ARK's Head of Research on How They Find the Next Huge Winner

the superstar stock picker. Because of course, back in the old days there were a lot of like star stock stock pickers, fund managers, you know, Peter Lynch comes to mind, some of the other tech investors back then. But these days, with e t s, with online brokerages that make it really easy for individuals to buy stocks on their own, it really sort of seemed to me like that era

was just gone. Right, So I suppose there was this idea that the time of stock picking has come and gone, and that if you want to make good returns in the market, you should just pour all your money into something like an smp F t F like a these sacks or something like that, and just stick with it and don't bother trying to outperform the market, because over a longer period of time, even the best stock pickers

UH had eventually underperformed. Right. I think this mantra of don't try to pick stocks a if you try to pick stocks, you're probably gonna underperform the index, and be if you come across a mutual fund or a fund manager who's good at picking stocks, oh it's probably just luck. It's not gonna last to you know. Even if even if there is someone who can beat the market, how are you going to know whether it's actually worth putting

her money with them? And so like this idea that everyone should just index, um they're trying to beat the market is kind of a loser's proposition. It's really been drilled into people's heads, and I think, like, you know, for years, they're really we just haven't had a sort

of another a new Peter Lynch or Buffet. You know, there's like Stark Quantz, maybe some bond fund managers who are known, but the idea of like someone who's just really associated with a great track record of picking individual stocks hasn't been a thing for a while. And yet

and yet a star stockpicker emerges over the horizon. Yeah, exactly right, So obviously that really Uh, that's for the first time in a long time, there is currently a fund manager, a stock picker who is a mass an incredible track record, an incredible following, And of course we're

talking about Cathy Wood. She is the CEO and chief investment officer of ARC invest and there is this a total fascination with ARC and this family of actively traded E t f s that have just done a phenomenally well in terms of returns, but also attracted an extraordinary amount of investor cash in the last couple of years. Right, So the ARK E t f s, I mean, I'm

looking at their performance. They have, you know, five different thematic portfolios alone that have basically doubled over the past year, which is pretty amazing if you think about it. Uh, it's amazing enough for just one stock to double in price like that and in just the space of twelve months, but to do it across multiple ETFs is really remarkable.

And I think within their actual portfolio there's a tiny, tiny number of stocks that haven't risen recently, and I'm not even sure there are any actually, Um, it's a really amazing performance. It's really true to Actually I'm looking at at the end of their performance of a r k K, which is the sort of flagship innovation E t F that ARC has was up a hundred and fifty two for the year. Extraordinary returns. And if you look at the holdings, they're just all of the companies

that have absolutely killed it in the recent environment. Tesla is the biggest one, but other names Square the Payments Company, Phenomenal, Roku, huge winner, Zillo, Spotify, tell Doc, which of course had an incredible year thanks to the rise of remote medicine and so forth. So it is a just extraordinary number of winners that this uh, this is a fund and the related funds, there's a related fund for finance and medicine that have that they've brought it just the track

records incredible. If if anyone follows Eric Bolkunis, who's sort of Bloomberg intelligence is E t F analysts, I feel like three quarters of his tweets these days are just about how extraordinary this family of funds and the performance of ARC invest has been lately. Yeah. Absolutely, and uh, you mentioned Kathy would already, but it's sort of it's

given rise to occults around her. I guess I don't want to say cult because that has negative connotations, but certainly there's been a lot of admiration and fascination with what she's been doing over at ARC. Yeah, and uh, we're recording this January twenty. I saw Erica bell Kuna's tweet just today that ARC has taken in is the third most popular fun family right now in terms of new money coming in so far, you're to date that that exceeds the money coming into Black Rocks. I share

his family, which is much bigger. I mean that's like, that's like the name brand. That's like basically the Coca Cola of e t F So to have a sort of small boutique fund firm with a few deply traded fund pulling in more than I shares, it's just it's staggering stuff. Yeah. Absolutely, So we are going to be talking to someone from Ark today, right we are. So the question is how do they do it? How do

they find how do they pick stocks? I mean, Tesla is obviously this huge winner, but it wouldn't have been a huge winner for them unless they had been in it for a lot longer than most people. So the question is how do they how do they find and pick great stocks that trouts the market? Everyone would like

to know. Well, I'm also I'm also interested in how they deal with inflows as they get bigger, and whether or not that makes it harder to have a sort of active E t F that is focused on stock picking. So this is going to be a really interesting conversation I can tell. Yeah, I'm super excited about this one. So we're going to be speaking with Brett Winton. He is the director of research at arc Um. He's been with the company since its founding in Previously to that,

he worked with Kathy Wood at Alliance Bernstein. They've worked together since two thousand seven. So with any luck, we're going to learn at least some of the secrets of um arc from Brett and how they do it. Although I should say, you know, to some extent, maybe it's not a secret because part of what they do is their research is very open, it's very transparent. They post models. So we're gonna we're gonna really learn, hopefully how it all works up. Brett, thank you very much for joining us,

Thank you for having me. Happy to be here. So you've worked with Kathy previously at Alliance Bernstein since two thousand seven with ar Sin. Why do you sort of compare and contrast big picture, and then we'll get into details what the research process looks like at a sort of traditional asset management firm versus the sort of open,

transparent research approach you take it our. I think it's interesting and that I was over hearing your intro and you were talking about stock picking, and I was hearing that, and and I don't think of what we do as stock picking at least at its inception level. So we

really look at the technology level first. And so we specifically seek to identify disruptive technologies that basically technology platforms that future historians will look back upon and say, oh my gosh, that was a signpost technology that was as big as the computer, that was as big as electrification. Uh. And and there's an established criteria for identifying these technologies

for it's called general purpose technology theory. But they all follow steep cost of clients, they all cut across sectors, and they all themselves or platforms of innovation. And so that actually matches that we're investing in those kinds of technologies matches with three critical weaknesses that I see in traditional fund management that create inefficiencies pricing inefficiencies that we

