to stay with that. We're going to 242 00:15:29,320 --> 00:15:35,280 Speaker 2: talk about the multiple ETFs you actively manage, but generally speaking, 243 00:15:35,480 --> 00:15:39,600 Speaker 2: after passive captured more than half of the mutual funds 244 00:15:39,600 --> 00:15:43,600 Speaker 2: and ETF assets, there has since been an explosion of 245 00:15:43,640 --> 00:15:47,760 Speaker 2: active ETFs as well as mutual funds. Some are thematic, 246 00:15:47,840 --> 00:15:52,120 Speaker 2: some are sector based, but they all have in common 247 00:15:52,600 --> 00:15:55,120 Speaker 2: that it's not relying on a passive index. What are 248 00:15:55,160 --> 00:15:58,880 Speaker 2: your thoughts on the future of active management in the 249 00:15:58,880 --> 00:15:59,680 Speaker 2: ETF space. 250 00:16:02,200 --> 00:16:04,520 Speaker 1: Well, I think the future of active management, you know, 251 00:16:05,000 --> 00:16:10,320 Speaker 1: as you correctly pointed out, I think there are generic 252 00:16:10,560 --> 00:16:13,840 Speaker 1: sections of the market where it is the broad market 253 00:16:13,880 --> 00:16:18,360 Speaker 1: exposure SMP. Those I think continue to be under pressure 254 00:16:18,600 --> 00:16:24,720 Speaker 1: as it moves to to those passive indices. But you 255 00:16:24,760 --> 00:16:29,200 Speaker 1: said something very interesting there. You know, the industry is specialized, 256 00:16:29,520 --> 00:16:35,040 Speaker 1: you know, sectors, thematics in the container of an active ETF. 257 00:16:35,960 --> 00:16:39,240 Speaker 1: I think that is more representative maybe where the future 258 00:16:39,240 --> 00:16:43,800 Speaker 1: of active industry is going where one can express a 259 00:16:44,280 --> 00:16:51,240 Speaker 1: differentiated view, and invariably that is a function of specialization, 260 00:16:51,440 --> 00:16:57,120 Speaker 1: I think, and of course I'm biased in that because 261 00:16:57,160 --> 00:17:01,000 Speaker 1: I am focused on a specialized area, which is the 262 00:17:01,000 --> 00:17:04,520 Speaker 1: technology area, and then within the technology are there are 263 00:17:04,560 --> 00:17:10,920 Speaker 1: many further subspecializations, and I think those that have broader 264 00:17:11,000 --> 00:17:14,960 Speaker 1: depth of domain knowledge hopefully that is the advantage, and 265 00:17:15,280 --> 00:17:18,120 Speaker 1: that that gets expressed in an active fund, an ETF 266 00:17:18,240 --> 00:17:22,520 Speaker 1: or a mutual fund or whatever. And you know, as 267 00:17:22,600 --> 00:17:24,840 Speaker 1: I as, I've been in this technology industry for a 268 00:17:24,920 --> 00:17:28,399 Speaker 1: long time. You know, twenty years ago, tech was twenty 269 00:17:28,400 --> 00:17:32,800 Speaker 1: percent of the SMP, it's over forty and it's probably 270 00:17:32,800 --> 00:17:36,760 Speaker 1: going higher as as now we're entering the AI era, 271 00:17:36,920 --> 00:17:43,200 Speaker 1: and so generalists, I think are at information asymmetry disadvantage 272 00:17:43,200 --> 00:17:46,600 Speaker 1: to those that have domain specificity. And if you have 273 00:17:47,440 --> 00:17:51,520 Speaker 1: better information, better knowledge, hopefully that leads to better decision making, 274 00:17:51,560 --> 00:17:54,800 Speaker 1: which is, you know, which will hopefully sustain the active management. 275 00:17:55,160 --> 00:17:57,720 Speaker 2: You know, I'm so glad you said that you think 276 00:17:57,800 --> 00:18:01,760 Speaker 2: the technology sector of the S and P five hundred 277 00:18:01,920 --> 00:18:05,080 Speaker 2: is going higher. Whenever people say to me, aren't you 278 00:18:05,160 --> 00:18:08,760 Speaker 2: concerned that tech is twenty nine percent of the s 279 00:18:08,840 --> 00:18:11,119 Speaker 2: and P five hundred or whatever the number happens to be. 280 00:18:11,560 --> 00:18:15,600 Speaker 2: My answer is always the magnificent seven are responsible for 281 00:18:16,160 --> 00:18:18,680 Speaker 2: something like two and a half trillion dollars in revenue 282 00:18:18,760 --> 00:18:23,120 Speaker 2: and five hundred billion dollars in profits. I'm shocked it's 283 00:18:23,160 --> 00:18:26,120 Speaker 2: only twenty nine percent. Why isn't it half of the 284 00:18:26,200 --> 00:18:28,560 Speaker 2: S and P five hundred. This is what's driving the 285 00:18:28,600 --> 00:18:32,439 Speaker 2: economy in the market. Doesn't it deserve a rich evaluation. 286 00:18:32,520 --> 00:18:34,000 Speaker 2: I'm curious as to your thoughts on. 287 00:18:33,960 --> 00:18:38,720 Speaker 1: That one hundred percent? Agree? Okay, I agree. You know, 288 00:18:38,800 --> 00:18:45,159 Speaker 1: the multiple in aggregate has not changed dramatically, but it 289 00:18:45,160 --> 00:18:50,359 Speaker 1: has driven by free cash flow and the forty percent 290 00:18:50,400 --> 00:18:53,520 Speaker 1: I quoting is a combination of comm services which they 291 00:18:53,560 --> 00:18:57,640 Speaker 1: carved out, which is really a tech company with classic 292 00:18:57,760 --> 00:19:03,000 Speaker 1: tech that's over forty percent. And when you look at 293 00:19:03,040 --> 00:19:05,960 Speaker 1: the contribution of free cash flow right, which is the 294 00:19:06,040 --> 00:19:10,919 Speaker 1: ultimate profit metrics it's followed, it is forty percent of 295 00:19:10,920 --> 00:19:12,719 Speaker 1: the free cash flow right. You know the other thing 296 00:19:12,760 --> 00:19:16,400 Speaker 1: about tech, I don't think people realize it has represented 297 00:19:16,440 --> 00:19:19,840 Speaker 1: the highest growth. It actually has the highest margin. It 298 00:19:19,880 --> 00:19:23,760 Speaker 1: is the highest free profitable margin. People think it's unprofitable. 299 00:19:23,800 --> 00:19:26,639 Speaker 1: It's like the ninety percent of technive profit. This is 300 00:19:26,920 --> 00:19:30,320 Speaker 1: the highest profit margin and the highest free cash flow growth. 301 00:19:30,680 --> 00:19:35,119 Speaker 1: And that's what's even the market cap appreciation, that is 302 00:19:35,160 --> 00:19:37,800 Speaker 1: the that is not not well understood. 303 00:19:38,240 --> 00:19:41,120 Speaker 2: Fair to say, this is not the late nineties dot 304 00:19:41,160 --> 00:19:47,040 Speaker 2: com no, you know, whimsical ideas with hardly any revenue 305 00:19:47,040 --> 00:19:51,080 Speaker 2: and no profits. These companies are printing money and are 306 00:19:51,160 --> 00:19:52,280 Speaker 2: wildly profitable. 307 00:19:52,480 --> 00:19:55,720 Speaker 1: Yeah, and in fact, I would even make another distinct, 308 00:19:55,720 --> 00:20:01,760 Speaker 1: you know, the MAC seven. The most profitable sector in 309 00:20:01,840 --> 00:20:07,360 Speaker 1: all the smp any is the semiconductor industry. They even 310 00:20:07,400 --> 00:20:09,760 Speaker 1: have higher margins now than the software industry, and the 311 00:20:09,760 --> 00:20:12,639 Speaker 1: software industry is amongst the highest. Right, so tech in general, 312 00:20:12,680 --> 00:20:16,199 Speaker 1: if you say software and semis are two thirds of 313 00:20:16,240 --> 00:20:19,560 Speaker 1: all of tech, right, they have the highest margins in 314 00:20:19,600 --> 00:20:19,960 Speaker 1: the world. 315 00:20:20,440 --> 00:20:20,640 Speaker 2: Huh. 316 00:20:21,000 --> 00:20:23,760 Speaker 1: So they have the most profitable companies with the most growth, 317 00:20:24,000 --> 00:20:27,240 Speaker 1: which generates the most free cash flow, which generates the returns, 318 00:20:27,280 --> 00:20:30,160 Speaker 1: which generates the forty percent of the market cap, which 319 00:20:30,200 --> 00:20:31,520 Speaker 1: is and most of those are max of. 320 00:20:31,600 --> 00:20:33,760 Speaker 2: It doesn't sound like a bad. 321 00:20:33,600 --> 00:20:36,800 Speaker 1: Placed place to keep your And now we have AI 322 00:20:37,080 --> 00:20:39,440 Speaker 1: and it probably goes higher. It's going to go higher. 323 00:20:39,520 --> 00:20:43,280 Speaker 2: Huh. Fascinating. So we were talking a little bit about 324 00:20:44,480 --> 00:20:47,639 Speaker 2: what makes technology so interesting. Share a little bit of 325 00:20:47,680 --> 00:20:52,480 Speaker 2: your perspective. How do you go about identifying technologies that 326 00:20:52,680 --> 00:20:56,280 Speaker 2: are going to drive future growth and as we've seen 327 00:20:56,640 --> 00:20:58,320 Speaker 2: reshape the entire economy. 328 00:20:59,640 --> 00:21:02,359 Speaker 1: You know, I guess I would say first, I'm a deconstructionist. 329 00:21:02,440 --> 00:21:08,440 Speaker 1: I like to deconstruct problems, deconstruct any kind of situation, 330 00:21:08,760 --> 00:21:11,760 Speaker 1: deconstruct sectors and industries. So I like to break things down. 331 00:21:12,440 --> 00:21:15,240 Speaker 1: And then even before breaking them down, and this kind 332 00:21:15,240 --> 00:21:18,600 Speaker 1: of goes to my childhood, I always had a fascination love. 333 00:21:18,480 --> 00:21:22,520 Speaker 2: Of maps, maps, maps that's interesting. 334 00:21:22,160 --> 00:21:25,719 Speaker 1: Cartography, ancient maps, and so I would I'd like to 335 00:21:25,760 --> 00:21:29,840 Speaker 1: map everything out, and so like the ancient mariners would say, 336 00:21:29,880 --> 00:21:33,840 Speaker 1: all the oceans, you'd want a map of where you're 337 00:21:33,920 --> 00:21:37,159 Speaker 1: navigating to. And so I start with that. I'd like 338 00:21:37,200 --> 00:21:41,720 Speaker 1: to break things down. I break technology down into five 339 00:21:41,800 --> 00:21:46,520 Speaker 1: or six major sub sectors, and then we just continually 340 00:21:46,600 --> 00:21:49,960 Speaker 1: deconstruct and break those down. And so once you start 341 00:21:50,000 --> 00:21:54,879 Speaker 1: breaking these things down, you then create a map of 342 00:21:54,920 --> 00:22:00,440 Speaker 1: the whole landscape, the semiconductor and lancecape, internet escape, the 343 00:22:00,480 --> 00:22:04,600 Speaker 1: software landscape, et cetera. And continually break things down and 344 00:22:04,640 --> 00:22:09,080 Speaker 1: so then they are digestible pieces, and then within those pieces, 345 00:22:09,320 --> 00:22:11,960 Speaker 1: then you interrogate all of the technologies that are going. 