1 00:00:02,520 --> 00:00:11,879 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Masters in 2 00:00:11,960 --> 00:00:15,480 Speaker 1: Business with Barry Ritholts on Bloomberg Radio. 3 00:00:17,040 --> 00:00:21,800 Speaker 2: This week we have an extra special bonus episode live 4 00:00:22,200 --> 00:00:27,800 Speaker 2: from future Proof. My conversation with Muddy Waters Carson Block 5 00:00:28,320 --> 00:00:34,560 Speaker 2: really a fascinating conversation, not only about how he developed 6 00:00:34,560 --> 00:00:39,520 Speaker 2: an interest in shorting fraudulent equities and companies, but how 7 00:00:39,560 --> 00:00:44,839 Speaker 2: his firm has evolved into a comprehensive research shop and 8 00:00:45,640 --> 00:00:49,480 Speaker 2: a long short hedge fund. I thought the conversation was fascinating, 9 00:00:49,680 --> 00:00:52,560 Speaker 2: and I think you will also, with no further ado. 10 00:00:53,240 --> 00:00:59,560 Speaker 2: My conversation with Muddy Waters Carson Block live at Citywide 11 00:00:59,640 --> 00:01:05,600 Speaker 2: future Proof Miami. I'm so fascinated by your career, what 12 00:01:05,640 --> 00:01:07,200 Speaker 2: you've done, what you've built. 13 00:01:08,120 --> 00:01:11,680 Speaker 3: I first kind of became aware of you. I don't know. 14 00:01:11,720 --> 00:01:13,760 Speaker 2: It seems like it was a long time ago with 15 00:01:13,840 --> 00:01:19,640 Speaker 2: the reverse Chinese mergers and Sino Forrest. Then wait, China 16 00:01:19,760 --> 00:01:22,319 Speaker 2: is doing what. I don't understand any of this. Tell 17 00:01:22,400 --> 00:01:25,920 Speaker 2: us how you kind of fell into that aspect of 18 00:01:26,760 --> 00:01:30,800 Speaker 2: markets and how you ended up becoming an activist short seller. 19 00:01:31,160 --> 00:01:35,120 Speaker 4: I will try to nutshell this, but I grew up 20 00:01:35,160 --> 00:01:40,160 Speaker 4: in investing. My father was an equity analyst and was 21 00:01:40,200 --> 00:01:45,600 Speaker 4: working with him from ninety nine to two alongside covering microcaps. 22 00:01:46,319 --> 00:01:50,320 Speaker 4: And we were just getting lied to incessantly by these managements. 23 00:01:50,400 --> 00:01:52,680 Speaker 4: And back then they had forty five days to file 24 00:01:52,720 --> 00:01:55,280 Speaker 4: their forms for so let me take them on non 25 00:01:55,360 --> 00:01:58,640 Speaker 4: deal roadshow to meet institutions. Stock would go up and 26 00:01:58,720 --> 00:02:01,720 Speaker 4: let me'd find out later that hit the bid. So 27 00:02:03,400 --> 00:02:06,520 Speaker 4: this is the same time that you had the largest 28 00:02:06,520 --> 00:02:11,119 Speaker 4: companies in the world like Enron, WorldCom Health South Hidelphia 29 00:02:11,160 --> 00:02:16,360 Speaker 4: blowing up an accounting scandals. So my my realization around 30 00:02:16,400 --> 00:02:19,440 Speaker 4: two thousand and two, I was really demoralized. It's like, look, 31 00:02:19,520 --> 00:02:22,760 Speaker 4: I want to be an investor, but this is this 32 00:02:22,919 --> 00:02:26,560 Speaker 4: market is riddled with financial predators from top to bottom. 33 00:02:27,240 --> 00:02:32,160 Speaker 4: How do I protect myself against that? So I went 34 00:02:32,200 --> 00:02:35,560 Speaker 4: to law school with just this amorphous idea that that 35 00:02:35,639 --> 00:02:38,919 Speaker 4: would give me some tools, and you know, fast forward, 36 00:02:39,480 --> 00:02:42,959 Speaker 4: decided to practice law, ended up in China. Jones Day 37 00:02:43,480 --> 00:02:46,480 Speaker 4: left to start the first self storage business in mainland China. 38 00:02:46,800 --> 00:02:50,880 Speaker 4: I don't recommend it, and anyway, I was just sort of, 39 00:02:50,919 --> 00:02:53,280 Speaker 4: you know, keep trying to keep that business from failing. 40 00:02:53,520 --> 00:02:56,800 Speaker 4: In two thousand and nine, when my father got really 41 00:02:56,800 --> 00:02:59,959 Speaker 4: excited about a bunch of these Chinese companies that had 42 00:03:00,040 --> 00:03:03,400 Speaker 4: had gone public in the US via reverse merger, and 43 00:03:04,120 --> 00:03:07,399 Speaker 4: like I had my own problems, I wasn't really that interested. 44 00:03:07,480 --> 00:03:09,240 Speaker 4: But he asked me to look and he asked me 45 00:03:09,240 --> 00:03:12,280 Speaker 4: to look at this first one called Orient Paper. And 46 00:03:12,600 --> 00:03:14,280 Speaker 4: the first thing he told me he'd been at a 47 00:03:14,360 --> 00:03:17,440 Speaker 4: conference that these guys had gone to, and you know 48 00:03:17,639 --> 00:03:20,640 Speaker 4: what he's hearing is, oh, you know, chairman Leo, he's 49 00:03:20,680 --> 00:03:24,240 Speaker 4: different from other Chinese company chairmen. He doesn't smoke, and 50 00:03:24,280 --> 00:03:27,080 Speaker 4: he doesn't chase women. And you know, my father's telling 51 00:03:27,120 --> 00:03:29,519 Speaker 4: me this woman, you know, late night in Shanghai, and 52 00:03:29,560 --> 00:03:32,400 Speaker 4: I'm like, I'm sure neither of those things is actually true. 53 00:03:32,560 --> 00:03:35,120 Speaker 4: But the fact that this is making its rounds at 54 00:03:35,160 --> 00:03:41,280 Speaker 4: the conference, somebody is trying to game Western investor psychology. 55 00:03:41,800 --> 00:03:45,800 Speaker 4: So that got my attention. Early twenty ten, went up 56 00:03:45,800 --> 00:03:48,640 Speaker 4: to see the company and it was a Potemkin factory, 57 00:03:48,680 --> 00:03:51,600 Speaker 4: Like I never believed that such a thing could exist, 58 00:03:51,880 --> 00:03:54,400 Speaker 4: where at the time it's market cap is one hundred 59 00:03:54,440 --> 00:03:57,360 Speaker 4: and fifty million, had just reported one hundred three million 60 00:03:57,400 --> 00:04:01,240 Speaker 4: dollars in revenue and the real revenue two to five 61 00:04:01,320 --> 00:04:05,880 Speaker 4: million dollars that it was these empty box So I 62 00:04:05,960 --> 00:04:08,840 Speaker 4: exposed that with the thirty some ond page report. And 63 00:04:08,880 --> 00:04:11,160 Speaker 4: the only reason I did that was I felt like 64 00:04:11,200 --> 00:04:13,280 Speaker 4: I was stuck in my business in Shanghai, like a 65 00:04:13,280 --> 00:04:15,560 Speaker 4: few hundred people who are leaving their stuff. 