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Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.

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Speaker 1: I'm Joe, Wasn'tal, and I'm Tracy all Away. So, Tracy,

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Speaker 1: here's something that I never thought I would see again.

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Speaker 1: So I first started following markets in the late nineties, um,

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Speaker 1: you know, dot Com era, and something that I never

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Speaker 1: thought I would see again in my career after that

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Speaker 1: ended was the superstar fund manager. Okay, why is that? Well,

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Speaker 1: actually that's a totally true. What I mean is more

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Speaker 1: the superstar stock picker. Because of course, back in the

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Speaker 1: old days there were a lot of like star stock

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Speaker 1: stock pickers, fund managers, you know, Peter Lynch comes to mind,

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Speaker 1: some of the other tech investors back then. But these days,

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Speaker 1: with e t s, with online brokerages that make it

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Speaker 1: really easy for individuals to buy stocks on their own,

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Speaker 1: it really sort of seemed to me like that era

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Speaker 1: was just gone. Right, So I suppose there was this

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Speaker 1: idea that the time of stock picking has come and gone,

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Speaker 1: and that if you want to make good returns in

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Speaker 1: the market, you should just pour all your money into

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Speaker 1: something like an smp F t F like a these

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Speaker 1: sacks or something like that, and just stick with it

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Speaker 1: and don't bother trying to outperform the market, because over

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Speaker 1: a longer period of time, even the best stock pickers

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Speaker 1: UH had eventually underperformed. Right. I think this mantra of

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Speaker 1: don't try to pick stocks a if you try to

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Speaker 1: pick stocks, you're probably gonna underperform the index, and be

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Speaker 1: if you come across a mutual fund or a fund

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Speaker 1: manager who's good at picking stocks, oh it's probably just luck.

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Speaker 1: It's not gonna last to you know. Even if even

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Speaker 1: if there is someone who can beat the market, how

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Speaker 1: are you going to know whether it's actually worth putting

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Speaker 1: her money with them? And so like this idea that

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Speaker 1: everyone should just index, um they're trying to beat the

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Speaker 1: market is kind of a loser's proposition. It's really been

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Speaker 1: drilled into people's heads, and I think, like, you know,

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Speaker 1: for years, they're really we just haven't had a sort

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Speaker 1: of another a new Peter Lynch or Buffet. You know,

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Speaker 1: there's like Stark Quantz, maybe some bond fund managers who

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Speaker 1: are known, but the idea of like someone who's just

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Speaker 1: really associated with a great track record of picking individual

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Speaker 1: stocks hasn't been a thing for a while. And yet

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Speaker 1: and yet a star stockpicker emerges over the horizon. Yeah,

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Speaker 1: exactly right, So obviously that really Uh, that's for the

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Speaker 1: first time in a long time, there is currently a

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Speaker 1: fund manager, a stock picker who is a mass an

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Speaker 1: incredible track record, an incredible following, And of course we're

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Speaker 1: talking about Cathy Wood. She is the CEO and chief

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Speaker 1: investment officer of ARC invest and there is this a

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Speaker 1: total fascination with ARC and this family of actively traded

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Speaker 1: E t f s that have just done a phenomenally

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Speaker 1: well in terms of returns, but also attracted an extraordinary

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Speaker 1: amount of investor cash in the last couple of years. Right,

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Speaker 1: So the ARK E t f s, I mean, I'm

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Speaker 1: looking at their performance. They have, you know, five different

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Speaker 1: thematic portfolios alone that have basically doubled over the past year,

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Speaker 1: which is pretty amazing if you think about it. Uh,

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Speaker 1: it's amazing enough for just one stock to double in

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Speaker 1: price like that and in just the space of twelve months,

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Speaker 1: but to do it across multiple ETFs is really remarkable.

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Speaker 1: And I think within their actual portfolio there's a tiny,

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Speaker 1: tiny number of stocks that haven't risen recently, and I'm

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Speaker 1: not even sure there are any actually, Um, it's a

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Speaker 1: really amazing performance. It's really true to Actually I'm looking

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Speaker 1: at at the end of their performance of a r

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Speaker 1: k K, which is the sort of flagship innovation E

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Speaker 1: t F that ARC has was up a hundred and

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Speaker 1: fifty two for the year. Extraordinary returns. And if you

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Speaker 1: look at the holdings, they're just all of the companies

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Speaker 1: that have absolutely killed it in the recent environment. Tesla

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Speaker 1: is the biggest one, but other names Square the Payments Company, Phenomenal, Roku,

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Speaker 1: huge winner, Zillo, Spotify, tell Doc, which of course had

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Speaker 1: an incredible year thanks to the rise of remote medicine

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Speaker 1: and so forth. So it is a just extraordinary number

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Speaker 1: of winners that this uh, this is a fund and

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Speaker 1: the related funds, there's a related fund for finance and

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Speaker 1: medicine that have that they've brought it just the track

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Speaker 1: records incredible. If if anyone follows Eric Bolkunis, who's sort

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Speaker 1: of Bloomberg intelligence is E t F analysts, I feel

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Speaker 1: like three quarters of his tweets these days are just

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Speaker 1: about how extraordinary this family of funds and the performance

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Speaker 1: of ARC invest has been lately. Yeah. Absolutely, and uh,

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Speaker 1: you mentioned Kathy would already, but it's sort of it's

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Speaker 1: given rise to occults around her. I guess I don't

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Speaker 1: want to say cult because that has negative connotations, but

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Speaker 1: certainly there's been a lot of admiration and fascination with

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Speaker 1: what she's been doing over at ARC. Yeah, and uh,

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Speaker 1: we're recording this January twenty. I saw Erica bell Kuna's

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Speaker 1: tweet just today that ARC has taken in is the

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Speaker 1: third most popular fun family right now in terms of

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Speaker 1: new money coming in so far, you're to date that

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Speaker 1: that exceeds the money coming into Black Rocks. I share

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Speaker 1: his family, which is much bigger. I mean that's like,

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Speaker 1: that's like the name brand. That's like basically the Coca

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Speaker 1: Cola of e t F So to have a sort

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Speaker 1: of small boutique fund firm with a few deply traded

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Speaker 1: fund pulling in more than I shares, it's just it's

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Speaker 1: staggering stuff. Yeah. Absolutely, So we are going to be

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Speaker 1: talking to someone from Ark today, right we are. So

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Speaker 1: the question is how do they do it? How do

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Speaker 1: they find how do they pick stocks? I mean, Tesla

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Speaker 1: is obviously this huge winner, but it wouldn't have been

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Speaker 1: a huge winner for them unless they had been in

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Speaker 1: it for a lot longer than most people. So the

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Speaker 1: question is how do they how do they find and

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Speaker 1: pick great stocks that trouts the market? Everyone would like

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Speaker 1: to know. Well, I'm also I'm also interested in how

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Speaker 1: they deal with inflows as they get bigger, and whether

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Speaker 1: or not that makes it harder to have a sort

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Speaker 1: of active E t F that is focused on stock picking.

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Speaker 1: So this is going to be a really interesting conversation

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Speaker 1: I can tell. Yeah, I'm super excited about this one.

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Speaker 1: So we're going to be speaking with Brett Winton. He

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Speaker 1: is the director of research at arc Um. He's been

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Speaker 1: with the company since its founding in Previously to that,

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Speaker 1: he worked with Kathy Wood at Alliance Bernstein. They've worked

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Speaker 1: together since two thousand seven. So with any luck, we're

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Speaker 1: going to learn at least some of the secrets of

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Speaker 1: um arc from Brett and how they do it. Although

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Speaker 1: I should say, you know, to some extent, maybe it's

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Speaker 1: not a secret because part of what they do is

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Speaker 1: their research is very open, it's very transparent. They post models.

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Speaker 1: So we're gonna we're gonna really learn, hopefully how it

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Speaker 1: all works up. Brett, thank you very much for joining us,

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Speaker 1: Thank you for having me. Happy to be here. So

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Speaker 1: you've worked with Kathy previously at Alliance Bernstein since two

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Speaker 1: thousand seven with ar Sin. Why do you sort of

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Speaker 1: compare and contrast big picture, and then we'll get into

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Speaker 1: details what the research process looks like at a sort

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Speaker 1: of traditional asset management firm versus the sort of open,

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Speaker 1: transparent research approach you take it our. I think it's

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Speaker 1: interesting and that I was over hearing your intro and

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Speaker 1: you were talking about stock picking, and I was hearing that,

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Speaker 1: and and I don't think of what we do as

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Speaker 1: stock picking at least at its inception level. So we

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Speaker 1: really look at the technology level first. And so we

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Speaker 1: specifically seek to identify disruptive technologies that basically technology platforms

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Speaker 1: that future historians will look back upon and say, oh

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Speaker 1: my gosh, that was a signpost technology that was as

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Speaker 1: big as the computer, that was as big as electrification. Uh.

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Speaker 1: And and there's an established criteria for identifying these technologies

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Speaker 1: for it's called general purpose technology theory. But they all

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Speaker 1: follow steep cost of clients, they all cut across sectors,

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Speaker 1: and they all themselves or platforms of innovation. And so

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Speaker 1: that actually matches that we're investing in those kinds of

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Speaker 1: technologies matches with three critical weaknesses that I see in

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Speaker 1: traditional fund management that create inefficiencies pricing inefficiencies that we

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Speaker 1: seek to exploit. So the technology fall follow steep cost

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Speaker 1: of clients, that the drama of those cost of declines

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Speaker 1: don't manifest over the next three or six months, So

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Speaker 1: it's it actually can look very linear over a short

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Speaker 1: time horizon, and so it doesn't really impact UH. An

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Speaker 1: understanding of that cost de client doesn't impact the way

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Speaker 1: analysts model the company on the cell side over the

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Speaker 1: next quarter or two. But if you take a step

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Speaker 1: back and you have an intentionally longer term point of view,

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Speaker 1: you can actually UH come to radically different conclusions about

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Speaker 1: what the future state of the world is likely to

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Speaker 1: look like relative to others, just by having an understanding

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Speaker 1: of of the mechanics of how a cost decline occurs

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Speaker 1: and then what the demand elasticity of that price difference

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Speaker 1: is going to be. So that's like from the beginning,

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Speaker 1: we set ourselves up to say, hey, we're not gonna

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Speaker 1: we're not gonna try to trade stocks or identify securities

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Speaker 1: that are mispriced on the basis of priced earnings or

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Speaker 1: price to sales or any kind of shorthand for valuation,

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Speaker 1: and we're not Also, we're also not going to try

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Speaker 1: to do a full um DCF because a full discounted

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Speaker 1: cash flow model, because then you get to cheat with

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Speaker 1: how you use the discount rate in your terminal rate

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Speaker 1: of growth. Instead, we're gonna say, if we own one

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Speaker 1: of these companies, uh five years from now, if we're

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Speaker 1: then forced to sell it to a technological pessimist, what

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Speaker 1: will that person be forced to pay given the cash

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Speaker 1: flow generation of the business at that time. And so,

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Speaker 1: just by underwriting the positions over a five year perspective,

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Speaker 1: we've been able to and still are able to identify

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Speaker 1: really radically under priced securities. Uh So, I think of

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Speaker 1: it as where value investors and intangible assets. Intangible assets

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Speaker 1: are very difficult to understand how much cash flow they

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Speaker 1: can can generate, but we really do the work of

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Speaker 1: trying to figure that out over the time horizon. That

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Speaker 1: matters in uh in the part of the capital structure

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Speaker 1: that we're in equities or infinite and duration, it's really

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Speaker 1: kind of dumb to underwrite them over a year or

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Speaker 1: two because the market volatility is you know, really high,

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Speaker 1: Like I can't tell you what the next twelve months

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Speaker 1: of equities is going to look like I can actually say,

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Speaker 1: with reasonable assurance over five years, this position is under priced. Uh.

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Speaker 1: And so that's like the first major inefficiency we exploit.

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Speaker 1: And at the technology level, what that means is, so

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Speaker 1: take Tesla, which is a position that everybody is well

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Speaker 1: aware of because we have a perspective on what the

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Speaker 1: cost declines of batteries is going to do. It allows

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Speaker 1: us to demonstrate to our satisfaction that we think by

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Speaker 1: there will be electric vehicles that are sticker priced comparable

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Speaker 1: to internal combustion engine vehicles and the average internal combustion

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Speaker 1: engine vehicle. So you'll walk into a dealership and say,

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Speaker 1: do I want a Toyota camera that you know it

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Speaker 1: costs me more over time it I have to take

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Speaker 1: it to the dealership more often because it breaks down

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Speaker 1: more often, and it costs me basically the same amount

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Speaker 1: of money out of pocket. Or do I want the

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Speaker 1: down market equivalent of a Model three, which is faster

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Speaker 1: off the line, cost me less money over time, and

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Speaker 1: it costs me less money today. It would be a

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Speaker 1: real surprise if people didn't shift over to buying electric vehicles.

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Speaker 1: So with that as your initial perspective, you can say, hey,

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Speaker 1: so if we are at we think they're going to

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Speaker 1: be forty million electric vehicles sold by If if we're

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Speaker 1: at forty million and the rest of the market, consultants, etcetera,

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Speaker 1: or something like seven million, well there's probably some inefficiently

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Speaker 1: priced assets exposed to that technology. So by identifying by

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Speaker 1: underwriting over a time horizon, that's that's reasonable, we believe.

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Speaker 1: And by identifying basically fertile terrain where technologies are misunderstood,

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Speaker 1: then we can basically concentrate our exposure into equities that

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Speaker 1: are more likely to be mispriced. So that's the I

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Speaker 1: just talked a lot, But that's the first of three

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Speaker 1: inefficiencies that we exploit. So I wanted to back up

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Speaker 1: for a second because one thing, one thing I wonder

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Speaker 1: a lot about in tech investing is what's the firm's

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Speaker 1: collective ground here? Does it come primarily from finance or

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Speaker 1: is there a lot of technological expertise? And what I

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Speaker 1: mean by that is you mentioned that you're looking at

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Speaker 1: technologies across a long term time horizon. Some of your

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Speaker 1: E t F s are very very technical. I know

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Speaker 1: you have a genomic revolution E t F for instance,

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Speaker 1: and I think you're looking at space exploration as well.

