1 00:00:02,240 --> 00:00:06,800 Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio. 2 00:00:09,920 --> 00:00:12,840 Speaker 1: This week on the podcast, I have an extra special guest. 3 00:00:13,119 --> 00:00:17,520 Speaker 1: His name is Matthew Grenade and he is a senior God. 4 00:00:17,560 --> 00:00:21,840 Speaker 1: How do I describe his role? His title really doesn't 5 00:00:21,840 --> 00:00:26,800 Speaker 1: do it justice. His official title is Chief Market Intelligence 6 00:00:26,840 --> 00:00:31,360 Speaker 1: Officer at Point seventy two, follow the progression that has 7 00:00:31,400 --> 00:00:34,320 Speaker 1: taken place. Stevie Cohen was running Sack Capital for a 8 00:00:34,360 --> 00:00:38,680 Speaker 1: long time. That was eventually converted into a family office 9 00:00:38,720 --> 00:00:42,960 Speaker 1: which was Point seventy two that reopened to outside investors 10 00:00:43,040 --> 00:00:49,240 Speaker 1: last year in UM and Grenade has been working there 11 00:00:49,720 --> 00:00:52,000 Speaker 1: for a good couple of years. Previously he was at 12 00:00:52,080 --> 00:00:55,920 Speaker 1: Bridgewater with Ray Dalio. You'll hear all about that during 13 00:00:55,920 --> 00:01:01,480 Speaker 1: our conversation, but more importantly, you'll hear about the intersection 14 00:01:01,560 --> 00:01:05,440 Speaker 1: between man and machine, between the way models can be 15 00:01:05,560 --> 00:01:10,200 Speaker 1: used to not only manage assets, but improve the entire process, 16 00:01:11,000 --> 00:01:15,840 Speaker 1: along with a variety of big data and other approaches 17 00:01:16,640 --> 00:01:20,040 Speaker 1: UH that are really quite fascinating if you are at 18 00:01:20,040 --> 00:01:26,479 Speaker 1: all interested in quantitative investing, machine learning, hedge funds, UH, 19 00:01:26,520 --> 00:01:30,200 Speaker 1: the state of investing today and what anybody who is 20 00:01:30,240 --> 00:01:33,880 Speaker 1: pursuing alpha must do to stay current, then you're gonna 21 00:01:33,920 --> 00:01:38,319 Speaker 1: find this to be an absolutely fascinating conversation. So, with 22 00:01:38,360 --> 00:01:41,440 Speaker 1: no further ado, here is my conversation with Point seventy 23 00:01:41,480 --> 00:01:49,200 Speaker 1: two's Matthew Grenade. My extra special guest this week is 24 00:01:49,280 --> 00:01:54,120 Speaker 1: Matthew Grenade. He is the chief market intelligence officer at 25 00:01:54,160 --> 00:01:58,240 Speaker 1: Point seventy two. That is Stevie Cohen's new hedge fund, 26 00:01:58,640 --> 00:02:04,360 Speaker 1: which employs about out people and manages about thirteen billion dollars. 27 00:02:04,800 --> 00:02:08,079 Speaker 1: Point seventy two asset management was converted into a hedge 28 00:02:08,080 --> 00:02:13,639 Speaker 1: fund in and last year it reopened to external investors. 29 00:02:14,280 --> 00:02:19,200 Speaker 1: Matthew comes to us by way of Bridgewater Associates Domino 30 00:02:19,360 --> 00:02:23,360 Speaker 1: Data Lab, and he got his both undergraduate and graduate 31 00:02:23,600 --> 00:02:27,920 Speaker 1: NBA at Harvard Business School, where at undergraduate he was 32 00:02:28,000 --> 00:02:32,840 Speaker 1: the president of the Harvard Crimson. Matthew Grenade, Welcome to Bloomberg. 33 00:02:33,000 --> 00:02:35,040 Speaker 1: Thank you for having me. So let's start. Let's start 34 00:02:35,080 --> 00:02:39,600 Speaker 1: with the most unusual thing on your um resume. You're 35 00:02:39,680 --> 00:02:44,120 Speaker 1: president of the Harvard Crimson, not exactly a hotbed of 36 00:02:44,200 --> 00:02:49,000 Speaker 1: future hedge funds officers. How did that come about? What 37 00:02:49,040 --> 00:02:52,480 Speaker 1: was that experience? Like, well, a couple of things, I 38 00:02:52,520 --> 00:02:55,120 Speaker 1: mean in terms of that experience. Running in newspaper is 39 00:02:55,160 --> 00:02:57,600 Speaker 1: one of the most amazing things in the world, and 40 00:02:57,639 --> 00:02:59,840 Speaker 1: I got to run a small one at Harvard. But I, 41 00:03:00,080 --> 00:03:02,320 Speaker 1: you know, I think it's just an incredible job because 42 00:03:02,760 --> 00:03:05,600 Speaker 1: you're in the middle of so much information, You're helping 43 00:03:05,639 --> 00:03:10,079 Speaker 1: shape the debate, you're investigating things, so many interesting people. Um, 44 00:03:10,160 --> 00:03:12,320 Speaker 1: Harvard's an awesome place to do that at. And so 45 00:03:12,600 --> 00:03:14,480 Speaker 1: there are there are a few jobs that I've loved 46 00:03:14,480 --> 00:03:16,600 Speaker 1: as much as I loved that one. It was incredible 47 00:03:17,320 --> 00:03:19,839 Speaker 1: how you get that job. There's sort of a couple 48 00:03:19,840 --> 00:03:22,000 Speaker 1: of things. One, there's a bit of a path um so, 49 00:03:22,160 --> 00:03:24,920 Speaker 1: it generally is a newsperson, a reporter. Um. So I 50 00:03:24,960 --> 00:03:26,800 Speaker 1: was a reporter for my first couple of years, and 51 00:03:26,800 --> 00:03:29,639 Speaker 1: then I was the head of the central what's called 52 00:03:29,639 --> 00:03:32,919 Speaker 1: the central Administration beat that covers the president of the university. 53 00:03:33,200 --> 00:03:35,720 Speaker 1: That's also kind of a traditional stepping stone. Then there's 54 00:03:35,720 --> 00:03:38,200 Speaker 1: a process called the Turkey Shoot. Um. The Turkey Shoot 55 00:03:38,240 --> 00:03:40,400 Speaker 1: runs for about a month leading up to Thanksgiving, where 56 00:03:40,440 --> 00:03:42,680 Speaker 1: they picked the next president. There's all sorts of sort 57 00:03:42,680 --> 00:03:45,320 Speaker 1: of arcane rules, but probably the most interesting is that 58 00:03:45,680 --> 00:03:48,480 Speaker 1: every outgoing member of the paper gets to vote, and 59 00:03:48,480 --> 00:03:51,240 Speaker 1: if more than three disagree, you're blackballed, and so you 60 00:03:51,320 --> 00:03:54,760 Speaker 1: resually hold in uh sort of and you know, in 61 00:03:54,800 --> 00:03:57,280 Speaker 1: sort of in veto mode for as long as that goes. 62 00:03:57,320 --> 00:04:02,680 Speaker 1: And so the deliberations generally run about teen hours straight exactly, 63 00:04:02,720 --> 00:04:04,560 Speaker 1: and then and then there's a big party and whatever 64 00:04:04,640 --> 00:04:06,880 Speaker 1: sort of once the sort of unlocking happens. But I 65 00:04:06,960 --> 00:04:09,200 Speaker 1: had I think six or seven opponents for the job, 66 00:04:09,280 --> 00:04:11,920 Speaker 1: and uh, you know, you have to you know, a 67 00:04:11,960 --> 00:04:14,760 Speaker 1: little politics, a little politics, a little message, a little 68 00:04:14,760 --> 00:04:16,919 Speaker 1: of this, um, and that's that's how it works. But 69 00:04:17,080 --> 00:04:20,160 Speaker 1: it was an amazing opportunity. So that's an unusual background 70 00:04:20,279 --> 00:04:23,680 Speaker 1: as a journalist and someone who's publishing the paper to 71 00:04:24,040 --> 00:04:28,080 Speaker 1: really being a data scientist for a financial services shop. 72 00:04:28,440 --> 00:04:32,760 Speaker 1: How did that career path unwind? Well, they're probably more 73 00:04:32,839 --> 00:04:34,479 Speaker 1: similar than you think, because I mean a lot of 74 00:04:34,520 --> 00:04:39,279 Speaker 1: it comes down to information, collecting information, using information, UM. 75 00:04:39,360 --> 00:04:40,840 Speaker 1: And so you know, I've always been someone who likes 76 00:04:40,880 --> 00:04:42,640 Speaker 1: to know what's going on, um, you know, what's going 77 00:04:42,640 --> 00:04:44,080 Speaker 1: on in the world. I like to sort of be 78 00:04:44,120 --> 00:04:46,679 Speaker 1: ahead of other people and knowing things, and so that's 79 00:04:46,720 --> 00:04:48,839 Speaker 1: the that's the similarity. But the you know, but the 80 00:04:48,880 --> 00:04:52,719 Speaker 1: career arc was um, I went from from college to Mackenzie, 81 00:04:53,120 --> 00:04:55,640 Speaker 1: was a business analyist there, uh, and then went to 82 00:04:55,720 --> 00:04:58,920 Speaker 1: business school like you mentioned, and then ended up at Bridgewater. 83 00:04:59,200 --> 00:05:02,520 Speaker 1: Um and which is also a fascinating place. Is a 84 00:05:02,560 --> 00:05:05,560 Speaker 1: fascinating place. So I was there for six years, um 85 00:05:05,640 --> 00:05:08,520 Speaker 1: and Uh, it's a phenomenal place to work. I'm a 86 00:05:08,560 --> 00:05:11,800 Speaker 1: big fan of Ray Dalio. I find his philosophy just 87 00:05:11,960 --> 00:05:15,560 Speaker 1: totally intriguing. I think Bridgewater kind of gets a bad rap. 88 00:05:15,640 --> 00:05:18,520 Speaker 1: People have called it a cult and have criticized the 89 00:05:18,600 --> 00:05:22,440 Speaker 1: radical transparency. You survived there for six years. Can't be 90 00:05:22,480 --> 00:05:24,600 Speaker 1: all bad, right, how to be pretty good? No, it's 91 00:05:24,600 --> 00:05:26,680 Speaker 1: not all bad at all. In fact, I think it's 92 00:05:26,880 --> 00:05:30,440 Speaker 1: you know one uh, you know as investors go, um, 93 00:05:30,480 --> 00:05:32,440 Speaker 1: you know they're they're as good as it gets um, 94 00:05:32,560 --> 00:05:35,000 Speaker 1: and you know, just phenomenal at it. And look, I 95 00:05:35,040 --> 00:05:40,520 Speaker 1: think the differences of the culture there get overstated, um, 96 00:05:40,680 --> 00:05:44,080 Speaker 1: meaning the radical transparency and meaning like how different it 97 00:05:44,120 --> 00:05:45,520 Speaker 1: is from you know. Look, I mean like you know, 98 00:05:45,600 --> 00:05:50,159 Speaker 1: I would say everywhere I've ever worked McKenzie point seventy two, Domino, Bridgewater, Um, 99 00:05:50,200 --> 00:05:52,600 Speaker 1: you know, they've all been ambitious people who are trying 100 00:05:52,640 --> 00:05:54,320 Speaker 1: to get to the right answer. Who wanted to do 101 00:05:54,360 --> 00:05:57,400 Speaker 1: great things. Um. And you know, like at core, like 102 00:05:57,480 --> 00:05:59,800 Speaker 1: that's that's a lot of what Bridgewater is about. And 103 00:06:00,160 --> 00:06:02,480 Speaker 1: you know, Ray and the team, they're are very thoughtful 104 00:06:02,520 --> 00:06:06,200 Speaker 1: about ways to um, you know, just sort of apply 105 00:06:06,560 --> 00:06:08,520 Speaker 1: certain ideas. Um. You know, like you want to. You 106 00:06:08,520 --> 00:06:10,600 Speaker 1: always want to make sure you're getting the best opinions right, 107 00:06:10,600 --> 00:06:12,919 Speaker 1: And so they're very explicit about you know, who should 108 00:06:12,920 --> 00:06:15,039 Speaker 1: you listen to about things? But you know, I see, 109 00:06:15,160 --> 00:06:17,039 Speaker 1: I see Steve asked that question all the time, you know, 110 00:06:17,080 --> 00:06:18,480 Speaker 1: like why am I listening to you? You know, I 111 00:06:18,480 --> 00:06:21,040 Speaker 1: should be listening to this person instead. Um. And so 112 00:06:21,400 --> 00:06:23,400 Speaker 1: I think Bridgewater is great at sort of scaling it. 113 00:06:23,480 --> 00:06:26,080 Speaker 1: But but um, but I think that the ideas are 114 00:06:26,800 --> 00:06:29,000 Speaker 1: are not not quite as radical as the media would 115 00:06:29,000 --> 00:06:32,039 Speaker 1: want you to believe. And then the transparency, Um, it's 116 00:06:32,080 --> 00:06:34,440 Speaker 1: just great. I mean I love the idea. Yeah, I 117 00:06:34,440 --> 00:06:35,520 Speaker 1: mean I was. I always just saying like it's a 118 00:06:35,560 --> 00:06:36,920 Speaker 1: very clean place to live. And the reason it's a 119 00:06:37,000 --> 00:06:39,000 Speaker 1: very clean place to live at Bridgewater is you just 120 00:06:39,080 --> 00:06:42,000 Speaker 1: don't say things behind people's back. You just say things 121 00:06:42,040 --> 00:06:46,479 Speaker 1: to their face. Um, and you're just He writes about 122 00:06:46,480 --> 00:06:49,200 Speaker 1: that in his first book in a chapter where he 123 00:06:49,279 --> 00:06:55,000 Speaker 1: describes raise people problem. I mean, most founders and chairman 124 00:06:55,600 --> 00:06:59,160 Speaker 1: don't spend the chapter describing the wrong people person. That's 125 00:06:59,240 --> 00:07:01,800 Speaker 1: fairly trying its parent. Yeah, I mean I think that's 126 00:07:01,839 --> 00:07:05,400 Speaker 1: fairly transparent. And and that's just how you're expected to operate, 127 00:07:05,440 --> 00:07:07,839 Speaker 1: you know. I mean, if you're gonna say something about Ray, 128 00:07:08,000 --> 00:07:11,080 Speaker 1: you say it to him. And um and I have 129 00:07:11,120 --> 00:07:13,840 Speaker 1: many stories of of of saying things to Ray that 130 00:07:13,880 --> 00:07:18,240 Speaker 1: I think people would find not horrifying. There's they were 131 00:07:18,280 --> 00:07:20,200 Speaker 1: me being honest and him and I trying to sort 132 00:07:20,200 --> 00:07:22,280 Speaker 1: of work out differences. But you know, the only rule 133 00:07:22,360 --> 00:07:24,360 Speaker 1: was just don't say it behind his back, and and 134 00:07:24,400 --> 00:07:27,400 Speaker 1: that's you know, it's it's interesting that that's considered so radical, 135 00:07:27,520 --> 00:07:29,440 Speaker 1: you know what I mean, It's not it's not that radical. 