66 00:04:15,320 --> 00:04:16,760 Speaker 3: In my facility. 67 00:04:16,800 --> 00:04:20,800 Speaker 4: The industry did not exist because it's a bad industry 68 00:04:20,839 --> 00:04:24,200 Speaker 4: to be in in China, and I don't know, there's 69 00:04:24,240 --> 00:04:26,520 Speaker 4: like a power and feeling like you have nothing to lose. 70 00:04:26,560 --> 00:04:28,200 Speaker 4: So I just threw the ball as far down the 71 00:04:28,200 --> 00:04:30,159 Speaker 4: field as I could wrote that report. 72 00:04:30,720 --> 00:04:31,520 Speaker 3: It went viral. 73 00:04:32,240 --> 00:04:34,840 Speaker 4: Quickly found out this was systemic, and one year later 74 00:04:34,920 --> 00:04:37,560 Speaker 4: Bloomberg's like, oh, he's one of the fifty most influential 75 00:04:37,600 --> 00:04:40,800 Speaker 4: in global finance with Ben Bernanki and Warren Buffett, and 76 00:04:40,800 --> 00:04:43,120 Speaker 4: it was like, well, yes, of course that's the natural 77 00:04:43,320 --> 00:04:44,599 Speaker 4: path from self storage. 78 00:04:44,640 --> 00:04:48,000 Speaker 2: That's every thirty page analyst report tends to lead to that. 79 00:04:48,320 --> 00:04:51,600 Speaker 2: So it's funny because you and I kind of came 80 00:04:51,640 --> 00:04:55,320 Speaker 2: of age in the markets similar period. The dot com 81 00:04:55,360 --> 00:04:59,880 Speaker 2: implosion was certainly fundamental to my view of both markets 82 00:05:00,240 --> 00:05:04,320 Speaker 2: and short sellers, and then the financial crisis. How do 83 00:05:04,400 --> 00:05:06,960 Speaker 2: you look at how the markets have changed over the 84 00:05:07,080 --> 00:05:12,920 Speaker 2: ensuing decade, as between QE and zero interest rate policy, 85 00:05:12,960 --> 00:05:17,560 Speaker 2: and more recently the Cares Act and the mascul fiscal stimulus. 86 00:05:18,040 --> 00:05:21,240 Speaker 2: I grew up thinking short sellers were the ones who 87 00:05:21,360 --> 00:05:25,680 Speaker 2: kept the market honest and responsible. That seems to be 88 00:05:25,720 --> 00:05:27,039 Speaker 2: a minority of you these days. 89 00:05:28,040 --> 00:05:33,200 Speaker 4: Yeah, well, I've come to the view that there's an 90 00:05:33,279 --> 00:05:38,440 Speaker 4: inverse relationship between interest rates and the amount of dishonesty 91 00:05:38,480 --> 00:05:42,760 Speaker 4: in society. So the lower your rates, the more easy 92 00:05:42,800 --> 00:05:46,120 Speaker 4: money is, the more dishonesty you get in society. And 93 00:05:46,200 --> 00:05:53,200 Speaker 4: so the emergency monetary policy outlived the emergency. And you 94 00:05:53,279 --> 00:05:55,400 Speaker 4: know what I felt is that just each year I've 95 00:05:55,400 --> 00:05:59,719 Speaker 4: been doing this, that investors are more and more anesthetized 96 00:05:59,760 --> 00:06:04,320 Speaker 4: to Now. The flip side of that is that behaviors 97 00:06:04,360 --> 00:06:08,720 Speaker 4: that were once really only present in microcap land will 98 00:06:08,760 --> 00:06:12,600 Speaker 4: those bubble up to MidCap land because of the inflation 99 00:06:12,680 --> 00:06:16,000 Speaker 4: of market caps. So on one hand, my business has 100 00:06:16,040 --> 00:06:21,400 Speaker 4: gotten harder because unless it's something really really egregious, like 101 00:06:21,520 --> 00:06:25,400 Speaker 4: people don't care. But then yes, you'll find that type 102 00:06:25,440 --> 00:06:29,400 Speaker 4: of behavior now even in mid cap companies, and you 103 00:06:29,400 --> 00:06:34,120 Speaker 4: know an environment where and just to be clear, it's 104 00:06:34,120 --> 00:06:37,120 Speaker 4: a small minority of companies that are frauds the world's 105 00:06:37,160 --> 00:06:41,000 Speaker 4: bigger problem is the gray zone, right, the things where yeah, 106 00:06:41,040 --> 00:06:44,760 Speaker 4: nobody's gonna get convicted. Tech you know, lawyers have signed off, 107 00:06:45,400 --> 00:06:49,160 Speaker 4: the auditor is okay with it, and that gray zone 108 00:06:49,279 --> 00:06:56,560 Speaker 4: behavior whereby you can significantly misrepresent economic reality that is 109 00:06:56,600 --> 00:06:59,240 Speaker 4: almost maybe the norm in many respects. 110 00:06:59,360 --> 00:07:02,040 Speaker 2: So this secret is to corrupt the attorneys and the 111 00:07:02,120 --> 00:07:03,560 Speaker 2: orderers and then it's home free. 112 00:07:04,040 --> 00:07:05,760 Speaker 3: Wait, corrupt the attorneys. 113 00:07:06,600 --> 00:07:11,760 Speaker 2: Right, So you mentioned microcaps, small cap mid caps. I 114 00:07:11,800 --> 00:07:14,680 Speaker 2: read a note of yours not too long ago talking 115 00:07:14,720 --> 00:07:18,200 Speaker 2: about the big caps and the megacaps, where so many 116 00:07:18,280 --> 00:07:20,440 Speaker 2: people have been calling this a bubble, and so many 117 00:07:20,440 --> 00:07:23,080 Speaker 2: people have saying, gee, I'd like to shorten video. 118 00:07:23,320 --> 00:07:24,800 Speaker 3: You kind of went out of your way. 119 00:07:24,600 --> 00:07:29,280 Speaker 2: To say, hey, maybe one day there's a downside play here, 120 00:07:29,360 --> 00:07:33,000 Speaker 2: but this freight train is really difficult to step in 121 00:07:33,040 --> 00:07:36,280 Speaker 2: front of. What are your thoughts on on the hyperscalers, 122 00:07:36,320 --> 00:07:38,160 Speaker 2: the megacap tech companies? 123 00:07:38,440 --> 00:07:39,080 Speaker 3: Three things? 