seek to exploit. So the technology fall follow steep cost of clients, that the drama of those cost of declines don't manifest over the next three or six months, So it's it actually can look very linear over a short time horizon, and so it doesn't really impact UH. An understanding of that cost de client doesn't impact the way analysts model the company on the cell side over the

next quarter or two. But if you take a step back and you have an intentionally longer term point of view, you can actually UH come to radically different conclusions about what the future state of the world is likely to look like relative to others, just by having an understanding of of the mechanics of how a cost decline occurs and then what the demand elasticity of that price difference

is going to be. So that's like from the beginning, we set ourselves up to say, hey, we're not gonna we're not gonna try to trade stocks or identify securities that are mispriced on the basis of priced earnings or price to sales or any kind of shorthand for valuation, and we're not Also, we're also not going to try to do a full um DCF because a full discounted cash flow model, because then you get to cheat with how you use the discount rate in your terminal rate

of growth. Instead, we're gonna say, if we own one of these companies, uh five years from now, if we're then forced to sell it to a technological pessimist, what will that person be forced to pay given the cash flow generation of the business at that time. And so, just by underwriting the positions over a five year perspective, we've been able to and still are able to identify really radically under priced securities. Uh So, I think of

it as where value investors and intangible assets. Intangible assets are very difficult to understand how much cash flow they can can generate, but we really do the work of

trying to figure that out over the time horizon. That matters in uh in the part of the capital structure that we're in equities or infinite and duration, it's really kind of dumb to underwrite them over a year or two because the market volatility is you know, really high, Like I can't tell you what the next twelve months of equities is going to look like I can actually say,

with reasonable assurance over five years, this position is under priced. Uh. And so that's like the first major inefficiency we exploit. And at the technology level, what that means is, so take Tesla, which is a position that everybody is well aware of because we have a perspective on what the

cost declines of batteries is going to do. It allows us to demonstrate to our satisfaction that we think by there will be electric vehicles that are sticker priced comparable to internal combustion engine vehicles and the average internal combustion

engine vehicle. So you'll walk into a dealership and say, do I want a Toyota camera that you know it costs me more over time it I have to take it to the dealership more often because it breaks down more often, and it costs me basically the same amount of money out of pocket. Or do I want the down market equivalent of a Model three, which is faster off the line, cost me less money over time, and

it costs me less money today. It would be a real surprise if people didn't shift over to buying electric vehicles. So with that as your initial perspective, you can say, hey, so if we are at we think they're going to be forty million electric vehicles sold by If if we're at forty million and the rest of the market, consultants, etcetera, or something like seven million, well there's probably some inefficiently

priced assets exposed to that technology. So by identifying by underwriting over a time horizon, that's that's reasonable, we believe. And by identifying basically fertile terrain where technologies are misunderstood, then we can basically concentrate our exposure into equities that are more likely to be mispriced. So that's the I just talked a lot, But that's the first of three

inefficiencies that we exploit. So I wanted to back up for a second because one thing, one thing I wonder a lot about in tech investing is what's the firm's collective ground here? Does it come primarily from finance or is there a lot of technological expertise? And what I mean by that is you mentioned that you're looking at technologies across a long term time horizon. Some of your

E t F s are very very technical. I know you have a genomic revolution E t F for instance, and I think you're looking at space exploration as well. So how do you build up the tech expertise in order to be confident in your calls on what's going to work out in the long term. Well, that actually leads right into the second inefficiency that I see us exploiting. Because all of these technologies are a cross sector technologies, they actually kind of cut across the skis of traditional

sector based analysts. Uh. You know, the auto analyst um had been told for years and years and years by the Tier one suppliers and by the automotive companies that electrically goals were actually not a meaningful technology. Not only that they're a niche technology, not only that the people trying to do it, we're crazy and we're probably going to bankrupt, and we'll be able to get the assets when we need them and we can invest in it

later on if required. Uh and so and and that auto analysts who's sitting and basically that echo chamber of information with the quarterly calls with the CFO, the annual calls with the CEO, who has spent his and in this case it's always a heat in autos his entire career basically being like, well, now Ford's better than Nissan,

and now GM is better than Ford. UH has has developed a pattern of thinking that basically doesn't allow him to UH really understand whether or not an electric vehicle will be competitive with these technology with the existing legacy technology, or or given him a good tool set by which

to assess whether or not it will UH. And and so when I was at Alliance Bernstein Alumina, which as a genomics company is UH, you know, nineties plus percent of genome sequence in the world go through illumino boxes. At Aligned Ferns Seins, it was covered by our industrials analysts, and you know, he didn't know what to do with it. He almost would have had to take on a whole new like set of learnings in order to successfully cover this company that to him looked extremely high priced. It

was not like Dana Her or Honeywell to him. And so he was perpetually a neutral. So the way that we approach kind of technology and markets is our analysts are assigned by technology rather than by sector. UH. And so it helps that our batteries analyst is able to Sam, who's great. He does the cost decline work on batteries.

He understands how it's going to feed into certainly the electric vehicle industry, but he also can look at kind of the energy storage within the utility space think about how that impacts kind of the propensity for utility spend. He can understand how it's gonna impact aerial drones and their ability to both delivered parcels from place to place

or or deliver people from place to place. By selecting for people that are expert in the technology, we believe we get an edge against basically sector experts who who are used to a kind of competitive landscape that that doesn't get dramatically upturned. Another example is in in banks, like I'm sure many bank analysts kind of poo pooed and overlooked Square. Well, our view is that you know you you have a digital bank branch in your pocket, and most of certainly the US is going to begin

banking in that way over the next five years. And uh Square is a choir and customers through its peer to peer transfer app, the cash app at something like twenty dollars per customer. Traditional banks pay upwards of a thousand dollars per customer account. The products that that Square is gonna offer is going to expand, and that retail bank branch infrastructure is going to depreciate much more rapidly than any of the executive are willing to acknowledge or

or maybe even understand. Uh and so a traditional banks analyst is not necessarily even empowered to turn around and suggest Square to his portfolio managers. It might be off limits to him. Uh and and and so kind of by focusing on the technologies that matter, with the analysts focused directly on those technologies, actually unlocks a lot of

