1 00:00:00,120 --> 00:00:04,360 Speaker 1: Bloomberg is now on your dashboard with Apple CarPlay and 2 00:00:04,360 --> 00:00:08,160 Speaker 1: Android Auto. It gives you access to every Bloomberg podcast, 3 00:00:08,280 --> 00:00:11,560 Speaker 1: live audio feeds from Bloomberg Radio, print stories from Bloomberg 4 00:00:11,640 --> 00:00:14,920 Speaker 1: News in audio form, and the latest headlines of the 5 00:00:14,920 --> 00:00:18,600 Speaker 1: click of a button with Bloomberg News. Now it's free 6 00:00:18,680 --> 00:00:21,439 Speaker 1: with the latest version of the Bloomberg Business App. That's 7 00:00:21,680 --> 00:00:24,400 Speaker 1: the Bloomberg Business App. Get it on your phone in 8 00:00:24,440 --> 00:00:27,760 Speaker 1: the Apple App Store or on Google Play. Just download 9 00:00:27,800 --> 00:00:30,560 Speaker 1: the app, connect your phone to your car and get started. 10 00:00:30,960 --> 00:00:34,400 Speaker 1: And it's all presented by our sponsor, Interactive Brokers. 11 00:00:35,400 --> 00:00:38,600 Speaker 2: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney. Alongside 12 00:00:38,640 --> 00:00:39,800 Speaker 2: my co host Matt Miller. 13 00:00:40,200 --> 00:00:44,279 Speaker 1: Every business day we bring you interviews from CEOs, market pros, 14 00:00:44,320 --> 00:00:48,160 Speaker 1: and Bloomberg experts, along with essential market moven News. 15 00:00:48,720 --> 00:00:51,839 Speaker 2: Find the Bloomberg Markets Podcast called Apple Podcasts or wherever 16 00:00:51,960 --> 00:00:54,560 Speaker 2: you listen to podcasts, and at Bloomberg dot Com slash 17 00:00:54,600 --> 00:00:57,560 Speaker 2: podcast catch funds. You know them, you love them? How 18 00:00:57,680 --> 00:01:00,000 Speaker 2: is performance in twenty twenty three? What to look for 19 00:01:00,120 --> 00:01:02,960 Speaker 2: in twenty twenty four? Elana Weinstein Joints and she's the 20 00:01:02,960 --> 00:01:07,440 Speaker 2: founder and CEO of the IDW Group and Bloomberg. Shanali Basset, 21 00:01:07,480 --> 00:01:09,679 Speaker 2: who covers all of Wall Street, joins, is how cool 22 00:01:09,760 --> 00:01:12,160 Speaker 2: is that Shanale that talks in hedge funds. 23 00:01:12,200 --> 00:01:14,560 Speaker 3: I've been waiting to have this conversation all week because 24 00:01:14,640 --> 00:01:17,360 Speaker 3: what a weird year, Paul in hedge funds, and Alana 25 00:01:17,440 --> 00:01:21,200 Speaker 3: is so plugged into this universe. Let's talk about performance first, though, 26 00:01:21,280 --> 00:01:23,880 Speaker 3: because what's stunning is you've seen this massive melt up 27 00:01:23,880 --> 00:01:27,080 Speaker 3: in markets, and the hedge funds, by and large across 28 00:01:27,120 --> 00:01:29,920 Speaker 3: the industry really have not kept up. Bloomberg's All Hedge 29 00:01:30,040 --> 00:01:32,880 Speaker 3: Index is only up about four point eight percent through 30 00:01:32,920 --> 00:01:35,800 Speaker 3: the year so far, and the multi strategies are supposed 31 00:01:35,800 --> 00:01:38,120 Speaker 3: to be the kings of the castle here are only 32 00:01:38,240 --> 00:01:41,400 Speaker 3: up less than three percent on the year. Alana, what's 33 00:01:41,440 --> 00:01:41,840 Speaker 3: going on? 34 00:01:42,319 --> 00:01:46,200 Speaker 4: Okay, So let me unpack those numbers. First off, the 35 00:01:46,200 --> 00:01:48,280 Speaker 4: hedge fund index, I think, is a blend of everything, 36 00:01:48,440 --> 00:01:50,880 Speaker 4: So we need to kind of go strategy by strategy 37 00:01:50,920 --> 00:01:54,320 Speaker 4: to really pull out what the themes are. Long short equities, 38 00:01:54,520 --> 00:01:58,000 Speaker 4: which had a really tough twenty one and twenty two. Finally, 39 00:01:58,040 --> 00:02:01,160 Speaker 4: Shanali had performance. I am please to sit here and 40 00:02:01,160 --> 00:02:03,520 Speaker 4: say something positive, which I have not been able to do. 41 00:02:03,600 --> 00:02:06,280 Speaker 4: For the last couple of years, those funds, which are 42 00:02:06,320 --> 00:02:09,200 Speaker 4: a big percentage of the hedge fund universe, the directional 43 00:02:09,360 --> 00:02:13,600 Speaker 4: concentrated Tiger cubs and similar are up as much as 44 00:02:13,840 --> 00:02:18,120 Speaker 4: twenty percent, thirty percent, even forty percent. The issue there, 45 00:02:18,560 --> 00:02:21,840 Speaker 4: which is really important is even though they're up that much, 46 00:02:22,000 --> 00:02:24,840 Speaker 4: they still have for the most part, an enormous high 47 00:02:24,919 --> 00:02:27,840 Speaker 4: watermark to have to get out of. And let's just 48 00:02:27,880 --> 00:02:31,080 Speaker 4: say it's a fund that's up thirty five percent this year. Okay, 49 00:02:31,919 --> 00:02:34,359 Speaker 4: you may think, and they were down about fifty percent 50 00:02:34,440 --> 00:02:37,720 Speaker 4: between twenty one and twenty two. You may think, okay, 51 00:02:37,960 --> 00:02:40,760 Speaker 4: ye'ar one this year, up thirty five they can get 52 00:02:40,760 --> 00:02:43,360 Speaker 4: out of that remaining fifteen or twenty next year and 53 00:02:43,400 --> 00:02:47,840 Speaker 4: then it's smooth sailing. But the reality is their AUM 54 00:02:48,000 --> 00:02:50,840 Speaker 4: maybe was thirteen billion at the beginning of twenty one, 55 00:02:51,040 --> 00:02:53,680 Speaker 4: it's six and a half billion now, So that thirty 56 00:02:53,720 --> 00:02:56,840 Speaker 4: five percent is on a much smaller base of capital, 57 00:02:57,120 --> 00:02:59,679 Speaker 4: and it's actually as a result, so there may very 58 00:02:59,760 --> 00:03:03,639 Speaker 4: much fewer dollars made than it would have been had 59 00:03:03,680 --> 00:03:06,440 Speaker 4: they made that money back up before they had the losses, 60 00:03:06,520 --> 00:03:09,120 Speaker 4: And as a result, there's really another sixty five percent 61 00:03:09,200 --> 00:03:16,000 Speaker 4: to go, So that's a huge headwind for that strategy. 62 00:03:16,160 --> 00:03:20,040 Speaker 3: How much more excitement is there around the industry, especially 63 00:03:20,080 --> 00:03:23,919 Speaker 3: because many of them have not met the market performance still, 64 00:03:24,240 --> 00:03:26,079 Speaker 3: and so even if they're up, they have two issues 65 00:03:26,080 --> 00:03:27,920 Speaker 3: here to your point the high water mark and the 66 00:03:28,000 --> 00:03:30,480 Speaker 3: issue is you could just invest in the S and 67 00:03:30,520 --> 00:03:32,720 Speaker 3: B or the Nasdaq or even the socks up sixty 68 00:03:32,760 --> 00:03:35,400 Speaker 3: five percent this year, how does that set them up 69 00:03:35,440 --> 00:03:39,680 Speaker 3: into appetite investor appetite for new hedge fund allocations next year. 70 00:03:39,800 --> 00:03:42,440 Speaker 4: Let me also touch on the multi managers which you cited. 71 00:03:42,720 --> 00:03:45,480 Speaker 4: It is true that blended it's not a great result, 72 00:03:45,760 --> 00:03:49,640 Speaker 4: but unpacking that, there's a lot of dispersion in that result. 73 00:03:49,800 --> 00:03:54,480 Speaker 4: If you look at the more established multi managers that 74 00:03:54,560 --> 00:03:56,920 Speaker 4: have been around a while and have built a mouse 75 00:03:56,960 --> 00:04:00,440 Speaker 4: trap that is very difficult to compete with. Fun like 76 00:04:00,680 --> 00:04:04,400 Speaker 4: Citadel leading the pack up fifteen percent, which is a 77 00:04:04,480 --> 00:04:07,920 Speaker 4: very different number than what you cited, or Millennium point 78 00:04:08,000 --> 00:04:12,080 Speaker 4: seventy two up double digits, and then the rest are struggling, 79 00:04:12,240 --> 00:04:14,040 Speaker 4: and that has a lot to do. So when we 80 00:04:14,200 --> 00:04:17,960 Speaker 4: to answer your question LP appetite, you know, the reality 81 00:04:18,120 --> 00:04:21,400 Speaker 4: is the reason the other multi managers grew so quickly 82 00:04:21,720 --> 00:04:25,400 Speaker 4: is because the more established players were either closed or 83 00:04:26,120 --> 00:04:29,960 Speaker 4: very deliberate about how they grew. And if you can't 84 00:04:30,000 --> 00:04:32,359 Speaker 4: allocate to the more established guys, or at least not 85 00:04:32,440 --> 00:04:34,840 Speaker 4: at the level you'd like, then you end up pouring 86 00:04:34,880 --> 00:04:38,000 Speaker 4: money into all these other funds that grew in the 87 00:04:38,080 --> 00:04:41,560 Speaker 4: last two years hand over fist. And when I say quickly, 88 00:04:41,800 --> 00:04:45,840 Speaker 4: I mean between three and eight x. Think about that 89 00:04:46,279 --> 00:04:49,200 Speaker 4: over a twenty four to thirty six month period. It 90 00:04:49,320 --> 00:04:53,800 Speaker 4: is very difficult to deploy that kind of capital growth 91 00:04:54,200 --> 00:04:58,240 Speaker 4: and hire a winning team. In order to do that, 92 00:04:58,720 --> 00:05:02,000 Speaker 4: talent is really fugual to hire and attract. No one 93 00:05:02,040 --> 00:05:05,480 Speaker 4: knows that better than myself and my team, and so 94 00:05:06,720 --> 00:05:09,560 Speaker 4: you can't get from one billion to eight billion, hire 95 00:05:09,600 --> 00:05:13,560 Speaker 4: one hundred and fifty pms and expect a great result. 