124 00:07:39,800 --> 00:07:43,640 Speaker 4: Number one, there are easier better shorts out there than 125 00:07:43,840 --> 00:07:47,360 Speaker 4: in video. Number two. The reason why I was making 126 00:07:47,400 --> 00:07:51,200 Speaker 4: those comments is that flows have driven so much of this, 127 00:07:51,320 --> 00:07:55,640 Speaker 4: Like you have obviously the passive bid, and it squeezes 128 00:07:55,720 --> 00:08:00,320 Speaker 4: floats and by squeezing floats. It's not a linear impact 129 00:08:00,360 --> 00:08:04,600 Speaker 4: on stock prices. It's a parabolic impact on stock prices 130 00:08:04,640 --> 00:08:09,120 Speaker 4: at least of the winners. So the idea that something 131 00:08:09,200 --> 00:08:12,440 Speaker 4: is overvalued, that's a reference to its fundamental value. But 132 00:08:12,640 --> 00:08:16,440 Speaker 4: I think you have to consider its technical value. And 133 00:08:16,480 --> 00:08:21,320 Speaker 4: that's what everybody fails to say when they're criticizing these 134 00:08:21,600 --> 00:08:23,000 Speaker 4: things on a fundamental basis. 135 00:08:23,080 --> 00:08:25,560 Speaker 3: It's like, well what are the technicals of this? Now? 136 00:08:26,280 --> 00:08:27,280 Speaker 3: That's my second point. 137 00:08:27,320 --> 00:08:30,080 Speaker 4: My third point is up till one month ago, I 138 00:08:30,120 --> 00:08:35,240 Speaker 4: was completely sanguine on the on SMP and markets in 139 00:08:35,240 --> 00:08:38,640 Speaker 4: general and the economy. And my view has one eight 140 00:08:38,960 --> 00:08:42,040 Speaker 4: And I think the wrong question is well what do 141 00:08:42,080 --> 00:08:44,760 Speaker 4: you think of the valuations or stock prices of XYZ 142 00:08:44,880 --> 00:08:48,160 Speaker 4: of these AI companies? And the right question is what 143 00:08:48,440 --> 00:08:51,920 Speaker 4: is about to happen to society and to the market 144 00:08:52,000 --> 00:08:55,160 Speaker 4: as a result of AI. And I and like I 145 00:08:55,200 --> 00:08:58,480 Speaker 4: said on this like a month ago, I wouldn't even 146 00:08:58,480 --> 00:09:01,600 Speaker 4: refer to these models as a I had this rule. 147 00:09:01,840 --> 00:09:04,319 Speaker 4: These are large language models. They are not going to 148 00:09:04,360 --> 00:09:08,920 Speaker 4: create new information. They merely process information that's already out there. 149 00:09:09,200 --> 00:09:12,440 Speaker 4: Don't call it AI, they're lllms. But I call it 150 00:09:12,480 --> 00:09:13,040 Speaker 4: AI now. 151 00:09:13,080 --> 00:09:17,520 Speaker 2: Right, they're neither artificial nor intelligence. They're something else. But 152 00:09:17,720 --> 00:09:20,880 Speaker 2: given that, and given this one eighty but we're going 153 00:09:20,920 --> 00:09:23,960 Speaker 2: to dive right into AI in a minute. But since 154 00:09:24,600 --> 00:09:28,520 Speaker 2: you built your reputation before the firm is pivoted into 155 00:09:28,760 --> 00:09:33,520 Speaker 2: a full service research shop, both long and short, you've 156 00:09:33,559 --> 00:09:39,320 Speaker 2: criticized SPACs, crypto thematic ETFs, NFTs. Where has there been 157 00:09:39,360 --> 00:09:43,640 Speaker 2: the biggest destruction of investor wealth in that group? 158 00:09:44,679 --> 00:09:47,240 Speaker 4: Well in that group, Okay, so it's a little bit 159 00:09:47,400 --> 00:09:51,520 Speaker 4: in vogue. I think to point toward private credit right now, 160 00:09:52,440 --> 00:09:56,440 Speaker 4: and the reality is like who knows, right, there's there's 161 00:09:56,480 --> 00:09:57,040 Speaker 4: no data. 162 00:09:57,160 --> 00:09:58,280 Speaker 3: It's really opaque. 163 00:09:58,800 --> 00:10:02,120 Speaker 4: My concerns there, and I recognize that data is not 164 00:10:02,240 --> 00:10:06,480 Speaker 4: the plural of anecdote. But in the past few months, 165 00:10:06,559 --> 00:10:09,360 Speaker 4: some of the research we've done, I've looked at some 166 00:10:10,000 --> 00:10:13,000 Speaker 4: abs issuers and this is not private credit, but I 167 00:10:13,120 --> 00:10:15,959 Speaker 4: was really surprised to see as I went down the 168 00:10:16,040 --> 00:10:19,720 Speaker 4: rabbit hole of abs issuances that a lot of the 169 00:10:19,720 --> 00:10:24,320 Speaker 4: paperwork that should be done to basically show release of 170 00:10:24,440 --> 00:10:28,080 Speaker 4: lians of loans that are being securitized and filed publicly, 171 00:10:28,880 --> 00:10:32,200 Speaker 4: that paperwork is not being filed publicly and at first, 172 00:10:32,240 --> 00:10:34,520 Speaker 4: I thought, oh my god, we've got these guys on fraud. 173 00:10:34,559 --> 00:10:37,760 Speaker 4: They're selling off loans that are still encumbered. And I 174 00:10:37,800 --> 00:10:41,880 Speaker 4: spoke with a couple of securitization attorneys and they said, no, no, no, 175 00:10:41,920 --> 00:10:45,640 Speaker 4: that's market practice. The warehouse lender just provides a letter 176 00:10:45,760 --> 00:10:48,560 Speaker 4: saying that the lians are released. It doesn't get filed publicly. 177 00:10:49,120 --> 00:10:51,120 Speaker 4: And so I asked each of them, well, what's to 178 00:10:51,160 --> 00:10:54,800 Speaker 4: stop the sponsor of the securitization from forging the letter 179 00:10:54,880 --> 00:10:57,240 Speaker 4: because he thinks, like, look, the loans are, the ABSs 180 00:10:57,280 --> 00:11:00,560 Speaker 4: are already over collateralized. I can double pledge these, you know, 181 00:11:00,600 --> 00:11:02,960 Speaker 4: because if the warehouse lender, if it's not public, the 182 00:11:02,960 --> 00:11:04,959 Speaker 4: warehouse lender wouldn't know if somebody forged it. 183 00:11:06,080 --> 00:11:09,360 Speaker 3: Huh yeah, that's a good point. 184 00:11:10,120 --> 00:11:13,120 Speaker 4: And so I when I see that the largest financial 185 00:11:13,160 --> 00:11:15,920 Speaker 4: institutions are not dotting eyes and crossing t's. 186 00:11:16,559 --> 00:11:16,720 Speaker 3: You know. 187 00:11:16,760 --> 00:11:19,480 Speaker 4: I've a conversation with somebody recently who gave me data 188 00:11:19,480 --> 00:11:22,200 Speaker 4: point from a private equity firm that's doing a lot 189 00:11:22,240 --> 00:11:25,160 Speaker 4: of clos The conversation from somebody who's on the p 190 00:11:25,520 --> 00:11:29,080 Speaker 4: side going to the CLO guys is like, hey, so 191 00:11:29,160 --> 00:11:31,560 Speaker 4: what do we know about the performance of the underlying loans, 192 00:11:31,880 --> 00:11:33,200 Speaker 4: like how are they performing? 