346 00:22:12,040 --> 00:22:14,840 Speaker 1: And so now you have this this giant, giant map 347 00:22:15,000 --> 00:22:19,800 Speaker 1: of all of technology, all reconfigured and mapped out, and 348 00:22:19,840 --> 00:22:22,439 Speaker 1: then you go into detail. And then this way you start, 349 00:22:22,640 --> 00:22:25,000 Speaker 1: it's kind of like a battlefield commander looking at a 350 00:22:25,040 --> 00:22:28,960 Speaker 1: giant war map and you see hot spots. This is hot, 351 00:22:29,040 --> 00:22:31,639 Speaker 1: this is cold, this is hot, this is cold. And 352 00:22:32,080 --> 00:22:35,040 Speaker 1: then you have systematized a way of looking at all 353 00:22:35,080 --> 00:22:40,000 Speaker 1: of those different categories and technologies and subsectors, and you 354 00:22:40,080 --> 00:22:41,600 Speaker 1: know all the companies that are there, you know the 355 00:22:41,600 --> 00:22:45,600 Speaker 1: competitors there, and then you're observing what's hot and what's not. 356 00:22:46,760 --> 00:22:51,240 Speaker 1: And so then so that's the current, that's the initial framework, 357 00:22:51,280 --> 00:22:54,400 Speaker 1: and so then you you start to see trends that 358 00:22:54,440 --> 00:22:56,480 Speaker 1: are that are happening and you think you see other 359 00:22:56,520 --> 00:22:58,160 Speaker 1: trends that are that are declining. 360 00:22:58,280 --> 00:23:01,080 Speaker 2: So what's so intriguing about that is we tend to 361 00:23:01,160 --> 00:23:07,320 Speaker 2: think of fundamental research CFP type research as very balance 362 00:23:07,359 --> 00:23:11,480 Speaker 2: sheet driven. What you're describing something that's much more holistic 363 00:23:11,560 --> 00:23:15,280 Speaker 2: and comprehensive. You're you're really looking at the whole echo 364 00:23:15,680 --> 00:23:21,720 Speaker 2: system of technology to see what is moving and use 365 00:23:21,760 --> 00:23:27,159 Speaker 2: the magic word systematize. How do you systematize that? Is 366 00:23:27,240 --> 00:23:33,119 Speaker 2: it just identifying what is on a mathematical basis popping 367 00:23:33,119 --> 00:23:33,639 Speaker 2: its head up. 368 00:23:33,840 --> 00:23:36,320 Speaker 1: Yeah, I think if we use AI as a great 369 00:23:36,680 --> 00:23:41,400 Speaker 1: framework as a test, as a case study. So if 370 00:23:41,400 --> 00:23:44,280 Speaker 1: I were to frame technology industry as we have this 371 00:23:44,359 --> 00:23:48,240 Speaker 1: hardware industry, and inside the hardware industry there are many 372 00:23:48,320 --> 00:23:53,199 Speaker 1: categories like smartphones and robotics and servers and things, and 373 00:23:53,200 --> 00:24:01,080 Speaker 1: then there's a semiconductor industry. There's different kinds of chips, accelerator, chips, memory, chips, foundry, lodge, analog. 374 00:24:01,440 --> 00:24:06,280 Speaker 1: And then let's say the software industry, there's security and applications, infrastructure, 375 00:24:06,280 --> 00:24:09,200 Speaker 1: et cetera. Once you have mapped all of these things 376 00:24:09,200 --> 00:24:13,840 Speaker 1: out and you know where all the companies or all 377 00:24:13,840 --> 00:24:16,720 Speaker 1: the bodies are buried, and you know who's competing with 378 00:24:16,800 --> 00:24:22,159 Speaker 1: whom and who's working on what. Along comes AI. AI 379 00:24:22,440 --> 00:24:27,439 Speaker 1: starts with Chat GPT in GPT three point five in 380 00:24:27,520 --> 00:24:31,240 Speaker 1: the end of twenty twenty two, early twenty twenty three, 381 00:24:31,480 --> 00:24:35,640 Speaker 1: and it shows up as an application at Chat application. Well, 382 00:24:35,680 --> 00:24:39,680 Speaker 1: the first thing you when I saw that, I said, Wow, 383 00:24:39,720 --> 00:24:41,040 Speaker 1: this is going to change the world. 384 00:24:42,080 --> 00:24:46,160 Speaker 2: And that was your initial response to the first demonstration, 385 00:24:46,240 --> 00:24:47,960 Speaker 2: you sort of chat gbtwo. 386 00:24:48,440 --> 00:24:53,040 Speaker 1: That and having a meeting with Jensen Huang in January 387 00:24:53,040 --> 00:24:56,040 Speaker 1: twenty twenty three. Those two things kind of triggered it. 388 00:24:56,200 --> 00:24:59,240 Speaker 1: Then once you see that, then you say, okay, how's 389 00:24:59,320 --> 00:25:01,239 Speaker 1: this going to cast k through? You know, it's kind 390 00:25:01,280 --> 00:25:03,639 Speaker 1: of like in biology there's a thing called I what 391 00:25:03,720 --> 00:25:08,920 Speaker 1: I call it trophic cascada, an ecological ecosystem, And then 392 00:25:08,920 --> 00:25:13,280 Speaker 1: you say, AI is the trigger. The first thing that 393 00:25:13,320 --> 00:25:16,000 Speaker 1: you see it. It's the first representation is well, you 394 00:25:16,080 --> 00:25:19,880 Speaker 1: got to build these models, and to build the models, 395 00:25:19,920 --> 00:25:23,120 Speaker 1: you need these chips, and so then you go well, 396 00:25:23,160 --> 00:25:26,720 Speaker 1: then you interrogate, well you need these kinds of GPUs 397 00:25:26,840 --> 00:25:29,280 Speaker 1: and memory and things. Then you say, well then you 398 00:25:29,400 --> 00:25:33,000 Speaker 1: need to well those are connected to the packaging systems, 399 00:25:33,240 --> 00:25:36,280 Speaker 1: and those packaging systems are connected then to foundries, and 400 00:25:36,320 --> 00:25:39,320 Speaker 1: then these foundaries are connected to the wayfer output, which 401 00:25:39,359 --> 00:25:41,240 Speaker 1: you need the equipment, and then you start to build 402 00:25:41,240 --> 00:25:45,280 Speaker 1: a chain of this is what's needed to build this part, 403 00:25:45,320 --> 00:25:49,400 Speaker 1: and then those chips get stonwn in servers and servers 404 00:25:49,480 --> 00:25:53,080 Speaker 1: need this whole eco supply chain, and then those servers 405 00:25:53,119 --> 00:25:57,000 Speaker 1: get then deployed in clouds, right and these clouds then 406 00:25:57,080 --> 00:25:58,720 Speaker 1: need Oh, by the way, these things generate a lot 407 00:25:58,760 --> 00:26:02,760 Speaker 1: of electricity, and that's spawned the whole power energy movement. 408 00:26:02,800 --> 00:26:05,399 Speaker 1: And then you but then you know what the power 409 00:26:05,480 --> 00:26:09,760 Speaker 1: transmission and grid and technical thermal equipment that needs to 410 00:26:10,800 --> 00:26:14,520 Speaker 1: power and cool these cloud data centers, and so you 411 00:26:14,560 --> 00:26:17,400 Speaker 1: have built that supply chain down. And then and then 412 00:26:17,440 --> 00:26:20,919 Speaker 1: after the AI is built, you bring the AI into 413 00:26:21,480 --> 00:26:24,080 Speaker 1: into the into business at Bloomberg and Blackrock, and you 414 00:26:24,119 --> 00:26:26,560 Speaker 1: bring those into software and then you embed that in 415 00:26:26,600 --> 00:26:30,080 Speaker 1: applications and then oh, by the way, that same AI 416 00:26:30,240 --> 00:26:33,640 Speaker 1: that's being we'll throw that into the self driving car 417 00:26:33,840 --> 00:26:37,520 Speaker 1: and robots. And so once you see that whole chain 418 00:26:38,840 --> 00:26:43,440 Speaker 1: and how that gets diffused, and then you have interrogator, 419 00:26:43,440 --> 00:26:47,240 Speaker 1: you've already built these maps effectively of every single one 420 00:26:47,280 --> 00:26:50,520 Speaker 1: of these little ecosystems and supply chains, and then you 421 00:26:50,560 --> 00:26:54,720 Speaker 1: see how diffusion works, and then then you say, well, 422 00:26:55,320 --> 00:26:57,919 Speaker 1: is it worth investing in these companies or not? And 423 00:26:58,000 --> 00:27:01,800 Speaker 1: that's when then you get into the financial announce Really interesting. 424 00:27:01,840 --> 00:27:05,960 Speaker 2: So I'm hearing infrastructure, which is everything from power to 425 00:27:06,160 --> 00:27:11,600 Speaker 2: cloud to database to intelligence which is the modeling, and 426 00:27:11,640 --> 00:27:16,800 Speaker 2: then software tools, applications, solutions. So This isn't you know. 427 00:27:16,920 --> 00:27:21,080 Speaker 2: I think people tend to think of oh Ai, that's Nvidia. 428 00:27:21,119 --> 00:27:24,520 Speaker 2: But what you're really saying is this is dozens of 429 00:27:24,640 --> 00:27:29,800 Speaker 2: not hundreds of companies working across a whole ecosystem. 430 00:27:28,880 --> 00:27:35,320 Speaker 1: That's exactly right now in the public stock market. The 431 00:27:36,560 --> 00:27:40,200 Speaker 1: first two years the manifestation of what I just describe, 432 00:27:40,240 --> 00:27:44,199 Speaker 1: but what you just eloquently described gets expressed in the 433 00:27:44,320 --> 00:27:48,560 Speaker 1: Mac seven. You know, if I were to, let's recompile 434 00:27:48,640 --> 00:27:55,520 Speaker 1: that as a as a nine layer cake. Okay. At 435 00:27:55,520 --> 00:27:58,080 Speaker 1: the bottom of this of this of this cake is 436 00:27:58,280 --> 00:28:02,120 Speaker 1: the power and the ender G and then that feeds 437 00:28:03,240 --> 00:28:06,440 Speaker 1: these servers and chips, and then those servers and chips 438 00:28:06,480 --> 00:28:10,280 Speaker 1: get live in a in a data center cloud. That 439 00:28:10,320 --> 00:28:14,280 Speaker 1: whole bottom layer, those three layers is what I call infrastructure. Okay, 440 00:28:14,440 --> 00:28:17,639 Speaker 1: So that's why you're seeing most of the MAX seven 441 00:28:17,680 --> 00:28:18,040 Speaker 1: are here. 442 00:28:18,720 --> 00:28:23,160 Speaker 2: So that's Google and Amazon and Microsoft and. 443 00:28:23,119 --> 00:28:29,399 Speaker 1: Now Tesla is building AII cloud. And then above that layer, 444 00:28:30,200 --> 00:28:33,800 Speaker 1: let's call it uh, that's the models and the data. 445 00:28:34,720 --> 00:28:37,920 Speaker 1: So this is where you also have more maximven, Microsoft, Google, 446 00:28:38,480 --> 00:28:41,800 Speaker 1: Open Ai, these some of the private companies and now 447 00:28:42,160 --> 00:28:45,320 Speaker 1: x Ai. And you know there are six or six 448 00:28:45,360 --> 00:28:48,080 Speaker 1: of these companies building these foundation models, and then the 449 00:28:48,160 --> 00:28:50,160 Speaker 1: data you're feeding the data. And then you have all 450 00:28:50,200 --> 00:28:55,440 Speaker 1: these data companies that have let's say legal data, healthcare data, 451 00:28:55,600 --> 00:28:59,360 Speaker 1: insurance data, and then some of them are proprietary data, 452 00:28:59,400 --> 00:29:01,480 Speaker 1: which are health being trained these models. Right. 