136 00:07:29,960 --> 00:07:32,280 Speaker 1: So now let's let's take this phote. You'll end up 137 00:07:32,320 --> 00:07:36,600 Speaker 1: at at point seventy two. Your title is Chief Market 138 00:07:36,640 --> 00:07:40,840 Speaker 1: Intelligence Officer. I've never even seen c M I O 139 00:07:41,040 --> 00:07:44,640 Speaker 1: as a abbreviation. What does a c M I O do? 140 00:07:45,360 --> 00:07:47,920 Speaker 1: That title was the title I had when I got there. Um, 141 00:07:48,000 --> 00:07:51,520 Speaker 1: and I was really focused at that point on proprietary research. 142 00:07:51,680 --> 00:07:53,440 Speaker 1: And so what we mean by that is how do 143 00:07:53,520 --> 00:07:57,800 Speaker 1: we take UM data sets or surveys or web scraping 144 00:07:57,880 --> 00:07:59,520 Speaker 1: or sort of all the different things you can do, 145 00:08:00,120 --> 00:08:03,840 Speaker 1: UM and make that useful to our portfolio managers and analysts. UM. 146 00:08:03,920 --> 00:08:06,040 Speaker 1: Since then, my job has evolved to include a couple 147 00:08:06,080 --> 00:08:09,040 Speaker 1: other things. So I also oversee our central book at 148 00:08:09,040 --> 00:08:11,680 Speaker 1: this point, UM, which is our sort of a systematic 149 00:08:11,680 --> 00:08:14,360 Speaker 1: best ideas book we have and also receive venture capital. 150 00:08:14,480 --> 00:08:17,640 Speaker 1: And we just haven't really changed the title. Quite fascinating. 151 00:08:18,040 --> 00:08:21,000 Speaker 1: Let's talk a little bit about big data and machine 152 00:08:21,080 --> 00:08:25,760 Speaker 1: learning and artificial intelligence. Help me make a little sense 153 00:08:25,800 --> 00:08:30,600 Speaker 1: about those buzzwords which have come into vogue for a while. 154 00:08:31,280 --> 00:08:34,360 Speaker 1: But but your shop has been using these things for 155 00:08:34,360 --> 00:08:39,520 Speaker 1: for quite a while. UM. What's the state of the industry, uh, 156 00:08:39,559 --> 00:08:44,400 Speaker 1: in terms of machine learning and big data and artificial intelligence? Well, 157 00:08:44,400 --> 00:08:46,840 Speaker 1: I think the you know, the thing to sort of 158 00:08:46,880 --> 00:08:50,040 Speaker 1: contextualize all those terms, UM. And you know, I agree 159 00:08:50,040 --> 00:08:52,320 Speaker 1: with you, they're they're very buzzy. UM. But but the 160 00:08:52,320 --> 00:08:53,760 Speaker 1: way I like to think about it as being model 161 00:08:53,800 --> 00:08:55,640 Speaker 1: what I call model driven UM. And so you can 162 00:08:55,640 --> 00:08:58,640 Speaker 1: talk about model driven businesses or model driven processes, and 163 00:08:58,720 --> 00:09:01,240 Speaker 1: really the idea of a model is it takes in data. 164 00:09:01,480 --> 00:09:04,240 Speaker 1: It could be big data, it could be not big data. UM. 165 00:09:04,280 --> 00:09:07,160 Speaker 1: It runs a certain set of logic on that UM 166 00:09:07,280 --> 00:09:10,360 Speaker 1: and then it produces a prediction of some variety UM. 167 00:09:10,520 --> 00:09:13,440 Speaker 1: And you know, basically it tries to close the loop 168 00:09:13,480 --> 00:09:16,960 Speaker 1: around that data so that you know, you're constantly improving 169 00:09:17,120 --> 00:09:19,800 Speaker 1: the logic or the algorithms. And so Netflix is a 170 00:09:19,800 --> 00:09:22,680 Speaker 1: model driven business intensents a model driven business UM. And 171 00:09:22,720 --> 00:09:25,120 Speaker 1: obviously finance and and you know the hedge funds we're 172 00:09:25,120 --> 00:09:27,679 Speaker 1: talking about there, you know, they're they're they're very model driven. 173 00:09:27,960 --> 00:09:29,719 Speaker 1: What I would you know, what I would say is that, 174 00:09:29,760 --> 00:09:32,320 Speaker 1: you know, the state of the industry, uh, in that 175 00:09:32,440 --> 00:09:35,720 Speaker 1: regard is that UM, you know, these techniques are highly 176 00:09:35,800 --> 00:09:38,840 Speaker 1: highly relevant to kind of almost everything we're doing, you know, 177 00:09:38,840 --> 00:09:41,960 Speaker 1: whether it be extracting signal from data sets or you know, 178 00:09:41,960 --> 00:09:44,360 Speaker 1: all the way up to making trading decisions. Uh. And 179 00:09:44,400 --> 00:09:46,280 Speaker 1: so you know, we're investing, you know, like a lot 180 00:09:46,320 --> 00:09:48,480 Speaker 1: of hedge funds were investing a lot in you know, 181 00:09:48,640 --> 00:09:52,280 Speaker 1: people with the data science capabilities and with the machine 182 00:09:52,360 --> 00:09:57,000 Speaker 1: learning capabilities as well. So ron course Ferry you famously said, 183 00:09:57,080 --> 00:09:59,640 Speaker 1: torture the data long enough and it will confess to 184 00:09:59,679 --> 00:10:02,400 Speaker 1: whatever you want. How do you avoid running into that 185 00:10:02,520 --> 00:10:06,280 Speaker 1: problem of when you're building models and putting a ton 186 00:10:06,320 --> 00:10:09,880 Speaker 1: of different quantitative information into it, how do you avoid 187 00:10:09,960 --> 00:10:12,960 Speaker 1: that bad outcome of Hey, if we back test this 188 00:10:13,120 --> 00:10:15,400 Speaker 1: enough and we make these tweaks, we could get this 189 00:10:15,480 --> 00:10:17,959 Speaker 1: to say whatever we want. Yes, I think there's I 190 00:10:18,000 --> 00:10:19,760 Speaker 1: think there's a couple of different ways you do that. 191 00:10:19,800 --> 00:10:22,720 Speaker 1: I mean one is UM. You know, you want to 192 00:10:22,760 --> 00:10:26,240 Speaker 1: have a fundamental intuition of some variety around what you're doing, 193 00:10:26,400 --> 00:10:27,920 Speaker 1: you know, I mean, you're not just sort of running 194 00:10:27,920 --> 00:10:30,280 Speaker 1: everything through a machine and some some people do, but 195 00:10:30,280 --> 00:10:32,200 Speaker 1: but not not. That's not how I like to do it. 196 00:10:32,320 --> 00:10:34,120 Speaker 1: You're not just sort of running everything through and sort 197 00:10:34,120 --> 00:10:36,400 Speaker 1: of seeing, you know, seeing what fits, because to your point, 198 00:10:36,480 --> 00:10:38,480 Speaker 1: something will fit UM, and it may be a real 199 00:10:38,520 --> 00:10:40,319 Speaker 1: thing or it maybe you know, a very short lived 200 00:10:40,360 --> 00:10:42,200 Speaker 1: thing UM. And then you know, you have to have 201 00:10:42,240 --> 00:10:45,080 Speaker 1: a lot of discipline in terms of looking at your UM. 202 00:10:45,280 --> 00:10:48,200 Speaker 1: You know it's called out a sample, uh sorry, basically 203 00:10:48,200 --> 00:10:50,560 Speaker 1: in sample, out of sample and live UM. And what 204 00:10:50,600 --> 00:10:53,080 Speaker 1: that basically means is where are you allowing yourself to 205 00:10:53,080 --> 00:10:55,520 Speaker 1: to fit the parameters where you're sort of just looking 206 00:10:55,559 --> 00:10:57,720 Speaker 1: at the results but still in a in a backwards 207 00:10:57,720 --> 00:10:59,960 Speaker 1: looking way, and when are you sort of really trying 208 00:11:00,080 --> 00:11:02,200 Speaker 1: it out? And you know, we have very strict rules 209 00:11:02,240 --> 00:11:05,720 Speaker 1: about how we segment those different things before we start, 210 00:11:05,840 --> 00:11:08,880 Speaker 1: you know, using you know, putting money against a certain strategy, 211 00:11:09,120 --> 00:11:11,360 Speaker 1: so and out of sample, just to put a little 212 00:11:11,360 --> 00:11:15,160 Speaker 1: flesh on that. If you're testing on a large cap us, hey, 213 00:11:15,240 --> 00:11:17,240 Speaker 1: let's see how the status in the past. Let's see 214 00:11:17,240 --> 00:11:19,960 Speaker 1: how it does overseas, not just the area you're looking 215 00:11:20,000 --> 00:11:22,520 Speaker 1: forward to see if it's really something to the model. 216 00:11:22,559 --> 00:11:24,600 Speaker 1: Is that a fair descriptor yeah. So let's say you 217 00:11:24,640 --> 00:11:27,680 Speaker 1: were using um, you know, credit card data to trade Chipotle, 218 00:11:27,880 --> 00:11:30,120 Speaker 1: you know, or something like that. Um. You know, what 219 00:11:30,160 --> 00:11:31,440 Speaker 1: you would do is you would sort of, you know, 220 00:11:31,440 --> 00:11:33,280 Speaker 1: you build some rules, um, and you would sort of 221 00:11:33,280 --> 00:11:36,240 Speaker 1: fit those rules to some sub some set of data 222 00:11:36,280 --> 00:11:38,360 Speaker 1: some time period, you know, three or four years. Then 223 00:11:38,520 --> 00:11:40,240 Speaker 1: you would stop fitting the rules and you would sort 224 00:11:40,240 --> 00:11:41,600 Speaker 1: of look at the next three or four years and 225 00:11:41,600 --> 00:11:43,920 Speaker 1: sort of see it, does that those two match. Do 226 00:11:43,960 --> 00:11:46,679 Speaker 1: they look the same or is the behavior very different? 227 00:11:46,920 --> 00:11:48,600 Speaker 1: And then you would and then you would basically start 228 00:11:48,679 --> 00:11:51,120 Speaker 1: running the model live from today and then see again 229 00:11:51,160 --> 00:11:53,040 Speaker 1: if those match the other two periods and so you're 230 00:11:53,160 --> 00:11:55,720 Speaker 1: looking sort of for a consistency across that and if 231 00:11:55,720 --> 00:11:57,439 Speaker 1: you're not seeing that, then that's a good sign that 232 00:11:57,480 --> 00:12:00,280 Speaker 1: you're overfitting it. It's also you know, because going back 233 00:12:00,320 --> 00:12:01,839 Speaker 1: to my original point, you know, you want to think 234 00:12:01,880 --> 00:12:04,200 Speaker 1: about whether or not there's a real intuition there. You know, 235 00:12:04,200 --> 00:12:07,200 Speaker 1: I mean, should credit card and chipotle a make sense together? Right? 236 00:12:07,360 --> 00:12:08,840 Speaker 1: It probably does because a lot of people use a 237 00:12:08,840 --> 00:12:10,679 Speaker 1: credit card at chipole. But you know, if you were 238 00:12:10,800 --> 00:12:13,600 Speaker 1: using uh, you know, credit card to trade ge you know, 239 00:12:13,640 --> 00:12:15,560 Speaker 1: you might you might start scratching your head about what 240 00:12:15,600 --> 00:12:18,000 Speaker 1: you're doing. Right, might just be a random correlation as 241 00:12:18,040 --> 00:12:21,800 Speaker 1: opposed to a real causal relationship. So so let's talk 242 00:12:21,840 --> 00:12:26,679 Speaker 1: about some of these unusual UM data sources. I know, 243 00:12:27,640 --> 00:12:31,520 Speaker 1: alternative satellite data is all the rage these days. People 244 00:12:31,559 --> 00:12:33,920 Speaker 1: are looking at parking lots, how filled they are. They're 245 00:12:33,960 --> 00:12:38,080 Speaker 1: looking at how deep transport ships are sitting, uh in 246 00:12:38,160 --> 00:12:40,720 Speaker 1: the water, how far below the waterline they might actually be. 247 00:12:41,440 --> 00:12:47,520 Speaker 1: How esoteric can we get with these alternative types of data? Well, 248 00:12:47,520 --> 00:12:50,160 Speaker 1: I think you can. I think you can get quite esoteric. 