96 00:05:13,760 --> 00:05:15,400 Speaker 4: There's a lot more that goes into this. 97 00:05:15,720 --> 00:05:18,240 Speaker 5: Okay, well, let's talk about talent, because I feel like 98 00:05:18,320 --> 00:05:20,440 Speaker 5: it was, you know, pandemic era where all we were 99 00:05:20,480 --> 00:05:23,960 Speaker 5: talking about was the competition to talent and compensation wars 100 00:05:24,000 --> 00:05:27,120 Speaker 5: who can offer the best starting salaries and the biggest bonuses. 101 00:05:27,200 --> 00:05:29,400 Speaker 5: We know that it has been a more difficult year 102 00:05:29,440 --> 00:05:33,440 Speaker 5: in terms of actually people getting paid, and we're probably 103 00:05:33,520 --> 00:05:35,880 Speaker 5: finding that out here at the tail end of the year. 104 00:05:35,960 --> 00:05:39,400 Speaker 5: So what is your view here on compensation and how 105 00:05:39,560 --> 00:05:42,080 Speaker 5: these firms are making themselves attractive. 106 00:05:42,600 --> 00:05:46,200 Speaker 4: I think compensation kind of falls into one of two buckets. 107 00:05:46,279 --> 00:05:49,520 Speaker 4: It's either formulaic and for example, if you're at a 108 00:05:49,560 --> 00:05:52,080 Speaker 4: multi manager with a pass through, you know exactly what 109 00:05:52,160 --> 00:05:55,560 Speaker 4: you're getting paid. There's no mystery there. Or you're at 110 00:05:55,560 --> 00:06:00,200 Speaker 4: a fund which is like a single manager, where if 111 00:06:00,200 --> 00:06:02,560 Speaker 4: you're a senior person, you have points in the fund 112 00:06:02,920 --> 00:06:05,599 Speaker 4: and then there's maybe a jump ball with respect to 113 00:06:05,800 --> 00:06:08,000 Speaker 4: being able to reward you on top of that, but 114 00:06:08,080 --> 00:06:10,840 Speaker 4: to the extent it's a fund that has a high 115 00:06:10,920 --> 00:06:14,400 Speaker 4: high water mark. No, there hasn't been a performance fee, 116 00:06:14,440 --> 00:06:17,320 Speaker 4: for example, the long short equity managers in twenty one 117 00:06:17,480 --> 00:06:19,839 Speaker 4: for the most part, there wasn't one in twenty two, 118 00:06:20,279 --> 00:06:23,760 Speaker 4: and even though they put up this incredible performance in 119 00:06:23,960 --> 00:06:27,320 Speaker 4: twenty three, they don't have a performance fee with which 120 00:06:27,360 --> 00:06:30,440 Speaker 4: to pay those people. And so to answer your question, 121 00:06:30,640 --> 00:06:34,520 Speaker 4: I think there's going to be a lot more frustration 122 00:06:35,000 --> 00:06:37,520 Speaker 4: this year than there even has been the last couple 123 00:06:37,560 --> 00:06:40,359 Speaker 4: of years. Those people are going to get paid more 124 00:06:40,440 --> 00:06:43,359 Speaker 4: because the manager is going the founder is going to 125 00:06:43,440 --> 00:06:46,120 Speaker 4: feel he or she needs to reach more deeply into 126 00:06:46,120 --> 00:06:48,560 Speaker 4: the management fee to pay them. But when you're someone 127 00:06:48,600 --> 00:06:51,599 Speaker 4: who's put up hundreds of millions of dollars, even billions 128 00:06:51,640 --> 00:06:54,839 Speaker 4: of dollars, the dispersion between what you're actually going to 129 00:06:54,839 --> 00:06:57,000 Speaker 4: get paid this year and what you've produced is going 130 00:06:57,040 --> 00:07:00,400 Speaker 4: to be very frustrating versus in previous Here is where 131 00:07:00,760 --> 00:07:03,120 Speaker 4: there wasn't performance for the fund. I didn't put up 132 00:07:03,160 --> 00:07:06,280 Speaker 4: performance as an individual, So whatever I get paid, I'm 133 00:07:06,320 --> 00:07:08,880 Speaker 4: kind of okay with because that's the ethos of this industry. 134 00:07:08,920 --> 00:07:12,520 Speaker 4: It's paid for performance. Now I've put up tremendous performance, 135 00:07:12,880 --> 00:07:15,800 Speaker 4: and my founder isn't really paying me something that feels 136 00:07:15,840 --> 00:07:18,920 Speaker 4: in any way proportionate to what I should get paid. 137 00:07:18,960 --> 00:07:21,480 Speaker 4: And I want to also highlight as much as some 138 00:07:21,520 --> 00:07:24,520 Speaker 4: of these funds lost a significant amount of AUM, we're 139 00:07:24,560 --> 00:07:27,920 Speaker 4: still talking about falls from Grace of thirty billion to 140 00:07:27,960 --> 00:07:31,320 Speaker 4: twenty billion, or in the case of Tiger Global, one 141 00:07:31,400 --> 00:07:34,520 Speaker 4: hundred billion to fifty five billion. But think about the 142 00:07:34,600 --> 00:07:37,560 Speaker 4: management fee. On fifty five billion, that's still a billion 143 00:07:37,920 --> 00:07:41,480 Speaker 4: of fees, or on twenty billion, that's four hundred million 144 00:07:41,480 --> 00:07:45,000 Speaker 4: of fees. So and these teams are relatively lean, particularly 145 00:07:45,080 --> 00:07:49,400 Speaker 4: at senior levels, and everyone knows that. So it's it's early, 146 00:07:49,480 --> 00:07:52,200 Speaker 4: still a visa vcamp. But what we're hearing, even though 147 00:07:52,240 --> 00:07:55,280 Speaker 4: the number is better, is an increased level of frustration, 148 00:07:55,440 --> 00:07:57,960 Speaker 4: and I think there's going to be more vulnerability at 149 00:07:58,000 --> 00:07:59,720 Speaker 4: these funds as a result. 150 00:08:00,040 --> 00:08:01,720 Speaker 2: When I left the street in the mid two thousands, 151 00:08:01,720 --> 00:08:03,960 Speaker 2: I studied long and hard about whether to transition to 152 00:08:03,960 --> 00:08:06,760 Speaker 2: the hedge fund business. And what I concluded then was 153 00:08:06,800 --> 00:08:11,320 Speaker 2: that long short equity, no alpha left done game had 154 00:08:11,360 --> 00:08:13,160 Speaker 2: been played out, so I can go Bloomberg. 155 00:08:13,520 --> 00:08:16,200 Speaker 4: So my question is how lucky we are? 156 00:08:16,400 --> 00:08:18,600 Speaker 2: And I was absolutely correct that the numbers bear that out. 157 00:08:19,080 --> 00:08:21,400 Speaker 2: My question is if I'm a really good trader, I 158 00:08:21,440 --> 00:08:24,560 Speaker 2: don't know, bonds, currency, something at Morgan, Stanle There, Goldman, Sachs. 159 00:08:24,880 --> 00:08:26,680 Speaker 2: Back in the day, I could just leave and go 160 00:08:26,760 --> 00:08:28,720 Speaker 2: raise a couple of billion dollars and then boom. That 161 00:08:28,880 --> 00:08:31,760 Speaker 2: was my path. Does that still exist or does a 162 00:08:31,760 --> 00:08:33,960 Speaker 2: lot of the new money coming into hedge funds go 163 00:08:34,000 --> 00:08:36,240 Speaker 2: to the point seventy two's and the citadels. 164 00:08:37,000 --> 00:08:37,200 Speaker 6: Well. 165 00:08:37,200 --> 00:08:40,280 Speaker 4: As I said earlier, some of those established managers are 166 00:08:40,280 --> 00:08:44,240 Speaker 4: closed or very carefully accepting new capital. I believe me, 167 00:08:44,280 --> 00:08:46,440 Speaker 4: there's a line around the block to allocate to those. 168 00:08:46,840 --> 00:08:50,000 Speaker 4: But two things. One the days of leaving the cell 169 00:08:50,080 --> 00:08:51,920 Speaker 4: side to go start a hedge fund, I think you'd 170 00:08:51,920 --> 00:08:54,160 Speaker 4: have to be completely nuts to try to attempt to 171 00:08:54,160 --> 00:08:56,160 Speaker 4: do that. Good luck getting a job at a hedge 172 00:08:56,160 --> 00:08:58,400 Speaker 4: fund period, because you've spent all this time on the 173 00:08:58,400 --> 00:09:01,600 Speaker 4: cell side and back in the day. Sorry not to 174 00:09:01,640 --> 00:09:04,880 Speaker 4: age you, but we got there. You could take risks, 175 00:09:04,960 --> 00:09:07,160 Speaker 4: sure right, you could take risk. You can't do that 176 00:09:07,200 --> 00:09:10,200 Speaker 4: post the financial crisis. So if you're still sitting on 177 00:09:10,280 --> 00:09:12,520 Speaker 4: the cell side, the reality is you're in more of 178 00:09:12,520 --> 00:09:14,719 Speaker 4: a managerial role than you are a risk taking role. 179 00:09:14,760 --> 00:09:18,040 Speaker 4: At a senior level, I think it's candidly become more 180 00:09:18,080 --> 00:09:22,560 Speaker 4: difficult to even launch coming from most hedge funds. Now, 181 00:09:22,559 --> 00:09:26,040 Speaker 4: coming from a top flight hedge fund which has a 182 00:09:26,080 --> 00:09:30,000 Speaker 4: great established track record and is doing well today, that's 183 00:09:30,040 --> 00:09:32,680 Speaker 4: exciting to LPs, but that group of funds has become 184 00:09:32,800 --> 00:09:35,520 Speaker 4: fewer and fewer. And yes, coming out of a fund 185 00:09:35,960 --> 00:09:38,520 Speaker 4: like a Citadel or a point seventy two sets you 186 00:09:38,640 --> 00:09:41,440 Speaker 4: up in good stead to be able to launch successfully, 187 00:09:41,760 --> 00:09:45,080 Speaker 4: But think about coming out of some of the tiger 188 00:09:45,120 --> 00:09:47,840 Speaker 4: cubs are related now that are still well below their 189 00:09:47,880 --> 00:09:51,199 Speaker 4: high water mark. That's not exactly an exciting value proposition 190 00:09:51,280 --> 00:09:53,200 Speaker 4: to Lpece to say, oh, I'm going to be different, 191 00:09:53,320 --> 00:09:56,520 Speaker 4: you know, and don't worry about the last twenty one 192 00:09:56,559 --> 00:10:00,080 Speaker 4: and twenty two. You know, we're doing better now and 193 00:10:01,840 --> 00:10:06,800 Speaker 4: my approach to risk management and managing volatility is better 194 00:10:06,840 --> 00:10:07,840 Speaker 4: than what you've experienced. 