193 00:11:33,600 --> 00:11:36,560 Speaker 3: Huh? Why did we track that we don't service them? 194 00:11:36,920 --> 00:11:38,120 Speaker 3: Like this is. 195 00:11:38,120 --> 00:11:42,640 Speaker 4: Reminiscent of right out of the Yeah, this is reminiscent 196 00:11:42,679 --> 00:11:45,360 Speaker 4: of some of the behaviors and credit. So then okay, 197 00:11:45,840 --> 00:11:48,720 Speaker 4: I combined again, it's not data, these are anecdotes, but 198 00:11:48,760 --> 00:11:51,080 Speaker 4: I combine that with also what I noticed starting a 199 00:11:51,080 --> 00:11:53,600 Speaker 4: few years ago. You know, every time I talk to 200 00:11:53,640 --> 00:11:56,880 Speaker 4: an allocator, you know, because we're actually we run a fund. 201 00:11:56,880 --> 00:11:58,719 Speaker 4: We're a hedge fund, so you know, we're talking to 202 00:11:58,720 --> 00:12:01,360 Speaker 4: allocators all the time. And you know what do you 203 00:12:01,360 --> 00:12:04,280 Speaker 4: guys investing in everybody all of a sudden starts saying 204 00:12:04,320 --> 00:12:06,480 Speaker 4: private credit, private credit, private credit. 205 00:12:06,960 --> 00:12:07,160 Speaker 3: You know. 206 00:12:07,280 --> 00:12:09,440 Speaker 4: That also reminds me of the Internet bubble, because the 207 00:12:09,440 --> 00:12:11,960 Speaker 4: way I used to think about that as it was 208 00:12:12,000 --> 00:12:16,600 Speaker 4: happening is there's too much money chasing too few investable companies. 209 00:12:16,640 --> 00:12:19,920 Speaker 4: So I don't think if there are problems in private credit, 210 00:12:20,000 --> 00:12:22,599 Speaker 4: Like my first bet is not that it's at Apollo, 211 00:12:23,160 --> 00:12:26,360 Speaker 4: But I also know we have looked at the insurance industry, 212 00:12:26,720 --> 00:12:29,120 Speaker 4: and so I know a lot of these insurers have 213 00:12:29,160 --> 00:12:32,640 Speaker 4: been bought by smaller pe firms. I mean they're basically 214 00:12:32,720 --> 00:12:35,440 Speaker 4: using these things to finance their deals. The Ft did 215 00:12:35,480 --> 00:12:38,400 Speaker 4: a great article a few Alphaville a few months ago 216 00:12:38,520 --> 00:12:41,280 Speaker 4: on the credit ratings agencies. I mean, it's not even 217 00:12:41,320 --> 00:12:44,360 Speaker 4: SMP in Moody's. The number one is Egan Jones. So 218 00:12:44,480 --> 00:12:47,280 Speaker 4: corporate headquarters is like an eight bedroom house in suburban 219 00:12:47,920 --> 00:12:51,720 Speaker 4: Boston literally, and I think they did like two thousand 220 00:12:51,760 --> 00:12:55,480 Speaker 4: deals last year that they raided. I mean, if you 221 00:12:56,040 --> 00:12:59,880 Speaker 4: lived through the GFC and we're paying attention, you know, 222 00:13:00,240 --> 00:13:03,520 Speaker 4: like starting to see some dots connect. So that does 223 00:13:03,559 --> 00:13:07,400 Speaker 4: have me worried also, and if that does intersect with 224 00:13:07,600 --> 00:13:10,640 Speaker 4: what I think could happen in the labor markets from 225 00:13:10,800 --> 00:13:13,600 Speaker 4: AI job displacement, you know, like I know you want 226 00:13:13,640 --> 00:13:15,079 Speaker 4: to pivot back to it, but I'm just going to 227 00:13:15,120 --> 00:13:18,120 Speaker 4: put it out there. I think it's not unrealistic to 228 00:13:18,200 --> 00:13:22,040 Speaker 4: say fifteen percent of knowledge work jobs in the US 229 00:13:22,440 --> 00:13:24,560 Speaker 4: in three years are gone. And if it's not three, 230 00:13:24,600 --> 00:13:26,959 Speaker 4: it's going to be five. But it might not be fifteen, 231 00:13:27,000 --> 00:13:30,040 Speaker 4: it could be twenty twenty five percent. I mean, that's 232 00:13:30,360 --> 00:13:32,000 Speaker 4: and I can work through how I got there. 233 00:13:32,320 --> 00:13:37,680 Speaker 2: Let's dive deeper into AI. It's both an investible asset 234 00:13:37,880 --> 00:13:42,400 Speaker 2: a shortable asset, and a tool that you're using to 235 00:13:42,520 --> 00:13:44,480 Speaker 2: run a business. So I want to hit each of 236 00:13:44,520 --> 00:13:48,400 Speaker 2: those things, starting with how are you using AI as 237 00:13:48,440 --> 00:13:54,520 Speaker 2: a tool to manage a business to identify long and 238 00:13:54,559 --> 00:13:59,839 Speaker 2: short opportunities? What is true AI not just lms me 239 00:14:00,280 --> 00:14:01,840 Speaker 2: for your daily work? 240 00:14:02,240 --> 00:14:05,320 Speaker 4: Well, I mean in the past again, since finding religion 241 00:14:05,440 --> 00:14:08,360 Speaker 4: just a few weeks ago. I mean, this is something 242 00:14:08,400 --> 00:14:11,160 Speaker 4: I've been hammering. Most of my employees have worked for 243 00:14:11,200 --> 00:14:15,040 Speaker 4: me for over ten years. We're an old firm for 244 00:14:15,080 --> 00:14:16,840 Speaker 4: a hedge fund. I mean I used to joke that 245 00:14:17,280 --> 00:14:20,080 Speaker 4: this is probably true that if you look at trips 246 00:14:20,080 --> 00:14:23,560 Speaker 4: and falls per dollar of AUM, we maybe are the 247 00:14:23,600 --> 00:14:26,640 Speaker 4: highest in the world. And that was kind of funny 248 00:14:26,640 --> 00:14:30,160 Speaker 4: to say until I started saying, guys, why are you 249 00:14:30,360 --> 00:14:33,680 Speaker 4: literally taking months to put together one hundred and fifty 250 00:14:33,840 --> 00:14:38,160 Speaker 4: page slide decks to discuss something internally? Stick it in 251 00:14:38,560 --> 00:14:42,280 Speaker 4: the machine. And so one of my analysts, my longest 252 00:14:42,280 --> 00:14:49,880 Speaker 4: serving employee, ex ex auditor, super bright, but al we 253 00:14:50,000 --> 00:14:53,360 Speaker 4: struggled with communication. I mean one of my skills was 254 00:14:54,200 --> 00:14:58,880 