249 00:12:50,200 --> 00:12:52,720 Speaker 1: I mean I think satellite, um you know, satellite has 250 00:12:52,720 --> 00:12:54,480 Speaker 1: been around for a while and to your point. I mean, 251 00:12:54,520 --> 00:12:58,000 Speaker 1: it's it's very widely used. Um. You know, you know 252 00:12:58,040 --> 00:13:01,000 Speaker 1: what we think much more about now is um, you know, 253 00:13:01,000 --> 00:13:04,080 Speaker 1: sort of much more specific data sets. UM. You know 254 00:13:04,160 --> 00:13:06,840 Speaker 1: kind of that that give you, you you know, a read 255 00:13:06,880 --> 00:13:10,360 Speaker 1: into a limited number of tickers, often via some sort 256 00:13:10,360 --> 00:13:14,120 Speaker 1: of payment system or something like that. UM. And uh, 257 00:13:14,160 --> 00:13:16,600 Speaker 1: you know I think that we're I think we're just 258 00:13:16,800 --> 00:13:19,160 Speaker 1: you know, we're probably in the third inning of something 259 00:13:19,240 --> 00:13:21,160 Speaker 1: or something like that in the in the in the 260 00:13:21,240 --> 00:13:26,600 Speaker 1: data movement in investing. That's that's quite fascinating. So let's 261 00:13:26,640 --> 00:13:29,240 Speaker 1: talk a little bit about complexity. You know, we could 262 00:13:29,240 --> 00:13:31,720 Speaker 1: go back a hundred years and just look at Graham 263 00:13:31,720 --> 00:13:36,040 Speaker 1: and Dodd simple p ratio and more expensive stocks over 264 00:13:36,080 --> 00:13:40,160 Speaker 1: time perform less well and have lower expected turns than 265 00:13:40,679 --> 00:13:44,440 Speaker 1: less expensive stocks. Are we running the risk of making 266 00:13:44,480 --> 00:13:49,120 Speaker 1: things too complex? At at what point does complexity get 267 00:13:49,200 --> 00:13:53,880 Speaker 1: outweighed by its own internal complications? Well, I think, um, 268 00:13:53,920 --> 00:13:55,560 Speaker 1: you know, I think this goes back to the point 269 00:13:55,600 --> 00:13:59,400 Speaker 1: I was making about, you know, about an intuition. Um. 270 00:13:59,480 --> 00:14:02,120 Speaker 1: And you know, at the end of the day a 271 00:14:02,200 --> 00:14:05,320 Speaker 1: point of two. You know, we are we are fundamental investors, 272 00:14:05,360 --> 00:14:07,839 Speaker 1: you know, we believe that Uh, that you know that 273 00:14:07,920 --> 00:14:11,320 Speaker 1: companies ultimately, you know, trade on how they're doing as 274 00:14:11,360 --> 00:14:13,160 Speaker 1: a business and the kind of cash flows they're going 275 00:14:13,200 --> 00:14:16,760 Speaker 1: to produce UM and you know, everything we do, I mean, 276 00:14:16,800 --> 00:14:20,040 Speaker 1: we will use very sophisticated data science to predict a 277 00:14:20,080 --> 00:14:23,000 Speaker 1: revenue stream or something like that, but we're at core 278 00:14:23,160 --> 00:14:25,480 Speaker 1: trying to do something fairly simple. You know, we're trying 279 00:14:25,480 --> 00:14:27,720 Speaker 1: to understand what the revenues are, what the costs are, 280 00:14:28,080 --> 00:14:31,320 Speaker 1: you know, what the growth profile of the earnings are UM, 281 00:14:31,440 --> 00:14:33,720 Speaker 1: and you know, we never sort of lose that grounding 282 00:14:34,160 --> 00:14:35,920 Speaker 1: UM and so you know, look, there's a lot of 283 00:14:35,920 --> 00:14:38,160 Speaker 1: ways to make money in the markets UM, and I'm 284 00:14:38,200 --> 00:14:40,400 Speaker 1: only I'm not an expert in a lot of them. 285 00:14:40,440 --> 00:14:43,480 Speaker 1: I'm only familiar with some of them. But but for us, 286 00:14:43,560 --> 00:14:47,680 Speaker 1: I think that grounding back to pretty simple principles U 287 00:14:48,160 --> 00:14:50,320 Speaker 1: is very important and not something that we lose track of. 288 00:14:50,920 --> 00:14:53,520 Speaker 1: It's interesting that you I think of you guys as 289 00:14:53,560 --> 00:14:57,920 Speaker 1: a quant shop, but you keep referring to intuition. What's 290 00:14:57,960 --> 00:15:02,520 Speaker 1: the intersection like between man machine? Is it really UM 291 00:15:03,400 --> 00:15:07,760 Speaker 1: technology aiding human decision making or is it mostly hey, 292 00:15:07,840 --> 00:15:10,560 Speaker 1: let's go and make the decisions and we'll just see 293 00:15:10,560 --> 00:15:13,320 Speaker 1: what happens, so it points in me too. We do UM, 294 00:15:13,360 --> 00:15:15,880 Speaker 1: we do UM. We do a mix of of three things. 295 00:15:15,920 --> 00:15:19,600 Speaker 1: We have a very large discretionary business that's global long 296 00:15:19,680 --> 00:15:23,120 Speaker 1: short equity you know, people driven its portfolio managers and 297 00:15:23,160 --> 00:15:26,800 Speaker 1: analysts UM looking at some subset of the of stock 298 00:15:26,920 --> 00:15:31,200 Speaker 1: universe UM, meeting with management teams, looking at data sets UH, 299 00:15:31,240 --> 00:15:35,280 Speaker 1: and then making decisions in a in a fairly discretionary fashion. UM. 300 00:15:35,320 --> 00:15:39,120 Speaker 1: We also have a systematic business that's running on algorithms UM. 301 00:15:39,280 --> 00:15:41,920 Speaker 1: And then we have a people plus machine business, which 302 00:15:41,960 --> 00:15:44,520 Speaker 1: is the one that I oversee, which is the you 303 00:15:44,520 --> 00:15:47,160 Speaker 1: know what what what you call the central book earlier UM, 304 00:15:47,360 --> 00:15:50,480 Speaker 1: where what we're doing there is we're looking at UM 305 00:15:50,520 --> 00:15:52,680 Speaker 1: what the behavior of all the people is as one 306 00:15:52,680 --> 00:15:55,280 Speaker 1: of the important inputs UM. But we're also looking at 307 00:15:55,280 --> 00:15:58,840 Speaker 1: the data sets and we're running algorithms to essentially helped 308 00:15:58,880 --> 00:16:01,000 Speaker 1: make decisions out of that. So one way of thinking 309 00:16:01,040 --> 00:16:04,080 Speaker 1: about it is that historically Steve had a best ideas 310 00:16:04,080 --> 00:16:07,000 Speaker 1: book that he he ran as a discretionary investor, and 311 00:16:07,080 --> 00:16:09,920 Speaker 1: over time we've built that up into a systematic best 312 00:16:09,960 --> 00:16:13,280 Speaker 1: ideas book UM. But but a lot of the input 313 00:16:13,280 --> 00:16:16,480 Speaker 1: of that is from discretionary investors and so UM. So 314 00:16:16,600 --> 00:16:18,400 Speaker 1: you know, one of the kind of key questions we're 315 00:16:18,440 --> 00:16:20,640 Speaker 1: always asking is what are the people best at and 316 00:16:20,680 --> 00:16:23,120 Speaker 1: what are the machines best at? And you know, our view, 317 00:16:23,520 --> 00:16:26,120 Speaker 1: UM is that you know, in terms of of really 318 00:16:26,160 --> 00:16:30,480 Speaker 1: being able to interpret fairly nuanced and complicated situations inside 319 00:16:30,520 --> 00:16:35,240 Speaker 1: a specific company, that people are still um, really really good. UM. 320 00:16:35,280 --> 00:16:37,440 Speaker 1: You know, there's other things that machines do very very well. 321 00:16:38,000 --> 00:16:39,440 Speaker 1: But you know, if you're going to meet with the 322 00:16:39,440 --> 00:16:42,400 Speaker 1: management team and interpret a large set of data that 323 00:16:42,400 --> 00:16:44,600 Speaker 1: that has a lot of sort of nuanced and specifics 324 00:16:44,640 --> 00:16:46,880 Speaker 1: to it, UM, the people still beat the machines at that. 325 00:16:47,080 --> 00:16:48,680 Speaker 1: And so we have a you know, we have several 326 00:16:48,720 --> 00:16:52,240 Speaker 1: hundred people that do that. Do you see that edge 327 00:16:52,320 --> 00:16:57,200 Speaker 1: of humans over machines continuing indefinitely or at at some 328 00:16:57,280 --> 00:17:02,400 Speaker 1: point in the future, will smart um computers and artificial 329 00:17:02,400 --> 00:17:06,680 Speaker 1: intelligence be able to do that also well? And definitely 330 00:17:06,760 --> 00:17:09,679 Speaker 1: is a very long time. So I'm gonna I'm not 331 00:17:09,720 --> 00:17:12,240 Speaker 1: gonna I'm not gonna comment on indefinitely. What I will 332 00:17:12,280 --> 00:17:15,080 Speaker 1: say is that our our thesis is a firm right 333 00:17:15,080 --> 00:17:19,000 Speaker 1: now over the next call it you know, seven to 334 00:17:19,080 --> 00:17:23,000 Speaker 1: ten years, is that UM, is that it is people 335 00:17:23,040 --> 00:17:26,879 Speaker 1: plus machines UM, and that the people are very good 336 00:17:26,920 --> 00:17:30,040 Speaker 1: at the nuanced situation, at the idea generation, at the 337 00:17:30,280 --> 00:17:34,000 Speaker 1: interpreting the thin data at the synthesis UM. And that 338 00:17:34,080 --> 00:17:37,840 Speaker 1: the machines are very good at conducting, UH, correcting for 339 00:17:38,119 --> 00:17:42,119 Speaker 1: behavioral bias at portfolio construction, at trade execution. And you know, 340 00:17:42,119 --> 00:17:43,600 Speaker 1: what we're trying to do is figure out how you 341 00:17:43,640 --> 00:17:46,040 Speaker 1: marry those two up in a really smart way UM. 342 00:17:46,119 --> 00:17:48,080 Speaker 1: And that that is essentially the you know, the next 343 00:17:48,119 --> 00:17:51,560 Speaker 1: wave of hedge fund but UM. But you know, like 344 00:17:51,600 --> 00:17:53,680 Speaker 1: where where we are ten or fifteen years in terms 345 00:17:53,720 --> 00:17:56,920 Speaker 1: of what people can do versus machines, I don't think 346 00:17:56,920 --> 00:18:00,600 Speaker 1: I can comment on that quite quite fascinating. Let's talk 347 00:18:00,600 --> 00:18:04,840 Speaker 1: about the venture capital work you guys do. UM. What 348 00:18:05,040 --> 00:18:09,960 Speaker 1: makes you different from traditional vcs? Well, I think a 349 00:18:10,040 --> 00:18:13,760 Speaker 1: couple of things make us UM different than traditional vcs, 350 00:18:13,800 --> 00:18:17,920 Speaker 1: But probably the most important is we we are extremely 351 00:18:18,200 --> 00:18:22,399 Speaker 1: expertise focused in how we are designed, so UM, we 352 00:18:22,440 --> 00:18:25,399 Speaker 1: have no generalists. UM. We have certain practice areas. Right now, 353 00:18:25,400 --> 00:18:28,440 Speaker 1: we have three different three or four different practice areas UM, 354 00:18:28,480 --> 00:18:31,479 Speaker 1: all of which are led by people who have worked 355 00:18:31,480 --> 00:18:33,720 Speaker 1: in that space and invested in that space for quite 356 00:18:33,760 --> 00:18:36,239 Speaker 1: some time, and kind of one of the standards I 357 00:18:36,400 --> 00:18:41,320 Speaker 1: use is, you know, when when when portfolio companies are 358 00:18:41,320 --> 00:18:44,560 Speaker 1: meeting with the investors on our team, do they believe 359 00:18:44,600 --> 00:18:47,240 Speaker 1: that the person they're sitting across from is the one 360 00:18:47,240 --> 00:18:49,480 Speaker 1: of the world's leading experts on the area that they're 361 00:18:49,520 --> 00:18:52,160 Speaker 1: working in. UM. So that that's one difference. I think 362 00:18:52,160 --> 00:18:54,399 Speaker 1: that the other different side point too is we're extremely 363 00:18:54,400 --> 00:18:57,960 Speaker 1: outbound in how we operate. So one of our challenges was, 364 00:18:58,320 --> 00:18:59,679 Speaker 1: you know, we don't have a we don't have a 365 00:18:59,720 --> 00:19:02,120 Speaker 1: brand end NVC you know the way a sequoia does 366 00:19:02,240 --> 00:19:04,480 Speaker 1: or something like that. And so, you know, one of 367 00:19:04,520 --> 00:19:06,520 Speaker 1: the biggest concerns you gotta have in venture investing is 368 00:19:06,560 --> 00:19:09,480 Speaker 1: adverse selection UH. And you probably don't want to be 369 00:19:09,520 --> 00:19:12,359 Speaker 1: taking what's coming through the door. UM. So you know, 370 00:19:12,400 --> 00:19:15,760 Speaker 1: what we focus on is um themes that we think 371 00:19:15,840 --> 00:19:18,880 Speaker 1: are gonna be big money makers, where we think real 372 00:19:19,160 --> 00:19:22,879 Speaker 1: change is happening, where technology is is um uh is 373 00:19:23,280 --> 00:19:26,160 Speaker 1: driving really important impact UH. And then we go try 374 00:19:26,200 --> 00:19:27,840 Speaker 1: to find the companies that we want to invest in 375 00:19:27,960 --> 00:19:32,119 Speaker 1: and knock on their door proactively, look extremely proactive. Almost 376 00:19:32,160 --> 00:19:35,800 Speaker 1: almost all of it is an outbound motion like ninety 377 00:19:36,320 --> 00:19:39,359 Speaker 1: eight percent of it UM and then UM, and so 378 00:19:39,400 --> 00:19:41,600 Speaker 1: that it would be the two big differences. I'd also 379 00:19:41,680 --> 00:19:45,000 Speaker 1: say that UM, you know, you know, probably as firms go, 380 00:19:45,440 --> 00:19:48,560 Speaker 1: our diligence is more intense than a lot of venture firms. 381 00:19:48,560 --> 00:19:50,919 Speaker 1: I think that comes from Steve Um. You know Steve 382 00:19:51,200 --> 00:19:54,520 Speaker 1: uh Um. Steve's one of Steve's sayings is do the 383 00:19:54,560 --> 00:19:57,679 Speaker 1: work um. And you know, when we go into an 384 00:19:57,680 --> 00:20:00,560 Speaker 1: investment committee to talk about something, uh, there's kind of 385 00:20:00,560 --> 00:20:02,840 Speaker 1: only one answer, which is I did the work um. 386 00:20:03,440 --> 00:20:05,960 Speaker 1: Otherwise the meaning's gonna end very soon. And so we 387 00:20:05,960 --> 00:20:07,600 Speaker 1: we hold a pretty high bar in terms of the 388 00:20:07,600 --> 00:20:09,480 Speaker 1: amount of research we're gonna do when we're looking into 389 00:20:09,480 --> 00:20:10,879 Speaker 1: a company. So there would be the three things I 390 00:20:10,960 --> 00:20:13,800 Speaker 1: point to. So, once you decide to make an investment 391 00:20:13,920 --> 00:20:17,960 Speaker 1: in a startup or an existing company, how actively involved 392 00:20:18,320 --> 00:20:22,040 Speaker 1: um with the corporate management are you? Are you guys 393 00:20:22,080 --> 00:20:25,919 Speaker 1: they're giving them advice assistance? Or is it more of 394 00:20:25,960 --> 00:20:29,439 Speaker 1: an arms length here's some money, now, now go do 395 00:20:29,560 --> 00:20:34,280 Speaker 1: something great. It varies, but I would say we're fairly active. 