1 00:00:02,720 --> 00:00:07,260 Speaker 1: Bloomberg Audio Studios. Podcasts. Radio. News. 2 00:00:08,070 --> 00:00:09,210 Speaker 2: What are you reading, Matt Levine? 3 00:00:09,730 --> 00:00:12,650 Speaker 1: You know, I'm reading Absalom, Absalom. It's a little much. 4 00:00:12,670 --> 00:00:15,470 Speaker 2: Many people think that's one of the greatest novels to 5 00:00:15,550 --> 00:00:16,080 Speaker 2: ever do it. 6 00:00:16,090 --> 00:00:16,570 Speaker 1: I know. I know. 7 00:00:16,610 --> 00:00:18,950 Speaker 2: Did you read it because of the Dwight Garner review 8 00:00:18,970 --> 00:00:21,569 Speaker 2: of The Disappearers that mentioned it as one of the best? 9 00:00:22,350 --> 00:00:22,670 Speaker 2: You did. 10 00:00:22,690 --> 00:00:26,140 Speaker 1: Well played. Wow. From downtown. This is like such not 11 00:00:26,160 --> 00:00:30,900 Speaker 1: money stuff content. Yes. Sylvia accurately identified why I am 12 00:00:30,920 --> 00:00:33,890 Speaker 1: reading Absalom, Absalom. It's like a little much, man. It's 13 00:00:33,909 --> 00:00:34,750 Speaker 1: a little much. Okay. 14 00:00:34,830 --> 00:00:36,809 Speaker 2: Absalom, Absalom is your fiction. What's your non? 15 00:00:37,150 --> 00:00:40,230 Speaker 1: My usual division is fiction, nonfiction, and math or finance. 16 00:00:40,590 --> 00:00:43,350 Speaker 1: But then also, I usually try to have two paper 17 00:00:43,370 --> 00:00:45,190 Speaker 1: books going and one book on my phone so I 18 00:00:45,210 --> 00:00:47,310 Speaker 1: can read it while walking the dog. And so right now, 19 00:00:47,350 --> 00:00:50,270 Speaker 1: my phone book is also fiction. It's Colson Whitehead's Sag Harbor. 20 00:00:50,400 --> 00:00:53,419 Speaker 2: What drove you to that? Wistful feelings about the end 21 00:00:53,440 --> 00:00:53,900 Speaker 2: of the summer? 22 00:00:54,540 --> 00:00:57,340 Speaker 1: So yes. So well-identified, but no, there's a much more 23 00:00:57,360 --> 00:00:57,740 Speaker 1: specific answer. 24 00:00:57,760 --> 00:00:58,440 Speaker 2: Give me one more. 25 00:00:58,560 --> 00:01:01,420 Speaker 1: Which is that I was In Montauk, Reading the East 26 00:01:01,440 --> 00:01:05,420 Speaker 1: Hampton Star, which featured a write-up of a book event 27 00:01:05,459 --> 00:01:07,240 Speaker 1: that Colson Whitehead did at, I don't know, the East 28 00:01:07,280 --> 00:01:09,480 Speaker 1: Hampton Public Library. Right, for the new one. And it 29 00:01:09,500 --> 00:01:12,240 Speaker 1: was just a good, he sounds fun, and so I 30 00:01:12,260 --> 00:01:15,920 Speaker 1: was like, I have late summer nostalgia, so I bought 31 00:01:15,959 --> 00:01:19,920 Speaker 1: Sag Harbor from my phone. Hello, and welcome to the 32 00:01:19,940 --> 00:01:22,360 Speaker 1: Money Slip Podcast. I'm Matt Levine. I wrote the Money 33 00:01:22,380 --> 00:01:25,840 Speaker 1: Slip column for Bloomberg Opinion. I'm joined by. 34 00:01:25,860 --> 00:01:28,840 Speaker 2: I'm Sylvia Killingsworth. I edit the OnBooks newsletter. 35 00:01:29,360 --> 00:01:32,300 Speaker 1: Sylvia is our returning guest co-host. 36 00:01:32,520 --> 00:01:33,899 Speaker 2: Had so much fun. 37 00:01:34,360 --> 00:01:44,610 Speaker 1: The last time. So in May of this year, China 38 00:01:44,709 --> 00:01:48,610 Speaker 1: announced a crackdown on basically cross-border trading from its brokerages. 39 00:01:48,910 --> 00:01:51,880 Speaker 1: So some Chinese brokerage firms, their stocks dropped a lot 40 00:01:51,920 --> 00:01:54,020 Speaker 1: because they got in trouble and they were restricted from 41 00:01:54,060 --> 00:01:55,860 Speaker 1: doing some of the trades that had made them a 42 00:01:55,860 --> 00:02:00,250 Speaker 1: lot of money. And just before that happened, people bought 43 00:02:00,270 --> 00:02:05,740 Speaker 1: a lot of puts on several Chinese brokerage stocks. And 44 00:02:05,760 --> 00:02:09,340 Speaker 1: Susquehanna International Group, which is a big options market maker, 45 00:02:09,940 --> 00:02:13,440 Speaker 1: sold a lot of those puts. And they went to 46 00:02:13,480 --> 00:02:17,130 Speaker 1: court in the US in June and said, people who 47 00:02:17,169 --> 00:02:21,770 Speaker 1: are buying these puts, we have statistical data that makes 48 00:02:21,810 --> 00:02:24,169 Speaker 1: it look really suspicious that they bought these puts. There's 49 00:02:24,230 --> 00:02:27,790 Speaker 1: no news. They just bought a lot of very short-dated. 50 00:02:27,639 --> 00:02:28,139 Speaker 2: They must have known. 51 00:02:28,160 --> 00:02:29,160 Speaker 1: Because they paid off a lot of money. 52 00:02:29,220 --> 00:02:29,639 Speaker 2: Somehow. 53 00:02:29,660 --> 00:02:31,280 Speaker 1: They must have known. They must have been. 54 00:02:31,300 --> 00:02:32,500 Speaker 2: Somebody must have leaked somewhere. 55 00:02:32,520 --> 00:02:34,720 Speaker 1: It guesses who they were. Right. Because like insider trading 56 00:02:34,760 --> 00:02:36,560 Speaker 1: laws are weird. You need to have a fiduciary duty. 57 00:02:37,100 --> 00:02:38,590 Speaker 1: That would be illegal. You need to be in breach 58 00:02:38,630 --> 00:02:40,730 Speaker 1: of some duty to someone. And so they're like, they 59 00:02:40,770 --> 00:02:43,230 Speaker 1: must have been either Chinese regulators or employees at these 60 00:02:43,250 --> 00:02:45,929 Speaker 1: brokerage firms and had some duty to keep it confidential. 61 00:02:46,470 --> 00:02:48,240 Speaker 1: And instead they traded on it. And so we want 62 00:02:48,280 --> 00:02:52,459 Speaker 1: the money back. And they sued like 100 John Doe defendants. 63 00:02:52,940 --> 00:02:53,640 Speaker 1: They don't know who they are. 64 00:02:53,639 --> 00:02:54,400 Speaker 2: 100 Johns Doe. 65 00:02:54,419 --> 00:02:57,210 Speaker 1: Yeah. they're like customers of a couple of like Chinese brokerages. 66 00:02:57,230 --> 00:02:59,150 Speaker 1: And I think of interactive brokers that you asked for. 67 00:02:59,730 --> 00:03:03,139 Speaker 1: And they basically like went to court. First of all, 68 00:03:03,150 --> 00:03:04,850 Speaker 1: you get the money back, but secondly to like immediately 69 00:03:04,889 --> 00:03:07,710 Speaker 1: get an injunction to like freeze these accounts. Yep. And 70 00:03:08,250 --> 00:03:13,410 Speaker 1: this month they lost the preliminary injunction. Basically the judge 71 00:03:13,430 --> 00:03:16,500 Speaker 1: was like, Well, the case can go forward maybe, like, 72 00:03:16,560 --> 00:03:18,120 Speaker 1: it's fine, but, like, we're not going to freeze all 73 00:03:18,160 --> 00:03:19,040 Speaker 1: these accounts because he. 74 00:03:19,060 --> 00:03:21,339 Speaker 2: Just didn't... It's impossible to know which one's who's what. 75 00:03:21,360 --> 00:03:23,839 Speaker 1: He just didn't, like, entirely believe that they were all 76 00:03:23,900 --> 00:03:24,540 Speaker 1: insider trading. 77 00:03:24,600 --> 00:03:26,920 Speaker 2: All 100 of them are holding hands and insider trading together. 78 00:03:26,940 --> 00:03:27,800 Speaker 1: Right, right. 79 00:03:27,820 --> 00:03:30,250 Speaker 2: So what was happening? If not all 100, some number 80 00:03:30,260 --> 00:03:32,070 Speaker 2: of them, but the rest were kind of just following 81 00:03:32,090 --> 00:03:32,450 Speaker 2: the pack. 82 00:03:33,169 --> 00:03:36,310 Speaker 1: Yeah, it's unclear, right? Like, there could be no insider trading. Like, 83 00:03:36,370 --> 00:03:40,130 Speaker 1: I think, like, the circumstantial evidence is reasonable that maybe 84 00:03:40,170 --> 00:03:41,270 Speaker 1: someone was insider trading. 85 00:03:41,890 --> 00:03:44,520 Speaker 2: But all 100 of them, eh. 100 is even kind 86 00:03:44,540 --> 00:03:46,500 Speaker 2: of a fake number. Sure, but we're calling it 100. 87 00:03:46,480 --> 00:03:48,280 Speaker 1: You just say John Does 1 through 100. Sure. But yeah, 88 00:03:48,600 --> 00:03:51,380 Speaker 1: all 100 love them, let's say. We don't know. I've 89 00:03:51,400 --> 00:03:54,160 Speaker 1: written about this before. If you get accused of insider 90 00:03:54,200 --> 00:03:58,300 Speaker 1: trading and you are not a U.S. national and they 91 00:03:58,340 --> 00:04:02,150 Speaker 1: freeze your account, it is probably not wise to get 92 00:04:02,190 --> 00:04:04,430 Speaker 1: on a plane and fly to New York and say, 93 00:04:04,470 --> 00:04:06,330 Speaker 1: I'd like my money back, because they might arrest you 94 00:04:06,370 --> 00:04:09,370 Speaker 1: for doing the insider trading. But if you're totally innocent, 95 00:04:09,410 --> 00:04:11,710 Speaker 1: you can take a shot. And so, in fact, a 96 00:04:11,750 --> 00:04:13,210 Speaker 1: couple of– I don't know if they flew to New York. 97 00:04:13,580 --> 00:04:17,560 Speaker 1: But a couple of the like John Doe's came to court. 98 00:04:18,260 --> 00:04:19,040 Speaker 2: And used their real names. 99 00:04:19,200 --> 00:04:20,560 Speaker 1: Yeah. And we're like, no, no, I'm not John Doe. 100 00:04:20,600 --> 00:04:24,690 Speaker 1: I'm this guy. And here's why I bought these put options, right? 101 00:04:25,060 --> 00:04:27,330 Speaker 1: And it's like, you know, I was on message boards 102 00:04:27,950 --> 00:04:32,330 Speaker 1: and I saw people worrying about a potential crackdown. And 103 00:04:32,830 --> 00:04:35,510 Speaker 1: I saw people buying put options and I thought, huh, 104 00:04:35,750 --> 00:04:39,690 Speaker 1: that's weird. I saw volatility going up. And so like 105 00:04:39,710 --> 00:04:41,430 Speaker 1: some of that, you know, it's always possible that that 106 00:04:41,470 --> 00:04:44,370 Speaker 1: is like self-serving, right? And that like people are Sure. 107 00:04:44,520 --> 00:04:47,970 Speaker 1: They can just say that. But to be fair, these 108 00:04:47,990 --> 00:04:51,270 Speaker 1: people are not like, I'm a Chinese securities regulator, just coincidentally. 