1 00:00:00,800 --> 00:00:04,040 Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney alongside 2 00:00:04,040 --> 00:00:06,920 Speaker 1: my co host Matt Miller. Every business day we bring 3 00:00:06,960 --> 00:00:11,520 Speaker 1: you interviews from CEOs, market pros, and Bloomberg experts, along 4 00:00:11,520 --> 00:00:15,600 Speaker 1: with essential market moving news. Find the Bloomberg Markets Podcast 5 00:00:15,600 --> 00:00:18,439 Speaker 1: on Apple Podcasts or wherever you listen to podcasts, and 6 00:00:18,480 --> 00:00:21,480 Speaker 1: at Bloomberg dot com slash podcast. I've been waiting all 7 00:00:21,560 --> 00:00:23,840 Speaker 1: week talk to Sarah Green Carmike, so we will good 8 00:00:23,920 --> 00:00:27,000 Speaker 1: night these two all right, and say thank you very much, Abigail, 9 00:00:27,040 --> 00:00:29,520 Speaker 1: Thank you very much. Brian. Let's bring in Sarah Green 10 00:00:29,560 --> 00:00:31,720 Speaker 1: Carmichael because she wrote an opinion piece at the beginning 11 00:00:31,720 --> 00:00:35,200 Speaker 1: of the week that really piqued my interest talking about 12 00:00:35,200 --> 00:00:37,600 Speaker 1: the fact that all these layoffs were reporting on and 13 00:00:37,640 --> 00:00:40,320 Speaker 1: now it's every day. UM, a few thousand at least 14 00:00:40,840 --> 00:00:45,200 Speaker 1: could be a really bad idea by the executive board. 15 00:00:45,240 --> 00:00:50,120 Speaker 1: So Sarah joins us now on Bloomberg Radio. UM. She's 16 00:00:50,159 --> 00:00:52,720 Speaker 1: a former executive editor at the Harvard Business Review, where 17 00:00:52,720 --> 00:00:55,360 Speaker 1: she hosted the HBr idea cast for mentor at Brown 18 00:00:55,440 --> 00:00:59,920 Speaker 1: University's Women's launch Pad, and of course you probably recognize 19 00:01:00,000 --> 00:01:03,639 Speaker 1: her from barrens um. Now she's writing for us. Sarah, 20 00:01:03,920 --> 00:01:06,400 Speaker 1: What's what's the idea here that these companies have just 21 00:01:06,480 --> 00:01:10,559 Speaker 1: gone through so much and spent a lot to hire people, 22 00:01:10,800 --> 00:01:14,959 Speaker 1: now they're starting to fire those very same people. Yeah, 23 00:01:15,000 --> 00:01:17,479 Speaker 1: it seems like a little bit of wasted resources. It's 24 00:01:17,480 --> 00:01:19,600 Speaker 1: sort of like you painted the room and now you've 25 00:01:19,600 --> 00:01:23,600 Speaker 1: decided to get renovate it. Um. So it makes the 26 00:01:23,640 --> 00:01:26,920 Speaker 1: company's look a little bit uh, shortsighted, I guess, and 27 00:01:27,200 --> 00:01:29,680 Speaker 1: I think you know, the big message that when I 28 00:01:29,720 --> 00:01:31,920 Speaker 1: looked into the research and talk to people was that 29 00:01:32,200 --> 00:01:34,640 Speaker 1: you've spent all this money to hire people. What's most 30 00:01:34,680 --> 00:01:38,680 Speaker 1: important is not the exact number of employees, but your culture. 31 00:01:38,720 --> 00:01:41,840 Speaker 1: Do you have a culture of innovation? Um? If you do, 32 00:01:42,040 --> 00:01:44,760 Speaker 1: you could weather the recession and ask yourself, you know, 33 00:01:44,959 --> 00:01:46,959 Speaker 1: what do we do with this extrac capacity? You know, 34 00:01:47,040 --> 00:01:49,080 Speaker 1: what new things do we create? How do we take 35 00:01:49,120 --> 00:01:52,000 Speaker 1: advantage of this time? Um? But instead it seems like 36 00:01:52,000 --> 00:01:54,920 Speaker 1: companies are being a little bit penny wise count foolish 37 00:01:55,040 --> 00:01:58,720 Speaker 1: by saying, you know, jettison ng um sometimes a small 38 00:01:58,760 --> 00:02:01,200 Speaker 1: percentage of their workforce, but still you know tens of 39 00:02:01,240 --> 00:02:05,080 Speaker 1: thousands of people, Sarah. Do we know a couple of things. 40 00:02:05,120 --> 00:02:08,080 Speaker 1: One is these companies. A lot of these tech companies 41 00:02:08,160 --> 00:02:11,079 Speaker 1: have almost doubled their workforces in the last three to 42 00:02:11,160 --> 00:02:14,080 Speaker 1: four to five years, so one could argue, boy, they overhired. 43 00:02:14,080 --> 00:02:16,680 Speaker 1: And second, there's also an argument that, you know, like 44 00:02:16,720 --> 00:02:19,040 Speaker 1: a lot of companies, a lot of industries, they didn't 45 00:02:19,040 --> 00:02:21,480 Speaker 1: really let people go during the pandemic. That would be 46 00:02:21,560 --> 00:02:23,679 Speaker 1: kind of heartless here, So maybe they got some ketchup 47 00:02:23,720 --> 00:02:26,440 Speaker 1: to do there. What do you do? You get a 48 00:02:26,480 --> 00:02:29,360 Speaker 1: sense that this is specific to the tech industry because 49 00:02:29,520 --> 00:02:31,600 Speaker 1: Michael Barr from Bloomberg News was just in here this 50 00:02:31,639 --> 00:02:35,680 Speaker 1: morning reporting Taco Bell they need employees. You know, Chipotle 51 00:02:35,760 --> 00:02:38,680 Speaker 1: they need twenty thousand employees. I don't know, it's kind 52 00:02:38,680 --> 00:02:42,280 Speaker 1: of tough to circle that square there. Yeah, you know, 53 00:02:42,440 --> 00:02:45,720 Speaker 1: we've been talking about the k saped economy for years now, 54 00:02:45,760 --> 00:02:48,200 Speaker 1: and it does seem a little bit like this is 55 00:02:48,480 --> 00:02:51,600 Speaker 1: you know, tech companies, it's also some financial firms UM 56 00:02:51,639 --> 00:02:54,919 Speaker 1: and consulting firm UM. You know, maybe some of these 57 00:02:54,960 --> 00:02:58,400 Speaker 1: firms are a little overstaffed, especially the tech companies, because 58 00:02:58,440 --> 00:03:00,600 Speaker 1: in some cases, you know, they still have many more 59 00:03:00,600 --> 00:03:02,800 Speaker 1: employees than they did at the start of the pandemic 60 00:03:02,880 --> 00:03:06,360 Speaker 1: even with the layoffs. Right, So they're still growing UM, 61 00:03:06,400 --> 00:03:09,200 Speaker 1: so that you could make it. I think a solid 62 00:03:09,240 --> 00:03:11,120 Speaker 1: argument is that the tech companies might be in a 63 00:03:11,200 --> 00:03:15,680 Speaker 1: slightly different position. UM. But I think, you know, for 64 00:03:15,760 --> 00:03:19,160 Speaker 1: so many companies, you know you mentioned food service, UM, 65 00:03:19,240 --> 00:03:23,040 Speaker 1: retail businesses. So many companies are having trouble just hiring anyone. 66 00:03:23,160 --> 00:03:25,400 Speaker 1: So it's a very sort of weird economy where you 67 00:03:25,440 --> 00:03:27,680 Speaker 1: can't get enough people at one level and other companies 68 00:03:27,760 --> 00:03:30,600 Speaker 1: think maybe they've got slightly too many. I do think 69 00:03:30,639 --> 00:03:35,040 Speaker 1: that if those tech companies are continuing to grow UM, 70 00:03:35,320 --> 00:03:37,800 Speaker 1: hiring freeze might have been better than the kind of 71 00:03:37,840 --> 00:03:41,680 Speaker 1: incredibly morale damaging prospect of LAOPP. That's the one of 72 00:03:41,720 --> 00:03:43,960 Speaker 1: the big problems, right, is the damage to morale, Because 73 00:03:44,760 --> 00:03:47,080 Speaker 1: you know, if you get fired, that's bad, UM. But 74 00:03:47,080 --> 00:03:49,680 Speaker 1: if if you're worried about getting fired for months and 75 00:03:49,680 --> 00:03:52,400 Speaker 1: then you narrowly avoid the acts, that's still not great. 76 00:03:53,160 --> 00:03:55,119 Speaker 1: Especially if some of the people out the door are 77 00:03:55,240 --> 00:03:57,000 Speaker 1: you're you know, you're still close with you might end 78 00:03:57,080 --> 00:03:59,560 Speaker 1: up hating your managers and what do you call it 79 00:03:59,640 --> 00:04:03,280 Speaker 1: quiet quitting? Yes, exactly, Yeah, I mean I think one 80 00:04:03,280 --> 00:04:05,720 Speaker 1: of the things that really comes through in the data 81 00:04:06,200 --> 00:04:08,880 Speaker 1: is that layoffs you lead to higher turnover. Do you 82 00:04:08,920 --> 00:04:11,520 Speaker 1: have the people that you fire, um, and then you 83 00:04:11,560 --> 00:04:13,520 Speaker 1: have the people who all then spend the next few 84 00:04:13,520 --> 00:04:16,240 Speaker 1: months dusting off their resumes and looking for work elsewhere. 85 00:04:16,320 --> 00:04:18,280 Speaker 1: So it really could lead to a lot of attrition 86 00:04:18,279 --> 00:04:21,039 Speaker 1: which could lead some of these firms to become understaffed, 87 00:04:21,080 --> 00:04:23,320 Speaker 1: which can be very costly. How much of this is 88 00:04:23,520 --> 00:04:25,600 Speaker 1: We used to have a function on the Bloomberg terminal 89 00:04:26,120 --> 00:04:29,120 Speaker 1: l O S S Go Lost Go, and I think 90 00:04:29,160 --> 00:04:31,040 Speaker 1: we got rid of it because it felt heartless. What 91 00:04:31,040 --> 00:04:33,240 Speaker 1: it would do is did put up a price chart 92 00:04:33,320 --> 00:04:35,600 Speaker 1: of the stock, and then it would put red circles 93 00:04:35,640 --> 00:04:39,800 Speaker 1: around the firing announcements. And of course usually when you 94 00:04:39,839 --> 00:04:43,720 Speaker 1: announce firings, Wall Street, being the heartless mob that it is, 95 00:04:44,400 --> 00:04:46,520 Speaker 1: goes and buys the stock. Right. How much of this 96 00:04:46,600 --> 00:04:48,120 Speaker 1: do you think is driven by that because these tech 97 00:04:48,120 --> 00:04:51,560 Speaker 1: stocks have gotten hit hard. I think it's very much 98 00:04:51,760 --> 00:04:56,119 Speaker 1: driven by quarterly expectations um so. And I think what's 99 00:04:56,480 --> 00:04:58,800 Speaker 1: a shame is that you will see if stock rise 100 00:04:58,839 --> 00:05:02,599 Speaker 1: a little bit, usually on announcing layoffs um. But then 101 00:05:02,760 --> 00:05:05,000 Speaker 1: you know what happens the next quarter or the quarter 102 00:05:05,120 --> 00:05:08,400 Speaker 1: after that, You know, the stock usually settles right back down. Again. UM, 103 00:05:08,480 --> 00:05:11,240 Speaker 1: so it does seem like it's actually very short term 104 00:05:11,279 --> 00:05:13,680 Speaker 1: thinking kind of saying, how can we play K Wall Street? 