396 00:20:34,320 --> 00:20:36,920 Speaker 1: And the reason we end up being active is goes 397 00:20:36,920 --> 00:20:40,160 Speaker 1: back to this expertise thing that I was describing, which 398 00:20:40,200 --> 00:20:42,760 Speaker 1: is that, Um, you know, because the team is made 399 00:20:42,840 --> 00:20:45,399 Speaker 1: up of people who are very deep experts, it tends 400 00:20:45,400 --> 00:20:48,360 Speaker 1: to be that the entrepreneurs want them on the boards 401 00:20:48,880 --> 00:20:51,240 Speaker 1: because you know, they're they're they're very useful and sort 402 00:20:51,240 --> 00:20:54,240 Speaker 1: of sorting through the strategic questions and knowing where the 403 00:20:54,280 --> 00:20:56,960 Speaker 1: business should go. Um. You know. It's interesting because when 404 00:20:56,960 --> 00:21:00,200 Speaker 1: we started out, I was actually, uh pretty really sucked 405 00:21:00,240 --> 00:21:02,159 Speaker 1: tot to take board seats because I actually, you know, 406 00:21:02,200 --> 00:21:03,879 Speaker 1: I think it can be a bit of a distraction 407 00:21:03,920 --> 00:21:07,000 Speaker 1: from doing the next investment. UM. But it turned out 408 00:21:07,040 --> 00:21:09,040 Speaker 1: it was an important ask from a lot of our entrepreneurs. 409 00:21:09,080 --> 00:21:10,600 Speaker 1: So we do end up taking a lot of board seats, 410 00:21:10,600 --> 00:21:14,600 Speaker 1: which means we're pretty involved. And we talked earlier about 411 00:21:15,200 --> 00:21:20,480 Speaker 1: the quantitative approach UM point seventy two. Often employees, how 412 00:21:20,560 --> 00:21:23,400 Speaker 1: much big data do you bring to bear when trying 413 00:21:23,440 --> 00:21:27,280 Speaker 1: to make a decision about either an area to invest 414 00:21:27,359 --> 00:21:32,320 Speaker 1: in or a specific company. Very little, very little, very little. Uh. 415 00:21:32,400 --> 00:21:34,040 Speaker 1: You know. Part part of it is the areas we're 416 00:21:34,080 --> 00:21:36,800 Speaker 1: investing in. I mean, we're generally investing in enterprise companies 417 00:21:37,080 --> 00:21:39,680 Speaker 1: uh in their early stage, and so you know, lots 418 00:21:39,680 --> 00:21:42,159 Speaker 1: of times they'll have three or four customers UM, and 419 00:21:42,200 --> 00:21:43,840 Speaker 1: there isn't a whole lot to sort of, you know, 420 00:21:43,880 --> 00:21:47,119 Speaker 1: torture the data for UM. Doesn't mean we don't do research. 421 00:21:47,119 --> 00:21:49,119 Speaker 1: We do a tremendous amount of research, but it tends 422 00:21:49,119 --> 00:21:52,040 Speaker 1: to be more interviews with people and UM, you know, 423 00:21:52,359 --> 00:21:56,879 Speaker 1: you know, customer follow ups with customers and probing on 424 00:21:56,920 --> 00:22:00,240 Speaker 1: how you know how a certain product works UM or 425 00:22:00,359 --> 00:22:04,000 Speaker 1: market sizing exercises or things like that UM. But we've 426 00:22:04,080 --> 00:22:05,879 Speaker 1: not brought a lot of the of the of the 427 00:22:05,880 --> 00:22:09,159 Speaker 1: big data to bear on on venture UM though I 428 00:22:09,200 --> 00:22:11,520 Speaker 1: do think you know, in the consumer space there could 429 00:22:11,520 --> 00:22:13,720 Speaker 1: be opportunities for that UM, and that that might be 430 00:22:13,760 --> 00:22:15,920 Speaker 1: something we explore down the road. So this might be 431 00:22:15,960 --> 00:22:19,760 Speaker 1: a little bit of a weird question. But how challenging 432 00:22:19,920 --> 00:22:25,680 Speaker 1: is it two manage two distinct businesses with two very 433 00:22:25,720 --> 00:22:30,560 Speaker 1: different approaches. One is so quantitative and data intensive, the 434 00:22:30,600 --> 00:22:33,679 Speaker 1: other seems to be a little more intuitive and subjective. 435 00:22:33,720 --> 00:22:36,840 Speaker 1: Do you find any sort of when you switch hats? 436 00:22:36,920 --> 00:22:39,800 Speaker 1: Is that a little bit different to get into that 437 00:22:40,280 --> 00:22:43,120 Speaker 1: a little bit challenging to get into that different headspace? 438 00:22:44,600 --> 00:22:47,920 Speaker 1: I wouldn't say so. I think the similarity between both 439 00:22:47,920 --> 00:22:50,600 Speaker 1: of them is that in both cases. You know, we're 440 00:22:50,720 --> 00:22:53,560 Speaker 1: very process driven. UM. You know in in the process 441 00:22:53,680 --> 00:22:56,320 Speaker 1: looks different in each case. But UH, you know, I'm 442 00:22:56,400 --> 00:22:58,560 Speaker 1: I'm a very big believer and I think this comes 443 00:22:58,600 --> 00:23:01,800 Speaker 1: from my my Bridgewater training UM in sort of process 444 00:23:01,800 --> 00:23:05,199 Speaker 1: over outcomes. UH. And you know you have to you know, 445 00:23:05,240 --> 00:23:06,919 Speaker 1: you have to think ahead of time about how you're 446 00:23:06,920 --> 00:23:08,680 Speaker 1: going to approach a problem and why that's going to 447 00:23:08,760 --> 00:23:11,920 Speaker 1: give you an advantage in uh in your approach um 448 00:23:12,000 --> 00:23:14,800 Speaker 1: and on on on both sides of the business UM 449 00:23:14,840 --> 00:23:17,040 Speaker 1: that I'm involved with. You know, that's how we how 450 00:23:17,080 --> 00:23:19,439 Speaker 1: we come at it. Uh. And you know when we 451 00:23:19,480 --> 00:23:23,520 Speaker 1: have very elaborate uh sort of you know, predesigned sort 452 00:23:23,520 --> 00:23:25,600 Speaker 1: of ways that we're going to develop algorithms, and we 453 00:23:25,640 --> 00:23:28,640 Speaker 1: have very uh clear ways that we're gonna make investment 454 00:23:28,640 --> 00:23:31,080 Speaker 1: decisions on the venture side. UM. And so for me 455 00:23:31,280 --> 00:23:33,199 Speaker 1: as a as a manager of both of those areas, 456 00:23:33,400 --> 00:23:35,120 Speaker 1: that's mainly what I'm trying to do is make sure 457 00:23:35,160 --> 00:23:38,639 Speaker 1: that process is really solid um and UH. And and 458 00:23:38,680 --> 00:23:43,320 Speaker 1: that's that's the similarity. How how significant uh portion of 459 00:23:43,400 --> 00:23:47,159 Speaker 1: the point seventy two book are the venture sides. So 460 00:23:47,200 --> 00:23:51,520 Speaker 1: the venture investments are all Steve's personal investments. UM, so 461 00:23:51,560 --> 00:23:53,800 Speaker 1: there's not point they're not well, I mean we use 462 00:23:53,920 --> 00:23:56,080 Speaker 1: it's points a ventures, we use the brand, but it's 463 00:23:56,080 --> 00:23:58,719 Speaker 1: not it's not in the fund. Uh, it's it's Steve's 464 00:23:58,720 --> 00:24:01,280 Speaker 1: personal money. UM. And it's you know, it's it's not 465 00:24:01,440 --> 00:24:03,159 Speaker 1: it's not super large. I mean it's a it's a 466 00:24:03,200 --> 00:24:05,119 Speaker 1: couple hundred million. So now I have to ask the 467 00:24:05,119 --> 00:24:09,080 Speaker 1: obvious question, if it's Steve's personal money, is there a 468 00:24:09,160 --> 00:24:13,560 Speaker 1: different UM thought process in terms of an exit. How 469 00:24:13,640 --> 00:24:18,440 Speaker 1: does that pressure or how does that structure affect how 470 00:24:18,520 --> 00:24:22,120 Speaker 1: you approach it or is it just a continuum across everything. 471 00:24:22,119 --> 00:24:26,199 Speaker 1: And his philosophy is the same whether it's public or 472 00:24:26,240 --> 00:24:30,399 Speaker 1: private investments. I think his philosophy is very similar across both. 473 00:24:30,680 --> 00:24:32,720 Speaker 1: You know, he is he is an I r R 474 00:24:32,800 --> 00:24:35,640 Speaker 1: focused investor UM. And you know he has a hedge 475 00:24:35,640 --> 00:24:38,800 Speaker 1: fund that does well and produces a good return every year. UM. 476 00:24:38,960 --> 00:24:41,399 Speaker 1: And you know he expects us to be the same, 477 00:24:41,600 --> 00:24:44,520 Speaker 1: to bring the same discipline to the private investments. And 478 00:24:44,560 --> 00:24:46,639 Speaker 1: so you know, we think about I r rs, We 479 00:24:46,680 --> 00:24:48,640 Speaker 1: think about exits, we think when we can get cash 480 00:24:48,680 --> 00:24:50,480 Speaker 1: back out, we think about applying leverage. We you know, 481 00:24:50,520 --> 00:24:52,879 Speaker 1: we think about all these different things UM. But but 482 00:24:52,920 --> 00:24:55,800 Speaker 1: it all comes back to you know, producing a you know, 483 00:24:55,840 --> 00:24:59,400 Speaker 1: a good rate of return UM and that's that's that's 484 00:24:59,400 --> 00:25:02,320 Speaker 1: how he thinks of about the world. Quite fascinating. So 485 00:25:02,600 --> 00:25:07,960 Speaker 1: you mentioned traditional UM forms of fundamental analysis. What what 486 00:25:08,080 --> 00:25:11,040 Speaker 1: metrics do you find important? Lots of people have talked 487 00:25:11,040 --> 00:25:12,800 Speaker 1: about price the book, and then it seems to have 488 00:25:12,840 --> 00:25:15,320 Speaker 1: fallen a little bit out of favor. Other people are 489 00:25:15,320 --> 00:25:19,760 Speaker 1: looking at various forms of valuation. What's the most important 490 00:25:19,760 --> 00:25:24,320 Speaker 1: fundamental approaches that that point seventy two is considering. It's 491 00:25:24,320 --> 00:25:26,880 Speaker 1: just very so widely. I mean, you know, we're trading 492 00:25:27,240 --> 00:25:28,840 Speaker 1: you know, in the U S we're trading almost eight 493 00:25:29,000 --> 00:25:31,480 Speaker 1: hundred names, and we also trade in Asia and Europe, 494 00:25:31,520 --> 00:25:35,240 Speaker 1: and so, uh, you know, there's I can't give sort 495 00:25:35,240 --> 00:25:37,960 Speaker 1: of a one size fits all answer to that question 496 00:25:37,960 --> 00:25:40,280 Speaker 1: because there's there's so many different sort of subsegments. So 497 00:25:40,400 --> 00:25:45,000 Speaker 1: following up on that, you have written that investing changes 498 00:25:45,040 --> 00:25:47,640 Speaker 1: over time and it's the role of the portfolio manager 499 00:25:48,119 --> 00:25:52,199 Speaker 1: to adapt to those changes. How have you seen recent 500 00:25:52,280 --> 00:25:55,359 Speaker 1: changes in the marketplace and what sort of adaptations do 501 00:25:55,440 --> 00:25:57,800 Speaker 1: people have to make? Well, I think, you know, I 502 00:25:57,840 --> 00:25:59,439 Speaker 1: think it's some of the things we're talking about them. 503 00:25:59,480 --> 00:26:02,320 Speaker 1: I think the UM you know, the explosion of big 504 00:26:02,440 --> 00:26:06,840 Speaker 1: data or what we call alternative data UM is you know, 505 00:26:07,080 --> 00:26:10,200 Speaker 1: a big impact. Uh. You know, it used to be 506 00:26:10,240 --> 00:26:13,920 Speaker 1: that most of the investing was a conversation between the investor, 507 00:26:13,920 --> 00:26:16,520 Speaker 1: the company and the cell side UM. And now you 508 00:26:16,560 --> 00:26:20,000 Speaker 1: know you have UM. You know, just you know, whether 509 00:26:20,000 --> 00:26:22,800 Speaker 1: it be credit card or geolocation or email receipts or 510 00:26:22,840 --> 00:26:25,000 Speaker 1: all these different satellite like you were talking about UM. 511 00:26:25,040 --> 00:26:27,399 Speaker 1: You know, all these different things that you know that 512 00:26:27,440 --> 00:26:29,040 Speaker 1: you can you can bring to bear. So I think 513 00:26:29,040 --> 00:26:31,440 Speaker 1: that's a really important trend. I think the other important trend, 514 00:26:31,560 --> 00:26:33,920 Speaker 1: like we're talking about earlier, is is people plus machine. 515 00:26:33,960 --> 00:26:36,600 Speaker 1: You know, what what are machines good at versus what 516 00:26:36,640 --> 00:26:39,880 Speaker 1: are people good at? UM? You know, machines uh, quite 517 00:26:39,880 --> 00:26:44,040 Speaker 1: good at UM at repetitive math and complicated math, and 518 00:26:44,359 --> 00:26:46,200 Speaker 1: UM you know have a lot to bring to bear 519 00:26:46,240 --> 00:26:49,480 Speaker 1: in terms of portfolio construction and trading and and and 520 00:26:49,640 --> 00:26:52,280 Speaker 1: those sorts of areas UM. So those are probably the 521 00:26:52,320 --> 00:26:55,840 Speaker 1: two most important trends that that we're seeing and thinking about. 522 00:26:56,640 --> 00:27:00,640 Speaker 1: Quite interesting. So you you talked earlier about the pursuit 523 00:27:00,680 --> 00:27:04,240 Speaker 1: of alpha for a lot of the hedge fund industry, 524 00:27:04,680 --> 00:27:07,040 Speaker 1: this has been a rough decade. Alpha has been hard 525 00:27:07,040 --> 00:27:10,639 Speaker 1: to come by. Lots and lots of other hedge funds 526 00:27:10,680 --> 00:27:16,480 Speaker 1: have had a hard time meaning their benchmark. Two questions 527 00:27:16,520 --> 00:27:22,960 Speaker 1: that come from that, what's behind alpha's um elusiveness these days? 528 00:27:23,440 --> 00:27:26,840 Speaker 1: And what must elusive alpha? You haven't thought of that previously? 