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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. Hello and welcome to

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Speaker 1: the Money Stuff Podcast. You're a weekly podcast. Where are

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Speaker 1: we talking about stuff related to money? I'm Matt Levine

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Speaker 1: and I write the Money Stuff column for Bloomberg Opinion.

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Speaker 2: And I'm Katie Greifeld, a reporter for Bloomberg News and

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Speaker 2: an anchor for Bloomberg Television.

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Speaker 1: What are we talking about today, Katie?

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Speaker 2: We're going to talk about how the US stock market

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Speaker 2: got deep seeked. We're going to talk about that sweet

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Speaker 2: Sweet ex debt, and then we're going to talk about trading.

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Speaker 3: In the dark.

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Speaker 2: Sounds good, deep seek Deep Seek. So I cannot believe.

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Speaker 2: I cannot believe that there is a money Stuff from

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Speaker 2: June that mentions deep seek because I would say ninety

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Speaker 2: seven percent of the people that I talked to on

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Speaker 2: Monday hadn't heard of it before this past weekend.

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Speaker 1: Where did they hear of it?

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Speaker 2: The people I talked to on Monday who hadn't heard

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Speaker 2: of it previously, Yeah, they heard about it when the

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Speaker 2: app shot up in the app store, Okay, and then

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Speaker 2: you had the cell side start writing about it. There

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Speaker 2: were a ton of tweets about it over the weekend.

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Speaker 2: Not that Twitter is real life, but in some cases

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Speaker 2: it kind of is.

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Speaker 1: Yeah, Like there's one guy who wrote a long report

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Speaker 1: on like his personal website and argues somewhat plausibly that

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Speaker 1: he influenced a lot of the investor reaction. It's like

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Speaker 1: interesting to see how like investor actions collas, right, because

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Speaker 1: like the model was kind of released like at the

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Speaker 1: Piniere last week. The catalyst is some combination of people

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Speaker 1: getting to think about it over the weekend and like

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Speaker 1: it shooting up in the app store, But at some

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Speaker 1: point there's like this big shift from like people using

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Speaker 1: the app to everyone having existential crisis about it. Nvidia, Yeah,

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Speaker 1: this is a digression. Yes, I wrote about it in June.

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Speaker 1: I'm prescient. I was like, yeah, this is going to

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Speaker 1: be really bad for the future of netin. But I

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Speaker 1: do love you know what I read about in June

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Speaker 1: is like the founder of deep seek it kind of

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Speaker 1: spun out of his quantitative hedge fund, and I love

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Speaker 1: that the skill sets of hedge funds and large language

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Speaker 1: models are like kind of overlapping, right. These are kind

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Speaker 1: of like, you know, using machine learning techniques to predict

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Speaker 1: somewhat unpredictable things, whether that's like the next word in

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Speaker 1: a sentence, or the stocks that will go up. And

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Speaker 1: classically people made billions of dollars, and by people I

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Speaker 1: mean like renaissance technologies made billions of dollars, but like

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Speaker 1: repurposing like people in the business of like natural language

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Speaker 1: generation into predicting stock prices. And it's nice to see

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Speaker 1: that come full circle and the people who are using

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Speaker 1: machine learning to predict stock prices are now getting back

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Speaker 1: into the natural language game and making billions of dollars

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Speaker 1: that way.

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Speaker 2: Yeah, I mean everything, everything is cyclical in that sense.

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Speaker 2: I will say I wish we had talked about it

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Speaker 2: on the podcast in June so that I could have

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Speaker 2: shared in some of this whole it was so early.

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Speaker 2: But in any case, it's fine.

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Speaker 1: The thing is not that like deep Seek is an

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Speaker 1: AI company in China. The thing is that the market

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Speaker 1: lost the trillion dollars of market cap on Monday. I

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Speaker 1: don't want to say that the US economy is based

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Speaker 1: on like building empowering data centers for AI companies, but

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Speaker 1: the projected incremental cash flows to the US economy, like

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Speaker 1: a lot of those are like, yes, we're going to

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Speaker 1: build a lot of data centers and a lot of

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Speaker 1: like power plants to power them, and deep Seek arguably

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Speaker 1: people think that it undermines that case, like quite significantly.

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Speaker 1: And yeah, if like you thought that all of economic

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Speaker 1: growth would come from like AI data centers, then now

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Speaker 1: you're like, yeah, the source of economic growth is gone,

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Speaker 1: which is a funny thing to think, but.

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Speaker 2: Yeah, I know, I think a lot of people actually

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Speaker 2: would agree with that logic because you take a look

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Speaker 2: at what happened in the bond market, like there was

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Speaker 2: this insane bid into bonds on Monday as well, and

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Speaker 2: sort of that was bonds being a haven.

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Speaker 1: Yeah, I saw Deep Seek is a macro event.

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Speaker 2: Yeah. Well one of the most credible reasons I got

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Speaker 2: was just because people are worried that this is going

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Speaker 2: to shave that incremental bid off of GDP, because there's

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Speaker 2: been plenty of risk off events where bonds didn't catch

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Speaker 2: a bid, but this was the one thing that spurred

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Speaker 2: like this haven bid across the treasury curve, which is

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Speaker 2: pretty funny.

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Speaker 1: I love AI as a as a subject matter because

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Speaker 1: it is so science fictional. And you know, one thing

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Speaker 1: people talk about sometimes is whether GDP is like a

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Speaker 1: bad index of like human flourishing, right, And so there's

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Speaker 1: an argument that like GDP measures understate GDP growth because

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Speaker 1: like you're getting all these like hedonic benefits from like

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Speaker 1: going on social media or whatever, and that's like captured

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Speaker 1: in GDP figures or whatever. The thesis here is something like,

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Speaker 1: if we have to build a lot of buildings and

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Speaker 1: burn a lot of coal or natural gas to make

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Speaker 1: really good AI models, then that's good for GDP. And

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Speaker 1: if you can get the same AIA benefits for free,

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Speaker 1: then that's bad for GDP. But like that's better, right,

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Speaker 1: It's clearly better to have those benefits without burning coal,

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Speaker 1: to have them, you know, and burn coal. I wrote

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Speaker 1: something like, you know, if you sort of like fully

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Speaker 1: believe the deep Seak thesis and that's like this magic

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Speaker 1: AI thing that is free, that's clearly better for human flourishing,

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Speaker 1: but it's like worse for stock market capitalization because no

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Speaker 1: one can make a profit from it.

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Speaker 2: And I mean, can we flourish if the stock market

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Speaker 2: doesn't go higher? I don't know, Matt, I'm scared to

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Speaker 2: find out the answer.

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Speaker 1: It's a real question. There's a Simpsons bit where Homer

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Speaker 1: has an auto dialer and it calls mister burns and

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Speaker 1: it says, hello, friend, would you trade one dollar for

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Speaker 1: eternal happiness? And mister Burns, thinking he's talking to a person,

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Speaker 1: thanks for when, and says, hmmm.

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Speaker 3: One dollar for eternal happiness.

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Speaker 1: May be happy with the dollar. I think about this

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Speaker 1: all the time, Like I sometimes feel that way too, right,

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Speaker 1: Like yeah, if you're a fron k goes down, but

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Speaker 1: like all of your future needs will be met by

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Speaker 1: like a little robot in your pocket, then you didn't

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Speaker 1: need the four own k, but like you want the

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Speaker 1: four oh and k.