109 00:04:52,070 --> 00:04:55,130 Speaker 1: It's like they're not Chinese securities regulators. Maybe they were 110 00:04:55,190 --> 00:04:59,790 Speaker 1: tipped by insiders, but quite possibly their story is true 111 00:05:00,130 --> 00:05:02,050 Speaker 1: and they saw someone else buying put options. 112 00:05:02,070 --> 00:05:03,600 Speaker 2: I was just doing what that guy was doing. I 113 00:05:03,620 --> 00:05:05,580 Speaker 2: don't know why he was doing it, but I have 114 00:05:05,600 --> 00:05:06,440 Speaker 2: a good guess. 115 00:05:06,640 --> 00:05:10,960 Speaker 1: Right. And people email me about this sometimes. To some people, 116 00:05:11,020 --> 00:05:14,470 Speaker 1: it feels wrong to buy options. Because you see a 117 00:05:15,089 --> 00:05:17,190 Speaker 1: big options trade and you have a good guess about why. 118 00:05:17,310 --> 00:05:22,000 Speaker 1: If you see, looking at public stock tapes, if you 119 00:05:22,020 --> 00:05:23,900 Speaker 1: just see a public trade and you're like, that must 120 00:05:23,940 --> 00:05:26,150 Speaker 1: be insider trading, and then you go trade on it, 121 00:05:26,160 --> 00:05:28,730 Speaker 1: people are like, that's insider trading too, but I disagree. 122 00:05:29,120 --> 00:05:30,540 Speaker 1: I think that is totally fine. 123 00:05:30,580 --> 00:05:33,760 Speaker 2: So trading on what appears from the outside to be 124 00:05:33,820 --> 00:05:36,779 Speaker 2: insider trading, you're saying is essentially still outsider. 125 00:05:36,940 --> 00:05:38,960 Speaker 1: Yeah. If you're trading on public information, that happens to be. 126 00:05:38,960 --> 00:05:40,850 Speaker 2: The public information is I saw it in the tape. 127 00:05:41,080 --> 00:05:43,870 Speaker 1: And like, you know, if you know, like if like 128 00:05:44,050 --> 00:05:45,850 Speaker 1: the CEO of the company calls you and like, hey, 129 00:05:45,890 --> 00:05:47,390 Speaker 1: I'm going to do a big options trade because we're 130 00:05:47,410 --> 00:05:50,289 Speaker 1: getting acquired. Then like, that's bad. Right. But if you just. 131 00:05:50,570 --> 00:05:52,089 Speaker 2: If you're looking at the shape of the trade. 132 00:05:52,130 --> 00:05:54,330 Speaker 1: If you just look at, if you're just a momentum trader, 133 00:05:54,370 --> 00:05:54,870 Speaker 1: then you're fine. 134 00:05:54,930 --> 00:05:55,610 Speaker 2: You're riding the wave. 135 00:05:56,190 --> 00:06:00,420 Speaker 1: And so, you know, some, you know, between. one in 136 00:06:00,420 --> 00:06:05,800 Speaker 1: 99 of these Susquehanna customers were probably doing that. And 137 00:06:06,740 --> 00:06:09,520 Speaker 1: Susquehanna's point, which I think is fair, is we can't 138 00:06:09,540 --> 00:06:10,940 Speaker 1: really tell who is who. Right. 139 00:06:11,320 --> 00:06:13,150 Speaker 2: And these people made money at our expense. 140 00:06:13,230 --> 00:06:15,270 Speaker 1: Right. And the judge's point, which I think is also fair, 141 00:06:15,370 --> 00:06:18,930 Speaker 1: is like, I didn't tell you to sue 100 John Does. Right. 142 00:06:19,190 --> 00:06:20,250 Speaker 1: You sued the people who did it. 143 00:06:20,330 --> 00:06:23,029 Speaker 2: Right. Tell me, give me the evidence of them actually 144 00:06:23,070 --> 00:06:23,930 Speaker 2: doing it and then we can talk. 145 00:06:24,290 --> 00:06:26,270 Speaker 1: I do think that like, you know, maybe the case 146 00:06:26,310 --> 00:06:29,850 Speaker 1: proceeds and they like, you know, subpoenaed the brokerages and 147 00:06:29,870 --> 00:06:31,770 Speaker 1: they get some information and maybe they can. 148 00:06:31,790 --> 00:06:33,450 Speaker 2: What are they looking for? They're looking for like, oh, 149 00:06:33,510 --> 00:06:36,029 Speaker 2: I texted my buddy from college and that's how I 150 00:06:36,170 --> 00:06:37,800 Speaker 2: learned that, you know, they're looking for. 151 00:06:37,810 --> 00:06:40,180 Speaker 1: Or just like literally the names of the accounts, right? 152 00:06:40,260 --> 00:06:42,720 Speaker 1: Where like, if you like match the name with like 153 00:06:42,740 --> 00:06:44,640 Speaker 1: the head of the Chinese securities regulator, like, oh, that 154 00:06:44,680 --> 00:06:47,099 Speaker 1: guy might be insider trading, right? But yeah, I think 155 00:06:47,120 --> 00:06:51,900 Speaker 1: it's very hard to like correlate a bunch of traders 156 00:06:51,940 --> 00:06:54,539 Speaker 1: in a foreign country with like who you think might 157 00:06:54,580 --> 00:06:57,320 Speaker 1: be insider trading. I think it's the sort of thing 158 00:06:57,360 --> 00:06:59,560 Speaker 1: that a securities regulator can look into. And I think 159 00:06:59,800 --> 00:07:02,520 Speaker 1: securities regulators might be looking into this, but it's very 160 00:07:02,560 --> 00:07:03,380 Speaker 1: hard for Susquehanna. 161 00:07:03,420 --> 00:07:05,940 Speaker 2: It's hard to identify the needle. 162 00:07:06,420 --> 00:07:08,460 Speaker 1: Yeah. It's hard to prove who insider traded and who didn't. 163 00:07:08,480 --> 00:07:10,100 Speaker 1: And like they have, you know, they do the best 164 00:07:10,120 --> 00:07:13,740 Speaker 1: they can with like statistical, like doesn't this trade look weird. Right. 165 00:07:13,820 --> 00:07:16,940 Speaker 2: But overall Susquehanna's case is like, but somebody did probably 166 00:07:16,980 --> 00:07:20,120 Speaker 2: most likely. And don't you see that that's why we 167 00:07:20,160 --> 00:07:22,820 Speaker 2: lost so much money. I mean, I know there are 168 00:07:22,850 --> 00:07:25,480 Speaker 2: market makers that they have to, but like, Isn't there 169 00:07:25,520 --> 00:07:29,260 Speaker 2: some point when you're selling those out-of-the-money put options where 170 00:07:29,280 --> 00:07:32,160 Speaker 2: you're like, hmm, we're selling a lot of these. This 171 00:07:32,220 --> 00:07:32,640 Speaker 2: is weird. 172 00:07:33,060 --> 00:07:35,380 Speaker 1: So, one, I think I talked about this with Kate 173 00:07:35,420 --> 00:07:39,740 Speaker 1: on this podcast. Ages ago, Jeff Yass, the founder of Susquehanna, 174 00:07:39,860 --> 00:07:42,320 Speaker 1: did an interview with the Market Wizards series of books 175 00:07:42,740 --> 00:07:45,960 Speaker 1: where he talks about this problem. In the 90s, getting 176 00:07:46,000 --> 00:07:48,360 Speaker 1: picked off by selling a bunch of call options right 177 00:07:48,400 --> 00:07:50,660 Speaker 1: before a merger. And then sometimes it was the insider 178 00:07:50,680 --> 00:07:52,880 Speaker 1: traders who then gave the money back because the SEC 179 00:07:53,020 --> 00:07:56,430 Speaker 1: came after them. And he's kind of like, yeah, but 180 00:07:56,470 --> 00:07:58,330 Speaker 1: like we're a market, like it's our job. 181 00:07:58,390 --> 00:07:59,720 Speaker 2: We have to do it. 182 00:07:59,750 --> 00:08:01,270 Speaker 1: We can't just be like, oh, that's a big trade. 183 00:08:01,310 --> 00:08:02,590 Speaker 2: We can't do it. They're not going to not do it, 184 00:08:02,630 --> 00:08:05,910 Speaker 2: but they surely have some higher order thought about it. 185 00:08:06,190 --> 00:08:08,250 Speaker 1: Right. But then the other thing is, and I've written 186 00:08:08,270 --> 00:08:10,910 Speaker 1: about this, like the simple story here is that people 187 00:08:10,950 --> 00:08:13,150 Speaker 1: kept coming to Susquehanna to buy puts and Susquehanna kept 188 00:08:13,170 --> 00:08:14,790 Speaker 1: selling them the puts and then it was short a 189 00:08:14,810 --> 00:08:16,710 Speaker 1: lot of puts and then like bad stuff happened and 190 00:08:16,730 --> 00:08:20,300 Speaker 1: I lost a lot of money. But that's not like true. 191 00:08:21,440 --> 00:08:23,900 Speaker 1: Like that's one part of the story. But the other 192 00:08:23,940 --> 00:08:25,800 Speaker 1: part of the story is like they're an options market maker. 193 00:08:25,900 --> 00:08:29,540 Speaker 1: Options market makers like just naturally immediately hedge. Not always. 194 00:08:29,620 --> 00:08:31,760 Speaker 1: It's not like, but like for the most part, if 195 00:08:31,780 --> 00:08:34,640 Speaker 1: you sell put options, you then go and short some 196 00:08:34,660 --> 00:08:37,140 Speaker 1: stock to hedge your risk on the put options. But 197 00:08:37,610 --> 00:08:40,050 Speaker 1: that's not the only possible hedge. You could lay off 198 00:08:40,070 --> 00:08:41,810 Speaker 1: the put options, right? That you could sell a hundred 199 00:08:41,830 --> 00:08:44,670 Speaker 1: put options to a customer and then buy back a 200 00:08:44,690 --> 00:08:48,190 Speaker 1: hundred put options from other customers or from another market 201 00:08:48,230 --> 00:08:49,170 Speaker 1: maker or something like that. 202 00:08:49,210 --> 00:08:49,370 Speaker 2: Right. 203 00:08:49,770 --> 00:08:52,190 Speaker 1: And so if you're a Cisco head and you're bringing 204 00:08:52,210 --> 00:08:55,880 Speaker 1: this lawsuit, You're counting each put option that you sold 205 00:08:55,900 --> 00:08:59,950 Speaker 1: to a customer and saying, this customer was maybe insider trading, 206 00:09:00,309 --> 00:09:01,810 Speaker 1: so they should give us the money back. But you're 207 00:09:01,830 --> 00:09:04,650 Speaker 1: only counting those. You're not counting the put options you bought. 208 00:09:04,830 --> 00:09:05,530 Speaker 2: Right, right. 209 00:09:05,570 --> 00:09:08,630 Speaker 1: Because those aren't insider trading. And so, okay, it's certain 210 00:09:08,880 --> 00:09:12,140 Speaker 1: that they're partially hedged, right? Sure. And the judge quotes 211 00:09:12,160 --> 00:09:15,420 Speaker 1: them admitting in the court papers, like, yeah, we don't 212 00:09:15,460 --> 00:09:20,000 Speaker 1: deny hedging, but that goes to damages. We'll discuss that later, right? 