Speaker 4: over months, weeks or months, many many hours trying to 255 00:14:59,000 --> 00:15:02,560 Speaker 4: draw out of her the patterns that she sees. But 256 00:15:02,680 --> 00:15:06,680 Speaker 4: she's just unable to elucidate. Now she's just pumping out 257 00:15:06,680 --> 00:15:09,800 Speaker 4: the memoranda from Claude, and I'm like, wow, this is 258 00:15:09,960 --> 00:15:12,480 Speaker 4: I mean, Cindy, this has saved us, like, you know, 259 00:15:12,560 --> 00:15:16,120 Speaker 4: probably five weeks of you know, like frustrated conversations in 260 00:15:16,160 --> 00:15:20,240 Speaker 4: the conference room like this. So from that perspective, it's helping, 261 00:15:20,760 --> 00:15:24,640 Speaker 4: I think hurting a little bit. You know. I've always 262 00:15:24,640 --> 00:15:27,600 Speaker 4: felt that we have an edge as active as short 263 00:15:27,640 --> 00:15:31,160 Speaker 4: sellers over our competition in terms of how I write 264 00:15:31,240 --> 00:15:32,000 Speaker 4: and communicate. 265 00:15:32,440 --> 00:15:33,480 Speaker 3: Like that edge has gone. 266 00:15:33,520 --> 00:15:35,760 Speaker 4: I mean, you can go to Claude and say, hey, 267 00:15:36,080 --> 00:15:39,080 Speaker 4: you know, write this up as a short report in 268 00:15:39,120 --> 00:15:39,960 Speaker 4: the voice of. 269 00:15:39,920 --> 00:15:43,160 Speaker 3: Carson Block, and you know it's not bad. I mean, 270 00:15:43,200 --> 00:15:46,280 Speaker 3: i it gets you a lot of the way there. 271 00:15:46,520 --> 00:15:49,160 Speaker 2: So so let me flip the question on you and 272 00:15:49,240 --> 00:15:56,080 Speaker 2: ask how has AI changed the fraudster's toolkit? What can 273 00:15:56,160 --> 00:16:00,680 Speaker 2: Claude do for a fabricated document, deep fakes, fake video, 274 00:16:00,800 --> 00:16:05,440 Speaker 2: fake voice, fake docs, fake everything. Has it just become 275 00:16:05,480 --> 00:16:08,320 Speaker 2: an arms race between the good guys and the bad guys? 276 00:16:10,360 --> 00:16:14,240 Speaker 4: Well, look, I mean the tools for forgery never really 277 00:16:14,280 --> 00:16:16,320 Speaker 4: needed to be that sophisticated. I Mean one of my 278 00:16:16,360 --> 00:16:20,400 Speaker 4: favorite examples of that was the Peregrine Financial That guy 279 00:16:20,520 --> 00:16:24,760 Speaker 4: had a post office box and an ink chat and 280 00:16:24,800 --> 00:16:27,640 Speaker 4: that's how he forged his auditor letters. And you know 281 00:16:27,680 --> 00:16:28,840 Speaker 4: that was a few hundred million. 282 00:16:28,880 --> 00:16:33,200 Speaker 2: But Bernie Madoff did not exactly use the most sophisticated technology. 283 00:16:33,360 --> 00:16:36,720 Speaker 4: Yeah, I think the thing that will get to be 284 00:16:36,800 --> 00:16:40,800 Speaker 4: more interesting is if you're a company CEO and you 285 00:16:40,840 --> 00:16:43,000 Speaker 4: know you're kind of you know, like you're pumping your 286 00:16:43,000 --> 00:16:46,520 Speaker 4: stock price, you're monetizing it and hitting the bid. I 287 00:16:46,520 --> 00:16:49,280 Speaker 4: think the more interesting thing is querying it. You know, 288 00:16:50,000 --> 00:16:53,520 Speaker 4: if I were the CEO of XYZ company, what would 289 00:16:53,560 --> 00:16:55,080 Speaker 4: a short sellar focus on? 290 00:16:55,560 --> 00:16:57,520 Speaker 3: You know, like what should I do about that? 291 00:16:57,840 --> 00:16:59,960 Speaker 4: So I think that we're going to get in this 292 00:17:00,080 --> 00:17:05,159 Speaker 4: cat and mouse game of preemption and reaction. But I mean, 293 00:17:05,200 --> 00:17:06,560 Speaker 4: at the end of the day, I mean, if you're 294 00:17:07,040 --> 00:17:09,240 Speaker 4: you know, if you're just ramping your stock price and 295 00:17:09,359 --> 00:17:12,160 Speaker 4: mortgaging your future, and I think as active as short 296 00:17:12,160 --> 00:17:15,040 Speaker 4: sellers will still get there, but you could at least 297 00:17:15,119 --> 00:17:19,520 Speaker 4: dig a wider moat around your you know, around what 298 00:17:19,560 --> 00:17:22,560 Speaker 4: you're doing if you use these AI tools. 299 00:17:23,240 --> 00:17:27,800 Speaker 2: So I always think of short sellers like Jim Chainos 300 00:17:27,880 --> 00:17:32,879 Speaker 2: and the original guys who who really pioneered forensic accounting 301 00:17:33,280 --> 00:17:37,560 Speaker 2: as being so deep into the documents. How useful are 302 00:17:37,600 --> 00:17:43,119 Speaker 2: tools like Claude Cowork as a forensic accountant to help 303 00:17:43,480 --> 00:17:50,080 Speaker 2: either identify patterns in every SEC filing or honing in 304 00:17:50,119 --> 00:17:54,040 Speaker 2: on a specific company's filings and seeing what doesn't smell 305 00:17:54,119 --> 00:17:55,040 Speaker 2: right for me. 306 00:17:55,200 --> 00:17:57,720 Speaker 3: That's a little TBD. I don't know yet. 307 00:17:57,760 --> 00:17:59,680 Speaker 4: I mean, I'm not sure that you can just upload 308 00:17:59,680 --> 00:18:02,919 Speaker 4: a ten and say, oh, you know, what's dodgy about this? 309 00:18:03,000 --> 00:18:06,320 Speaker 4: I mean, first of all, one of the one of 310 00:18:06,359 --> 00:18:09,040 Speaker 4: the secrets. It's not a secret, but of what we 311 00:18:09,119 --> 00:18:13,760 Speaker 4: do is we obtain documents from places people never look. 312 00:18:14,240 --> 00:18:17,960 Speaker 4: So again, like we're you know, we're pulling UCC files 313 00:18:18,200 --> 00:18:22,239 Speaker 4: to look at asset securitizations. We love companies that have 314 00:18:22,320 --> 00:18:27,720 Speaker 4: overseas subsidiaries because most overseas jurisdictions there are publicly filed 315 00:18:27,760 --> 00:18:28,800 Speaker 4: financial statements. 316 00:18:29,119 --> 00:18:31,240 Speaker 3: So we'll pull those and upload them. 317 00:18:31,520 --> 00:18:33,800 Speaker 4: And then also, I'm sure it could really help connect 318 00:18:33,800 --> 00:18:35,800 Speaker 4: the dots with people. I mean right now or up 319 00:18:35,880 --> 00:18:39,360 Speaker 4: till now, we've had to rely on memory, like oh wait, yeah, 320 00:18:39,400 --> 00:18:41,920 Speaker 4: this dude. You know, we've looked at fifty eight entities. 