1 00:00:02,720 --> 00:00:14,000 Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. 2 00:00:17,960 --> 00:00:21,480 Speaker 2: Hello and welcome to another episode of the All Thoughts Podcast. 3 00:00:21,600 --> 00:00:23,079 Speaker 2: I'm Tracy Alloway. 4 00:00:22,800 --> 00:00:24,520 Speaker 3: And I'm Joe. Why isn't thal Joe? 5 00:00:24,600 --> 00:00:27,520 Speaker 2: It is the holiday season. I am very much looking 6 00:00:27,560 --> 00:00:30,480 Speaker 2: forward to it. One reason I'm looking forward to it 7 00:00:30,520 --> 00:00:35,200 Speaker 2: is because we always do an annual ask Us Anything show. 8 00:00:35,680 --> 00:00:39,000 Speaker 3: Yeah, you know, I think I liked it in the podcast. 9 00:00:39,120 --> 00:00:40,800 Speaker 3: You know, it's true not about us. I mean, the 10 00:00:40,840 --> 00:00:43,200 Speaker 3: show is about what we're interested in, but it really 11 00:00:43,360 --> 00:00:46,559 Speaker 3: is about the perfect guests. But every once in a while, 12 00:00:46,640 --> 00:00:49,159 Speaker 3: I suppose it's nice to sort of have the microphone 13 00:00:49,159 --> 00:00:51,000 Speaker 3: turned in the other direction, so to speak. Yeah. 14 00:00:51,040 --> 00:00:54,360 Speaker 2: The other thing I would say is we're already pretty accessible, 15 00:00:55,160 --> 00:00:58,360 Speaker 2: Like there's a whole All Thoughts discord that we're on. 16 00:00:58,920 --> 00:01:03,120 Speaker 2: There's Twitter, obviously, there's instant messages on ib and mostly 17 00:01:03,200 --> 00:01:07,040 Speaker 2: we respond depending on how busy we are, so feel 18 00:01:07,040 --> 00:01:10,119 Speaker 2: free to contact us there too. But we did get 19 00:01:10,240 --> 00:01:13,440 Speaker 2: some pretty interesting questions from a bunch of listeners. 20 00:01:13,640 --> 00:01:15,880 Speaker 3: Yeah, and I like the fact that we can put 21 00:01:15,920 --> 00:01:19,400 Speaker 3: in many cases listeners voices on the air bring back 22 00:01:19,440 --> 00:01:22,959 Speaker 3: some of the old time radio call in shows, the 23 00:01:23,080 --> 00:01:25,000 Speaker 3: likes of which I used to listen to when I 24 00:01:25,040 --> 00:01:26,479 Speaker 3: was a young kid writing in the car. 25 00:01:26,360 --> 00:01:26,840 Speaker 4: With my dad. 26 00:01:27,000 --> 00:01:28,200 Speaker 2: Colin shows are Lindy. 27 00:01:28,360 --> 00:01:31,560 Speaker 3: The Colin shows are definitely Lindy. All right, shall we start, Yeah, 28 00:01:31,600 --> 00:01:32,600 Speaker 3: let's take a listen to some. 29 00:01:33,319 --> 00:01:38,840 Speaker 5: John sorrow A sixty three, Berkeley, California. Sometimes when you 30 00:01:38,840 --> 00:01:41,520 Speaker 5: finish recording an episode, do you look at each other 31 00:01:41,560 --> 00:01:44,240 Speaker 5: and say this doesn't meet our standards and kill the 32 00:01:44,280 --> 00:01:47,560 Speaker 5: episode or is it more like skiing? Once you commit 33 00:01:47,600 --> 00:01:49,920 Speaker 5: to writing the chair lift, you commit to skiing down 34 00:01:49,920 --> 00:01:50,400 Speaker 5: the mountain. 35 00:01:52,280 --> 00:01:55,040 Speaker 2: Well, I can't ski. I'm the only Austrian in the 36 00:01:55,080 --> 00:01:59,440 Speaker 2: world possibly who cannot ski. So I'm we have killed 37 00:01:59,520 --> 00:02:03,440 Speaker 2: episode in the past. In fact, there's kind of a 38 00:02:03,440 --> 00:02:06,440 Speaker 2: funny story where we did an episode it really wasn't 39 00:02:06,480 --> 00:02:10,040 Speaker 2: going very well. The guests seem very nervous and just 40 00:02:10,120 --> 00:02:14,480 Speaker 2: sort of like not at his top performance. And in 41 00:02:14,560 --> 00:02:18,120 Speaker 2: the end, after we recorded, we told him, you know, 42 00:02:18,520 --> 00:02:21,520 Speaker 2: we're just going to kill this episode, and he was 43 00:02:21,600 --> 00:02:24,760 Speaker 2: actually really happy about that because he felt he hadn't 44 00:02:24,760 --> 00:02:27,840 Speaker 2: done his best work either. And then the other thing 45 00:02:27,840 --> 00:02:31,040 Speaker 2: I would say is I actually think journalists should kill 46 00:02:31,200 --> 00:02:34,160 Speaker 2: a lot more stories. We should be more selective and 47 00:02:34,200 --> 00:02:38,680 Speaker 2: if you think about the German word for reductor, reductor, 48 00:02:39,360 --> 00:02:43,359 Speaker 2: it sounds a lot like redact, right, which gives you, 49 00:02:43,360 --> 00:02:46,760 Speaker 2: you know, some sense of what editors are supposed to 50 00:02:46,800 --> 00:02:49,799 Speaker 2: be doing, which is taking away as opposed to adding. 51 00:02:50,280 --> 00:02:53,160 Speaker 3: Yeah, it is very rare we have. I mean, the 52 00:02:53,280 --> 00:02:57,560 Speaker 3: number of episodes that we've fully recorded that we actually 53 00:02:57,560 --> 00:02:59,959 Speaker 3: did not release. I think it's less than you could 54 00:03:00,040 --> 00:03:02,240 Speaker 3: on one hand, Right, it's not that many, but there 55 00:03:02,280 --> 00:03:04,760 Speaker 3: have been a few just for whatever reason I guess 56 00:03:04,880 --> 00:03:07,320 Speaker 3: was not on their game or whatever, it didn't work. 57 00:03:07,400 --> 00:03:09,960 Speaker 3: And I agree. I agree with Tracy. I think we 58 00:03:09,960 --> 00:03:12,799 Speaker 3: should do more in general. I mean, I think journalists 59 00:03:12,800 --> 00:03:14,640 Speaker 3: as a whole should do more. It's like, just release 60 00:03:14,680 --> 00:03:17,160 Speaker 3: the good stuff, you know, just released ones you're you're 61 00:03:17,200 --> 00:03:19,880 Speaker 3: genuinely proud of. I don't want to release anything anywhere 62 00:03:20,080 --> 00:03:22,400 Speaker 3: when I can't say I'm really proud of this work. 63 00:03:22,440 --> 00:03:24,760 Speaker 3: And normally we are proud, but occasionally it's like, no, 64 00:03:25,360 --> 00:03:27,560 Speaker 3: this is this is not awesome. All right, let's go 65 00:03:27,600 --> 00:03:28,280 Speaker 3: on to the next one. 66 00:03:29,280 --> 00:03:34,320 Speaker 6: Thanks Joe, Thanks Tracy. I was really inspired by Joe's 67 00:03:34,400 --> 00:03:37,640 Speaker 6: tweets on Moby Dick, so this question is for him. 68 00:03:39,640 --> 00:03:43,800 Speaker 6: What did you read after Moby Dick. Thanks guys, thanks 69 00:03:43,880 --> 00:03:44,360 Speaker 6: for listening. 70 00:03:44,680 --> 00:03:46,680 Speaker 3: That's a very easy one. Because I was such in 71 00:03:46,840 --> 00:03:49,880 Speaker 3: such a mood to keep after reading Moby Dick, I 72 00:03:49,880 --> 00:03:52,440 Speaker 3: didn't want to read anything besides Moby Dick. I sort 73 00:03:52,480 --> 00:03:54,920 Speaker 3: of just thought about rereading it again, and I probably 74 00:03:54,960 --> 00:03:57,240 Speaker 3: will sometime the next year or two. But because I 75 00:03:57,280 --> 00:04:00,640 Speaker 3: was in that mood, I read a book by the 76 00:04:00,800 --> 00:04:04,680 Speaker 3: historian writer CLR. James, who wrote a book about Moby Dick. 77 00:04:04,880 --> 00:04:08,160 Speaker 3: Is a book called Mirrorer's Renegades and Castaways by CLR. James, 78 00:04:08,200 --> 00:04:11,920 Speaker 3: and it basically made the argument that Herman Melville, with 79 00:04:11,960 --> 00:04:16,640 Speaker 3: the Ahab character, specifically anticipated many of the pathologies of 80 00:04:16,680 --> 00:04:21,320 Speaker 3: the twentieth century, the rise of fascism, dictators, and so forth, 81 00:04:21,520 --> 00:04:23,880 Speaker 3: all of the crises of sort of Western civilization that 82 00:04:23,920 --> 00:04:27,320 Speaker 3: we experienced in the twentieth century. Fantastic book, almost as 83 00:04:27,360 --> 00:04:28,960 Speaker 3: good as Moby Dick itself. 