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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the All Thoughts podcast.

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Speaker 2: I'm Tracy Alloway.

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Speaker 3: And I'm Joe. Why isn't thal Joe?

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Speaker 2: Do you think it's fair to say that things seem

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Speaker 2: a little uncertain at the moment?

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Speaker 4: Well?

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Speaker 3: Yes, Well what do you have in mind?

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Speaker 4: Okay?

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Speaker 3: Well, of course yes, and big things going on.

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Speaker 2: So I was thinking particularly of three things. So number

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Speaker 2: one is obviously US presidential elections, and things seem very

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Speaker 2: uncertain on that front. We're not sure who's going to

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Speaker 2: win in November. And more than that, at this point,

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Speaker 2: I think we're not entirely sure who the Democratic candidate

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Speaker 2: is going to be. There's uncertainty there. But then secondly,

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Speaker 2: there's uncertainty about the direction of the stock market in

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Speaker 2: a number of ways. So we have seen some of

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Speaker 2: the error kicked out of the tires of big outperformers

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Speaker 2: from earlier in the year. I'm thinking specifically of Nvidia

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Speaker 2: and some of those big tech companies. They've come down recently.

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Speaker 2: There are people talking about how AI is the future

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Speaker 2: of industry, but there are also people talking about how

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Speaker 2: AI is a massive bubble, and I think Goldman Sachs

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Speaker 2: had that analysis out earlier this week talking about how

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Speaker 2: there was no actual use case for AI, which seems

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Speaker 2: to be the polar opposite of some of the enthusiasm

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Speaker 2: in the market. And then thirdly, we still have that

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Speaker 2: underlying economic tension where it seems like there are some

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Speaker 2: signs of a slowdown, but the Fed so far is

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Speaker 2: kind of resisting any pressure to cut interest rates. So

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Speaker 2: lots of things up in the air at the moment.

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Speaker 5: Yeah, I mean, like you said, uncertainty is always with

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Speaker 5: in the market, but there are some really big questions.

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Speaker 3: About things that are going on. The AI one in.

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Speaker 5: Particular, because a lot of people, as you notice, like wow,

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Speaker 5: they're really spending a lot of money on all this

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Speaker 5: chips and electricity, and the payoff is ambiguous, and.

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Speaker 3: So there are a lot of questions about that.

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Speaker 5: Clearly, the political landscape, although you know, on questions related

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Speaker 5: to the market. Look, the stock market's done really well

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Speaker 5: under Joe Biden, it did really well under President Trump,

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Speaker 5: seems to mostly go up over time. But yes, the

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Speaker 5: age of uncertainty, I would not disagree with you.

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Speaker 2: So all of this had me thinking, you know, if

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Speaker 2: you're an investor, and particularly a thematic investor. At the moment,

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Speaker 2: I think you're facing some tough choices because it's not

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Speaker 2: just a range of uncertainties. In some respects, it's like

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Speaker 2: polar opposite outcomes. You know, either you get Trump and

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Speaker 2: everything that comes with it, or you get Biden and

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Speaker 2: everything that comes with that, and some of that is

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Speaker 2: very different totally.

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Speaker 5: Well, the other thing that I've been thinking about in

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Speaker 5: the AI conversation really has driven this home, which is

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Speaker 5: that if you're a discretionary investor, now me, I'm just

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Speaker 5: spy and die all the way. So I don't have

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Speaker 5: to think about all this stuff because I own Nvidia

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Speaker 5: and through my S and P five hundred index fund.

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Speaker 4: But if in.

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Speaker 5: Nvidia falls out of favor and something else rises, well

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Speaker 5: I'll have that too, So I'm spy and die. But

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Speaker 5: I understand that some people have to like beat the

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Speaker 5: benchmark or beat the index, or justify why someone else

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Speaker 5: would pay them fees to invest money on their behalf.

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Speaker 5: Then of course you really have to get the call correct,

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Speaker 5: because the only way that you beat the market over

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Speaker 5: this year or last year is if you got the

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Speaker 5: AI call, and if you weren't heavy on Nvidia or

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Speaker 5: some of these other names. You almost certainly did not

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Speaker 5: justify someone giving paying you money to invest, and so

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Speaker 5: these are really tough questions for investors who have to

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Speaker 5: like do well so to speak relatively to answer yes.

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Speaker 2: So I'm very pleased to say that we have the

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Speaker 2: perfect guest we are going to bring back on, James

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Speaker 2: van Gielan. He is, of course the fault owner of

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Speaker 2: Satrini Research. We spoke to him last year about one

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Speaker 2: of his big investment themes, which at the time was

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Speaker 2: ozembic and the associated GLP one weight loss drugs. That

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Speaker 2: was a huge winner in the months since. We're going

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Speaker 2: to get a handle on what he's looking at now

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Speaker 2: and also his thought process for developing new investment themes

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Speaker 2: at a time when a lot of things seem to

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Speaker 2: be very uncertain. So James, thanks so much for coming

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Speaker 2: back on all thoughts.

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Speaker 4: Thanks for having me back.

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Speaker 2: What are you looking at nowadays? To start with the

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Speaker 2: easy question, Yeah.

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Speaker 4: Well, you know, AI obviously is a huge theme in

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Speaker 4: the market, and I think part of it, Yeah, have

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Speaker 4: you really Yeah, it's this little thing. But I think

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Speaker 4: that the most important way to basically track this theme

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Speaker 4: is it's one of those technologically innovation driven kind of themes.

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Speaker 4: And the way that I viewed thematic investing is along

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Speaker 4: this spectrum of you have disruption or continuation, and then

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Speaker 4: you have macro drivers or micro drivers. And I'm not

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Speaker 4: talking about necessarily macro and the sense of macroeconomics, but

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Speaker 4: you know, demographic shifts, even like consumer attitudes, the overton

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Speaker 4: windows shifting, just big things changing versus big things staying

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Speaker 4: the same. So over the past twelve months, you know,

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Speaker 4: there have been probably ten or twelve themes that I

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Speaker 4: think have really made themselves down in the market and

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Speaker 4: the way that's as it's moved, and right now, there

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Speaker 4: have been a few that I've probably overstayed my welcoming.

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Speaker 4: I think that some of them are getting a little

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Speaker 4: bit frothy.

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Speaker 2: And you were early to AI, yeah.

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Speaker 4: Yeah, and that's one of the risks of doing this

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Speaker 4: kind of investing. You know, there's a great quote from

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Speaker 4: Robert Schiller. Nobody has ever made a decision based on

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Speaker 4: a number. They always need a story. I don't know

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Speaker 4: if that's the exact quote, but another one of my

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Speaker 4: favorites is Kyle Harrison said that price is a number

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Speaker 4: today multiplied by a story tomorrow. And the way that

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Speaker 4: I ve be investing is it's always going to prioritize

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Speaker 4: a good story and a good narrative. And that's what

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Speaker 4: has the making of a theme, because you need people

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Speaker 4: to make the decision to execute a trade and move price,

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Speaker 4: and then you also need on the other side something

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Speaker 4: to actually happen in the real economy. And as far

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Speaker 4: as artificial intelligence, this kind of fast moving innovative theme

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Speaker 4: that we don't really you know, you can say what

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Speaker 4: is AI going to look like five years out and

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Speaker 4: you'll get five different answers from five different people. And

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Speaker 4: I started from a place of you know, which became

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Speaker 4: consensus very quickly with the picks and shovels, right, and

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Speaker 4: then the picks and shovels of the picks and shovels,

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Speaker 4: and then you get this thing where you know, during

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Speaker 4: the gold Rush, it wasn't just everyone showing up to

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Speaker 4: sell picks and shovels, just everyone waiting there. So we

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Speaker 4: have to kind of look now for this controversial thing

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Speaker 4: of does AI have use cases? And I think it's

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Speaker 4: pretty clear that it does if you're looking for it.

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Speaker 4: But in terms of like monitoring the way that it's

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Speaker 4: going to progress. I think the biggest component here is

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Speaker 4: going to be going from B to B to B

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Speaker 4: two C. And what I said about two months ago

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Speaker 4: was that the company that was going to make this

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Speaker 4: really happen, it's not going to be Microsoft, you know

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Speaker 4: with Chat, GBT and open AI. I think it's going

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Speaker 4: to be Apple. And you know, if any company can

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Speaker 4: get it done, it's going to be Apple. And you

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Speaker 4: have this kind of dynamic where you need something to

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Speaker 4: happen that forces people to use it. Right. It's kind

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Speaker 4: of like like Facebook, right, or social media in general,

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Speaker 4: where you know there was only so long you could

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Speaker 4: go without being connected to everything and everyone. And Apple

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Speaker 4: is you know, have you ever texted someone like, Hey,

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Speaker 4: I'm coming over and then you get in the car

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Speaker 4: and if it connects the car play, it'll give you

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Speaker 4: a notification that says, like, you know, directions to Joe's house.

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Speaker 3: Yeah.

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Speaker 4: Your phone knows everything about you, right, And your phone

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Speaker 4: knows who you are, it knows what you're doing, it

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Speaker 4: knows what you want to do, it knows who your

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Speaker 4: mom is. And that information combined with Apple's trust in

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Speaker 4: the consumer, trust and the idea that they place of

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Speaker 4: a high premium on privacy. You can't have this AI

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Speaker 4: assistant that knows everything about you if you think that

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Speaker 4: it's going to be stored on a server in Kubertino.