other sort of like misunderstood opportunities. Companies that fall through the cracks and UM and sectors that are really right for getting disrupted U that have people who have built their whole careers on understanding that the structure of the sector as it currently exists rather than as it likely

is to exist. So this is this is really interesting, this sort of this sort of structural problem in stock identification as a result of people being bucketed into traditional UM sectors rather than starting at the technology technology level. You know, you, I think that's two inefficiencies. What is the third inefficiency that you seek to exploit? Uh, that you see within the sort of traditional investment selection approach, Well,

you alluded to it, Joe, but we, Uh. All of these technologies are themselves platforms a top which other innovations are going to be built, and so it's really easy to suffer from a failure of the imagination. You really can't like. So some people try to model these technologies. They say, these are the three areas where it's selling today, and we're just going to drag out the growth rate, and that's going to be the size of the market. You can't pre imagine all the things that are going

to happen on top of it. And so the only hope to begin to understand the potential scope and breadth, the areas where it will apply apply and where it won't is to um expand your information footprint, to be totally transparent about what you believe is going to happen. And so we publish blogs, we publish white papers, we talk on podcasts. We have our own podcast, it's f

y I for your innovation. You should listen to it, uh, And we do that because when we produce this information, when we're transparent with our forecasts, that information attracts other information. People come back at us and say, I don't understand how you got to that conclusion you're wrong about solid

state batteries. They really seek to combat us to to argue from first principles whether or not we're right and why and and so this helps us to understand both the limitations are of our ability to tell what's going to happen in the future and to get a sense for some things that we may not have imagined that we should be underwriting in to our fund mental models.

Uh and so and that's very different. You know, just being able to access Twitter uh is not something that was is allowed in a lot of fun management shops. That seems crazy to me. Our analysts are on Twitter, they're interacting with the community. You know, this is the way you understand how the world is going to work. We believe that it gives us a competitive edge that from the beginning we've designed ourselves to be able to

compliantly do that. Uh and and to kind of operate in the technology circles where these technologies are actually being grappled with and built and um and deployed into the market. So I wonder if you could bring a lot of the discussion that we've been having and sort of try to solidify it with a single stock example. I wanted to talk about Tesla because, of course, Kathy made a

pretty famous call a couple of years ago. I think it was for Tesla's stock to go to four thousand dollars, and it has since hit that on a split adjusted basis, and you have a new price target on it. What is it that your methodology and your organizational structure was seeing about Tesla in particular that others aren't seeing. There was a lot of criticism and incredulity about that call when you made it a couple of years ago. So

what was it that you saw? For one thing? So we have a cost A client on lithium I AM batteries. We think we have a better understanding of what goes into an electric vehicle than probably probably any other shop on the street. I don't know, certainly anybody than anybody publishing that informs both our top line forecasts I alluded to it. We we believe forty million units will be sold by of electric vehicles uh and and because they'll

be cheaper than traditional cars. When we first began that top line forecasting, the EI A and and you know the OPEC, these policy agencies thought that electric vehicles were gonna sell in the two hundred three hundred thousand unit annually through the end of their forecast through. So first of all, you do that and you say, well, we must be doing something wrong, like what are we misunderstanding here? Uh, And it turns out I don't think we were misunderstanding anything.

It's just that there's not actually a great set of incentive structures for people to make reasonable, first principles long term forecasts. I've been surprised. I thought that we would basically from inception, I thought we would be kind of duplicating the work of consultants like Mackenzie Global Institute, but we would understand the mechanics of how those forecasts were built, and so that would give us an edge. But we

got totally different results. Sometimes sometimes we got similar results, in which case it's very easy to say, wow, this is kind of priced in. You know, there's probably not much that's of interest to us here. Um. But sometimes we got very different results. And I think it's for consultants because they actually are paid to to cater to

the biases of the executives that hire them. But that aside. So, first of all, we think Tesla is you know, right now share of the electric vehicle industry forty million units. If they maintain their share, which we think they can scale production at that rate, then that would put them at ten million units in um we think that the

electric vehicle industry is gonna consolidate. It's actually the products are differentiated on a software basis, much more so than internal combustion, So you could get to a naturally higher margin in that industry relative to traditional automotive And so you can do pretty simple math to to actually say Tesla as an electric vehicle manufacturer, just leaving aside robotaxi and vertical integration into ride hail, and the insurance product

is still a compelling position even at these valuation levels. So it's given kind of the state of their technology stack versus others, you could end up in a situation where, just like Apple um, they're extracting most of the profitability out of the industry even though they have something like a share of the industry. Uh, And so that's possible.

From the beginning, we've always thought that Tesla was interesting not just for the initial vehicle sale, but because of their ability to um monetize the fleet of assets that they have in the field. You know, you can say that Tesla has the largest deployed fleet of robots in the world. Uh, and that those robots improve over time. Uh and conditional on them delivering the ability for one of those cars to drive itself around, which is you know,

actually quite a difficult technical challenge. UM. You know, you can imagine that those vehicles that Wall Street still believes is uh, I sell a car for fifty thou dollars, maybe even optimistic Wall Street thinks I get like five thousand dollars of operating earnings off of that. If instead I, the owner of that vehicle, am able to turn it into a taxi, it could do a hundred thousand miles a and might generate to me, uh, you know, twenty dollars in operating earnings per year to to Tesla, to

me the owner. Uh, you know, additional cash flow on top of that. And so you go from uh, a single sale operating earnings event to every asset they've ever sold generates cash flow for them year after year after year. Uh. And we don't think that's a probability. In fact, we think it's a relatively low probability. We think there's a chance within our model that they're able to deliver robotaxi

capability to the deployed vehicles in fleet. But that call option is worth quite a bit because you have, depending on how aggressive they are at building electric vehicle factories, they basically get an uber like model in a natural monopoly type position in all of the vehicles that they have deployed. So, you know, speaking of that business, I

mean I was looking you guys. You put your models, your financial models on geth hub, or at least some of them, and so anyone can go to your site and then go to your geth hub and find the assumptions that you use to figure out the economics basically of UH robotaxis and all and all of this and how much is worth and how much that's gonna cost and how compelling UH that model of car usage would