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Speaker 1: So how do you build up the tech expertise in

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Speaker 1: order to be confident in your calls on what's going

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Speaker 1: to work out in the long term. Well, that actually

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Speaker 1: leads right into the second inefficiency that I see us exploiting.

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Speaker 1: Because all of these technologies are a cross sector technologies,

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Speaker 1: they actually kind of cut across the skis of traditional

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Speaker 1: sector based analysts. Uh. You know, the auto analyst um

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Speaker 1: had been told for years and years and years by

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Speaker 1: the Tier one suppliers and by the automotive companies that

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Speaker 1: electrically goals were actually not a meaningful technology. Not only

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Speaker 1: that they're a niche technology, not only that the people

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Speaker 1: trying to do it, we're crazy and we're probably going

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Speaker 1: to bankrupt, and we'll be able to get the assets

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Speaker 1: when we need them and we can invest in it

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Speaker 1: later on if required. Uh and so and and that

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Speaker 1: auto analysts who's sitting and basically that echo chamber of

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Speaker 1: information with the quarterly calls with the CFO, the annual

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Speaker 1: calls with the CEO, who has spent his and in

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Speaker 1: this case it's always a heat in autos his entire

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Speaker 1: career basically being like, well, now Ford's better than Nissan,

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Speaker 1: and now GM is better than Ford. UH has has

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Speaker 1: developed a pattern of thinking that basically doesn't allow him

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Speaker 1: to UH really understand whether or not an electric vehicle

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Speaker 1: will be competitive with these technology with the existing legacy technology,

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Speaker 1: or or given him a good tool set by which

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Speaker 1: to assess whether or not it will UH. And and

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Speaker 1: so when I was at Alliance Bernstein Alumina, which as

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Speaker 1: a genomics company is UH, you know, nineties plus percent

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Speaker 1: of genome sequence in the world go through illumino boxes.

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Speaker 1: At Aligned Ferns Seins, it was covered by our industrials analysts,

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Speaker 1: and you know, he didn't know what to do with it.

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Speaker 1: He almost would have had to take on a whole

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Speaker 1: new like set of learnings in order to successfully cover

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Speaker 1: this company that to him looked extremely high priced. It

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Speaker 1: was not like Dana Her or Honeywell to him. And

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Speaker 1: so he was perpetually a neutral. So the way that

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Speaker 1: we approach kind of technology and markets is our analysts

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Speaker 1: are assigned by technology rather than by sector. UH. And

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Speaker 1: so it helps that our batteries analyst is able to Sam,

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Speaker 1: who's great. He does the cost decline work on batteries.

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Speaker 1: He understands how it's going to feed into certainly the

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Speaker 1: electric vehicle industry, but he also can look at kind

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Speaker 1: of the energy storage within the utility space think about

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Speaker 1: how that impacts kind of the propensity for utility spend.

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Speaker 1: He can understand how it's gonna impact aerial drones and

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Speaker 1: their ability to both delivered parcels from place to place

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Speaker 1: or or deliver people from place to place. By selecting

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Speaker 1: for people that are expert in the technology, we believe

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Speaker 1: we get an edge against basically sector experts who who

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Speaker 1: are used to a kind of competitive landscape that that

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Speaker 1: doesn't get dramatically upturned. Another example is in in banks,

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Speaker 1: like I'm sure many bank analysts kind of poo pooed

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Speaker 1: and overlooked Square. Well, our view is that you know

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Speaker 1: you you have a digital bank branch in your pocket,

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Speaker 1: and most of certainly the US is going to begin

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Speaker 1: banking in that way over the next five years. And

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Speaker 1: uh Square is a choir and customers through its peer

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Speaker 1: to peer transfer app, the cash app at something like

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Speaker 1: twenty dollars per customer. Traditional banks pay upwards of a

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Speaker 1: thousand dollars per customer account. The products that that Square

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Speaker 1: is gonna offer is going to expand, and that retail

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Speaker 1: bank branch infrastructure is going to depreciate much more rapidly

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Speaker 1: than any of the executive are willing to acknowledge or

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Speaker 1: or maybe even understand. Uh and so a traditional banks

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Speaker 1: analyst is not necessarily even empowered to turn around and

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Speaker 1: suggest Square to his portfolio managers. It might be off

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Speaker 1: limits to him. Uh and and and so kind of

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Speaker 1: by focusing on the technologies that matter, with the analysts

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Speaker 1: focused directly on those technologies, actually unlocks a lot of

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Speaker 1: other sort of like misunderstood opportunities. Companies that fall through

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Speaker 1: the cracks and UM and sectors that are really right

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Speaker 1: for getting disrupted U that have people who have built

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Speaker 1: their whole careers on understanding that the structure of the

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Speaker 1: sector as it currently exists rather than as it likely

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Speaker 1: is to exist. So this is this is really interesting,

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Speaker 1: this sort of this sort of structural problem in stock

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Speaker 1: identification as a result of people being bucketed into traditional

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Speaker 1: UM sectors rather than starting at the technology technology level.

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Speaker 1: You know, you, I think that's two inefficiencies. What is

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Speaker 1: the third inefficiency that you seek to exploit? Uh, that

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Speaker 1: you see within the sort of traditional investment selection approach, Well,

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Speaker 1: you alluded to it, Joe, but we, Uh. All of

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Speaker 1: these technologies are themselves platforms a top which other innovations

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Speaker 1: are going to be built, and so it's really easy

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Speaker 1: to suffer from a failure of the imagination. You really

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Speaker 1: can't like. So some people try to model these technologies.

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Speaker 1: They say, these are the three areas where it's selling today,

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Speaker 1: and we're just going to drag out the growth rate,

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Speaker 1: and that's going to be the size of the market.

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Speaker 1: You can't pre imagine all the things that are going

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Speaker 1: to happen on top of it. And so the only

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Speaker 1: hope to begin to understand the potential scope and breadth,

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Speaker 1: the areas where it will apply apply and where it

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Speaker 1: won't is to um expand your information footprint, to be

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Speaker 1: totally transparent about what you believe is going to happen.

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Speaker 1: And so we publish blogs, we publish white papers, we

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Speaker 1: talk on podcasts. We have our own podcast, it's f

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Speaker 1: y I for your innovation. You should listen to it, uh,

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Speaker 1: And we do that because when we produce this information,

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Speaker 1: when we're transparent with our forecasts, that information attracts other information.

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Speaker 1: People come back at us and say, I don't understand

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Speaker 1: how you got to that conclusion you're wrong about solid

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Speaker 1: state batteries. They really seek to combat us to to

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Speaker 1: argue from first principles whether or not we're right and

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Speaker 1: why and and so this helps us to understand both

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Speaker 1: the limitations are of our ability to tell what's going

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Speaker 1: to happen in the future and to get a sense

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Speaker 1: for some things that we may not have imagined that

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Speaker 1: we should be underwriting in to our fund mental models.

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Speaker 1: Uh and so and that's very different. You know, just

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Speaker 1: being able to access Twitter uh is not something that

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Speaker 1: was is allowed in a lot of fun management shops.

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Speaker 1: That seems crazy to me. Our analysts are on Twitter,

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Speaker 1: they're interacting with the community. You know, this is the

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Speaker 1: way you understand how the world is going to work.

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Speaker 1: We believe that it gives us a competitive edge that

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Speaker 1: from the beginning we've designed ourselves to be able to

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Speaker 1: compliantly do that. Uh and and to kind of operate

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Speaker 1: in the technology circles where these technologies are actually being

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Speaker 1: grappled with and built and um and deployed into the market.

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Speaker 1: So I wonder if you could bring a lot of

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Speaker 1: the discussion that we've been having and sort of try

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Speaker 1: to solidify it with a single stock example. I wanted

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Speaker 1: to talk about Tesla because, of course, Kathy made a

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Speaker 1: pretty famous call a couple of years ago. I think

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Speaker 1: it was for Tesla's stock to go to four thousand dollars,

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Speaker 1: and it has since hit that on a split adjusted basis,

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Speaker 1: and you have a new price target on it. What

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Speaker 1: is it that your methodology and your organizational structure was

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Speaker 1: seeing about Tesla in particular that others aren't seeing. There

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Speaker 1: was a lot of criticism and incredulity about that call

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Speaker 1: when you made it a couple of years ago. So

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Speaker 1: what was it that you saw? For one thing? So

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Speaker 1: we have a cost A client on lithium I AM batteries.

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Speaker 1: We think we have a better understanding of what goes

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Speaker 1: into an electric vehicle than probably probably any other shop

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Speaker 1: on the street. I don't know, certainly anybody than anybody

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Speaker 1: publishing that informs both our top line forecasts I alluded

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Speaker 1: to it. We we believe forty million units will be

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Speaker 1: sold by of electric vehicles uh and and because they'll

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Speaker 1: be cheaper than traditional cars. When we first began that

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Speaker 1: top line forecasting, the EI A and and you know

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Speaker 1: the OPEC, these policy agencies thought that electric vehicles were

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Speaker 1: gonna sell in the two hundred three hundred thousand unit

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Speaker 1: annually through the end of their forecast through. So first

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Speaker 1: of all, you do that and you say, well, we

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Speaker 1: must be doing something wrong, like what are we misunderstanding here? Uh,

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Speaker 1: And it turns out I don't think we were misunderstanding anything.

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Speaker 1: It's just that there's not actually a great set of

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Speaker 1: incentive structures for people to make reasonable, first principles long

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Speaker 1: term forecasts. I've been surprised. I thought that we would

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Speaker 1: basically from inception, I thought we would be kind of

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Speaker 1: duplicating the work of consultants like Mackenzie Global Institute, but

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Speaker 1: we would understand the mechanics of how those forecasts were built,

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Speaker 1: and so that would give us an edge. But we

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Speaker 1: got totally different results. Sometimes sometimes we got similar results,

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Speaker 1: in which case it's very easy to say, wow, this

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Speaker 1: is kind of priced in. You know, there's probably not

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Speaker 1: much that's of interest to us here. Um. But sometimes

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Speaker 1: we got very different results. And I think it's for

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Speaker 1: consultants because they actually are paid to to cater to

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Speaker 1: the biases of the executives that hire them. But that aside. So,

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Speaker 1: first of all, we think Tesla is you know, right

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Speaker 1: now share of the electric vehicle industry forty million units.

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Speaker 1: If they maintain their share, which we think they can

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Speaker 1: scale production at that rate, then that would put them

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Speaker 1: at ten million units in um we think that the

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Speaker 1: electric vehicle industry is gonna consolidate. It's actually the products

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Speaker 1: are differentiated on a software basis, much more so than

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Speaker 1: internal combustion, So you could get to a naturally higher

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Speaker 1: margin in that industry relative to traditional automotive And so

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Speaker 1: you can do pretty simple math to to actually say

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Speaker 1: Tesla as an electric vehicle manufacturer, just leaving aside robotaxi

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Speaker 1: and vertical integration into ride hail, and the insurance product

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Speaker 1: is still a compelling position even at these valuation levels.

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Speaker 1: So it's given kind of the state of their technology

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Speaker 1: stack versus others, you could end up in a situation where,

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Speaker 1: just like Apple um, they're extracting most of the profitability

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Speaker 1: out of the industry even though they have something like

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Speaker 1: a share of the industry. Uh, And so that's possible.

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Speaker 1: From the beginning, we've always thought that Tesla was interesting

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Speaker 1: not just for the initial vehicle sale, but because of

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Speaker 1: their ability to um monetize the fleet of assets that

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Speaker 1: they have in the field. You know, you can say

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Speaker 1: that Tesla has the largest deployed fleet of robots in

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Speaker 1: the world. Uh, and that those robots improve over time.

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Speaker 1: Uh and conditional on them delivering the ability for one

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Speaker 1: of those cars to drive itself around, which is you know,

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Speaker 1: actually quite a difficult technical challenge. UM. You know, you

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Speaker 1: can imagine that those vehicles that Wall Street still believes

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Speaker 1: is uh, I sell a car for fifty thou dollars,

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Speaker 1: maybe even optimistic Wall Street thinks I get like five

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Speaker 1: thousand dollars of operating earnings off of that. If instead I,

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Speaker 1: the owner of that vehicle, am able to turn it

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Speaker 1: into a taxi, it could do a hundred thousand miles

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Speaker 1: a and might generate to me, uh, you know, twenty

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Speaker 1: dollars in operating earnings per year to to Tesla, to

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Speaker 1: me the owner. Uh, you know, additional cash flow on

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Speaker 1: top of that. And so you go from uh, a

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Speaker 1: single sale operating earnings event to every asset they've ever

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Speaker 1: sold generates cash flow for them year after year after year. Uh.