321 00:18:42,359 --> 00:18:45,480 Speaker 4: This dude was in that entity. Oh interesting. So the 322 00:18:45,520 --> 00:18:48,040 Speaker 4: AI is going to get rid of it's going to 323 00:18:48,240 --> 00:18:50,399 Speaker 4: make that part easy. But you still have to go 324 00:18:50,480 --> 00:18:52,679 Speaker 4: out and do the legwork. And you know, a lot 325 00:18:52,720 --> 00:18:54,639 Speaker 4: of times you have to send people in person to 326 00:18:54,720 --> 00:18:58,399 Speaker 4: do document retrieval. So I don't think it's as simple 327 00:18:58,440 --> 00:19:00,600 Speaker 4: as you know, Hey, I woke up today and I 328 00:19:00,600 --> 00:19:02,840 Speaker 4: want to be an activist short seller, like you know, 329 00:19:03,080 --> 00:19:05,760 Speaker 4: which you know, claude, which company should I write about? 330 00:19:05,800 --> 00:19:08,359 Speaker 4: And you know, what's my thesis? Like, you have to 331 00:19:08,359 --> 00:19:10,960 Speaker 4: still do a lot of leg work. But the accounting 332 00:19:10,960 --> 00:19:16,280 Speaker 4: stuff your claude has demonstrated internally again, turning my you know, 333 00:19:16,280 --> 00:19:21,639 Speaker 4: my accounting analysts, the former auditor, turning her poorly expressed 334 00:19:21,760 --> 00:19:25,760 Speaker 4: thoughts into actual words, you know. And of course she's 335 00:19:25,840 --> 00:19:27,840 Speaker 4: iterating with it. No, no, no, that's not what I mean. 336 00:19:28,160 --> 00:19:30,679 Speaker 4: But it's got real accounting knowledge. Oh well, according to 337 00:19:30,720 --> 00:19:32,480 Speaker 4: ASC blah blah blah, and this does not meet the 338 00:19:32,520 --> 00:19:34,560 Speaker 4: definition of the da da da N wow. 339 00:19:34,720 --> 00:19:36,560 Speaker 3: I mean, that's it's really interesting. 340 00:19:36,640 --> 00:19:38,120 Speaker 4: I mean, look, I think at the end of the day, 341 00:19:38,920 --> 00:19:40,800 Speaker 4: part of the edge of being an active as short 342 00:19:40,800 --> 00:19:44,200 Speaker 4: seller is the willingness to be sued, or the tolerance 343 00:19:44,240 --> 00:19:46,640 Speaker 4: for being sued. So I'm not worried that a bunch 344 00:19:46,680 --> 00:19:49,119 Speaker 4: of people are going to run out and like commoditize this. 345 00:19:49,320 --> 00:19:50,800 Speaker 4: And and I think at the end of the day, 346 00:19:50,920 --> 00:19:55,520 Speaker 4: also having a brand where if everybody can produce skeptical 347 00:19:56,200 --> 00:20:00,240 Speaker 4: you know pieces, it's also knowing when the model is 348 00:20:00,320 --> 00:20:02,960 Speaker 4: kind of bullying you, right, like no, no, because that 349 00:20:03,040 --> 00:20:06,000 Speaker 4: happens too. I mean, these things are sickophantic, like wow, 350 00:20:06,040 --> 00:20:09,960 Speaker 4: that's brilliant. Of course it's a fraud person, you know, no. 351 00:20:10,520 --> 00:20:15,439 Speaker 2: To say nothing of double checking for hallucinations. And a 352 00:20:15,480 --> 00:20:18,320 Speaker 2: bunch of lawyers who have been using AI keep getting 353 00:20:18,320 --> 00:20:21,879 Speaker 2: into trouble because it's citing cases that don't really exist, 354 00:20:22,400 --> 00:20:26,240 Speaker 2: and the judge's clerk, also using AI, discovers these cases 355 00:20:26,240 --> 00:20:30,240 Speaker 2: don't exist. But you mentioned a word that's really fascinating. 356 00:20:30,800 --> 00:20:36,920 Speaker 2: You mentioned edge. If all the participants in the markets 357 00:20:37,000 --> 00:20:41,160 Speaker 2: long and short are using the same tools, is there 358 00:20:41,200 --> 00:20:43,760 Speaker 2: any edge to be found that had there or is 359 00:20:43,800 --> 00:20:46,560 Speaker 2: it really about how you're applying these tools where the 360 00:20:46,640 --> 00:20:47,400 Speaker 2: edge comes from. 361 00:20:47,640 --> 00:20:48,720 Speaker 3: That's an interesting question. 362 00:20:48,840 --> 00:20:50,879 Speaker 4: I mean, look, on the on the short side, you 363 00:20:50,960 --> 00:20:53,480 Speaker 4: only make money if people care, right, And so I 364 00:20:53,480 --> 00:20:58,639 Speaker 4: think that's been the problem that most so since the GFC. 365 00:20:59,640 --> 00:21:03,280 Speaker 4: Almost everybody who is running money principally focused on short 366 00:21:03,280 --> 00:21:06,840 Speaker 4: strategies has gone out of business. I mean some you know, 367 00:21:06,920 --> 00:21:08,920 Speaker 4: they rode off into the sunset. You know, I made 368 00:21:08,920 --> 00:21:12,399 Speaker 4: a bunch of money, most carried out, you know. I 369 00:21:12,400 --> 00:21:15,040 Speaker 4: think the problem that a lot of the short sellers 370 00:21:15,160 --> 00:21:18,600 Speaker 4: have had and I had this too, you know, and 371 00:21:18,680 --> 00:21:20,960 Speaker 4: have if I have to actively correct for it is 372 00:21:21,800 --> 00:21:23,800 Speaker 4: you try. You tend to view the world the way 373 00:21:23,840 --> 00:21:26,320 Speaker 4: it should be, not the way it is. And so 374 00:21:26,480 --> 00:21:29,920 Speaker 4: that's the problem. Like when people short you know, Tesla 375 00:21:30,000 --> 00:21:33,400 Speaker 4: because you know Elon Musk this and that, like nobody 376 00:21:33,440 --> 00:21:36,800 Speaker 4: cares right and that that's that's the thing. So I 377 00:21:37,520 --> 00:21:40,960 Speaker 4: think you to be good on the short side, you 378 00:21:41,080 --> 00:21:44,719 Speaker 4: have to understand why people are buying the stock, and 379 00:21:44,760 --> 00:21:48,240 Speaker 4: you have to have a view that goes directly against 380 00:21:48,240 --> 00:21:50,840 Speaker 4: that or that undermines that thesis. And I think so 381 00:21:50,960 --> 00:21:54,240 Speaker 4: much of the time that doesn't happen. So I don't know. 382 00:21:54,280 --> 