195 00:10:07,960 --> 00:10:10,240 Speaker 3: Also em bettered in Paul's question here is there's a 196 00:10:10,440 --> 00:10:14,360 Speaker 3: long short equation has been doing better, but macro has 197 00:10:14,440 --> 00:10:17,840 Speaker 3: been such an exciting prospect. Currencies, fixed income, the interest 198 00:10:17,920 --> 00:10:21,360 Speaker 3: rate environment so drastically changing. When you're looking at the 199 00:10:21,520 --> 00:10:25,680 Speaker 3: types of managers. How much is the macro story part 200 00:10:25,679 --> 00:10:27,760 Speaker 3: of twenty twenty four and what does that mean for 201 00:10:27,800 --> 00:10:28,680 Speaker 3: the talent story. 202 00:10:28,800 --> 00:10:31,960 Speaker 4: That's a good question, Shanali, because every year I feel 203 00:10:31,960 --> 00:10:34,800 Speaker 4: like it's a different story in macro. Twenty two is great, 204 00:10:35,720 --> 00:10:38,720 Speaker 4: twenty three has not been so great, So we'll see 205 00:10:38,760 --> 00:10:42,760 Speaker 4: what twenty four holds. It's a strategy which embodies as 206 00:10:42,760 --> 00:10:47,160 Speaker 4: we've seen a lot of volatility, so I think it's 207 00:10:47,200 --> 00:10:53,679 Speaker 4: TBD in terms of how they will perform. And you know, 208 00:10:53,760 --> 00:11:00,199 Speaker 4: talent follows where there is an ecosystem which can navigate 209 00:11:00,280 --> 00:11:01,920 Speaker 4: that volatility best. 210 00:11:02,559 --> 00:11:05,080 Speaker 2: We're now in an environment, an entry environment that haven't 211 00:11:05,080 --> 00:11:07,000 Speaker 2: been in a long time where rates went up so 212 00:11:07,120 --> 00:11:09,080 Speaker 2: much now they're going to be coming down. Is there 213 00:11:09,120 --> 00:11:11,800 Speaker 2: a feeling within the hedge fund community that certain strategies 214 00:11:11,840 --> 00:11:14,240 Speaker 2: are going to work better in twenty four than maybe 215 00:11:14,240 --> 00:11:15,280 Speaker 2: the past couple of years. 216 00:11:15,960 --> 00:11:21,719 Speaker 4: Well, I think the reality is the uh, you know, 217 00:11:22,360 --> 00:11:27,079 Speaker 4: the bar has gone up in terms of not just 218 00:11:27,080 --> 00:11:30,559 Speaker 4: just the market environment, but also what LPs expect for 219 00:11:30,640 --> 00:11:33,600 Speaker 4: what they're paying. And it's really the focus is of 220 00:11:33,640 --> 00:11:38,400 Speaker 4: course on alpha and running in a way which neutralizes 221 00:11:38,559 --> 00:11:41,640 Speaker 4: a lot of the market risk inherent right, that has 222 00:11:41,679 --> 00:11:46,000 Speaker 4: whip sawed a lot of the return for funds. Factor 223 00:11:46,080 --> 00:11:52,000 Speaker 4: rotations really has obscured great fundamental stock picking and so 224 00:11:52,160 --> 00:12:01,360 Speaker 4: being somewhere which can isolate what is value, growth, momentum, 225 00:12:02,400 --> 00:12:05,839 Speaker 4: macro cross currents and hedge those things out so that 226 00:12:05,920 --> 00:12:10,720 Speaker 4: great idiosyncratic alpha driven stock picking can shine through. That 227 00:12:10,720 --> 00:12:13,000 Speaker 4: that's the most important thing, because then it doesn't matter 228 00:12:13,000 --> 00:12:14,040 Speaker 4: what the market's doing. 229 00:12:14,120 --> 00:12:14,280 Speaker 2: You know. 230 00:12:14,559 --> 00:12:17,240 Speaker 3: Interestingly, I want to comment on something you had talked 231 00:12:17,280 --> 00:12:19,280 Speaker 3: about a little earlier. We were talking about the success of 232 00:12:19,440 --> 00:12:21,840 Speaker 3: sit it out in multi strat but it's also worth 233 00:12:21,880 --> 00:12:25,120 Speaker 3: talking about the undertone of the other side of that story. 234 00:12:25,160 --> 00:12:28,959 Speaker 3: You had mentioned that some firms grew so fast, so quickly, 235 00:12:29,360 --> 00:12:31,880 Speaker 3: and the poster child of that this year was really 236 00:12:31,920 --> 00:12:34,680 Speaker 3: Sean Feld. This idea that a lot of money was pulled, 237 00:12:34,720 --> 00:12:37,480 Speaker 3: billions of dollars was pulled from the firm. You know, 238 00:12:37,520 --> 00:12:40,000 Speaker 3: they really had to look to raise capital to really 239 00:12:40,080 --> 00:12:43,920 Speaker 3: fill that whole. They even considered merging with another large 240 00:12:44,040 --> 00:12:49,120 Speaker 3: multi strategy firm. Is Seanfeld alone? Is there other struggles 241 00:12:49,200 --> 00:12:51,320 Speaker 3: that we're seeing in firms like Schonfeld and how does 242 00:12:51,320 --> 00:12:52,400 Speaker 3: that play out into next year? 243 00:12:52,440 --> 00:12:52,840 Speaker 7: Totally? 244 00:12:53,240 --> 00:12:56,079 Speaker 4: You know, all these funds that grew so quickly, whether 245 00:12:56,960 --> 00:12:59,920 Speaker 4: I mean, I'm just not to just to talk about 246 00:13:00,040 --> 00:13:04,480 Speaker 4: who's grown hand over fist. These funds are multiples aum 247 00:13:04,559 --> 00:13:08,079 Speaker 4: wise where they were even two years ago. Lmr Hudson 248 00:13:08,120 --> 00:13:13,120 Speaker 4: Bay sinctive Schoenfeld, as you mentioned, fruition, and you can't 249 00:13:13,120 --> 00:13:15,920 Speaker 4: do that. You have to be far more deliberate with 250 00:13:16,000 --> 00:13:18,600 Speaker 4: respect to thinking about what is the end state, how 251 00:13:18,600 --> 00:13:22,359 Speaker 4: many pms do I want so that I can maximize 252 00:13:22,360 --> 00:13:27,679 Speaker 4: idea generation and minimize crowding risk and then execute against 253 00:13:27,679 --> 00:13:31,560 Speaker 4: that and only grow as you've been able to attract talent, 254 00:13:31,720 --> 00:13:34,560 Speaker 4: and I don't mean bodies, I mean talent, and talent 255 00:13:34,720 --> 00:13:39,360 Speaker 4: always has options, right if you're that good. The reality is, 256 00:13:40,440 --> 00:13:44,160 Speaker 4: as one of those multimanagers that grew very quickly, you're 257 00:13:44,280 --> 00:13:47,760 Speaker 4: up against the Citadels, the point seventy two's, the Ballyasns 258 00:13:47,800 --> 00:13:50,520 Speaker 4: of this world, the millenniums competing for those people. So 259 00:13:50,640 --> 00:13:54,640 Speaker 4: what are you offering that's really a differentiated value proposition? 260 00:13:54,960 --> 00:13:56,840 Speaker 4: And when I hear of a fund and this is 261 00:13:56,880 --> 00:14:00,000 Speaker 4: an actual fund that grew eight x in that period 262 00:14:00,240 --> 00:14:02,840 Speaker 4: and hired and is now has one hundred and fifty 263 00:14:02,880 --> 00:14:06,480 Speaker 4: pms with no real team underneath any of those pms 264 00:14:06,960 --> 00:14:10,120 Speaker 4: and the pms they attracted or really science experiments. Most 265 00:14:10,160 --> 00:14:12,760 Speaker 4: of them have not been pms before, don't know how 266 00:14:12,760 --> 00:14:15,720 Speaker 4: to run risk in a market neutral model, and there's 267 00:14:15,760 --> 00:14:18,160 Speaker 4: no real resources at the firm to make them better. 268 00:14:18,520 --> 00:14:21,760 Speaker 4: That's a recipe for disaster. And what we've learned is 269 00:14:22,160 --> 00:14:26,560 Speaker 4: being a successful multi manager is not as simple as capital, 270 00:14:26,760 --> 00:14:29,920 Speaker 4: a pass through and PMS. It is far more nuanced 271 00:14:29,960 --> 00:14:30,320 Speaker 4: than that. 272 00:14:31,240 --> 00:14:33,240 Speaker 5: All right, really great to have you join us today, 273 00:14:33,240 --> 00:14:35,920 Speaker 5: Alana and Shanali Basik as well. Thank you so much. 274 00:14:35,960 --> 00:14:44,200 Speaker 5: We really appreciated. Alana Einstein there from IWDIDW group. It's 275 00:14:44,200 --> 00:14:46,200 Speaker 5: the holiday week, guys, We're almost through it. We're like 276 00:14:46,240 --> 00:14:48,560 Speaker 5: almost at the end of the year. Thank you very much. 277 00:14:50,120 --> 00:14:53,960 Speaker 8: You're listening to the Team Can't Live program Bloomberg Markets 278 00:14:54,000 --> 00:14:57,080 Speaker 8: weekdays at ten am Eastern on Bloomberg dot Com, the 279 00:14:57,160 --> 00:15:01,160 Speaker 8: iHeartRadio app, and the Bloomberg Business app non demand wherever 280 00:15:01,200 --> 00:15:02,360 Speaker 8: you get your podcasts. 281 00:15:04,120 --> 00:15:07,880 Speaker 2: I want to pivot to Cam Harvey. He's professor finance 282 00:15:07,960 --> 00:15:11,360 Speaker 2: at the Fuqua School of Business at Duke University, my 283 00:15:11,480 --> 00:15:14,520 Speaker 2: alma mater. I took a couple classes from Professor Harvey. 284 00:15:14,560 --> 00:15:17,760 Speaker 2: I survived. I'm proud to say I can't claim any 285 00:15:17,920 --> 00:15:21,480 Speaker 2: more success than survival, but some good stuff there. Hey, Cam, 286 00:15:21,560 --> 00:15:23,600 Speaker 2: I look at the ten year treasury. I mean, I'm 287 00:15:23,600 --> 00:15:25,440 Speaker 2: not a bond guy, but I mean, just a couple 288 00:15:25,480 --> 00:15:27,800 Speaker 2: of coffee ago, the ten year treasury was at five percent. 289 00:15:27,840 --> 00:15:30,840 Speaker 2: Now we're at three point eighty two percent. What's going 290 00:15:30,880 --> 00:15:31,440 Speaker 2: on out there? 