84 00:04:29,400 --> 00:04:32,159 Speaker 2: Also, I've been recommending two books to you. One of 85 00:04:32,160 --> 00:04:34,479 Speaker 2: them is The Heart of the Sea, which is about 86 00:04:34,560 --> 00:04:38,839 Speaker 2: the whale ship Essex, which is the ship that mobi 87 00:04:39,160 --> 00:04:42,200 Speaker 2: that actually inspired Moby Dick. And then there's another one 88 00:04:42,279 --> 00:04:45,520 Speaker 2: I'm reading just now called A Marriage at Sea, and 89 00:04:45,600 --> 00:04:50,000 Speaker 2: it's about a couple who are out, you know, sailing 90 00:04:50,040 --> 00:04:53,680 Speaker 2: around the world, and their ship gets destroyed by a 91 00:04:53,760 --> 00:04:56,360 Speaker 2: whale and they have to survive on like a little raft. 92 00:04:56,680 --> 00:04:58,640 Speaker 2: I actually don't know if they survive yet, so I 93 00:04:58,680 --> 00:04:59,480 Speaker 2: guess we'll find out. 94 00:05:00,000 --> 00:05:01,719 Speaker 3: I promise I will read both of those. 95 00:05:02,000 --> 00:05:03,279 Speaker 2: Both of them are excellent. 96 00:05:03,360 --> 00:05:06,200 Speaker 3: I promise I'll read both. Let's hear the next question. 97 00:05:06,800 --> 00:05:10,479 Speaker 7: Hi Yang, longtime listener here. My name is Sturm. I'm 98 00:05:10,520 --> 00:05:13,320 Speaker 7: thirty two and I live in Puerto Rico, and I 99 00:05:13,360 --> 00:05:15,960 Speaker 7: have a question for Joe. I know you're an efficient 100 00:05:16,000 --> 00:05:19,840 Speaker 7: markets guy, but if EMH is true, how have portfolio 101 00:05:19,920 --> 00:05:22,240 Speaker 7: managers accumulated such vast wealth. 102 00:05:25,600 --> 00:05:27,479 Speaker 3: There's a really good question. I wonder about why the 103 00:05:27,760 --> 00:05:32,640 Speaker 3: financial system overall exists. Why are people putting targets on prices, 104 00:05:32,680 --> 00:05:35,360 Speaker 3: and why are their pundits on TV? And why is 105 00:05:35,400 --> 00:05:38,440 Speaker 3: their financial TV? And why is their trading and so forth. 106 00:05:38,640 --> 00:05:40,440 Speaker 3: I still don't really know why the answer of why 107 00:05:40,480 --> 00:05:43,160 Speaker 3: anyone has made any money. But my guess is that 108 00:05:43,160 --> 00:05:47,520 Speaker 3: there are other services that people on finance provide other 109 00:05:47,640 --> 00:05:52,200 Speaker 3: than high quality security selection that whether it's like okay, 110 00:05:52,240 --> 00:05:55,679 Speaker 3: I don't really know how to manage risk or whatever 111 00:05:55,720 --> 00:05:58,680 Speaker 3: it is, or I'm anxious about the thought of investing 112 00:05:58,760 --> 00:06:01,800 Speaker 3: all kinds of things. I guess, you know, there probably 113 00:06:01,880 --> 00:06:06,920 Speaker 3: are people who are able to obtain information that maybe 114 00:06:06,960 --> 00:06:10,440 Speaker 3: counts as alpha probably exists. I don't know. I remain unsatisfied. 115 00:06:10,480 --> 00:06:13,440 Speaker 3: I've asked this question myself. I don't fully know the answer, 116 00:06:13,480 --> 00:06:17,440 Speaker 3: but my guests, my best guess, intuitively is that within 117 00:06:17,560 --> 00:06:20,480 Speaker 3: this world that we call finance, there are things that 118 00:06:20,600 --> 00:06:24,520 Speaker 3: produce value other than sort of you know, making good 119 00:06:24,560 --> 00:06:27,560 Speaker 3: trades and so forth, that maybe are harder to articulate 120 00:06:27,640 --> 00:06:30,920 Speaker 3: and so forth, and thus they are providing a valuable 121 00:06:31,080 --> 00:06:32,080 Speaker 3: service at that front. 122 00:06:32,120 --> 00:06:34,599 Speaker 2: But imling your customers. 123 00:06:34,120 --> 00:06:35,560 Speaker 3: You're counseling something. 124 00:06:35,600 --> 00:06:38,800 Speaker 2: I don't know, but I mean, I would just add 125 00:06:38,839 --> 00:06:41,560 Speaker 2: on to that, like it it seems just on the 126 00:06:41,640 --> 00:06:46,920 Speaker 2: EMH stuff, it seems on a simplistic level, if EMH 127 00:06:47,120 --> 00:06:48,920 Speaker 2: was real, you would never get. 128 00:06:48,960 --> 00:06:50,560 Speaker 8: A bubble, right, this is what? 129 00:06:50,640 --> 00:06:50,880 Speaker 1: Yeah. 130 00:06:50,960 --> 00:06:53,839 Speaker 2: And in fact, one of my favorite moments on the 131 00:06:53,880 --> 00:06:57,960 Speaker 2: podcast was when we interviewed Eugene Fama, the father of 132 00:06:58,000 --> 00:07:02,960 Speaker 2: the EMH I and I think I got him to 133 00:07:03,000 --> 00:07:06,240 Speaker 2: say that like bubbles exist, right, which I was surprised. 134 00:07:06,360 --> 00:07:09,640 Speaker 3: Yeah, I know, they hate like they really wrestle with it, 135 00:07:09,640 --> 00:07:12,040 Speaker 3: like this is there one thing they hate this phenomenon, 136 00:07:12,200 --> 00:07:15,600 Speaker 3: so they come up with all this stuff. But yeah, 137 00:07:15,640 --> 00:07:18,840 Speaker 3: we will continue asking the question of why why do 138 00:07:18,960 --> 00:07:22,000 Speaker 3: we exist? Why does financial media and coverage in pundits 139 00:07:22,040 --> 00:07:24,880 Speaker 3: and all this stuff exist. We will continue answering this question. 140 00:07:24,920 --> 00:07:27,400 Speaker 3: In twenty twenty six, maybe we'll have an answer. 141 00:07:27,120 --> 00:07:29,080 Speaker 2: Only the big questions why are we here? 142 00:07:29,160 --> 00:07:29,760 Speaker 3: Why are we here? 143 00:07:30,520 --> 00:07:32,720 Speaker 2: All right, let's listen to the next question. 144 00:07:35,400 --> 00:07:38,920 Speaker 8: Hi, I'm Max Niederman. I'm nineteen and I live in 145 00:07:38,960 --> 00:07:43,240 Speaker 8: San Francisco. Supposing that AI does end up being as 146 00:07:43,280 --> 00:07:46,720 Speaker 8: transformative as people claim, where do you think the value 147 00:07:46,800 --> 00:07:51,080 Speaker 8: that that creates will accrue? From their valuations and how 148 00:07:51,080 --> 00:07:54,119 Speaker 8: people talk about it, At least in SF, it seems 149 00:07:54,160 --> 00:07:57,160 Speaker 8: people expect the labs to capture a massive chunk of 150 00:07:57,160 --> 00:08:01,440 Speaker 8: that value. On the other hand, the have never been profitable, 151 00:08:01,960 --> 00:08:05,600 Speaker 8: and even then, hardware companies like Navidia and TSMC seem 152 00:08:05,680 --> 00:08:09,560 Speaker 8: to trade at lower multiples despite being near monopolies with 153 00:08:09,800 --> 00:08:13,400 Speaker 8: very good margins. So my question is this, when all 154 00:08:13,440 --> 00:08:16,120 Speaker 8: things are sudden done, which parts of the supply chain 155 00:08:16,120 --> 00:08:19,400 Speaker 8: will be commoditized and which will capture all the value. 156 00:08:20,560 --> 00:08:24,440 Speaker 2: Yeah, I think that's a very fair question. And I think, 157 00:08:25,280 --> 00:08:28,880 Speaker 2: you know, it's difficult to predict if tech at some 158 00:08:29,000 --> 00:08:33,040 Speaker 2: point is going to produce the or invent the proverbial 159 00:08:33,559 --> 00:08:36,760 Speaker 2: AI god, like this thing that solves all of our problems. 