213 00:09:20,020 --> 00:09:20,420 Speaker 2: Yeah, yeah. 214 00:09:20,690 --> 00:09:23,450 Speaker 1: But the customers made on the order of $ 70 million. 215 00:09:23,490 --> 00:09:26,750 Speaker 1: This is what they allege. So Susquehanna lost $ 70 million 216 00:09:27,090 --> 00:09:29,670 Speaker 1: just on those trades, right? Just on the puts that 217 00:09:29,700 --> 00:09:32,140 Speaker 1: they sold to the customers. How much did they make 218 00:09:32,220 --> 00:09:35,179 Speaker 1: on their hedges? It would be shocking if it was 219 00:09:35,220 --> 00:09:38,220 Speaker 1: less than $ 35 million, right? But it would not be 220 00:09:38,260 --> 00:09:41,819 Speaker 1: shocking if it was more than $ 70 million, right? There's 221 00:09:41,880 --> 00:09:45,439 Speaker 1: no allegation that they were like, net short these puts, right? 222 00:09:45,460 --> 00:09:47,620 Speaker 1: They're buying some puts from some people, selling some puts 223 00:09:47,640 --> 00:09:50,179 Speaker 1: to some other people. Maybe, maybe they made money that day. 224 00:09:50,200 --> 00:09:51,699 Speaker 1: I don't know. But they still can sue for the 225 00:09:51,720 --> 00:09:52,420 Speaker 1: money they lost. 226 00:09:52,490 --> 00:09:55,809 Speaker 2: Sure, sure, sure. But it's slightly obscuring the fact that 227 00:09:56,210 --> 00:09:58,790 Speaker 2: they were also having a hand Right. 228 00:09:58,809 --> 00:10:01,270 Speaker 1: Like what I wrote is that like, okay, so like 229 00:10:01,740 --> 00:10:05,600 Speaker 1: Citadel Securities, another big options market maker, joined Susquehanna. They're like, 230 00:10:05,700 --> 00:10:07,400 Speaker 1: we also sold options. 231 00:10:07,480 --> 00:10:08,100 Speaker 2: Yeah, yeah, yeah. 232 00:10:08,220 --> 00:10:10,199 Speaker 1: And we also got taken by this, right? 233 00:10:11,460 --> 00:10:11,880 Speaker 2: Pity them. 234 00:10:12,200 --> 00:10:16,020 Speaker 1: So both Citadel Securities and Susquehanna were selling options that day, right? 235 00:10:16,059 --> 00:10:19,240 Speaker 1: Because they're market makers, right? Also, they were both presumably hedging. 236 00:10:19,610 --> 00:10:21,750 Speaker 1: I don't know how they hatched. Classically, you delta hedge, 237 00:10:22,070 --> 00:10:24,350 Speaker 1: you sell some stock, right? So maybe they just sold stock. 238 00:10:24,730 --> 00:10:28,490 Speaker 1: But it's very possible that if you sell a lot 239 00:10:28,510 --> 00:10:30,850 Speaker 1: of options, you'll be like, I'm going to buy back 240 00:10:30,870 --> 00:10:34,090 Speaker 1: some options, right? Maybe they just have customer flow both ways. 241 00:10:34,130 --> 00:10:36,030 Speaker 1: They have customers buying, they have customers selling, so they 242 00:10:36,050 --> 00:10:38,910 Speaker 1: buy back from the customers who are selling. Maybe not, though. 243 00:10:39,190 --> 00:10:41,750 Speaker 1: Maybe one or both of these market makers was like, 244 00:10:42,630 --> 00:10:46,090 Speaker 1: we have sold a lot of put options. We don't 245 00:10:46,130 --> 00:10:48,570 Speaker 1: like our risk position. Let's buy back some put options. 246 00:10:49,820 --> 00:10:52,780 Speaker 1: put a bid in the market. It's not impossible that 247 00:10:52,800 --> 00:10:57,119 Speaker 1: like Citadel Securities bought some put options from SIG or 248 00:10:57,140 --> 00:10:58,100 Speaker 1: that SIG. 249 00:10:58,140 --> 00:10:59,910 Speaker 2: Bought some put options from Citadel. 250 00:10:59,710 --> 00:11:02,350 Speaker 1: Securities, right? And then like you have a mess, right? 251 00:11:02,510 --> 00:11:05,010 Speaker 1: And it's like they should be suing each other. So 252 00:11:05,230 --> 00:11:05,870 Speaker 1: I don't have any evidence. 253 00:11:05,890 --> 00:11:07,070 Speaker 2: Some of the John Does are named. 254 00:11:07,130 --> 00:11:08,890 Speaker 1: I don't have any evidence of that happened, but like it's, 255 00:11:09,110 --> 00:11:11,810 Speaker 1: you know, totally possible part of the story, right? It's 256 00:11:11,870 --> 00:11:14,210 Speaker 1: not like everyone who was buying put options that day 257 00:11:14,630 --> 00:11:16,730 Speaker 1: was an insider trader, right? Like some of them were, 258 00:11:16,770 --> 00:11:37,160 Speaker 1: you know, just people in the market. Like possibly... Pacing 259 00:11:37,240 --> 00:11:37,939 Speaker 1: the frontier. 260 00:11:38,200 --> 00:11:40,620 Speaker 2: Pacing the frontier. What a terrible... You should talk about that. 261 00:11:40,640 --> 00:11:41,100 Speaker 1: No, it's great. 262 00:11:41,580 --> 00:11:43,140 Speaker 2: Pacing is one of those verbs where it could mean 263 00:11:43,210 --> 00:11:45,390 Speaker 2: either one, right? Like tabling? 264 00:11:45,910 --> 00:11:46,209 Speaker 1: Yeah. 265 00:11:46,250 --> 00:11:47,910 Speaker 2: They mean it to say slowing down. 266 00:11:47,929 --> 00:11:48,010 Speaker 1: Sure. 267 00:11:48,530 --> 00:11:50,930 Speaker 2: But pacing can also mean keeping up with, like in 268 00:11:50,970 --> 00:11:51,450 Speaker 2: a running. 269 00:11:51,850 --> 00:11:54,030 Speaker 1: To me, pacing as a verb mainly means like walking 270 00:11:54,070 --> 00:11:55,170 Speaker 1: back and forth because you're nervous. 271 00:11:56,090 --> 00:11:57,510 Speaker 2: Oh, huh. 272 00:11:58,610 --> 00:11:59,130 Speaker 1: Like you're pacing. 273 00:11:59,150 --> 00:12:01,720 Speaker 2: Pacing the frontier, like, oh, oh no. 274 00:12:02,010 --> 00:12:03,890 Speaker 1: Oh, the air is going to kill us. 275 00:12:04,460 --> 00:12:06,140 Speaker 2: It's going to kill us all. It's going to turn 276 00:12:06,179 --> 00:12:07,319 Speaker 2: us into paperclips. Right. 277 00:12:07,500 --> 00:12:10,079 Speaker 1: No, anyway, sorry. Dario Amadei. 278 00:12:10,400 --> 00:12:14,420 Speaker 2: Furiously walking back and forth on the frontier. Wearing a 279 00:12:14,420 --> 00:12:15,060 Speaker 2: cowboy hat. 280 00:12:15,080 --> 00:12:18,530 Speaker 1: The CEO of Anthropic. published an essay over the weekend 281 00:12:18,570 --> 00:12:23,530 Speaker 1: called Pacing the Frontier, basically being like, we should slow down, 282 00:12:23,610 --> 00:12:25,730 Speaker 1: take a deep breath, and take our time a little 283 00:12:25,770 --> 00:12:29,950 Speaker 1: bit on the continuing development of frontier AI models because 284 00:12:30,030 --> 00:12:31,449 Speaker 1: otherwise they might kill us all. I don't think he 285 00:12:31,470 --> 00:12:33,090 Speaker 1: said otherwise they might kill us all, but that was 286 00:12:33,130 --> 00:12:33,690 Speaker 1: kind of implied. 287 00:12:33,730 --> 00:12:37,980 Speaker 2: Well, this is, to be fair, sort of like the founding, theoretically, 288 00:12:38,010 --> 00:12:40,580 Speaker 2: was supposed to be the founding ethos of, at the time, OpenAI, 289 00:12:40,620 --> 00:12:42,520 Speaker 2: which Dario Amadei was a part of. I'm in the 290 00:12:42,540 --> 00:12:45,910 Speaker 2: middle of reading Kevin Roos' AGI Chronicles, and... First of all, 291 00:12:45,990 --> 00:12:47,650 Speaker 2: all of them all used to work together at the 292 00:12:47,690 --> 00:12:50,770 Speaker 2: same place, whether it was Google or OpenAI. But they all, 293 00:12:50,850 --> 00:12:53,929 Speaker 2: Dario Amadei especially, their main thing was like, we're so 294 00:12:53,980 --> 00:12:57,380 Speaker 2: concerned with how AI is going to potentially ruin everything. 295 00:12:57,420 --> 00:12:58,900 Speaker 2: So we need to be the ones to do it 296 00:12:59,100 --> 00:13:00,559 Speaker 2: so that Google doesn't do it. 297 00:13:01,020 --> 00:13:05,420 Speaker 1: Every AI developer is someone who's worried about AI killing 298 00:13:05,460 --> 00:13:05,610 Speaker 1: us all. 299 00:13:05,620 --> 00:13:06,440 Speaker 2: That's like the main identity. 300 00:13:06,460 --> 00:13:09,500 Speaker 1: You can't get into AI without starting from the thought. 301 00:13:09,520 --> 00:13:11,580 Speaker 2: What if AI kills us all? Exactly. Right. That is 302 00:13:11,620 --> 00:13:13,040 Speaker 2: the number one starting point. 303 00:13:13,330 --> 00:13:15,349 Speaker 1: There's so much about this essay. So many people are 304 00:13:15,390 --> 00:13:19,910 Speaker 1: being like, this is all cynical marketing, and they don't 305 00:13:19,980 --> 00:13:20,440 Speaker 1: believe this. 306 00:13:20,460 --> 00:13:21,579 Speaker 2: What if it's genuine marketing? 307 00:13:21,900 --> 00:13:24,140 Speaker 1: People have different views on this, but I think there's 308 00:13:24,240 --> 00:13:29,000 Speaker 1: clearly a marketing benefit to conveying how powerful your AI 309 00:13:29,020 --> 00:13:30,920 Speaker 1: is by saying, oh, what if it goes a little? 310 00:13:31,220 --> 00:13:35,179 Speaker 1: I think, is there cynicism in the AI industry? Is 311 00:13:35,240 --> 00:13:37,199 Speaker 1: Sam Altman being somewhat cynical? Maybe. 312 00:13:37,220 --> 00:13:39,140 Speaker 2: Sam is a different person. 313 00:13:39,160 --> 00:13:41,780 Speaker 1: Do I think that most people at The big AI 314 00:13:41,820 --> 00:13:44,340 Speaker 1: labs are genuinely worried about AI killing us all? Yes. 315 00:13:44,360 --> 00:13:46,080 Speaker 1: I don't think it's all marketing. Right. 316 00:13:46,820 --> 00:13:50,100 Speaker 2: No, that's like, you definitely get that sense reading Kevin's 317 00:13:50,140 --> 00:13:52,800 Speaker 2: book that like, that's the starting point for everyone is like, 318 00:13:52,920 --> 00:13:55,469 Speaker 2: oh my God, this is so powerful. It's going to 319 00:13:55,490 --> 00:13:57,670 Speaker 2: obliterate us. How do we control it? Also, I need 320 00:13:57,690 --> 00:14:00,470 Speaker 2: to be first. Right. It's very paradoxical. 321 00:14:00,730 --> 00:14:02,610 Speaker 1: I joke about this because like, I think a lot 322 00:14:02,630 --> 00:14:06,030 Speaker 1: about how much AI researchers get paid. Oh God. 323 00:14:06,110 --> 00:14:09,070 Speaker 2: And how- More than- everything. 