105 00:05:13,720 --> 00:05:16,159 Speaker 1: What can we do to show them that we're taking 106 00:05:16,160 --> 00:05:19,479 Speaker 1: it seriously? Um And unfortunately, I think, you know, the 107 00:05:19,600 --> 00:05:22,520 Speaker 1: message so often to the remaining employees is you know, 108 00:05:22,600 --> 00:05:24,599 Speaker 1: we'll just have to do more with less. And I 109 00:05:24,640 --> 00:05:26,960 Speaker 1: think what actually might happen at some of these companies 110 00:05:27,040 --> 00:05:29,279 Speaker 1: is people will have to do less with less. You know, 111 00:05:29,320 --> 00:05:32,000 Speaker 1: it will affect revenue, it will affect other opportunities that 112 00:05:32,080 --> 00:05:34,039 Speaker 1: they you know, I would hope to achieve in the 113 00:05:34,080 --> 00:05:37,120 Speaker 1: long run. Alright, great stuff. I really appreciate you taking 114 00:05:37,120 --> 00:05:39,760 Speaker 1: the time. Sarah Green Carmichael, she is the editor Bloomberg 115 00:05:39,800 --> 00:05:42,560 Speaker 1: Opinion talking about layoffs and we've seen it again. It's 116 00:05:42,640 --> 00:05:45,000 Speaker 1: it's tough to kind of, you know, kind of take 117 00:05:45,040 --> 00:05:47,120 Speaker 1: a look at all the data with the the global 118 00:05:47,680 --> 00:05:50,040 Speaker 1: labor market is so so strong. But these tech companies, 119 00:05:50,480 --> 00:05:53,160 Speaker 1: we've seen a lot of headline noise here in terms 120 00:05:53,200 --> 00:05:55,359 Speaker 1: of layingoff the panel and it hasn't been driving the 121 00:05:55,400 --> 00:05:59,120 Speaker 1: price up recently. Maybe initially yeah, maybe, uh, you know, 122 00:05:59,200 --> 00:06:01,840 Speaker 1: the non tech comes especially when three M says we 123 00:06:01,960 --> 00:06:03,960 Speaker 1: gotta fire thousands of people, or in IBM says we 124 00:06:04,000 --> 00:06:06,560 Speaker 1: gotta fire thousands of people. People say, ohs, what's what's 125 00:06:06,560 --> 00:06:09,000 Speaker 1: the deal with your demand? Exactly right, So we'll see 126 00:06:09,000 --> 00:06:10,360 Speaker 1: if we're gonna get more of that. We a lot 127 00:06:10,400 --> 00:06:13,160 Speaker 1: of tech earnings next week and we'll stay on top 128 00:06:13,160 --> 00:06:14,480 Speaker 1: of that. We'll see what they want to do with 129 00:06:14,520 --> 00:06:21,040 Speaker 1: their head count. This is Bloomberg Ben Slaven, global head 130 00:06:21,040 --> 00:06:25,080 Speaker 1: of E T F S. Yeah, exactly, Ben Slaven, He's 131 00:06:25,080 --> 00:06:26,400 Speaker 1: a global head of E t S or B and 132 00:06:26,520 --> 00:06:29,400 Speaker 1: Y Melon Assets Servicing. He's here in a Bloomberg Interactive 133 00:06:29,440 --> 00:06:31,719 Speaker 1: Broker studio. So he gets the gold star today for 134 00:06:31,760 --> 00:06:34,640 Speaker 1: showing up when most people don't show up at work. 135 00:06:34,640 --> 00:06:37,600 Speaker 1: And literally I'm looking around, I don't see a body here. Um, 136 00:06:37,640 --> 00:06:40,000 Speaker 1: but that's kind of how the world is these days. Ben. 137 00:06:40,080 --> 00:06:43,480 Speaker 1: I mean it's Friday, right, I know, I guess that's whatever. 138 00:06:43,800 --> 00:06:46,240 Speaker 1: So Ben, you at E T F S, I mean, 139 00:06:46,400 --> 00:06:48,919 Speaker 1: you can't help but grow. It's just a question of 140 00:06:49,080 --> 00:06:52,479 Speaker 1: how much you do grow in the E T F business. 141 00:06:52,720 --> 00:06:54,360 Speaker 1: Talk to us about B and Y Melon. What are 142 00:06:54,360 --> 00:06:56,920 Speaker 1: you guys seeing in the business these days? Well, first, 143 00:06:57,000 --> 00:06:59,480 Speaker 1: thanks for having me. Great to be here. And before 144 00:06:59,480 --> 00:07:01,320 Speaker 1: we begin, I just want to give a quick happy 145 00:07:01,360 --> 00:07:04,800 Speaker 1: birthday shout out to Spy. So we're thirty. We are. 146 00:07:04,880 --> 00:07:08,440 Speaker 1: We are thirty years into the e t F journey 147 00:07:08,760 --> 00:07:15,040 Speaker 1: and years maybe Nate Nate most was sort of the 148 00:07:15,200 --> 00:07:18,880 Speaker 1: considered the godfather all those years ago. Um, he actually 149 00:07:18,920 --> 00:07:23,520 Speaker 1: recently passed, but the legacy lives on. But here we are, 150 00:07:25,000 --> 00:07:29,080 Speaker 1: Oh is that right? Okay? Yet long gone? At this point, 151 00:07:29,120 --> 00:07:32,800 Speaker 1: I actually think it's condos. But in in this environment, 152 00:07:32,920 --> 00:07:37,520 Speaker 1: we've seen an acceleration of not only product launches, but 153 00:07:37,560 --> 00:07:40,720 Speaker 1: also adoption by investors. So last year was a record 154 00:07:40,840 --> 00:07:43,680 Speaker 1: for the e t F industry. UM. Actually, in the 155 00:07:43,760 --> 00:07:45,600 Speaker 1: last two years at B and Y Melon, on our 156 00:07:45,640 --> 00:07:50,000 Speaker 1: platform that we service a large percentage of the industry, 157 00:07:50,200 --> 00:07:52,720 Speaker 1: we were launching almost one e t F a day 158 00:07:52,840 --> 00:07:56,240 Speaker 1: on our platform. Now that is slowed in. Our expectation 159 00:07:57,000 --> 00:07:59,680 Speaker 1: is that it will slow a bit and also closures 160 00:07:59,680 --> 00:08:01,600 Speaker 1: will pickup, so on a net basis, we might not 161 00:08:01,720 --> 00:08:04,120 Speaker 1: hit that record, but the que looks strong and one 162 00:08:04,120 --> 00:08:07,280 Speaker 1: of those drivers is really the mutual funds starting to 163 00:08:07,320 --> 00:08:10,400 Speaker 1: convert to e t reasons right, we had a regulatory 164 00:08:10,520 --> 00:08:13,480 Speaker 1: change that allowed mutual fund conversions to e t f 165 00:08:13,560 --> 00:08:16,440 Speaker 1: s and frankly, e t F s are just a 166 00:08:16,480 --> 00:08:18,680 Speaker 1: lot cheaper. I mean, if you're an investor, it makes 167 00:08:18,720 --> 00:08:21,680 Speaker 1: a lot more sense depending on obviously your goals and 168 00:08:21,720 --> 00:08:23,920 Speaker 1: what you want to deal with, your risk holrange, ceter But, uh, 169 00:08:24,000 --> 00:08:28,160 Speaker 1: you're looking at twenty five or fifty basis points maybe 170 00:08:29,000 --> 00:08:31,480 Speaker 1: or a hundred rather than you know, one and a 171 00:08:31,480 --> 00:08:34,640 Speaker 1: half or two percent. Fees matter um, and that has 172 00:08:34,679 --> 00:08:36,960 Speaker 1: been one of the drivers of e t F adoption. 173 00:08:37,440 --> 00:08:41,200 Speaker 1: But look, you know, from an asset manager standpoint, investors 174 00:08:41,240 --> 00:08:45,560 Speaker 1: are clearly preferring the e t F structure, and that 175 00:08:45,679 --> 00:08:47,880 Speaker 1: is putting a lot of pressure on the industry. So 176 00:08:48,160 --> 00:08:51,520 Speaker 1: some of the mutual fund sponsors are saying, hey, if 177 00:08:51,520 --> 00:08:54,320 Speaker 1: we can't beat them, join them. And now that the 178 00:08:54,440 --> 00:08:56,960 Speaker 1: path is open to convert a mutual fund to an 179 00:08:56,960 --> 00:09:01,440 Speaker 1: e t F, it just provides another way for large 180 00:09:01,520 --> 00:09:04,400 Speaker 1: mutual fund complexes to enter the e t F space 181 00:09:04,440 --> 00:09:07,199 Speaker 1: and continue to grow their funds and to some degree 182 00:09:07,240 --> 00:09:09,280 Speaker 1: stop some of the bleeding that we've seen coming out 183 00:09:09,320 --> 00:09:11,480 Speaker 1: of mutual funds at the expense of e t F. 184 00:09:11,640 --> 00:09:13,360 Speaker 1: I don't. I don't. When I was a sell side 185 00:09:13,360 --> 00:09:16,240 Speaker 1: of analysts, I made a living with the mutual funds. 