be verse traditional? Is that something like putting it out there like that, having a open source model that anyone can play with? Is that something that previously, you know, your old shop, Is that something that you wanted to do and there's just sort of like no way, you know, that sort of openness what you're describing being able to

tweet social media argue about this stuff, get feedback. I mean, I recall I think a couple of years ago, seeing you or maybe your firm get into like going back with like someone in ft Alphaville, like arguing about your tesla mode might have been you like, is that something that prior to art that you had wanted to do

and felt like was missing? And talk to us a little bit more about that aspect of the of the approach, It's always been clear to me that you get more out of the information ecosystem by providing information into it. I can't say that at Alliance Bernstein, I like wanted

to do that. It wasn't even within the realm of possibility of a thing that I could have done, right, Like think about the way in which traditional fund management, the analysts themselves are are are buried there like not allowed to speak on behalf of the firm in any way like and and whereas we think that the analysts need to be able to converse at lee discuss what they believe, both in written form and orally, so that

they uncover their own weaknesses. Like being having to publish to the world makes you a much better and more diligent modeler than if you're not going to do so. You know, if you dig into the like underpinnings of models that aren't published, they're always a mess. They're always

poorly documented. There are always things where people have been like, I just picked that because I wanted to write, whereas um, both the kind of auditing process we have to go through in order to get something ready to externalize and the research process along the way, in which you're always seeking to distill it to its most elemental and understandable form,

actually forces us to be better at our jobs. And then once you publish, you get better again because you get this great feedback loop of people telling you what you got wrong, which at the time is kind of like having your eyes gouged out, but at the end of which you know you you are stronger and more certain about the things that you were right about, and you uncovered the critical weaknesses and how you underwrote the

business or the technology or whatever you're talking about. And so it really has it helps us internally and externally in the iteration cycle of trying to get closer to what's going to be the ultimate truth of how things play out. We have a much more open format than other organizations that that I've been involved with, and I think it has helped both in strategic decision making internally

and certainly in portfolio management and research. UM. I think that the hard part has not been getting people to to share their work, but it's really to do the

hard first principles work. In the first place, I think there's a lot of people, particularly within the financial industry, who are not used to having to be intellectually ambitious and and and except that you're going to forecast something with it with over a time prison, where it's unfair to try to forecast it over that time prison, but it's actually fair so long as you understand the error

bands around what you're forecasting. Uh and and so people prefer because it's more comfortable to pay five thousand dollars for the you know, the market's report that tells you what the market is going to be five years from

now and be like, okay, well I'm just gonna use that. Well, if you think about within equities and particularly high priced equities, which is the train that we operate in, you know, most of the value of that businesses in whatever the terminal rate of growth you put on the DCF and

so effectively. What they're doing is they are outsourcing the most critically sensitive part of the valuation work to an entity that they haven't necessarily due diligence at all, and it is not really well incentivized to create a forecast that's correct. The hard part is not getting people to share. It's getting people to make a reasonable first principles forecast

that is different. So it's really like the I think forecasting within the financial industry for the most part, is people taking other forecasts and going plus or minus from that other forecast, right, And that's not that's just not how we approach it, and having having a process and a discipline of not approaching it that way, not saying why I want to see what everybody else has done, and then I feel better about this, so I'm gonna go slightly higher or worse about this, so I'm gonna

go slightly lower. Instead, it's like, well, this is what we think it's gonna be. And sometimes you do all that work and you're like, Okay, well that's not that interesting because it's similar to what everybody else thinks. And sometimes it's wildly interesting because you're like, what is everybody else thinking? And so then the process of discovery of either what did you overlook or what are they overlooking?

That's where all of the inefficiency lies. Now. Early on we tried to do I think we like the analysts didn't run their own Twitter accounts. They were like they shared them and they were more corporate, and that didn't work very well. Like you do have to have somebody publishing under their own name, speaking in their own voice to a certain extent, so that they can both own the mistakes and own the triumphs of oh I understand

this thing. Uh, and to um kind of the create creating an environment where analysts are owning kind of that intellectual accomplishment. And then and and the learning process and how it filters into the learning process is a big part of the secret sauce. And that's not available. I mean an alliance FIRSTY and I do it a lot of work, and then somebody else's name would go on it. You know, that doesn't feel very good, Like, what's your incentive structure for doing all that work? If if like

it's it's it's just published under somebody else's name. So I think that the you know, having kind of a real like having the analysts as close to the metal as possible of what's going on in the world, and having their perspective on what's going to happen in the world, like come right up against that interface is really critical

to to just thinking about things better. Do you have a different approach to finding and hiring analysts at ARC then at a place like Alliance Bernstein and can a different type of person get an interview or get their foot in the door there? Then might say the traditional filter at a traditional asset management company, Well, I mean we're likely to hire someone soon who doesn't have a

college degree, so that probably gives you a sense. The usual route is you you hire N B A S or you you know, hire our sell side analysts you know, and those are often very smart people. Um, but there is I think a selection bias that happens early on within kind of the financial ecosystem that um, you know, cuts out some of the creativity that you need in order to end up with the different result another like

nuanced it. I think it's interesting about our processes if you if you imagine how do you make money in our do well by your clients when managing equities? Well, if you are wrong that's fine as long as you're uniquely wrong, right, right, Like, if you're uniquely wrong, everybody thought you were crazy anyway, and so it's not priced in. It's when you're wrong with everybody else that you get into trouble. And it's when you're uniquely right that you

actually you know, compound your holdings. Uh. And so if if your forecasts were on average worse but unique, that is better than having forecasts that are closer to the actual truth but the same as everybody else. And so you know, you need to have a diversity of kind of cognitive perspective in some way relative to everybody that

you're competing against. Even if it yields kind of like more volatile results, you you on average have more differentiated results, which then gives you both downside protection everybody thought you were crazy anyway and upside potential. Well nobody expected this. So um, I think having in and that requires a degree of um kind of hardness against some of the social pressures that I think operate in a lot of