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Speaker 2: That's actually all I could think about on Monday was

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Speaker 2: how I will ever financially recover from this trading session?

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Speaker 1: Like we did not air, but I.

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Speaker 2: Was like, no, I need to get to the podcast.

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Speaker 2: You know, the show must go on. So you boldly

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Speaker 2: asked the question in a money stuff this week, what

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Speaker 2: if this Chinese quant hedge funds that you know then

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Speaker 2: went on to develop deep Seek had a bunch of

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Speaker 2: InVideo puts and made a bunch of money that way.

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Speaker 2: And then friend of the show Bill Ackman tweeted the

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Speaker 2: exact same question, which was fun.

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Speaker 1: I assume that other people independently came to this idea

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Speaker 1: I had several readers emailing about it before I wrote it.

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Speaker 2: No, no, no, you were the first one to ever write

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Speaker 2: about deep Seek, first one to ever pose the hypothetical.

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Speaker 1: Sure, And I should also say that the idea of

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Speaker 1: like disruptors funding their business by giving away their product

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Speaker 1: for free and shorting incumbents. I learned of it from

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Speaker 1: Joe Wisenthal or fellow Bloomberg podcast, like a decade ago.

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Speaker 1: He was writing about it, you know, before everyone. But

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Speaker 1: in any case, yeah, well it's a really funny idea. Two,

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Speaker 1: it has really kind of percolated up. Apparently Howard Nick

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Speaker 1: is getting questions about it in Congress. Oh my, whether

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Speaker 1: that's there's no evidence for it, but it would be

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Speaker 1: very funny if he did it.

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Speaker 2: Also, is it illegal? I mean, is it insider trading?

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Speaker 1: So here's what I'll say. One, this is not legal advice.

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Speaker 1: Two I think in the US it's like clearly fine.

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Speaker 1: The way it works in the US is like you

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Speaker 1: are allowed to trade on your own information, but you're

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Speaker 1: not allowed to misappropriate information from someone else. So like

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Speaker 1: if he was buying in Vidia puts in his personal

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Speaker 1: account while you know, running deep Seek, then that like

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Speaker 1: might look bad depending on exactly what his arrangement was

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Speaker 1: with deep Seek. But if like the corporate complex of

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Speaker 1: like Deep Seek and the hedge fund was trading on

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Speaker 1: deep Seek's own information to make a profit for that complex,

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Speaker 1: and it seems fine, Like it seems like you're not

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Speaker 1: misappropriating an information and you're really only trading on your

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Speaker 1: own knowledge of your own business right and your own

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Speaker 1: like extrapolation of what that will mean for other businesses.

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Speaker 1: I think it's totally fine. Now. The two caveats that

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Speaker 1: are one, I'm not sure that every jurisdiction sees it

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Speaker 1: that way. The US law is much more about this

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Speaker 1: like sort of misappropriation theory, whereas like in in other

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Speaker 1: places it's like more common to just be like a

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Speaker 1: fairness focused, like you can't trade on information that the

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Speaker 1: market doesn't have. And then two, if you did this,

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Speaker 1: people would get mad and they would say, oh, that's

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Speaker 1: insider trading. And then you'd say, no, no, it's not inside

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Speaker 1: of trading. It's fine. They're like, okay, fine, but it's

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Speaker 1: market manipulation and like it's market univation because they'll find

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Speaker 1: something you said wrong, right and so here in deep set,

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Speaker 1: because of in a lot of controversy about like is

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Speaker 1: it really the case that like its training cost was

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Speaker 1: as low as it was, or was it like distilling

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Speaker 1: models or rather AI companies. You know, you find something

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Speaker 1: that you can seize on to be like, oh, this

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Speaker 1: is a misrepresentation, and then it's like market mauntivation. You

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Speaker 1: were like saying false things to bring down in video stock.

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Speaker 1: So it would be a risky thing to do to

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Speaker 1: tank in video stuck in such a high profile way

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Speaker 1: while learning puts on it, like people would get mad

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Speaker 1: and like might find a thing to try you with.

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Speaker 1: But like, no, I don't think it's inside her jetty.

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Speaker 2: Yeah, well, I mean again, this is all hypothetical. We

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Speaker 2: have no idea if he actually did this.

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Speaker 1: But I don't think he did. I just like it's fun.

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Speaker 1: He should have done it. I would have done it,

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Speaker 1: because like the other thing is like they don't like

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Speaker 1: I don't know what their business model is, Like public

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Speaker 1: reporting is like some combination of he's just you know,

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Speaker 1: making money on his hedge fund, or he's doing this

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Speaker 1: out of like the goodness of his heart, and like

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Speaker 1: I want to wanting to contribute to AI research because

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Speaker 1: it's like open source. It's cheap, how are they gonna

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Speaker 1: make money? And I like get shortened in video.

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Speaker 2: Yeah, I did also want to bring up that the

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Speaker 2: narrative around this week has been that basically deep seek

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Speaker 2: was able to recreate open ai with six million dollars

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Speaker 2: and it's just been fun watching that get picked apart.

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Speaker 2: I've spoken to so many invidiables in the past couple

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Speaker 2: of days who have made this into a bowl case

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Speaker 2: for in video, which I find really interesting. And there

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Speaker 2: is a lot of skepticism around the numbers here. And

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Speaker 2: one of the news stories that came about is that

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Speaker 2: Microsoft and open ai are taking a look at whether

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Speaker 2: this group used open AI's API to basically get a

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Speaker 2: large amount of data, which would violate open AI's terms

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Speaker 2: of service. Because open Ai, as we were reminded this week,

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Speaker 2: isn't actually open source. It's closed source, but Metaslama is

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Speaker 2: open source.

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Speaker 1: Here's how I think about this. There are a lot

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Speaker 1: of businesses that have been or like look likely to

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Speaker 1: be really badly disrupted by AI. Right, Like there's some

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Speaker 1: margin where like if you're an accountant, let's say podcaster, podcaster, Yes,

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Speaker 1: if you're a podcaster, they're coming for you. And in

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Speaker 1: the next few years, an AI will be able to

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Speaker 1: sit here and talk into a microphone. I mean not literally,

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Speaker 1: but like we'll be able to generate voice in a

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Speaker 1: way that is better than I can do for a

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Speaker 1: fraction of my very high price, and therefore I will

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Speaker 1: be out of work. Right, And this is true like

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Speaker 1: across a range of like sort of knowledge industries, right,

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Speaker 1: And so AI is like undercutting a lot of people

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Speaker 1: jobs in a way that like increases abundance for like

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Speaker 1: the people who want to listen to podcasts or get

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Speaker 1: accounting services. But then like undercuts like the earning potential

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Speaker 1: of the people who are providing it cheap. AI is

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Speaker 1: to ai what AI is to humans. Right, It's like

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Speaker 1: open ai is like, ah, we can do your accounting

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Speaker 1: for a tenth of the price of accountants and now

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Speaker 1: deep things like we can do it for a tenth

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Speaker 1: of the price of open ai. It's great. But like

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Speaker 1: the other interesting thing is like you know you talk

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Speaker 1: about like were they using open AI's APIs to train

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Speaker 1: their model to like distill open AI's model, Like get

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Speaker 1: a corpus of open ai outputs and you use that

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Speaker 1: to train your your model. We use it to like

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Speaker 1: to refine the training of your model. That to me

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Speaker 1: is a little bit analogous to the complaints that publishers

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Speaker 1: have about open ai using their data to train its models. Right.