529 00:27:27,240 --> 00:27:30,960 Speaker 1: And what do active managers have to do to stay 530 00:27:31,040 --> 00:27:34,840 Speaker 1: relevant and at the top of their game? Yeah, well, Steve, 531 00:27:34,840 --> 00:27:36,680 Speaker 1: Steve always jokes that he'd just like to go back 532 00:27:36,680 --> 00:27:38,960 Speaker 1: to the nineties, Um, you know, when it was easy, 533 00:27:39,040 --> 00:27:41,760 Speaker 1: when it was when it was a lot easier. And 534 00:27:41,880 --> 00:27:45,000 Speaker 1: uh and look, I mean, you know, success straws competition, 535 00:27:45,080 --> 00:27:48,320 Speaker 1: that's just capitalism. And I think that um, you know, 536 00:27:49,119 --> 00:27:51,320 Speaker 1: you know, I think there's not a whole lot of 537 00:27:51,440 --> 00:27:53,520 Speaker 1: mystery to why it's harder. I think it's harder mainly 538 00:27:53,520 --> 00:27:56,320 Speaker 1: because a lot more people are doing it. Um, you know, 539 00:27:56,400 --> 00:27:59,439 Speaker 1: there's there's certain i'd say, sort of boogemen in the market, 540 00:27:59,480 --> 00:28:01,480 Speaker 1: you know, like et F flows and things like that 541 00:28:01,480 --> 00:28:03,960 Speaker 1: that people also talk about. But but I think the 542 00:28:04,440 --> 00:28:06,399 Speaker 1: core thing that makes Alpha harder is just, you know, 543 00:28:06,440 --> 00:28:09,040 Speaker 1: the scale at which it all takes place today. Um, 544 00:28:09,160 --> 00:28:11,520 Speaker 1: and you know, I think in terms of of of 545 00:28:11,560 --> 00:28:16,600 Speaker 1: maintaining an advantage. UM. You know, I uh, I remember 546 00:28:16,840 --> 00:28:18,800 Speaker 1: the very first time I met Steve, I asked him 547 00:28:18,840 --> 00:28:20,480 Speaker 1: the question of how he had been able to sustain 548 00:28:20,560 --> 00:28:24,239 Speaker 1: his fund for so long UM and he's at such 549 00:28:24,280 --> 00:28:26,719 Speaker 1: a high level, and he said, well, because I've rebuilt 550 00:28:26,760 --> 00:28:29,520 Speaker 1: it four or five times UM. And you know, and 551 00:28:29,520 --> 00:28:31,960 Speaker 1: and you know, the point he made is that this 552 00:28:32,040 --> 00:28:35,880 Speaker 1: is just a constantly changing game that's always attracting competitors. 553 00:28:36,160 --> 00:28:38,640 Speaker 1: And if you think that whatever success you have today 554 00:28:38,680 --> 00:28:42,520 Speaker 1: is going to be true tomorrow, you are really naive UM. 555 00:28:42,600 --> 00:28:44,680 Speaker 1: And so you know, there's it's part of what I 556 00:28:44,960 --> 00:28:46,560 Speaker 1: like so much about working with him, And there's just 557 00:28:46,600 --> 00:28:49,880 Speaker 1: a restless energy to him because he knows that that's 558 00:28:49,920 --> 00:28:52,360 Speaker 1: what's required to continue to survive. And so that's how 559 00:28:52,360 --> 00:28:55,360 Speaker 1: we approach the firm we have UM. You know, always 560 00:28:55,400 --> 00:28:57,720 Speaker 1: you know, tons of new initiatives and experiments going on, 561 00:28:57,800 --> 00:28:59,720 Speaker 1: and things will succeed or you know, and things will 562 00:28:59,760 --> 00:29:01,880 Speaker 1: fail and will kill them, and things that will succeed 563 00:29:01,920 --> 00:29:04,360 Speaker 1: will scale UM. But that but you know, I think 564 00:29:04,400 --> 00:29:07,040 Speaker 1: his his view, and I agree with it, is that 565 00:29:07,280 --> 00:29:09,840 Speaker 1: it's that you know, it's that activity that's how you 566 00:29:09,920 --> 00:29:12,880 Speaker 1: maintain an advantage um, because the business you know, in 567 00:29:13,320 --> 00:29:15,400 Speaker 1: three or four years isn't gonna look anything like it does, 568 00:29:15,960 --> 00:29:18,040 Speaker 1: you know, three or four years prior to now. So 569 00:29:18,320 --> 00:29:22,000 Speaker 1: Michael Mobison calls that the paradox of skill, that the 570 00:29:22,080 --> 00:29:25,680 Speaker 1: success of the hedge fund industry and other sectors of 571 00:29:25,720 --> 00:29:30,600 Speaker 1: finance have attracted so many intelligent, talented people that the 572 00:29:30,680 --> 00:29:33,479 Speaker 1: easy money has gone away and it's becomes so much harder. Well, 573 00:29:33,520 --> 00:29:35,040 Speaker 1: that's what makes it fun, right, I mean, that's what 574 00:29:35,080 --> 00:29:37,760 Speaker 1: makes it. It's the you know, it's the competitive drive 575 00:29:37,880 --> 00:29:39,680 Speaker 1: and the and the knowing that the bar is always 576 00:29:39,720 --> 00:29:42,080 Speaker 1: going up. Um. You know, it's that challenge that I 577 00:29:42,080 --> 00:29:44,400 Speaker 1: think draws a lot of people to the industry to 578 00:29:44,680 --> 00:29:48,360 Speaker 1: to say the very least. So look around at some 579 00:29:48,400 --> 00:29:50,960 Speaker 1: of the other hedge funds out there, like the Show 580 00:29:51,120 --> 00:29:55,440 Speaker 1: or Citadel or Renaissance Technologies, and they were pretty early 581 00:29:55,600 --> 00:30:00,560 Speaker 1: onto the high frequency trading and other computer driven UH approaches. 582 00:30:00,800 --> 00:30:04,440 Speaker 1: Is that anything that is UH in point seventy two's 583 00:30:04,520 --> 00:30:07,680 Speaker 1: field of interest or is that something that hey, let 584 00:30:07,760 --> 00:30:11,360 Speaker 1: the computer driven guys do that. You have your own 585 00:30:11,680 --> 00:30:15,440 Speaker 1: specific skill set, So we don't do any high frequency trading. UM, 586 00:30:15,640 --> 00:30:18,160 Speaker 1: we do a fair bit of computer driven trading in 587 00:30:18,160 --> 00:30:21,080 Speaker 1: our systematic unit, and then in some of the units 588 00:30:21,120 --> 00:30:24,920 Speaker 1: I oversee their systematic as well, so driven by computers. Um. 589 00:30:24,960 --> 00:30:29,640 Speaker 1: But uh, but but nothing that would constitute high frequency um. Uh. 590 00:30:29,680 --> 00:30:33,160 Speaker 1: You know, it's certainly an area where a bunch of 591 00:30:33,160 --> 00:30:34,960 Speaker 1: people made a bunch of money, but it wasn't something 592 00:30:35,360 --> 00:30:38,240 Speaker 1: that we did. One of the things I didn't ask 593 00:30:38,320 --> 00:30:43,360 Speaker 1: you earlier but is relevant here is the Domino Data Lab. 594 00:30:43,760 --> 00:30:47,400 Speaker 1: What was the thinking behind that? And how have you 595 00:30:47,560 --> 00:30:52,920 Speaker 1: used that experience at Bridgewater and at point seventy two? Yes, 596 00:30:53,000 --> 00:30:55,840 Speaker 1: So the thinking behind that was really sort of two 597 00:30:55,840 --> 00:30:58,360 Speaker 1: big ideas. One was that we were moving to a 598 00:30:58,400 --> 00:31:03,000 Speaker 1: model driven world, um, where you know, we're algorithms that 599 00:31:03,080 --> 00:31:07,640 Speaker 1: were trained, fed and trained data that made predictions or 600 00:31:07,680 --> 00:31:10,600 Speaker 1: decisions for businesses. That that was going to be a 601 00:31:10,720 --> 00:31:14,400 Speaker 1: very important um thing that took place, and so you know, 602 00:31:14,440 --> 00:31:16,360 Speaker 1: you see the rise of Netflix and Amazon and all 603 00:31:16,360 --> 00:31:19,840 Speaker 1: these things that I would call model driven businesses. Uh. 604 00:31:19,880 --> 00:31:22,680 Speaker 1: And then the second sort of big idea was that, 605 00:31:23,040 --> 00:31:26,320 Speaker 1: um that as that happened, the people who did that work, 606 00:31:26,480 --> 00:31:30,280 Speaker 1: the data scientist needed a system of record. So salespeople 607 00:31:30,280 --> 00:31:32,920 Speaker 1: work in salesforce HR people work in work day. There 608 00:31:33,000 --> 00:31:35,600 Speaker 1: was not an equivalent for data science, and so we 609 00:31:35,600 --> 00:31:38,880 Speaker 1: were building and in our building, uh, the system of 610 00:31:38,920 --> 00:31:43,200 Speaker 1: record for UM for data scientists and and those were 611 00:31:43,240 --> 00:31:45,360 Speaker 1: those were really the two big big ideas behind it. 612 00:31:45,720 --> 00:31:48,560 Speaker 1: And whatever happens to Domino Data LAMB. Does it still exist? 613 00:31:48,640 --> 00:31:51,600 Speaker 1: It still exists doing great UM you know, just you know, 614 00:31:51,640 --> 00:31:54,640 Speaker 1: continues to grow leaps and bounds. I'm on the board. UM. 615 00:31:54,680 --> 00:31:57,280 Speaker 1: It's still an independent company, still an independent company backed 616 00:31:57,280 --> 00:32:02,040 Speaker 1: by Sequoia and COT primarily UM and some others actually 617 00:32:02,040 --> 00:32:06,360 Speaker 1: including Bloomberg, Beta, UM and uh, you know, and it's uh, 618 00:32:06,760 --> 00:32:08,920 Speaker 1: it's it's been. It's been very successful. And probably one 619 00:32:08,920 --> 00:32:11,240 Speaker 1: of the most interesting things about it is just the 620 00:32:11,320 --> 00:32:14,080 Speaker 1: diversity of industries now that are representing the client base. 621 00:32:14,120 --> 00:32:16,400 Speaker 1: You know, it started out a lot of finance firms, 622 00:32:16,440 --> 00:32:18,760 Speaker 1: insurance firms were interested, but now we have everything from 623 00:32:19,040 --> 00:32:23,680 Speaker 1: retailers to grocery stores, to auto makers to pharmaceutical makers. 624 00:32:24,160 --> 00:32:27,800 Speaker 1: Because you know, basically the thesis we were betting on 625 00:32:27,880 --> 00:32:30,160 Speaker 1: was that the world was going to become model driven. 626 00:32:30,440 --> 00:32:32,600 Speaker 1: And this is a tool set. This is a tool 627 00:32:32,640 --> 00:32:36,600 Speaker 1: set to help track how effectively you're deploying your model. 628 00:32:36,960 --> 00:32:40,200 Speaker 1: It's a it's a tool set that um you know, basically, 629 00:32:40,280 --> 00:32:43,640 Speaker 1: data scientists build their models using the languages and tools 630 00:32:43,640 --> 00:32:46,960 Speaker 1: they want in Domino, and then Domino revisions those things. 631 00:32:47,000 --> 00:32:48,720 Speaker 1: Means they keep track of the data and the code 632 00:32:48,760 --> 00:32:51,000 Speaker 1: and the results, and then you can also publish out 633 00:32:51,640 --> 00:32:53,680 Speaker 1: so you can run models from that, and so it's 634 00:32:53,840 --> 00:32:57,000 Speaker 1: sort of the your central repository, your system of record 635 00:32:57,040 --> 00:33:01,360 Speaker 1: for models. Quite interesting, and I keep coming back to 636 00:33:01,400 --> 00:33:06,520 Speaker 1: the idea of of man and machine. When you're evaluating talent, 637 00:33:06,920 --> 00:33:13,200 Speaker 1: be it a startup management team or a a potential 638 00:33:13,280 --> 00:33:16,880 Speaker 1: higher or a portfolio manager, how much of that is 639 00:33:17,040 --> 00:33:19,320 Speaker 1: data driven and how much of that is your own 640 00:33:19,400 --> 00:33:24,600 Speaker 1: human intuition? Well, in in people processes, you know, look, 641 00:33:24,640 --> 00:33:29,200 Speaker 1: I think there's still a lot of human intuition into it. Uh. 642 00:33:29,240 --> 00:33:31,719 Speaker 1: We we do try to be as rigorous and as 643 00:33:31,760 --> 00:33:34,320 Speaker 1: systematic as possible. And what I mean by that is, 644 00:33:34,760 --> 00:33:36,680 Speaker 1: you know, we we try to start with the job 645 00:33:36,840 --> 00:33:39,320 Speaker 1: and the outcomes we expect. And as you think about 646 00:33:39,320 --> 00:33:42,200 Speaker 1: those outcomes, what capabilities are required? And you think about 647 00:33:42,200 --> 00:33:44,760 Speaker 1: those capabilities, you know, what's the best way to evaluate 648 00:33:44,800 --> 00:33:47,440 Speaker 1: those capabilities? I personally don't like interviews. I don't think 649 00:33:47,480 --> 00:33:50,760 Speaker 1: they're particularly useful. UM. I think that work samples and 650 00:33:50,840 --> 00:33:53,320 Speaker 1: projects and these sort of and more testing and those 651 00:33:53,320 --> 00:33:55,800 Speaker 1: sorts of things are very valuable. UM. But you know, 652 00:33:55,800 --> 00:33:57,720 Speaker 1: obviously there's also you know, you do need to meet 653 00:33:57,720 --> 00:33:59,640 Speaker 1: the people. And that's that's a part of it. Um 654 00:33:59,800 --> 00:34:02,880 Speaker 1: by it for us, the hiring process or the evaluation 655 00:34:02,920 --> 00:34:05,800 Speaker 1: process of people adventure, UM, you know, just has a 656 00:34:05,800 --> 00:34:09,280 Speaker 1: certain methodical nous to it. That's that's very important, quite 657 00:34:09,360 --> 00:34:12,680 Speaker 1: quite fascinating. We have been speaking with Matthew Grenade. He 658 00:34:13,000 --> 00:34:17,879 Speaker 1: is the chief market intelligence officer at Point seventy two. UH. 659 00:34:17,920 --> 00:34:20,239 Speaker 1: If you enjoy this conversation, we'll be sure and come 660 00:34:20,239 --> 00:34:23,120 Speaker 1: back for the podcast extras, where we keep the tape 661 00:34:23,200 --> 00:34:28,120 Speaker 1: rolling and continue discussing all things quant and hedge fund investing. 662 00:34:28,680 --> 00:34:32,120 Speaker 1: We love your comments, feedback and suggestions. You can write 663 00:34:32,120 --> 00:34:35,799 Speaker 1: to us at m IB podcast at Bloomberg dot net. 664 00:34:36,520 --> 00:34:39,279 Speaker 1: Be sure and check out my daily column at Bloomberg 665 00:34:39,320 --> 00:34:42,400 Speaker 1: dot com slash opinion. You can follow me on Twitter 666 00:34:42,640 --> 00:34:47,160 Speaker 1: at rid Holts. I'm Barry Ridholts. You're listening to Masterson Business. 