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Speaker 1: And we don't think that's a probability. In fact, we

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Speaker 1: think it's a relatively low probability. We think there's a

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Speaker 1: chance within our model that they're able to deliver robotaxi

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00:25:35,760 --> 00:25:40,040
Speaker 1: capability to the deployed vehicles in fleet. But that call

433
00:25:40,119 --> 00:25:45,000
Speaker 1: option is worth quite a bit because you have, depending

434
00:25:45,080 --> 00:25:48,480
Speaker 1: on how aggressive they are at building electric vehicle factories,

435
00:25:48,920 --> 00:25:52,600
Speaker 1: they basically get an uber like model in a natural

436
00:25:52,680 --> 00:25:56,479
Speaker 1: monopoly type position in all of the vehicles that they

437
00:25:56,520 --> 00:26:00,920
Speaker 1: have deployed. So, you know, speaking of that business, I

438
00:26:00,960 --> 00:26:03,480
Speaker 1: mean I was looking you guys. You put your models,

439
00:26:03,960 --> 00:26:06,200
Speaker 1: your financial models on geth hub, or at least some

440
00:26:06,359 --> 00:26:09,359
Speaker 1: of them, and so anyone can go to your site

441
00:26:09,440 --> 00:26:11,480
Speaker 1: and then go to your geth hub and find the

442
00:26:11,560 --> 00:26:16,640
Speaker 1: assumptions that you use to figure out the economics basically

443
00:26:16,960 --> 00:26:20,240
Speaker 1: of UH robotaxis and all and all of this and

444
00:26:20,280 --> 00:26:22,080
Speaker 1: how much is worth and how much that's gonna cost

445
00:26:22,160 --> 00:26:26,840
Speaker 1: and how compelling UH that model of car usage would

446
00:26:26,840 --> 00:26:31,720
Speaker 1: be verse traditional? Is that something like putting it out

447
00:26:31,760 --> 00:26:34,840
Speaker 1: there like that, having a open source model that anyone

448
00:26:35,040 --> 00:26:39,720
Speaker 1: can play with? Is that something that previously, you know,

449
00:26:39,880 --> 00:26:42,240
Speaker 1: your old shop, Is that something that you wanted to

450
00:26:42,359 --> 00:26:45,359
Speaker 1: do and there's just sort of like no way, you know,

451
00:26:45,440 --> 00:26:47,520
Speaker 1: that sort of openness what you're describing being able to

452
00:26:47,560 --> 00:26:50,560
Speaker 1: tweet social media argue about this stuff, get feedback. I mean,

453
00:26:50,600 --> 00:26:53,080
Speaker 1: I recall I think a couple of years ago, seeing

454
00:26:53,840 --> 00:26:56,280
Speaker 1: you or maybe your firm get into like going back

455
00:26:56,359 --> 00:26:59,280
Speaker 1: with like someone in ft Alphaville, like arguing about your

456
00:26:59,480 --> 00:27:01,720
Speaker 1: tesla mode might have been you like, is that something

457
00:27:01,840 --> 00:27:04,520
Speaker 1: that prior to art that you had wanted to do

458
00:27:04,680 --> 00:27:07,359
Speaker 1: and felt like was missing? And talk to us a

459
00:27:07,400 --> 00:27:10,560
Speaker 1: little bit more about that aspect of the of the approach,

460
00:27:12,000 --> 00:27:15,640
Speaker 1: It's always been clear to me that you get more

461
00:27:15,760 --> 00:27:20,040
Speaker 1: out of the information ecosystem by providing information into it.

462
00:27:20,560 --> 00:27:24,280
Speaker 1: I can't say that at Alliance Bernstein, I like wanted

463
00:27:24,359 --> 00:27:27,399
Speaker 1: to do that. It wasn't even within the realm of

464
00:27:27,520 --> 00:27:31,520
Speaker 1: possibility of a thing that I could have done, right,

465
00:27:31,640 --> 00:27:34,520
Speaker 1: Like think about the way in which traditional fund management,

466
00:27:34,920 --> 00:27:38,320
Speaker 1: the analysts themselves are are are buried there like not

467
00:27:38,480 --> 00:27:41,440
Speaker 1: allowed to speak on behalf of the firm in any

468
00:27:41,520 --> 00:27:45,480
Speaker 1: way like and and whereas we think that the analysts

469
00:27:45,560 --> 00:27:48,800
Speaker 1: need to be able to converse at lee discuss what

470
00:27:48,960 --> 00:27:53,119
Speaker 1: they believe, both in written form and orally, so that

471
00:27:53,600 --> 00:27:58,600
Speaker 1: they uncover their own weaknesses. Like being having to publish

472
00:27:58,680 --> 00:28:01,560
Speaker 1: to the world makes you a much better and more

473
00:28:01,680 --> 00:28:05,119
Speaker 1: diligent modeler than if you're not going to do so.

474
00:28:05,680 --> 00:28:09,040
Speaker 1: You know, if you dig into the like underpinnings of

475
00:28:09,200 --> 00:28:12,240
Speaker 1: models that aren't published, they're always a mess. They're always

476
00:28:12,280 --> 00:28:15,440
Speaker 1: poorly documented. There are always things where people have been like,

477
00:28:15,640 --> 00:28:19,119
Speaker 1: I just picked that because I wanted to write, whereas um,

478
00:28:19,560 --> 00:28:23,280
Speaker 1: both the kind of auditing process we have to go

479
00:28:23,480 --> 00:28:27,800
Speaker 1: through in order to get something ready to externalize and

480
00:28:28,080 --> 00:28:30,639
Speaker 1: the research process along the way, in which you're always

481
00:28:30,680 --> 00:28:36,520
Speaker 1: seeking to distill it to its most elemental and understandable form,

482
00:28:37,200 --> 00:28:41,280
Speaker 1: actually forces us to be better at our jobs. And

483
00:28:41,400 --> 00:28:43,680
Speaker 1: then once you publish, you get better again because you

484
00:28:43,760 --> 00:28:46,920
Speaker 1: get this great feedback loop of people telling you what

485
00:28:47,080 --> 00:28:49,640
Speaker 1: you got wrong, which at the time is kind of

486
00:28:49,760 --> 00:28:52,600
Speaker 1: like having your eyes gouged out, but at the end

487
00:28:52,680 --> 00:28:55,960
Speaker 1: of which you know you you are stronger and more

488
00:28:56,080 --> 00:28:59,400
Speaker 1: certain about the things that you were right about, and

489
00:28:59,760 --> 00:29:03,640
Speaker 1: you uncovered the critical weaknesses and how you underwrote the

490
00:29:03,720 --> 00:29:06,760
Speaker 1: business or the technology or whatever you're talking about. And

491
00:29:06,840 --> 00:29:11,680
Speaker 1: so it really has it helps us internally and externally

492
00:29:11,920 --> 00:29:15,360
Speaker 1: in the iteration cycle of trying to get closer to

493
00:29:15,440 --> 00:29:17,720
Speaker 1: what's going to be the ultimate truth of how things

494
00:29:17,840 --> 00:29:21,120
Speaker 1: play out. We have a much more open format than

495
00:29:21,800 --> 00:29:24,400
Speaker 1: other organizations that that I've been involved with, and I

496
00:29:24,440 --> 00:29:27,640
Speaker 1: think it has helped both in strategic decision making internally

497
00:29:27,800 --> 00:29:32,080
Speaker 1: and certainly in portfolio management and research. UM. I think

498
00:29:32,120 --> 00:29:36,120
Speaker 1: that the hard part has not been getting people to

499
00:29:36,800 --> 00:29:40,800
Speaker 1: to share their work, but it's really to do the

500
00:29:40,920 --> 00:29:43,960
Speaker 1: hard first principles work. In the first place, I think

501
00:29:44,000 --> 00:29:47,240
Speaker 1: there's a lot of people, particularly within the financial industry,

502
00:29:47,720 --> 00:29:54,480
Speaker 1: who are not used to having to be intellectually ambitious

503
00:29:54,680 --> 00:29:57,600
Speaker 1: and and and except that you're going to forecast something

504
00:29:57,680 --> 00:30:00,400
Speaker 1: with it with over a time prison, where it's unfair

505
00:30:00,520 --> 00:30:02,960
Speaker 1: to try to forecast it over that time prison, but

506
00:30:03,080 --> 00:30:05,880
Speaker 1: it's actually fair so long as you understand the error

507
00:30:05,960 --> 00:30:09,960
Speaker 1: bands around what you're forecasting. Uh and and so people

508
00:30:10,560 --> 00:30:15,200
Speaker 1: prefer because it's more comfortable to pay five thousand dollars

509
00:30:15,280 --> 00:30:18,280
Speaker 1: for the you know, the market's report that tells you

510
00:30:18,760 --> 00:30:20,360
Speaker 1: what the market is going to be five years from

511
00:30:20,400 --> 00:30:23,240
Speaker 1: now and be like, okay, well I'm just gonna use that. Well,

512
00:30:23,320 --> 00:30:27,000
Speaker 1: if you think about within equities and particularly high priced equities,

513
00:30:27,040 --> 00:30:29,440
Speaker 1: which is the train that we operate in, you know,

514
00:30:29,720 --> 00:30:32,160
Speaker 1: most of the value of that businesses in whatever the

515
00:30:32,280 --> 00:30:34,520
Speaker 1: terminal rate of growth you put on the DCF and

516
00:30:34,600 --> 00:30:38,080
Speaker 1: so effectively. What they're doing is they are outsourcing the

517
00:30:38,200 --> 00:30:42,080
Speaker 1: most critically sensitive part of the valuation work to an

518
00:30:42,280 --> 00:30:45,800
Speaker 1: entity that they haven't necessarily due diligence at all, and

519
00:30:45,920 --> 00:30:49,360
Speaker 1: it is not really well incentivized to create a forecast

520
00:30:49,440 --> 00:30:52,120
Speaker 1: that's correct. The hard part is not getting people to share.

521
00:30:52,240 --> 00:30:58,000
Speaker 1: It's getting people to make a reasonable first principles forecast

522
00:30:58,120 --> 00:31:02,240
Speaker 1: that is different. So it's really like the I think

523
00:31:02,360 --> 00:31:06,920
Speaker 1: forecasting within the financial industry for the most part, is

524
00:31:07,040 --> 00:31:11,560
Speaker 1: people taking other forecasts and going plus or minus from

525
00:31:11,640 --> 00:31:14,720
Speaker 1: that other forecast, right, And that's not that's just not

526
00:31:14,840 --> 00:31:17,760
Speaker 1: how we approach it, and having having a process and

527
00:31:17,840 --> 00:31:20,440
Speaker 1: a discipline of not approaching it that way, not saying

528
00:31:20,560 --> 00:31:22,360
Speaker 1: why I want to see what everybody else has done,

529
00:31:22,600 --> 00:31:24,600
Speaker 1: and then I feel better about this, so I'm gonna

530
00:31:24,600 --> 00:31:27,320
Speaker 1: go slightly higher or worse about this, so I'm gonna

531
00:31:27,360 --> 00:31:30,160
Speaker 1: go slightly lower. Instead, it's like, well, this is what

532
00:31:30,280 --> 00:31:32,440
Speaker 1: we think it's gonna be. And sometimes you do all

533
00:31:32,520 --> 00:31:34,160
Speaker 1: that work and you're like, Okay, well that's not that

534
00:31:34,280 --> 00:31:36,840
Speaker 1: interesting because it's similar to what everybody else thinks. And

535
00:31:36,960 --> 00:31:40,240
Speaker 1: sometimes it's wildly interesting because you're like, what is everybody

536
00:31:40,280 --> 00:31:42,920
Speaker 1: else thinking? And so then the process of discovery of

537
00:31:43,000 --> 00:31:46,280
Speaker 1: either what did you overlook or what are they overlooking?

538
00:31:46,920 --> 00:31:50,800
Speaker 1: That's where all of the inefficiency lies. Now. Early on

539
00:31:51,880 --> 00:31:54,480
Speaker 1: we tried to do I think we like the analysts

540
00:31:54,480 --> 00:31:56,680
Speaker 1: didn't run their own Twitter accounts. They were like they

541
00:31:56,800 --> 00:31:59,320
Speaker 1: shared them and they were more corporate, and that didn't

542
00:31:59,360 --> 00:32:02,920
Speaker 1: work very well. Like you do have to have somebody

543
00:32:03,440 --> 00:32:06,360
Speaker 1: publishing under their own name, speaking in their own voice

544
00:32:06,400 --> 00:32:09,920
Speaker 1: to a certain extent, so that they can both own

545
00:32:10,040 --> 00:32:13,680
Speaker 1: the mistakes and own the triumphs of oh I understand

546
00:32:13,760 --> 00:32:17,600
Speaker 1: this thing. Uh, and to um kind of the create

547
00:32:17,720 --> 00:32:21,080
Speaker 1: creating an environment where analysts are owning kind of that

548
00:32:21,840 --> 00:32:25,680
Speaker 1: intellectual accomplishment. And then and and the learning process and

549
00:32:25,760 --> 00:32:28,360
Speaker 1: how it filters into the learning process is a big

550
00:32:28,480 --> 00:32:31,200
Speaker 1: part of the secret sauce. And that's not available. I

551
00:32:31,240 --> 00:32:33,479
Speaker 1: mean an alliance FIRSTY and I do it a lot

552
00:32:33,560 --> 00:32:36,000
Speaker 1: of work, and then somebody else's name would go on it.