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Speaker 1: Ai synthesizes the output of like all these humans and

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Speaker 1: then comes up with a thing that is that they

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Speaker 1: can produce that sort of output more cheaply than humans can.

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Speaker 1: And they're like, wow, what we were just like learning

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Speaker 1: from humans. It's fine, and like you know, deep Seek

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Speaker 1: kind of did that to open Ai maybe, like argue, yeah,

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Speaker 1: like people, there's a suspicion which I think violence in

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Speaker 1: terms of service, but it is like a certain product

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Speaker 1: justice to it. Well.

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Speaker 2: Deep Seek, for its part, says that it has distilled

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Speaker 2: models for R one based on other open source systems,

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Speaker 2: not necessarily on open Ai. But again, met Islama is

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Speaker 2: open source. It's freely available for use, and I don't know,

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Speaker 2: maybe that's not a bad thing. They're all just building

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Speaker 2: on each other. That doesn't seem terrible, right.

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Speaker 1: It's like like there's this notion that if deep seak

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Speaker 1: is like piggybacking on the work of other models, then

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Speaker 1: like then somehow the like baircase against like us AI

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Speaker 1: infrastructure spending is like weaker, But I'm not sure that's true. Right,

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Speaker 1: It's possible that it's just like the answer is that

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Speaker 1: you've made it cheaper to scale AI because like you

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Speaker 1: can build impressively on prior work and so you don't

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Speaker 1: need all those data centers, don't it.

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Speaker 2: I am curious what this means for this date of

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Speaker 2: CAPEX spending when it comes to all these big tech giants,

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Speaker 2: And we do have a lot of tech giants reporting

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Speaker 2: this week, and you know it's going to come up

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Speaker 2: on the earnings calls, which haven't happened yet as we're

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Speaker 2: recording this podcast, but we're like.

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Speaker 1: Two days out from like the US government like we're

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Speaker 1: gonna spend a trillion dollars on AI Capex.

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Speaker 2: Right, It's like you think about last week.

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Speaker 1: I don't know, people are saying news driven like you

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Speaker 1: could imagine everyone being like, ah, we're just getting out

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Speaker 1: all the capex, Like can you imagine a really hard

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Speaker 1: pivot in the next week. It just that doesn't seem

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Speaker 1: that there's.

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Speaker 2: A lot of awkward timing here because you think about Stargate,

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Speaker 2: which was announced last week with open AI with soft

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Speaker 2: Bank one hundred billion dollars Meta last week announced it

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Speaker 2: was boosting its capex to up to sixty five billion dollars.

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Speaker 2: Microsoft is spending eighty billion dollars. It's just felt like

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Speaker 2: this arms race to see who can spend the most

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Speaker 2: money on this and then to have everyone again get

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Speaker 2: deep seaked on Monday was as someone with a four

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Speaker 2: toh one K brutal but pretty fun to watch.

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Speaker 1: I'm sorry this is rude, but it's such a soft

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Speaker 1: thanks story. So I think announcing like we're gonna been

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Speaker 1: ae hundred billion dollars on data centers like ten minutes before,

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Speaker 1: like that becomes an obsolete thesis, Like yeah, that's.

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Speaker 3: Like, that's a good story, a good story.

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Speaker 1: Speaking of old times traits.

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Speaker 2: Man, let's talk about X debt. It seems like it's

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Speaker 2: getting a lot sweeter potentially. Well, this is also weird.

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Speaker 1: I never know how to interpret.

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Speaker 2: This because is this sweetener?

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Speaker 1: So X twitter X, the company formerly known as Twitter

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Speaker 1: Elion Mask bought it in twenty twenty two, and when

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Speaker 1: he signed his deal to buy it, for a variety

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Speaker 1: of reasons, it looked like a better deal than it

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Speaker 1: turned out to be like months later, and so all

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Speaker 1: these banks, led by Morgan Stanley agreed to provide them

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Speaker 1: thirteen billion dollars of debt to buy Twitter, and by

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Speaker 1: the time that closed, you know, ordinarily like they would

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Speaker 1: sell that debt to investors before the deal closed, but

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Speaker 1: for a variety of reasons, mainly that he was suing

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Speaker 1: to try to stop the deal from closing to the

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Speaker 1: last part and so wouldn't help out on the dead cell.

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Speaker 1: For a variety of reasons, they didn't sell the debt

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Speaker 1: before the deal closed. And by the time the deal closed,

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Speaker 1: it looked bad, both because Twitter's business had deteriorated and

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Speaker 1: because Elon Musk deteriorated it further, and because he was

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Speaker 1: like talking smack about Twitter for the whole time he

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Speaker 1: was trying not to buy it, and so like no

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Speaker 1: one wanted the debt, they couldn't sell the debt, And

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Speaker 1: there were occasional news stories being like they tried to

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Speaker 1: sell the debt at like sixty cents on the dollar

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Speaker 1: and they couldn't or whatever, like they got bids at six. Yeah,

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Speaker 1: so like numbers like sixty cents on the dollar were

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Speaker 1: floating around and now they're apparently outloading some of the

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Speaker 1: debt and like numbers like ninety to ninety five cents

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Speaker 1: on the dollar are floating around so pretty good. That's

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Speaker 1: like not necessarily apples to apples, because it's like they're

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Speaker 1: varying levels of seniority and it's possible that they couldn't

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Speaker 1: sell the worst that at sixty and now they're signing

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Speaker 1: the best that at ninety.

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Speaker 2: But still so this Xai stake that Twitter has, I mean,

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Speaker 2: I have questions. I was already questioning, you know who

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Speaker 2: knows on pricing, but is that worth you know, up

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Speaker 2: to thirty cents per bond or whatever, and whether it

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Speaker 2: still is after of course deep Seek came about on Monday.

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Speaker 1: After Elon Musk bought Twitter. The next thing quickly became

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Speaker 1: large language models. Like when he signed the deal to

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Speaker 1: by Twitter, like no one was talking about open ai

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Speaker 1: or whatever. But shortly after the closing, like large language

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Speaker 1: models were the thing. And so he started an AI

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Speaker 1: company called Xai. It has the same first letter as

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Speaker 1: Twitter does now, which is X and it clearly shared

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Speaker 1: some resources with x and you know who like made

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Speaker 1: enough noise about doing AI out of x that the

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Speaker 1: upshot is that Xai is not just owned by him personally,

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Speaker 1: and like people who put money into Xai and he's

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Speaker 1: raised a lot of money for It's also partly owned

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Speaker 1: by x by Twitter, So that company that he bought

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Speaker 1: one of its assets is apparently a six billion dollar

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Speaker 1: stake in Xai that's measured it it's like most recent evaluation,

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Speaker 1: it's like fifty billion dollars. Now maybe that valuation has

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Speaker 1: gone way down since Deepsek was released. There's not a

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Speaker 1: lot of public market comps for like pure play AI companies,

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Speaker 1: so like, who knows what the valuation is of Xai

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Speaker 1: or open Ai or anything else. But you know, last

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Speaker 1: time we checked, that stake was worth six billion dollars,

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Speaker 1: which sure is worth a lot of money to the

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Speaker 1: debt because the debt is like thirteen billion dollars, and

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Speaker 1: like you know, if they have collateral, then you know

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Speaker 1: that's worth you know, forty five cents on the dollar.