186 00:09:16,400 --> 00:09:19,680 Speaker 1: I go up to Boston, see you know, Fidelity and 187 00:09:19,840 --> 00:09:23,400 Speaker 1: Putnam and all those guys. So that's because they had 188 00:09:23,400 --> 00:09:26,160 Speaker 1: a ga jillion analysts covering chemicals and media and all 189 00:09:26,200 --> 00:09:29,000 Speaker 1: that kind of stuff, and lots of portfolio managers. If 190 00:09:29,000 --> 00:09:31,160 Speaker 1: they were to convert to an e t F and 191 00:09:31,320 --> 00:09:34,640 Speaker 1: cut the fees by like a jillion, what happens to 192 00:09:34,679 --> 00:09:37,640 Speaker 1: all those all that cost, the analyst, that portfolio manages 193 00:09:37,679 --> 00:09:40,360 Speaker 1: all that stuff, what happens to that cost? It's putting 194 00:09:40,360 --> 00:09:43,439 Speaker 1: an incredible amount of pressure on those asset managers. In fact, 195 00:09:43,440 --> 00:09:45,840 Speaker 1: I read an analysis UM and I believe it was 196 00:09:46,040 --> 00:09:49,199 Speaker 1: one of the Bloomberg analysts who made an assumption that 197 00:09:49,480 --> 00:09:53,920 Speaker 1: roughly twenty billion in revenue has been sucked out of 198 00:09:53,960 --> 00:09:57,360 Speaker 1: the asset management industry sector, and a large degree of 199 00:09:57,400 --> 00:09:59,520 Speaker 1: that is due to e t f s, whether it 200 00:09:59,600 --> 00:10:03,960 Speaker 1: be simply the cost as you were mentioning, but also um, 201 00:10:04,000 --> 00:10:06,600 Speaker 1: you know, really the fact that it's a lot of 202 00:10:06,640 --> 00:10:09,880 Speaker 1: that asset is passive UM that has come in to 203 00:10:10,040 --> 00:10:14,079 Speaker 1: the industry which doesn't require those those analysts. Although there's 204 00:10:14,080 --> 00:10:16,880 Speaker 1: a lot of actively managed ETFs and they're really gaining 205 00:10:16,880 --> 00:10:20,320 Speaker 1: in popularity. I mean, Jack Bogel would be ironically, I 206 00:10:20,360 --> 00:10:24,160 Speaker 1: think celebrating in his grave right now because this is 207 00:10:24,720 --> 00:10:27,360 Speaker 1: part of his legacy as well, even though he turned 208 00:10:27,400 --> 00:10:30,520 Speaker 1: down uh what is it, Nate most when he first 209 00:10:30,559 --> 00:10:33,440 Speaker 1: came to his office. Yeah, I mean the old the 210 00:10:33,480 --> 00:10:36,559 Speaker 1: old e t f adage is the whole passive is massive, 211 00:10:37,120 --> 00:10:40,560 Speaker 1: you know, sort of tagline. But you're right, it's changing 212 00:10:40,800 --> 00:10:43,400 Speaker 1: from a product development standpoint. If you think at what's 213 00:10:43,440 --> 00:10:46,640 Speaker 1: happening now versus where the assets are, more than half 214 00:10:46,720 --> 00:10:48,960 Speaker 1: of the products in the industry that we're launched in 215 00:10:49,000 --> 00:10:51,640 Speaker 1: this sort of latest sort of wave of new products 216 00:10:51,720 --> 00:10:54,240 Speaker 1: are actively managed. In fact, that that's what we saw 217 00:10:54,240 --> 00:10:56,200 Speaker 1: on our own platform and if you look at our 218 00:10:56,240 --> 00:10:59,160 Speaker 1: que going forward again, the majority of the products are 219 00:10:59,200 --> 00:11:01,559 Speaker 1: active and if you see what is going on just 220 00:11:01,640 --> 00:11:04,520 Speaker 1: this year. At this point in the market cycle, actively 221 00:11:04,559 --> 00:11:08,240 Speaker 1: managed ETFs are about three percent roughly of the industry, 222 00:11:08,600 --> 00:11:12,079 Speaker 1: but they've accounted for about forty of the flows here 223 00:11:12,360 --> 00:11:16,320 Speaker 1: in three in January. So again small base, but you 224 00:11:16,400 --> 00:11:19,240 Speaker 1: are seeing, um, you know, quite a bit of investor interest. 225 00:11:19,360 --> 00:11:21,560 Speaker 1: Part of that is the market cycle, and part of 226 00:11:21,559 --> 00:11:23,880 Speaker 1: that is there's just frankly more product and choice for 227 00:11:24,000 --> 00:11:26,720 Speaker 1: investors to to choose from and and that's driving it. 228 00:11:26,920 --> 00:11:31,280 Speaker 1: So does the corporations to companies. Did they offer e 229 00:11:31,360 --> 00:11:34,000 Speaker 1: t f s for the four one case because some 230 00:11:34,040 --> 00:11:37,320 Speaker 1: people study that's that's kind of where that's for mutual funds. Yeah, 231 00:11:37,360 --> 00:11:40,000 Speaker 1: it's a it's a great question. Um. You know, four 232 00:11:40,000 --> 00:11:42,360 Speaker 1: o one case have been kind of the last stand 233 00:11:42,679 --> 00:11:45,679 Speaker 1: um really for mutual funds, and that is where the 234 00:11:45,720 --> 00:11:48,600 Speaker 1: stickiest and and sort of mutual funds remain dominant. But 235 00:11:48,720 --> 00:11:50,760 Speaker 1: the bottom line is e t f s are starting 236 00:11:50,760 --> 00:11:53,839 Speaker 1: to crack into that market. The fact is for more 237 00:11:53,880 --> 00:11:56,200 Speaker 1: than a decade you can have e t f s 238 00:11:56,200 --> 00:11:58,599 Speaker 1: and four oh one case. I've had one myself and 239 00:11:58,679 --> 00:12:00,480 Speaker 1: invested in e t f I don't think you simply 240 00:12:00,520 --> 00:12:02,840 Speaker 1: need a broke rach account right to be able to 241 00:12:03,520 --> 00:12:06,160 Speaker 1: access those ETFs. And so it really is a matter 242 00:12:06,200 --> 00:12:09,719 Speaker 1: of the four one k plan itself allowing investors to 243 00:12:09,960 --> 00:12:13,959 Speaker 1: access ETFs, and so we can by the way he 244 00:12:14,280 --> 00:12:20,040 Speaker 1: manages doesn't fidelity dolom my my guy Rick at a 245 00:12:20,120 --> 00:12:24,720 Speaker 1: Meret prize UM. He sent me recently a few um 246 00:12:24,840 --> 00:12:27,120 Speaker 1: options and s junk was one of them. So I 247 00:12:27,120 --> 00:12:28,880 Speaker 1: told him, I told him, I want high yield, but 248 00:12:29,200 --> 00:12:33,040 Speaker 1: so you can and I think the real um One 249 00:12:33,080 --> 00:12:35,440 Speaker 1: of the other big drivers though, is that the kids 250 00:12:36,000 --> 00:12:38,839 Speaker 1: are investing in apps right, and they don't want as 251 00:12:38,880 --> 00:12:41,520 Speaker 1: much of the red tape when they're using robin Hood 252 00:12:41,600 --> 00:12:44,480 Speaker 1: or we Bowl or whatever. And this is just a 253 00:12:44,600 --> 00:12:48,760 Speaker 1: much easier way to invest in the diversified product if 254 00:12:48,920 --> 00:12:50,920 Speaker 1: that's what you're after. You can also besting single stock 255 00:12:50,960 --> 00:12:54,840 Speaker 1: ETFs and leverage up, but this just seems like so 256 00:12:54,960 --> 00:13:00,280 Speaker 1: much cleaner and uh than than a mutual fund, right Lukely. 257 00:13:00,320 --> 00:13:02,400 Speaker 1: And again there's the other benefits we talked about. Certainly 258 00:13:02,440 --> 00:13:04,400 Speaker 1: liquidity in the low cost is a piece of it, 259 00:13:04,440 --> 00:13:08,400 Speaker 1: but remember even when we're talking about retirement accounts, you know, 260 00:13:08,520 --> 00:13:13,400 Speaker 1: it's also about the ability to construct with precision asset 261 00:13:13,400 --> 00:13:17,360 Speaker 1: allocation models that fit a variety of not only time horizons, 262 00:13:17,360 --> 00:13:21,600 Speaker 1: but profiles. And again, the ETFs continue to proliferate, and 263 00:13:21,679 --> 00:13:25,520 Speaker 1: that precision allows the asset allocators or those putting that 264 00:13:25,600 --> 00:13:28,400 Speaker 1: kind of investment advice out there for investors, whether it 265 00:13:28,440 --> 00:13:32,360 Speaker 1: be packaged or not, with an incredible amount of precision 266 00:13:32,800 --> 00:13:36,320 Speaker 1: and the ability to again customize those portfolios. That really 267 00:13:36,320 --> 00:13:38,960 Speaker 1: makes sense for investors, which is just very difficult to do, 268 00:13:39,400 --> 00:13:43,640 Speaker 1: uh in a mutual fund rapper. I mean, I can't 269 00:13:43,640 --> 00:13:46,520 Speaker 1: even think of any reason to be long the asset 270 00:13:46,520 --> 00:13:50,640 Speaker 1: management business at all. This ETFs thing, it's a long 271 00:13:50,760 --> 00:13:54,679 Speaker 1: term story, it seems to me. I mean, is Boston Fidelity, 272 00:13:54,760 --> 00:13:58,440 Speaker 1: Putnam Wellington? You know those guys, what do they do? 273 00:13:59,240 --> 00:14:00,719 Speaker 1: I mean, a lot of them are trying to jump 274 00:14:00,720 --> 00:14:04,439 Speaker 1: into If you can't beat them, join them. I mean, 275 00:14:04,440 --> 00:14:06,600 Speaker 1: Fidelity is a great example. I mean they have come 276 00:14:06,640 --> 00:14:09,600 Speaker 1: in to the e t F market UM and now 277 00:14:09,640 --> 00:14:12,040 Speaker 1: they're really starting to ramp up not only the product 278 00:14:12,040 --> 00:14:14,880 Speaker 1: development but also the distribution of those products, and you're 279 00:14:14,920 --> 00:14:17,720 Speaker 1: starting to see the assets move forward. JP Morgan another 280 00:14:17,800 --> 00:14:20,720 Speaker 1: large firm that's really trying to lean into the e 281 00:14:20,800 --> 00:14:24,400 Speaker 1: t F effort. That's again had a legacy mutual fund business, 282 00:14:24,400 --> 00:14:26,040 Speaker 1: so it's it's changed. What do you guys do it? 283 00:14:26,560 --> 00:14:29,000 Speaker 1: And y Mel and assets Servicing? How do you So? 284 00:14:29,200 --> 00:14:34,080 Speaker 1: We we provide infrastructure to the entire industry, so roughly 285 00:14:34,120 --> 00:14:36,760 Speaker 1: we have a quarter of the industry's assets UM and 286 00:14:36,840 --> 00:14:39,400 Speaker 1: we saw last year because of the record volume, we 287 00:14:39,440 --> 00:14:43,360 Speaker 1: saw close to one point a trillion dollars in notional 288 00:14:43,440 --> 00:14:46,080 Speaker 1: volume flowed through our pipes. And these are the creation 289 00:14:46,080 --> 00:14:48,720 Speaker 1: and redemption orders that are coming through all the e 290 00:14:48,800 --> 00:14:51,720 Speaker 1: t f s we service and company we have, you know, 291 00:14:51,840 --> 00:14:55,560 Speaker 1: again provide all of that back middle office services to 292 00:14:55,720 --> 00:14:59,080 Speaker 1: power up our sponsors e t f s and again 293 00:14:59,160 --> 00:15:02,000 Speaker 1: that businesses grow going for sure, and at the same 294 00:15:02,040 --> 00:15:04,520 Speaker 1: time the complexity is growing as well as these products 295 00:15:04,520 --> 00:15:08,560 Speaker 1: proliferate UM and you know, again covering almost every asset class, 296 00:15:08,920 --> 00:15:11,480 Speaker 1: every market in the world that you can possibly think 297 00:15:11,520 --> 00:15:12,960 Speaker 1: of putting in an e t F at this point. 