324 00:14:09,250 --> 00:14:11,750 Speaker 1: Right. And like how in the financial industry, there's like 325 00:14:11,790 --> 00:14:14,410 Speaker 1: a traditional, like people have their number, right? And if 326 00:14:14,429 --> 00:14:16,330 Speaker 1: you get paid $ 30 million, you can stop working. And 327 00:14:16,410 --> 00:14:19,550 Speaker 1: not everyone, right? But like you need some method of 328 00:14:19,630 --> 00:14:21,290 Speaker 1: motivating people to continue working. 329 00:14:21,910 --> 00:14:24,600 Speaker 2: But they don't care about money. Well, they do and 330 00:14:24,620 --> 00:14:24,860 Speaker 2: they don't. 331 00:14:25,760 --> 00:14:28,300 Speaker 1: The fact that they get paid $ 30 million just proves 332 00:14:28,320 --> 00:14:28,760 Speaker 1: that they do. 333 00:14:29,500 --> 00:14:30,520 Speaker 2: Isn't it like 75 sometimes? 334 00:14:30,540 --> 00:14:32,200 Speaker 1: Whatever, yeah. I mean, it's huge numbers. But like the 335 00:14:32,240 --> 00:14:34,859 Speaker 1: point is that In the financial industry, people think very 336 00:14:34,940 --> 00:14:37,440 Speaker 1: hard about how to calibrate salaries so that you keep 337 00:14:37,480 --> 00:14:38,220 Speaker 1: working even. 338 00:14:38,080 --> 00:14:40,330 Speaker 2: Though you're making $ 30 million a year. 339 00:14:40,630 --> 00:14:44,370 Speaker 1: And I don't understand how it works in AI. And 340 00:14:44,430 --> 00:14:46,990 Speaker 1: I have joked that one way it works is that 341 00:14:47,190 --> 00:14:50,670 Speaker 1: because the AI researchers believe that their product will kill 342 00:14:50,710 --> 00:14:53,670 Speaker 1: everyone on Earth, that is somehow motivating. I see. So 343 00:14:53,720 --> 00:14:54,600 Speaker 1: it's not the money. 344 00:14:54,700 --> 00:14:56,180 Speaker 2: It's the singularity. 345 00:14:56,460 --> 00:14:59,680 Speaker 1: Yeah. And I've joked that when I worked at Goldman, 346 00:15:00,080 --> 00:15:01,900 Speaker 1: if you had told everyone that our work was going 347 00:15:01,920 --> 00:15:04,260 Speaker 1: to kill everyone on Earth, That would probably not make 348 00:15:04,300 --> 00:15:05,700 Speaker 1: us more of a way to do it. But then 349 00:15:05,740 --> 00:15:09,590 Speaker 1: I thought about it for a minute. I was like, well, maybe, maybe, maybe. 350 00:15:09,610 --> 00:15:12,210 Speaker 2: It's going to be the derivative. 351 00:15:12,230 --> 00:15:14,790 Speaker 1: But it's just like a weird, right. A weird culture 352 00:15:14,850 --> 00:15:17,410 Speaker 1: of everyone being like, oh man, I'm so excited to 353 00:15:17,490 --> 00:15:21,630 Speaker 1: go to work at the Killing Everyone on Earth factory today. 354 00:15:21,650 --> 00:15:21,950 Speaker 1: But yeah. 355 00:15:22,870 --> 00:15:26,070 Speaker 2: Okay. So Dario wrote this memo where he's like, we 356 00:15:26,090 --> 00:15:28,530 Speaker 2: got to hold hands and not kill us all. 357 00:15:28,630 --> 00:15:31,920 Speaker 1: Right. So. if you want to be cynical, there's like 358 00:15:31,960 --> 00:15:35,590 Speaker 1: a very simple cynical story, which is that like, like 359 00:15:35,690 --> 00:15:37,450 Speaker 1: one model for what's going on in AI is that 360 00:15:37,930 --> 00:15:40,850 Speaker 1: the model capabilities are kind of ahead of deployment in 361 00:15:40,890 --> 00:15:42,989 Speaker 1: a lot of areas, right? Like there's just like so 362 00:15:43,030 --> 00:15:49,940 Speaker 1: much stuff where companies could add economic value by using 363 00:15:50,380 --> 00:15:52,980 Speaker 1: existing AI models that they just haven't gotten around to yet. 364 00:15:53,060 --> 00:15:55,980 Speaker 1: Cause like it's too new and they're not trained on 365 00:15:56,000 --> 00:15:58,320 Speaker 1: it and they need more forward deployed engineers to like, 366 00:15:58,340 --> 00:16:01,040 Speaker 1: you know, so you have like, Anthropic and OpenAI doing 367 00:16:01,060 --> 00:16:03,720 Speaker 1: all these partnerships with private equity firms to basically roll 368 00:16:03,840 --> 00:16:10,050 Speaker 1: out AI to companies, right? So if you just turned 369 00:16:10,170 --> 00:16:14,490 Speaker 1: off model development for the next year, Anthropic and OpenAI 370 00:16:14,730 --> 00:16:16,570 Speaker 1: could do a lot of business. They could sell a 371 00:16:16,610 --> 00:16:18,650 Speaker 1: lot of products, right? And they'd make a lot of 372 00:16:18,710 --> 00:16:21,250 Speaker 1: money because their unit economics are pretty good. 373 00:16:21,270 --> 00:16:24,630 Speaker 2: That's going to work at the making... Money factory. That's 374 00:16:24,650 --> 00:16:26,730 Speaker 2: not going to work at the killing everyone factory. 375 00:16:27,050 --> 00:16:29,410 Speaker 1: Like AI right now is in kind of a great place, 376 00:16:29,450 --> 00:16:30,850 Speaker 1: which is that it's commercially useful. 377 00:16:31,090 --> 00:16:31,990 Speaker 2: There's all these applications. 378 00:16:32,010 --> 00:16:34,940 Speaker 1: It has barely scratched the surface of its commercial use. 379 00:16:34,980 --> 00:16:36,740 Speaker 1: I mean, people will disagree with that, but I think 380 00:16:36,920 --> 00:16:39,359 Speaker 1: a lot of people think that. And it's probably not 381 00:16:39,380 --> 00:16:41,960 Speaker 1: going to kill us all right this second. And so 382 00:16:42,640 --> 00:16:45,660 Speaker 1: what a great place to be. But because there is 383 00:16:45,900 --> 00:16:50,250 Speaker 1: a competitive pressure to build the best model every frontier lab, 384 00:16:50,680 --> 00:16:54,400 Speaker 1: instead of only just going around sending out forward deployed 385 00:16:54,440 --> 00:16:58,660 Speaker 1: engineers to like sell more compute to accounting firms is 386 00:16:58,760 --> 00:17:02,740 Speaker 1: spending bajillions of dollars training their next model. And will 387 00:17:02,760 --> 00:17:03,880 Speaker 1: the next model kill us all? 388 00:17:04,000 --> 00:17:06,540 Speaker 2: Right. They're on the frontier specifically seeking AGI. 389 00:17:06,920 --> 00:17:07,040 Speaker 1: Sure. 390 00:17:07,060 --> 00:17:08,240 Speaker 2: Trying to get to whatever point. 391 00:17:08,480 --> 00:17:09,880 Speaker 1: But like, but like, so right. 392 00:17:09,900 --> 00:17:12,760 Speaker 2: So one, you can have the factory doing the making 393 00:17:12,780 --> 00:17:14,540 Speaker 2: money over here, but everyone always needs to have the 394 00:17:14,600 --> 00:17:17,340 Speaker 2: engineers at the frontier. Like the competition is like still 395 00:17:17,460 --> 00:17:17,960 Speaker 2: being the best. 396 00:17:18,280 --> 00:17:20,139 Speaker 1: Yeah. But like, but like one aspect of that is 397 00:17:20,180 --> 00:17:23,020 Speaker 1: like that competition might lead to robots that kill us all. 398 00:17:23,470 --> 00:17:26,590 Speaker 1: regardless of that, that competition is very expensive. It requires 399 00:17:26,630 --> 00:17:27,070 Speaker 1: a lot. 400 00:17:26,930 --> 00:17:27,990 Speaker 2: Of data centers, right? 401 00:17:28,010 --> 00:17:29,450 Speaker 1: It requires a lot of training. 402 00:17:29,470 --> 00:17:29,869 Speaker 2: So many chips. 403 00:17:30,369 --> 00:17:33,730 Speaker 1: And if you just turned off that competition, first of all, 404 00:17:33,910 --> 00:17:35,810 Speaker 1: we'd have one more year before robots kill us all. 405 00:17:36,290 --> 00:17:40,250 Speaker 1: But second of all, they'd be much more profitable, right? 406 00:17:40,650 --> 00:17:44,229 Speaker 1: And so the simple cynical story is that the frontier 407 00:17:44,330 --> 00:17:47,710 Speaker 1: AI labs would have a better business if they didn't 408 00:17:47,760 --> 00:17:50,800 Speaker 1: have to race to train their models, right? Right. 409 00:17:50,900 --> 00:17:53,720 Speaker 2: So there's a business case you're saying for what Amadeus Yeah. 410 00:17:54,240 --> 00:17:59,820 Speaker 1: And by the way, the biggest critic of this is 411 00:18:00,020 --> 00:18:05,300 Speaker 1: Donald Trump, who called Jensen Huang to be like, it's 412 00:18:05,340 --> 00:18:08,879 Speaker 1: a hoax. Donald Trump saying something is a hoax is 413 00:18:08,920 --> 00:18:13,169 Speaker 1: the most terrifying thing I've heard about AI existential risk. 414 00:18:13,600 --> 00:18:15,560 Speaker 1: But no, another critic of it is Jensen Huang, who 415 00:18:15,680 --> 00:18:17,560 Speaker 1: does not run a Frontier ALAP. Right. 416 00:18:17,820 --> 00:18:18,560 Speaker 2: He's the chip man. 417 00:18:18,580 --> 00:18:22,550 Speaker 1: He sells chips. And his business is much better if 418 00:18:22,609 --> 00:18:24,870 Speaker 1: they're constantly training the next model and buying new chips. 419 00:18:25,350 --> 00:18:28,330 Speaker 1: So he is a critic of this for straightforward. I mean, 420 00:18:29,050 --> 00:18:33,310 Speaker 1: I think that everyone has genuine beliefs. Sure. But also, like, 421 00:18:33,609 --> 00:18:37,780 Speaker 1: his straightforward business interests are in racing to train models 422 00:18:37,820 --> 00:18:41,280 Speaker 1: as much as possible. And Dario's and Sam Albin's are 423 00:18:41,300 --> 00:18:43,920 Speaker 1: maybe not doing that. The other thing I wrote about 424 00:18:43,980 --> 00:18:49,530 Speaker 1: is like, okay, so like arguably OpenAI and Anthropic have 425 00:18:49,570 --> 00:18:53,429 Speaker 1: some business interest in not training models that fast so 426 00:18:53,450 --> 00:18:56,350 Speaker 1: that they can, you know, capture some revenue from their 427 00:18:56,369 --> 00:19:00,690 Speaker 1: existing models. And as several people, including David Sack said, 428 00:19:00,710 --> 00:19:02,230 Speaker 1: you can just not, you just do that. You don't 429 00:19:02,270 --> 00:19:04,609 Speaker 1: have to train the next model that will kill us all. 430 00:19:04,630 --> 00:19:07,470 Speaker 1: You can just not do that. But there is competitive 431 00:19:07,490 --> 00:19:09,090 Speaker 1: pressure to train the next model that will kill us 432 00:19:09,170 --> 00:19:13,940 Speaker 1: all because one, Some customers want the most advanced model, 433 00:19:14,000 --> 00:19:15,840 Speaker 1: and it really helps to be on the top of 434 00:19:15,859 --> 00:19:18,380 Speaker 1: the leaderboard of who's got the best model. And then two, 435 00:19:18,400 --> 00:19:21,090 Speaker 1: you're competing for researchers too, and the researchers want to 436 00:19:21,119 --> 00:19:22,530 Speaker 1: be at the best lab, not the worst lab. 437 00:19:22,550 --> 00:19:24,869 Speaker 2: It feels like a crucible of capitalism. 