Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.
Speaker 1: I'm Joe, Wasn'tal, and I'm Tracy all Away. So, Tracy,
Speaker 1: here's something that I never thought I would see again.
Speaker 1: So I first started following markets in the late nineties, um,
Speaker 1: you know, dot Com era, and something that I never
Speaker 1: thought I would see again in my career after that
Speaker 1: ended was the superstar fund manager. Okay, why is that? Well,
Speaker 1: actually that's a totally true. What I mean is more
Speaker 1: the superstar stock picker. Because of course, back in the
Speaker 1: old days there were a lot of like star stock
Speaker 1: stock pickers, fund managers, you know, Peter Lynch comes to mind,
Speaker 1: some of the other tech investors back then. But these days,
Speaker 1: with e t s, with online brokerages that make it
Speaker 1: really easy for individuals to buy stocks on their own,
Speaker 1: it really sort of seemed to me like that era
Speaker 1: was just gone. Right, So I suppose there was this
Speaker 1: idea that the time of stock picking has come and gone,
Speaker 1: and that if you want to make good returns in
Speaker 1: the market, you should just pour all your money into
Speaker 1: something like an smp F t F like a these
Speaker 1: sacks or something like that, and just stick with it
Speaker 1: and don't bother trying to outperform the market, because over
Speaker 1: a longer period of time, even the best stock pickers
Speaker 1: UH had eventually underperformed. Right. I think this mantra of
Speaker 1: don't try to pick stocks a if you try to
Speaker 1: pick stocks, you're probably gonna underperform the index, and be
Speaker 1: if you come across a mutual fund or a fund
Speaker 1: manager who's good at picking stocks, oh it's probably just luck.
Speaker 1: It's not gonna last to you know. Even if even
Speaker 1: if there is someone who can beat the market, how
Speaker 1: are you going to know whether it's actually worth putting
Speaker 1: her money with them? And so like this idea that
Speaker 1: everyone should just index, um they're trying to beat the
Speaker 1: market is kind of a loser's proposition. It's really been
Speaker 1: drilled into people's heads, and I think, like, you know,
Speaker 1: for years, they're really we just haven't had a sort
Speaker 1: of another a new Peter Lynch or Buffet. You know,
Speaker 1: there's like Stark Quantz, maybe some bond fund managers who
Speaker 1: are known, but the idea of like someone who's just
Speaker 1: really associated with a great track record of picking individual
Speaker 1: stocks hasn't been a thing for a while. And yet
Speaker 1: and yet a star stockpicker emerges over the horizon. Yeah,
Speaker 1: exactly right, So obviously that really Uh, that's for the
Speaker 1: first time in a long time, there is currently a
Speaker 1: fund manager, a stock picker who is a mass an
Speaker 1: incredible track record, an incredible following, And of course we're
Speaker 1: talking about Cathy Wood. She is the CEO and chief
Speaker 1: investment officer of ARC invest and there is this a
Speaker 1: total fascination with ARC and this family of actively traded
Speaker 1: E t f s that have just done a phenomenally
Speaker 1: well in terms of returns, but also attracted an extraordinary
Speaker 1: amount of investor cash in the last couple of years. Right,
Speaker 1: So the ARK E t f s, I mean, I'm
Speaker 1: looking at their performance. They have, you know, five different
Speaker 1: thematic portfolios alone that have basically doubled over the past year,
Speaker 1: which is pretty amazing if you think about it. Uh,
Speaker 1: it's amazing enough for just one stock to double in
Speaker 1: price like that and in just the space of twelve months,
Speaker 1: but to do it across multiple ETFs is really remarkable.
Speaker 1: And I think within their actual portfolio there's a tiny,
Speaker 1: tiny number of stocks that haven't risen recently, and I'm
Speaker 1: not even sure there are any actually, Um, it's a
Speaker 1: really amazing performance. It's really true to Actually I'm looking
Speaker 1: at at the end of their performance of a r
Speaker 1: k K, which is the sort of flagship innovation E
Speaker 1: t F that ARC has was up a hundred and
Speaker 1: fifty two for the year. Extraordinary returns. And if you
Speaker 1: look at the holdings, they're just all of the companies
Speaker 1: that have absolutely killed it in the recent environment. Tesla
Speaker 1: is the biggest one, but other names Square the Payments Company, Phenomenal, Roku,
Speaker 1: huge winner, Zillo, Spotify, tell Doc, which of course had
Speaker 1: an incredible year thanks to the rise of remote medicine
Speaker 1: and so forth. So it is a just extraordinary number
Speaker 1: of winners that this uh, this is a fund and
Speaker 1: the related funds, there's a related fund for finance and
Speaker 1: medicine that have that they've brought it just the track
Speaker 1: records incredible. If if anyone follows Eric Bolkunis, who's sort
Speaker 1: of Bloomberg intelligence is E t F analysts, I feel
Speaker 1: like three quarters of his tweets these days are just
Speaker 1: about how extraordinary this family of funds and the performance
Speaker 1: of ARC invest has been lately. Yeah. Absolutely, and uh,
Speaker 1: you mentioned Kathy would already, but it's sort of it's
Speaker 1: given rise to occults around her. I guess I don't
Speaker 1: want to say cult because that has negative connotations, but
Speaker 1: certainly there's been a lot of admiration and fascination with
Speaker 1: what she's been doing over at ARC. Yeah, and uh,
Speaker 1: we're recording this January twenty. I saw Erica bell Kuna's
Speaker 1: tweet just today that ARC has taken in is the
Speaker 1: third most popular fun family right now in terms of
Speaker 1: new money coming in so far, you're to date that
Speaker 1: that exceeds the money coming into Black Rocks. I share
Speaker 1: his family, which is much bigger. I mean that's like,
Speaker 1: that's like the name brand. That's like basically the Coca
Speaker 1: Cola of e t F So to have a sort
Speaker 1: of small boutique fund firm with a few deply traded
Speaker 1: fund pulling in more than I shares, it's just it's
Speaker 1: staggering stuff. Yeah. Absolutely, So we are going to be
Speaker 1: talking to someone from Ark today, right we are. So
Speaker 1: the question is how do they do it? How do
Speaker 1: they find how do they pick stocks? I mean, Tesla
Speaker 1: is obviously this huge winner, but it wouldn't have been
Speaker 1: a huge winner for them unless they had been in
Speaker 1: it for a lot longer than most people. So the
Speaker 1: question is how do they how do they find and
Speaker 1: pick great stocks that trouts the market? Everyone would like
Speaker 1: to know. Well, I'm also I'm also interested in how
Speaker 1: they deal with inflows as they get bigger, and whether
Speaker 1: or not that makes it harder to have a sort
Speaker 1: of active E t F that is focused on stock picking.
Speaker 1: So this is going to be a really interesting conversation
Speaker 1: I can tell. Yeah, I'm super excited about this one.
Speaker 1: So we're going to be speaking with Brett Winton. He
Speaker 1: is the director of research at arc Um. He's been
Speaker 1: with the company since its founding in Previously to that,
Speaker 1: he worked with Kathy Wood at Alliance Bernstein. They've worked
Speaker 1: together since two thousand seven. So with any luck, we're
Speaker 1: going to learn at least some of the secrets of
Speaker 1: um arc from Brett and how they do it. Although
Speaker 1: I should say, you know, to some extent, maybe it's
Speaker 1: not a secret because part of what they do is
Speaker 1: their research is very open, it's very transparent. They post models.
Speaker 1: So we're gonna we're gonna really learn, hopefully how it
Speaker 1: all works up. Brett, thank you very much for joining us,
Speaker 1: Thank you for having me. Happy to be here. So
Speaker 1: you've worked with Kathy previously at Alliance Bernstein since two
Speaker 1: thousand seven with ar Sin. Why do you sort of
Speaker 1: compare and contrast big picture, and then we'll get into
Speaker 1: details what the research process looks like at a sort
Speaker 1: of traditional asset management firm versus the sort of open,
Speaker 1: transparent research approach you take it our. I think it's
Speaker 1: interesting and that I was over hearing your intro and
Speaker 1: you were talking about stock picking, and I was hearing that,
Speaker 1: and and I don't think of what we do as
Speaker 1: stock picking at least at its inception level. So we
Speaker 1: really look at the technology level first. And so we
Speaker 1: specifically seek to identify disruptive technologies that basically technology platforms
Speaker 1: that future historians will look back upon and say, oh
Speaker 1: my gosh, that was a signpost technology that was as
Speaker 1: big as the computer, that was as big as electrification. Uh.