160 00:08:37,080 --> 00:08:40,000 Speaker 2: I would be a little bit skeptical of that. It 161 00:08:40,080 --> 00:08:44,040 Speaker 2: seems to me like it's going to be a productivity improvement, 162 00:08:44,480 --> 00:08:48,000 Speaker 2: kind of at the margin for now. So maybe you 163 00:08:48,200 --> 00:08:51,000 Speaker 2: use it instead of Google Search. You can use it 164 00:08:51,040 --> 00:08:55,120 Speaker 2: to explain certain terms much more quickly. Some industries are 165 00:08:55,160 --> 00:08:57,880 Speaker 2: probably going to use it to design new products. I 166 00:08:57,920 --> 00:09:02,160 Speaker 2: know there's a lot of excitement around medicine, but where 167 00:09:02,200 --> 00:09:06,320 Speaker 2: the value will accrue. I mean, some of the big 168 00:09:06,360 --> 00:09:09,920 Speaker 2: tech companies are so expensive right now in the US. 169 00:09:10,080 --> 00:09:14,080 Speaker 2: I keep describing this as the coffee pod theory of AI, 170 00:09:14,280 --> 00:09:19,960 Speaker 2: which is, some tech companies are choosing to basically produce 171 00:09:20,280 --> 00:09:25,800 Speaker 2: the world's most expensive, sophisticated cappuccino machine, and they're saying that, 172 00:09:26,040 --> 00:09:29,240 Speaker 2: you know, it produces the best coffee that you've ever had. 173 00:09:30,000 --> 00:09:34,360 Speaker 2: And crucially, what they're doing is targeting a market for 174 00:09:34,400 --> 00:09:37,080 Speaker 2: this machine. Let's say it costs like two thousand dollars. 175 00:09:37,480 --> 00:09:41,600 Speaker 2: That's basically the entire world, and I'm not sure that 176 00:09:41,679 --> 00:09:44,959 Speaker 2: approach works. And then you know, some other companies are 177 00:09:45,000 --> 00:09:50,240 Speaker 2: taking a very different strategy, and they're producing something that's 178 00:09:50,480 --> 00:09:54,400 Speaker 2: relatively cheap and standardized, sort of like a coffee pod 179 00:09:54,559 --> 00:09:59,280 Speaker 2: coffee maker, and it's much cheaper and again targeting the 180 00:09:59,320 --> 00:10:02,080 Speaker 2: total market of the world. And you know, if you 181 00:10:02,080 --> 00:10:04,080 Speaker 2: think about it that way, I kind of have a 182 00:10:04,160 --> 00:10:06,000 Speaker 2: sense of where it's going. 183 00:10:07,360 --> 00:10:09,040 Speaker 3: I really have no idea, you know. The one thing 184 00:10:09,080 --> 00:10:13,400 Speaker 3: I'll say is, I think there is this view that 185 00:10:13,559 --> 00:10:17,080 Speaker 3: okay Ai can replace human labor, and therefore lots of 186 00:10:17,120 --> 00:10:20,280 Speaker 3: workers are very vulnerable, and therefore there is gonna be 187 00:10:20,320 --> 00:10:24,640 Speaker 3: this tremendous like it'll be this very a force of inequality, 188 00:10:24,720 --> 00:10:26,680 Speaker 3: so to speak. Right, So it's gonna be there's gonna 189 00:10:26,720 --> 00:10:29,000 Speaker 3: be the model makers. And I think in this vision 190 00:10:29,120 --> 00:10:32,600 Speaker 3: that you're asking about these model makers and they're gonna 191 00:10:32,600 --> 00:10:34,400 Speaker 3: make a fortune and the rest of us are going 192 00:10:34,480 --> 00:10:37,680 Speaker 3: to have to get by on Ubi or whatever because 193 00:10:37,760 --> 00:10:39,679 Speaker 3: they've put us all the models have put us all 194 00:10:39,679 --> 00:10:42,920 Speaker 3: out of work, and look, I think that's possible. The 195 00:10:42,960 --> 00:10:46,160 Speaker 3: one thing I'll say, though, is that like the tech 196 00:10:46,280 --> 00:10:49,880 Speaker 3: is a force for like financial inequality, that's already been 197 00:10:49,880 --> 00:10:51,640 Speaker 3: the story. We don't even need to talk about AI. 198 00:10:52,000 --> 00:10:55,319 Speaker 3: With the existing tech giants that we've seen that have 199 00:10:55,440 --> 00:10:58,199 Speaker 3: built over the last fifteen twenty years out of San Francisco, 200 00:10:58,360 --> 00:11:02,160 Speaker 3: we've already seen this incredible rush of sort of wealth 201 00:11:02,200 --> 00:11:04,960 Speaker 3: and income, et cetera to this fairly like small, you know, 202 00:11:05,000 --> 00:11:09,160 Speaker 3: this one industry. This is very like, you know, very small, 203 00:11:09,200 --> 00:11:13,400 Speaker 3: fairly small concentration of software engineers and executives and vcs 204 00:11:13,480 --> 00:11:16,360 Speaker 3: and so forth. And as such, I do wonder, like, 205 00:11:16,600 --> 00:11:19,280 Speaker 3: could it be that somehow it's like totally the opposite, 206 00:11:19,320 --> 00:11:22,040 Speaker 3: that it goes in the other trajectory, and that really 207 00:11:22,080 --> 00:11:24,440 Speaker 3: it's sort of like a force free quality or something, 208 00:11:24,559 --> 00:11:28,000 Speaker 3: and that it ends up being a situation in which 209 00:11:28,040 --> 00:11:31,400 Speaker 3: people were like, capital itself is not as valuable as 210 00:11:31,400 --> 00:11:33,880 Speaker 3: it used to be. I don't know. I just think 211 00:11:33,920 --> 00:11:38,839 Speaker 3: we should possibly consider the idea that what people are 212 00:11:38,880 --> 00:11:41,800 Speaker 3: predicting that will happen with AI is that what is 213 00:11:41,840 --> 00:11:44,960 Speaker 3: literally just an extension of what we've already seen, and 214 00:11:45,000 --> 00:11:47,880 Speaker 3: that to the extent that AI is something new, maybe 215 00:11:47,880 --> 00:12:05,560 Speaker 3: that'll put us on a new trajector. All right, let's 216 00:12:05,559 --> 00:12:06,480 Speaker 3: take another question. 217 00:12:07,440 --> 00:12:10,960 Speaker 4: Hi, Tracy and Joe, this is Jennifer thirty calling from Seattle, 218 00:12:11,160 --> 00:12:15,120 Speaker 4: long time, first time. This question is for Joe. Joe, 219 00:12:15,160 --> 00:12:18,240 Speaker 4: what drew you to your interest in Chinese history, particularly 220 00:12:18,240 --> 00:12:21,200 Speaker 4: it's modern era. Have you ever considered going more back 221 00:12:21,200 --> 00:12:24,960 Speaker 4: in time? I asked, because I'm Chinese American with parents 222 00:12:25,000 --> 00:12:27,360 Speaker 4: who said very little about their lives during the Cultural 223 00:12:27,360 --> 00:12:30,640 Speaker 4: Revolution and the eighties when the country started opening up. 224 00:12:30,679 --> 00:12:34,079 Speaker 4: So I often learned through Joe's tweets and book recommendations. 225 00:12:34,400 --> 00:12:37,000 Speaker 4: But I had also noticed in my last trip to 226 00:12:37,040 --> 00:12:39,680 Speaker 4: China that the one historical figure cities pay the most 227 00:12:39,679 --> 00:12:44,240 Speaker 4: homage to is not Mao or Dungchowping, but actually Sonyette Sen, 228 00:12:44,400 --> 00:12:47,599 Speaker 4: who was a precursor to these leaders. Thanks love the 229 00:12:47,640 --> 00:12:48,120 Speaker 4: show as. 230 00:12:48,080 --> 00:12:52,120 Speaker 3: Always, so obviously we do a lot of contemporary China episodes, 231 00:12:52,160 --> 00:12:53,559 Speaker 3: and it sort of seemed like, you know, I should 232 00:12:53,559 --> 00:12:56,800 Speaker 3: probably understand a little bit more about the history. I 233 00:12:56,880 --> 00:12:58,600 Speaker 3: started with the ancient stuff. I was like, oh, I'm 234 00:12:58,640 --> 00:13:00,400 Speaker 3: just going to I'm just going to go back and 235 00:13:00,400 --> 00:13:02,360 Speaker 3: I'm going to read about the old dynasties and then 236 00:13:02,400 --> 00:13:04,440 Speaker 3: I'll get to the present. And I sort of like, 237 00:13:04,679 --> 00:13:07,440 Speaker 3: I found it a little bit hard. It wasn't grabbing 238 00:13:07,480 --> 00:13:10,040 Speaker 3: my attention to ancient stuff. But then I remembered a 239 00:13:10,040 --> 00:13:12,199 Speaker 3: couple of years ago. We did an episode with Adam 240 00:13:12,200 --> 00:13:14,520 Speaker 3: Posen at the Peterson Institute a couple of years ago 241 00:13:14,559 --> 00:13:16,600 Speaker 3: at Jackson Hall, and he had just sort of mentioned 242 00:13:16,640 --> 00:13:20,600 Speaker 3: offhand that he had read asra Vogel's biography of Don Chopeg, 243 00:13:21,120 --> 00:13:22,760 Speaker 3: and I was like, oh, maybe I'll just start there. 