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Speaker 1: So I think that like there was reporting that X's

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Speaker 1: banks were shopping the debt, specifically telling people, hey, you'd

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Speaker 1: have a priority claim on all these Xai shares. Isn't

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Speaker 1: that nice? And what I wrote about this is like

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Speaker 1: it's very hard to do a credit analysis of x

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Speaker 1: Twitter because if Twitter doesn't pay interest on its debt,

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Speaker 1: like the lenders can foreclose, but like what good does

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Speaker 1: that do them? Right, Like Elon Musk can kind of

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Speaker 1: trash Twitter on the way out the door, and Twitter

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Speaker 1: seems like a hard like seems like a hard company

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Speaker 1: to run before Elon Musk bought it, and now it's

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Speaker 1: gotten even harder. So it's a company that has a

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Speaker 1: lot of potential upside, particularly for Elon Musk, who can

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Speaker 1: use it to like get presidents elected. But in terms

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Speaker 1: of like downside production for lenders, it's like, I don't

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Speaker 1: know whereas like you know, again a week ago, saying

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Speaker 1: we own a big stake in an AI company seems

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Speaker 1: like really great downside production because one AA companies are valuable.

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Speaker 1: Two Elon Musk is like kind of unlikely to walk

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Speaker 1: away from an AI company in a way that he

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Speaker 1: tried to walk away from Twitter. And three people do

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Speaker 1: credit analysis of AA companies where they did where they're like, oh, look,

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Speaker 1: it has like all these like Nvidia chips. Even if

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Speaker 1: like something goes horribly wrong at this company, those chips

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Speaker 1: are super valuable because there's such an AI gold rush,

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Speaker 1: So like there's really good collateral here right again, like

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Speaker 1: that has been undermined by the events of the last week.

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Speaker 1: But it does feel like the Xai stake was pretty

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Speaker 1: credit enhancing for the Twitter bonds.

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Speaker 2: Yeah, and I mean you say that el Musk is

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Speaker 2: less likely to walk away from the Ai company in

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Speaker 2: the same way he might be tempted to walk away

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Speaker 2: from Twitter. I mean, how confident are you when you

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Speaker 2: say that? Because who really knows, Matt.

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Speaker 1: Yeah, nobody knows anything. But like Elon Musk is kind

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Speaker 1: of always sad, including when he was buying it, that

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Speaker 1: Twitter was not a great business decision, but yeah, he

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Speaker 1: thought it was important for the future of the world.

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Speaker 1: Blah blah, blah blah. And like that, by the way,

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Speaker 1: turned out to be right. Like he's had a ton

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Speaker 1: of political influence with Twitter without it necessarily making him

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Speaker 1: a lot of money. Again a week ago, everyone's like, wow,

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Speaker 1: Ai is real gusher of money, right, Like who knows now, right,

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Speaker 1: But like, you're right, I'm not saying personally, I think

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Speaker 1: there's no chance of him getting bored of Ai, just

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Speaker 1: like you know, I think that was a reasonable thing

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Speaker 1: for the market to think, are Weekiah.

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Speaker 2: Yeah, you know what, it could be more fun than owning.

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Speaker 1: Twitter virtually anything.

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Speaker 2: I don't know. I mean I'm speaking specifically from Elon

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Speaker 2: Musk's shoes perhaps owning TikTok, but that's a yeah, different conversation.

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Speaker 1: Yeah, yeah, I would have read that, right, he buys TikTok.

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Speaker 1: Can you imagine him doing that separately? Like that'd be

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Speaker 1: so rude. It didn't even occur to me because he's

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Speaker 1: been like talked about as a potential buyer of TikTok.

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Speaker 1: I assumed that he would do that out of the

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Speaker 1: vehicle that is X. But I guess there's no law

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Speaker 1: saying that. I mean whatever, there are like standard views

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Speaker 1: of like corporate you know, fiducie duties and corporate opportunities.

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Speaker 1: Were like, yeah, it would be really weird of him

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Speaker 1: to buy a competitor. If he owns X, he could

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Speaker 1: do that, and then he could on them say.

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Speaker 2: Very bold of you to assume there are laws here,

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Speaker 2: but yeah, yeah right. No.

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Speaker 1: I started by saying there, no, there's no law that

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Speaker 1: he can't do it, but of course there is, but

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Speaker 1: there's not anyway, if he buys TikTok, the natural thing

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Speaker 1: to do would be to have X by TikTok. But

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Speaker 1: that doesn't mean he'll do that.

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Speaker 2: Yeah, I feel like TikTok is way more valuable than X.

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Speaker 2: But what do I know?

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Speaker 1: Yeah, but it's like a weird situation, right, because you're

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Speaker 1: buying the US operations and you're buying it under the gun, right,

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Speaker 1: you'd be righting literally, like Donald Trump is saying to TikTok,

434
00:21:03,720 --> 00:21:05,879
Speaker 1: either you shut down or yourself to my friend, right, like,

435
00:21:05,920 --> 00:21:07,880
Speaker 1: how much leverage do you have to negotiate a price there?

436
00:21:08,200 --> 00:21:10,880
Speaker 2: That's true. Something that was in the main bar story

437
00:21:10,920 --> 00:21:14,800
Speaker 2: about this X debt with the Xai sweetener was just

438
00:21:14,960 --> 00:21:19,000
Speaker 2: on X's annual interest expenses, And I knew that they

439
00:21:19,040 --> 00:21:21,160
Speaker 2: had gone higher, but I didn't appreciate by how much.

440
00:21:21,520 --> 00:21:25,679
Speaker 2: So with this debt that they were saddled with, the

441
00:21:25,680 --> 00:21:28,399
Speaker 2: annual interest expense went from around fifty million dollars to

442
00:21:28,480 --> 00:21:34,040
Speaker 2: well over a billion dollars for X. That is gargantuan.

443
00:21:34,520 --> 00:21:37,040
Speaker 1: Yeah, it's thirteen you know. Well, because they didn't have

444
00:21:37,080 --> 00:21:40,000
Speaker 1: debt before, because they were like, yeah, they were like

445
00:21:40,040 --> 00:21:43,040
Speaker 1: a not particularly lucrative public tech company. They were not

446
00:21:43,119 --> 00:21:44,040
Speaker 1: running with a lot of debt.

447
00:21:44,280 --> 00:21:47,120
Speaker 2: Elon Musbo, you're just a bird app Yeah, yeah, so.

448
00:21:47,040 --> 00:21:49,000
Speaker 1: He bought them with thirteen billion dollars of debt, right,

449
00:21:49,040 --> 00:21:52,000
Speaker 1: like thirteen billion times, you know, eight percent is a

450
00:21:52,040 --> 00:21:52,679
Speaker 1: billion dollars.

451
00:21:53,000 --> 00:21:55,040
Speaker 2: Yeah, I mean you touched on a little bit. Like

452
00:21:55,080 --> 00:21:56,800
Speaker 2: Elon Musk could probably say, I'm good for it. But

453
00:21:57,320 --> 00:22:00,640
Speaker 2: so twitter X theoretically is like barely breaking even.