Speaker 1: And and there's an established criteria for identifying these technologies
Speaker 1: for it's called general purpose technology theory. But they all
Speaker 1: follow steep cost of clients, they all cut across sectors,
Speaker 1: and they all themselves or platforms of innovation. And so
Speaker 1: that actually matches that we're investing in those kinds of
Speaker 1: technologies matches with three critical weaknesses that I see in
Speaker 1: traditional fund management that create inefficiencies pricing inefficiencies that we
Speaker 1: seek to exploit. So the technology fall follow steep cost
Speaker 1: of clients, that the drama of those cost of declines
Speaker 1: don't manifest over the next three or six months, So
Speaker 1: it's it actually can look very linear over a short
Speaker 1: time horizon, and so it doesn't really impact UH. An
Speaker 1: understanding of that cost de client doesn't impact the way
Speaker 1: analysts model the company on the cell side over the
Speaker 1: next quarter or two. But if you take a step
Speaker 1: back and you have an intentionally longer term point of view,
Speaker 1: you can actually UH come to radically different conclusions about
Speaker 1: what the future state of the world is likely to
Speaker 1: look like relative to others, just by having an understanding
Speaker 1: of of the mechanics of how a cost decline occurs
Speaker 1: and then what the demand elasticity of that price difference
Speaker 1: is going to be. So that's like from the beginning,
Speaker 1: we set ourselves up to say, hey, we're not gonna
Speaker 1: we're not gonna try to trade stocks or identify securities
Speaker 1: that are mispriced on the basis of priced earnings or
Speaker 1: price to sales or any kind of shorthand for valuation,
Speaker 1: and we're not Also, we're also not going to try
Speaker 1: to do a full um DCF because a full discounted
Speaker 1: cash flow model, because then you get to cheat with
Speaker 1: how you use the discount rate in your terminal rate
Speaker 1: of growth. Instead, we're gonna say, if we own one
Speaker 1: of these companies, uh five years from now, if we're
Speaker 1: then forced to sell it to a technological pessimist, what
Speaker 1: will that person be forced to pay given the cash
Speaker 1: flow generation of the business at that time. And so,
Speaker 1: just by underwriting the positions over a five year perspective,
Speaker 1: we've been able to and still are able to identify
Speaker 1: really radically under priced securities. Uh So, I think of
Speaker 1: it as where value investors and intangible assets. Intangible assets
Speaker 1: are very difficult to understand how much cash flow they
Speaker 1: can can generate, but we really do the work of
Speaker 1: trying to figure that out over the time horizon. That
Speaker 1: matters in uh in the part of the capital structure
Speaker 1: that we're in equities or infinite and duration, it's really
Speaker 1: kind of dumb to underwrite them over a year or
Speaker 1: two because the market volatility is you know, really high,
Speaker 1: Like I can't tell you what the next twelve months
Speaker 1: of equities is going to look like I can actually say,
Speaker 1: with reasonable assurance over five years, this position is under priced. Uh.
Speaker 1: And so that's like the first major inefficiency we exploit.
Speaker 1: And at the technology level, what that means is, so
Speaker 1: take Tesla, which is a position that everybody is well
Speaker 1: aware of because we have a perspective on what the
Speaker 1: cost declines of batteries is going to do. It allows
Speaker 1: us to demonstrate to our satisfaction that we think by
Speaker 1: there will be electric vehicles that are sticker priced comparable
Speaker 1: to internal combustion engine vehicles and the average internal combustion
Speaker 1: engine vehicle. So you'll walk into a dealership and say,
Speaker 1: do I want a Toyota camera that you know it
Speaker 1: costs me more over time it I have to take
Speaker 1: it to the dealership more often because it breaks down
Speaker 1: more often, and it costs me basically the same amount
Speaker 1: of money out of pocket. Or do I want the
Speaker 1: down market equivalent of a Model three, which is faster
Speaker 1: off the line, cost me less money over time, and
Speaker 1: it costs me less money today. It would be a
Speaker 1: real surprise if people didn't shift over to buying electric vehicles.
Speaker 1: So with that as your initial perspective, you can say, hey,
Speaker 1: so if we are at we think they're going to
Speaker 1: be forty million electric vehicles sold by If if we're
Speaker 1: at forty million and the rest of the market, consultants, etcetera,
Speaker 1: or something like seven million, well there's probably some inefficiently
Speaker 1: priced assets exposed to that technology. So by identifying by
Speaker 1: underwriting over a time horizon, that's that's reasonable, we believe.
Speaker 1: And by identifying basically fertile terrain where technologies are misunderstood,
Speaker 1: then we can basically concentrate our exposure into equities that
Speaker 1: are more likely to be mispriced. So that's the I
Speaker 1: just talked a lot, But that's the first of three
Speaker 1: inefficiencies that we exploit. So I wanted to back up
Speaker 1: for a second because one thing, one thing I wonder
Speaker 1: a lot about in tech investing is what's the firm's
Speaker 1: collective ground here? Does it come primarily from finance or
Speaker 1: is there a lot of technological expertise? And what I
Speaker 1: mean by that is you mentioned that you're looking at
Speaker 1: technologies across a long term time horizon. Some of your
Speaker 1: E t F s are very very technical. I know
Speaker 1: you have a genomic revolution E t F for instance,
Speaker 1: and I think you're looking at space exploration as well.
Speaker 1: So how do you build up the tech expertise in
Speaker 1: order to be confident in your calls on what's going
Speaker 1: to work out in the long term. Well, that actually
Speaker 1: leads right into the second inefficiency that I see us exploiting.
Speaker 1: Because all of these technologies are a cross sector technologies,
Speaker 1: they actually kind of cut across the skis of traditional
Speaker 1: sector based analysts. Uh. You know, the auto analyst um
Speaker 1: had been told for years and years and years by
Speaker 1: the Tier one suppliers and by the automotive companies that
Speaker 1: electrically goals were actually not a meaningful technology. Not only
Speaker 1: that they're a niche technology, not only that the people
Speaker 1: trying to do it, we're crazy and we're probably going
Speaker 1: to bankrupt, and we'll be able to get the assets
Speaker 1: when we need them and we can invest in it
Speaker 1: later on if required. Uh and so and and that
Speaker 1: auto analysts who's sitting and basically that echo chamber of
Speaker 1: information with the quarterly calls with the CFO, the annual
Speaker 1: calls with the CEO, who has spent his and in
Speaker 1: this case it's always a heat in autos his entire
Speaker 1: career basically being like, well, now Ford's better than Nissan,
Speaker 1: and now GM is better than Ford. UH has has
Speaker 1: developed a pattern of thinking that basically doesn't allow him
Speaker 1: to UH really understand whether or not an electric vehicle
Speaker 1: will be competitive with these technology with the existing legacy technology,
Speaker 1: or or given him a good tool set by which
Speaker 1: to assess whether or not it will UH. And and
Speaker 1: so when I was at Alliance Bernstein Alumina, which as
Speaker 1: a genomics company is UH, you know, nineties plus percent
Speaker 1: of genome sequence in the world go through illumino boxes.
Speaker 1: At Aligned Ferns Seins, it was covered by our industrials analysts,
Speaker 1: and you know, he didn't know what to do with it.
Speaker 1: He almost would have had to take on a whole
Speaker 1: new like set of learnings in order to successfully cover
Speaker 1: this company that to him looked extremely high priced. It
Speaker 1: was not like Dana Her or Honeywell to him. And
Speaker 1: so he was perpetually a neutral. So the way that
Speaker 1: we approach kind of technology and markets is our analysts
Speaker 1: are assigned by technology rather than by sector. UH. And
Speaker 1: so it helps that our batteries analyst is able to Sam,
Speaker 1: who's great. He does the cost decline work on batteries.
Speaker 1: He understands how it's going to feed into certainly the
Speaker 1: electric vehicle industry, but he also can look at kind
Speaker 1: of the energy storage within the utility space think about
Speaker 1: how that impacts kind of the propensity for utility spend.
Speaker 1: He can understand how it's gonna impact aerial drones and
Speaker 1: their ability to both delivered parcels from place to place
Speaker 1: or or deliver people from place to place. By selecting
Speaker 1: for people that are expert in the technology, we believe
Speaker 1: we get an edge against basically sector experts who who
Speaker 1: are used to a kind of competitive landscape that that
Speaker 1: doesn't get dramatically upturned. Another example is in in banks,
Speaker 1: like I'm sure many bank analysts kind of poo pooed
Speaker 1: and overlooked Square. Well, our view is that you know
Speaker 1: you you have a digital bank branch in your pocket,
Speaker 1: and most of certainly the US is going to begin
Speaker 1: banking in that way over the next five years. And
Speaker 1: uh Square is a choir and customers through its peer
Speaker 1: to peer transfer app, the cash app at something like
Speaker 1: twenty dollars per customer. Traditional banks pay upwards of a
Speaker 1: thousand dollars per customer account. The products that that Square
Speaker 1: is gonna offer is going to expand, and that retail
Speaker 1: bank branch infrastructure is going to depreciate much more rapidly
Speaker 1: than any of the executive are willing to acknowledge or
Speaker 1: or maybe even understand. Uh and so a traditional banks
Speaker 1: analyst is not necessarily even empowered to turn around and
Speaker 1: suggest Square to his portfolio managers. It might be off
Speaker 1: limits to him. Uh and and and so kind of
Speaker 1: by focusing on the technologies that matter, with the analysts
Speaker 1: focused directly on those technologies, actually unlocks a lot of
Speaker 1: other sort of like misunderstood opportunities. Companies that fall through
Speaker 1: the cracks and UM and sectors that are really right
Speaker 1: for getting disrupted U that have people who have built
Speaker 1: their whole careers on understanding that the structure of the
Speaker 1: sector as it currently exists rather than as it likely
Speaker 1: is to exist. So this is this is really interesting,
Speaker 1: this sort of this sort of structural problem in stock
Speaker 1: identification as a result of people being bucketed into traditional
Speaker 1: UM sectors rather than starting at the technology technology level.