438 00:19:25,030 --> 00:19:28,530 Speaker 1: Right. The interesting thing is that because there are competitive pressures, 439 00:19:29,030 --> 00:19:33,070 Speaker 1: no one entirely feels like they can unilaterally disarm. But 440 00:19:33,590 --> 00:19:36,490 Speaker 1: there's some chance that if you got the four leading 441 00:19:36,670 --> 00:19:40,360 Speaker 1: AI labs in a room, they could agree to disarm. Right. 442 00:19:40,600 --> 00:19:43,159 Speaker 1: And there's other non frontier competitors who could catch up 443 00:19:43,180 --> 00:19:45,649 Speaker 1: to them, but maybe it'd be okay. But if you 444 00:19:45,670 --> 00:19:47,389 Speaker 1: got like the four competitors in a room and they 445 00:19:47,410 --> 00:19:50,609 Speaker 1: agreed to like slow the rollout of their products. that 446 00:19:50,650 --> 00:19:54,700 Speaker 1: looks like a very classic antitrust conspiracy and so it's 447 00:19:54,720 --> 00:19:55,780 Speaker 1: not clear they can do. 448 00:19:55,720 --> 00:19:58,879 Speaker 2: That and so conspiracy to save humanity well this is 449 00:19:58,900 --> 00:19:59,260 Speaker 2: the thing. 450 00:19:59,140 --> 00:20:02,060 Speaker 1: Like so i read about this like there's this amazing 451 00:20:02,320 --> 00:20:04,460 Speaker 1: lawsuit it's like against blackrock because. 452 00:20:04,359 --> 00:20:06,399 Speaker 2: It's like yeah it's incomprehensible but. 453 00:20:06,300 --> 00:20:10,510 Speaker 1: Like basically there's this idea that like ESG investors led 454 00:20:10,530 --> 00:20:14,480 Speaker 1: by BlackRock, tell coal companies to stop producing coal. And 455 00:20:14,640 --> 00:20:16,879 Speaker 1: why do they tell coal companies to stop producing coal? Well, 456 00:20:17,200 --> 00:20:20,500 Speaker 1: they will tell you because coal creates pollution and climate 457 00:20:20,520 --> 00:20:23,560 Speaker 1: change and is bad for us. And so as responsible 458 00:20:23,640 --> 00:20:27,450 Speaker 1: stewards of capital and of the environment, they would like 459 00:20:27,550 --> 00:20:32,050 Speaker 1: to produce less coal. But the plaintiffs in this lawsuit, 460 00:20:32,090 --> 00:20:35,080 Speaker 1: which is several state attorney generals, They say maybe, but 461 00:20:35,119 --> 00:20:37,280 Speaker 1: the effect of telling coal companies to produce less coal 462 00:20:37,320 --> 00:20:42,909 Speaker 1: is that coal prices go up. And the coal companies have- Colluded. Yeah. Essentially, 463 00:20:42,930 --> 00:20:45,750 Speaker 1: you have a cartel to sell less coal at higher prices. 464 00:20:45,770 --> 00:20:48,790 Speaker 1: It's like OPEC for coal. And the takeaway from this 465 00:20:48,830 --> 00:20:52,150 Speaker 1: lawsuit is that even if you have a good pro-humanity 466 00:20:52,190 --> 00:20:55,500 Speaker 1: reason for conspiring to restrict the supply of your product, 467 00:20:55,800 --> 00:20:57,920 Speaker 1: it's still potentially an antitrust conspiracy. 468 00:20:58,000 --> 00:21:00,159 Speaker 2: And I quote, trying to save the world is no 469 00:21:00,220 --> 00:21:02,080 Speaker 2: excuse for an antitrust conspiracy. 470 00:21:02,160 --> 00:21:09,409 Speaker 1: Right. And, like, by the way, that has to be wrong. Like, obviously, 471 00:21:10,330 --> 00:21:12,730 Speaker 1: trying to save the world is a very good excuse 472 00:21:12,770 --> 00:21:13,370 Speaker 1: for an antitrust conspiracy. 473 00:21:13,390 --> 00:21:14,130 Speaker 2: Yeah, the main excuse. 474 00:21:14,210 --> 00:21:17,770 Speaker 1: Yeah. Maybe not under, like, traditional antitrust law. But, like, 475 00:21:18,170 --> 00:21:20,290 Speaker 1: if you're like, the robots were an hour away from 476 00:21:20,350 --> 00:21:23,100 Speaker 1: killing everyone on Earth, so I'd... Turn them off. Me 477 00:21:23,150 --> 00:21:25,400 Speaker 1: and the CEO of my competitor held hands and turned 478 00:21:25,420 --> 00:21:28,000 Speaker 1: them off. Like, great, do that. Yeah. And then you 479 00:21:28,020 --> 00:21:30,200 Speaker 1: go to jail for antitrust conspiracy. 480 00:21:30,260 --> 00:21:30,649 Speaker 2: Worth it. 481 00:21:31,000 --> 00:21:34,760 Speaker 1: But, right, like... So Darius SA does have a footnote 482 00:21:34,780 --> 00:21:39,300 Speaker 1: being like, with appropriate changes to antitrust law, right? They 483 00:21:39,340 --> 00:21:41,330 Speaker 1: need permission to get together in a room to agree to. 484 00:21:41,310 --> 00:21:42,530 Speaker 2: Turn off the killer alerts. Right, which is why you 485 00:21:42,570 --> 00:21:44,270 Speaker 2: wrote that they can't just do it themselves. They sort 486 00:21:44,290 --> 00:21:46,150 Speaker 2: of need the government to nod at them and say. 487 00:21:46,350 --> 00:21:48,090 Speaker 1: Yeah, but it's like this weird situation where the people 488 00:21:48,450 --> 00:21:50,340 Speaker 1: building AI are like, this is going to kill us all. 489 00:21:50,660 --> 00:21:52,760 Speaker 2: And Donald Trump is like, no, it's a hoax. 490 00:21:52,880 --> 00:21:55,800 Speaker 1: So I don't think they're going to get regulated. 491 00:21:56,090 --> 00:21:57,510 Speaker 2: A couple of things that are really interesting about the 492 00:21:57,530 --> 00:21:59,570 Speaker 2: Kevin Ruse book, which I'm only halfway through, but one 493 00:21:59,590 --> 00:22:00,610 Speaker 2: of the really amazing. 494 00:22:00,630 --> 00:22:01,250 Speaker 1: Is it out by the way? 495 00:22:01,270 --> 00:22:03,710 Speaker 2: It is not out. The publication date is October 6th. 496 00:22:03,950 --> 00:22:05,830 Speaker 2: I got an advanced copy because, you know, I edit 497 00:22:05,869 --> 00:22:07,889 Speaker 2: a newsletter. But one of the more interesting... I mean, 498 00:22:07,930 --> 00:22:10,729 Speaker 2: it is a sort of narrative history of how we 499 00:22:10,750 --> 00:22:13,850 Speaker 2: got here and the race to AGI. And something that 500 00:22:13,890 --> 00:22:17,710 Speaker 2: is really kind of funny to me is Amadei's Pacing 501 00:22:17,730 --> 00:22:20,199 Speaker 2: the Frontier memo. Like, the entire book is like, oh, 502 00:22:20,220 --> 00:22:21,920 Speaker 2: and then there was the big this memo, and then 503 00:22:21,940 --> 00:22:24,000 Speaker 2: the big that memo, and the say AGI memo, and 504 00:22:24,040 --> 00:22:28,760 Speaker 2: the all of these instances are marked by documents written 505 00:22:28,859 --> 00:22:33,300 Speaker 2: by humans, which is so interesting to me because all 506 00:22:33,320 --> 00:22:34,879 Speaker 2: that we talk about now with AI is like, oh, 507 00:22:35,040 --> 00:22:39,240 Speaker 2: was it written with AI? The foundational inflection points and 508 00:22:39,280 --> 00:22:42,800 Speaker 2: dramatic tensions in the book, every moment there is an 509 00:22:42,859 --> 00:22:44,040 Speaker 2: associated memo. 510 00:22:44,070 --> 00:22:46,430 Speaker 1: I was thinking about that. I find myself really allergic 511 00:22:46,470 --> 00:22:50,110 Speaker 1: to writing that sounds like stereotypical AI writing. And so 512 00:22:50,150 --> 00:22:53,320 Speaker 1: as I was reading Daria's memo, I was like, did 513 00:22:53,340 --> 00:22:53,880 Speaker 1: Clara write this? 514 00:22:54,119 --> 00:22:57,030 Speaker 2: And I'm like, no, it doesn't sound like AI at all. 515 00:22:57,040 --> 00:22:58,500 Speaker 1: All of these people are good writers. 516 00:22:58,520 --> 00:23:01,320 Speaker 2: Yes, no, there is zero. I mean, they're all philosophers, right? 517 00:23:01,359 --> 00:23:03,200 Speaker 2: And that's like the most of what they're doing is 518 00:23:03,220 --> 00:23:04,940 Speaker 2: they're getting together. They all live in this one house 519 00:23:04,980 --> 00:23:06,820 Speaker 2: on Delano Street or whatever. And they're all like talking 520 00:23:06,859 --> 00:23:09,050 Speaker 2: and staying up through the night. And like, they all 521 00:23:09,350 --> 00:23:13,790 Speaker 2: write these original foundational pieces of text. And I just 522 00:23:13,810 --> 00:23:15,969 Speaker 2: think it's just such an interesting idea that like some 523 00:23:16,010 --> 00:23:18,330 Speaker 2: of the most important documents of like the race to 524 00:23:19,410 --> 00:23:22,510 Speaker 2: building this thing that is intelligent are these like human 525 00:23:22,810 --> 00:23:28,260 Speaker 2: handwritten hand sewn hand forged you know flags planted in 526 00:23:28,280 --> 00:23:30,580 Speaker 2: the ground about you know this must be done or 527 00:23:30,619 --> 00:23:32,300 Speaker 2: in order to do this this is where we are 528 00:23:32,320 --> 00:23:34,280 Speaker 2: this is a watershed moment yeah. 529 00:23:34,200 --> 00:23:36,600 Speaker 1: But that's just like just we're just in like a 530 00:23:36,720 --> 00:23:40,930 Speaker 1: like a phase transition right like most of the code 531 00:23:41,230 --> 00:23:46,510 Speaker 1: most of the algorithms behind modern ai are were invented 532 00:23:46,530 --> 00:23:48,250 Speaker 1: by humans because who else would have done it, right? 