298 00:15:13,320 --> 00:15:14,600 Speaker 1: One of the things that drew me to e t 299 00:15:14,800 --> 00:15:18,240 Speaker 1: f s to begin with was well, Eric Valtunists and 300 00:15:18,520 --> 00:15:21,840 Speaker 1: um uh Tony Santa Tara Vegas, who who do research 301 00:15:21,880 --> 00:15:24,480 Speaker 1: for us, are on the money. UM. They run through 302 00:15:24,520 --> 00:15:27,080 Speaker 1: the flows and you can really read so much what's 303 00:15:27,080 --> 00:15:29,320 Speaker 1: going in the broader market by e t F flows. 304 00:15:29,600 --> 00:15:31,680 Speaker 1: And that's what I thought initially was so cool. What 305 00:15:31,720 --> 00:15:35,040 Speaker 1: do you see in the flows right now? Lack of conviction. 306 00:15:35,760 --> 00:15:37,800 Speaker 1: That's not a great answer. I knew you were gonna 307 00:15:37,880 --> 00:15:40,040 Speaker 1: I knew you were gonna You're gonna be unhappy with that, 308 00:15:40,120 --> 00:15:43,080 Speaker 1: but that is the fact. It is interesting to see. 309 00:15:43,120 --> 00:15:45,360 Speaker 1: So if you just take something like fixed income, which 310 00:15:45,680 --> 00:15:49,800 Speaker 1: again we saw a very large churn in fixed income 311 00:15:49,960 --> 00:15:52,000 Speaker 1: last year. I mean obviously due to the bond market 312 00:15:52,080 --> 00:15:55,040 Speaker 1: route again money coming out of mutual funds into e 313 00:15:55,200 --> 00:15:57,280 Speaker 1: t F s UM where E t F again picked 314 00:15:57,320 --> 00:15:59,200 Speaker 1: up market share. And you look at what's happening in 315 00:15:59,280 --> 00:16:02,160 Speaker 1: January and I just took a look at the leaderboard 316 00:16:02,280 --> 00:16:06,360 Speaker 1: and you see UM not only short duration. You see 317 00:16:06,360 --> 00:16:08,600 Speaker 1: the long bond tilt. You know that's one of year 318 00:16:08,600 --> 00:16:11,200 Speaker 1: plus picking up flow. You see high yield, you see 319 00:16:11,240 --> 00:16:13,720 Speaker 1: investment grade UM. I see a g G which is 320 00:16:13,760 --> 00:16:17,200 Speaker 1: the total bond market all picking up flow. Hence my 321 00:16:17,360 --> 00:16:19,960 Speaker 1: comment at this point lack of conviction. The one thing 322 00:16:20,000 --> 00:16:22,800 Speaker 1: we are seeing that's unique as international UM, and that's 323 00:16:22,800 --> 00:16:24,800 Speaker 1: been a sort of hot you know, coming into the 324 00:16:24,840 --> 00:16:26,960 Speaker 1: beginning of this year. All right, Ben, great stuff, Ben Slaving, 325 00:16:27,000 --> 00:16:29,120 Speaker 1: Global head of e t S b N Y Melon 326 00:16:29,200 --> 00:16:35,560 Speaker 1: Assets Servicing chat GPT. I don't know what it is, 327 00:16:35,600 --> 00:16:37,920 Speaker 1: but you know, you build. You take a look, what's 328 00:16:37,920 --> 00:16:40,320 Speaker 1: it take to build a website these days? You just 329 00:16:40,720 --> 00:16:42,480 Speaker 1: hire some kid and he does it for you. I 330 00:16:42,480 --> 00:16:45,000 Speaker 1: think chat GPT now is that kid and it does 331 00:16:45,040 --> 00:16:47,800 Speaker 1: it in thirty seconds. I mean it depends again like 332 00:16:47,920 --> 00:16:52,720 Speaker 1: the AI told us when Sam Potter and Katie Greifeld 333 00:16:52,760 --> 00:16:55,640 Speaker 1: tried to put an e t F together. It really 334 00:16:55,680 --> 00:16:58,240 Speaker 1: depends on your parameters, right, You've got to be a 335 00:16:58,280 --> 00:17:00,640 Speaker 1: little more specific, and you just build me a website. 336 00:17:00,640 --> 00:17:02,280 Speaker 1: All right, Let's talk to someone you kind of in 337 00:17:02,320 --> 00:17:07,320 Speaker 1: this space. James Cliff, founder of the firms called Durable. Uh, James, 338 00:17:07,359 --> 00:17:09,119 Speaker 1: thanks so much for joining us here. Tell us what 339 00:17:09,359 --> 00:17:12,240 Speaker 1: Durable does, and then we'll get into the whole chat. 340 00:17:12,280 --> 00:17:14,959 Speaker 1: GPT MAC can talk about it, because I really don't know. 341 00:17:16,119 --> 00:17:19,520 Speaker 1: Thanks for having me. Yeah, So, Durable helps small businesses 342 00:17:19,520 --> 00:17:23,560 Speaker 1: get online in less than thirty seconds. So with three inputs, 343 00:17:23,680 --> 00:17:28,080 Speaker 1: your location, your business category. Um, wait, there's two inputs, 344 00:17:28,480 --> 00:17:30,879 Speaker 1: you essentially have a working website. We rate the copy, 345 00:17:30,960 --> 00:17:32,880 Speaker 1: we pick your images, we pick your colors, we pick 346 00:17:32,880 --> 00:17:35,320 Speaker 1: your layout. Give you everything you need to launch a 347 00:17:35,440 --> 00:17:38,760 Speaker 1: beautiful website in less than thirty seconds. Get a customed 348 00:17:38,800 --> 00:17:41,200 Speaker 1: domain name, get analytics built in, and then you're off 349 00:17:41,200 --> 00:17:42,959 Speaker 1: to the races. And what's that called? And what does 350 00:17:43,000 --> 00:17:47,160 Speaker 1: that cost me? Right now? It's free for thirty days 351 00:17:47,280 --> 00:17:50,399 Speaker 1: and ten dollars a month. So I so exactly. So 352 00:17:50,440 --> 00:17:52,960 Speaker 1: I had read about Durable. I've heard about this, and 353 00:17:52,960 --> 00:17:55,520 Speaker 1: that's why my guest thirty seconds was pretty much on 354 00:17:55,560 --> 00:17:58,719 Speaker 1: the money. Um, and then what's the next step. I mean, 355 00:17:58,760 --> 00:18:00,679 Speaker 1: I assume you can find two in it at some 356 00:18:00,760 --> 00:18:05,200 Speaker 1: point after you give those just those two variables exactly. 357 00:18:05,280 --> 00:18:07,439 Speaker 1: There's a there's a custom editor within it. We actually 358 00:18:07,440 --> 00:18:09,520 Speaker 1: have AI within the platform as well, so you can 359 00:18:09,600 --> 00:18:14,440 Speaker 1: regenerate different components, you can test different copy. Essentially, our 360 00:18:14,440 --> 00:18:18,040 Speaker 1: whole product is thinking it from an AI perse perspective, 361 00:18:18,160 --> 00:18:20,240 Speaker 1: So just making it really easier for people to play 362 00:18:20,280 --> 00:18:23,480 Speaker 1: around with different layouts, different images, getting to a point 363 00:18:23,480 --> 00:18:25,720 Speaker 1: where they're just excited about what they're creating and what 364 00:18:25,720 --> 00:18:29,960 Speaker 1: they're launching. So typically this website building process for an individual, 365 00:18:30,000 --> 00:18:32,040 Speaker 1: one you're doing on your phone when you're in a 366 00:18:32,160 --> 00:18:34,840 Speaker 1: McDonald's drive through half the time, So like, how do 367 00:18:34,840 --> 00:18:37,560 Speaker 1: you make that experience fun and enjoyable as opposed to 368 00:18:37,600 --> 00:18:40,800 Speaker 1: this kind of clunky experience where you're trying to get 369 00:18:40,840 --> 00:18:43,480 Speaker 1: a pixel to be in the right place. Um. And 370 00:18:43,520 --> 00:18:45,240 Speaker 1: that's really how we're thinking about, is how do we 371 00:18:45,280 --> 00:18:49,280 Speaker 1: make this accessible to the folks out there that I 372 00:18:49,359 --> 00:18:52,000 Speaker 1: want a website but don't have one yet. The cool 373 00:18:52,040 --> 00:18:54,920 Speaker 1: thing is I've owned a domain name for I'm gonna 374 00:18:54,920 --> 00:18:57,159 Speaker 1: say over fifteen years. Have you that? I just have 375 00:18:57,240 --> 00:18:59,320 Speaker 1: never acted a few of them actually because I had 376 00:18:59,320 --> 00:19:01,080 Speaker 1: this idea way back in the day. So I bought 377 00:19:01,080 --> 00:19:04,399 Speaker 1: go to hell pants dot com and Cocktail party pants 378 00:19:04,440 --> 00:19:07,520 Speaker 1: dot com and Mr fancy Pants where you're going, okay exactly, 379 00:19:07,560 --> 00:19:09,879 Speaker 1: but I never I was like, I don't know what 380 00:19:10,040 --> 00:19:14,239 Speaker 1: ht ellie even stands for. So I've never acted on that. 381 00:19:14,320 --> 00:19:15,919 Speaker 1: So now I guess I could do it with UH 382 00:19:16,160 --> 00:19:22,400 Speaker 1: with durable exactly. Who who is your your typical customer James? Yeah, 383 00:19:22,400 --> 00:19:25,639 Speaker 1: So typically we're we're working with solo business owners, so 384 00:19:25,760 --> 00:19:28,840 Speaker 1: someone who's I'm an owner operator of their business. So 385 00:19:28,920 --> 00:19:32,200 Speaker 1: primarily in the services space, so we have a very 386 00:19:32,200 --> 00:19:37,240 Speaker 1: wide variety of those folks though, So personal trainers, creatives, consultants, 387 00:19:37,359 --> 00:19:41,240 Speaker 1: home services, essentially anywhere where you're trading your hours for 388 00:19:41,320 --> 00:19:44,320 Speaker 1: dollars or hours for projects. Were really great fit because 389 00:19:44,359 --> 00:19:47,360 Speaker 1: typically these folks don't have the time or the budget 390 00:19:47,440 --> 00:19:51,119 Speaker 1: to hire expensive web development firms, and they're pretty simple 391 00:19:51,119 --> 00:19:53,040 Speaker 1: businesses that you're trying to operate. You're not trying to 392 00:19:53,119 --> 00:19:57,199 Speaker 1: run these complex e commerce inventory. It's Hey, I'm offering 393 00:19:57,200 --> 00:19:59,680 Speaker 1: a service, how do I get that online? Get more 394 00:19:59,720 --> 00:20:02,120 Speaker 1: cost mers and make my life easier. UM. That's really 395 00:20:02,160 --> 00:20:05,679 Speaker 1: who we're working with. All right, So chat GPT you 396 00:20:05,760 --> 00:20:09,359 Speaker 1: say it's dominated your inbox and news feeds. Talk to 397 00:20:09,400 --> 00:20:11,760 Speaker 1: us about that business and kind of the impact of 398 00:20:11,840 --> 00:20:18,440 Speaker 1: chat GPT on your Um, they're a partner, Yeah, I think, Um. 399 00:20:18,480 --> 00:20:21,000 Speaker 1: I mean it's the most exciting technology that I've seen 400 00:20:21,000 --> 00:20:23,639 Speaker 1: in my fifteen years working on the internet. UM. And 401 00:20:23,680 --> 00:20:27,840 Speaker 1: I think the world agrees. Um. I'm excited about it. 402 00:20:27,880 --> 00:20:30,880 Speaker 1: And I think it's this new evolution of software. Right. 