Speaker 1: You know, you, I think that's two inefficiencies. What is
Speaker 1: the third inefficiency that you seek to exploit? Uh, that
Speaker 1: you see within the sort of traditional investment selection approach, Well,
Speaker 1: you alluded to it, Joe, but we, Uh. All of
Speaker 1: these technologies are themselves platforms a top which other innovations
Speaker 1: are going to be built, and so it's really easy
Speaker 1: to suffer from a failure of the imagination. You really
Speaker 1: can't like. So some people try to model these technologies.
Speaker 1: They say, these are the three areas where it's selling today,
Speaker 1: and we're just going to drag out the growth rate,
Speaker 1: and that's going to be the size of the market.
Speaker 1: You can't pre imagine all the things that are going
Speaker 1: to happen on top of it. And so the only
Speaker 1: hope to begin to understand the potential scope and breadth,
Speaker 1: the areas where it will apply apply and where it
Speaker 1: won't is to um expand your information footprint, to be
Speaker 1: totally transparent about what you believe is going to happen.
Speaker 1: And so we publish blogs, we publish white papers, we
Speaker 1: talk on podcasts. We have our own podcast, it's f
Speaker 1: y I for your innovation. You should listen to it, uh,
Speaker 1: And we do that because when we produce this information,
Speaker 1: when we're transparent with our forecasts, that information attracts other information.
Speaker 1: People come back at us and say, I don't understand
Speaker 1: how you got to that conclusion you're wrong about solid
Speaker 1: state batteries. They really seek to combat us to to
Speaker 1: argue from first principles whether or not we're right and
Speaker 1: why and and so this helps us to understand both
Speaker 1: the limitations are of our ability to tell what's going
Speaker 1: to happen in the future and to get a sense
Speaker 1: for some things that we may not have imagined that
Speaker 1: we should be underwriting in to our fund mental models.
Speaker 1: Uh and so and that's very different. You know, just
Speaker 1: being able to access Twitter uh is not something that
Speaker 1: was is allowed in a lot of fun management shops.
Speaker 1: That seems crazy to me. Our analysts are on Twitter,
Speaker 1: they're interacting with the community. You know, this is the
Speaker 1: way you understand how the world is going to work.
Speaker 1: We believe that it gives us a competitive edge that
Speaker 1: from the beginning we've designed ourselves to be able to
Speaker 1: compliantly do that. Uh and and to kind of operate
Speaker 1: in the technology circles where these technologies are actually being
Speaker 1: grappled with and built and um and deployed into the market.
Speaker 1: So I wonder if you could bring a lot of
Speaker 1: the discussion that we've been having and sort of try
Speaker 1: to solidify it with a single stock example. I wanted
Speaker 1: to talk about Tesla because, of course, Kathy made a
Speaker 1: pretty famous call a couple of years ago. I think
Speaker 1: it was for Tesla's stock to go to four thousand dollars,
Speaker 1: and it has since hit that on a split adjusted basis,
Speaker 1: and you have a new price target on it. What
Speaker 1: is it that your methodology and your organizational structure was
Speaker 1: seeing about Tesla in particular that others aren't seeing. There
Speaker 1: was a lot of criticism and incredulity about that call
Speaker 1: when you made it a couple of years ago. So
Speaker 1: what was it that you saw? For one thing? So
Speaker 1: we have a cost A client on lithium I AM batteries.
Speaker 1: We think we have a better understanding of what goes
Speaker 1: into an electric vehicle than probably probably any other shop
Speaker 1: on the street. I don't know, certainly anybody than anybody
Speaker 1: publishing that informs both our top line forecasts I alluded
Speaker 1: to it. We we believe forty million units will be
Speaker 1: sold by of electric vehicles uh and and because they'll
Speaker 1: be cheaper than traditional cars. When we first began that
Speaker 1: top line forecasting, the EI A and and you know
Speaker 1: the OPEC, these policy agencies thought that electric vehicles were
Speaker 1: gonna sell in the two hundred three hundred thousand unit
Speaker 1: annually through the end of their forecast through. So first
Speaker 1: of all, you do that and you say, well, we
Speaker 1: must be doing something wrong, like what are we misunderstanding here? Uh,
Speaker 1: And it turns out I don't think we were misunderstanding anything.
Speaker 1: It's just that there's not actually a great set of
Speaker 1: incentive structures for people to make reasonable, first principles long
Speaker 1: term forecasts. I've been surprised. I thought that we would
Speaker 1: basically from inception, I thought we would be kind of
Speaker 1: duplicating the work of consultants like Mackenzie Global Institute, but
Speaker 1: we would understand the mechanics of how those forecasts were built,
Speaker 1: and so that would give us an edge. But we
Speaker 1: got totally different results. Sometimes sometimes we got similar results,
Speaker 1: in which case it's very easy to say, wow, this
Speaker 1: is kind of priced in. You know, there's probably not
Speaker 1: much that's of interest to us here. Um. But sometimes
Speaker 1: we got very different results. And I think it's for
Speaker 1: consultants because they actually are paid to to cater to
Speaker 1: the biases of the executives that hire them. But that aside. So,
Speaker 1: first of all, we think Tesla is you know, right
Speaker 1: now share of the electric vehicle industry forty million units.
Speaker 1: If they maintain their share, which we think they can
Speaker 1: scale production at that rate, then that would put them
Speaker 1: at ten million units in um we think that the
Speaker 1: electric vehicle industry is gonna consolidate. It's actually the products
Speaker 1: are differentiated on a software basis, much more so than
Speaker 1: internal combustion, So you could get to a naturally higher
Speaker 1: margin in that industry relative to traditional automotive And so
Speaker 1: you can do pretty simple math to to actually say
Speaker 1: Tesla as an electric vehicle manufacturer, just leaving aside robotaxi
Speaker 1: and vertical integration into ride hail, and the insurance product
Speaker 1: is still a compelling position even at these valuation levels.
Speaker 1: So it's given kind of the state of their technology
Speaker 1: stack versus others, you could end up in a situation where,
Speaker 1: just like Apple um, they're extracting most of the profitability
Speaker 1: out of the industry even though they have something like
Speaker 1: a share of the industry. Uh, And so that's possible.
Speaker 1: From the beginning, we've always thought that Tesla was interesting
Speaker 1: not just for the initial vehicle sale, but because of
Speaker 1: their ability to um monetize the fleet of assets that
Speaker 1: they have in the field. You know, you can say
Speaker 1: that Tesla has the largest deployed fleet of robots in
Speaker 1: the world. Uh, and that those robots improve over time.
Speaker 1: Uh and conditional on them delivering the ability for one
Speaker 1: of those cars to drive itself around, which is you know,
Speaker 1: actually quite a difficult technical challenge. UM. You know, you
Speaker 1: can imagine that those vehicles that Wall Street still believes
Speaker 1: is uh, I sell a car for fifty thou dollars,
Speaker 1: maybe even optimistic Wall Street thinks I get like five
Speaker 1: thousand dollars of operating earnings off of that. If instead I,
Speaker 1: the owner of that vehicle, am able to turn it
Speaker 1: into a taxi, it could do a hundred thousand miles
Speaker 1: a and might generate to me, uh, you know, twenty
Speaker 1: dollars in operating earnings per year to to Tesla, to
Speaker 1: me the owner. Uh, you know, additional cash flow on
Speaker 1: top of that. And so you go from uh, a
Speaker 1: single sale operating earnings event to every asset they've ever
Speaker 1: sold generates cash flow for them year after year after year. Uh.