553
00:32:36,400 --> 00:32:38,320
Speaker 1: You know, that doesn't feel very good, Like, what's your

554
00:32:38,360 --> 00:32:41,040
Speaker 1: incentive structure for doing all that work? If if like

555
00:32:41,160 --> 00:32:44,720
Speaker 1: it's it's it's just published under somebody else's name. So

556
00:32:45,800 --> 00:32:47,880
Speaker 1: I think that the you know, having kind of a

557
00:32:47,960 --> 00:32:50,920
Speaker 1: real like having the analysts as close to the metal

558
00:32:51,280 --> 00:32:54,160
Speaker 1: as possible of what's going on in the world, and

559
00:32:55,080 --> 00:32:58,240
Speaker 1: having their perspective on what's going to happen in the world,

560
00:32:58,600 --> 00:33:02,720
Speaker 1: like come right up against that interface is really critical

561
00:33:02,880 --> 00:33:06,960
Speaker 1: to to just thinking about things better. Do you have

562
00:33:07,520 --> 00:33:12,640
Speaker 1: a different approach to finding and hiring analysts at ARC

563
00:33:13,160 --> 00:33:16,200
Speaker 1: then at a place like Alliance Bernstein and can a

564
00:33:16,280 --> 00:33:19,280
Speaker 1: different type of person get an interview or get their

565
00:33:19,320 --> 00:33:22,640
Speaker 1: foot in the door there? Then might say the traditional

566
00:33:23,240 --> 00:33:27,959
Speaker 1: filter at a traditional asset management company, Well, I mean

567
00:33:28,040 --> 00:33:31,400
Speaker 1: we're likely to hire someone soon who doesn't have a

568
00:33:31,440 --> 00:33:34,360
Speaker 1: college degree, so that probably gives you a sense. The

569
00:33:34,800 --> 00:33:37,040
Speaker 1: usual route is you you hire N B A S

570
00:33:37,120 --> 00:33:39,560
Speaker 1: or you you know, hire our sell side analysts you know,

571
00:33:39,680 --> 00:33:42,720
Speaker 1: and those are often very smart people. Um, but there

572
00:33:42,840 --> 00:33:46,160
Speaker 1: is I think a selection bias that happens early on

573
00:33:46,640 --> 00:33:51,920
Speaker 1: within kind of the financial ecosystem that um, you know,

574
00:33:52,400 --> 00:33:55,480
Speaker 1: cuts out some of the creativity that you need in

575
00:33:55,680 --> 00:33:59,000
Speaker 1: order to end up with the different result another like

576
00:33:59,240 --> 00:34:02,440
Speaker 1: nuanced it. I think it's interesting about our processes if

577
00:34:02,520 --> 00:34:05,720
Speaker 1: you if you imagine how do you make money in

578
00:34:06,040 --> 00:34:09,960
Speaker 1: our do well by your clients when managing equities? Well,

579
00:34:10,800 --> 00:34:14,520
Speaker 1: if you are wrong that's fine as long as you're

580
00:34:14,640 --> 00:34:19,400
Speaker 1: uniquely wrong, right, right, Like, if you're uniquely wrong, everybody

581
00:34:19,480 --> 00:34:23,400
Speaker 1: thought you were crazy anyway, and so it's not priced in.

582
00:34:24,280 --> 00:34:26,759
Speaker 1: It's when you're wrong with everybody else that you get

583
00:34:26,800 --> 00:34:30,320
Speaker 1: into trouble. And it's when you're uniquely right that you

584
00:34:30,480 --> 00:34:35,759
Speaker 1: actually you know, compound your holdings. Uh. And so if

585
00:34:35,920 --> 00:34:40,920
Speaker 1: if your forecasts were on average worse but unique, that

586
00:34:41,160 --> 00:34:43,880
Speaker 1: is better than having forecasts that are closer to the

587
00:34:43,960 --> 00:34:46,560
Speaker 1: actual truth but the same as everybody else. And so

588
00:34:47,440 --> 00:34:51,239
Speaker 1: you know, you need to have a diversity of kind

589
00:34:51,280 --> 00:34:54,920
Speaker 1: of cognitive perspective in some way relative to everybody that

590
00:34:55,000 --> 00:34:58,400
Speaker 1: you're competing against. Even if it yields kind of like

591
00:34:58,600 --> 00:35:03,960
Speaker 1: more volatile results, you you on average have more differentiated results,

592
00:35:04,040 --> 00:35:07,400
Speaker 1: which then gives you both downside protection everybody thought you

593
00:35:07,440 --> 00:35:11,800
Speaker 1: were crazy anyway and upside potential. Well nobody expected this.

594
00:35:12,880 --> 00:35:16,680
Speaker 1: So um, I think having in and that requires a

595
00:35:16,800 --> 00:35:20,719
Speaker 1: degree of um kind of hardness against some of the

596
00:35:20,800 --> 00:35:24,160
Speaker 1: social pressures that I think operate in a lot of

597
00:35:24,680 --> 00:35:28,000
Speaker 1: Wall Street. And so we don't, Yeah, we don't select

598
00:35:28,080 --> 00:35:30,680
Speaker 1: from the same pool of candidates, or at least we haven't.

599
00:35:31,360 --> 00:35:34,560
Speaker 1: We've often end up screening out kind of more traditional

600
00:35:34,680 --> 00:35:38,719
Speaker 1: financial candidates just because they don't have as idiosyncratic a

601
00:35:38,840 --> 00:35:41,280
Speaker 1: point of view. I wanted to ask you about another

602
00:35:41,400 --> 00:35:44,719
Speaker 1: specific well I don't want to say specific thing, but

603
00:35:44,960 --> 00:35:49,680
Speaker 1: another specific technology. Since you group yourselves by technology. You

604
00:35:49,840 --> 00:35:54,360
Speaker 1: have a crypto analyst who looks at blockchain and bitcoin,

605
00:35:54,600 --> 00:35:57,480
Speaker 1: and I think Cathy has been quite bullish on the

606
00:35:57,560 --> 00:36:01,479
Speaker 1: technological potential of blockchain as a whole. Could you maybe

607
00:36:01,560 --> 00:36:04,760
Speaker 1: walk us through the thinking behind that and again connect

608
00:36:04,840 --> 00:36:08,480
Speaker 1: it to your overall methodology and structure, because I think

609
00:36:08,520 --> 00:36:11,880
Speaker 1: there were quite a few cell side analysts talking about,

610
00:36:12,400 --> 00:36:15,759
Speaker 1: you know, how bitcoins a bubble, but blockchain is the

611
00:36:15,840 --> 00:36:20,799
Speaker 1: technological future. But you at ARC took a different approach

612
00:36:21,040 --> 00:36:25,759
Speaker 1: and basically said by bitcoin and by blockchain related technology.

613
00:36:25,960 --> 00:36:28,719
Speaker 1: So what was the thinking there? Yeah, I think you

614
00:36:28,800 --> 00:36:32,680
Speaker 1: can from a very high level think about, um, how

615
00:36:33,400 --> 00:36:37,120
Speaker 1: all contracts that we sign actually have this failed mode

616
00:36:37,239 --> 00:36:40,840
Speaker 1: where the political entity that enforces them sometimes just decides

617
00:36:40,920 --> 00:36:44,000
Speaker 1: not to enforce them or changes the rules. Like imagine

618
00:36:44,040 --> 00:36:47,120
Speaker 1: you've signed a contract and then suddenly you know somebody,

619
00:36:47,239 --> 00:36:50,160
Speaker 1: the other counterparty in the contract rnegs, he doesn't pay up,

620
00:36:50,400 --> 00:36:51,719
Speaker 1: and you go to the government and say, well, you

621
00:36:51,800 --> 00:36:53,680
Speaker 1: have to force this guy to pay because he didn't pay,

622
00:36:54,080 --> 00:36:56,200
Speaker 1: and the guy is actually you know, as an end

623
00:36:56,239 --> 00:36:58,719
Speaker 1: with the government. The government's like, no, thank you. Uh.

624
00:36:58,840 --> 00:37:02,280
Speaker 1: And so the pro amss of of crypto assets generally

625
00:37:02,760 --> 00:37:07,320
Speaker 1: is basically, uh that that final layer of kind of

626
00:37:07,400 --> 00:37:12,640
Speaker 1: contract settlement happens regardless of the underlying political circumstances. So

627
00:37:12,920 --> 00:37:15,440
Speaker 1: you can broaden that across. Think of like all of

628
00:37:15,520 --> 00:37:18,640
Speaker 1: the various contracts in the economy. Think of a structuring

629
00:37:18,719 --> 00:37:22,959
Speaker 1: deskin and investment bank. It's basically set up to create

630
00:37:23,040 --> 00:37:27,400
Speaker 1: complex contracts with counterparties where they are are assured that

631
00:37:27,480 --> 00:37:30,440
Speaker 1: those those contracts will actually be made good because the

632
00:37:30,520 --> 00:37:35,080
Speaker 1: counterparties are really well respected established institutions. Well, kind of

633
00:37:35,200 --> 00:37:39,520
Speaker 1: smart contracting platforms like Ethereum and others allows for an

634
00:37:39,560 --> 00:37:42,960
Speaker 1: experimentation layer where you can, you know, instead of having

635
00:37:43,000 --> 00:37:45,440
Speaker 1: to work in Morgan Stanley in order to structure those products,

636
00:37:45,520 --> 00:37:48,800
Speaker 1: you can be Joe uh, you know, coder and and

637
00:37:49,040 --> 00:37:53,120
Speaker 1: create kind of those contracts with the protocol itself serving

638
00:37:53,280 --> 00:37:56,160
Speaker 1: as the ultimate counterparty that that will see that those

639
00:37:56,560 --> 00:38:00,919
Speaker 1: contracts get executed upon. Well, currencies are all contracts. In fact,

640
00:38:00,960 --> 00:38:04,520
Speaker 1: you could argue that the most valuable contracts and there's

641
00:38:04,560 --> 00:38:07,200
Speaker 1: a social contract between me and the US government that

642
00:38:07,440 --> 00:38:10,080
Speaker 1: somehow the purchasing price of the dollar is not going

643
00:38:10,160 --> 00:38:12,640
Speaker 1: to diminish more than I guess two percent a year

644
00:38:12,719 --> 00:38:17,160
Speaker 1: or whatever their target is. Right. So bitcoin basically supplants

645
00:38:17,280 --> 00:38:21,200
Speaker 1: that social contract with with kind of its protocol for

646
00:38:21,440 --> 00:38:25,480
Speaker 1: security of the asset. So I think it's a profoundly

647
00:38:25,719 --> 00:38:30,040
Speaker 1: interesting kind of set of ideas across the entire crypto

648
00:38:30,080 --> 00:38:34,160
Speaker 1: asset space that over of all the technologies we look at,

649
00:38:34,440 --> 00:38:38,880
Speaker 1: over probably the longest adoption time will have actually the

650
00:38:39,000 --> 00:38:44,000
Speaker 1: most dramatic uh financial and technological impact. Uh. So you know,

651
00:38:44,200 --> 00:38:46,840
Speaker 1: from the beginning, we thought it was interesting. We had

652
00:38:46,880 --> 00:38:52,520
Speaker 1: a crypto a blockchain analyst in I believe is when

653
00:38:52,600 --> 00:38:56,040
Speaker 1: we hired him. Uh, And it was because even though

654
00:38:56,360 --> 00:39:01,880
Speaker 1: there were not necessarily many investable as it's we understood

655
00:39:01,920 --> 00:39:04,440
Speaker 1: that the technology at that time, we understood that the

656
00:39:04,520 --> 00:39:08,440
Speaker 1: technology was within within our product suite. We understood that

657
00:39:08,480 --> 00:39:12,360
Speaker 1: the technology was interesting enough and going to create sufficient

658
00:39:12,480 --> 00:39:16,880
Speaker 1: disruption that it was worth beginning to invest in understanding it,

659
00:39:17,080 --> 00:39:19,920
Speaker 1: Understanding where it was going to go, Understanding what the

660
00:39:20,000 --> 00:39:23,279
Speaker 1: best mechanism by which to create client exposure to it,

661
00:39:23,680 --> 00:39:26,160
Speaker 1: and so that's what we did. We have vehicles in

662
00:39:26,200 --> 00:39:29,160
Speaker 1: which we can get client exposure to crypto assets. We

663
00:39:29,640 --> 00:39:32,480
Speaker 1: you know, it was a smart, smart move, both at

664
00:39:32,520 --> 00:39:35,719
Speaker 1: the time and going forward. And I think that if

665
00:39:35,800 --> 00:39:39,000
Speaker 1: you're within the financial services industry and you're not thinking

666
00:39:39,160 --> 00:39:44,120
Speaker 1: very deeply about how digital wallets, crypto assets and and

667
00:39:44,640 --> 00:39:48,080
Speaker 1: UH neural NEETs and artificial intelligence are going to change

668
00:39:48,480 --> 00:39:51,880
Speaker 1: what you're doing over the medium term, then you're not

669
00:39:52,480 --> 00:39:56,680
Speaker 1: operating intelligently within the firm that you're operating. So I

670
00:39:56,760 --> 00:39:58,880
Speaker 1: have a follow up question. I'm going to try to

671
00:39:58,920 --> 00:40:05,800
Speaker 1: phrase this UH as diplomatically as possible. Your outperformance speaks

672
00:40:05,880 --> 00:40:09,719
Speaker 1: for itself. Your returns have been absolutely excellent in recent years.

673
00:40:09,840 --> 00:40:12,520
Speaker 1: But there are people out there who would point to

674
00:40:12,640 --> 00:40:16,520
Speaker 1: that performance and say that you've been writing a tech

675
00:40:16,600 --> 00:40:19,719
Speaker 1: bubble or you're buying into the stocks that have very

676
00:40:19,840 --> 00:40:24,239
Speaker 1: compelling narratives that seem to capture the wider imagination, but

677
00:40:24,719 --> 00:40:28,880
Speaker 1: that haven't actually been proven yet in terms of earnings.

678
00:40:29,200 --> 00:40:32,120
Speaker 1: They're just training at you know, massive valuations and getting

679
00:40:32,120 --> 00:40:35,320
Speaker 1: more expensive, but the earnings haven't actually kept up. So

680
00:40:36,120 --> 00:40:40,040
Speaker 1: what do you say to those people? Two people who

681
00:40:40,160 --> 00:40:45,400
Speaker 1: say that you're basically momentum training on enthusiasm for unproved technology, right.

682
00:40:45,719 --> 00:40:49,160
Speaker 1: I didn't phrase that very diplomatically, did I? No, that's fine,

683
00:40:49,440 --> 00:40:52,040
Speaker 1: I mean listen to. Our job is to understand what

684
00:40:52,160 --> 00:40:55,080
Speaker 1: the value of something is going to be, as we

685
00:40:55,239 --> 00:40:59,000
Speaker 1: currently phrase it, five years from now. And um. Sometimes

686
00:40:59,239 --> 00:41:02,800
Speaker 1: so fuel cells is a great example where you know,

687
00:41:02,880 --> 00:41:07,120
Speaker 1: we did our first work on fuel cells in we

688
00:41:07,200 --> 00:41:09,200
Speaker 1: did a cost declient on it. We determined that within

689
00:41:09,320 --> 00:41:12,799
Speaker 1: passenger vehicles, we don't think they are gonna be cost

690
00:41:12,880 --> 00:41:16,400
Speaker 1: competitive with electric vehicles until the early ties, and that

691
00:41:16,560 --> 00:41:19,800
Speaker 1: was contingent on something like the Toyota Marai selling in

692
00:41:19,840 --> 00:41:22,560
Speaker 1: Toyota Prius like volumes over the course of a decade.