403 00:20:30,920 --> 00:20:33,840 Speaker 1: So previously is there's this world where um, the user 404 00:20:33,880 --> 00:20:36,520 Speaker 1: has to do the work, UM, which is you gotta 405 00:20:36,560 --> 00:20:38,280 Speaker 1: log in, you gotta click the buttons, you got to 406 00:20:38,280 --> 00:20:39,760 Speaker 1: think of what to say, you gotta think of what 407 00:20:39,840 --> 00:20:43,680 Speaker 1: image to choose. UM. For a knowledge worker, it's hey, 408 00:20:43,680 --> 00:20:46,080 Speaker 1: I need to create this formula and excel, I need 409 00:20:46,119 --> 00:20:49,639 Speaker 1: to write this document. Um. Whereas now this new AI 410 00:20:49,680 --> 00:20:53,480 Speaker 1: revolution is creating this um different world where the AI 411 00:20:53,560 --> 00:20:56,879 Speaker 1: does the work and your skill and knowledge is what 412 00:20:56,960 --> 00:20:59,119 Speaker 1: can actually tell the A what to do. UM. So 413 00:20:59,160 --> 00:21:01,640 Speaker 1: it's this new paradigm shift that I'm just so excited 414 00:21:01,680 --> 00:21:05,239 Speaker 1: about because so much of work and even for our users, right, 415 00:21:05,320 --> 00:21:07,919 Speaker 1: it's UM, it frustrates me that they have to do 416 00:21:07,960 --> 00:21:10,199 Speaker 1: these things within the app. Um, why don't we just 417 00:21:10,480 --> 00:21:12,320 Speaker 1: make it automated? Why can't you just talk to your 418 00:21:12,320 --> 00:21:14,679 Speaker 1: app to ask it to do things? How much smarter 419 00:21:14,720 --> 00:21:17,560 Speaker 1: can software get? Um? So I'm just looking at this 420 00:21:17,600 --> 00:21:20,720 Speaker 1: as a brand new user experience. UM, that's gonna open 421 00:21:20,840 --> 00:21:24,640 Speaker 1: up just so many opportunities for folks to UM make 422 00:21:24,680 --> 00:21:26,679 Speaker 1: their jobs better and easier. I mean, this kind of 423 00:21:26,680 --> 00:21:29,800 Speaker 1: technology is finally starting to work in places where we've 424 00:21:29,800 --> 00:21:32,000 Speaker 1: been trying to make it work for a long time. 425 00:21:32,119 --> 00:21:35,560 Speaker 1: Right with sirie and you know your car, My car 426 00:21:35,680 --> 00:21:38,320 Speaker 1: can finally call my mom when I push the talk 427 00:21:38,359 --> 00:21:40,840 Speaker 1: button and say call mom. It hasn't been able to 428 00:21:40,880 --> 00:21:43,639 Speaker 1: do it for a decade. Um, is this going to 429 00:21:43,720 --> 00:21:47,159 Speaker 1: replace a lot of you know, for example, what me 430 00:21:47,240 --> 00:21:49,439 Speaker 1: and Paul do, I saw the Wall Street Journal reporting 431 00:21:49,480 --> 00:21:54,159 Speaker 1: earlier that UM BuzzFeed, which is you know, journalistic news site, 432 00:21:54,280 --> 00:21:57,040 Speaker 1: is gonna rely on chat GPT to write some of 433 00:21:57,080 --> 00:22:02,160 Speaker 1: its content. Yeah. I think if you look at any 434 00:22:02,240 --> 00:22:06,360 Speaker 1: evolution of technology, there's always this sphere of job replacement. UM. 435 00:22:06,440 --> 00:22:09,240 Speaker 1: What I believe in is it's gonna let people focus 436 00:22:09,280 --> 00:22:12,120 Speaker 1: on what they're really good at. So y'all are very 437 00:22:12,119 --> 00:22:14,040 Speaker 1: good at your jobs. You've got an audience, you've got 438 00:22:14,119 --> 00:22:17,720 Speaker 1: a personality. UM one, you could train your own AI 439 00:22:17,800 --> 00:22:21,080 Speaker 1: model to scale yourself, so maybe you start doing fifteen 440 00:22:21,080 --> 00:22:24,600 Speaker 1: different radio shows and fifteen different languages in real time. UM. 441 00:22:24,680 --> 00:22:27,800 Speaker 1: So I think the the individual actually becomes more important. 442 00:22:28,200 --> 00:22:32,080 Speaker 1: And everyone says everyone's worry about A replacing their jobs. UM, 443 00:22:32,119 --> 00:22:34,800 Speaker 1: I think it's gonna replace a lot of employers, whereas 444 00:22:34,840 --> 00:22:37,400 Speaker 1: the individual can now become their own business of one 445 00:22:37,520 --> 00:22:40,600 Speaker 1: and scale themselves and solve all the things that UM 446 00:22:40,720 --> 00:22:44,119 Speaker 1: employers or corporations do well. I think individuals will have 447 00:22:44,160 --> 00:22:46,199 Speaker 1: the power to do that. So I think it opens 448 00:22:46,280 --> 00:22:49,880 Speaker 1: up a lot more opportunities than it takes away. And yeah, 449 00:22:50,000 --> 00:22:52,000 Speaker 1: I think we again, we haven't seen what this can 450 00:22:52,040 --> 00:22:54,560 Speaker 1: do yet. But I think for folks like anyone with 451 00:22:54,600 --> 00:22:58,200 Speaker 1: a skill and who can think critically and use this technology, 452 00:22:58,440 --> 00:23:00,879 Speaker 1: I think it makes them a lot or powerful as 453 00:23:00,880 --> 00:23:04,120 Speaker 1: an individual and gives them a lot more economic opportunity 454 00:23:04,400 --> 00:23:06,720 Speaker 1: across the world to UM. And there's a lot of 455 00:23:06,760 --> 00:23:09,720 Speaker 1: a lot of markets that we're seeing. Even UM that 456 00:23:10,040 --> 00:23:12,600 Speaker 1: English is their second language. They're building a website in English, 457 00:23:12,600 --> 00:23:14,120 Speaker 1: and now they can offer their skills to a brand 458 00:23:14,119 --> 00:23:17,640 Speaker 1: new market and communicate in not their first language. It's 459 00:23:17,640 --> 00:23:20,560 Speaker 1: like it's been compared to Calculator a lot, right, So 460 00:23:21,359 --> 00:23:23,760 Speaker 1: Paul Universe, you guys grew up with that Hewlett Packard 461 00:23:23,920 --> 00:23:27,960 Speaker 1: you know, yeah exactly, that didn't replace you, that made 462 00:23:27,960 --> 00:23:31,160 Speaker 1: you better at your job exactly. Alright, So I think 463 00:23:31,160 --> 00:23:34,040 Speaker 1: it's pretty fascinating. I guess Max Headroom isn't coming to 464 00:23:34,040 --> 00:23:38,359 Speaker 1: to replace us anytime soon. Um. So what's the uh 465 00:23:38,640 --> 00:23:41,359 Speaker 1: what's the path forward look like for durable then? Um? 466 00:23:41,520 --> 00:23:43,640 Speaker 1: You know, how do how do you scale up from here? 467 00:23:43,680 --> 00:23:47,720 Speaker 1: When's the I p O? Yeah, I mean we'll we'll 468 00:23:47,760 --> 00:23:49,720 Speaker 1: ask the AI when the I p O is. But 469 00:23:50,040 --> 00:23:53,439 Speaker 1: the traction has been incredible. So there's so much market 470 00:23:53,480 --> 00:23:57,320 Speaker 1: demand for what we're doing. We're growing like month over 471 00:23:57,400 --> 00:24:00,840 Speaker 1: month um and the numbers are not mall at this point. 472 00:24:00,880 --> 00:24:04,600 Speaker 1: So it's really just trying to catch up on building technology. 473 00:24:04,720 --> 00:24:08,440 Speaker 1: So we've got a massive backlog for engineers to work through. 474 00:24:08,480 --> 00:24:12,000 Speaker 1: We're retraining and retraining our own AI models. Um, we're 475 00:24:12,000 --> 00:24:15,520 Speaker 1: building these brand new user experiences. UM. So again thinking 476 00:24:15,600 --> 00:24:19,480 Speaker 1: AI first, what what? How how minimal can these inputs 477 00:24:19,480 --> 00:24:22,160 Speaker 1: be to to build workflows for our customers. So really 478 00:24:22,160 --> 00:24:25,840 Speaker 1: looking at abstracting as much as possible UM for this 479 00:24:26,000 --> 00:24:28,840 Speaker 1: solo business owner. So one part of that marketing and websites, 480 00:24:29,080 --> 00:24:32,119 Speaker 1: and then you have UM workflows like invoicing and scheduling 481 00:24:32,480 --> 00:24:34,879 Speaker 1: and then accounting and kind of the back office stuff. 482 00:24:34,920 --> 00:24:38,160 Speaker 1: So every piece of this workflow for an individual business owner, 483 00:24:38,359 --> 00:24:40,080 Speaker 1: how do you make their job so easy that all 484 00:24:40,119 --> 00:24:42,359 Speaker 1: they have to do is show up do the work? 485 00:24:42,400 --> 00:24:44,280 Speaker 1: And I think, in my ideal world, you can say, hey, 486 00:24:44,320 --> 00:24:47,280 Speaker 1: I've got this skill, Um, I pressed two buttons, I've 487 00:24:47,320 --> 00:24:49,640 Speaker 1: got a business, I've got a customer lined up tomorrow, 488 00:24:49,960 --> 00:24:52,800 Speaker 1: and all of a sudden, I just have this complete 489 00:24:52,800 --> 00:24:56,840 Speaker 1: freedom over my pressing, my schedule flexibility, um. And to me, 490 00:24:56,920 --> 00:24:59,760 Speaker 1: that's the utopian world that we're trying to create, all right, James, 491 00:24:59,760 --> 00:25:02,680 Speaker 1: great stuff. James Clift is the founder of a durable 492 00:25:02,760 --> 00:25:06,320 Speaker 1: creating websites. It's like a matter of seconds. How cool 493 00:25:06,400 --> 00:25:11,520 Speaker 1: is that this move to somebody who does this stuff 494 00:25:11,560 --> 00:25:13,399 Speaker 1: for living and he is working in a huge scam. 495 00:25:13,520 --> 00:25:16,600 Speaker 1: This is don Steinbruger. He's been covering markets for a 496 00:25:16,640 --> 00:25:19,240 Speaker 1: long time. He's a CEO and founder of agecol Partners. 