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Speaker 4: It needs to be on your phone. So the next

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Speaker 4: thing is how do we start having these big models

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Speaker 4: that you know that are in these very big data

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Speaker 4: centers and you know all this CAPEX that's spent on that.

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Speaker 4: Now we have to just like every other technological trend,

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Speaker 4: we have to miniaturize it, right, and we have to

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Speaker 4: basically have your phone doing inference, you know, sending to

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Speaker 4: those models, but also these smaller models.

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Speaker 5: Can I push back on that real quickly because I've

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Speaker 5: given up every piece of information about my entire life

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Speaker 5: already to the internet at all. Yeah, of course I

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Speaker 5: have just by existing in the world, right and I'm

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Speaker 5: not and it all exists on servers somewhere in either

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Speaker 5: Kupertino or Amazon servers or whatever. What is it about

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Speaker 5: AI specifically that changes that need? Because like I said,

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Speaker 5: I've given I didn't I don't want to. But just

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Speaker 5: the reality of life in the world is my information

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Speaker 5: is already out there.

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Speaker 4: So AI has two sides, training and inference, right, And

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Speaker 4: you can train on. You know, all this data that

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Speaker 4: you've already given up to the faceless kind of entity

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Speaker 4: that controls the world. But what AI brings about is

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Speaker 4: in real time drawing conclusions from that data. And it's

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Speaker 4: two sided. There's convenience and then there's privacy concerns. And

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Speaker 4: I agree with you that convenience definitely outweighs privacy concerns

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Speaker 4: when it comes to the consumer. But when you have

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Speaker 4: this ecosystem that Apple has created with this walled garden,

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Speaker 4: and then you kind of alleviate the privacy concerns and

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Speaker 4: you have this constant inference that's going on, and there

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Speaker 4: will come a time where you know, you'll be walking

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Speaker 4: down the street and you'll say, I just called Tracy,

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Speaker 4: you know a week ago. What did we talk about

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Speaker 4: and when did we say we're going to meet?

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Speaker 1: Oh?

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Speaker 4: Okay, can you text Tracy and tell her, actually, I

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Speaker 4: have a thesis that just got destroyed, so I don't

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Speaker 4: really want to talk about that one. And you know,

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Speaker 4: can you text Joe and let him know too, And

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Speaker 4: then can you look at my calendar when am my velvement?

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Speaker 4: You can be walking down the street. Yeah, And the

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Speaker 4: productivity gains from your phone doing inference and inferring what

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Speaker 4: it knows about you from that data.

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Speaker 3: That's huge, real quickly.

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Speaker 5: So you mentioned the picks and shovels and video, than

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Speaker 5: the picks and shovels who provide the picks and shovels

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Speaker 5: and people got to tell you about super micro and

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Speaker 5: the racks and maybe Dell super Micro.

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Speaker 4: I never heard of it.

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Speaker 3: I've heard of it, just so we're clear your long Apple.

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Speaker 4: Yeah, I'm any name that I mentioned inherently, I'm automatically

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Speaker 4: going to be longer shot.

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Speaker 2: Yeah, you mentioned the privacy concerns, and I just thought

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Speaker 2: back to this moment. My husband and I were in

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Speaker 2: our kitchen and we were talking about something really innocuous,

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Speaker 2: like corn or something, and suddenly Alexa started telling us

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Speaker 2: a joke about corn, and like we hadn't said Alexa

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Speaker 2: or anything like that, it just started talking like clearly

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Speaker 2: had been recording everything that we were saying. And I

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Speaker 2: will admit that that was that was kind of a

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Speaker 2: jarring experience. I would be less surprised if my Apple

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Speaker 2: phone did that versus my Alexa that I use to

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Speaker 2: turn on the lights and things like that.

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Speaker 4: Yeah, it turns out that large language models are really

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Speaker 4: good at language.

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Speaker 2: Yeah, surprising.

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Speaker 4: I think that the next kind of picks and shovels idea.

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Speaker 4: The thing is everyone's always well, what are we going

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Speaker 4: to invest in that's doing the use cases? And then

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Speaker 4: those same people will come around and say, why are

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Speaker 4: you know these five stocks driving the entire market? And

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Speaker 4: the thing is the latency between a company starting and

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Speaker 4: becoming successful and doing an IPO has gotten a lot

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Speaker 4: longer in the past, like two decades, So if you're

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Speaker 4: a public equity investor, you might not really get to

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Speaker 4: the use cases that you can invest in are those

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Speaker 4: five stocks and combine that with you know, everything else,

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Speaker 4: and like you said, the uncertainty. When there's uncertainty, people

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Speaker 4: tend to crowd into things that they know are going

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Speaker 4: to work. I think that really the picks and shovels

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Speaker 4: thing will probably play out again. But when it comes

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Speaker 4: to EDJI and doing inference onto and kind of I

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Speaker 4: mean this is probably a controversial statement, but I think

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Speaker 4: that we might see the first real true replacement cycle,

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Speaker 4: like real replacement cycle where Apple comes out with the

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Speaker 4: new phone and it has all these productivity gains, and

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Speaker 4: it has all this new hardware, and you know, it

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Speaker 4: can do these things, and if you don't have that phone,

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Speaker 4: you can't do it, and it becomes kind of like

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Speaker 4: that sci fi movie Gatica, where you know, yeah, if

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Speaker 4: you're left out, you're you're just you're just left behind.

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Speaker 5: I've been wondering about this because at some point in

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Speaker 5: my life it's stopped being important to upgrade. I mean,

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Speaker 5: at some point in my life it's stopped being important

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Speaker 5: or interesting to even tune into, like the new Apple events,

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Speaker 5: and those used to be big events online, and then

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Speaker 5: people stopped. And then at one point, if someone asked me,

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Speaker 5: what iPhone do you have, I would have told them

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Speaker 5: the anti Zycle. I have the iPhone five for whatever.

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Speaker 5: I have no idea which one I have right now.

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Speaker 5: I got a couple of years ago. It does everything

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Speaker 5: I need. I have zero idea which one it is.

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Speaker 5: So I've been sort of wondering. Yeah, So you think

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Speaker 5: that there's the potential, at least for some unlock of

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Speaker 5: new capabilities where the people who don't have that new

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Speaker 5: phone actually feel, oh, I really just can't interact with

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Speaker 5: the same way that people who have it do.

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Speaker 4: Yeah, and that kind of replacement cycle drives that new

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Speaker 4: picks and shovels thesis where you're looking at all these

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Speaker 4: kind of you know, system on chip names, and that's

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Speaker 4: where I see this theme that you know, everyone is

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Speaker 4: talking about going But the thing about thematic equity is

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Speaker 4: that if you let it, it just becomes momentum investing,

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Speaker 4: because themes are stories, and stories translate into momentum very easily.

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Speaker 4: And when people do an association with momentum, they're always

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Speaker 4: thinking about kind of momentum inable market like what we've

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Speaker 4: been seeing. And you know that's not the case at all.

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Speaker 4: There was plenty of themes in twenty twenty two when

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Speaker 4: everything else was going down. I mean, tech going down

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Speaker 4: was a theme. You know, the interest rate hikes, the

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Speaker 4: inflation for the first time that you know, that's a theme.

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Speaker 4: That's a story that grabs people's attention and makes them

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Speaker 4: go to their terminal and execute a trade. And same

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Speaker 4: thing with energy, and you know, the war in Ukraine.

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Speaker 4: And so I try my hardest to it's super cliche,

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Speaker 4: but think a few months ahead at least.

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Speaker 2: So for something like the US presidential elections. You know,

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Speaker 2: again things are currently up in the air, and you

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Speaker 2: can look at the polls and have a sense of

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Speaker 2: who might have a lead, but overall it's pretty difficult

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Speaker 2: to tell who's going to be in office come next year.

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Speaker 2: How do you start preparing for something like that from

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Speaker 2: an perspective.

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Speaker 4: So if the question how do you start preparing the

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Speaker 4: answer early early, you start preparing early. Right. No, I

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Speaker 4: don't think that, you know, was anyone surprised that we

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Speaker 4: have an election this year? Yeah?

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Speaker 3: Some people, Yeah, you know, some people really are because

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Speaker 3: of how few people have been paying attention.

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Speaker 4: I mean, that's It's one of those things where I

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Speaker 4: think it's like when you're a kid and you touch

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Speaker 4: the stove and you get burned, and you're well, I'm

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Speaker 4: not doing that again. That's how I think the twenty

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Speaker 4: twenty election was for me. So as soon as I

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Speaker 4: saw that the year was an election year, I started

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Speaker 4: kind of preparing for this. This was back in January

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Speaker 4: when the odds of Trump even being a nominee, right,

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Speaker 4: or like being the Republican nominee, were like twenty percent.

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Speaker 4: It was relatively easy back then to look at these

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Speaker 4: very kind of exposed equities and say, this is a

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Speaker 4: swap I guess on Trump being a nominee.

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Speaker 5: So I've ied some cell side research about who would

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Speaker 5: be exposed. One clever one the company five below. He

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Speaker 5: sells a bunch of cheap stuff, which you can't sell

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Speaker 5: cheap stuff if there's a massive tariffs on cheap stuff.

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Speaker 5: And that stock has been Oh that's a total dog.

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Speaker 5: That stock was over two hundred back in March and

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Speaker 5: now it's at one hundred and two. Of the time

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Speaker 5: we're talking about this, what stands out to you is.