244 00:13:22,840 --> 00:13:25,120 Speaker 3: That seems like more recent and tractable and I could 245 00:13:25,120 --> 00:13:28,120 Speaker 3: sort of slot that into my head into something. And 246 00:13:28,200 --> 00:13:31,080 Speaker 3: I read that and I found it very very compelling, 247 00:13:31,160 --> 00:13:34,240 Speaker 3: as Adam suggested, and I think that sort of set 248 00:13:34,280 --> 00:13:38,240 Speaker 3: off like it. I think reading twentieth century Chinese history 249 00:13:38,720 --> 00:13:42,560 Speaker 3: helps me to some extent understand the present day. I've 250 00:13:42,600 --> 00:13:46,760 Speaker 3: never read a Sunyatsen biography, but I'm very interested in him. 251 00:13:46,880 --> 00:13:49,360 Speaker 3: One of the rare figures who sort of revered in 252 00:13:49,440 --> 00:13:54,160 Speaker 3: both Taiwan and Mainland, and so therefore someone who is 253 00:13:54,200 --> 00:13:56,960 Speaker 3: an inspiration to both the nationalists and the communists, which 254 00:13:57,000 --> 00:13:58,640 Speaker 3: is kind of weird. And he was like sort of 255 00:13:58,640 --> 00:14:02,559 Speaker 3: a for you know, a an early pioneer of land 256 00:14:02,600 --> 00:14:05,040 Speaker 3: reform and so forth. So I think maybe that'll be 257 00:14:05,080 --> 00:14:07,440 Speaker 3: another thing I aim to read in twenty twenty six 258 00:14:07,480 --> 00:14:09,000 Speaker 3: A good signette symbiography. 259 00:14:09,360 --> 00:14:12,480 Speaker 2: I really like the older Chinese history. I read a 260 00:14:12,559 --> 00:14:14,680 Speaker 2: really good book. I can't remember the title now, but 261 00:14:14,760 --> 00:14:19,600 Speaker 2: it was all about the Chinese bureaucracy and government system 262 00:14:20,200 --> 00:14:23,160 Speaker 2: how it used to be, and you know, one of 263 00:14:23,200 --> 00:14:26,520 Speaker 2: the most rigorous systems in the world. Arguably you had 264 00:14:26,560 --> 00:14:30,120 Speaker 2: to take this insane test to get the position. And 265 00:14:30,400 --> 00:14:33,560 Speaker 2: I remember I also did a bunch of research around 266 00:14:33,600 --> 00:14:37,520 Speaker 2: the sort of late eighteen hundred's, early nineteen hundreds, the 267 00:14:37,600 --> 00:14:40,320 Speaker 2: fall of the Imperial family and what it means for 268 00:14:40,480 --> 00:14:43,600 Speaker 2: debt markets. That was a really fun story. 269 00:14:44,160 --> 00:14:46,960 Speaker 3: Yeah, I should go back and read read the ancient 270 00:14:46,960 --> 00:14:49,720 Speaker 3: stuff as well. I'm going to add that to the 271 00:14:49,920 --> 00:14:51,040 Speaker 3: twenty twenty six list. 272 00:14:51,200 --> 00:14:53,240 Speaker 2: Okay, let's take another question. 273 00:14:53,680 --> 00:14:56,280 Speaker 7: I joined Tracy. My name is Danny Bessov. I'm twenty 274 00:14:56,280 --> 00:14:58,480 Speaker 7: six years old and currently based in New York where 275 00:14:58,480 --> 00:15:01,320 Speaker 7: I'm doing my masses at Columbian Financial Economics. I'm a 276 00:15:01,440 --> 00:15:04,080 Speaker 7: huge fan of the pot and Spotify told me in 277 00:15:04,120 --> 00:15:05,920 Speaker 7: the last year I was in the top half percent 278 00:15:05,920 --> 00:15:08,720 Speaker 7: of listeners and maybe now the first time caller as well. 279 00:15:09,440 --> 00:15:12,240 Speaker 7: My question is this, you have such a wide range 280 00:15:12,280 --> 00:15:14,800 Speaker 7: of guests with very different backgrounds, and you cover an 281 00:15:14,920 --> 00:15:17,880 Speaker 7: enormous amount of information. How much of that are you 282 00:15:17,920 --> 00:15:20,760 Speaker 7: able to retain and not really call back in everyday 283 00:15:20,800 --> 00:15:24,360 Speaker 7: conversations or when you do follow up episodes? And how 284 00:15:24,440 --> 00:15:27,000 Speaker 7: much do you rely on not taking or revisiting past 285 00:15:27,040 --> 00:15:29,320 Speaker 7: episodes to connect your ideas over time? 286 00:15:31,040 --> 00:15:33,240 Speaker 2: This is a really good question. This is a sort 287 00:15:33,280 --> 00:15:36,480 Speaker 2: of journalistic process question. By the way, thank you for 288 00:15:36,520 --> 00:15:39,680 Speaker 2: being in the top top percentage of Odd Lots listeners. 289 00:15:39,680 --> 00:15:40,560 Speaker 2: We appreciate that. 290 00:15:40,520 --> 00:15:41,560 Speaker 3: It's good coffee sometime. 291 00:15:42,400 --> 00:15:47,480 Speaker 2: But going back to the question, you know it kind 292 00:15:47,480 --> 00:15:50,160 Speaker 2: of this is obvious, but it kind of depends on 293 00:15:50,280 --> 00:15:54,240 Speaker 2: the guest, and if the guest says something memorable or 294 00:15:54,280 --> 00:15:57,520 Speaker 2: something that you really think is insightful. I think we 295 00:15:57,600 --> 00:16:00,400 Speaker 2: do retain some of that information. I used to retain 296 00:16:00,480 --> 00:16:03,680 Speaker 2: a lot more when we were editing our own transcripts 297 00:16:03,720 --> 00:16:07,920 Speaker 2: and publishing them. I really value transcripts, but it was 298 00:16:08,040 --> 00:16:11,240 Speaker 2: just so much work and took forever, so we stopped 299 00:16:11,240 --> 00:16:13,760 Speaker 2: doing it. We're hoping to come up with some sort 300 00:16:13,800 --> 00:16:18,200 Speaker 2: of solution soon. And the biggest compliment I can pay 301 00:16:18,200 --> 00:16:20,800 Speaker 2: a guest is saying that, like, I'm going to go 302 00:16:20,880 --> 00:16:23,960 Speaker 2: back and read the transcript because there's so much knowledge, there, 303 00:16:24,160 --> 00:16:29,359 Speaker 2: so much information. I just have to absorb all of it. 304 00:16:29,360 --> 00:16:31,080 Speaker 3: It was funny. I was a dinner the other night 305 00:16:31,160 --> 00:16:33,720 Speaker 3: with someone and I said to them, have you ever 306 00:16:33,760 --> 00:16:36,480 Speaker 3: actually learned anything from listening to a podcast? Because I 307 00:16:36,520 --> 00:16:39,440 Speaker 3: do sometimes wonder if I ever remember anything. No, that's 308 00:16:39,440 --> 00:16:42,960 Speaker 3: not totally true. I do, but I also sometimes wonder 309 00:16:43,200 --> 00:16:47,400 Speaker 3: setting a side podcasts and recall the specific facts. I 310 00:16:47,440 --> 00:16:49,400 Speaker 3: also have the same thing with books. You know, we 311 00:16:49,400 --> 00:16:52,120 Speaker 3: were just talking about that Dunk Show Pang biography. And 312 00:16:52,160 --> 00:16:54,560 Speaker 3: if someone's like, oh, Joe, you just spent three weeks 313 00:16:54,600 --> 00:16:57,360 Speaker 3: of your life reading a biography of Dunk