497 00:25:19,880 --> 00:25:22,240 Speaker 1: But he's working down and he does it from Richmond, Virginia, 498 00:25:22,240 --> 00:25:26,560 Speaker 1: which is probably my favorite town in America. I love Richmond, 499 00:25:26,560 --> 00:25:29,680 Speaker 1: went to school there, my twins were born there. Great 500 00:25:29,720 --> 00:25:33,240 Speaker 1: great town. Somehow, Don who is the proud member of 501 00:25:33,240 --> 00:25:36,919 Speaker 1: the Summit High School, New Jersey Class of Night, somehow 502 00:25:36,960 --> 00:25:39,520 Speaker 1: he's working his scam down there in Richmond. Don, Thanks 503 00:25:39,600 --> 00:25:42,159 Speaker 1: what for joining us again here? I mean, what are 504 00:25:42,160 --> 00:25:44,640 Speaker 1: you telling your clients about these markets? Here? I mean, 505 00:25:44,680 --> 00:25:47,639 Speaker 1: earnings are coming in, the Fed Reserve is moving a 506 00:25:47,720 --> 00:25:51,879 Speaker 1: lot of moving pieces here. What are you telling your clients? So, Paul, 507 00:25:52,440 --> 00:25:55,440 Speaker 1: I think it's a good time for the hedge fund industry. Uh, 508 00:25:55,480 --> 00:25:58,520 Speaker 1: you know, I am not optimistic on the capitol markets 509 00:25:58,560 --> 00:26:01,680 Speaker 1: this year, as you know that that is said they're 510 00:26:01,680 --> 00:26:03,920 Speaker 1: going to continue to raise rates or that though at 511 00:26:03,920 --> 00:26:08,159 Speaker 1: a slower pace. Uh. You know, people have known for 512 00:26:08,200 --> 00:26:10,000 Speaker 1: a long time. You don't want to fight the fet 513 00:26:10,119 --> 00:26:13,159 Speaker 1: theF THATT is not going to lower rates probably until 514 00:26:13,920 --> 00:26:16,880 Speaker 1: at least two thousand and twenty four. I think there's 515 00:26:16,920 --> 00:26:20,000 Speaker 1: gonna be a recession. It's going to cause some headwinds 516 00:26:20,040 --> 00:26:22,440 Speaker 1: for the equity market. So I'm not looking at the 517 00:26:22,480 --> 00:26:25,960 Speaker 1: equity market oring back up interest rates go up a 518 00:26:26,000 --> 00:26:28,560 Speaker 1: little bit more, it's not going to be good for 519 00:26:28,600 --> 00:26:34,000 Speaker 1: the bond market. So, you know, relative value UH hedge 520 00:26:34,000 --> 00:26:36,320 Speaker 1: fund strategies, I think it make a lot of sense. 521 00:26:37,040 --> 00:26:40,879 Speaker 1: UH hedge fund strategies that aren't correlated to the U 522 00:26:41,200 --> 00:26:44,800 Speaker 1: SMP five hundred, I think helped diversify a portfolio, like 523 00:26:45,160 --> 00:26:50,760 Speaker 1: global macro and UH quantitative global macro strategies like C 524 00:26:50,960 --> 00:26:53,600 Speaker 1: T A S. I think it's gonna be a year 525 00:26:53,680 --> 00:26:58,720 Speaker 1: where UM active management outperforms indexes. You know, we've seen 526 00:26:58,800 --> 00:27:03,119 Speaker 1: volatility come down a lot across people tell us that 527 00:27:03,200 --> 00:27:07,160 Speaker 1: every single year, like active management is going to outperform 528 00:27:07,240 --> 00:27:09,040 Speaker 1: every year. That's that's the beginning of the year mantra 529 00:27:09,119 --> 00:27:12,560 Speaker 1: for people in the financial industry. Well, you know, for 530 00:27:12,600 --> 00:27:15,479 Speaker 1: a decade, it didn't happen for a decade. You just 531 00:27:15,560 --> 00:27:19,479 Speaker 1: had everybody putting money in index funds and that just 532 00:27:19,600 --> 00:27:24,000 Speaker 1: caused index funds to outperform. So last year you did 533 00:27:24,040 --> 00:27:28,399 Speaker 1: see UH long shirt equity managers at a lot of 534 00:27:28,440 --> 00:27:32,160 Speaker 1: alpha relative to the SMP five hundred, and I think 535 00:27:32,160 --> 00:27:34,040 Speaker 1: you're going to see the same thing this year because 536 00:27:34,280 --> 00:27:36,600 Speaker 1: I think you're going to see more volatility in the 537 00:27:36,720 --> 00:27:43,240 Speaker 1: marketplace and volatility is good for active management because active 538 00:27:43,240 --> 00:27:46,520 Speaker 1: managers are you know, picking a target price to sell at, 539 00:27:46,560 --> 00:27:50,320 Speaker 1: and if you have volatility, it reaches that target price quicker. 540 00:27:50,720 --> 00:27:53,919 Speaker 1: You also are able to buy securities at a lower 541 00:27:53,960 --> 00:27:57,679 Speaker 1: price because you have high volatility, prices go below the 542 00:27:57,680 --> 00:28:01,960 Speaker 1: intrinsic value. So what about then, value versus growth or 543 00:28:02,240 --> 00:28:07,480 Speaker 1: small versus big. I think a small and mid cap 544 00:28:07,560 --> 00:28:11,720 Speaker 1: or where you want to be, relative valuations look very strong. 545 00:28:12,160 --> 00:28:17,160 Speaker 1: I also like outside the US, so international small cap 546 00:28:17,520 --> 00:28:23,159 Speaker 1: um much greater inefficiencies, better valuation standpoint. I think you're 547 00:28:23,200 --> 00:28:26,959 Speaker 1: going to see a weekend into the dollar. There's greater inefficiencies, 548 00:28:26,960 --> 00:28:31,040 Speaker 1: not as many Wall Street coverage of companies over in Asia. 549 00:28:31,600 --> 00:28:35,280 Speaker 1: So that's an area we're focused on. Hey, donn it 550 00:28:35,359 --> 00:28:37,320 Speaker 1: used to be, you know, back in the day, if 551 00:28:37,359 --> 00:28:39,840 Speaker 1: I had two or three good years on the bond desk, 552 00:28:39,920 --> 00:28:43,240 Speaker 1: or equity desk or commadi's desk, a Goldman or fixed income, 553 00:28:43,600 --> 00:28:45,400 Speaker 1: I could just go out and hang my own shingles, 554 00:28:45,400 --> 00:28:48,760 Speaker 1: start a hedge fund, raise a billion dollars. Is that still? 555 00:28:49,360 --> 00:28:53,040 Speaker 1: Is that still a thing? It is not. I mean, 556 00:28:53,080 --> 00:28:57,440 Speaker 1: it's the most competitive industry there is. There's probably fifteen 557 00:28:57,560 --> 00:29:01,240 Speaker 1: thousand hedge funds and they're all calling on the same 558 00:29:01,360 --> 00:29:05,240 Speaker 1: institutional investors to get money. So these pension funds and 559 00:29:05,320 --> 00:29:08,400 Speaker 1: Downmond funds, foundations, I mean, they are literally getting called 560 00:29:08,400 --> 00:29:12,120 Speaker 1: by thousands of people that want to sell hedge funds, 561 00:29:12,200 --> 00:29:16,440 Speaker 1: private equity, real estate, long only. So brand is becoming 562 00:29:16,560 --> 00:29:19,880 Speaker 1: more and more important in the industry and you're seeing, 563 00:29:20,240 --> 00:29:23,280 Speaker 1: you know, for example, a majority of hedge funds have 564 00:29:23,480 --> 00:29:27,760 Speaker 1: less than five hundred million assets, and probably five cent 565 00:29:28,080 --> 00:29:31,400 Speaker 1: of flows are going to hedge funds with less than 566 00:29:31,400 --> 00:29:34,600 Speaker 1: five million assets, which is a problem because these firms 567 00:29:34,640 --> 00:29:36,760 Speaker 1: are getting too big, and the bigger you are, the 568 00:29:36,800 --> 00:29:39,280 Speaker 1: harderness to generate the terms. Well, who are the biggest 569 00:29:39,320 --> 00:29:44,160 Speaker 1: The biggest winners last year were the absolute behemoths, right, Um, 570 00:29:44,680 --> 00:29:51,840 Speaker 1: Bridgewater and Citadel, Right Citadel, Yeah, and all those guys. 571 00:29:51,880 --> 00:29:54,640 Speaker 1: So so if you look at the HFR index that 572 00:29:54,720 --> 00:29:58,400 Speaker 1: they have one index that equally waits hedge funds, and 573 00:29:58,600 --> 00:30:01,040 Speaker 1: that was down about four. They have another one that's 574 00:30:01,160 --> 00:30:04,560 Speaker 1: asset weighted, kind of like the SMP asset weights companies. 575 00:30:05,120 --> 00:30:08,320 Speaker 1: The asset weight of one was flat performance wise, but 576 00:30:08,440 --> 00:30:11,680 Speaker 1: that's really unusual if you go back over long periods 577 00:30:11,680 --> 00:30:16,880 Speaker 1: of time, smaller hedge funds significantly outperform large hedge funds, 578 00:30:16,920 --> 00:30:18,920 Speaker 1: and I think that's going to be the case going forward. 579 00:30:19,160 --> 00:30:20,880 Speaker 1: So if you really want to generate a lot of 580 00:30:20,920 --> 00:30:23,600 Speaker 1: returns for hedge funds, you don't want to invest in 581 00:30:23,680 --> 00:30:25,880 Speaker 1: the largest hedge funds. You want to find the up 582 00:30:25,920 --> 00:30:30,240 Speaker 1: and commerce. How about the commodity space? How does investors 583 00:30:30,240 --> 00:30:32,160 Speaker 1: play that? Because commodities have had a big run and 584 00:30:32,200 --> 00:30:40,240 Speaker 1: we're talking about inflation here and China's reopening, China's reopening, well, obviously, Uh, 585 00:30:40,440 --> 00:30:43,800 Speaker 1: commodities did really well last year. There are a couple 586 00:30:43,800 --> 00:30:45,440 Speaker 1: of different ways you can play it. I mean they're 587 00:30:45,600 --> 00:30:49,560 Speaker 1: they're pure commodity managers that you can invest in. You 588 00:30:49,560 --> 00:30:53,120 Speaker 1: could also invest in you know, global macro that's allocating 589 00:30:53,160 --> 00:30:57,400 Speaker 1: the equities, fixed income, curgencies, commodities and let them choose, 590 00:30:57,920 --> 00:31:02,080 Speaker 1: you know, what weight commodity should be. There's also, uh, 591 00:31:02,360 --> 00:31:05,320 Speaker 1: these quantitative managers I mentioned before, C T A. S. 592 00:31:05,400 --> 00:31:09,560 Speaker 1: It's probably the hedge fund industry. They're quantitatively trying to 593 00:31:09,720 --> 00:31:12,680 Speaker 1: determine where the best value is and again you let 594 00:31:12,720 --> 00:31:16,160 Speaker 1: them determine whether they should be heavily weighted in commodities 595 00:31:16,240 --> 00:31:18,680 Speaker 1: or not. Alright, good stuff all right, don appreciate it 596 00:31:18,720 --> 00:31:21,000 Speaker 1: as always giving us a lay the land of the 597 00:31:21,040 --> 00:31:25,400 Speaker 1: Hedge Fund biz Don Steinbrug, founder and CEO of age Croft. 