667 00:34:47,440 --> 00:35:04,120 Speaker 1: I'm Bloomberg rad Ye. Welcome to the podcast. Matthew thank 668 00:35:04,160 --> 00:35:06,400 Speaker 1: you so much for doing this. UM. I've been looking 669 00:35:06,400 --> 00:35:12,080 Speaker 1: forward to this conversation. I have followed Stevie Cohen's career 670 00:35:12,200 --> 00:35:15,920 Speaker 1: from AFAR for since the nineties, and I find him 671 00:35:15,920 --> 00:35:22,440 Speaker 1: to be an absolutely intriguing individual, both as a investor 672 00:35:22,440 --> 00:35:26,640 Speaker 1: and an art collector, and a person who has managed 673 00:35:26,840 --> 00:35:33,400 Speaker 1: to um thrive despite a lot of really fascinating challenges. So, UM, 674 00:35:33,480 --> 00:35:36,560 Speaker 1: when we first made contact with your office, I was 675 00:35:36,600 --> 00:35:40,440 Speaker 1: really excited about this. UM, so thank you for doing this. 676 00:35:41,120 --> 00:35:43,440 Speaker 1: One of the things we did not get to talk 677 00:35:43,520 --> 00:35:47,120 Speaker 1: about during the broadcast portion was the OpEd that you 678 00:35:47,239 --> 00:35:51,839 Speaker 1: and Steve wrote in the Wall Street Journal. And UM, 679 00:35:51,880 --> 00:35:56,560 Speaker 1: it's not software is eating the world, it's models will 680 00:35:56,640 --> 00:35:58,719 Speaker 1: run the world. Tell us a little bit about that. 681 00:35:59,840 --> 00:36:03,160 Speaker 1: So so Mark Andresen wrote a piece several years ago 682 00:36:03,280 --> 00:36:05,400 Speaker 1: and called software is Eating the World, and it's basically 683 00:36:05,400 --> 00:36:09,200 Speaker 1: the idea that software is going to change every business. UM, 684 00:36:09,320 --> 00:36:12,319 Speaker 1: and Steve and I were thinking about, you know, kind 685 00:36:12,360 --> 00:36:14,080 Speaker 1: of what's the equivalent today, because I think that was 686 00:36:14,120 --> 00:36:17,560 Speaker 1: written almost seven or eight years ago. Uh. And you know, 687 00:36:17,760 --> 00:36:19,880 Speaker 1: the thing that we zero in on was this idea 688 00:36:20,000 --> 00:36:23,600 Speaker 1: that that really models we're going to change the change 689 00:36:23,640 --> 00:36:26,120 Speaker 1: the business landscape and you know, you know the idea 690 00:36:26,120 --> 00:36:28,719 Speaker 1: of a model um, you know, think about Netflix, right. 691 00:36:28,719 --> 00:36:31,160 Speaker 1: So I think Netflix is a great model driven business 692 00:36:31,200 --> 00:36:34,160 Speaker 1: where you know, eight percent of the content consumption there 693 00:36:34,160 --> 00:36:37,200 Speaker 1: comes from their recommendation engine, right, and so basically what 694 00:36:37,239 --> 00:36:38,759 Speaker 1: they're what they're trying to do is they're trying to 695 00:36:38,760 --> 00:36:41,640 Speaker 1: build the best recommend or possible. There you know, you're 696 00:36:41,680 --> 00:36:44,120 Speaker 1: signing up, they're taking in data about you there, you 697 00:36:44,120 --> 00:36:46,800 Speaker 1: know your zip code, and but then they watch everything 698 00:36:46,840 --> 00:36:49,440 Speaker 1: you do. They watch you know how you um, you 699 00:36:49,480 --> 00:36:51,319 Speaker 1: know what shows do you jump on right away? Which 700 00:36:51,320 --> 00:36:53,160 Speaker 1: shows do you finish? Which shows do you not? And 701 00:36:53,200 --> 00:36:55,520 Speaker 1: that lets them recommend better and better content for you. 702 00:36:55,600 --> 00:36:57,960 Speaker 1: And then basically at the core of their business is 703 00:36:58,000 --> 00:37:02,120 Speaker 1: this engine that's that's you know, holding or basically recommending 704 00:37:02,160 --> 00:37:04,279 Speaker 1: content for you, um that you're going to enjoy more 705 00:37:04,320 --> 00:37:06,239 Speaker 1: and more. And now they're using that same data in 706 00:37:06,239 --> 00:37:08,880 Speaker 1: that same approach to build content as well. Um. So 707 00:37:09,040 --> 00:37:10,799 Speaker 1: I think we think about that as a model driven 708 00:37:10,800 --> 00:37:12,920 Speaker 1: business and it's a it's a really sort of powerful 709 00:37:13,000 --> 00:37:15,359 Speaker 1: mode because once you get the loop going where you're 710 00:37:15,400 --> 00:37:18,240 Speaker 1: collecting the data and seeing the outcomes that you're driven, 711 00:37:18,360 --> 00:37:21,359 Speaker 1: you're driving you can make the model better and better. Um. 712 00:37:21,480 --> 00:37:23,279 Speaker 1: And so you know we in the in the out 713 00:37:23,400 --> 00:37:25,960 Speaker 1: ed what we talk about is uh, some public and 714 00:37:25,960 --> 00:37:29,719 Speaker 1: some private companies. Um that Uh you know that that 715 00:37:29,760 --> 00:37:32,319 Speaker 1: our model driven and and and some of the implications 716 00:37:32,360 --> 00:37:35,320 Speaker 1: of this trend um and um and so Yeah, it 717 00:37:35,400 --> 00:37:38,239 Speaker 1: was a fun piece to write. Yeah, and it's still 718 00:37:38,280 --> 00:37:40,920 Speaker 1: available if you anybody wants to go see it. Models 719 00:37:40,960 --> 00:37:44,400 Speaker 1: will run the world. It's in the Wall Street Journal. UM. 720 00:37:44,480 --> 00:37:47,879 Speaker 1: So when you see something like and Reeson's peace, Uh, 721 00:37:48,040 --> 00:37:51,000 Speaker 1: software is in in the world. I want to say 722 00:37:51,000 --> 00:37:55,280 Speaker 1: that he's half right. Software had started to eat the world. 723 00:37:55,920 --> 00:38:00,160 Speaker 1: But we run into problems all the time. That software, 724 00:38:00,040 --> 00:38:02,160 Speaker 1: it only gets you half the way there. And and 725 00:38:02,920 --> 00:38:07,480 Speaker 1: the entire infrastructure of everything from the hardware to the 726 00:38:07,600 --> 00:38:11,600 Speaker 1: network too, everything else that's involved has to work seamlessly. 727 00:38:12,200 --> 00:38:15,920 Speaker 1: Doesn't quite feel like we're in the future yet. How 728 00:38:16,000 --> 00:38:19,920 Speaker 1: do you am I overstating that or how do you 729 00:38:20,000 --> 00:38:23,080 Speaker 1: how do you perceive the world where you know, a 730 00:38:23,239 --> 00:38:25,960 Speaker 1: robot butler doesn't take you to work each day, but 731 00:38:26,000 --> 00:38:28,879 Speaker 1: it's not too far off in the future. I can't 732 00:38:28,880 --> 00:38:31,239 Speaker 1: remember who said it, but somebody said, uh, you know, 733 00:38:31,280 --> 00:38:33,719 Speaker 1: the future is here. It's just unevenly distributed, you know. 734 00:38:33,880 --> 00:38:36,400 Speaker 1: William Gibson, Yeah, I think there's I think there's a 735 00:38:36,440 --> 00:38:38,319 Speaker 1: lot of truth to that, you know. I mean when 736 00:38:38,320 --> 00:38:41,520 Speaker 1: you're in uh, you know, San Francisco and you you know, 737 00:38:41,560 --> 00:38:44,280 Speaker 1: you see the self driving cars that you know, Cruise 738 00:38:44,360 --> 00:38:48,839 Speaker 1: and and Google and others are making. Um, you know, 739 00:38:48,120 --> 00:38:52,720 Speaker 1: then that that feels that feels very in the future. Uh. 740 00:38:52,760 --> 00:38:54,600 Speaker 1: And then you know, like you said, you look at 741 00:38:54,600 --> 00:38:56,279 Speaker 1: some other industries and you sort of scratch your head 742 00:38:56,320 --> 00:38:58,399 Speaker 1: about you know, why can't I get a good cell 743 00:38:58,440 --> 00:39:00,880 Speaker 1: signal in Manhattan? It's exactly why point why can I 744 00:39:01,160 --> 00:39:03,239 Speaker 1: maintain the still signal on the train back to back 745 00:39:03,280 --> 00:39:07,480 Speaker 1: to Connecticut? Um? But um so I certainly, I certainly 746 00:39:07,520 --> 00:39:11,000 Speaker 1: agree that it's it's unevenly distributed. But but you know, 747 00:39:11,040 --> 00:39:14,240 Speaker 1: there's also a tremendous amount of very exciting things happening. 748 00:39:14,520 --> 00:39:16,960 Speaker 1: Um and uh And and look, that's what makes the 749 00:39:17,040 --> 00:39:19,480 Speaker 1: venture investing so much fun, you know, is seeing all 750 00:39:19,480 --> 00:39:24,240 Speaker 1: that and being involved in that world, having that view 751 00:39:24,520 --> 00:39:27,120 Speaker 1: of upcoming technologies. How does it affect the way you 752 00:39:27,160 --> 00:39:31,759 Speaker 1: look at the world of existing public companies. That's a 753 00:39:31,760 --> 00:39:35,239 Speaker 1: great question. I look, I think, um, uh, you know, 754 00:39:35,320 --> 00:39:40,680 Speaker 1: it makes you um much more skeptical about their advantages 755 00:39:40,880 --> 00:39:45,640 Speaker 1: and about the durability of their um of their moats 756 00:39:45,680 --> 00:39:48,520 Speaker 1: quote unquote right uh and um, you know, you look 757 00:39:48,560 --> 00:39:51,239 Speaker 1: at how fast the change has happened in retail and 758 00:39:51,280 --> 00:39:54,920 Speaker 1: how and how deep and dramatic some of that took place, um, 759 00:39:55,000 --> 00:39:56,239 Speaker 1: you know, and you go back and you look at 760 00:39:56,239 --> 00:39:57,880 Speaker 1: some of these companies and all the moats they were 761 00:39:57,880 --> 00:40:00,400 Speaker 1: talking about and the customer loyalty, and then you know, 762 00:40:00,960 --> 00:40:02,400 Speaker 1: um and so you know, one of the things we 763 00:40:02,440 --> 00:40:04,080 Speaker 1: try to do at at points of ME two is 764 00:40:04,120 --> 00:40:06,279 Speaker 1: we we try to sort of cross pollinate some of 765 00:40:06,320 --> 00:40:09,880 Speaker 1: the big thematic learnings um from the venture work in 766 00:40:10,040 --> 00:40:12,719 Speaker 1: with them, in with the public market investors. We had 767 00:40:12,760 --> 00:40:15,120 Speaker 1: a dinner a few months ago on robotics UM, and 768 00:40:15,160 --> 00:40:17,840 Speaker 1: we had through four CEOs of robotics companies, and we 769 00:40:17,880 --> 00:40:20,000 Speaker 1: had our industrial a couple of our industrials pms, and 770 00:40:20,000 --> 00:40:22,880 Speaker 1: our healthcare pms, you know, and it's essentially a discussion, 771 00:40:22,920 --> 00:40:24,920 Speaker 1: you know, exactly along the lines you said of you know, 772 00:40:25,120 --> 00:40:27,560 Speaker 1: how is how is robotics going to And obviously there's 773 00:40:27,600 --> 00:40:29,520 Speaker 1: gonna be a bunch of private companies that get created, 774 00:40:29,680 --> 00:40:32,239 Speaker 1: but it's also going to really change, you know, in 775 00:40:32,280 --> 00:40:34,479 Speaker 1: those two areas. You know a lot of companies as well. 776 00:40:35,160 --> 00:40:37,239 Speaker 1: How often do you guys have dinners like that? It 777 00:40:37,320 --> 00:40:40,720 Speaker 1: sounds like that's an intriguing evening. We do them about 778 00:40:40,760 --> 00:40:43,880 Speaker 1: once a month. We're doing one tonight actually um and 779 00:40:44,080 --> 00:40:47,560 Speaker 1: uh um, you know it's what's the topic tonight tonight? 780 00:40:47,880 --> 00:40:52,479 Speaker 1: Topics actually talent evaluation. So Angela Duckworth is going to join. 781 00:40:52,719 --> 00:40:56,160 Speaker 1: Um wrote a book, Grita. Have you gotten to Have 782 00:40:56,239 --> 00:40:58,160 Speaker 1: you read that yet? I have read gret And how 783 00:40:58,200 --> 00:41:00,520 Speaker 1: do you like it? I think it's great. It's been 784 00:41:00,560 --> 00:41:02,480 Speaker 1: at the top of a number of people's lists for 785 00:41:03,360 --> 00:41:06,000 Speaker 1: for quite a while. Yeah, I have a you know, 786 00:41:06,719 --> 00:41:09,399 Speaker 1: I think it's a it's an interesting way to sort 787 00:41:09,440 --> 00:41:12,840 Speaker 1: of think about, you know, why people are successful. Also, 788 00:41:12,920 --> 00:41:15,360 Speaker 1: as a parent, it's something you know, you you you 789 00:41:15,400 --> 00:41:17,840 Speaker 1: think a lot about, uh, you know, what can you 790 00:41:17,840 --> 00:41:20,399 Speaker 1: actually teach your kids? And you know how and and 791 00:41:20,560 --> 00:41:22,799 Speaker 1: you know, probably at the top of my list of 792 00:41:22,840 --> 00:41:26,239 Speaker 1: things I realized my children to have and to learn. Um. 793 00:41:26,320 --> 00:41:28,680 Speaker 1: And so we have we have rules now about sticking 794 00:41:28,719 --> 00:41:31,600 Speaker 1: with things and stuff like that, largely because of her books. 