533 00:23:48,550 --> 00:23:50,629 Speaker 1: But like now we're at a point of recursive self-improvement 534 00:23:50,650 --> 00:23:52,590 Speaker 1: where like now like the AI is programming the AI. 535 00:23:53,030 --> 00:23:55,429 Speaker 1: All these people grew up in a, they didn't grow 536 00:23:55,450 --> 00:23:57,520 Speaker 1: up in a world where AI would write papers for them. 537 00:23:57,560 --> 00:23:59,780 Speaker 1: And like they've thought a lot about this. And the 538 00:23:59,840 --> 00:24:02,180 Speaker 1: idea of going to Claude and being like, write a 539 00:24:02,220 --> 00:24:05,380 Speaker 1: memo about restricting Claude is stupid. But like the next 540 00:24:05,420 --> 00:24:08,420 Speaker 1: generation will just do that. They'll be like, hey Claude, 541 00:24:08,440 --> 00:24:08,929 Speaker 1: what should I do? 542 00:24:08,950 --> 00:24:10,170 Speaker 2: I think Claude will be great at that. 543 00:24:11,050 --> 00:24:14,800 Speaker 1: He'll be like, no, like great for like, Claude will 544 00:24:14,820 --> 00:24:15,909 Speaker 1: do a perfectly adequate job. 545 00:24:15,920 --> 00:24:17,460 Speaker 2: We won't need to write the memos because Claude will 546 00:24:17,500 --> 00:24:19,090 Speaker 2: already be our overlord. I know. 547 00:24:19,180 --> 00:24:21,190 Speaker 1: Like Claude will do a perfectly adequate job of laying 548 00:24:21,230 --> 00:24:24,449 Speaker 1: out the reasons for restricting the pace of AI improvement, 549 00:24:24,490 --> 00:24:27,530 Speaker 1: but not stylishly. No, no. 550 00:24:27,869 --> 00:24:29,669 Speaker 2: I mean, unless you trained it on, you know, some 551 00:24:29,690 --> 00:24:32,189 Speaker 2: of the most stylish writing out there, like money stuff columns. 552 00:24:33,880 --> 00:24:35,359 Speaker 1: Claude has ingested a lot of money stuff. 553 00:24:35,840 --> 00:24:53,630 Speaker 2: Claude has ingested all of it. Let's talk about pensions. 554 00:24:53,650 --> 00:24:54,010 Speaker 1: Okay. 555 00:24:54,450 --> 00:24:55,970 Speaker 2: I was told that this was a thing that used 556 00:24:56,020 --> 00:24:58,600 Speaker 2: to exist. You used to have a job in one 557 00:24:58,660 --> 00:25:03,280 Speaker 2: place for 40 years. And then you would retire. And 558 00:25:04,140 --> 00:25:06,280 Speaker 2: in exchange, you got a pension at the end. 559 00:25:07,460 --> 00:25:07,740 Speaker 1: Yeah. 560 00:25:07,859 --> 00:25:11,830 Speaker 2: It was very simple. Everyone had them. The world worked. 561 00:25:12,109 --> 00:25:14,780 Speaker 1: Right. There were some problems with it. One of them 562 00:25:15,180 --> 00:25:17,359 Speaker 1: is that if you didn't have a job for 40 years, 563 00:25:17,500 --> 00:25:20,820 Speaker 1: if you moved jobs, pensions were more complicated. Another problem 564 00:25:20,859 --> 00:25:23,859 Speaker 1: is actually like there's this like notion of a golden 565 00:25:23,900 --> 00:25:25,960 Speaker 1: age where everyone had a defined benefit pension, but like, 566 00:25:26,180 --> 00:25:28,680 Speaker 1: in fact, like the majority of people did not have 567 00:25:29,119 --> 00:25:32,090 Speaker 1: defined benefit pensions. And then another problem is that occasionally 568 00:25:32,119 --> 00:25:34,109 Speaker 1: pensions would go bankrupt, right? Because the idea of a 569 00:25:34,130 --> 00:25:36,590 Speaker 1: pension fund is like, you know, you work at a company, 570 00:25:36,970 --> 00:25:40,170 Speaker 1: the company agrees to pay you some number of dollars, 571 00:25:40,810 --> 00:25:45,780 Speaker 1: some formula, something every month after you retire. And how 572 00:25:45,859 --> 00:25:49,419 Speaker 1: it meets those obligations is like sort of in the 573 00:25:49,460 --> 00:25:53,050 Speaker 1: first instance up to the company. And so it like 574 00:25:53,090 --> 00:25:54,909 Speaker 1: sets aside some money and invests it. And if it 575 00:25:54,950 --> 00:25:56,930 Speaker 1: invests it in dumb stuff and loses all the money, 576 00:25:57,310 --> 00:25:59,250 Speaker 1: then you don't get your pension, which is bad. And 577 00:25:59,270 --> 00:26:03,369 Speaker 1: so like ERISA, the retirement security law was passed to 578 00:26:03,850 --> 00:26:06,810 Speaker 1: kind of clamp down on pension fund bankruptcies. But there 579 00:26:06,850 --> 00:26:11,730 Speaker 1: was a set stream of liabilities. And so the company 580 00:26:11,750 --> 00:26:14,300 Speaker 1: had to invest to meet those liabilities. And over time 581 00:26:14,460 --> 00:26:17,619 Speaker 1: in the U.S., that has been like largely replaced by 582 00:26:17,640 --> 00:26:18,550 Speaker 1: like the 401k. 583 00:26:18,770 --> 00:26:21,480 Speaker 2: Why did that happen? What was wrong with the defined 584 00:26:21,590 --> 00:26:23,870 Speaker 2: benefit pension besides the fact that it sort of never 585 00:26:23,890 --> 00:26:26,010 Speaker 2: really existed in this imaginary wonderful way that we thought? 586 00:26:26,030 --> 00:26:27,890 Speaker 1: Well, a couple of reasons. One is that if you're 587 00:26:27,930 --> 00:26:29,730 Speaker 1: moving jobs, like a 401k is yours. 588 00:26:29,810 --> 00:26:31,929 Speaker 2: Yeah, you're like, wait, I need to leave this company 589 00:26:31,990 --> 00:26:33,869 Speaker 2: to go to different jobs. But like you can't take 590 00:26:33,890 --> 00:26:34,629 Speaker 2: your pension with you. 591 00:26:34,650 --> 00:26:36,510 Speaker 1: Right, because the pension like belongs to the company. So 592 00:26:36,570 --> 00:26:39,110 Speaker 1: it's complicated to have a pension if you're moving jobs 593 00:26:39,130 --> 00:26:41,070 Speaker 1: a lot. Whereas a 401k, you can move with you. 594 00:26:41,090 --> 00:26:43,609 Speaker 1: It's just an account that belongs to you. Another reason 595 00:26:43,869 --> 00:26:48,830 Speaker 1: is that the pension itself is a risk, right? Because 596 00:26:48,869 --> 00:26:52,230 Speaker 1: there is a mismatch between the company has promised you 597 00:26:52,260 --> 00:26:57,040 Speaker 1: a monthly dollar amount of benefits, and those benefits are 598 00:26:57,040 --> 00:26:58,480 Speaker 1: 20 years in the future. And so it has to 599 00:26:58,520 --> 00:27:00,880 Speaker 1: invest such that it will be able to meet that 600 00:27:00,920 --> 00:27:01,960 Speaker 1: dollar amount of benefits. 601 00:27:02,320 --> 00:27:02,920 Speaker 2: Better get it right. 602 00:27:03,340 --> 00:27:05,460 Speaker 1: Yeah. One way to do that is to buy treasury 603 00:27:05,500 --> 00:27:07,600 Speaker 1: bonds that will pay out exactly the amount of benefits, 604 00:27:07,619 --> 00:27:09,889 Speaker 1: but that's very expensive, right? Another way to do it 605 00:27:09,930 --> 00:27:12,370 Speaker 1: is to buy stocks that you hope will grow to 606 00:27:12,550 --> 00:27:14,470 Speaker 1: meet the amount of benefits. Don't get it wrong. But 607 00:27:14,490 --> 00:27:18,050 Speaker 1: you have, there's risk there. The thing about a 401k 608 00:27:18,530 --> 00:27:22,390 Speaker 1: is that it's not, in the general case, a promised 609 00:27:22,450 --> 00:27:24,990 Speaker 1: stream of benefits. It's just a dollar amount. It's just 610 00:27:25,010 --> 00:27:26,690 Speaker 1: a big pool. Yeah. So if you put money in 611 00:27:26,730 --> 00:27:28,830 Speaker 1: a 401k, you have the money in the 401k. If 612 00:27:28,850 --> 00:27:30,910 Speaker 1: the stocks go down, they go down. But there's no, like, 613 00:27:31,369 --> 00:27:34,800 Speaker 1: funding gap, right? Right. You're never, like, I mean, there is. Sure. 614 00:27:34,840 --> 00:27:35,300 Speaker 1: There is, right? 615 00:27:35,320 --> 00:27:36,899 Speaker 2: Maybe the number is as big as you want. 616 00:27:36,740 --> 00:27:39,520 Speaker 1: It to be. Yeah. You can convert. either like literally 617 00:27:39,570 --> 00:27:42,770 Speaker 1: by buying annuities or just mathematically by like multiplying by 4% 618 00:27:42,770 --> 00:27:46,390 Speaker 1: or whatever, you can convert your balance in your 401k 619 00:27:46,770 --> 00:27:49,369 Speaker 1: into a stream of income in retirement. And if that 620 00:27:49,390 --> 00:27:51,290 Speaker 1: stream of income is not sufficient to support your lifestyle, 621 00:27:51,310 --> 00:27:52,810 Speaker 1: then you have a funding gap. But it doesn't like 622 00:27:52,869 --> 00:27:54,949 Speaker 1: look like that. It's not like on financial statements. It's 623 00:27:54,970 --> 00:27:56,340 Speaker 1: not like a company being like, oh, we have a 624 00:27:56,710 --> 00:27:59,119 Speaker 1: billion dollar funding gap. It's just like, oh, the number 625 00:27:59,160 --> 00:28:02,100 Speaker 1: in your 401k is lower than you'd like. So it 626 00:28:02,480 --> 00:28:04,659 Speaker 1: creates less concentrated risk, right? There's no, you're never gonna 627 00:28:04,680 --> 00:28:07,340 Speaker 1: have a pension bankruptcy if you have 401ks. But then 628 00:28:07,359 --> 00:28:09,260 Speaker 1: the other reason And I know this is true, but 629 00:28:09,280 --> 00:28:11,419 Speaker 1: it's just like a thing people used to say is 630 00:28:11,460 --> 00:28:15,379 Speaker 1: that the move from pension funds to 401ks was like 631 00:28:15,420 --> 00:28:17,100 Speaker 1: good for Wall Street, right? Because the idea is that 632 00:28:17,140 --> 00:28:19,120 Speaker 1: you take all of these people who are like, you know, 633 00:28:19,680 --> 00:28:23,369 Speaker 1: working and like hoping, you know, counting on a pension 634 00:28:23,410 --> 00:28:26,409 Speaker 1: for retirement and you turn them into individual investors. You 635 00:28:26,450 --> 00:28:29,230 Speaker 1: turn them into people who, instead of just like counting 636 00:28:29,270 --> 00:28:32,060 Speaker 1: on their employer to fund their retirement, they're now playing 637 00:28:32,080 --> 00:28:34,410 Speaker 1: the stock market and they become customers of wall street. 