Shopang, what 314 00:16:57,440 --> 00:17:00,440 Speaker 3: did you learn? And I'd be like, uh, he sort 315 00:17:00,440 --> 00:17:02,640 Speaker 3: of opened up and for a while he was sent 316 00:17:02,720 --> 00:17:06,680 Speaker 3: down to work on a tractor factory, and then he 317 00:17:06,760 --> 00:17:08,840 Speaker 3: opened up, and I would be like, you know, there's 318 00:17:08,960 --> 00:17:10,320 Speaker 3: like I was like, oh, did I really need to 319 00:17:10,359 --> 00:17:12,119 Speaker 3: spend three weeks of my life to be able to 320 00:17:12,160 --> 00:17:15,960 Speaker 3: recall four basic facts. I suppose that there are things 321 00:17:16,000 --> 00:17:17,800 Speaker 3: that I don't know that I remember, and like my 322 00:17:17,880 --> 00:17:20,600 Speaker 3: deep storage part of my memory that would be you know, 323 00:17:20,880 --> 00:17:24,280 Speaker 3: that would come out in a conversation. But I do 324 00:17:24,560 --> 00:17:27,040 Speaker 3: going back to questions of like why are we all 325 00:17:27,080 --> 00:17:29,840 Speaker 3: here and so forth. I do sometimes have these thoughts 326 00:17:29,880 --> 00:17:32,639 Speaker 3: of like do you know how much? How much do 327 00:17:32,680 --> 00:17:35,679 Speaker 3: we really learn and internalize? And how much is it 328 00:17:35,760 --> 00:17:37,440 Speaker 3: just fun to have these conversations. 329 00:17:37,680 --> 00:17:41,119 Speaker 2: Sometimes we surprise ourselves, yeah, right, Like sometimes we come 330 00:17:41,200 --> 00:17:43,840 Speaker 2: up with some random fact that's kind of interesting that 331 00:17:43,880 --> 00:17:47,320 Speaker 2: we learned from somewhere. Just on the note taking part 332 00:17:47,359 --> 00:17:51,240 Speaker 2: of the question. We generally don't take notes in the interview. 333 00:17:51,240 --> 00:17:54,000 Speaker 2: We might jot down like a word or two just 334 00:17:54,320 --> 00:17:57,960 Speaker 2: to remember to come back and ask a follow up question. 335 00:17:58,560 --> 00:18:04,479 Speaker 2: But yeah, it's a genuine conversation between three people, and 336 00:18:04,520 --> 00:18:06,760 Speaker 2: we try to keep it as natural as possible. 337 00:18:07,560 --> 00:18:11,160 Speaker 9: Hey, Joe and Tracy aj Toss here. I'm thirty seven 338 00:18:11,240 --> 00:18:13,679 Speaker 9: years old and I'm from Lafayette, Louisiana. 339 00:18:13,800 --> 00:18:14,760 Speaker 5: Go Cajun's. 340 00:18:15,119 --> 00:18:17,160 Speaker 9: I have a question for you about bitcoin. You guys 341 00:18:17,200 --> 00:18:21,600 Speaker 9: have spoken about how the narrative around bitcoin changes every cycle, 342 00:18:22,200 --> 00:18:24,399 Speaker 9: and so I'd actually like to ask you to make 343 00:18:24,440 --> 00:18:27,280 Speaker 9: a prediction whether you think there will be another cycle 344 00:18:27,320 --> 00:18:31,360 Speaker 9: for bitcoin where it way out performs everything else, and 345 00:18:32,800 --> 00:18:36,600 Speaker 9: assuming that it does, what would the narrative be this time? 346 00:18:37,600 --> 00:18:38,240 Speaker 5: Thanks guys. 347 00:18:40,000 --> 00:18:43,440 Speaker 2: That is a really good question. So the narrative theory 348 00:18:43,520 --> 00:18:47,880 Speaker 2: of bitcoin is this idea that because bitcoin is essentially nothing, 349 00:18:48,040 --> 00:18:52,440 Speaker 2: it is able to transform itself into anything. So we've 350 00:18:52,480 --> 00:18:58,080 Speaker 2: seen it go through these various cycles of transformation, reinventing itself. 351 00:18:58,359 --> 00:19:01,080 Speaker 2: You know, in the beginning, it was supposed to be 352 00:19:01,320 --> 00:19:04,680 Speaker 2: just a digital payment system. And there's the famous incident 353 00:19:04,800 --> 00:19:08,040 Speaker 2: of the guy that bought a pizza and lost a 354 00:19:08,080 --> 00:19:11,239 Speaker 2: lot of money that he otherwise would have gained. And 355 00:19:11,280 --> 00:19:13,240 Speaker 2: then it kind of, you know, it turned into an 356 00:19:13,240 --> 00:19:19,160 Speaker 2: inflation hedge. More recently, it's turned into a Trump vehicle. 357 00:19:20,200 --> 00:19:23,159 Speaker 2: I actually had it. I had an idea for a 358 00:19:23,280 --> 00:19:27,160 Speaker 2: narrative that comes next. But I'm blanking out on it totally. 359 00:19:27,400 --> 00:19:28,320 Speaker 2: I feel really bad. 360 00:19:29,160 --> 00:19:31,600 Speaker 3: You'll tweet it if yeah, I'll tweet it. 361 00:19:31,600 --> 00:19:33,480 Speaker 2: It was like two days ago, oh man, and I 362 00:19:33,520 --> 00:19:35,720 Speaker 2: had this idea and sorry, I'm very tired. 363 00:19:35,760 --> 00:19:36,520 Speaker 3: It'll come back to it. 364 00:19:36,520 --> 00:19:38,240 Speaker 2: It's the end of a busy season. 365 00:19:38,320 --> 00:19:40,800 Speaker 3: It's it's been a busy season, busy year. Look, I 366 00:19:40,840 --> 00:19:43,320 Speaker 3: have no you know. I was like, oh, now I'm 367 00:19:43,320 --> 00:19:46,440 Speaker 3: going to give the official odd lots for Bitcoin price 368 00:19:46,480 --> 00:19:48,760 Speaker 3: target for twenty twenty six. No, I really have no idea. 369 00:19:48,760 --> 00:19:52,159 Speaker 3: I don't have any idea of it'll outperform again. You know, 370 00:19:52,200 --> 00:19:56,040 Speaker 3: of course it has been pronounced dead several times many 371 00:19:56,080 --> 00:19:59,280 Speaker 3: times over the years, so perhaps that's always the reason 372 00:19:59,400 --> 00:20:01,880 Speaker 3: to disc out the idea of okay, this time is dead. 373 00:20:02,119 --> 00:20:06,080 Speaker 3: That being said, you know, look like the I thought 374 00:20:06,240 --> 00:20:09,159 Speaker 3: one of the most compelling arguments that had of the 375 00:20:09,200 --> 00:20:11,200 Speaker 3: various narratives over time, I thought one of the most 376 00:20:11,520 --> 00:20:13,480 Speaker 3: compelling ones had been that it would be sort of 377 00:20:13,520 --> 00:20:17,919 Speaker 3: this you know, post sovereign money and therefore kind of 378 00:20:17,920 --> 00:20:21,040 Speaker 3: like a digital gold safe haven. It has not been 379 00:20:21,119 --> 00:20:24,080 Speaker 3: behaving like one in twenty twenty five. I mean, this 380 00:20:24,200 --> 00:20:27,120 Speaker 3: is one of the most volatile years on memory. From 381 00:20:27,119 --> 00:20:30,840 Speaker 3: like a de globalization and geopolitical standpoint. If you'd think 382 00:20:30,880 --> 00:20:33,960 Speaker 3: there'd be one year where it's like, okay, people want 383 00:20:34,000 --> 00:20:38,200 Speaker 3: some sort of money that is disconnected from governments, maybe 384 00:20:38,240 --> 00:20:40,080 Speaker 3: this would be the year. Maybe they do, but they're 385 00:20:40,119 --> 00:20:43,840 Speaker 3: choosing for gold. They're choosing the actual safe haven. Bitcoin meanwhile, 386 00:20:43,840 --> 00:20:47,080 Speaker 3: has underperformed US treasuries. So the US treasuries yet another 387 00:20:47,240 --> 00:20:50,360 Speaker 3: safe haven that is outperforming Bitcoin in twenty twenty five. 388 00:20:50,760 --> 00:20:53,600 Speaker 3: So I really don't know, but I do think that 389 00:20:53,680 --> 00:20:59,480 Speaker 3: the poor performance in twenty twenty five should be something 390 00:20:59,560 --> 00:21:02,320 Speaker 3: of a certain Again, people have always said, oh, it's 391 00:21:02,359 --> 00:21:04,320 Speaker 3: a tech stock hitch well, techtocks