291 00:15:32,880 --> 00:15:36,240 Speaker 9: Yeah, So it overall is very good news that that 292 00:15:36,320 --> 00:15:41,520 Speaker 9: rate has gone down, and I think that that investors 293 00:15:41,560 --> 00:15:46,960 Speaker 9: and consumers have revised their expectations of inflation and this 294 00:15:47,000 --> 00:15:49,960 Speaker 9: is good news. So it means that the Fed has 295 00:15:50,000 --> 00:15:53,760 Speaker 9: stopped and is now talking about decreasing rates, and that 296 00:15:53,920 --> 00:15:58,200 Speaker 9: pressure on the long term rates seems to abate it. 297 00:15:59,040 --> 00:16:03,800 Speaker 2: So I mean the market's pricing in multiple rate cuts 298 00:16:04,120 --> 00:16:07,000 Speaker 2: for twenty twenty forty. I think the market's maybe a 299 00:16:07,000 --> 00:16:09,080 Speaker 2: little over at skis here or is that something that 300 00:16:09,120 --> 00:16:10,360 Speaker 2: the Fed should be considering? 301 00:16:12,400 --> 00:16:12,680 Speaker 6: Well? 302 00:16:13,920 --> 00:16:16,160 Speaker 9: Last time, Ozon I was making the case that the 303 00:16:16,600 --> 00:16:21,440 Speaker 9: cuts should have begun in twenty twenty three. So the 304 00:16:21,520 --> 00:16:25,440 Speaker 9: Fed overshot by pushing the short rates up so high. 305 00:16:26,200 --> 00:16:30,400 Speaker 9: And the sooner that they take down some of these cuts, 306 00:16:30,480 --> 00:16:34,240 Speaker 9: the better, because it does cause a lot of stress 307 00:16:34,320 --> 00:16:39,280 Speaker 9: in our economy, and that stress is unnecessary given that inflation, 308 00:16:39,720 --> 00:16:43,360 Speaker 9: according to my calculations, is running well below two percent. 309 00:16:43,600 --> 00:16:46,720 Speaker 10: And professor, let's dig into those calculations because they're fascinating. 310 00:16:46,760 --> 00:16:49,080 Speaker 10: I love reading your work, whether it's across LinkedIn or 311 00:16:49,160 --> 00:16:52,360 Speaker 10: more broadly, and for you, it's housing, right, This is 312 00:16:52,400 --> 00:16:54,560 Speaker 10: where the misinterpretation is coming in. 313 00:16:56,040 --> 00:16:56,560 Speaker 6: Exactly. 314 00:16:56,720 --> 00:17:01,160 Speaker 9: So housing is operating with the lag, so it's not 315 00:17:01,280 --> 00:17:05,600 Speaker 9: like current rents or current housing prices. We change the 316 00:17:05,640 --> 00:17:09,080 Speaker 9: way that inflation is calculated back in nineteen eighty two, 317 00:17:09,760 --> 00:17:13,920 Speaker 9: so before eighty two, it was real time inflation and housing, 318 00:17:14,440 --> 00:17:18,520 Speaker 9: but now it operates with the LAG. So the housing 319 00:17:18,600 --> 00:17:23,959 Speaker 9: inflation that's reported in the CPI is inflation that happened 320 00:17:24,000 --> 00:17:28,679 Speaker 9: a year ago, and that component of inflation is thirty 321 00:17:28,720 --> 00:17:32,920 Speaker 9: five percent of the overall inflation. And the reason inflation 322 00:17:33,000 --> 00:17:36,840 Speaker 9: has been high is that the shelter component has been 323 00:17:36,960 --> 00:17:40,960 Speaker 9: running at seven percent. That's what goes into the CPI, 324 00:17:41,280 --> 00:17:43,720 Speaker 9: but we know that's not the case. If you look 325 00:17:43,760 --> 00:17:48,639 Speaker 9: in real time rental inflation or house price inflation is 326 00:17:48,760 --> 00:17:53,159 Speaker 9: near zero or one percent, And if you recalculate the 327 00:17:53,240 --> 00:17:59,280 Speaker 9: CPI with real time shelter costs, then what you see 328 00:17:59,760 --> 00:18:04,640 Speaker 9: is a real time inflation rate below two percent. And 329 00:18:04,960 --> 00:18:11,439 Speaker 9: this has been very frustrating because this shelter component caused 330 00:18:11,440 --> 00:18:14,800 Speaker 9: the FED to make a mistake early on in declaring 331 00:18:14,920 --> 00:18:20,400 Speaker 9: that housing was no big deal and inflation was transitory, 332 00:18:20,640 --> 00:18:23,920 Speaker 9: and it caused them not to move to keep rates 333 00:18:23,920 --> 00:18:27,479 Speaker 9: so low for so long, and then the same mistake 334 00:18:27,640 --> 00:18:31,760 Speaker 9: was made in twenty twenty three. It was obvious that 335 00:18:32,040 --> 00:18:36,399 Speaker 9: the only reason inflation was high was because of the 336 00:18:36,400 --> 00:18:40,800 Speaker 9: shelter inflation that had happened in the past. Policy needs 337 00:18:40,840 --> 00:18:45,520 Speaker 9: to be executed based upon real time data, not on 338 00:18:45,680 --> 00:18:48,200 Speaker 9: data that happened like a year ago. 339 00:18:48,640 --> 00:18:50,840 Speaker 10: In the world I cover more which is AI we 340 00:18:50,960 --> 00:18:54,000 Speaker 10: say a lot garbage in, garbage out. So how are 341 00:18:54,000 --> 00:18:57,120 Speaker 10: you having conversations as a professor of course over there? 342 00:18:57,400 --> 00:19:00,520 Speaker 10: Do you trying to get this message across to use 343 00:19:00,640 --> 00:19:04,520 Speaker 10: hearing that it's falling on death is or more is 344 00:19:04,560 --> 00:19:06,359 Speaker 10: that are willing to listen to some of the data 345 00:19:06,359 --> 00:19:07,960 Speaker 10: that should be taken into account when it comes to 346 00:19:07,960 --> 00:19:09,120 Speaker 10: in flrastory pressures. 347 00:19:10,119 --> 00:19:13,560 Speaker 9: So this has been a more than a year long 348 00:19:13,640 --> 00:19:16,639 Speaker 9: campaign for me, and again I'm very pleased that the 349 00:19:16,640 --> 00:19:20,280 Speaker 9: FED has changed the conversation. And the sooner they move 350 00:19:20,800 --> 00:19:24,200 Speaker 9: the better. There is still a lot of risk out there. 351 00:19:24,560 --> 00:19:27,840 Speaker 9: So like I know, people are patting the FED on 352 00:19:27,840 --> 00:19:33,639 Speaker 9: the back, congratulations, mission accomplished. No, it's way way too early. 353 00:19:34,560 --> 00:19:38,439 Speaker 9: My inverted Yell curved indicator which is eight out of 354 00:19:38,440 --> 00:19:43,479 Speaker 9: eight in predicting recessions, that's the nineteen sixties. We are 355 00:19:43,560 --> 00:19:47,360 Speaker 9: at the average lead time to a recession. So if 356 00:19:47,359 --> 00:19:50,040 Speaker 9: you look at the last four recessions, the Yell curve 357 00:19:50,080 --> 00:19:55,280 Speaker 9: inverted on average thirteen months before a recession, and we 358 00:19:55,320 --> 00:19:58,960 Speaker 9: are at month thirteen breakdown. We're at the average, So 359 00:19:59,040 --> 00:20:04,160 Speaker 9: again it's too early. There are significant headwinds. The FED 360 00:20:04,200 --> 00:20:08,879 Speaker 9: can help things by reducing the FED funds rate as 361 00:20:08,920 --> 00:20:09,760 Speaker 9: soon as possible. 362 00:20:10,119 --> 00:20:12,000 Speaker 2: Hey, Kim, I know you also do a lot of 363 00:20:12,000 --> 00:20:16,320 Speaker 2: work on blockchain, crypto fintech. You teach a course down 364 00:20:16,359 --> 00:20:19,600 Speaker 2: there at DO Innovation and Crypto Ventures. What's the theme 365 00:20:19,800 --> 00:20:22,040 Speaker 2: that you think we should be on the lookout for 366 00:20:22,040 --> 00:20:23,439 Speaker 2: for twenty twenty four in this space. 367 00:20:24,880 --> 00:20:29,480 Speaker 9: So it's interesting that we've got the two simultaneous disruptions happening. 368 00:20:29,960 --> 00:20:34,240 Speaker 9: So one is then decentralized finance to change our payment 369 00:20:34,320 --> 00:20:39,920 Speaker 9: system and to allow for functionality that we've never had before, 370 00:20:40,359 --> 00:20:43,159 Speaker 9: and we've got AI going on at the same time. 371 00:20:43,760 --> 00:20:46,719 Speaker 9: So I look at this in kind of the big picture, 372 00:20:47,280 --> 00:20:50,080 Speaker 9: and the big picture the thing that worries me a 373 00:20:50,119 --> 00:20:55,640 Speaker 9: lot is just the size of the US debt. So 374 00:20:55,840 --> 00:20:59,639 Speaker 9: the service on that debt is about seven hundred billion 375 00:20:59,680 --> 00:21:03,760 Speaker 9: dollars a year, and that service will increase in twenty 376 00:21:03,800 --> 00:21:07,359 Speaker 9: twenty four to be the second largest spending category for 377 00:21:07,440 --> 00:21:11,320 Speaker 9: the government. So the way to get out of this 378 00:21:11,480 --> 00:21:16,720 Speaker 9: problem is, well, there's three different ways. One way is 379 00:21:16,760 --> 00:21:20,320 Speaker 9: to increase taxes, and that just kills growth and nobody 380 00:21:20,359 --> 00:21:25,000 Speaker 9: wants increased taxes. The second way is to print money 381 00:21:25,080 --> 00:21:27,480 Speaker 9: and to inflate our way out. So you print the 382 00:21:27,480 --> 00:21:29,919 Speaker 9: money to pay off the debt, and that is just 383 00:21:30,000 --> 00:21:33,720 Speaker 9: like a tax also, so nobody really wants that. Nobody 384 00:21:33,760 --> 00:21:37,080 Speaker 9: wants to go back to that inflation. The third way 385 00:21:37,800 --> 00:21:42,080 Speaker 9: is with growth. So if we increase our growth rate, 386 00:21:42,320 --> 00:21:46,239 Speaker 9: then it naturally increases the tax revenue and allows us 387 00:21:46,240 --> 00:21:49,120 Speaker 9: to pay down some of that debt. So we are 388 00:21:49,160 --> 00:21:52,520 Speaker 9: at a cusp of a pivot point, in my opinion, 389 00:21:52,800 --> 00:21:57,000 Speaker 9: in the economy with these two disruptions going on, and 390 00:21:57,400 --> 00:22:01,880 Speaker 9: they potentially allow us to get on a different growth path. 391 00:22:02,200 --> 00:22:05,600 Speaker 9: And it's really important that we do not fumble these 392 00:22:05,720 --> 00:22:12,920 Speaker 9: opportunities by overregulating either decentralized finance or AI. We need 393 00:22:12,960 --> 00:22:17,320 Speaker 9: to embrace these disruptions and to look at the benefits, 394 00:22:17,400 --> 00:22:20,320 Speaker 9: not just the costs. And I'm not saying this is 395 00:22:20,359 --> 00:22:24,919 Speaker 9: without risk. There is risk, but the key thing is 396 00:22:24,960 --> 00:22:27,679 Speaker 9: to get on a higher growth path. I don't think 397 00:22:27,720 --> 00:22:30,240 Speaker 9: it's unreasonable, all. 398 00:22:30,240 --> 00:22:33,600 Speaker 2: Right, Kim, that's great stuff. As always, Campbell Harvey, Professor 399 00:22:33,640 --> 00:22:38,520 Speaker 2: of Finance at the Fuqual School of Business at Duke University, 400 00:22:39,560 --> 00:22:41,359 Speaker 2: Great great Place, highly recommend it. 401 00:22:41,600 --> 00:22:44,680 Speaker 8: You're listening to the tape. Can's our live program Bloomberg 402 00:22:44,760 --> 00:22:48,320 Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, the 403 00:22:48,400 --> 00:22:51,640 Speaker 8: tune in app, Bloomberg dot Com, and the Bloomberg Business App. 404 00:22:51,680 --> 00:22:54,479 Speaker 8: You can also listen live on Amazon Alexa from our 405 00:22:54,520 --> 00:22:59,520 Speaker 8: flagship New York station, Just say Alexa Play Bloomberg eleven thirty. 