Wall Street. And so we don't, Yeah, we don't select from the same pool of candidates, or at least we haven't. We've often end up screening out kind of more traditional financial candidates just because they don't have as idiosyncratic a point of view. I wanted to ask you about another specific well I don't want to say specific thing, but

another specific technology. Since you group yourselves by technology. You have a crypto analyst who looks at blockchain and bitcoin, and I think Cathy has been quite bullish on the technological potential of blockchain as a whole. Could you maybe walk us through the thinking behind that and again connect it to your overall methodology and structure, because I think there were quite a few cell side analysts talking about, you know, how bitcoins a bubble, but blockchain is the

technological future. But you at ARC took a different approach and basically said by bitcoin and by blockchain related technology. So what was the thinking there? Yeah, I think you can from a very high level think about, um, how all contracts that we sign actually have this failed mode where the political entity that enforces them sometimes just decides

not to enforce them or changes the rules. Like imagine you've signed a contract and then suddenly you know somebody, the other counterparty in the contract rnegs, he doesn't pay up, and you go to the government and say, well, you have to force this guy to pay because he didn't pay, and the guy is actually you know, as an end with the government. The government's like, no, thank you. Uh. And so the pro amss of of crypto assets generally is basically, uh that that final layer of kind of

contract settlement happens regardless of the underlying political circumstances. So you can broaden that across. Think of like all of the various contracts in the economy. Think of a structuring deskin and investment bank. It's basically set up to create complex contracts with counterparties where they are are assured that those those contracts will actually be made good because the

counterparties are really well respected established institutions. Well, kind of smart contracting platforms like Ethereum and others allows for an experimentation layer where you can, you know, instead of having to work in Morgan Stanley in order to structure those products, you can be Joe uh, you know, coder and and create kind of those contracts with the protocol itself serving as the ultimate counterparty that that will see that those

contracts get executed upon. Well, currencies are all contracts. In fact, you could argue that the most valuable contracts and there's a social contract between me and the US government that somehow the purchasing price of the dollar is not going to diminish more than I guess two percent a year or whatever their target is. Right. So bitcoin basically supplants that social contract with with kind of its protocol for

security of the asset. So I think it's a profoundly interesting kind of set of ideas across the entire crypto asset space that over of all the technologies we look at, over probably the longest adoption time will have actually the most dramatic uh financial and technological impact. Uh. So you know, from the beginning, we thought it was interesting. We had a crypto a blockchain analyst in I believe is when

we hired him. Uh, And it was because even though there were not necessarily many investable as it's we understood that the technology at that time, we understood that the technology was within within our product suite. We understood that the technology was interesting enough and going to create sufficient disruption that it was worth beginning to invest in understanding it, Understanding where it was going to go, Understanding what the best mechanism by which to create client exposure to it,

and so that's what we did. We have vehicles in which we can get client exposure to crypto assets. We you know, it was a smart, smart move, both at the time and going forward. And I think that if you're within the financial services industry and you're not thinking very deeply about how digital wallets, crypto assets and and UH neural NEETs and artificial intelligence are going to change what you're doing over the medium term, then you're not

operating intelligently within the firm that you're operating. So I have a follow up question. I'm going to try to phrase this UH as diplomatically as possible. Your outperformance speaks for itself. Your returns have been absolutely excellent in recent years.

But there are people out there who would point to that performance and say that you've been writing a tech bubble or you're buying into the stocks that have very compelling narratives that seem to capture the wider imagination, but that haven't actually been proven yet in terms of earnings. They're just training at you know, massive valuations and getting more expensive, but the earnings haven't actually kept up. So

what do you say to those people? Two people who say that you're basically momentum training on enthusiasm for unproved technology, right. I didn't phrase that very diplomatically, did I? No, that's fine, I mean listen to. Our job is to understand what the value of something is going to be, as we currently phrase it, five years from now. And um. Sometimes so fuel cells is a great example where you know, we did our first work on fuel cells in we

did a cost declient on it. We determined that within passenger vehicles, we don't think they are gonna be cost competitive with electric vehicles until the early ties, and that was contingent on something like the Toyota Marai selling in Toyota Prius like volumes over the course of a decade.

Uh and so um. You know, having gone through that exercise, it was very easy for us to kind of, you know, look at that entire stack of assets and and every time one comes up and says, oh, well, this is what's different, we've already done the work to understand, you know, the key cost assumptions you need to make, um and how there's costs are declining and what that means for the future unit economics of that technology. And then you

can say Okay, that's not something we're going to invest in. Now. We could be wrong, right, and and we're wrong all the time, right. But but but I think that actually doing the work to underwrite the asset this is really difficult. It's not easy. Uh. And so there's a difference between I am going to invest in the blockchain iced tea company because they say the word blockchain, and and invest in a company that I think is under priced over

time horizon that is meaningful to my clients. I'm really glad you brought up fuel cells because I was actually gonna go there next. So maybe probably probably people are aware some of the hottest stocks right now are fuel cell stocks. Plug Power is one, fuel Cell Energy is another. One.

Major winners, and I have a personal interest in this area because and I'm not saying this to brag or anything like that, but I actually like traded these exact stocks when I was in college in the late nineties and early two thousands, the same exact stocks plug Power and fuel Cell. They've been around forever, and I you know, they were crazy overvalued then, but I got kind of lucky and that helped pay for college. Anyway, The point is,

I'm not trying to brag it just anyway. My point is at the time, it was like, Okay, this is right around the corner and fuel cell fuel cells are gonna be on the road by Obviously that didn't happen. So you're saying it's not different this time, that this is just yet another series in an extremely long history of people getting over excited and over optimistic about this technology, and that once again it's further off than people think.