453 00:29:01,560 --> 00:29:04,560 Speaker 2: So we've seen a couple of stories about the Wall 454 00:29:04,560 --> 00:29:09,600 Speaker 2: Street Journal and Reuters leasing their entire corpus of all 455 00:29:09,640 --> 00:29:13,240 Speaker 2: their content to various AM models to work. 456 00:29:13,040 --> 00:29:15,200 Speaker 1: On correct and you know, companies like Reddit have done 457 00:29:15,240 --> 00:29:19,560 Speaker 1: a deal like that Wall Street Journal. There's some lawsuits. 458 00:29:19,600 --> 00:29:23,720 Speaker 2: Even New York Times, well they haven't in some instances 459 00:29:23,720 --> 00:29:26,440 Speaker 2: seem to have borrowed stuff that was you know, you 460 00:29:26,680 --> 00:29:31,719 Speaker 2: your ninety nine dollars a year subscription to the Washington 461 00:29:31,760 --> 00:29:34,680 Speaker 2: Post doesn't entitle you arguably to scrape all that data. 462 00:29:35,120 --> 00:29:37,880 Speaker 2: But hey, they're cutting checks and cutting deals, and I 463 00:29:37,920 --> 00:29:40,400 Speaker 2: think everybody just wants their piece of the pie. 464 00:29:40,200 --> 00:29:42,720 Speaker 1: That's right. And then there are some companies you mentioned 465 00:29:42,760 --> 00:29:47,360 Speaker 1: Thompson Reuters, which was you know, they have they run 466 00:29:47,440 --> 00:29:49,360 Speaker 1: one of the one of they have one of their 467 00:29:49,360 --> 00:29:55,160 Speaker 1: biggest legal data sets, you know, and they control that 468 00:29:55,240 --> 00:29:58,360 Speaker 1: legal data and so then they're putting AI on top 469 00:29:58,400 --> 00:30:00,600 Speaker 1: of that. So that's that that's it ellgents and the 470 00:30:00,640 --> 00:30:03,960 Speaker 1: data layer, and then above that layer you have the applications, 471 00:30:04,320 --> 00:30:07,920 Speaker 1: the tools and data infrastructure, and then the services the 472 00:30:08,000 --> 00:30:12,440 Speaker 1: human it labor to implement and to the age. 473 00:30:12,480 --> 00:30:14,400 Speaker 2: Give us some give us some names. I have a 474 00:30:14,400 --> 00:30:15,680 Speaker 2: couple of things on my phone. 475 00:30:15,720 --> 00:30:16,480 Speaker 1: What do you like? 476 00:30:17,440 --> 00:30:20,160 Speaker 2: Oh, on the app side, Yeah, I mean I'm using Perplexity. 477 00:30:20,440 --> 00:30:21,040 Speaker 1: Perplexity. 478 00:30:21,480 --> 00:30:23,120 Speaker 2: It's so clean and so simple. 479 00:30:23,880 --> 00:30:25,640 Speaker 1: I love chat GIPT. 480 00:30:27,240 --> 00:30:31,240 Speaker 2: They're slightly differently different, right, just the output, but they're 481 00:30:31,320 --> 00:30:36,000 Speaker 2: still and I'm finding far fewer hallucinations than than I 482 00:30:36,200 --> 00:30:39,200 Speaker 2: used to. Yes, Like I had Bill Dudley from the 483 00:30:39,240 --> 00:30:41,600 Speaker 2: New York fed in who was born in you know, 484 00:30:41,640 --> 00:30:46,120 Speaker 2: the late nineteen fifties, and chat GBT mentioned he happened 485 00:30:46,160 --> 00:30:49,160 Speaker 2: to be a linebacker for the Detroit Lions in nineteen 486 00:30:49,200 --> 00:30:51,680 Speaker 2: fifty two. It took it away, and there was a 487 00:30:51,680 --> 00:30:54,480 Speaker 2: guy named Bill Dudley who was It took it a 488 00:30:54,480 --> 00:30:57,280 Speaker 2: while for it to figure out, and like, after a 489 00:30:57,320 --> 00:31:01,240 Speaker 2: certain period that eventually got clean. Wait, if you're born 490 00:31:01,240 --> 00:31:04,960 Speaker 2: in fifty seven, you're probably not a pro football player 491 00:31:04,960 --> 00:31:08,920 Speaker 2: in fifty five. But it took it definitely took months, yes, 492 00:31:08,960 --> 00:31:12,080 Speaker 2: for it to kind of somehow recognize that. 493 00:31:12,560 --> 00:31:14,760 Speaker 1: Yeah, and that's on the consumer side, and there'll be 494 00:31:14,760 --> 00:31:17,840 Speaker 1: a lot more consumer apps coming, you know. You know, 495 00:31:17,880 --> 00:31:21,320 Speaker 1: companies like Apple have this Apple intelligence right if anything 496 00:31:21,320 --> 00:31:24,840 Speaker 1: in there, absolutely locked in on your private seed. But 497 00:31:24,880 --> 00:31:27,320 Speaker 1: they're gonna know you the best, and so there will 498 00:31:27,320 --> 00:31:28,880 Speaker 1: be AI assistants coming. 499 00:31:29,320 --> 00:31:31,240 Speaker 2: I hope it'll be better than Siri, which was a 500 00:31:31,320 --> 00:31:34,960 Speaker 2: huge disappointment for sure. But I would trust an Apple agent. 501 00:31:35,160 --> 00:31:37,280 Speaker 2: You would exactly to be able to say, hey, make 502 00:31:37,280 --> 00:31:40,280 Speaker 2: dinner reservations for Friday at this restaurant. Here's my calendar, 503 00:31:40,640 --> 00:31:44,760 Speaker 2: and invite Bob Smith and Mary and hopefully it can manage. 504 00:31:44,440 --> 00:31:49,320 Speaker 1: That absolutely and even more things even more difficult. Then 505 00:31:49,440 --> 00:31:51,520 Speaker 1: let's say that like, oh, I need to help them, 506 00:31:51,720 --> 00:31:55,400 Speaker 1: I need to do my taxes. I want my taxes 507 00:31:55,480 --> 00:31:56,600 Speaker 1: help or I need. 508 00:31:56,520 --> 00:32:00,440 Speaker 2: So I'm skeptical on really complex things. And at the 509 00:32:00,480 --> 00:32:04,720 Speaker 2: same time, I just read yesterday the latest comparison of 510 00:32:04,840 --> 00:32:09,840 Speaker 2: AI diagnostics versus doctors. AI just moved ahead. They moved 511 00:32:09,840 --> 00:32:13,719 Speaker 2: ahead on things like X rays and MRIs a while ago. Correct, 512 00:32:13,760 --> 00:32:17,320 Speaker 2: but now on here's twenty data points diagnose this illness. 513 00:32:17,880 --> 00:32:22,000 Speaker 2: It just moved ahead of the accuracy rate of human 514 00:32:22,080 --> 00:32:22,920 Speaker 2: day you. 515 00:32:23,120 --> 00:32:29,160 Speaker 1: Said exactly, the complexity of the tasks will only go 516 00:32:29,320 --> 00:32:31,320 Speaker 1: higher in terms of what they will be capable to do. 517 00:32:31,760 --> 00:32:36,440 Speaker 1: So and these ais are following these what we call 518 00:32:36,480 --> 00:32:41,440 Speaker 1: the scaling laws of scaling intelligence. But the things that 519 00:32:41,480 --> 00:32:44,640 Speaker 1: they will be capable of, it's not just booking a restaurant. 520 00:32:44,680 --> 00:32:47,680 Speaker 1: It'll be doing very complex tasks. And so we are 521 00:32:47,880 --> 00:32:50,040 Speaker 1: just at the very, very very beginning of that. 522 00:32:50,640 --> 00:32:55,000 Speaker 2: Huh, that's really fascinating. So given the mapping you do 523 00:32:55,080 --> 00:32:59,000 Speaker 2: of the whole ecosystem and then the dive into the 524 00:32:59,120 --> 00:33:04,360 Speaker 2: financial background, what strategies the end do you then use 525 00:33:04,400 --> 00:33:08,080 Speaker 2: in saying, Okay, I understand the whole ecosystem, I understand 526 00:33:08,400 --> 00:33:12,040 Speaker 2: the various balance sheets of these companies. How do you 527 00:33:12,080 --> 00:33:13,920 Speaker 2: then pick which stock you want to own? 528 00:33:14,160 --> 00:33:21,120 Speaker 1: Ah? So I have a certain small you know, rules, 529 00:33:21,160 --> 00:33:22,920 Speaker 1: I guess if you could call it that, that I've 530 00:33:23,040 --> 00:33:27,360 Speaker 1: I've or observations that I've made over many years, especially 531 00:33:27,400 --> 00:33:29,640 Speaker 1: in tech, right because this is a very dynamic industry. 532 00:33:31,120 --> 00:33:36,600 Speaker 1: One of those is like, there's a power law. What 533 00:33:36,720 --> 00:33:39,960 Speaker 1: I believe in power laws, And it seems like every 534 00:33:40,000 --> 00:33:43,920 Speaker 1: industry I've ever looked at, there's number one and number 535 00:33:43,920 --> 00:33:46,400 Speaker 1: two and that maybe in number three, so. 536 00:33:46,920 --> 00:33:49,560 Speaker 2: Very fat head, and then a long yeah, minor. 537 00:33:49,440 --> 00:33:53,720 Speaker 1: Let's just say fifty market chair number one, twenty five, 538 00:33:53,800 --> 00:33:56,560 Speaker 1: number two, and then cats and dogs, right, winner takes 539 00:33:56,600 --> 00:34:00,280 Speaker 1: all go everywhere, and it doesn't matter if you're selling 540 00:34:00,480 --> 00:34:05,680 Speaker 1: frozen pizza to search advertising. Okay, these power laws and 541 00:34:06,440 --> 00:34:09,359 Speaker 1: then because but the thing is that you could have 542 00:34:09,400 --> 00:34:15,000 Speaker 1: power laws that apply to hundreds of categories, right, doesn't 543 00:34:15,040 --> 00:34:18,319 Speaker 1: have to be all encompassing in one. And so when 544 00:34:18,320 --> 00:34:21,520 Speaker 1: I look at tech and that those all those different categories, 545 00:34:22,600 --> 00:34:25,719 Speaker 1: I firmly believe in these power law concepts that you 546 00:34:25,800 --> 00:34:27,719 Speaker 1: want to be betting on number one or number two, 547 00:34:28,280 --> 00:34:31,120 Speaker 1: especially number one, not even number two. You want number 548 00:34:31,160 --> 00:34:35,959 Speaker 1: one ideally, and so are you. And so in many 549 00:34:36,000 --> 00:34:40,200 Speaker 1: cases they're already existing players, okay. And so if they 550 00:34:40,239 --> 00:34:44,360 Speaker 1: are already existing players and then their their hegemony is 551 00:34:44,400 --> 00:34:47,200 Speaker 1: not being challenged, that's kind of an easy answer. You 552 00:34:47,600 --> 00:34:50,040 Speaker 1: keep riding the wave. And that's why people are always 553 00:34:50,040 --> 00:34:51,400 Speaker 1: complaining about Max seven. 