have had or a 392 00:21:04,400 --> 00:21:07,359 Speaker 3: textdoc proxy textocks have done great this year, So it 393 00:21:07,400 --> 00:21:11,119 Speaker 3: hasn't even it hasn't even satisfied that requirement. And then 394 00:21:11,160 --> 00:21:13,280 Speaker 3: the last thing I'll say is that there's a lot 395 00:21:13,320 --> 00:21:17,600 Speaker 3: of I think legitimate enthusiasm about crypto, particularly in the 396 00:21:17,640 --> 00:21:21,480 Speaker 3: realm of stable coins, getting back to maybe the original idea, 397 00:21:21,480 --> 00:21:24,560 Speaker 3: as Tracy mentioned, of a payments platform, but all that 398 00:21:24,680 --> 00:21:28,800 Speaker 3: is happening on other chains besides Bitcoin, and so even 399 00:21:28,840 --> 00:21:31,840 Speaker 3: if crypto becomes more of a thing in the financial system, 400 00:21:32,119 --> 00:21:35,879 Speaker 3: it's not obvious possible, but it's certainly not obvious that 401 00:21:35,960 --> 00:21:38,000 Speaker 3: Bitcoin would see any benefit from it. 402 00:21:38,359 --> 00:21:42,480 Speaker 2: I do think, though, we shouldn't underestimate bitcoin's ability to 403 00:21:42,520 --> 00:21:45,359 Speaker 2: reinvent itself. And this actually took me a long time 404 00:21:45,560 --> 00:21:49,800 Speaker 2: to realize. And you know, he mentioned the obituaries for crypto. 405 00:21:50,080 --> 00:21:53,480 Speaker 2: I wrote some of those, like I think in twenty twelve, 406 00:21:53,600 --> 00:21:54,359 Speaker 2: I wrote one. 407 00:21:55,200 --> 00:21:56,680 Speaker 3: Yea, so we've all learned our list. 408 00:21:56,840 --> 00:21:58,920 Speaker 2: May I kulpa on that. We'll see what happens. 409 00:22:14,520 --> 00:22:15,720 Speaker 3: All right, let's take another question. 410 00:22:15,960 --> 00:22:20,320 Speaker 10: Hello, Joan Tracy. This is Cynthia from Vancouver, Canada. What 411 00:22:20,480 --> 00:22:23,639 Speaker 10: is something that Marqua seems really confident about that you 412 00:22:23,760 --> 00:22:27,160 Speaker 10: think deserves more questioning or more nuanced discussion. 413 00:22:28,000 --> 00:22:31,520 Speaker 3: You know, first of all, thanks Cynthia for calling in. 414 00:22:31,840 --> 00:22:34,959 Speaker 3: The first thing that comes to my mind is I 415 00:22:35,000 --> 00:22:37,240 Speaker 3: have been I wrote about this recently in the Odd 416 00:22:37,320 --> 00:22:40,800 Speaker 3: Lots newsletter. I am kind of surprised about how well 417 00:22:40,840 --> 00:22:45,320 Speaker 3: anchored market based expectations for inflation have been, because there 418 00:22:45,400 --> 00:22:48,560 Speaker 3: is I think pretty legitimate. There's two things going on. 419 00:22:48,560 --> 00:22:51,520 Speaker 3: One is, you know, the FED hasn't missed its inflation 420 00:22:51,840 --> 00:22:55,200 Speaker 3: target for years, and so there's perhaps reasons to think 421 00:22:55,240 --> 00:22:59,000 Speaker 3: that just already that the FED hasn't been as serious 422 00:22:59,040 --> 00:23:02,120 Speaker 3: about two percent as it had been before. But then 423 00:23:02,160 --> 00:23:04,520 Speaker 3: also I do think there are good reasons to think 424 00:23:04,640 --> 00:23:07,800 Speaker 3: that in the future the FED will not have the 425 00:23:07,840 --> 00:23:10,920 Speaker 3: same capacity to target stable prices the way it used 426 00:23:10,960 --> 00:23:14,679 Speaker 3: to due to politicization, due to the general sort of 427 00:23:14,760 --> 00:23:18,760 Speaker 3: like populist trend in politics, which is not something distinct 428 00:23:18,800 --> 00:23:21,639 Speaker 3: to the Trump administration. That certainly I think the Trump 429 00:23:21,680 --> 00:23:25,080 Speaker 3: administration has accelerated that process with a lot of the 430 00:23:25,119 --> 00:23:30,000 Speaker 3: criticism of the chairman and so forth, and so like, 431 00:23:30,400 --> 00:23:32,719 Speaker 3: I am a bit surprised that if you look at 432 00:23:32,800 --> 00:23:36,080 Speaker 3: various market based measures of future inflation, five year, five 433 00:23:36,160 --> 00:23:40,480 Speaker 3: year break events, and so forth, that it has been 434 00:23:40,600 --> 00:23:44,920 Speaker 3: pretty stable, because that feels disconnected from the way that 435 00:23:45,000 --> 00:23:47,480 Speaker 3: we basically talk about this topic. 436 00:23:47,880 --> 00:23:51,480 Speaker 2: Mine is also related to inflation. I think far more 437 00:23:51,520 --> 00:23:55,400 Speaker 2: people need to figure out how the CPI official numbers 438 00:23:55,400 --> 00:24:00,280 Speaker 2: for inflation are actually calculated and produced. And you know, 439 00:24:00,640 --> 00:24:04,480 Speaker 2: ultimately a lot of it is a series of numbers 440 00:24:04,600 --> 00:24:08,359 Speaker 2: that are estimated. Those are called imputed numbers, and a 441 00:24:08,400 --> 00:24:12,680 Speaker 2: lot of them are actual observations of prices which Bureau 442 00:24:12,840 --> 00:24:16,000 Speaker 2: of Labor Services people go out and collect. It's actually 443 00:24:16,080 --> 00:24:22,000 Speaker 2: a really labor intensive thing to do, and most people, 444 00:24:22,160 --> 00:24:25,520 Speaker 2: you know, they treat them like as a simplistic number, 445 00:24:25,560 --> 00:24:27,560 Speaker 2: and I think it's much more important to look at 446 00:24:27,600 --> 00:24:33,520 Speaker 2: the different components. And actually we're recording this on December eighteenth, 447 00:24:33,520 --> 00:24:35,840 Speaker 2: and we're seeing a really good example of why it's 448 00:24:35,880 --> 00:24:40,959 Speaker 2: important to know how CPI is actually calculated, because you know, 449 00:24:41,440 --> 00:24:44,920 Speaker 2: the number just came out much lower than expected, and 450 00:24:45,200 --> 00:24:47,960 Speaker 2: it's interesting if you start to break it down to 451 00:24:48,040 --> 00:24:52,320 Speaker 2: see what assumptions were made and what's actually moving all. 452 00:24:52,280 --> 00:24:54,520 Speaker 3: Right, for the next question. This one was just written 453 00:24:54,960 --> 00:24:56,720 Speaker 3: as a post on audio note, but it's a really 454 00:24:56,720 --> 00:24:59,000 Speaker 3: fun one where Tracy and I kind of see the 455 00:24:59,000 --> 00:25:01,560 Speaker 3: world a little bit different, so we have a chance 456 00:25:01,600 --> 00:25:05,680 Speaker 3: to get into that. This question comes from Diego Aguilar Cannibell. 457 00:25:05,920 --> 00:25:08,600 Speaker 3: He has where did my intellectual beef with the idea 458 00:25:08,640 --> 00:25:11,600 Speaker 3: of the term premium start? And look, so this is 459 00:25:11,640 --> 00:25:14,360 Speaker 3: something that's come up. People talk about the term premium 460 00:25:14,680 --> 00:25:17,560 Speaker 3: in the in the yield curve, and I'm always like, 461 00:25:17,640 --> 00:25:20,920 Speaker 3: what exactly does that mean? So you know the way, 462 00:25:21,080 --> 00:25:23,920 Speaker 3: like I guess, Tracy, let's tart with you. What is 463 00:25:23,960 --> 00:25:26,520 Speaker 3: the term premium? Don't ask me, I'm not gonna define it. 464 00:25:26,680 --> 00:25:28,120 Speaker 3: How do you What is the term premium? 465 00:25:28,520 --> 00:25:33,080 Speaker 2: It is basically the extra compensation or premium that investors 466 00:25:33,080 --> 00:25:36,120 Speaker 2: demand to hold debt, you know, out longer term. 467 00:25:36,440 --> 00:25:38,439 Speaker 3: Okay, so here's what I don't get. Why do they 468 00:25:38,480 --> 00:25:41,919 Speaker 3: deserve extra compensation? Why don't they just deserve the appropriate compensation? 