I mean, there are niche applications where you can underwrite it. I'm not going to disparage or endorse plug power, but there are clearly buyers of fuel cell driven forklifts because you can have the hydrogen right there on site. And makes sense that to you know, our understanding of how you would have to underwrite that asset to justify its prices. You would have to think it's going to get into

the truck or passenger vehicle business. You would have to think that the cost declient on the technology would carry it into a competitive position with alternative um mode of technologies in those domains. And like just on the electric vehicle side, it's really hard. You have to make a lot of assumptions about somehow the assets or the technology being bought up to drive the costs efficiently low to

make it unit economic compelling. Uh. And so there there you can like, you have to generate the hydrogen somewhere first of all, so that costs money. You're operating costs are much much higher. Uh. Then you have to fund the build out of the hydrogen fueling infrastructure, which is really it's not easy. It's like a hard coordination problem. Tesla. Even from the beginning, we thought I was skeptical of Tesla's supercharger network build out. I thought that that was

not a layer that they needed to compete in. As it turned out, I was dead wrong. Like that definitely differentiates their product because range doesn't become as much of an issue in making the sale because people can imagine doing the road trip they want to do with their new car, which is, if you can't do the road trip, what's the point of getting the new car? Right? Uh?

And Uh, But a supercharger costs a tenth what a hydrogen station does to like a full supercharger station versus a hydrogen station, So you know, you're talking on the order of a hundred to two hundred thousand dollars versus

a million to two million. You have to do a ton of execution, have like a ton of selling of the technology that it's hard to see how it happens because you can't chicken and egg the infrastructure in place sufficient to drive demand for the underlying technology sufficient to get it low enough in price that it's cost competitive with existing modes of transport. Uh So is it possible, yes, can we reasonably underwrite it? Not at this time? HM. That was a good answer. But that's what I mean.

You know, when Facebook oculus right suddenly, all these analysts are coming out with VR is going to be you know, eighteen million units by ten or whatever, and and we looked at it, we did a model on it, and and we couldn't get enough people to buy the headsets for a triple A game developer to justify underwriting a game developed specific for the headsets, And so you just couldn't. It didn't make sense, right, Like, you go through and you try to say, what is this market going to be?

And if it doesn't, like, if the modeling that you do doesn't make sense, then you're not going to take aggressive positions on the basis of it making sense so and that you know, so we never built kind of VR heavily into our own video model because that you know, it was a dry hole and and a lot of I think our role is to figure out actually the things too that you can dismiss a whole category of

by doing a single piece of work. Like that's really important because you have you know, now there's a gazillion SPACs coming at us right and and um, you need to have some some lens by which you approach these assets and and say what they are fundamentally worth. That allows you to easily establish whether or not something is of potential interest to portfolio management and to your clients.

I wanted to go back to something we mentioned in the intro, which is the extraordinary inflows that we've seen into ARC alongside the extraordinary performance that we've been discussing. Have those inflows change the way you invest at all or your research process? Does it perhaps become harder to

identify new opportunities? Um, the more money you have to put into a certain company or a technology, every investment decision that you make, you would want to make frictionlessly at the exact size that you want at the instant that you want to do it, and that's you know, not possible. No matter how much you're managing, the research process has always remained the same. We always start at the technology level. We understand the direction that the technology

is going. Because all of the technologies that we're investing in are are are exponential. You're actually creating a lot more opportunity. I mean, if if you look at our tests are open source Tesla model, you can you can drag out, you can see what our next year needs to be given our expectation for EV sales, and it actually meaningfully increases your expectation for value of the company just dragging out to the right by one year, you know.

And and kind of the spack phenomenon is creating more publicly traded equities that we could potentially invest in within the technology areas that we're interested in. And you know, taking in a lot of flows uh into our assets. Luckily within the E T F construct that's relatively easy. Uh and uh, And we are always selective about, you know, how we deploy and what's the most efficient way to

get exposure to the inefficiencies that we see. You know, you mentioned the big picture UM technology families that you start with. So you have this like top down approach to figuring out the big areas. Genomics is one, robotics, How did you come up with those? I mean, what is there? Is there a methodological process for figuring out and planning a flag on the ground and saying, Okay, this is going to be really big. Yeah, so they all have to uh and and so I alluded to it.

But there's this theory called general purpose technolo oology theory where it's like these academics have agreed upon what the criteria are for really meaningful technologies. They all have steep cost of clients, they all cut across sectors, and they're all themselves platforms of innovation. Uh. And so we try to apply that framework to the technologies that we're interested in. We think there are five fundamental technology platforms that are

all entering the economic marketplace today. Uh. Gene sequencing and editing, AI and particularly neuron NEETs, robots, particularly collaborative robots, energy storage and the advances and battery technology, and then blockchain cryptocurrency. And so we believe that yet future historians will look back and identify all of those as big technological buckets. But you know, within taxonomys there are always weaknesses, right, and you could draw the lines in a slightly different area.

So we tried to look back and see, like, what technologies did historians agree upon where these general purpose technology platforms over time? And there's not consensus at even looking backwards. So of course there's not consensus today what are the

major technology platforms? But I think it's a so the other um from those five technology platforms, there are also we have fourteen underlying technologies that are discreetly model able, where we have a good understanding of the cost a client of kind of how it cuts across sectors, and and the equity market capit cruel that we expect those technologies to achieve over time. I'm I'm a big believer in getting really dirt simple with your assumptions of things

so that you can tell if they make sense. So it's it's kind of like, you know, what what are robots going to be worth? Well? What if we start out and say what about every manual laborer employee in the world, and we say, well, we're going to supplement this person with a ten dollar tool that's a robot, Like, what would that market be worth? What would be the cash flow cruel to the about manufacturers in that instance.