693
00:41:22,920 --> 00:41:26,719
Speaker 1: Uh and so um. You know, having gone through that exercise,

694
00:41:27,000 --> 00:41:30,160
Speaker 1: it was very easy for us to kind of, you know,

695
00:41:30,239 --> 00:41:33,160
Speaker 1: look at that entire stack of assets and and every

696
00:41:33,200 --> 00:41:35,120
Speaker 1: time one comes up and says, oh, well, this is

697
00:41:35,160 --> 00:41:38,040
Speaker 1: what's different, we've already done the work to understand, you know,

698
00:41:38,120 --> 00:41:41,120
Speaker 1: the key cost assumptions you need to make, um and

699
00:41:41,320 --> 00:41:43,840
Speaker 1: how there's costs are declining and what that means for

700
00:41:43,880 --> 00:41:46,600
Speaker 1: the future unit economics of that technology. And then you

701
00:41:46,640 --> 00:41:49,400
Speaker 1: can say Okay, that's not something we're going to invest in. Now.

702
00:41:49,480 --> 00:41:52,200
Speaker 1: We could be wrong, right, and and we're wrong all

703
00:41:52,239 --> 00:41:56,440
Speaker 1: the time, right. But but but I think that actually

704
00:41:56,560 --> 00:42:02,640
Speaker 1: doing the work to underwrite the asset this is really difficult.

705
00:42:03,239 --> 00:42:07,080
Speaker 1: It's not easy. Uh. And so there's a difference between

706
00:42:08,200 --> 00:42:11,319
Speaker 1: I am going to invest in the blockchain iced tea

707
00:42:11,360 --> 00:42:15,040
Speaker 1: company because they say the word blockchain, and and invest

708
00:42:15,280 --> 00:42:18,439
Speaker 1: in a company that I think is under priced over

709
00:42:19,320 --> 00:42:22,359
Speaker 1: time horizon that is meaningful to my clients. I'm really

710
00:42:22,440 --> 00:42:25,000
Speaker 1: glad you brought up fuel cells because I was actually

711
00:42:25,040 --> 00:42:28,840
Speaker 1: gonna go there next. So maybe probably probably people are

712
00:42:28,840 --> 00:42:31,400
Speaker 1: aware some of the hottest stocks right now are fuel

713
00:42:31,400 --> 00:42:35,920
Speaker 1: cell stocks. Plug Power is one, fuel Cell Energy is another. One.

714
00:42:36,520 --> 00:42:40,960
Speaker 1: Major winners, and I have a personal interest in this

715
00:42:41,239 --> 00:42:44,759
Speaker 1: area because and I'm not saying this to brag or

716
00:42:44,760 --> 00:42:48,560
Speaker 1: anything like that, but I actually like traded these exact

717
00:42:48,719 --> 00:42:52,520
Speaker 1: stocks when I was in college in the late nineties

718
00:42:52,520 --> 00:42:56,359
Speaker 1: and early two thousands, the same exact stocks plug Power

719
00:42:56,400 --> 00:43:00,360
Speaker 1: and fuel Cell. They've been around forever, and I you know,

720
00:43:00,480 --> 00:43:03,680
Speaker 1: they were crazy overvalued then, but I got kind of

721
00:43:03,760 --> 00:43:06,080
Speaker 1: lucky and that helped pay for college. Anyway, The point is,

722
00:43:06,120 --> 00:43:08,319
Speaker 1: I'm not trying to brag it just anyway. My point

723
00:43:08,440 --> 00:43:10,640
Speaker 1: is at the time, it was like, Okay, this is

724
00:43:10,719 --> 00:43:13,840
Speaker 1: right around the corner and fuel cell fuel cells are

725
00:43:13,880 --> 00:43:17,400
Speaker 1: gonna be on the road by Obviously that didn't happen.

726
00:43:17,800 --> 00:43:19,920
Speaker 1: So you're saying it's not different this time, that this

727
00:43:20,120 --> 00:43:24,800
Speaker 1: is just yet another series in an extremely long history

728
00:43:25,280 --> 00:43:29,799
Speaker 1: of people getting over excited and over optimistic about this technology,

729
00:43:30,080 --> 00:43:32,960
Speaker 1: and that once again it's further off than people think.

730
00:43:33,040 --> 00:43:36,200
Speaker 1: I mean, there are niche applications where you can underwrite it.

731
00:43:36,560 --> 00:43:40,160
Speaker 1: I'm not going to disparage or endorse plug power, but

732
00:43:40,640 --> 00:43:44,600
Speaker 1: there are clearly buyers of fuel cell driven forklifts because

733
00:43:44,719 --> 00:43:48,120
Speaker 1: you can have the hydrogen right there on site. And

734
00:43:48,320 --> 00:43:52,520
Speaker 1: makes sense that to you know, our understanding of how

735
00:43:52,640 --> 00:43:56,280
Speaker 1: you would have to underwrite that asset to justify its prices.

736
00:43:56,320 --> 00:43:58,120
Speaker 1: You would have to think it's going to get into

737
00:43:58,360 --> 00:44:01,960
Speaker 1: the truck or passenger vehicle business. You would have to

738
00:44:02,040 --> 00:44:05,040
Speaker 1: think that the cost declient on the technology would carry

739
00:44:05,080 --> 00:44:09,360
Speaker 1: it into a competitive position with alternative um mode of

740
00:44:09,440 --> 00:44:14,680
Speaker 1: technologies in those domains. And like just on the electric

741
00:44:14,760 --> 00:44:18,960
Speaker 1: vehicle side, it's really hard. You have to make a

742
00:44:19,040 --> 00:44:22,680
Speaker 1: lot of assumptions about somehow the assets or the technology

743
00:44:22,760 --> 00:44:27,520
Speaker 1: being bought up to drive the costs efficiently low to

744
00:44:27,760 --> 00:44:31,560
Speaker 1: make it unit economic compelling. Uh. And so there there

745
00:44:31,640 --> 00:44:34,240
Speaker 1: you can like, you have to generate the hydrogen somewhere

746
00:44:34,360 --> 00:44:37,799
Speaker 1: first of all, so that costs money. You're operating costs

747
00:44:37,840 --> 00:44:40,600
Speaker 1: are much much higher. Uh. Then you have to fund

748
00:44:41,120 --> 00:44:44,279
Speaker 1: the build out of the hydrogen fueling infrastructure, which is

749
00:44:44,880 --> 00:44:48,080
Speaker 1: really it's not easy. It's like a hard coordination problem. Tesla.

750
00:44:48,440 --> 00:44:51,120
Speaker 1: Even from the beginning, we thought I was skeptical of

751
00:44:51,200 --> 00:44:54,759
Speaker 1: Tesla's supercharger network build out. I thought that that was

752
00:44:54,920 --> 00:44:57,680
Speaker 1: not a layer that they needed to compete in. As

753
00:44:57,719 --> 00:45:00,480
Speaker 1: it turned out, I was dead wrong. Like that definitely

754
00:45:00,600 --> 00:45:05,080
Speaker 1: differentiates their product because range doesn't become as much of

755
00:45:05,120 --> 00:45:07,640
Speaker 1: an issue in making the sale because people can imagine

756
00:45:07,920 --> 00:45:09,680
Speaker 1: doing the road trip they want to do with their

757
00:45:09,719 --> 00:45:11,960
Speaker 1: new car, which is, if you can't do the road trip,

758
00:45:12,040 --> 00:45:14,160
Speaker 1: what's the point of getting the new car? Right? Uh?

759
00:45:14,280 --> 00:45:18,279
Speaker 1: And Uh, But a supercharger costs a tenth what a

760
00:45:18,360 --> 00:45:22,560
Speaker 1: hydrogen station does to like a full supercharger station versus

761
00:45:22,640 --> 00:45:24,880
Speaker 1: a hydrogen station, So you know, you're talking on the

762
00:45:25,000 --> 00:45:27,359
Speaker 1: order of a hundred to two hundred thousand dollars versus

763
00:45:27,400 --> 00:45:30,000
Speaker 1: a million to two million. You have to do a

764
00:45:30,120 --> 00:45:33,480
Speaker 1: ton of execution, have like a ton of selling of

765
00:45:33,560 --> 00:45:35,920
Speaker 1: the technology that it's hard to see how it happens

766
00:45:35,920 --> 00:45:38,680
Speaker 1: because you can't chicken and egg the infrastructure in place

767
00:45:38,719 --> 00:45:42,880
Speaker 1: sufficient to drive demand for the underlying technology sufficient to

768
00:45:42,920 --> 00:45:45,719
Speaker 1: get it low enough in price that it's cost competitive

769
00:45:45,760 --> 00:45:50,680
Speaker 1: with existing modes of transport. Uh So is it possible, yes,

770
00:45:51,040 --> 00:45:54,719
Speaker 1: can we reasonably underwrite it? Not at this time? HM.

771
00:45:55,719 --> 00:45:58,040
Speaker 1: That was a good answer. But that's what I mean.

772
00:45:58,280 --> 00:46:02,000
Speaker 1: You know, when Facebook oculus right suddenly, all these analysts

773
00:46:02,040 --> 00:46:04,480
Speaker 1: are coming out with VR is going to be you know,

774
00:46:04,680 --> 00:46:07,920
Speaker 1: eighteen million units by ten or whatever, and and we

775
00:46:08,080 --> 00:46:09,840
Speaker 1: looked at it, we did a model on it, and

776
00:46:10,160 --> 00:46:14,799
Speaker 1: and we couldn't get enough people to buy the headsets

777
00:46:15,200 --> 00:46:18,560
Speaker 1: for a triple A game developer to justify underwriting a

778
00:46:18,719 --> 00:46:23,160
Speaker 1: game developed specific for the headsets, And so you just couldn't.

779
00:46:23,400 --> 00:46:26,200
Speaker 1: It didn't make sense, right, Like, you go through and

780
00:46:26,320 --> 00:46:29,239
Speaker 1: you try to say, what is this market going to be?

781
00:46:29,960 --> 00:46:33,600
Speaker 1: And if it doesn't, like, if the modeling that you

782
00:46:33,719 --> 00:46:36,319
Speaker 1: do doesn't make sense, then you're not going to take

783
00:46:36,400 --> 00:46:40,040
Speaker 1: aggressive positions on the basis of it making sense so

784
00:46:40,200 --> 00:46:42,520
Speaker 1: and that you know, so we never built kind of

785
00:46:42,640 --> 00:46:47,360
Speaker 1: VR heavily into our own video model because that you know,

786
00:46:47,560 --> 00:46:50,120
Speaker 1: it was a dry hole and and a lot of

787
00:46:51,040 --> 00:46:55,160
Speaker 1: I think our role is to figure out actually the

788
00:46:55,320 --> 00:46:58,000
Speaker 1: things too that you can dismiss a whole category of

789
00:46:58,640 --> 00:47:01,560
Speaker 1: by doing a single piece of work. Like that's really

790
00:47:01,680 --> 00:47:05,360
Speaker 1: important because you have you know, now there's a gazillion

791
00:47:05,480 --> 00:47:09,879
Speaker 1: SPACs coming at us right and and um, you need

792
00:47:09,960 --> 00:47:13,719
Speaker 1: to have some some lens by which you approach these

793
00:47:13,800 --> 00:47:17,640
Speaker 1: assets and and say what they are fundamentally worth. That

794
00:47:17,800 --> 00:47:21,480
Speaker 1: allows you to easily establish whether or not something is

795
00:47:21,920 --> 00:47:26,480
Speaker 1: of potential interest to portfolio management and to your clients.

796
00:47:42,360 --> 00:47:44,160
Speaker 1: I wanted to go back to something we mentioned in

797
00:47:44,200 --> 00:47:47,399
Speaker 1: the intro, which is the extraordinary inflows that we've seen

798
00:47:47,520 --> 00:47:52,120
Speaker 1: into ARC alongside the extraordinary performance that we've been discussing.

799
00:47:52,719 --> 00:47:56,080
Speaker 1: Have those inflows change the way you invest at all

800
00:47:56,400 --> 00:48:00,600
Speaker 1: or your research process? Does it perhaps become harder to

801
00:48:00,960 --> 00:48:06,520
Speaker 1: identify new opportunities? Um, the more money you have to

802
00:48:06,680 --> 00:48:11,000
Speaker 1: put into a certain company or a technology, every investment

803
00:48:11,080 --> 00:48:13,720
Speaker 1: decision that you make, you would want to make frictionlessly

804
00:48:14,000 --> 00:48:16,360
Speaker 1: at the exact size that you want at the instant

805
00:48:16,600 --> 00:48:18,600
Speaker 1: that you want to do it, and that's you know,

806
00:48:18,880 --> 00:48:22,480
Speaker 1: not possible. No matter how much you're managing, the research

807
00:48:22,600 --> 00:48:26,120
Speaker 1: process has always remained the same. We always start at

808
00:48:26,160 --> 00:48:29,799
Speaker 1: the technology level. We understand the direction that the technology

809
00:48:29,920 --> 00:48:32,320
Speaker 1: is going. Because all of the technologies that we're investing

810
00:48:32,360 --> 00:48:37,279
Speaker 1: in are are are exponential. You're actually creating a lot

811
00:48:37,360 --> 00:48:39,880
Speaker 1: more opportunity. I mean, if if you look at our

812
00:48:39,960 --> 00:48:42,600
Speaker 1: tests are open source Tesla model, you can you can

813
00:48:42,719 --> 00:48:45,759
Speaker 1: drag out, you can see what our next year needs

814
00:48:45,840 --> 00:48:48,600
Speaker 1: to be given our expectation for EV sales, and it

815
00:48:48,680 --> 00:48:52,920
Speaker 1: actually meaningfully increases your expectation for value of the company

816
00:48:53,320 --> 00:48:55,880
Speaker 1: just dragging out to the right by one year, you know.