00:21:57,480 Speaker 4: I think the I don't know that this erodes the 383 00:21:57,720 --> 00:22:01,000 Speaker 4: edge of just of judgment when it comes the longside 384 00:22:01,240 --> 00:22:03,320 Speaker 4: or short side. And then on the long side, I 385 00:22:03,359 --> 00:22:05,879 Speaker 4: mean kind of buy what the smart money is buying 386 00:22:05,920 --> 00:22:07,919 Speaker 4: seems to have worked like I hate to say it, 387 00:22:07,960 --> 00:22:11,680 Speaker 4: but you know, again, technicals versus fundamental value, the technical 388 00:22:11,760 --> 00:22:14,880 Speaker 4: value of something. You know, I don't know that AI 389 00:22:15,240 --> 00:22:17,960 Speaker 4: helps tongue with that right now, but it certainly can. 390 00:22:18,480 --> 00:22:22,320 Speaker 2: Let's talk about AI pretenders, and I'm pulling something else 391 00:22:22,359 --> 00:22:25,320 Speaker 2: you had written a while ago, and there's a little 392 00:22:25,320 --> 00:22:29,000 Speaker 2: bit of a rhyme with late nineties every company edit 393 00:22:29,040 --> 00:22:31,800 Speaker 2: a dot com and their stock would see a pop. 394 00:22:32,520 --> 00:22:36,000 Speaker 2: What are you noticing amongst the fake AI stories, the 395 00:22:36,040 --> 00:22:39,919 Speaker 2: fake pivots, the companies that really have nothing whatsoever to 396 00:22:40,040 --> 00:22:43,320 Speaker 2: use to do with AI other than two or three 397 00:22:43,320 --> 00:22:45,960 Speaker 2: people who work for the company have a Perplexity app 398 00:22:46,000 --> 00:22:50,520 Speaker 2: on their phone. Tell us about the narrative of AI pretenders. 399 00:22:50,840 --> 00:22:52,959 Speaker 4: Well, I look, I think if all of a sudden 400 00:22:52,960 --> 00:22:56,480 Speaker 4: the company has started talking. So I first did this 401 00:22:57,040 --> 00:23:01,760 Speaker 4: shortly after I started on the short side. Cloud became 402 00:23:01,800 --> 00:23:05,119 Speaker 4: the thing, right, and so then I saw some companies 403 00:23:05,119 --> 00:23:08,359 Speaker 4: that had just done a global find and replaced, like, 404 00:23:08,480 --> 00:23:11,600 Speaker 4: you know, find this, replace it with cloud. And so 405 00:23:11,640 --> 00:23:14,919 Speaker 4: if you see that with AI in a company's filings 406 00:23:15,200 --> 00:23:18,080 Speaker 4: and in their statements, then yeah, I mean they're they're 407 00:23:18,080 --> 00:23:21,359 Speaker 4: probably a pretender. I mean, I think it's pretty clear. 408 00:23:22,160 --> 00:23:25,600 Speaker 4: You know, AI, real AI requires scale. I mean, whether 409 00:23:25,680 --> 00:23:28,840 Speaker 4: you're on the process on the hardware side, or you're 410 00:23:29,080 --> 00:23:32,080 Speaker 4: you know, or you're actually producing the AI models. So 411 00:23:32,920 --> 00:23:36,280 Speaker 4: I don't know everybody who's like AI enabled it, And look, 412 00:23:36,320 --> 00:23:38,320 Speaker 4: I'm out over my skis here because I don't really 413 00:23:38,400 --> 00:23:41,919 Speaker 4: understand the technology. But everybody who's AI enabled, it's like, well, 414 00:23:41,960 --> 00:23:44,159 Speaker 4: what's what's the foundation? You know, if you are an 415 00:23:44,200 --> 00:23:47,240 Speaker 4: AI enabled this, what are you running? Are you running perplexity, 416 00:23:47,359 --> 00:23:50,240 Speaker 4: Claude chat GPT? And if you're like, well I made 417 00:23:50,240 --> 00:23:53,800 Speaker 4: it myself, like no, man, like I'm not going to 418 00:23:53,840 --> 00:23:56,040 Speaker 4: buy that, you know, like these things have cost way 419 00:23:56,119 --> 00:23:58,840 Speaker 4: too many billions of dollars to develop for you to 420 00:23:58,840 --> 00:23:59,760 Speaker 4: be able to vibe code. 421 00:23:59,800 --> 00:24:04,480 Speaker 2: You're short Sellers are always looking for a downside catalyst 422 00:24:05,000 --> 00:24:09,520 Speaker 2: when you start thinking about the megacaps, the hyperscalers, or 423 00:24:09,560 --> 00:24:13,280 Speaker 2: anybody else that's really blown up in value and in 424 00:24:13,400 --> 00:24:16,320 Speaker 2: price that's created a little bit of an air pocket. 425 00:24:16,760 --> 00:24:20,240 Speaker 2: Has that catalyst come in yet or is there something 426 00:24:20,280 --> 00:24:24,720 Speaker 2: off in the future that's gonna lead people to say, hey, 427 00:24:24,760 --> 00:24:26,720 Speaker 2: this has gone too far, we need to take something 428 00:24:26,760 --> 00:24:27,399 Speaker 2: off the table. 429 00:24:27,680 --> 00:24:30,760 Speaker 4: Well, if you take my view that a lot of 430 00:24:30,800 --> 00:24:34,240 Speaker 4: these names have traded based on their technical values as 431 00:24:34,240 --> 00:24:38,639 Speaker 4: opposed to fundamental. Then you need to get into okay, 432 00:24:38,760 --> 00:24:42,760 Speaker 4: like what are the underlying technicals here. So I'm a 433 00:24:42,800 --> 00:24:45,600 Speaker 4: fan of a guy named Mike Green, So I think 434 00:24:45,680 --> 00:24:48,679 Speaker 4: Mike is going to prove to be really prophetic. And 435 00:24:48,720 --> 00:24:52,160 Speaker 4: so what he's been saying since maybe eighteen or nineteen 436 00:24:52,800 --> 00:24:55,959 Speaker 4: is that so passive has broken the market. You know, 437 00:24:56,200 --> 00:25:01,120 Speaker 4: don't disagree, but that when the flows, actually the buying 438 00:25:01,320 --> 00:25:04,960 Speaker 4: from the target date funds tapers off and then you 439 00:25:05,040 --> 00:25:09,800 Speaker 4: get net outflows, that's when you get as he calls it, 440 00:25:10,119 --> 00:25:13,600 Speaker 4: or was calling it nineteen twenty nine magnitude crash at 441 00:25:13,880 --> 00:25:15,960 Speaker 4: you know, filling the year, you know, filling the blank 442 00:25:16,000 --> 00:25:20,520 Speaker 4: with the year speed. And that's what scares me about 443 00:25:20,880 --> 00:25:24,840 Speaker 4: AI because you know, like I said, if I if 444 00:25:24,840 --> 00:25:28,600 Speaker 4: this thesis is correct, that then three