454
00:22:01,280 --> 00:22:03,760
Speaker 1: I hesitate to speculate, like the wre Ald Street Journal

455
00:22:03,880 --> 00:22:07,679
Speaker 1: report memos from Elon Musk saying that, but then he

456
00:22:07,680 --> 00:22:09,200
Speaker 1: did not he wrote it, so I don't I don't

457
00:22:09,240 --> 00:22:13,840
Speaker 1: even know, man, But like, okay, but right, I mean,

458
00:22:13,880 --> 00:22:15,280
Speaker 1: like the other thing I was thinking about is like

459
00:22:15,880 --> 00:22:19,040
Speaker 1: when Donald Trump was elected, there was a lot of

460
00:22:19,040 --> 00:22:23,840
Speaker 1: writing to the effect of, like, this investment looks a

461
00:22:23,840 --> 00:22:27,880
Speaker 1: lot better for Elon Musk, right because one like twitter

462
00:22:28,040 --> 00:22:30,360
Speaker 1: x might actually be more valuable now because it's now

463
00:22:30,440 --> 00:22:32,280
Speaker 1: like platform with a lot of political power. You can

464
00:22:32,320 --> 00:22:36,480
Speaker 1: probably sell more ads. But two, you know, it's hugely

465
00:22:36,480 --> 00:22:38,440
Speaker 1: increased the value of the rest of his corporate empire,

466
00:22:38,880 --> 00:22:41,760
Speaker 1: right because like he now runs a space company that

467
00:22:41,880 --> 00:22:43,760
Speaker 1: like runs the government, he runs a car company that

468
00:22:44,359 --> 00:22:46,960
Speaker 1: he has all these powers. So the investment looks really

469
00:22:46,960 --> 00:22:49,040
Speaker 1: good for him. Does any of that help the debt?

470
00:22:49,320 --> 00:22:51,840
Speaker 1: I don't know. I think that Elon Musk, being so

471
00:22:51,960 --> 00:22:55,000
Speaker 1: close to the levers of power is probably a small

472
00:22:55,080 --> 00:22:56,440
Speaker 1: negative for lenders.

473
00:22:56,960 --> 00:22:57,240
Speaker 2: M H.

474
00:22:57,840 --> 00:23:01,760
Speaker 1: Banks don't like to lend to politically exposed persons, right

475
00:23:01,800 --> 00:23:03,639
Speaker 1: because it makes you a worse credit, right because like,

476
00:23:03,680 --> 00:23:06,439
Speaker 1: if you end money to Donald Trump and like he

477
00:23:06,480 --> 00:23:08,280
Speaker 1: doesn't pay you, then you for close on his property.

478
00:23:08,320 --> 00:23:10,000
Speaker 1: But you end money to Donald Trump and he becomes

479
00:23:10,000 --> 00:23:12,239
Speaker 1: the president, then like you can't foreclose on him, right,

480
00:23:12,320 --> 00:23:14,240
Speaker 1: So like it makes your credit a little bit worse.

481
00:23:14,400 --> 00:23:16,080
Speaker 1: And I think there's something of that with Elon Musk,

482
00:23:16,119 --> 00:23:17,840
Speaker 1: where like, on the one hand, he's more likely to

483
00:23:17,840 --> 00:23:19,280
Speaker 1: be good for the money now that he's like so

484
00:23:19,359 --> 00:23:21,679
Speaker 1: much richer and his businesses are so much better. But

485
00:23:21,720 --> 00:23:23,280
Speaker 1: on the other hand, if he doesn't want to pay you, like,

486
00:23:23,320 --> 00:23:25,119
Speaker 1: there's not a lot you can do about it. So

487
00:23:25,640 --> 00:23:37,560
Speaker 1: it's a mixed proposition for the lenders.

488
00:23:43,640 --> 00:23:46,639
Speaker 2: I'm gonna slam this laptop shot at two thirty, just

489
00:23:46,640 --> 00:23:49,639
Speaker 2: so you know, no matter what happens, I have a

490
00:23:49,720 --> 00:23:53,680
Speaker 2: horse to ride, I have miles to run in New Jersey.

491
00:23:54,840 --> 00:23:56,600
Speaker 1: Let's talk about darkpools.

492
00:23:56,880 --> 00:24:01,280
Speaker 2: Okay, dark pools. So, according to Zomberg News, most US

493
00:24:01,359 --> 00:24:06,480
Speaker 2: equity trading isn't done publicly anymore. Matt Levine specifically off

494
00:24:06,520 --> 00:24:10,280
Speaker 2: exchange activity is on course to account for a record

495
00:24:10,320 --> 00:24:14,159
Speaker 2: fifty one point eight percent of traded volume in January.

496
00:24:14,320 --> 00:24:17,000
Speaker 2: That would be the fifth monthly record in a row,

497
00:24:17,320 --> 00:24:21,200
Speaker 2: and the third month running that actually when off exchange

498
00:24:21,240 --> 00:24:25,280
Speaker 2: volume was greater than half of all volume. Are you scared?

499
00:24:26,440 --> 00:24:28,879
Speaker 1: I would say I'm scared. But it is like a

500
00:24:28,880 --> 00:24:31,400
Speaker 1: little bit like the index fund tipping point. Right. Off

501
00:24:31,480 --> 00:24:35,080
Speaker 1: exchange volume is sort of to exchanges, what like index

502
00:24:35,080 --> 00:24:38,480
Speaker 1: funds are to active management. Right, It's like you trade

503
00:24:38,480 --> 00:24:42,840
Speaker 1: on the exchange, you produce a public good. You produce information.

504
00:24:43,240 --> 00:24:45,920
Speaker 1: The particular thing you produce is like stock prices, right

505
00:24:45,920 --> 00:24:48,879
Speaker 1: Like when you trade on exchange, like you trade against

506
00:24:48,920 --> 00:24:51,639
Speaker 1: a lit order, and the order book on the exchange

507
00:24:51,720 --> 00:24:53,840
Speaker 1: is public. People can see what the stock is trading for,

508
00:24:54,160 --> 00:24:57,199
Speaker 1: and you're producing information. If you trade off exchange, the

509
00:24:57,240 --> 00:25:00,719
Speaker 1: off exchange mechanism, you know, if you're hedge fund, it's

510
00:25:00,760 --> 00:25:02,960
Speaker 1: called the dark pool. If you're an individual, it's called

511
00:25:03,040 --> 00:25:05,760
Speaker 1: like a like a wholesaler or an internalizer. But the

512
00:25:05,800 --> 00:25:08,920
Speaker 1: off exchange place where you trade is kind of free

513
00:25:09,000 --> 00:25:10,959
Speaker 1: riding on the public exchanges, right, Like they're looking at

514
00:25:10,960 --> 00:25:15,480
Speaker 1: the exchanges for pricing and then they're probably giving you

515
00:25:15,560 --> 00:25:18,600
Speaker 1: a better price. Right, there's probably some reason that you're

516
00:25:18,640 --> 00:25:20,560
Speaker 1: on the off exchange venue, and it's probably that you're

517
00:25:20,560 --> 00:25:21,320
Speaker 1: getting a better price.

518
00:25:21,400 --> 00:25:21,520
Speaker 3: Right.