598 00:31:25,760 --> 00:31:29,280 Speaker 1: He is uh proud of lum of the University of Richmond. 599 00:31:29,280 --> 00:31:32,200 Speaker 1: He's a fellow Spider like me, and he's a lum 600 00:31:32,280 --> 00:31:36,080 Speaker 1: of Summit High School. In Summit class of night. Some 601 00:31:36,160 --> 00:31:38,200 Speaker 1: stellar people came out of that class, I can tell 602 00:31:38,200 --> 00:31:43,239 Speaker 1: you right away. One of the most read stories on 603 00:31:43,280 --> 00:31:46,080 Speaker 1: the terminal deals with our good friends of Goldman Sachs, 604 00:31:46,160 --> 00:31:51,600 Speaker 1: the CEO David Solomon. His pay is getting cut by 605 00:31:52,000 --> 00:31:54,760 Speaker 1: twenty five million dollars. And that's during a year which 606 00:31:54,760 --> 00:31:57,560 Speaker 1: the share price and profit tumbled and the firm retreated 607 00:31:57,560 --> 00:32:00,760 Speaker 1: from a highly public effort to create a consumer. Who 608 00:32:00,760 --> 00:32:03,680 Speaker 1: broke that news only Bassett and Steve Dixon. She's only 609 00:32:03,720 --> 00:32:06,680 Speaker 1: backs bass She covers all things Wall Street for Bloomberg News. 610 00:32:06,680 --> 00:32:09,360 Speaker 1: She joins us here in our Bloomberg Interactive Broker studio. 611 00:32:09,760 --> 00:32:12,720 Speaker 1: When I first read your reportingly, I said, good for 612 00:32:12,880 --> 00:32:16,600 Speaker 1: Goldman Sachs, Good for David Solomon. The rank and file 613 00:32:16,640 --> 00:32:19,120 Speaker 1: are taking pay cuts this year. Bonuses are down, and 614 00:32:19,200 --> 00:32:22,600 Speaker 1: here's my leader participating in the pain too. Yeah, I 615 00:32:22,600 --> 00:32:24,000 Speaker 1: think that was my read. Well, you have to look 616 00:32:24,040 --> 00:32:25,960 Speaker 1: at two things here. According to people familiar with the matter, 617 00:32:26,040 --> 00:32:29,640 Speaker 1: this is definitely in line with cuts you're gonna see 618 00:32:29,720 --> 00:32:32,720 Speaker 1: for partners and senior managers at Goldman. But then you 619 00:32:32,720 --> 00:32:34,200 Speaker 1: also have to take a look at what the filing 620 00:32:34,320 --> 00:32:38,600 Speaker 1: says here and what it says about why. Look, I 621 00:32:38,640 --> 00:32:40,960 Speaker 1: look at a lot of filings. It's the only place 622 00:32:41,000 --> 00:32:44,080 Speaker 1: anymore after f t X that I could trust anything anymore. 623 00:32:44,920 --> 00:32:46,640 Speaker 1: But I think it's really important to look at it 624 00:32:46,680 --> 00:32:49,480 Speaker 1: because board determines compensation at the end of the day, 625 00:32:50,040 --> 00:32:52,120 Speaker 1: his basse page stay the same as a two million, 626 00:32:52,120 --> 00:32:54,720 Speaker 1: which is often done with the variable compensation was what 627 00:32:54,920 --> 00:32:58,440 Speaker 1: dropped off meaningfully. Last year was a record year. But 628 00:32:59,120 --> 00:33:01,200 Speaker 1: the one thing that they said also is that this 629 00:33:01,280 --> 00:33:04,200 Speaker 1: is kind of in line with his ability to strategically 630 00:33:04,280 --> 00:33:07,200 Speaker 1: reposition the firm. So both the fact that you have 631 00:33:07,520 --> 00:33:09,680 Speaker 1: come down from a record year last year you had 632 00:33:09,720 --> 00:33:12,440 Speaker 1: record levels of pay. You also had special awards tied 633 00:33:12,480 --> 00:33:15,160 Speaker 1: to last year's pay for David Solomon. But this year 634 00:33:15,520 --> 00:33:17,640 Speaker 1: you have to remember that this thirty percent drop in 635 00:33:17,680 --> 00:33:20,360 Speaker 1: his total compensation package is just so much steeper than 636 00:33:20,400 --> 00:33:23,440 Speaker 1: you're seeing at the other banks, Morgan Stanley's drop is 637 00:33:23,480 --> 00:33:26,920 Speaker 1: only ten percent. Jamie Diaman at JP Morgan, it's stable. 638 00:33:27,240 --> 00:33:29,920 Speaker 1: They did get rid of his special award package that 639 00:33:30,000 --> 00:33:32,640 Speaker 1: shareholders kind of revolted against last year, so it's not 640 00:33:32,680 --> 00:33:35,800 Speaker 1: like there was no pressure on Jamie Diamonds pay. But 641 00:33:36,680 --> 00:33:40,400 Speaker 1: you do see this kind of a typical compensation package 642 00:33:40,400 --> 00:33:42,360 Speaker 1: really follow a lot more Goldman Sacks than you saw 643 00:33:42,360 --> 00:33:45,760 Speaker 1: at the other banks. So how much does the do 644 00:33:45,840 --> 00:33:48,760 Speaker 1: we say the failure of Marcus? That is that safe 645 00:33:48,800 --> 00:33:50,760 Speaker 1: to say? The reason you could say that is because 646 00:33:50,800 --> 00:33:52,680 Speaker 1: there are parts of Marcus that they're unwinding. I think 647 00:33:52,680 --> 00:33:55,760 Speaker 1: it's confusing because it's really like a lending business. But 648 00:33:55,800 --> 00:33:58,000 Speaker 1: they have Green Sky, which is a separate lending business, 649 00:33:58,000 --> 00:34:00,360 Speaker 1: So it's not like there's not a consumer of this there. 650 00:34:00,360 --> 00:34:02,400 Speaker 1: They just bought a huge one. Yeah. No. But when 651 00:34:02,440 --> 00:34:06,200 Speaker 1: they first announced these plans, um some of us who 652 00:34:06,400 --> 00:34:08,680 Speaker 1: know less about Wall Street than you and Alice and 653 00:34:08,680 --> 00:34:11,680 Speaker 1: Williams thought, Okay, Goldman Sax is gonna come in and rule. 654 00:34:12,200 --> 00:34:14,520 Speaker 1: You know, everyone's going to be using Marcus. They're gonna 655 00:34:14,600 --> 00:34:17,160 Speaker 1: own the high end consumer segments. Let me do something, 656 00:34:17,200 --> 00:34:19,680 Speaker 1: do you really use Marcus? Who? How many people. Do 657 00:34:19,719 --> 00:34:22,520 Speaker 1: you know that uses Marcus to borrow money from, or 658 00:34:22,600 --> 00:34:24,400 Speaker 1: even Goldman Sacks to borrow money from. What you did 659 00:34:24,480 --> 00:34:27,440 Speaker 1: at Marcus was put your cash in at an interest 660 00:34:27,520 --> 00:34:30,160 Speaker 1: rate that was way higher than you got anywhere else 661 00:34:30,200 --> 00:34:32,520 Speaker 1: for a long amount of time. So when they engage 662 00:34:32,560 --> 00:34:34,400 Speaker 1: with consumers, because remember they still have a lot of 663 00:34:34,440 --> 00:34:37,080 Speaker 1: high net worth individuals that are really important to their 664 00:34:37,160 --> 00:34:40,320 Speaker 1: going forward strategy. Goldman has always managed money for the 665 00:34:40,400 --> 00:34:42,480 Speaker 1: richest of the rich in the world. Do they keep 666 00:34:42,520 --> 00:34:45,000 Speaker 1: a business like Marcus where they're working with so many 667 00:34:45,000 --> 00:34:47,279 Speaker 1: companies and they want to get these younger folks that 668 00:34:47,320 --> 00:34:49,279 Speaker 1: are going to get wealthier over time that work for 669 00:34:49,320 --> 00:34:52,759 Speaker 1: those companies and you know, manage their money, have them 670 00:34:52,880 --> 00:34:55,200 Speaker 1: saved with them. I think these are big existential questions 671 00:34:55,200 --> 00:34:57,799 Speaker 1: for Goldman Sacks, which is very different than like, hey, 672 00:34:57,880 --> 00:34:59,560 Speaker 1: let me get a Chase card. But at the time 673 00:35:00,040 --> 00:35:02,640 Speaker 1: time it seemed like they were gonna beat Out and 674 00:35:02,760 --> 00:35:06,360 Speaker 1: twenty six and Revolute. I didn't At the time, it 675 00:35:06,360 --> 00:35:07,920 Speaker 1: seemed like they were going to take they were going 676 00:35:07,960 --> 00:35:11,120 Speaker 1: to be the American Express Platinum card of the future, 677 00:35:11,320 --> 00:35:13,720 Speaker 1: and they're not. I think that that was a pipe 678 00:35:13,800 --> 00:35:15,799 Speaker 1: dream always. Frankly I think that that was a bit 679 00:35:15,800 --> 00:35:18,200 Speaker 1: of hype around. Well. I was excited about it, and 680 00:35:18,320 --> 00:35:20,839 Speaker 1: now I know you never were. I sat next to 681 00:35:20,840 --> 00:35:24,000 Speaker 1: you reporting on Matt Miller, who always said that the 682 00:35:24,000 --> 00:35:27,600 Speaker 1: Goldmen card sucks. He always did him and Tom Keane, 683 00:35:27,600 --> 00:35:29,520 Speaker 1: but there was never really that much of a product. 