406 00:23:00,119 --> 00:23:03,600 Speaker 10: We dig into the geopolitical news of the week of 407 00:23:03,680 --> 00:23:06,760 Speaker 10: the month. Of course, the narrative continues to build about 408 00:23:06,760 --> 00:23:09,240 Speaker 10: the Red Sea, and of course Suthi Rebel has been 409 00:23:09,280 --> 00:23:13,000 Speaker 10: seen to me attacking certain vessels and it basically redirecting. 410 00:23:13,040 --> 00:23:17,720 Speaker 10: We understand now half of all containership fleets now currently 411 00:23:17,960 --> 00:23:20,680 Speaker 10: no longer going through the Red Sea. They're avoiding the route. 412 00:23:20,960 --> 00:23:24,480 Speaker 10: This is according to new industry data that overall is 413 00:23:24,520 --> 00:23:26,680 Speaker 10: one that we can probably cooperate with. Our next guest, 414 00:23:26,720 --> 00:23:28,600 Speaker 10: we're please to welcome to the show. Anton Post, who 415 00:23:28,600 --> 00:23:32,640 Speaker 10: CEO Mercury Resources, and you're all about negotiating skills. You're 416 00:23:32,640 --> 00:23:36,160 Speaker 10: about professionals from an industry basis helping you plot out 417 00:23:36,200 --> 00:23:39,960 Speaker 10: your dry cargo issues. When you are looking at such 418 00:23:40,040 --> 00:23:43,080 Speaker 10: a difficult supply chain management conundrum, What do you advise 419 00:23:43,119 --> 00:23:44,240 Speaker 10: at this moment Anton. 420 00:23:44,840 --> 00:23:48,520 Speaker 11: Yeah, thanks Carolyn. So at this point it's a lot 421 00:23:48,520 --> 00:23:53,160 Speaker 11: of wait and see in what's happening with the international coalition. 422 00:23:54,119 --> 00:23:57,720 Speaker 11: We are partners at have Court Gallbery's Melory Alexander. On 423 00:23:57,760 --> 00:24:01,639 Speaker 11: the container side, it's right now a lot of handholding 424 00:24:01,720 --> 00:24:06,119 Speaker 11: with our client base on the commodities sector to guide 425 00:24:06,160 --> 00:24:10,480 Speaker 11: them through what's happening in the markets at this point. 426 00:24:10,480 --> 00:24:15,719 Speaker 11: So we're seeing of course increased freight, increased insurance, a 427 00:24:15,720 --> 00:24:20,760 Speaker 11: lot of uncertainty and mixed signals from ship owners, container 428 00:24:20,880 --> 00:24:26,840 Speaker 11: lines and from governments on what's happening to basically mitigate 429 00:24:27,000 --> 00:24:28,719 Speaker 11: the risk and the threats that are out there from 430 00:24:28,720 --> 00:24:30,280 Speaker 11: these many art groups. 431 00:24:30,440 --> 00:24:32,080 Speaker 10: I mean, yeah, talk to us about some of the 432 00:24:32,160 --> 00:24:34,439 Speaker 10: mitigation of the risks of how the US Navy is 433 00:24:34,520 --> 00:24:36,800 Speaker 10: leading this military partnership. You are out there talking to 434 00:24:36,800 --> 00:24:39,760 Speaker 10: the brocus the ship is the government support authorities. Do 435 00:24:39,840 --> 00:24:43,160 Speaker 10: they take this as some sort of easing of concern 436 00:24:43,520 --> 00:24:45,320 Speaker 10: or are they still wanting to hold back. 437 00:24:46,080 --> 00:24:48,640 Speaker 6: Yeah, we're seeing some easing of concern. 438 00:24:49,320 --> 00:24:53,359 Speaker 11: Chip operators on the container side, like Marrisk CGM are 439 00:24:53,440 --> 00:24:55,880 Speaker 11: starting to signal a return to going into the Red 440 00:24:55,920 --> 00:25:00,600 Speaker 11: Sea and the Suez Canal. Some ship operators on the 441 00:25:00,640 --> 00:25:04,320 Speaker 11: container side like HAPAG Lloyd are still holding back and 442 00:25:04,359 --> 00:25:09,080 Speaker 11: not even announcing any kind of return to that to 443 00:25:09,119 --> 00:25:11,000 Speaker 11: that market on the dry bulk side, which is a 444 00:25:11,040 --> 00:25:13,200 Speaker 11: huge part of a huge part of our. 445 00:25:13,080 --> 00:25:16,119 Speaker 6: Business dry commodities ship owners. 446 00:25:16,200 --> 00:25:19,560 Speaker 11: Right now, it's naturally a time of year where it's 447 00:25:19,880 --> 00:25:23,480 Speaker 11: where things have slowed down. Uh, So we're seeing ship 448 00:25:23,480 --> 00:25:26,679 Speaker 11: owners of the dry bulk side diverting ships that are 449 00:25:26,720 --> 00:25:31,800 Speaker 11: already en route and ship raiding new cargos. 450 00:25:31,359 --> 00:25:32,959 Speaker 6: For January February. 451 00:25:33,000 --> 00:25:37,080 Speaker 11: Timing is more of a let's see what happens after 452 00:25:37,440 --> 00:25:39,960 Speaker 11: New Year's and what's happening on the h on the 453 00:25:40,040 --> 00:25:42,920 Speaker 11: side to U, you know, on the on the news 454 00:25:42,960 --> 00:25:46,920 Speaker 11: to mitigate what's happening there with the with with the 455 00:25:46,960 --> 00:25:49,680 Speaker 11: military response. So of course we've seen the Navy move 456 00:25:49,720 --> 00:25:53,679 Speaker 11: the usswy D Eisenhower into the Gulf of Baden and 457 00:25:53,840 --> 00:25:54,919 Speaker 11: off them Andy Coast. 458 00:25:55,200 --> 00:25:55,320 Speaker 6: Uh. 459 00:25:55,760 --> 00:26:01,119 Speaker 11: This twenty nation coalition that the United States it's announced 460 00:26:01,640 --> 00:26:05,200 Speaker 11: is seemingly coming together, but there's been some hesitancy from 461 00:26:05,240 --> 00:26:07,400 Speaker 11: a couple of you know, some of the US allies 462 00:26:07,440 --> 00:26:10,480 Speaker 11: and some nations have been reluctant to even put their 463 00:26:10,560 --> 00:26:13,880 Speaker 11: name attached to it. Yet so definitely some still some 464 00:26:13,960 --> 00:26:16,560 Speaker 11: concerns and not a lot of comfort. 465 00:26:17,000 --> 00:26:21,920 Speaker 2: Anton. Who ultimately decides whether ship does transit the Red 466 00:26:21,960 --> 00:26:25,600 Speaker 2: Sea and the Suez Is it the insurance company, the 467 00:26:25,640 --> 00:26:28,600 Speaker 2: ship owner, maybe the captain himself or herself. I mean, 468 00:26:28,600 --> 00:26:30,360 Speaker 2: who really at theen today makes that decision? 469 00:26:31,160 --> 00:26:34,320 Speaker 11: Yes, yes, and yes, right, it's a it's a very 470 00:26:34,400 --> 00:26:37,800 Speaker 11: much a combined situation. Ultimately, the master of the ship, 471 00:26:37,840 --> 00:26:41,040 Speaker 11: the capture of the ship has the as the ultimate say, right, 472 00:26:41,080 --> 00:26:44,159 Speaker 11: but they're taking instructions from the ship owner. But in 473 00:26:44,200 --> 00:26:48,280 Speaker 11: this situation, really in all situations regarding commercial and merchant shipping, 474 00:26:48,600 --> 00:26:50,800 Speaker 11: you have many layers on top, right, you have the 475 00:26:50,920 --> 00:26:53,760 Speaker 11: ultimate ship owner, the company that owns the ship. You 476 00:26:53,760 --> 00:26:56,840 Speaker 11: have the master that has ultimate responsibility for the ship. 477 00:26:56,840 --> 00:26:59,120 Speaker 11: And then you have the ship operator, the company that's 478 00:26:59,200 --> 00:27:03,239 Speaker 11: often time chartering the vessel and has some say over it. 479 00:27:03,359 --> 00:27:06,640 Speaker 11: So it's very much a combined decision. So even if 480 00:27:06,640 --> 00:27:10,560 Speaker 11: a ship owner wants to order that ship to transit 481 00:27:10,600 --> 00:27:12,880 Speaker 11: through the through the Suez Canal and the Red Sea, 482 00:27:13,440 --> 00:27:16,520 Speaker 11: if the master is not comfortable, right, the master can 483 00:27:16,560 --> 00:27:19,520 Speaker 11: basically say we're not taking We're not taking her through. 484 00:27:19,480 --> 00:27:21,479 Speaker 2: You know, I just kind of learned today through some 485 00:27:21,520 --> 00:27:25,399 Speaker 2: other discussions that while yes, the having a aircraft strike 486 00:27:25,440 --> 00:27:28,680 Speaker 2: carrier group in the area is certainly intimidating, certainly represents 487 00:27:28,880 --> 00:27:31,440 Speaker 2: a tremendous show of force, at the end of the day, 488 00:27:31,480 --> 00:27:33,720 Speaker 2: what can it really do against a bunch of terrorists? 489 00:27:35,560 --> 00:27:37,960 Speaker 2: You know, what's the what's the belief in the industry 490 00:27:37,960 --> 00:27:39,560 Speaker 2: about what it can really achieve? 