519
00:25:21,520 --> 00:25:24,720
Speaker 1: If you're a retail trader, like gets pretty straightforward, people

520
00:25:24,800 --> 00:25:26,840
Speaker 1: get mad about it. But like, the idea of payment

521
00:25:26,880 --> 00:25:30,080
Speaker 1: for order flow is that I fregency trading firm doesn't

522
00:25:30,119 --> 00:25:32,600
Speaker 1: take nearly as much risk trading with retail traders as

523
00:25:32,640 --> 00:25:34,720
Speaker 1: they do trading with fancy hedge funds, and so they're

524
00:25:34,720 --> 00:25:36,119
Speaker 1: willing to give you a better price. It's called the

525
00:25:36,160 --> 00:25:38,960
Speaker 1: price improvement. Right. If you're on a dark pool, you're

526
00:25:39,000 --> 00:25:41,480
Speaker 1: an institution looking for a better price than you get

527
00:25:41,520 --> 00:25:44,080
Speaker 1: on the exchange, and you might get that for some

528
00:25:44,200 --> 00:25:46,080
Speaker 1: variety of reasons, one of which is just like the

529
00:25:46,160 --> 00:25:49,240
Speaker 1: fees might be lower, and so you know, you're trading

530
00:25:49,240 --> 00:25:51,040
Speaker 1: in the dark to get a better price, and so

531
00:25:51,119 --> 00:25:54,520
Speaker 1: you're not producing the sort of pubably good byproduct of that,

532
00:25:54,560 --> 00:25:59,440
Speaker 1: which is like making prices for everyone else. And if

533
00:25:59,440 --> 00:26:02,280
Speaker 1: everyone does that then it can get kind of weird.

534
00:26:02,440 --> 00:26:06,280
Speaker 1: The very article about this quoted Larry tab saying that

535
00:26:06,359 --> 00:26:09,080
Speaker 1: the more trading that goes off exchange is the fuel

536
00:26:09,160 --> 00:26:11,880
Speaker 1: orders that are on exchange competing to determine the best price,

537
00:26:11,880 --> 00:26:14,280
Speaker 1: and this means the pricing on and off exchange could

538
00:26:14,320 --> 00:26:17,159
Speaker 1: get worse, right. I think if there's no one publicly trading,

539
00:26:17,400 --> 00:26:19,119
Speaker 1: then like no one knows what the price is, and

540
00:26:19,160 --> 00:26:22,480
Speaker 1: so the off exchange pricing deteriorates. I don't think there's

541
00:26:22,520 --> 00:26:25,120
Speaker 1: any reason to think that fifty percent is any sort

542
00:26:25,160 --> 00:26:28,040
Speaker 1: of magical tipping point, but it's interesting.

543
00:26:28,720 --> 00:26:31,960
Speaker 2: Yeah, and I'm glad you've framed it as a public good.

544
00:26:32,000 --> 00:26:34,720
Speaker 2: It's a public good that we would be trading on exchange,

545
00:26:34,800 --> 00:26:37,399
Speaker 2: But you could get worried about price discovery, maybe not

546
00:26:37,640 --> 00:26:40,760
Speaker 2: at fifty two versus forty eight percent, But it is

547
00:26:40,920 --> 00:26:43,399
Speaker 2: kind of a fun thought exercise to imagine a world

548
00:26:43,440 --> 00:26:47,879
Speaker 2: where you have ninety percent of trades happening in the dark,

549
00:26:48,240 --> 00:26:51,680
Speaker 2: that their prices are based and extrapolated on the ten

550
00:26:51,760 --> 00:26:54,359
Speaker 2: percent that's happening on exchange. I don't know what that

551
00:26:54,400 --> 00:26:55,160
Speaker 2: world looks like.

552
00:26:55,720 --> 00:26:58,000
Speaker 1: But it's the same thought experiment as index ons, Right,

553
00:26:58,000 --> 00:27:00,439
Speaker 1: It's the same thing where people are like, you know,

554
00:27:00,520 --> 00:27:02,440
Speaker 1: if forty fund the stock market is own by indixed funds,

555
00:27:02,440 --> 00:27:04,800
Speaker 1: that's fine, but ninety percent gets weird, right, Like, you know,

556
00:27:04,920 --> 00:27:06,520
Speaker 1: like there's no real way to know what the magic

557
00:27:06,560 --> 00:27:09,440
Speaker 1: number is. But it's the same idea of like there

558
00:27:09,480 --> 00:27:13,280
Speaker 1: is this like informational good and it's efficient for a

559
00:27:13,280 --> 00:27:15,520
Speaker 1: lot of people to free write on it, but at

560
00:27:15,520 --> 00:27:16,880
Speaker 1: some point it becomes a problem.

561
00:27:17,880 --> 00:27:20,000
Speaker 2: Yeah. Well, I think there is an important caveat here.

562
00:27:20,000 --> 00:27:22,080
Speaker 2: And this was mentioned lower down in the Bloomberg News

563
00:27:22,160 --> 00:27:25,560
Speaker 2: article which was written by Catherine Dougherty. It's super good,

564
00:27:26,040 --> 00:27:28,480
Speaker 2: but so you're talking about dark pools when it comes

565
00:27:28,520 --> 00:27:31,440
Speaker 2: to institutional investors in hedge funds, but when it comes

566
00:27:31,560 --> 00:27:36,960
Speaker 2: to retail trading in penny stocks. If you strip out

567
00:27:37,200 --> 00:27:40,960
Speaker 2: penny stocks from this data, according to Jeffries, then off

568
00:27:41,000 --> 00:27:46,080
Speaker 2: exchange trading volume remains below forty percent, which is less scary,

569
00:27:46,280 --> 00:27:49,280
Speaker 2: but it is interesting that it's sort of both sides

570
00:27:49,320 --> 00:27:52,200
Speaker 2: of the spectrum chipping away at on exchange volume. It's

571
00:27:52,240 --> 00:27:53,720
Speaker 2: like you have dark pools over here with all the

572
00:27:53,760 --> 00:27:57,080
Speaker 2: fancy hedge funds, and then you have retail playing in

573
00:27:57,160 --> 00:27:58,480
Speaker 2: penny stocks as well.

574
00:27:58,800 --> 00:28:01,119
Speaker 1: You think of hedge funds as the banks sophisticated investors,

575
00:28:01,160 --> 00:28:04,800
Speaker 1: and they are, but I think if you're a hedge fund,

576
00:28:05,240 --> 00:28:10,520
Speaker 1: you often think of yourself as an innocent victim of

577
00:28:10,560 --> 00:28:13,640
Speaker 1: the public stock markets, Like I think at some time frame,

578
00:28:13,840 --> 00:28:17,639
Speaker 1: like there are particular firms who are like good at

579
00:28:17,960 --> 00:28:20,760
Speaker 1: getting the last penny of price on the stock exchange,

580
00:28:21,160 --> 00:28:24,120
Speaker 1: and that's a particular skill set. Those firms are called

581
00:28:24,160 --> 00:28:26,919
Speaker 1: high frequency traders, right, And like there are a lot

582
00:28:26,920 --> 00:28:29,159
Speaker 1: of hedge funds who are really mad about high percncruit traders,

583
00:28:29,200 --> 00:28:31,440
Speaker 1: who feel like they're being front run by higher concutrators,

584
00:28:31,560 --> 00:28:33,879
Speaker 1: who feel like the public stock markets are an evil

585
00:28:33,880 --> 00:28:37,080
Speaker 1: and predatory place. And you see this in like Michael

586
00:28:37,119 --> 00:28:40,000
Speaker 1: Lewis's book Flashboys, were like big time hedge fund managers,

587
00:28:40,040 --> 00:28:42,360
Speaker 1: like he like goes into their office and they're like,

588
00:28:42,360 --> 00:28:44,880
Speaker 1: look at look at these higherxcy traders. They're fleecing me. Right,