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Speaker 4: Being exposed depending on what the theme is there, I

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Speaker 4: create either a long short basket to kind of gain exposure,

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Speaker 4: because I don't ever want that kind of idiosyncratic like

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Speaker 4: single name equity risk where a company ends up doing

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Speaker 4: really well and has nothing to do with my actual thesis.

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Speaker 4: So I constructed a basket that kind of played on

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Speaker 4: a few different themes. Tariffs was a big one of them. Right,

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Speaker 4: So you look at how much comes from China. First,

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Speaker 4: you look at Trump's what he says, right, one hundred

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Speaker 4: percent tariffs, sixty percent tariffs that you know, and the

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Speaker 4: thing is protectionism from China. Trade that's not that's not

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Speaker 4: a part is an issue anymore. Right, There are no

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Speaker 4: China doves in the government. But Trump is certainly a

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Speaker 4: unique brand of that when it comes to the trade policy.

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Speaker 4: So that seemed like the most asymmetric way to kind

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Speaker 4: of capture that. So you look at five below for example, restoration, hardware,

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Speaker 4: floor and decor, you know, best Buy, these companies that

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Speaker 4: really do a lot of business when it comes to China,

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Speaker 4: and that's a lot of their impacosts and tariffs really

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Speaker 4: would affect them. And that's your short side. And then

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Speaker 4: you say, okay, well who is going to be all

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Speaker 4: right here? And that comes down to pricing power. You know,

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Speaker 4: if consumers start consolidating their spending because of tariff's, who

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Speaker 4: can kind of keep that pricing power regardless? And like Costco, Walmart,

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Speaker 4: some of the automotive parts like O'Reilly, that's your longside.

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Speaker 4: So that makes up one component of the basket and

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Speaker 4: then move on. Okay, And the thing is, for the

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Speaker 4: past three three years, one of my exposures to a

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Speaker 4: theme has been bidnomics. So you know, that was big

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Speaker 4: on electrification and infrastructure spending and generally fading the idea

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Speaker 4: that we could have a recession when fiscal policy is

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Speaker 4: spending like a drunken sailor. And we said, okay, well,

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Speaker 4: now we need some kind of offsetting beta. Here, we

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Speaker 4: need some Trump beta. And there are these orphan equities

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Speaker 4: like Fannie May and Freddie Mack. And you know, I

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Speaker 4: looked at the story and I said, I don't think

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Speaker 4: that that's going to happen. He didn't do it the

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Speaker 4: first time, but it doesn't matter because it's a story.

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Speaker 4: So people are going to take action based on that story.

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Speaker 4: Then looking at things like private prisons and law enforcement.

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Speaker 2: I remember that from twenty sixteen that was like a

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Speaker 2: big Trump winner.

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Speaker 4: Yeah, and that but that was when I was kind

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Speaker 4: of looking at this. That I think was the first

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Speaker 4: place that I start is basically sell side research is

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Speaker 4: to see what is being said. And there were a

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Speaker 4: lot of kind of well, here's how you know, these

365
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Speaker 4: equities performed when Trump was president, and here's how they

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Speaker 4: performed when Biden was president. And if you go into

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Speaker 4: anything like that with that kind of thinking where you're

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Speaker 4: just overfitting to like two periods of time, I mean

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Speaker 4: when Biden was elected, if you had that kind of thinking,

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Speaker 4: you would have went long clean energy right in short

371
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Speaker 4: and short oil, and you would have gotten destroyed because like,

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Speaker 4: no matter who's the president, there are going to be

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Speaker 4: bear of force as at work, so you really have

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Speaker 4: to try to specify and get rid of as much

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Speaker 4: incongruous kind of correlation. So names like Axin or GEO

376
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Speaker 4: Group or you know, Corcivic, those will probably benefit. I

377
00:20:43,080 --> 00:20:44,879
Speaker 4: had kind of a fun one that was a company

378
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Speaker 4: that makes the storefront glass for retail stores, because I figured,

379
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Speaker 4: no matter who wins this election, there's going to be

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Speaker 4: people that are angry.

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Speaker 5: It's funny you mentioned fan you may equity because it

382
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Speaker 5: still exists there. It's like a cockroach. But I've always

383
00:20:59,840 --> 00:21:02,040
Speaker 5: though it's like the Boomer's crypto.

384
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Speaker 4: It's like that.

385
00:21:02,840 --> 00:21:04,600
Speaker 3: It's like if you look at like there is.

386
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Speaker 5: A Fanny and Freddy Twitter, Yeah, and it's like they

387
00:21:08,200 --> 00:21:09,840
Speaker 5: talk the same way that people are talking about the

388
00:21:09,920 --> 00:21:10,760
Speaker 5: random token.

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Speaker 4: But their money spends just as well, right, and it

390
00:21:12,920 --> 00:21:18,840
Speaker 4: moves the bid up. Yeah. So I think that I

391
00:21:18,920 --> 00:21:22,679
Speaker 4: came into this year all prepared, Right, here's how the

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Speaker 4: basket is going to look. The thing that really made

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Speaker 4: me the most satisfied about this, I mean, the basket

394
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Speaker 4: has done quite well because Trump odds went from twenty

395
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Speaker 4: percent of being the nominee to you know, sixty three

396
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Speaker 4: percent of being the president.

397
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Speaker 3: Uh huh.

398
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Speaker 4: And the performances tracked that pretty well. And there are

399
00:21:37,440 --> 00:21:41,359
Speaker 4: a few other aspects of that basket, but the thing

400
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Speaker 4: that obviously didn't make me happy because something that I

401
00:21:44,400 --> 00:21:48,000
Speaker 4: owned went down. But right after that debate, which I think,

402
00:21:48,119 --> 00:21:51,280
Speaker 4: you know, there were a lot of people that were

403
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Speaker 4: surprised by that, And I guess you could if you

404
00:21:53,800 --> 00:21:57,440
Speaker 4: were drawing a chart of like should I be concerned

405
00:21:57,440 --> 00:21:59,600
Speaker 4: about Biden's performance in a debate after the State of

406
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Speaker 4: the Union, and you you know, maybe you had some

407
00:22:01,600 --> 00:22:04,320
Speaker 4: moments where like he's talking about you know, where's that

408
00:22:04,440 --> 00:22:07,639
Speaker 4: lady that and the lady died three weeks ago or something,

409
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Speaker 4: or or you know.

410
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Speaker 3: That there were signs, there were.

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Speaker 4: Signs, but you know, I wasn't necessarily surprised by that.

412
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Speaker 4: And when that happened, the Bidenomics basket, you know, goes

413
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Speaker 4: down by five percent, and then the Trump basket goes

414
00:22:21,600 --> 00:22:25,520
Speaker 4: up by ten, and you know, that's kind of reinforcing

415
00:22:25,560 --> 00:22:27,520
Speaker 4: where it shows you that you know, you're not just

416
00:22:27,600 --> 00:22:31,200
Speaker 4: riding beta, You've been allocated to the right thing.

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Speaker 2: I was going to ask, how you wait those respective baskets,

418
00:22:35,800 --> 00:22:38,480
Speaker 2: like a Biden long short basket versus a Trump long

419
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Speaker 2: short basket, but maybe maybe you equal weight it and

420
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Speaker 2: then the returns are asymmetric as you just mentioned.

421
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Speaker 4: Yeah, there's some discretionary aspect where I think that you know,

422
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Speaker 4: something matters more than or that investors are paying more attention,

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Speaker 4: you know, don't. I didn't think back in March that

424
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Speaker 4: this tariff aspect was getting as much attention. I figured

425
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Speaker 4: that would kind of progress and get more attention as

426
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Speaker 4: the year went on, as Trump said more about trade policy,

427
00:23:03,240 --> 00:23:06,360
Speaker 4: whereas something that's you know, as first order thinking, as

428
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Speaker 4: you know, if Trump's president, We're going to need more

429
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Speaker 4: you know, border prisons or you know that. So the

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Speaker 4: waiting kind of changes throughout the course of the narrative progressing.

431
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Speaker 5: It's kind of weird, like going back to if we

432
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Speaker 5: had a one hundred percent teariff on imported goods, and

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Speaker 5: this is a little bit of a sidetrack. I feel

434
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Speaker 5: like that would be incredibly disruptive to the way we

435
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Speaker 5: do economics in this country. And I don't have any

436
00:23:31,119 --> 00:23:33,000
Speaker 5: opinion on whether it be good or bad. I'll leave

437
00:23:33,040 --> 00:23:35,160
Speaker 5: that to others. It is sort of funny to think

438
00:23:35,240 --> 00:23:38,160
Speaker 5: we're going to have this big teariff, this big change

439
00:23:38,200 --> 00:23:40,280
Speaker 5: and how we interact with the world, and so we're

440
00:23:40,320 --> 00:23:43,240
Speaker 5: just going to take it all out on you know,

441
00:23:43,320 --> 00:23:45,919
Speaker 5: five billion dollar company five below. Like, I just have

442
00:23:46,000 --> 00:23:49,720
Speaker 5: a feeling that if we did have this wholesale orientation

443
00:23:49,800 --> 00:23:51,919
Speaker 5: of how we trade, like, they're not going to be

444
00:23:51,920 --> 00:23:53,760
Speaker 5: the only company that no effects.