638 00:28:34,470 --> 00:28:36,150 Speaker 2: Right. And they're like, is some part of this also 639 00:28:36,170 --> 00:28:38,110 Speaker 2: that like, Oh, now I can do the dials on 640 00:28:38,130 --> 00:28:38,590 Speaker 2: my own 401k. 641 00:28:38,630 --> 00:28:41,060 Speaker 1: Oh yeah, totally. Right. And so not everyone does the 642 00:28:41,080 --> 00:28:43,280 Speaker 1: dials on their own 401k. Like the most popular 401k 643 00:28:44,100 --> 00:28:45,160 Speaker 1: investment is target date funds. 644 00:28:45,180 --> 00:28:45,920 Speaker 2: Set it and forget it. 645 00:28:46,080 --> 00:28:48,240 Speaker 1: Yeah. Target date funds, which are truly set it for their, 646 00:28:48,890 --> 00:28:51,210 Speaker 1: They're like not even index funds. They're like a slider 647 00:28:51,270 --> 00:28:53,330 Speaker 1: of index funds where it's like, you know, mostly stock 648 00:28:53,350 --> 00:28:55,070 Speaker 1: index funds and becomes mostly bond index. 649 00:28:54,890 --> 00:28:55,550 Speaker 2: Funds over time. Right. 650 00:28:55,930 --> 00:28:58,590 Speaker 1: So it's not turning the dials on your own 401k, 651 00:28:58,610 --> 00:29:01,270 Speaker 1: but like you could, you could. There's all sorts of 652 00:29:01,310 --> 00:29:05,170 Speaker 1: like possibilities for investments in 401ks. And so like the 653 00:29:05,230 --> 00:29:07,070 Speaker 1: idea back in the day was like, oh, this is 654 00:29:07,110 --> 00:29:08,840 Speaker 1: like creating a lot of customers for Wall Street. 655 00:29:08,860 --> 00:29:08,990 Speaker 2: Got it. 656 00:29:09,340 --> 00:29:12,920 Speaker 1: But what I've argued is that first of all, pensions, 657 00:29:13,220 --> 00:29:15,260 Speaker 1: it doesn't really create customers for Wall Street because if 658 00:29:15,620 --> 00:29:19,350 Speaker 1: a company goes from having a pension to having 401ks, Sure, 659 00:29:19,370 --> 00:29:23,370 Speaker 1: all those 401k investors are now customers of Wall Street. 660 00:29:23,390 --> 00:29:26,100 Speaker 2: Right, it doesn't change the shape of the customers. 661 00:29:26,300 --> 00:29:27,880 Speaker 1: So you don't have the big pension customer, you have 662 00:29:27,900 --> 00:29:31,860 Speaker 1: like a thousand small 401k customers. And in some ways 663 00:29:31,900 --> 00:29:34,830 Speaker 1: that's better for Wall Street because you can sell the 664 00:29:35,210 --> 00:29:36,710 Speaker 1: 401k customers all sorts of other services. 665 00:29:36,750 --> 00:29:37,310 Speaker 2: More customers. 666 00:29:38,050 --> 00:29:40,630 Speaker 1: Yeah, and maybe they'll be less sophisticated customers. in some ways. 667 00:29:41,760 --> 00:29:45,660 Speaker 1: But the advantage of the pension is it actually turns 668 00:29:45,700 --> 00:29:47,460 Speaker 1: out to be a really good customer for Wall Street. 669 00:29:48,140 --> 00:29:52,140 Speaker 1: Because the pension can, because it has a very long 670 00:29:52,180 --> 00:29:56,230 Speaker 1: time horizon, can do kind of spicy stuff that a 671 00:29:56,290 --> 00:30:00,060 Speaker 1: retail stock investor wouldn't do. And so pensions are classically 672 00:30:00,120 --> 00:30:03,800 Speaker 1: great customers for private equity and private credit and all 673 00:30:03,820 --> 00:30:07,790 Speaker 1: sorts of other investments. And I think that 30 years ago, 674 00:30:07,830 --> 00:30:11,490 Speaker 1: you would have said, well, probably these retail investors will 675 00:30:11,510 --> 00:30:14,270 Speaker 1: pay higher fees to Wall Street than a big, sophisticated 676 00:30:14,310 --> 00:30:16,750 Speaker 1: pension that can bargain for its fees. But what has 677 00:30:16,790 --> 00:30:21,150 Speaker 1: happened in the last few decades is that the 401k 678 00:30:21,170 --> 00:30:27,640 Speaker 1: has become just index funds, targeted, cheap indexed. public equity 679 00:30:27,660 --> 00:30:30,580 Speaker 1: investment and like in fact it turns out retail investors 680 00:30:30,620 --> 00:30:34,080 Speaker 1: pay very low fees like over time because there has 681 00:30:34,120 --> 00:30:36,300 Speaker 1: just been a lot of pricing pressure from like indexing 682 00:30:37,120 --> 00:30:40,190 Speaker 1: and pension funds like sure like they get a volume 683 00:30:40,210 --> 00:30:43,550 Speaker 1: discount and their indexing is very cheap but like they 684 00:30:43,590 --> 00:30:47,830 Speaker 1: can do you know put money into a 2 in 685 00:30:47,830 --> 00:30:50,610 Speaker 1: 20 private equity fund and so the thing that I've 686 00:30:50,630 --> 00:30:53,870 Speaker 1: been writing about recently is that I feel like there 687 00:30:53,930 --> 00:30:57,190 Speaker 1: is a financial industry push to like revive the. 688 00:30:57,140 --> 00:30:59,850 Speaker 2: Pension fund reinvent the pension fund yeah. 689 00:30:59,730 --> 00:31:03,210 Speaker 1: Because like what you see is like this huge push 690 00:31:04,390 --> 00:31:08,070 Speaker 1: to put private credit and private equity into retirement funds 691 00:31:09,090 --> 00:31:12,730 Speaker 1: and yep how do you do that well you can just. 692 00:31:12,750 --> 00:31:14,350 Speaker 2: You get trump to say it's allowed so. 693 00:31:14,330 --> 00:31:17,600 Speaker 1: That happened right legally it's fine you can put whatever 694 00:31:17,630 --> 00:31:21,240 Speaker 1: you want you put crypto sports betting everything um seriously 695 00:31:21,780 --> 00:31:24,900 Speaker 1: But there's a question of what vehicle you use. And 696 00:31:24,940 --> 00:31:27,360 Speaker 1: so you just be like, well, we'll sell private equity 697 00:31:27,420 --> 00:31:32,600 Speaker 1: to retail investors. How did that go? Well, so I've 698 00:31:32,620 --> 00:31:34,240 Speaker 1: been writing about this for a while. And you hear 699 00:31:34,320 --> 00:31:40,180 Speaker 1: people at private asset managers say, look, retirement savers can 700 00:31:40,320 --> 00:31:43,120 Speaker 1: take some risk. They can take some illiquidity risk because 701 00:31:43,140 --> 00:31:45,670 Speaker 1: they have a long time horizon. They don't need their 702 00:31:45,690 --> 00:31:47,730 Speaker 1: money for 30 years, so they should be able to 703 00:31:47,810 --> 00:31:50,740 Speaker 1: take some illiquidity risk. They should be buying illiquid stuff. 704 00:31:50,760 --> 00:31:53,280 Speaker 1: They should be buying private equity, private credit that pays 705 00:31:53,320 --> 00:31:55,880 Speaker 1: some premium in exchange for not being able to sell 706 00:31:55,940 --> 00:31:57,440 Speaker 1: it that day because they don't need to sell it 707 00:31:57,460 --> 00:32:00,020 Speaker 1: that day. They're retirement savers. And like that has a 708 00:32:00,060 --> 00:32:04,880 Speaker 1: certain theoretical appeal. Sure. It's true. And that's why pension 709 00:32:04,900 --> 00:32:06,970 Speaker 1: funds can do it, right? Pension funds also have 30-year 710 00:32:07,010 --> 00:32:07,290 Speaker 1: time horizons. 711 00:32:07,330 --> 00:32:09,190 Speaker 2: But don't people like to, you know, maybe borrow a 712 00:32:09,190 --> 00:32:10,370 Speaker 2: little bit from their 401k sometimes? 713 00:32:10,550 --> 00:32:13,790 Speaker 1: Yeah, like it turns out that people do want liquidity 714 00:32:13,870 --> 00:32:15,670 Speaker 1: in their retirement savings, even though like, you know, at 715 00:32:15,690 --> 00:32:18,110 Speaker 1: some like first principles level they shouldn't because like, yeah, 716 00:32:18,130 --> 00:32:22,010 Speaker 1: like emergencies happen Or just like the experience of being 717 00:32:22,050 --> 00:32:24,450 Speaker 1: able to manage your own money means that you might 718 00:32:24,470 --> 00:32:24,930 Speaker 1: change your mind. 719 00:32:25,030 --> 00:32:26,370 Speaker 2: You might be like, oh, I want to move to 720 00:32:26,390 --> 00:32:26,690 Speaker 2: something else. 721 00:32:27,410 --> 00:32:30,230 Speaker 1: And when you go to a like institutional asset manager 722 00:32:30,410 --> 00:32:32,510 Speaker 1: and you say, we'll pay you a premium for locking 723 00:32:32,530 --> 00:32:34,150 Speaker 1: up your money. They're like, oh yes, we understand that. 724 00:32:34,490 --> 00:32:36,990 Speaker 1: With retail investors, it's somewhat harder to convey that idea. 725 00:32:37,310 --> 00:32:39,330 Speaker 1: And we've seen that this year with like the private 726 00:32:39,370 --> 00:32:43,230 Speaker 1: credit PDC stuff where all these private business development companies 727 00:32:44,010 --> 00:32:46,630 Speaker 1: raise money from retail investors. And they're like, we'll earn 728 00:32:46,650 --> 00:32:49,350 Speaker 1: a liquidity premium by investing your money in private credit 729 00:32:49,840 --> 00:32:52,500 Speaker 1: And then there was some bad news about software companies, 730 00:32:52,700 --> 00:32:54,500 Speaker 1: and all these investors were like, we'd like our money back. 731 00:32:54,860 --> 00:32:56,790 Speaker 1: And the BDCs were like, no, no, you're not supposed to. 732 00:32:56,790 --> 00:32:58,450 Speaker 2: No, no, we told you only 5% of you are 733 00:32:58,470 --> 00:32:59,550 Speaker 2: allowed to take them out every quarter. 734 00:32:59,570 --> 00:33:01,850 Speaker 1: They were sort of premised on the idea that you 735 00:33:01,890 --> 00:33:04,550 Speaker 1: wouldn't really want your money back, and then everyone wanted 736 00:33:04,570 --> 00:33:07,370 Speaker 1: their money back. And there's just a misunderstanding, right? Where 737 00:33:07,710 --> 00:33:08,810 Speaker 1: the private credit funds are like. 738 00:33:10,090 --> 00:33:11,430 Speaker 2: Is that a liquidity mismatch? 739 00:33:12,180 --> 00:33:13,820 Speaker 1: It's not. I don't like that. 740 00:33:13,880 --> 00:33:15,320 Speaker 2: It's a psychological liquidity mismatch. 