554 00:34:51,880 --> 00:34:54,200 Speaker 2: You anticipated where I was going to go next. Yeah, 555 00:34:54,239 --> 00:34:58,840 Speaker 2: what you're essentially saying is Max seven is they're focusing 556 00:34:58,880 --> 00:35:02,760 Speaker 2: on the number seven and while ignoring the magnificent side. 557 00:35:03,040 --> 00:35:05,760 Speaker 2: You want to be in the number one stock everywhere, 558 00:35:05,800 --> 00:35:10,160 Speaker 2: which is going to naturally force the crowd investors to 559 00:35:10,280 --> 00:35:12,800 Speaker 2: the top five, ten, fifteen companies. 560 00:35:13,120 --> 00:35:18,280 Speaker 1: That's exactly what's been happening. The strong get stronger unless 561 00:35:18,600 --> 00:35:22,439 Speaker 1: unless there are signs of weakness, right there. 562 00:35:22,560 --> 00:35:27,000 Speaker 2: Is it competition, is it missteps by management? Is it 563 00:35:27,120 --> 00:35:31,680 Speaker 2: some new disruptive technology that thrusts the winners aside? What 564 00:35:31,760 --> 00:35:34,920 Speaker 2: do you look for to say, hey, XYZ has been 565 00:35:35,000 --> 00:35:38,359 Speaker 2: killing it for five ten years, but their run is over. 566 00:35:40,000 --> 00:35:48,120 Speaker 1: That's exactly right. Usually, usually these companies do not get disruptive, 567 00:35:48,239 --> 00:35:51,280 Speaker 1: but on occasion they do. I think the most obvious 568 00:35:51,320 --> 00:35:56,279 Speaker 1: one recently was the ascendency of Nvidia versus Intel for 569 00:35:56,400 --> 00:36:02,520 Speaker 1: thirty years Intel read and legion and and then there 570 00:36:02,560 --> 00:36:06,160 Speaker 1: was a transition. There are several several reasons, but there 571 00:36:06,200 --> 00:36:11,080 Speaker 1: was a transition too to accelerate computing from CPUs and 572 00:36:11,080 --> 00:36:13,600 Speaker 1: and then they've lost leadership on foundry to T S 573 00:36:13,680 --> 00:36:17,200 Speaker 1: m C and then mobile and they didn't they didn't 574 00:36:17,200 --> 00:36:20,160 Speaker 1: engage in mobile. And so there are times there are 575 00:36:20,239 --> 00:36:25,799 Speaker 1: times where companies, you know, different different transitions, like if 576 00:36:25,840 --> 00:36:30,399 Speaker 1: Microsoft did not pivot to the cloud from Windows right, 577 00:36:30,440 --> 00:36:34,279 Speaker 1: and the government you know, went after them on Windows, 578 00:36:34,640 --> 00:36:38,319 Speaker 1: but they were they were litigating yesterday's war, right, Microsoft 579 00:36:38,360 --> 00:36:41,719 Speaker 1: found Azure and then and then history was rewritten. 580 00:36:42,520 --> 00:36:45,440 Speaker 2: And what do you think of the job Saudia and 581 00:36:45,520 --> 00:36:47,480 Speaker 2: the Dalla has been that, you know, people. 582 00:36:47,239 --> 00:36:49,680 Speaker 1: Forget that's got to be one of the great great 583 00:36:49,800 --> 00:36:54,200 Speaker 1: great ceo uh and and and what he has mastered 584 00:36:54,239 --> 00:36:55,440 Speaker 1: in the history of business. 585 00:36:55,760 --> 00:36:59,640 Speaker 2: Microsoft was dead money for a decade. That sounds ridiculous 586 00:36:59,640 --> 00:37:02,200 Speaker 2: to say. I know, people don't remember there not that 587 00:37:02,320 --> 00:37:06,000 Speaker 2: Bohmer was a terrible CEO, but he was a founder 588 00:37:06,239 --> 00:37:10,560 Speaker 2: and maybe just wasn't nimble enough to see the next generation. 589 00:37:11,160 --> 00:37:14,080 Speaker 2: He was, you know, like many founders, they're stuck in 590 00:37:14,640 --> 00:37:18,120 Speaker 2: you know Microsoft one point oh, yes, and Nadella is 591 00:37:18,239 --> 00:37:20,000 Speaker 2: I don't know, maybe he's three point oh or four 592 00:37:20,040 --> 00:37:20,600 Speaker 2: point oh. 593 00:37:20,480 --> 00:37:23,360 Speaker 1: But yeah, definitely he's gotta it's gotta be one of 594 00:37:23,400 --> 00:37:27,160 Speaker 1: the greatest unbelieved business turnarounds in history. That doesn't get 595 00:37:27,200 --> 00:37:28,600 Speaker 1: that much enough recognition. 596 00:37:29,080 --> 00:37:31,160 Speaker 2: I I totally totally agree. 597 00:37:32,480 --> 00:37:34,880 Speaker 1: So they had this power law concept. Going back to 598 00:37:34,920 --> 00:37:37,520 Speaker 1: your idea, The other one is you need a second act. 599 00:37:38,560 --> 00:37:41,279 Speaker 1: You know, you need multiple acts. If you even look 600 00:37:41,320 --> 00:37:44,720 Speaker 1: at these great companies, right, you know, Microsoft, for example, 601 00:37:44,800 --> 00:37:46,640 Speaker 1: you had the Windows and then you had a second 602 00:37:46,680 --> 00:37:50,160 Speaker 1: act which is Azure right, and Azure it's been driving 603 00:37:50,880 --> 00:37:56,040 Speaker 1: the company, right. Even Apple found the iPhone after Mac right. 604 00:37:56,200 --> 00:37:58,759 Speaker 1: And so you need companies that have the Amazon. I 605 00:37:58,800 --> 00:38:00,520 Speaker 1: don't even know how many acts they've had so many 606 00:38:00,560 --> 00:38:05,879 Speaker 1: different acts, right, And so the great established companies can 607 00:38:06,680 --> 00:38:12,279 Speaker 1: continually add multiple new businesses. Not only what you're currently doing. 608 00:38:12,320 --> 00:38:15,760 Speaker 1: You got to anticipate the next So these power laws, 609 00:38:16,239 --> 00:38:19,359 Speaker 1: do you have, you know, multiple acts, because then that 610 00:38:19,520 --> 00:38:24,960 Speaker 1: helps you have duration that you can endure and then 611 00:38:25,160 --> 00:38:28,160 Speaker 1: are you differentiated enough? But then there is a whole 612 00:38:28,200 --> 00:38:30,360 Speaker 1: new class of companies, right, So there you have the 613 00:38:30,440 --> 00:38:34,680 Speaker 1: max seven, these power law companies. But there's always history 614 00:38:34,960 --> 00:38:37,880 Speaker 1: for tech has always given you the opportunity for the 615 00:38:37,920 --> 00:38:40,640 Speaker 1: new companies, the new companies to come. And so it's 616 00:38:40,680 --> 00:38:44,000 Speaker 1: really the combination of let's continue to ride the power 617 00:38:44,080 --> 00:38:48,080 Speaker 1: laws of the established companies and then let's find those 618 00:38:48,120 --> 00:38:54,440 Speaker 1: new companies that can rise and become the new challenger. 619 00:38:54,520 --> 00:38:57,759 Speaker 1: So it's that those two those are the two components 620 00:38:57,800 --> 00:38:59,960 Speaker 1: of a technology absolutely fault. 621 00:39:01,040 --> 00:39:03,480 Speaker 2: Before we get into the funds, I really want to 622 00:39:03,600 --> 00:39:07,400 Speaker 2: just touch base on two really interesting things you said 623 00:39:07,480 --> 00:39:13,680 Speaker 2: earlier one is just generally on the valuation question with 624 00:39:13,840 --> 00:39:19,920 Speaker 2: technology and similarly the market concentration of the Magnificent seven. 625 00:39:20,320 --> 00:39:21,440 Speaker 2: Share your thoughts on that. 626 00:39:23,239 --> 00:39:28,520 Speaker 1: Yeah, I think valuation right, if I were to broadly say, 627 00:39:28,600 --> 00:39:32,640 Speaker 1: is at a fair level. Now there's this dispersion in 628 00:39:32,719 --> 00:39:36,640 Speaker 1: that you mentioned the Mac seven and the crowding and 629 00:39:36,280 --> 00:39:43,399 Speaker 1: these giant winners. They have valuations that are higher than 630 00:39:43,440 --> 00:39:47,000 Speaker 1: the rest of tech. The rest of tech has not, 631 00:39:48,400 --> 00:39:52,560 Speaker 1: for the most part, recovered from the depression that we had, 632 00:39:52,560 --> 00:39:56,399 Speaker 1: the recession we had in twenty twenty two. They went, 633 00:39:56,560 --> 00:40:01,400 Speaker 1: they were way exaggerated in twenty one. You crashed in 634 00:40:01,480 --> 00:40:05,440 Speaker 1: twenty two, and there's been not that much of a recovery. 635 00:40:05,520 --> 00:40:09,880 Speaker 1: So a large part of tech is still at depressed levels. 636 00:40:10,920 --> 00:40:14,000 Speaker 1: I would say we're back to pre you know, twenty 637 00:40:14,040 --> 00:40:18,399 Speaker 1: eighteen seventeen levels, except the Max seven and a few 638 00:40:18,400 --> 00:40:22,360 Speaker 1: companies like that that have that are at higher levels, 639 00:40:22,360 --> 00:40:24,000 Speaker 1: but their performance have been better. 640 00:40:24,600 --> 00:40:28,399 Speaker 2: Right, And you know, it's funny, we still have over 641 00:40:28,440 --> 00:40:31,080 Speaker 2: a month ago this year, this could be the first 642 00:40:31,120 --> 00:40:33,200 Speaker 2: year the S and P five hundred beats the Nazaq 643 00:40:33,239 --> 00:40:37,320 Speaker 2: one hundred in a long time. I'm trying to remember 644 00:40:37,400 --> 00:40:38,600 Speaker 2: the last time we saw that. 645 00:40:39,520 --> 00:40:44,160 Speaker 1: Yeah, because a large part of the of the nastach, 646 00:40:44,440 --> 00:40:49,719 Speaker 1: especially non Mac seven, they've not done well. You know, 647 00:40:49,719 --> 00:40:52,759 Speaker 1: you large parts of software, large parts of semiconductors. Even 648 00:40:54,000 --> 00:40:58,160 Speaker 1: if you're not in the AI class, you know you've 649 00:40:58,239 --> 00:40:59,000 Speaker 1: been left behind. 650 00:41:00,040 --> 00:41:03,040 Speaker 2: Interesting, So I want to talk about something that you 651 00:41:03,200 --> 00:41:07,479 Speaker 2: do with your team. Every year, you conduct a tour 652 00:41:07,560 --> 00:41:11,320 Speaker 2: of Silicon Valley. You meet with leaders of both public 653 00:41:11,400 --> 00:41:16,480 Speaker 2: and private technology companies, often twenty five thirty different companies 654 00:41:16,600 --> 00:41:20,080 Speaker 2: and their senior management. Tell us a little bit about 655 00:41:20,120 --> 00:41:22,600 Speaker 2: what that experience is, Like, what do you learn? Does 656 00:41:22,640 --> 00:41:26,840 Speaker 2: it actually help you with your investing process? 657 00:41:27,080 --> 00:41:30,480 Speaker 1: Yeah? I think you're referring to our annual every summer 658 00:41:30,560 --> 00:41:37,200 Speaker 1: we do a bus tour. Effectively we bring thirty Blackrock investors. Now, 659 00:41:37,239 --> 00:41:40,920 Speaker 1: that said, we do, you know, two thousand meetings a 660 00:41:41,000 --> 00:41:44,160 Speaker 1: year with companies on my team. Wow, I personally do 661 00:41:44,239 --> 00:41:47,759 Speaker 1: almost a thousand meetings with companies. Now, this is a 662 00:41:47,760 --> 00:41:53,200 Speaker 1: special event because it pulls together seven, eight, nine, ten 663 00:41:53,239 --> 00:41:59,000 Speaker 1: different teams at black Rock, thirty plus execs and investors. 