And then so how much would you assume, uh it's occupied in terms of enterprise value by the companies that are catering to that economic opportunity. Uh And and so if you do kind of that very high level assumptions about the technologies that we track, you would assume that there's gonna be fifty trillion dollars in market cap accruel to our technologies over the next decade. Uh And and

so the this gets back to the capacity question. There's gonna be a lot of economic value created, and there's gonna be major, major businesses that accrue out of it, you know, like with it. If you look at how we've modeled autonomous robot taxis globally, uh we think that autonomous robot taxis the platforms that enable that are going to be worth more than the global energy sector as a whole within five years. Just and and you actually

don't have to make radical assumptions to get there. You say, well, look at global miles driven and these are going to price it something likes a mile, So they're going to be cheaper than actually buying and owning and operating a vehicle in the US that costs yous a mile if you buy a new one, right, and so it's going to be the default way by which people get around. These autonomous taxi platforms are going to scrape a platform

feed just like an uber or lift. If you back into the aggregate cash flow that you expect given your adoption curves and everything else, it's it's uh, you know, measured in hundreds of billions of dollars within five years, So it's natural that the market would pay at least a reasonable cash flow multiple on that cash. So there's there's a combination of like, what's the cost aclient look like, how is it cross sector? Uh? And when has it gone cross sector? What other things are going to be

built on top of it? And thinking about how meaningful is this going to be economically over the medium to long term, And if you can kind of dimension that it's meaningful and cross sector and steep cost decline and itself a platform of innovation, it's very likely that this thing is going there's gonna be a lot of value

created here. So it's worth devoting the intellectual capital to understanding and understanding that puts and takes of how it's going to get to market, and which part of the value chain is going to be the most um cash a creative and and and how that part of the value chain is underwritten today relative to how you think it should be. Would you ever consider starting a SPAC or is it too much of a departure from the current model? Um? Well, I mean I think that there

are pluses and minuses of SPACs. I think that there is a degree of nervousness that at least I have right now that there that when people raise spacts like they are heavily incentivized to figure out something to buy with them. Nobody returns the money, right, And it's almost like a you create a time bomb of I p O right, like U I p O. But the real I p O is when you merge with the other

entity and the people who are controlling whether or not emerged. Yes, you have to get the shareholders to vote, but the people who are controlling it, like it's basically like you know, buy something or or you lose it. It seems in some ways backwards to how companies should come to the capital markets, and that they should come to the capital markets when they're ready not because there's a pool of money that's going around trying to find everything that could

possibly go to the capital markets. I'm nervous about that. On the other hand, if you think about UM, what has happened with late stage venture, which is where a lot of these companies would otherwise have been funded, is that there you are only allowing accredited investors to invest in kind of these technology companies and uh, you know, the late stage venture capitalists get money through their carry

and their management fee. Uh, that's also quite punitive to the end he old or, and you're cutting out the entire you know, Joe investor who's not accredited, And so you could argue that this is a way to democratize access to these late stage venture type assets. I think that to me, there seems like there's a lot of misbehavior going on in the space. And usually our bias is too when there's a lot of capital going after

something to be wary of it. UM. But I can't you know, comment you know directly, but the lack of disclosure for the underlying companies I think could lead misbehavior. On top of misbehavior, I talked about how consultants UM like Mackenzie and stuff. They they their forecasts weren't as good as I thought they were going to be. Well, we also look at the forecast of the management teams within these facts and and that is a difference from an I P O. And you know and S one.

You you're not going to get management team telling you what they think they're going to print in revenue five years from now. Within the facts the management teams are and and there as a general rule so far, looking at what management teams have forecast, we have a hard time, um, hitting that. So let me sort of and you know, I think we can wrap up soon. Um, but let me just sort of this sort of gets to a

bigger question, and Tracy sort of hinted at it. Well, you know, this sort of the claim among our detractors that you've done a really good job basically riding this big bubble. What happens if at some point we are in a bubble And some people would say we're in one now, but you know, there are times in which, in retrospect you're like, oh, there's definitely or in a bubble, there was no good tech to buy in December of anything that you bought then pretty much in anything related

to tech was probably gonna be underwater for years. If you had purchased then maybe some of them, you know I've obviously done well instance could then what what do you do if you come across that environment where all of your models are saying, in these areas of innovation, in these areas of tech that we're into, we just can't make the numbers work for anything that's of like quality. Is that a concern? Is that a situation that you've thought about? Like, how do you think about that question?

I can say that right now we can still find a lot of inefficiently priced assets. So at least as we model or as we expect the world to unveil itself. Um, I don't see it, you know, within the context of the positions that we put client money into. I think that uh, you know, financial markets are full of in some ways saying a bubble, I think is is you know, it's lots of burbl ng and sometimes you get a

you know a bigger degree of burbling. But there there are always you know, there's the I C O boom in there's if today or you know, three months from now, the equity markets are down, you know, then we would all look back and say, oh, well, the SPACs were the sign. It was obvious, didn't you see? You know?

And and if you are investing money in the equity markets, equities are infinite in duration, you should not be doing that on the basis that the one year result is going to be meaningfully indicative of whether or not it was a good decision, that it's the wrong time horizon, right, and so like, I like my comfort level is that we look out five years and I say this looks very reasonable over five years, because we're not making We're not going out five years and saying then I'm going

to pay an elevated multiple. I'm going out five years and saying I'm going to be a forced seller to someone who only pays the market multiple for the cash flow coming off of business with this kind of margin profile and capital intensity, and and you know our return hurdle for for the positions we underwrite, as so we think it's going to roughly double over five years. Well, you know, so I have a lot of ways in which to get exposure where that's at least the way

we forecast the world. Now, could we look really dumb twelve months from now? Yes, In fact, I think it's likely that at some point people will think that ARC was a scam and that we were, um, you know, we don't know our left from our right, and we're

doing things wrong. And our discipline and our our mission is to continue to say what we think is going to happen, and to try to you know, buy basically intangible assets at at deep value, regardless of the market environment, the tenure rates, and that they came down as much

as they did during the pandemic. It has it provides the highest leverage to the longer duration assets, right and and so naturally if you know over ten years you can only get one percent compounded on your money, well, then something that's not going to produce cash flow for you until ten years from now, but that cash flow could be monumental looks a lot more attractive because you're you know, the competitive rate of of of cash flow

generation is is just much lower. That had an effect on the overall market multiple, and we don't try to take a stance against the overall market multiple as and we don't try to position ourselves. You know, I'm not gonna I'm not trying to allocate between equities and fixed income, right, and so I just try to underwrite the equities, you know,

qua another equity exposure, you know, financial markets. I was being accused of having committed career suicide because of our Tesla position, and that was you know, that was Memorial Day of like, you know that that was that was not that long ago. The markets mania is much more volatile than our fundamental valuing of the company. So the way in which we manage the portfolios is were typically short term contrarian. If something is rallying, it's often rallying.