445
00:23:54,600 --> 00:23:57,640
Speaker 4: And I think when people kind of look at when

446
00:23:57,680 --> 00:24:00,800
Speaker 4: I share my portfolio with people, which you know, I

447
00:24:00,840 --> 00:24:02,760
Speaker 4: know some people are kind of neurotic about that, but

448
00:24:02,800 --> 00:24:04,919
Speaker 4: I'm not big enough to where I think that someone's

449
00:24:04,920 --> 00:24:07,080
Speaker 4: going to, you know, attack me. But when I show

450
00:24:07,080 --> 00:24:09,760
Speaker 4: my portfolio to people, they are kind of taken aback

451
00:24:09,800 --> 00:24:11,960
Speaker 4: by how many securities are in there. But the way

452
00:24:12,000 --> 00:24:14,360
Speaker 4: that I view it is, you know, I have diversification

453
00:24:14,440 --> 00:24:17,200
Speaker 4: at the security level, but I'm very concentrated in.

454
00:24:17,200 --> 00:24:18,800
Speaker 3: These themes in the somatic level.

455
00:24:18,880 --> 00:24:23,000
Speaker 4: Yeah, so like GLP ones might make up fifteen to

456
00:24:23,040 --> 00:24:25,760
Speaker 4: twenty percent of the portfolio, but it's not just Lily

457
00:24:25,880 --> 00:24:28,600
Speaker 4: and it's not just Novo Nordisk. It's also you know,

458
00:24:29,119 --> 00:24:32,960
Speaker 4: something like Torrid, which is a woman's kind of fashion

459
00:24:33,080 --> 00:24:34,520
Speaker 4: for plus size clothing.

460
00:24:34,640 --> 00:24:35,880
Speaker 2: Oh, you would be short that one.

461
00:24:36,000 --> 00:24:38,920
Speaker 4: No, So well think about it, right, you don't teleport

462
00:24:38,920 --> 00:24:44,520
Speaker 4: your weight if you're three hundred pounds and you go

463
00:24:44,600 --> 00:24:48,760
Speaker 4: down six sizes. First off, you're happy that you're losing weight, right,

464
00:24:48,960 --> 00:24:51,199
Speaker 4: so you want to show off, show that off, but

465
00:24:51,280 --> 00:24:52,359
Speaker 4: you're still plus sized.

466
00:24:52,480 --> 00:24:54,080
Speaker 2: Oh I see, Okay, so you're.

467
00:24:53,880 --> 00:24:55,560
Speaker 4: Buying clothes the whole way down.

468
00:24:55,800 --> 00:24:56,080
Speaker 2: Ah.

469
00:24:56,440 --> 00:24:58,359
Speaker 4: And then there is in fact, you know, like this

470
00:24:58,480 --> 00:25:00,719
Speaker 4: is like well documented, there is a re bound effect

471
00:25:01,240 --> 00:25:06,360
Speaker 4: from GLP one drugs. If you stop them, you regain

472
00:25:06,520 --> 00:25:07,320
Speaker 4: a portion of the weight.

473
00:25:07,560 --> 00:25:10,840
Speaker 2: Oh so it's actually like the weight fluctuation that matters here.

474
00:25:10,920 --> 00:25:14,399
Speaker 4: Okay. I mean, if you know anyone that's overweight that

475
00:25:15,240 --> 00:25:18,439
Speaker 4: struggled with their weight, they have these closets full of

476
00:25:18,560 --> 00:25:21,639
Speaker 4: just you know, various sizes of clothes. So you know

477
00:25:21,720 --> 00:25:26,800
Speaker 4: that the general idea is applying second order thinking to

478
00:25:26,920 --> 00:25:30,120
Speaker 4: capture a theme in not just the most obvious way,

479
00:25:30,119 --> 00:25:31,840
Speaker 4: because that's going to be the most crowded way. And

480
00:25:31,880 --> 00:25:36,399
Speaker 4: that's that's like I said before, you really want to

481
00:25:36,440 --> 00:25:39,960
Speaker 4: try to not just have this be story based trend

482
00:25:40,000 --> 00:25:40,399
Speaker 4: of following.

483
00:25:41,240 --> 00:25:44,399
Speaker 2: Wait, I love talking about second order effects and I

484
00:25:44,400 --> 00:25:47,440
Speaker 2: have a bunch of favorite examples that I have probably

485
00:25:47,440 --> 00:25:49,840
Speaker 2: said on this podcast before. So I'll spare all our

486
00:25:49,880 --> 00:25:53,359
Speaker 2: listeners from repeating those. But do you have a favorite

487
00:25:53,480 --> 00:25:54,960
Speaker 2: sort of second order play.

488
00:25:55,080 --> 00:25:56,159
Speaker 4: I want to hear yours first.

489
00:25:56,720 --> 00:26:00,160
Speaker 2: Well, the one I always think about, Well, there's the

490
00:26:00,160 --> 00:26:03,680
Speaker 2: gummy Bear one, and then there's also like the Sawdust one,

491
00:26:03,800 --> 00:26:06,399
Speaker 2: Gummy Bear. Hold on, I'm gonna have to look this up.

492
00:26:06,480 --> 00:26:07,560
Speaker 3: No, I remember what it was.

493
00:26:08,240 --> 00:26:10,080
Speaker 2: Do you remember the gummy bear one? Do you remember

494
00:26:10,119 --> 00:26:10,560
Speaker 2: both of them?

495
00:26:10,680 --> 00:26:10,880
Speaker 1: Yeah?

496
00:26:11,000 --> 00:26:13,360
Speaker 3: Okay, well I don't remember the sawdust one. I'll look

497
00:26:13,400 --> 00:26:16,120
Speaker 3: up the chickens or something like that. And there wasn't

498
00:26:16,280 --> 00:26:18,520
Speaker 3: cows in milk, right, So after the.

499
00:26:18,480 --> 00:26:21,000
Speaker 5: Housing bus, then there wasn't enough wood and there wasn't

500
00:26:21,040 --> 00:26:23,239
Speaker 5: enough sawdust, and that wasn't good for cows. And then

501
00:26:23,280 --> 00:26:24,680
Speaker 5: that was so there was less milk.

502
00:26:25,080 --> 00:26:27,600
Speaker 2: So no one would have expected as a result of

503
00:26:27,640 --> 00:26:30,000
Speaker 2: the two thousand and eight housing collapse that you would

504
00:26:30,040 --> 00:26:32,880
Speaker 2: see milk prices go up because there was less milk.

505
00:26:33,080 --> 00:26:35,840
Speaker 2: But apparently that's what happened. Because the cows didn't have

506
00:26:35,880 --> 00:26:38,840
Speaker 2: as much sawdust, they weren't as comfortable, they weren't as productive,

507
00:26:39,240 --> 00:26:41,680
Speaker 2: and so there was less milk. And then what was

508
00:26:41,720 --> 00:26:42,439
Speaker 2: the gummy bear.

509
00:26:42,440 --> 00:26:43,320
Speaker 3: Gummy Bear one?

510
00:26:43,760 --> 00:26:46,680
Speaker 5: Was that this is what someone told us that due

511
00:26:46,720 --> 00:26:49,760
Speaker 5: to the collapse an auto production during the pandemic, that

512
00:26:49,840 --> 00:26:52,720
Speaker 5: there wasn't as much demand for leather for the seating,

513
00:26:52,960 --> 00:26:55,439
Speaker 5: and that a lot of the I guess gelatine that

514
00:26:55,600 --> 00:26:58,439
Speaker 5: was needed for gummy bears came out of that supply

515
00:26:58,560 --> 00:27:01,000
Speaker 5: chain or something like that, so it affected gummy bear

516
00:27:01,040 --> 00:27:03,000
Speaker 5: production when the chip stopped coming.

517
00:27:04,119 --> 00:27:05,959
Speaker 4: I mean, I would love to meet the person that

518
00:27:06,160 --> 00:27:10,320
Speaker 4: predicted that. Yeah, right, right, if you. And so when

519
00:27:10,359 --> 00:27:13,360
Speaker 4: it comes to what I do, there is kind of

520
00:27:13,400 --> 00:27:17,480
Speaker 4: like a you get kind of deferred in your ability

521
00:27:17,480 --> 00:27:20,240
Speaker 4: to be like, yeah, called it, you know, because I

522
00:27:20,240 --> 00:27:23,720
Speaker 4: don't forego security analysis entirely. I don't, you know, I

523
00:27:23,800 --> 00:27:25,840
Speaker 4: make sure that if I'm shorting a company that it's

524
00:27:25,840 --> 00:27:27,679
Speaker 4: a bad company, or if I'm buying a company, that

525
00:27:27,680 --> 00:27:31,399
Speaker 4: it's mispriced and you know that it has upside. So

526
00:27:31,520 --> 00:27:34,800
Speaker 4: you never really know for sure. And sometimes even you know,

527
00:27:35,119 --> 00:27:37,720
Speaker 4: they'll get on the earnings call, especially if it's like

528
00:27:37,760 --> 00:27:40,800
Speaker 4: a short and you know what or you think you

529
00:27:40,920 --> 00:27:43,439
Speaker 4: know why it went down. You know, this is like

530
00:27:43,480 --> 00:27:44,840
Speaker 4: the last time I was on, we talked about the

531
00:27:44,840 --> 00:27:49,919
Speaker 4: Titan Stabler. Oh yeah, right, And obviously they eventually started saying, well,

532
00:27:50,520 --> 00:27:52,600
Speaker 4: this probably has to do with those weight loss drugs.

533
00:27:52,640 --> 00:27:54,360
Speaker 4: But you know, in the beginning, when it's going down

534
00:27:54,359 --> 00:27:56,920
Speaker 4: and when the company's you know, seeing decreased revenue, sometimes

535
00:27:56,960 --> 00:27:58,600
Speaker 4: it takes them a little bit to like realize what's

536
00:27:58,640 --> 00:28:01,040
Speaker 4: going on, and sometimes they never do.