491 00:27:40,520 --> 00:27:44,520 Speaker 11: Yeah, I mean right now, without having the international will 492 00:27:44,800 --> 00:27:49,160 Speaker 11: to do something, and the geopolitics going on over over 493 00:27:49,240 --> 00:27:52,879 Speaker 11: being seen to align you know, for some of our 494 00:27:53,000 --> 00:27:55,679 Speaker 11: NATO allies in this case, right to see themselves to 495 00:27:55,840 --> 00:27:59,760 Speaker 11: align with the United States on the mission to deal 496 00:27:59,800 --> 00:28:03,080 Speaker 11: with it's been it's been walking a tight tightrope, right, 497 00:28:03,160 --> 00:28:06,520 Speaker 11: So we're not even seeing strong rhetoric from. 498 00:28:06,280 --> 00:28:07,960 Speaker 6: Some nations on the issue, let. 499 00:28:07,880 --> 00:28:12,480 Speaker 11: Alone, let alone deploying actual military assets, enable assets to 500 00:28:12,520 --> 00:28:15,760 Speaker 11: the situation. You know, some of what's been written about this, 501 00:28:16,400 --> 00:28:19,399 Speaker 11: about the about the coalition that's coming together, as some 502 00:28:19,560 --> 00:28:23,560 Speaker 11: NATO allies have instead of deploying a destroyer of frigate 503 00:28:24,000 --> 00:28:27,280 Speaker 11: let alone, you know, any kind of hardcore military assets, 504 00:28:27,560 --> 00:28:31,520 Speaker 11: they're deploying one staff officer to the team, right, So 505 00:28:31,800 --> 00:28:36,040 Speaker 11: what signal is that sending to ye many armed groups here. 506 00:28:37,920 --> 00:28:38,960 Speaker 6: Operas I mean. 507 00:28:38,840 --> 00:28:42,120 Speaker 10: To that end, I mean what's fascinating is just how 508 00:28:42,360 --> 00:28:45,360 Speaker 10: global and interconnected the shipping world is. I think in 509 00:28:45,480 --> 00:28:47,640 Speaker 10: some of your notes you were talking about one vessel 510 00:28:47,840 --> 00:28:52,560 Speaker 10: which is Liberia flagged, Dutch operated, Japanese owned. To that end, 511 00:28:53,240 --> 00:28:57,880 Speaker 10: how does do politics in and of itself affect the 512 00:28:57,920 --> 00:29:01,200 Speaker 10: business of shipping? Are people having to decide whether or 513 00:29:01,280 --> 00:29:05,800 Speaker 10: not they work alongside certain alliances that they've always usually done, 514 00:29:05,880 --> 00:29:07,480 Speaker 10: So how is that playing out in the world in 515 00:29:07,520 --> 00:29:08,080 Speaker 10: which you work? 516 00:29:08,880 --> 00:29:14,080 Speaker 11: Yeah, absolutely, Caroline, And what we're seeing is situations in 517 00:29:14,200 --> 00:29:18,680 Speaker 11: hardcore examples now of ship owners and dry cargo market. 518 00:29:18,680 --> 00:29:20,960 Speaker 11: One example that comes to mind, right, a dry cargo 519 00:29:21,080 --> 00:29:26,000 Speaker 11: ship operator that's working on a voyage of a dry 520 00:29:26,080 --> 00:29:30,960 Speaker 11: cargo a dry commodity is actually asking a large international 521 00:29:31,040 --> 00:29:35,840 Speaker 11: trading firm that's their counterparty on that if there's any 522 00:29:35,960 --> 00:29:41,240 Speaker 11: Israeli involvement in the trade, let alone, put aside the 523 00:29:41,280 --> 00:29:44,960 Speaker 11: ownership of the vessel, the operations of the vessel, where 524 00:29:45,000 --> 00:29:46,960 Speaker 11: the ship is going to, put that all aside, just 525 00:29:47,000 --> 00:29:51,000 Speaker 11: whether or not there's any involvement by Israeli companies in 526 00:29:51,040 --> 00:29:55,280 Speaker 11: the actual trade of the commodity in that particular sense. 527 00:29:55,360 --> 00:30:00,360 Speaker 11: So this is really really reach far reaching, right, something 528 00:30:00,360 --> 00:30:02,920 Speaker 11: else that does always researching and getting ready for today. 529 00:30:03,680 --> 00:30:07,320 Speaker 11: Interestingly enough, we're seeing ships now transiting through the Red 530 00:30:07,360 --> 00:30:11,920 Speaker 11: Sea that are using their AIS system that's usually used 531 00:30:11,920 --> 00:30:16,560 Speaker 11: to transmit data on the ship's position and load port 532 00:30:16,680 --> 00:30:20,960 Speaker 11: and discharge port and ETA. They're using that instead of 533 00:30:21,000 --> 00:30:25,520 Speaker 11: showing a discharge port of let's say Peaeus Grease, they've 534 00:30:25,560 --> 00:30:31,760 Speaker 11: replaced that with information that says armed security aboard or 535 00:30:32,200 --> 00:30:36,560 Speaker 11: no Israeli involvement in the ship in the in the 536 00:30:37,000 --> 00:30:39,360 Speaker 11: slot where you would put the discharge port or the 537 00:30:39,400 --> 00:30:43,120 Speaker 11: destination port, so that it transmits through to the MNY 538 00:30:43,920 --> 00:30:48,000 Speaker 11: armed groups, right, that they could see that information as 539 00:30:48,000 --> 00:30:52,000 Speaker 11: they're tracking the ship. In addition to what's allegedly an 540 00:30:52,040 --> 00:30:56,640 Speaker 11: Iranian spy vessel you know, that's been lingering there too, and. 541 00:30:56,760 --> 00:30:58,840 Speaker 2: Touch about thirty seconds left. I mean, is there any 542 00:30:59,400 --> 00:31:03,440 Speaker 2: realistics solution here other than I guess the cessation of 543 00:31:03,520 --> 00:31:04,600 Speaker 2: hostilities in the region. 544 00:31:05,720 --> 00:31:07,800 Speaker 6: Yeah, I think the others. 545 00:31:07,920 --> 00:31:11,479 Speaker 11: The other solution is potentially in very unsavory, right, a 546 00:31:11,480 --> 00:31:16,520 Speaker 11: military solution to dealing with what's going on on the 547 00:31:16,560 --> 00:31:20,480 Speaker 11: ground in Yemen, and of course, I'm no expert in 548 00:31:21,440 --> 00:31:24,680 Speaker 11: military matters in terms of how to deal with that, 549 00:31:24,720 --> 00:31:27,720 Speaker 11: so I wouldn't even I wouldn't even vent venture that. 550 00:31:27,680 --> 00:31:28,400 Speaker 6: But you could. 551 00:31:28,560 --> 00:31:30,840 Speaker 11: You can imagine if we're dealing with a situation where 552 00:31:30,840 --> 00:31:35,200 Speaker 11: we're United States NATO allies are not even willing to 553 00:31:35,800 --> 00:31:38,479 Speaker 11: send more than a staff officer to a joint naval 554 00:31:38,560 --> 00:31:42,000 Speaker 11: task force, what's gonna you know, where's the will to 555 00:31:42,120 --> 00:31:45,120 Speaker 11: deal with this on the situation on the ground where 556 00:31:45,160 --> 00:31:46,719 Speaker 11: the attacks are originating from. 557 00:31:47,240 --> 00:31:49,880 Speaker 10: Well, you as an officer in the US Naval Reserve, 558 00:31:49,960 --> 00:31:51,760 Speaker 10: so you know a bit more than we do here 559 00:31:51,760 --> 00:31:55,000 Speaker 10: in the radio studio. But we really appreciate your time. Anton, 560 00:31:55,000 --> 00:31:57,240 Speaker 10: absolutely fascinated to get and on the ground feel of 561 00:31:57,640 --> 00:32:00,360 Speaker 10: all the negotiations you're currently having. Anton Posners, of course, 562 00:32:00,400 --> 00:32:02,760 Speaker 10: the CEO of Mercury Resources. 563 00:32:03,400 --> 00:32:07,040 Speaker 8: You're listening to the tape Kent Live program Bloomberg Markets 564 00:32:07,080 --> 00:32:10,480 Speaker 8: weekdays at ten am Eastern on Bloomberg Radio, the tune 565 00:32:10,520 --> 00:32:13,480 Speaker 8: in app, Bloomberg dot Com, and the Bloomberg Business App. 566 00:32:13,520 --> 00:32:16,320 Speaker 8: You can also listen live on Amazon Alexa from our 567 00:32:16,360 --> 00:32:21,360 Speaker 8: flagship New York station, Just say Alexa play Bloomberg eleven thirty. 568 00:32:22,120 --> 00:32:23,920 Speaker 10: We want to get some expertise in this area where 569 00:32:23,920 --> 00:32:26,480 Speaker 10: the risks are also where the opportunities are to be 570 00:32:26,520 --> 00:32:29,320 Speaker 10: investing in this sort of technology. We'll please to welcome 571 00:32:29,600 --> 00:32:34,080 Speaker 10: Jake Sapo's general partner at Emergence Capital, and Jake, you know, 572 00:32:34,120 --> 00:32:36,560 Speaker 10: the retort coming from the likes of open ai and 573 00:32:36,600 --> 00:32:39,760 Speaker 10: the other foundational models has been fair use, and ultimately 574 00:32:39,800 --> 00:32:41,960 Speaker 10: also the fact that they've been trying to get ahead 575 00:32:41,960 --> 00:32:44,040 Speaker 10: of this in some way by striking deals with the 576 00:32:44,080 --> 00:32:47,400 Speaker 10: actual springer. I think the key publicists over in Germany 577 00:32:47,440 --> 00:32:50,760 Speaker 10: where they've sort of licensed some of their particular use 578 00:32:50,960 --> 00:32:57,240 Speaker 10: of publications and writings. What will the New York Times 579 00:32:57,320 --> 00:32:59,520 Speaker 10: case mean do you think for the training of these 580 00:32:59,520 --> 00:33:02,560 Speaker 10: models when you put the genie back in, can you 581 00:33:02,560 --> 00:33:05,800 Speaker 10: in any way take out New York Times's own articles 582 00:33:05,800 --> 00:33:07,960 Speaker 10: from the training of open AI's model. 583 00:33:09,000 --> 00:33:09,880 Speaker 6: Yeah, it's a great question. 