If it goes up because it beat on earnings, that doesn't change what we think the company is going to look like five years from now. So we'll often sell off that gain to buy into something that you know, suddenly was investing too much in R and D so they missed on earnings and it's like, yes, give me

more of that, uh. And so that that actually doing the work over five years provides us a lot of um kind of anchoring that allows us to manage positions within the portfolio as they respond to news that we don't think is actually fundamentally meaningful. In in the event that the markets start to sell off for whatever reason, because rates are going up because I don't know, geopolitical risk diminishes. But you know, you you all are the ones that get to explain daily why markets do what

they do. You know, then we'll respond to that. That's why we actively manage the portfolios. But um, I certainly I would hate to have to say what going to happen in three months. I think that's much harder than than saying what's going to happen over five years. Actually, um, I think it's it's a really it's a really challenging game because it requires you anticipating what other people are going to then think, rather than trying to forecast what's

going to happen kind of objectively in the world. Uh. And um, I think I think a lot of people play that game, but I it's not that interesting to me, and I think it's really hard. Brett, that was that was fantastic. Really. Uh, I'm really glad we got a chance to talk to you. I learned a ton in that conversation, and I appreciate you taking the time my pleasure. Joe Ye, thank you, Thank you so much. Thanks Brette, Tracy,

that was really cool. I mean, obviously I've been aware of arc and they're amazing stock picks, and particularly um they're sort of vindication on the Tesla pick. But I'm hearing overall like they're sort of like general approach. That

was very useful and interesting. Yeah, I agree. There were two things that stuck out from that conversation for me, and again I don't mean to naval gays in the media too much, but one of them was the way they organized themselves around technologies rather than traditional sort of

analyst or industry sectors. And I have to say, I think that's something that a lot of media companies have struggled with over the years, you know, particularly when bitcoin came out, for instance, there was a lot of discussion about it. Should it be done by market reporters, should it be done by commodities reporters, Does it fit into an investment team or the tech team, and everyone kind

of struggled to fit it into a traditional category. But had you just looked at it as a sort of um sort of cross beats technology like blockchain, maybe it would have been easier to conceptualize, I suppose, and the same thing for you know, electric batteries and things like that. So that was really interesting and the second thing about refining your work through public interaction and discourse also strikes a chord. Both you and I are very active on

Twitter and social media. I think journalism in itself is a very public activity, since every time you publish something, you're probably going to get some sort of reaction or feedback to it, and in the end you can use that to refine your thought process, you think more strategically about your model or your subject matter or whatever. And I don't know, I just see a lot of parallels between what his analysts that are are doing and what some journalists are doing or could be doing. Yeah, now

that that definitely stood out to me. And it's one of these things where, like I get as a journalist so much value from interacting on social media, arguing with people, having people like try to like pick apart my point. And it's very intuitive after he describes it. It's not something i'd like really thought about, like I was. I do think endless or bysiders or cell siders like I

do think it's like good to publicly interact. But after hearing him like describe it, that benefits what they do with like posting all of their theses, uh there, um, their their models making them public that you can sort of instantly see how an asset management firm could really use that to the advantage. And it's also good marketing. I mean, uh, you know, it's it stands out. I mean,

it's it's good for refining your arguments. You know. I remember those sort of like fights about Tesla, especially as he mentioned back in eighteen when there were serious questions about whether the company was going to make it. But it's all a good marketing and it's uh, it makes it stand out. And I can't think of any other firm right now that's doing anything similar, but I could

see a lot more more sort of embracing that model absolutely. Uh. The other thing that I was thinking about was our conversation around value investing and this idea that actually, if you kind of redefine how you're looking at a company's value, then maybe your universe of value stock starts to look

very different. So this idea that you know, the way ARC is looking at it, Tesla probably was a value stock back in and I suppose also to Brett's point, it depends on your time horizon, right, Yeah, I mean, you know, like you've got to be pretty confident and I think I thought that was really interesting about the sort of like the value of genuinely original ideas because there is a lot of um, you know, within the space of like the million people who say cover Apple

or cover Facebook. As he put it, you know, it's like maybe the bullish ones take the consensus and at ten the bearish ones subtract ten or But the idea that's like coming out a problem where you genuinely seek to uncover ideas that aren't just some deviation from consensus

is a sort of a very interesting challenge. But again, you can see if you're like really confident about it and you like feel like you understand it, you can come up with interesting ideas that you can have some conviction for make a meaningfully sized bet, so to speak. So I'm trying to think if there was one other thing that stood it out to me, But yeah, let's leave it there. This has been another episode of the

All Thoughts podcast. I'm Tracy Alloway. You can follow me on Twitter at Tracy Alloway and I'm Joe wisn't thought you could follow me on Twitter? Oh, I remember what I was gonna say? Can I just say it real quickly I'm just gonna say it right here in the outro. I thought that was, you know, I'm I'm personally biased because I'm interested. I've always I've been interested in fuel

cell companies for a long time. But I did think that was a pretty interesting example of the case that they're not just bubble riders, that there are like these sort of sexy areas of the stock market that they're not participating in. And then if they were just sort of a firm that was like writing bubbles or writing how trends, that they would be participating in that area. So I thought that was interesting. It's like, here's the thing, a bunch of investors are super excited about it. They

are not. It's sort of like a sort of I thought a useful counter example of this idea that they're just in all the sexy areas. Anyway, I just wanted to say that. Uh, I'm Joe Wisan though. You can follow me on Twitter at the Stalwart, Follow our guests Brett Winton, He's at Winton a r K on Twitter, and of course I check out all of their white papers and models at their website. Follow our producer Laura Carlson,

She's at Laura M. Carlson. Follow the Bloomberg head of podcast, Francesca Levi at Francesca Today, and check out all of our podcasts at Bloomberg under the handle at podcasts. Thanks for listening,