817
00:48:56,000 --> 00:48:59,479
Speaker 1: And and kind of the spack phenomenon is creating more

818
00:49:00,080 --> 00:49:03,759
Speaker 1: publicly traded equities that we could potentially invest in within

819
00:49:03,880 --> 00:49:07,200
Speaker 1: the technology areas that we're interested in. And you know,

820
00:49:07,360 --> 00:49:11,360
Speaker 1: taking in a lot of flows uh into our assets.

821
00:49:11,440 --> 00:49:14,680
Speaker 1: Luckily within the E T F construct that's relatively easy.

822
00:49:15,160 --> 00:49:19,080
Speaker 1: Uh and uh, And we are always selective about, you know,

823
00:49:19,160 --> 00:49:21,279
Speaker 1: how we deploy and what's the most efficient way to

824
00:49:21,360 --> 00:49:25,960
Speaker 1: get exposure to the inefficiencies that we see. You know,

825
00:49:26,239 --> 00:49:30,800
Speaker 1: you mentioned the big picture UM technology families that you

826
00:49:30,880 --> 00:49:34,040
Speaker 1: start with. So you have this like top down approach

827
00:49:34,120 --> 00:49:39,520
Speaker 1: to figuring out the big areas. Genomics is one, robotics,

828
00:49:39,840 --> 00:49:42,560
Speaker 1: How did you come up with those? I mean, what

829
00:49:42,840 --> 00:49:47,160
Speaker 1: is there? Is there a methodological process for figuring out

830
00:49:47,640 --> 00:49:50,399
Speaker 1: and planning a flag on the ground and saying, Okay,

831
00:49:50,520 --> 00:49:55,160
Speaker 1: this is going to be really big. Yeah, so they

832
00:49:55,239 --> 00:49:57,880
Speaker 1: all have to uh and and so I alluded to it.

833
00:49:58,000 --> 00:50:00,680
Speaker 1: But there's this theory called general purpose technolo oology theory

834
00:50:00,920 --> 00:50:04,239
Speaker 1: where it's like these academics have agreed upon what the

835
00:50:04,360 --> 00:50:08,240
Speaker 1: criteria are for really meaningful technologies. They all have steep

836
00:50:08,280 --> 00:50:10,799
Speaker 1: cost of clients, they all cut across sectors, and they're

837
00:50:10,800 --> 00:50:14,480
Speaker 1: all themselves platforms of innovation. Uh. And so we try

838
00:50:14,560 --> 00:50:18,480
Speaker 1: to apply that framework to the technologies that we're interested in.

839
00:50:18,680 --> 00:50:22,520
Speaker 1: We think there are five fundamental technology platforms that are

840
00:50:22,560 --> 00:50:27,560
Speaker 1: all entering the economic marketplace today. Uh. Gene sequencing and editing,

841
00:50:27,880 --> 00:50:32,920
Speaker 1: AI and particularly neuron NEETs, robots, particularly collaborative robots, energy

842
00:50:33,000 --> 00:50:36,960
Speaker 1: storage and the advances and battery technology, and then blockchain cryptocurrency.

843
00:50:37,160 --> 00:50:40,359
Speaker 1: And so we believe that yet future historians will look

844
00:50:40,400 --> 00:50:45,560
Speaker 1: back and identify all of those as big technological buckets.

845
00:50:46,480 --> 00:50:50,319
Speaker 1: But you know, within taxonomys there are always weaknesses, right,

846
00:50:50,440 --> 00:50:53,440
Speaker 1: and you could draw the lines in a slightly different area.

847
00:50:53,680 --> 00:50:56,640
Speaker 1: So we tried to look back and see, like, what

848
00:50:57,040 --> 00:51:01,400
Speaker 1: technologies did historians agree upon where these general purpose technology

849
00:51:01,440 --> 00:51:06,400
Speaker 1: platforms over time? And there's not consensus at even looking backwards.

850
00:51:06,480 --> 00:51:09,480
Speaker 1: So of course there's not consensus today what are the

851
00:51:09,560 --> 00:51:13,480
Speaker 1: major technology platforms? But I think it's a so the

852
00:51:13,600 --> 00:51:17,359
Speaker 1: other um from those five technology platforms, there are also

853
00:51:17,480 --> 00:51:21,839
Speaker 1: we have fourteen underlying technologies that are discreetly model able,

854
00:51:22,040 --> 00:51:24,040
Speaker 1: where we have a good understanding of the cost a

855
00:51:24,120 --> 00:51:26,960
Speaker 1: client of kind of how it cuts across sectors, and

856
00:51:27,200 --> 00:51:31,080
Speaker 1: and the equity market capit cruel that we expect those

857
00:51:31,120 --> 00:51:35,160
Speaker 1: technologies to achieve over time. I'm I'm a big believer

858
00:51:35,520 --> 00:51:39,839
Speaker 1: in getting really dirt simple with your assumptions of things

859
00:51:40,400 --> 00:51:42,640
Speaker 1: so that you can tell if they make sense. So

860
00:51:42,840 --> 00:51:45,040
Speaker 1: it's it's kind of like, you know, what what are

861
00:51:45,160 --> 00:51:47,279
Speaker 1: robots going to be worth? Well? What if we start

862
00:51:47,320 --> 00:51:51,080
Speaker 1: out and say what about every manual laborer employee in

863
00:51:51,160 --> 00:51:53,040
Speaker 1: the world, and we say, well, we're going to supplement

864
00:51:53,120 --> 00:51:56,320
Speaker 1: this person with a ten dollar tool that's a robot, Like,

865
00:51:56,440 --> 00:51:58,320
Speaker 1: what would that market be worth? What would be the

866
00:51:58,800 --> 00:52:01,600
Speaker 1: cash flow cruel to the about manufacturers in that instance.

867
00:52:01,640 --> 00:52:05,279
Speaker 1: And then so how much would you assume, uh it's

868
00:52:05,360 --> 00:52:09,440
Speaker 1: occupied in terms of enterprise value by the companies that

869
00:52:09,520 --> 00:52:13,560
Speaker 1: are catering to that economic opportunity. Uh And and so

870
00:52:13,920 --> 00:52:17,960
Speaker 1: if you do kind of that very high level assumptions

871
00:52:17,960 --> 00:52:22,560
Speaker 1: about the technologies that we track, you would assume that

872
00:52:23,120 --> 00:52:26,680
Speaker 1: there's gonna be fifty trillion dollars in market cap accruel

873
00:52:26,760 --> 00:52:30,320
Speaker 1: to our technologies over the next decade. Uh And and

874
00:52:30,440 --> 00:52:34,080
Speaker 1: so the this gets back to the capacity question. There's

875
00:52:34,080 --> 00:52:37,960
Speaker 1: gonna be a lot of economic value created, and there's

876
00:52:37,960 --> 00:52:42,440
Speaker 1: gonna be major, major businesses that accrue out of it,

877
00:52:42,760 --> 00:52:44,600
Speaker 1: you know, like with it. If you look at how

878
00:52:44,640 --> 00:52:48,640
Speaker 1: we've modeled autonomous robot taxis globally, uh we think that

879
00:52:48,880 --> 00:52:53,279
Speaker 1: autonomous robot taxis the platforms that enable that are going

880
00:52:53,320 --> 00:52:56,080
Speaker 1: to be worth more than the global energy sector as

881
00:52:56,120 --> 00:53:00,360
Speaker 1: a whole within five years. Just and and you actually

882
00:53:00,360 --> 00:53:03,640
Speaker 1: don't have to make radical assumptions to get there. You say, well,

883
00:53:04,000 --> 00:53:07,440
Speaker 1: look at global miles driven and these are going to

884
00:53:07,520 --> 00:53:09,880
Speaker 1: price it something likes a mile, So they're going to

885
00:53:09,920 --> 00:53:12,440
Speaker 1: be cheaper than actually buying and owning and operating a

886
00:53:12,560 --> 00:53:16,000
Speaker 1: vehicle in the US that costs yous a mile if

887
00:53:16,040 --> 00:53:18,040
Speaker 1: you buy a new one, right, and so it's going

888
00:53:18,080 --> 00:53:20,520
Speaker 1: to be the default way by which people get around.

889
00:53:21,000 --> 00:53:24,160
Speaker 1: These autonomous taxi platforms are going to scrape a platform

890
00:53:24,239 --> 00:53:26,600
Speaker 1: feed just like an uber or lift. If you back

891
00:53:26,640 --> 00:53:29,600
Speaker 1: into the aggregate cash flow that you expect given your

892
00:53:29,600 --> 00:53:33,040
Speaker 1: adoption curves and everything else, it's it's uh, you know,

893
00:53:33,719 --> 00:53:36,960
Speaker 1: measured in hundreds of billions of dollars within five years,

894
00:53:37,040 --> 00:53:40,240
Speaker 1: So it's natural that the market would pay at least

895
00:53:40,280 --> 00:53:43,960
Speaker 1: a reasonable cash flow multiple on that cash. So there's

896
00:53:44,120 --> 00:53:47,160
Speaker 1: there's a combination of like, what's the cost aclient look like,

897
00:53:47,560 --> 00:53:50,560
Speaker 1: how is it cross sector? Uh? And when has it

898
00:53:50,640 --> 00:53:52,800
Speaker 1: gone cross sector? What other things are going to be

899
00:53:52,880 --> 00:53:56,600
Speaker 1: built on top of it? And thinking about how meaningful

900
00:53:56,680 --> 00:53:59,840
Speaker 1: is this going to be economically over the medium to

901
00:54:00,000 --> 00:54:03,520
Speaker 1: long term, And if you can kind of dimension that

902
00:54:03,680 --> 00:54:08,920
Speaker 1: it's meaningful and cross sector and steep cost decline and

903
00:54:09,200 --> 00:54:12,440
Speaker 1: itself a platform of innovation, it's very likely that this

904
00:54:12,600 --> 00:54:15,040
Speaker 1: thing is going there's gonna be a lot of value

905
00:54:15,080 --> 00:54:18,560
Speaker 1: created here. So it's worth devoting the intellectual capital to

906
00:54:18,760 --> 00:54:22,200
Speaker 1: understanding and understanding that puts and takes of how it's

907
00:54:22,239 --> 00:54:24,040
Speaker 1: going to get to market, and which part of the

908
00:54:24,120 --> 00:54:26,520
Speaker 1: value chain is going to be the most um cash

909
00:54:26,560 --> 00:54:29,560
Speaker 1: a creative and and and how that part of the

910
00:54:29,640 --> 00:54:32,719
Speaker 1: value chain is underwritten today relative to how you think

911
00:54:32,760 --> 00:54:35,640
Speaker 1: it should be. Would you ever consider starting a SPAC

912
00:54:35,800 --> 00:54:37,600
Speaker 1: or is it too much of a departure from the

913
00:54:37,640 --> 00:54:42,319
Speaker 1: current model? Um? Well, I mean I think that there

914
00:54:42,360 --> 00:54:46,439
Speaker 1: are pluses and minuses of SPACs. I think that there

915
00:54:46,560 --> 00:54:48,880
Speaker 1: is a degree of nervousness that at least I have

916
00:54:49,400 --> 00:54:53,160
Speaker 1: right now that there that when people raise spacts like

917
00:54:53,320 --> 00:54:56,359
Speaker 1: they are heavily incentivized to figure out something to buy

918
00:54:56,520 --> 00:55:00,840
Speaker 1: with them. Nobody returns the money, right, And it's almost

919
00:55:00,920 --> 00:55:03,920
Speaker 1: like a you create a time bomb of I p

920
00:55:04,080 --> 00:55:06,439
Speaker 1: O right, like U I p O. But the real

921
00:55:06,520 --> 00:55:08,000
Speaker 1: I p O is when you merge with the other

922
00:55:08,160 --> 00:55:11,200
Speaker 1: entity and the people who are controlling whether or not emerged. Yes,

923
00:55:11,480 --> 00:55:13,279
Speaker 1: you have to get the shareholders to vote, but the

924
00:55:13,320 --> 00:55:16,279
Speaker 1: people who are controlling it, like it's basically like you know,

925
00:55:16,440 --> 00:55:19,480
Speaker 1: buy something or or you lose it. It seems in

926
00:55:19,640 --> 00:55:22,880
Speaker 1: some ways backwards to how companies should come to the

927
00:55:22,960 --> 00:55:25,080
Speaker 1: capital markets, and that they should come to the capital

928
00:55:25,120 --> 00:55:27,719
Speaker 1: markets when they're ready not because there's a pool of

929
00:55:27,800 --> 00:55:31,040
Speaker 1: money that's going around trying to find everything that could

930
00:55:31,080 --> 00:55:34,480
Speaker 1: possibly go to the capital markets. I'm nervous about that.