years or a 445 00:25:28,640 --> 00:25:31,880 Speaker 4: few years, fifteen percent of knowledge workers have lost their jobs. 446 00:25:32,320 --> 00:25:34,360 Speaker 4: It's not like they're going to find new jobs. It's 447 00:25:34,400 --> 00:25:37,920 Speaker 4: not like you know, fiscal or monetary stimulus creates new 448 00:25:37,960 --> 00:25:41,160 Speaker 4: knowledge work jobs. They are going to and they all 449 00:25:41,200 --> 00:25:47,000 Speaker 4: have college debt, mortgages, car leases. Eventually they're going to 450 00:25:47,040 --> 00:25:50,000 Speaker 4: start hitting up their four oh one k's and so 451 00:25:50,160 --> 00:25:53,760 Speaker 4: first they stop contributing as they're laid off. Then they 452 00:25:53,760 --> 00:25:57,479 Speaker 4: need that money and they start taking early redemptions. And 453 00:25:57,560 --> 00:26:02,000 Speaker 4: so if Mike Green is correct, when that happens, there's 454 00:26:02,040 --> 00:26:05,719 Speaker 4: nobody there to catch the falling knife, and the technicals 455 00:26:05,760 --> 00:26:10,000 Speaker 4: have basically for you know better, since the GFC just 456 00:26:10,160 --> 00:26:15,879 Speaker 4: created this tremendous amount of air, that's when it's really 457 00:26:15,920 --> 00:26:19,160 Speaker 4: time to panic, because that that's what I think, that's 458 00:26:19,240 --> 00:26:21,760 Speaker 4: what's going to make his thesis. That's what's going to 459 00:26:21,800 --> 00:26:25,200 Speaker 4: test his thesis. And if you asked me five weeks ago, 460 00:26:26,359 --> 00:26:29,040 Speaker 4: you know, did I think that we're in any danger 461 00:26:29,160 --> 00:26:32,560 Speaker 4: anything on the horizon in terms of seeing you know, 462 00:26:32,600 --> 00:26:36,080 Speaker 4: reversal of four oh one K flows and rise significant 463 00:26:36,160 --> 00:26:37,960 Speaker 4: rise in unemployment, I would have said no. 464 00:26:38,600 --> 00:26:38,959 Speaker 3: I was. 465 00:26:39,119 --> 00:26:43,240 Speaker 4: I was completely sanguine five weeks ago, and I've, like 466 00:26:43,280 --> 00:26:44,920 Speaker 4: I said, I've won eight So. 467 00:26:45,119 --> 00:26:48,160 Speaker 2: I can't leave on that much of a downbeat note. 468 00:26:48,600 --> 00:26:52,800 Speaker 2: So for the last question, you run along short funds. 469 00:26:53,520 --> 00:26:57,560 Speaker 2: If that's the downside of AI, if that's the negative 470 00:26:57,600 --> 00:27:02,320 Speaker 2: we're seeing in the world of why collar work, what's 471 00:27:02,520 --> 00:27:07,280 Speaker 2: the long side of the portfolio, What looks attractive through 472 00:27:07,359 --> 00:27:10,680 Speaker 2: and on the other side of whatever happens with either 473 00:27:10,840 --> 00:27:13,160 Speaker 2: the AI thesis or Green's thesis. 474 00:27:13,320 --> 00:27:17,080 Speaker 4: Well, okay, the tough thing is there obviously are going 475 00:27:17,119 --> 00:27:20,639 Speaker 4: to be companies that benefit from AI, right, and so 476 00:27:20,680 --> 00:27:23,840 Speaker 4: they're the hyperscalers. But if you have but if they're 477 00:27:23,880 --> 00:27:25,840 Speaker 4: in the index, which they all are, and they're major 478 00:27:25,880 --> 00:27:28,320 Speaker 4: parts of the index, and you have index fund flows, 479 00:27:29,200 --> 00:27:31,640 Speaker 4: well the bull cases that over the long term they're 480 00:27:31,680 --> 00:27:33,560 Speaker 4: going to be you know, there's gonna be a great 481 00:27:33,600 --> 00:27:36,760 Speaker 4: buying opportunity. So I'm not sure that's where you hide out. 482 00:27:36,800 --> 00:27:40,240 Speaker 4: I mean, what we've been doing for the past month 483 00:27:40,640 --> 00:27:43,240 Speaker 4: is creating a set of what I think are highly 484 00:27:43,280 --> 00:27:49,080 Speaker 4: convexed trades in the book, especially basically shorten credit. So 485 00:27:49,119 --> 00:27:51,119 Speaker 4: it's not like the lead up to the GFC there's 486 00:27:51,119 --> 00:27:55,480 Speaker 4: not you know, deep CDs market, but I think, I mean, 487 00:27:55,520 --> 00:27:59,960 Speaker 4: credit spreads are stupidly tight and credit ball is stupid 488 00:28:00,119 --> 00:28:04,159 Speaker 4: Lee Love. So to me, you want convexity and there 489 00:28:04,160 --> 00:28:06,120 Speaker 4: are lots of ways to pay it where you're capping 490 00:28:06,200 --> 00:28:09,520 Speaker 4: your potential loss. That's how we're that's how we're approaching it. 491 00:28:09,760 --> 00:28:11,720 Speaker 4: So like and look, I hope this doesn't play out. 492 00:28:11,760 --> 00:28:13,879 Speaker 4: I mean, I be cause I don't have like a 493 00:28:13,920 --> 00:28:17,399 Speaker 4: plan B or C after AI takes, you know, like 494 00:28:17,520 --> 00:28:20,320 Speaker 4: my job. But you know, at least as it does 495 00:28:20,359 --> 00:28:22,080 Speaker 4: play out, we're we're positioning for it. 496 00:28:22,359 --> 00:28:26,560 Speaker 2: That was my conversation live at future Proof Citywide, Miami 497 00:28:27,080 --> 00:28:31,320 Speaker 2: with Muddy Waters Carson Block. If you enjoy this conversation, 498 00:28:31,840 --> 00:28:34,480 Speaker 2: well check out any of the six hundred we've done 499 00:28:34,520 --> 00:28:38,800 Speaker 2: over the past almost fourteen years. You can find those 500 00:28:38,840 --> 00:28:44,640 Speaker 2: at iTunes, YouTube, Spotify, Bloomberg, wherever you get your favorite 501 00:28:44,680 --> 00:28:47,560 Speaker 2: podcasts from. I would be remiss if I didn't thank 502 00:28:47,640 --> 00:28:51,160 Speaker 2: the crack team that helps put these conversations. 503 00:28:50,680 --> 00:28:52,800 Speaker 3: Together each week. 504 00:28:53,080 --> 00:28:57,080 Speaker 2: Alexis Noriega is my video producer. Anna Luke is my 505 00:28:57,200 --> 00:29:00,680 Speaker 2: regular producer. Sean Russo is my head of research. I'm 506 00:29:00,720 --> 00:29:04,800 Speaker 2: Barry Ritolts. You've been listening to Masters in Business on 507 00:29:04,920 --> 00:29:10,280 Speaker 2: Bloomberg Radio.