537
00:28:01,280 --> 00:28:01,480
Speaker 1: You know.

538
00:28:01,640 --> 00:28:04,639
Speaker 2: Well, I remember that about restoration hardware too, because it

539
00:28:04,920 --> 00:28:07,520
Speaker 2: started going down like a year or two ago, and

540
00:28:07,560 --> 00:28:10,920
Speaker 2: people were worried about like, oh, this is a recessionary signal.

541
00:28:11,400 --> 00:28:14,760
Speaker 2: And I remember seeing this online and basically saying like, well,

542
00:28:14,760 --> 00:28:17,880
Speaker 2: maybe it has more to do about with restoration hardware

543
00:28:17,960 --> 00:28:23,639
Speaker 2: specific things and issues there rather than macroeconomic conditions. And

544
00:28:23,680 --> 00:28:26,120
Speaker 2: people went nuts. But that's exactly what it was, right,

545
00:28:26,160 --> 00:28:29,480
Speaker 2: Like the renovation boom and the furniture boom continued for

546
00:28:29,680 --> 00:28:30,480
Speaker 2: some time after that.

547
00:28:30,800 --> 00:28:33,719
Speaker 4: Restoration hardware, Yeah, it wasn't a recessionary signal, but there

548
00:28:33,760 --> 00:28:35,760
Speaker 4: is a great signal to be had in the story

549
00:28:35,800 --> 00:28:38,360
Speaker 4: of restoration hardware, which is if you own a company

550
00:28:38,800 --> 00:28:40,920
Speaker 4: and the CEO starts coming on and spends half the

551
00:28:40,960 --> 00:28:43,800
Speaker 4: earnings call talking about macroeconomics, Oh yeah, sell that.

552
00:28:43,960 --> 00:28:45,360
Speaker 2: God, that's a bad sign.

553
00:28:45,640 --> 00:28:47,600
Speaker 5: That's a good yeah that It was like a famous

554
00:28:47,640 --> 00:28:49,880
Speaker 5: conference call, and it was right in twenty twenty one,

555
00:28:50,240 --> 00:28:52,600
Speaker 5: and it was sort of like an equivalent of although

556
00:28:52,640 --> 00:28:54,920
Speaker 5: one of the best moments on TV. Ever, like the

557
00:28:55,040 --> 00:28:57,440
Speaker 5: Kramer they are nuts or whatever, They're blind at the

558
00:28:57,480 --> 00:28:59,640
Speaker 5: FED in two thousand and six or seven about Bear

559
00:28:59,680 --> 00:29:01,560
Speaker 5: Stearns and he went on, He's like, they have no.

560
00:29:01,520 --> 00:29:03,360
Speaker 3: Idea what's coming on recession. Recession.

561
00:29:03,400 --> 00:29:05,680
Speaker 5: A bunch of people took that seriously, but it seemed

562
00:29:05,680 --> 00:29:08,440
Speaker 5: like a restoration hardware story, and then the rest of

563
00:29:08,480 --> 00:29:10,000
Speaker 5: the economy just kept chugging along.

564
00:29:11,480 --> 00:29:15,520
Speaker 2: Wait, there are times when uncertainty kind of rears its

565
00:29:15,600 --> 00:29:20,200
Speaker 2: ugly head and does wreck some investment theses. Maybe wreck

566
00:29:20,320 --> 00:29:22,600
Speaker 2: is a strong word, but I know you were, for instance,

567
00:29:22,920 --> 00:29:27,200
Speaker 2: looking into how to play water and water shortages in

568
00:29:27,280 --> 00:29:30,680
Speaker 2: the future. But lo and behold, a couple of weeks ago,

569
00:29:31,200 --> 00:29:34,520
Speaker 2: the Supreme Court made a decision having to do with

570
00:29:34,640 --> 00:29:39,400
Speaker 2: something called the Chevron deference that seems to have caused

571
00:29:39,400 --> 00:29:42,719
Speaker 2: additional uncertainty for that particular thesis. Can you walk us

572
00:29:42,720 --> 00:29:43,680
Speaker 2: through what happened there?

573
00:29:44,240 --> 00:29:47,760
Speaker 4: Well? First off, water in the realm of thematic equity,

574
00:29:48,240 --> 00:29:51,880
Speaker 4: water is waterloo kind of it's this battle that it's

575
00:29:51,960 --> 00:29:55,000
Speaker 4: always a good idea to be long water. If you

576
00:29:55,000 --> 00:29:57,520
Speaker 4: think the GDP is going to go up, unless you're

577
00:29:57,560 --> 00:30:00,200
Speaker 4: in China, where water use has actually declined as VP

578
00:30:00,320 --> 00:30:02,520
Speaker 4: has gone up because they've been really serious about efficiency.

579
00:30:02,560 --> 00:30:06,480
Speaker 4: But the idea is, if you buy water, you're never

580
00:30:06,560 --> 00:30:09,480
Speaker 4: really going to blow up, you know, unless like really,

581
00:30:09,520 --> 00:30:12,880
Speaker 4: unless it's like global financial crisis. You'll be pretty safe.

582
00:30:13,000 --> 00:30:16,280
Speaker 4: You can. There's always going to be some doom and

583
00:30:16,280 --> 00:30:18,440
Speaker 4: gloom about water. So if you get on the phone

584
00:30:18,440 --> 00:30:20,280
Speaker 4: with an investor, you can say, well, we own water

585
00:30:20,360 --> 00:30:23,640
Speaker 4: because of you know, here's this big report from whatever.

586
00:30:23,720 --> 00:30:26,400
Speaker 4: You know. The end it says that you know, water

587
00:30:26,480 --> 00:30:28,680
Speaker 4: scarcity and this then and the other thing, and you

588
00:30:28,720 --> 00:30:30,800
Speaker 4: ever see well, you guys, obviously have you've seen the

589
00:30:30,800 --> 00:30:33,120
Speaker 4: movie The Big Short? Correct? Yes, so you know how

590
00:30:33,120 --> 00:30:35,920
Speaker 4: that movie ends the last scene less?

591
00:30:35,960 --> 00:30:38,120
Speaker 5: Oh yeah, And I didn't understand it. It was like

592
00:30:38,160 --> 00:30:42,120
Speaker 5: these like title cards and they're like next Water and

593
00:30:42,120 --> 00:30:43,560
Speaker 5: then it faced them like I was like yeah, and

594
00:30:43,560 --> 00:30:45,680
Speaker 5: then he's like oh and he's buying Almond Firms and

595
00:30:45,720 --> 00:30:47,680
Speaker 5: I was like they like left it hanging. I was like, yeah,

596
00:30:47,840 --> 00:30:50,360
Speaker 5: what's the I didn't get it. That was Michael Burry

597
00:30:50,760 --> 00:30:54,320
Speaker 5: Michael and now Next Water Firms and then they fade

598
00:30:54,320 --> 00:30:56,040
Speaker 5: to black we're just ending to a movie.

599
00:30:56,040 --> 00:30:58,160
Speaker 4: Ever, so yeah, I think like when the levee breaks

600
00:30:58,240 --> 00:31:01,080
Speaker 4: is playing in the background, and you know, but it's

601
00:31:01,160 --> 00:31:04,640
Speaker 4: really dramatic, right, Michael Burry that you know, our protagonist

602
00:31:05,520 --> 00:31:10,680
Speaker 4: is only investing in one thing, ellipses water. Yeah, And

603
00:31:10,960 --> 00:31:13,320
Speaker 4: you know that's the way that this thesis has kind

604
00:31:13,320 --> 00:31:17,480
Speaker 4: of been presented for decades, where it's this kind of

605
00:31:17,520 --> 00:31:20,160
Speaker 4: apocalyptic you have. You have this idea in your head

606
00:31:20,160 --> 00:31:23,120
Speaker 4: where I mean everybody has this, and it's like an

607
00:31:23,240 --> 00:31:26,920
Speaker 4: evolutionary fear. The water has to be okay, the water

608
00:31:27,000 --> 00:31:28,320
Speaker 4: quality has to be okay, and there has to be

609
00:31:28,400 --> 00:31:31,640
Speaker 4: enough of it or else we can't do anything. So

610
00:31:32,680 --> 00:31:36,120
Speaker 4: it's always been that kind of thing. And I really

611
00:31:37,680 --> 00:31:40,640
Speaker 4: didn't want to just write about water because it's like, well,

612
00:31:40,680 --> 00:31:42,160
Speaker 4: you know, here's what's going on with water. So I

613
00:31:42,160 --> 00:31:46,000
Speaker 4: wanted to focus on the non apocalyptic parts. And I

614
00:31:46,000 --> 00:31:48,680
Speaker 4: don't want to buy anything because of pessimism. I want

615
00:31:48,680 --> 00:31:55,160
Speaker 4: to you know. And what had happened was these microplastics,

616
00:31:55,280 --> 00:31:59,720
Speaker 4: these forever chemicals. The EPA handed down a law that

617
00:32:00,040 --> 00:32:03,560
Speaker 4: mandated that you have to control the level of microplastics

618
00:32:03,600 --> 00:32:07,360
Speaker 4: serve as their you know, p fas, right, so I

619
00:32:07,400 --> 00:32:10,840
Speaker 4: spoke with people in the industry, and everyone is really

620
00:32:11,040 --> 00:32:14,480
Speaker 4: really excite. If you are in the business of you know,

621
00:32:14,600 --> 00:32:19,240
Speaker 4: remediation or you know, making sure that the water is clean,

622
00:32:19,440 --> 00:32:22,240
Speaker 4: you were really excited about this and probably still are.