931
00:55:35,200 --> 00:55:37,520
Speaker 1: On the other hand, if you think about UM, what

932
00:55:38,400 --> 00:55:41,279
Speaker 1: has happened with late stage venture, which is where a

933
00:55:41,360 --> 00:55:44,560
Speaker 1: lot of these companies would otherwise have been funded, is

934
00:55:44,719 --> 00:55:48,880
Speaker 1: that there you are only allowing accredited investors to invest

935
00:55:49,160 --> 00:55:53,600
Speaker 1: in kind of these technology companies and uh, you know,

936
00:55:53,760 --> 00:55:57,680
Speaker 1: the late stage venture capitalists get money through their carry

937
00:55:58,320 --> 00:56:02,800
Speaker 1: and their management fee. Uh, that's also quite punitive to

938
00:56:02,840 --> 00:56:04,680
Speaker 1: the end he old or, and you're cutting out the

939
00:56:04,920 --> 00:56:08,440
Speaker 1: entire you know, Joe investor who's not accredited, And so

940
00:56:08,640 --> 00:56:11,280
Speaker 1: you could argue that this is a way to democratize

941
00:56:11,400 --> 00:56:15,759
Speaker 1: access to these late stage venture type assets. I think

942
00:56:15,800 --> 00:56:17,759
Speaker 1: that to me, there seems like there's a lot of

943
00:56:17,800 --> 00:56:22,080
Speaker 1: misbehavior going on in the space. And usually our bias

944
00:56:22,320 --> 00:56:24,759
Speaker 1: is too when there's a lot of capital going after

945
00:56:24,920 --> 00:56:27,640
Speaker 1: something to be wary of it. UM. But I can't

946
00:56:28,040 --> 00:56:32,279
Speaker 1: you know, comment you know directly, but the lack of

947
00:56:32,360 --> 00:56:37,680
Speaker 1: disclosure for the underlying companies I think could lead misbehavior.

948
00:56:37,760 --> 00:56:41,040
Speaker 1: On top of misbehavior, I talked about how consultants UM

949
00:56:41,200 --> 00:56:44,440
Speaker 1: like Mackenzie and stuff. They they their forecasts weren't as

950
00:56:44,440 --> 00:56:45,960
Speaker 1: good as I thought they were going to be. Well,

951
00:56:46,000 --> 00:56:48,280
Speaker 1: we also look at the forecast of the management teams

952
00:56:48,320 --> 00:56:51,239
Speaker 1: within these facts and and that is a difference from

953
00:56:51,280 --> 00:56:53,399
Speaker 1: an I P O. And you know and S one.

954
00:56:54,200 --> 00:56:56,680
Speaker 1: You you're not going to get management team telling you

955
00:56:56,760 --> 00:56:58,399
Speaker 1: what they think they're going to print in revenue five

956
00:56:58,480 --> 00:57:01,040
Speaker 1: years from now. Within the facts the management teams are

957
00:57:01,520 --> 00:57:06,800
Speaker 1: and and there as a general rule so far, looking

958
00:57:06,840 --> 00:57:10,440
Speaker 1: at what management teams have forecast, we have a hard time, um,

959
00:57:11,080 --> 00:57:14,840
Speaker 1: hitting that. So let me sort of and you know,

960
00:57:14,880 --> 00:57:18,080
Speaker 1: I think we can wrap up soon. Um, but let

961
00:57:18,120 --> 00:57:20,080
Speaker 1: me just sort of this sort of gets to a

962
00:57:20,120 --> 00:57:22,840
Speaker 1: bigger question, and Tracy sort of hinted at it. Well,

963
00:57:22,960 --> 00:57:26,000
Speaker 1: you know, this sort of the claim among our detractors

964
00:57:26,120 --> 00:57:29,760
Speaker 1: that you've done a really good job basically riding this

965
00:57:29,920 --> 00:57:34,800
Speaker 1: big bubble. What happens if at some point we are

966
00:57:35,040 --> 00:57:37,680
Speaker 1: in a bubble And some people would say we're in

967
00:57:37,760 --> 00:57:39,880
Speaker 1: one now, but you know, there are times in which,

968
00:57:39,920 --> 00:57:43,400
Speaker 1: in retrospect you're like, oh, there's definitely or in a bubble,

969
00:57:43,440 --> 00:57:48,000
Speaker 1: there was no good tech to buy in December of

970
00:57:49,280 --> 00:57:52,120
Speaker 1: anything that you bought then pretty much in anything related

971
00:57:52,160 --> 00:57:55,720
Speaker 1: to tech was probably gonna be underwater for years. If

972
00:57:55,800 --> 00:57:58,680
Speaker 1: you had purchased then maybe some of them, you know

973
00:57:58,920 --> 00:58:02,320
Speaker 1: I've obviously done well instance could then what what do

974
00:58:02,360 --> 00:58:04,560
Speaker 1: you do if you come across that environment where all

975
00:58:04,600 --> 00:58:08,880
Speaker 1: of your models are saying, in these areas of innovation,

976
00:58:08,920 --> 00:58:11,240
Speaker 1: in these areas of tech that we're into, we just

977
00:58:11,360 --> 00:58:15,200
Speaker 1: can't make the numbers work for anything that's of like quality.

978
00:58:15,600 --> 00:58:17,840
Speaker 1: Is that a concern? Is that a situation that you've

979
00:58:17,880 --> 00:58:19,959
Speaker 1: thought about? Like, how do you think about that question?

980
00:58:21,000 --> 00:58:24,240
Speaker 1: I can say that right now we can still find

981
00:58:24,280 --> 00:58:27,640
Speaker 1: a lot of inefficiently priced assets. So at least as

982
00:58:27,720 --> 00:58:32,960
Speaker 1: we model or as we expect the world to unveil itself. Um,

983
00:58:33,360 --> 00:58:36,360
Speaker 1: I don't see it, you know, within the context of

984
00:58:36,880 --> 00:58:41,040
Speaker 1: the positions that we put client money into. I think

985
00:58:41,080 --> 00:58:44,320
Speaker 1: that uh, you know, financial markets are full of in

986
00:58:44,400 --> 00:58:47,560
Speaker 1: some ways saying a bubble, I think is is you know,

987
00:58:47,800 --> 00:58:50,960
Speaker 1: it's lots of burbl ng and sometimes you get a

988
00:58:51,520 --> 00:58:54,640
Speaker 1: you know a bigger degree of burbling. But there there

989
00:58:54,640 --> 00:58:56,640
Speaker 1: are always you know, there's the I C O boom

990
00:58:56,680 --> 00:59:02,280
Speaker 1: in there's if today or you know, three months from now,

991
00:59:02,360 --> 00:59:06,080
Speaker 1: the equity markets are down, you know, then we would

992
00:59:06,080 --> 00:59:08,000
Speaker 1: all look back and say, oh, well, the SPACs were

993
00:59:08,040 --> 00:59:11,000
Speaker 1: the sign. It was obvious, didn't you see? You know?

994
00:59:11,360 --> 00:59:15,120
Speaker 1: And and if you are investing money in the equity markets,

995
00:59:15,360 --> 00:59:19,480
Speaker 1: equities are infinite in duration, you should not be doing

996
00:59:19,560 --> 00:59:22,600
Speaker 1: that on the basis that the one year result is

997
00:59:22,680 --> 00:59:25,800
Speaker 1: going to be meaningfully indicative of whether or not it

998
00:59:25,960 --> 00:59:30,440
Speaker 1: was a good decision, that it's the wrong time horizon, right,

999
00:59:30,520 --> 00:59:34,840
Speaker 1: and so like, I like my comfort level is that

1000
00:59:35,440 --> 00:59:38,080
Speaker 1: we look out five years and I say this looks

1001
00:59:38,200 --> 00:59:41,400
Speaker 1: very reasonable over five years, because we're not making We're

1002
00:59:41,440 --> 00:59:42,960
Speaker 1: not going out five years and saying then I'm going

1003
00:59:43,000 --> 00:59:45,360
Speaker 1: to pay an elevated multiple. I'm going out five years

1004
00:59:45,400 --> 00:59:47,640
Speaker 1: and saying I'm going to be a forced seller to

1005
00:59:47,720 --> 00:59:51,439
Speaker 1: someone who only pays the market multiple for the cash

1006
00:59:51,480 --> 00:59:53,560
Speaker 1: flow coming off of business with this kind of margin

1007
00:59:53,640 --> 00:59:57,600
Speaker 1: profile and capital intensity, and and you know our return

1008
00:59:57,680 --> 01:00:01,520
Speaker 1: hurdle for for the positions we underwrite, as so we

1009
01:00:01,600 --> 01:00:04,360
Speaker 1: think it's going to roughly double over five years. Well,

1010
01:00:04,640 --> 01:00:06,840
Speaker 1: you know, so I have a lot of ways in

1011
01:00:06,920 --> 01:00:10,840
Speaker 1: which to get exposure where that's at least the way

1012
01:00:10,920 --> 01:00:14,560
Speaker 1: we forecast the world. Now, could we look really dumb

1013
01:00:14,840 --> 01:00:17,360
Speaker 1: twelve months from now? Yes, In fact, I think it's

1014
01:00:17,840 --> 01:00:21,600
Speaker 1: likely that at some point people will think that ARC

1015
01:00:21,960 --> 01:00:24,720
Speaker 1: was a scam and that we were, um, you know,

1016
01:00:24,920 --> 01:00:27,240
Speaker 1: we don't know our left from our right, and we're

1017
01:00:27,280 --> 01:00:31,480
Speaker 1: doing things wrong. And our discipline and our our mission

1018
01:00:32,520 --> 01:00:34,640
Speaker 1: is to continue to say what we think is going

1019
01:00:34,680 --> 01:00:38,960
Speaker 1: to happen, and to try to you know, buy basically

1020
01:00:39,040 --> 01:00:43,520
Speaker 1: intangible assets at at deep value, regardless of the market environment,

1021
01:00:43,840 --> 01:00:48,160
Speaker 1: the tenure rates, and that they came down as much

1022
01:00:48,200 --> 01:00:52,000
Speaker 1: as they did during the pandemic. It has it provides

1023
01:00:52,040 --> 01:00:55,520
Speaker 1: the highest leverage to the longer duration assets, right and

1024
01:00:55,760 --> 01:00:59,400
Speaker 1: and so naturally if you know over ten years you

1025
01:00:59,440 --> 01:01:02,520
Speaker 1: can only get one percent compounded on your money, well,

1026
01:01:02,600 --> 01:01:05,520
Speaker 1: then something that's not going to produce cash flow for

1027
01:01:05,760 --> 01:01:07,919
Speaker 1: you until ten years from now, but that cash flow

1028
01:01:07,960 --> 01:01:11,280
Speaker 1: could be monumental looks a lot more attractive because you're

1029
01:01:11,480 --> 01:01:15,720
Speaker 1: you know, the competitive rate of of of cash flow

1030
01:01:15,760 --> 01:01:18,520
Speaker 1: generation is is just much lower. That had an effect

1031
01:01:18,560 --> 01:01:20,600
Speaker 1: on the overall market multiple, and we don't try to

1032
01:01:20,640 --> 01:01:24,400
Speaker 1: take a stance against the overall market multiple as and

1033
01:01:24,520 --> 01:01:27,160
Speaker 1: we don't try to position ourselves. You know, I'm not

1034
01:01:27,240 --> 01:01:30,720
Speaker 1: gonna I'm not trying to allocate between equities and fixed income, right,

1035
01:01:30,800 --> 01:01:34,120
Speaker 1: and so I just try to underwrite the equities, you know,

1036
01:01:34,280 --> 01:01:38,840
Speaker 1: qua another equity exposure, you know, financial markets. I was

1037
01:01:39,560 --> 01:01:43,400
Speaker 1: being accused of having committed career suicide because of our

1038
01:01:43,480 --> 01:01:47,040
Speaker 1: Tesla position, and that was you know, that was Memorial

1039
01:01:47,200 --> 01:01:50,800
Speaker 1: Day of like, you know that that was that was

1040
01:01:50,920 --> 01:01:55,960
Speaker 1: not that long ago. The markets mania is much more

1041
01:01:56,080 --> 01:02:00,160
Speaker 1: volatile than our fundamental valuing of the company. So the

1042
01:02:00,240 --> 01:02:02,880
Speaker 1: way in which we manage the portfolios is were typically

1043
01:02:02,920 --> 01:02:06,800
Speaker 1: short term contrarian. If something is rallying, it's often rallying.

1044
01:02:07,120 --> 01:02:09,720
Speaker 1: If it goes up because it beat on earnings, that

1045
01:02:09,840 --> 01:02:11,960
Speaker 1: doesn't change what we think the company is going to

1046
01:02:12,040 --> 01:02:14,479
Speaker 1: look like five years from now. So we'll often sell

1047
01:02:14,560 --> 01:02:17,880
Speaker 1: off that gain to buy into something that you know,

1048
01:02:18,240 --> 01:02:20,800
Speaker 1: suddenly was investing too much in R and D so

1049
01:02:20,880 --> 01:02:22,800
Speaker 1: they missed on earnings and it's like, yes, give me

1050
01:02:22,880 --> 01:02:26,840
Speaker 1: more of that, uh. And so that that actually doing

1051
01:02:26,960 --> 01:02:30,640
Speaker 1: the work over five years provides us a lot of

1052
01:02:31,280 --> 01:02:34,200
Speaker 1: um kind of anchoring that allows us to manage positions

1053
01:02:34,280 --> 01:02:37,320
Speaker 1: within the portfolio as they respond to news that we

1054
01:02:37,360 --> 01:02:40,760
Speaker 1: don't think is actually fundamentally meaningful. In in the event

1055
01:02:40,920 --> 01:02:43,280
Speaker 1: that the markets start to sell off for whatever reason,

1056
01:02:43,320 --> 01:02:46,680
Speaker 1: because rates are going up because I don't know, geopolitical

1057
01:02:46,880 --> 01:02:49,080
Speaker 1: risk diminishes. But you know, you you all are the

1058
01:02:49,160 --> 01:02:51,400
Speaker 1: ones that get to explain daily why markets do what

1059
01:02:51,480 --> 01:02:53,680
Speaker 1: they do. You know, then we'll respond to that. That's

1060
01:02:53,720 --> 01:02:57,720
Speaker 1: why we actively manage the portfolios. But um, I certainly

1061
01:02:57,960 --> 01:03:00,200
Speaker 1: I would hate to have to say what going to

1062
01:03:00,280 --> 01:03:03,600
Speaker 1: happen in three months. I think that's much harder than

1063
01:03:03,960 --> 01:03:07,800
Speaker 1: than saying what's going to happen over five years. Actually, um,

1064
01:03:08,120 --> 01:03:10,800
Speaker 1: I think it's it's a really it's a really challenging

1065
01:03:10,880 --> 01:03:14,440
Speaker 1: game because it requires you anticipating what other people are

1066
01:03:14,480 --> 01:03:17,960
Speaker 1: going to then think, rather than trying to forecast what's

1067
01:03:18,000 --> 01:03:20,960
Speaker 1: going to happen kind of objectively in the world. Uh.