469 00:25:42,119 --> 00:25:43,040 Speaker 3: Like I buy stocks? 470 00:25:43,119 --> 00:25:43,399 Speaker 7: Do I? 471 00:25:43,440 --> 00:25:46,480 Speaker 3: Oh, I deserve extra compensation because they're risky. No, I 472 00:25:46,960 --> 00:25:50,680 Speaker 3: deserve appropriate compensation. I deserve to be compensated, I suppose, 473 00:25:51,000 --> 00:25:53,119 Speaker 3: But I hate this idea of like extra compensation. We 474 00:25:53,160 --> 00:25:56,560 Speaker 3: always want extra compensation. But then also, I guess my 475 00:25:56,640 --> 00:26:00,439 Speaker 3: other issue is that, like I think, if you knew, 476 00:26:00,680 --> 00:26:04,000 Speaker 3: if you if you had a crystal ball and you 477 00:26:04,080 --> 00:26:06,679 Speaker 3: knew for sure what the FED was going to do 478 00:26:06,880 --> 00:26:10,520 Speaker 3: in every policy meeting, it would make for the next 479 00:26:10,800 --> 00:26:13,440 Speaker 3: ten years. You know, there's eighty years, so the next 480 00:26:13,480 --> 00:26:15,560 Speaker 3: eighty meetings, if you knew what that was going to be, 481 00:26:16,119 --> 00:26:18,879 Speaker 3: I think you would know the exact price that ten 482 00:26:18,960 --> 00:26:21,120 Speaker 3: year yields should treat it. And so I don't really 483 00:26:21,200 --> 00:26:25,560 Speaker 3: understand why we can't just say that the yield curve 484 00:26:25,880 --> 00:26:29,400 Speaker 3: is the market's expectation of overnight rates for the next 485 00:26:29,440 --> 00:26:30,120 Speaker 3: ten years. 486 00:26:30,200 --> 00:26:33,919 Speaker 2: Well, it's it's in comparison, so the extra premium is 487 00:26:33,960 --> 00:26:37,119 Speaker 2: in comparison to shorter term bonds, and there are some 488 00:26:37,240 --> 00:26:40,840 Speaker 2: calculations that go into that. I think it's it's, you know, 489 00:26:41,320 --> 00:26:44,080 Speaker 2: not the be all and end hall of the market, 490 00:26:44,160 --> 00:26:46,679 Speaker 2: but it's certainly an interesting thing to look at. And 491 00:26:46,720 --> 00:26:50,360 Speaker 2: when we get big moments in the term premium's movement, 492 00:26:50,520 --> 00:26:54,879 Speaker 2: you know, if it flips from positive to negative, it 493 00:26:55,240 --> 00:26:57,800 Speaker 2: kind of tells you a lot about where the market's going. 494 00:26:58,040 --> 00:27:00,880 Speaker 3: Okay, on this point, though, let's just press further there. 495 00:27:00,920 --> 00:27:03,280 Speaker 3: I think there's really key, which is so they're right, 496 00:27:03,840 --> 00:27:07,800 Speaker 3: everyone admits these are unobservable, right, these are models. There's 497 00:27:07,840 --> 00:27:11,000 Speaker 3: are models that indicate what this notional term premium is. 498 00:27:11,480 --> 00:27:13,719 Speaker 3: The fact that it can flip to negative and it 499 00:27:13,800 --> 00:27:17,000 Speaker 3: was negative for much of the twenty tens suggests to 500 00:27:17,040 --> 00:27:19,160 Speaker 3: me like that's another reason, Like I don't understand how 501 00:27:19,240 --> 00:27:22,560 Speaker 3: this idea that there is holders of long term debt 502 00:27:22,840 --> 00:27:26,280 Speaker 3: per se need extra because of extra protection or whatever, 503 00:27:26,640 --> 00:27:29,840 Speaker 3: because apparently they were actually going to a discount on 504 00:27:29,920 --> 00:27:32,399 Speaker 3: it if we go by the model, what the model 505 00:27:32,440 --> 00:27:35,040 Speaker 3: said in the twenty tens. So I just find the 506 00:27:35,080 --> 00:27:37,880 Speaker 3: whole thing. I just find it all very unsatisfying. 507 00:27:38,640 --> 00:27:41,720 Speaker 2: I would also say the term premium beef. We play 508 00:27:41,720 --> 00:27:43,480 Speaker 2: it up a little bit for the show. 509 00:27:43,560 --> 00:27:44,520 Speaker 3: So let up for the show. 510 00:27:44,560 --> 00:27:47,040 Speaker 2: We hope you enjoy. All right, this is going to 511 00:27:47,080 --> 00:27:48,400 Speaker 2: be our final question. 512 00:27:48,640 --> 00:27:48,920 Speaker 8: Again. 513 00:27:49,000 --> 00:27:52,199 Speaker 2: This one was written in rather than a voice note, 514 00:27:52,400 --> 00:27:55,399 Speaker 2: so here we go. It is from Michael Kahn. He 515 00:27:55,760 --> 00:27:59,800 Speaker 2: asks a non markets question, more specifically for me to 516 00:28:00,160 --> 00:28:03,880 Speaker 2: see what was your favorite magic the gathering card and 517 00:28:03,920 --> 00:28:08,600 Speaker 2: what critical function did it serve in your deck? Bonus question, 518 00:28:08,840 --> 00:28:12,080 Speaker 2: if you and Joe were a magic card, which ones 519 00:28:12,119 --> 00:28:17,119 Speaker 2: would you be? That's a very fun question. Okay, so 520 00:28:17,400 --> 00:28:22,480 Speaker 2: my favorite was probably my Chevon Dragon. I like the 521 00:28:22,560 --> 00:28:25,320 Speaker 2: idea of dragons, and it had really nice art and 522 00:28:25,560 --> 00:28:29,439 Speaker 2: at the time it was a really valuable and powerful card. 523 00:28:30,000 --> 00:28:33,400 Speaker 2: By the way, I don't know if I'm pronouncing Chavon right, 524 00:28:33,520 --> 00:28:37,119 Speaker 2: whether it's Chavon or Chyvon. Like, we didn't have a 525 00:28:37,160 --> 00:28:40,480 Speaker 2: lot of pronunciation guides when I was in middle school. 526 00:28:40,520 --> 00:28:43,320 Speaker 2: And I also just don't remember how people were saying 527 00:28:43,360 --> 00:28:46,000 Speaker 2: these things. It was like thirty years ago. And I 528 00:28:46,040 --> 00:28:50,720 Speaker 2: say that just because someone someone on Twitter or read 529 00:28:50,760 --> 00:28:54,120 Speaker 2: it was criticizing me for not knowing that a card 530 00:28:54,160 --> 00:28:57,840 Speaker 2: that Joe mentioned was an actual card. Yeah, I didn't 531 00:28:57,880 --> 00:28:59,480 Speaker 2: know that. I thought you were making it up. I 532 00:28:59,480 --> 00:29:02,680 Speaker 2: mean sounds like, oh, you know, an obvious magic card. 533 00:29:02,800 --> 00:29:06,040 Speaker 3: I know nothing at all about magic. I've never played it. 534 00:29:06,160 --> 00:29:08,920 Speaker 3: I really, I just I don't know how the game works, 535 00:29:09,000 --> 00:29:12,080 Speaker 3: et cetera. All I only remember one fact, which is 536 00:29:12,120 --> 00:29:14,520 Speaker 3: that one of my good friend's brother younger brother in 537 00:29:14,600 --> 00:29:17,760 Speaker 3: high school had a call called Wall of Brambles. Actually, 538 00:29:17,920 --> 00:29:21,080 Speaker 3: this gets to like the question about memorization. Who the 539 00:29:21,160 --> 00:29:23,600 Speaker 3: heck knows how that random fact got stuck in my 540 00:29:23,680 --> 00:29:26,160 Speaker 3: head or what. But this is I hear magic and 541 00:29:26,160 --> 00:29:28,120 Speaker 3: I just remember this card. I just looked it up. 542 00:29:29,000 --> 00:29:31,520 Speaker 3: It looks like this card would cost somewhere between twenty 543 00:29:31,560 --> 00:29:34,920 Speaker 3: five and thirty cents, so I'm certainly not memorizing it 544 00:29:34,960 --> 00:29:37,320 Speaker 3: because there's a particularly valuable or important card. 545 00:29:37,640 --> 00:29:42,160 Speaker 2: And then what role did the