Speaker 1: And we don't think that's a probability. In fact, we
Speaker 1: think it's a relatively low probability. We think there's a
Speaker 1: chance within our model that they're able to deliver robotaxi
Speaker 1: capability to the deployed vehicles in fleet. But that call
Speaker 1: option is worth quite a bit because you have, depending
Speaker 1: on how aggressive they are at building electric vehicle factories,
Speaker 1: they basically get an uber like model in a natural
Speaker 1: monopoly type position in all of the vehicles that they
Speaker 1: have deployed. So, you know, speaking of that business, I
Speaker 1: mean I was looking you guys. You put your models,
Speaker 1: your financial models on geth hub, or at least some
Speaker 1: of them, and so anyone can go to your site
Speaker 1: and then go to your geth hub and find the
Speaker 1: assumptions that you use to figure out the economics basically
Speaker 1: of UH robotaxis and all and all of this and
Speaker 1: how much is worth and how much that's gonna cost
Speaker 1: and how compelling UH that model of car usage would
Speaker 1: be verse traditional? Is that something like putting it out
Speaker 1: there like that, having a open source model that anyone
Speaker 1: can play with? Is that something that previously, you know,
Speaker 1: your old shop, Is that something that you wanted to
Speaker 1: do and there's just sort of like no way, you know,
Speaker 1: that sort of openness what you're describing being able to
Speaker 1: tweet social media argue about this stuff, get feedback. I mean,
Speaker 1: I recall I think a couple of years ago, seeing
Speaker 1: you or maybe your firm get into like going back
Speaker 1: with like someone in ft Alphaville, like arguing about your
Speaker 1: tesla mode might have been you like, is that something
Speaker 1: that prior to art that you had wanted to do
Speaker 1: and felt like was missing? And talk to us a
Speaker 1: little bit more about that aspect of the of the approach,
Speaker 1: It's always been clear to me that you get more
Speaker 1: out of the information ecosystem by providing information into it.
Speaker 1: I can't say that at Alliance Bernstein, I like wanted
Speaker 1: to do that. It wasn't even within the realm of
Speaker 1: possibility of a thing that I could have done, right,
Speaker 1: Like think about the way in which traditional fund management,
Speaker 1: the analysts themselves are are are buried there like not
Speaker 1: allowed to speak on behalf of the firm in any
Speaker 1: way like and and whereas we think that the analysts
Speaker 1: need to be able to converse at lee discuss what
Speaker 1: they believe, both in written form and orally, so that
Speaker 1: they uncover their own weaknesses. Like being having to publish
Speaker 1: to the world makes you a much better and more
Speaker 1: diligent modeler than if you're not going to do so.
Speaker 1: You know, if you dig into the like underpinnings of
Speaker 1: models that aren't published, they're always a mess. They're always
Speaker 1: poorly documented. There are always things where people have been like,
Speaker 1: I just picked that because I wanted to write, whereas um,
Speaker 1: both the kind of auditing process we have to go
Speaker 1: through in order to get something ready to externalize and
Speaker 1: the research process along the way, in which you're always
Speaker 1: seeking to distill it to its most elemental and understandable form,
Speaker 1: actually forces us to be better at our jobs. And
Speaker 1: then once you publish, you get better again because you
Speaker 1: get this great feedback loop of people telling you what
Speaker 1: you got wrong, which at the time is kind of
Speaker 1: like having your eyes gouged out, but at the end
Speaker 1: of which you know you you are stronger and more
Speaker 1: certain about the things that you were right about, and
Speaker 1: you uncovered the critical weaknesses and how you underwrote the
Speaker 1: business or the technology or whatever you're talking about. And
Speaker 1: so it really has it helps us internally and externally
Speaker 1: in the iteration cycle of trying to get closer to
Speaker 1: what's going to be the ultimate truth of how things
Speaker 1: play out. We have a much more open format than
Speaker 1: other organizations that that I've been involved with, and I
Speaker 1: think it has helped both in strategic decision making internally
Speaker 1: and certainly in portfolio management and research. UM. I think
Speaker 1: that the hard part has not been getting people to
Speaker 1: to share their work, but it's really to do the
Speaker 1: hard first principles work. In the first place, I think
Speaker 1: there's a lot of people, particularly within the financial industry,
Speaker 1: who are not used to having to be intellectually ambitious
Speaker 1: and and and except that you're going to forecast something
Speaker 1: with it with over a time prison, where it's unfair
Speaker 1: to try to forecast it over that time prison, but
Speaker 1: it's actually fair so long as you understand the error
Speaker 1: bands around what you're forecasting. Uh and and so people
Speaker 1: prefer because it's more comfortable to pay five thousand dollars
Speaker 1: for the you know, the market's report that tells you
Speaker 1: what the market is going to be five years from
Speaker 1: now and be like, okay, well I'm just gonna use that. Well,
Speaker 1: if you think about within equities and particularly high priced equities,
Speaker 1: which is the train that we operate in, you know,
Speaker 1: most of the value of that businesses in whatever the
Speaker 1: terminal rate of growth you put on the DCF and
Speaker 1: so effectively. What they're doing is they are outsourcing the
Speaker 1: most critically sensitive part of the valuation work to an
Speaker 1: entity that they haven't necessarily due diligence at all, and
Speaker 1: it is not really well incentivized to create a forecast
Speaker 1: that's correct. The hard part is not getting people to share.
Speaker 1: It's getting people to make a reasonable first principles forecast
Speaker 1: that is different. So it's really like the I think
Speaker 1: forecasting within the financial industry for the most part, is
Speaker 1: people taking other forecasts and going plus or minus from
Speaker 1: that other forecast, right, And that's not that's just not
Speaker 1: how we approach it, and having having a process and
Speaker 1: a discipline of not approaching it that way, not saying
Speaker 1: why I want to see what everybody else has done,
Speaker 1: and then I feel better about this, so I'm gonna
Speaker 1: go slightly higher or worse about this, so I'm gonna
Speaker 1: go slightly lower. Instead, it's like, well, this is what
Speaker 1: we think it's gonna be. And sometimes you do all
Speaker 1: that work and you're like, Okay, well that's not that
Speaker 1: interesting because it's similar to what everybody else thinks. And
Speaker 1: sometimes it's wildly interesting because you're like, what is everybody
Speaker 1: else thinking? And so then the process of discovery of
Speaker 1: either what did you overlook or what are they overlooking?
Speaker 1: That's where all of the inefficiency lies. Now. Early on
Speaker 1: we tried to do I think we like the analysts
Speaker 1: didn't run their own Twitter accounts. They were like they
Speaker 1: shared them and they were more corporate, and that didn't
Speaker 1: work very well. Like you do have to have somebody
Speaker 1: publishing under their own name, speaking in their own voice
Speaker 1: to a certain extent, so that they can both own
Speaker 1: the mistakes and own the triumphs of oh I understand
Speaker 1: this thing. Uh, and to um kind of the create
Speaker 1: creating an environment where analysts are owning kind of that
Speaker 1: intellectual accomplishment. And then and and the learning process and
Speaker 1: how it filters into the learning process is a big
Speaker 1: part of the secret sauce. And that's not available. I
Speaker 1: mean an alliance FIRSTY and I do it a lot
Speaker 1: of work, and then somebody else's name would go on it.
Speaker 1: You know, that doesn't feel very good, Like, what's your
Speaker 1: incentive structure for doing all that work? If if like
Speaker 1: it's it's it's just published under somebody else's name. So
Speaker 1: I think that the you know, having kind of a
Speaker 1: real like having the analysts as close to the metal
Speaker 1: as possible of what's going on in the world, and
Speaker 1: having their perspective on what's going to happen in the world,
Speaker 1: like come right up against that interface is really critical
Speaker 1: to to just thinking about things better. Do you have
Speaker 1: a different approach to finding and hiring analysts at ARC
Speaker 1: then at a place like Alliance Bernstein and can a
Speaker 1: different type of person get an interview or get their
Speaker 1: foot in the door there? Then might say the traditional
Speaker 1: filter at a traditional asset management company, Well, I mean
Speaker 1: we're likely to hire someone soon who doesn't have a
Speaker 1: college degree, so that probably gives you a sense. The
Speaker 1: usual route is you you hire N B A S
Speaker 1: or you you know, hire our sell side analysts you know,
Speaker 1: and those are often very smart people. Um, but there
Speaker 1: is I think a selection bias that happens early on
Speaker 1: within kind of the financial ecosystem that um, you know,
Speaker 1: cuts out some of the creativity that you need in
Speaker 1: order to end up with the different result another like
Speaker 1: nuanced it. I think it's interesting about our processes if
Speaker 1: you if you imagine how do you make money in
Speaker 1: our do well by your clients when managing equities? Well,
Speaker 1: if you are wrong that's fine as long as you're
Speaker 1: uniquely wrong, right, right, Like, if you're uniquely wrong, everybody
Speaker 1: thought you were crazy anyway, and so it's not priced in.
Speaker 1: It's when you're wrong with everybody else that you get
Speaker 1: into trouble. And it's when you're uniquely right that you
Speaker 1: actually you know, compound your holdings. Uh. And so if
Speaker 1: if your forecasts were on average worse but unique, that
Speaker 1: is better than having forecasts that are closer to the
Speaker 1: actual truth but the same as everybody else. And so
Speaker 1: you know, you need to have a diversity of kind
Speaker 1: of cognitive perspective in some way relative to everybody that
Speaker 1: you're competing against. Even if it yields kind of like
Speaker 1: more volatile results, you you on average have more differentiated results,
Speaker 1: which then gives you both downside protection everybody thought you
Speaker 1: were crazy anyway and upside potential. Well nobody expected this.