623
00:32:23,880 --> 00:32:28,240
Speaker 4: But the thing about thematic investing is that uncertainty is

624
00:32:28,360 --> 00:32:32,920
Speaker 4: kind of anathema to it. Where people want to buy

625
00:32:33,040 --> 00:32:37,600
Speaker 4: on a story. You know that drugn Miller quote. I

626
00:32:37,640 --> 00:32:39,440
Speaker 4: made one hundred and twenty percent of my money on

627
00:32:39,480 --> 00:32:42,400
Speaker 4: the simple obvious ideas and lost twenty percent on everything else.

628
00:32:42,880 --> 00:32:45,920
Speaker 4: And that's kind of the way that I viewed these

629
00:32:46,000 --> 00:32:49,600
Speaker 4: themes where you want this cultural touchstone. You want everyone

630
00:32:49,640 --> 00:32:52,080
Speaker 4: to realize that, oh, this is going to be big,

631
00:32:52,360 --> 00:32:54,080
Speaker 4: this is a big deal. And whether that's you know,

632
00:32:54,240 --> 00:32:57,120
Speaker 4: in a niche area or in a wide ranging area,

633
00:32:57,200 --> 00:32:59,920
Speaker 4: that is less important. But it has to have that moment.

634
00:33:00,360 --> 00:33:03,720
Speaker 4: And when you have something that adds uncertainty, even if

635
00:33:03,760 --> 00:33:06,959
Speaker 4: you disagree. Right when it comes to what happened with

636
00:33:07,240 --> 00:33:10,920
Speaker 4: the Chevron deference, in the simplest possible terms, basically the

637
00:33:11,040 --> 00:33:19,160
Speaker 4: stated policy has been for the judiciary to defer to experts.

638
00:33:19,720 --> 00:33:23,800
Speaker 4: So if you have you know, yes, exactly, and obviously

639
00:33:23,880 --> 00:33:26,880
Speaker 4: the campus small government doesn't love this because they view

640
00:33:26,960 --> 00:33:29,520
Speaker 4: this as you know, unelected technocrats that are kind of

641
00:33:29,560 --> 00:33:33,719
Speaker 4: driving policy. And what happened with Chevron defference was for

642
00:33:33,760 --> 00:33:36,680
Speaker 4: a long time, if you have the EPA saying no,

643
00:33:36,840 --> 00:33:39,960
Speaker 4: this is bad, they would say, okay, yeah, probably you know,

644
00:33:41,120 --> 00:33:44,200
Speaker 4: you know more than I do. Now with this ruling,

645
00:33:44,360 --> 00:33:46,960
Speaker 4: that is not the case anymore, right, and that has

646
00:33:47,240 --> 00:33:51,680
Speaker 4: wide ranging impacts that I don't know because I am

647
00:33:51,720 --> 00:33:53,040
Speaker 4: not an attorney.

648
00:33:53,720 --> 00:33:56,680
Speaker 5: And so the basic gist with water here is that, okay,

649
00:33:56,680 --> 00:33:59,320
Speaker 5: there was a theme, not the apocalypse and of water,

650
00:33:59,360 --> 00:34:03,080
Speaker 5: but this assistant demand for water cleaning due to microplastics.

651
00:34:03,320 --> 00:34:04,840
Speaker 3: The EPA had put down this.

652
00:34:04,840 --> 00:34:09,160
Speaker 5: Rule, and the Chevron ruling by the Supreme Court raises

653
00:34:09,239 --> 00:34:12,560
Speaker 5: ambiguity about the teeth of that, or the enforceability or

654
00:34:12,560 --> 00:34:14,840
Speaker 5: the persistence of that. And this is the kind of

655
00:34:14,840 --> 00:34:17,040
Speaker 5: thing that's toxic to a thematic investor.

656
00:34:17,200 --> 00:34:20,560
Speaker 4: Yeah, and you know, I still believe that that thesis

657
00:34:20,560 --> 00:34:23,120
Speaker 4: is fine. I mean, I look at it, and I've

658
00:34:23,200 --> 00:34:25,880
Speaker 4: spoken to attorneys and they say, well, you have these

659
00:34:25,880 --> 00:34:29,319
Speaker 4: two existing appeals, but this happened before the Chevron deference ruling,

660
00:34:29,440 --> 00:34:32,920
Speaker 4: so you have to track those appeals. But that's the

661
00:34:32,960 --> 00:34:35,719
Speaker 4: only thing that matters. This isn't going to change. You know,

662
00:34:36,080 --> 00:34:38,839
Speaker 4: whatever was going to happen before this ruling, that's what's

663
00:34:38,840 --> 00:34:43,080
Speaker 4: going to happen now. If those appeals are you know,

664
00:34:43,120 --> 00:34:47,680
Speaker 4: overturned or upheld, that's what matters. And I think that

665
00:34:47,719 --> 00:34:50,359
Speaker 4: it's still going to work. But the thing is, I

666
00:34:50,480 --> 00:34:53,799
Speaker 4: was playing this story and I'm playing the fact that

667
00:34:53,920 --> 00:34:56,640
Speaker 4: you know, like a week after I wrote about it

668
00:34:56,640 --> 00:34:58,880
Speaker 4: started becoming this like cultural thing. You see these memes

669
00:34:58,920 --> 00:35:02,480
Speaker 4: on Twitter about like microplastics and.

670
00:35:02,760 --> 00:35:05,799
Speaker 2: In certain parts of the Male anatomy, Yeah, this goes

671
00:35:05,800 --> 00:35:08,160
Speaker 2: on the radio. So I think we can't say yeah, I.

672
00:35:08,080 --> 00:35:10,319
Speaker 4: Mean, I stop myself. You're the one that said it.

673
00:35:11,280 --> 00:35:14,239
Speaker 4: But you know, it's the idea where nobody can be

674
00:35:14,280 --> 00:35:18,440
Speaker 4: okay with the water. And there's always been an issue

675
00:35:18,480 --> 00:35:23,919
Speaker 4: of payers like to see things. They like to pay

676
00:35:23,960 --> 00:35:26,040
Speaker 4: for things that they can see, so they like to

677
00:35:26,040 --> 00:35:28,040
Speaker 4: pay for the roads being fixed, or they like to

678
00:35:28,040 --> 00:35:30,759
Speaker 4: pay for like new things being built, but you don't

679
00:35:30,760 --> 00:35:34,040
Speaker 4: really see the water. But with social media and the

680
00:35:34,080 --> 00:35:37,400
Speaker 4: idea that you can make everyone aware of like microplastics,

681
00:35:37,400 --> 00:35:41,320
Speaker 4: and there's so many emerging contaminants out there just from

682
00:35:41,800 --> 00:35:47,360
Speaker 4: you know, the on a bashed capitalists, progress and industry.

683
00:35:47,440 --> 00:35:50,839
Speaker 4: So I still think that there will be companies that

684
00:35:50,920 --> 00:35:54,600
Speaker 4: will make a lot more money because of pfas. But

685
00:35:54,640 --> 00:35:57,160
Speaker 4: for now I have to say, well, I don't know

686
00:35:57,200 --> 00:35:59,359
Speaker 4: if the risk is worth the reward here. Maybe I'll

687
00:35:59,400 --> 00:36:02,279
Speaker 4: just wait until these appeals are decided and then the

688
00:36:02,320 --> 00:36:14,160
Speaker 4: story can progress.

689
00:36:19,160 --> 00:36:20,600
Speaker 5: I want to talk a little bit more about the

690
00:36:20,640 --> 00:36:23,839
Speaker 5: AI story where we started, and so, first of all,

691
00:36:24,239 --> 00:36:26,799
Speaker 5: Druck and Miller, there were two instances in the last year.

692
00:36:26,880 --> 00:36:28,719
Speaker 5: I was just like, so he said something about like

693
00:36:29,080 --> 00:36:31,640
Speaker 5: Chad GPT came out. He knew that they were training

694
00:36:31,640 --> 00:36:33,319
Speaker 5: on in video chips and he went long in video

695
00:36:33,320 --> 00:36:35,400
Speaker 5: and killed it. And then there was the story of

696
00:36:35,520 --> 00:36:39,360
Speaker 5: Javier Mila having won in Argentina and claims that he

697
00:36:39,400 --> 00:36:43,480
Speaker 5: went on perplexity AI and should I invest with Argentina

698
00:36:43,520 --> 00:36:45,120
Speaker 5: and it gave him some ETFs and he bought them

699
00:36:45,120 --> 00:36:49,319
Speaker 5: and he did well. So much admiration for this sort

700
00:36:49,320 --> 00:36:53,440
Speaker 5: of simplistic thinking, and well done to him. But here, okay,

701
00:36:53,680 --> 00:36:56,719
Speaker 5: the sort of Chad GPT AI and video thing very

702
00:36:56,719 --> 00:37:00,640
Speaker 5: well known, and now there are all these questions about, Okay,

703
00:37:00,680 --> 00:37:04,200
Speaker 5: a lot of money is being spent on chips and electricity,

704
00:37:05,040 --> 00:37:07,480
Speaker 5: and is there going to be this payoff or will

705
00:37:07,560 --> 00:37:09,880
Speaker 5: who will get the payoff from that? How do you

706
00:37:10,080 --> 00:37:12,759
Speaker 5: think about that specific question outside of Apple and I

707
00:37:12,840 --> 00:37:16,040
Speaker 5: understand your argument about why they're in the position to

708
00:37:16,200 --> 00:37:21,480
Speaker 5: actually deliver valuable services in the broader world, businesses feeling

709
00:37:21,480 --> 00:37:23,640
Speaker 5: like they need an AI strategy or needing it to

710
00:37:23,680 --> 00:37:26,799
Speaker 5: like complement their software, how do you sort of begin

711
00:37:26,880 --> 00:37:27,680
Speaker 5: to answer that question.