664 00:41:59,840 --> 00:42:01,799 Speaker 1: And then we get on a bus and we go 665 00:42:01,880 --> 00:42:05,320 Speaker 1: visit the top managements and CEOs both public and private companies. 666 00:42:05,360 --> 00:42:08,080 Speaker 1: Every year. This has been I've been running this now 667 00:42:08,600 --> 00:42:09,320 Speaker 1: eleven years. 668 00:42:09,480 --> 00:42:09,760 Speaker 2: Wow. 669 00:42:10,120 --> 00:42:15,560 Speaker 1: And what that does is that you know, you're on site. 670 00:42:15,760 --> 00:42:18,600 Speaker 1: You know, it's a little it's a little less formal. 671 00:42:20,440 --> 00:42:23,279 Speaker 1: The companies feel more comfortable because they're they're hosting you, 672 00:42:23,760 --> 00:42:26,879 Speaker 1: and it's and it's really more of a strategic discussions 673 00:42:27,480 --> 00:42:29,120 Speaker 1: than relitigating the quarter. 674 00:42:29,600 --> 00:42:33,440 Speaker 2: Right, So it is and much longer term. 675 00:42:33,280 --> 00:42:35,960 Speaker 1: Than yeah yeah, yeah yeah. And then you know, it's 676 00:42:36,000 --> 00:42:41,279 Speaker 1: always a great barometer of like what what what were 677 00:42:41,320 --> 00:42:46,720 Speaker 1: the topics of the tour in twenty fourteen versus twenty 678 00:42:46,760 --> 00:42:50,520 Speaker 1: twenty four and you could really see an evolutionary of 679 00:42:50,560 --> 00:42:53,799 Speaker 1: what was topical every year, and it's so it's a 680 00:42:53,800 --> 00:42:57,880 Speaker 1: great way. It's also great for the people because many times, 681 00:42:58,280 --> 00:43:01,120 Speaker 1: even you know, within a firm like Black Cruct, any 682 00:43:01,120 --> 00:43:04,040 Speaker 1: of the teams don't get that much time to be 683 00:43:04,160 --> 00:43:08,320 Speaker 1: with each other. So it's both for representing a unified 684 00:43:08,320 --> 00:43:11,759 Speaker 1: front to the company and then also within within the 685 00:43:11,960 --> 00:43:16,520 Speaker 1: interpersonal relationships that are strengthened. And then it's a really 686 00:43:16,600 --> 00:43:21,239 Speaker 1: a great barometer of what are the key topics. And 687 00:43:21,239 --> 00:43:23,120 Speaker 1: then if you looked at the last two years of 688 00:43:23,520 --> 00:43:26,440 Speaker 1: the bus tour, there's only one topic AI. 689 00:43:26,600 --> 00:43:30,719 Speaker 2: Yeah, so let's go before the previous two years. Give 690 00:43:30,800 --> 00:43:34,040 Speaker 2: us some examples of ideas that were surfaced via this 691 00:43:34,080 --> 00:43:34,600 Speaker 2: bus tour. 692 00:43:35,440 --> 00:43:40,200 Speaker 1: So I'll give you some specific example. I remember distinctly 693 00:43:41,880 --> 00:43:46,279 Speaker 1: there was one about a m d uh huh, when 694 00:43:46,320 --> 00:43:51,200 Speaker 1: a MD had just announced its new chiplet based you know, 695 00:43:51,680 --> 00:43:54,279 Speaker 1: Jim Keller was still working there, and it was one 696 00:43:54,280 --> 00:43:57,719 Speaker 1: of the famed chip designers, and they had redesigned the 697 00:43:57,800 --> 00:44:03,400 Speaker 1: processor and the CPU and that Zen architecture was the 698 00:44:03,440 --> 00:44:07,000 Speaker 1: basis in which ten years later they've gained all that 699 00:44:07,040 --> 00:44:10,239 Speaker 1: market share from Intel. But that was that day, and 700 00:44:10,800 --> 00:44:13,360 Speaker 1: I remember because a MD was on its. 701 00:44:13,160 --> 00:44:19,759 Speaker 2: Back perennially, always a laggard, always short of capital, always like, hey, 702 00:44:19,920 --> 00:44:21,640 Speaker 2: these guys are going to be here in five years. 703 00:44:21,680 --> 00:44:25,919 Speaker 1: But they made that seminal bet to really change that 704 00:44:26,080 --> 00:44:29,160 Speaker 1: chip architecture and that and then another one I remember 705 00:44:29,280 --> 00:44:33,919 Speaker 1: distinctly when there was lots of questions around Tesla, right, 706 00:44:34,800 --> 00:44:37,319 Speaker 1: can they get the Model three? There they were, they were, 707 00:44:37,680 --> 00:44:40,160 Speaker 1: They had warehouse, you know, not even a warehouse, a 708 00:44:40,239 --> 00:44:44,120 Speaker 1: tent to make remember that, and everyone was saying twenty four, 709 00:44:45,280 --> 00:44:48,520 Speaker 1: it had a tent to make the Model three. And 710 00:44:48,600 --> 00:44:51,319 Speaker 1: I think that kind of unlocked. That's like, well, we're 711 00:44:51,360 --> 00:44:53,600 Speaker 1: about to We're about to turn, We're about to make it. 712 00:44:54,400 --> 00:44:57,160 Speaker 1: This production is about to scale. And that was another 713 00:44:57,200 --> 00:44:59,839 Speaker 1: seminal moment. So you have these these events like that 714 00:44:59,840 --> 00:45:00,399 Speaker 1: that come through. 715 00:45:00,560 --> 00:45:05,520 Speaker 2: Let me ask you relative to Tesla an echosystem question. 716 00:45:06,160 --> 00:45:09,200 Speaker 2: So for the longest time, Tesla had the market all 717 00:45:09,239 --> 00:45:12,960 Speaker 2: to itself. Recently I saw a chart that showed for 718 00:45:13,000 --> 00:45:17,080 Speaker 2: the first time, Tesla's market share drop below fifty percent, 719 00:45:17,280 --> 00:45:20,520 Speaker 2: not because their sales have fallen, but because there are 720 00:45:20,520 --> 00:45:25,680 Speaker 2: so many other players in the EV space. I can't 721 00:45:25,680 --> 00:45:29,560 Speaker 2: help but give either credit or blame to Jeff Bezos, 722 00:45:29,920 --> 00:45:34,640 Speaker 2: who so totally destroyed sector after sector after sector that 723 00:45:35,120 --> 00:45:39,080 Speaker 2: when Musk came along, the automobile industry said, hey, we 724 00:45:39,160 --> 00:45:42,279 Speaker 2: saw what Amazon did, We better, you know, get our 725 00:45:42,320 --> 00:45:47,640 Speaker 2: act together pretty quickly. Any truth to that urban legend, I. 726 00:45:47,600 --> 00:45:55,560 Speaker 1: Would say, in EV just pure EV cars, Tesla's share 727 00:45:55,880 --> 00:45:59,440 Speaker 1: and its ascendency the entire market is especially in the US, 728 00:45:59,600 --> 00:46:04,360 Speaker 1: especially the West, not China, it's definitely slowed, if not stalled. 729 00:46:04,560 --> 00:46:08,439 Speaker 2: Okay, arguably I had the CEO of Lucid in here 730 00:46:08,840 --> 00:46:12,560 Speaker 2: who made a very aggressive claim that whether it was 731 00:46:12,719 --> 00:46:17,719 Speaker 2: battery technology motors range software. Tesla was a leader, and 732 00:46:17,840 --> 00:46:21,799 Speaker 2: Lucid as as leapfrog them. You could, you can, we 733 00:46:21,840 --> 00:46:24,520 Speaker 2: could debate that would but at least but it's a 734 00:46:24,640 --> 00:46:27,160 Speaker 2: credible whether it's true or not, it's a credible claim 735 00:46:27,600 --> 00:46:30,759 Speaker 2: which would not have been remotely credible five years ago, 736 00:46:30,800 --> 00:46:32,400 Speaker 2: even three years ago. 737 00:46:32,560 --> 00:46:34,920 Speaker 1: I would say to that. And I don't want to 738 00:46:35,000 --> 00:46:39,800 Speaker 1: comment on that specific company, but you know companies like that, 739 00:46:39,920 --> 00:46:42,839 Speaker 1: they're selling one hundred thousand dollars car, right, Tesla selling 740 00:46:42,880 --> 00:46:47,759 Speaker 1: it forty thousand dollars car the fifty thousand dollars and 741 00:46:48,000 --> 00:46:52,239 Speaker 1: up market is very different. Which is which is most vs? Right? 742 00:46:56,280 --> 00:46:58,000 Speaker 1: You know, if you remember you go in the past, 743 00:46:58,120 --> 00:47:01,840 Speaker 1: the greatest, the best selling single was like the Toto Corolla, 744 00:47:02,600 --> 00:47:06,160 Speaker 1: you know, like a couple million a year, and you 745 00:47:06,160 --> 00:47:09,760 Speaker 1: look at Tesla's Model three and Y and they're also 746 00:47:09,840 --> 00:47:13,680 Speaker 1: in that range. So basically, if you're in that kind 747 00:47:13,719 --> 00:47:17,480 Speaker 1: of category, you get to a certain market level, a 748 00:47:17,600 --> 00:47:22,080 Speaker 1: saturation level. And I think that in the West and 749 00:47:22,120 --> 00:47:25,560 Speaker 1: then you know, with the more reticence to adopt EV 750 00:47:25,680 --> 00:47:28,000 Speaker 1: and still in the United States, you kind of have 751 00:47:28,160 --> 00:47:32,319 Speaker 1: a certain ceiling you need. And this is why there's 752 00:47:32,320 --> 00:47:36,759 Speaker 1: so much discussion about Tesla either having a lower cost 753 00:47:36,880 --> 00:47:39,320 Speaker 1: robotex or lower cost car to get at the market 754 00:47:39,440 --> 00:47:43,200 Speaker 1: sub fifty thousand dollars where you have you know that 755 00:47:43,320 --> 00:47:46,000 Speaker 1: unlocks some market three times bigger. It's like a thirty 756 00:47:46,040 --> 00:47:49,319 Speaker 1: thousand dollars car or twenty five thousand dollars car. But 757 00:47:49,400 --> 00:47:54,439 Speaker 1: I think Tesla's main pivot really and even Elon would 758 00:47:54,440 --> 00:47:56,799 Speaker 1: will tell you it's not about the car. The car 759 00:47:56,880 --> 00:48:01,359 Speaker 1: is a mere means to deliver autonomy, right. And it's 760 00:48:01,360 --> 00:48:05,839 Speaker 1: a robotics company, right, It's and and and autonomy is 761 00:48:05,840 --> 00:48:09,360 Speaker 1: to meet big onlock, not not selling the car itself. 762 00:48:09,880 --> 00:48:15,240 Speaker 2: That'll be interesting. We've been waiting autonomy for a while. 763 00:48:15,840 --> 00:48:18,439 Speaker 2: One can't help but wonder how much easier it would 764 00:48:18,480 --> 00:48:22,200 Speaker 2: be if if build into the roads and other vehicles 765 00:48:22,280 --> 00:48:29,520 Speaker 2: where some form of our device that allows other cars 766 00:48:29,560 --> 00:48:32,439 Speaker 2: to know where here's where the exit is, here's where 767 00:48:32,440 --> 00:48:35,000 Speaker 2: the lanes are, here's where other cars are, like, there 768 00:48:35,040 --> 00:48:37,560 Speaker 2: could be an infrastructure build out that makes that. 769 00:48:38,239 --> 00:48:39,880 Speaker 1: Have you brought it? When's the last time you were 770 00:48:39,920 --> 00:48:42,759 Speaker 1: in La or this year? This year? Okay, did you 771 00:48:42,800 --> 00:48:44,120 Speaker 1: see Waymo's running around? 