Speaker 1: So um, I think having in and that requires a
Speaker 1: degree of um kind of hardness against some of the
Speaker 1: social pressures that I think operate in a lot of
Speaker 1: Wall Street. And so we don't, Yeah, we don't select
Speaker 1: from the same pool of candidates, or at least we haven't.
Speaker 1: We've often end up screening out kind of more traditional
Speaker 1: financial candidates just because they don't have as idiosyncratic a
Speaker 1: point of view. I wanted to ask you about another
Speaker 1: specific well I don't want to say specific thing, but
Speaker 1: another specific technology. Since you group yourselves by technology. You
Speaker 1: have a crypto analyst who looks at blockchain and bitcoin,
Speaker 1: and I think Cathy has been quite bullish on the
Speaker 1: technological potential of blockchain as a whole. Could you maybe
Speaker 1: walk us through the thinking behind that and again connect
Speaker 1: it to your overall methodology and structure, because I think
Speaker 1: there were quite a few cell side analysts talking about,
Speaker 1: you know, how bitcoins a bubble, but blockchain is the
Speaker 1: technological future. But you at ARC took a different approach
Speaker 1: and basically said by bitcoin and by blockchain related technology.
Speaker 1: So what was the thinking there? Yeah, I think you
Speaker 1: can from a very high level think about, um, how
Speaker 1: all contracts that we sign actually have this failed mode
Speaker 1: where the political entity that enforces them sometimes just decides
Speaker 1: not to enforce them or changes the rules. Like imagine
Speaker 1: you've signed a contract and then suddenly you know somebody,
Speaker 1: the other counterparty in the contract rnegs, he doesn't pay up,
Speaker 1: and you go to the government and say, well, you
Speaker 1: have to force this guy to pay because he didn't pay,
Speaker 1: and the guy is actually you know, as an end
Speaker 1: with the government. The government's like, no, thank you. Uh.
Speaker 1: And so the pro amss of of crypto assets generally
Speaker 1: is basically, uh that that final layer of kind of
Speaker 1: contract settlement happens regardless of the underlying political circumstances. So
Speaker 1: you can broaden that across. Think of like all of
Speaker 1: the various contracts in the economy. Think of a structuring
Speaker 1: deskin and investment bank. It's basically set up to create
Speaker 1: complex contracts with counterparties where they are are assured that
Speaker 1: those those contracts will actually be made good because the
Speaker 1: counterparties are really well respected established institutions. Well, kind of
Speaker 1: smart contracting platforms like Ethereum and others allows for an
Speaker 1: experimentation layer where you can, you know, instead of having
Speaker 1: to work in Morgan Stanley in order to structure those products,
Speaker 1: you can be Joe uh, you know, coder and and
Speaker 1: create kind of those contracts with the protocol itself serving
Speaker 1: as the ultimate counterparty that that will see that those
Speaker 1: contracts get executed upon. Well, currencies are all contracts. In fact,
Speaker 1: you could argue that the most valuable contracts and there's
Speaker 1: a social contract between me and the US government that
Speaker 1: somehow the purchasing price of the dollar is not going
Speaker 1: to diminish more than I guess two percent a year
Speaker 1: or whatever their target is. Right. So bitcoin basically supplants
Speaker 1: that social contract with with kind of its protocol for
Speaker 1: security of the asset. So I think it's a profoundly
Speaker 1: interesting kind of set of ideas across the entire crypto
Speaker 1: asset space that over of all the technologies we look at,
Speaker 1: over probably the longest adoption time will have actually the
Speaker 1: most dramatic uh financial and technological impact. Uh. So you know,
Speaker 1: from the beginning, we thought it was interesting. We had
Speaker 1: a crypto a blockchain analyst in I believe is when
Speaker 1: we hired him. Uh, And it was because even though
Speaker 1: there were not necessarily many investable as it's we understood
Speaker 1: that the technology at that time, we understood that the
Speaker 1: technology was within within our product suite. We understood that
Speaker 1: the technology was interesting enough and going to create sufficient
Speaker 1: disruption that it was worth beginning to invest in understanding it,
Speaker 1: Understanding where it was going to go, Understanding what the
Speaker 1: best mechanism by which to create client exposure to it,
Speaker 1: and so that's what we did. We have vehicles in
Speaker 1: which we can get client exposure to crypto assets. We
Speaker 1: you know, it was a smart, smart move, both at
Speaker 1: the time and going forward. And I think that if
Speaker 1: you're within the financial services industry and you're not thinking
Speaker 1: very deeply about how digital wallets, crypto assets and and
Speaker 1: UH neural NEETs and artificial intelligence are going to change
Speaker 1: what you're doing over the medium term, then you're not
Speaker 1: operating intelligently within the firm that you're operating. So I
Speaker 1: have a follow up question. I'm going to try to
Speaker 1: phrase this UH as diplomatically as possible. Your outperformance speaks
Speaker 1: for itself. Your returns have been absolutely excellent in recent years.
Speaker 1: But there are people out there who would point to
Speaker 1: that performance and say that you've been writing a tech
Speaker 1: bubble or you're buying into the stocks that have very
Speaker 1: compelling narratives that seem to capture the wider imagination, but
Speaker 1: that haven't actually been proven yet in terms of earnings.
Speaker 1: They're just training at you know, massive valuations and getting
Speaker 1: more expensive, but the earnings haven't actually kept up. So
Speaker 1: what do you say to those people? Two people who
Speaker 1: say that you're basically momentum training on enthusiasm for unproved technology, right.
Speaker 1: I didn't phrase that very diplomatically, did I? No, that's fine,
Speaker 1: I mean listen to. Our job is to understand what
Speaker 1: the value of something is going to be, as we
Speaker 1: currently phrase it, five years from now. And um. Sometimes
Speaker 1: so fuel cells is a great example where you know,
Speaker 1: we did our first work on fuel cells in we
Speaker 1: did a cost declient on it. We determined that within
Speaker 1: passenger vehicles, we don't think they are gonna be cost
Speaker 1: competitive with electric vehicles until the early ties, and that
Speaker 1: was contingent on something like the Toyota Marai selling in
Speaker 1: Toyota Prius like volumes over the course of a decade.
Speaker 1: Uh and so um. You know, having gone through that exercise,
Speaker 1: it was very easy for us to kind of, you know,
Speaker 1: look at that entire stack of assets and and every
Speaker 1: time one comes up and says, oh, well, this is
Speaker 1: what's different, we've already done the work to understand, you know,
Speaker 1: the key cost assumptions you need to make, um and
Speaker 1: how there's costs are declining and what that means for
Speaker 1: the future unit economics of that technology. And then you
Speaker 1: can say Okay, that's not something we're going to invest in. Now.
Speaker 1: We could be wrong, right, and and we're wrong all
Speaker 1: the time, right. But but but I think that actually
Speaker 1: doing the work to underwrite the asset this is really difficult.
Speaker 1: It's not easy. Uh. And so there's a difference between
Speaker 1: I am going to invest in the blockchain iced tea
Speaker 1: company because they say the word blockchain, and and invest
Speaker 1: in a company that I think is under priced over
Speaker 1: time horizon that is meaningful to my clients. I'm really
Speaker 1: glad you brought up fuel cells because I was actually
Speaker 1: gonna go there next. So maybe probably probably people are
Speaker 1: aware some of the hottest stocks right now are fuel
Speaker 1: cell stocks. Plug Power is one, fuel Cell Energy is another. One.
Speaker 1: Major winners, and I have a personal interest in this
Speaker 1: area because and I'm not saying this to brag or
Speaker 1: anything like that, but I actually like traded these exact
Speaker 1: stocks when I was in college in the late nineties
Speaker 1: and early two thousands, the same exact stocks plug Power
Speaker 1: and fuel Cell. They've been around forever, and I you know,
Speaker 1: they were crazy overvalued then, but I got kind of
Speaker 1: lucky and that helped pay for college. Anyway, The point is,
Speaker 1: I'm not trying to brag it just anyway. My point
Speaker 1: is at the time, it was like, Okay, this is
Speaker 1: right around the corner and fuel cell fuel cells are
Speaker 1: gonna be on the road by Obviously that didn't happen.
Speaker 1: So you're saying it's not different this time, that this
Speaker 1: is just yet another series in an extremely long history
Speaker 1: of people getting over excited and over optimistic about this technology,
Speaker 1: and that once again it's further off than people think.
Speaker 1: I mean, there are niche applications where you can underwrite it.
Speaker 1: I'm not going to disparage or endorse plug power, but
Speaker 1: there are clearly buyers of fuel cell driven forklifts because
Speaker 1: you can have the hydrogen right there on site. And
Speaker 1: makes sense that to you know, our understanding of how
Speaker 1: you would have to underwrite that asset to justify its prices.