795 00:41:31,600 --> 00:41:35,120 Speaker 1: So that's that's quite fascinating. UM, I could talk about 796 00:41:35,160 --> 00:41:38,080 Speaker 1: this stuff forever, but I know only have you for 797 00:41:38,120 --> 00:41:40,680 Speaker 1: a finite amount of time, and I wanted to get 798 00:41:41,120 --> 00:41:45,359 Speaker 1: to my favorite questions. UM so let me jump right 799 00:41:45,360 --> 00:41:48,680 Speaker 1: into this, So feel free to answer these as longer 800 00:41:48,719 --> 00:41:52,600 Speaker 1: as short as you want. These are pretty straightforward, um, 801 00:41:52,640 --> 00:41:56,759 Speaker 1: but they usually are a little uh insightful into who 802 00:41:56,800 --> 00:41:59,280 Speaker 1: you are. Tell us the first car you ever owned, 803 00:41:59,440 --> 00:42:03,680 Speaker 1: you're making model? It was a Volvo S forty two thousand, 804 00:42:04,960 --> 00:42:07,520 Speaker 1: sort of, the two door with the hatchback. Is that 805 00:42:07,520 --> 00:42:10,799 Speaker 1: the one I'm I'm thinking of it? Four door? It was? 806 00:42:10,920 --> 00:42:13,040 Speaker 1: It was it was a new model year. Um, so, yeah, 807 00:42:13,040 --> 00:42:15,719 Speaker 1: it was a four door. It was blue. What's the 808 00:42:15,760 --> 00:42:21,160 Speaker 1: most important thing people don't know about Matthew Grenade? Um. 809 00:42:21,200 --> 00:42:24,600 Speaker 1: People are usually surprised to learn that I'm from the South, Um, 810 00:42:24,640 --> 00:42:27,120 Speaker 1: you know, having gone to Harvard twice and worked at 811 00:42:27,120 --> 00:42:30,000 Speaker 1: hedge funds and things like that, and uh, and my 812 00:42:30,080 --> 00:42:32,160 Speaker 1: family has been from the South from for a very 813 00:42:32,160 --> 00:42:35,880 Speaker 1: long time. Um. You have the slightest wisp of an accent, 814 00:42:35,960 --> 00:42:39,239 Speaker 1: but not a heavy the slightest whisp. And then you know, 815 00:42:39,320 --> 00:42:42,640 Speaker 1: and and and I think, uh, you know, certainly affects 816 00:42:42,719 --> 00:42:46,160 Speaker 1: my my manners and that kind of thing. So are 817 00:42:46,200 --> 00:42:48,480 Speaker 1: you a courtly southern gentleman? Is that I wouldn't go 818 00:42:48,560 --> 00:42:53,839 Speaker 1: that far. But but but but my my my mom 819 00:42:53,960 --> 00:42:57,080 Speaker 1: raised me right, she would say, so, So, tell us 820 00:42:57,120 --> 00:42:59,280 Speaker 1: about some of your early mentors. Who are the people 821 00:42:59,320 --> 00:43:05,600 Speaker 1: who helped guide your career. Yeah so, um uh so 822 00:43:05,719 --> 00:43:08,640 Speaker 1: Bo Jones, who was publisher of the Washington Post. Um 823 00:43:08,680 --> 00:43:10,480 Speaker 1: he had been a president of the Crimson as well. 824 00:43:10,840 --> 00:43:14,080 Speaker 1: Um he uh. I worked for him for a summer 825 00:43:14,320 --> 00:43:17,279 Speaker 1: and uh, you know, one of the things, one of 826 00:43:17,280 --> 00:43:19,000 Speaker 1: the things that a couple of things very interesting about 827 00:43:19,000 --> 00:43:21,480 Speaker 1: working from one was, you know, he and and Don 828 00:43:21,560 --> 00:43:25,360 Speaker 1: Graham um in the Graham family in general sort of 829 00:43:25,400 --> 00:43:29,439 Speaker 1: really understood the ecosystem of their business well and and 830 00:43:29,520 --> 00:43:33,160 Speaker 1: sort of how all the parts interconnected. Um uh in 831 00:43:33,239 --> 00:43:37,200 Speaker 1: the in sort of you know, the basically how the 832 00:43:37,320 --> 00:43:41,719 Speaker 1: subscription revenue, um you know was important, but you didn't 833 00:43:41,719 --> 00:43:43,239 Speaker 1: want to You wanted to make sure that you kept 834 00:43:43,239 --> 00:43:45,919 Speaker 1: that price low enough. You have the advertisers, and they'd 835 00:43:45,960 --> 00:43:48,279 Speaker 1: a very holistic way of thinking about the business. And 836 00:43:48,320 --> 00:43:51,360 Speaker 1: then the second uh thing that I thought they you know, 837 00:43:51,400 --> 00:43:54,600 Speaker 1: they're very principal based leaders. Um. You know, a new 838 00:43:54,640 --> 00:43:56,759 Speaker 1: a newsrooms of place, things can run quite a mock 839 00:43:56,840 --> 00:43:59,120 Speaker 1: and and the Washington Post has the backs of their 840 00:43:59,160 --> 00:44:02,720 Speaker 1: reporters and that was always interesting to watch. UM. Another 841 00:44:02,760 --> 00:44:06,200 Speaker 1: would be Tom Barkin, who Um, Tom uh is now 842 00:44:06,239 --> 00:44:09,320 Speaker 1: president of the Richmond Fed UM and on the FOMC 843 00:44:09,480 --> 00:44:11,840 Speaker 1: at the moment, but he was a very senior partner 844 00:44:11,840 --> 00:44:14,640 Speaker 1: at McKenzie UH and one of the people who I 845 00:44:15,040 --> 00:44:18,279 Speaker 1: worked with the closest and most when I was there 846 00:44:18,360 --> 00:44:20,719 Speaker 1: right out of college. UM. And you know, I think 847 00:44:20,719 --> 00:44:23,040 Speaker 1: the thing Tom taught me was the uh sort of 848 00:44:23,040 --> 00:44:25,719 Speaker 1: seeing the essence of the of a problem. UM. You know, 849 00:44:25,880 --> 00:44:28,040 Speaker 1: when you're when you're first out of school and and 850 00:44:28,320 --> 00:44:30,560 Speaker 1: you can think of a two thousand analyzes to do, 851 00:44:30,640 --> 00:44:32,879 Speaker 1: you know, let's do all these things. And Tom Tom 852 00:44:32,920 --> 00:44:35,000 Speaker 1: was great at knowing what the what the right question 853 00:44:35,080 --> 00:44:37,120 Speaker 1: was to ask and the and the right one to answer. 854 00:44:38,120 --> 00:44:41,800 Speaker 1: So what investors influenced the way you look at markets 855 00:44:41,840 --> 00:44:46,520 Speaker 1: and your approach to deploying risk capital. Well, look, I 856 00:44:46,560 --> 00:44:49,120 Speaker 1: mean it's it's really the two I've worked you know, 857 00:44:49,239 --> 00:44:51,280 Speaker 1: closely with. It would be it would be Ray and 858 00:44:51,480 --> 00:44:55,960 Speaker 1: Steve Um. And that's quite a pair of mentors his uh. 859 00:44:56,080 --> 00:44:59,399 Speaker 1: You know, with Ray, I think UM sort of two 860 00:44:59,400 --> 00:45:04,600 Speaker 1: big lessons. One is um um being systematic, being process 861 00:45:04,719 --> 00:45:07,320 Speaker 1: driven that you know, you don't you don't look at outcomes, 862 00:45:07,320 --> 00:45:09,680 Speaker 1: you look at how you got to those outcomes. Uh. 863 00:45:09,719 --> 00:45:12,399 Speaker 1: And then also being fundamental um. And you know, as 864 00:45:12,400 --> 00:45:14,480 Speaker 1: we're talking about earlier, in the world of data science, 865 00:45:14,719 --> 00:45:16,640 Speaker 1: you can torture the data to say anything, and so 866 00:45:16,719 --> 00:45:18,160 Speaker 1: you really have to think about how the how the 867 00:45:18,160 --> 00:45:21,720 Speaker 1: world actually works and why what you're finding matters. Um. 868 00:45:21,760 --> 00:45:25,360 Speaker 1: And then with Steve UM, you know, it's it's the 869 00:45:25,440 --> 00:45:28,319 Speaker 1: sort of tenacity to to really dig in and do 870 00:45:28,400 --> 00:45:30,160 Speaker 1: the work, you know, which, as I mentioned, is one 871 00:45:30,160 --> 00:45:33,400 Speaker 1: of the things he he says over and over UM. Uh. 872 00:45:33,440 --> 00:45:36,160 Speaker 1: You know, you you don't go talk to Steve about 873 00:45:36,160 --> 00:45:39,320 Speaker 1: a name or a venture, investment, or a new strategy 874 00:45:39,440 --> 00:45:42,560 Speaker 1: without having sort of turned it over a hundred different ways. Um. 875 00:45:42,640 --> 00:45:44,960 Speaker 1: And you know his bar for just having you dig 876 00:45:45,000 --> 00:45:48,680 Speaker 1: deep is very high. Um. And Uh, there's probably the 877 00:45:48,760 --> 00:45:53,480 Speaker 1: lessons I've learned most from those guys. So we mentioned, um, 878 00:45:53,520 --> 00:45:56,960 Speaker 1: grit tell us about some of your favorite books fiction, 879 00:45:57,000 --> 00:46:01,640 Speaker 1: non fiction, FINANCEI related whatever. Yeah, So, UM, I mean 880 00:46:01,760 --> 00:46:05,120 Speaker 1: some of my favorite books of all times. Uh, let's see. 881 00:46:05,160 --> 00:46:07,879 Speaker 1: So and just so you know, just so you know, 882 00:46:08,800 --> 00:46:14,840 Speaker 1: the feedback I get on this question is consistently the 883 00:46:14,880 --> 00:46:17,520 Speaker 1: most asked about question, and people say to me, I'm 884 00:46:17,560 --> 00:46:21,320 Speaker 1: always looking for a well thought out suggestion for a book, 885 00:46:21,880 --> 00:46:24,799 Speaker 1: and it's my favorite question you ask people because I've 886 00:46:24,840 --> 00:46:27,719 Speaker 1: created a reading list off of that question, so it's 887 00:46:27,760 --> 00:46:30,440 Speaker 1: not just a random Hey, what are you thinking about 888 00:46:31,920 --> 00:46:35,239 Speaker 1: the books people recommend? Other people say, he seems like 889 00:46:35,280 --> 00:46:37,560 Speaker 1: an intelligent guy. I want to read the books that 890 00:46:37,600 --> 00:46:40,759 Speaker 1: he likes to read. So I'm just I'm just annotating 891 00:46:41,080 --> 00:46:43,520 Speaker 1: before you. So let's try to do three from fairly 892 00:46:43,560 --> 00:46:47,840 Speaker 1: diverse areas. So uh, so you know more finance data science. E. 893 00:46:47,960 --> 00:46:51,560 Speaker 1: I love super Forecasters, which you know is basically tetlock, 894 00:46:51,600 --> 00:46:54,759 Speaker 1: which talks about how you, you know, essentially get good predictions. 895 00:46:54,800 --> 00:46:56,560 Speaker 1: And he's spent his life studying how you get good 896 00:46:56,560 --> 00:46:58,480 Speaker 1: predictions or someone in the markets. You know, it's it's 897 00:46:58,520 --> 00:47:02,719 Speaker 1: it's critical. Um, then let's go outside of investing in 898 00:47:02,760 --> 00:47:05,440 Speaker 1: financing those sorts of things. One of my favorite sort 899 00:47:05,480 --> 00:47:09,560 Speaker 1: of historical books is Wild Swans UM Wild swan Swands, 900 00:47:09,560 --> 00:47:13,880 Speaker 1: which chronicles the life of three women in China and 901 00:47:13,960 --> 00:47:17,000 Speaker 1: the twentieth century. UM. I think I think China is 902 00:47:17,160 --> 00:47:20,640 Speaker 1: such an interesting story because it just you know, it's, it's, 903 00:47:20,760 --> 00:47:23,919 Speaker 1: it's there's been so much dramatic change. And you look 904 00:47:23,960 --> 00:47:27,839 Speaker 1: at those three lives and uh you know, uh, you know, 905 00:47:28,280 --> 00:47:30,120 Speaker 1: one of which is a fair bit of which has 906 00:47:30,160 --> 00:47:32,359 Speaker 1: been on the cultural revolution, and you sort of think 907 00:47:32,400 --> 00:47:34,239 Speaker 1: the world you're living in is the world you're living in, 908 00:47:34,280 --> 00:47:36,960 Speaker 1: and it can just change so dramatically. I want to 909 00:47:36,960 --> 00:47:39,480 Speaker 1: make sure I have the right book Wild Swans Three 910 00:47:39,560 --> 00:47:42,279 Speaker 1: Daughters of China by Jung Chang. Is that it? That's 911 00:47:42,320 --> 00:47:45,840 Speaker 1: it quite interesting? Uh. And then we'll go for a classic, 912 00:47:46,160 --> 00:47:50,520 Speaker 1: uh I Um, I love The Tempest by Shakespeare. Um, 913 00:47:50,600 --> 00:47:52,239 Speaker 1: and uh you know it's where I me there's a 914 00:47:52,280 --> 00:47:53,920 Speaker 1: lot of things goes on go on in that book, 915 00:47:53,920 --> 00:47:56,560 Speaker 1: but uh, that's where you know he he wrote, you 916 00:47:56,600 --> 00:47:59,480 Speaker 1: know what was past his prologue, UM, which I think 917 00:47:59,600 --> 00:48:02,960 Speaker 1: is really true. The past is prologue could really be 918 00:48:03,040 --> 00:48:07,520 Speaker 1: the slogan for anybody who creates models. So so that 919 00:48:07,560 --> 00:48:10,799 Speaker 1: works out. That works out pretty well. Also, UM, tell 920 00:48:10,880 --> 00:48:13,520 Speaker 1: us about a time you failed and what you learned 921 00:48:13,680 --> 00:48:19,560 Speaker 1: from the experience. There's been a bunch, but uh, you know, 922 00:48:20,480 --> 00:48:24,880 Speaker 1: well I'll do this one. So um, Before we started 923 00:48:24,880 --> 00:48:28,239 Speaker 1: Domino Data Labs, my co founders and either two of us, 924 00:48:28,800 --> 00:48:32,200 Speaker 1: three of us total, all all of us from from Bridgewater. 