584 00:33:10,160 --> 00:33:13,720 Speaker 7: It's great to be here. What I'd say is this 585 00:33:13,800 --> 00:33:17,200 Speaker 7: is going to create a lot of movement towards exploring 586 00:33:17,280 --> 00:33:21,400 Speaker 7: open source models. So open ai is the champion of 587 00:33:21,400 --> 00:33:24,560 Speaker 7: the kind of closed source model ecosystem, which means that 588 00:33:24,600 --> 00:33:26,640 Speaker 7: the data that they're training on and the code that 589 00:33:26,640 --> 00:33:29,400 Speaker 7: they actually generate from it is not publicly available, and 590 00:33:29,440 --> 00:33:31,800 Speaker 7: so there's lots of questions now around transparency, what data 591 00:33:31,800 --> 00:33:34,160 Speaker 7: are they using? What can we use as a business 592 00:33:34,160 --> 00:33:36,600 Speaker 7: if we're going to use their opena models. What is 593 00:33:36,600 --> 00:33:39,360 Speaker 7: happening now is an increased interest by people that consume 594 00:33:39,600 --> 00:33:43,360 Speaker 7: models to explore open source models, where the data that 595 00:33:43,440 --> 00:33:46,080 Speaker 7: is used to train those models is clearer and you, 596 00:33:46,320 --> 00:33:47,880 Speaker 7: as the user of the model, have the ability to 597 00:33:47,880 --> 00:33:51,200 Speaker 7: manipulate the model themselves. So we think that twenty twenty four, 598 00:33:51,280 --> 00:33:53,400 Speaker 7: and certainly the advent of this lawsuit is going to 599 00:33:53,400 --> 00:33:56,480 Speaker 7: push businesses who are using this technology to consider open 600 00:33:56,520 --> 00:33:57,880 Speaker 7: source models more seriously. 601 00:33:58,400 --> 00:34:02,640 Speaker 2: So I think I'm getting comfortable with what AI is, 602 00:34:03,000 --> 00:34:04,440 Speaker 2: and that's a big leap for me because it took 603 00:34:04,480 --> 00:34:06,880 Speaker 2: me in most of the year. Here now my question. 604 00:34:06,680 --> 00:34:07,800 Speaker 7: The last time we talked about it. 605 00:34:07,920 --> 00:34:10,680 Speaker 2: Yeah, I just need some help on what do you 606 00:34:10,719 --> 00:34:12,759 Speaker 2: think in twenty twenty four, Jakes might be some of 607 00:34:12,800 --> 00:34:16,360 Speaker 2: the use cases that maybe will help the general public 608 00:34:16,400 --> 00:34:19,000 Speaker 2: get a better feel for what generative AI is and 609 00:34:19,000 --> 00:34:19,640 Speaker 2: what it can mean. 610 00:34:20,719 --> 00:34:20,919 Speaker 6: Yeah. 611 00:34:20,960 --> 00:34:23,040 Speaker 7: Well, I think the good news is in twenty twenty four, 612 00:34:23,040 --> 00:34:26,760 Speaker 7: we're going to move from kind of prototyping land into production. 613 00:34:27,080 --> 00:34:28,960 Speaker 7: A lot of these applications are going to actually get 614 00:34:29,000 --> 00:34:32,279 Speaker 7: launched and are going to have real impacts for consumers, 615 00:34:32,760 --> 00:34:34,120 Speaker 7: you know. As you know, we focus on B to 616 00:34:34,160 --> 00:34:36,680 Speaker 7: B software investing in emergence, and we think there's going 617 00:34:36,760 --> 00:34:39,160 Speaker 7: to be a bunch of exciting applications of AI in 618 00:34:39,200 --> 00:34:42,239 Speaker 7: twenty twenty four. Give you one example, we work with 619 00:34:42,239 --> 00:34:47,320 Speaker 7: a company called Docimity, which is shorthand is LinkedIn for doctors, 620 00:34:47,440 --> 00:34:49,680 Speaker 7: and they provide a whole suite of tools for doctors 621 00:34:49,719 --> 00:34:52,120 Speaker 7: in addition to just a social networking application, but they 622 00:34:52,120 --> 00:34:54,440 Speaker 7: help doctors serve their patients. One of the things that 623 00:34:54,440 --> 00:34:57,480 Speaker 7: they're using AI to do is to help doctors draft 624 00:34:57,560 --> 00:35:00,080 Speaker 7: letters to insurance companies to get approval for treatments. 625 00:35:00,880 --> 00:35:01,040 Speaker 6: Right. 626 00:35:01,080 --> 00:35:04,040 Speaker 7: That has massive implications for not just the doctors, but 627 00:35:04,080 --> 00:35:06,640 Speaker 7: also obviously consumers and getting you know, much more efficient. 628 00:35:07,160 --> 00:35:09,239 Speaker 7: That type of thing wasn't possible a year ago and 629 00:35:09,400 --> 00:35:12,080 Speaker 7: is increasingly possible today. So you're going to see those 630 00:35:12,080 --> 00:35:12,960 Speaker 7: types of things. 631 00:35:12,960 --> 00:35:13,600 Speaker 6: Come to fruition. 632 00:35:13,680 --> 00:35:16,319 Speaker 7: We have worked with a company called Ironclade that's in 633 00:35:16,320 --> 00:35:19,680 Speaker 7: the legal contracting space and they help they help companies 634 00:35:19,760 --> 00:35:22,800 Speaker 7: draft contracts using AI more effectively, which allows people to 635 00:35:22,800 --> 00:35:25,399 Speaker 7: process contracts more quickly and ensure they're correct. So you're 636 00:35:25,400 --> 00:35:27,799 Speaker 7: going to see them kind of seep into all sorts 637 00:35:27,800 --> 00:35:31,080 Speaker 7: of applications across the business ecosystem. 638 00:35:31,200 --> 00:35:32,719 Speaker 2: A new phrase for me, and there's a lot of 639 00:35:32,760 --> 00:35:35,080 Speaker 2: new phrases I'm learning as it relates to AI co 640 00:35:35,320 --> 00:35:39,160 Speaker 2: pilots that ensure humans stay engaged. Here explain what that 641 00:35:39,239 --> 00:35:42,160 Speaker 2: concept is and of what are some interesting companies that 642 00:35:42,160 --> 00:35:43,239 Speaker 2: maybe have seen with that tech. 643 00:35:44,480 --> 00:35:50,480 Speaker 7: Yeah, so imagine that you are on your computer and 644 00:35:50,480 --> 00:35:53,960 Speaker 7: you're searching for some piece of information. The idea is 645 00:35:54,880 --> 00:35:58,360 Speaker 7: historically you would go, you know, to Google and look externally, 646 00:35:58,480 --> 00:36:00,560 Speaker 7: or you would kind of scour all your documents internally, 647 00:36:01,200 --> 00:36:03,480 Speaker 7: a copilot will actually pop up and give you not 648 00:36:03,640 --> 00:36:06,359 Speaker 7: just a link to something, but actually an answer. We 649 00:36:06,400 --> 00:36:08,759 Speaker 7: work with a company called Guru that does this exact thing. 650 00:36:09,040 --> 00:36:11,120 Speaker 7: So the idea is, instead of searching across all of 651 00:36:11,160 --> 00:36:14,400 Speaker 7: your applications, the information has actually serviced to you directly. 652 00:36:14,400 --> 00:36:16,319 Speaker 7: So the concept of a copilot, to boil it down, is, 653 00:36:16,680 --> 00:36:19,200 Speaker 7: as you're doing your job, you have an assistant that 654 00:36:19,320 --> 00:36:20,960 Speaker 7: is kind of next to you that helps coach you 655 00:36:21,560 --> 00:36:24,759 Speaker 7: on the right direction to go. Now, critically, there's a 656 00:36:24,800 --> 00:36:29,799 Speaker 7: difference between a copilot and an autopilot. Autopilot is dangerous, right, 657 00:36:29,840 --> 00:36:31,840 Speaker 7: because that's when the human shuts off their brain and 658 00:36:31,880 --> 00:36:34,200 Speaker 7: the plane flies itself, and that could be bad, or 659 00:36:34,400 --> 00:36:37,080 Speaker 7: you know, it could be good. As you're building these 660 00:36:37,120 --> 00:36:39,279 Speaker 7: co pilots, As you're building these coaches, you need to 661 00:36:39,360 --> 00:36:41,960 Speaker 7: ensure that the human stays engaged and is actually in 662 00:36:42,000 --> 00:36:45,360 Speaker 7: putting their new their thoughts and creativity and double checking 663 00:36:45,360 --> 00:36:48,160 Speaker 7: into the software. Because, as we all know, AI is 664 00:36:48,200 --> 00:36:51,040 Speaker 7: not perfect. There are errors that are committed all the time. 665 00:36:51,840 --> 00:36:54,600 Speaker 7: That's okay in consumer applications. You know, if you're chatting 666 00:36:54,640 --> 00:36:58,359 Speaker 7: with a dead celebrity using AI, it's okay if it's wrong, right, 667 00:36:58,760 --> 00:37:01,520 Speaker 7: But if I'm drafting a important legal contract or if 668 00:37:01,560 --> 00:37:04,920 Speaker 7: I'm communicating with an insurance company, I need to be 669 00:37:05,000 --> 00:37:07,279 Speaker 7: ensured that it's a bulletproof. And a core way to 670 00:37:07,320 --> 00:37:09,600 Speaker 7: do that today is to ensure that the human is 671 00:37:09,640 --> 00:37:12,520 Speaker 7: engaged with the copilot and not just clicking accept, accept, 672 00:37:12,520 --> 00:37:14,440 Speaker 7: accept on whatever it suggests. 673 00:37:14,920 --> 00:37:17,319 Speaker 10: When you're looking at the valuations of the companies that 674 00:37:17,320 --> 00:37:19,880 Speaker 10: you're ruying in the B to B space, or more broadly, 675 00:37:19,920 --> 00:37:22,600 Speaker 10: when we're talking of the latest one hundred billion dollar 676 00:37:22,680 --> 00:37:25,600 Speaker 10: valuation of open AI. When we're thinking of Vanthropic reaching 677 00:37:25,640 --> 00:37:27,520 Speaker 10: a run rate revenue run rate of eight hundred and 678 00:37:27,560 --> 00:37:31,000 Speaker 10: fifty million and potentially being valued more than eighteen billion. 679 00:37:31,960 --> 00:37:35,719 Speaker 10: Are those valuations realistic if open source is going to 680 00:37:35,760 --> 00:37:41,040 Speaker 10: become more of the valued foundational model direction of choice 681 00:37:41,360 --> 00:37:44,719 Speaker 10: or do you think, really still these valuations are going 682 00:37:44,760 --> 00:37:45,680 Speaker 10: to be the right source of. 683 00:37:45,640 --> 00:37:50,000 Speaker 7: Amount for them. It's a really great question. There are 684 00:37:50,040 --> 00:37:52,760 Speaker 7: lots of great closed source companies like the ones you named. 