Speaker 1: You would have to think it's going to get into
Speaker 1: the truck or passenger vehicle business. You would have to
Speaker 1: think that the cost declient on the technology would carry
Speaker 1: it into a competitive position with alternative um mode of
Speaker 1: technologies in those domains. And like just on the electric
Speaker 1: vehicle side, it's really hard. You have to make a
Speaker 1: lot of assumptions about somehow the assets or the technology
Speaker 1: being bought up to drive the costs efficiently low to
Speaker 1: make it unit economic compelling. Uh. And so there there
Speaker 1: you can like, you have to generate the hydrogen somewhere
Speaker 1: first of all, so that costs money. You're operating costs
Speaker 1: are much much higher. Uh. Then you have to fund
Speaker 1: the build out of the hydrogen fueling infrastructure, which is
Speaker 1: really it's not easy. It's like a hard coordination problem. Tesla.
Speaker 1: Even from the beginning, we thought I was skeptical of
Speaker 1: Tesla's supercharger network build out. I thought that that was
Speaker 1: not a layer that they needed to compete in. As
Speaker 1: it turned out, I was dead wrong. Like that definitely
Speaker 1: differentiates their product because range doesn't become as much of
Speaker 1: an issue in making the sale because people can imagine
Speaker 1: doing the road trip they want to do with their
Speaker 1: new car, which is, if you can't do the road trip,
Speaker 1: what's the point of getting the new car? Right? Uh?
Speaker 1: And Uh, But a supercharger costs a tenth what a
Speaker 1: hydrogen station does to like a full supercharger station versus
Speaker 1: a hydrogen station, So you know, you're talking on the
Speaker 1: order of a hundred to two hundred thousand dollars versus
Speaker 1: a million to two million. You have to do a
Speaker 1: ton of execution, have like a ton of selling of
Speaker 1: the technology that it's hard to see how it happens
Speaker 1: because you can't chicken and egg the infrastructure in place
Speaker 1: sufficient to drive demand for the underlying technology sufficient to
Speaker 1: get it low enough in price that it's cost competitive
Speaker 1: with existing modes of transport. Uh So is it possible, yes,
Speaker 1: can we reasonably underwrite it? Not at this time? HM.
Speaker 1: That was a good answer. But that's what I mean.
Speaker 1: You know, when Facebook oculus right suddenly, all these analysts
Speaker 1: are coming out with VR is going to be you know,
Speaker 1: eighteen million units by ten or whatever, and and we
Speaker 1: looked at it, we did a model on it, and
Speaker 1: and we couldn't get enough people to buy the headsets
Speaker 1: for a triple A game developer to justify underwriting a
Speaker 1: game developed specific for the headsets, And so you just couldn't.
Speaker 1: It didn't make sense, right, Like, you go through and
Speaker 1: you try to say, what is this market going to be?
Speaker 1: And if it doesn't, like, if the modeling that you
Speaker 1: do doesn't make sense, then you're not going to take
Speaker 1: aggressive positions on the basis of it making sense so
Speaker 1: and that you know, so we never built kind of
Speaker 1: VR heavily into our own video model because that you know,
Speaker 1: it was a dry hole and and a lot of
Speaker 1: I think our role is to figure out actually the
Speaker 1: things too that you can dismiss a whole category of
Speaker 1: by doing a single piece of work. Like that's really
Speaker 1: important because you have you know, now there's a gazillion
Speaker 1: SPACs coming at us right and and um, you need
Speaker 1: to have some some lens by which you approach these
Speaker 1: assets and and say what they are fundamentally worth. That
Speaker 1: allows you to easily establish whether or not something is
Speaker 1: of potential interest to portfolio management and to your clients.
Speaker 1: I wanted to go back to something we mentioned in
Speaker 1: the intro, which is the extraordinary inflows that we've seen
Speaker 1: into ARC alongside the extraordinary performance that we've been discussing.
Speaker 1: Have those inflows change the way you invest at all
Speaker 1: or your research process? Does it perhaps become harder to
Speaker 1: identify new opportunities? Um, the more money you have to
Speaker 1: put into a certain company or a technology, every investment
Speaker 1: decision that you make, you would want to make frictionlessly
Speaker 1: at the exact size that you want at the instant
Speaker 1: that you want to do it, and that's you know,
Speaker 1: not possible. No matter how much you're managing, the research
Speaker 1: process has always remained the same. We always start at
Speaker 1: the technology level. We understand the direction that the technology
Speaker 1: is going. Because all of the technologies that we're investing
Speaker 1: in are are are exponential. You're actually creating a lot
Speaker 1: more opportunity. I mean, if if you look at our
Speaker 1: tests are open source Tesla model, you can you can
Speaker 1: drag out, you can see what our next year needs
Speaker 1: to be given our expectation for EV sales, and it
Speaker 1: actually meaningfully increases your expectation for value of the company
Speaker 1: just dragging out to the right by one year, you know.
Speaker 1: And and kind of the spack phenomenon is creating more
Speaker 1: publicly traded equities that we could potentially invest in within
Speaker 1: the technology areas that we're interested in. And you know,
Speaker 1: taking in a lot of flows uh into our assets.
Speaker 1: Luckily within the E T F construct that's relatively easy.
Speaker 1: Uh and uh, And we are always selective about, you know,
Speaker 1: how we deploy and what's the most efficient way to
Speaker 1: get exposure to the inefficiencies that we see. You know,
Speaker 1: you mentioned the big picture UM technology families that you
Speaker 1: start with. So you have this like top down approach
Speaker 1: to figuring out the big areas. Genomics is one, robotics,
Speaker 1: How did you come up with those? I mean, what
Speaker 1: is there? Is there a methodological process for figuring out
Speaker 1: and planning a flag on the ground and saying, Okay,
Speaker 1: this is going to be really big. Yeah, so they
Speaker 1: all have to uh and and so I alluded to it.
Speaker 1: But there's this theory called general purpose technolo oology theory
Speaker 1: where it's like these academics have agreed upon what the
Speaker 1: criteria are for really meaningful technologies. They all have steep
Speaker 1: cost of clients, they all cut across sectors, and they're
Speaker 1: all themselves platforms of innovation. Uh. And so we try
Speaker 1: to apply that framework to the technologies that we're interested in.
Speaker 1: We think there are five fundamental technology platforms that are
Speaker 1: all entering the economic marketplace today. Uh. Gene sequencing and editing,
Speaker 1: AI and particularly neuron NEETs, robots, particularly collaborative robots, energy
Speaker 1: storage and the advances and battery technology, and then blockchain cryptocurrency.
Speaker 1: And so we believe that yet future historians will look
Speaker 1: back and identify all of those as big technological buckets.
Speaker 1: But you know, within taxonomys there are always weaknesses, right,
Speaker 1: and you could draw the lines in a slightly different area.
Speaker 1: So we tried to look back and see, like, what
Speaker 1: technologies did historians agree upon where these general purpose technology
Speaker 1: platforms over time? And there's not consensus at even looking backwards.
Speaker 1: So of course there's not consensus today what are the
Speaker 1: major technology platforms? But I think it's a so the
Speaker 1: other um from those five technology platforms, there are also
Speaker 1: we have fourteen underlying technologies that are discreetly model able,
Speaker 1: where we have a good understanding of the cost a
Speaker 1: client of kind of how it cuts across sectors, and
Speaker 1: and the equity market capit cruel that we expect those
Speaker 1: technologies to achieve over time. I'm I'm a big believer
Speaker 1: in getting really dirt simple with your assumptions of things
Speaker 1: so that you can tell if they make sense. So
Speaker 1: it's it's kind of like, you know, what what are
Speaker 1: robots going to be worth? Well? What if we start
Speaker 1: out and say what about every manual laborer employee in
Speaker 1: the world, and we say, well, we're going to supplement
Speaker 1: this person with a ten dollar tool that's a robot, Like,
Speaker 1: what would that market be worth? What would be the
Speaker 1: cash flow cruel to the about manufacturers in that instance.
Speaker 1: And then so how much would you assume, uh it's
Speaker 1: occupied in terms of enterprise value by the companies that
Speaker 1: are catering to that economic opportunity. Uh And and so
Speaker 1: if you do kind of that very high level assumptions
Speaker 1: about the technologies that we track, you would assume that
Speaker 1: there's gonna be fifty trillion dollars in market cap accruel
Speaker 1: to our technologies over the next decade. Uh And and
Speaker 1: so the this gets back to the capacity question. There's
Speaker 1: gonna be a lot of economic value created, and there's
Speaker 1: gonna be major, major businesses that accrue out of it,
Speaker 1: you know, like with it. If you look at how
Speaker 1: we've modeled autonomous robot taxis globally, uh we think that
Speaker 1: autonomous robot taxis the platforms that enable that are going
Speaker 1: to be worth more than the global energy sector as
Speaker 1: a whole within five years. Just and and you actually
Speaker 1: don't have to make radical assumptions to get there. You say, well,
Speaker 1: look at global miles driven and these are going to
Speaker 1: price it something likes a mile, So they're going to
Speaker 1: be cheaper than actually buying and owning and operating a
Speaker 1: vehicle in the US that costs yous a mile if
Speaker 1: you buy a new one, right, and so it's going
Speaker 1: to be the default way by which people get around.