772 00:48:44,560 --> 00:48:45,000 Speaker 2: I did not. 773 00:48:45,520 --> 00:48:49,560 Speaker 1: I did not, So weymo is now operating in Los Angeles, 774 00:48:49,640 --> 00:48:55,560 Speaker 1: and they're everywhere in San Francisco, Phoenix, and the futures here. 775 00:48:55,560 --> 00:48:57,160 Speaker 2: It's just it. 776 00:48:57,080 --> 00:48:59,960 Speaker 1: Is, it is, it's within grasps. Finally, it's it's always 777 00:49:00,080 --> 00:49:02,399 Speaker 1: been three years in the future, but it really is now. 778 00:49:02,400 --> 00:49:05,080 Speaker 2: I think so. So now let's let's bring this conversation 779 00:49:05,600 --> 00:49:09,359 Speaker 2: full circle back to the funds you run. Let's talk 780 00:49:09,360 --> 00:49:12,120 Speaker 2: about b I b AI, which is the I shares 781 00:49:12,719 --> 00:49:17,879 Speaker 2: AI Innovation and Technology Active ETF. Tell us a little 782 00:49:17,880 --> 00:49:20,880 Speaker 2: bit about that. That that's a fairly concentrated portfolio, isn't it. 783 00:49:21,440 --> 00:49:25,160 Speaker 1: That's right, this is we just launched this. This is 784 00:49:25,200 --> 00:49:30,080 Speaker 1: our first fora first we have two ETFs. Now we're 785 00:49:30,120 --> 00:49:33,480 Speaker 1: jumping on that that ETF bandwagon will. 786 00:49:33,360 --> 00:49:35,839 Speaker 2: Yeah, I think that that might work out a black Yeah, 787 00:49:35,840 --> 00:49:36,759 Speaker 2: that's right here, I hear. 788 00:49:38,000 --> 00:49:41,759 Speaker 1: But this this one is you know, I think, you know, 789 00:49:41,840 --> 00:49:44,759 Speaker 1: hopefully we look back, uh, this is the second year 790 00:49:44,800 --> 00:49:48,560 Speaker 1: of AI as we would as I would say, and 791 00:49:48,920 --> 00:49:51,520 Speaker 1: I think this is going to be a decade long, 792 00:49:51,760 --> 00:49:57,040 Speaker 1: if not longer, trend, and we are trying to express 793 00:49:57,120 --> 00:50:02,000 Speaker 1: in a concentrated way thirty plus companies in an ETF 794 00:50:02,200 --> 00:50:04,560 Speaker 1: that represents this whole stack of AI. 795 00:50:05,680 --> 00:50:07,799 Speaker 2: From Nvidia down to the small the. 796 00:50:07,800 --> 00:50:10,279 Speaker 1: Way up to the apps, from the compute to the 797 00:50:10,800 --> 00:50:15,719 Speaker 1: apps and everything in between. And I do know one thing. 798 00:50:16,680 --> 00:50:20,200 Speaker 1: So we want a concentrated exposure to the builders of AI, 799 00:50:21,280 --> 00:50:25,840 Speaker 1: companies building the key elements of AI. And I do 800 00:50:25,920 --> 00:50:29,600 Speaker 1: know one thing it will be. It's going to change dramatically. 801 00:50:30,400 --> 00:50:33,799 Speaker 1: What we think is the companies that today might not 802 00:50:33,920 --> 00:50:36,799 Speaker 1: be and so we need I feel like, especially when 803 00:50:36,840 --> 00:50:40,000 Speaker 1: there's high rate of change in the early days of 804 00:50:40,000 --> 00:50:44,480 Speaker 1: an industry like this, we need dynamic adaptation. We need 805 00:50:44,520 --> 00:50:47,960 Speaker 1: to be flexibly an adaptive and so to lock yourself 806 00:50:48,000 --> 00:50:52,960 Speaker 1: into a fixed passive structure versus a dynamically changing structure. 807 00:50:53,280 --> 00:50:55,360 Speaker 1: That's really the goal of this ETF. 808 00:50:55,520 --> 00:51:00,200 Speaker 2: Let's talk about I Shares Technology Opportunities Active ETF or 809 00:51:00,280 --> 00:51:05,120 Speaker 2: t e K broader portfolio fifty to seventy global tech companies. 810 00:51:05,320 --> 00:51:06,640 Speaker 2: Tell us what that focus is. 811 00:51:06,920 --> 00:51:12,520 Speaker 1: That is basically the ETF version of our mutual fund. 812 00:51:13,080 --> 00:51:16,799 Speaker 1: And so that includes tech companies, not only ETF, not 813 00:51:16,840 --> 00:51:21,400 Speaker 1: only AI companies, but broad tech globally larger companies. But 814 00:51:22,760 --> 00:51:25,000 Speaker 1: you know, there's lots of tech companies that don't really 815 00:51:25,640 --> 00:51:27,720 Speaker 1: that don't really have that much to do with AI 816 00:51:27,840 --> 00:51:30,959 Speaker 1: building AI, and so you're going to get the whole 817 00:51:31,000 --> 00:51:32,640 Speaker 1: totality of tech that in that. 818 00:51:33,200 --> 00:51:36,840 Speaker 2: So you said something before that has stayed with me 819 00:51:37,120 --> 00:51:40,000 Speaker 2: about looking at the entire map of the ecosystem and 820 00:51:40,520 --> 00:51:46,520 Speaker 2: watching what becomes hot and what fades. Technological change today 821 00:51:46,800 --> 00:51:51,319 Speaker 2: is just so rapid. It changes at light speed. How 822 00:51:51,360 --> 00:51:54,239 Speaker 2: do you keep up? How do you stay aligned with 823 00:51:55,040 --> 00:51:58,600 Speaker 2: the industry dynamics as they evolve in real time? It 824 00:51:58,680 --> 00:52:01,919 Speaker 2: seems like it's not even quarter to quarter anymore, it's 825 00:52:02,320 --> 00:52:03,680 Speaker 2: minute to minute. 826 00:52:04,440 --> 00:52:06,759 Speaker 1: Maybe not minute to minute, but you're you're absolutely right 827 00:52:06,800 --> 00:52:11,239 Speaker 1: in AI. So there are different time scales according to the 828 00:52:11,239 --> 00:52:13,840 Speaker 1: different industry. So let's say in AI, you're right, it 829 00:52:13,920 --> 00:52:16,440 Speaker 1: might literally be minute to minute day to day. Okay. 830 00:52:18,560 --> 00:52:24,120 Speaker 1: On the smartphone, you know, things are more staid, they're 831 00:52:24,160 --> 00:52:30,560 Speaker 1: slower paced, and so you have a spectrum of rates 832 00:52:30,560 --> 00:52:36,560 Speaker 1: of change. That's number one. So number two, how do 833 00:52:36,600 --> 00:52:39,440 Speaker 1: we keep keep up? I mean, you know, I've I 834 00:52:39,520 --> 00:52:44,200 Speaker 1: read a lot, and not only read. You have to 835 00:52:44,360 --> 00:52:48,040 Speaker 1: stay attuned to all this new multimedia. There's so many 836 00:52:48,640 --> 00:52:52,960 Speaker 1: experts and podcasts like yours, and and scientists and and 837 00:52:53,000 --> 00:52:56,280 Speaker 1: then we do like I do personally a thousand company 838 00:52:56,280 --> 00:52:57,800 Speaker 1: meetings a year. That's amazing. 839 00:52:57,960 --> 00:53:02,520 Speaker 2: So that's for a day. You're working fifty weeks a year. 840 00:53:02,719 --> 00:53:06,040 Speaker 1: Yes, I mean yes, I do many, many, many mini 841 00:53:06,080 --> 00:53:11,440 Speaker 1: meetings a week. Huh so, and so then you assimilate 842 00:53:11,440 --> 00:53:15,920 Speaker 1: all this information and then you are all I'm always 843 00:53:16,680 --> 00:53:19,920 Speaker 1: doing the calculus. Who's winning, who's losing, who's winning, who's losing, 844 00:53:22,040 --> 00:53:23,120 Speaker 1: what's changing, what's not. 845 00:53:23,960 --> 00:53:27,440 Speaker 2: So how do you balance having a long term perspective 846 00:53:28,000 --> 00:53:32,280 Speaker 2: for a technology like AI with you run a fund, 847 00:53:32,320 --> 00:53:34,920 Speaker 2: you run a couple of funds. Yeah, you get judged 848 00:53:35,000 --> 00:53:38,840 Speaker 2: every quarter. There's a very short term and Wall Street 849 00:53:38,880 --> 00:53:43,040 Speaker 2: is notorious for being too short term focused. How do 850 00:53:43,160 --> 00:53:46,440 Speaker 2: you manage the tradeoff between hey, this is going to 851 00:53:46,440 --> 00:53:49,560 Speaker 2: be a dominant technology over the next five years to 852 00:53:50,040 --> 00:53:53,920 Speaker 2: uh oh it's September thirtieth and we know what happens 853 00:53:54,239 --> 00:53:57,120 Speaker 2: starting in October. How do you manage that trade off? 854 00:53:57,440 --> 00:54:00,680 Speaker 1: That is the central question because we are being challenged 855 00:54:01,080 --> 00:54:06,640 Speaker 1: all the time. You know, I feel you get some 856 00:54:06,800 --> 00:54:12,719 Speaker 1: latitude if you have already a historical track record. So 857 00:54:12,800 --> 00:54:16,520 Speaker 1: for example, twenty twenty two was just brutal hell on 858 00:54:16,560 --> 00:54:17,239 Speaker 1: Earth for tech. 859 00:54:17,680 --> 00:54:19,600 Speaker 2: It was you know, not only was it hell on Earth. 860 00:54:19,640 --> 00:54:22,280 Speaker 2: For tech, it was the first year in over forty 861 00:54:22,360 --> 00:54:26,040 Speaker 2: years where both stocks and bonds were down double digits, 862 00:54:26,520 --> 00:54:31,320 Speaker 2: like once every half century. And then the only saving 863 00:54:31,360 --> 00:54:35,360 Speaker 2: grace was twenty twenty one was so spectacular that it 864 00:54:35,440 --> 00:54:37,960 Speaker 2: felt like, all right, we're giving back some profits. But 865 00:54:38,040 --> 00:54:40,719 Speaker 2: it's not, you know, it didn't feel like it was 866 00:54:41,480 --> 00:54:43,160 Speaker 2: seven eight o nine, which was. 867 00:54:43,280 --> 00:54:47,719 Speaker 1: Twenty twenty two was worse than was worse than two 868 00:54:47,760 --> 00:54:48,600 Speaker 1: thousand and eight. 869 00:54:49,120 --> 00:54:52,239 Speaker 2: For technology tech, Oh yeah, for sure. Really that's a 870 00:54:52,280 --> 00:54:53,560 Speaker 2: big statement. 871 00:54:53,280 --> 00:54:57,320 Speaker 1: Because in two thousand and nine it was a universal collepse, 872 00:54:57,600 --> 00:55:02,759 Speaker 1: that's correct. It centered mostly you know, real estate. Tech 873 00:55:02,800 --> 00:55:05,439 Speaker 1: went down, of course, but it didn't go down more. 874 00:55:05,800 --> 00:55:12,240 Speaker 1: In twenty twenty two, it was predominantly a tech collapse. 875 00:55:12,440 --> 00:55:15,160 Speaker 2: But it wasn't like the dot com implosion where the 876 00:55:15,239 --> 00:55:17,720 Speaker 2: nasty one hundred fell eighty plus percent. 