741 00:33:15,340 --> 00:33:19,120 Speaker 1: Yeah, it's like the retail investors, it turns out, wanted 742 00:33:19,160 --> 00:33:21,390 Speaker 1: more liquidity than the funds were offering them. And the 743 00:33:21,430 --> 00:33:24,290 Speaker 1: funds weren't offering them that much liquidity because the funds 744 00:33:24,370 --> 00:33:26,570 Speaker 1: really were making long-term investments. And it really was a 745 00:33:26,610 --> 00:33:28,810 Speaker 1: bad idea for them to sell all their long-term investments 746 00:33:29,250 --> 00:33:32,970 Speaker 1: to meet redemptions. And so you have that problem where 747 00:33:33,130 --> 00:33:37,860 Speaker 1: it's actually hard to sell illiquid private investments to individual 748 00:33:37,900 --> 00:33:41,600 Speaker 1: retirement savers. But selling illiquid private investments to retirement savers 749 00:33:41,900 --> 00:33:44,780 Speaker 1: is both theoretically a good idea because they have long 750 00:33:44,820 --> 00:33:48,280 Speaker 1: time horizons. They should not care about liquidity. They should 751 00:33:48,300 --> 00:33:51,120 Speaker 1: be willing to get paid to take some liquidity risk. 752 00:33:51,600 --> 00:33:54,650 Speaker 1: And it's also just like a good economic proposition for 753 00:33:54,670 --> 00:33:58,010 Speaker 1: the financial industry because you can't make money giving people 754 00:33:58,410 --> 00:34:01,350 Speaker 1: public equity exposure and charging them one basis point. So 755 00:34:01,770 --> 00:34:04,210 Speaker 1: the way to like make money in active management, the 756 00:34:04,230 --> 00:34:06,350 Speaker 1: way to make money as like an investment manager is 757 00:34:06,370 --> 00:34:08,910 Speaker 1: to do something in the private's world. And so you 758 00:34:08,950 --> 00:34:11,600 Speaker 1: see this big push for investment managers to do stuff 759 00:34:11,640 --> 00:34:13,200 Speaker 1: in the private's world. And the way to do that 760 00:34:13,500 --> 00:34:15,029 Speaker 1: is to make it look more like a pension. So 761 00:34:15,050 --> 00:34:16,820 Speaker 1: there's a Wall Street Journal article today, kind of following 762 00:34:16,860 --> 00:34:19,080 Speaker 1: up on a Bloomberg article from March that I also 763 00:34:19,100 --> 00:34:22,960 Speaker 1: read about, about basically BlackRock pitching, I don't want to 764 00:34:22,980 --> 00:34:23,990 Speaker 1: say a pension plan. No, no, no, no. 765 00:34:24,020 --> 00:34:25,739 Speaker 2: I want to read to you the exact Wall Street 766 00:34:25,760 --> 00:34:30,540 Speaker 2: Journal headline. BlackRock plans to make the corporate 401k look 767 00:34:30,900 --> 00:34:31,780 Speaker 2: more like a pension. 768 00:34:31,880 --> 00:34:32,080 Speaker 1: Yes. 769 00:34:32,180 --> 00:34:34,160 Speaker 2: It's still a 401k, but it looks like a pension. 770 00:34:34,180 --> 00:34:35,500 Speaker 1: Yeah. Okay. Right. 771 00:34:35,900 --> 00:34:37,399 Speaker 2: It's a pension-like 401k. 772 00:34:37,920 --> 00:34:40,000 Speaker 1: Well, what they say is they want to keep the 773 00:34:40,040 --> 00:34:43,379 Speaker 1: benefits of a 401k, which is portability. 774 00:34:43,520 --> 00:34:43,640 Speaker 2: Yep. 775 00:34:44,489 --> 00:34:46,320 Speaker 1: Meaning that you don't have to do that one job forever. 776 00:34:46,640 --> 00:34:50,180 Speaker 1: And also some amount of individual control with the benefits 777 00:34:50,200 --> 00:34:52,820 Speaker 1: of the pension, which is professional management and long time horizons. 778 00:34:53,260 --> 00:34:54,680 Speaker 1: And so what does that mean? I don't know. I'm 779 00:34:54,700 --> 00:34:58,060 Speaker 1: not sure they know. But my general view is that 780 00:34:58,100 --> 00:35:01,009 Speaker 1: it means putting private assets into it. I think the 781 00:35:01,030 --> 00:35:05,650 Speaker 1: main thing that it means conceptually is that instead of 782 00:35:06,230 --> 00:35:10,089 Speaker 1: telling you the number of dollars in your account, the 783 00:35:10,130 --> 00:35:13,140 Speaker 1: long-term goal is to tell you the number of dollars 784 00:35:13,180 --> 00:35:14,950 Speaker 1: you'll get each month in retirement. 785 00:35:15,290 --> 00:35:15,810 Speaker 2: An annuity? 786 00:35:16,290 --> 00:35:16,509 Speaker 1: Yeah. 787 00:35:16,830 --> 00:35:18,700 Speaker 2: Which is a maybe more practical way to focus your 788 00:35:18,719 --> 00:35:19,200 Speaker 2: mind on. 789 00:35:19,280 --> 00:35:21,759 Speaker 1: Sure, sure. But like the way you implement that in 790 00:35:21,800 --> 00:35:25,620 Speaker 1: a 401k is like you have some investment account, let's 791 00:35:25,660 --> 00:35:28,259 Speaker 1: say a target date fund, maybe a target date fund 792 00:35:28,280 --> 00:35:32,859 Speaker 1: that includes a great heaping helping of private assets. And 793 00:35:32,880 --> 00:35:36,350 Speaker 1: your target date fund targets your retirement date. And when 794 00:35:36,370 --> 00:35:38,509 Speaker 1: you hit the retirement date, it switches from a target 795 00:35:38,530 --> 00:35:41,890 Speaker 1: date fund into an annuity. Like you just use the 796 00:35:41,930 --> 00:35:43,670 Speaker 1: balance in the target date fund to buy an annuity. 797 00:35:44,000 --> 00:35:46,540 Speaker 1: And the annuity pays you X thousand dollars a month 798 00:35:46,580 --> 00:35:49,580 Speaker 1: until you die or for some fixed period of years. 799 00:35:50,360 --> 00:35:52,259 Speaker 1: That's a pension, right? I mean, that's like to you 800 00:35:52,280 --> 00:35:54,730 Speaker 1: that has the experience of a pension. Except that you 801 00:35:54,770 --> 00:35:56,989 Speaker 1: don't know what the number of dollars every month is 802 00:35:57,610 --> 00:36:01,129 Speaker 1: until you retire because they buy the annuity when you retire. 803 00:36:01,290 --> 00:36:03,590 Speaker 1: But there are ways around that. You can estimate. You 804 00:36:03,610 --> 00:36:06,420 Speaker 1: can buy forward starting annuities. You can structure around that 805 00:36:06,860 --> 00:36:09,120 Speaker 1: if you are a professional asset manager. 806 00:36:09,469 --> 00:36:09,879 Speaker 2: I am not. 807 00:36:10,570 --> 00:36:13,359 Speaker 1: Right. So, like, I think the idea is, like, this 808 00:36:13,420 --> 00:36:16,399 Speaker 1: is all baby steps because it's all optional because people 809 00:36:16,420 --> 00:36:18,480 Speaker 1: have 401ks and some people are used to 401ks and 810 00:36:18,500 --> 00:36:20,339 Speaker 1: they're like, I just want to own index funds. 811 00:36:20,360 --> 00:36:20,540 Speaker 2: Right. 812 00:36:20,880 --> 00:36:23,040 Speaker 1: But, like, the idea, I think, would have to be 813 00:36:23,500 --> 00:36:27,779 Speaker 1: that in a perfect world, a company would offer its 814 00:36:28,420 --> 00:36:32,240 Speaker 1: employees to push a button and the button is, like, 815 00:36:32,820 --> 00:36:36,540 Speaker 1: BlackRock will give you X thousand dollars in retirement and, like, 816 00:36:36,800 --> 00:36:40,319 Speaker 1: that's that. And, like, BlackRock... figures out how to get there. 817 00:36:40,600 --> 00:36:40,779 Speaker 2: Right. 818 00:36:40,840 --> 00:36:42,520 Speaker 1: And the way they get there is keeping helpings of 819 00:36:42,540 --> 00:36:43,000 Speaker 1: private assets. 820 00:36:43,040 --> 00:36:43,700 Speaker 2: Right, right, right. 821 00:36:44,160 --> 00:36:47,299 Speaker 1: And the structure of it is they buy assets and 822 00:36:47,320 --> 00:36:49,780 Speaker 1: hold them until retirement and then they- It's like. 823 00:36:49,580 --> 00:36:53,419 Speaker 2: An elegant way to say don't worry about the short-term 824 00:36:53,760 --> 00:36:56,739 Speaker 2: kind of stuff. Don't worry about the software stocks in the- Right. 825 00:36:57,180 --> 00:37:02,510 Speaker 1: It's like focusing people's minds away from the dollar balance 826 00:37:02,530 --> 00:37:04,549 Speaker 1: in their 401k, which went down today because of AI. 827 00:37:04,550 --> 00:37:05,589 Speaker 2: Right, right. Yeah, yeah, yeah. 828 00:37:06,230 --> 00:37:08,370 Speaker 1: Focus it on, like, you get a long-term pension, don't 829 00:37:08,390 --> 00:37:08,850 Speaker 1: worry about it. 830 00:37:09,090 --> 00:37:11,470 Speaker 2: Yeah, yeah, yeah. So psychologically a pension. 831 00:37:11,570 --> 00:37:21,190 Speaker 1: Yeah, right. And that was the Money Stuff Podcast. I'm 832 00:37:21,230 --> 00:37:21,710 Speaker 1: Matt Levine. 833 00:37:22,150 --> 00:37:23,290 Speaker 2: And I'm Sylvia Killingsworth. 834 00:37:23,810 --> 00:37:26,180 Speaker 1: We'll have fill-in guest hosts for a few months while 835 00:37:26,219 --> 00:37:29,779 Speaker 1: my usual co-host Katie Greifeld is on parental leave. You 836 00:37:29,820 --> 00:37:31,819 Speaker 1: can find my work by subscribing to the Money Stuff 837 00:37:31,840 --> 00:37:33,259 Speaker 1: newsletter on Bloomberg.com. 838 00:37:33,760 --> 00:37:35,860 Speaker 2: And you can find me by subscribing to the OnBooks 839 00:37:35,920 --> 00:37:37,440 Speaker 2: newsletter at Bloomberg.com. 840 00:37:37,940 --> 00:37:39,520 Speaker 1: We'd love to hear from you. You can send an 841 00:37:39,580 --> 00:37:43,120 Speaker 1: email to moneypod at Bloomberg.net. Ask us a question and 842 00:37:43,140 --> 00:37:45,540 Speaker 1: we might answer it on the air. You can also 843 00:37:45,580 --> 00:37:48,730 Speaker 1: subscribe to our show wherever you're listening right now. And 844 00:37:48,750 --> 00:37:50,810 Speaker 1: leave us a review. It helps more people find the show. 845 00:37:51,430 --> 00:37:54,210 Speaker 1: The Money Stuff podcast is produced by Anna Mazarakis and 846 00:37:54,270 --> 00:37:57,970 Speaker 1: Moses Andam. Our theme music was composed by Blake Maples. 847 00:37:58,750 --> 00:38:01,910 Speaker 1: Amy Keene is our executive producer. And Cheryl Brumley is 848 00:38:01,969 --> 00:38:05,180 Speaker 1: Bloomberg's head of podcasts. Thanks for listening to the Money 849 00:38:05,200 --> 00:38:08,209 Speaker 1: Stuff Podcast. We'll be back next week with more stuff.