685 00:37:53,760 --> 00:37:57,080 Speaker 7: As open source becomes more and more viable in twenty 686 00:37:57,120 --> 00:37:58,960 Speaker 7: four and twenty twenty four and beyond, work with a 687 00:37:58,960 --> 00:38:02,120 Speaker 7: company called together that helps you know companies actually use 688 00:38:02,440 --> 00:38:05,080 Speaker 7: closed source model open source models more effectively. There is 689 00:38:05,080 --> 00:38:06,880 Speaker 7: going to be pressure put on these open source models, 690 00:38:07,200 --> 00:38:09,759 Speaker 7: and so the key thing to check as you're evaluating 691 00:38:09,800 --> 00:38:11,960 Speaker 7: an investment in a close source or really any a 692 00:38:12,120 --> 00:38:13,800 Speaker 7: opportunity is retention. 693 00:38:14,920 --> 00:38:15,279 Speaker 6: You want. 694 00:38:15,719 --> 00:38:18,960 Speaker 7: These things are getting incredible adoption right initial adoption. People 695 00:38:19,000 --> 00:38:21,600 Speaker 7: are playing with this stuff, experimenting with their prototyping, et cetera. 696 00:38:22,000 --> 00:38:23,840 Speaker 7: But the core thing that will matter in twenty twenty 697 00:38:23,840 --> 00:38:28,120 Speaker 7: four and AI is retention. Are people that start using 698 00:38:28,120 --> 00:38:30,560 Speaker 7: the product continuing to use the product, and the key 699 00:38:30,600 --> 00:38:33,920 Speaker 7: questions you alluded to is in these foundational models, it's 700 00:38:34,000 --> 00:38:35,920 Speaker 7: easiest to start with the closed source model because it's 701 00:38:35,920 --> 00:38:37,680 Speaker 7: all packaged for you and you can just kind of 702 00:38:37,880 --> 00:38:40,640 Speaker 7: get it up and running quickly. But if open source 703 00:38:40,680 --> 00:38:45,799 Speaker 7: models become more preferred, either because they're more easily manipulable 704 00:38:45,920 --> 00:38:48,720 Speaker 7: or because of the New York Times, you know, driven 705 00:38:48,840 --> 00:38:51,920 Speaker 7: concerns around where's your data coming from, there is going 706 00:38:51,960 --> 00:38:54,040 Speaker 7: to be some downward pressure on these close source models, 707 00:38:54,040 --> 00:38:56,279 Speaker 7: which could put pressure on retention, which could ultimately pu 708 00:38:56,280 --> 00:38:57,560 Speaker 7: pressure on some of those valuations. 709 00:38:57,800 --> 00:39:00,719 Speaker 10: And Jake, how are you trying to add analyze the 710 00:39:00,760 --> 00:39:03,319 Speaker 10: regulatory outlook for twenty to twenty four as you make 711 00:39:03,360 --> 00:39:07,400 Speaker 10: these investment decisions? How you trying to preempt It feels 712 00:39:07,560 --> 00:39:09,640 Speaker 10: sort of like crypto all over again. Everyone's trying to 713 00:39:09,640 --> 00:39:12,200 Speaker 10: get ahead of where the SEC or indeed, more broadly, 714 00:39:12,560 --> 00:39:14,479 Speaker 10: you know, we've had an executive order here in the US, 715 00:39:14,480 --> 00:39:16,640 Speaker 10: but this is going to come down to global regulation. 716 00:39:16,719 --> 00:39:20,040 Speaker 10: We've seen the EU with its AI Act. How do 717 00:39:20,040 --> 00:39:20,719 Speaker 10: you front run that? 718 00:39:22,000 --> 00:39:25,560 Speaker 7: Yeah, well, I think transparency is really important, Like understanding 719 00:39:25,560 --> 00:39:27,960 Speaker 7: where you get your data from is really important. You 720 00:39:27,960 --> 00:39:30,719 Speaker 7: don't want to be using copyrighted data to train your models, right, 721 00:39:30,719 --> 00:39:33,520 Speaker 7: that's like it's obvious, But I think people are going 722 00:39:33,560 --> 00:39:35,439 Speaker 7: to be paying much more attention to where that data 723 00:39:35,440 --> 00:39:37,680 Speaker 7: comes from. I think similarly, trust is going to become 724 00:39:37,760 --> 00:39:39,239 Speaker 7: much more important, not just in terms of where your 725 00:39:39,320 --> 00:39:43,480 Speaker 7: data comes from, but is that data actually believable? You know, 726 00:39:44,120 --> 00:39:46,520 Speaker 7: bad data going into a good model is going to 727 00:39:46,520 --> 00:39:50,760 Speaker 7: create a bad output. I work with the CEO of Guru, 728 00:39:50,800 --> 00:39:54,080 Speaker 7: Rick Nucci, calls this truth washing, which is where, Okay, 729 00:39:54,160 --> 00:39:56,880 Speaker 7: I'm using data that comes in and the model spits 730 00:39:56,920 --> 00:39:59,520 Speaker 7: out an answer that sounds really confident. 731 00:39:59,560 --> 00:40:00,000 Speaker 6: What's wrong. 732 00:40:01,000 --> 00:40:02,960 Speaker 7: I think going forward there's going to be a much 733 00:40:03,320 --> 00:40:05,799 Speaker 7: greater impetus put on how do you validate the data 734 00:40:05,800 --> 00:40:07,760 Speaker 7: that went into training the model is not only legally 735 00:40:07,760 --> 00:40:10,160 Speaker 7: for you, legal for you to use, but also accurate 736 00:40:10,239 --> 00:40:12,160 Speaker 7: so that the input or the output that comes out 737 00:40:12,160 --> 00:40:15,000 Speaker 7: of the model can be trusted. So as we think 738 00:40:15,040 --> 00:40:18,239 Speaker 7: about evaluating investment opportunities, we're thinking about it from a 739 00:40:18,239 --> 00:40:21,239 Speaker 7: first principle's perspective, which is a belief that regulators are 740 00:40:21,280 --> 00:40:23,200 Speaker 7: going to move in the direction. Thank you Apple for 741 00:40:23,239 --> 00:40:26,960 Speaker 7: the use of the thumbs up there. This technology is 742 00:40:26,960 --> 00:40:30,600 Speaker 7: going to move in a direction where regulators are concerned about. Okay, 743 00:40:30,960 --> 00:40:32,640 Speaker 7: is the data? Are you allowed to use the data 744 00:40:32,680 --> 00:40:35,560 Speaker 7: that you are using? And is the output that's coming 745 00:40:35,600 --> 00:40:37,920 Speaker 7: out of these models trustworthy such that good things can 746 00:40:37,960 --> 00:40:38,400 Speaker 7: come from it? 747 00:40:38,840 --> 00:40:41,880 Speaker 2: Thirty seconds Jake, if I bring a cool AI idea 748 00:40:41,920 --> 00:40:43,640 Speaker 2: to Sandhill Road, can I raise money? 749 00:40:44,960 --> 00:40:45,120 Speaker 6: Well? 750 00:40:45,160 --> 00:40:47,080 Speaker 7: As we talked about last time, Paul, Standhill Road is 751 00:40:47,360 --> 00:40:50,839 Speaker 7: that's the past. The future is San Francisco, the Embarcadero. 752 00:40:50,880 --> 00:40:52,560 Speaker 7: That's where the question nice. 753 00:40:52,800 --> 00:40:53,279 Speaker 2: I like that. 754 00:40:55,160 --> 00:40:58,000 Speaker 7: But to answer your question, can you raise money? Well, first, 755 00:40:58,040 --> 00:41:01,160 Speaker 7: I would look at your background, and I think your 756 00:41:01,160 --> 00:41:04,759 Speaker 7: an incredible interviewer. I don't know how oh technically savvy, you. 757 00:41:04,680 --> 00:41:06,520 Speaker 2: Are, right, but good. 758 00:41:06,520 --> 00:41:09,000 Speaker 7: I mean the reality is there is going to be 759 00:41:09,040 --> 00:41:12,239 Speaker 7: more media and companies twenty four yeah, exactly, covering your 760 00:41:12,239 --> 00:41:13,759 Speaker 7: the media company, I'll writ a check all day long. 761 00:41:14,480 --> 00:41:18,120 Speaker 7: There is going to be more scrutiny of AI investments 762 00:41:18,160 --> 00:41:20,120 Speaker 7: going forward, and I think the key thing people are 763 00:41:20,160 --> 00:41:22,120 Speaker 7: going to be looking for is that retention point. In 764 00:41:22,120 --> 00:41:25,440 Speaker 7: twenty twenty three, so many AI applications, we're getting so 765 00:41:25,520 --> 00:41:27,520 Speaker 7: much tire kicking, and lots of people were using the 766 00:41:27,560 --> 00:41:29,480 Speaker 7: product for a week, two weeks, three weeks and then 767 00:41:29,480 --> 00:41:31,319 Speaker 7: they would get bored or try the next hot thing, 768 00:41:31,360 --> 00:41:34,319 Speaker 7: et cetera. What investors are going to be increasingly looking 769 00:41:34,360 --> 00:41:36,440 Speaker 7: for in twenty twenty four is Okay, yeah, you've got 770 00:41:36,480 --> 00:41:38,319 Speaker 7: a lot of people to start using your thing, but 771 00:41:38,360 --> 00:41:40,960 Speaker 7: are people continuing to use the thing indefinitely and continue 772 00:41:40,960 --> 00:41:41,799 Speaker 7: to get value out of it? 773 00:41:42,000 --> 00:41:42,160 Speaker 6: Right? 774 00:41:42,200 --> 00:41:44,560 Speaker 2: All right, Jake, once again, thanks so much for joining us. 775 00:41:44,600 --> 00:41:48,320 Speaker 2: Really appreciate getting your insight there. Jake Sayper, general partner 776 00:41:48,360 --> 00:41:51,920 Speaker 2: at Emergency Capital, Thanks. 777 00:41:51,680 --> 00:41:55,160 Speaker 1: For listening to the Bloomberg Markets podcast. You can subscribe 778 00:41:55,200 --> 00:41:58,920 Speaker 1: and listen to interviews at Apple Podcasts or whatever podcast 779 00:41:58,960 --> 00:42:02,520 Speaker 1: platform you prefer. I'm Matt Miller. I'm on Twitter at 780 00:42:02,560 --> 00:42:04,279 Speaker 1: Matt Miller nineteen seventy three. 781 00:42:04,719 --> 00:42:07,120 Speaker 2: And I'm Paul Sweeney. I'm on Twitter at pt Sweeney. 782 00:42:07,239 --> 00:42:09,880 Speaker 2: Before the podcast, you can always catch us worldwide at 783 00:42:09,880 --> 00:42:10,880 Speaker 2: Bloomberg Radio