684 00:35:29,960 --> 00:35:34,440 Speaker 1: Just anyway, do you think he from shareholder's perspective, the 685 00:35:34,480 --> 00:35:37,839 Speaker 1: board perspective, maybe the partnership of perspective, does he get 686 00:35:38,440 --> 00:35:42,280 Speaker 1: mark it down for Marcus And if so, how much? Well, listen, 687 00:35:42,440 --> 00:35:44,080 Speaker 1: at the end of the day, there were parts of 688 00:35:44,080 --> 00:35:46,880 Speaker 1: the bank that we're having record numbers, really just blow out, blowout, 689 00:35:46,880 --> 00:35:49,879 Speaker 1: blowout performance. Remember Goldman's acts, their equities trading desk last 690 00:35:49,960 --> 00:35:53,120 Speaker 1: year had some quarters where the even surpass Morgan Stanley. 691 00:35:53,200 --> 00:35:56,640 Speaker 1: Their dealmakers blew through the roof by a margin though 692 00:35:56,680 --> 00:35:58,960 Speaker 1: I have not seen in many, many many years relative 693 00:35:58,960 --> 00:36:02,040 Speaker 1: to the other banks, but up markets, this seems reputational 694 00:36:02,200 --> 00:36:04,160 Speaker 1: to me. And I don't know if that was blank find? 695 00:36:04,360 --> 00:36:06,200 Speaker 1: Was that blank find was that? I can't remember really 696 00:36:06,280 --> 00:36:09,200 Speaker 1: because I'm wondering if I'm aboard, if I'm Solomon. I mean, 697 00:36:09,200 --> 00:36:10,440 Speaker 1: do I have to worry a little bit here that 698 00:36:10,440 --> 00:36:13,239 Speaker 1: I'm going to be painted by this marketing is really 699 00:36:13,239 --> 00:36:15,320 Speaker 1: going to be an issue for me longer term. This 700 00:36:15,360 --> 00:36:18,319 Speaker 1: consumer business lost two billion dollars last year. It lost 701 00:36:18,360 --> 00:36:20,640 Speaker 1: four billion dollars over a couple of years, a few 702 00:36:20,719 --> 00:36:23,440 Speaker 1: years there, and so even beyond the reputational issue, it 703 00:36:23,480 --> 00:36:26,560 Speaker 1: is a money issue. And not for nothing. Million bucks 704 00:36:27,320 --> 00:36:30,080 Speaker 1: for the CEO of Goldman SAX, that's nothing. You walk 705 00:36:30,120 --> 00:36:33,439 Speaker 1: across the street to Carlisle Group that KKR, those guys 706 00:36:33,480 --> 00:36:35,959 Speaker 1: are really making the bank. So one thing we didn't 707 00:36:36,000 --> 00:36:38,200 Speaker 1: see yet this was just the ceo paychecks that we're 708 00:36:38,239 --> 00:36:42,160 Speaker 1: seeing the compensation packages that are coming out through regulatory findings. 709 00:36:42,360 --> 00:36:44,680 Speaker 1: What has not come out yet is my favorite filing, 710 00:36:44,719 --> 00:36:48,680 Speaker 1: which is the proxy where you see the total executive 711 00:36:48,680 --> 00:36:51,560 Speaker 1: and directors compensation. So what is his number two, number three, 712 00:36:51,640 --> 00:36:54,600 Speaker 1: number four making? Are they facing the same types of 713 00:36:54,640 --> 00:36:56,600 Speaker 1: pressure on their compensations? If the trader makes a hundred 714 00:36:56,640 --> 00:36:59,080 Speaker 1: million bucks, Goldman Sax doesn't report that in any filing, right, 715 00:36:59,080 --> 00:37:00,920 Speaker 1: and and there are plenty of people who make more 716 00:37:00,920 --> 00:37:03,000 Speaker 1: money than the CEOs at these banks. To your point, 717 00:37:03,480 --> 00:37:06,240 Speaker 1: and to the other point you're making, that's interesting speaking 718 00:37:06,239 --> 00:37:09,280 Speaker 1: of Wall Street talent and hiring, you know, Jonathan Great 719 00:37:09,480 --> 00:37:11,560 Speaker 1: black Stone this week had told me they're slowing hiring 720 00:37:11,560 --> 00:37:15,080 Speaker 1: at black Stone, and they had a huge compensation package 721 00:37:15,080 --> 00:37:17,080 Speaker 1: for their private equity guys last year, but that was 722 00:37:17,160 --> 00:37:19,160 Speaker 1: mostly because they were able to sell a lot of 723 00:37:19,200 --> 00:37:21,720 Speaker 1: companies at the beginning of last year. The Goldman Saxes 724 00:37:21,760 --> 00:37:23,040 Speaker 1: of the world. Are they going to bring in their 725 00:37:23,080 --> 00:37:25,960 Speaker 1: two hundred analysts to their investment bank or three hundred 726 00:37:25,960 --> 00:37:28,520 Speaker 1: anar whatever it is like City and Goldman Sex Works. 727 00:37:28,520 --> 00:37:30,479 Speaker 1: Are the big banks still doing it or they paired 728 00:37:30,560 --> 00:37:32,600 Speaker 1: that back. That's a great question. I don't know what 729 00:37:32,800 --> 00:37:36,120 Speaker 1: younger people hiring looks like this year. My friend hughes 730 00:37:36,120 --> 00:37:38,480 Speaker 1: Son who works at the NBC, what does Tom King 731 00:37:38,560 --> 00:37:43,239 Speaker 1: call it? The death star air? I'm agnostic, and so 732 00:37:43,440 --> 00:37:45,680 Speaker 1: a news is news is news, and so when you 733 00:37:45,920 --> 00:37:48,520 Speaker 1: look at what he had kind of calculated, it was 734 00:37:48,560 --> 00:37:51,520 Speaker 1: interesting if you look at the severance packages for all 735 00:37:51,560 --> 00:37:53,800 Speaker 1: the people who are being laid off at Goldman that 736 00:37:54,080 --> 00:37:55,840 Speaker 1: if you average it out based on the number of 737 00:37:55,840 --> 00:37:58,560 Speaker 1: people who are being let go, it's like a hundred 738 00:37:58,640 --> 00:38:00,879 Speaker 1: forty dollars, I want to say, which is a very 739 00:38:00,920 --> 00:38:04,439 Speaker 1: low end um in terms of pay scale, which means 740 00:38:04,560 --> 00:38:06,840 Speaker 1: either younger workers are being laid off. Her back office 741 00:38:06,840 --> 00:38:09,080 Speaker 1: workers are being laid off for the most part. They're 742 00:38:09,120 --> 00:38:11,920 Speaker 1: definitely retirements happening across Wall Street at a faster rate. 743 00:38:11,960 --> 00:38:14,440 Speaker 1: So you are seeing people leaving on the higher end 744 00:38:14,440 --> 00:38:17,920 Speaker 1: of the pay scale. But do you then hire a 745 00:38:17,960 --> 00:38:21,680 Speaker 1: lot more young people coming in? I don't. I'm gonna 746 00:38:21,800 --> 00:38:23,239 Speaker 1: I'll go down to Duke in a couple of months 747 00:38:23,239 --> 00:38:25,560 Speaker 1: for my board meeting. I'll get the first hand skinny 748 00:38:25,560 --> 00:38:28,319 Speaker 1: because that we get those numbers. You're they're nervous. I 749 00:38:28,360 --> 00:38:30,560 Speaker 1: hear MBAs and young people are nervous. Can I just 750 00:38:30,560 --> 00:38:33,040 Speaker 1: talk about something else? Question? Talking about next week? Yes, 751 00:38:33,080 --> 00:38:36,799 Speaker 1: I'm going out in Miami tomorrow. Nice it's hedge fund week. 752 00:38:37,000 --> 00:38:39,720 Speaker 1: Oh of course. And they're not meeting in Cleveland, meeting 753 00:38:39,719 --> 00:38:44,120 Speaker 1: in It's called Singer's Kim Kardashian, who is now wait 754 00:38:44,120 --> 00:38:49,960 Speaker 1: wait all Singer makes sense to me, Elliott. Kim Kardashian 755 00:38:50,080 --> 00:38:53,040 Speaker 1: now has a private equity firm. My newsletters coming out 756 00:38:53,080 --> 00:38:55,640 Speaker 1: calls her the buyout queen because remember, there's not that 757 00:38:55,680 --> 00:38:59,480 Speaker 1: many women leading private equity firms, and so she's doing 758 00:38:59,560 --> 00:39:02,080 Speaker 1: kind of like the keynote like end speech at this 759 00:39:02,120 --> 00:39:04,800 Speaker 1: big conference. There are four conferences down there in Miami 760 00:39:05,160 --> 00:39:08,680 Speaker 1: starting this weekend. Morgan Stanley's Big conference, a huge prime broker, 761 00:39:09,120 --> 00:39:12,600 Speaker 1: the m f A conference, that Eye Connections conference. There's 762 00:39:12,640 --> 00:39:15,280 Speaker 1: Tiger twenty one, which is a lot high worth individuals. 763 00:39:15,280 --> 00:39:17,799 Speaker 1: Oh yeah, that's right. We talked to the Tiger guy. Yeah. Yeah, 764 00:39:17,840 --> 00:39:19,719 Speaker 1: So I'm gonna be kind of hanging out in a 765 00:39:19,719 --> 00:39:25,000 Speaker 1: lot of lobbies. Nobody better that we would want down 766 00:39:25,000 --> 00:39:28,080 Speaker 1: there than no. I'm excited. Uh and I'm Sanale is 767 00:39:28,120 --> 00:39:29,880 Speaker 1: one of the best reporters that I know here. Can 768 00:39:29,920 --> 00:39:32,520 Speaker 1: I carry your bags? Can I I would do that? Guys? 769 00:39:32,760 --> 00:39:35,160 Speaker 1: So nice to me. Well, we look forward to we 770 00:39:35,239 --> 00:39:38,359 Speaker 1: look forward to getting the download the skinny or whatever 771 00:39:38,400 --> 00:39:41,040 Speaker 1: you call it from Miami from Shanale next week when 772 00:39:41,080 --> 00:39:43,200 Speaker 1: she is down in Miami. You'll call in right, call 773 00:39:43,280 --> 00:39:46,879 Speaker 1: in excellent, excellent. Mike mcgloane's down there too. Maybe we'll 774 00:39:46,880 --> 00:39:49,000 Speaker 1: get him to go over there and do some you know, 775 00:39:49,120 --> 00:39:51,440 Speaker 1: frontline reporting on maybe you know, the E T F 776 00:39:51,520 --> 00:39:53,799 Speaker 1: S and crypto and all. Maybe Mike mcglogan can talk 777 00:39:53,840 --> 00:40:00,000 Speaker 1: to Kim Kardashian exactly. I'm sure you like that. Thanks 778 00:40:00,000 --> 00:40:03,440 Speaker 1: for listening to the Bloomberg Markets Podcast. You can subscribe 779 00:40:03,480 --> 00:40:07,239 Speaker 1: and listen to interviews with Apple Podcasts or whatever podcast 780 00:40:07,239 --> 00:40:10,799 Speaker 1: platform you prefer. I'm Matt Miller. I'm on Twitter at 781 00:40:10,840 --> 00:40:14,440 Speaker 1: Matt Miller three. Put on fall Sweeney I'm on Twitter 782 00:40:14,520 --> 00:40:17,359 Speaker 1: at pt Sweeney. Before the podcast, you can always catch 783 00:40:17,400 --> 00:40:18,959 Speaker 1: us worldwide at Bloomberg Radio