589
00:28:45,040 --> 00:28:48,280
Speaker 1: Like people get really mad and like not like unsophisticated

590
00:28:48,360 --> 00:28:51,520
Speaker 1: retail investors, like sophisticated investment managers whose time frame is

591
00:28:51,560 --> 00:28:54,320
Speaker 1: longer than five seconds, right, and like those people feel

592
00:28:54,320 --> 00:28:56,400
Speaker 1: like they're getting fleeced by the people whose time frame

593
00:28:56,480 --> 00:28:59,719
Speaker 1: is less than five seconds. And so retail institutions are

594
00:28:59,720 --> 00:29:01,720
Speaker 1: in the same boat here where they both feel like

595
00:29:01,760 --> 00:29:04,280
Speaker 1: the public markets are predatory, and so they want to

596
00:29:04,320 --> 00:29:07,400
Speaker 1: find a more protected place where they won't be subject

597
00:29:07,560 --> 00:29:10,600
Speaker 1: to the predators of the public markets. And the more

598
00:29:10,640 --> 00:29:13,120
Speaker 1: that happens, the more the public market is just full

599
00:29:13,160 --> 00:29:15,360
Speaker 1: of predators, right, Like it's just full of the most

600
00:29:15,360 --> 00:29:18,120
Speaker 1: sophisticated trading firms trading against each other and trying to

601
00:29:18,120 --> 00:29:20,360
Speaker 1: make my buck coffee each other, and like that makes

602
00:29:20,400 --> 00:29:22,920
Speaker 1: them less and less appealing for both retail and for

603
00:29:23,640 --> 00:29:25,800
Speaker 1: like a hedge fund that has a you know, fundamental

604
00:29:25,880 --> 00:29:26,880
Speaker 1: investment thesis.

605
00:29:27,160 --> 00:29:31,680
Speaker 2: Yeah. Sorry, I'm still thinking about like the comparison to

606
00:29:32,120 --> 00:29:35,560
Speaker 2: passive versus active, and we did reach that tipping point

607
00:29:35,600 --> 00:29:39,040
Speaker 2: where passive is now the majority of invested assets.

608
00:29:39,080 --> 00:29:40,680
Speaker 1: Yeah, but it's fine. I mean, like people say it's

609
00:29:40,720 --> 00:29:42,440
Speaker 1: not finely people get mad, right, Yeah. I think there's

610
00:29:42,480 --> 00:29:45,800
Speaker 1: a reasonable case that like some aspects of like you know,

611
00:29:45,880 --> 00:29:50,120
Speaker 1: capital allocation efficiency have been undermined by the rise of indexing. Again,

612
00:29:50,200 --> 00:29:52,480
Speaker 1: not because fifty percent was the tipping point, but just

613
00:29:52,520 --> 00:29:55,400
Speaker 1: because it's bigger than it used to be. But so far,

614
00:29:55,440 --> 00:29:58,280
Speaker 1: it seems like there's a lot of competition to find

615
00:29:58,400 --> 00:30:00,680
Speaker 1: the right price for security, and there are a lot

616
00:30:00,680 --> 00:30:04,280
Speaker 1: of hedge funds who make like really quite a lot

617
00:30:04,320 --> 00:30:07,560
Speaker 1: of money trying to make press efficient. So it's not

618
00:30:07,640 --> 00:30:10,600
Speaker 1: obvious that indexing has like ended that. Yeah, you could

619
00:30:10,640 --> 00:30:13,640
Speaker 1: probably tell a similar story for the sort of microstructure

620
00:30:13,680 --> 00:30:17,360
Speaker 1: level of public slock markets, where again, like the trading

621
00:30:17,360 --> 00:30:22,360
Speaker 1: firms that trade in public markets do pretty well, so fine, yeah,

622
00:30:22,440 --> 00:30:24,120
Speaker 1: and also like spreads are tied and everything like that,

623
00:30:24,120 --> 00:30:24,640
Speaker 1: So it's like.

624
00:30:25,000 --> 00:30:27,720
Speaker 2: Yeah, yeah, Well, I don't know if passive will ever

625
00:30:28,360 --> 00:30:31,440
Speaker 2: take up ninety percent of invested assets. And I'm not

626
00:30:31,560 --> 00:30:34,360
Speaker 2: saying that we could get to ninety percent of trading

627
00:30:34,400 --> 00:30:36,120
Speaker 2: happening in the dark, but it does seem I mean,

628
00:30:36,200 --> 00:30:38,640
Speaker 2: taking a look at, you know, the various people that

629
00:30:38,680 --> 00:30:41,240
Speaker 2: are already closet in this piece that obviously this trend

630
00:30:41,240 --> 00:30:45,120
Speaker 2: has been years in the making and now we're we're

631
00:30:45,160 --> 00:30:48,360
Speaker 2: at fifty two percent roughly. Who knows where that goes.

632
00:30:48,600 --> 00:30:52,040
Speaker 2: This is something that the SEC under Gary Gensler did

633
00:30:52,200 --> 00:30:55,280
Speaker 2: try to address and try to push more of that

634
00:30:55,320 --> 00:30:58,440
Speaker 2: trading to the public exchanges. And the wild card here

635
00:30:58,520 --> 00:31:01,280
Speaker 2: in terms of the trajectory of where this goes is

636
00:31:01,400 --> 00:31:04,480
Speaker 2: Paul Atkins. I don't know what his view is. He

637
00:31:04,560 --> 00:31:08,120
Speaker 2: seems like an interesting guy, he seems like a contrarian.

638
00:31:08,200 --> 00:31:11,320
Speaker 2: I don't know where he lies on this, but it'll

639
00:31:11,320 --> 00:31:12,120
Speaker 2: be fun to find out.

640
00:31:12,480 --> 00:31:14,920
Speaker 1: I think some people in the industry would have comments

641
00:31:14,960 --> 00:31:17,480
Speaker 1: about the idea that the gangsler SEC tried to push

642
00:31:17,560 --> 00:31:19,840
Speaker 1: more training in public exchange, Like that's true, but like

643
00:31:20,360 --> 00:31:23,480
Speaker 1: he also tried to like wildly complicate aspects of public

644
00:31:23,480 --> 00:31:25,760
Speaker 1: exchanges and ways that might have made them pust appealing.

645
00:31:25,880 --> 00:31:28,240
Speaker 1: But no, I think, like broadly speaking, that's true. And

646
00:31:28,760 --> 00:31:31,600
Speaker 1: my guess is that in general, there is not a

647
00:31:31,600 --> 00:31:34,080
Speaker 1: ton of incentive for the SEC to do a ton

648
00:31:34,120 --> 00:31:39,000
Speaker 1: of huge market structure overhauls because while no one kind

649
00:31:39,000 --> 00:31:42,360
Speaker 1: of loves us equity market structure, it seems to work fine,

650
00:31:42,760 --> 00:31:47,080
Speaker 1: and any potential change would be very complicated. And in fact,

651
00:31:47,160 --> 00:31:51,880
Speaker 1: the gangster SEC proposed pretty radical changes and like got

652
00:31:52,000 --> 00:31:54,520
Speaker 1: pushed back pretty hard and like didn't end up actually

653
00:31:54,560 --> 00:31:56,800
Speaker 1: doing anything, not doing much in like the very radical

654
00:31:56,840 --> 00:31:59,840
Speaker 1: like auctioning kind of ideas. Yeah, and it's hard for

655
00:31:59,880 --> 00:32:04,600
Speaker 1: me imagine will surely be a very strange SEC being

656
00:32:04,640 --> 00:32:07,920
Speaker 1: like we need to reform darkpool training, but maybe who.