877 00:55:17,840 --> 00:55:18,680 Speaker 1: That's right, it wasn't. 878 00:55:18,760 --> 00:55:21,959 Speaker 2: It wasn't, but it was still no fun. You were down, Yeah, 879 00:55:22,040 --> 00:55:24,920 Speaker 2: it was down thirty plus percent, lost a third of 880 00:55:24,960 --> 00:55:26,160 Speaker 2: its value. That's a big hit. 881 00:55:26,400 --> 00:55:31,160 Speaker 1: But in my in my in my career, twenty twenty 882 00:55:31,160 --> 00:55:34,960 Speaker 1: two was the worst year. And so do you have 883 00:55:35,400 --> 00:55:40,440 Speaker 1: the latitude and the confidence and support by by investors 884 00:55:40,440 --> 00:55:44,759 Speaker 1: and management to allow you to continue, you know, and 885 00:55:45,040 --> 00:55:49,000 Speaker 1: and you know, and then obvious the last couple of 886 00:55:49,040 --> 00:55:53,480 Speaker 1: years has been good, right and so but do you 887 00:55:53,680 --> 00:55:58,200 Speaker 1: does everybody get that avail that opportunity to and that 888 00:55:58,239 --> 00:56:01,359 Speaker 1: goes to the short term long term, but I try 889 00:56:01,440 --> 00:56:05,680 Speaker 1: not to focus on the short term. And you know, 890 00:56:05,719 --> 00:56:09,560 Speaker 1: we're trying to make systematic bets to the best of 891 00:56:09,600 --> 00:56:14,279 Speaker 1: our ability with you know, especially an active manager. You 892 00:56:14,360 --> 00:56:19,279 Speaker 1: know it is you need to show because we're we 893 00:56:19,440 --> 00:56:23,680 Speaker 1: hold generally fewer companies, and you need you need a 894 00:56:23,719 --> 00:56:30,880 Speaker 1: couple of years to show that those longer duration bets 895 00:56:30,880 --> 00:56:36,200 Speaker 1: start to manifest. And so if I was always chasing 896 00:56:36,360 --> 00:56:39,279 Speaker 1: the quarter, you would you know, you're you're now you're 897 00:56:39,320 --> 00:56:43,200 Speaker 1: trying to be You're not a momentum trade, you would 898 00:56:43,400 --> 00:56:46,719 Speaker 1: yeah or yeah, exactly we And that's really kind of 899 00:56:46,719 --> 00:56:50,640 Speaker 1: that at the end, we're saying our decisions that are 900 00:56:50,920 --> 00:56:53,600 Speaker 1: born out of all of this domain and expertise and 901 00:56:53,640 --> 00:56:57,440 Speaker 1: all of this analytical rigor, and then we express that 902 00:56:57,560 --> 00:57:00,640 Speaker 1: for a multi year basis, and then that ultimately comes through. 903 00:57:01,160 --> 00:57:04,280 Speaker 1: And if we were to continually shift by the wind 904 00:57:04,360 --> 00:57:09,239 Speaker 1: every quarter, you kind of lose your soul effectively of 905 00:57:09,280 --> 00:57:11,880 Speaker 1: what you stand for, and so we try not to 906 00:57:11,920 --> 00:57:13,719 Speaker 1: do that. Obviously, twenty twenty two we had to make 907 00:57:13,760 --> 00:57:16,360 Speaker 1: a lot of adjustments, but other than that, we kind 908 00:57:16,360 --> 00:57:17,720 Speaker 1: of stick to that same framework. 909 00:57:17,960 --> 00:57:21,280 Speaker 2: H really fascinating. All right, so I only have you 910 00:57:21,360 --> 00:57:25,520 Speaker 2: for another few minutes. Let's jump to our favorite questions. 911 00:57:25,560 --> 00:57:28,560 Speaker 2: Okay that we ask all of our guests, starting with 912 00:57:29,240 --> 00:57:32,160 Speaker 2: what's keeping you entertained these days? What are you listening 913 00:57:32,200 --> 00:57:33,960 Speaker 2: to watching, streaming, et cetera. 914 00:57:34,960 --> 00:57:38,840 Speaker 1: Okay, I don't get the chance to watch that much 915 00:57:39,480 --> 00:57:44,400 Speaker 1: TV and streaming, but streaming shows, the ones I've recently 916 00:57:44,440 --> 00:57:49,480 Speaker 1: seen I seen. I really like Showgun New one, the 917 00:57:49,520 --> 00:57:51,720 Speaker 1: new One, the remake from the from the eighties, or 918 00:57:53,120 --> 00:57:55,120 Speaker 1: three Body Problem I enjoyed. 919 00:57:55,240 --> 00:57:57,520 Speaker 2: I love that. I couldn't get through the book, but 920 00:57:57,560 --> 00:57:58,440 Speaker 2: the show was great. 921 00:57:58,520 --> 00:58:03,240 Speaker 1: Yeah. And then but I watch a lot more. I'm 922 00:58:03,240 --> 00:58:08,320 Speaker 1: a history guy, so I love Epic History on YouTube. 923 00:58:08,320 --> 00:58:12,720 Speaker 1: It is absolutely fantastic Epic History Epic History TV. Yeah, 924 00:58:12,840 --> 00:58:18,480 Speaker 1: it's fantastic. I watch a lot of science stuff like 925 00:58:18,560 --> 00:58:24,560 Speaker 1: World Science Festival of Columbia. Professor here Brian Green. Oh sure, yeah, 926 00:58:24,720 --> 00:58:28,320 Speaker 1: I also like chess. I watched like chess. 927 00:58:28,600 --> 00:58:29,480 Speaker 2: You watch chess. 928 00:58:29,520 --> 00:58:32,200 Speaker 1: Yes, I love watching chess so like Chess, Dog is 929 00:58:32,360 --> 00:58:36,280 Speaker 1: just a great show, especially the old old matches of 930 00:58:36,320 --> 00:58:40,360 Speaker 1: the of the great the great players like Bobby Fisher 931 00:58:40,440 --> 00:58:43,800 Speaker 1: and Paul Morphy and things. And the podcast. I think 932 00:58:43,880 --> 00:58:46,360 Speaker 1: the best podcast for me is The Ancients. 933 00:58:46,760 --> 00:58:48,240 Speaker 2: The Ancients Ancient. 934 00:58:48,480 --> 00:58:53,640 Speaker 1: This is an ancient civilizations in ancient history. So those 935 00:58:53,680 --> 00:58:55,840 Speaker 1: are what Yeah, that's what kind of occupies me. I 936 00:58:56,360 --> 00:59:00,560 Speaker 1: don't do as much business shows and buses this pods. 937 00:59:00,760 --> 00:59:04,640 Speaker 1: I'll listened to yours a few times and a few others, 938 00:59:04,680 --> 00:59:08,160 Speaker 1: but I'm more about you know, I'm in finance all 939 00:59:08,240 --> 00:59:12,600 Speaker 1: day long. I don't really need more finance, so I 940 00:59:13,040 --> 00:59:17,640 Speaker 1: go for my love of history. It's probably that I. 941 00:59:17,560 --> 00:59:19,560 Speaker 2: Have the same issue. It's like I don't want to 942 00:59:19,600 --> 00:59:22,520 Speaker 2: hear a guest I'm going to interview one another show 943 00:59:22,880 --> 00:59:24,880 Speaker 2: I want to I don't want to repeat questions or 944 00:59:24,920 --> 00:59:28,160 Speaker 2: steal questions. I want to bring a fresh approach, and 945 00:59:28,280 --> 00:59:31,000 Speaker 2: when you're immersed in it all day, you just don't 946 00:59:31,040 --> 00:59:34,600 Speaker 2: want to go that way. Next question, Yeah, who were 947 00:59:34,640 --> 00:59:37,560 Speaker 2: your early mentors who helped to shape your career? 948 00:59:39,080 --> 00:59:44,840 Speaker 1: The you know mentor would imbue a personal one on 949 00:59:44,840 --> 00:59:48,480 Speaker 1: one like tutor and tutoring and things. I didn't have 950 00:59:48,560 --> 00:59:53,800 Speaker 1: too many of those. I would say my earliest mentors 951 00:59:53,880 --> 00:59:56,280 Speaker 1: I go to high school. That's of my formative years 952 00:59:56,320 --> 01:00:01,600 Speaker 1: in Illinois. My English teacher who was also my debate coach, 953 01:00:02,360 --> 01:00:06,200 Speaker 1: my history teacher, and my chemistry teacher. I look back 954 01:00:06,240 --> 01:00:10,240 Speaker 1: and they really helped form who I am today. And 955 01:00:10,280 --> 01:00:13,480 Speaker 1: then in the professional world, I would say, I go 956 01:00:13,560 --> 01:00:16,440 Speaker 1: to and this is like black Rock. When I and 957 01:00:16,480 --> 01:00:19,080 Speaker 1: I joined, it was Tom Callen who hired me. And 958 01:00:19,160 --> 01:00:22,640 Speaker 1: Tom said, not so much as a mentor, but he said, 959 01:00:24,240 --> 01:00:29,160 Speaker 1: here are the keys, and you express your creativity and 960 01:00:29,200 --> 01:00:33,120 Speaker 1: build the business. And he gave me that latitude. And 961 01:00:33,160 --> 01:00:37,760 Speaker 1: so I give credit to Tom Callen. But I didn't 962 01:00:37,760 --> 01:00:40,240 Speaker 1: have too many people mentoring me of doing this. It 963 01:00:40,360 --> 01:00:44,520 Speaker 1: was more most of my mentors are dead. I have 964 01:00:44,680 --> 01:00:49,400 Speaker 1: people that I have influenced me, like like Napoleon and 965 01:00:49,760 --> 01:00:53,440 Speaker 1: Frank Lloyd Wright and and and Beethoven and others. 966 01:00:54,120 --> 01:00:57,800 Speaker 2: So you grew up in Illinois. Did you do any 967 01:00:57,840 --> 01:00:59,840 Speaker 2: of the Frank Lloyd Wright tours? Oh? 968 01:01:00,280 --> 01:01:00,919 Speaker 1: I did, all right. 969 01:01:00,960 --> 01:01:03,360 Speaker 2: So we spent every Thanksgiving and we'll met and so 970 01:01:03,480 --> 01:01:06,560 Speaker 2: I've done that whole run. And I have to assume 971 01:01:06,600 --> 01:01:07,080 Speaker 2: you've been to. 972 01:01:07,160 --> 01:01:09,240 Speaker 1: Falling Waters, right, I've been Falling. 973 01:01:09,000 --> 01:01:14,040 Speaker 2: Water, so I I twenty oh really that's on my list. 974 01:01:14,080 --> 01:01:18,160 Speaker 2: In twenty seventeen, I bought a car in Indianapolis, flew 975 01:01:18,200 --> 01:01:22,720 Speaker 2: out testrovit, signed the papers, drove home, and halfway home 976 01:01:23,160 --> 01:01:26,560 Speaker 2: was Falling Waters. And we were there the first day 977 01:01:26,600 --> 01:01:28,280 Speaker 2: it was open, and I want to say it was 978 01:01:29,040 --> 01:01:32,600 Speaker 2: early March and it was like a light coaching. Oh yeah, 979 01:01:32,640 --> 01:01:37,120 Speaker 2: we did the whole tour. It's absolutely astonishing, not just 980 01:01:37,800 --> 01:01:43,280 Speaker 2: because how delightful the building is, but never before and 981 01:01:43,360 --> 01:01:48,520 Speaker 2: probably never since, will a house be so ideally suited 982 01:01:48,600 --> 01:01:49,640 Speaker 2: to its surroundings. 983 01:01:49,640 --> 01:01:52,000 Speaker 1: It's just absolutely Yeah. 984 01:01:51,200 --> 01:01:55,320 Speaker 2: It's always interesting when you see, oh you could see 985 01:01:55,360 --> 01:01:59,720 Speaker 2: the thought that went into every curve, every line, every detail. 986 01:01:59,720 --> 01:02:01,240 Speaker 2: It's really it's really amazing. 