Speaker 1: These autonomous taxi platforms are going to scrape a platform
Speaker 1: feed just like an uber or lift. If you back
Speaker 1: into the aggregate cash flow that you expect given your
Speaker 1: adoption curves and everything else, it's it's uh, you know,
Speaker 1: measured in hundreds of billions of dollars within five years,
Speaker 1: So it's natural that the market would pay at least
Speaker 1: a reasonable cash flow multiple on that cash. So there's
Speaker 1: there's a combination of like, what's the cost aclient look like,
Speaker 1: how is it cross sector? Uh? And when has it
Speaker 1: gone cross sector? What other things are going to be
Speaker 1: built on top of it? And thinking about how meaningful
Speaker 1: is this going to be economically over the medium to
Speaker 1: long term, And if you can kind of dimension that
Speaker 1: it's meaningful and cross sector and steep cost decline and
Speaker 1: itself a platform of innovation, it's very likely that this
Speaker 1: thing is going there's gonna be a lot of value
Speaker 1: created here. So it's worth devoting the intellectual capital to
Speaker 1: understanding and understanding that puts and takes of how it's
Speaker 1: going to get to market, and which part of the
Speaker 1: value chain is going to be the most um cash
Speaker 1: a creative and and and how that part of the
Speaker 1: value chain is underwritten today relative to how you think
Speaker 1: it should be. Would you ever consider starting a SPAC
Speaker 1: or is it too much of a departure from the
Speaker 1: current model? Um? Well, I mean I think that there
Speaker 1: are pluses and minuses of SPACs. I think that there
Speaker 1: is a degree of nervousness that at least I have
Speaker 1: right now that there that when people raise spacts like
Speaker 1: they are heavily incentivized to figure out something to buy
Speaker 1: with them. Nobody returns the money, right, And it's almost
Speaker 1: like a you create a time bomb of I p
Speaker 1: O right, like U I p O. But the real
Speaker 1: I p O is when you merge with the other
Speaker 1: entity and the people who are controlling whether or not emerged. Yes,
Speaker 1: you have to get the shareholders to vote, but the
Speaker 1: people who are controlling it, like it's basically like you know,
Speaker 1: buy something or or you lose it. It seems in
Speaker 1: some ways backwards to how companies should come to the
Speaker 1: capital markets, and that they should come to the capital
Speaker 1: markets when they're ready not because there's a pool of
Speaker 1: money that's going around trying to find everything that could
Speaker 1: possibly go to the capital markets. I'm nervous about that.
Speaker 1: On the other hand, if you think about UM, what
Speaker 1: has happened with late stage venture, which is where a
Speaker 1: lot of these companies would otherwise have been funded, is
Speaker 1: that there you are only allowing accredited investors to invest
Speaker 1: in kind of these technology companies and uh, you know,
Speaker 1: the late stage venture capitalists get money through their carry
Speaker 1: and their management fee. Uh, that's also quite punitive to
Speaker 1: the end he old or, and you're cutting out the
Speaker 1: entire you know, Joe investor who's not accredited, And so
Speaker 1: you could argue that this is a way to democratize
Speaker 1: access to these late stage venture type assets. I think
Speaker 1: that to me, there seems like there's a lot of
Speaker 1: misbehavior going on in the space. And usually our bias
Speaker 1: is too when there's a lot of capital going after
Speaker 1: something to be wary of it. UM. But I can't
Speaker 1: you know, comment you know directly, but the lack of
Speaker 1: disclosure for the underlying companies I think could lead misbehavior.
Speaker 1: On top of misbehavior, I talked about how consultants UM
Speaker 1: like Mackenzie and stuff. They they their forecasts weren't as
Speaker 1: good as I thought they were going to be. Well,
Speaker 1: we also look at the forecast of the management teams
Speaker 1: within these facts and and that is a difference from
Speaker 1: an I P O. And you know and S one.
Speaker 1: You you're not going to get management team telling you
Speaker 1: what they think they're going to print in revenue five
Speaker 1: years from now. Within the facts the management teams are
Speaker 1: and and there as a general rule so far, looking
Speaker 1: at what management teams have forecast, we have a hard time, um,
Speaker 1: hitting that. So let me sort of and you know,
Speaker 1: I think we can wrap up soon. Um, but let
Speaker 1: me just sort of this sort of gets to a
Speaker 1: bigger question, and Tracy sort of hinted at it. Well,
Speaker 1: you know, this sort of the claim among our detractors
Speaker 1: that you've done a really good job basically riding this
Speaker 1: big bubble. What happens if at some point we are
Speaker 1: in a bubble And some people would say we're in
Speaker 1: one now, but you know, there are times in which,
Speaker 1: in retrospect you're like, oh, there's definitely or in a bubble,
Speaker 1: there was no good tech to buy in December of
Speaker 1: anything that you bought then pretty much in anything related
Speaker 1: to tech was probably gonna be underwater for years. If
Speaker 1: you had purchased then maybe some of them, you know
Speaker 1: I've obviously done well instance could then what what do
Speaker 1: you do if you come across that environment where all
Speaker 1: of your models are saying, in these areas of innovation,
Speaker 1: in these areas of tech that we're into, we just
Speaker 1: can't make the numbers work for anything that's of like quality.
Speaker 1: Is that a concern? Is that a situation that you've
Speaker 1: thought about? Like, how do you think about that question?
Speaker 1: I can say that right now we can still find
Speaker 1: a lot of inefficiently priced assets. So at least as
Speaker 1: we model or as we expect the world to unveil itself. Um,
Speaker 1: I don't see it, you know, within the context of
Speaker 1: the positions that we put client money into. I think
Speaker 1: that uh, you know, financial markets are full of in
Speaker 1: some ways saying a bubble, I think is is you know,
Speaker 1: it's lots of burbl ng and sometimes you get a
Speaker 1: you know a bigger degree of burbling. But there there
Speaker 1: are always you know, there's the I C O boom
Speaker 1: in there's if today or you know, three months from now,
Speaker 1: the equity markets are down, you know, then we would
Speaker 1: all look back and say, oh, well, the SPACs were
Speaker 1: the sign. It was obvious, didn't you see? You know?
Speaker 1: And and if you are investing money in the equity markets,
Speaker 1: equities are infinite in duration, you should not be doing
Speaker 1: that on the basis that the one year result is
Speaker 1: going to be meaningfully indicative of whether or not it
Speaker 1: was a good decision, that it's the wrong time horizon, right,
Speaker 1: and so like, I like my comfort level is that
Speaker 1: we look out five years and I say this looks
Speaker 1: very reasonable over five years, because we're not making We're
Speaker 1: not going out five years and saying then I'm going
Speaker 1: to pay an elevated multiple. I'm going out five years
Speaker 1: and saying I'm going to be a forced seller to
Speaker 1: someone who only pays the market multiple for the cash
Speaker 1: flow coming off of business with this kind of margin
Speaker 1: profile and capital intensity, and and you know our return
Speaker 1: hurdle for for the positions we underwrite, as so we
Speaker 1: think it's going to roughly double over five years. Well,
Speaker 1: you know, so I have a lot of ways in
Speaker 1: which to get exposure where that's at least the way
Speaker 1: we forecast the world. Now, could we look really dumb
Speaker 1: twelve months from now? Yes, In fact, I think it's
Speaker 1: likely that at some point people will think that ARC
Speaker 1: was a scam and that we were, um, you know,
Speaker 1: we don't know our left from our right, and we're
Speaker 1: doing things wrong. And our discipline and our our mission
Speaker 1: is to continue to say what we think is going
Speaker 1: to happen, and to try to you know, buy basically
Speaker 1: intangible assets at at deep value, regardless of the market environment,
Speaker 1: the tenure rates, and that they came down as much
Speaker 1: as they did during the pandemic. It has it provides
Speaker 1: the highest leverage to the longer duration assets, right and
Speaker 1: and so naturally if you know over ten years you
Speaker 1: can only get one percent compounded on your money, well,
Speaker 1: then something that's not going to produce cash flow for
Speaker 1: you until ten years from now, but that cash flow
Speaker 1: could be monumental looks a lot more attractive because you're
Speaker 1: you know, the competitive rate of of of cash flow
Speaker 1: generation is is just much lower. That had an effect
Speaker 1: on the overall market multiple, and we don't try to
Speaker 1: take a stance against the overall market multiple as and
Speaker 1: we don't try to position ourselves. You know, I'm not
Speaker 1: gonna I'm not trying to allocate between equities and fixed income, right,
Speaker 1: and so I just try to underwrite the equities, you know,
Speaker 1: qua another equity exposure, you know, financial markets. I was
Speaker 1: being accused of having committed career suicide because of our
Speaker 1: Tesla position, and that was you know, that was Memorial
Speaker 1: Day of like, you know that that was that was
Speaker 1: not that long ago. The markets mania is much more
Speaker 1: volatile than our fundamental valuing of the company. So the
Speaker 1: way in which we manage the portfolios is were typically
Speaker 1: short term contrarian. If something is rallying, it's often rallying.