dragon play in my deck? 546 00:29:42,960 --> 00:29:46,200 Speaker 2: I think I said this on the podcast. I really 547 00:29:46,760 --> 00:29:51,280 Speaker 2: enjoyed the collecting side of magic, the gathering, more than 548 00:29:51,360 --> 00:29:55,800 Speaker 2: I enjoyed actually playing it. You know, there's some gender 549 00:29:55,880 --> 00:29:58,800 Speaker 2: imbalance at the time, and it like sometimes felt weird to, 550 00:29:59,480 --> 00:30:02,080 Speaker 2: you know, go to war with a bunch of guys. 551 00:30:02,520 --> 00:30:05,520 Speaker 2: But I really enjoyed putting together the decks and sort 552 00:30:05,560 --> 00:30:09,560 Speaker 2: of strategizing, and I guess I didn't enjoy testing them 553 00:30:09,560 --> 00:30:12,840 Speaker 2: out that much. What would we be if we were 554 00:30:13,640 --> 00:30:14,760 Speaker 2: a card? 555 00:30:14,760 --> 00:30:16,400 Speaker 3: Tra you got to you gotta answer this then. 556 00:30:16,480 --> 00:30:18,960 Speaker 2: If all thoughts or if us It says if you 557 00:30:19,040 --> 00:30:25,720 Speaker 2: and Joe were a magic card, So give me a second. Okay, 558 00:30:26,040 --> 00:30:29,600 Speaker 2: full disclosure. I just asked chat GPT because this seems 559 00:30:29,680 --> 00:30:34,080 Speaker 2: like a kind of fun question, and the AI generated 560 00:30:34,120 --> 00:30:38,800 Speaker 2: answer is we would be a blue red legendary creature 561 00:30:39,360 --> 00:30:43,719 Speaker 2: that rewards drawing cards, talking about weird market mechanics, and 562 00:30:43,760 --> 00:30:47,360 Speaker 2: turning every small data point into a big compounding advantage. 563 00:30:48,320 --> 00:30:52,840 Speaker 2: I'm not sure how you would incorporate discussions about markets 564 00:30:52,880 --> 00:30:55,600 Speaker 2: into a Magic the Gathering game, but you know, it's 565 00:30:55,680 --> 00:30:59,280 Speaker 2: nice to hear we could be a dragon. I would say, like, 566 00:30:59,560 --> 00:31:04,320 Speaker 2: maybe we would be a Vesuven Doppelganger. And I say 567 00:31:04,360 --> 00:31:08,560 Speaker 2: that partly because I'm sort of biased to the older cards, 568 00:31:08,640 --> 00:31:11,640 Speaker 2: the ice age cards that came out in the mid 569 00:31:11,720 --> 00:31:16,600 Speaker 2: nineties for obvious reasons, and I think the doppelganger, you know, 570 00:31:17,040 --> 00:31:22,760 Speaker 2: they obviously compliment each other, and their actual power in 571 00:31:22,800 --> 00:31:25,400 Speaker 2: the game from what I remember, is they can take 572 00:31:25,440 --> 00:31:30,440 Speaker 2: on the characteristics of any creature that's actually in play, 573 00:31:30,520 --> 00:31:33,840 Speaker 2: or most of the characteristics, And I think that's you know, 574 00:31:34,240 --> 00:31:37,160 Speaker 2: that's kind of what we do. We try to adapt 575 00:31:37,240 --> 00:31:41,000 Speaker 2: that's right to every conversation to get the best result. 576 00:31:41,480 --> 00:31:43,880 Speaker 3: That's right. I also asked you a gpetitas. I didn't 577 00:31:43,880 --> 00:31:47,959 Speaker 3: really understand. I don't I don't even understand it's results. 578 00:31:47,960 --> 00:31:50,440 Speaker 3: But then it said why this works to Joe generates 579 00:31:50,480 --> 00:31:57,000 Speaker 3: flow motion liquidity, and Tracy imposes structure, friction and narrative discipline, 580 00:31:57,040 --> 00:31:59,480 Speaker 3: which I think many listeners would actually sort of agree 581 00:31:59,520 --> 00:32:02,000 Speaker 3: with it sort of. Yeah, it doesn't off, so. 582 00:32:02,080 --> 00:32:04,360 Speaker 2: That's not bad. But it's not a Magic the Gathering. 583 00:32:04,120 --> 00:32:07,120 Speaker 3: No, no, no, I didn't even understand like all this stuff. 584 00:32:07,120 --> 00:32:10,120 Speaker 3: I can't even I don't know too you are legend. 585 00:32:10,120 --> 00:32:11,400 Speaker 3: I don't know what any of this stuff is. 586 00:32:11,720 --> 00:32:11,880 Speaker 4: Oh. 587 00:32:12,120 --> 00:32:15,160 Speaker 2: I do have some good news, which is remember I 588 00:32:15,200 --> 00:32:17,560 Speaker 2: said that I had lost all my Magic the Gathering 589 00:32:17,560 --> 00:32:20,160 Speaker 2: cards and I didn't know where they were. My mom 590 00:32:20,240 --> 00:32:25,040 Speaker 2: actually found them in her basement. They were sort of 591 00:32:25,160 --> 00:32:28,880 Speaker 2: hidden away. The bad news is I really don't have 592 00:32:28,960 --> 00:32:32,120 Speaker 2: any valuable ones. I still have the Shaffon Dragon, and 593 00:32:32,200 --> 00:32:34,960 Speaker 2: I have one that was signed by the artist, but 594 00:32:35,280 --> 00:32:35,959 Speaker 2: that's about it. 595 00:32:36,280 --> 00:32:38,600 Speaker 3: I'm surprised there's not one in there, Like, is there 596 00:32:38,640 --> 00:32:41,840 Speaker 3: like an antiques road show style show for Magic the 597 00:32:41,880 --> 00:32:44,160 Speaker 3: Gathering Cards? Like maybe we could do an episode like 598 00:32:44,200 --> 00:32:46,960 Speaker 3: it's some guy and hear some dealer and like talk 599 00:32:47,040 --> 00:32:48,880 Speaker 3: through the cards or something like that. I would do that. 600 00:32:48,960 --> 00:32:52,960 Speaker 2: Yeah, that would be really So this is the end 601 00:32:53,280 --> 00:32:56,320 Speaker 2: of the AMA. Thank you everyone for sending in all 602 00:32:56,360 --> 00:32:59,280 Speaker 2: these great questions. We had a lot of fun answering them. 603 00:32:59,480 --> 00:33:01,800 Speaker 3: Yeah, and thank you everyone, just for an end of 604 00:33:01,880 --> 00:33:04,920 Speaker 3: your crazy year, great year, So really thank you to 605 00:33:05,000 --> 00:33:07,719 Speaker 3: everyone who everyone who's listened. 606 00:33:07,920 --> 00:33:10,680 Speaker 2: And yeah, we definitely hope you listen in twenty twenty 607 00:33:10,720 --> 00:33:14,360 Speaker 2: six because we have some excellent episodes coming up. So 608 00:33:15,320 --> 00:33:16,120 Speaker 2: shall we leave it there. 609 00:33:16,200 --> 00:33:16,960 Speaker 3: Let's leave it there. 610 00:33:17,200 --> 00:33:20,160 Speaker 2: This has been another episode of the Odd Lots podcast. 611 00:33:20,320 --> 00:33:23,840 Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway and. 612 00:33:23,800 --> 00:33:26,480 Speaker 3: I'm Jill Wisenthal. You can follow me at the Stalwart. 613 00:33:26,680 --> 00:33:30,080 Speaker 3: Definitely follow our producers Carmen Rodriguez at Carman Arman dash 614 00:33:30,120 --> 00:33:33,080 Speaker 3: Ol Bennett at Dashbot and kill Brooks at Kilbrooks. For 615 00:33:33,160 --> 00:33:35,840 Speaker 3: more Odd Laws content, gode Bloomberg dot com, slash odd 616 00:33:35,880 --> 00:33:38,360 Speaker 3: Lots were a daily newsletter and all of our episodes, 617 00:33:38,560 --> 00:33:40,520 Speaker 3: and you can chat about all of these topics twenty 618 00:33:40,520 --> 00:33:43,920 Speaker 3: four to seven in our discord Discord dot gg slash 619 00:33:43,920 --> 00:33:44,600 Speaker 3: out Lots. 620 00:33:44,720 --> 00:33:47,360 Speaker 2: And if you enjoy odd Lots, if you want us 621 00:33:47,400 --> 00:33:51,640 Speaker 2: to go on an antiques roadshow for magic gathering cards, 622 00:33:51,680 --> 00:33:54,360 Speaker 2: then please leave us a positive review on your favorite 623 00:33:54,360 --> 00:33:58,320 Speaker 2: podcast platform. 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