987 01:02:01,520 --> 01:02:06,480 Speaker 1: The genesis of that, my interest in architecture. Yeah, I 988 01:02:06,560 --> 01:02:10,360 Speaker 1: read The fountain Head. You read the book. 989 01:02:11,160 --> 01:02:15,960 Speaker 2: I slogged through it in college and basically gave up 990 01:02:15,960 --> 01:02:18,760 Speaker 2: on her because of that book you gave But like that, 991 01:02:18,880 --> 01:02:20,640 Speaker 2: it's really such a painful book. 992 01:02:20,680 --> 01:02:24,560 Speaker 1: It is, yeah, but it spawned there's some ideas in 993 01:02:24,680 --> 01:02:27,640 Speaker 1: it that are interesting. The idea, especially the architecture that 994 01:02:27,680 --> 01:02:29,440 Speaker 1: really triggered, oh, architecture. 995 01:02:30,560 --> 01:02:33,919 Speaker 2: But so since you mentioned the Fountain Head, let's talk 996 01:02:33,960 --> 01:02:36,240 Speaker 2: about books. What are some of your favorites? What are 997 01:02:36,280 --> 01:02:37,160 Speaker 2: you reading right now? 998 01:02:37,840 --> 01:02:41,520 Speaker 1: Okay, there are certain books that are influential to me. 999 01:02:41,720 --> 01:02:46,360 Speaker 1: I was grew up in just people on the show, 1000 01:02:46,960 --> 01:02:48,640 Speaker 1: they don't I grew up before the Internet. 1001 01:02:51,040 --> 01:02:52,040 Speaker 2: I don't think we're that far up. 1002 01:02:52,800 --> 01:02:56,640 Speaker 1: And and I was a nerd. I was a total nerd. 1003 01:02:56,800 --> 01:02:58,120 Speaker 2: Same and so the. 1004 01:02:58,040 --> 01:02:59,640 Speaker 1: Lord of the Rings and I knew you were going 1005 01:02:59,680 --> 01:03:00,960 Speaker 1: to go there. How did you do it? 1006 01:03:01,240 --> 01:03:04,080 Speaker 2: Because that was the I reread The Hobbit and The 1007 01:03:04,120 --> 01:03:06,919 Speaker 2: Lord of the Rings every summer throughout my teen year. 1008 01:03:06,960 --> 01:03:07,480 Speaker 1: Oh my god. 1009 01:03:07,760 --> 01:03:11,760 Speaker 2: And someone just told me that the character actor who 1010 01:03:11,800 --> 01:03:18,080 Speaker 2: played Smigele in the movie actually narrates the book on 1011 01:03:18,120 --> 01:03:21,720 Speaker 2: the audible version. And people have told me it's not 1012 01:03:21,960 --> 01:03:23,480 Speaker 2: like listening to a book on tape. 1013 01:03:23,520 --> 01:03:24,680 Speaker 1: It's like a. 1014 01:03:24,600 --> 01:03:29,000 Speaker 2: Full radio play that he had his voice. That's right, 1015 01:03:29,600 --> 01:03:31,000 Speaker 2: it's supposed to be fantastic. 1016 01:03:31,200 --> 01:03:34,320 Speaker 1: Yeah, I even go, Yeah, I loved it. Uh, And 1017 01:03:34,360 --> 01:03:37,840 Speaker 1: then I went even. I went really deep the Silmarillion 1018 01:03:38,000 --> 01:03:40,160 Speaker 1: and the twenty thousand year prehistory to the Lord of 1019 01:03:40,200 --> 01:03:42,640 Speaker 1: the Rings. I went that, how far afield. 1020 01:03:42,640 --> 01:03:45,840 Speaker 2: Did you go in sci fi? Heinland? Philip K. Dick, 1021 01:03:49,760 --> 01:03:57,600 Speaker 2: Pride of Strong recommends Pride of Sok just fascinating book. 1022 01:03:57,600 --> 01:03:59,960 Speaker 2: Give us one or two more books and then we'll. 1023 01:03:59,640 --> 01:04:02,040 Speaker 1: Currently I'm reading, I read a lot of history books. 1024 01:04:02,080 --> 01:04:05,720 Speaker 1: I'm reading three books I browse. I read a lot parallel, 1025 01:04:06,320 --> 01:04:08,360 Speaker 1: and I tend to not to finish it all. But 1026 01:04:08,400 --> 01:04:10,960 Speaker 1: I'm reading right now Campaigns of Napoleon by David Chandler. 1027 01:04:11,440 --> 01:04:15,760 Speaker 1: I reading The Fall of Carthage by Adrian Goldsworthy and SPQR. 1028 01:04:15,880 --> 01:04:21,000 Speaker 1: Mary Beard. And I just bought my sixty Memorable Games 1029 01:04:21,000 --> 01:04:23,160 Speaker 1: by Bobby Fischer. I just wanted to go read. 1030 01:04:23,000 --> 01:04:25,800 Speaker 2: All did you read? I forgot who the author was, 1031 01:04:25,840 --> 01:04:31,040 Speaker 2: but there's a great Youngis Khan biography. Oh yes, really interesting. 1032 01:04:31,080 --> 01:04:34,720 Speaker 2: I could see the book. Yes, But I have one other. 1033 01:04:34,800 --> 01:04:35,920 Speaker 2: I have a book recommendation. 1034 01:04:36,000 --> 01:04:38,160 Speaker 1: Okay, you tell me, you tell me, And it's. 1035 01:04:38,000 --> 01:04:41,520 Speaker 2: Called How to Invent Everything, A Survival Guide to the 1036 01:04:41,560 --> 01:04:46,320 Speaker 2: Stranded Time Traveler. And it's just a history of technology, 1037 01:04:46,960 --> 01:04:51,720 Speaker 2: but they use the watch McCall. The cheat is they're 1038 01:04:51,840 --> 01:04:55,120 Speaker 2: using the guide for time travel as Hey, if you 1039 01:04:55,160 --> 01:04:57,680 Speaker 2: ever get stuck in ancient history, here are the tools 1040 01:04:57,760 --> 01:05:00,200 Speaker 2: you can build, and here's how you should do. And 1041 01:05:00,240 --> 01:05:03,800 Speaker 2: it's just just a history of technology ten thousand years 1042 01:05:03,800 --> 01:05:06,680 Speaker 2: ago to today, absolutely fast, ten thousand years ago, right, 1043 01:05:06,720 --> 01:05:09,560 Speaker 2: going back to the invention of class. 1044 01:05:09,000 --> 01:05:12,360 Speaker 1: I have I like to collect some of those ancient artifacts. 1045 01:05:12,440 --> 01:05:14,400 Speaker 2: Oh, that would be that sounds like fun. All right, 1046 01:05:14,440 --> 01:05:16,240 Speaker 2: so I only have you for two minutes. You can 1047 01:05:16,280 --> 01:05:18,560 Speaker 2: get to my last two yes, last questions, like that's 1048 01:05:18,560 --> 01:05:19,840 Speaker 2: the problem with sci fi geeks? 1049 01:05:20,080 --> 01:05:22,040 Speaker 1: Yes, okay, I didn't know you're sci fi gee. 1050 01:05:22,120 --> 01:05:24,720 Speaker 2: Oh absolutely. What sort of advice would you give to 1051 01:05:24,760 --> 01:05:29,720 Speaker 2: a recent college grad interested in a career in technology investing. 1052 01:05:30,360 --> 01:05:34,280 Speaker 1: Not so much technology, let's say investing in general. I 1053 01:05:34,280 --> 01:05:37,560 Speaker 1: think you've got to be a great thinker. It's not 1054 01:05:37,600 --> 01:05:42,360 Speaker 1: so much the finance. Finance can be taught easy. It's 1055 01:05:42,400 --> 01:05:51,160 Speaker 1: about thinking, and it is about a flexibility to have 1056 01:05:51,400 --> 01:05:58,200 Speaker 1: a to be reason and plan and think at a 1057 01:06:00,880 --> 01:06:02,600 Speaker 1: you know, in a kind of a holistic and a 1058 01:06:02,960 --> 01:06:05,480 Speaker 1: flexible manner. Because AI is going to do so many 1059 01:06:05,560 --> 01:06:10,400 Speaker 1: of the tasks and and and they will often know 1060 01:06:10,520 --> 01:06:13,280 Speaker 1: more than you about any specific donain So you need 1061 01:06:13,320 --> 01:06:18,080 Speaker 1: to be above that in a way, almost like an 1062 01:06:18,200 --> 01:06:19,880 Speaker 1: architect would. 1063 01:06:20,000 --> 01:06:23,040 Speaker 2: It makes makes a lot of sense and our final question, yes, 1064 01:06:23,160 --> 01:06:25,520 Speaker 2: what do you know about the world of technology today? 1065 01:06:25,680 --> 01:06:28,440 Speaker 2: You wish you knew back in the mid nineties when 1066 01:06:28,440 --> 01:06:29,760 Speaker 2: you were really starting out. 1067 01:06:31,080 --> 01:06:34,640 Speaker 1: You know, if I knew what how this would unfold 1068 01:06:35,440 --> 01:06:38,200 Speaker 1: in the Silicon Valley, has it, I would have just 1069 01:06:38,240 --> 01:06:42,320 Speaker 1: gone straight to Silicon Valley of the company. Maybe maybe 1070 01:06:42,360 --> 01:06:47,320 Speaker 1: instead of being on the investment side. Huh. I don't know. 1071 01:06:47,440 --> 01:06:51,000 Speaker 1: It is a it's a double edged question because I 1072 01:06:51,080 --> 01:06:55,400 Speaker 1: like the I like the dynamic exposure to many companies, 1073 01:06:55,400 --> 01:06:55,840 Speaker 1: but like. 1074 01:06:56,360 --> 01:06:58,480 Speaker 2: Plus, the path you've taken is so fascinating. 1075 01:06:59,800 --> 01:07:02,520 Speaker 1: I'll say another point for the young people. 1076 01:07:02,800 --> 01:07:03,600 Speaker 2: Uh huh. 1077 01:07:03,880 --> 01:07:06,840 Speaker 1: Always bet on the future, not on the current past. 1078 01:07:06,920 --> 01:07:07,760 Speaker 1: Bet on the future. 1079 01:07:07,840 --> 01:07:10,200 Speaker 2: What a great way to wrap this up, Tony, thank 1080 01:07:10,200 --> 01:07:13,120 Speaker 2: you for being so generous with your time. We have 1081 01:07:13,280 --> 01:07:18,240 Speaker 2: been speaking with Tony Kim, managing director at Blackrock, where 1082 01:07:18,240 --> 01:07:23,360 Speaker 2: he heads the Fundamental Equity Technology group. Blackrock manages about 1083 01:07:23,400 --> 01:07:28,840 Speaker 2: eleven trillion dollars in assets. If you enjoy this conversation, well, 1084 01:07:28,920 --> 01:07:31,040 Speaker 2: be sure and check out any of the five hundred 1085 01:07:31,080 --> 01:07:34,280 Speaker 2: previous discussions we've had over the past ten years. You 1086 01:07:34,320 --> 01:07:38,240 Speaker 2: can find those at iTunes, Spotify, YouTube, Bloomberg, wherever you 1087 01:07:38,360 --> 01:07:41,400 Speaker 2: find your favorite podcast, and be sure and check out 1088 01:07:41,400 --> 01:07:46,000 Speaker 2: my new podcast, At the Money, short conversations with experts 1089 01:07:46,360 --> 01:07:50,520 Speaker 2: about topics affecting your money, earning it, spending it, and 1090 01:07:50,600 --> 01:07:53,680 Speaker 2: most of all, investing it. At the Money wherever you 1091 01:07:53,720 --> 01:07:57,440 Speaker 2: find your favorite podcast and in the Masters in Business 1092 01:07:57,440 --> 01:07:59,800 Speaker 2: feed I would be remiss if I did not thank 1093 01:07:59,800 --> 01:08:02,720 Speaker 2: the pract team that helps with these conversations together each week. 1094 01:08:03,000 --> 01:08:06,240 Speaker 2: My audio engineer is Meredith Frank, My producer is Anna Luke. 1095 01:08:07,000 --> 01:08:10,480 Speaker 2: Sean Russo is my researcher. Sage Bauman is the head 1096 01:08:10,480 --> 01:08:14,560 Speaker 2: of Podcasts at Bloomberg. I'm Barry Ridolts. You've been listening 1097 01:08:14,600 --> 01:08:17,160 Speaker 2: to Masters in Business on Bloomberg Radio.