Speaker 1: If it goes up because it beat on earnings, that
Speaker 1: doesn't change what we think the company is going to
Speaker 1: look like five years from now. So we'll often sell
Speaker 1: off that gain to buy into something that you know,
Speaker 1: suddenly was investing too much in R and D so
Speaker 1: they missed on earnings and it's like, yes, give me
Speaker 1: more of that, uh. And so that that actually doing
Speaker 1: the work over five years provides us a lot of
Speaker 1: um kind of anchoring that allows us to manage positions
Speaker 1: within the portfolio as they respond to news that we
Speaker 1: don't think is actually fundamentally meaningful. In in the event
Speaker 1: that the markets start to sell off for whatever reason,
Speaker 1: because rates are going up because I don't know, geopolitical
Speaker 1: risk diminishes. But you know, you you all are the
Speaker 1: ones that get to explain daily why markets do what
Speaker 1: they do. You know, then we'll respond to that. That's
Speaker 1: why we actively manage the portfolios. But um, I certainly
Speaker 1: I would hate to have to say what going to
Speaker 1: happen in three months. I think that's much harder than
Speaker 1: than saying what's going to happen over five years. Actually, um,
Speaker 1: I think it's it's a really it's a really challenging
Speaker 1: game because it requires you anticipating what other people are
Speaker 1: going to then think, rather than trying to forecast what's
Speaker 1: going to happen kind of objectively in the world. Uh.
Speaker 1: And um, I think I think a lot of people
Speaker 1: play that game, but I it's not that interesting to me,
Speaker 1: and I think it's really hard. Brett, that was that
Speaker 1: was fantastic. Really. Uh, I'm really glad we got a
Speaker 1: chance to talk to you. I learned a ton in
Speaker 1: that conversation, and I appreciate you taking the time my pleasure.
Speaker 1: Joe Ye, thank you, Thank you so much. Thanks Brette, Tracy,
Speaker 1: that was really cool. I mean, obviously I've been aware
Speaker 1: of arc and they're amazing stock picks, and particularly um
Speaker 1: they're sort of vindication on the Tesla pick. But I'm
Speaker 1: hearing overall like they're sort of like general approach. That
Speaker 1: was very useful and interesting. Yeah, I agree. There were
Speaker 1: two things that stuck out from that conversation for me,
Speaker 1: and again I don't mean to naval gays in the
Speaker 1: media too much, but one of them was the way
Speaker 1: they organized themselves around technologies rather than traditional sort of
Speaker 1: analyst or industry sectors. And I have to say, I
Speaker 1: think that's something that a lot of media companies have
Speaker 1: struggled with over the years, you know, particularly when bitcoin
Speaker 1: came out, for instance, there was a lot of discussion
Speaker 1: about it. Should it be done by market reporters, should
Speaker 1: it be done by commodities reporters, Does it fit into
Speaker 1: an investment team or the tech team, and everyone kind
Speaker 1: of struggled to fit it into a traditional category. But
Speaker 1: had you just looked at it as a sort of
Speaker 1: um sort of cross beats technology like blockchain, maybe it
Speaker 1: would have been easier to conceptualize, I suppose, and the
Speaker 1: same thing for you know, electric batteries and things like that.
Speaker 1: So that was really interesting and the second thing about
Speaker 1: refining your work through public interaction and discourse also strikes
Speaker 1: a chord. Both you and I are very active on
Speaker 1: Twitter and social media. I think journalism in itself is
Speaker 1: a very public activity, since every time you publish something,
Speaker 1: you're probably going to get some sort of reaction or
Speaker 1: feedback to it, and in the end you can use
Speaker 1: that to refine your thought process, you think more strategically
Speaker 1: about your model or your subject matter or whatever. And
Speaker 1: I don't know, I just see a lot of parallels
Speaker 1: between what his analysts that are are doing and what
Speaker 1: some journalists are doing or could be doing. Yeah, now
Speaker 1: that that definitely stood out to me. And it's one
Speaker 1: of these things where, like I get as a journalist
Speaker 1: so much value from interacting on social media, arguing with people,
Speaker 1: having people like try to like pick apart my point.
Speaker 1: And it's very intuitive after he describes it. It's not
Speaker 1: something i'd like really thought about, like I was. I
Speaker 1: do think endless or bysiders or cell siders like I
Speaker 1: do think it's like good to publicly interact. But after
Speaker 1: hearing him like describe it, that benefits what they do
Speaker 1: with like posting all of their theses, uh there, um,
Speaker 1: their their models making them public that you can sort
Speaker 1: of instantly see how an asset management firm could really
Speaker 1: use that to the advantage. And it's also good marketing.
Speaker 1: I mean, uh, you know, it's it stands out. I mean,
Speaker 1: it's it's good for refining your arguments. You know. I
Speaker 1: remember those sort of like fights about Tesla, especially as
Speaker 1: he mentioned back in eighteen when there were serious questions
Speaker 1: about whether the company was going to make it. But
Speaker 1: it's all a good marketing and it's uh, it makes
Speaker 1: it stand out. And I can't think of any other
Speaker 1: firm right now that's doing anything similar, but I could
Speaker 1: see a lot more more sort of embracing that model absolutely. Uh.
Speaker 1: The other thing that I was thinking about was our
Speaker 1: conversation around value investing and this idea that actually, if
Speaker 1: you kind of redefine how you're looking at a company's value,
Speaker 1: then maybe your universe of value stock starts to look
Speaker 1: very different. So this idea that you know, the way
Speaker 1: ARC is looking at it, Tesla probably was a value
Speaker 1: stock back in and I suppose also to Brett's point,
Speaker 1: it depends on your time horizon, right, Yeah, I mean,
Speaker 1: you know, like you've got to be pretty confident and
Speaker 1: I think I thought that was really interesting about the
Speaker 1: sort of like the value of genuinely original ideas because
Speaker 1: there is a lot of um, you know, within the
Speaker 1: space of like the million people who say cover Apple
Speaker 1: or cover Facebook. As he put it, you know, it's
Speaker 1: like maybe the bullish ones take the consensus and at
Speaker 1: ten the bearish ones subtract ten or But the idea
Speaker 1: that's like coming out a problem where you genuinely seek
Speaker 1: to uncover ideas that aren't just some deviation from consensus
Speaker 1: is a sort of a very interesting challenge. But again,
Speaker 1: you can see if you're like really confident about it
Speaker 1: and you like feel like you understand it, you can
Speaker 1: come up with interesting ideas that you can have some
Speaker 1: conviction for make a meaningfully sized bet, so to speak.
Speaker 1: So I'm trying to think if there was one other
Speaker 1: thing that stood it out to me, But yeah, let's
Speaker 1: leave it there. This has been another episode of the
Speaker 1: All Thoughts podcast. I'm Tracy Alloway. You can follow me
Speaker 1: on Twitter at Tracy Alloway and I'm Joe wisn't thought
Speaker 1: you could follow me on Twitter? Oh, I remember what
Speaker 1: I was gonna say? Can I just say it real
Speaker 1: quickly I'm just gonna say it right here in the outro.
Speaker 1: I thought that was, you know, I'm I'm personally biased
Speaker 1: because I'm interested. I've always I've been interested in fuel
Speaker 1: cell companies for a long time. But I did think
Speaker 1: that was a pretty interesting example of the case that
Speaker 1: they're not just bubble riders, that there are like these
Speaker 1: sort of sexy areas of the stock market that they're
Speaker 1: not participating in. And then if they were just sort
Speaker 1: of a firm that was like writing bubbles or writing
Speaker 1: how trends, that they would be participating in that area.
Speaker 1: So I thought that was interesting. It's like, here's the thing,
Speaker 1: a bunch of investors are super excited about it. They
Speaker 1: are not. It's sort of like a sort of I
Speaker 1: thought a useful counter example of this idea that they're
Speaker 1: just in all the sexy areas. Anyway, I just wanted
Speaker 1: to say that. Uh, I'm Joe Wisan though. You can
Speaker 1: follow me on Twitter at the Stalwart, Follow our guests
Speaker 1: Brett Winton, He's at Winton a r K on Twitter,
Speaker 1: and of course I check out all of their white
Speaker 1: papers and models at their website. Follow our producer Laura Carlson,
Speaker 1: She's at Laura M. Carlson. Follow the Bloomberg head of podcast,
Speaker 1: Francesca Levi at Francesca Today, and check out all of
Speaker 1: our podcasts at Bloomberg under the handle at podcasts. Thanks
Speaker 1: for listening,