657
00:32:07,720 --> 00:32:10,720
Speaker 2: Knows, who knows, I don't know. I would imagine it's

658
00:32:10,720 --> 00:32:12,320
Speaker 2: not high on the priority list.

659
00:32:12,400 --> 00:32:16,440
Speaker 1: Yes, there is like a weird populist appeal to saying

660
00:32:16,440 --> 00:32:18,920
Speaker 1: We're gonna end payment for order flow and send all

661
00:32:18,960 --> 00:32:21,480
Speaker 1: of your orders to the stock exchange. It's like almost

662
00:32:21,480 --> 00:32:24,040
Speaker 1: certainly bad for retail investors, but like if you say it,

663
00:32:24,080 --> 00:32:26,160
Speaker 1: people are like, oh, yeah, that'll be good for retail investors.

664
00:32:26,320 --> 00:32:30,440
Speaker 1: So there's some populist appeal to that as a platform,

665
00:32:30,480 --> 00:32:32,840
Speaker 1: and this arguably why Gary Yanser tried to do it.

666
00:32:33,120 --> 00:32:36,040
Speaker 1: But you get bogged down very quickly in the weeds,

667
00:32:36,040 --> 00:32:37,120
Speaker 1: and it's hard to actually.

668
00:32:36,880 --> 00:32:38,080
Speaker 3: Do it so well.

669
00:32:38,480 --> 00:32:41,080
Speaker 2: Taking a look at just the ETF filings that are

670
00:32:41,080 --> 00:32:43,640
Speaker 2: coming across, there's a lot of hopeus and dreams that

671
00:32:44,080 --> 00:32:46,360
Speaker 2: Atkins is going to be a huge crypto bole but

672
00:32:47,080 --> 00:32:50,040
Speaker 2: I don't know. We'll see. Well that seems true, but

673
00:32:50,880 --> 00:32:53,680
Speaker 2: does it. I don't know. I remember you think about

674
00:32:53,800 --> 00:32:56,520
Speaker 2: Gary Gensler, People were like, oh my god, he taught

675
00:32:56,720 --> 00:32:59,719
Speaker 2: an MIT course on the blockchain, and then turns out

676
00:32:59,720 --> 00:33:03,840
Speaker 2: he t into like enemy number one for the crypto crowd.

677
00:33:04,120 --> 00:33:07,760
Speaker 1: I just think that, like fail actins has orders from

678
00:33:07,760 --> 00:33:12,520
Speaker 1: the top to be a crypto friendly And I also think,

679
00:33:12,680 --> 00:33:15,560
Speaker 1: you know, you're talking about ETF filings, like you could

680
00:33:15,560 --> 00:33:18,680
Speaker 1: file an ETF for any meume quin in the world,

681
00:33:19,160 --> 00:33:22,360
Speaker 1: and people are people are, but in particular they're filing

682
00:33:22,400 --> 00:33:26,280
Speaker 1: for like trump quin and milaniaquint. Now is the SEC

683
00:33:26,520 --> 00:33:31,240
Speaker 1: going to say, well, these tokens are two subjects to

684
00:33:31,280 --> 00:33:33,880
Speaker 1: manipulation and like don't have a real investment thesis, so

685
00:33:33,920 --> 00:33:35,600
Speaker 1: we can't approve an ETF on them, Like.

686
00:33:37,520 --> 00:33:41,360
Speaker 2: Well, well, actually, are you slamming your lap clop closed? Wait,

687
00:33:41,400 --> 00:33:44,040
Speaker 2: hold on, okay, here we go. Actually to that point,

688
00:33:44,360 --> 00:33:49,520
Speaker 2: there were filings for double leverage Millenia and trump ETFs.

689
00:33:50,200 --> 00:33:52,960
Speaker 2: But they.

690
00:33:54,120 --> 00:33:58,400
Speaker 1: They what that was a joke? What do you mean?

691
00:33:58,960 --> 00:34:01,200
Speaker 1: I mean, like I wish I had filed a double

692
00:34:01,280 --> 00:34:03,600
Speaker 1: leverge of melai ATF as a joke, because like that's

693
00:34:03,720 --> 00:34:07,760
Speaker 1: very funny. But like, imagine I was joking.

694
00:34:08,400 --> 00:34:09,080
Speaker 2: I don't think this.

695
00:34:09,080 --> 00:34:11,360
Speaker 1: Is probably right, but I'm gonna I'm going to just

696
00:34:11,480 --> 00:34:13,280
Speaker 1: choose to believe that the issuer was joking.

697
00:34:13,800 --> 00:34:16,239
Speaker 2: Yeah, well, assuming that they weren't, and I'm pretty sure

698
00:34:16,280 --> 00:34:19,439
Speaker 2: that they weren't joking, they did withdraw the filing.

699
00:34:19,080 --> 00:34:21,600
Speaker 1: And typical it's fine, problem solved.

700
00:34:22,000 --> 00:34:26,399
Speaker 2: No, they definitely weren't joking. Anyway, That suggests they got

701
00:34:26,400 --> 00:34:29,239
Speaker 2: a call from the SEC that said, maybe this is

702
00:34:29,280 --> 00:34:31,839
Speaker 2: too far, so we'll find out where the lines are.

703
00:34:33,960 --> 00:34:35,799
Speaker 1: I am not convinced that in the next four years

704
00:34:35,800 --> 00:34:38,680
Speaker 1: we'll find out where any lines are. Wow, I suspect that,

705
00:34:38,800 --> 00:34:41,000
Speaker 1: Like You'll be like, is the line over there? Like Nope,

706
00:34:41,040 --> 00:34:41,399
Speaker 1: not here.

707
00:34:41,760 --> 00:34:44,360
Speaker 2: You think a lineless world.

708
00:34:50,360 --> 00:34:51,800
Speaker 1: And that was The Money Stuff Podcast.

709
00:34:51,960 --> 00:34:54,440
Speaker 2: I'm Matt Livian and I'm Katie Gresel.

710
00:34:54,840 --> 00:34:56,919
Speaker 1: You can find my work by subscribing to The Money

711
00:34:56,920 --> 00:34:59,400
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712
00:34:59,160 --> 00:35:01,640
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713
00:35:01,719 --> 00:35:04,800
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714
00:35:05,200 --> 00:35:06,880
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715
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Speaker 1: email to Moneypot at Bloomberg dot m ask us a

716
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717
00:35:11,880 --> 00:35:14,080
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718
00:35:14,120 --> 00:35:16,160
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719
00:35:16,160 --> 00:35:17,040
Speaker 2: people find the show.

720
00:35:17,160 --> 00:35:20,759
Speaker 1: The Money Stuff Podcast is produced by Anna Maserakis and Moses.

721
00:35:20,400 --> 00:35:23,720
Speaker 2: Ondeng Our theme music was composed by Blake Maples, Brandon

722
00:35:23,760 --> 00:35:27,400
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723
00:35:27,400 --> 00:35:28,279
Speaker 2: head of Podcasts.

724
00:35:28,640 --> 00:35:30,960
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725
00:35:31,040 --> 00:35:32,560
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