1068
01:03:21,120 --> 01:03:23,520
Speaker 1: And um, I think I think a lot of people

1069
01:03:23,600 --> 01:03:26,640
Speaker 1: play that game, but I it's not that interesting to me,

1070
01:03:26,720 --> 01:03:30,440
Speaker 1: and I think it's really hard. Brett, that was that

1071
01:03:30,640 --> 01:03:33,680
Speaker 1: was fantastic. Really. Uh, I'm really glad we got a

1072
01:03:33,760 --> 01:03:36,680
Speaker 1: chance to talk to you. I learned a ton in

1073
01:03:36,800 --> 01:03:41,120
Speaker 1: that conversation, and I appreciate you taking the time my pleasure.

1074
01:03:41,240 --> 01:04:03,840
Speaker 1: Joe Ye, thank you, Thank you so much. Thanks Brette, Tracy,

1075
01:04:03,960 --> 01:04:07,720
Speaker 1: that was really cool. I mean, obviously I've been aware

1076
01:04:07,840 --> 01:04:12,160
Speaker 1: of arc and they're amazing stock picks, and particularly um

1077
01:04:12,840 --> 01:04:17,160
Speaker 1: they're sort of vindication on the Tesla pick. But I'm

1078
01:04:17,360 --> 01:04:20,080
Speaker 1: hearing overall like they're sort of like general approach. That

1079
01:04:20,240 --> 01:04:24,120
Speaker 1: was very useful and interesting. Yeah, I agree. There were

1080
01:04:24,200 --> 01:04:27,400
Speaker 1: two things that stuck out from that conversation for me,

1081
01:04:27,520 --> 01:04:29,880
Speaker 1: and again I don't mean to naval gays in the

1082
01:04:29,960 --> 01:04:32,000
Speaker 1: media too much, but one of them was the way

1083
01:04:32,040 --> 01:04:36,640
Speaker 1: they organized themselves around technologies rather than traditional sort of

1084
01:04:36,760 --> 01:04:40,240
Speaker 1: analyst or industry sectors. And I have to say, I

1085
01:04:40,320 --> 01:04:42,840
Speaker 1: think that's something that a lot of media companies have

1086
01:04:42,920 --> 01:04:46,440
Speaker 1: struggled with over the years, you know, particularly when bitcoin

1087
01:04:46,560 --> 01:04:48,600
Speaker 1: came out, for instance, there was a lot of discussion

1088
01:04:48,640 --> 01:04:52,960
Speaker 1: about it. Should it be done by market reporters, should

1089
01:04:52,960 --> 01:04:56,560
Speaker 1: it be done by commodities reporters, Does it fit into

1090
01:04:56,880 --> 01:04:59,840
Speaker 1: an investment team or the tech team, and everyone kind

1091
01:04:59,880 --> 01:05:03,440
Speaker 1: of struggled to fit it into a traditional category. But

1092
01:05:03,640 --> 01:05:06,520
Speaker 1: had you just looked at it as a sort of

1093
01:05:06,840 --> 01:05:12,120
Speaker 1: um sort of cross beats technology like blockchain, maybe it

1094
01:05:12,160 --> 01:05:16,440
Speaker 1: would have been easier to conceptualize, I suppose, and the

1095
01:05:16,520 --> 01:05:19,000
Speaker 1: same thing for you know, electric batteries and things like that.

1096
01:05:20,040 --> 01:05:22,560
Speaker 1: So that was really interesting and the second thing about

1097
01:05:22,600 --> 01:05:26,480
Speaker 1: refining your work through public interaction and discourse also strikes

1098
01:05:26,520 --> 01:05:29,000
Speaker 1: a chord. Both you and I are very active on

1099
01:05:29,080 --> 01:05:32,920
Speaker 1: Twitter and social media. I think journalism in itself is

1100
01:05:32,960 --> 01:05:36,680
Speaker 1: a very public activity, since every time you publish something,

1101
01:05:36,720 --> 01:05:39,520
Speaker 1: you're probably going to get some sort of reaction or

1102
01:05:39,560 --> 01:05:41,960
Speaker 1: feedback to it, and in the end you can use

1103
01:05:42,120 --> 01:05:46,040
Speaker 1: that to refine your thought process, you think more strategically

1104
01:05:46,080 --> 01:05:48,960
Speaker 1: about your model or your subject matter or whatever. And

1105
01:05:49,240 --> 01:05:51,040
Speaker 1: I don't know, I just see a lot of parallels

1106
01:05:51,120 --> 01:05:54,720
Speaker 1: between what his analysts that are are doing and what

1107
01:05:55,080 --> 01:05:58,040
Speaker 1: some journalists are doing or could be doing. Yeah, now

1108
01:05:58,120 --> 01:06:01,160
Speaker 1: that that definitely stood out to me. And it's one

1109
01:06:01,200 --> 01:06:05,120
Speaker 1: of these things where, like I get as a journalist

1110
01:06:05,360 --> 01:06:09,560
Speaker 1: so much value from interacting on social media, arguing with people,

1111
01:06:09,720 --> 01:06:12,840
Speaker 1: having people like try to like pick apart my point.

1112
01:06:13,360 --> 01:06:16,440
Speaker 1: And it's very intuitive after he describes it. It's not

1113
01:06:16,600 --> 01:06:18,640
Speaker 1: something i'd like really thought about, like I was. I

1114
01:06:18,840 --> 01:06:21,920
Speaker 1: do think endless or bysiders or cell siders like I

1115
01:06:22,000 --> 01:06:24,840
Speaker 1: do think it's like good to publicly interact. But after

1116
01:06:25,000 --> 01:06:28,520
Speaker 1: hearing him like describe it, that benefits what they do

1117
01:06:28,800 --> 01:06:32,720
Speaker 1: with like posting all of their theses, uh there, um,

1118
01:06:33,080 --> 01:06:36,280
Speaker 1: their their models making them public that you can sort

1119
01:06:36,320 --> 01:06:41,439
Speaker 1: of instantly see how an asset management firm could really

1120
01:06:41,520 --> 01:06:44,360
Speaker 1: use that to the advantage. And it's also good marketing.

1121
01:06:44,480 --> 01:06:47,440
Speaker 1: I mean, uh, you know, it's it stands out. I mean,

1122
01:06:47,520 --> 01:06:50,520
Speaker 1: it's it's good for refining your arguments. You know. I

1123
01:06:50,760 --> 01:06:54,360
Speaker 1: remember those sort of like fights about Tesla, especially as

1124
01:06:54,400 --> 01:06:57,320
Speaker 1: he mentioned back in eighteen when there were serious questions

1125
01:06:57,360 --> 01:06:59,560
Speaker 1: about whether the company was going to make it. But

1126
01:06:59,680 --> 01:07:01,919
Speaker 1: it's all a good marketing and it's uh, it makes

1127
01:07:02,000 --> 01:07:04,080
Speaker 1: it stand out. And I can't think of any other

1128
01:07:04,200 --> 01:07:07,160
Speaker 1: firm right now that's doing anything similar, but I could

1129
01:07:07,160 --> 01:07:11,680
Speaker 1: see a lot more more sort of embracing that model absolutely. Uh.

1130
01:07:11,840 --> 01:07:13,520
Speaker 1: The other thing that I was thinking about was our

1131
01:07:13,600 --> 01:07:18,080
Speaker 1: conversation around value investing and this idea that actually, if

1132
01:07:18,200 --> 01:07:21,120
Speaker 1: you kind of redefine how you're looking at a company's value,

1133
01:07:21,520 --> 01:07:26,400
Speaker 1: then maybe your universe of value stock starts to look

1134
01:07:26,520 --> 01:07:29,919
Speaker 1: very different. So this idea that you know, the way

1135
01:07:30,200 --> 01:07:33,760
Speaker 1: ARC is looking at it, Tesla probably was a value

1136
01:07:33,800 --> 01:07:38,160
Speaker 1: stock back in and I suppose also to Brett's point,

1137
01:07:38,280 --> 01:07:42,200
Speaker 1: it depends on your time horizon, right, Yeah, I mean,

1138
01:07:42,600 --> 01:07:45,280
Speaker 1: you know, like you've got to be pretty confident and

1139
01:07:45,360 --> 01:07:47,680
Speaker 1: I think I thought that was really interesting about the

1140
01:07:47,760 --> 01:07:53,720
Speaker 1: sort of like the value of genuinely original ideas because

1141
01:07:53,760 --> 01:07:56,480
Speaker 1: there is a lot of um, you know, within the

1142
01:07:56,600 --> 01:07:59,640
Speaker 1: space of like the million people who say cover Apple

1143
01:08:00,040 --> 01:08:02,480
Speaker 1: or cover Facebook. As he put it, you know, it's

1144
01:08:02,480 --> 01:08:04,680
Speaker 1: like maybe the bullish ones take the consensus and at

1145
01:08:04,720 --> 01:08:11,280
Speaker 1: ten the bearish ones subtract ten or But the idea

1146
01:08:11,400 --> 01:08:15,520
Speaker 1: that's like coming out a problem where you genuinely seek

1147
01:08:15,600 --> 01:08:20,240
Speaker 1: to uncover ideas that aren't just some deviation from consensus

1148
01:08:21,240 --> 01:08:24,519
Speaker 1: is a sort of a very interesting challenge. But again,

1149
01:08:24,560 --> 01:08:26,640
Speaker 1: you can see if you're like really confident about it

1150
01:08:26,680 --> 01:08:28,960
Speaker 1: and you like feel like you understand it, you can

1151
01:08:29,240 --> 01:08:32,920
Speaker 1: come up with interesting ideas that you can have some

1152
01:08:33,040 --> 01:08:36,759
Speaker 1: conviction for make a meaningfully sized bet, so to speak.

1153
01:08:37,000 --> 01:08:39,320
Speaker 1: So I'm trying to think if there was one other

1154
01:08:39,760 --> 01:08:42,080
Speaker 1: thing that stood it out to me, But yeah, let's

1155
01:08:42,120 --> 01:08:45,120
Speaker 1: leave it there. This has been another episode of the

1156
01:08:45,200 --> 01:08:47,960
Speaker 1: All Thoughts podcast. I'm Tracy Alloway. You can follow me

1157
01:08:48,240 --> 01:08:52,240
Speaker 1: on Twitter at Tracy Alloway and I'm Joe wisn't thought

1158
01:08:52,240 --> 01:08:54,519
Speaker 1: you could follow me on Twitter? Oh, I remember what

1159
01:08:54,600 --> 01:08:56,000
Speaker 1: I was gonna say? Can I just say it real

1160
01:08:56,080 --> 01:08:58,280
Speaker 1: quickly I'm just gonna say it right here in the outro.

1161
01:08:59,160 --> 01:09:01,559
Speaker 1: I thought that was, you know, I'm I'm personally biased

1162
01:09:01,560 --> 01:09:03,679
Speaker 1: because I'm interested. I've always I've been interested in fuel

1163
01:09:03,680 --> 01:09:05,880
Speaker 1: cell companies for a long time. But I did think

1164
01:09:05,920 --> 01:09:09,720
Speaker 1: that was a pretty interesting example of the case that

1165
01:09:09,800 --> 01:09:13,000
Speaker 1: they're not just bubble riders, that there are like these

1166
01:09:13,040 --> 01:09:16,840
Speaker 1: sort of sexy areas of the stock market that they're

1167
01:09:16,920 --> 01:09:19,840
Speaker 1: not participating in. And then if they were just sort

1168
01:09:19,880 --> 01:09:22,000
Speaker 1: of a firm that was like writing bubbles or writing

1169
01:09:22,080 --> 01:09:25,880
Speaker 1: how trends, that they would be participating in that area.

1170
01:09:25,920 --> 01:09:27,840
Speaker 1: So I thought that was interesting. It's like, here's the thing,

1171
01:09:28,160 --> 01:09:30,960
Speaker 1: a bunch of investors are super excited about it. They

1172
01:09:31,000 --> 01:09:33,439
Speaker 1: are not. It's sort of like a sort of I

1173
01:09:33,520 --> 01:09:36,160
Speaker 1: thought a useful counter example of this idea that they're

1174
01:09:36,200 --> 01:09:38,719
Speaker 1: just in all the sexy areas. Anyway, I just wanted

1175
01:09:38,760 --> 01:09:41,439
Speaker 1: to say that. Uh, I'm Joe Wisan though. You can

1176
01:09:41,479 --> 01:09:44,439
Speaker 1: follow me on Twitter at the Stalwart, Follow our guests

1177
01:09:44,760 --> 01:09:48,559
Speaker 1: Brett Winton, He's at Winton a r K on Twitter,

1178
01:09:48,960 --> 01:09:50,559
Speaker 1: and of course I check out all of their white

1179
01:09:50,560 --> 01:09:54,320
Speaker 1: papers and models at their website. Follow our producer Laura Carlson,

1180
01:09:54,520 --> 01:09:58,160
Speaker 1: She's at Laura M. Carlson. Follow the Bloomberg head of podcast,

1181
01:09:58,240 --> 01:10:01,920
Speaker 1: Francesca Levi at Francesca Today, and check out all of

1182
01:10:01,960 --> 01:10:06,040
Speaker 1: our podcasts at Bloomberg under the handle at podcasts. Thanks

1183
01:10:06,080 --> 01:10:06,559
Speaker 1: for listening,