712
00:37:28,200 --> 00:37:32,040
Speaker 4: Well, it seems like the question that you're asking really

713
00:37:32,080 --> 00:37:33,040
Speaker 4: is is it a bubble?

714
00:37:33,880 --> 00:37:36,719
Speaker 5: Well, there's two ways that it could be a bubble, right.

715
00:37:36,719 --> 00:37:39,040
Speaker 5: There could be people who are just overpaying for the

716
00:37:39,080 --> 00:37:41,319
Speaker 5: stock because they feel like I want to ride this wave,

717
00:37:41,719 --> 00:37:45,560
Speaker 5: or businesses overpaying for the capex because they think that

718
00:37:45,600 --> 00:37:47,680
Speaker 5: they need to do something with AI to be in

719
00:37:47,719 --> 00:37:49,520
Speaker 5: the game, and it turns out that maybe they don't.

720
00:37:49,880 --> 00:37:52,160
Speaker 5: So I'm actually kind of interested in the second one

721
00:37:52,200 --> 00:37:55,280
Speaker 5: of not whether there's an equity market bubble in AI,

722
00:37:55,640 --> 00:37:59,160
Speaker 5: but the question of whether the business investment which is

723
00:37:59,200 --> 00:38:03,920
Speaker 5: pouring into AI related things will materialize as results in

724
00:38:03,960 --> 00:38:05,200
Speaker 5: revenues for the company spend.

725
00:38:05,239 --> 00:38:07,520
Speaker 2: It's sort of the idea that Okay, everyone has to

726
00:38:07,560 --> 00:38:12,800
Speaker 2: develop their own in house AI offering to compete, but like, actually,

727
00:38:12,840 --> 00:38:15,000
Speaker 2: maybe in five years time we find that everyone just

728
00:38:15,040 --> 00:38:17,920
Speaker 2: buys an off the shelf AI thing and plugs it

729
00:38:17,960 --> 00:38:21,160
Speaker 2: in like they do Microsoft Word or whatever, rather than

730
00:38:21,360 --> 00:38:23,520
Speaker 2: spend billions like on their own thing.

731
00:38:23,880 --> 00:38:27,440
Speaker 5: And whether the sort of quality of the services that

732
00:38:27,520 --> 00:38:30,040
Speaker 5: an AIOD could produce for the companies, whether it's a

733
00:38:30,040 --> 00:38:33,760
Speaker 5: software company like Salesforce or whatever it else, actually turns

734
00:38:33,800 --> 00:38:36,279
Speaker 5: out to be something valuable that they can resell and

735
00:38:36,440 --> 00:38:38,400
Speaker 5: get capture value from their end customers.

736
00:38:39,360 --> 00:38:44,520
Speaker 4: So I think the answer to your question, or at

737
00:38:44,560 --> 00:38:48,080
Speaker 4: least some color on that question, is I don't think

738
00:38:48,080 --> 00:38:53,279
Speaker 4: that there's ever been a truly revolutionary technology that has

739
00:38:53,360 --> 00:38:56,879
Speaker 4: not been accompanied by some excess capex that we look

740
00:38:56,920 --> 00:39:00,720
Speaker 4: back and we say, well, that was wasteful, right, because

741
00:39:01,000 --> 00:39:02,960
Speaker 4: when we look back, we also have the benefit of

742
00:39:03,000 --> 00:39:06,799
Speaker 4: seeing which areas get commoditized, and we look at how

743
00:39:06,880 --> 00:39:10,400
Speaker 4: cheap you know, whether it's broadband or you know, you

744
00:39:10,440 --> 00:39:14,120
Speaker 4: can go all the way back to the railwayman roads, Yeah, yeah,

745
00:39:14,400 --> 00:39:17,080
Speaker 4: you can. You know. The thing is, you know, without

746
00:39:17,120 --> 00:39:19,960
Speaker 4: steam engines, we wouldn't have trains, and without trains we

747
00:39:20,000 --> 00:39:23,440
Speaker 4: wouldn't have planes, and without you know, and there was

748
00:39:23,520 --> 00:39:27,759
Speaker 4: this progress that was made and it catapulted forward the

749
00:39:27,840 --> 00:39:32,279
Speaker 4: human race. But that doesn't necessarily mean that they were

750
00:39:32,480 --> 00:39:36,600
Speaker 4: responsible about it, right, that they built too much, they

751
00:39:36,760 --> 00:39:41,360
Speaker 4: built too fast, they financed capital projects through equity sales

752
00:39:41,400 --> 00:39:45,920
Speaker 4: at extremely elevated prices, and you know, I mean, but

753
00:39:46,080 --> 00:39:48,680
Speaker 4: that doesn't change the fact that during the railway Mania

754
00:39:49,040 --> 00:39:52,399
Speaker 4: the total route mileage of railways in the UK went

755
00:39:52,440 --> 00:39:56,040
Speaker 4: from you know, like eighteen hundred miles in eighteen forty

756
00:39:56,080 --> 00:39:59,520
Speaker 4: three to six thousand miles in eighteen fifty, so two

757
00:39:59,640 --> 00:40:03,400
Speaker 4: hundred increase in seven years. And you know, maybe we

758
00:40:03,480 --> 00:40:07,840
Speaker 4: have to ask ourselves sometimes, do you need this CAPEX

759
00:40:07,880 --> 00:40:10,879
Speaker 4: bubble in order to progress the technology?

760
00:40:11,080 --> 00:40:11,360
Speaker 3: Sure?

761
00:40:11,920 --> 00:40:15,880
Speaker 4: And I think that yeah, there are certainly areas, and

762
00:40:16,239 --> 00:40:20,719
Speaker 4: I don't think that we'll know exactly where we say

763
00:40:20,719 --> 00:40:23,000
Speaker 4: well that was irresponsible or where we say well that

764
00:40:23,080 --> 00:40:27,120
Speaker 4: was a great business decision because you look at you know,

765
00:40:27,160 --> 00:40:29,360
Speaker 4: it obviously is intertwined. You can say, well, I care

766
00:40:29,400 --> 00:40:30,920
Speaker 4: about the CABEC side, but I don't care about the

767
00:40:30,920 --> 00:40:33,640
Speaker 4: stock market side. But you know, the companies can sell

768
00:40:33,640 --> 00:40:37,200
Speaker 4: equity and fund cabex with that. And you look at

769
00:40:37,239 --> 00:40:42,880
Speaker 4: some of the cell side estimates on electricity for example,

770
00:40:43,480 --> 00:40:48,200
Speaker 4: and I have a great screenshot or actually it might

771
00:40:48,320 --> 00:40:50,360
Speaker 4: just be an actual picture because I think this was

772
00:40:50,400 --> 00:40:53,520
Speaker 4: in nineteen ninety eight or and it's of a cell

773
00:40:53,640 --> 00:40:57,680
Speaker 4: side report that is talking about the info electric revolution

774
00:40:58,239 --> 00:41:00,719
Speaker 4: and the fact that if everyone is going to have

775
00:41:00,760 --> 00:41:03,120
Speaker 4: a personal computer, and every personal computer is going to

776
00:41:03,120 --> 00:41:05,560
Speaker 4: be connected to the internet, well, you know, that's going

777
00:41:05,600 --> 00:41:08,440
Speaker 4: to be a billion dollars or a trillion dollars of

778
00:41:08,480 --> 00:41:10,480
Speaker 4: spending on computers, and then there's going to be a

779
00:41:10,480 --> 00:41:15,040
Speaker 4: trillion dollars of spending on the electric grid, and the

780
00:41:15,080 --> 00:41:17,240
Speaker 4: electricity demand is going to grow at a thirteen percent

781
00:41:17,280 --> 00:41:18,319
Speaker 4: keger for the next two days.

782
00:41:18,360 --> 00:41:19,360
Speaker 2: It sounds very familiar.

783
00:41:19,440 --> 00:41:21,560
Speaker 4: Yeah, yeah, it sounds super familiar. And then you look

784
00:41:21,560 --> 00:41:24,360
Speaker 4: at electricity demand over the last twenty years and it's flat.

785
00:41:25,440 --> 00:41:30,160
Speaker 4: And I think that that's just a natural progression of

786
00:41:30,160 --> 00:41:33,720
Speaker 4: a new technology. That's something that almost has to happen

787
00:41:33,800 --> 00:41:36,360
Speaker 4: where you have you know, if you want to deliver

788
00:41:36,480 --> 00:41:39,920
Speaker 4: the promise in certain places, it's going to be overstated.

789
00:41:39,960 --> 00:41:42,960
Speaker 4: And do we need a better electric grid? Yeah, we

790
00:41:43,400 --> 00:41:46,840
Speaker 4: really need better electric grid. It's it's terrible, especially in

791
00:41:46,880 --> 00:41:49,799
Speaker 4: some places, it's just terrible. Maybe what we need is

792
00:41:49,920 --> 00:41:52,480
Speaker 4: for data centers to get built in areas where they

793
00:41:52,480 --> 00:41:55,879
Speaker 4: can just revise the electric grid. But I do think

794
00:41:55,920 --> 00:41:58,520
Speaker 4: that there are areas where we'll look back and say

795
00:41:59,400 --> 00:42:01,120
Speaker 4: that was arissible cap E spending.