925 00:48:32,760 --> 00:48:36,000 Speaker 1: We started a previous business called Cerebro UH and Cerebro 926 00:48:36,160 --> 00:48:38,440 Speaker 1: was in the talent evaluation space, and so I was 927 00:48:38,480 --> 00:48:41,399 Speaker 1: trying to sort of figure out smarter ways to help 928 00:48:41,440 --> 00:48:44,520 Speaker 1: companies assess their talent. And we had some great clients 929 00:48:44,800 --> 00:48:48,720 Speaker 1: UH in tech, mainly technology firms UH, and we mainly 930 00:48:48,800 --> 00:48:52,399 Speaker 1: had leaders from the business lines, and so we would 931 00:48:52,400 --> 00:48:53,960 Speaker 1: sort of do this work, they would love it, and 932 00:48:53,960 --> 00:48:57,160 Speaker 1: then we would get past to the recruiting department and 933 00:48:57,200 --> 00:48:59,160 Speaker 1: the project would just die. And we did this like 934 00:48:59,200 --> 00:49:01,239 Speaker 1: over and over and over again. UM. And what we 935 00:49:01,280 --> 00:49:04,120 Speaker 1: finally realized was realized a couple of things. One was 936 00:49:04,160 --> 00:49:07,319 Speaker 1: that at a micro level, that the incentives between the 937 00:49:07,320 --> 00:49:09,799 Speaker 1: recruiters and the business people were very different. That the 938 00:49:09,840 --> 00:49:14,279 Speaker 1: recruiters wanted to put people in seats and that the UH, 939 00:49:14,320 --> 00:49:16,120 Speaker 1: and that the business people wanted to have great people 940 00:49:16,160 --> 00:49:18,640 Speaker 1: in those seats. But then more deeply, what we learned 941 00:49:18,719 --> 00:49:20,239 Speaker 1: was that we actually had no idea what we were 942 00:49:20,239 --> 00:49:22,680 Speaker 1: doing UM, and that you know, that we were really 943 00:49:22,760 --> 00:49:25,080 Speaker 1: trying to build a business in an area that we 944 00:49:25,080 --> 00:49:28,239 Speaker 1: weren't experts in and that you you know that that 945 00:49:28,400 --> 00:49:31,000 Speaker 1: is starting a business is just so so so hard 946 00:49:31,040 --> 00:49:33,560 Speaker 1: in like a thousand different ways, and uh, you know, 947 00:49:33,600 --> 00:49:35,960 Speaker 1: and so you have to you have to take advantages 948 00:49:35,960 --> 00:49:38,360 Speaker 1: where you can. And so what we uh, you know 949 00:49:38,360 --> 00:49:40,440 Speaker 1: what we've we started asking ourselves, so what do we 950 00:49:40,480 --> 00:49:43,000 Speaker 1: actually know about? And in those areas of what we 951 00:49:43,000 --> 00:49:45,000 Speaker 1: actually know about, where are their actual problems? And that 952 00:49:45,080 --> 00:49:48,440 Speaker 1: led us do Domino in the data science space. So 953 00:49:48,440 --> 00:49:51,200 Speaker 1: so you come from the school of Ray Dalio's use 954 00:49:51,320 --> 00:49:55,200 Speaker 1: failure as a learning experience to improve your next uh, 955 00:49:55,280 --> 00:49:58,200 Speaker 1: your next attempt at whatever it is. Oh. Absolutely, so 956 00:49:59,400 --> 00:50:03,439 Speaker 1: he told me a funny story about the inside of 957 00:50:03,560 --> 00:50:08,640 Speaker 1: his uh of his book with with the failure cycle, 958 00:50:09,239 --> 00:50:11,799 Speaker 1: and someone who will remain nameless said to him, Ray, 959 00:50:11,840 --> 00:50:15,279 Speaker 1: what sort of signature is that? They obviously hadn't read 960 00:50:15,320 --> 00:50:19,160 Speaker 1: the book, but quite quite hilarious. Um, so tell us 961 00:50:19,200 --> 00:50:20,960 Speaker 1: what you do for fun when you're out of the office. 962 00:50:21,000 --> 00:50:25,279 Speaker 1: Would you do to kickback, relax, have a good time. Um. 963 00:50:25,320 --> 00:50:28,160 Speaker 1: I like to cook um. And this is like going 964 00:50:28,160 --> 00:50:31,600 Speaker 1: back to being from the South. So my my grandmother 965 00:50:31,640 --> 00:50:34,960 Speaker 1: taught me to cook um and uh, and so my 966 00:50:35,040 --> 00:50:37,799 Speaker 1: wife and I will throw parties and we'll cook, in 967 00:50:37,840 --> 00:50:41,880 Speaker 1: particular fried chicken and things like that, and that's probably 968 00:50:41,880 --> 00:50:44,480 Speaker 1: what I enjoy. You work off a cookbookers at all 969 00:50:44,560 --> 00:50:48,319 Speaker 1: grandma's recipes, it's usually a combination. Um. So I like to, 970 00:50:48,440 --> 00:50:51,160 Speaker 1: you know, kind of mix in some more modern cooking 971 00:50:51,920 --> 00:50:54,960 Speaker 1: with some of the more traditional recipes. So give us 972 00:50:54,960 --> 00:50:58,520 Speaker 1: a few dishes. Uh. Well, you know, a traditional dinner 973 00:50:58,600 --> 00:51:02,240 Speaker 1: party would be, um, you know, fried chicken with macaroni 974 00:51:02,320 --> 00:51:06,799 Speaker 1: and cheese and biscuits and blueberry cobbler. But southern, real 975 00:51:06,840 --> 00:51:08,760 Speaker 1: southern cooking. But I'll also do you know, like maybe 976 00:51:08,840 --> 00:51:13,080 Speaker 1: some molecular astronomy with like a watermelon drop or something. Um. 977 00:51:13,440 --> 00:51:16,560 Speaker 1: So you gotta keep it, keep it modern. But um, 978 00:51:16,680 --> 00:51:21,960 Speaker 1: did you see Nathan Revold's Get gast Row cookbook? I 979 00:51:22,000 --> 00:51:25,319 Speaker 1: have all those it's supposed to be a fascinating Have 980 00:51:25,400 --> 00:51:27,920 Speaker 1: you tried any of those dishes? So he has so 981 00:51:27,960 --> 00:51:30,600 Speaker 1: he has his his five volumes, five or six volume 982 00:51:30,640 --> 00:51:33,520 Speaker 1: set that's very intense and completely overwhelming. And then he 983 00:51:33,560 --> 00:51:36,239 Speaker 1: has a home version, um, which I have done a 984 00:51:36,239 --> 00:51:38,759 Speaker 1: couple of things out of the home version. Do they work? 985 00:51:39,280 --> 00:51:42,320 Speaker 1: They work? Um? But he's he's he's much more serious 986 00:51:42,320 --> 00:51:45,120 Speaker 1: than I am. So he's he's he's very intense yet 987 00:51:45,239 --> 00:51:47,600 Speaker 1: to say, to say the least. So what are you 988 00:51:47,680 --> 00:51:52,040 Speaker 1: most excited about within the financial industry today? Well, I 989 00:51:52,040 --> 00:51:54,280 Speaker 1: think you know the thing that the most interesting question 990 00:51:54,360 --> 00:51:57,840 Speaker 1: right now is the people plus machine question. You know, what, 991 00:51:57,840 --> 00:52:00,200 Speaker 1: what are the people good at? How do you the 992 00:52:00,239 --> 00:52:04,319 Speaker 1: most out of them? How do you um uh, how 993 00:52:04,360 --> 00:52:07,920 Speaker 1: do how do you think about those capabilities? And how 994 00:52:07,920 --> 00:52:10,239 Speaker 1: do you couple those with what machines are good at? Um? 995 00:52:10,280 --> 00:52:12,919 Speaker 1: And I um, you know, I think that, Like I said, 996 00:52:12,920 --> 00:52:15,520 Speaker 1: I think the next generation hedge fund is going to 997 00:52:15,600 --> 00:52:18,839 Speaker 1: be a mixture of those two things and um. And 998 00:52:18,880 --> 00:52:21,200 Speaker 1: that's a it's a really it's hard in a lot 999 00:52:21,200 --> 00:52:23,920 Speaker 1: of ways, but it's a very exciting question. So a 1000 00:52:23,960 --> 00:52:26,400 Speaker 1: millennial or a recent college grad comes up to you 1001 00:52:26,480 --> 00:52:31,239 Speaker 1: and says they're interested in a career in either investing 1002 00:52:31,520 --> 00:52:37,000 Speaker 1: or quant what sort of career advice would you give them? Well, 1003 00:52:37,040 --> 00:52:40,600 Speaker 1: I'm not sure it would be so specific to any field. 1004 00:52:40,640 --> 00:52:43,720 Speaker 1: I mean, I think, uh, I think the career advice 1005 00:52:44,000 --> 00:52:46,600 Speaker 1: I would give and I'm I'm not a huge fan 1006 00:52:46,640 --> 00:52:48,799 Speaker 1: of giving advice, but since I'm on the show and 1007 00:52:48,920 --> 00:52:56,640 Speaker 1: on the spot. Um. Look, I Number one would be, um, hm, 1008 00:52:57,120 --> 00:53:00,239 Speaker 1: set your goals as preposterously as you can set them. Um, 1009 00:53:00,280 --> 00:53:03,080 Speaker 1: you will continuously surprise yourself and what you can do. 1010 00:53:03,239 --> 00:53:07,239 Speaker 1: And UM I think uh, um you know so so 1011 00:53:07,360 --> 00:53:11,040 Speaker 1: aim big and dream really big. Um. So that would 1012 00:53:11,040 --> 00:53:17,120 Speaker 1: be one I think. Second, Um, the is work hard. Um. 1013 00:53:17,320 --> 00:53:21,000 Speaker 1: The you know, no no one I've ever met, uh 1014 00:53:21,360 --> 00:53:23,799 Speaker 1: doesn't know, no one, no one who I've ever worked for, 1015 00:53:23,880 --> 00:53:29,160 Speaker 1: you know, Ray, Steve, these guys, none of them are slackers. Um. 1016 00:53:29,200 --> 00:53:31,640 Speaker 1: You know, I mean Steve starts every he starts the 1017 00:53:31,640 --> 00:53:34,960 Speaker 1: week on Sunday morning at um and you know, uh, 1018 00:53:35,000 --> 00:53:37,200 Speaker 1: and and that's when that's when the then he works 1019 00:53:37,239 --> 00:53:38,960 Speaker 1: all day Sunday and he works a fair bit today 1020 00:53:39,000 --> 00:53:42,400 Speaker 1: Saturday and so um, so you know, I think I 1021 00:53:42,400 --> 00:53:46,280 Speaker 1: think it would be to set really almost preposterous goals. Uh, 1022 00:53:46,400 --> 00:53:48,719 Speaker 1: you know, be willing to work really really hard. And 1023 00:53:48,760 --> 00:53:51,480 Speaker 1: then I think the third would probably be, um, you know, 1024 00:53:51,960 --> 00:53:54,359 Speaker 1: love what you do. Um. I've also never really met 1025 00:53:54,360 --> 00:53:57,239 Speaker 1: someone who was successful who didn't really love what they did. Um. 1026 00:53:57,280 --> 00:53:59,360 Speaker 1: And I think you know, Steve Jobs had something that 1027 00:53:59,400 --> 00:54:01,959 Speaker 1: he said I think in the Stanford commincement speech. It's 1028 00:54:02,000 --> 00:54:04,640 Speaker 1: like if you haven't found, if you haven't found what 1029 00:54:04,719 --> 00:54:07,319 Speaker 1: you love yet, just keep looking. UM. And I think 1030 00:54:07,360 --> 00:54:09,040 Speaker 1: that's I think it's right. I think all those things 1031 00:54:09,040 --> 00:54:13,600 Speaker 1: are true, good good advice. UM. And our final question, 1032 00:54:13,760 --> 00:54:15,520 Speaker 1: what is it that you know about the world of 1033 00:54:15,560 --> 00:54:19,439 Speaker 1: investing today? Did that you wish you knew twenty years 1034 00:54:19,560 --> 00:54:23,439 Speaker 1: or so when you were first getting out of college? Well, 1035 00:54:24,000 --> 00:54:28,279 Speaker 1: stay long, Microsoft right, that that was a good time 1036 00:54:28,360 --> 00:54:32,480 Speaker 1: to not not panic, right exactly, But I mean we're 1037 00:54:32,560 --> 00:54:35,640 Speaker 1: as opposed to crystal ball, more processed. Absolutely. Look, I 1038 00:54:35,880 --> 00:54:38,560 Speaker 1: think the UM uh, you know, I think I think 1039 00:54:38,600 --> 00:54:42,160 Speaker 1: one of the most interesting things is just how different um, 1040 00:54:42,239 --> 00:54:45,200 Speaker 1: different periods of time will feel and be UM. And 1041 00:54:45,239 --> 00:54:47,279 Speaker 1: this goes a little bit too, you know what has 1042 00:54:47,280 --> 00:54:49,759 Speaker 1: passed his prologue and using history and things like that, 1043 00:54:49,840 --> 00:54:52,560 Speaker 1: you know, I mean, UM, when you know I graduated 1044 00:54:52,600 --> 00:54:55,440 Speaker 1: in from college in two thousand, you know that was 1045 00:54:55,480 --> 00:54:58,799 Speaker 1: the just the bubble was peaking and UM and the 1046 00:54:58,800 --> 00:55:01,680 Speaker 1: tech bubble, and that sort of felt one very one 1047 00:55:01,719 --> 00:55:03,600 Speaker 1: certain way. And then you know, you get to two 1048 00:55:03,600 --> 00:55:06,960 Speaker 1: thousand eight and you're just in a very very different regime. 1049 00:55:07,000 --> 00:55:09,839 Speaker 1: And I think, UM, I think the differences between these 1050 00:55:09,840 --> 00:55:12,360 Speaker 1: regimes and how what's gonna work in these regimes is 1051 00:55:13,160 --> 00:55:15,520 Speaker 1: quite different. Um. You know, you really have to kind 1052 00:55:15,520 --> 00:55:18,520 Speaker 1: of get your your head around that, um and and 1053 00:55:18,600 --> 00:55:23,319 Speaker 1: kind of really appreciate that quite quite fascinating. We have 1054 00:55:23,400 --> 00:55:27,680 Speaker 1: been speaking with Matthew Grenade. He is the chief market 1055 00:55:27,760 --> 00:55:32,400 Speaker 1: intelligence officer at Point seventy two, where he also oversees 1056 00:55:32,560 --> 00:55:36,560 Speaker 1: their main book as well as helping to manage their 1057 00:55:36,640 --> 00:55:41,239 Speaker 1: venture capital business. If you enjoy this conversation, we'll be 1058 00:55:41,280 --> 00:55:43,319 Speaker 1: sure and look up an inch or down an inch 1059 00:55:43,600 --> 00:55:48,840 Speaker 1: on Apple iTunes, overcast at your Bloomberg dot com wherever 1060 00:55:49,120 --> 00:55:52,160 Speaker 1: final podcasts are sold and you can see any of 1061 00:55:52,200 --> 00:55:54,920 Speaker 1: the other let's call it two dred and forty or 1062 00:55:55,000 --> 00:55:59,280 Speaker 1: so past conversations we have had. We love your comments, 1063 00:55:59,320 --> 00:56:03,520 Speaker 1: feedback and suggestions right to us at m IB podcast 1064 00:56:03,520 --> 00:56:06,680 Speaker 1: at Bloomberg dot net. I would be remiss if I 1065 00:56:06,719 --> 00:56:09,200 Speaker 1: did not thank the crack staff that helps put together 1066 00:56:09,239 --> 00:56:13,720 Speaker 1: this conversation each week. Medina Parwana is my producer slash 1067 00:56:13,760 --> 00:56:18,560 Speaker 1: audio engineer. Taylor Riggs is our booker. Attica val Broun 1068 00:56:18,840 --> 00:56:22,840 Speaker 1: is our project manager. Michael Batnick is my head of research. 1069 00:56:23,600 --> 00:56:27,160 Speaker 1: I'm Barry Riholts. You've been listening to Masters in Business 1070 00:56:27,560 --> 00:56:28,640 Speaker 1: on Bloomberg Radio