796
00:42:02,200 --> 00:42:05,160
Speaker 2: What would you need to see to take money off

797
00:42:05,360 --> 00:42:07,560
Speaker 2: the table from some of your AI plays or have

798
00:42:07,640 --> 00:42:08,400
Speaker 2: you done that already?

799
00:42:08,480 --> 00:42:10,960
Speaker 4: I'm always taking money off the table. It's not even

800
00:42:11,000 --> 00:42:14,760
Speaker 4: a question of like, I mean, my job is to invest,

801
00:42:15,040 --> 00:42:17,799
Speaker 4: not to not invest, and it takes a lot for

802
00:42:17,880 --> 00:42:21,360
Speaker 4: me to sell something and go to cash like a lot.

803
00:42:21,760 --> 00:42:24,680
Speaker 4: You know. I'm a strong believer that there's always an

804
00:42:24,719 --> 00:42:27,640
Speaker 4: opportunity somewhere. That got put to the test in twenty

805
00:42:27,680 --> 00:42:29,960
Speaker 4: twenty two, and there were there were opportunities in planning

806
00:42:29,960 --> 00:42:32,600
Speaker 4: places on the long side of the short side. And

807
00:42:33,800 --> 00:42:38,200
Speaker 4: I think that when I make that decision, I'm not

808
00:42:38,280 --> 00:42:40,840
Speaker 4: saying do I think that this has reached a peak.

809
00:42:41,200 --> 00:42:43,640
Speaker 4: I'm saying, do I think that there are better opportunities elsewhere?

810
00:42:44,040 --> 00:42:48,799
Speaker 4: And sometimes that is just the natural progression of some

811
00:42:49,120 --> 00:42:52,200
Speaker 4: a narrative in the market or just real things happening.

812
00:42:52,520 --> 00:42:55,960
Speaker 4: I mean, in Nvidia has pretty much priced in the

813
00:42:56,000 --> 00:42:58,680
Speaker 4: idea that we need to build out these data centers

814
00:42:59,120 --> 00:43:00,959
Speaker 4: and now we have to say, Okay, well what else

815
00:43:01,120 --> 00:43:04,239
Speaker 4: are we gonna do? And you know, I mean, it

816
00:43:04,280 --> 00:43:06,560
Speaker 4: doesn't matter if I'm a believer or non believer. It

817
00:43:06,600 --> 00:43:09,759
Speaker 4: only matters if the market is all right.

818
00:43:10,160 --> 00:43:13,080
Speaker 2: James Vanguila and from Succrini Research, thank you so much

819
00:43:13,160 --> 00:43:14,320
Speaker 2: for coming back on all lots.

820
00:43:14,560 --> 00:43:14,880
Speaker 4: Thank you.

821
00:43:14,960 --> 00:43:30,320
Speaker 2: That's fun. Yeah, Joe, I enjoyed that conversation. I like

822
00:43:30,480 --> 00:43:32,560
Speaker 2: just thinking through investment ideas.

823
00:43:32,680 --> 00:43:35,120
Speaker 5: No, I like that one, just getting to like think

824
00:43:35,560 --> 00:43:39,319
Speaker 5: about you know. So, actually what really struck me was

825
00:43:39,960 --> 00:43:41,279
Speaker 5: his comment actually in.

826
00:43:41,200 --> 00:43:42,360
Speaker 3: The water part of it.

827
00:43:42,560 --> 00:43:45,200
Speaker 5: Yeah, well, what becomes the fly and the ointment of

828
00:43:45,400 --> 00:43:48,480
Speaker 5: a thesis? And why when does it become like, Okay,

829
00:43:48,480 --> 00:43:51,840
Speaker 5: here's a theme, but it's just not going to take hold,

830
00:43:52,000 --> 00:43:55,920
Speaker 5: yes hard enough, so to speak, such that it becomes

831
00:43:55,920 --> 00:43:58,399
Speaker 5: a real investing theme. And so the idea is like, yeah,

832
00:43:58,400 --> 00:44:00,760
Speaker 5: there probably is going to be a lot more demand

833
00:44:01,280 --> 00:44:04,319
Speaker 5: for filtration in the future to clean the water. But

834
00:44:04,360 --> 00:44:07,160
Speaker 5: with a little bit of a regulatory ambiguity now in

835
00:44:07,200 --> 00:44:10,440
Speaker 5: the wake of the Chevron decision, then suddenly like this

836
00:44:10,560 --> 00:44:13,080
Speaker 5: is just not going to take hold the way it

837
00:44:13,200 --> 00:44:15,920
Speaker 5: might have in a different decision, and therefore it's not

838
00:44:16,000 --> 00:44:18,920
Speaker 5: going to be quite as powerful as an investing theme is,

839
00:44:18,960 --> 00:44:19,520
Speaker 5: Like it.

840
00:44:19,400 --> 00:44:20,160
Speaker 3: Was very useful.

841
00:44:20,360 --> 00:44:20,520
Speaker 1: Well.

842
00:44:20,560 --> 00:44:24,520
Speaker 2: I thought the framing of price and story mattering to

843
00:44:24,640 --> 00:44:28,640
Speaker 2: thematic investment was important because there is a situation where

844
00:44:28,680 --> 00:44:31,000
Speaker 2: you can be a little bit too smart and you

845
00:44:31,080 --> 00:44:34,720
Speaker 2: see something that no one else possibly sees, and then

846
00:44:35,160 --> 00:44:39,080
Speaker 2: you know that something just never gets reflected in the price,

847
00:44:39,280 --> 00:44:43,600
Speaker 2: like there happened instance of that, instances of that throughout history,

848
00:44:44,080 --> 00:44:47,040
Speaker 2: and so you really need both the I guess the

849
00:44:47,160 --> 00:44:50,000
Speaker 2: underpricing or you know, at least the price to be

850
00:44:50,120 --> 00:44:54,680
Speaker 2: like relatively attractive, plus the momentum provided by that story

851
00:44:55,200 --> 00:44:58,279
Speaker 2: to make really compelling investment. And then if you get

852
00:44:58,320 --> 00:45:01,040
Speaker 2: something like in video where you know, at least up

853
00:45:01,080 --> 00:45:04,520
Speaker 2: until maybe the beginning of last year, it was underpriced

854
00:45:04,520 --> 00:45:07,200
Speaker 2: certainly relative to where it is right now, plus all

855
00:45:07,239 --> 00:45:10,120
Speaker 2: that attention from investors, then you got this massive winner.

856
00:45:10,640 --> 00:45:13,960
Speaker 5: I'm so impressed by those drunken Miller comments where he's like, oh, yeah,

857
00:45:14,000 --> 00:45:17,239
Speaker 5: they used in video chips for chat GBTAI seems like

858
00:45:17,280 --> 00:45:17,640
Speaker 5: a big deal.

859
00:45:17,680 --> 00:45:21,080
Speaker 3: I'm gonna go along, and whereas me, I'm like, oh,

860
00:45:21,080 --> 00:45:21,640
Speaker 3: they already know.

861
00:45:21,760 --> 00:45:24,200
Speaker 5: Everyone knew that they were like using in video chips

862
00:45:24,239 --> 00:45:25,960
Speaker 5: so it's all priced into the market.

863
00:45:26,040 --> 00:45:28,200
Speaker 2: But that's why there are a lot of people saying,

864
00:45:28,239 --> 00:45:30,920
Speaker 2: like a year ago, that it was all priced in.

865
00:45:31,239 --> 00:45:31,439
Speaker 4: Right.

866
00:45:32,040 --> 00:45:35,479
Speaker 2: Yeah, I hadn't seen that five below chart either.

867
00:45:35,640 --> 00:45:38,480
Speaker 5: That's no, so I hadn't looked at the chart in

868
00:45:38,560 --> 00:45:41,080
Speaker 5: a while. But it really look if you.

869
00:45:41,000 --> 00:45:44,280
Speaker 2: Have a one hundred percent over two hundred to like one hundred,

870
00:45:44,400 --> 00:45:46,439
Speaker 2: if you a few months, lety.

871
00:45:46,440 --> 00:45:48,319
Speaker 5: Just put that there is not much you could sell

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00:45:48,360 --> 00:45:51,520
Speaker 5: for five dollars in below in the world, in a

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00:45:51,560 --> 00:45:53,239
Speaker 5: world of one hundred percent tariffs.

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00:45:53,480 --> 00:45:56,200
Speaker 2: Yes, I'm sure there's a pun you could do then

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00:45:56,239 --> 00:46:00,080
Speaker 2: about the stock price being below. Oh yeah something, But okay,

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00:46:00,239 --> 00:46:01,000
Speaker 2: shall we leave it there.

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00:46:01,040 --> 00:46:01,680
Speaker 3: Let's leave it there.

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00:46:01,800 --> 00:46:04,560
Speaker 2: This has been another episode of the All Thoughts podcast.

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00:46:04,640 --> 00:46:07,920
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.

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00:46:07,760 --> 00:46:10,400
Speaker 5: And I'm Jill Wisenthal. You can follow me at the Stalwart.

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Speaker 5: producer Moses Ondam for more Odd Lots content. Go to

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